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Forest Pulse: The Latest on the World’s Forests

Last Updated on April 4, 2024

The Forest Pulse draws on the most recent data and analysis to reveal the latest trends in global forest loss and deforestation.

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How much forest was lost in 2023?

This section of the Forest Pulse is updated annually  using annual tree cover loss data to provide a comprehensive overview on where forests have been lost around the world. Annual updates are released each year and cover the previous year’s trends. 

Tropical Forest Loss Drops Steeply in Brazil and Colombia, but High Rates Persist Overall

By Mikaela Weisse ,  Elizabeth Goldman  and  Sarah Carter

Data created and updated by Peter Potapov, Svetlana Turubanova and Sasha Tyukavina –  University of Maryland’s GLAD lab

Between 2022 and 2023, Brazil and Colombia experienced a remarkable 36% and 49% decrease in primary forest loss, respectively. Yet despite these dramatic reductions, the rate of tropical primary forest loss in 2023 remained stubbornly consistent with recent years, according to new data from the University of Maryland’s GLAD lab and available on WRI’s Global Forest Watch platform.

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As some countries show political will to reduce forest loss and others do not, the frontiers of forest loss are shifting: the notable reductions in Brazil and Colombia were counteracted by sharp increases in forest loss in Bolivia, Laos and Nicaragua, and more modest increases in other countries.

Total tropical primary forest loss in 2023 totaled 3.7 million hectares, the equivalent of losing almost 10 football (soccer) fields of forest per minute. While this represents a 9% decrease from 2022, the rate in 2023 was nearly identical to that of 2019 and 2021. All this forest loss produced 2.4 gigatonnes (Gt) of carbon dioxide emissions in 2023, equivalent to almost half of the annual fossil fuel emissions of the United States. 

Tropical primary forest loss, 2002-2023

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Why Do We Focus on Tropical Primary Forests?

Though the tree cover loss data from the University of Maryland has global coverage, Global Forest Watch primarily focuses on loss in the tropics because that is where  more than 96%  of deforestation, or human-caused, permanent removal of forest cover occurs. This piece largely focuses on primary forests in the humid tropics, which are areas of mature rainforest that are especially important for biodiversity, carbon storage and regulating  regional and local climate effects .

There are just six years remaining until 2030, by which time leaders of 145 countries promised to halt and reverse forest loss . While the declines in forest loss in Brazil and Colombia show promise towards that commitment, it’s clear that the world is falling far short of its targets .

Top 10 countries for tropical primary forest loss in 2022 and 2023

The Many Benefits of Forests

Forests are critical ecosystems for fighting climate change, supporting livelihoods and protecting biodiversity.

Climate: As the world faces a “ final warning ” on the climate crisis, reducing deforestation is one of the most cost-effective land-based measures for mitigating climate change. Forests are both a sink and a source for carbon, removing carbon dioxide from the air when standing or regrowing and emitting it when cleared or degraded.

Human well-being:  Some 1.6 billion people , including nearly 70 million Indigenous Peoples , rely on forest resources for their livelihoods. Deforestation, especially in the tropics, also impacts local temperatures and rainfall in ways that can compound the local effects of global climate change, with consequences for human health and agricultural productivity.

Biodiversity: Forests harbor the most biodiversity of any ecosystem on Earth, and in turn, species that live within forests play an important role in maintaining healthy ecosystems and resources and services that people depend on .

Here’s a deeper look at some of the trends in forest loss in 2023:

Dramatic reductions in primary forest loss in Brazil and Colombia coincide with political changes

Brazil lost 36% less primary forest in 2023 than in 2022, hitting its lowest level since 2015. That decline translates to a dramatic decrease in Brazil’s share of total primary forest loss in the tropics — from 43% of the tropical total in 2022 to only 30% of the total in 2023.

Brazil primary forest loss, 2002-2023 

The Amazon biome experienced the largest decline, with 39% less primary forest loss in 2023 than in 2022. This is largely consistent with official government figures, which found a 22% decrease in Amazonian deforestation from 2022 to 2023 ( read more about how the GFW data compares to Brazil’s official data). As the world’s largest rainforest, the Amazon holds outsized importance for global biodiversity and climate change mitigation.

The reduction in forest loss coincides with the transition of government leadership from President Jair Bolsonaro to President Luiz Inácio Lula da Silva, known as Lula, at the beginning of 2023. During Bolsonaro’s tenure, his administration eroded environmental protections and gutted enforcement agencies . In contrast, Lula has pledged to end deforestation in the Amazon and other biomes by 2030, and already had a proven track record on this issue from his previous administration .

Since his reelection, President Lula has taken action to reduce forest loss, including revoking anti-environmental measures , recognizing new Indigenous territories , and bolstering law enforcement efforts  (though some enforcement employees are currently  on   strike , saying they are overworked and undercompensated). These changes appear to be having an impact on the rate of forest loss, though it still remains higher than its low point in the early 2010s.

This positive news comes at a time when the Amazon is experiencing its worst drought on record . Though forest loss from fire did not increase in the Brazilian Amazon as a whole in 2023, the area surrounding the city of Manaus saw unprecedented fires , and the state of Roraima experienced a record-breaking number of fires in February 2024. Concern is growing that feedback loops between deforestation, warming temperatures and drought could result in a “tipping point” beyond which parts of the Amazon can no longer support rainforest and would turn into savannah.

And not all biomes in Brazil saw the reduction in forest loss seen in the Amazon: both the Cerrado and Pantanal biomes saw increased forest loss in 2023.

Select Brazil biomes tree cover loss, 2001-2023 

The Cerrado biome, a tropical savannah to the southeast of the Amazon, experienced a 6% increase in tree cover loss    from 2022 to 2023, continuing a five-year increasing trend. The Cerrado is the epicenter of agricultural production in the country and the extent of its soy production has more than doubled over the past 20 years. Civil society organizations are calling on companies to commit to deforestation and conversion free farming as a way to ensure that their supply chains are not contributing to ecosystem loss in this valuable biome.

Meanwhile, the Pantanal biome, the world’s largest tropical wetland, experienced a spike in forest loss in 2023 due to fires. Fire is a normal feature of this ecosystem, however, a multi-year “ megadrought ” caused in part by climate change has resulted in repeated burning over large areas, which has experts worried about the ability of this ecosystem to recover.

Colombia also experienced a dramatic decline in primary forest loss in 2023, with a 49% reduction in primary forest loss compared to 2022.

Colombia primary forest loss, 2002-2023

Colombia's rate of primary forest loss increased significantly starting in 2016, coinciding with the country's peace agreement with the Revolutionary Armed Forces of Colombia (FARC). That agreement included resettling FARC members to new areas, leaving accessible large tracts of remote forests where they previously maintained strict control over land use. As a result, clearing by other armed groups and land speculators increased. The new figures for 2023 indicate a possible return to pre-peace agreement levels of forest loss.

Like Brazil, Colombia also recently experienced a change in leadership,  inaugurating President Gustavo Petro Urrego in August 2022. His administration focuses on the environment, rural reform and peace to bring better quality of life for people. As part of those efforts, President Petro’s government is  negotiating with different armed groups , with forest conservation as an explicit goal of the discussions. One of the armed groups currently in the negation process, the Estado Mayor Central (EMC), included  penalties on forest clearing as a “gesture of peace.” However,  it is unclear when or if negotiations will reach a final peace agreement with each of these groups.

Local communities have also been promoting the sustainable management of natural resources and the conservation of forests.

Primary forest loss surges in Bolivia, Laos and Nicaragua

Not all tropical countries have seen reductions in primary forest loss like Brazil and Colombia. Bolivia, Laos and Nicaragua all experienced rapid increases in forest loss in 2023, largely due to fires (in the case of Bolivia) and expansion of agricultural land.

In Bolivia, primary forest loss increased by 27%, reaching its highest year on record for the third year in a row. Bolivia had the third most primary forest loss of any tropical country, despite having less than half the forest area of either the Democratic Republic of the Congo or Indonesia.

Bolivia primary forest loss, 2002-2023 

Fire continues to play a major role in the country, accounting for just over half (51%) of the primary forest loss in 2023. Forest fires in tropical nations like Bolivia are usually  set by humans  for agricultural purposes, such as regenerating grasslands for grazing and clearing for cropland, or to  claim land . Under hot and dry conditions, those fires can spread out of control into forested areas. Bolivia experienced record-breaking heat in 2023 due to the combination of human-caused climate change and the natural El Niño phenomenon. In 2023, forest loss due to fire was most prevalent in the department of Beni, which experienced more than double its rate of primary forest loss from 2022. Forest fires again were prevalent within Noel Kempff Mercado Park and other protected areas in the country.

Agricultural expansion was the other major driver of primary forest loss in Bolivia. Soy expansion has resulted in  nearly a million hectares  of deforestation in the country since the turn of the century, nearly a quarter of which can be attributed to Mennonite colonies. Though Bolivia has much less soy production than neighboring countries, most of its expansion has come  at the expense of forests . The government continues to promote the agribusiness industry by setting ambitious targets for soy and beef exports, promoting the expansion of biodiesel and subsidizing agricultural activity.

New hot spots of forest loss in Bolivia in areas ravaged by fires

Laos also experienced its highest rate on record (since 2001) of primary forest loss in 2023, with a 47% increase from an already record-high in 2022. In 2023 alone, 1.9% of Laos’ remaining primary forests were lost, a rate of loss that’s 5 times faster than Brazil’s proportional to its forest area.

Laos primary forest loss, 2002-2023

Primary forest loss in Laos is mostly driven by agricultural expansion . This expansion is fueled in part by demand and investment in Laos’ agriculture sector by China , which is the largest importer of Laos’ agricultural products. Laos’ economic situation may also be contributing to increased forest loss. High unemployment, a persistent rate of hyperinflation, depreciating currency and a rise in the price of cooking oil have driven up the cost of basic needs , spurring farmers to carve new agricultural plots from forests.

Nicaragua has also seen an increase in primary forest loss in 2023 and recent years, with 60,000 hectares lost in 2023. While the country had the eleventh highest area of primary forest loss in the tropics in 2023, it had the highest rate of primary forest loss relative to its size,    losing 4.2% of its remaining primary forest in a single year.

Nicaragua primary forest loss, 2002-2023

The expansion of agriculture and cattle ranching is the main cause of forest loss in Nicaragua. Gold mining is also a driver: the area of mining concessions has also nearly doubled since 2021 to cover around 15% of the country. Beef and gold are both major exports for Nicaragua, with much of their production bound for the United States. In many cases, deforestation has been accompanied by violent land invasions in Indigenous territories. Sources say the government is complicit in deforestation and land invasions, also shuttering environmental and Indigenous advocacy groups in the country.

Primary forest loss in the Democratic Republic of the Congo continues apace

Persistently high rates of primary forest loss in the Democratic Republic of the Congo (DRC) are cause for concern. The Congo Basin is the last major tropical forest that remains a carbon sink , meaning the forest absorbs more carbon than it emits. Most Congo Basin countries experience consistently low levels of primary forest loss, such as Gabon and the Republic of the Congo — both High Forest Low Deforestation (HFLD) countries — which continued to see low levels in 2023. However, more than half of the Congo Basin’s forest is located in DRC, which is losing half a million hectares of primary rainforest each year. And while the rate in 2023 increased by only 3%, the continued small increases over many years are adding up.

Democratic Republic of the Congo primary forest loss, 2015-2023

This graph shows primary forest loss since 2015, when the integration of Landsat 8 satellite data and updates to the loss detection algorithm from UMD resulted in better capturing of small-scale loss, which is common in Congo Basin countries. Primary forest loss prior to 2015 is underreported in the data.

The drivers of loss in DRC are predominantly shifting cultivation (where land is cleared and burned for the short-term cultivation of crops and left fallow for forests and soil nutrients to regenerate) and charcoal production , the dominant form of energy in the country (generated by cutting and burning timber). Poverty is widespread and access to electricity is limited — about 62% of the population lives on approximately US$2 a day and 81% does not have access to electricity — with local populations relying on forests for food and energy demands.

The rate of primary forest loss was higher in the eastern part of DRC, where new hot spots of loss appeared in 2023. Armed groups have impacted this region for more than two decades by selling timber and other forest products to fund their operations, harming local populations . Additionally, approximately 5.6 million people have been displaced and survive by clearing the forest for fuel and to open land for agriculture.

New hot spots show expansion of forest loss frontiers in eastern Democratic Republic of the Congo

Another driver of primary forest loss seen in DRC in 2023 was artisanal and semi-industrial mining. Though a relatively small driver of forest loss, mining that is not conducted responsibly can contribute to deforestation and degradation at local scales, as well as to human rights abuses  for workers and other negative impacts to communities and ecosystems .

While high rates of primary forest loss continue, the government of DRC has promised to invest in an economy not based entirely on resource exploitation. Work as part of the New Climate Economy is set to commence in 2024, which promises resources to protect DRC’s forests and enable the economic transformation needed to reduce pressure on forests. 

Primary forest loss in Indonesia remains historically low despite an uptick in 2023

Indonesia experienced a 27% uptick in primary forest loss in 2023, an El Niño year, though the rate remains well below that of the mid-2010s.

Indonesia primary forest loss, 2002-2023

Much of the primary forest loss in Indonesia according to the GFW analysis is within areas that Indonesia classifies as secondary forest and other land cover (e.g., mixed dry land agriculture, estate crop, plantation forest, shrub and others). This is because the  GFW primary forest definition is different than Indonesia’s official primary forest definition and classification. GFW’s statistics on loss of primary forests in Indonesia are therefore considerably higher than the official Indonesian statistics on deforestation in primary forest. 

The emergence of El Niño conditions led to concerns that Indonesia might experience another fire season like 2015; however, fires in 2023 had a less severe impact than  initially predicted . In rural areas, fire is used to clear land for agriculture and can escape beyond property boundaries into trees and peat soils, releasing stored carbon into the atmosphere. Wetter conditions than the 2015 El Niño and investments made by the government in fire prevention capabilities, as well as efforts to suppress fire by local communities, all contributed to a quieter than expected fire season.

Primary forest loss in patches greater than 100 hectares made up 15% of the loss in Indonesia in 2023. The expansion of industrial plantations took place in several locations adjacent to existing oil palm and pulp and paper plantations in Central Kalimantan, West Kalimantan and West Papua. According to the Ministry of Environment and Forestry, this expansion occurred in concessions granted prior to 2014 when the current administration took office.

Small scale primary forest loss was also prevalent throughout the country in 2023. Small clearings for agriculture contributed to ongoing losses within several protected areas, including Tesso Nilo National Park and Rawa Singkil Wildlife Reserve. Other losses linked to mining could be seen in Sumatra, Maluku, Central Kalimantan and Sulawesi.

Fires once again drive tree cover loss outside the tropics

As in previous years , the global trend in tree cover loss depends largely on annual fire dynamics in boreal forests. 2023 saw a 24% increase in global tree cover loss, from 22.8 million hectares in 2022 to 28.3 million hectares in 2023, which can entirely be explained by a huge increase in fire-driven tree cover loss in Canada. In the rest of the world, tree cover loss decreased overall by 4%. 

Canada tree cover loss, 2001-2023  

Like in many areas of the world , extensive drought and increased temperatures driven by climate change were widespread across Canada. This led to the worst fire season on record, with a 5-fold increase in tree cover loss due to fire between 2022 and 2023. High temperatures create dry and extremely flammable fuel for fires, meaning that fires are more likely to start, and also more likely to turn into megafires .

While fires can be a natural part of the ecosystem in northern forests and the forests can often regrow, more intense and more frequent fires can lead to permanent changes to forests. Smoldering fires can also persist below the ground and reignite again, causing more damage.

The  consequences of Canada’s 2023 fires also go beyond forests — resulting in destroyed homes and fatalities, and temporarily causing some of the worst air quality in the world across some of the most populous parts of Canada and the United States.

Progress is possible, but needs to happen everywhere

The data from 2023 demonstrates that countries can cut rates of tropical forest loss if they garner the political will to do so, and the countries that have accomplished this can provide lessons for others. However, past experience in Brazil shows that such progress can be reversed when political winds change.

Enduring incentives and financial mechanisms that place a value on standing forests are needed to make forests less vulnerable to depletion from farms, mines, infrastructure or other economic activities. REDD+ and other performance-based payment mechanisms can provide a financial incentive for forest protection and restoration by valuing forest carbon, and regulatory or voluntary measures to eliminate deforestation from commodity supply chains can help to counter economic drivers of tropical deforestation. Investments in the bioeconomy can also lead to progress on reducing deforestation while promoting economic growth and ensuring the livelihoods of those who rely on forests.

Ultimately, solutions that are truly adapted to the local context, alongside global solutions for climate change and sustainability, must work hand in hand to reduce forest loss everywhere. 

Explore the data yourself on  Global Forest Watch  

• 2022 Forest Pulse

“Tropical Forest Loss Drops Steeply in Brazil and Colombia, but High Rates Persist Overall.” Global Forest Review, updated April 4, 2024. Washington, DC: World Resources Institute. Available online at https://research.wri.org/gfr/latest-analysis-deforestation-trends . 

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Characterizing Community Forests in the United States

Current affiliation: University of North Carolina Wilmington, Environmental Science, Wilmington, NC, USA

Current affiliation: US Department of the Interior, Office of Collaborative Action and Dispute Resolution Washington, DC, USA

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Reem Hajjar, Kathleen McGinley, Susan Charnley, Gregory E Frey, Meredith Hovis, Frederick W Cubbage, John Schelhas, Kailey Kornhauser, Characterizing Community Forests in the United States, Journal of Forestry , Volume 122, Issue 3, May 2024, Pages 273–284, https://doi.org/10.1093/jofore/fvad054

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Research on community forests (CFs), primarily governed and managed by local forest users in the United States, is limited, despite their growth in numbers over the past decade. We conducted a survey to inventory CFs in the United States and better understand their ownership and governance structures, management objectives, benefits, and financing. The ninety-eight CFs in our inventory are on private, public, and tribal lands. They had various ways of soliciting input from, or sharing decision-making authority with, local groups, organizations, and citizens. Recreation and environmental services were the most important management goals, but timber production occurred on more than two-thirds of CFs, contributing to income on many CFs, along with a diversity of other income sources to fund operations. We discuss the difficulties in creating a comprehensive CF inventory and typology given the diversity of models that exist, reflecting local social and environmental conditions and the bottom-up nature of community forestry in the United States.

Study Implications: Despite their small footprint in the United States, community forests are a rapidly developing model of forest ownership, governance, and management that helps protect forestlands and open space and demonstrates how market and nonmarket forest goods and services can be produced for broad and enduring community benefits. This study inventories and characterizes community forests in the United States to increase understanding of this model, its prevalence, and its potential. It provides a baseline of information that serves as a foundation for further exploration and research on the impacts and contributions of community forests.

Over the past few decades, many countries have increasingly promoted community forests (CFs) as a way to conserve forests, enhance rural livelihoods, and recognize the traditional and customary rights of local forest users to access, use, and manage forests ( Hajjar et al. 2021 ; Lund et al. 2018 ). CFs are delineated forest areas where community members have access to natural resources, are engaged in their governance, and receive indirect and direct benefits from their management ( Charnley and Poe 2007 ; McDermott and Schreckenberg 2009 ). Although CFs have existed in many forms across the globe for centuries, these more recent efforts are typically formal, government-sanctioned, and often government-sponsored. A total 14% of the world’s forests, and 28% of forests in low- and middle-income countries, are currently owned or managed by Indigenous peoples and local communities ( Rights and Resources Initiative 2018 ). Internationally, CF initiatives span a broad range of tenure regimes, institutional arrangements, relationships between communities and higher levels of government, activities, and outcomes that have evolved in line with local contexts, conditions, needs, and goals ( Charnley and Poe 2007 ; Hajjar and Molnar 2016 ).

In the United States, CFs have also existed in diverse forms for centuries ( Baker and Kusel 2003 ; McCullough 1995 ), although as elsewhere, formally designated community forests have been increasing in number since the 1990s. This relatively recent trend is likely driven by several factors. First, vertically integrated forest products companies nationwide have been divesting of their industrial timberlands since the late 1980s for economic reasons ( Zhang 2021 ), causing a large-scale shift in timberland ownership from industrial to institutional investors ( Zhang 2021 ). To prevent residential development, maintain access to local forests, conserve forest resources, and keep working forests working to provide economic opportunities for local residents, initiatives to acquire industrial timberland and manage it as CFs have proliferated ( Belsky 2008 ). Second, private family forest owners are aging; the average age of the primary decision-maker over family forestlands is 65 and, for about 20% of these ownerships, 75 or older ( Butler et al. 2021 ). Keeping their family forestland intact for future generations is a top concern for family forest owners ( Butler et al. 2021 ). If their descendants are uninterested or unable to keep this land in the family, community groups or municipalities may wish to acquire it as a CF to prevent its subdivision and fragmentation and provide community benefits.

Third, Indigenous peoples in the United States have regained greater control over ancestral lands, including forestlands, both on and off tribal trust lands over the past several decades ( McGinley et al. 2022 ). Some tribes have acquired forestland through fee simple purchase, including with funding designated for CF creation, and established CFs on those lands ( McGinley et al. 2022 ). Fourth, the 1990s saw a dramatic increase in citizen participation in decision-making about the management of public forestlands ( Baker and Kusel 2003 ; Charnley and Poe 2007 ). This trend has persisted, with community-based organizations, community members, forest collaborative groups, and other stakeholders playing a greater role in management decision-making and collaborative forest stewardship on federal lands ( Davis et al. 2020 ). In some cases, these arrangements may exhibit the characteristics of a CF.

Simultaneously, several programs providing funding for land acquisition to create CFs have arisen in the past two decades ( McGinley et al. 2022 ). Access to funding along with the emergence of supportive policies, organizations providing technical assistance, and practitioner networks have fostered a more favorable environment for CF creation since the 2000s ( Frey et al. forthcoming ). These trends have played out somewhat differently in different locations, but together they have contributed to a nationwide rise in CFs in the United States.

Unlike many other countries around the world with communal property systems, CFs in the United States do not exist as a distinct land tenure or ownership class. CFs have been established on a variety of public, private, and tribal lands and have diverse land tenure arrangements ( McGinley et al. 2022 ). Furthermore, there is no universally accepted definition of a CF in the United States ( Frey et al. forthcoming ). These two facts make studying CFs in the United States, as a distinct form of forest tenure, management, and governance, challenging. Literature on US CFs, most of it published since the 1990s, has primarily been descriptive in nature, relying on limited numbers of case studies to elaborate on the various motivations for creating CFs and the institutional and political context that pushed them forward ( Belsky 2015 ; McCarthy 2006 ); development of mechanisms and institutional arrangements for governance ( Abrams 2023 ; Abrams et al. 2015 ; Belsky and Barton 2018 ); and their potential benefits ( Christoffersen et al. 2008 ; Lyman et al. 2014 ). Belsky (2008) proposed a typology of CFs defined by who owns the CF—Indigenous groups, towns or municipalities, or community-based conservation organizations. A key message of the scientific literature is that a vast diversity of CFs exists in the United States, reflecting the diverse social, economic, and ecological contexts in which they occur.

To our knowledge, no prior research has attempted to document or characterize the full suite of CFs in the United States. Thus, the goals of this paper are to (1) identify, inventory, and characterize CFs in the United States; (2) enhance understanding of their ownership and governance structures, management objectives, and sources of income; (3) extend the discussion of the variability in forms of CFs and build on previous work to refine a CF typology; and (4) problematize how we recognize CFs in the United States (i.e., what is included, what is not, and why).

Defining CFs 1

A common but broad premise of CFs internationally is that place-based communities have some role in determining how local forests are to be managed for community benefit ( Hajjar et al. 2021 ). In the United States, communities associated with CFs are frequently not only place-based but also communities of interest and practice or some combination of these ( McGinley et al. 2022 ), complicating the notion of “community” and “local” (see Brosius et al. [2005 ] for a discussion). For purposes of deciding what to include in this study, we considered the following attributes of CFs, which are prevalent in the literature on US CFs (see Frey et al. forthcoming ): (1) ownership or tenure by a local governmental or nongovernmental organization (NGO) on behalf of the community; (2) communities are substantively involved in forest management and governance; (3) communities have secure rights to access and benefit from the forest; (4) social and economic benefits for local communities are a management priority; and (5) forest conservation values are permanently protected.

Creating a CF Inventory

To catalogue and characterize CFs in the United States, we first undertook an inventory of existing CFs, aiming to be as comprehensive as possible. Given the lack of a consistent definition or model of CFs, we used a hybrid approach to identify them ( Frey et al. forthcoming ). This meant first searching for entities that self-identify their property or initiative as a CF and for those that have participated in programs or policies related to CFs. Then we overlaid a series of inclusion criteria based on the attributes of CFs outlined above. Therefore, to be included in our study, local communities had to have rights of access and use and some form of management responsibility or decision-making authority (beyond consultation) over local forests. Additionally, these forests were managed to promote ecological sustainability and contribute to conservation while creating tangible local community benefits as a management priority.

We began by compiling a list of CFs and related information from a US Endowment for Forestry and Communities study ( Christoffersen et al. 2008 ) and a previous exploratory project ( Hovis et al. 2022 ). We then added to this list, drawing from CF lists provided by organizations that work with and support them, such as the Ford Foundation, the Northwest Community Forest Coalition, the Northern Forest Center, the Trust for Public Lands, the Open Space Institute, and the USDA Forest Service (Forest Service) Community Forest and Open Space Conservation Program. We also used Google Search Engine to identify any additional CFs not already included in our list. Search terms included: state name AND community forest OR community managed forest OR community-based forest OR town forest. We further consulted with various professionals in our networks involved with CFs (e.g., via the Northwest Community Forest Coalition annual meeting) to ensure the comprehensiveness of our list. Finally, we consulted with two project advisory committees that we set up at the start of the funded project under which this research was undertaken: one, a research advisory committee consisting of CF professionals across select government agencies, CF coalitions, and networks; the other, a tribal forestry advisory committee consisting of representatives of tribes with CFs and tribal natural resources networks.

We also used Google Search Engine to record any information on the identified CFs, usually landing on the websites of CF owners or their supporters. This information typically included the group name, forest location, acreage, landowner, governance, management objectives, history, URL, and contact information. Searches and consultations took place between 2019 and 2023, with more CFs identified and added continually as we heard of cases that were missed in our searches or that were being newly created. We examine the limitations of this approach in the Discussion section.

We initially located 136 possible CFs in the United States using these methods. Of these, thirty-two clearly did not meet our criteria, and we were unable to find additional information or contacts for eleven. To the remaining ninety-three CFs that met our inclusion criteria and for which we had contact information, we sent an internet-based survey using Qualtrics. We requested that a CF manager or other person familiar with the CF fill out the survey. The survey included questions about the CF, such as size, forest type, ownership, decision-making, who is involved in day-to-day management, management priorities, rules of access and use, and financing. Although most survey questions were designed to capture objective characteristics of the CF (i.e., size, ownership, etc.), we acknowledge that answers to a question asking about “management priorities” may not reflect the diversity of priorities a community may have for its forests. Rather, we expected that a CF manager responding to the survey would choose priorities that were being explicitly managed for, consistent with their management plan or mission statement.

To increase response rates ( Dillman et al. 2014 ), we followed up by sending reminder emails after 2 and 4 weeks and then through phone calls where phone numbers were available. Following this, for all nonresponses or cases where contact information could not be located, we filled out the survey ourselves to the extent possible using CF websites and other available resources. Not all survey questions had responses readily available from website sources, and so these surveys were not as complete. This resulted in some topic areas having smaller sample sizes, as displayed in the Results section. We also followed this protocol for newly identified CFs throughout the time period of the research (either newly created CFs or CFs discovered through our networks that met our criteria), for a combined total of ninety-eight CFs recorded up to April 2023. Survey responses were tabulated in SPSS, where descriptive statistics (frequencies and crosstabs) were used to show patterns across various CF characteristics.

We refer to three regions in discussing our results based on the Forest Service Resources Planning Act Assessment (RPA) regions: the West, combining the Pacific Coast and Rocky Mountain RPA regions, including CFs in Montana, Idaho, Washington, Oregon, California, and Arizona; the North, which includes CFs found in Maine, Vermont, New Hampshire, Massachusetts, New York, Connecticut, Michigan, and Wisconsin; and the South, which includes CFs in Georgia, North and South Carolina, Virginia, and Puerto Rico.

We collected data on ninety-eight CFs across the United States, constituting the sample used for this study ( SI Table 1 ). The survey response rate was 87% (eighty-five of ninety-eight); for the remaining thirteen survey nonresponses, we gathered information from internet sources. We expect the number of CFs to continue to grow in the coming years: after closing the survey in April 2023, we learned of at least four additional initiatives that were close to acquiring CF lands and nine that were seeking funds to purchase CF land. We believe that ninety-eight is close to the current total number of self-identifying CFs in the United States but acknowledge that it is likely an undercount of actual CFs that meet our inclusion criteria. We discuss the difficulties in accurately capturing all US CFs in the Discussion section. Rather than thinking of our sample as a complete inventory of all US CFs, we consider it sufficient for characterizing different types of CFs in the United States.

Location, Year Established, and Size

The greatest number of CFs per state were found in West Coast states ( figure 1 ; Washington, fourteen CFs; Oregon, twelve; California, nine); northeastern states (Maine, twelve CFs; Vermont, nine; New Hampshire, eight); and the upper midwestern states of Michigan and Wisconsin (five each). Fewer were located in southern states, with a handful spread across Georgia, North and South Carolina, and Virginia. The earliest recorded CFs in our sample were created in the 1930s and 1940s ( figure 2 ), mostly city and county forests in the northwestern United States (Montesano Community Forest, Hood River County Forest, Ashland Forestlands, Arcata Community Forest), and two town forests that self-describe as CFs in the Northeast (Gorham Town Forest, Mendon Town Forest). Most CFs in our sample were created after 2010 when there was a sharp increase in the number of CFs in all regions. This time period corresponded with new legislative support for CFs in some states (e.g., Washington State’s 2011 Community Forest Trust legislation) and at the federal level (e.g., the Forest Service's 2011 Community Forest Program), which have helped tribes, local governments, and nonprofit organizations acquire land at risk of development to establish CFs.

Location of CFs in our database. In this article, we refer to three regions in discussing our results: the West, which includes CFs found in Montana, Idaho, Washington, Oregon, California, and Arizona; the North, which includes Maine, Vermont, New Hampshire, Massachusetts, New York, Connecticut, Michigan, and Wisconsin; and the South, which includes Georgia, North and South Carolina, Virginia, and Puerto Rico.

Number of CFs in the United States since 1930.

The total area covered by CFs in our inventory is 436,411 acres (ac). Of this total, 87% of CFs were smaller than 5,000 ac ( figure 3 ), and 63% were less than 1,000 ac. By region, median sizes of CFs were: 1,360 ac in the West, 375 ac in the North, and 334 ac in the South. Nine CFs in the West were 5,000 ac or larger, compared to four in the North and none in the South. The majority of CFs less than 1,000 ac (thirty-nine of sixty-one CFs) were located in the North, with over half of those being between 100 and 500 ac (twenty-five of thirty-nine CFs). A total of 76% of reporting CFs said their forests were located on one contiguous parcel and 24% were on multiple unconnected parcels (varying from two to seventeen parcels).

Acreage of CFs across regions.

Ownership, Decision-Making Authority, and Management

As indicated in figure 4 , CFs in our sample were primarily owned by either a local government body (town, city, or county government) or by an NGO (e.g., a community-based organization, land trust, or other nonprofit). In the West, CFs were mostly purchased from private corporate owners (industrial timber companies, timber investment management organizations [TIMOs], or real estate investment trusts). In the North, CF lands were mostly acquired from private family forest owners. Ownership types included CFs of various sizes, although CFs larger than 1,000 ac tended to be held by a government body, whereas the majority of NGO-held CFs were smaller than 1,000 ac ( SI Table 2 ).

Ownership of CFs: (a) current landowner of forestlands designated as CFs and (b) previous landowners from whom the current landowner acquired the CF land. “Joint ownership” in (a) were parcels jointly owned by a local government body and a land trust ( n  = 3), a private utilities firm ( n  = 1), or a university ( n  = 1); a tribe and a conservancy ( n  = 1); and a land trust and private equity firm ( n  = 1). “Other” in (b) were parcels that were pieced together from multiple ownerships.

Land ownership largely corresponded with the entity with ultimate decision-making authority over management, access, and use of the CF ( SI Figure 1 ). Government agencies largely had authority over government-owned CFs, tribes over tribally owned CFs, and NGOs over the land they owned. These entities had various ways of soliciting input from, or sharing decision-making authority with, local groups, organizations, and citizens. In some cases, this was institutionalized through formal joint decision-making processes. For example, there were eleven cases of local government ownership (town, city, or county-owned forests) where decision-making authority was jointly held by both that government body and formal citizen councils or committees established for this purpose. In other cases, although respondents did not describe decision-making as “joint,” they involved community members through mechanisms such as advisory committees and boards made up of local citizens, formal community and public consultation processes (mostly for city or town government ownerships), or various events, regular meetings, and other formal and informal mechanisms that sought community input (mostly for NGO ownerships). Local groups and volunteers contributed to day-to-day management of CFs across most ownerships ( SI Figure 2 ). In particular, various recreation-related volunteer groups helped to maintain trail systems. Otherwise, in many cases, forest consultants or forestry professionals from government agencies or NGOs contributed to forest planning and stewardship.

Management Goals and Allowed Activities

Survey respondents were asked to select the top four goals, from a list of options, that the CF was managed for ( figure 5 ). Across the country, the vast majority of CFs stated that recreation was a top management goal (82% of ninety-five reporting CFs). Collectively, conservation-oriented goals (watershed, habitat or open space protection, biodiversity conservation and restoration, and carbon sequestration, totaling 98% of reporting CFs), as well as other nonextractive goals (education, recreation, and cultural heritage protection, totaling 93% of reporting CFs) were much more prominent than extraction-oriented goals (timber production, nontimber forest products (NTFPs) management, agroforestry, and livestock grazing, totaling 47% of reporting CFs). However, timber production was among the top four management goals reported across the sample as a whole.

Primary management goals. Respondents were asked to list top four management goals for their CF.

There were few strong patterns between ownership type and management goals ( SI Table 3 ). Recreation was listed as one of the top management goals in over 75% of cases across CF ownership types, except for tribal (two of five CFs) and private corporate (one of two) owned CFs. All but two CFs owned by local governments listed a conservation-oriented goal. Half of local government-owned (twenty-two of forty-four CFs) and half of NGO-owned CFs (fifteen of thirty-two CFs) listed an extractive-oriented goal. Of the five tribal-owned CFs in our sample, only one listed an extractive-oriented goal (agroforestry) as a primary management goal and only one indicated that timber was produced but not as a primary goal. Local government and NGO-owned CFs reported slightly more often that producing timber was a primary goal (government: nineteen CFs listed it as a primary goal, twelve as a nonprimary goal, and twelve do not produce timber; NGO: thirteen, ten, and six, respectively).

Although timber production occurred on 70% of reporting CFs (sixty-five of ninety-three reporting CFs; figure 6A ), in almost half of those cases (twenty-eight of sixty-five cases) timber production was not one of the top four primary management goals of the CF. Geographically, no CFs in the southern region produced timber as a primary goal. In the North, slightly more CFs produced timber as a primary goal than not as a primary goal (twenty versus sixteen CFs), with only nine reporting no timber production. In the West, seventeen CFs reported producing timber as a primary goal, with ten producing but not primary and ten not producing. Timber production occurred across all acreages ( figure 6B ), including on almost two-thirds of the smallest CFs in our study (<1,000 ac) and on all CFs larger than 5,000 ac (although not always as a primary goal). Similarly, timber production occurred across all ownership types ( figure 6C ), whether as a primary goal or not. Of those engaged in timber production, a private consulting forester was used to oversee timber sales in 43% of fifty-one reporting cases, and internal staff from the CF owner in 26% of cases. Across ownerships, the entity that did the logging was most often a private contracting company (70% of fifty reporting cases). These entities were located at a place within 25 miles of the CF in 52% of forty-two reporting cases, or 26–50 miles in 33% of cases.

Status of timber production across CFs (a) by region, (b) CF size, and (c) ownership type.

Community forests had a variety of rules related to which activities were allowed and whether permits from CF owners were needed if allowed ( SI Figure 3 ). Motorized recreation, camping, and commercial uses of firewood or NTFPs were only allowed in a handful of CFs, often with the requirement of a free or paid permit. Hunting and fishing, in accordance with state regulations, were allowed in more than half of the reporting cases (69% of sixty-five reporting cases and 78% of sixty reporting cases, respectively) and rarely required a permit from CF owners. Personal use of firewood and other NTFPs were allowed in 22% and 40% of sixty-three reporting cases, respectively, although firewood use often required a permit. Altogether, 85% of sixty-six reporting cases allowed some nontimber extractive activities for personal use (either firewood, other NTFPs, hunting, or fishing). Only one CF did not allow recreation and four allowed it only with a free permit. In almost all cases, the same rules applied to the local community as to the general public, except for a few instances where NTFP and firewood use were limited to local community members.

Income Generation and Budgetary Support

A number of CFs across ownerships generated revenue from forest products and services (80% of forty-nine reporting CFs), mostly from timber sales ( figure 7 ; SI Table 4 ), although two-thirds of those reporting revenue generation stated that timber contributed to less than 30% of their budget. The few instances of revenue from hunting leases and payments for ecosystem services (mainly carbon offsets) were mostly reported in CFs owned by private nonprofits, whereas grazing permits or agriculture revenue were only reported in three state or local government-owned CFs ( SI Table 4 ). Timber revenue was reported across all ownerships where timber harvest occurred, except for the two cases of state government ownership, where it is anticipated in the future, once the forest regains commercial value following harvest by the previous owner.

Main sources of revenue generated from forest activities in 48 reporting CFs by region.

Grants from federal or state governments were the most frequently cited sources of annual budgetary support from 2018 to 2020, the period we asked about (70% of fifty-three reporting CFs; figure 8 ; SI Figure 4 ), although almost two-thirds of those CFs stated that grants contributed to less than 30% of their budget. Unsurprisingly, government-owned CFs more often reported (federal, state, or local) government sources for budgetary support. Local government-owned CFs were more reliant on local government funds: 71% of twenty-four reporting local government CFs stated they received funding from local governments (50% of them stating that they received more than 60% of their budget from this source), with only a handful of nongovernment owned CFs reporting support from this source. The NGO-owned CFs reported relying on donations from local community members and fundraiser events much more often than government-owned CFs (in three cases, community donations made up more than 60% of the budget). We did not track sources of funds used for acquiring forestlands in our survey.

Sources of budgetary support 2018–2020 by ownership type. Public ownership includes federal, state, and local governments, and private ownership includes both corporate and nonprofits. Polygons indicate largest differences between private and public ownerships.

As the results indicate, there are a variety of ownership and governance forms that CFs currently take in the United States, a variety of benefits that they provide, and a diversity of income sources that they rely on. As stated above, one goal of this study was to discuss the variability in CFs and develop a robust typology of them. Although Belsky (2008) proposed a CF typology based on ownership types, given the diversity of CFs we encountered in our survey (including within ownership types), we intended to develop a typology based on key characteristics, including ownership, decision-making, operational management, goals, size, and income sources. Two-step cluster analyses and Pearson’s χ 2 tests were performed to assess whether the CFs in our dataset could be empirically grouped according to various combinations of these characteristics. However, limited patterns emerged for creating definitive statistical typologies. Instead, we discuss here some emergent qualitative patterns based on the descriptive statistics reported in the Results section, reflect on the diversity of CFs in the United States, and propose a basic typology for practical purposes. Finally, we discuss the difficulties in creating a comprehensive CF inventory for the United States, given this diversity.

Ownership type emerged as a factor that seemed to shape some key functions of a CF—specifically, decision-making authority and sources of budgetary support. Publicly owned CFs (mostly by local city or town government) more often reported having either a government entity as ultimate decision-making authority or joint authority between local government and citizen councils or other local groups. They were also more reliant on government funding for budgetary support, either through federal or state grants, local government funds, or combinations of these. Privately owned CFs (mostly community-based organizations and local land trusts) more often reported having those same owners make decisions about the CF and less often reported that they formally engage in joint decision-making (although it is difficult to ascertain actual community participation in governance with our survey research design). They also more often reported relying on community donations and fundraiser events than local government funds. All five tribally owned CFs in our dataset were run by tribes themselves, including decision-making authority and operational management. Besides these basic characteristics, however, ownership type seems to have little influence on the size of CFs, management goals, allowed activities, timber production (equally present in public and private CFs), or earned income sources.

We saw moderate regional differences in ownership and size (more government ownership and larger sizes in the West), and who the CF owner bought their forestland from. Ownership history may help explain why the median size of CFs in the West was considerably larger than in the North. The majority of CF lands in the West were purchased from private corporate forest owners, whose holdings are often in the hundreds of thousands of acres ( Sass et al. 2021 ), and from TIMOs in particular, which typically sell land every 10 to 15 years ( Zhang 2021 ). In contrast, the majority of CF lands in the North were purchased from family forest owners; approximately 90% of these ownerships in the United States are under 50 ac ( Butler et al. 2021 ). Yet CFs larger than 5,000 ac occur in both the North and the West.

In both these regions, timber production often occurred across CFs of all sizes and was a primary management goal in roughly equal frequency, although not in our small sample of southern CFs. Timber production was not limited to any particular ownership type, or size class, of CF; rather, the potential to harvest timber as a management goal and source of revenue generation is likely influenced by the nature of the forest assets contained in a particular CF. Those with productive timberlands are presumably more likely than those lacking them to have timber production as a primary management goal. However, it may take years for this goal to be realized if the former owner recently harvested a substantial amount of commercial timber. All CFs across regions emphasized conservation goals, but forest restoration (phrased in the survey as “forest restoration, including wildfire management”) was cited more often in the West. Almost all CFs allowed public access for recreation and many for nontimber extractive activities for personal use. It is likely that some CFs regulate access more than others, but we could not capture this variation in our survey.

The difficulty in creating a typology of CFs is unsurprising given that, by definition, CFs reflect the values and priorities of the communities in which they are situated. Other historical, social, economic, and environmental factors also likely influence their characteristics. Additionally, policies and programs that provide funding opportunities to support CFs and their operations vary by state, influencing their sources of budgetary support. Investigating underlying factors that lead to the diversity in CF models and characteristics is a rich area for further research.

The second phase of our research (a larger project than reported here, aiming to better understand how CFs contribute to conservation and rural prosperity in the United States) uses a case-study sampling approach based on two characteristics that we postulated would be important distinguishing features of a typology: ownership of the CF and whether timber production is a primary management goal of the CF ( Table 1 ). We acknowledge that our survey results do not show that these two characteristics are statistically related to many other factors examined here but reasoned that ownership can influence CF governance and financing mechanisms, and that the role (or lack thereof) of timber production reflects the CF’s management goals, forest resources, financing mechanisms, and benefit streams. We recognize that CFs produce a host of benefits for communities beyond timber production. However, whether a CF prioritizes timber, harvests timber but does not prioritize it, or does not harvest timber emerged as an effective way to distinguish groups of CFs from each other in terms of their management priorities and resulting benefit streams. Otherwise, most CFs shared recreation and conservation-related goals.

A basic typology based on ownership and whether timber is a primary management goal of the CF. Percentages (in parentheses) reflect percentage of eighty-two CFs in our inventory that reported on timber status and ownership.

The diversity of CFs in the United States also reflects the grassroots nature of community forests across the country, making them somewhat unique relative to community forests globally. In many low- and middle-income countries, community forests are forests managed using a top-down model imposed and defined by national CF policies or land reforms and extensive financial and technical support from external donor organizations (e.g., national or international NGOs, multilateral/bilateral aid agencies), with communities receiving some rights and many responsibilities for forest management ( Charnley 2023 ; Hajjar et al. 2021 ; Ribot et al. 2006 ). In contrast, in the United States, CF establishment is typically driven from the bottom up, in most cases through local governments, locally based NGOs, or groups of citizens that come together to protect their local forests. There is no distinct CF tenure category at the national level and few national or state-level policies associated with community forests in the United States. Exceptions include Washington and New York states, where there are legislatively approved funding sources 2 to support CF acquisition and associated policy requirements once established, and the national-level Forest Service Community Forest and Open Space Conservation Program, which has supported the acquisition of numerous CFs in our inventory. This more grassroots approach results in a broad range of ownership, management, governance types, and rights and responsibilities among community members relative to many other countries. It also makes CFs somewhat hard to pinpoint in the United States, posing challenges for efforts to inventory them.

Stemming from this diversity in CFs, a key difficulty we faced in undertaking this inventory was determining what to include. Our approach to including CFs that self-identify as such or had participated in a program or policy related to CFs and met our criteria was naturally limiting. Although this approach was necessary to make an inventory possible, we acknowledge that many more CFs potentially exist than we included here, depending on how a CF is defined. In particular, our inventory captured many town forests and land trust forestlands, some tribal forests, and some state and federal forests. Yet these general ownership categories need further examination.

Town forests are local government-owned forests common across much of New England and the Northeast and in many cases may be considered CFs. They have long been established to generate income from timber and other resources for town budgets or specific projects and public services, to protect water, soil, and wildlife habitat, and to provide recreation and education opportunities for local community members and others ( Baker and Kusel 2003 ; Brown 1941 ; Hovis et al. 2022 ; McCullough 1995 ). The local ownership, management, and benefits of many town forests fulfill most of the criteria of CFs as laid out above. However, the acquisition and designation of a town forest does not guarantee its long-term protection from sale or development, and depending on how much the community participates in governance, it may or may not fulfil the governance criterion of CFs ( McGinley et al. 2022 ).

Similarly, many land trusts own forestlands that could be considered CFs, depending on how these forests are governed and managed, potentially increasing the number and extent of CFs in the United States. However, land trusts may not provide access for local communities or the general public to their forested land, may not provide for local community participation in decision-making, or may not manage their forests specifically for local benefits.

The extent to which tribal forests should be considered CFs is also complicated. Most tribal lands are trust lands, with about 56 million acres of land held in trust for tribes by the federal government (2.3% of US land area; DOI 2023 ). Although these lands are managed for the benefit of individual tribes, forest management activities take place under the direction of forest management and integrated resource management plans developed under the federal Bureau of Indian Affairs (BIA) guidelines and are subject to BIA approval. Since the passage of the Indian Self-Determination and Education Assistance Act of 1975 (Public Law 93-638), an increasing number of tribes have established contracts, known as 638 contracts, with the BIA by which tribal government forestry departments assume management responsibilities for forests on trust lands. These contracts are initiated by a formal request by a tribe to the BIA. By 2011, 112 tribes had taken advantage of these self-determination/self-governance opportunities for forest management, compared to 187 that relied on BIA to manage their lands directly ( Gordon et al. 2013 ). Given this complexity in governance, it is unclear to what extent the trust lands of individual tribes meet the criteria of CFs; such classification should be undertaken by tribes themselves. Tribes can also purchase and own fee lands to which they hold title. The five tribally owned CFs in our sample (they self-identify as such) were purchased this way from private landowners. Further research on tribal forests could explore the variations in ownership, benefits, and management of these forests on trust and fee lands.

Our inventory includes two CFs owned by Washington State and one that occurs on federal lands in California. These cases may appear to contradict our defining attributes of a CF, namely that they have local, long-term ownership or tenure, and that communities have significant decision-making authority. We included the state and federal CFs in our inventory primarily because they self-identified as CFs. However, they also display several attributes of a CF. The two state-owned CFs were acquired through Washington’s 2011 Community Forest Trust Program ( WA DNR, n.d. ). The legislation that created the program stipulated that CFs acquired with program funds (from state budget appropriations) be state-owned, and that state agencies have ultimate decision-making authority. But the legislation also stipulated that state-owned CFs have an advisory committee composed of roughly twenty members representing diverse stakeholder interests to inform those decisions and co-develop forest management plans with citizen input, and that CF management objectives should reflect the values of local communities ( WA Legislature 2011 ).

Regarding the federally owned case, Weaverville CF, the community manages the CF through a 10-year renewable cooperative stewardship agreement between the Forest Service and Bureau of Land Management (who own and administer different parts of the CF), and the local county resource conservation district (RCD) ( Frost 2014 ; Kelly 2018 ). The RCD is responsible for implementing forest management activities and is governed by a board of directors that oversees CF management, with input from a steering committee composed of ten to fifteen members, including local citizens and agency and RCD staff. Local residents have opportunities to provide input at community meetings that occur once or twice annually. The CF is managed to meet local community needs and priorities, such as wildfire risk reduction, habitat improvement for fish and wildlife, and recreation ( Frost 2014 ; Kelly 2018 ).

The question of whether CFs in the United States that self-identify as such should be considered CFs if they occur on land that is state- or federally owned—with the government retaining ultimate decision-making authority—deserves more attention and is a matter of debate among some practitioners and scholars (see Frey et al. forthcoming ). The international literature recognizes CFs that occur on national government-owned land where communities have concessions to manage the forests for a specified time period (e.g., several CFs in Canada [ Teitelbaum et al. 2006 ], Cameroon [ Piabuo et al. 2018 ], Guatemala [ Taylor 2010 ]); and CFs on national government land that are comanaged by the state and local communities (e.g., Tanzania; Blomley and Ramadhani, 2006 ). This highlights the importance of taking into account the governance criterion in defining CFs in the United States—the level of community involvement in decision-making—just as with town forests and land trusts, and opens the door for potential additional CFs on public lands that might fit the criteria but were not captured here.

The CFs we identified comprise less than 0.1% of all forests in the United States but are a rapidly developing model of forest ownership, governance, and management that provides local community benefits. They take a creative approach to funding and managing local forestlands through public, NGO, or tribal structures, generated income sources, and grant and donor fundraising. They have continued long-standing town and tribal forest ownership and management, helped protect forestlands and open space from imminent development, and offered innovative ways to form explicit community partnerships to manage existing public and private landscapes. As they solidify income sources and management capability, they also might serve as a new model of how market and nonmarket goods and services can be produced on forestlands for broad and enduring community benefits.

We have likely not included all individual CFs in the United States in this study and may have significant undercounts of certain types of CFs. Potential undercounts stem largely from ambiguity over which town, tribal, and private (e.g., land trust-held) forests meet our CF definition and criteria and lingering questions over whether CFs exist on federal lands. Nevertheless, the inventory will increase continually as communities develop proposals for CFs and obtain acquisition funding each year and new research is carried out. To help address this research limitation, we plan to create a centralized, publicly accessible repository that can serve as a living inventory to be updated as more CFs are either acknowledged as such or created. Although incomplete, our current inventory captures a fair representation of the variety of CF models in the United States, reflecting a diversity of ownerships, governance structures, management goals, benefit streams, and more.

This initial research to inventory and describe US CFs provides a sound base for further exploration. Future research could further explore levels of local participation in forest management and governance and when and how these variables would qualify a forest as a CF on public, private, or tribal lands. More in-depth research could also help refine our CF typology to include characteristics hard to ascertain from a survey instrument, such as level of community involvement or capacity and organizational development stage (e.g., incipient or mature). Furthermore, as more NGO-owned and town-owned forests self-identify with the label “community forest,” the consequences, advantages, and disadvantages of using that label will need further examination.

Future research could also compare CF models with traditional (noncommunity based) private and public forest ownerships to highlight their relative differences, advantages, and disadvantages. For example, some CF models share similarities but also have important differences with private family forest ownerships in terms of priority management objectives and timber production ( Butler et al. 2021 ; Shanafelt et al. 2023 ), warranting a systematic comparison of ownership types. Finally, we began this exercise of inventorying CFs in the United States to better understand their contributions to conservation and rural prosperity. Better understanding the ability of communities to capture CF monetary and nonmonetary benefits (and to do so equitably) can help inform the design of policies, programs, and actions to best support CFs.

Supplementary data are available at Journal of Forestry online.

This study was funded in part by the USDA National Institute of Food and Agriculture, Agriculture and Food Research Initiative (AFRI) Award number 2021-67023-34426. Partial funding was also provided by the USDA Forest Service’s Southern Research Station, Pacific Northwest Research Station, and International Institute of Tropical Forestry as well as Oregon State University and North Carolina State University.

The authors are developing a publicly accessible repository of community forests. In the meantime, the data used in this study will be made available upon reasonable request.

Abrams , Jesse. 2023 . Forest Policy and Governance in the United States . New York, NY : Routledge .

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Piabuo , Serge Mandiefe , Divine Foundjem-Tita , and Peter A. Minang . 2018 . “Community Forest Governance in Cameroon: A Review.” Ecology and Society 23 ( 3 ): 15 . doi: 10.5751/ES-10330-230334 .

Ribot , Jesse C. , Arun Agrawal , and Anne M. Larson . 2006 . “Recentralizing While Decentralizing: How National Governments Reappropriate Forest Resources.” World Development 34 ( 11 ): 1864 – 1886 . doi: 10.1016/j.worlddev.2005.11.020 .

Rights and Resources Initiative . 2018 . At a Crossroads: Consequential Trends in Recognition of Community-Based Forest Tenure . Washington, DC : Rights and Resources Initiative .

Sass , Emma M. , Marla Markowski-Lindsay , Brett J. Butler , Jesse Caputo , Andrew Hartsell , Emily Huff , and Amanda Robillard . 2021 . “Dynamics of Large Corporate Forestland Ownerships in the United States.” Journal of Forestry 119 ( 4 ): 363 – 375 . doi: 10.1093/jofore/fvab013 .

Shanafelt , David W. , Jesse Caputo , Jens Abildtrup , and Brett J. Butler . 2023 . “If A Tree Falls in A Forest, Why Do People Care? An Analysis of Private Family Forest Owners’ Reasons for Owning Forest in the United States National Woodland Owner Survey.” Small-Scale Forestry 22 ( 2 ): 303 – 321 . doi: 10.1007/s11842-022-09530-y .

Taylor , Peter Leigh. 2010 . “Conservation, Community, and Culture? New Organizational Challenges of Community Forest Concessions in the Maya Biosphere Reserve of Guatemala.” Journal of Rural Studies 26 ( 2 ): 173 – 184 . doi: 10.1016/j.jrurstud.2009.09.006 .

Teitelbaum , S. , T.M. Beckely , and S. Nadeau . 2006 . “A National Portrait of Community Forestry in Canada.” The Forestry Chronicle 82 ( 3 ): 416 – 428 .

US Department of the Interior (DOI) . 2023 . “Native American Ownership and Governance of Natural Resources.” Natural Resources Revenue Data 2023 . https://revenuedata.doi.gov/how-revenue-works/native-american-ownership-governance/ .

Washington Department of Natural Resources (WA DNR) . n.d . “Washington Community Forest Trust Program.” https://www.dnr.wa.gov/managed-lands/washington-community-forest-trust-program .

Washington Legislature (WA Legislature) . 2011 . “Senate Bill Report ESHB 1421.” Washington State Legislature . https://lawfilesext.leg.wa.gov/biennium/2011-12/Pdf/Bill%20Reports/Senate/1421-S.E%20SBA%20NRMW%2011.pdf?q=20231016011547 .

Zhang , Daowei. 2021 . From Backwoods to Boardrooms: The Rise of Institutional Investment in Timberland . Corvallis, OR : Oregon State University Press .

Community forests, community forestry, and community-based forestry are terms that are often used interchangeably in the U.S. literature; however, see Frey et al. (forthcoming) and Belsky (2008) for a discussion of important differences.

The Washington State Community Forests Program was established by the state legislature in 2019 to provide grant funding for CF acquisition ( https://rco.wa.gov/grant/community-forests-program/ ). The New York Community Forest Conservation Grant program similarly funds municipal land acquisitions for community forests ( https://www.dec.ny.gov/lands/124345.html#:~:text=and%20contact%20information-,Program%20Overview,Leadership%20and%20Community%20Protection%20Act ).

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• 15 May 2024

Forestry social science is failing the needs of the people who need it most

You have full access to this article via your institution.

Jorge Lengua and his son (also Jorge) do not need to cut down trees to harvest Brazil nuts in the Bolivian Amazon. Credit: Martin Silva/AFP/Getty

The world’s forests are vital to its future. In terms of climate change, they are increasingly seen as key to both mitigation — in their role as carbon sinks — and adaptation, through sustainable management of forests. Tied in with both is the funding provided by those looking to offset carbon dioxide emissions by planting trees, a source of much-needed climate finance.

It is, therefore, unsurprising that ‘climate change’ and ‘climate finance’ are terms that dominate studies in forestry policy, according to a review of the literature published last week (see go.nature.com/4decszc ). That, in itself, need not be a problem. But one stark conclusion from the report is that too few studies focus on the people who live in, or who make a living from, forests.

Swathes of Earth are turning into desert — but the degradation can be stopped

This finding should be taken on board by science funding agencies and the United Nations-affiliated research networks for biodiversity and climate change. And it should be taken into account when research priorities are set and collaborations are formed. Quite aside from the ethical case for more community-focused forestry policy, forest conservation is unlikely to succeed without the involvement of those most closely associated with forests.

The review is published by the International Union of Forest Research Organizations (IUFRO), a global body representing more than 600 institutions across over 100 countries. It assesses mostly English-language social-science literature published between 2011 and 2022 — covering the period since the last such review, in 2010 . The authors find that the literature is dominated by the climate-mitigation interests of governments in high-income countries. They dub this the ‘financialization’ and ‘climatization’ of the literature surrounding forest policy.

This trend can be explained partly by the fact that forests are increasingly being incorporated into climate policies at all levels of governance — not least because of legally binding targets set by the 2015 Paris climate agreement. Forests are seen as providing the path of least resistance to achieving these targets, because their involvement requires little in the way of behaviour change from high-income countries. This has led to an expanding array of forest-related climate agreements at both regional and global levels. The largest of these is REDD+, through which low- and middle-income countries are paid (by companies and governments in high-income countries) to protect their forests. In return, contributors benefit from associated carbon credits. By the end of 2023, projects covered by REDD+ encompassed more than 60% of the forested area of developing countries. The scheme is not without controversy, with studies showing that carbon offsets can be overstated 1 and have little impact on the economic well-being of forest communities 2 .

Biodiversity thrives in Ethiopia’s church forests

Forest agreements rely on the research community for support. Take REDD+ again. Some scientists are looking at ways to measure how carbon is stored in different forests; others are working on verifying that countries comply with climate commitments. Researchers also sit on scientific advisory committees.

But there’s more to the study of forest governance than climate. For example, there’s the matter of how Indigenous and local knowledge contribute to biodiversity conservation today. And there are studies of the various ecotourism schemes being set up. But these subjects are less well-represented in the literature.

Researchers in such fields do advise on forest-related international agreements not linked directly to climate change. These include the UN Convention on Biological Diversity and the UN Forum on Forests (UNFF), a global body dedicated to discussing a wide range of forest-related issues. But the UNFF is a voluntary arrangement; unlike the UN conventions on biodiversity and climate change, its decisions have no legal force.

We must get a grip on forest science — before it’s too late

The UN biodiversity convention, whose member states have agreed to conserve 30% of Earth’s land, waters and coasts by 2030, draws on a wider set of research disciplines — not least through its scientific advisory body, IPBES, which incorporates studies in Indigenous and local knowledge into its work 3 . The convention also contains an explicit mandate to provide benefits for the people who rely on biodiversity for their livelihoods. However, the IUFRO review’s authors found that there is little coordination between the biodiversity convention and the UN’s climate convention — or between the researchers who advise these two bodies.

The review is far from the first to highlight that research that should aim to benefit all stakeholders instead focuses on areas that are priorities for the governments of high-income countries. This is an important and timely reminder. It should not be difficult for the researchers involved in the world’s largest scientific networks — the IPCC for climate and IPBES for biodiversity — to create a shared agenda for the study of forests that extends beyond climate change and climate finance. And, given the need for such action, funders should respond positively to such a proposal.

Earth’s forests have the potential to benefit people everywhere. Researchers, policymakers and funders must ensure that everyone’s needs are taken into account.

Nature 629 , 503 (2024)

doi: https://doi.org/10.1038/d41586-024-01411-y

West, T. A. P., Börner, J., Sills, E. O. & Kontoleon, A. Proc. Natl Acad. Sci. USA 117 , 24188–24194 (2020).

Article   PubMed   Google Scholar  

Malan, M. et al. Nature Sustain. 7 , 120–129 (2024).

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Pascual, U. et al. Nature 620 , 813–823 (2023).

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• Published: 27 January 2020

Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA

• Jessica E. Halofsky   ORCID: orcid.org/0000-0002-1502-4895 1 ,
• David L. Peterson 2 &
• Brian J. Harvey 2  

Fire Ecology volume  16 , Article number:  4 ( 2020 ) Cite this article

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Wildfires in the Pacific Northwest (Washington, Oregon, Idaho, and western Montana, USA) have been immense in recent years, capturing the attention of resource managers, fire scientists, and the general public. This paper synthesizes understanding of the potential effects of changing climate and fire regimes on Pacific Northwest forests, including effects on disturbance and stress interactions, forest structure and composition, and post-fire ecological processes. We frame this information in a risk assessment context, and conclude with management implications and future research needs.

Large and severe fires in the Pacific Northwest are associated with warm and dry conditions, and such conditions will likely occur with increasing frequency in a warming climate. According to projections based on historical records, current trends, and simulation modeling, protracted warmer and drier conditions will drive lower fuel moisture and longer fire seasons in the future, likely increasing the frequency and extent of fires compared to the twentieth century. Interactions between fire and other disturbances, such as drought and insect outbreaks, are likely to be the primary drivers of ecosystem change in a warming climate. Reburns are also likely to occur more frequently with warming and drought, with potential effects on tree regeneration and species composition. Hotter, drier sites may be particularly at risk for regeneration failures.

Resource managers will likely be unable to affect the total area burned by fire, as this trend is driven strongly by climate. However, fuel treatments, when implemented in a spatially strategic manner, can help to decrease fire intensity and severity and improve forest resilience to fire, insects, and drought. Where fuel treatments are less effective (wetter, high-elevation, and coastal forests), managers may consider implementing fuel breaks around high-value resources. When and where post-fire planting is an option, planting different genetic stock than has been used in the past may increase seedling survival. Planting seedlings on cooler, wetter microsites may also help to increase survival. In the driest topographic locations, managers may need to consider where they will try to forestall change and where they will allow conversions to vegetation other than what is currently dominant.

Antecedentes

Los incendios de vegetación en el Noroeste del pacífico (Washington, Oregon, Idaho, y el oeste de Montana, EEUU), han sido inmensos en años recientes, capturando la atención de los gestores de recursos, de científicos dedicados a los incendios, y del público en general. Este trabajo sintetiza el conocimiento de los efectos potenciales del cambio climático y de los regímenes de fuego en bosques del noroeste del Pacífico, incluyendo los efectos sobre las interacciones entre disturbios y distintos estreses, la estructura y composición de los bosques, y los procesos ecológicos posteriores. Encuadramos esta información en el contexto de la determinación del riesgo, y concluimos con implicancias en el manejo y la necesidad de futuras investigaciones.

Los incendios grandes y severos en el Noroeste del Pacífico están asociados con condiciones calurosas y secas, y tales condiciones muy probablemente ocurran con el incremento en la frecuencia del calentamiento global. De acuerdo a proyecciones basadas en registros históricos, tendencias actuales y modelos de simulación, condiciones prolongadas de aumento de temperaturas y sequías conducirán a menores niveles de humedad, incrementando probablemente la frecuencia y extensión de fuegos en el futuro, en comparación con lo ocurrido durante el siglo XX. Las interacciones entre el fuego y otros disturbios, son probablemente los principales conductores de cambios en los ecosistemas en el marco del calentamiento global. Los incendios recurrentes podrían ocurrir más frecuentemente con aumentos de temperatura y sequías, con efectos potenciales en la regeneración de especies forestales y en la composición de especies. Los sitios más cálidos y secos, pueden estar particularmente en riesgo por fallas en la regeneración.

Conclusiones

Los gestores de recursos no podrían tener ningún efecto sobre el área quemada, ya que esta tendencia está fuertemente influenciada por el clima. Sin embargo, el tratamiento de combustibles, cuando está implementado de una manera espacialmente estratégica, puede ayudar a reducir la intensidad y severidad de los incendios, y mejorar la resiliencia de los bosques al fuego, insectos, y sequías. En lugares en los que el tratamiento de combustibles es menos efectivo (áreas más húmedas, elevadas, y bosques costeros) los gestores deberían considerar implementar barreras de combustible alrededor de valores a proteger. Cuando y donde la plantación post fuego sea una opción, plántulas provenientes de diferentes stocks genéticos de aquellos que han sido usados en el pasado pueden incrementar su supervivencia. La plantación de plántulas en micrositios más húmedos y fríos podría ayudar también a incrementar la supervivencia de plántulas. En ubicaciones topográficas más secas, los gestores deberían considerar evitar cambios y donde estos sean posibles, permitir conversiones a tipos de vegetación diferentes a las actualmente dominantes.

Abbreviations

ENSO: El Niño-Southern Oscillation

MPB: Mountain Pine Beetle

PDO: Pacific Decadal Oscillation

Introduction

Large fires are becoming a near-annual occurrence in many regions globally as fire regimes are changing with warming temperatures and shifting precipitation patterns. The US Pacific Northwest (states of Washington, Oregon, Idaho, and western Montana, USA; hereafter the Northwest) is no exception. In 2014, the largest wildfire in recorded history for Washington State occurred, the 103 640 ha Carlton Complex Fire (Fig. 1 ). In 2015, an extreme drought year with very low snowpack across the Northwest (Marlier et al. 2017 ), 688 000 ha burned in Oregon and Washington (Fig. 2 ), with over 3.6 million ha burned in the western United States. Several fires in 2015 occurred in conifer forests on the west ( i.e. , wet) side of the Cascade Range, including a rare fire event in coastal temperate rainforest on the Olympic Peninsula. In some locations, short-interval reburns have occurred. For example, one location on Mount Adams in southwestern Washington burned three times between 2008 and 2015 (Fig. 3 ). Similarly, during the summer of 2017 in southwestern Oregon, the 77 000 ha Chetco Bar Fire burned over 40 000 ha of the 2002 Biscuit Fire, including a portion of the Biscuit Fire that had burned over part of the 1987 Silver Fire. At over 200 000 ha, the Biscuit Fire was the largest fire in the recorded history of Oregon.

Large wildfires, such as the 2014 Carlton Complex Fire in Washington, USA (103 640 ha), have occurred throughout western North America during the past several decades. These disturbances have a significant effect on landscape pattern and forest structure and will likely become more common in a warmer climate, especially in forests with heavy fuel loadings. Photo credit: Morris Johnson

Fires burning across the Pacific Northwest, USA, on 25 August 2015. This natural-color satellite image was collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. Actively burning areas, detected by MODIS’s thermal bands, are outlined in red. National Aeronautics and Space Administration image courtesy of Jeff Schmaltz, MODIS Rapid Response Team

( a ) Large fires around Mount Adams in Gifford Pinchot National Forest in southwestern Washington, USA, between 2001 and 2015 (area in orange burned twice, and area in red burned three times); and ( b ) area in Gifford Pinchot National Forest that has burned three times since 2008 (2008 Cold Springs fire, 2012 Cascade Creek fire, and 2015 Cougar Creek fire). Map credit: Robert Norheim; photo credit: Darryl Lloyd

Over the twentieth century in the Northwest, years with relatively warm and dry conditions have generally corresponded with larger fires and greater area burned (Trouet et al. 2006 ; Westerling et al. 2006 ; Littell et al. 2009 ; Littell et al. 2010 ; Abatzoglou and Kolden 2013 ; Cansler and McKenzie 2014 ; Dennison et al. 2014 ; Stavros et al. 2014 ; Westerling 2016 ; Kitzberger et al. 2017 ; Reilly et al. 2017 ; Holden et al. 2018 ). Decreasing fuel moisture and increasing duration of warm, dry weather creates large areas of dry fuels that are more likely to ignite and carry fire over a longer period of time (Littell et al. 2009 ).

A warming climate will have profound effects on fire frequency, extent, and possibly severity in the Northwest. Increased temperatures are projected to lengthen fire and growing seasons, increase evaporative demand, decrease soil and fuel moisture, increase likelihood of large fires, and increase area burned by wildfire (McKenzie et al. 2004 ; Littell et al. 2010 ; Stavros et al. 2014 ; Westerling 2016 ). Decreased summer precipitation is also projected to increase area burned (Holden et al. 2018 ).

Interactions between fire and other disturbance agents ( e.g. , drought, insect outbreaks) will likely catalyze ecosystem changes in a warming climate. Increased tree stress and interacting effects of drought may also contribute to increasing wildfire severity (damage to vegetation and soils) and area burned (McKenzie et al. 2009 ; Stavros et al. 2014 ; Littell et al. 2016 ; Reilly et al. 2017 ).

Climatic changes and associated stressors can interact with altered vegetation conditions ( e.g. , those resulting from historical management practices) to affect fire frequency, extent, and severity, as well as forest conditions in the future (Keeley and Syphard 2016 ). Human influence through domestic livestock grazing, road construction, conversion of land to agriculture, and urbanization has resulted in (direct or indirect) exclusion of fires in dry forests (Hessburg et al. 2005 ). Many larger, fire-resistant trees have been removed by selective logging. These activities, along with active fire suppression, have resulted in increased forest density and fuel buildup in forests historically characterized by frequent, low-severity and mixed-severity fires (Hessburg et al. 2005 ). Although landscape pattern and fuel limitations were key factors that limited fire size and severity historically, these limitations have been largely removed from many contemporary landscapes, thus increasing the potential for large high-severity fires, particularly in a warming climate.

Facing such changes, land managers need information on the magnitude and likelihood of altered fire regimes and forest conditions in a warming climate to help guide long-term sustainable resource management. Many published studies have explored the potential effects of climate change on forest fire in the Northwest, including paleoecological, modeling, and local- to regional-scale empirical studies. However, to our knowledge, there is no single resource that synthesizes these varied studies for the Northwest region. A synthesis of this information can help managers better understand the potential effects of climate change on ecosystem processes, assess risks, and implement actions to reduce the negative effects of climate change and transition systems to new conditions.

In this synthesis, we draw from relevant published literature to discuss potential effects of changing climate on fire frequency, extent, and severity in Northwest forests. Sources of information include: (1) long-term (centuries to millennia) paleoecological studies of climate, fire, and species distribution; (2) medium-term (decades to centuries) fire history studies; (3) near-term (years to decades) studies on trends in vegetation and fire associated with recent climatic variability and change; (4) forward-looking studies using simulation models to project future fire and vegetation change; and (5) recent syntheses focused on potential climate change effects.

We used regionally specific information where possible, including information from adjacent regions with forests of similar structure and function when relevant. Following an overview of climate projections, we (1) identified risks related to wildfire as affected by climate change in three broad ecosystem types; (2) explored the magnitude and likelihood of those risks; and (3) concluded with a discussion of uncertainties about future climate and fire, potential future research, and implications for resource management.

Overview of climate projections

Warming temperatures and changing precipitation patterns will affect amount, timing, and type of precipitation; snowmelt timing and rate (Luce et al. 2012 ; Luce et al. 2013 ; Safeeq et al. 2013 ); streamflow magnitude (Hidalgo et al. 2009 ; Mantua et al. 2010 ); and soil moisture content (McKenzie and Littell 2017 ). Compared to the historical period from 1976 to 2005, 32 global climate models project increases in mean annual temperature for the middle and end of the twenty-first century in the Northwest. These projected increases range from 2.0 to 2.6 °C for mid-century (2036 to 2065) and 2.8 to 4.7 °C for the end of the century (2071 to 2100), depending on future greenhouse gas emissions (specifically representative concentration pathway 4.5 or 8.5; Vose et al. 2017 ). Warming is expected to occur during all seasons, although most models project the largest temperature increases in summer (Mote et al. 2014 ). All models suggest a future increase in heat extremes (Vose et al. 2017 ).

Changes in precipitation are less certain than those for temperature. Global climate model projections for annual average precipitation range from −4.7 to +13.5%, averaging about +3% among models (Mote et al. 2014 ). A majority of models project decreases in summer precipitation, but projections for precipitation vary for other seasons. However, models agree that extreme precipitation events ( i.e. , number of days with precipitation >2.5 cm) will likely increase, and that the length of time between precipitation events will increase (Mote et al. 2014 ; Easterling et al. 2017 ).

Risk assessment

A risk-based approach to climate change vulnerability assessments provides a common framework to evaluate potential climate change effects and identify a structured way to choose among adaptation actions or actions to mitigate climate change risks (EPA 2014 ). Risk assessment is linked with risk management by (1) identifying risks—that is, how climate change may prevent an agency or other entity from reaching its goals; (2) analyzing the potential magnitude of consequences and likelihood for each risk; (3) selecting a set of risk-reducing actions to implement; and (4) prioritizing those actions that address risks with the highest likelihood and magnitude of consequences (EPA 2014 ).

Here, we summarized potential risks that are relevant for natural resource management associated with climate–fire interactions, including: wildfire frequency, extent, and severity; reburns; stress interactions; and regeneration for (1) moist coniferous forest (low to mid elevation), (2) dry coniferous forest and woodland (low to mid elevation), and (3) subalpine coniferous forest and woodland (high elevation). The likelihood and magnitude of consequences, and confidence in inferences are described for each risk. Although the information provided here does not constitute risk management, as described in the previous paragraph, this information can be used to inform more site- and resource-specific risk assessments and risk management.

The risks identified here were inferred from the authors’ review of the published literature described below, as well as experience with developing climate change vulnerability assessments in the study region over the past decade (Halofsky et al. 2011a , b ; Raymond et al. 2014 ; Halofsky and Peterson 2017a , b ; Halofsky et al. 2019 ; Hudec et al. 2019 ). These assessments encompassed all ecosystems and species addressed in this synthesis, and included extensive discussion of the effects of wildfire and other disturbances. Climate change effects and adaptation options in the assessments were greatly informed by input from resource managers as well as by scientific information. Thus, many fire-related vulnerabilities identified in the assessments are relevant to the risk assessment discussed here.

Risk in moist coniferous forests

Most climate–fire risks in moist coniferous forests are relatively low (Table 1 ). These forests occur west of the Cascade Range in Oregon and Washington and are frequently dominated by Douglas-fir ( Pseudotstuga menziesii [Mirb.] Franco) and western hemlock ( Tsuga heterophylla [Raf.] Sarg.). Moist coniferous forests are characterized by an infrequent, stand-replacing ( i.e. , high-severity) fire regime (Agee 1993 ). Although fire frequency and severity may increase with climate change, the frequency of fire in these moist ecosystems will likely remain relatively low.

Risk in dry coniferous forests

Climate–fire risks in dry coniferous forests and woodlands are high for increased fire frequency, extent, and severity (Table 2 ). Dry coniferous forests and woodlands occur at lower elevations in southwestern Oregon, east of the Cascade Range in Oregon and Washington, and at lower elevations in the Rocky Mountains in Idaho and Montana. Fire regimes in these forests and woodlands range from moderate frequency and mixed severity to frequent and low severity. Ponderosa pine ( Pinus ponderosa Douglas ex P. Lawson & C. Lawson) is a characteristic species, along with Douglas-fir, grand fir ( Abies grandis [Douglas ex D. Don] Lindl.), and white fir ( Abies concolor [Gordon & Glend.] Lindl. ex Hildebr.). These forests and woodlands are also at risk from interacting disturbances and hydrologic change (moderate to high likelihood and magnitude of consequences), and post-fire regeneration failures are likely to occur on some sites.

Risk in high-elevation forests

Climate–fire risks in high-elevation forests are moderate, with a primary factor being increased fire frequency and extent in lower-elevation forests spreading to higher-elevation systems (Table 3 ). Regeneration could be challenging in locations where seed availability is low due to very large fires. High-elevation forests occur in mountainous areas across the Northwest. They are characterized by species such as subalpine fir ( Abies lasiocarpa [Hook.] Nutt.), mountain hemlock ( Tsuga mertensiana [Bong.] Carrière), and lodgepole pine ( Pinus contorta var. contorta Engelm. ex S. Watson). High-elevation forests are characterized by infrequent, stand-replacement fire regimes (Agee 1993 ). Risks of stress interactions are also moderate, because drought and insect outbreaks will likely affect high-elevation forests with increasing frequency.

Historical and contemporary fire–climate relationships

Paleoclimate and fire data.

Wildfire-derived charcoal deposited in lake sediments can be used to identify individual fire events and to estimate fire frequency over hundreds to thousands of years (Itter et al. 2017 ). In combination with sediment pollen records, charcoal records help to determine how vegetation and fire frequency and severity shifted with climatic variability in the past (Gavin et al. 2007 ). Existing paleoecological reconstructions of the Northwest are based mostly on pollen and charcoal records from lakes in forested areas west of the Cascade Range, with few studies in the dry interior of the region (Kerns et al. 2017 ).

The early Holocene ( circa 10 500 to 5000 years BP) was the warmest post-glacial period in the Northwest (Whitlock 1992 ). During the early Holocene, summers were warmer and drier relative to recent historical conditions, with more intense droughts (Whitlock 1992 ; Briles et al. 2005 ). In many parts of the Northwest, these warmer and drier summer conditions were associated with higher fire frequency (Whitlock 1992 ; Walsh et al. 2008 ; Walsh et al. 2015 ).

Sediment charcoal analysis documented relatively frequent (across the paleoecological record) fire activity during the early Holocene in eight locations: North Cascade Range (Prichard et al. 2009 ), Olympic Peninsula (Gavin et al. 2013 ), Puget Lowlands (Crausbay et al. 2017 ), southwestern Washington (Walsh et al. 2008 ), Oregon Coast Range (Long et al. 1998 ), Willamette Valley (Walsh et al. 2010 ), Siskiyou Mountains (Briles et al. 2005 ), and Northern Rocky Mountains in Idaho (Brunelle and Whitlock 2003 ) (Table 4 ). Higher fire frequency in these locations was generally associated with higher abundance of tree species adapted to survive fire or regenerate soon after fire, including Douglas-fir, lodgepole pine, and Oregon white oak ( Quercus garryana Douglas ex Hook.) (Table 4 ). Other pollen analyses (without parallel charcoal analysis) support the expansion of these species during the early Holocene ( e.g. , Sea and Whitlock 1995 ; Worona and Whitlock 1995 ), in addition to the expansion of ponderosa pine and oak in drier interior forests (Hansen 1943 ; Whitlock and Bartlein 1997 ). Relatively frequent fire (across the paleoecological record) during the early Holocene likely resulted in a mosaic of forest successional stages, with species such as red alder ( Alnus rubra Bong.) dominating early-successional stages in mesic forest types (Cwynar 1987 ).

Paleoecological studies (covering the early Holocene and other time periods) indicate that climate has been a major control on fire in the Northwest over millennia, with interactions between fire and vegetation. During times of high climatic variability and fire frequency ( e.g. , the early Holocene), fires were catalysts for large-scale shifts in forest composition and structure (Prichard et al. 2009 ; Crausbay et al. 2017 ). Species that persisted during these times of rapid change have life history traits that facilitate survival in frequently disturbed environments (Brubaker 1988 ; Whitlock 1992 ), including red alder, Douglas-fir, lodgepole pine, ponderosa pine, and Oregon white oak, which suggests that these species may be successful in a warmer future climate (Whitlock 1992 ; Prichard et al. 2009 ).

Fire-scar and tree-ring records

Fire-scar studies indicate that climate was historically a primary determinant of fire frequency and extent in the Northwest. Years with increased fire frequency and area burned were generally associated with warmer and drier spring and summer conditions in the Northwest (Hessl et al. 2004 ; Wright and Agee 2004 ; Heyerdahl et al. 2008 ; Taylor et al. 2008 ). Climate of previous years does not have a demonstrated effect on fire, unlike other regions such as the Southwest, most likely because fuels are not as limiting for fire across the Northwest (Heyerdahl et al. 2002 ; Hessl et al. 2004 ).

Warmer and drier conditions in winter and spring are more common during the El Niño phase of the El Niño-Southern Oscillation (ENSO) in the Northwest (Mote et al. 2014 ). The Pacific Decadal Oscillation (PDO) is an ENSO-like pattern in the North Pacific, resulting in sea surface temperature patterns that appeared to occur in 20- to 30-year phases during the twentieth century (Mantua et al. 1997 ). Positive phases of the PDO are associated with warmer and drier winter conditions in the Northwest.

Associations between large fire years and El Niño have been found in the interior Northwest ( e.g. , Heyerdahl et al. 2002 ), as have associations between large fire years and the (warm, dry) positive phase of the PDO (Hessl et al. 2004 ). Other studies have found ambiguous or non-significant relationships between fire and these climate cycles in the Northwest ( e.g. , Hessl et al. 2004 ; Taylor et al. 2008 ). However, interactions between ENSO and PDO (El Niño plus positive phase PDO) were associated with increased area burned (Westerling and Swetnam 2003 ) and synchronized fire in some years in dry forests across the inland Northwest (Heyerdahl et al. 2008 ).

The PDO and ENSO likely affect fire extent by influencing the length of the fire season (Heyerdahl et al. 2002 ). Warmer and drier winter and spring conditions increase the length of time that fuels are flammable (Wright and Agee 2004 ). Although climate change effects on the PDO and ENSO are uncertain, both modes of climatic variation influence winter and spring conditions in the Northwest, whereas summer drought during the year of a fire has the strongest association with major fire years at the site and regional scales (Hessl et al. 2004 ). Summer drought conditions are likely more important than in other regions where spring conditions are more strongly related to fire, because the Northwest has a winter-dominant precipitation regime; fire season occurs primarily in late summer (August through September), and summer drought reduces fuel moisture (Hessl et al. 2004 ; Littell et al. 2016 ).

Contemporary climate and fire records

In the twentieth century, wildfire area burned in the Northwest was positively related to low precipitation, drought, and temperature (Littell et al. 2009 ; Abatzoglou and Kolden 2013 ; Holden et al. 2018 ). Warmer spring and summer temperatures across the western United States cause early snowmelt, increased evapotranspiration, lower summer soil and fuel moisture, and thus longer fire seasons (Westerling 2016 ). Precipitation during the fire season also exerts a strong control on area burned through wetting effects and feedbacks to vapor pressure deficit (a measure of humidity; Holden et al. 2018 ). Between 2000 and 2015, warmer temperatures and vapor pressure deficit decreased fuel moisture during the fire season in 75% of the forested area in the western US and added about nine days per year of high fire potential (defined using several measures of fuel aridity; Abatzoglou and Williams 2016 ).

Periods of high annual area burned in the Northwest are also associated with high (upper atmosphere) blocking ridges over western North America and the North Pacific Ocean. Blocking ridges occur when centers of high pressure occur over a region in such a way that they prevent other weather systems from moving through. These blocking ridges, typical in the positive phase of the PDO (Trouet et al. 2006 ), divert moisture away from the region, increasing temperature and reducing relative humidity (Gedalof et al. 2005 ). Prolonged blocking and more severe drought (Brewer et al. 2012 ) are needed to dry out fuels in mesic to wet forest types ( e.g. , Sitka spruce [ Picea sitchensis {Bong.} Carrière], western hemlock) along coastal Oregon and Washington. With increased concentrations of carbon dioxide in the atmosphere, the persistence of high blocking ridges that divert moisture from the region may increase (Lupo et al. 1997 , as cited in Flannigan et al. 2009 ), further enhancing drought conditions and the potential for fire.

Lightning ignitions also affect wildfire frequency. However, research on lightning with recent and future climate change is equivocal. Some studies suggest that lightning will increase up to 40% globally in a warmer climate (Price and Rind 1994 ; Reeve and Toumi 1999 ; Romps et al. 2014 ), although a recent study suggests that lightning may decrease by as much as 15% globally (Finney et al. 2018 ).

Increases in annual area burned are generally associated with increases in area burned at high severity. Fire size, fire severity, and high-severity burn patch size were positively correlated in 125 fires in the North Cascades of Washington over a recent 25-year period (Cansler and McKenzie 2014 ). Other analyses have similarly shown a positive correlation between annual area burned and area burned severely (in large patches) in the Northwest (Dillon et al. 2011 ; Abatzoglou et al. 2017 ; Reilly et al. 2017 ). The annual extent of fire has increased slightly in the Northwest, although the proportion of area burning at high severity did not increase over the 1985 to 2010 period, either for the region as a whole or for any subregion (Reilly et al. 2017 ). Similarly, an analysis of recent fires (1984 to 2014) in the Northwest found no decrease in the proportion of unburned area within fire perimeters (Meddens et al. 2018 ).

Many studies have found that bottom-up controls such as vegetation, fuels, and topography are more important drivers of fire severity than climate in Western forests ( e.g. , Dillon et al. 2011 ; Parks et al. 2014 ). The direct influence of climate on fire severity is intrinsically much stronger in moister and higher-elevation forests, because drying of fuels in these systems requires extended warm and dry periods. Fire severity in many dry forest types is influenced primarily by fuel quantity and structure (Parks et al. 2014 ). However, fuel accumulations associated with fire exclusion in dry forests may be strengthening the influence of climate on fire severity, likely resulting in increased fire severity in drier forest types (Parks et al. 2016a ).

Wildfire projections under changing climate

Historical patterns suggest that higher temperatures, stable or decreasing summer precipitation, and increased drought severity in the Northwest will likely increase the frequency and extent of fire. Models can help to explore potential future fire frequency and severity in a changing climate, with several types of models being used to project future fire (McKenzie et al. 2004 ). We focused here on models for which output is available in the Northwest—empirical (statistical) models and mechanistic (process-based) models. Both types of models have limitations as well as strengths, but they are conceptually useful to assess potential changes in fire with climate change.

Fire projections by empirical models

Empirical models use the statistical relationship between observed climate and area burned during the historical record (the past 100 years or so) to project future area burned. Future area burned is based on projections of future temperature and precipitation, usually from global climate models. These models do not account for the potential decreases in burn probability in areas that have recently burned, or for long-term changes in vegetation (and thus flammability) with climate change (Parks et al. 2015 ; McKenzie and Littell 2017 ; Littell et al. 2018 ). They also do not account for human influence on fire ignitions (Syphard et al. 2017 ).

Numerous studies have developed empirical models to project future area burned or fire potential at both global (Krawchuk et al. 2009 ; Moritz et al. 2012 ) and regional scales ( e.g. , western US; McKenzie et al. 2004 ; Littell et al. 2010 ; Yue et al. 2013 ; Kitzberger et al. 2017 ). All studies suggest that fire potential, area burned, or both will increase in the western US in the future with warming climate. Below we highlight a few examples that explicitly address the Northwest. These examples provide future fire projections at relatively coarse spatial scales, with changes in area burned being variable across landscapes.

McKenzie et al. ( 2004 ) projected that, with a mean temperature increase of 2 °C, area burned by wildfire will increase by a factor of 1.4 to 5 for most Western states, including Idaho, Montana, Oregon, and Washington. Kitzberger et al. ( 2017 ) projected increases in annual area burned of 5 times the median in 2010 to 2039 compared to 1961 to 2004 for the 11 conterminous Western states. Models developed by Littell et al. ( 2010 ) for Idaho, Montana, Oregon, and Washington suggested that area burned will double or triple by the 2080s, based on future climate projections for two global climate models (Fig. 4 ). Median area burned was projected to increase from about 0.2 million ha historically to 0.3 million ha in the 2020s, 0.5 million ha in the 2040s, and 0.8 million ha in the 2080s. The projections cited here are coarse scale, and area burned can be expected to vary from place to place within the area of the projections.

Conceptual model showing that indirect effects of climate change via disturbance cause faster shifts in vegetation than do direct effects of climate change. Adapted from McKenzie et al. ( 2004 )

Littell et al. ( 2010 ) also developed empirical models at a finer (ecosection) scale for the state of Washington. The relatively low frequency of fire in coastal forests makes development of empirical models difficult, so the output from these models for coastal forests is uncertain. For drier forest types, potential evapotranspiration and water balance deficit were the most important variables explaining area burned. In forested ecosystems (Western and Eastern Cascades, Okanogan Highlands, and Blue Mountains ecosections), the mean area burned was projected to increase by a factor of 3.8 in the 2040s compared to 1980 to 2006. An updated version of these models, expanded to the western US (Littell et al. 2018 ), also suggests that area burned will increase in the future for most forested ecosections of the Northwest, but increases in area burned may be tempered, or area burned may decrease, in areas that are more fuel limited ( e.g. , in non-forest vegetation types).

Another application of empirical models is to project the future incidence of very large fires, often defined as the largest 5 to 10% of fires or fires >5000 ha. Barbero et al. ( 2015 ) projected that the annual probability of very large fires will increase by a factor of 4 in 2041 to 2070 compared to 1971 to 2000. Projections by Davis et al. ( 2017 ) suggested that the proportion of forests highly suitable for fires >40 ha will increase by >20% in the next century for most of Oregon and Washington, but less so for the Coast Range and Puget Lowlands. The largest projected increases were in the Blue Mountains, Klamath Mountains, and East Cascades. The number of fires that escape initial attack will also likely increase (Fried et al. 2008 ).

Few empirical model projections are available for future fire severity. Using empirical models, Parks et al. ( 2016a ) suggested that fire severity in a warming climate may not change significantly in the Northwest, because fuels limit fire severity. However, altered fire severity will depend partly on vegetation composition and structure (as they affect fuels), and climate change is expected to alter vegetation composition and structure both directly and indirectly (through disturbance). Empirical models do not account for these potential changes in vegetation and fuels (among other limitations; see McKenzie and Littell 2017 ). In the near term, high stem density as a result of fire exclusion and past management may increase fire severity in dry, historically frequent-fire forests (Haugo et al. 2019 ).

Fire projections by mechanistic models

Mechanistic models allow for exploration of potential interactions between vegetation and fire under changing and potentially novel climate. Mechanistic models can also account for elevated carbon dioxide concentration on vegetation, which could result in increased vegetation productivity (and fuel loading). Examples of mechanistic models that simulate fire include dynamic global vegetation models, such as MC1 (Bachelet et al. 2001 ), LANDIS-II (Scheller and Mladenoff 2008 ), and Fire-BioGeoChemical (Fire-BGC; Keane et al. 1996 ).

Using the MC1 dynamic global vegetation model for the western three quarters of Oregon and Washington, Rogers et al. ( 2011 ) projected a 76 to 310% increase in annual area burned and a 29 to 41% increase in burn severity (measured as aboveground carbon consumed by fire) by the end of the twenty-first century, with the degree of increase depending on climate scenario. These projected changes were largely driven by increased summer drought. Under a hot and dry climate scenario (with more frequent droughts), large fires were projected to occur throughout the twenty-first century (including the early part), primarily in mesic forests west of the Cascade crest.

Using the MC2 model (an updated version of MC1), Sheehan et al. ( 2015 ) also projected increasing fire activity in Idaho, Oregon, Washington, and western Montana. Mean fire return interval was projected to decrease across all forest-dominated subregions, with or without fire suppression. Projected decreases in mean fire interval were as high as 82% in the interior subregions without fire suppression; projected decreases in mean fire interval for the westernmost subregion were as high as 48% without fire suppression.

The MC1 and MC2 models have also been calibrated and run for smaller subregions in the Northwest. For the Willamette Valley, Turner et al. ( 2015 ) projected (under a high temperature increase scenario) increased fire frequency, with average area burned per year increasing by a factor of nine relative to the recent historical period (1986 to 2010); area burned over the recent historical period was very low (0.2% of the area per year). For a western Washington study region, MC2 projected a 400% increase in annual area burned in the twenty-first century compared to 1980 to 2010 (Halofsky et al. 2018a ). Although the projected average annual area burned was still only 1.2% of the landscape, some fire years were very large, burning 10 to 25% of the study region.

The MC1 model projected increased fire frequency and extent in forested lands east of the Cascade crest (Halofsky et al. 2013 ; Halofsky et al. 2014 ). Fire was projected to burn more than 75% of forested lands several times between 2070 and 2100. On average, projected future fires burned the most forest under a hot, dry scenario. Applying the MC2 model to a larger south-central Oregon region, Case et al. ( 2019 ) suggested that future fire will become more frequent in most vegetation types, increasing most in dry and mesic forest types. For forested vegetation types, fire severity was projected to remain similar or increase slightly compared to historical fire severity.

The LANDIS-II model has been applied to the Oregon Coast Range in the Northwest. Creutzburg et al. ( 2017 ) found that area burned over the twenty-first century did not increase significantly with climate change compared to historical levels, but fire severity and extreme fire weather did increase.

Fire-BGC models have mostly been applied in the northern US Rocky Mountains, which overlaps with the Northwest. For northwestern Montana (Glacier National Park), Keane et al. ( 1999 ) used Fire-BGC in a warmer, wetter climate scenario to project higher vegetation productivity and fuel accumulations that contribute to more intense crown fires and larger fire sizes. Fire frequency also increased over a 250-year simulation period: fire rotation decreased from 276 to 213 years, and reburns occurred in 37% of the study area (compared to 17% under historical conditions). In drier locations (low-elevation south-facing sites), low-severity surface fires were more common, with fire return intervals of 50 years.

Mechanistic modeling suggests that fire frequency and area burned will increase in the Northwest. Fire severity may also increase, depending partly on forest composition, structure, and productivity over time. Warmer temperatures in winter and spring, and increased precipitation during the growing season (even early in the growing season), could increase forest productivity. This increase in productivity would maintain or increase fuel loadings and promote high-severity fires when drought and ignitions occur. In mechanistic model projections for the region, some of the largest increases in fire severity (Keane et al. 1999 ; Case et al. 2019 ) and the largest single fire years (Halofsky et al. 2013 ; Halofsky et al. 2018a ) occurred in wetter scenarios with increased forest productivity. Future increased fire frequency without increased vegetation productivity is likely to result in decreased fire severity because of reduction in fuels as well as the potential for type conversion to vegetation characterized by less woody biomass. However, in highly productive systems such as forests west of the Cascade crest, future fires will probably be high severity (as they were historically) and more frequent (Rogers et al. 2011 ; Halofsky et al. 2018a ).

Short-interval reburns

A reburn occurs when the perimeter of a recent past fire is breached by a subsequent fire, something that all fire-prone forests have experienced. In the Northwest, reburns in the early twentieth century were documented in some of the earliest forestry publications ( e.g. , Isaac and Meagher 1936 ). However, under a warming climate, increased frequency and extent of fire will increase the likelihood of reburns, increasing the need to understand how earlier fires affect subsequent overlapping fires and how forests respond to multiple fires. Recent concern about reburns centers on projections that short-interval, high-severity ( i.e. , stand-replacing) reburns may become more common (Westerling et al. 2011 ; Prichard et al. 2017 ). Multiple fires can interact as linked disturbances (Simard et al. 2011 ), whereby the first fire affects the likelihood of occurrence, size, or magnitude (intensity, severity) of a reburn. Multiple fires can also interact to produce compound disturbance effects (Paine et al. 1998 ), in which ecological response after a reburn is qualitatively different than after the first fire.

Effects of past fire on future fire occurrence

Interactions between past forest fires and the occurrence of subsequent fires are generally characterized by negative feedbacks: fires are less likely to start within or spread into recently burned areas ( i.e. , within the last 5 to 25 years) compared to similar areas that have not experienced recent fire. For example, lightning-strike fires within the boundary of recently burned areas in the US Rocky Mountains (Idaho, Montana) were less likely to grow to fires larger than 20 ha than were lightning-strike fires in comparable areas outside recent fire boundaries (Parks et al. 2016b ). This negative relation between past fires and likelihood of future fires is generally attributed to limits on ignition potential and initial spread of fires through fine woody fuels, which are sparse following fire. Fine fuels are consumed by the first fire and do not recover to sufficient levels until at least a decade later in many interior forest systems in the Northwest (Isaac 1940 ; Donato et al. 2013 ) and US Rocky Mountains (Nelson et al. 2016 , 2017 ). However, negative feedbacks can be short-lived (or non-existent) in productive west-side forests in the Northwest, where fuels are abundant in early-successional forests (Isaac 1940 ; Agee and Huff 1987 ; Gray and Franklin 1997 ).

Past fires in the northern US Rocky Mountains have also been effective at preventing the spread of subsequent fires into their perimeters (Teske et al. 2012 ; Parks et al. 2015 ). Similar results have been found in mixed-conifer forests of the interior Northwest, where past wildfire perimeters inhibited the spread of the 2007 Tripod Complex Fire in eastern Washington (Prichard and Kennedy 2014 ). This limitation of fire spread decreases with time. The probability that reburns will be inhibited by earlier fires is near 100% in the first year post fire, but is only 30% by 15 to 20 years post fire (Parks et al. 2015 ). However, extreme fire weather can dampen buffering effects of reburns at any interval between fires, such that past fire perimeters become less effective at inhibiting reburns during warm, dry, and windy conditions (Parks et al. 2015 ).

Effects of past fire on future fire severity

Fire severity (fire-caused vegetation mortality) in a reburn is affected by interactions among severity of the first fire, climate setting and forest type, interval between fires, and weather at the time of the reburn. Reburns are typically less severe when the interval between fires is shorter than 10 to 15 years (Parks et al. 2014 ; Harvey et al. 2016b ; Stevens-Rumann et al. 2016 ). After 10 to 15 years, the effects of past fires on reburn severity diverge in different ecological contexts.

In areas where tree and shrub regeneration is prolific following one severe fire ( e.g. , moist Douglas-fir forests, subalpine forests dominated by lodgepole pine, some mixed-conifer forests [ e.g. , southwest Oregon mixed conifer forests with a hardwood component]), fire severity can be greater in reburns than in comparable single burns once the interval between fires exceeds 10 to 12 years (Thompson et al. 2007 ; Harvey et al. 2016b ). In lower-elevation, drier, and more fuel-limited forests ( e.g. , ponderosa pine forests and woodlands, areas with slower woody plant establishment following fire), past fire limits future fire severity, often for 20 to 30 years (Parks et al. 2015 ; Harvey et al. 2016b ; Stevens-Rumann et al. 2016 ). In these lower-productivity forests, the severity of past fire has been found to be the best predictor of reburn severity (Parks et al. 2014 ; Harvey et al. 2016b ), but this is not necessarily the case in higher-productivity forests (Thompson et al. 2007 ; Stevens-Rumann et al. 2016 ). Surface fuel treatment followed by tree planting can greatly reduce the intensity of a reburn and allow most newly established trees to survive (Lyons-Tinsley and Peterson 2012 ).

Of particular concern for forest resilience is how and why forests may experience two severe fires in short succession. In the northern US Rocky Mountains, the likelihood of experiencing two successive stand-replacing fires ( i.e. , a severe fire followed by a severe reburn) is greatest (1) in areas with high post-fire regeneration capacity ( e.g. , higher-elevation subalpine forests on moist sites), and (2) when the reburn occurs during warm, dry conditions (Harvey et al. 2016b ). In high-productivity west-side forests of Oregon and Washington, the potential for two successive high-severity burns may always exist ( e.g. , Isaac 1940 ), but occurrence depends on ignition and low fuel moisture.

Effects of reburns on forest species composition and structure

Short-interval reburns can produce compound effects on tree regeneration, altering species composition in some cases and shifting to non-forest vegetation in others. For example, thin-barked species, which do not survive fire but instead regenerate from seed following fire-induced mortality ( e.g. , lodgepole pine), can face “immaturity risk” if the interval between one fire and a reburn is too short to produce a sufficient canopy seedbank (Keeley et al. 1999 ; Turner et al. 2019 ). In northern US Rocky Mountain systems, low- and moderate-severity reburns have shifted dominance from lodgepole pine toward thick-barked species that can resist fire, such as ponderosa pine (Larson et al. 2013 ; Stevens-Rumann and Morgan 2016 ).

In the western Cascades of southern Washington, areas that burned in the 1902 Yacolt Burn and subsequently reburned within 30 years were characterized by much lower conifer regeneration than areas that burned only once (Gray and Franklin 1997 ). However, in the Klamath and Siskiyou mountains of southwestern Oregon, a short-interval (15 years between fires), high-severity reburn had no compound effect on regeneration (two years post fire) of Douglas-fir, the dominant tree species (Donato et al. 2009b ), with no difference from areas that burned once at a longer interval (>100 years between fires). Plant species diversity and avian diversity were higher in reburns compared to once-burned areas, with hardwoods contributing to habitat diversity in the reburn areas (Donato et al. 2009b ; Fontaine et al. 2009 ).

The effects of reburns on post-fire conifer regeneration seem to depend on legacy trees that survive both fires, providing seed across fire events (Donato et al. 2009b ). In systems where legacy trees are rare ( i.e. , thin-barked species easily killed by fire) or where shrubs and hardwoods can outcompete trees for long durations, reburns are more likely to produce lasting compound effects on forest structure and composition, possibly resulting in a shift to non-forest vegetation.

Disturbance and stress interactions

Combinations of biotic and abiotic stressors, or stress complexes, will likely be major drivers of shifts in forest ecosystems with changing climate (Manion 1991 ). A warmer climate will affect forests directly through soil moisture stress and indirectly through increased extent and severity of disturbances, particularly fire and insect outbreaks (McKenzie et al. 2009 ).

Water deficit and disturbance interactions

Although water deficit (the condition in which potential summer atmospheric and plant demands exceed available soil moisture) is rarely fatal by itself, it is a predisposing factor that can exacerbate the forest stress complex (Manion 1991 ; McKenzie et al. 2009 ). Water deficit directly contributes to potentially lethal stresses in forest ecosystems by intensifying negative water balances (Stephenson 1998 ; Milne et al. 2002 ; Littell et al. 2008 ; Restaino et al. 2016 ). Water deficit also indirectly increases the frequency, extent, and severity of disturbances, especially wildfire and insect outbreaks (McKenzie et al. 2004 ; Logan and Powell 2009 ). These indirect disturbances alter forest ecosystem structure and function, at least temporarily, much faster than do chronic effects of water deficit ( e.g. , Loehman et al. 2017 ; Fig. 4 ).

Interactions among drought, insect outbreaks, and fire

During the past few decades, wildfires and insect outbreaks have affected a large area across the Northwest (Fig. 5 ). Increased area burned has been at least partly caused by extreme drought–wildfire dynamics, which will likely become more prominent as drought severity and area burned increase in the future (Parks et al. 2014 ; McKenzie and Littell 2017 ). Insect disturbance has likewise expanded across the Northwest since 1990, catalyzed by higher temperature and the prevalence of dense, low-vigor forests. Cambium feeders, such as bark beetles, are associated with prolonged droughts, in which tree defenses are compromised (Logan and Bentz 1999 ; Carroll et al. 2004 ; Hicke et al. 2006 ). Patches of fire–insect disturbance mosaic are starting to run into each other (Fig. 5 ), and similar to reburns, are an inevitable consequence of increasing disturbance activity, even in the absence of mechanistic links among disturbances.

Recent disturbances in the Northwest, USA, showing wildfire extent for 1984 to 2017 (orange), and insect and disease extent for 1997 to 2017 (brown). Data sources: Monitoring Trends in Burn Severity ( https://www.mtbs.gov ) and US Forest Service Insect and Disease Detection Survey ( https://www.fs.fed.us/foresthealth/applied-sciences/mapping-reporting/gis-spatial-analysis/detection-surveys.shtml ). Map credit: Robert Norheim

In a review of the fire–bark beetle literature, Hicke et al. ( 2012 ) noted that, despite varying research approaches and questions, much agreement existed on fire hazard (defined as changes to fuels and potential fire behavior) after bark beetle outbreaks. There was strong agreement that surface fire and torching potential increased during the gray phase ( e.g. , 5 to 10 years following outbreaks, when snags remain standing; but see Woolley et al. 2019 ), but that crown fire potential was reduced in this phase. Similarly, there was agreement that fire hazard was lower in the old phase ( i.e. , silver phase), which occurs one to several decades after outbreak, when beetle-killed snags have fallen, understory vegetation increases, and seedlings establish. However, there was disagreement regarding fire potential during the red phase (0 to 4 years after outbreak initiation), when trees retain their drying needles and changes in foliar chemistry can increase flammability. Many studies have concluded that during this approximately 1- to 4-year period, fire hazard increases (Klutsch et al. 2011 [but see Simard et al. 2011 ], Hoffman et al. 2012 , Jolly et al. 2012 ; Jenkins et al. 2014 ). Fire hazard has been found to increase as the proportion of the stand killed by bark beetles increases, regardless of forest type (Page and Jenkins 2007 ; DeRose and Long 2009 ; Hoffman et al. 2012 ).

Concern has also risen as to whether fire occurrence and severity will increase following outbreaks of bark beetles ( e.g. , Hoffman et al. 2013 ), although empirical support for such interactions has been lacking (Parker et al. 2006 ; Hicke et al. 2012 ). Insect outbreaks have not been shown to increase the likelihood of fire or area burned (Kulakowski and Jarvis 2011 ; Flower et al. 2014 ; Hart et al. 2015 ; Meigs et al. 2015 ). Further, when fire occurs in post-outbreak forests, most measures of fire severity related to fire-caused vegetation mortality are generally similar between beetle-affected forests and areas that were unaffected by pre-fire outbreaks. Field studies in Oregon showed that burn severity (fire-caused vegetation mortality) was actually lower in lodgepole pine forests affected by mountain pine beetle (MPB; Dendroctonus ponderosae Hopkins) than analogous unaffected forests that burned (Agne et al. 2016 ). In an analysis of recent (1987 to 2011) fires across the Northwest, Meigs et al. ( 2016 ) also found that burn severity (from satellite-derived burn severity indices) was lower in forests with higher pre-fire insect outbreak severity.

Field studies in California, the Rocky Mountains, and interior British Columbia, Canada, conducted in a range of forest types have also explored the relationship between beetle outbreak severity (pre-fire basal area killed by beetles) and burn severity (fire-caused vegetation mortality), and suggest relatively minor effects of beetle outbreaks on burn severity. When fire burned through red stages (1 to 4 years post outbreak, when trees retain red needles) in dry conifer forests of California, small increases ( e.g. , 8 to 10% increase in fire-caused tree mortality) in burn severity were observed in areas of high outbreak severity (Stephens et al. 2018 ). In dry Douglas-fir forests in Wyoming, fire severity in the gray phase (4 to 10 years post outbreak) of Douglas-fir beetle ( Dendroctonus pseudotsugae Hopkins) outbreak was unaffected by beetle outbreak severity (Harvey et al. 2013 ). Similar results of minimal beetle effect on fire severity were reported in gray-stage spruce–fir forests in Colorado, USA (Andrus et al. 2016 ). In lodgepole pine-dominated forests affected by MPB, outbreak effects on burn severity differed by weather and stage of outbreak. For example, in both green and red phases (when most beetle-killed trees retained crowns fading from green to red), fire severity increased with pre-fire beetle outbreak severity under moderate but not extreme ( e.g. , hot, dry, windy) weather (Harvey et al. 2014a ). Conversely, in the red and gray stages, fire severity increased with pre-fire outbreak severity under extreme but not moderate weather (Harvey et al. 2014b ).

In British Columbia, gray-stage post-outbreak stands did not burn more severely than unaffected stands for most measures of burn severity (Talucci et al. 2019 ). The effects of beetle outbreaks on fire severity in forest types typified by stand-replacing fire regimes seem to be overall variable and minor, especially given that such forest types are inherently characterized by severe fire. The key exception to the otherwise modest effects of pre-fire beetle outbreaks on burn severity is the effect of deep wood charring and combustion on beetle-killed snags that burn. This effect has been reported across stages and forest type when measured, and consistently increases with pre-fire beetle outbreak severity (Harvey et al. 2014b ; Talucci et al. 2019 ). Because fire intensity and thus severity are driven by topography, weather, and fuels, beetle-outbreak-induced changes to fuel structures may play a minor role in affecting fire severity. In all cases in studies above where topography and weather were quantified, fire severity responded strongly and consistently to these factors irrespective of pre-fire beetle outbreaks.

In the Northwest, lodgepole pine forests have been affected by MPB outbreaks, with high mortality in some locations ( e.g. , Okanogan-Wenatchee Forest; Fig. 6 ). Widely distributed at mid to higher elevations in the Rocky Mountains, lodgepole pine is the dominant species over much of its range there, forming nearly monospecific stands. In the Northwest, lodgepole pine occurs at mid to higher elevations in the Cascade Range and eastward, and monospecific stands are limited to early seral stages and specific soil conditions ( e.g. , Pumice Plateau in central Oregon). In some populations in the Northwest, lodgepole pine forests have also adapted to stand-replacing fires via cone serotiny.

Number of trees killed by beetles in Okanogan-Wenatchee National Forest, Washington, USA, from 1980 to 2016. Data source: C. Mehmel, Okanogan-Wenatchee National Forest, Washington, USA

Bark beetle outbreaks and subsequent fire may interact to affect post-fire forest recovery, but results differ depending on the dominant regeneration mechanism of the tree attacked by beetles. Species with a persistent canopy seedbank, such as lodgepole pine, are minimally affected by compound disturbances between beetle outbreaks and fire. For example, in the Cascade Range and Rocky Mountains, areas that experienced beetle outbreaks prior to fire had similar levels of post-fire lodgepole pine seedling establishment compared to areas that had fire only (Harvey et al. 2014a ; Harvey et al. 2014b ; Edwards et al. 2015 ; Agne et al. 2016 ). Species such as Douglas-fir, which do not have a persistent canopy seedbank, have been shown to have lower post-fire seedling establishment in areas affected by Douglas-fir beetle outbreaks and fire (Harvey et al. 2013 ), although effects may be transient and disappear with time since fire (Stevens-Rumann et al. 2015 ).

Interactions among fungal pathogens and other stressors

The effects of weather and climate on fungal pathogens vary by species, with the spread of some pathogens facilitated by drought and others by wet periods (Klopfenstein et al. 2009 ; Sturrock et al. 2011 ; Ayres et al. 2014 ). Forests with low vigor and physiologically stressed trees ( e.g. , dense stands) are generally more susceptible to fungal pathogens. In the Northwest, a wide range of root rots and other native fungal pathogens exists in all forest types. For example, on the west side of the Cascade Range, laminated root rot ( Phellinus weirii [Murrill] Gilb.) is widespread, causing small pockets of mortality in Douglas-fir (Agne et al. 2018 ). However, no evidence exists that this pathogen has been or will be accelerated by a warmer climate. Other pathogens, such as Swiss needle cast ( Phaeocryptopus gaeumannii [T. Rohde] Petrak), may be favored by warmer and wetter winters (Agne et al. 2018 ). Fungal pathogens stress trees and may increase susceptibility to insect infestations. For example, Douglas-fir beetle is closely associated with laminated root rot centers in forests on the west side of the Cascades in Oregon and Washington (Goheen and Hansen 1993 ). Overall, interactions between fungal pathogens and fire with climate change are uncertain.

Stress complexes and forest mortality

Recent large-scale tree mortality events in the Southwest (Breshears et al. 2005 ), Texas (Schwantes et al. 2016 ), and California, USA (Young et al. 2017 ), have been caused by multi-year droughts weakening trees, followed by various beetle species acting as the mortality agents. It is likely that more intense and longer droughts will increase in the future under changing climate (Trenberth et al. 2014 ), and interactions between drought and other disturbance agents are likely to cause tree mortality. As noted above, fungal pathogens may contribute to increasing insect outbreaks (Goheen and Hansen 1993 ), along with increasing temperatures, shorter winters, and tree stress. Fire-caused tree mortality will also likely be affected by interacting disturbances. In some cases, fire severity has been marginally higher in areas affected by beetle mortality (Harvey et al. 2014a ; Harvey et al. 2014b ; Stephens et al. 2018 ). However, empirical studies examining the effects of large-scale tree mortality events on fire behavior are limited (Stephens et al. 2018 ). Modeling studies suggest that fire rate of spread may increase after mortality events ( e.g. , Perrakis et al. 2014 ).

Effects of changing disturbance regimes on forest structure and composition

In Northwest forest ecosystems, warming climate and changing disturbance regimes are likely to lead to changes in species composition and structure, probably over many decades. In general, increased fire frequency will favor plant species with life history traits that allow for survival with more frequent fire (Chmura et al. 2011 ). These include (1) species that can resist fires ( e.g., thick-barked species such as Douglas-fir, western larch [ Larix laricina Nutt.], and ponderosa pine); (2) species with high dispersal ability that can establish after fires ( e.g., Douglas-fir); and (3) species with serotinous cones that allow seed dispersal from the canopy after fire ( e.g., lodgepole pine) (Rowe 1983 ; Agee 1993 ).

In the forest understory, increased fire frequency and extent will likely create more opportunities for establishment by invasive species (Hellmann et al. 2008 ). Species that can endure fires (sprouters) and seedbank species (evaders) are also likely to increase with more frequent fire. For example, sprouting shrubs and hardwoods are prolific after fire in southwest Oregon (Halofsky et al. 2011). However, high-intensity fire can consume or kill seeds stored in the upper soil layers and kill shallow belowground plant parts, and repeated fires at short intervals can deplete seed stores and belowground plant resources (Zedler et al. 1983 ).

More frequent fire will likely decrease abundance of avoider species, including shade-tolerant species, species with thin bark, and slow invaders after fire (Chmura et al. 2011 ). Forest stands composed primarily of fire-susceptible evader species, such as western hemlock, subalpine fir, and Engelmann spruce ( Picea engelmannii Parry ex Engelm.), will likely have higher mortality for a given fire intensity than stands composed of more fire-resistant species, such as mature Douglas-fir and western larch. If fire-sensitive species are not able to re-seed into burned areas and re-establish themselves (because of short fire intervals, competition, or harsh conditions for seedling establishment), these species can be lost from a site (Stevens-Rumann and Morgan 2016 ). Direct mortality or lack of regeneration of fire-sensitive species with more frequent fire will favor more fire-adapted species that can survive fire or regenerate after fire. For example, in southwest Oregon, shrubs and hardwoods are likely to increase in abundance with increased fire frequency and reduced conifer regeneration in some locations (Tepley et al. 2017 ).

Changes in disturbance regimes can influence the structure of forests at multiple spatial scales (Reilly et al. 2018 ). Within forest stands, more frequent fire will likely decrease tree density in dry forests, and open savannas may increase in area. Forest understories may shift from being duff- or forb-dominated to shrub- or grass-dominated. Tree canopy base heights will likely increase as frequent fires remove lower branches. Across forested landscapes ( i.e., among stands), fire directly influences the spatial mosaic of forest patches (Agee 1993 ). More extreme fire conditions with climate change may initially lead to larger and more frequent fires, resulting in larger burn patch sizes and greater landscape homogeneity (Harvey et al. 2016a ). More frequent severe fire will likely decrease forest age, the fraction of old-growth forest patches, and the landscape connectivity of old-growth forest patches (Baker 1995 ; McKenzie et al. 2004 ). However, more frequent low- and mixed-severity fires may eventually reduce fuels in drier forest ecosystems ( e.g., dry mixed conifer), leading to lower-intensity fires and a finer-scale patch mosaic (Chmura et al. 2011 ).

Effects of climate change on post-fire processes

Forest regeneration.

Changing climate and fire frequency, extent, and severity are likely to influence forest regeneration processes, thus affecting the structural and compositional trajectories of forest ecosystems. First, climate change is expected to affect regeneration through increased fire frequency. As fire-free intervals shorten, the time available for plants to mature and produce seed before the next fire will be limited. Such changes in fire-free intervals can have significant effects on post-fire regeneration, because different plants have varied adaptations to fire. Species that resprout following fire may decline in density, but species that are fire-killed and thus require reproduction from seed may be locally eliminated.

Second, climate change may result in increased fire severity. If the size of high-severity fire patches increases, seed sources to regenerate these patches will be limited. Regeneration of non-serotinous species will require long-distance seed dispersal and may be slower in large, high-severity patches (Little et al. 1994 ; Donato et al. 2009a ; Downing et al. 2019 ).

Third, climate change will likely result in increased forest drought stress. Warmer temperatures, lower snowpack, and increased evapotranspiration will increase summer drought stress. Warmer and drier conditions after fire events may cause recruitment failures, particularly at the seedling stage (Dodson and Root 2013 ). In this way, fire can accelerate species turnover when climatic conditions are unfavorable for establishment of dominant species (Crausbay et al. 2017 ) and seed sources are available for alternative species.

Regeneration in dry forests in the Northwest ( e.g., ponderosa pine) may be particularly sensitive to changing climate. Hotter and drier sites ( e.g., on southwestern aspects) may be particularly at risk for regeneration failures (Nitschke et al. 2012 ; Dodson and Root 2013 ; Donato et al. 2016 ; Rother and Veblen 2017 ; Tepley et al. 2017 ). High soil surface temperatures can also cause mortality (Minore and Laacke 1992 ). Forest structure (mainly shade from an existing canopy) can ameliorate harsh conditions and allow for regeneration (Dobrowski et al. 2015 ). However, after high-severity disturbance, dry forests at the warm and dry edges of their distribution (ecotones) may convert to grasslands or shrublands in a warming climate (Johnstone et al. 2010 ; Jiang et al. 2013 ; Savage et al. 2013 ; Donato et al. 2016 ; Stevens-Rumann et al. 2017 ).

In the Klamath-Siskiyou ecoregion of southwestern Oregon and northern California, Tepley et al. ( 2017 ) found that conifer regeneration was reduced by low soil moisture after fires. With lower soil moisture, greater propagule pressure (smaller high-severity patches with more live seed trees) was needed to achieve a given level of regeneration. This suggests that, at high levels of climatic water deficit, even small high-severity patches are at risk for low post-fire conifer regeneration. Successive fires could further limit conifer seed sources, thus favoring shrubs and hardwoods.

Germination of ponderosa pine is favored by moderate temperatures and low moisture stress, and survival increases when maximum temperatures are warm (but not hot) and when growing season rainfall is above average (Petrie et al. 2016 ; Rother and Veblen 2017 ). Empirical modeling by Petrie et al. ( 2017 ) projected that, with warming temperature in the middle of the twenty-first century, regeneration potential of ponderosa pine may increase slightly on many sites. However, by the end of the century, with decreased moisture availability, regeneration potential in the Northwest decreased by 67% in 2060 to 2099 compared to 1910 to 2014. In the eastern Cascade Range of Oregon, Dodson and Root ( 2013 ) found decreasing ponderosa pine regeneration with decreasing elevation and moisture availability, suggesting that moisture stress would limit regeneration.

Several studies in the Rocky Mountains have also found decreased post-fire regeneration with increased water deficits on drier, lower-elevation sites (Rother et al. 2015 ; Donato et al. 2016 ; Stevens-Rumann et al. 2017 ; Davis et al. 2019 ). Donato et al. ( 2016 ) found decreased regeneration of Douglas-fir 24 years after fire on drier, lower-elevation sites compared to more mesic sites at higher elevations. Regeneration declined with higher burn severity and was minimal beyond 100 to 200 m from a seed source. Similarly, Harvey et al. ( 2016c ) found that post-fire tree seedling establishment decreased with greater post-fire drought severity in subalpine forests of the northern US Rocky Mountains; post-fire subalpine fir and Engelmann spruce regeneration were both negatively affected by drought. Davis et al. ( 2019 ) modeled post-fire recruitment probability for ponderosa pine and Douglas-fir on sites in the Rocky Mountains, and found that recruitment probability decreased between 1988 and 2015 for both species, suggesting a decline in climatic suitability for post-fire tree regeneration.

In a study of annual regeneration and growth for 10 years following wildfire in the eastern Cascade Range of Washington, Littlefield ( 2019 ) found that establishment rates of lodgepole pine (and other species) were highest when growing seasons were cool and moist. A lagged climate signal was apparent in annual growth rates, but standardized climate–growth relationships did not vary across topographic settings, suggesting that topographic setting did not decouple site conditions from broader climatic trends to a degree that affected growth patterns. These results underscore the importance of favorable post-fire climatic conditions in promoting robust establishment and growth while highlighting the importance of topography and stand-scale processes ( e.g., seed availability and delivery). Although concerns about post-fire regeneration failure may be warranted under some conditions, failure is not a general phenomenon in all places and at all times (Littlefield 2019 ).

If warming climate trends continue as projected, without (or even with) tree planting, loss of forests may occur on the driest sites in the Northwest (Donato et al. 2016 ; Harvey et al. 2016c ; Stevens-Rumann et al. 2017 ), particularly east of the Cascade crest and in southwestern Oregon. Individual drought years are not likely to alter post-fire successional pathways, especially if wet years occur between dry years (Tepley et al. 2017 ; Littlefield 2019 ). Recruitment of conifers following a disturbance can require years to decades in the Northwest (Little et al. 1994 ; Shatford et al. 2007 ; Tepley et al. 2014 ). Thus, shrubs or grasses may dominate during drought periods, but conifers could establish and overtop shrubs and grasses during wetter and cooler periods (Dugan and Baker 2015 ; Donato et al. 2016 ).

Management actions

More frequent and larger wildfires in Northwest forests will likely be a major challenge facing resource managers of public and private lands in future decades (Peterson et al. 2011a ). Adapting forest management to climate change will help forest ecosystems transition to new conditions, while continuing to provide timber, water, recreation, habitat, and other benefits to society. Starting the process of adaptation now, before the marked increase in wildfire expected by the mid twenty-first century, will likely improve options for successful outcomes. Fortunately, some current forest management practices, including stand density management and surface fuel reduction in dry forests, and control of invasive species, are “climate smart” because they increase resilience to changing climate and disturbances (Peterson et al. 2011a ; Peterson et al. 2011b ).

Resource managers will likely be unable to prevent increasing broad-scale trends in area burned with climate change, but fuel treatments can decrease fire intensity and severity locally (Agee and Skinner 2005 ; Peterson et al. 2005 ). In drought- and fire-prone forests of the Northwest ( e.g., ponderosa pine and dry mixed-conifer forests east of the Cascades and in southwestern Oregon), reducing forest density can decrease crown fire potential (Agee and Skinner 2005 ; Safford et al. 2012 ; Martinson and Omi 2013 ; Shive et al. 2013 ), and negative effects of drought on tree growth (Clark et al. 2016 ; Sohn et al. 2016 ). Even in wetter forest types, reducing stand density can increase water availability, tree growth, and tree vigor by reducing competition (Roberts and Harrington 2008 ). Decreases in forest stand density, coupled with hazardous fuel treatments, can also increase forest resilience to wildfire in dry forest types (Agee and Skinner 2005 ; Stephens et al. 2013 ; Hessburg et al. 2015 ).

In dry forests, forest thinning prescriptions may need to reduce forest density to increase forest resistance and resilience to fire, insects, and drought (Peterson et al. 2011a ; Sohn et al. 2016 ). For example, in anticipation of a warmer climate and increased fire frequency, managers in Okanogan-Wenatchee National Forest in eastern Washington are currently basing stocking levels for thinning and fuel treatments on the next driest forest type. Thinning and fuel treatments could also be prioritized in (1) locations where climate change effects, particularly increased summer drought, are expected to be most pronounced ( e.g., on south-facing slopes); (2) high-value habitats; and (3) high-risk locations such as the wildland–urban interface. Fuel treatments must be maintained over time to remain effective (Agee and Skinner 2005 ; Peterson et al. 2005 ). Insufficient financial resources, agency capacity constraints, and air quality constraints on prescribed burning are harsh realities that will in most cases limit the extent of fuel treatments (Melvin 2018 ), necessitating strategic implementation of treatments in locations where fuel reduction will maximize ecological, economic, and political benefits.

Fewer options exist for reducing fire severity in wetter, high-elevation and coastal forests of the Northwest, historically characterized by infrequent, stand-replacement fire regimes (Halofsky et al. 2018b ). In these ecosystems, thinning and hazardous fuel treatments are unlikely to significantly affect fire behavior, because fires typically occur under extreme weather conditions ( i.e., during severe drought). However, managers may consider installing fuel breaks around high-value resources, such as municipal watersheds, key wildlife habitats, and valuable infrastructure, to reduce fire intensity and facilitate fire suppression efforts (Syphard et al. 2011 ). In addition, ecosystem resilience to a warmer climate is likely to improve by promoting landscape heterogeneity with diverse species and stand structures, and by reducing the effects of existing non-climatic stressors on ecosystems, such as landscape fragmentation and invasive species (Halofsky et al. 2018b ).

The future increase in fire will put late-successional forest at risk, potentially reducing habitat structures (large trees, snags, downed wood) that are important for many plant and animal species. In dry forests, some structures can be protected from fire by thinning around them and reducing organic material at their base (Halofsky et al. 2016 ). To increase habitat quality and connectivity, increasing the density of these structures may be particularly effective in younger forests, especially where young forests are in close proximity to late-successional forest.

Regeneration failures after fire are a risk with changing climate, particularly for drier forests. A primary method to help increase natural post-fire regeneration is to increase seed sources by both reducing fire severity (through fuel treatments and prescribed fire) and increasing the number of live residual trees (Dodson and Root 2013 ). In areas adjacent to green trees, natural regeneration may be adequate. In locations farther than 200 m from living trees, managers may want to supplement natural regeneration with planting where costs are not prohibitive because of remoteness or topography (North et al. 2019 ). Where post-fire planting is desirable, managers may consider changes from current practices. For example, they may want to consider lowering stocking density and increasing the spatial heterogeneity of plantings to increase resilience to fire and drought (North et al. 2019 ). Planting seedlings on cooler, wetter microsites will also likely help to increase survival (Rother et al. 2015 ). Managers may also consider different genetic stock than has been used in the past to increase seedling survival (Chmura et al. 2011 ). Tools such as the Seedlot Selection Tool ( https://seedlotselectiontool.org/sst ) can help identify seedling stock that will be best adapted to a given site in the future.

In general, regeneration in the driest topographic locations may be slower in a warming climate than it has been in the past. Some areas are likely to convert from conifer forest to hardwoods or non-forest (shrubland or grassland) vegetation, particularly at lower treeline. Managers may need to consider where they will try to forestall change and where they may need to allow conversions to occur (Rother et al. 2015 ).

Finally, collaboration among many groups—land management agencies, rural communities, private forest landowners, tribes, and conservation groups—is needed for successful adaptation to the effects of a warmer climate on wildfire (Joyce et al. 2009 ; Spies et al. 2010 ; Stein et al. 2013 ). Working together will ensure a common vision for stewardship of forest resources, and help produce a consistent, effective strategy for fuel treatments and other forest practices across large forest landscapes.

Uncertainties and future research needs

Changing disturbance regimes will accompany climate change in the Northwest (Tables 1 , 2 and 3 ). However, uncertainties remain, many related to future human behavior relative to greenhouse gas emissions, the rate and magnitude of climate change, and effects on vegetation and fire regimes. Human activities will also affect fire through land use and management, fire ignitions, and fire suppression, all of which are difficult to predict. For example, societal priorities may change, affecting forest management and vegetation conditions. Fire suppression is likely to continue in the future, but may become less effective under more extreme fire weather conditions (Fried et al. 2008 ), affecting area burned.

Historical relationships between climate and fire in the Northwest indicate that the ENSO and PDO can influence area burned. However, it is unclear how climate change will affect these modes of climatic variability or how they may interact with the effects of climate change on natural resources; global climate models differ in how these cycles are represented and in how they are projected to change. The frequency and persistence of high blocking ridges in summer (which divert moisture from the region) will also affect fire frequency and severity in the region, and climate change may affect the frequency of these blocking ridges (Lupo et al. 1997 ).

The lack of fire over the last few centuries in forests with low-frequency and high-severity fire regimes creates uncertainty in fire projections for the future. Although the likelihood of a large fire event in these forests is low, if large fire events start occurring as frequently as some models project ( e.g., Rogers et al. 2011 ), then major ecological changes are likely. Updating models as events occur over time may help to adjust projections in the future.

Shifts in forest productivity and composition are highly likely to occur with climate change in the region, which could affect fuel levels. However, it is uncertain how carbon dioxide fertilization will interact with moisture stress and disturbance regimes to affect forest productivity (Chmura et al. 2011 ) and thus fuel levels. Increased forest productivity, combined with hot and dry conditions in late summer, would likely produce large and severe fires (Rogers et al. 2011 ). Continued research on the potential effects of carbon dioxide fertilization on forest productivity will help to improve fire severity projections.

Other high-priority research needs include determining forest ecosystem response to multiple disturbances and stressors ( e.g., effects of repeated fire and drought on forest regeneration), and determining post-fire regeneration controls across a range of forest types and conditions. Identifying locations where vegetation type shifts ( e.g., forest to woodland or shrubland) are likely because of changing climate and disturbance regimes will help managers determine where to prioritize efforts. Managers will also benefit from evaluation of pre- and post-fire forest treatments to increase resilience or facilitate transition to new conditions in different forest types.

Although this synthesis is focused on the effects of climate change on fire and vegetation, many secondary effects are expected for natural resources and ecosystem services, some of which are already occurring. Climate change is reducing snowpack (Mote et al. 2018 ) and affecting hydrologic function in the Northwest, including more flooding in winter and lower streamflow in summer (Luce and Holden 2009 ). Higher stream temperatures are degrading cold-water fish habitat (Isaak et al. 2010 ). Altered vegetation and snowpack are expected to have long-term implications for animal habitat (Singleton et al. 2019 ). Recreational opportunities (Hand et al. 2019 ), infrastructure on public lands (Furniss et al. 2018 ), and cultural values (Davis 2018 ) will likely also be affected by changing climate, fire, and other disturbances.

Uncertainties associated with climate change require an experimental approach to resource management; using an adaptive management framework can help address uncertainties and adjust management over time. In the context of climate change adaptation, adaptive management involves: (1) defining management goals, objectives, and timeframes; (2) analyzing vulnerabilities and determining priorities; (3) developing adaptation options; (4) implementing plans and projects; and (5) monitoring, reviewing, and adjusting (Millar et al. 2014 ). Scientists and managers can work together to implement an adaptive management framework and ensure that the best available science is used to inform management actions on the ground.

Availability of data and materials

Please contact the corresponding author for data requests.

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Acknowledgements

We thank D. Donato, L. Evers, B. Glenn, M. Johnson, V. Kane, M. Reilly, and three anonymous reviewers for providing helpful suggestions that improved the manuscript. P. Loesche provided valuable editorial assistance, J. Ho assisted with literature compilation and figures, and R. Norheim developed several maps.

Funding was provided by the US Department of the Interior, Northwest Climate Adaptation Science Center, and the US Forest Service Pacific Northwest Research Station and Office of Sustainability and Climate. None of the funding bodies played any role in the design of the study, interpretation of data, or writing the manuscript.

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JH led the study and contributed to information collection, analysis, and interpretation, and co-wrote the paper. DP contributed to information collection, analysis, and interpretation, and co-wrote the paper. BH contributed to information collection, analysis, and interpretation, and co-wrote the paper. All authors read and approved the final manuscript.

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Halofsky, J.E., Peterson, D.L. & Harvey, B.J. Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA. fire ecol 16 , 4 (2020). https://doi.org/10.1186/s42408-019-0062-8

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Estimating Carbon Sequestration Potential of Forest and Its Influencing Factors at Fine Spatial-Scales: A Case Study of Lushan City in Southern China

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Accurate prediction of forest carbon sequestration potential requires a comprehensive understanding of tree growth relationships. However, the studies for estimating carbon sequestration potential concerning tree growth relationships at fine spatial-scales have been limited. In this paper, we assessed the current carbon stock and predicted sequestration potential of Lushan City, where a region has rich vegetation types in southern China, by introducing parameters of diameter at breast height (DBH) and tree height in the method of coupling biomass expansion factor (BEF) and tree growth equation. The partial least squares regression (PLSR) was used to explore the role of combined condition factors (e.g., site, stand, climate) on carbon sequestration potential. The results showed that (1) in 2019, the total carbon stock of trees in Lushan City was 9.22 × 10 5 t, and the overall spatial distribution exhibited a decreasing tendency from northwest to south-central, and the carbon density increased with elevation; (2) By 2070, the carbon density of forest in Lushan City will reach a relatively stable state, and the carbon stock will continue to rise to 2.15 × 10 6 t, which is 2.33 times of the current level, indicating that Lushan forest will continue to serve as a carbon sink for the next fifty years; (3) Excluding the effect of tree growth, regional forest carbon sequestration potential was significantly influenced on site characteristics, which achieved the highest Variable Importance in Projection (VIP) value (2.19) for slope direction. Our study provided a better understanding of the relationships between forest growth and carbon sequestration potential at fine spatial-scales. The results regarding the condition factors and how their combination characteristics affect the potential for carbon sequestration could provide crucial insights for Chinese carbon policy and global carbon neutrality goals.

1. Introduction

As an integral component of terrestrial ecosystems, forest ecosystems are a massive global carbon reservoir [ 1 ]. Forests sequester 2/3 of the total terrestrial carbon sequestration annually [ 2 ]. They perform a critical and irreplaceable function in lowering the rate of accumulation of greenhouse gases in the atmosphere, which helps to mitigate global warming [ 3 ]. Since the 1980s, due to large-scale afforestation programs, forests in southern China have accounted for more than 65% of the national carbon sink [ 4 , 5 ], much higher than in northern regions. At the present stage, China’s strategic goal of “reaching a carbon peak by 2030 and achieving carbon neutrality by 2060” requires a focus on emission reduction and sink enhancement, so it is necessary to quantify the current carbon stock and sequestration potential, i.e., the maximum carbon capacity that can be stored in forest ecosystems without human interference [ 6 ]. For a thorough understanding of the role of forest ecosystems in the carbon cycle, an accurate calculation of the carbon sequestration potential of forest ecosystems is required. Not only does it aid in quantifying the impacts of forests on global warming, it also aids forest management decision-making processes [ 7 , 8 ].

Existing methods for estimating carbon sequestration potential are shaped by the extension of carbon stock estimates. The estimation of carbon stocks using the biomass expansion factor (BEF) is considered relatively reliable [ 9 ], which determines the forest biomass and forest volume as a fixed ratio and estimates the carbon stock of the region by the mean ratio method (MRM) [ 10 ]. The continuous BEF method was proposed by Fang et al. and was used to estimate the carbon stock of forests in China [ 11 ]. The forest carbon sequestration potential is the difference between the maximum forest carbon capacity and the current (or a given year) forest carbon stock. Since the carbon density of mature forests can represent the maximum carbon density of forests in similar regions, the carbon stock at this time is frequently assumed to be the maximum forest carbon capacity [ 12 ]. In the natural state, the carbon stock of forest vegetation usually increases rapidly with the increase of forest age (successional stage), then slows down and reaches a steady state [ 13 ]. This increasing trend, described as S-shaped, was also reported by Taylor et al. [ 14 ] and Rothstein et al. [ 15 ]. The carbon stock of existing forests in China increases with the age of the forest, and all types of forests at different age stages can sustain carbon sequestration [ 12 ].

Current studies have introduced age into the estimation, using the relationship between biomass density and tree age to estimate carbon sequestration potential. Mostly used for large-scale study areas, such as the whole of China [ 16 ] and Finland [ 17 ], the estimation method has been thoroughly developed. Due to the large geographical span, diverse climate types, and complex tree growth in the large-scale areas, it is feasible to use this simplified connection to estimate carbon sequestration potential as a reference value. However, for the fine-scale regions, such as the county-level study areas in Hebei Province [ 18 ] and Tibet Autonomous Region [ 19 ], the carbon density and carbon sequestration potential of forest vegetation in 2050 were estimated by directly fitting the biomass-forest age relationship using the biomass converted from storage volume, ignoring the fact that the change of forest stock volume is disturbed by various conditions and is an artificial estimate during the survey [ 20 ]. This does not accurately reflect the growth of trees, and the estimation for this scale is still questionable, affecting the actual forestry carbon sink project design. As a result, we take the more accurate depiction of tree growth as an entry point. According to the widely established model for estimating storage volume, i.e., the binary standing volume model, DBH and tree height can visually represent the growth of volume [ 21 ], which can be combined with the tree growth equation [ 22 ] to reduce the uncertainty in estimation. The stochastic simulation is used to more accurately represent the change in accumulation volume during the growth of trees and to improve the accuracy of estimating forest carbon sequestration potential.

The forest carbon sequestration potential is not only influenced by forest growth but also by climatic factors [ 23 ], topographic factors [ 24 ], land use change [ 25 ], management measures [ 26 ], etc. Since the carbon stock of forest ecosystems is the fundamental parameter for studying the carbon exchange between forest ecosystems and the atmosphere [ 27 ], these existing studies mostly choose the current state of carbon stock as the variable and analyze its influencing factors, ignoring the growth status of the forest and focusing solely on the impact of environmental conditions under the current state of carbon stock [ 24 , 28 ]. The current carbon stock is influenced by age group composition and dominant species type, so the carbon stock is often not stable [ 12 ]. The carbon sequestration potential is the maximum possible growth of forest carbon stock under the current scenario, which is the predicted result after the dynamic growth of the forest. At this time, the average age of all tree species has reached the mature forest stage, and the carbon stock is relatively stable, which is convenient to reveal the relationship between the carbon sequestration potential and the current condition factors in the study area. Furthermore, most previous research has concentrated on single components such as elevation factor, canopy density, rainfall factor, and so on [ 23 , 24 , 28 ], and less attention has been paid to the combination characteristics among factors. The forest growth condition of the carbon sequestration potential is used in our study as an entry point to analyze the effect of single factors of the condition factor on forest carbon sequestration. Based on this, we also try to combine single factors of the same type and examine the magnitude to which influence on carbon sequestration potential among various combined features, this will assist in eliminating the interference of uninterpretable information such as multiple correlations and better analyze the influence of multiple condition factors such as site, stand, and climate in a comprehensive manner.

Given the above, Lushan City in southern China, a “natural laboratory” for studying forest ecology [ 29 ], was selected as a case region of the study. The specific objectives of the study were to (1) better understand the relationships between forest growth and carbon sequestration potential at the fine-scale study area; (2) estimate the region’s carbon sequestration potential by analyzing the current characteristics of carbon stock; and (3) reveal the influence of condition factors and their combination characteristics (site, stand, and climate) on carbon sequestration potential without forest growth disturbances.

2. Materials and Methods

2.1. study area.

Lushan City (115°49′42″–116°8′18″ E, 29°9′6″–29°38′32″ N) is located in the north of Jiangxi Province, with a total area of 764.54 km 2 ( Figure 1 ). The city, in the East Asian monsoon region, has a humid subtropical climate influenced by both Lushan Mountain and Poyang Lake. The annual average temperature is 15.3~17.3 °C, the precipitation is uneven in all seasons, and the dry and wet seasons are obvious. Lushan City owns Lushan Nature Reserve, which has been operating for 41 years since 1981 and has a subtropical forest ecosystem as its main conservation object. The forest coverage rate has increased from 42.00% before the establishment of the nature reserve to 80.70% at present, and the forest resources are abundant and well-maintained, with a wide diversity of tree species [ 29 , 30 ]. The total area of arboreal woodland in Lushan is 274.47 km², consisting of (i) natural forests (formed by natural underplanting, artificially promoted renewal or sprouting after a disturbance such as natural forest harvesting) and (ii) planted forests (formed entirely by machine seeding or artificial sowing, such as seedling planting, seeding and fly sowing). Among them, the area of natural forest is 201.30 km 2 (73.34%), and the area of planted forest is 73.17 km 2 (26.66%). Based on the main dominant species of the forest fine patches in the forest management inventory, the forest patches were classified into 7 types: Pinus massoniana , Pinus taiwanensis , and Pinus elliottii constitute pine forest (PF); Cunninghamia lanceolata and Cryptomeria japonica constitute Chinese fir forest (CFF); Cinnamomum camphora , Quercus L. and other hard broad species constitute broadleaf hardwood (BLH); Populus L., Paulownia fortunei and other soft broad species constitute broadleaf softwood (BLS); and mixed coniferous forest (MCF), mixed broadleaf forests (MBF) and mixed conifer-broadleaf forests (MCBF). Among them, PF (41.86%), MCF (18.30%), and CFF (17.95%) accounted for a higher percentage. According to the age of trees, the patches of forests in Lushan are mainly young and middle-aged, with the majority of trees between 20–40 years old (59.33%) and very few patches with an average age of more than 60 years old (0.72%). The complex and varied mountainous landscape of Lushan presents an elevation difference of about 1465 m. The vegetation shows more obvious vertical distribution characteristics, and 81% of the forest patches have an elevation greater than 100 m. Furthermore, initiatives including closing hills for afforestation, rehabilitating degraded forests, and tending to forests have been taken seriously in Lushan City to increase their capacity as carbon sinks (For example, the above projects involved 400 ha of forest in 2019). In conclusion, Lushan City has a diverse range of forest types and a considerable mountain microclimate, with the typical characteristics of subtropical mountain forests in southern China.

Location, topography, basic forest information, and forest patch distribution in the study area. ( a ) shows the location and topography of the area; ( b ) shows the distribution of different forest types. Based on the main dominant species of the forest fine patches in the forest management inventory, the forest patches were classified into 7 types: PF refers to pine forest composed of Pinus massoniana , Pinus taiwanensis , and Pinus elliottii ; CFF refers to Chinese fir forest composed of Cunninghamia lanceolata and Cryptomeria japonica ; BLH refers to hard broad forest composed of Cinnamomum camphora , Quercus L. and other hard broad species; BLS refers to soft broad forest composed of Populus L., Paulownia fortunei and other soft broad species; and three types of mixed forests: mixed coniferous forest (MCF), mixed broadleaf forests (MBF) and mixed conifer-broadleaf forests (MCBF); ( c ) shows the area share of different forest patches; ( d ) shows average age composition of forest patches; ( e ) shows the origin of the forest patches (natural forest/planted forest).

2.2. Data Sources

Forest management inventory is an important basic task for understanding the current state of forest resources and the ecological environment, providing a foundation for a scientific formulation of forestry development planning. The base data for this study was obtained from the forest management inventory data (FMID) in Lushan City, Jiangxi Province. Excluding economic forest, shrub forest, bamboo forest, and other forest patch types (The above types are incomplete in FMID), there were 5162 forest patches. After removing invalid data, 5077 valid forest survey patches were obtained. The survey recorded 76 forest class factors, including (i) site conditions (e.g., average elevation, slope direction, slope gradient, soil thickness, etc.); (ii) stand characteristics (average tree age, average DBH, average tree height, canopy density, etc.); and (iii) evaluation factors (forest naturalness, stand protection class, etc.), which provide sufficient variables and considerations for modeling. The survey accuracy of the sampled volume and forest patch factors is ensured through systematic sampling, and the overall regional volume accuracy reaches 80–85%; the error of the average tree height does not exceed 10%, and the error of the average DBH does not exceed 1 cm; the allowable error of the average age of natural forests is less than one age class period (10 years for PF, BLH, 5 years for CFF, BLS, mixed forests depending on the actual species composition), and the average age of planted forests is basically error-free. Furthermore, the FMID was completed in 2019, and we traveled to the field in September 2021 to conduct research and select some typical sample sites to verify the data’s legitimacy.

Climate data were obtained from nine meteorological stations in and around Lushan city from 2014 to 2018 daily rainfall and average temperature data from the China Meteorological Science Data Sharing Service ( http://data.cma.cn/ , accessed on 15 June 2022). Radiation data were high-temporal (3 h) surface solar radiation data from 2014 to 2017 sunshine hours in the Lushan area from the National Qinghai-Tibet Plateau Scientific Data Center [ 31 ] ( http://www.tpdc.ac.cn , accessed on 15 June 2022). Data points were extracted using the fishnet extraction tool and then spatially interpolated using the inverse distance weighting (IDW) approach in ArcGIS Pro2.5 [ 32 ] to create the grid data of multi-year average temperature, precipitation, and radiation. We compared the effect of IDW interpolation with other spatial interpolation of climate data in the Poyang Lake basin study [ 33 ] and finally chose to use the result of the IDW for influence factor analysis. All raster spatial resolutions were unified at 30 m, and the projection coordinate system was unified at CGCS2000_3_Degree_GK_Zone_39.

2.3. Methods for Estimating Carbon Sequestration Potential

2.3.1. dbh-tree height growth model.

There is an obvious positive correlation between tree standing volume and its DBH and tree height [ 11 ]. We used the binary standing volume model, which has sufficient accuracy and is the most widely used [ 21 ], to describe the functional relationship, as shown in Equation (1).

where V is the stumpage volume (m 3 ), D is the average DBH (cm), H is the tree height (m); a 0 , a 1 , a 2 are the parameters to be fitted. The FMID were counted by forest patches, and 3–5 standard trees of the dominant species were selected in each forest patch for measurement, and the average DBH ( D ) of the cross-sectional area was used as the DBH data, and the average tree height ( H ) was used as the tree height data. The classifications were fitted to a 0 , a 1 , and a 2 to obtain model parameters that better fit this study area.

Changes in tree DBH and tree height are distinctive features of the performance with increasing tree age, and we selected samples of forest patches with similar natural conditions, divided into age groups, and proposed a simplified model of DBH and tree height growth. After a sufficient number of data samples passed the Shapiro-Wilk normality test [ 34 ], it can be assumed that the mean DBH and mean tree height of forest patches of the same mean age follow a normal distribution, and the trend of the model normal distribution parameters with mean age can be studied further. The relationship between DBH-tree height and age is difficult to construct with a uniform expression, so an attempt was made to employ the existing growth models Gompertz, Logistic, Korf, Mitscherlich, and Richards growth functions [ 22 , 35 ]. The above models were used to fit nonlinear curves for the expectation of DBH-tree height and their age, respectively. Using the highest R² and lowest RMSE as the test criteria, the best growth model for the forest type in this region was determined to be the logistic [ 36 ], which was selected for subsequent analysis, as shown in Equation (2).

where Y is the DBH or tree height, T is the age of the tree, e is the natural exponential, c 0 , c 1 , and c 2 are the parameters to be fitted. This equation describes a three-parameter S-shaped growth curve, with c 0 showing the exact upper boundary of growth and c 1 , c 2 jointly determining the growth rate of the curve, which is an ideal population growth model with important ecological significance and is widely used.

2.3.2. Stochastic Simulation of Volume Growth

Under the premise that the mean DBH and tree height of the same mean age forest patch obey normal distribution, respectively, the mean volume of the forest patches should satisfy some joint probability distribution function of DBH and tree height according to the binary standing volume model (Equation (1)). Since the form of the distribution obtained from the solution of this function is complicated, it is not conducive to practical application. Therefore, we use MATLAB R2020b (9.9) and Origin 2019b to conduct a stochastic simulation. Based on the age series and the DBH-tree height growth function, the samples of DBH and tree height were drawn reflecting normal distribution. Further, we got a sample matrix of volume. The sample means were used as point estimates, leading to the expectation of volume under different forest types and ages. By stochastic simulation of volume growth, we gained a more accurate fit to the logistic growth function. More details about stochastic simulation can be found in Appendix A .

2.3.3. Estimation of Carbon Sequestration Potential

Tree biomass density was significantly and linearly positively correlated with volume density [ 11 ] (Equation (3)), and forest carbon stock estimates were derived by multiplying forest biomass by the amount of elemental carbon in the biomass (i.e., the carbon content factor). Carbon density is the amount of carbon stored per unit area of forest biomass.

where W is the biomass density (kg/ha), V is the volume density (m³/ha), β 1 , β 0 are model parameters, mainly based on the forest type conversion model proposed by Fang et al. [ 11 ] and Zeng et al. [ 37 ]. Due to the different tree species composition, age, and population structure of different vegetation types [ 38 ], the carbon content conversion coefficients may vary greatly. In this study, forest carbon stocks were measured based on the carbon content coefficients of each tree species (group) in the “Guidelines for carbon sink measurement and monitoring in afforestation projects” issued by the State Forestry Administration and previous research results [ 39 , 40 ] ( Table 1 ).

BEF parameters and carbon content coefficients of forest types.

PF refers to pine forest; CFF refers to Chinese fir forest; BLH refers to hard broad forest; BLS refers to soft broad forest; and three types of mixed forests: MCF refers to mixed coniferous forest, MBF refers to mixed broadleaf forests, and MCBF refers to mixed conifer-broadleaf forests.

Carbon sink capacity indicates the ability of vegetation to fix carbon per unit time, expressed as the increment of carbon stock in a certain time (Equation (4)). When the carbon density of the forest is relatively stable, the carbon sequestration potential is the difference between the carbon stock tending to the maximum and the carbon stock in the current year.

where the annual carbon sink C S is the difference between the corresponding carbon stocks of C t and C t − 1 in adjacent years and is equivalent to the product of the carbon content factor and the biomass. Equations (3) and (4) were implemented on Origin 2019b software.

2.4. Influencing Factors Analysis Method

In this study, the PLSR was used to explore the conditional factors of carbon sequestration potential [ 41 , 42 ] to effectively remove the interference of non-interpretative information. The results of carbon sequestration potential values were used as dependent variables to analyze the influence of single trait factors. A combination of single traits of the same type was attempted to construct the combined traits separately ( Table 2 ) to reflect the degree of influence of a certain factor type comprehensively.

Selection of single-factor and combined characteristics of the conditional factors.

X ˜ is the normalization of single factor X in the table.

Multiple correlation diagnostics were first performed to calculate the variance inflation factor ( V I F ). It is generally considered that when V I F > 10, multiple correlations among the factors will seriously affect the estimates of partial least squares. After testing, all single and combined factors satisfy V I F < 10, indicating no significant linear correlation between the factors and can be used for PLSR. The factors that passed the diagnostic were selected for PLSR, and Variable Importance in Projection ( V I P ) was calculated to indicate the degree of explanation of the standard deviation by the factors, as shown in Equation (5).

where q denotes the number of variables involved in the analysis, m denotes the number of iterations, and in the i th iteration, r ( Y , x i ) is calculated as the correlation coefficient between the dependent variable and all variables. w i j is the weight of variable j , which reflects the degree of explanation of the variables in the model. The sum of squares of V I P values of all variables is equal to 1. Factors with V I P < 1 are considered to have a low degree of explanation of the model, and factors with V I P ≥ 1 have a high degree of explanation. All the above tests were performed with Python 3.9.

3.1. Modeling Results of Tree Forest Volume Growth

The fitted parameters of the binary standing volume model and growth simulation for each type of forest in Lushan City are shown in Table 3 . The fitted parameters of the binary standing volume model and the DBH-Tree Height growth model generally had R² values above 0.90, which were well fitted, as shown in Figure 2 . For each age group of different forest types, the growth function of volume expectation with tree age was fitted. It was found that the fitted logistic curves using continuous derivable logistic curves yielded good fitting results for the volume expectancy as a function of mean age. For the curves of relative tree height, relative DBH, and forest volume with age for specific forest types, please refer to Figure A1 in Appendix B . The fitted models for the major forest types showed statistical significance at the 0.01 level, and the R² values close to 1 confirmed the good applicability of the model for estimating tree forest volume in Lushan. This volume growth model illustrated the relationship between the volume of a dominant species forest with age in a simplified form, which provides a good basis for the estimation and prediction of carbon sequestration potential.

Fitting the growth model of accumulation volume of the forest types in Lushan City. ( a ) shows the tree height-standardized age fitting relationship; ( b ) shows the DBH-standardized age fitting relationship; ( c ) shows the accumulation volume expectation-mean age fitting relationship after random simulation. ( a – c ) compare the relative tree height, relative DBH, and accumulation volume with age curves of different forest types, respectively, which illustrates that the Logistic function has a good fitting effect and also describes the differences between the curves of different forest types, fully reflecting the growth characteristics of forest types in Lushan City. (The points in the graph were forest patches sampling in FMID data).

Fitting results of the volume growth models in different forest types.

3.2. Characteristics of the Current Carbon Sequestration Capacity of Tree Forests

The average carbon density of tree forests in Lushan City in 2019 was 33.59 t/ha. The current state of carbon density showed an overall distribution pattern of high in the northwest and low in the south, decreasing from north to south, as shown in Figure 3 . The carbon density contribution of different age groups of forest types at various altitudes was analyzed. Forest patches were more distributed at 0–100 m and 100–300 m altitudes, and the carbon density was 26.41–28.97 t/ha here, which was lower than the average carbon density of the study area (33.59 t/ha). The carbon density increased with elevation in the four gradient intervals higher than 300 m, closely related to the forest types at various elevations. The main contributing forest types for carbon density were CFF and MCF in the 300–600 m and 600–900 m gradient intervals. PF was the most significant contributory species in the other four gradient intervals, particularly in the highest elevation interval (1200–1465 m), where PF accounted for a considerable proportion under all conditions.

Characteristics of the current carbon sequestration capacity of forests in Lushan City. The carbon sequestration capacity of forests in Lushan City was calculated from four aspects: spatial distribution, elevation, age group, and forest types. The geographic distribution map in the middle shows the spatial distribution of carbon density of forests in 2019; the carbon density stacking figures on both sides show the carbon density share of each age group of forest types in Lushan City at (0, 100], (100, 300], (300, 600], (600, 900], (900, 1200], and (1200, 1465] altitude gradients.

In 2019, the volume of forest storage in Lushan City was 2.34 × 10 6 m³, the biomass was about 1.73 × 10 6 t, and the total carbon stock was 9.22 × 10 5 t ( Table 4 ). The carbon stock indicates the overall state of a forest type, and the percentage of carbon stock contributed by each type of forest varies. Among them, the four forest types of PF, CFF, MCF, and MCBF provided 86.12% of carbon stock, with PF mainly providing 33.39% of carbon stock. The annual carbon sink of the Lushan forest was 3.02 × 10 4 t from 2019 to 2020, and its main contributing sources were PF (40.66%), CFF (15.39%), and MCF (19.50%). The average carbon density of tree forests in Lushan had grown about 1.10 t/ha/a with a growth rate of 3.28% from 2019 to 2020. The lowest growth rate of BLS was 0.74 t/ha/a with a growth rate of 1.84%, and the highest growth rate of MBF was 1.35 t/ha/a with a growth rate of 3.41%.

Status of carbon density/carbon stock in forest patches of different types.

3.3. Predicted Carbon Sequestration Potential of Tree Forests

The relationship between the carbon density of forests and tree age was examined based on the distribution of current forest age groups. A significant increase in carbon density will experience in the next 20 to 50 years, and it will achieve a stable state after 50 years. This relationship indicates that the upper limit of carbon density will be between 55 and 75 years (The year here refers to the average age of the forest stands). The carbon density of Lushan City will reach a relatively stable state in 2070, achieving the maximum carbon sequestration potential in the study area ( Figure 4 ). The change of overall carbon stock in tree forests from 2019 to 2070 shows an upward trend of decreasing growth rate: The carbon stock continuously will increase from 9.22 × 10 5 t to 2.15 × 10 6 t, and the overall carbon density will raise from 33.59 t/ha in 2019 to 78.33 t/ha in 2070, increasing to 2.33 times of the original one. The potential carbon sequestration is about 1.23 × 10 6 t, with a higher contribution from PF and MCF ( Figure 5 ). PF has the highest carbon sequestration potential because of its absolute dominance of the land area. The annual carbon sink of tree forests shows a trend of increasing and then decreasing: the highest annual carbon sink will occur in 2030 with 3.39 × 10 4 t, and will decrease to 8.06 × 10 3 t in 2070 ( Figure 5 ). The peak yearly carbon sink of diverse dominant species forest patches occurs in different years due to the varied tree species structure and age composition. Among them, the peak annual carbon sink of CFF is the earliest, reaching the maximum in 2019; the peak annual carbon sink of MCF is the latest, reaching the maximum in 2039; the remaining dominant tree types will reach the peak annual carbon sink in 2022–2032.

Box-and-whisker plot for forests carbon density by age in Lushan City. The figure shows the median, 25th and 75th percentile, mean (triangles), range, and extreme values outside the range (the proportion of the interquartile range past the low and high quartiles is 1.5, points outside this range will be identified as outliers).

Carbon sequestration potential of different forest types in Lushan City from 2019–2070: ( a ) depicts the change of carbon stock of forest types in Lushan, ( b ) records the contribution of different forest types to the total carbon stock more visually in percentage; ( c ) depicts the change of annual carbon sink of forests types in Lushan; and the contribution of different forest types to the total annual carbon sink is visually represented in ( d ); ( e ) compares the change of carbon stock of different forest types, and ( f ) compares the annual carbon sink changes of different forest types and records the peak and arrival years.

The carbon sequestration potential of natural forests is significantly higher than that of planted forests ( Figure 6 ). And the carbon stock of natural forests is about 2.31 times higher than that of planted forests in 2019, increasing to 3.15 by 2070. The annual carbon sinks in planted forests will peak between 2025 and 2026, while that of natural forests will peak between 2031 and 2032. The growth rate of carbon density in natural forests is also consistently higher than that in planted forests, with both reaching the same level between 2035 and 2036. By 2070, the carbon density of natural forests will reach 80.03 t/ha, higher than that of planted forests at 69.99 t/ha, indicating that natural forests can provide a more effective carbon sequestration function for Lushan City.

Carbon sequestration potential of natural and planted forests in Lushan City from 2019–2070: ( a ) shows the change of carbon stock, the two curves show an upward trend of decreasing growth rate, and the natural forest curve is always above the planted forest; ( b ) shows the change of annual carbon sink, the two curves show an increasing and then decreasing trend; ( c ) shows the change of carbon density, the two curves show an upward trend, and the natural forest carbon density exceeds the planted forest in 2040. ( d ) is the change of carbon density growth rate, and the change trend is similar to ( b ).

3.4. Exploration of Factors Influencing Carbon Sequestration Potential

As shown in Figure 7 , we analyzed the single factors of all samples, in which the VIP values of slope direction (2.19), slope gradient (1.24), and soil thickness (1.02) were greater than 1. Slope direction (SD) had the highest importance, indicating that the carbon sequestration potential was significantly influenced by site characteristics. Adding the combination factors for analysis, the VIP value of stand characteristics was 1.29 based on the original key factors. All were higher than their three single factors (forest density (1.28), vegetation cover (0.44), and canopy density (0.42)), indicating that the combination of stand characteristics had stronger explanations than the single factors. The effect of combined factors of site conditions and climatic factors was average and less important than some single factors. When the effect sizes of the combined factors were compared, the explanatory effects of both stand characteristics (1.27) and climatic factors (1.16) were larger than 1, with the explanatory effects of stand characteristics being stronger than those of site characteristics.

Variable Importance in Projection (VIP) for condition factors. ( a ) shows the comparison of VIP values between single factors for overall, natural forest, and planted forest; ( b ) shows the comparison of VIP values between combined features for overall, natural forest, and planted forest; ( c ) shows the comparison of VIP values between single factors and combined features. Mean elevation (ELE), Slope direction (SD), Slope gradient (SG), Soil thickness (ST), Humus thickness (HT), Forest density (FD), Vegetation cover (VC), Canopy density (CD), Precipitation (PRE), Radiation (RAD), Temperature (TEM).

Furthermore, the parameters impacting carbon sequestration capability varied depending on the forest type. The VIP values of single factors of natural forests and the overall regional forests were not significantly different, and their key factors were all slope direction factors in site characteristics. It is worth noting that the influencing factors of carbon sequestration potential of planted forests are different from the overall regional forests, and the VIP values of two factors, soil thickness (1.67) and vegetation cover (1.42), are greater than 1. Regarding the combination of characteristics, the climatic characteristics of both natural and planted forests had stronger explanatory effects than the single factors. Comparing the effect sizes among the combination characteristics, climate characteristics had higher explanatory effects on natural and planted forests, respectively. The explanatory role of site characteristics on the carbon sequestration potential of natural forests was high (1.18).

4. Discussion

4.1. estimation methodology and estimation results.

The biomass-forest age relationship has become a frequently utilized method for predicting future forest carbon pools and estimating forest biomass carbon stocks [ 16 , 43 , 44 ]. Existing research has mostly employed biomass-forest age connections to estimate carbon sequestration potential in larger-scale study areas, such as national [ 16 ] and provincial [ 45 ] scales, as well as incorporating stand age in the present stand growth model framework to reduce estimation bias [ 46 ]. However, unlike the predictive growth equation with DBH and tree height factors, Liu et al. [ 20 ] and Zhou et al. [ 47 ] showed that, while many studies have reported successful applications of fitting biomass-forest age relationships directly using biomass converted from forest volume [ 48 , 49 ], there are still questions about the accuracy and precision of volume estimates, particularly concerning reducing the uncertainty of model parameters [ 50 ]. As a result, explicitly fitting the biomass-age relationship using biomass transformed from volume fails to appropriately depict tree growth [ 51 ]. Since the actual forestry carbon sink projects are frequently carried out for fine scales, more accurate forecast results of carbon sequestration potential are required. In this study, considering the complexity of the forest survey samples in the fine-scale study area, the tree growth equation was re-fitted based on the characteristics of the binary standing volume model in which the DBH and tree height can visually represent the growth of storage volume, using the relationship between DBH, tree height, standing volume storage, and tree age. Compared with the original model, the model constructed in this study further refines the relationship between volume and age of a forest type using a stochastic simulation process, which can be applied even with limited forest biomass data and forest age observation, and provides a reference for the prediction of forest carbon sequestration potential at fine-scale regions.

In addition, our estimating results are consistent with previous research literature [ 52 , 53 , 54 ]. Excluding differences in the age structure of the study area and study methods, they are generally consistent with the results of previous research on carbon density estimation in Jiangxi Province ( Table 5 ). Compared with carbon density estimates at the same study scale in Jiangxi Province, the average carbon density in Lushan City estimated in this study was higher than the carbon density in Taihe County estimated by Wu et al. [ 55 ], and the carbon density in Xingguo County estimated by Li et al. [ 56 ]. A possible reason is that our study was investigated 16 years later than those two, during which the tree forest maintained stable growth and carbon density continued to increase. The average carbon density in Lushan City is close to 36.0 t/ha, which was estimated by Zhang et al. [ 24 ] in the whole of Jiangxi Province. Compared with the carbon density in Jiangxi Province estimated by Li et al. [ 52 ] and Wu et al. [ 53 ] (23.87–27.2 t/ha), the average carbon density in Lushan City is slightly higher, and its contribution to the forest carbon sequestration function in Jiangxi Province is greater. Compared with the predicted carbon sequestration potential of arboreal forests based on biomass-age relationships in previous literature, the results were similar to those predicted by Wu et al. [ 55 ] and Qiu et al. [ 54 ], indicating the reasonableness of the model. Additionally, compared with the national data, the carbon density in Lushan City in 2020 is lower than the 50.51 t/ha predicted by Zhang et al. [ 46 ] and the 59.8 t/ha predicted by Xu et al. [ 16 ], which may be mainly because the tree forests in Lushan City are dominated by middle-aged and young forests, and the forest management in Jiangxi Province is primarily rough management with slow growth [ 57 ]. The predicted carbon density in Lushan City in 2050 is close to the predicted values for the national forest carbon density in 2050, which indicates that the forest vegetation in Lushan City has significant potential for carbon sequestration.

Comparison with the estimated and predicted values of forest carbon density in Jiangxi Province from previous studies.

4.2. Factors Influencing Carbon Sequestration Potential

Based on the growth of forest age of different forest types, we quantified the future carbon sequestration potential of Lushan City forests. After incorporating information on stand developmental stages into predicting future forest carbon sequestration potential, this study found that forest carbon stocks accumulated rapidly at young ages and gradually saturated at later stages, which is consistent with He et al. [ 43 , 59 ]. After changes in forest carbon density have stabilized, mature and over-mature forests can also continue to accumulate carbon as stand age increases [ 60 ], and still hold a crucial role in the carbon cycle despite decreasing growth efficiency. Therefore, the carbon sequestration benefits given by forests as they grow and expand are ongoing. In addition to forest growth and development, forest carbon sequestration capability is intimately tied to large-scale afforestation and regional extension of ecological restoration efforts. In the next five decades, ecological restoration programs and sustainable forest management in China will increase forest area and biomass carbon intensity, making forests of various ages a carbon sink [ 46 ]. And according to the China Forestry Sustainable Development Strategy Research Group, the quantity and quality of China’s forests are expected to enter a phase of steady development, which implies that the capacity of increasing forest carbon sequestration potential may be limited. As a result, to acquire better forest carbon sequestration potential assuming normal forest growth and development, it is required to investigate the influence of condition factors on carbon sequestration potential.

The predicted carbon sequestration potential value was used as the dependent variable in this study. The site characteristics had a significant impact on carbon sequestration potential, with slope direction having the most impact, which was significantly and positively correlated with the value of carbon sequestration potential. This result is consistent with the previous regional research findings in Jiangxi Province. Wu et al. [ 61 ] examined the vegetation carbon density of major forests in the Poyang Lake basin. They discovered that slope direction and gradient had a substantial impact on vegetation carbon density. Since the slope direction, slope gradient, elevation, and other site features have redistribution effects on surface light, heat, and water resources, which affect the forest growth and, consequently, the carbon pool. The findings imply that the research area’s carbon sequestration capacity is greatly influenced by the azimuth of solar irradiation, and the sunny slope (i.e., south slope) may yield stronger carbon sequestration [ 24 ].

The key factors influencing the carbon sequestration capability of various origins’ forests are diverse, resulting in various management strategies. Natural forests and the overall forests in the region have comparable crucial features, and they are all tied to site characteristics. The protection of natural forests should be encouraged, and the slope direction and slope gradient should be emphasized in the implementation of natural forest protection projects, which will avoid the reduction of forest carbon sink capacity caused by problems such as soil erosion. On the other hand, the key factors of planted forests are soil thickness and vegetation cover. Relatively thicker soil and relatively higher vegetation cover can provide a higher carbon sink. Therefore, when predicting the carbon sequestration potential of planted forests in the future, the above factors can be considered as the main control factors for modeling to improve the prediction accuracy. To provide favorable conditions for the expansion of carbon sink in a planted forest, more consideration should also be given to the aforementioned components when developing planted forest initiatives. Furthermore, when the findings of the multifactor combination were compared, the climatic combination had a greater impact than the site and stand characteristics. The growing season was effectively extended by the rises in temperature and precipitation, which also increased microbial activity, photosynthetic capacity, and plant growth and respiration [ 62 ]. This improved the capacity of forests to store carbon [ 5 ]. Therefore, the climatic combination characteristics can be considered to incorporate into the prediction model, allowing multiple climate condition scenarios to be established to more correctly estimate the future carbon sequestration potential of forests.

4.3. Uncertainties and Potential Constraints

Carbon stocks in forest ecosystems are primarily influenced by two aspects. On the one hand, changes in forest biomass and the accompanying changes in the carbon cycle, and on the other hand, changes in the forest soil carbon pool, namely the balance between imports and losses of organic carbon into the soil [ 9 ]. Solar radiation also plays an important role in plant carbon sequestration. For example, sunny slopes can lead to strong soil mineralization and evapotranspiration, which may limit plant carbon sequestration. The estimation of carbon sequestration potential is somewhat biased because actual measurements of soil nutrient mineralization and evapotranspiration have not been carried out. Due to the lack of data on understory vegetation, herbaceous layer, deadwood layer, dead wood, and soil layer in the FMID, this study did not cover the carbon stocks of the categories mentioned above and only considered the carbon stocks of live trees, so the estimation of forest ecosystem carbon stocks in Lushan City was quite underestimated.

The predictions in this study are also based on certain assumptions, which lead to some uncertainties in the results: first, the maximum carbon sequestration potential is an estimate based on spatial and temporal intergeneration, assuming no forest disease or mortality, and that existing forests grow naturally according to the growth equation, which only represents the maximum potential that a forest type or age can achieve under ideal conditions. In actuality, forests are affected by disease and mortality during the growth process, which may result in exaggerated estimations of carbon sequestration potential [ 38 ]. Second, if China’s forestry development and forest cover expand, the fraction of newly generated forests may fluctuate in the forecast process [ 6 ]. On the other hand, there are high uncertainties in the tree species composition and age groups of newly created forests, which may lead to inaccurate prediction results [ 58 ]. Hence, the newly created forests are not included in the estimation, and the prediction of carbon sequestration potential is slightly underestimated.

Finally, the impacts of anthropogenic and natural disturbances on forest carbon sequestration were not considered. With the increasing emphasis on forest protection through regulations such as “peak carbon dioxide emissions and carbon neutrality”, it is reasonable to expect that human activities such as logging will cause minimal direct disruption of natural forests in the future [ 6 ]. However, for the disturbance of planted forests under the influence of various anthropogenic activities (e.g., afforestation, logging, irrigation), the future carbon sequestration potential of forests still varies greatly [ 16 , 23 ]. Factors such as climate change, elevated atmospheric CO 2 concentration, and nitrogen deposition may also affect the accumulation process of forest biomass density, and estimating forest carbon sequestration capability based on current climate circumstances may also introduce some uncertainty [ 23 , 54 ]. A more comprehensive study, including climate changes such as warming and drought, as well as the effects of other anthropogenic disturbances on future forest carbon sequestration, should be conducted.

5. Conclusions

Our study provided a better understanding of the relationships between forest growth and carbon sequestration potential at fine spatial-scales by introducing BEF and tree growth equations. Moreover, we further explored the effect of the combination of factor characteristics on the carbon sequestration potential, excluding forest growth effects, which provides crucial insights for Chinese carbon policy and global carbon neutrality goals.

By 2070, the carbon density of forests in Lushan City will reach a relatively stable state, and its carbon stock will be close to the maximum, indicating that Lushan forests will serve as a long-term carbon sink in the next fifty years. Among them, pine forests and mixed coniferous forests have a higher carbon sequestration contribution. In addition, the carbon sequestration potential of natural forests was much higher than that of planted forests, with the gap widening as the woods aged. Thus, conserving natural forests should be encouraged to sustain carbon sequestration capacity in future afforestation projects, and replantation site characteristics should be carefully considered in the afforestation projects to increase carbon sequestration capacity. Slope direction, slope gradient, soil thickness, and vegetation cover factors are important factors of forestry carbon sink, which should be paid attention to in implementing forestry carbon sink projects.

More importantly, incorporating DBH and tree height data from the binary standing volume model can better represent forest growth changes. A stochastic simulation process could be used to further refine the relationship between the standing volume of forest types and the age of the trees, which improved the accuracy of the prediction of carbon sequestration potential at the fine-scale areas. It can also be applied in the case of limited forest biomass data and stand age observation, enriching the ways of predicting forest carbon sequestration potential. Future work should also consider climate changes on future forest carbon sequestration for better achieving global carbon neutrality goals.

Appendix A contains the specific process of stochastic simulation of volume growth. The details are as follows:

Under the premise that the mean DBH and tree height of a stand at the same mean age are normally distributed, the mean volume of a stand should satisfy some joint probability distribution model of DBH and tree height according to the binary standing volume model (Equation (1)). In practical applications, we are more interested in the expectation of volume as a function of tree age. The expectation of volume at a certain average age can be calculated based on probability density, which can be integrated as shown in Equation (A1).

where, E ( V ) ( t ) is the expected volume at the age of t . f ( v ) ( t ) is the probability density distribution function of the volume at the age of t . Therefore, the probability distribution model of volume, expectation, and mean age function models are theoretically uniquely determined and solvable; however, their solution process is complex and tedious. To solve this problem, we construct the stochastic simulation algorithm in the following steps:

(1) Construct the time series vector: t → = ( t 1 , t 2 , ⋯ , t M ) ( M is the number of age groups). In turn, the two normal distribution models obeyed by DBH and tree height are randomly sampled (each group has a large enough sample size, N = 500) to obtain two M × N sample matrices: ( d i j ) M × N and ( h i j ) M × N , respectively. The sample matrix of volume ( v i j ) M × N is obtained by matrix operation of Equation (A2).

where, d i j , h i j and v i j are the i th DBH, tree height, and volume sampling data of the j th age group. a 0 , a 1 , a 2 are the parameters obtained by fitting the binary standing volume model (Equation (1)). From Equation (A2), we got a sample matrix of volume ( v i j ) M × N . M is the length of time series, and N is the number of samples simulated.

(2) μ ^ is the mean vector calculated as the point estimate of μ , indicating the accumulation expectation under the year series as shown in Equation (A3).

The j th element e v j in vector is the average of the accumulation volume in j th column, indicating the accumulation expectation under a single year.

(3) A nonlinear fit to t → and e v → using a continuously derivable logistic curve was performed in Origin 2019b to obtain the accumulation expectation v i j as a function of mean age: the volume-tree age growth model.

Variation curves of tree height, DBH, and volume expectation with age for different forest types. ( a ) shows the tree height-standardized age fitting relationship; ( b ) shows the DBH-standardized age fitting relationship; ( c ) shows the accumulation volume expectation-mean age fitting relationship after random simulation. PF refers to pine forest; CFF refers to Chinese fir forest; BLH refers to hard broad forest; BLS refers to soft broad forest; and three types of mixed forests: MCF refers to mixed coniferous forest, MBF refers to mixed broadleaf forests, and MCBF refers to mixed conifer-broadleaf forests. (The points in the graph represent forest patches sampling data).

Funding Statement

This research was funded by Big Data-Driven Ecological Security and Natural Resources Early Warning Plan, Key Projects of Philosophy and Social Science Research, Chinese Ministry of Education (Grant No. 19JZD023), and Analysis of Ecological Characteristics and Ecological Value Transformation of the Whole Area of Lushan City.

Author Contributions

Conceptualization, G.H., Z.Z. and Y.C.; methodology, G.H. and Z.Z.; formal analysis, G.H. and Z.Z.; investigation, G.H. and Y.C.; writing—original draft preparation, G.H.; writing—review and editing, Q.Z., W.W. and W.P.; visualization, Z.Z.; supervision, Y.C.; funding acquisition, Y.C. and W.P.; G.H. and Z.Z. contributed equally to this paper. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Restoring forests: regeneration and ecosystem function for the future

• Published: 28 March 2019
• Volume 50 , pages 139–151, ( 2019 )

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• Magnus Löf 1 ,
• Palle Madsen 2 ,
• Marek Metslaid 3 , 4 ,
• Johanna Witzell 1 &
• Douglass F. Jacobs 5  

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Conventions and policies for biodiversity conservation and climate change mitigation state the need for increased protection, restoration and climate change adaptation of forests. Much degraded land may be targeted for large-scale forest restoration, yet challenges include costs, a shortage of regeneration material and the need for restored forests to serve as a resource for communities. To ensure ecosystem function for the future, forest restoration programs must: (1) learn from the past; (2) integrate ecological knowledge; (3) advance regeneration techniques and systems; (4) overcome biotic and abiotic disturbances and (5) adapt for future forest landscapes. Historical forest conditions, while site-specific, may help to identify the processes that leave long-term legacies in current forests and to understand tree migration biology/population dynamics and their relationship with climate change. Ecological theory around plant–plant interactions has shown the importance of negative (competition) and positive (facilitation) interactions for restoration, which will become more relevant with increasing drought due to climate change. Selective animal browsing influences plant–plant interactions and challenges restoration efforts to establish species-rich forests; an integrated approach is needed to simultaneously manage ungulate populations, landscape carrying capacity and browse-tolerant regeneration. A deeper understanding of limiting factors that affect plant establishment will facilitate nursery and site preparation systems to overcome inherent restoration challenges. Severe anthropogenic disturbances connected to global change have created unprecedented pressure on forests, necessitating novel ecological engineering, genetic conservation of tree species and landscape-level approaches that focus on creating functional ecosystems in a cost-effective manner.

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Need for forest restoration programs, and purpose of the congress

Forests around the globe provide a wide variety of ecosystem services. International conventions and national policies for biodiversity conservation and climate change mitigation state the need for increased forest protection, forest restoration and adaptation of forest management to climate change. However, global forest cover continues to decline. According to the last Forest Resources Assessment, the world’s forest area decreased by ca 3% between 1990 and 2015 (FAO 2018 ). Against this background, forest restoration activities have become increasingly common around the world and regeneration of trees is often a key component in these projects. Much degraded land may be targeted for forest restoration. In an analysis from 2011, the World Resource Institute suggested that ca 2 billion hectares of land is suitable for different kinds of forest restoration (Minnemayer et al. 2011 ; Silva et al. 2019 ).

Several global, regional and national organizations have set targets during the last decade for large-scale forest landscape restoration. For example, the Great Green Wall of the Sahara and the Sahel Initiative in Africa aim to surround the region with vegetation, and China alone aims to plant 6.7 million hectares of forest per year (Cernansky 2018 ). The Bonn Challenge, started in 2011, aims to have 150 million hectares of degraded land in process of restoration by 2020, and 350 million hectares by 2030 according to the New York Declaration of 2014. Additionally, the UN REDD + program is attempting to encourage restoration of forests by creating a market value for the carbon stored therein (Jacobs et al. 2015 ). The United Nations recently recognized the critical role of ecosystem restoration as a tool for improving environmental conditions and enhancing human communities by designating 2021–2030 the UN Decade of Ecosystem Restoration (UN 2019 ). Restoration ambitions are high, and these initiatives have received much support from countries and organizations around the world.

Successful large-scale restoration faces many challenges. First, the costs of achieving restoration are often high. A conservative estimate of 2000 $USD per hectare means that at least 700 billion $USD is required to restore 350 million hectares by 2030. This represents a significant barrier for scaling up restoration worldwide and there is a risk that the interest from policymakers will wane over time as the costs and difficulties becomes apparent (Löf 2017 ). Restoration costs vary depending on methods used, from lower-cost alternatives using natural regeneration with native tree species to higher-cost approaches for active restoration using site preparation and planting. Recently, there has been a debate regarding advantages, disadvantages and the degree of success with these approaches (e.g. Crouzeilles et al. 2017 ; Meli et al. 2017 ; Reid et al. 2018 ) but no one-size-fits all solution regarding methods for restoration exists. Instead a broad palette of methods adapted to the local context needs to be utilized to accomplish the ambitious targets. Normally, costs for restoration increase with the degree of degradation of an ecosystem (Stanturf et al. 2001 ; Chazdon 2008 ) (Fig.  1 ). Thus, if appropriate seed sources are available, natural regeneration with native tree species may represent a cost-effective option that may be applied across large areas. In other cases, when no such seed sources exist or when sites are too harsh, active restoration using planting (sometimes with non-natives) is the only option to establish forests (Jacobs et al. 2015 ).

A model for restoration, degradation and costs (adapted from Stanturf et al. 2001 ). The state of the forest condition ranges from natural to degraded (x-axis) and levels of ecosystem function (left y-axis) change in response to disturbance along a solid line (f1–f2). Cost of forest restoration (right y-axis) follows the dashed line (c1–c2). The costs increase with degree of degradation, e.g. higher costs using artificial regeneration (direct seeding, and especially high costs during planting) when less mother trees are available during afforestation and mine reclamation. When the degree of degradation is lower, i.e. when scattered trees and near-natural forests are left; restoration can rely on low-cost natural regeneration

Second, an important obstacle for successful active restoration is often a shortage of regeneration material (Cernansky 2018 ). Restoration projects involve the re-establishment of native tree species, but there is often little knowledge about how, where and when to obtain genetic material (species and provenances) with desired properties for the sites under restoration, and how to collect, store and pretreat seeds followed by cost-efficient cultivation of the seedlings in nurseries. In addition, we have little experience with establishing such regeneration; efficient regeneration systems currently exist for only a few tree species, mostly for use in industrial plantations and these techniques are not always suited for restoration programs (Löf et al. 2012 ). In addition, climate change raises questions about where different tree species may thrive in the future. Assisted migration, i.e. the human-assisted movement of plants or animals to more climatically suitable habitats, may be one way forward (Stanturf et al. 2014a , b ; Dumroese et al. 2015 ). Much knowledge in this field is, however, still missing. The same can be said about how climate change will alter relationships among trees, insects and various diseases.

A third major constraint for successful cost-effective restoration in the long term is that the restored forests needs to be a positive resource for the local and regional communities. Thus, forest restoration success depends much on people and their ability to utilize restored forests in different ways. The mounting demands from a growing human population increase the already huge pressure on the world’s natural resources. If society’s needs are not considered, the risk is that public interest to protect forests in a sustainable way will be insufficient. Multiple-use forestry, such as with multi-purpose mixed forest systems and agroforestry systems that provide food and other benefits for people, is therefore important. In addition, increasing evidence suggests that integrating exotic commercial tree species with native ones can support biodiversity, the environment and local economies. One such approach is the new generation plantation (NGP) Platform that was launched in 2007 (Silva et al. 2019 ). This concept aims to complement timber production with effective maintenance and enhancement of ecosystems, and simultaneously contribute to socio-economic development and climate change mitigation (Fig.  2 ). Plantations, with a current share of ca 7% of total global forests, can help to reduce pressure on natural forests (FAO 2010 ) as they contribute to ca 33% of global demand for industrial round wood, a demand that is predicted to increase (Silva et al. 2019 ). The NGP concept is one way to, at least partly, meet these growing demands.

Example of a new generation plantations initiative by Veracel in Bahia, Brazil where a degraded rain forest dominated landscape ( a ) can be restored with native tree species forming ecological corridors of rain forest in-between highly productive eucalyptus plantations ( b ) (Copyright Stora Enso)

In September 2011, we held the 1st IUFRO Restoring Forests Congress in Madrid, Spain to address recent advances in forest restoration techniques and theory. Selected papers from this symposium were published in New Forests (vol. 43, Issues 5–6). The 2nd IUFRO Restoring Forests Congress occurred in October 2014 in Indiana, USA and the corresponding Special Issue of this symposium was published in New Forests (vol. 46, Issues 5–6). To continue to communicate and advance upon emerging issues and themes in forest restoration, we held the 3rd IUFRO Restoring Forests Congress on 12–14 September 2017 in Lund, Sweden. The symposium was organized by the Swedish University of Agricultural Sciences and Southern Swedish Forest Research Centre, Estonian University of Life Sciences, University of Copenhagen and Purdue University, as well as IUFRO Divisions 1.01.00 (Temperate and Boreal Silviculture), 1.06.00 (Restoration of Degraded Sites), 2.01.15 (Whole Plant Physiology), and 1.01.06 (Ecology and Silviculture of Oak). A total of 109 scientists, representing 31 countries in five continents attended the Congress, which included 12 invited seminars, 38 offered oral presentations and 46 poster presentations. Field tours to Söderåsens National Park and Herrevads Monastery area highlighted ongoing research and management programs to promote forest restoration of broadleaves. The overall theme of the 3 rd Congress was Restoring Forests — Regeneration and Ecosystem Function for the Future. The Congress was arranged according to the following five topics/sessions and within each topic, several key issues emerged (see below—Focal topics of the congress): (1) learning from the past; (2) ecological knowledge supporting forest restoration; (3) advances in restoration and regeneration techniques and systems; (4) forest restoration following biotic and abiotic disturbances; and (5) restoring forest landscapes for the future. The purpose of this international congress was to support this process by knowledge sharing and communication of the state-of-the-art in the research field of regeneration and forest restoration ecology.

Focal topics of the congress

The Congress was arranged according to the following centralized topics, and within each topic, several key issues emerged.

Learning from the past

Scientists in restoration ecology continue to debate the usefulness of historical ecosystem conditions as targets and reference for practical restoration (e.g. Harris et al. 2006 ; Corlett 2016 ). An ecosystem condition is a response to past land use and past climate; restoring ecosystems is unlikely to be easy or even possible, especially under a rapidly changing climate. Even though historical conditions may, however, not always be useful as targets, research into long-term forest dynamics can support forest restoration in many ways. During this session, Richard Bradshaw listed three topics of major importance for restoration (Löf 2017 ). First, past historical forest conditions are often site-specific. Human pressure on forests may have varied much between sites and landscapes and, thus, restoration practitioners must be aware of this when defining any restoration targets. Second, through studies of the past we may identify the processes that leave long-term legacies in current forests (Johnstone et al. 2016 ; Jõgiste et al. 2017 ). Human impact through forest management and land-use patterns strongly influence current forest dynamics (Giesecke et al. 2017 ). For example, past forest fire regimes have been greatly altered by human activity; humans first greatly increased fire frequency and subsequently almost eradicated fire in parts of the world, therefore possibly influencing the fire regime to an extent that may be unprecedented for millennia (Krawchuk et al. 2009 ; Bradshaw and Sykes 2014 ). Similarities exist for past browsing and grazing regimes by both domestic animals and wild ungulates. Finally, understanding tree migration biology and population dynamics and their relationship with climate change is important. It can be very expensive for restoration practitioners trying to resist natural trends in forest dynamics. Restoration strategies should therefore look forward to solve future needs of society, but their success will be enhanced by looking backwards on information about the outcomes of past human impact, climate dynamics and forest response.

Ecological knowledge supporting forest restoration

During the last two decades, ecological theory concerning plant–plant interactions has developed with potential for great implications for cost-effective restoration (Löf 2017 ). In particular, it has been stressed that negative (competition) and positive (facilitation) interactions co-exist to a larger degree than previously thought (e.g. Bruno et al. 2003 ). Earlier, there was a strong focus on negative interactions, and still the most common vegetation management techniques during regeneration and restoration are measures that reduce competition from non-desired vegetation. Examples of such measures include prescribed fire, herbicides and mechanical site preparation. Concepts, such as the stress-gradient-hypothesis (e.g. Bertness and Callaway 1994 ; He et al. 2013 ) and nurse-plants (e.g. Gómez-Aparicio 2009 ) have, however, the potential to guide restoration toward methods that improve seedling survival and growth while simultaneously keeping restoration costs low. The former concept states that facilitation relative to competition increases in importance with the degree of abiotic and biotic stress, and the latter that one plant species can facilitate growth and development of another species. In addition, managing plant–plant interactions for restoration will be particularly relevant in scenarios of increasing drought due to climate change, with potential for an increasing role of facilitation to secure regeneration. However, these concepts have so far most frequently been applied to restoration in dry land ecosystems dominated by perennial herbs or shrubs (Gómez-Aparicio et al. 2004 ). Knowledge is still lacking for forest ecosystems where vegetation dynamics are more complex, i.e. temperate or tropical regions. To succeed, it is not only necessary to select good combinations of nurse- and target-tree species with complementary species traits, but also to manage plant densities and community composition as plant cover and competition increase with time.

Direct or indirect biotic interactions involving herbivores may also play a fundamental role for cost-effective forest restoration. High deer populations in Europe and North America, for example, pose one of the greatest challenges to the forest regeneration of many plant species, which further affects species composition, forest structure and function (e.g. Côté et al. 2004 ). Selective ungulate browsing influences plant–plant interactions to such an extent that browse-tolerant or less palatable tree species often are favored and, thus, challenge restoration efforts to establish more species-rich forests (Kuijper et al. 2010 ; Metslaid et al. 2013 ). Physical or chemical protection of regeneration by fences, repellents or individual tree shelters is usually effective, but the costs of establishing, maintaining and removing these measures are high. Low-cost strategies may include providing physical protection for the target trees by using dense natural regeneration, direct seeding of species that are more browse tolerant or using nurse trees/shrubs. Ungulate impact can also be lessened by reducing the populations (by hunting), increasing the forest landscape carrying capacity (more food for the deer) or combining these two approaches. In addition, fodder crops of grasses, herbs or woody species, may be established to release browsing pressure from more desired species on restoration sites; and supplementary feeding might be an option if this food improves nutrition of the ungulate (Felton et al. 2016 ). However, it may be difficult to translate new research findings about low-cost browsing-tolerant regeneration alternatives to restoration practice. An integrated approach is needed that simultaneously manages populations of deer, landscape carrying capacity and browsing-tolerant regeneration, which requires good cooperation among various stakeholders.

Advances in restoration and regeneration techniques and systems

Forest restoration and regeneration practices must continuously adapt to meet changing needs of society, human and natural disturbances, policies, markets, technologies and climate (Wagner et al. 2018 ). Much of the past science in this area emphasized reforestation following timber harvest for industrial purposes, but an increasing focus on restoration of harsh, degraded environments demands new seedling production and planting techniques (Oliet and Jacobs 2012 ). Improved understanding of biotic and abiotic factors that affect ecophysiology of plant establishment is required to overcome inherent challenges on restoration sites. Nursery systems aim to produce high quality, stress resistant planting stock that will withstand stresses following field establishment (Haase and Davis 2017 ). Stocktypes, fertilization or irrigation regimes and hardening practices can be manipulated in the nursery to tailor morphological and physiological seedling attributes to optimize performance under the conditions of a given outplanting site (Grossnickle and El-Kassaby 2015 ; Dumroese et al. 2016 ). Seedling size is positively correlated with plant content of nitrogen and carbohydrates and seedlings may remobilize these stored reserves in order to resist environmental stresses on the planting site (Villar-Salvador et al. 2015 ; Uscola et al. 2015 ). Thus, while a shift away from planting older and larger seedlings toward younger and smaller seedlings has occurred in some boreal regions (Löf 2017 ), the opposite trend prevails for restoration under harsh, dry site conditions, such as in Mediterranean regions (Villar-Salvador et al. 2012 ).

Site preparation is often necessary on restoration sites to ensure natural or artificial regeneration success, with requirements often dependent upon severity of prior disturbance. For instance, severe landscape disturbance associated with mine reclamation may require novel innovations in geomorphic restoration and site reconstruction (MacDonald et al. 2015 ; Zapico et al. 2018 ) to ensure functioning soil. Technological advances of mechanical site preparation have improved the efficiency of control of site limiting factors (i.e. target vegetation, compaction, mounding), costs, environmental impact and worker safety (Löf et al. 2012 ; Löf 2017 ). Continued enhancements in application of field fertilization, soil amendments and tree shelters (Earnshaw et al. 2016 ; Löf 2017 ; Oliet et al. 2019 ) will further promote regeneration and restoration success. While fencing is generally effective against ungulate damage, the high cost of installation and maintenance limits its practical use. Thus, continued evaluation of alternatives such tree shelters, nurse plants and silvicultural planting schemes that account for browse risk (Owings et al. 2017 ; Burney and Jacobs 2018 ; Maltoni et al. 2019 ) will help to ensure cost-effective restoration.

Forest restoration following biotic and abiotic disturbances

Disturbances are an essential part of forest ecosystems. Ecological processes that lead to stand structural complexity are in fact dependent upon natural disturbances (Löf 2017 ), which may include windthrow, forest fire and pest or disease outbreaks. Although the economics of fiber production is the main driver of management in commercial forests, there are opportunities to use alternative forestry methods, such as retention trees, variable density thinning and prescribed burning that mimic natural disturbances to promote heterogeneous forest stand structure and biodiversity (Puettmann et al. 2016 ; Löf 2017 ). Degradation and loss of forests due to anthropogenic disturbances, however, have resulted in a significant reduction of forest biodiversity and the need for restoration of millions of hectares of degraded forests worldwide (Lindenmayer 2019 ). In degraded environments, such as with grazing lands, abandoned crop lands or following severe wildfire, structural elements and sources of microsite diversity are often limited (Cortina et al. 2011 ), adding to restoration challenges. Mining sites are particularly difficult to restore to functioning forest ecosystems because of the need for massive landscape-level reconstruction of soils, hydrology and biota, as well as the associated forest fragmentation, erosion and disposal of mine tailing wastes (Stanturf et al. 2014a , b ; Macdonald et al. 2015 ).

Simultaneously, global change, including an exponential increase in the introduction of invasive species and a rapidly shifting climate puts unprecedented pressure on forests (Martín et al. 2013 ; Dumroese et al. 2015 ). The number of threatened and endangered forest tree species continues to rise, and the practical restoration of such species is complex due to the need for effective integration of technology, ecology and society (Jacobs et al. 2013 ). Thus, the genetic conservation of tree species has become an urgent global necessity as this provides the essential basis for the adaptation and resilience of tree species to environmental stress and change (Potter et al. 2017 ). While knowledge of historical species range distributions can help to guide restoration targets (Dalgleish et al. 2015 ), shifts in frost hardiness zones, drought severity indices and presence or absence of species competitors will drive species regional adaptiveness in the future. We are still in the infancy of navigating these dynamic and critical issues that will have long-lasting consequences for success of forest restoration programs.

Restoring forest landscapes of the future

In addition to goals for mitigating climate change and improving local economies, restoration provides great hope that biodiversity losses can be minimized and sometimes reversed. However, there is a polarized debate among conservation, forest management and restoration in relation to, e.g. non-native species and novel ecosystems, but also any need for continued management (Hobbs 2013 ). This debate will proceed even though all disciplines will be transformed in order to address rapid climate change both from theoretical- and practical perspectives (e.g. Colloff et al. 2017 ; Spathelf et al. 2018 ). The traditional use of past ecosystems as targets and criteria for success will probably be replaced over time by an increased orientation towards an uncertain future. Conflicts are sometimes rooted in discrepancies regarding definitions of concepts, and in other cases concepts are context-dependent (Corlett 2016 ). For example, retention approaches at final harvest, whereby individual trees, tree patches and dead wood are left on a site with a primary aim to promote biodiversity (Löf 2017 ), were introduced a few decades ago on several continents. Retention can be seen as a conservation action, as natural forest elements are saved for the future (in a forest landscape previously not much affected by humans). But it can also be seen as restoration because it creates structurally richer stands (in a forest landscape that has been homogenized by human operations, i.e. clearcutting and plantation forestry). Other practices, such as forest enrichment by underplanting, contribute to enhance structural complexity of managed forests yet their effectiveness is dependent upon choice of thinning intensity and underplanted species attributes (Gavinet et al. 2016 ; Lesko and Jacobs 2018 ; Lu et al. 2018 ). Because several disciplines are involved in the discussion, any attempts to formulate a universal definition of restoration will continue to generate discussion (Mansourian 2018 ; Stanturf et al. 2014a , b ).

Large areas require forest restoration and so landscape-level approaches that focus on functional ecosystems, rather than historical conditions in small areas, may be most cost-effective (Stanturf et al. 2014a , b ; Perring et al. 2015 ). In addition, a need to broaden the scope of forest restoration, from the site and stand level to the landscape level, is often required (Gustafson et al. 2018 ). Otherwise it will be difficult to accommodate diverse objectives from multiple owners and incorporate livelihood needs. The concept of New Generation Plantations (Silva et al. 2019 ) is one promising way forward, but such multi-purpose approaches create new challenges for research and management. While our understanding of mixed forest systems has increased over the last decades, the formulation of silvicultural and restoration guidelines for practical management of more complex forest stands and landscapes is lacking in many cases (Coll et al. 2018 ). Thus, despite ambitious goals and governmental funding initiatives for restoration including native tree species, such stands may not receive timely thinning treatments for sustained timber production or sustained compositional diversity.

Brief presentation of special issue content

A total of 12 original research or review articles covering a wide range of issues in forest restoration are included in this Special Issue and some of these are highlighted here. In accordance with his inaugural address from the Congress, Luis Neves Silva describes the potential for new generation plantations (NGP) to conserve natural forests and biodiversity, while also providing timber and helping to mitigate climate change (Silva et al. 2019 ). Approximately 11.1 million hectares of land are being managed as NGP, which results in economic return while contributing positively to ecosystems and socio-economic development. David Lindenmayer’s inaugural address detailed how the case study of Eucalyptus regnans in southeastern Australia can be applied toward four guiding principles that strategically help to conserve biodiversity and accomplish effective forest restoration. These general principles focus on key species and their habitats, attributes of stand structure, landscape heterogeneity and ecological processes (Lindenmayer 2019 ).

Addressing the theme of Ecological Knowledge Supporting Forest Restoration , Merlin et al. ( 2019 ) report results of an experiment conducted in the boreal forests of northern Alberta, Canada on a site that was heavily reconstructed following oil sands mining. They demonstrated the importance of physical and chemical properties of the reconstructed soils as determinants of the performance of planted boreal tree species and how limiting factors to seedling performance may vary by species and shift over time. It is well known that granivorous rodents are a major threat to successful regeneration of broadleaves. To help identify control mechanisms, Villalobos et al. ( 2019 ) demonstrated that repellents may help to reduce predation of oak acorns and beech nuts by voles. Meta-analyses provide an important tool to identify generalized trends across research studies globally. In seedling quality and field performance research, however, about one-third of publications cannot be used for such purposes due a lack of methodological details and statistical results. Andivia et al. ( 2019 ) present guidelines for improving article presentation so that future research can be more readily incorporated into such meta-analyses.

Oliet et al. ( 2019 ) contributed toward the session on Advances in Restoration and Regeneration Techniques and Systems. Tree shelters are commonly used as a means to improve survival of planted seedlings in Mediterranean restoration systems, but the eco-physiological mechanisms for species differences are not well understood. By examining solid versus mesh wall tubes that vary in light transmissivity, they concluded that microclimatic factors may primarily drive responses.

Juan A. Martín delivered an invited presentation to launch the session titled Forest Restoration Following Biotic and Abiotic Disturbances . Through traditional hybridization and selection of native clones, significant progress has been made toward restoration of European elms that were devastated by Dutch elm disease (Martín et al. 2019 ). The researchers recognized the importance of improving knowledge in elm ecology and society’s acceptance of disease resistant hybrid elms in order for such species restoration to be successful, similar to the case of American chestnut in the US (Jacobs et al. 2013 ). As also presented in this session, Anderson et al. ( 2019 ) describe accumulation of soil organic matter under re-vegetation treatments following oil sands reclamation in northern Alberta, Canada. They linked treatment differences to varying macro-faunal activity and suggested that planting of aspen would result in more rapid carbon sequestration through soil organic matter accumulation. Berg et al. ( 2019 ) studied advance regeneration responses in forest gaps over a 20-year period following hurricane disturbance in the central Appalachian Mountains region of the US. They observed strong temporal variation in tree survival associated with distance from gap center, with survivorship increasing from gap center to forest edge during the first half of the study, and the opposite trend by the end of the experiment.

Contributing to the theme of Restoring Forest Landscapes of the Future, Riccioli et al. ( 2019 ) evaluated the willingness to pay for restoration management to maintain the recreational values of forests. Forest users preferred the use of management leading to high forests versus natural evolution, suggesting the increasing importance of recreational value toward motivating forest restoration in the future.

In conclusion, we recognized the importance of continued commitment to multidisciplinary, global collaboration in forest restoration research. Regular gatherings through symposia were identified as an instrumental tool in exchange of experiences and research information, and it was decided that the 4th IUFRO Restoring Forests would be held during 2020 in Chile.

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Acknowledgements

We are most grateful to all authors of this Special Issue of the 3rd IUFRO Restoring Forests Congress, and for support from The Swedish Research Council for Sustainable Development, EFINORD-SNS Nordic Network of Forest Regeneration, and grant IUT21-4 from the Estonian Ministry of Education and Research. We appreciate constructive comments from three reviewers on this manuscript.

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Löf, M., Madsen, P., Metslaid, M. et al. Restoring forests: regeneration and ecosystem function for the future. New Forests 50 , 139–151 (2019). https://doi.org/10.1007/s11056-019-09713-0

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The global issue of ecological resource scarcity, worsened by climate change, necessitates effective methods to promote resource conservation. One commonly used approach is presenting ecological resource scarcity information. However, the effectiveness of this method remains uncertain, particularly in an unpredictable world. This research aims to examine the role of perceived environmental unpredictability in moderating the impact of ecological resource scarcity information on pro-environmental behavior (PEB).

We conducted three studies to test our hypothesis on moderation. Study 1 ( N  = 256) measured perceived general environmental unpredictability, perceived resource scarcity and daily PEB frequencies in a cross-sectional survey. Study 2 ( N  = 107) took it a step further by manipulating resource scarcity. Importantly, to increase ecological validity, Study 3 ( N  = 135) manipulated the information on both ecological resource scarcity and nature-related environmental unpredictability, and measured real water and paper consumption using a newly developed washing-hands paradigm.

In Study 1, we discovered that perceived resource scarcity positively predicted PEB, but only when individuals perceive the environment as less unpredictable (interaction effect: 95% CI  = [-0.09, -0.01], Δ R 2  = 0.018). Furthermore, by manipulating scarcity information, Study 2 revealed that only for individuals with lower levels of environmental unpredictability presenting ecological resource scarcity information could decrease forest resource consumption intention (interaction effect: 95% CI  = [-0.025, -0.031], Δ R 2  = .04). Moreover, Study 3 found that the negative effect of water resource scarcity information on actual water and (interaction effect: 95%CI = [3.037, 22.097], η p 2  = .050) paper saving behaviors (interaction effect: 95%CI = [0.021, 0.275], η p 2  = .040), as well as hypothetical forest resource consumption (interaction effect: 95%CI = [-0.053, 0.849], η p 2  = .023) emerged only for people who receiving weaker environmental unpredictability information.

Across three studies, we provide evidence to support the moderation hypothesis that environmental unpredictability weakens the positive effect of ecological resource scarcity information on PEB, offering important theoretical and practical implications on the optimal use of resource scarcity to enhance PEB.

Peer Review reports

Introduction

Ecological resource scarcity, such as water and energy, poses significant challenges in our current times. The reduction of renewable freshwater resources per capita by 55% from 1993 to 2014 emphasizes the urgency of addressing this issue [ 1 ]. According to the World Economic Forum (2019), water shortages remain a top concern for policymakers and business leaders worldwide. In response to resource scarcity, various entities, including governments, water utilities, and community-based organizations, have employed different strategies to promote resource conservation [ 2 ]. One of the most common approaches is to raise problem awareness by conveying information about resource scarcity [ 2 ]. For example, the fact that billions of people lack access to safe water is utilized in the World Water Day campaign in 2023 to encourage more people to take action. Additionally, the Hong Kong SAR Government’s “Let’s Save 10L Water 2.0” campaign emphasizes the importance of conserving water by highlighting the limited availability of this resource.

Despite these efforts, it is important to recognize the complexity and interconnectedness of the world we live in, which makes predicting future environmental conditions challenging. Unforeseen events such as pathogen prevalence, natural disasters, wars, and financial crises illustrate the dynamic nature of our environment. In such an unpredictable world, can simply providing information about ecological resource scarcity lead to a significant increase in pro-environmental behaviors?

In the current research, we aimed to explore whether ecological resource scarcity information could promote pro-environmental behaviors effectively in the unpredictable world. We argued that ecological resource scarcity information is not necessarily useful in promoting pro-environmental behaviors and proposed that environmental unpredictability is a vital factor weakening the effect of ecological resource scarcity on resource consumption.

Uncertain association between ecological resource scarcity information and pro-environmental behaviors

Based on the information-motivation-behavioral skills (IMB) model, individuals are more likely to change their behavior when they are informed about a problem, along with being motivated to act and have skills to act [ 3 ]. In the environmental protection domain, there is a general lack of problem awareness about ecological resource scarcity [ 4 , 5 ]. This lack of awareness hinders individuals from engaging in pro-environmental behaviors (PEB), which refers to the actions that enhance the quality of the environment, regardless of the intent behind them [ 6 ]. Resource conservation campaigns often focus on resource scarcity information to encourage PEB [ 7 ]. In some empirical studies, the resource scarcity information was found to be effective. For example, individuals living in regions that experience drought have a higher tendency to make behavioral changes to conserve water [ 8 , 9 ]. People who perceived stronger ecological resource scarcity reported higher resource-saving behavioral frequencies [ 10 ], and indicated a higher frequency of PEB [ 11 ]. And water scarcity information was linked to a significant decrease in water use [ 12 , 13 , 14 ].

However, we identified some conflicting evidence. Information about resource scarcity is often not sufficient to reduce resource consumption in intervention [ 15 ], and the effectiveness of awareness campaigns is unclear [ 16 ]. For example, presenting the information about water resource scarcity only was evaluated as ineffective to promote water-saving behaviors by lay people [ 10 ]. Energy scarcity information was not strong enough to affect attitudes, intentions, and behaviors toward electricity energy saving [ 17 ]. Moreover, resource scarcity information failed to modify resource consumption behaviors in experimental settings [ 2 , 18 ].

The uncertain relationship between resource scarcity and PEB can be understood through an evolutionary psychological approach. According to the life history theory, individuals may adopt various strategies for allocating resources [ 19 , 20 , 21 , 22 , 23 ]. Those who choose a slow life history strategy prioritize long-term benefits and future planning, which leads them to behave in an environmentally friendly manner for the sake of future generations. On the other hand, individuals adopting a fast life history strategy prioritize immediate gains over long-term consequences [ 24 ], resulting in less PEB.

This theory, combined with empirical evidence, suggest that the impact of resource scarcity on PEB may vary depending on the situation, implying that promoting pro-environmental actions may require considering factors beyond simply informing individuals about scarcity. If PEB is seen as an investment in the environment, people engaging in PEB expect long-term benefits from it. However, the environment does not always provide consistent long-term benefits, particularly in today’s unpredictable world. When the expected advantages of environmental protection become uncertain, individuals may prioritize immediate gains, exploit natural resources, and reduce their commitment to PEB. This study hence focuses on the situational factor related to the unpredictable environment, testing its importance in influencing individuals’ PEB under resource scarcity.

Moderating role of environmental unpredictability

Environmental unpredictability is defined as the level of spatial–temporal variation in environmental harshness [ 24 ]. Past empirical studies measured environmental unpredictability in diverse ways [ 25 ]. In the current research, we tried to capture both individual-related and nature-related environmental unpredictability in temporal or spatial dimensions. Individual-related environmental unpredictability is mostly indicated by residential changes, and changes in parental financial status for children [ 19 , 24 , 26 ]. It shows whether the structure of an environment, such as the social or economic environment in which one lives, changes over time. Nature-related environmental unpredictability focuses on the pattern of variation that makes environments unpredictable, such as unpredictability of weather and the unpredictability of natural disasters [ 25 ].

Based on the life history theory, the environment plays a crucial role in shaping individuals’ life history strategies [ 19 , 20 , 21 , 22 , 23 ]. In predictable environments individuals are more likely to adopt a slow life-history strategy, while highly unpredictable environments promote a fast life-history strategy [ 24 ]. Importantly, environmental unpredictability during childhood can influence short-sighted tendencies [ 27 , 28 , 29 , 30 ], and this effect can also be observed in adulthood [ 31 ]. In an unpredictable environment, individuals prioritize immediate desires over future needs because investing in long-term environmental protection may not yield future benefits. This has implications for PEB, as present efforts on environmental protection may not be effective in improving resource scarcity in the future when the environment is unpredictable.

There are two aspects that illustrate the expectation that PEB efforts may not pay off in unpredictable environments. Firstly, in an unpredictable environment, there is a flow of uncontrollable information, which makes it challenging for individuals to maintain strong beliefs that their actions can bring about positive outcomes, such as improving resource scarcity [ 32 ]. According to the theories of reasoned action and planned behavior, the impact of awareness of the problem on behavior is greater when individuals perceive a higher level of control over their actions [ 33 ]. Hence, environmental unpredictability not only reduces the perceived personal control but also creates a barrier between scarcity awareness and PEB.

Secondly, in unpredictable environments, individuals are more likely to fear free riders, which further hinders behavioral change towards environmental protection under resource scarcity. When deciding whether to take action to protect the environment, people consider whether others will cooperate. However, in unpredictable environments, the likelihood of others investing in PEB becomes uncertain as well, which induces a heightened fear of free riders. For instance, experimental games have shown that individuals behave less cooperatively and invest fewer public goods when the probability of benefiting from them is uncertain [ 34 ]. Moreover, studies have demonstrated that individuals are less likely to prioritize the interests of others over their own when environmental unpredictability is primed [ 31 , 35 ]. Due to the fear that others will not take action in an unpredictable environment, individual efforts to protect the environment may appear less effective in solving the issue of resource scarcity.

Taken together, stronger environmental unpredictability is associated with a fast life-history strategy characterized by low self-efficacy and high fear of free riders, which ultimately leads to less PEB performance in the face of resource scarcity. Both multilevel and individual-level studies have indicated that psychological traits similar to the fast life history strategy weaken the association between environmental problem awareness and actual PEB [ 10 , 36 ]. Besides, some indirect evidence revealed that resource scarcity and environmental unpredictability could lead to some psychological outcomes that go against promoting PEB. Specifically, poorer childhood and economic uncertainty jointly increase the present orientation and decrease the sense of control [ 37 , 38 ]. A strong present orientation and low sense of control discourage people from taking actions to save resources [ 39 ]. With the above in mind, the following moderation hypothesis was proposed:

Hypothesis: Environmental unpredictability will moderate the effect of ecological resource scarcity on PEB. Specifically, ecological resource scarcity information would play a less effective role in promoting PEB when environmental unpredictability is stronger.

Current research

In the current research, we conducted three studies to test our hypothesis on moderation. In Study 1, we examined whether perceived general environmental unpredictability would moderate the relationship between perceived resource scarcity and daily PEB frequencies. Study 2 took it a step further by manipulating resource scarcity to test whether the positive effect of ecological resource scarcity information on forest resource consumption intention would be weakened by individual-related environmental unpredictability, specifically the frequency of residential changes. Importantly, to increase ecological validity, Study 3 manipulated the information on both ecological resource scarcity and nature-related environmental unpredictability, and measured real water and paper consumption using a newly developed washing-hands paradigm.

To examine the moderating effect of environmental unpredictability on the relationship between ecological resource scarcity and daily PEB frequency, we conducted a cross-sectional survey for Study 1. We hypothesised that ecological resource scarcity would predict higher frequencies of daily PEB for individuals who perceived the environment as predictable. However, we expected this positive association to diminish for individuals who perceived high levels of environmental unpredictability.

Participants

To ensure sufficient statistical power (80% power, α = .05) to detect a small-to-medium-sized effect for our moderation hypothesis, based on previous research in the same domain [ 10 ], we estimated that a sample size of 256 participants would be required using G*Power 3.1 [ 40 ]. Participants were recruited from a Chinese online survey platform ( www.wjx.cn ) and received monetary compensation for their participation. The survey platform utilized a voluntary opt-in panel, inviting users to complete the questionnaire. A total of 263 participants from China completed the survey. It is important to note that data collection was planned to conclude once 256 observations were collected within a three-week period.

The average age of the participants was 32.21 ± 7.11 years (ranging from 18 to 66 years), with 44.1% of them being male ( N  = 116). In terms of educational attainment, 1.9% held a middle-school degree or below, 1.9% had a high school degree, 8.7% held a junior college degree, 79.8% had a bachelor’s degree, and 7.6% had a master’s degree or higher. The average annual family income was 23.65 ± 21.04 ten thousand yuan.

Procedure and measures

To address the potential influence of priming participants’ perceived resource scarcity through items expressing the seriousness of resource scarcity [ 11 , 41 , 42 ], we carefully structured the data collection process. Firstly, we measured the dependent variable, PEB frequencies. Following this, participants completed the measure of perceived environmental unpredictability, and subsequently rated their perceived ecological resource scarcity. Additionally, to account for potential bias in self-reported PEB due to social desirability [ 43 ], we included a measurement of social desirability as a control variable. Finally, participants provided their demographic information, including age, gender, educational attainment, and annual personal income.

Perceived resource scarcity

The measurement of perceived ecological resource scarcity, consisting of 5 items, was adapted from a previous study conducted by Gu and her colleagues [ 10 ] (Cronbach’s α in the current study is 0.79). Participants were asked to indicate their level of agreement with statements such as “There are not enough resources for everyone in the place where I live” and “In the place where I live, I have already noticed some signs of resource scarcity.” Each item was rated on a 7-point Likert scale, ranging from 1 ( strongly disagree ) to 7 ( strongly agree ). The mean score of the entire scale was computed. Higher scores on this scale indicated higher levels of perceived ecological resource scarcity.

Perceived general environmental unpredictability

The item “For me, the environment we live in is unpredictable” developed by Reynolds and McCrea [ 44 ], was used to measure how participants perceived the general unpredictability of their environment. Participants rated this item on a 7-point Likert scale, ranging from 1 ( strongly disagree ) to 7 ( strongly agree ). Higher score indicated stronger perceived unpredictability.

Daily PEB frequency

Participants were asked to rate the frequency of PEB in their daily lives on a scale from 1 ( never ) to 5 ( always ). They were presented with six common resource conservation actions and asked to consider their behaviors in the year prior to the survey. The items are “do not turn the tap to the maximum when using water”, “switch off the lights when you leave”, “set the air conditioner’s temperature to 26–28 degrees centigrade in summer”, “buy and use energy-efficient appliances”, “avoid using disposable tableware whenever possible”. These six PEB were then converted into a PEB frequency scale, and a mean score was calculated for each participant. Higher scores indicated a higher frequency of PEB. Although the Cronbach’s α for the PEB scale was relatively low at .50, we decided to keep the measure because the items were face-valid. It is worth noting that removing any of the items did not improve the Cronbach’s alpha. Consistent with findings from previous studies, different types of PEB were not completely consistent [ 45 , 46 ]. And importantly, using the common score derived from the six items did not significantly alter the results.

Social desirability

Social desirability was measured using the liar subscale of the Eysenck Personality Questionnaire (EPQ) [ 47 ]. This subscale consists of 12 items, with participants answering each question with a “ Yes ” or “ No ” response. A code of 1 was assigned to “ Yes ” and 0 to “ No ”. Higher scores on this subscale indicated a stronger tendency towards social desirability. The measure demonstrated good internal consistency with a Cronbach’s α of .75.

Correlation analyses

Prior to conducting hypothesis testing, all variables exhibited normal distributions, as indicated by skewness values ranging from -0.89 to + 0.05 and kurtosis values ranging from -0.72 to + 0.77. We computed Pearson’s correlation coefficients to explore the relations among the studied variables (see Table  1 for descriptive statistics and intercorrelation coefficients). We found a marginally significant positive relationship between perceived ecological resource scarcity and PEB frequency ( r  = 0.12, p  = .058). And there was no correlation between environmental unpredictability and PEB frequency ( r  = -0.07, p  = .29). Importantly, as expected, social desirability was positively associated with PEB ( r  = 0.30, p  < .001), indicating that it should be controlled for in subsequent analyses.

Moderation analyses

To examine the impact of environmental unpredictability on the relationship between perceived ecological resource scarcity and PEB, we used the PROCESS macro for SPSS [ 48 ]. Controlling for social desirability, we found a significant interaction effect between perceived ecological resource scarcity and environmental unpredictability ( b  = -0.05, SE  = 0.02, t  = -2.26, p  = .025, 95% CI  = [-0.09, -0.01], Δ R 2  = 0.018). To further understand this interaction, we conducted a floodlight analysis [ 49 ]. The results showed that perceived ecological resource scarcity was positively and significantly associated with PEB when environmental unpredictability was below 4.41 ( b  = 0.07, SE  = 0.03, t  = 1.97, p  = .05, 95% CI  = [0.000, 0.136]), but not when it was above 4.41.

Additionally, we performed a simple slope analysis to examine the relationship between perceived ecological resource scarcity and PEB for individuals with different levels of perceived environmental unpredictability with social desirability controlled (see Fig.  1 ). The results indicated that perceived ecological resource scarcity positively predicted PEB for individuals with lower levels of environmental unpredictability (-1 SD ), b  = 0.13, SE  = 0.05, t  = 2.83, p  = .005, 95% CI  = [0.039, 0.219]. However, this relationship was not significant for individuals with higher levels of environmental unpredictability (+ 1 SD ), ( b  = -0.02, SE  = 0.05, t  = -0.35, p  = 0.73, 95% CI  = [-0.114, 0.079]).

The effect of resource scarcity on PEB at different levels of environmental unpredictability (Study 1)

Furthermore, controlling for demographic variables did not significantly change the results of moderation analysis. In summary, individuals who perceived the environment as more predictable were more likely to engage in PEB when facing ecological resource scarcity.

Brief discussion

Study 1 identified a moderating effect of environmental unpredictability on associations between perceived ecological resource scarcity and daily PEB. Individuals who perceived the environment as less unpredictable were more likely to adopt environmentally friendly ways to respond to ecological resource scarcity. However, it is important to consider the potential influence of responding to the PEB items on participants’ perceptions of ecological resource scarcity. The act of responding to these items may have directed participants’ attention towards environmental issues, potentially leading to an implicit increase in their perceived ecological resource scarcity. Therefore, it is not possible to infer the direction of the causal relationship between perceived ecological resource scarcity and PEB frequencies solely from correlational data. In addition, using a single item for measuring environmental unpredictability may raise concerns about the comprehensiveness of measurement. To address these limitations, we conducted Study 2, where we manipulated perceived ecological resource scarcity in order to demonstrate its causal effect, and further explore the moderating effect of environmental unpredictability by using another measurement.

Furthermore, it is important to note that the observed moderation effect size was small, which could be attributed to the fact that we measured various types of PEB in this study. According to the Goal System Theory, PEB can be motivated by multiple goals. In the context of resource scarcity, individuals who perceive the environment as more predictable are more likely to prioritize environmental protection for the benefit of future generations, especially if they themselves also stand to gain [ 50 ]. For instance, engaging in electricity-saving behaviors not only benefits the environment in the long run but also reduces personal electricity bills. In other words, personal benefits may matter. In our subsequent studies, we will focus on examining PEB that does not involve salient personal benefits in order to highlight the moderating effect of environmental unpredictability.

In Study 2, we sought to replicate the moderating effect of environmental unpredictability on the link between ecological resource scarcity and PEB by manipulating resource scarcity information. We proposed that receiving ecological resource scarcity information would increase PEB intention for individuals with lower levels of environmental unpredictability but that the effect would disappear for individuals with higher levels of environmental unpredictability.

To test our moderation hypothesis, we determined that a sample size of 107 would be necessary to achieve 80% power (α = .05) in order to detect a small-to-medium-size effect ( f 2  = .075) based on previous research [ 10 ] using G*Power 3.1 [ 40 ]. We established the rule for ending data collection prior to gathering data, stipulating that the survey link would be closed after obtaining more than 150 observations. Ultimately, we recruited 155 Chinese adults who completed an anonymous online questionnaire and all of these responses were valid.

The participants had an average age of 32.91 ± 10.10 years (range = 18–59 years) and 41.90% of them were males ( N  = 65). In terms of educational attainment, 9.70% held a high school degree, 16.1% held a junior college degree, 66.6% held a bachelor’s degree, and 13.5% held a master’s degree or higher. The average annual personal income was 11.18 ± 44.58 ten thousand yuan.

In the present study, participants reported their demographic information first. Then, environmental unpredictability was measured. Next, participants were randomly assigned to one of two experimental conditions to read a news article, where exposure to the information of resource scarcity (vs. control condition) was the manipulated factor. Finally, PEB intention was measured using a forest management task.

Manipulation of ecological resource scarcity information

Participants were assigned at random to read one of two news articles. The articles were created specifically to manipulate perceptions of ecological resource scarcity. In the scarcity group ( n  = 77), participants read an article titled “Interpretation of China’s Resources through Big Data: Invisible Resource Scarcity in China”, which highlighted the severity of natural resource scarcity in China. In the control group ( n  = 78), participants read an article of similar length that aimed to evoke similar levels of negative arousal. This article was titled “Interpretation of Sleep through Big Data: Invisible Sleeping Problems in China” and discussed sleep issues in China. To ensure the credibility of the mock news articles, participants were informed that the articles were sourced from The People’s Daily , a reputable Chinese newspaper.

Immediately after reading their respective article, participants rated their perception of ecological resource scarcity using a 7-point Likert scale ranging from “ strongly disagree ” (1) to “ strongly agree ” (7). The item presented was: “Currently, I believe that we live in an environment where natural resources are extremely scarce.” Besides, participants also responded to one item on their mood at the moment for the manipulation check on a 7-point Likert scale (1 = “ very negative ” to 7 = “ very positive ”).

• Environmental unpredictability

At the individual level, environmental unpredictability is mostly indicated by residential changes [ 24 , 25 ]. The frequency of residential changes showed whether the structure of an environment one lives in changes over time, which is the important aspect of environmental unpredictability. Therefore, Study 2 used the frequency individuals moved in the past to represent their environmental unpredictability. Higher score indicates stronger environmental unpredictability ( M  = 3.59, SD  = 2.17, Min  = 0, Max  = 11). The variable showed approximately normal distribution, with skewness = 0.64 and kurtosis = 0.65. Hence, the raw data of moving frequency are used for analysis.

PEB intention

A forest management task was used to measure PEB intention, specifically in relation to forest resource conservation intention [ 51 ]. Participants were asked to imagine that they were the owner of a timber company and must compete with three other companies to harvest timber in the same forest. They need to cut down as many trees as possible for their companies to profit and thrive. However, the rapid deforestation could lead to forest destruction. Then, participants were asked to answer one question about deforestation rate on a 7-point Likert scale, ranging from 1 ( very slow ) to 7 ( very fast ), which asked, ‘How fast do you want your company to cut down trees?’ Additionally, they were asked one question about forest resource consumption, ranging from 1 to 100 acres, which asked, ‘How many acres of trees do you expect your company to cut down?’. Give that both questions indicate greedy for forest resources, the average of participants’ reversed standardized scores on the two questions was computed to represent PEB intention. Higher scores indicate stronger forest resource conservation intention. We also treated the two items separately to test our hypothesis, which can be found in the Additional file 1 .

Manipulation checks

The manipulation of resource scarcity information was successful. Specifically, participants in the scarcity condition ( M  = 5.17, SD  = 1.25) compared to those in the control condition ( M  = 4.55, SD  = 1.56), reported higher levels of awareness on ecological resource scarcity, t (153) = 2.72, p  = .007, 95%CI = [0.169, 1.066], d  = 0.44. Furthermore, there was no difference of mood between the two conditions ( M scarcity  = 5.06, SD scarcity  = 1.19; M control  = 4.92, SD control  = 1.23), t (153) = 0.73, p  > .05, 95%CI = [-0.526, 0.242].

Hypothesis test

To test for the moderating effect of environmental unpredictability, we regressed the forest resource conservation intention on ecological resource scarcity information (dummy coded: 1 =  scarcity condition, 0 =  control condition), environmental unpredictability and their interaction by employing the PROCESS macro (Model 1, 5000 bootstrap samples) for SPSS [ 48 ]. The results showed a significant main effect of ecological resource scarcity information ( b  = 0.63, SE  = 0.23, t  = 2.78, p  = .006, 95% CI  = [0.183, 1.078]). And there was no main effect of environmental unpredictability ( b  = 0.04, SE  = .03, t  = 1.23, p  > .05, 95% CI  = [-0.026, 0.109]).

Results showed a significant interaction effect ( b  = -0.14, SE  = 0.06, t  = -2.54, p  = .012, 95% CI  = [-0.025, -0.031], Δ R 2  = .04), meaning that the effect of ecological resource scarcity information on forest resource conservation intention was moderated by environmental unpredictability. Specifically, for individuals with lower levels of environmental unpredictability (below 1 SD ), participants in the scarcity condition exhibited stronger forest resource conservation intention relative to those in the control condition, b  = 0.43, SE  = 0.16, t  = 2.63, p  = .0095, 95% CI = [0.107, 0.755]. In contrast, for individuals with higher levels of environmental unpredictability (above 1 SD ), the ecological resource scarcity manipulation had no effect on forest resource conservation intention, b  = -0.17, SE  = 0.17, t  = -1.04, p  > .05, 95% CI = [-0.512, 0.158] (see Fig.  2 ).

The effect of resource scarcity × environmental unpredictability on forest resource conservation intention (Study 2)

Besides, a floodlight analysis was performed to decompose the interaction [ 49 ]. It revealed that ecological resource scarcity manipulation increased forest resource conservation intention for any value of environmental unpredictability less than 2.78 ( b  = 0.24, SE  = 0.12, t  = 1.98, p  = .05, 95% CI = [0.000, 0.487]), but not for any value greater than 2.78. More importantly, the above findings did not significantly differ after controlling for demographic variables.

Study 2 replicated results of Study 1 and identified that environmental unpredictability weakened the positive effect of ecological resource scarcity information on resource conservation. Presenting ecological resource scarcity information could effectively increase forest conservation intention, particularly for individuals who move less frequently, indicating lower levels of environmental unpredictability.

However, the results of Study 2 were limited in several aspects. First, environmental unpredictability can be caused either by individuals themselves, such as frequent relocation, or by nature, such as unforeseen natural disasters. The present study focused on individual-related environmental unpredictability only. Secondly, the measurement of resource conservation intention instead of actual behaviors may have restricted the ecological validity of the findings. Thirdly, it is possible that the moderation effect was underestimated. In the forest management task, the psychological experience of forest resource scarcity may have been primed in both conditions, as participants were informed about the need to compete with other companies for limited forest resources. Consequently, participants’ decisions may have been heavily influenced by the forest management scenario.

Based on above discussions of Study 2, in Study 3, actual PEB was measured to increase ecological validity, and nature-caused environmental unpredictability was focused to improve generalizability. In addition, hypothetical forest resource conservation was also measured to replicate findings of Study 2. We proposed that receiving ecological resource scarcity information would increase actual resource conservation and forest resource conservation intention under predictable environmental conditions but that this effect would disappear under unpredictable environmental conditions.

We conducted a power analysis through G*Power 3.1 with the moderating effect size in Study 2, which suggested that a sample size of 135 would be required to achieve 80% power ( α  = .05) [ 40 ]. A total of 142 college students in Beijing, China was recruited to participate in the experiment in exchange for monetary compensation. Six participants who failed to finish all experimental tasks were excluded from data analysis. It is worth noting that the rule for terminating data collection was decided before data collection began: the experiment was terminated when more than 135 observations were collected in two weeks.

The average age of the participants was 21.87 ± 2.67 years (range = 17–29 years), and 75.00% of them were female ( N  = 102). The average annual household income was 12.37 ± 17.10 thousand yuan .

Research design and procedure

A 2 (water resource scarcity vs. control) × 2 (unpredictable vs. predictable environment) between-subject design was used.

Before arriving at the lab, participants were asked to fill out their demographic information in an online survey. Upon arrival at the lab, participants were randomly assigned into one of four groups to read a newspaper. These newspapers were designed to be looked like real Beijing Daily newspapers. In each type of newspaper, there were two pieces of news. One was designed to manipulate the water resource scarcity information, and another was designed to manipulate environmental unpredictability information. Then, actual water and paper consumption data was recorded in a washing-hands paradigm. Finally, forest resource consumption intention was measured.

Manipulation of water resource scarcity information

Similar to Study 2, in the scarcity condition ( n  = 67), the news article described the seriousness of water resource scarcity in Beijing. While, in the control condition ( n  = 69), the news article described Beijing residents’ sleep problems. After reading the article, participants responded to 1 item on perceived ecological resource scarcity on a 7-point Likert scale (1 = “ strongly disagree ” to 7 = “ strongly agree ”), which was adapted from new ecological paradigm scale (NEP): “The earth has plenty of natural resources if we just learn how to develop them” [ 52 ].

Manipulation of environmental unpredictability information

In the unpredictable condition ( n  = 68), the news article was titled “Natural Disasters are Unpredictable and Difficult to Prevent: 9.578 million People were Affected by Various Natural Disasters in January”. The news conveyed the information that natural disasters happened frequently, which caused many people to be affected in January, and there was no way to predict and prevent disasters. By contrast, in the predictable condition ( n  = 68), the news stated that even though natural disasters are frequent in China and many people were affected, now some devices can help predict and prevent disasters. The title was “Predication and Prevention of the Occurrence of Natural Disasters is Possible: 9.578 million People were Affected by Various Natural Disasters in January”.

Manipulation check items were rated right after reading the news article. Participants responded to 2 items about perceived unpredictability on a 7-point Likert scale (1 = “ strongly disagree ” to 7 = “ strongly agree ”): “The environment where I live is unstable”, and “The environment where I live is unpredictable”. The average score of the two items was computed such that a higher score indicated stronger perceived unpredictability.

Actual water and paper resource consumption

To cover up our real purpose, participants were told that the research was attempting to study palms, so that we would collect their fingerprints in the study. In the washing-hands paradigm, participants were asked to use the inkpad and leave their fingerprints on a sheet of white paper to study their palms. After that, they had to wash their hands in the lab. The amount of water and paper they used was recorded.

To measure the water consumption, the experimenter placed one measuring cylinder under the washbasin, and the measuring cylinder was linked to the washbasin’s outlet pipe. Importantly, participants could not see the cylinder. To measure their paper consumption, a bag of paper was placed on the washbasin for the participants to use. Besides, to exclude the experimenter effect, participants washed their hands without experimenter observation. Importantly, participants did not know that their behaviors were recorded, and participants were not aware of the real purpose of the study (see Fig.  3 ). All of the participants were debriefed at the end of the study.

Set-up of washing-hands paradigm

Considering that water and paper consumption for washing ink from hands might be affected by palm size, we recorded the palm area for each participant based on their fingerprints. Then, actual resource consumption was represented by average water consumption and average paper consumption, calculated by water or paper consumption divided by palm area.

Hypothetical forest resource conservation

Same as Study 2, the forest management task was used. After reading the scenario, participants were asked to answer the question, “How many acres of trees do you expect your company to cut down?”, ranging from 1 to 100 acres. A higher score on the measurement indicates a lower intention for forest resource conservation.

Perceived resource scarcity was significantly greater in the scarcity condition ( n  = 67, M  = 5.61, SD  = 1.19) than that in the control condition ( n  = 69, M  = 5.13, SD  = 1.38), t (134) = 2.17, p  = .032, 95%CI = [0.043, 0.920], d  = 0.37. Perceived unpredictability was also significantly greater in the unpredictable condition ( n  = 68, M  = 5.13, SD  = 1.28) compared to the predictable condition ( n  = 68, M  = 4.55, SD  = 1.39), t (134) = 2.51, p  = .013, 95%CI = [0.121, 1.026], d  = 0.43. Overall, the manipulations were successful and valid.

To examine the interaction effect between water resource scarcity and environmental unpredictability on resource conservation, two-factor MANOVAs were conducted.

Concerning the average water consumption, gender, age, household income, and cleanliness habits are included as control variables. The findings revealed that the main effect of scarcity was significant ( F (1,128) = 5.44, p  = 0.021, 95%CI = [-14.168, -0.673], η p 2  = .041), and the main effect of environmental unpredictability was not significant ( F (1,128) = 0.23, p  > .05, 95%CI = [-7.437, 5.984]). As expected, the interaction was significant ( F (1,128) = 6.81, p  = .01, 95%CI = [3.037, 22.097], η p 2  = .050). Then, simple effect analysis revealed that under the predictable condition, the average water consumption was significantly less under the scarcity condition ( M  = 25.29, SD  = 13.87) than under the control condition ( M  = 37.13, SD  = 13.91), F (1,128) = 12.25, p  < .001, 95%CI = [-18.535, -5.146], η p 2  = .087. However, under the unpredictable condition, there was no significant difference between the scarcity condition ( M  = 32.71, SD  = 13.93) and control condition ( M  = 31.98, SD  = 13.90), F (1,128) = 0.05, p  > .05, 95%CI = [-5.984, 7.437] (see Fig.  4 ).

Average water consumption as a function of resource scarcity and environmental unpredictability manipulations (Study 3)

Moreover, the results in average paper consumption showed a similar pattern. Main effects of scarcity ( F (1,128) = 0.42, p  > .05, 95%CI = [-0.137, 0.042]) and environmental unpredictability ( F (1,128) = 0.70, p  > .05, 95%CI = [-0.143, 0.036]) were not significant. A significant interaction effect was detected, F (1,128) = 5.30, p  = .023, 95%CI = [0.021, 0.275], η p 2  = .040. As predicted, in the predictable condition, paper consumption in the scarcity condition ( M  = 0.28, SD  = 0.18) was significantly less than in the control condition ( M  = 0.38, SD  = 0.18), F (1,128) = 4.39, p  = .038, 95%CI = [-0.184, -0.005], η p 2  = .033. No significant difference in paper consumption were observed between scarcity condition ( M  = 0.33, SD  = 0.19) and control condition ( M  = 0.28, SD  = 0.19) in unpredictable condition, F (1,128) = 1.39, p  > .05, 95%CI = [-0.036, 0.143] (see Fig.  5 ).

Average paper consumption as a function of resource scarcity and environmental unpredictability manipulations (Study 3)

More importantly, the above findings did not significantly differ without control variables in data analysis, and also did not significantly differ using the raw scores of water and paper consumption. Detailed results can be found in the Additional file 1 .

Hypothetical forest resource consumption was log transformed as it showed non-normal distribution. The findings showed that the main effects of scarcity ( F (1,130) = 1.800, p  > .05, 95%CI = [-0.363, 0.271]) and environmental unpredictability ( F (1,130) = 2.189, p  > .05, 95%CI = [-0.688, 0.049]) were not significant. A marginally significant interaction effect was detected, F (1, 130) = 3.04, p  = .084, 95%CI = [-0.053, 0.849], η p 2  = .023. As predicted, in the predictable condition, forest resource consumption in the scarcity condition ( M raw  = 30.76, SD raw  = 14.97) was significantly less than the control condition ( M raw  = 40.74, SD raw  = 25.41), F (1,130) = 4.71, p  = .032, 95%CI = [-0.672, -0.031], η p 2  = .035. No significant difference of forest resource consumption was observed between scarcity condition ( M raw  = 42.15, SD raw  = 20.41) and control condition ( M raw  = 44.00, SD raw  = 28.17) in unpredictable condition, F (1,130) = 0.08, p  > .05, 95%CI = [-0.027, 0.363] (see Fig.  6 ).

Forest resource consumption as a function of resource scarcity and environmental unpredictability manipulations (Study 3)

As expected, Study 3 replicated the findings of the previous two studies. We identified a moderating effect of nature-caused environmental unpredictability on ecological resource scarcity information’s effect on actual PEB. Specifically, individuals who received lower levels of environmental unpredictability information exhibited more water-saving and paper-saving behaviors, and were inclined to harvest fewer forest resources in the face of water scarcity. Interestingly, even though our manipulation focused solely on water scarcity, both paper consumption and forest resource consumption were affected as well, despite their lack of direct association with water. These results highlight the robust influence of resource scarcity information and environmental unpredictability on PEB, thereby enhancing the ecological validity of our findings.

General discussion

Focusing on the global issue of environmental unpredictability, the current research explored when does showing resource scarcity information promote PEB. In Study 1, a cross-sectional study, we discovered that resource scarcity information effectively enhances PEB, but only when individuals perceive the environment as less unpredictable. Furthermore, by manipulating scarcity information, Study 2 revealed that only for individuals with lower levels of environmental unpredictability could presenting ecological resource scarcity information decrease forest resource consumption intention. Moreover, an experiment with high ecological validity was conducted in Study 3 and found that the negative effect of water resource scarcity information on actual water and paper saving behaviors, as well as hypothetical forest resource consumption emerged only for people who receiving weaker environmental unpredictability information.

Theoretical contribution and practical implication

Environmental unpredictability is an important concept in life history theory. Numerous studies have verified that childhood environmental unpredictability plays a crucial role in shaping life history strategies [ 27 , 28 , 30 , 37 , 53 ]. However, little is known about how adulthood environmental unpredictability functions. The current research provided preliminary evidence that unpredictability in adulthood can also function in shaping behaviors. Adulthood unpredictability, including both individual- and nature-related environmental unpredictability, demotivates individuals to sacrifice present interests for future environmental benefits when facing scarcity.

Some psychological factors, including those discussed earlier (such as short-sighted tendency, fear of free riders, and perceived lack of control), as well as self-interest and competitive orientation, can serve as potential mechanisms underlying the moderating effect of environmental unpredictability. Self-interest and competitive orientation are important ways for individuals to survive in a harsh environment. Individuals may adopt a competitive orientation to obtain more benefits for themselves to survive during periods of scarcity. In addition, they may also seek to weaken others’ interests. These factors have been identified as “Stone Age” psychological biases leading to environment destruction [ 54 ]. To better respond to ecological resource scarcity, the current research demonstrated the importance of creating a predictable and peaceful world by removing the psychological barriers to mitigate ecological resource scarcity.

The IMB model provides a comprehensive framework advancing resource conservation research and intervention implements [ 3 ]. Even though the IMB model captures three vital components, information, motivation, and behavioral skills on behavior change, the psychological barriers caused by environmental unpredictability were ignored. As illustrated in a recent meta-analysis [ 15 ], compared with the control group, of the 38 interventions including IMB components, water use was reduced by only 5.9% in average with a small effect size, and the magnitude of effect varied widely in different interventions. According to the findings in the current research, levels of environmental unpredictability may be the underlying reason for the varied efficacy. Therefore, to best strengthen reducing resource consumption interventions based on the IMB model, it’s necessary to take environmental unpredictability into consideration.

Importantly, the current research developed a new paradigm, washing-hands paradigm, to measure actual resource consumption in the lab. As illustrated in previous studies, there are gaps between self-reported behaviors, and objective behaviors [ 43 ]. However, over 80% of recent studies only relied on self-reported data [ 55 ]. The washing-hands paradigm sets up a situation to capture actual water and paper resource consumption data. Importantly, the confounding variables can be controlled in the paradigm, such as habits, individual difference on palm size, and social desirability. This paradigm can help to establish causality and improve ecological validity of lab experiments, advancing resource conservation research.

The current research also provides some vital practical implications for both policy makers and environmental organizations. Our data suggested that creating a predictable environment can help promote resource conservation when facing ecological resource scarcity information. Governments should try to eliminate unpredictable factors. However, some unpredictable factors are difficult to address, such as natural disasters and virus spread. In such conditions, individual-level practices appear to be more important. For countries with a predictable environment, the strategy of the reminders of the ecological resource scarcity information is effective. However, for countries with an unpredictable environment, governments and organizations can consider using public media to decrease residents’ perceived unpredictability. Moreover, inspired by our Study 2, emphasizing predictable environmental information when reminding residents of scarcity should be encouraged. Environmental organizations should provide information that the environment is predictable when calling for resource conservation to respond to scarcity.

Limitations and future directions

The current research faces the limitation that the measurement in the correlation study is restricted due to the use of only one item to measure the moderator, and the alpha level of the PEB measure is low. For future studies, one aspect to consider is the exploration potential mechanisms of the moderation hypothesis. The current research did not delve into psychological mechanisms. It is suggested that future research could investigate underlying potential mechanisms of the moderation hypothesis to enrich the framework. Another related issue pertains to the IMB model. In the current research, we mainly focused on the effectiveness of scarcity information component but didn’t include motivation and behavioral skills components. It’s worthy for future research to test if creating a predictable environment can still strengthen the effect of IMB intervention. Besides, there are various types of resource conservation behaviors that individuals can engage in. Importantly, different behaviors are not necessarily highly relevant. For example, factors predicting shutting down electronics at night could not predict upgrading to energy-efficient appliances because these behaviors may cluster into distinct dimensions [ 56 , 57 ]. In the current research, we may not be able to generalize our findings to other types of behaviors. Thus, future research is encouraged to investigate whether the moderation hypothesis can be verified in other types of resource conservation behaviors.

Across three studies, we provided evidence to support the moderation hypothesis that environmental unpredictability weakens the positive effect of ecological resource scarcity information on PEB. Moving forward, it would be valuable to delve deeper into the underlying mechanisms, examine the moderation effect across various types of PEB, and investigate its potential application in PEB interventions.

Availability of data and materials

No datasets were generated or analysed during the current study.

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The authors acknowledge the financial support from the National Natural Science Foundation of China (31871126), Chongqing Normal University (23XWB043) and Social Science Fund of Chongqing, China (2023BS076).

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GD and JJ contributed to the study conception and design. Material preparation, data collection and analysis were performed by GD. GD and JJ contributed to the interpretation of data. The first draft of the manuscript was written by GD. JJ commented on previous versions of the manuscript or revised it critically for important intellectual content. All authors approved the final version of the manuscript.

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Gu, D., Jiang, J. Navigating an unpredictable environment: the moderating role of perceived environmental unpredictability in the effectiveness of ecological resource scarcity information on pro-environmental behavior. BMC Psychol 12 , 261 (2024). https://doi.org/10.1186/s40359-024-01762-1

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FER @ National Conference on Undergraduate Research

The FER Department had four outstanding students that represented the college in this past National Conference on Undergraduate Research (NCUR) held in Long Beach, CA. This conference allows selected students to display their research, scholarship, and creative activity in all fields of study. This is an annual event that gives our students the opportunity to learn from all disciplines through promoting a culture of development of networking, gaining professional development, and honing into their research skills. 

Nick Berenson (he, him, his) – Environmental Sciences (BS), Economics (BS). Nick worked with Dr. Erin Sills, Dr. Rajan Parajuli, and 2 PhD candidates.

Nick’s research project and experience @ NCUR: My research was on Urbanization and Community Forestry: Assessing Changing Forest Dynamics and Livelihoods, which studies how people change their dependence on community forests as their proximity to urban influence shifts. I learned a lot about how to accurately present scientific information, communicate with peers about my research, and how to interact with other researchers. Additionally, I got to network with other undergraduates and read about their projects, which helps me gain more insight in the collaboration and connectedness of the scientific community. I was impressed with my peers’ presentations and it made me realize how grateful and lucky I am to have this opportunity, I will definitely cherish it forever.

Andrea Putri – Environmental Technology & Management (BS), Minor: Environmental Toxicology. Andrea worked with Dr. Angela Allen.

Andrea’s research project and experience @ NCUR: I have been working on the water quality issues with Dr. Allen for almost a year. We are focusing on continuous monitoring of several parameters at Little Rock Creek in Southeast Raleigh. Myself and other students (Dom Zecca and Jada West) have been performing and analyzing data to not only present to the community but at conferences as well. NCUR Conference was my first national conference, so I am grateful for that opportunity to present our work. These types of experiences give me the chance to create new connections with other like-minded students/professionals. 

Dominic Zecca  – Environmental Technology & Management (BS), Minors: Biological Sciences and Anthropology. Dominic worked with Dr. Angela Allen.

Dominic’s research project and experience @ NCUR: I did my research with Dr. A and Andrea Putri, and we focused on water quality in Southeast Raleigh. We wanted to see what the effects of urbanization are in this historically marginalized community of Raleigh and if there was a difference in water quality between our sites in Southeast Raleigh and other sites around Raleigh. At the conference, we were able to present our research to a wide variety of students and professors with varying degrees of environmental health knowledge. Furthermore, we were able to connect with everyone we talked to in some way, as water quality and urbanization are issues many places around the US are addressing. This conference was an exciting opportunity to represent NC State and meet new people from all over the US, and it is an experience that I would recommend to other people who are thinking about getting into research or wanting to attend conferences while being a part of the NC State experience. 

Text provided by Dr. Angela Allen, Nick Berenson, Andrea Putri and Dominic Zecca.

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Can we revolutionize the chemical industry and create a circular economy? Yes, with the help of catalysts

The chemical industry is a cornerstone of global development, driving innovation, and providing essential products that support our modern way of life.

However, its reliance on unsustainable fossil resources has posed significant threats to global ecosystems through climate change and chemical pollution.

A new commentary published in Cell Press' One Earth co-authored by Griffith University researchers puts forth a transformative solution: catalysis to leverage sustainable waste resources, ushering the industry from a linear to a circular economy.

"If we look at recent statistics, the chemical industry contributes a staggering US$5.7 trillion to the global economy and sustains 150 million jobs worldwide, excluding refined fossil fuels," said Professor Karen Wilson, one of the lead authors and Director of Griffith's Centre for Catalysis and Clean Energy.

"But it remains the largest industrial energy consumer and the third-largest emitter of direct CO 2 emissions globally."

In 2022, the industry emitted 935 million metric tons of CO 2 during primary chemicals production. Moreover, its operations have led to significant water contamination and the release of toxic chemicals into the environment, perpetuating a cycle of ecological harm.

Co-lead author Professor Adam Lee, also based at Griffith, said: "Catalytic processes could minimise reliance on finite fossil fuels and curb CO 2 emissions significantly by harnessing agricultural, municipal, and plastic waste as feedstocks.

"This feedstock transition not only mitigates environmental damage but also addresses vulnerabilities in the industry's supply chain, which are susceptible to geopolitical and natural disruptions."

Professor Wilson added: "Catalysis has historically played a key role in transforming fossil resources into essential fuels and products, and now offers a beacon of hope for revolutionising the chemical industry and promoting a circular economy."

However, the authors acknowledge that this vision demands concerted innovation in catalyst formulation and process integration.

"Prioritising Earth-abundant elements over precious metals will unlock sustainable catalytic systems for the efficient conversion of organic waste into benign and recyclable products," Professor Wilson said.

"Already, pioneering initiatives such as the co-location of different industries in Kalundborg, Denmark to foster symbiosis have demonstrated new collaborative models to improve resource efficiency and waste reduction."

"Catalysis offers a pathway towards sustainability, enabling us to transform waste into valuable resources and pave the way for a circular economy," Professor Lee added.

In the OneEarth commentary, the team explored sources of catalysis for sustainable and circular chemical processes through the following lenses:

Catalysis to enable waste biomass utilisation

Catalysis for circular polymers

Catalysis to remediate chemical pollution

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Story Source:

Materials provided by Griffith University . Original written by Carley Rosengreen. Note: Content may be edited for style and length.

Journal Reference :

• Ali Abbas, Megan Cross, Xiaoguang Duan, Steffen Jeschke, Muxina Konarova, George W. Huber, Adam F. Lee, Emma C. Lovell, Jason Y.C. Lim, Anastasios Polyzos, Ryan Richards, Karen Wilson. Catalysis at the intersection of sustainable chemistry and a circular economy . One Earth , 2024; 7 (5): 738 DOI: 10.1016/j.oneear.2024.04.018

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