research paper on biodiversity conservation

Advertisement

An overview of remote monitoring methods in biodiversity conservation

• Review Article
• Published: 05 October 2022
• Volume 29 , pages 80179–80221, ( 2022 )

Cite this article

• Rout George Kerry   ORCID: orcid.org/0000-0002-2943-3681 1 ,
• Francis Jesmar Perez Montalbo   ORCID: orcid.org/0000-0002-1493-5080 2 ,
• Rajeswari Das   ORCID: orcid.org/0000-0002-9367-3954 3 ,
• Sushmita Patra   ORCID: orcid.org/0000-0002-8624-3323 4 ,
• Gyana Prakash Mahapatra 5 ,
• Ganesh Kumar Maurya 6 ,
• Vinayak Nayak   ORCID: orcid.org/0000-0002-1516-5161 4 ,
• Atala Bihari Jena   ORCID: orcid.org/0000-0002-1690-7913 7 ,
• Kingsley Eghonghon Ukhurebor   ORCID: orcid.org/0000-0001-7595-3939 8 ,
• Ram Chandra Jena 9 ,
• Sushanto Gouda   ORCID: orcid.org/0000-0001-5117-683X 10 ,
• Sanatan Majhi 1 &
• Jyoti Ranjan Rout   ORCID: orcid.org/0000-0001-9717-4901 11  

8004 Accesses

7 Citations

8 Altmetric

Explore all metrics

Conservation of biodiversity is critical for the coexistence of humans and the sustenance of other living organisms within the ecosystem. Identification and prioritization of specific regions to be conserved are impossible without proper information about the sites. Advanced monitoring agencies like the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) had accredited that the sum total of species that are now threatened with extinction is higher than ever before in the past and are progressing toward extinct at an alarming rate. Besides this, the conceptualized global responses to these crises are still inadequate and entail drastic changes. Therefore, more sophisticated monitoring and conservation techniques are required which can simultaneously cover a larger surface area within a stipulated time frame and gather a large pool of data. Hence, this study is an overview of remote monitoring methods in biodiversity conservation via a survey of evidence-based reviews and related studies, wherein the description of the application of some technology for biodiversity conservation and monitoring is highlighted. Finally, the paper also describes various transformative smart technologies like artificial intelligence (AI) and/or machine learning algorithms for enhanced working efficiency of currently available techniques that will aid remote monitoring methods in biodiversity conservation.

Similar content being viewed by others

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets.

The Use of Remote Sensing to Enhance Biodiversity Monitoring and Detection: A Critical Challenge for the Twenty-First Century

Remote sensing of biodiversity: what to measure and monitor from space to species?

Avoid common mistakes on your manuscript.

Introduction

Biological diversity “biodiversity” entails the assortment of earthly life forms heterogeneously ranging from genetic to ecosystem level. It can embrace the evolutionary, ecological, and cultural aspects that uphold life in various forms (McQuatters-Gollop et al. 2019 ). It fosters ecological functioning that paves the path for fundamental ecosystem services comprising food, water, preservation of soil fertility, and management of pests and diseases (Avigliano et al. 2019 ; Whitehorn et al. 2019 ). The plasticity of co-existence between mankind and nature is irreversible because of the symbiotic relationship that sustains the co-survival of humans with other living organisms (Arias-Maldonado 2016 ).

Biological diversity of forest originating from gene to ecosystem, through species, supports forest habitat that gives rise to fodders and other goods and services in a wide array of diverse biophysical and socio-economic ambience. Despite the applicability and significance of biodiversity, its conservation is vaguely acknowledged. Presently, human invasions have distorted around 75% of the land-based territory and about 66% of the marine ecosystem. Further to this, over a third of the global terrestrial regions are now devoted to domestic pursuit (FAO 2019 ). Moreover, since 1970, the significance of agricultural crop yield has increased by about 300%, and harvesting of raw timber has hiked by 45%. Moreover, renewable and non-renewable resources roughly of 60 billion tons are presently extracted annually across the globe. Exploitation of land has abridged the prolificacy of 23% of the global land area, annually, up to US$577 billion in worldwide crops are in jeopardy from pollinator loss, and about 100–300 million people are at elevated threat of natural disaster due to loss of coastal habitats and protection (IPBES 2019 ). If such trends continue then by 2050, the transformative change in nature can lead to an unprecedented devastating irreversible impact on mankind, which will take centuries to recover.

These atrocities of biodiversity need to be averted through proper monitoring and conservation measures. Based on the present advancements in technology, a combination of system-based smart techniques, remote sensing, and molecular approaches will be necessary for implementation of such ambitious conservation drives. Computer-based simulation techniques such as geographic information system (GIS), active and passive radio detection and ranging (RADAR) system, and light detection and ranging (LiDAR) system are playing a crucial role for monitoring biodiversity in real time (Bouvier et al. 2017 ; Bae et al. 2019 ; Bakx et al. 2019 ). Further to this, the application of recent advancements like artificial intelligence (AI) (Kwok 2019 ) and/or machine learning algorithms (Fernandes et al. 2020 ) have also been exploited for the same (Hu et al. 2015 ). These systems are not only reliable in monitoring biodiversity globally but can also help prevent further biodiversity loss worldwide. Besides monitoring tools, conservation of individual species and genetic biodiversity as a whole will require the use of recent molecular techniques. Conservation genomics revolves around the concept that genome-scale data will meliorate the competence of resource proprietors to conserve species. Despite the decades-long utilization of genetic approaches for conservation research, it has only recently been implied for generating genome-wide data which is functional for conservation (Supple and Shapiro 2018 ). The revolutionary molecular tools like restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP), random amplified polymorphic DNA (RAPD), sequence characterized amplified region (SCAR), microsatellites and mini-satellites, expressed sequence tags (ESTs), inter-simple sequence repeat (ISSR), and single nucleotide polymorphisms (SNPs) have transformed the hierarchy of biodiversity conservation to a higher level (Mosa et al. 2019 ).

Evidently, more sophisticated monitoring methods such as system-based simulation techniques, remote sensing, artificial intelligence, and geographic information system as well as molecular-based techniques facilitate the monitoring methods in biodiversity conservation and restoration. Therefore, the present study is an overview of remote monitoring methods in biodiversity conservation via a survey of evidence-based reviews and related studies, wherein the description of the application of some technology for biodiversity conservation and monitoring. Finally, the paper also describes various transformative smart technologies like artificial intelligence (AI) and/or machine learning algorithms for enhanced working efficiency of currently available techniques that will aid remote monitoring methods in biodiversity conservation.

Methodology of literature search

The relevant literature search was done electronically by using Google Search Engine, PubMed, ScienceDirect, SpringerLink, Frontiers Media, and MDPI databases. The most importantly searched keywords were biodiversity and potential threats, techniques to monitoring biodiversity, geographic information system, remote sensing, active remote sensing system, radio detection and ranging (RADAR) system, light detection and ranging (LiDAR) systems, passive remote sensing systems, techniques for identification and genetic conservation of species, restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP), random amplified polymorphic DNA (RAPD), sequence characterized amplified region (SCAR), mini- and micro-satellites, expressed sequence tags (ESTs), inter-simple sequence repeat (ISSR), single nucleotide polymorphisms (SNPs), and artificial intelligence in biodiversity monitoring which were used and placed repeatedly within the text.

Biodiversity and potential threats

Biodiversity in simple terms is a heterogenic distribution of flora and fauna throughout the world or in a particular niche (Naeem et al. 2016 ). The number of species described around the world as per IUCN ( 2020 ) accounts for 2,137,939, of which 72,327 are vertebrates, 1,501,581 are invertebrates, 422,756 are plants, and 141,275 are identified as fungi and protists. It is now acknowledged that biodiversity is a major indicator of community ecosystem fluctuations and functioning (Tilman et al. 2014 ). These include provisioning of food, pollination, cultural recreation, and supporting nutrient cycling (Harrison et al. 2014 ; Bartkowski et al. 2015 ). Biodiversity as a whole is represented by two major components that are species richness and species evenness. A biogeographic region with a significant level of endemic species and with a higher loss of habitat is generally depicted as a biodiversity hotspot (Marchese 2015 ). These areas have proven themselves as a tool for establishing conservation priorities and orchestrate vital rationale in decision-making for cost-effective tactics to safeguard biodiversity in its natural conserved state. Usually, the hotspots are marked by single or multiple species-based metrics or concentrate on phylogenetic and functional diversity to shield species that sustain exclusive and inimitable functions inside the ecosystem (Marchese 2015 ).

Currently, as per the IUCN Red List of Species 2020–2021, of the 2,137,939 species around the world, about 31,030 species are categorized as “threatened” species. Among these, plants with 16,460 numbers contribute the most followed by vertebrates (9063), invertebrates (5333), and fungi and protists (174) [IUCN 2020 ]. Many of the species are still not assessed due to a lack of reliable identification tools or techniques. Biodiversity is mostly threatened by over-population, habitat and landscape modification, indiscriminate exploitation of resources, pollution, and lack of proper documentation (Marchese 2015 ; Liu et al. 2020 ; Reid et al. 2019 ). Demographic changes can be considered an imperative module for assisting the indirect drivers of biodiversity alternations specifically associated with land use patterns (Newbold et al. 2015 ). Population explosion, central demographic developments, and urbanization impact both ecosystems and the species it harbors (Mehring et al. 2020 ). As the changing demographic pattern is associated with population explosion, this may pose a pessimistic impact on food availability, restricted emission of greenhouse gases, control of invasive species and diseases, etc. (Lampert 2019 ; Manisalidis et al. 2020 ; Hoban et al. 2020 ; Reid et al. 2019 ). To generate such massive data over a stipulated time frame and process them simultaneously to extrude applicable information requires cutting-edge tools and multidisciplinary scientific input (Randin et al. 2020 ). With recent advancements in mapping software, large-scale data processors, and monitoring tools and genetics, artificial intelligence for generating accurate data over a larger area as a part of a global monitoring strategy has now become feasible (Wetzel et al. 2015 ; Randin et al. 2020 ). Hence, an assortment of the above mention techniques and tools will be essential for the conservation and restoration of biodiversity.

Techniques for monitoring biodiversity

Mapping and monitoring techniques have been frontiers in predicting and modeling anthropogenic activities, habitat use, and pattern of land use over time in a particular region. These advanced physical techniques include GIS, LiDAR, and RADAR systems (Bouvier et al. 2017 ; Bae et al. 2019 ; Bakx et al. 2019 ).

Geographic information system (GIS)

Understanding functional geography and making intelligent decisions is widely beneficial for naturalists. GIS is a popular tool for analyzing possible and current spatial-temporal distribution, location, distribution patterns, population assessment, and identification of priority areas for their conservation and management (Krigas et al. 2012 ; Salehi and Ahmadian 2017 ). Currently, development of ecological niche models based on topographic, bioclimatic, soil, and land use variables was mapped and predicted for species such as Clinopodium nepeta , Thymbra capitata , Melissa officinalis , Micromeria juliana , Origanum dictamnus , O. vulgare , O. onites , Salvia fruticosa , S. pomifera , and Satureja thymbra (Bariotakis et al. 2019 ). With the assistance of digitally integrated video and audio-GIS (DIVA-GIS), actual geographic distribution and the future potential assortments of several Zingiber sp. like Z. mioga , Z. officinale , Z. striolatum , and Z. cochleariforme were analyzed (Huang et al. 2019 ). Most recently, important climatic inconsistencies distressing the geographical dispersion of wild Akebia trifoliate based on the formation of spatial database were successfully determined with the help of GIS (Wang et al. 2020 ). However, GIS possesses certain limitations such as expensive software, hardware, capturing GIS data, and difficulty in their use (Bearman et al. 2016 ) (Fig. 1 , Table 1 ).

Application of Geographic Information Systems (GIS), remote sensing technologies like radio detection and ranging (RADAR) and satellite-based light detection and ranging (LiDAR) for wildlife monitoring in the forest ecosystem. The figure describes global forest distribution (Our World In Data 2020 ), wherein displayed GIS and different levels of GIS data, schismatic of passive remote sensing, active remote sensing segregated into primary RADAR system, and a block diagram of satellite-based LiDAR system for generation of DCHM (digital canopy height model) image and 3-D point cloud image of the whole organism is outlined. Global positioning system (GPS), Inertial measurement unit (IMU). The figure is inspired by the following sources: Omasa et al. ( 2007 ), Admin ( 2017 ), Bhatta and Priya ( 2017 ), Jahncke et al. ( 2018 ), Martone et al. ( 2018 ), Srivastava et al. ( 2020 ). The components of the figure are modifications of Portree ( 2006 ), Organikos ( 2012 ); Smithsonian’s National Zoo and Conservation Biology Institute Smithsonian’s National Zoo ( 2016 ), and Freepik ( 2021 ). Abbreviations: FP-mode, first-pulse mode; LP-mode, last-pulse mode; DEM, digital elevation model; DTM, digital terrain model

• Remote sensing

The ability to extract information about the environment without physical contact from a large distance by a sensor that reflects and/ or emits electromagnetic spectrum (visible, infrared, and microwave spectra) is defined as remote sensing. Based on the source of radiation emitted, which comes in contact with the object, remote sensing can be categorized as active or passive remote sensing systems (Höppler et al. 2020 ). Remote sensing of biodiversity can be used for habitat mapping including species area curve and habitat heterogeneity, species mapping/distribution, plant functional diversity/ traits, spectral diversity including vegetation indices and spectral species (Cavender-Bares et al. 2020 ; Wang and Gamon 2019 ).

Active remote sensing system

An active remote sensor emits energy pulses and records the return time and amplitude of the backscattered energy pulses from the object to generate the required information about it (Vogeler and Cohen 2016 ). Currently used active remote sensing technologies like RADAR and LiDAR systems can be used to determine the location, speed, and direction of any wildlife form.

Radio detection and ranging (RADAR) system

As a sub-set of the active remote sensing system, RADAR, operates in the microwave of wavelengths of 1 mm to 1 m. Additionally, the modern RADAR systems are incorporated with software routines to mathematically enhance spatial resolution and manage multiple pictures of the same object, also as Synthetic Aperture RADARs (SARs) (Fig. 1 ). These systems can be used to determine the polarization of the emitted and receive electromagnetic rays which provides a better understanding of the analyzed surface properties (Hay 2000 ; Valbuena et al. 2020 ; Barlow and O’Neill 2020 ) (Table 2 ).

There are two basic types of RADAR systems, namely, primary and secondary. In the primary system, the signal is transmitted in all directions however some of the signals are reflected back to the receiver after colliding with the target thereby defining or detecting the location of the target (Hirst 2008 ; Bhatta and Priya 2017 ). In this system, the transmitted signal needs to be of high power to ensure that the reflected signal is sufficient enough to provide accurate and precise information about the target (Bhatta and Priya 2017 ). Again, noise and signal attenuation due to some factors might disrupt the reflected signal which can also be regarded as a limitation (Bhatta and Priya 2017 ; Martone et al. 2018 ). In the secondary RADAR system, an active answering signal system has been installed for accuracy, where the transmitted signal is received by a compatible transponder that retrieves the signal and further sends a signal comprising the useful information in a coded form (Hirst 2008 ; Bhatta and Priya 2017 ). The receiver receives the coded signal, and after decryption of the code, the information about the target is transcribed, thereby providing information about the real-time spatial orientation (Bhatta and Priya 2017 ; Jahncke et al. 2018 ).

Further to this, ultra wideband (UWB) RADAR is one of the traditional methods used for life detection that analyzes the reflected/echo signal received after hitting the target. Micro-motions by humans, nearby environment, and clutter signals can modulate the reflected signal. As per evidence, it is a reasonable, effective, and complete non-invasive life detection method (Chunming and Guoliang 2012 ; Karthikeyan and Preethi 2018 ; Yin and Zhou 2019 ). With the growth of scientific innovation in the field of remote sensing, a hybrid (On-Chip Split-Ring-Based Sensor) RADAR system has emerged with high-resolution range and sensitivity. This system can easily detect multiple life forms simultaneously even across obstacles (Liu et al. 2016 ).

One of the major applications of RADAR is range detection and to date, the replacement of the sensing and detection efficiency and accuracy by RADAR has not been possible by any other electronic system (Bhatta and Priya 2017 ; Parrens et al. 2019 ). Extension of the sensing capability with respect to atmospheric conditions such as rain, snow, smoke, darkness, and fog and collecting the data makes it unexceptional and advantageous. At present, RADARs have broad areas of applications in defense and control systems, monitoring and forecast systems, astronomy, target-locating system and remote sensing, etc. (Bhatta and Priya 2017 ).

Light detection and ranging (LiDAR) systems

LiDAR is a widely recognized technology, especially the airborne laser scanner (ALS), which focuses on the emission and receipt of laser pulses. During field surveys, LiDAR technology offers the potential to establish variables, representing forest structures that are distinct from those detected or assessed. Bitemporal airborne LiDAR with field survey is widely used for systematic assessment of uncertainties in satellite imagery-based vegetation (Ma et al. 2018 ). Ground-based field survey with airborne LiDAR is applicable for daily tracking of bats on foot-to-roost trees using various radio receivers and antennas and the location of tree cavities using directional antennas and binoculars from the ground (Carr et al. 2018 ; Stephenson 2020 ) (Fig. 1 , Table 2 ).

Unmanned aerial vehicle (UAV) camera with LiDAR data is used for surveying mangrove-inundation spatial patterns in a subtropical intertidal wetland in southeast China (Zhu et al. 2019 ). With the support of artificial intelligence (AI), LiDAR remote sensing is found to be successful in predicting models for efficient biodiversity study by covering more areas with a clear database in a very span of time. LiDAR plot extracted information and Landsat pixel-based composites along with time-series were effective in modeling sets of reflectance images of forest structure across Canada’s forest-dominated ecosystem (Matasci et al. 2018 ). More advanced LiDAR technologies have now overtaken the previous ones in terms of both efficacy and accuracy. Almeida et al. ( 2019 ) in their research on three seasonal semi-deciduous natural forest cover types in the Atlantic forest biome of Southern Brazil have considered ALS with portable ground LiDAR remote sensing as a proxy for analysis of structural hallmarks of forest canopies enduring restoration. Airborne LiDAR with principal component analysis (PCA) assessed the estimation of canopy structure and biomass of Moso bamboo ( Phyllostachys pubescens ) in widely distributed subtropical forests of south China (Cao et al. 2019 ). High-resolution vertical Scheimpflug LiDAR has proven to resolve hypothesis for insects flying over Ostra Herrestad wind farm near the town Shimrishamn in southern Sweden (Jansson et al. 2020 ).

To conserve plant diversity information on various forest attributes, aboveground biomass (AGB), canopy structure, canopy cover, and leaf area index (LAI) are considered important and can be assessed most efficiently with a technique like LiDAR (Bolton et al. 2020 ). Forest canopy height could be an important indicator of biodiversity, productivity, and carbon storage (Li et al. 2020b ). LiDAR when combined with other RS techniques, i.e., high spatial resolution with hyperspectral sensors, thermal remote sensing, and satellite RS are yielding eye-catching results, particularly in biodiversity and ecosystem conservation.

Passive remote sensing systems

Passive remote sensing is often understood as a system that operates by passive sensors which can only be used for detection in presence of the natural source of energy, i.e., sunlight (visible to shortwave spectrum and infrared thermal radiation) (Srivastava et al. 2020 ). These sensors have a specialty to detect natural energy (radiation) that is either emitted or reflected from the source of energy or object (Earthdata 2021 ). Limitations of the applicability of passive remote sensing for biodiversity and ecosystem conservation are its dependency on sunlight as a source of radiation which is again dependent completely on the season, region, and climatic conditions (Fig. 1 ).

Techniques for identification and genetic conservation of species

Restriction fragment length polymorphism (rflp).

RFLP is a biallelic, polymorphic genetic marker characterized by hybrid labeled probes of DNA fragments and digested with restriction endonucleases for estimations of genetic diversity (Vignal et al. 2002 ; Amom and Nongdam 2017 ). Different restriction sites in DNA represent the genetic divergence between different populations or related species within a population. Özdil et al. ( 2018 ) demonstrated genetic diversity among 11 donkey species in Turkey by conducting PCR-RFLP of two genes. The restriction sites of DraII, MboI, and EagI on the lactoferrin gene (LTF) and PstI on the κ-casein gene (CSN3) have been validated to identify the polymorphism among the donkey population (Özdil et al. 2018 ). Meikasari et al. ( 2019 ) have also made elucidation of low genetic diversity among the seahorse ( Hippocampus comes ) found in Bintan waters. Another recent study indicates the utilization of PCR-RFLP in genetic-based sex determination of Sebastes rockfish (Vaux et al. 2020 ). The PCR-RFLP application in identification of durum ( Triticum durum L.) and bread wheat ( T. aestivum ) species has also been studied by analyzing chloroplast DNA (Haider and Nabulsi 2020 ) (Table 3 , Fig. 2A ).

Molecular techniques for conservation of biodiversity. A Restriction fragment length polymorphism; the genomic DNA extracted from different organisms is PCR amplified and subjected to restriction digestion using specific restriction enzymes, then DNA fragments are separated by electrophoresis and hybridized with radiolabeled probes (Özdil et al. 2018 ; Chaudhary and Maurya 2019 ; Panigrahi et al. 2019 ; Haider and Nabulsi 2020 ). B Amplified fragment length polymorphism; the restriction fragments of genomic DNA was ligated with compatible adapters and PCR amplified using selective primers against adapters, and the amplified fragments were separated by electrophoresis for DNA fingerprint analysis (Blears et al. 1998 ; Malik et al. 2018 ; Wu et al. 2019b ; Zimmermann et al. 2019 ; Neiber et al. 2020 ). C Random amplified polymorphic DNA; random PCR fragments were amplified from the genome of different species using primers with random sequences and separated by gel electrophoresis to determine the difference between species based on RAPD markers (Panigrahi et al. 2019 ; Saikia et al. 2019 ). D Sequence characterized amplified region; PCR products are generated using primers for RAPD markers from the genome of different varieties and separated in the gel. The polymorphic DNA band is gel extracted and processed for cloning and sequencing for getting specific amplification by designing SCAR primers (Yang et al. 2014 ; Bhagyawant 2015 ; Ganie et al. 2015 ; Cunha and Domingues 2017 ). E Micro-satellites; the microsatellite repeats were PCR amplified using primers that flank the repeated sequence and separated by gel electrophoresis. The individual bands were cloned and sequenced for analysis of genetic and population diversity (Kim 2019 ; Touma et al. 2019 ). F Expressed sequence tag-simple sequence repeat; cDNA library was prepared from cDNA synthesized from isolated mRNAs, and then end sequencing of cDNA library was performed by EST primers followed by its assembly. The SSR regions were amplified in assembled ESTs using specific primers and the products were analyzed in gel (Rudd 2003 ; Sun et al. 2019 ; Wagutu et al. 2020 ). G Inter-simple sequence repeat; the isolated genomic DNA from organisms was subjected to PCR amplification using primers specific to microsatellite, and the PCR products were separated by electrophoresis for analysis (Sarwat 2012 ; El Hentati et al. 2019 ; Tiwari et al. 2020 ). H Single nucleotide polymorphism; the genomic DNA from different samples was digested with suitable restriction enzyme followed by adapter ligation and PCR amplification using selective primers. Further to this, PCR products were fragmented with DNase I and labeled with fluorescent probes followed by hybridization in an SNP array. The wells were scanned and data were analyzed (Alsolami et al. 2013 ; Scionti et al. 2018 ; Cendron et al. 2020 ; Kyrkjeeide et al. 2020 )

Amplified fragment length polymorphism (AFLP)

AFLP is considered an effective means of detecting polymorphism in DNA without having any prior information regarding the genome. Being a dominant marker, it can analyze multiple loci through amplification of DNA performing PCR reaction (Bryan et al. 2017 ). The method employs restriction digestion of DNA and amplification of fragments through ligation of adapters on both ends and using primers specific to adapters (Malik et al. 2018 ). Genetic differences can be identified from the disparity in the number and length of bands on electrophoretic separation. Its application ranges from the assessment of genetic diversity within species to generate of genetic maps for disease diagnosis and phylogenetic studies. AFLP data analysis study represents the genetic diversity in E. tangutorum population contributed by geographical and environmental factors (Wu et al. 2019b ). The phylogenetic relationships and genetic distances among A. platensis populations and other distinct related species such as A. georginae and A. ludwigi in southern Brazil were also investigated through AFLP (Zimmermann et al. 2019 ). Population structure and differentiation among Melanopsis etrusca were clearly distinct between the eastern, western, and central regions populations in Italy (Neiber et al. 2020 ) (Table 3 , Fig. 2B ).

Random amplified polymorphic DNA (RAPD)

The RAPD is a PCR-based technique in which 8–10 short nucleotides comprise both forward and reverse primers that bind arbitrary nucleotide sequences of chromosomal DNA to generate random fragments. Due to this random nature of primers, no prior knowledge about genome sequence is needed. The annealing sites of these random primers vary for different species or individual to individual. Discrimination can be identified or determined from the amplified DNA fragments (RAPD markers) separated by agarose gel electrophoresis (Freigoun et al. 2020 ). RAPD markers are dominant and involved in various applications such as genome mapping, molecular evolutionary genetics, genetic diversity analysis, and population genetics as well as determining taxonomic identity (Qamer et al. 2021 ). Saikia et al. ( 2019 ) deduced genetic variation among the different morphs of muga silkworm of Northeast India through RAPD analysis. Moreover, Sulistyahadi et al. ( 2020 ) studied the locus diversity as well as genetic polymorphism of the endemic species Rhacophorus margaritifer population by this technique. It has also been used to elucidate the genetic variation in a medicinal plant species found in the south of Jordan named Artemisia judaica (Al-Rawashdeh 2011 ) (Table 3 , Fig. 2C ).

Sequence characterized amplified region (SCAR)

SCAR markers are DNA fragments generated by PCR amplification using specific 15–30-bp long primers derived from RAPD markers through cloning and sequencing (Bhagyawant 2015 ). Usually, RAPD markers are associated with low reproducibility and are dominant in nature, making it inappropriate for species identification (Sairkar et al. 2016 ). To overcome this disadvantage, RAPD markers are converted to SCAR markers which are locus-specific and co-dominant in nature (Bhagyawant 2015 ; Feng et al. 2018 ). Due to the specificity of primers, PCR amplification of SCARs is less sensitive to reaction condition and thus are easy to perform (Yuskianti and Shiraishi 2010 ). SCAR markers provide authenticate information both for species identification and population genetic diversity analysis. Researchers have successfully developed SCAR markers for the medicinal plant V. serpens using 1135-bp long amplicon through RAPD obtained by six accessions of the plant, thereby preventing it from extinction (Jha et al. 2020 ) (Table 3 , Fig. 2D ).

Mini- and micro-satellites

Mini-satellites (variable number of tandem repeats (VNTRs) 6–100 bp) and micro-satellites (1–6 bp) (simple sequence repeats (SSR) and short tandem repeats (STR)) are randomly repetitive DNA sequences widely dispersed in all eukaryotic species genomes. These multi-allelic markers are co-dominantly inherited with species-specific location and size within the genome (Vergnaud and Denoeud 2000 ; Vieira et al. 2016 ). Due to the high level of polymorphism associated with mini and microsatellites, it is extensively utilized in genetic analysis and population studies. Microsatellites are interspersed all over the genome and therefore represent high variability and their identification show great variation among species of the different population (Abdul-Muneer 2014 ). Its analysis includes PCR amplification of loci by using primers that flank the repeated sequence. By using microsatellite markers, genetic structure of Agu pigs has been elucidated along with its correlation with Ryukyu wild boar, two Chinese breeds and five European breeds (Touma et al. 2019 ). Similarly, De Góes Maciel et al. ( 2019 ) analyzed 13 microsatellite loci of 361 white-lipped peccaries for assessment of their population structure and level of genetic diversity (Table 3 , Fig. 2E ).

Expressed sequence tags (ESTs)

ESTs are small sequences of DNA usually 200 to 500 nucleotides long that act as tags for the expressed genes in certain cells, tissues, or organs. ESTs are generated by sequencing either the 3′ end or 5′ end of a segment derived from random clones from the cDNA library and long enough for the identity illustration of the expressed gene (Behera et al. 2013 ). ESTs are widely involved in gene discovery, determining the phylogenetic relationship between individuals, genetic diversity, and proteomic analysis as well as transcriptome profiling (Cai et al. 2015 ). EST-derived SSR markers are more informative than genomic SSRs for genetic diversity analysis due to several advantages such as high conserved nature, variation in coding sequence, and high heritability to closely related species (Parthiban et al. 2018 ). Sun et al. ( 2019 ) have conducted the structure analyses of expressed sequence tag-simple sequence repeat (EST-SSR) markers in Juglans sigillata and demonstrated the genetic structure based on its geographic feature. Moreover, EST-SSR analyses have provided information regarding the genetic distance between the J. regia and J. sigillata populations. By considering EST-SSRs and genotype sequencing data, they have interpreted iron walnut as the subspecies of J. regia (Sun et al. 2019 ). Investigation of evolutionary relations and genotypic relatedness are essential for the conservation of endangered species. Recently the genetic variability of an endangered species Magnolia patungensis was studied by analyzing the EST-SSR polymorphic markers (Wagutu et al. 2020 ) (Table 3 , Fig. 2F ).

Inter-simple sequence repeat (ISSR)

ISSR markers are used in diversified analyses such as species identification, evolutionary and taxonomic studies, genome mapping, genetic diversity, and gene tagging because of their high polymorphic nature (Arif et al. 2011 ; Abdelaziz et al. 2020 ). These multilocus markers are generated through PCR amplification by using microsatellites as primers. Prior sequence knowledge is not required for primer designing as repeat sequence is used to amplify these inter-microsatellite regions (Ng and Tan 2015 ). It overcomes all the limitations possessed by other markers such as RAPD and AFLP which are associated with low reproducibility (Najafzadeh et al. 2014 ). Genetic diversity and population structure analysis have been performed among 11 populations of Bergenia ciliata using 15 ISSR markers. The analysis shows a high level of polymorphism among this medicinal plant species, found in the Indian Himalayan Region (Tiwari et al. 2020 ). El Hentati et al. ( 2019 ) have studied genetic diversity and phylogenetic relationships among 20 samples of three geographical local cattle populations using ISSR primers. They found a significant variation and geographical separation among the cattle from the north, northeast, and northwest of Tunisia (Table 3 , Fig. 2G ).

Single nucleotide polymorphisms (SNPs)

Single nucleotide variation in genetic sequences defines the Single nucleotide polymorphism (SNP) among individuals, generated due to point mutation or replication errors, giving rise to different alleles within a locus (Van den Broeck et al. 2014 ). SNPs are the most common form of variation present extensively in the non-coding, coding, and inter-genic regions of DNA (Vallejos-Vidal et al. 2019 ). SNPs are mainly exploited for population structure, genetic diversity, genetic map construction, and identification of particular traits, etc. (Xia et al. 2019 ). Their abundance in coding regions makes them more attractive markers for the detection of mutations associated with diseases. SNP markers are however less polymorphic than SSR markers due to their biallelic or triallelic nature (Casci 2010 ; Mammadov et al. 2012 ). Cendron et al. ( 2020 ) demonstrated the population structure and genetic diversity of local Italian chicken breeds by using SNPs for conservation purposes which revealed lower genetic diversity among the local breeds. In another study, genetic diversity and differentiation among the D. ruyschiana populations of the Norwegian region were investigated by analyzing 96 SNPs derived from 43 sites that reported the existence of four distinct genetic groups within the population (Kyrkjeeide et al. 2020 ) (Table 3 , Fig. 2H ).

Artificial intelligence in biodiversity monitoring

With the growing performance of computing power and DL in recent years, machines had become significantly more intelligent and reliable than ever. Modern machines can handle more extensive data and more complex DL models than before (Dean 2019 ; Chen et al. 2020a ). Through this progress, machines had achieved the ability to replicate human expertise (Liu et al. 2019b ). Currently, several problems exist within our diverse planet. Researchers began to accelerate the development of several AI solutions with DL to preserve the earth for the later generations to come. In most studies, DL method’s employment provided an automated capability for machines to recognize, classify, and detect images, sounds, and behavior of animals, plants, and even humans (Abeßer 2020 ). According to Klein et al. ( 2015 ), one of the primary methods of preserving our biodiversity consists of monitoring and manual data collection. However, frequent conduct of such practices can become tedious and cause disturbances to sensitive wildlife habitats. With that said, monitoring became less reliable and brief (Table 4 ). AI-based methods have shown that even at its pre-mature level, biodiversity can have an improvement by reducing animal extinction, prolonged and in-depth monitoring of various life forms, unlocking and accessing unexplored areas, and faster and easier classification of species. With the continuing efforts in data duration, transparency, and research collaborations, these technology types may reach far beyond our expectations. These solutions, if appropriately handled, can yield a massive impact to preserve the planet and its resources without involving humans. Furthermore, the implementation of AI-based methods also extends humans’ capability to explore locations that our biological composition cannot handle, leading to discoveries of new species and life. Due to the accessibility of various capture devices, a wide range of collected data through images, videos, audio, and other forms of data fast-tracked DL and AI development. The problematic method and reliance on organic experts to perform a small to large-scale monitoring of animals, plants, and insects became less challenging as automation systems have improved significantly over a short period (Bergslien 2013 ; Buxton et al. 2018 ; Willi et al. 2018 ) (Fig. 3 ).

Application of advanced computer-added physical instruments and associated smart technologies for biodiversity monitoring. The figure describes in a conserved forest ecosystem wildlife and forest ecosystem can be monitored by using autonomous acoustic recording units, animal tracking units (GIS-based), X-ray fluorescence (XRF) analyzer, global positioning system (GPS), and camera trapping units. Configuration of these data from multiple sources and data heterogeneity can be monitored, processed, interpreted, analyzed, and distributed (encrypted) via artificial intelligence and machine-learning-based technologies. Using these technologies wildlife morphology (camera), behavior, phenology, distribution, abundance, phylogeny (acoustic), and diversity with respect to human invasion can be observed. The figure is inspired by the following sources: Bergslien ( 2013 ), Buxton et al. ( 2018 ), Willi et al. ( 2018 ). The components of the figure are modifications of The RAFOS group at the Graduate School of Oceanography, University of Rhode Island, Kingston, RI 02881 ( 2001 ), Mudgineer ( 2011 ); Leapfrog ( 2021 ), Pixabay ( 2021 ), Pngtree ( 2021 ), and Vecteezy ( 2021a , b )

Recently, a wide range of low-cost yet powerful sensors, microphones, and cameras have become available, giving aid to alleviating the problem of collecting data. Such extensive data collections from the said technologies fueled DL models to learn more patterns that generated solutions to better monitor and manage biodiversity. The common uses include automated recognition, classification, and detection of people (Kim and Moon 2016 ), animals (Verma and Gupta 2018 ), plants (Saleem et al. 2019 ), fish (Jalal et al. 2020 ), and even insects (Xia et al. 2018 ) based on their sound or image (Christin et al. 2019 ). Even with DL’s promising capabilities, it still exhibits some caveats that limit its full potential in biodiversity monitoring, specifically in real time. Monitoring wildlife through video became an exponential and popular recent development that improved interpretability with less comprehension to researchers and the like (Chen et al. 2019 ). However, it became difficult and expensive due to the challenging deployment of capable computers or capture devices to perform the task (Willi et al. 2019 ). While operating with DL models in urban areas is relatively easy due to the availability of sufficient data on infrastructure, functioning in remote areas still relies on post-monitoring systems (He et al. 2016 ; Zhang et al. 2019a ).

Researchers are also on for finding more efficient data collection techniques that will require less computational cost and fewer complexes. Currently, the computer on a hardware basis still rigorously improves and becomes more affordable and independently deployable. With that said, DL can become more efficient and reliable over time that can produce real-time wildlife monitoring in remote areas through a more visual aspect like videos without much constraint from the limited infrastructure.

Challenges and future prospects

Approximately, 1 million of the 10 million species that exist in the world are threatened with extinction (Bawa et al. 2020 ). Besides monitoring tools, a combination of efforts from varied disciplines will be essential for the safeguard of individual species and biodiversity as a whole. Computer model-based technologies like the GIS, RADAR, remote sensing, and LiDAR are actively used for the monitoring of habitats, state of threats, land uses, and conversion. Molecular approaches such as Mitochondrial DNA ( Cyt b ), SNPs, RFLP, microsatellites, etc. are also playing a pivotal role in identifying, tracking, and determining the impact of anthropogenic and environmental factors on wildlife (Krestoff et al. 2021 ; Gouda et al. 2020 ; Ridley et al. 2020 ). However, many of these techniques face challenges in form of cost-efficiency and expert handling and have single or limited focal species at the ecosystem level. Some of the possible changes and prospects in biodiversity monitoring systems that can be implemented in near future on broader aspects are discussed below.

The science of chorology with advances in GIS and remote sensing techniques in recent times has better presented the landscape as a functional unit for biodiversity management. Visualizations of spatial-temporal changes and development of biotic and abiotic threats to species also known as “threat maps” emerged as multipurpose techniques for the implementation of conservation activities at the ground level (Ridley et al. 2020 ). InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) is a newly develop modeling software with set parameters for screening and quantification of ecosystem services such as carbon stock, changes in land use, landscape, forest cover, etc. SolVES is a modern-day ArcGIS-dependent tool that provides the user with easy access to several functions of the Ecosystem Services (ES), human perceptions associated with social and cultural beliefs, socio-economic values, usage of resources, etc. even without conducting questionnaires or other ground surveys of the local people and other stakeholders (Neugarten et al. 2020 ).

ARIES (ARtificial Intelligence for Ecosystem Services) is a series of algorithm processes which are generated through detecting or recognizing and keeping the track of living systems. It is a software-based platform that solves complex and arduous social or bio-geographical dimensions by integrating biodiversity data (Silvestro et al. 2022 ). It has been successfully tested for carbon emission, climate change, water levels, and ethnic/recreational values (Bagstad et al. 2018 ). Costing Nature is another easy-to-use rapid and reliable web-based technique used for screening protected areas, land use and land cover (LULC), trends of habitation, biodiversity assessment, and possible future threats using global database. It has been used for testing ES for timber, fuel wood, grazing/fodder, and non-wood forest products (Thessen 2016 ; Dominguez-Morales et al. 2021 ; Neugarten et al. 2018 ).

As rightly pointed out by Malavasi ( 2020 ), biodiversity maps are always selective and do not necessarily display all values that are known about any given region or ecosystem. They are often inevitably affected by personal views or scientific blindness and it is therefore important to strive and rate maps not only in terms of scientific accuracy but also on their “viability.” The use of Public Participation Geographic Information Systems (PPGIS) over conventional screening systems can act as a bottom-up approach to empower concern agencies about the threats and conservation priorities by providing visual tools. Similarly, the use of a counter map can prove as a possible substitute for mitigating the loss of biodiversity in a more “systemic” manner (Schägner et al. 2013 ; Malavasi 2020 ).

Genomics models and concepts are widely applied for biodiversity sustenance, from ideal seed selection for preservation to assessing the degree of impact at community-level effects. The concept of population genomics has provided valuable information on population size, demographic history, ability of the populations to evolve and adapt to the changing environment, etc. (Miraldo et al. 2016 ; Hu et al. 2020 , 2021 ; Hohenlohe et al. 2021 ). They have been able to successfully develop large sets of markers that increase the ability to detect and quantify low levels of hybridization or admixture. Techniques such as intron sequences with assistance from Transcriptome Ortholog Alignment Sequence Tools (TOASTs), Next-Generation Sequencing (NGS), and Comparative Anchor Tagged Sequences (CATs) may represent a good proxy to assess functional adaptive potential or functional diversity in future genomic studies (Forcina et al. 2021 ).

With continuous advances in technology, more precise and reliable techniques have been designed for biodiversity conservation. However, association mapping and expanding knowledge on “omics” will help in identifying morphological traits and bring together intellectual minds to a platform for developing advanced gene traits. It also helps identify high biodiversity conservation priority areas or hotspots. Working closely with international agencies like the Convention on Biological Diversity (CBD) and UN Framework Convention on Climate Change (UNFCCC) and achieving its targets will be important for the conservation of biodiversity on the planet. Lastly, it is the human who understands the importance of coexistence and cohabitation with other forms of living beings that will help implement conservation measures and create a sense of protecting the ecosystem. Therefore, it is suggested that a combination of sophisticated monitoring methods including system-based smart techniques, transformative smart technologies, remote sensing, geographical information system, and artificial intelligence in combination with molecular approaches will smartly keep the track of living organisms and will help in biodiversity conservation and restoration.

Availability of data and materials

All data generated or analyzed during this study are included in this article.

Abdelaziz SM, Medraoui L, Alami M, Pakhrou O, Makkaoui M, Boukhary OMS, Filali-Maltouf A (2020) Inter simple sequence repeat markers to assess genetic diversity of the desert date (Balanites aegyptiaca Del.) for Sahelian ecosystem restoration. Sci Rep 10:14948

Abdul-Muneer PM (2014) Application of microsatellite markers in conservation genetics and fisheries management: recent advances in population structure analysis and conservation strategies. Gen Res Int 2014:691759

CAS   Google Scholar  

Abeßer J (2020) A review of deep learning based methods for acoustic scene classification. Appl Sci 10:2020

Article   Google Scholar  

Ade FY, Hakim L, Arumingtyas EL, Azrianingsih R (2019) The detection of Anaphalis spp. genetic diversity based on molecular character (using ITS, ETS, and EST-SSR markers). Int J Adv Sci Eng Inform Technol 9:1695–1702

Admin (2017) My blog, GIS Layers, Environmental Science and Resource Management.  http://heleneloyan.cikeys.com/update/gis-layers/ . Accessed 11 Aug 2021

Akçay HG, Kabasakal B, Aksu D, Demir N, Öz M, Erdoğan A (2020) Automated bird counting with deep learning for regional bird distribution mapping. Animals : an Open Access Journal from MDPI 10:1207

Al-Allak ZS, Dragh MA, Hussain AS (2020) Genetic polymorphism and diversity of Iraqi Awassi sheep using PCR-RAPD technique. Basrah J Vet Res 19:147–154

Alemu A, Feyissa T, Letta T, Abeyo B (2020) Genetic diversity and population structure analysis based on the high density SNP markers in Ethiopian durum wheat (Triticum turgidum ssp. durum). BMC Genet 21:18

Article   CAS   Google Scholar  

Alexander C, Korstjens AH, Usher G, Nowak MG, Fredriksson G, Hill RA (2018) LiDAR patch metrics for object-based clustering of forest types in a tropical rainforest. Int J Appl Earth Obs Geoinf 73:253–261

Google Scholar  

Ali AM, Darvishzadeh R, Skidmore A, Gara TW, Heurich M (2021) Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest. Int J Digital Earth 14:106–120

Almeida DRA, Broadbent EN, Zambrano AMA, Wilkinson BE, Ferreira ME, Chazdon R, Meli P, Gorgens EB, Silva CA, Stark SC, Valbuena R, Papa DA, Brancalion PHS (2019) Monitoring the structure of forest restoration plantations with a drone-lidar system. Int J Appl Earth Obs Geoinf 79:192–198

Al-Rawashdeh IM (2011) Genetic variability in a medicinal plant Artemisia judaica using random amplified polymorphic DNA (RAPD) markers. Int J Agr Biol 13:279–282

Alsolami R, Knight SJ, Schuh A (2013) Clinical application of targeted and genome-wide technologies: can we predict treatment responses in chronic lymphocytic leukemia? Person Med 10:361–376

Amom T, Nongdam P (2017) The use of molecular marker methods in plants: a review. Int J Curr Res Rev 9:01–07

Arias-Maldonado M (2016) The anthropocenic turn: theorizing sustainability in a postnatural age. Sustainability 8:10

Arif IA, Khan HA, Bahkali AH, Al Homaidan AA, Al Farhan AH, Al Sadoon M, Shobrak M (2011) DNA marker technology for wildlife conservation. Saudi J Biol Sci 18:219–225

Arshad B, Barthelemy J, Pilton E, Perez P (2020) Where is my deer?-wildlife tracking and counting via edge computing and deep learning. In: 2020 IEEE SENSORS. IEEE, Rotterdam, Netherlands, pp 1–4

Avigliano E, Rosso JJ, Lijtmaer D, Ondarza P, Piacentini L, Izquierdo M, Cirigliano A, Romano G, Nuñez Bustos E, Porta A, Mabragaña E, Grassi E, Palermo J, Bukowski B, Tubaro P, Schenone N (2019) Biodiversity and threats in non-protected areas: a multidisciplinary and multi-taxa approach focused on the Atlantic Forest. Heliyon 5:e02292

Bae S, Levick SR, Heidrich L, Magdon P, Leutner BF, Wöllauer S, Serebryanyk A, Nauss T, Krzystek P, Gossner MM, Schall P, Heibl C, Bässler C, Doerfler I, Schulze E-D, Krah F-S, Culmsee H, Jung K, Heurich M et al (2019) Radar vision in the mapping of forest biodiversity from space. Nat Commun 10:4757

Bagstad KJ, Cohen E, Ancona ZH, McNulty SG, Sun G (2018) The sensitivity of ecosystem service models to choices of input data and spatial resolution. Appl Geogr 93:25–36

Bakx TRM, Koma Z, Seijmonsbergen AC, Kissling WD (2019) Use and categorization of light detection and ranging vegetation metrics in avian diversity and species distribution research. Divers Distrib 25:1045–1059

Bariotakis M, Georgescu L, Laina D, Oikonomou I, Ntagounakis G, Koufaki M-I, Souma M, Choreftakis M, Zormpa OG, Smykal P, Sourvinos G, Lionis C, Castanas E, Karousou R, Pirintsos SA (2019) From wild harvest towards precision agriculture: use of ecological niche modelling to direct potential cultivation of wild medicinal plants in Crete. Sci Total Environ 694:133681

Barlow SE, O’Neill MA (2020) Technological advances in field studies of pollinator ecology and the future of e-ecology. Curr Opin Insect Sci 38:15–25

Bartkowski B, Lienhoop N, Hansjürgens B (2015) Capturing the complexity of biodiversity: a critical review of economic valuation studies of biological diversity. Ecol Econ 113:1–14

Basak S, Chakrabartty I, Hedaoo V, Shelke RG, Rangan L (2019) Assessment of genetic variation among wild Alpinia nigra (Zingiberaceae) population: an approach based on molecular phylogeny. Mol Biol Rep 46:177–189

Baumann M, Levers C, Macchi L, Bluhm H, Waske B, Gasparri NI, Kuemmerle T (2018) Mapping continuous fields of tree and shrub cover across the Gran Chaco using Landsat 8 and Sentinel-1 data. Remote Sens Environ 216:201–211

Bawa KS, Nawn N, Chellam R, Krishnaswamy J, Mathur V, Olsson SB, Pandit N, Rajagopal P, Sankaran M, Shaanker RU, Shankar D, Ramakrishnan U, Vanak AT, Quader S (2020) Opinion: envisioning a biodiversity science for sustaining human well-being. Proc Natl Acad Sci 117:25951–25955

Bearman N, Jones N, André I, Cachinho HA, DeMers M (2016) The future role of GIS education in creating critical spatial thinkers. J Geogr High Educ 40:394–408

Behera PM, Behera DK, Panda A, Dixit A, Padhi P (2013) In silico expressed sequence tag analysis in identification of probable diabetic genes as virtual therapeutic targets. Biomed Res Int 2013:704818

Belenguer-Plomer MA, Tanase MA, Fernandez-Carrillo A, Chuvieco E (2019) Burned area detection and mapping using Sentinel-1 backscatter coefficient and thermal anomalies. Remote Sens Environ 233:111345

Bergslien ET (2013) X-ray diffraction and field portable X-ray fluorescence analysis and screening of soils: project design. Geol Soc Lond, Spec Publ 384:27–46

Bhagyawant SS (2015) RAPD-SCAR Markers: an interface tool for authentication of traits. J Biosci Med 4:1–9

Bhatta NP, Priya MG (2017) Radar and its applications. Int J Control Theory Appl 10:1–9

Bispo PDC, Pardini M, Papathanassiou KP, Kugler F, Balzter H, Rains D, dos Santos JR, Rizaev IG, Tansey K, dos Santos MN, Spinelli Araujo L (2019) Mapping forest successional stages in the Brazilian Amazon using forest heights derived from TandEM-X SAR interferometry. Remote Sens Environ 232:111194

Bjerge K, Nielsen JB, Sepstrup MV, Helsing-Nielsen F, Høye TT (2021) An automated light trap to monitor moths (lepidoptera) using computer vision-based tracking and deep learning. Sensors 21:343

Blears MJ, De Grandis SA, Lee H, Trevors JT (1998) Amplified fragment length polymorphism (AFLP): a review of the procedure and its applications. J Ind Microbiol Biotechnol 21:99–114

Bolton DK, Tompalski P, Coops NC, White JC, Wulder MA, Hermosilla T, Queinnec M, Luther JE, van Lier OR, Fournier RA, Woods M, Treitz PM, van Ewijk KY, Graham G, Quist L (2020) Optimizing Landsat time series length for regional mapping of lidar-derived forest structure. Remote Sens Environ 239:111645

Bouvier M, Durrieu S, Gosselin F, Herpigny B (2017) Use of airborne lidar data to improve plant species richness and diversity monitoring in lowland and mountain forests. PLoS One 12:e0184524

Bowler E, Fretwell PT, French G, Mackiewicz M (2020) Using deep learning to count albatrosses from space: assessing results in light of ground truth uncertainty. Remote Sens 12:2026

Bryan GJ, McLean K, Waugh R, Spooner DM (2017) Levels of Intra-specific AFLP diversity in tuber-bearing potato species with different breeding systems and ploidy levels. Front Genet 8:119

Buss MEF, Leizica E, Peinetti R, Noellemeyer E (2020) Relationships between landscape features, soil properties, and vegetation determine ecological sites in a semiarid savanna of central Argentina. J Arid Environ 173:104038

Buxton RT, Lendrum PE, Crooks KR, Wittemyer G (2018) Pairing camera traps and acoustic recorders to monitor the ecological impact of human disturbance. Global Ecol Conserv 16:e00493

Cai C, Yang Y, Cheng L, Tong C, Feng J (2015) Development and assessment of EST-SSR marker for the genetic diversity among tobaccos (Nicotiana tabacum L.). Russ J Genet 51:591–600

Cao L, Coops NC, Sun Y, Ruan H, Wang G, Dai J, She G (2019) Estimating canopy structure and biomass in bamboo forests using airborne LiDAR data. ISPRS J Photogramm Remote Sens 148:114–129

Carr A, Zeale MRK, Weatherall A, Froidevaux JSP, Jones G (2018) Ground-based and LiDAR-derived measurements reveal scale-dependent selection of roost characteristics by the rare tree-dwelling bat Barbastella barbastellus. For Ecol Manag 417:237–246

Carreiras JMB, Jones J, Lucas RM, Shimabukuro YE (2017) Mapping major land cover types and retrieving the age of secondary forests in the Brazilian Amazon by combining single-date optical and radar remote sensing data. Remote Sens Environ 194:16–32

Casci T (2010) SNPs that come in threes. Nat Rev Genet 11:8–8

Cavender-Bares J, Gamon JA, Townsend PA (2020) The use of remote sensing to enhance biodiversity monitoring and detection: a critical challenge for the twenty-first century. In: Cavender-Bares J, Gamon JA, Townsend PA (eds) Remote sensing of plant biodiversity. Springer International Publishing, Cham, pp 1–12

Chapter   Google Scholar  

Cendron F, Perini F, Mastrangelo S, Tolone M, Criscione A, Bordonaro S, Iaffaldano N, Castellini C, Marzoni M, Buccioni A, Soglia D, Schiavone A, Cerolini S, Lasagna E, Cassandro M (2020) Genome-wide SNP analysis reveals the population structure and the conservation status of 23 Italian chicken breeds. Animals : an Open Access Journal from MDPI 10:1441

Chang J, Shoshany M (2017) Radar polarization and ecological pattern properties across Mediterranean-to-arid transition zone. Remote Sens Environ 200:368–377

Chaudhary R, Maurya GK (2019) In: Vonk J, Shackelford T (eds) Restriction fragment length polymorphism. Encyclopedia of Animal Cognition and Behavior. Springer International Publishing, Cham, pp 1–3

Chen R, Little R, Mihaylova L, Delahay R, Cox R (2019) Wildlife surveillance using deep learning methods. Ecol Evol 9:9453–9466

Chen M-Y, Chiang H-S, Lughofer E, Egrioglu E (2020a) Deep learning: emerging trends, applications and research challenges. Soft Comput 24:7835–7838

Chen X, Zhao J, Chen Y, Zhou W, Hughes AC (2020b) Automatic standardized processing and identification of tropical bat calls using deep learning approaches. Biol Conserv 241:108269

Christin S, Hervet É, Lecomte N (2019) Applications for deep learning in ecology. Methods Ecol Evol 10:1632–1644

Chunming W, Guoliang D (2012) The study of UWB RADAR life-detection for searching human subjects. Energy Procedia 17:1028–1033

Clapham M, Miller E, Nguyen M, Darimont CT (2020) Automated facial recognition for wildlife that lack unique markings: a deep learning approach for brown bears. Ecol Evol 10:12883–12892

Crabbe RA, Lamb D, Edwards C (2020) Discrimination of species composition types of a grazed pasture landscape using Sentinel-1 and Sentinel-2 data. Int J Appl Earth Obs Geoinf 84:101978

Cunha JT, Domingues L (2017) RAPD/SCAR Approaches for identification of adulterant breeds’ milk in dairy products. Methods Mol Biol (Clifton NJ) 1620:183–193

Curry CJ, Davis BW, Bertola LD, White PA, Murphy WJ, Derr JN (2021) Spatiotemporal genetic diversity of lions reveals the influence of habitat fragmentation across Africa. Mol Biol Evol 38:48–57

Dalponte M, Jucker T, Liu S, Frizzera L, Gianelle D (2019) Characterizing forest carbon dynamics using multi-temporal lidar data. Remote Sens Environ 224:412–420

de Góes Maciel F, Rufo DA, Keuroghlian A, Russo AC, Brandt NM, Vieira NF, da Nóbrega BM, Nava A, Nardi MS, de Almeida Jácomo AT, Silveira L, Furtado MM, Tôrres NM, Miyaki CY, Tambosi LR, Biondo C (2019) Genetic diversity and population structure of white-lipped peccaries (Tayassu pecari) in the Pantanal, Cerrado and Atlantic Forest from Brazil. Mamm Biol 95:85–92

Dean J (2019) The deep learning revolution and its implications for computer architecture and chip design.  http://arxiv.org/abs/1911.05289 . Accessed 12 Aug 2021

Ditria EM, Lopez-Marcano S, Sievers M, Jinks EL, Brown CJ, Connolly RM (2020) Automating the analysis of fish abundance using object detection: optimizing animal ecology with deep learning. Front Mar Sci 7:429

Dominguez-Morales JP, Duran-Lopez L, Gutierrez-Galan D, Rios-Navarro A, Linares-Barranco A, Jimenez-Fernandez A (2021) Wildlife monitoring on the edge: a performance evaluation of embedded neural networks on microcontrollers for animal behavior classification. Sensors 21:2975

Dube T, Shoko C, Sibanda M, Madileng P, Maluleke XG, Mokwatedi VR, Tibane L, Tshebesebe T (2020) Remote Sensing of Invasive Lantana camara (Verbenaceae) in Semiarid Savanna Rangeland Ecosystems of South Africa. Rangel Ecol Manag 73:411–419

Duporge I, Isupova O, Reece S, Macdonald DW, Wang T (2020) Using very-high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes. Remote Sens Ecol Conserv 7(3):369–381 n/a

Earthdata (2021) Remote Sensors. Earthdata.

Ebrahimi R, Hassandokht MR, Zamani Z, Roldan-Ruiz I, Muylle H, Van Glabeke S, Van Bockstaele E, Kashi A (2019) Genetic characterization of Allium stipitatum accessions: an economically wild edible Allium species with unique flavor. Braz J Bot 42:83–96

El Hentati H, Thamri N, Derouich W, Hadhli M, Boukhorsa T (2019) Study of genetic diversity in Tunisian local cattle populations using ISSR markers. J Anim Plant Sci 42(3):7296–7302

El-Demerdash E-SS, Elsherbeny EA, Salama YAM, Ahmed MZ (2019) Genetic diversity analysis of some Egyptian origanum and Thymus species using AFLP markers. J Gen Eng Biotechnol 17:13

Erinjery JJ, Singh M, Kent R (2018) Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery. Remote Sens Environ 216:345–354

Esmaeili H, Karami A, Hadian J, Nejad Ebrahimi S, Otto L-G (2020) Genetic structure and variation in Iranian licorice (Glycyrrhiza glabra L.) populations based on morphological, phytochemical and simple sequence repeats markers. Ind Crop Prod 145:112140

FAO, A (2019) The state of food and agriculture. 2019. In: 2019, Moving forward on food loss and waste reduction. Food and Agriculture Organization of the United Nations, Rome

Fauvel M, Lopes M, Dubo T, Rivers-Moore J, Frison P-L, Gross N, Ouin A (2020) Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series. Remote Sens Environ 237:111536

Fedrigo M, Newnham GJ, Coops NC, Culvenor DS, Bolton DK, Nitschke CR (2018) Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar. ISPRS J Photogramm Remote Sens 136:106–119

Feng S, Zhu Y, Yu C, Jiao K, Jiang M, Lu J, Shen C, Ying Q, Wang H (2018) Development of species-specific SCAR markers, based on a SCoT analysis, to authenticate Physalis (Solanaceae) species. Front Genet 9:192

Fernandes ACM, Gonzalez RQ, Lenihan-Clarke MA, Trotter EFL, Arsanjani JJ (2020) Machine learning for conservation planning in a changing climate. Sustainability 12:7657

Fernandez-Carrillo A, McCaw L, Tanase MA (2019) Estimating prescribed fire impacts and post-fire tree survival in eucalyptus forests of Western Australia with L-band SAR data. Remote Sens Environ 224:133–144

Ferreira RC, Piredda R, Bagnoli F, Bellarosa R, Attimonelli M, Fineschi S, Schirone B, Simeone MC (2011) Phylogeography and conservation perspectives of an endangered macaronesian endemic: Picconia azorica (Tutin) Knobl. (Oleaceae). Eur J For Res 130:181–195

Forcina G, Camacho-Sanchez M, Tuh FYY, Moreno S, Leonard JA (2021) Markers for genetic change. Heliyon 7:e05583

Freepik (2021) Download 3d isometric terrain of a mountainous landscape for free. In: Freepik. href=' https://www.freepik.com/photos/map '>Map photo created by kjpargeter - www.freepik.com . Accessed 9 Aug 2021

Freigoun SAB, Elagib TY, Raddad EYA (2020) Analysis of genetic diversity in four Sudanese provenances of Balanites aegyptiaca (L.) Del. based on random amplified polymorphic DNA (RAPD) marker. Afr J Biotechnol 19:408–414

Gallardo-Alvárez MI, Lesher-Gordillo JM, Machkour-M’Rabet S, Zenteno-Ruiz CE, Olivera-Gómez LD, del Rosario Barragán-Vázquez M, Ríos-Rodas L, Valdés-Marín A, Vázquez-López HG, Arriaga-Weiss SL (2019) Genetic diversity and population structure of founders from wildlife conservation management units and wild populations of critically endangered Dermatemys mawii. Global Ecol Conserv 19:e00616

Gamal E, Khdery G, Morsy A, Ali M, Hashim A, Saleh H (2020) Using GIS based modelling to aid conservation of two endangered plant species (Ebenus armitagei and Periploca angustifolia) at Wadi Al-Afreet, Egypt. Remote Sens Appl: Soc Environ 19:100336

Ganie SH, Upadhyay P, Das S, Prasad Sharma M (2015) Authentication of medicinal plants by DNA markers. Plant Gene 4:83–99

García M, Saatchi S, Ustin S, Balzter H (2018) Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery. Int J Appl Earth Obs Geoinf 66:159–173

Ge Z, Dai Z, Pang W, Li S, Wei W, Mei X, Huang H, Gu J (2017) LIDAR-based detection of the post-typhoon recovery of a meso-macro-tidal beach in the Beibu Gulf, China. Mar Geol 391:127–143

Goncalves AL, García MV, Heuertz M, González-Martínez SC (2019) Demographic history and spatial genetic structure in a remnant population of the subtropical tree Anadenanthera colubrina var. cebil (Griseb.) Altschul (Fabaceae). Ann For Sci 76:18

González-Rivero M, Beijbom O, Rodriguez-Ramirez A, Bryant DEP, Ganase A, Gonzalez-Marrero Y, Herrera-Reveles A, Kennedy EV, Kim CJS, Lopez-Marcano S, Markey K, Neal BP, Osborne K, Reyes-Nivia C, Sampayo EM, Stolberg K, Taylor A, Vercelloni J, Wyatt M, Hoegh-Guldberg O (2020) Monitoring of coral reefs using artificial intelligence: a feasible and cost-effective approach. Remote Sens 12:489

Gouda S, Kerry RG, Das A, Chauhan NS (2020) Wildlife forensics: a boon for species identification and conservation implications. Forensic Sci Int 317:110530

Griffiths P, Nendel C, Pickert J, Hostert P (2020) Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series. Remote Sens Environ 238:111124

Große-Stoltenberg A, Hellmann C, Thiele J, Werner C, Oldeland J (2018) Early detection of GPP-related regime shifts after plant invasion by integrating imaging spectroscopy with airborne LiDAR. Remote Sens Environ 209:780–792

Guo X, Coops NC, Tompalski P, Nielsen SE, Bater CW, John Stadt J (2017) Regional mapping of vegetation structure for biodiversity monitoring using airborne lidar data. Ecol Inform 38:50–61

Guo Y, Liao J, Shen G (2021) Mapping large-scale mangroves along the maritime silk road from 1990 to 2015 using a novel deep learning model and landsat data. Remote Sens 13:245

Haas J, Ban Y (2017) Sentinel-1A SAR and sentinel-2A MSI data fusion for urban ecosystem service mapping. Remote Sens Appl: Soc Environ 8:41–53

Haider N, Nabulsi I (2020) Identification of bread and durum wheats from their diploid ancestral species based on chloroplast DNA. Agriculture (Pol’nohospodárstvo) 66:56–66

Hao J, Jiao K, Yu C, Guo H, Zhu Y, Yang X, Zhang S, Zhang L, Feng S, Song Y, Dong M, Wang H, Shen C (2018) Development of SCoT-based SCAR marker for rapid authentication of Taxus media. Biochem Genet 56:255–266

Harrison PA, Berry PM, Simpson G, Haslett JR, Blicharska M, Bucur M, Dunford R, Egoh B, Garcia-Llorente M, Geamănă N, Geertsema W, Lommelen E, Meiresonne L, Turkelboom F (2014) Linkages between biodiversity attributes and ecosystem services: a systematic review. Ecosyst Serv 9:191–203

Hay SI (2000) An overview of remote sensing and geodesy for epidemiology and public health application. Adv Parasitol 47:1–35

He Z, Kays R, Zhang Z, Ning G, Huang C, Han TX, Millspaugh J, Forrester T, McShea W (2016) Visual informatics tools for supporting large-scale collaborative wildlife monitoring with citizen scientists. IEEE Circuits SystMag 16:73–86

Hirst M (2008) Operational environment. The air transport system. Woodhead Publishing, Cambridge, pp 72–101

Hoban S, Bruford M, D’Urban Jackson J, Lopes-Fernandes M, Heuertz M, Hohenlohe PA, Paz-Vinas I, Sjögren-Gulve P, Segelbacher G, Vernesi C, Aitken S, Bertola LD, Bloomer P, Breed M, Rodríguez-Correa H, Funk WC, Grueber CE, Hunter ME, Jaffe R et al (2020) Genetic diversity targets and indicators in the CBD post-2020 Global biodiversity framework must be improved. Biol Conserv 248:108654

Hohenlohe PA, Funk WC, Rajora OP (2021) Population genomics for wildlife conservation and management. Mol Ecol 30:62–82

Höppler L, Gödde F, Gutleben M et al (2020) Synergy of active- and passive remote sensing: An approach to reconstruct three-dimensional cloud macro- and microphysics. https://www.atmos-meas-tech-discuss.net/amt-2020-49/ . Accessed 12 Aug 2021

Høye TT, Ärje J, Bjerge K, Hansen OLP, Iosifidis A, Leese F, Mann HMR, Meissner K, Melvad C, Raitoharju J (2021) Deep learning and computer vision will transform entomology. Proc Natl Acad Sci 118:e2002545117

Hu J, Rampitsch C, Bykova NV (2015) Advances in plant proteomics toward improvement of crop productivity and stress resistancex. Front Plant Sci 6:209

Hu C, Pan T, Wu Y, Zhang C, Chen W, Chang Q (2020) Spatial genetic structure and historical demography of East Asian wild boar. Anim Genet 51:557–567

Hu C, Yuan S, Sun W, Chen W, Liu W, Li P, Chang Q (2021) Spatial genetic structure and demographic history of the wild boar in the Qinling Mountains, China. Animals 11:346

Huang Z, Xie L, Wang H, Zhong J, Li Y, Liu J, Ou Z, Liang X, Li Y, Huang H, Lin Z, Zhang K, Zhang L, Zheng X (2019) Geographic distribution and impacts of climate change on the suitable habitats of Zingiber species in China. Ind Crop Prod 138:111429

Igawa T, Takahara T, Lau Q, Komaki S (2019) An application of PCR-RFLP species identification assay for environmental DNA detection. PeerJ 7:e7597

Inanaga M, Hasegawa Y, Mishima K, Takata K (2020) Genetic diversity and structure of Japanese endemic genus Thujopsis (Cupressaceae) using EST-SSR markers. Forests 11:935

IPBES (2019) The IPBES’ 2019 global assessment report on biodiversity and ecosystem services. UN Report: Nature’s Dangerous Decline “Unprecedented”; Species Extinction Rates “Accelerating.” In: United Nations Sustainable Development. https://www.un.org/sustainabledevelopment/blog/2019/05/nature-decline-unprecedentedreport . Accessed 7 Jul 2020

Irina L-T, Javier B-P, Teresa C-BM, Eurídice L-A, María L, del Carmen C-I (2019) Integrating ecological and socioeconomic criteria in a GIS-based multicriteria-multiobjective analysis to develop sustainable harvesting strategies for Mexican oregano Lippia graveolens Kunth, a non-timber forest product. Land Use Policy 81:668–679

IUCN (2020) IUCN 2020. The IUCN red list of threatened species. Version 2020-1. In: IUCN red list of threatened species. https://www.iucnredlist.org/en . Accessed 6 Jul 2020

Jahncke R, Leblon B, Bush P, LaRocque A (2018) Mapping wetlands in Nova Scotia with multi-beam RADARSAT-2 Polarimetric SAR, optical satellite imagery, and Lidar data. Int J Appl Earth Obs Geoinf 68:139–156

Jalal A, Salman A, Mian A, Shortis M, Shafait F (2020) Fish detection and species classification in underwater environments using deep learning with temporal information. Ecol Inform 57:101088

Jamil S, Fawad, Abbas MS et al (2020) Deep learning and computer vision-based a novel framework for himalayan bear, marco polo sheep and snow leopard detection. In: 2020 International Conference on Information Science and Communication Technology (ICISCT). IEEE, Karachi, Pakistan, pp 1–6

Jansson S, Malmqvist E, Brydegaard M, Åkesson S, Rydell J (2020) A Scheimpflug lidar used to observe insect swarming at a wind turbine. Ecol Indic 117:106578

Jha SR, Naz R, Asif A, Okla MK, Soufan W, Al-Ghamdi AA, Ahmad A (2020) Development of an in vitro propagation protocol and a sequence characterized amplified region (SCAR) marker of Viola serpens Wall ex. Ging. Plants (Basel, Switzerland) 9:246

Karsli BA, Demir E, Fidan HG, Karsli T (2020) Assessment of genetic diversity and differentiation among four indigenous Turkish sheep breeds using microsatellites. Arch Anim Breed 63:165–172

Karthikeyan S, Preethi NSR (2018) (2018) Life detection system using UWB RADAR during disaster. Second Int Conf Green Comput Interne Things (ICGCIoT) 2:361–365

Kasprzak-Filipek K, Sawicka-Zugaj W, Litwińczuk Z, Chabuz W, Šveistienė R, Bulla J (2019) Assessment of the genetic structure of Central European cattle breeds based on functional gene polymorphism. Global Ecol Conserv 17:e00525

Khalighifar A, Brown RM, Goyes Vallejos J, Peterson AT (2021) Deep learning improves acoustic biodiversity monitoring and new candidate forest frog species identification (genus Platymantis) in the Philippines. Biodivers Conserv 30:643–657

Kim S-K (2019) Genetic diversity and DNA markers in fish. In: Kim S-K (ed) Essentials of Marine Biotechnology. Springer International Publishing, Cham, pp 109–144

Kim Y, Moon T (2016) Human detection and activity classification based on micro-doppler signatures using deep convolutional neural networks. IEEE Geosci Remote Sens Lett 13:8–12

Kittichai V, Pengsakul T, Chumchuen K, Samung Y, Sriwichai P, Phatthamolrat N, Tongloy T, Jaksukam K, Chuwongin S, Boonsang S (2021) Deep learning approaches for challenging species and gender identification of mosquito vectors. Sci Rep 11:4838

Klein DJ, McKown MW, Tershy BR (2015) Deep learning for large scale biodiversity monitoring. Bloomberg Data for Good, New York, p 7

Knapp N, Fischer R, Huth A (2018) Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states. Remote Sens Environ 205:199–209

Knapp N, Fischer R, Cazcarra-Bes V, Huth A (2020) Structure metrics to generalize biomass estimation from lidar across forest types from different continents. Remote Sens Environ 237:111597

Kovács I, Tóth B, Schally G, Csányi S, Bleier N (2020) The assessment of wildlife damage estimation methods in maize with simulation in GIS environment. Crop Prot 127:104971

Koyama CN, Watanabe M, Hayashi M, Ogawa T, Shimada M (2019) Mapping the spatial-temporal variability of tropical forests by ALOS-2 L-band SAR big data analysis. Remote Sens Environ 233:111372

Krestoff ES, Creecy JP, Lord WD, Haynie ML, Coyer JA, Sampson K (2021) Mitochondrial DNA evaluation and species identification of Kemp’s Ridley Sea Turtle (Lepidochelys kempii) bones after a 3-year exposure to submerged marine and terrestrial environments. Front Mar Sci 8:646455

Krigas N, Papadimitriou K, Mazaris AD (2012) GIS and ex situ plant conservation. In: Alam BM (ed) Application of Geographic Information Systems. IntechOpen, London, SW1P 1WG, UK

Kumar A, Kishore BSPC, Saikia P, Deka J, Bharali S, Singha LB, Tripathi OP, Khan ML (2019) Tree diversity assessment and above ground forests biomass estimation using SAR remote sensing: a case study of higher altitude vegetation of North-East Himalayas, India. Physics Chem Earth, Parts A/B/C 111:53–64

Kwok R (2019) AI empowers conservation biology. Nature 567:133–134

Kyrkjeeide MO, Westergaard KB, Kleven O, Evju M, Endrestøl A, Brandrud MK, Stabbetorp O (2020) Conserving on the edge: genetic variation and structure in northern populations of the endangered plant Dracocephalum ruyschiana L. (Lamiaceae). Conserv Genet 21:707–718

Labouisse J-P, Cubry P, Austerlitz F, Rivallan R, Nguyen HA (2020) New insights on spatial genetic structure and diversity of Coffea canephora(Rubiaceae) in Upper Guinea based on old herbaria. Plant Ecol Evol 153:82–100

Lambert M-J, Traoré PCS, Blaes X, Baret P, Defourny P (2018) Estimating smallholder crops production at village level from Sentinel-2 time series in Mali’s cotton belt. Remote Sens Environ 216:647–657

Lampert A (2019) Over-exploitation of natural resources is followed by inevitable declines in economic growth and discount rate. Nat Commun 10:1419

Lang N, Schindler K, Wegner JD (2019) Country-wide high-resolution vegetation height mapping with Sentinel-2. Remote Sens Environ 233:111347

Laurin GV, Puletti N, Grotti M, Stereńczak K, Modzelewska A, Lisiewicz M, Sadkowski R, Kuberski Ł, Chirici G, Papale D (2020) Species dominance and above ground biomass in the Białowieża Forest, Poland, described by airborne hyperspectral and lidar data. Int J Appl Earth Obs Geoinf 92:102178

Leapfrog (2021) GIS Data, Maps and Images. https://help.seequent.com/Geothermal/4.1/en-GB/Content/gisdata/gis-data.htm . Accessed 2 Aug 2021

Lei Y, Treuhaft R, Keller M, dos-Santos M, Gonçalves F, Neumann M (2018) Quantification of selective logging in tropical forest with spaceborne SAR interferometry. Remote Sens Environ 211:167–183

Li J, Zhao B, Chen Y, Zhao B, Yang N, Hu S, Shen J, Wu X (2020a) A genetic evaluation system for New Zealand white rabbit germplasm resources based on SSR markers. Animals : an Open Access Journal from MDPI 10:1258

Li W, Niu Z, Shang R, Qin Y, Wang L, Chen H (2020b) High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data. Int J Appl Earth Obs Geoinf 92:102163

Liu L, Guo C, Li J, Xu H, Zhang J, Wang B (2016) Simultaneous life detection and localization using a wideband chaotic signal with an embedded tone. Sensors 16:1866

Liu J, Skidmore AK, Jones S, Wang T, Heurich M, Zhu X, Shi Y (2018) Large off-nadir scan angle of airborne LiDAR can severely affect the estimates of forest structure metrics. ISPRS J Photogramm Remote Sens 136:13–25

Liu F-M, Zhang N-N, Liu X-J, Yang Z-J, Jia H-Y, Xu D-P (2019a) Genetic diversity and population structure analysis of Dalbergia odorifera germplasm and development of a core collection using microsatellite markers. Genes 10

Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C, Ledsam JR, Schmid MK, Balaskas K, Topol EJ, Bachmann LM, Keane PA, Denniston AK (2019b) A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. Lancet Digit Health 1:e271–e297

Liu J, Yong DL, Choi C-Y, Gibson L (2020) Transboundary frontiers: An emerging priority for biodiversity conservation. Trends Ecol Evol 35:679–690. https://doi.org/10.1016/j.tree.2020.03.004

Lucas R, Van De Kerchove R, Otero V, Lagomasino D, Fatoyinbo L, Omar H, Satyanarayana B, Dahdouh-Guebas F (2020) Structural characterisation of mangrove forests achieved through combining multiple sources of remote sensing data. Remote Sens Environ 237:111543

Luo S, Wang C, Xi X, Pan F, Peng D, Zou J, Nie S, Qin H (2017) Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation. Ecol Indic 73:378–387

Ma J, Xiao X, Qin Y, Chen B, Hu Y, Li X, Zhao B (2017) Estimating aboveground biomass of broadleaf, needleleaf, and mixed forests in Northeastern China through analysis of 25-m ALOS/PALSAR mosaic data. For Ecol Manag 389:199–210

Ma Q, Su Y, Luo L, Li L, Kelly M, Guo Q (2018) Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data. Ecol Indic 95:298–310

Ma X, Mahecha MD, Migliavacca M, van der Plas F, Benavides R, Ratcliffe S, Kattge J, Richter R, Musavi T, Baeten L, Barnoaiea I, Bohn FJ, Bouriaud O, Bussotti F, Coppi A, Domisch T, Huth A, Jaroszewicz B, Joswig J et al (2019) Inferring plant functional diversity from space: the potential of Sentinel-2. Remote Sens Environ 233:111368

Malavasi M (2020) The map of biodiversity mapping. Biol Conserv 252:108843

Malik MH, Moaeen-Ud-Din M, Bilal G, Ghaffar A, Muner RD, Raja GK, Khan WA (2018) Development of amplified fragment length polymorphism (AFLP) markers for the identification of Cholistani cattle. Arch Anim Breed 61:387–394

Mammadov J, Aggarwal R, Buyyarapu R, Kumpatla S (2012) SNP Markers and their impact on plant breeding. Int J Plant Genomics 2012:728398

Manisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E (2020) Environmental and health impacts of air pollution: a review. Front Public Health 8:14

Marchese C (2015) Biodiversity hotspots: a shortcut for a more complicated concept. Global Ecol Conserv 3:297–309

Martin-Abadal M, Ruiz-Frau A, Hinz H, Gonzalez-Cid Y (2020) Jellytoring: real-time jellyfish monitoring based on deep learning object detection. Sensors 20:1708

Martone M, Rizzoli P, Wecklich C, González C, Bueso-Bello J-L, Valdo P, Schulze D, Zink M, Krieger G, Moreira A (2018) The global forest/non-forest map from TandEM-X interferometric SAR data. Remote Sens Environ 205:352–373

Matasci G, Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW, Bolton DK, Tompalski P, Bater CW (2018) Three decades of forest structural dynamics over Canada’s forested ecosystems using Landsat time-series and lidar plots. Remote Sens Environ 216:697–714

McQuatters-Gollop A, Mitchell I, Vina-Herbon C, Bedford J, Addison PFE, Lynam CP, Geetha PN, Vermeulan EA, Smit K, Bayley DTI, Morris-Webb E, Niner HJ, Otto SA (2019) From science to evidence – how biodiversity indicators can be used for effective marine conservation policy and management. Front Mar Sci 6:109

Meena B, Singh N, Mahar KS, Sharma YK, Rana TS (2019) Molecular analysis of genetic diversity and population genetic structure in Ephedra foliata: an endemic and threatened plant species of arid and semi-arid regions of India. Physiol Mol Biol Plants: An International Journal of Functional Plant Biology 25:753–764

Mehring M, Mehlhaus N, Ott E, Hummel D (2020) A systematic review of biodiversity and demographic change: a misinterpreted relationship? Ambio 49:1297–1312

Mei Z, Khan MA, Zhang X, Fu J (2017) Rapid and accurate genetic authentication of Penthorum chinense by improved RAPD-derived species-specific SCAR markers. Biodivers J Biol Diver 18:1243–1249

Meikasari NS, Nurilmala M, Butet NA, Sudrajat AO (2019) PCR-RFLP as a detection method of allelic diversity seahorse Hippocampus comes (Cantor, 1849) from Bintan waters, Riau Island. IOP Conf Series: Earth Environ Sci 404:012046

Mercier A, Betbeder J, Baudry J, Le Roux V, Spicher F, Lacoux J, Roger D, Hubert-Moy L (2020) Evaluation of Sentinel-1 and 2 time series for predicting wheat and rapeseed phenological stages. ISPRS J Photogramm Remote Sens 163:231–256

Miller W, Hayes VM, Ratan A, Petersen DC, Wittekindt NE, Miller J, Walenz B, Knight J, Qi J, Zhao F, Wang Q, Bedoya-Reina OC, Katiyar N, Tomsho LP, Kasson LM, Hardie R-A, Woodbridge P, Tindall EA, Bertelsen MF et al (2011) Genetic diversity and population structure of the endangered marsupial Sarcophilus harrisii (Tasmanian devil). Proc Natl Acad Sci 108:12348–12353

Minh NTA, Van TT, Hau HV, Trieu LN, Tien CV, Vinh TT, Van DN (2019) Genetic diversity and variation of Huperzia serrata (Thunb. ex Murray) Trevis. population in Vietnam revealed by ISSR and SCoT markers. Biotechnol Biotechnol Equip 33:1525–1534

Mir AH, Tyub S, Kamili AN (2020) Ecology, distribution mapping and conservation implications of four critically endangered endemic plants of Kashmir Himalaya. Saudi J Biol Sci 27:2380–2389

Miraldo A, Li S, Borregaard MK, Flórez-Rodríguez A, Gopalakrishnan S, Rizvanovic M, Wang Z, Rahbek C, Marske KA, Nogués-Bravo D (2016) An anthropocene map of genetic diversity. Science 353:1532–1535

Molerović N, Rašković B, Đedović R, Andrić OD, Marković Z, Marić S (2019) Characterization of the genetic structure of the brown trout (Salmo trutta) from “Braduljica” fish farm, Serbia. Biotechnol Anim Husband 35:289–299

Mosa KA, Gairola S, Jamdade R, El-Keblawy A, Al Shaer KI, Al Harthi EK, Shabana HA, Mahmoud T (2019) The promise of molecular and genomic techniques for biodiversity research and DNA barcoding of the arabian peninsula flora. Front Plant Sci 9:1929

Mudgineer O jpg: *derivative work: (2011) Oil drilling rig, simple illustration. https://commons.wikimedia.org/wiki/File:Oil_Rig_NT8.svg . Accessed 9 Aug 2021

Naczk AM and Ziętara MS (2019) Genetic diversity in Dactylorhiza majalis subsp. majalis populations (Orchidaceae) of northern Poland. Nordic J Bot 37.  https://doi.org/10.1111/njb.01989

Naeem S, Prager C, Weeks B, Varga A, Flynn DFB, Griffin K, Muscarella R, Palmer M, Wood S, Schuster W (2016) Biodiversity as a multidimensional construct: a review, framework and case study of herbivory’s impact on plant biodiversity. Proc Biol Sci 283:20153005

Najafzadeh R, Arzani K, Bouzari N, Saei A (2014) Genetic diversity assessment and identification of new sour cherry genotypes using intersimple sequence repeat markers. Int J Biodivers 2o14:308398

Neiber MT, Cianfanelli S, Bartolini F, Glaubrecht M (2020) Not a marginal loss: genetic diversity of the endangered freshwater snail Melanopsis etrusca (Brot, 1862) from thermal springs in Tuscany, Italy. Conserv Genet 21:199–216

Neugarten RA, Langhammer PF, Osipova E et al (2018) Tools for measuring, modelling, and valuing ecosystem services: guidance for key biodiversity areas, natural world heritage sites, and protected areas, 1st edn. (ed. by Groves C) IUCN, International Union for Conservation of Nature, USA

Neugarten RA, Moull K, Martinez NA, Andriamaro L, Bernard C, Bonham C, Cano CA, Ceotto P, Cutter P, Farrell TA, Gibb M, Goedschalk J, Hole D, Honzák M, Kasecker T, Koenig K, Larsen TH, Ledezma JC, McKinnon M et al (2020) Trends in protected area representation of biodiversity and ecosystem services in five tropical countries. Ecosyst Serv 42:101078

Newbold T, Hudson LN, Hill SLL, Contu S, Lysenko I, Senior RA, Börger L, Bennett DJ, Choimes A, Collen B, Day J, De Palma A, Díaz S, Echeverria-Londoño S, Edgar MJ, Feldman A, Garon M, Harrison MLK, Alhusseini T et al (2015) Global effects of land use on local terrestrial biodiversity. Nature 520:45–50

Ng WL, Tan SG (2015) Inter-Simple Sequence Repeat (ISSR) markers: are we doing it right? ASM Sci J 9:30–39

Omasa K, Hosoi F, Konishi A (2007) 3D lidar imaging for detecting and understanding plant responses and canopy structure. J Exp Bot 58:881–898

Oon A, Ngo KD, Azhar R, Ashton-Butt A, Lechner AM, Azhar B (2019) Assessment of ALOS-2 PALSAR-2L-band and Sentinel-1 C-band SAR backscatter for discriminating between large-scale oil palm plantations and smallholdings on tropical peatlands. Remote Sens Appl: Soc Environ 13:183–190

Organikos (2012) Thermal imaging, elephant listening. In: Organikos. https://organikos.net/2012/09/23/thermalimaging-elephant-listening/ . Accessed 9 Aug 2021

Our World In Data (2020) Forest area as share of land area, Source: UN Food and Agriculture Organization (FAO). https://commons.wikimedia.org/wiki/File:Forest_area_as_share_of_land_area,_OWID.svg . Accessed 9 Aug 2021

Özdil F, İlhan F, Işık R (2018) Genetic characterization of some Turkish sheep breeds based on the sequencing of the Ovar-DRB1 gene in the major histocompatibility complex (MHC) gene region. Arch Anim Breed 61:475–480

Özdil F, Bulut H, Işık R (2019) Genetic diversity of κ-casein (CSN3) and lactoferrin (LTF) genes in the endangered Turkish donkey (Equus asinus) populations. Arch Anim Breed 62:77–82

Panicz R, Napora-Rutkowski Ł, Keszka S, Skuza L, Szenejko M, Śmietana P (2019) Genetic diversity in natural populations of noble crayfish (Astacus astacus L.) in north-western Poland on the basis of combined SSR and AFLP data. PeerJ 7:e7301

Panigrahi S, Velraj P, Subba Rao T (2019) Chapter 21 - Functional microbial diversity in contaminated environment and application in bioremediation. In: Das S, Dash HR (eds) Microbial Diversity in the Genomic Era. Academic Press, London, pp 359–385

Parrens M, Bitar AA, Frappart F, Paiva R, Wongchuig S, Papa F, Yamasaki D, Kerr Y (2019) High resolution mapping of inundation area in the Amazon basin from a combination of L-band passive microwave, optical and radar datasets. Int J Appl Earth Obs Geoinf 81:58–71

Parthiban S, Govindaraj P, Senthilkumar S (2018) Comparison of relative efficiency of genomic SSR and EST-SSR markers in estimating genetic diversity in sugarcane. 3. Biotech 8:144

Pixabay (2021) 110,000+ free vector stock art images, hand selected - pixabay. https://pixabay.com/vectors/ . Accessed 11 Aug 2021

Pngtree (2021) Millions of PNG images, backgrounds and vectors for free download. In: Pngtree. href=' https://pngtree.com/so/shouting-horn '> shouting horn png from pngtree.com . Accessed 2 Aug 2021

Portree D (2006) A diagram showing the orbital configuration of an Almaz radar satellite, a type of Soviet reconnaissance satellite based on the Almaz OPS space stations.  https://commons.wikimedia.org/wiki/File:Almaz_radar_satellite.svg . Accessed 9 Aug 2021

Prošek J, Gdulová K, Barták V, Vojar J, Solský M, Rocchini D, Moudrý V (2020) Integration of hyperspectral and LiDAR data for mapping small water bodies. Int J Appl Earth Obs Geoinf 92:102181

Qamer S, Al-Abbadi AA, Sajid M, Asad F, Khan MF, Khan NA, Sthanadar AA, Akhtar MN, Mahmoud AH, Mohammed OB (2021) Genetic analysis of honey bee, Apis dorsata populations using random amplified polymorphic DNA (RAPD) markers. J King Saud Univ - Sci 33:101218

Qi W, Saarela S, Armston J, Ståhl G, Dubayah R (2019) Forest biomass estimation over three distinct forest types using TandEM-X InSAR data and simulated GEDI lidar data. Remote Sens Environ 232:111283

Qiao Y, Guo F, Huo N, Zhan L, Sun J, Zuo X, Guo Z, Gu YQ, Wang Y, Liu Y (2021) Genotyping-by-sequencing to determine the genetic structure of a Tibetan medicinal plant Swertia mussotii Franch. Genet Resour Crop Evol 68:469–484

Rajah P, Odindi J, Mutanga O (2018) Feature level image fusion of optical imagery and Synthetic Aperture Radar (SAR) for invasive alien plant species detection and mapping. Remote Sens Appl: Soc Environ 10:198–208

Randin CF, Ashcroft MB, Bolliger J, Cavender-Bares J, Coops NC, Dullinger S, Dirnböck T, Eckert S, Ellis E, Fernández N, Giuliani G, Guisan A, Jetz W, Joost S, Karger D, Lembrechts J, Lenoir J, Luoto M, Morin X et al (2020) Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote Sens Environ 239:111626

Rappaport DI, Royle JA, Morton DC (2020) Acoustic space occupancy: combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes. Ecol Indic 113:106172

Ray A, Jena S, Haldar T, Sahoo A, Kar B, Patnaik J, Ghosh B, Chandra Panda P, Mahapatra N, Nayak S (2019) Population genetic structure and diversity analysis in Hedychium coronarium populations using morphological, phytochemical and molecular markers. Ind Crop Prod 132:118–133

Reeth CV, Michel N, Bockstaller C, Caro G (2019) Influences of oilseed rape area and aggregation on pollinator abundance and reproductive success of a co-flowering wild plant. Agric Ecosyst Environ 280:35–42

Reid AJ, Carlson AK, Creed IF, Eliason EJ, Gell PA, Johnson PTJ, Kidd KA, MacCormack TJ, Olden JD, Ormerod SJ, Smol JP, Taylor WW, Tockner K, Vermaire JC, Dudgeon D, Cooke SJ (2019) Emerging threats and persistent conservation challenges for freshwater biodiversity. Cambridge Philos Soc 94:849–873

Ridley FA, McGowan PJ, Mair L (2020) The scope and extent of literature that maps threats to species: a systematic map protocol. Environ Evid 9:23

Righi T, Splendiani A, Fioravanti T, Petetta A, Candelma M, Gioacchini G, Gillespie K, Hanke A, Carnevali O, Caputo Barucchi V (2020) Mediterranean swordfish (Xiphias gladius Linnaeus, 1758) population structure revealed by microsatellite DNA: genetic diversity masked by population mixing in shared areas. PeerJ 8:e9518

Rodríguez-Peña RA, Johnson RL, Johnson LA, Anderson CD, Ricks NJ, Farley KM, Robbins MD, Wolfe AD, Stevens MR (2018) Investigating the genetic diversity and differentiation patterns in the Penstemon scariosus species complex under different sample sizes using AFLPs and SSRs. Conserv Genet 19:1335–1348

Rudd S (2003) Expressed sequence tags: alternative or complement to whole genome sequences? Trends Plant Sci 8:321–329

Saikia M, Devi D (2019) Analysis of genetic diversity and phylogeny of Philosamia ricini (Lepidoptera: Saturniidae) by using RAPD and internal transcribed spacer DNA1. Mol Biol Rep 46:3035–3048

Saikia M, Haloi K, Nath R, Devi D (2019) Genetic diversity among the morphs of Antheraea assamensis Helfer: study using RAPD and internal transcribed spacer DNA1. Indian J Exp Biol 57:418–426

Sairkar PK, Sharma A, Shukla NP (2016) SCAR marker for identification and discrimination of Commiphora wightii and C. myrrha. Mol Biol Int 2016:1482796. https://doi.org/10.1155/2016/1482796

Saleem MH, Potgieter J, Arif KM (2019) Plant disease detection and classification by deep learning. Plants 8:468

Saleem MH, Potgieter J, Arif KM (2020) Plant disease classification: a comparative evaluation of convolutional neural networks and deep learning optimizers. Plants 9:1319

Salehi F, Ahmadian L (2017) The application of geographic information systems (GIS) in identifying the priority areas for maternal care and services. BMC Health Serv Res 17:482

Sarwat M (2012) ISSR: a reliable and cost-effective technique for detection of DNA polymorphism. In: Sucher NJ, Hennell JR, Carles MC (eds) Plant DNA Fingerprinting and Barcoding: Methods and Protocols Methods in Molecular Biology. Humana Press, Totowa, pp 103–121

Schägner JP, Brander L, Maes J, Hartje V (2013) Mapping ecosystem services’ values: current practice and future prospects. Ecosyst Serv 4:33–46

Schlund M, Erasmi S (2020) Sentinel-1 time series data for monitoring the phenology of winter wheat. Remote Sens Environ 246:111814

Scionti F, Di Martino MT, Pensabene L, Bruni V, Concolino D (2018) The cytoscan HD array in the diagnosis of neurodevelopmental disorders. High-Throughput 7:E28

Sedano F, Lisboa S, Duncanson L, Ribeiro N, Sitoe A, Sahajpal R, Hurtt G, Tucker C (2020) Monitoring intra and inter annual dynamics of forest degradation from charcoal production in Southern Africa with Sentinel – 2 imagery. Int J Appl Earth Obs Geoinf 92:102184

Selvaraj MG, Vergara A, Montenegro F, Alonso Ruiz H, Safari N, Raymaekers D, Ocimati W, Ntamwira J, Tits L, Omondi AB, Blomme G (2020) Detection of banana plants and their major diseases through aerial images and machine learning methods: a case study in DR Congo and Republic of Benin. ISPRS J Photogramm Remote Sens 169:110–124

Senn HV, Ghazali M, Kaden J, Barclay D, Harrower B, Campbell RD, Macdonald DW, Kitchener AC (2019) Distinguishing the victim from the threat: SNP-based methods reveal the extent of introgressive hybridization between wildcats and domestic cats in Scotland and inform future in situ and ex situ management options for species restoration. Evol Appl 12:399–414

Sereshkeh FM, Azizi A, Noroozisharaf A (2019) Structure of genetic diversity among and within populations of the endemic Iranian plant Dracocephalum kotschyi. Hortic Environ Biotechnol 60:767–777

Shen L, Li X-W, Meng X-X, Wu J, Tang H, Huang L-F, Xiao S-M, Xu J, Chen S-L (2019) Prediction of the globally ecological suitability of Panax quinquefolius by the geographic information system for global medicinal plants (GMPGIS). Chin J Nat Med 17:481–489

Silvestro D, Goria S, Sterner T, Antonelli A (2022) Improving biodiversity protection through artificial intelligence. Nat Sustain 5:415–424

Slagter B, Tsendbazar N-E, Vollrath A, Reiche J (2020) Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: a case study in the St. Lucia wetlands, South Africa. Int J Appl Earth Obs Geoinf 86:102009

Smithsonian’s National Zoo and Conservation Biology Institute Smithsonian’s National Zoo (2016) Asian elephant. In: Smithsonian’s National Zoo. https://nationalzoo.si.edu/animals/asian-elephant . Accessed 9 Aug 2021

Srikanth K, Kim N-Y, Park W, Kim J-M, Kim K-D, Lee K-T, Son J-H, Chai H-H, Choi J-W, Jang G-W, Kim H, Ryu Y-C, Nam J-W, Park J-E, Kim J-M, Lim D (2019) Comprehensive genome and transcriptome analyses reveal genetic relationship, selection signature, and transcriptome landscape of small-sized Korean native Jeju horse. Sci Rep 9:16672

Srivastava PK, Malhi RKM, Pandey PC, Anand A, Singh P, Pandey MK, Gupta A (2020) 1 - Revisiting hyperspectral remote sensing: origin, processing, applications and way forward. In: Pandey PC, Srivastava PK, Balzter H, Bhattacharya B, Petropoulos GP (eds) Hyperspectral Remote Sensing Earth Observation. Elsevier, Amsterdam, pp 3–21

Stephenson PJ (2020) Technological advances in biodiversity monitoring: applicability, opportunities and challenges. Curr Opin Environ Sustain 45:36–41

Sulistyahadi FN, Puspitasari IGAAR, Nuryanto A (2020) Diversity analysis of Rhacophorus margaritifer (Schlegel, 1837) in Baturraden based on RAPD markers. J Trop Biodiversi Biotechnol 5:44–52

Sun Y-W, Hou N, Woeste K, Zhang C, Yue M, Yuan X-Y, Zhao P (2019) Population genetic structure and adaptive differentiation of iron walnut Juglans regia subsp. sigillata in southwestern China. Ecol Evol 9:14154–14166

Supple MA, Shapiro B (2018) Conservation of biodiversity in the genomics era. Genome Biol 19:131

Tanase MA, Villard L, Pitar D, Apostol B, Petrila M, Chivulescu S, Leca S, Borlaf-Mena I, Pascu I-S, Dobre A-C, Pitar D, Guiman G, Lorent A, Anghelus C, Ciceu A, Nedea G, Stanculeanu R, Popescu F, Aponte C, Badea O (2019) Synthetic aperture radar sensitivity to forest changes: a simulations-based study for the Romanian forests. Sci Total Environ 689:1104–1114

Tani N, Kawahara T, Yoshimaru H, Hoshi Y (2003) Development of SCAR markers distinguishing pure seedlings of the endangered species Morus boninensis from M. boninensis × M. acidosa hybrids for conservation in Bonin (Ogasawara) Islands. Conserv Genet 4:605–612

Tarazona Y, Miyasiro-López M (2020) Monitoring tropical forest degradation using remote sensing. Challenges and opportunities in the Madre de Dios region, Peru. Remote Sens Appl: Soc Environ 19:100337

Tashayo B, Honarbakhsh A, Akbari M, Eftekhari M (2020) Land suitability assessment for maize farming using a GIS-AHP method for a semi- arid region, Iran. J Saudi Soc Agric Sci 19(5):332–338

Teobaldelli M, Cona F, Saulino L, Migliozzi A, D’Urso G, Langella G, Manna P, Saracino A (2017) Detection of diversity and stand parameters in Mediterranean forests using leaf-off discrete return LiDAR data. Remote Sens Environ 192:126–138

The RAFOS group at the Graduate School of Oceanography, University of Rhode Island, Kingston, RI 02881 (2001) The ideal signal received from moored SOFAR emitters and several recorded signals from the float. The arrival time can be measured very accurately. https://commons.wikimedia.org/wiki/File:Sound_wave_Correlation.jpg . Accessed 11 Aug 2021

Thessen A (2016) Adoption of machine learning techniques in ecology and earth science. One Ecosyst 1:e8621

Thuy MTP, Ha TTT, Quang TH (2020) Analysis of genetic diversity in Pa Co pine (Pinus kwangtungensis Chun ex Tsiang) using RAPD and ISSR markers. Vietnam J Sci Technol Eng 62:62–68

Tilman D, Isbell F, Cowles JM (2014) Biodiversity and ecosystem functioning. Annu Rev Ecol Evol Syst 45:471–493

Tiwari V, Meena B, Nair NK, Rana TS (2020) Molecular analyses of genetic variability in the populations of Bergenia ciliata in Indian Himalayan Region (IHR). Physiol Mol Biol Plants: An International Journal of Functional Plant Biology 26:975–984

Torresani M, Rocchini D, Sonnenschein R, Zebisch M, Hauffe HC, Heym M, Pretzsch H, Tonon G (2020) Height variation hypothesis: a new approach for estimating forest species diversity with CHM LiDAR data. Ecol Indic 117:106520

Touma S, Arakawa A, Oikawa T (2019) Evaluation of the genetic structure of indigenous Okinawa Agu pigs using microsatellite markers. Asian Australas J Anim Sci 33:212–218

Tuisima-Coral LL, Hlásná Čepková P, Weber JC, Lojka B (2020) Preliminary evidence for domestication effects on the genetic diversity of Guazuma crinita in the Peruvian Amazon. Forests 11:795

Valbuena R, O’Connor B, Zellweger F, Simonson W, Vihervaara P, Maltamo M, Silva CA, Almeida DRA, Danks F, Morsdorf F, Chirici G, Lucas R, Coomes DA, Coops NC (2020) Standardizing ecosystem morphological traits from 3D information sources. Trends Ecol Evol 35(8):656–667

Vallejos-Vidal E, Reyes-Cerpa S, Rivas-Pardo JA, Maisey K, Yáñez JM, Valenzuela H, Cea PA, Castro-Fernandez V, Tort L, Sandino AM, Imarai M, Reyes-López FE (2019) Single-nucleotide polymorphisms (SNP) mining and their effect on the tridimensional protein structure prediction in a set of immunity-related expressed sequence tags (EST) in Atlantic salmon (Salmo salar). Front Genet 10:1406

Van den Broeck T, Joniau S, Clinckemalie L, Helsen C, Prekovic S, Spans L, Tosco L, Van Poppel H, Claessens F (2014) The role of single nucleotide polymorphisms in predicting prostate cancer risk and therapeutic decision making. Biomed Res Int 2014:627510

Vaux F, Aycock HM, Bohn S, Rasmuson LK, O’Malley KG (2020) Sex identification PCR–RFLP assay tested in eight species of Sebastes rockfish. Conserv Genet Resour 12:541–544

Vecteezy (2021a) Set of wild animal. In: Vecteezy.com . href=" https://www.vecteezy.com/freevector/vector "> Vector Vectors by Vecteezy. Accessed 25 May 2021

Vecteezy (2021b) Forest scene with tall trees. In: Vecteezy.com . https://www.vecteezy.com/vector-art/298788-forestscene-with-tall-trees . Accessed 25 May 2021

Vergnaud G, Denoeud F (2000) Minisatellites: mutability and genome architecture. Genome Res 10:899–907

Verma GK, Gupta P (2018) Wild animal detection using deep convolutional neural network. In: Chaudhuri BB, Kankanhalli MS, Raman B (eds) Proceedings of 2nd International Conference on Computer Vision and Image Processing Advances in Intelligent Systems and Computing. Springer, Singapore, pp 327–338

Vidaña-Vila E, Navarro J, Alsina-Pagès RM, Ramírez Á (2020) A two-stage approach to automatically detect and classify woodpecker (Fam. Picidae) sounds. Appl Acoust 166:107312

Vieira MLC, Santini L, Diniz AL, de Freitas Munhoz C (2016) Microsatellite markers: what they mean and why they are so useful. Genet Mol Biol 39:312–328

Vignal A, Milan D, SanCristobal M, Eggen A (2002) A review on SNP and other types of molecular markers and their use in animal genetics. Gen Select Evol: GSE 34:275–305

Vogeler JC, Cohen WB (2016) A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models. Span J Remote Sens 45:1–14

Wagutu GK, Fan X-R, Njeri HK, Wen X-Y, Liu Y-L, Chen Y-Y (2020) Development and characterization of EST-SSR markers for the endangered tree Magnolia patungensis (Magnoliaceae). Ann Bot Fenn 57:97–107

Wang R, Gamon JA (2019) Remote sensing of terrestrial plant biodiversity. Remote Sens Environ 231:111218

Wang J, Xiao X, Qin Y, Doughty RB, Dong J, Zou Z (2018) Characterizing the encroachment of juniper forests into sub-humid and semi-arid prairies from 1984 to 2010 using PALSAR and Landsat data. Remote Sens Environ 205:166–179

Wang J, Xiao X, Bajgain R, Starks P, Steiner J, Doughty RB, Chang Q (2019a) Estimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images. ISPRS J Photogramm Remote Sens 154:189–201

Wang X, Chen W, Luo J, Yao Z, Yu Q, Wang Y, Zhang S, Liu Z, Zhang M, Shen Y (2019b) Development of EST-SSR markers and their application in an analysis of the genetic diversity of the endangered species Magnolia sinostellata. Mol Gen Genomics: MGG 294:135–147

Wang L, Deng H, Qiu X, Wang P, Yang F (2020) Determining the impact of key climatic factors on geographic distribution of wild Akebia trifoliata. Ecol Indic 112:106093

Wetzel FT, Saarenmaa H, Regan E, Martin CS, Mergen P, Smirnova L, Tuama ÉÓ, Camacho FAG, Hoffmann A, Vohland K, Häuser CL (2015) The roles and contributions of Biodiversity Observation Networks (BONs) in better tracking progress to 2020 biodiversity targets: a European case study. Biodiversity 16:137–149

Whitehorn PR, Navarro LM, Schröter M, Fernandez M, Rotllan-Puig X, Marques A (2019) Mainstreaming biodiversity: a review of national strategies. Biol Conserv 235:157–163

Whyte A, Ferentinos KP, Petropoulos GP (2018) A new synergistic approach for monitoring wetlands using Sentinels-1 and 2 data with object-based machine learning algorithms. Environ Model Softw 104:40–54

Willi M, Pitman RT, Cardoso AW, Locke C, Swanson A, Boyer A, Veldthuis M, Fortson L (2018) Software, data and models used in “Identifying animal species in camera trap images using deep learning and citizen science

Willi M, Pitman RT, Cardoso AW, Locke C, Swanson A, Boyer A, Veldthuis M, Fortson L (2019) Identifying animal species in camera trap images using deep learning and citizen science. Methods Ecol Evol 10:80–91

Wu K, Rodriguez GA, Zajc M, Jacquemin E, Clément M, De Coster A, Lambot S (2019a) A new drone-borne GPR for soil moisture mapping. Remote Sens Environ 235:111456

Wu W-D, Liu W-H, Sun M, Zhou J-Q, Liu W, Zhang C-L, Zhang X-Q, Peng Y, Huang L-K, Ma X (2019b) Genetic diversity and structure of Elymus tangutorum accessions from western China as unraveled by AFLP markers. Hereditas 156:8

Xia D, Chen P, Wang B, Zhang J, Xie C (2018) Insect detection and classification based on an improved convolutional neural network. Sensors, Basel, p 18

Xia W, Luo T, Zhang W, Mason AS, Huang D, Huang X, Tang W, Dou Y, Zhang C, Xiao Y (2019) Development of high density SNP markers and their application in evaluating genetic diversity and population structure in Elaeis guineensis. Front Plant Sci 10:130

Xu F, Lei P, Jiang M, Sang L, Guan F, Meng F, Quan H (2019) Genetic diversity of Herpetospermum caudigerum (Ser.) Baill using AFLP and chloroplast microsatellites. Biotechnol Biotechnol Equip 33:1260–1268

Yang L, Khan MA, Mei Z, Yang M, Zhang T, Wei C, Yang W, Zhu L, Long Y, Fu J (2014) Development of RAPD-SCAR markers for Lonicera japonica (Caprifolicaceae) variety authentication by improved RAPD and DNA cloning. Revista De Biol Trop 62:1649–1657

Yin L and Zhou YM (2019) Life detection strategy based on infrared vision and ultra-wideband radar data fusion. Elect Eng Syst Sci 1–7

Yuskianti V, Shiraishi S (2010) Sequence characterized amplified region (SCAR) markers in Sengon (Paraseriathes falcataria ( L .)) Nielsen. Hayati J Biosci 17:167–172

Zeng Z, Gan Y, Kettner AJ, Yang Q, Zeng C, Brakenridge GR, Hong Y (2020) Towards high resolution flood monitoring: an integrated methodology using passive microwave brightness temperatures and Sentinel synthetic aperture radar imagery. J Hydrol 582:124377

Zhang C, Patras P, Haddadi H (2019a) Deep learning in mobile and wireless networking: a survey. IEEE Commun Surv Tutor 21:2224–2287

Zhang P, Nascetti A, Ban Y, Gong M (2019b) An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data. ISPRS J Photogramm Remote Sens 158:50–62

Zhang W, Brandt M, Wang Q, Prishchepov AV, Tucker CJ, Li Y, Lyu H, Fensholt R (2019c) From woody cover to woody canopies: how Sentinel-1 and Sentinel-2 data advance the mapping of woody plants in savannas. Remote Sens Environ 234:111465

Zhang Y, Ling F, Foody GM, Ge Y, Boyd DS, Li X, Du Y, Atkinson PM (2019d) Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016. Remote Sens Environ 224:74–91

Zhang Y, Zhang M, Hu Y, Zhuang X, Xu W, Li P, Wang Z (2019e) Mining and characterization of novel EST-SSR markers of Parrotia subaequalis (Hamamelidaceae) from the first Illumina-based transcriptome datasets. PLoS One 14:e0215874

Zheng Y, Lan S, Chen WY, Chen X, Xu X, Chen Y, Dong J (2019) Visual sensitivity versus ecological sensitivity: an application of GIS in urban forest park planning. Urban For Urban Green 41:139–149

Zhu X, Hou Y, Weng Q, Chen L (2019) Integrating UAV optical imagery and LiDAR data for assessing the spatial relationship between mangrove and inundation across a subtropical estuarine wetland. ISPRS J Photogramm Remote Sens 149:146–156

Zimmermann BL, De Vargas Machado JV, Santos S, Bartholomei-Santos ML (2019) Genetic diversity of three aegla species (Decapoda, Anomura) revealed by AFLP and mtDNA markers. Crustaceana 19:445–462

Zizka A, Silvestro D, Vitt P, Knight TM (2020) Automated conservation assessment of the orchid family with deep learning. Conserv Biol 35:897–908 n/a

Download references

Author information

Authors and affiliations.

Department of Biotechnology, Utkal University, Vani Vihar, Bhubaneswar, Odisha, 751004, India

Rout George Kerry & Sanatan Majhi

College of Informatics and Computing Sciences, Batangas State University, Batangas, Philippines

Francis Jesmar Perez Montalbo

Department of Soil Science and Agricultural Chemistry, School of Agriculture, GIET University, Gunupur, Rayagada, Odisha, 765022, India

Rajeswari Das

Indian Council of Agricultural Research-Directorate of Foot and Mouth Disease-International Centre for Foot and Mouth Disease, Arugul, Bhubaneswar, Odisha, 752050, India

Sushmita Patra & Vinayak Nayak

Department of Anatomy and Cell Biology, McGill University, Montreal, Quebec, H3A 0C7, Canada

Gyana Prakash Mahapatra

Zoology Section, Mahila MahaVidyalya, Banaras Hindu University, Varanasi, 221005, India

Ganesh Kumar Maurya

Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA

Atala Bihari Jena

Department of Physics, Edo State University Uzairue, P.B.M. 04, Auchi, Edo State, 312101, Nigeria

Kingsley Eghonghon Ukhurebor

Department of Pharmaceutical Sciences, Utkal University, Vani Vihar, Bhubaneswar, Odisha, 751004, India

Ram Chandra Jena

Department of Zoology, Mizoram University, Aizawl, 796009, India

Sushanto Gouda

School of Biological Sciences, AIPH University, Bhubaneswar, Odisha, 752101, India

Jyoti Ranjan Rout

You can also search for this author in PubMed   Google Scholar

Contributions

RGK, FJPM, RD, and SP wrote the initial draft, GPM, GKM, VN, ABJ, KEU, RCJ, and SG edited and reviewed the whole manuscript, and SM and JRR comprehensively edited the manuscript which was then reviewed and approved by all authors.

Corresponding authors

Correspondence to Sanatan Majhi or Jyoti Ranjan Rout .

Ethics declarations

Ethics approval.

Not applicable.

Consent to participate

Consent to publish, conflict of interest.

The authors declare no competing interests.

Additional information

Responsible Editor: Philippe Garrigues

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Kerry, R.G., Montalbo, F.J.P., Das, R. et al. An overview of remote monitoring methods in biodiversity conservation. Environ Sci Pollut Res 29 , 80179–80221 (2022). https://doi.org/10.1007/s11356-022-23242-y

Download citation

Received : 10 December 2021

Accepted : 20 September 2022

Published : 05 October 2022

Issue Date : November 2022

DOI : https://doi.org/10.1007/s11356-022-23242-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Artificial intelligence
• Geographic information system
• Molecular techniques
• Wildlife conservation
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• F1000 Biol Rep

Climate change and biodiversity conservation: impacts, adaptation strategies and future research directions

Shannon m hagerman.

Institute for Resources, Environment and Sustainability, University of British Columbia, Aquatic Ecosystems Research Laboratory, 4 th Floor, 2202 Main Mall, Vancouver, British Columbia, V6T 1Z4, Canada

Kai MA Chan

The impacts of climate change pose fundamental challenges for current approaches to biodiversity conservation. Changing temperature and precipitation regimes will interact with existing drivers such as habitat loss to influence species distributions despite their protection within reserve boundaries. In this report we summarize a suite of current adaptation proposals for conservation, and highlight some key issues to be resolved.

Introduction and context

Changing temperature and precipitation regimes [ 1 ] are expected to interact with other drivers to impact a range of biological processes and influence species distributions [ 2 , 3 ] ( Figure 1 ). In the past 5 years a growing body of empirical evidence has documented climate-change-attributed changes in processes, including phenology [ 4 - 6 ], net primary production [ 7 ], and species interactions [ 8 ]. Changes in species distributions have also been observed in both above-ground [ 3 , 9 - 11 ] and below-ground communities [ 12 ].

Within and between each of these levels, the global change drivers, mediating drivers and responses can interact and feed back to each other.

This situation poses fundamental challenges to existing approaches for biodiversity conservation because targets (for example, species) are currently managed within spatially and temporally static reserves [ 13 - 18 ]. As a result of changing species distributions, some populations and species will no longer be viable in reserves created for their protection. Additionally, altered disturbance regimes may enhance the ability of invasive species to colonize reserves more easily [ 19 ].

Thus, a central unresolved question in conservation biology is: how can we manage for biodiversity objectives in an era of accelerated climate change? In this report we provide a brief overview of a current suite of proposed adaptation approaches, and identify some future challenges and key issues to be resolved. Both mitigation and adaptation strategies are crucial to respond to climate change. Although reserves can play a role in carbon storage and sequestration - for example, through initiatives such as reducing emissions from deforestation and degradation (one aspect of climate change mitigation) - here we focus solely on adaptation strategies.

Major recent advances

Below we highlight four commonly proposed adaptation strategies for biodiversity conservation given climate change. In this overview report we focus on a selection of commonly proposed in situ adaptation strategies in response to the impacts of climate change. For a journalistic overview of ex situ strategies, such as captive breeding, seed and gene banking, in the context of responding to climate change, the reader is referred to [ 20 ].

The first three approaches seek to reduce extinction risk primarily by addressing the effects of climate change on species distributions (the pattern), and in part by passively influencing mediating drivers (for example, providing corridors for movement). The last considers a more controversial interventionist option ( Table 1 ).

Managing the matrix as a buffer should both protect core populations (but often not in the matrix, rather by insulating reserves) and also facilitate shifts across a landscape; new and dynamic reserves function primarily by protecting core populations and also by accommodating (rather than facilitating) target movement.

New reserves and corridors

The most common proposed approach for conservation adaptation is to expand linked networks of protected areas including migration corridors [ 15 , 17 , 18 , 21 - 23 ]. These researchers argue that the existing network does not provide enough area to allow for organisms to respond autonomously to changing climatic conditions.

The principal purpose of new protected areas is to mitigate the risk of extinction by providing the potential for species distributions to shift; a secondary contribution is that they may also enhance micro-evolutionary potential through enhanced population size and diversity. Therefore, corridors may reduce extinction risk by enabling the passive shifting of some species to new geographic ranges, and by reinforcing species distributions (in a metapopulation context).

A crucial challenge for this approach is determining where to site corridors and new reserve areas. The current state-of-the-science is to use species distribution models or bioclimate envelope models to generate projections of future species’ responses to various climate scenarios [ 24 - 27 ]. Many view this information as providing essential insight into the strategic siting of new protected areas [ 28 ]. At the same time, myriad uncertainties impact the validity of these projections [ 29 - 34 ]. Efforts to address these uncertainties are ongoing [ 27 , 35 ], but many uncertainties may remain (or even increase) within decision-making time frames nonetheless.

Schemes for siting new areas may be more robust to uncertainties by incorporating coarse scale environmental gradients, such as edaphic and elevational ranges (for example, [ 21 ]).

Matrix as buffers

As a complement to protected areas expansion, many researchers highlight the importance of matrix areas [ 36 , 37 ] or the wider landscape, as being particularly crucial for biological adaptation in an era of change [ 15 , 21 ]. For example, some land uses, such as forestry or agro-forestry (or lower impact marine activities), may provide a spatial buffer for populations as they respond to climate change and move outside core reserves. In order for this proposal to be effective, matrix areas must be of sufficient size, and landowners must be willing to adjust their activities as monitoring indicates [ 21 ]. Incentives may increase the viability of this proposal. The logic of this approach is similar to new protected areas and corridors: more benign matrix areas may passively facilitate species shifts by promoting movement across land- and seascapes; they may also reinforce species distributions at fine scales (around reserves).

Dynamic reserves

The management of matrix areas for biodiversity objectives further supports a third proposal. Dynamic reserves implemented on managed landscapes (or seascapes) are areas whose locations and levels of protection change through time and space [ 18 , 22 , 38 , 39 ]. This approach may be particularly important in areas where there is little spatial opportunity available for new core protected areas. At the same time, the issue of ownership and property rights requires further examination in different contexts in order to more fully understand the implementation challenges of this potential approach in particular localities. This approach involves the future passive facilitation of shifting species distributions in response to future conditions, rather than prediction of conditions.

Assisted colonization

More controversial is the interventionist proposal for ‘assisted migration’ [ 40 , 41 ] or ‘assisted colonization’ [ 42 ]. Both describe a management option in which species are deliberately introduced into an area where it has not existed in recent history for the purpose of achieving a conservation objective. This proposal has emerged in response to the mounting evidence that some species may not be able to track changing climatic conditions quickly enough [ 3 , 43 ], or because there are natural or human barriers in the way. This approach would involve actively shifting species distributions.

The assisted colonization proposal is at odds with current reserve management in which substantial efforts are directed at keeping non-native species out. It also carries with it substantial risks because introduced species may become invasive and displace other valued ecosystem elements. Nevertheless, assisted colonization may be seen as a necessary last resort in some cases. In anticipation of this, Hoegh-Guldberg et al . [ 42 ] have proposed a framework for decision making within which the costs, benefits and risks of the translocation event would be evaluated. Other researchers have inferred the risk of potential invasion of assisted colonization from comparisons of intra-continental and inter-continental past invasions [ 44 ].

Future directions

In this last section we identify a collection of key challenges and issues to be resolved for reserve management suited for an era of change. We divide these challenges into five categories: focus on processes, projections and uncertainties, monitoring, implementation, and norms and expectations.

Focus on processes

In the main, conservation activities have focussed on maintaining biodiversity patterns and indirectly enabling natural processes: for example, by protecting space for species to exist (represented by the first three categories referred to above). As climate change influences mediating drivers, the attributes that make certain places conducive to species flourishing (critical habitat) will change, and in some cases disappear. For species whose critical habitat changes dramatically or disappears, it will be increasingly necessary to consider approaches that involve the active management of mediating drivers.

Restoration activities have long involved management of disturbance regimes, ecosystem function, and species interactions. Adapting to the impacts of climate change may require more such active management, including assisted colonization, and other interventions, such as enhancement of evolutionary adaptation [ 45 ], and active maintenance of pre-climate change processes and conditions.

Projections and uncertainties

A key area of future research is to improve our capacity for forecasting species responses to changing climate - for example, by incorporating biotic interactions in bio-climate models [ 46 ], and refining species-specific process-based models [ 47 ]. Other areas include the longstanding scientific challenge of understanding when a given species will become invasive in a given context [ 44 ]. Efforts to reduce the ecological uncertainties just mentioned will represent a key contribution to the literature on adaptive reserve management.

In addition to ecological uncertainties, there are various parametric and model uncertainties relating to species distribution models. This includes uncertainties relating to so-called ‘unknown unknowns’; where key processes are not yet recognized, understood or incorporated into model structure, or as parameters. Yet such processes may play critical roles in ecosystem dynamics nonetheless. Moreover, there are uncertainties relating to the climate scenario models that influence the outputs of envelope models [ 48 ]. Lastly, there are critical socio-political uncertainties (in values, impacts, responses and feedbacks).

Thus, a second key area of future research is the development of conservation approaches that are robust to uncertainty, recognizing that many of the above uncertainties are irreducible. As ecological and social systems co-adapt, non-linear dynamics will lead to perpetually surprising outcomes [ 49 ]. Therefore, even with the best scientific research and most comprehensive models, species responses may surprise us. Indeed, uncertainties may also increase with new research and insights [ 50 ]. Thus, the implementation of safe-to-fail adaptive management policies may be as or more important than efforts to reduce uncertainties.

In many ways, conservation adaptation requires recognition of what is changing and where (for example, assisted migration, dynamic reserves). Thus, there is an urgent need for monitoring of impacts. While existing monitoring programs could be adapted and used for this purpose, programs specifically targeted to assessing the impacts of climate change would support the most effective adaptation responses possible under highly uncertain circumstances.

Implementation

So far, the adaptation proposals outlined above have focussed primarily on biological dimensions. This effort has provided a critical foundation, but land-use decisions, including reserves, are social decisions made in the context specific places. Therefore, a key area of future research is to identify through applied case studies the factors that determine the relative receptivity or resistance of communities to new and additional conservation measures. This effort will provide crucial insights by which conservationists can foster socially sustainable conservation action.

Changing norms and expectations for reserve management

To date, core protected areas have been managed with a preferred minimum intervention (with exceptions for active management including controlled burns, programs to limit grazers, and efforts to minimize the impacts and distributions of invasive species, for example). Proposals for more widespread intervention, including assisted colonization, raise many unanswered questions. When do we intervene and to what extent? To what extent and under what circumstances are we willing to sacrifice the persistence of one species to save another? Who decides? And by what decision process? Addressing these questions, including latent and even more controversial proposals for conservation triage [ 51 ], will be a key challenge moving forward.

Ultimately, one of the biggest challenges to fostering biological adaptation may be a willingness across stakeholders, scientists and managers to re-calibrate existing expectations of nature and reserves in responding to an era of global change.

Acknowledgments

Funding for this work was provided by the National Science Foundation (SES-0345798) through the Climate Decision Making Center (CDMC) at Carnegie Mellon University, and a University Graduate Fellowship from the University of British Columbia.

The electronic version of this article is the complete one and can be found at: http://F1000.com/Reports/Biology/content/1/16

Competing interests

The authors declare that they have no competing interests.

• UNESCO's commitment
• Culture and values
• Conservation and sustainable use
• Local, indigenous and scientific knowledge
• Education and awareness
• Ocean Sciences
• UNESCO Biodiversity Portal
• International governance mechanisms
• United Nations Decade on Ecosystem Restoration
• United Nations Decade of Ocean Science for Sustainable Development
• Ocean Biodiversity Information System

Conservation and sustainable use of biodiversity

Biodiversity is currently being lost at up to 1,000 times the natural rate. Some scientists are now referring to the crisis as the ‘Earth’s sixth mass extinction’, comparable to the last great extinction crisis 65 million years ago. These extinctions are irreversible and pose a serious threat to our health and wellbeing. Designation and management of protected areas is the cornerstone of biodiversity conservation. However, despite an increase in the total number of protected areas in the world, biodiversity continues to decline.

An integrated landscape approach to conservation planning plays a key role in ensuring suitable habitats for species. However, many protected areas are not functioning as effectively as originally intended, due in part to limited resources to maintain these areas and/or enforce relevant legal frameworks. In addition, current protected area networks may need to be re-aligned to account for climate change. Efforts to preserve biodiversity must take into account not only the physical environment, but also social and economic systems that are well connected to biodiversity and ecosystem services. For protected areas to contribute effectively to a secure future for biodiversity, there is a need for measures to enhance the representativeness of networks, and to improve management effectiveness.

• Growth in protected areas in many countries is helping to maintain options for the future, but sustainable use and management of territory outside protected areas remains a priority.
• Measures to improve environmental status within conservation areas, combined with landscape-scale approaches, are urgently needed if their efficiency is to be improved.
• Lack of adequate technical and financial resources and capacity can limit the upscaling of innovative solutions, demonstrating further the need for regional and subregional co-operation.
• Capacity building is a key factor in the successful avoidance and reduction of land degradation and informed restoration.
• Capacity development needs should be addressed at three levels: national, provincial and local.
• There is a need for capacity building to enable sources outside government to inform relevant departments and policies on biodiversity (e.g. through consultancies, academia and think tanks).

Sites, connected landscapes and networks

Conserving biodiversity and promoting sustainable use.

UNESCO works on the conservation of biodiversity and the sustainable use of its components through UNESCO designated sites, including biosphere reserves , World Heritage sites and UNESCO Global Geoparks . In 2018, UNESCO designated sites protected over 10 million km 2 , an area equivalent to the size of China. These conservation instruments have adopted policies and strategies that aim to conserve these sites, while supporting the broader objectives of sustainable development. One such example is the policy on the integration of a sustainable development perspective into the processes of the World Heritage Convention.

UNESCO is also the depository of the Convention on Wetlands of International Importance . Countless species of plants and animals depend on these delicate habitats for survival.

The first comprehensive assessment of species that live within World Heritage sites reveals just how critical they are to preserving the diversity of life on Earth.

The MAB Programme and the World Network of Biosphere Reserves: connecting landscapes and reconciling conservation with development

Biosphere reserves are designated under UNESCO’s Man and the Biosphere (MAB) Programme and promote solutions reconciling the conservation of biodiversity with its sustainable use at local and regional scales.

This dynamic and interactive network of sites works to foster the harmonious integration of people and nature for sustainable development through participatory dialogue, knowledge sharing, poverty reduction, human wellbeing improvements, respect or cultural values and efforts to improve society’s ability to cope with climate change. Progress has been achieved in connecting landscapes and protected areas through biosphere reserves, however further efforts are needed.

• World Network of Biosphere Reserves (WNBR)
• BIOsphere and Heritage of Lake Chad (BIOPALT)
• Women for Bees - a joint Guerlain and UNESCO programme
• Protecting Great Apes and their habitats
• Ecosystem restoration for sustainable development in Haiti ( Français | Español )
• Green Economy in Biosphere Reserves project in Ghana, Nigeria and Tanzania *
• More activities and projects

and the sustainable use of its components through UNESCO designated sites

Capacity building

Capacity building is needed to provide adequate support to Member States to attain the international biodiversity goals and the SDGs. In some countries, technical, managerial and institutional capacity to define guidelines for the conservation and sustainable use of biodiversity is inadequate. Additionally, existing institutional and technical capacity is often fragmented and uncoordinated. As new ways of interacting with biodiversity emerge, it is essential that stakeholders are trained and have sufficient capacity to implement new and varied approaches. Further efforts will be needed therefore to facilitate capacity building by fostering learning and leadership skills.

UNESCO is mandated to assist Member States in the design and implementation of national policies on education, culture, science, technology and innovation including biodiversity.

The BIOPALT project: integrated management of ecosystems

More than 30 million people live in the Lake Chad Basin. The site is highly significant in terms of  biodiversity and natural and cultural heritage. The cross-border dimension of the basin also presents opportunities for sub-regional integration. The  BIOsphere and Heritage of Lake Chad (BIOPALT) project focuses on poverty reduction and peace promotion, and aims to strengthen the capacities of the Lake Chad Basin Commission member states to safeguard  and  manage sustainably the water resources, socio-ecosystems and cultural resources of the region.

Women for bees: Women’s empowerment and biodiversity conservation

Women for Bees is a state-of-the-art female beekeeping entrepreneurship programme launched by UNESCO and Guerlain. Implemented in UNESCO designated biosphere reserves around the world with the support of the French training centre, the Observatoire Français d’Apidologie (OFA), the programme has actor, film maker and humanitarian activist Angelina Jolie for a Godmother, helping promote its twin objectives of women’s empowerment and biodiversity conservation.

Intergovernmental Oceanographic Commission (IOC) and capacity development

Capacity development is  present in all areas of IOC ’s work, at the global programme level as well as  within  each of its three sub-commissions and  the IOC-INDIO regional committee. In 2015, IOC adopted its Capacity Development Strategy. IOC is the custodian agency for SDG 14A.

In collaboration with the International Oceanographic Data and Information Exchange (IODE) , IOC has implemented a network of Regional Training Centres under the OceanTeacher Global Academy (OTGA) project, which has seven such centres around the world (Belgium, Colombia, India, Kenya, Malaysia, Mozambique and Senegal). Through its network of centres, OTGA provides a  programme of training courses related to IOC programmes, which contribute to the sustainable management of  oceans and coastal areas worldwide. OTGA has developed an e-Learning  Platform that hosts all training  resources for the training courses and makes them freely available to any interested parties.

Since 2012,  270 scientists from 69 countries have been trained to  manage  marine  biodiversity  data,  publish  data  through the Ocean Biogeographic Information System (OBIS) , and perform scientific data analysis for reporting and assessment. Since 1990, IOC West Pacific Regional Training and  Research  Centres  have  trained  more  than  1,000 people in a variety of topics including: 

• monitoring the ecological impacts of ocean acidification on coral reef ecosystems,
• harmful algal blooms,
• traditional and molecular taxonomy,
• reef health monitoring, and
• seagrass and mangrove ecology and management.

Most courses take place in a face-to-face classroom environment,  however training can also be conducted online using ICTs and the OceanTeacher e-Learning Platform, thereby increasing the number of people reached.

and peace-building through the promotion of green economy and the valorization of the basin's natural resources

Governance and connecting the scales

Governance systems in many countries function as indirect drivers of changes to ecosystems and biodiversity. At present, most policies that address biodiversity are fragmented and target specific. Additionally, the current design of governance, institutions and policies rarely takes into account the diverse values of biodiversity. There are also substantial challenges to the design and implementation of effective transboundary and regional initiatives to halt biodiversity loss, ecosystem degradation, climate change and unsustainable development. Another key challenge to successful policy-making is adequate mobilization of financial resources. Increased funding from both public and private sources, together with innovative financing mechanisms such as ecological fiscal transfers, would help to strengthen institutional capacities.

• Governance options that harness synergies are the best option for achieving the SDGs.
• There is a need to develop engagement and actions with diverse stakeholders in governance through regional cooperation and partnerships with the private sector.  
• Mainstreaming biodiversity into development policies, plans and programmes can improve efforts to achieve both the Aichi Targets and the SDGs.

UNESCO works to engage with new governance schemes at all levels through the LINKS Programme , the MAB Programme , the UNESCO-CBD Joint Programme and integrated management of ecosystems linking local to regional scales.

UNESCO supports the integrated management of ecosystems linking local to regional scales, especially through transboundary biosphere reserves, World Heritage sites and UNESCO Global Geoparks. The governance and management of a biosphere reserve places special emphasis on the crucial role that combined knowledge, learning and capacity building play in creating and sustaining a dynamic and mutually beneficial interactions between the conservation and development objectives at local and regional scales.

A transboundary biosphere reserve is defined by the following elements: a shared ecosystem; a common culture and shared traditions, exchanges and cooperation at local level; the will to manage jointly the territory along the bio-sphere reserve values and principles; a political commitment resulting in an official agreement between governmental authorities of the countries concerned. The transboundary biosphere reserve establishes a coordinating structure representative of various administrations and scientific boards, the authorities in charge of the different areas included the protected areas, the representatives of local communities, private sector, and NGOs. A permanent secretariat and a budget are devoted to its functioning. Focal points for co-operation are designated in each country participating.

Transboundary conservation and cooperation

The Trifinio Fraternidad Transboundary Biosphere Reserve is located between El Salvador, Guatemala and Honduras. It is the first transboundary biosphere reserve in Central America and represents a major contribution to the implementation of the Mesoamerican Corridor. It includes key biodiversity areas, such as Montecristo National Park and a variety of forest ecosystems.

Trifinio Fraternidad Transboundary Biosphere Reserve (El Salvador/Guatemala/Honduras)

Related items

• Information and communication
• Social and human sciences
• Natural sciences
• Programme implementation
• See more add

Conservation of nature's strongholds needed to halt biodiversity loss

Researchers argue for scaling-up area-based conservation to maintain ecological integrity.

To achieve global biodiversity targets, conservationists and governments must prioritize the establishment and effective management of large, interconnected protected areas with high ecological integrity, John G. Robinson from the Wildlife Conservation Society, US, and colleagues argue in an essay publishing May 21 in the open-access journal PLOS Biology .

The Kunming-Montreal Global Biodiversity Framework (GBF), signed at the 2022 Conference of Parties to the UN Convention on Biological Diversity in Montreal, recognized the importance of protecting large areas of natural habitat to maintain the resilience and integrity of ecosystems. To halt biodiversity loss, these protected and conserved areas need to be in the right places, connected to one another, and well managed. One of the GBF targets is to protect at least 30% of the global land and ocean by 2030, known as the 30x30 target.

To achieve GBF targets, the authors propose prioritizing large, interconnected protected areas with high ecological integrity, that are effectively managed and equitably governed. They emphasize the importance of conserving landscapes at scales large enough to encompass functioning ecosystems and the biodiversity they contain. In many cases, this will require interconnected groups of protected areas that are managed together. Effective governance means that the diversity of stakeholders and rights holders are recognized and that the costs and benefits are shared equitably between them. The authors argue that protected and conservation areas that meet all four criteria -- which they name "Nature's Strongholds" -- will be disproportionately important for biodiversity conservation. They identify examples of Nature's Strongholds in the high-biodiversity tropical forest regions of Central Africa and the Amazon.

By applying the four criteria presented in this essay to identify Nature's Strongholds around the world, governments and conservationists can coordinate their efforts to best address threats to biodiversity, the authors say.

The authors add, "'Nature's Strongholds' -- large, interconnected, ecologically intact areas that are well managed and equitably governed -- are identified in Amazonia and Central Africa. The approach offers an effective way to conserve biodiversity at a global scale."

• Ecology Research
• Endangered Plants
• Biodiversity
• Rainforests
• Land Management
• Urbanization
• Environmental Policies
• Biodiversity hotspot
• Organic farming
• Agroecology
• Conservation biology
• Sustainable land management
• Deforestation
• Unified neutral theory of biodiversity

Story Source:

Materials provided by PLOS . Note: Content may be edited for style and length.

Journal Reference :

• John G. Robinson, Danielle LaBruna, Tim O’Brien, Peter J. Clyne, Nigel Dudley, Sandy J. Andelman, Elizabeth L. Bennett, Avecita Chicchon, Carlos Durigan, Hedley Grantham, Margaret Kinnaird, Sue Lieberman, Fiona Maisels, Adriana Moreira, Madhu Rao, Emma Stokes, Joe Walston, James EM Watson. Scaling up area-based conservation to implement the Global Biodiversity Framework’s 30x30 target: The role of Nature’s Strongholds . PLOS Biology , 2024; 22 (5): e3002613 DOI: 10.1371/journal.pbio.3002613

Cite This Page :

Explore More

• Brain Network Responsible for Stuttering
• When Should You Neuter or Spay Your Dog?
• Why Do Dyeing Poison Frogs Tap Dance?
• Cold Supply Chains for Food: Huge Savings
• Genetic Mosaicism More Common Than Thought
• How Killifish Embryos Survive 8 Month Drought
• Simple Food Swaps to Cut Greenhouse Gases
• Fossil Porcupine in a Prickly Dilemma
• Future Climate Impacts Put Whale Diet at Risk
• Charge Your Laptop in a Minute?

Trending Topics

Strange & offbeat.

share this!

May 21, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

peer-reviewed publication

trusted source

Conservation of 'Nature's Strongholds' needed to halt biodiversity loss, say researchers

by Public Library of Science

To achieve global biodiversity targets, conservationists and governments must prioritize the establishment and effective management of large, interconnected protected areas with high ecological integrity, John G. Robinson from the Wildlife Conservation Society, US, and colleagues argue in an essay published May 21 in the open-access journal PLOS Biology .

The Kunming–Montreal Global Biodiversity Framework (GBF), signed at the 2022 Conference of Parties to the UN Convention on Biological Diversity in Montreal, recognized the importance of protecting large areas of natural habitat to maintain the resilience and integrity of ecosystems.

To halt biodiversity loss , these protected and conserved areas need to be in the right places, connected to one another, and well managed. One of the GBF targets is to protect at least 30% of the global land and ocean by 2030, known as the 30x30 target.

To achieve GBF targets, the authors propose prioritizing large, interconnected protected areas with high ecological integrity, that are effectively managed and equitably governed. They emphasize the importance of conserving landscapes at scales large enough to encompass functioning ecosystems and the biodiversity they contain.

In many cases, this will require interconnected groups of protected areas that are managed together. Effective governance means that the diversity of stakeholders and rights holders is recognized and that the costs and benefits are shared equitably between them.

The authors argue that protected and conservation areas that meet all four criteria—which they name "Nature's Strongholds"—will be disproportionately important for biodiversity conservation. They identify examples of Nature's Strongholds in the high-biodiversity tropical forest regions of Central Africa and the Amazon.

By applying the four criteria presented in this essay to identify Nature's Strongholds around the world, governments and conservationists can coordinate their efforts to best address threats to biodiversity, the authors say.

The authors add, "Nature's Strongholds—large, interconnected, ecologically intact areas that are well managed and equitably governed—are identified in Amazonia and Central Africa. The approach offers an effective way to conserve biodiversity at a global scale."

Journal information: PLoS Biology

Provided by Public Library of Science

Explore further

Feedback to editors

Living fast, dying young: Study reveals impact of habitat disturbance on social organization of Arabian babblers

9 minutes ago

Researchers develop efficient tandem catalyst to enhance nitrate electroreduction to ammonia

22 minutes ago

Researchers find high magnetic field facilitates novel intrinsic ferromagnetic polar metals

25 minutes ago

New strategy leverages lignin condensation for biomass utilization

31 minutes ago

Study finds environmental conditions influenced how early humans migrated across continents

33 minutes ago

'Living fossils' are unique, not ancient, say researchers

42 minutes ago

First detection of magnetic massive stars outside our galaxy

Computational lens unmasks hidden 3D information from a single 2D micrograph

Some species may better tolerate climate change than expected

47 minutes ago

Our political debates may not be as antagonistic as we think, study shows

56 minutes ago

Relevant PhysicsForums posts

A dna animation.

6 hours ago

Probability, genetic disorder related

19 hours ago

Looking For Today's DNA Knowledge

May 27, 2024

Covid Vaccines Reducing Infections

Human sperm, egg cells mass-generated using ips, and now, here comes covid-19 version ba.2, ba.4, ba.5,....

May 25, 2024

More from Biology and Medical

Related Stories

Alarm bells ring as scientists uncover major flaws in ability to track changes to global conservation areas

Mar 18, 2024

Report: Governments are falling short on promises of effective biodiversity protection

May 9, 2024

World meets protected areas target, quality needs improving

May 19, 2021

More for less: A smarter way to protect biodiversity

Mar 6, 2024

Protected areas cover a sixth of Earth's land and freshwater

Time for a new global protected area target

Apr 11, 2019

Recommended for you

Scientists count emperor penguins in the Antarctic to investigate factors affecting population

3 hours ago

Marine Protected Areas don't line up with core habitats of rare migratory fish, finds new research

12 hours ago

Key factors that orchestrate mysterious synchronized coral spawning revealed

17 hours ago

New insights into juvenile crown-of-thorns starfish behaviors that threaten reefs

Salty soil sensitizes plants to an unconventional mode of bacterial toxicity

May 28, 2024

New research shows soil microorganisms could produce additional greenhouse gas emissions from thawing permafrost

Let us know if there is a problem with our content.

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Phys.org in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

• Latest news
• UCL in the media
• Services for media
• Student news
• Tell us your story

Genes provide hope for the survival of Arabia’s last big cat

28 May 2024

The release of captive bred Arabian leopards carefully selected for their genes could make a significant contribution to the successful recovery of the Critically Endangered wild population and avert extinction, according to new research involving UCL.

An international team of scientists, from the University of Kent, University of East Anglia, UCL, Nottingham-Trent University and the Diwan of Royal Court in Oman, surveyed the remote Dhofar mountain range of southern Oman to determine how many of Arabia’s last big cat survive.

By deploying camera traps to identify individual leopards and performing DNA analyses from wild leopard scat alongside samples from the captive population, the team estimates there could be only 51 wild leopards remaining in Oman, distributed between three isolated, genetically impoverished but distinct subpopulations.

Despite revealing extremely low levels of genetic diversity in the wild leopard population in Oman, the team discovered higher levels of genetic diversity in captive leopards across the region, in particular among several individuals originating from neighbouring Yemen that helped found today’s captive-breeding population. This important genetic resource has the potential for a major role in successful recovery of the Arabian leopard.

The team’s research, published in Evolutionary Applications , showed that the dwindling regional wild population could most effectively be recovered thorough ‘genetic rescue’, namely, the introduction of offspring from captive-bred leopards — which harbour the greatest amount of genetic diversity — into the wild population. However, their predictions indicate that for genetic rescue to establish the most viable populations through leopard reintroductions, the benefit that new genes can bring needs to be carefully assessed, in particular because captive leopards may already be in-bred.

The study used conservation genetic analysis, cutting-edge computer simulations, and extensive fieldwork in Oman to closely examine Arabian leopard DNA and assess the risk of future extinction, as well as forecasting how genetic rescue can secure the leopard’s viability. The authors say their findings could help other threatened species.

Co-author Dr Jim Labisko (UCL Centre for Biodiversity and Environment Research, UCL Biosciences) said: “Combining multiple methods of surveying, monitoring, and sampling leopard populations has been crucial for us to now determine that genetic rescue could now be an achievable means by which to help recover the Arabian leopard.

“Camera trapping meant we could both count and individually identify wild leopards, analyses of droppings from wild leopards and sampling material from captive animals provided insight as to the current levels of genetic diversity within the extant population, and the use of material from crucially important museum collections indicates that significant levels of genetic diversity were already lost by the end of the 20th century, due primarily to the targeted killing of leopards.

“Our combined modelling of these data puts us in a considerably better-informed position to determine next steps in the long-term recovery of the iconic Arabian leopard, the region’s last remaining big cat.”

Professor Jim Groombridge, who led the research at the University of Kent’s Durrell Institute of Conservation and Ecology, explained how the genetic analysis was carried out: “In collaboration with the Diwan of Royal Court in Oman, we surveyed and collected leopard scats from across the Dhofar mountain range, and extracted DNA from them which we analysed using microsatellite DNA markers to quantify genetic diversity.”

First author Dr Hadi Al Hikmani, Arabian leopard Conservation Lead at the Royal Commission for AlUla in Saudi Arabia, described the motivation for this study: “The Arabian leopard is one of the world’s rarest carnivores and is extraordinarily elusive. The only way to monitor these leopards in the wild is to deploy camera traps high up across the mountain ranges where the leopards live, and to collect the scats they leave behind on the mountain passes, for DNA analysis.”

Professor Cock van Oosterhout, of the School of Environmental Sciences at UEA, added: “The problem is that all individuals are somehow related to each other. They are the descendants of the few ancestors that managed to survive a major population crash. Hence, it becomes virtually impossible to stop inbreeding, and this exposes ‘bad’ mutations, what we call genetic load. In turn, this can increase the mortality rate, causing further population collapse.

“The genetic load poses a severe threat, but it can be alleviated by genetic rescue, and our study has projected the best way to do this. The wild population needs ‘genetic rescue’ from more genetically diverse leopards bred in captivity.

“However, there is a risk that we could introduce other bad mutations from the captive population into the wild, so we will need a careful balance.”

• Research paper in Evolutionary Applications
• Dr Jim Labisko’s academic profile
• UCL Centre for Biodiversity & Environment Research
• UCL Genetics, Evolution & Environment
• UCL Biosciences
• University of Kent
• Critically Endangered Arabian leopard in Oman. Credit: Dr Hadi Al Hikmani

Media contact 

tel: +44 20 7679 9222  / +44 (0) 7717 728648

E: chris.lane [at] ucl.ac.uk

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Published: 06 February 2023

Prioritizing India’s landscapes for biodiversity, ecosystem services and human well-being

• Arjun Srivathsa   ORCID: orcid.org/0000-0003-2935-3857 1 , 2   na1 ,
• Divya Vasudev 3   na1 ,
• Tanaya Nair   ORCID: orcid.org/0000-0003-2622-8612 1 , 4   na1 ,
• Stotra Chakrabarti 5 ,
• Pranav Chanchani 6 ,
• Ruth DeFries   ORCID: orcid.org/0000-0002-3332-4621 7 ,
• Arpit Deomurari   ORCID: orcid.org/0000-0001-5267-9789 6 , 8 ,
• Sutirtha Dutta 9 ,
• Dipankar Ghose 6 ,
• Varun R. Goswami 3 ,
• Rajat Nayak 10 ,
• Amrita Neelakantan 11 ,
• Prachi Thatte 6 ,
• Srinivas Vaidyanathan   ORCID: orcid.org/0000-0003-3642-0309 10 ,
• Madhu Verma   ORCID: orcid.org/0000-0002-7982-9182 12 ,
• Jagdish Krishnaswamy   ORCID: orcid.org/0000-0001-7985-0005 13 , 14 , 15   na1 ,
• Mahesh Sankaran   ORCID: orcid.org/0000-0002-1661-6542 1 , 15 &
• Uma Ramakrishnan   ORCID: orcid.org/0000-0002-5370-5966 1 , 15   na1  

Nature Sustainability volume  6 ,  pages 568–577 ( 2023 ) Cite this article

23k Accesses

17 Citations

52 Altmetric

Metrics details

Biodiversity

• Developing world
• Environmental impact
• Sustainability

An Author Correction to this article was published on 20 February 2023

This article has been updated

Biodiversity conservation and human well-being are tightly interlinked. Yet, mismatches in the scale at which these two priority issues are planned and implemented have exacerbated biodiversity loss, erosion of ecosystem services and declining human quality of life. India houses the second largest human population on the planet, while < 5% of the country’s land area is effectively protected for conservation. This warrants landscape-level conservation planning through a judicious mix of land-sharing and land-sparing approaches combined with the co-production of ecosystem services. Through a multifaceted assessment, we prioritize spatial extents of land parcels that, in the face of anthropogenic threats, can safeguard conservation landscapes across India’s biogeographic zones. We found that only a fraction (~15%) of the priority areas identified here are encompassed under India’s extant Protected Area network, and furthermore, that several landscapes of high importance were omitted from all previous global-scale assessments. We then examined the spatial congruence of priority areas with administrative units earmarked for economic development by the Indian government and propose management zoning through state-driven and participatory approaches. Our spatially explicit insights can help meet the twin goals of biodiversity conservation and sustainable development in India and other countries across the Global South.

Similar content being viewed by others

A bolder conservation future for Indonesia by prioritising biodiversity, carbon and unique ecosystems in Sulawesi

Effectiveness of protected areas influenced by socio-economic context

Challenges beyond reaching a 30% of area protection

The concomitant impacts of biodiversity decline, climate change, unsustainable land use and inequitable extraction of natural resources have degraded the quality of human life 1 , 2 , 3 . Biodiversity underpins several provisioning, regulatory and cultural or aesthetic ecosystem services. Ecosystem services linked to biodiversity, in turn, are crucial for ensuring long-term human well-being 4 . Recent discourse on nature-based solutions acknowledges nature’s contribution as humanity’s ‘safety net’ 5 , 6 and that ecological processes are shaped by a complex interplay between ecological and social systems 7 , 8 . A sustainable future can only be ensured by adopting an ecosystem approach to conservation that emphasizes the links between human and natural systems to address global-, regional- and biome-scale threats to their functioning 9 . Siloed management approaches to meet conservation targets that ignore this interplay have failed to yield equitable and sustainable progress 10 , 11 . A renewed global resolve will need to consider nature in the Anthropocene and explicitly reconcile land-use planning for economic development with ecosystem functioning, biodiversity conservation and long-term human well-being.

For nearly two decades, several priority-setting tools have been proposed the world over to identify and rank species, habitats and locations based on their relative importance, vulnerability or ease of management intervention 12 . These approaches generally adopt systematic spatial conservation planning—a set of decision-making tools that help determine the spatial locations where resources and actions need to be directed to optimize conservation gains 13 , 14 . Earlier iterations of these tools largely involved identifying locations to demarcate Protected Areas (PAs), determine optimal reserve design or propose conservation corridors 15 . However, human interests such as economic aspirations, infrastructure development and agricultural expansion to ensure food security must be integrated with conservation goals to enhance traction with policymakers 16 . This necessitates the adoption of ‘landscape approaches’ to conservation 17 so as to offer pragmatic solutions in the Anthropocene. Such considerations led to the idea of ‘zoning’ landscapes, whereby assessors not only identify priority locations, but also stratify them into various zones and determine appropriate management interventions 18 , 19 . This combination of prioritization and zoning therefore holds promise to guide a better management of landscapes in an increasingly human-modified world.

Since its conception, the idea of prioritizing areas for conservation at the global level has seen wide application in scientific studies. While these investigations can offer important insights at the macroscale, their real-world applications have been limited 20 . One reason for this is perhaps the lack of alignment between scientific ‘boundaries’ (sampling units) and administrative jurisdictions; yet, it is within these jurisdictions that implementation typically occurs 4 . Aligning ecological findings with administrative boundaries, or existing policy, may thus increase the utility of prioritization exercises. A second reason may be that making assessments at global scales potentially compromises the spatial resolution of analyses, hampered by the lack of availability and comparability of data at scale, or miss out on local socio-political nuances 21 , 22 . Finally, macro-scale analyses could entail intrinsic biases in the representation of key features or attributes. For example, certain historically overlooked biomes may continue to remain ignored 23 , habitats presumed to be ‘unproductive’ may not be prioritized (for example, grasslands 24 ), indices such as species richness, which ignore community composition, may discount rarity or endemism 25 , PAs or ‘intact’ wilderness areas may take precedence over heterogeneous multi-use conservation landscapes 26 , 27 and important dimensions, such as human populations, may be excluded altogether 28 .

Achieving conservation goals, sustaining ecosystem services and ensuring human well-being while balancing economic development present formidable challenges in implementation 3 . India exemplifies this premise for the following reasons: (1) it is a large, diverse country with ten distinct biogeographic zones and four biodiversity hotspots, (2) its PA network, conservation landscapes and riverscapes support several ecologically important and evolutionarily distinct species assemblages, (3) it has the second largest human population in the world, with a large proportion of the people directly dependent on resources drawn from natural ecosystems and (4) with rapid infrastructure development and liberal investment policies, India is currently among the fastest-growing economies in Asia. In this study we (1) identified spatial scale(s), resolutions and thematic dimensions for a priority-setting exercise across the country (Fig. 1 ), (2) used a systematic spatial prioritization approach to optimize landscapes for habitat protection, biodiversity conservation and ecosystem service gains while penalizing locations facing negative anthropogenic impacts, and (3) aligned our results with global biodiversity targets (Post-2020 Global Biodiversity Framework 29 , COP15) and the Indian government’s development initiative to identify ‘aspirational’ economic districts. We provide insights that can guide future environmental planning and policy to help meet the twin goals of biodiversity conservation and sustainable development in a strategically important country for Asia. More broadly, our framework can be adapted and applied to conservation planning in ecoregions of other developing countries.

Themes and input layers identified for spatial prioritization: (1) Habitats (28 vegetation classes depicted as five composite panels), (2) Ecosystem services (blue water flux, above- and below-ground carbon and green water flux), (3) Biodiversity (PAs, key biodiversity areas and beta-diversity index of threatened mammals, birds, reptiles and amphibians) and (4) Threats (human population density, livestock population density, urbanization, linear infrastructure, mines, river fragmentation, agricultural expansion, vegetation greening, vegetation browning, future climate warming and future rainfall anomaly).

Overlap in priority sites across themes

Priority maps generated independently for the three themes of Habitats, Ecosystem Services and Biodiversity (Fig. 2a ) represented sites of high value in isolation, that is, sites that are (1) representative of important and rare natural habitats, (2) responsible for the provision of key ecosystem services, namely, water and carbon, and (3) important solely from the perspective of threatened species diversity and turnover. We found moderate overlap between these three layers, which varied across biogeographic zones (hereafter, ‘zones’; Fig. 2 ). The overlap of priority sites for biodiversity (defined as the top 30% rankings) and habitats was 37% across the country, ranging from 30% in deserts to 48% in the Western Ghats (Supplementary Fig. 1 ). There was a substantial increase in overlap sites when ecosystem services were included as a criterion in addition to biodiversity and habitats. Around 38% of priority sites for ecosystem services were not covered by either biodiversity or habitat priority sites; these locations collectively contain a population of 2.3 million people (based on spatial overlap alone) who are directly dependent on these watersheds (Supplementary Fig. 1 ).

a , Priority maps based on Habitats (top left), Ecosystem Services (centre left), Biodiversity (bottom left) and the composite analysis of the three themes (right). b , Theme-specific results from the prioritization analysis of Threats.

Conservationists often highlight the mismatches in priority sites when different criteria are used to assign a ‘conservation value’ 30 , 31 . Our findings support this concern (Fig. 2 ) and point to the value of incorporating multiple contributors to conservation value. Of significance, our multicriterion approach selected for India’s Open Natural Ecosystems 32 (prioritized on the basis of habitats, but not biodiversity), encompassing open grasslands, savannas, hot and cold deserts, ravines, rocky boulders and escarpments. These systems are extremely fragile, with unique endemic flora and fauna, but are inappropriately classified as ‘wastelands’ as per India’s land-use and conservation policy 33 . Similarly, areas ranked high for habitat and biodiversity, but low in terms of ecosystem services represent some locations in the arid/semi-arid dry zones of western India, the cold deserts of the Trans-Himalayas and parts of the Terai grasslands along the India/Nepal border (Fig. 2a ). Conversely, areas ranked high for ecosystem services, critical for water and carbon storage, did not rank high for biodiversity or habitats in some places (for example, parts of Northeast India). This, perhaps, is due to the low coverage of traditional PAs or insufficient data on biodiversity in the region.

Appraisal of anthropogenic pressures

Reconciling biodiversity conservation concerns with the demands of economic development is one of the greatest challenges of the twenty-first century. We explicitly incorporated Threats (Fig. 2b ) as a factor that makes conservation more costly and also such that the final priority ranks represent such a reconciliation (Fig. 3 ). Across zones, we observed as a consistent pattern the compounding effects of agricultural expansion and urbanization, coupled with vegetation greening (indicative of increased year-round irrigation in agriculture areas) and linear infrastructure (Fig. 2b ). Thus, urban hotspots, representing major cities and the agricultural belts of (1) the northern semi-arid zone, (2) the lowland plains of Northeast India and (3) the western and southern parts of the Deccan peninsula were ranked the highest in terms of anthropogenic pressures (Fig. 2b and Supplementary Fig. 1 ). Of these, the northern semi-arid zone and the western parts of the Deccan peninsula also had pronounced signatures of vegetation browning and greening, respectively. Vegetation browning could arise from forest degradation, deforestation or forest fires, but it could also be due to natural shifts in woody vegetation to grass-dominated systems or the loss of green foliar biomass due to climate change impacts. Vegetation greening could be due to ecological restoration or natural regeneration of native species, but the more ubiquitous source is CO 2 fertilization and the proliferation of invasive alien species (for example, Prosopis and Lantana 34 , 35 , 36 ).

a , Conservation priority ranks for India, computed zone-wise and combined. b , Top 30% priority areas in each zone demarcated as a set of three 10% blocks. Designated PAs are overlaid to show the extent of overlap and spatial congruence with high priority locations.

Our results pertaining to threats, when viewed in the context of projected human population growth and the demands of meeting future food security concerns, are representative of areas that either have or will soon surpass thresholds beyond which interventions for sustainable land-use practices may not be feasible 37 . However, our analyses could not fully elicit the broader deleterious impacts of certain human activities. These include threats that are (1) rapidly evolving in terms of scale, extent and impacts, for example, hydropower dams and road networks 38 , 39 , (2) peculiar to certain regions of the country (for example, the expansion of oil palm plantations 40 ) or (3) difficult to quantify in terms of their long-term consequences (for example, loss of connectivity 41 ). While we did incorporate projected temperature increases and future rainfall anomalies to account for climate change impacts, zones that are likely to be the most vulnerable to these threats, that is, coastal areas and island systems, were not part of our assessment. Nevertheless, climate change is still projected to impact various ecosystems considered in our analysis in significant ways. For example, predicted increases in the magnitude and frequency of major floods can shape the distribution of biodiversity on the floodplains of Northeast India 42 . Such influences underscore the importance of spatial conservation planning based on prioritization efforts of the kind we undertook in this study. We also note that climatic influences on terrestrial systems are extremely dynamic and are pivotally linked to future national and global policy changes.

Landscape-level approach to conservation

Recent conservation literature has highlighted the importance of viewing shared habitats as coupled human–natural systems that are encompassed within ‘conservation landscapes’ 41 , 43 . Across zones, we found that most designated PAs, which constitute ~5% of India’s land area and span an average area of ~300 km 2 (ref. 44 ), were included as priority sites. But these were embedded within larger landscapes that also included high priority non-PA locations. Contrary to our expectation that PAs would be over-represented in our results, 85% of the top 30% priority sites were outside PAs (Fig. 3 ). This finding was reinforced by the specific inclusion of ecosystems (such as the Open Natural Ecosystems referred to above) that are not part of India’s PA network and sites critical for the supply of ecosystem services. Interestingly, locations that constituted the top 30% priority ranks in our study were largely connected (Fig. 3 ), even when we did not explicitly impose connectivity parameters via the prioritization model. To examine this further, we generated the ‘clumpiness’ index, which compares priority adjacencies with what would be expected at random 45 . The index ranges from −1 (disaggregated) to +1 (clumped). When examined at the countrywide level, the index value was 0.80; within-zone analyses produced index values ranging from 0.77 to 0.88, indicating the high aggregation of priority sites at both spatial scales. Although our zone-wise analyses allowed for better geographic representation of sites, they still indicated a lack of contiguity across some zone boundaries (Fig. 3a ). These aspects together emphasize that functional connectivity is an important consideration when implementing landscape-scale conservation interventions within our priority sites 46 .

Traditional PAs, which typify a land-sparing approach to conservation, are mostly focused on forested habitats in the country. A substantially large proportion of biodiversity continues to inhabit unprotected, human-use landscapes, warranting a land-sharing approach. Realizing conservation and human well-being goals in the landscapes that encompass the 30% priority areas will therefore necessitate invoking other effective area-based conservation measures (OECMs). The core tenets of these approaches hinge fundamentally on effective and equitable models of governance that are fully cognizant of the complexities of socio-ecological systems, which are only beginning to be recognized in environmental and conservation policy 47 . In India, this may be achieved through the implementation of existing frameworks, for instance, in locations where communities are granted Community Forest Rights, and by declaring areas as Critical Wildlife Habitats under the Forest Rights Act, provisions that, at present, remain extremely underused. Our spatially explicit landscape approach that considers that linked biodiversity–ecosystem service dimensions can only succeed if the beneficiaries of ecological restoration, and those who may be otherwise impacted (typically the marginalized sections of society), are addressed in policies and implementation.

Synergies and trade-offs within and between themes

In the final prioritization assessment, we assigned equal weighting to all input themes (equal but negative weight for Threats). We ascertained the synergies and trade-offs between themes by examining alternative scenarios in which (1) areas with high human impacts were prioritized rather than penalized, that is, Threats-focused assessment, and (2) Habitats, Ecosystem Services and Biodiversity were each iteratively afforded higher weighting than the others (see the Methods section for details). While there was reasonable concordance between the results from our balanced scenario (themes with equal weights) and those from individual theme-focused scenarios, some locations showed stark mismatches indicative of trade-offs (Supplementary Figs. 3–5 ). These trade-offs may reflect regional peculiarities: biodiversity conservation could constrain provisioning services such as non-timber forest products or livestock grazing in certain locations (such as PAs), and an increase in tree cover through habitat (mis)management could alter hydrological services 48 . Such trade-offs could also be spatially asynchronous: upstream water abstraction from rivers and disruption of sediment transport by dams can have deleterious impacts on aquatic biodiversity and ecosystem services downstream. All these cases collectively highlight the problems of prioritizing areas based on single themes or biasing prioritization on one theme over the other(s). Of course, there is also a trade-off in choosing locations that are of high conservation value and relatively secure (avoiding regions of high Threats) or those that are vulnerable (prioritizing Threats). Ideally, the former are suited to interventions involving preservation (for example, reserve design and OECMs), and the latter, to interventions involving mitigation or restoration (for example, mitigation of linear infrastructure impacts).

Besides the differences between the themes described above, we acknowledge that there could be trade-offs even within the thematic dimensions considered here. For instance, we found very low spatial concordance between carbon and blue water flux, and a marginally higher correlation between carbon and green water flux; the spatial mismatch was more pronounced at locations where values of above- and below-ground carbon reached very high levels (Supplementary Fig. 6 ). This relationship is not unique to our study area, with a previous report indicating that carbon sequestration and hydrological services do not necessarily work in synergy 49 . Regulatory services, such as hydrological or water services, can be generated only for a certain size of catchment, depending on factors such as climate, soil, geology and vegetation type. And while there could be synergy between carbon and water services to people at a local or regional scale 50 , transpiration from a large patch of forest can increase rainfall in other regions and benefit agriculture and communities elsewhere 51 . These considerations of synergies and trade-offs are particularly relevant when viewed in the context of the drastic biodiversity declines documented in the recent Living Planet Report 52 . This report reiterates the important links between biodiversity loss, climate change, ecosystem services (water) and food security—aspects addressed in this study through our multicriteria assessment and by allocating equal importance (weights) to the constituent input attributes.

Linking landscape prioritization and administration

Overlaying administrative (district) boundaries, we highlight 338 districts that play a key role in maintaining India’s biodiversity and ecosystem services (Fig. 4a,b ). Of these, 169 are ‘high priority’ districts, where natural habitats, biodiversity and ecosystem services are currently at optimal levels and span a large spatial extent. The next 169 are ‘potential priority’ districts (Fig. 4b ), where the three aspects are currently at suboptimal levels in terms of the extent of coverage. At this point, our aim was also to link our results with India’s aspirational districts, identified by the Indian government for economic development (that is, the National Institute for Transforming India (NITI) Aayog aspirational districts; see Methods for details).

a , Proportions of each district covered by areas identified as the top 30% priority locations. b , Districts in the top quantile (25%) identified as ‘high priority’ districts (169) and the next 25% quantile identified as 169 ‘potential priority’ districts (169). c , NITI Aayog aspirational districts (112) earmarked by the Government of India and their overlap with the 338 priority districts identified in this study (72 districts overlap). The darker lines in b and c denote state boundaries.

Considered in conjunction with our results, we recommend that for locations where the NITI aspirational districts overlap with ‘high priority’ districts (Fig. 4c ), the management focus needs to be on the retention of habitats, ecosystem services and biodiversity through both state-driven and participatory approaches. This will require deprioritizing mega-infrastructure projects while promoting equitable models of nature protection in addition to the demarcation of PAs. Such approaches may entail community stewardship for biodiversity protection, co-management of habitats outside PAs and nature-friendly livelihood development within larger conservation landscapes. In locations where aspirational districts overlap with ‘potential priority’ districts, the management focus, in addition to the retention of important sites, should also aim for proactive rewilding and ecological restoration efforts. Here, targeted actions are essential to mitigate the negative impacts of infrastructure development through economically incentivized instruments, such as paying for ecosystem services, promoting agroforestry and, where appropriate, demarcating conservation and/or community reserves (PA categories that are not exclusionary but mandate sustainable use of resources by local communities). These localized efforts and interventions need to be synergistically integrated into district- and state-level plans to ensure tangible impacts. For further deliberation, we provide maps showing the spatial overlaps between NITI aspirational districts and (1) the top 30% priority areas and (2) threat ranks across the country in Supplementary Fig. 7 .

Practical considerations for on-ground implementation

This study represents a country-level assessment that attempts to link eco-socio-administrative dimensions for landscape-level prioritization in India. We submit, however, that the spatial scale and resolution at which we carried out this assessment were constrained by several limitations associated with data availability that are commonplace across developing countries as well as for global-level datasets alike. Our metric of species diversity, for instance, relied on range maps derived from the International Union for Conservation of Nature (IUCN) that do not have uniform accuracy across species 53 . In fact, range maps for many rare, endemic and data-deficient species in India were either unavailable or excluded from our assessment because they were completely inaccurate. We also used only a subset of ecosystem services deemed important for human well-being; services such as pollination, forest produce extraction, rangeland services or freshwater dependence could not be included due to the unavailability of spatially explicit data. Incorporating these features (when such information becomes available) may yield a more accurate and comprehensive assessment of priority locations and landscapes.

The relationship between ecosystem services and poverty alleviation (especially in the aspirational and poorer districts of India, as defined by the NITI Aayog discussed above) is also driven by the political economy of negotiations between stakeholders and those who manage or regulate ecosystem services 54 . A detailed understanding of the associated vulnerabilities is important to enable ecosystem services to benefit the poor. If not internalized, an ecosystem services approach to conservation planning may fail because of resistance from those who are excluded or those who stand to lose most from such undertakings. The actions discussed above will also be critical in other countries of the Global South, where a large proportion of the people are directly dependent on forests and other natural ecosystems for their livelihoods. The nuances and location-based differences in prescriptive actions that we have discussed here reiterate the importance of (1) spatially explicit prioritization assessments incorporating regional expertise on biodiversity and threats, and (2) mainstreaming biodiversity and ecosystem service goals into district and state management plans for socio-economic development. In this context, India’s ambitious endeavours on climate change mitigation through the reversal of land degradation (26 million ha), the Green India Mission 55 and renewable energy projects initiated under the intended Nationally Determined Contribution (NDC) will require a degree of reorientation to include biodiversity and ecosystem service considerations.

The recognition of links between ecosystem integrity and human health has led to broad and ambitious goals under global targets such as the Post-2020 Global Biodiversity Framework. However, repercussions of the COVID-19 pandemic have underscored the wide geographical imbalance in the capacities, resources and vulnerabilities of different countries to achieve such goals. The Global South, in particular, aims to conserve biodiversity and meet climate change goals under scenarios in which people have high dependency on natural resources 56 , coupled with aspirations of economic progress and better standards of human life. To address these expectations, national-scale prioritization exercises such as ours, which combine conservation priorities, human well-being indices, economic development and infrastructure considerations, can guide countries and governments towards meeting international targets set for the next decade (for example, see ref. 57 ); linkages between prioritization exercises and existing government schemes and administrative boundaries are crucial in this regard 53 , 58 . In the context of elevated competition for scarce land and water, exacerbated by climate change, the importance of our framework lies in its ability to objectively and effectively address trade-offs at the intersection of sustainable development goals, conservation of biodiversity and ecosystem services.

Framework for prioritization

The main goal of our study was to prioritize representative landscapes for biodiversity conservation, securing ecosystem services and human well-being targets, while balancing these with the economic aspirations of 1.4 billion people. Our prioritization exercise is part of a larger programme aimed at mainstreaming biodiversity conservation into the discourse on development and human well-being—the Government of India’s National Mission for Biodiversity and Human Well-Being (NMBHWB) 59 . Recognizing the importance of delineating, characterizing and evaluating functional landscapes, the NMBHWB set up a working group in 2020 that was tasked with deliberating on and determining landscape-level approaches to biodiversity conservation, keeping nature’s benefits to human well-being as the central theme. Through consultations with 42 experts from across the country, the working group first outlined the need for, salient features of and prerequisites for a priority-setting exercise. Subsequently, the working group engaged with 18 field and domain experts (the authors of the present study) to undertake the prioritization exercise in India.

Prioritization approach

We used a two-step prioritization approach (Fig. 1 ) to ensure representation of geographies and ecoregions across India. We first chose biogeographic zones (‘zones’) as described by Rodgers and Panwar 60 as an appropriate level of primary spatial classification. This level of classification splits the country into zones that have some similarity in biogeography, while across zones, they capture diversity of species, ecosystems and human–nature relationships. The country has ten zones: (1) Trans-Himalayas, (2) Himalayas, (3) deserts, (4) semi-arid, (5) Western Ghats, (6) Deccan plateau, (7) Gangetic plains, (8) Northeast India, (9) coasts and (10) islands. We excluded coasts and islands from our analyses as we deemed them to be unique and requiring a separate treatment of biodiversity, ecosystem services and threats. The eight selected zones are shown in Fig. 1 (additional descriptions are provided in Supplementary Table 1 ). We then identified three broad focus themes, each of which encompassed a set of criteria for prioritization.

We considered natural habitats that require inclusion in priority landscapes. These layers, with zone-specific sets of priority ecosystems, represented our Habitats theme.

Recognizing the increasing emphasis on human–nature links and the joint well-being of biodiversity and people, we considered Ecosystem Services as the second theme.

Species diversity, biodiversity hotspots and locations of species population sources collectively formed the Biodiversity theme.

Lastly, we included a Threats theme to recalibrate conservation priorities based on spatial variation of anthropogenic pressures and impacts on natural ecosystems and biodiverse areas that provision essential ecosystem services. The full list of input layers is presented in Fig. 1 and detailed in Supplementary Table 2 .

Spatial prioritization

There are several approaches to conducting spatial prioritization analyses, depending on the objectives of the study, type(s) of spatial data available and the optimization functions of interest, along with the corresponding software programs, of which MARXAN and Zonation are the most widely used 18 , 61 , 62 , 63 , 64 . Zonation takes information on multiple input features to produce conservation ranks across the entire area of interest. Depending on the metrics used and the analytical specifications, it is possible to use either program to yield comparable results 65 . We found Zonation to be well-suited for our analysis because it allowed us to seamlessly combine feature data (0/1) with quantitative data on ecosystem services across large regions at a relatively fine spatial resolution. The program is also robust to differences in scale (range of values) across different input layers. The program’s algorithm follows an iterative process to rank cells (in our case, 1 km 2 pixels) within the area of interest by excluding cells one at a time and measuring the collective conservation value of retained cells. In other words, cells with the lowest values are removed first and those with the highest values are retained until the end. The final output thus generated is an optimized map whose pixels are ranked according to relative priority values. For additional details, see Moilanen et al. 62 . For each zone, we conducted a set of six prioritization analyses (four theme-wise runs and two composite runs) as explained below.

We used data from Roy et al. 66 with 156 land-cover classes across the country. We first reduced the data into 28 habitat classes by grouping together similar land-cover classes, broadly following the forest-type classification of Champion and Seth 67 . For instance, the land-cover types labelled ‘Grassland’, ‘Human-made grassland’, ‘ Lasiurus–Panicum grassland’, ‘ Cenchrus–Dactyloctenium grassland’, ‘ Aristidia–Oropetium grassland’ and ‘ Sehima – Dichanthium grassland’ were reclassified collectively as ‘Grasslands’. Data from 25 m 2 pixels of the original dataset were resampled at a 1 km 2 resolution; the value assigned to each 1 km 2 pixel for each of the 28 habitat classes reflected the proportion of 25 m 2 pixels of the corresponding class. For each zone, we further subselected habitat classes to retain (1) the dominant vegetation classes (covering > 1% of the total zone area) and (2) select, rare, vulnerable habitats, determined on the basis of our experience and knowledge of the landscape(s). For example, we included wet grasslands in the Northeast India zone due to their importance for ecology and livelihood, even though this habitat class covers only 0.25% of the zone. The final number of habitat types chosen for each zone thus varied between three (deserts zone) and ten (Western Ghats zone; Supplementary Table 2 ). All habitats were assigned equal weights for prioritization. We chose the ‘core-area zonation’ cell removal rule as it prioritizes pixels with the most important yet rare features; this way, pixels that contained high proportions of rare habitats were retained even if the other zone-wide dominant habitats within these pixels were low.

Ecosystem services

We identified and used three layers that collectively reflect key ecosystem services, for which we were able to obtain or generate spatially explicit metrics across the country. These were (1) blue water flux, the amount of flowing water or ground water available to help meet utilitarian needs and for ecological flows in rivers, (2) green water flux, the amount of water lost through surface evapo-transpiration, which contributes to multiple ecosystem services, from flood control in very wet regions to recycling as rainfall in other regions, and (3) carbon stock, a harmonized metric of above- and below-ground biomass carbon density calculated using woody plant, grassland and cropland biomass (Supplementary Table 2 ). While these do not represent a comprehensive set of ecosystem services, we believe that they capture two critical policy mandates for India: water security and climate change mitigation. We assigned equal weights to all three layers and chose the ‘additive benefit function’ (ABF) cell removal rule in Zonation to ensure that locations with relatively high values of all three features are prioritized higher than areas where individual features or subsets of the three features had high values.

India supports an extremely high diversity of wildlife (inside and outside designated PAs); most of these species are found in higher densities here than elsewhere across their range. In considering attributes that best represent biodiversity, our goal was twofold. First, we wanted to include areas that support high populations of species and, second, those areas that support and adequately represent the wide diversity of species outside PAs. We therefore used PAs, reasonably assuming that these locations currently harbour source populations of many species. In addition, we also used Key Biodiversity Areas (KBAs) 68 to include locations outside designated PAs with a high diversity of species that are of ecological or conservation importance (that is, PAs and KBAs did not spatially overlap in our analysis and were 0/1 data). Next, we collated distribution ranges of imperilled mammals, birds, amphibians and reptiles—species categorized as Critically Endangered, Endangered, Vulnerable or Threatened as per the IUCN Red List (IUCN 2020). We limited our criteria to only include species for which the range distribution data were reliable; for a subset of species, we also corrected and modified the IUCN maps (based on expert consultation) before inclusion in our analyses. For each zone, we stacked the species maps and generated a beta-diversity index, calculated as the Bray–Curtis distance between a focal cell and a hypothetical reference cell that had all threatened species of the corresponding zone. This gave us an index of zone-wise species turnover. We assigned equal weights to all three layers (treating PAs and KBAs with high population densities with the same importance as the layer characterizing species turnover) and chose the ABF cell removal rule in Zonation to prioritize areas based on this theme.

We identified 11 attributes that negatively impact biodiversity, ecosystem services and habitats to varying degrees and that are likely to reduce the ease of implementing conservation actions. These were human population density, livestock population density, urbanization, linear infrastructure, mines, river fragmentation, agricultural expansion, vegetation greening and vegetation browning (both of which can have positive or negative impacts on ecosystem services and biodiversity), future climate warming and future rainfall anomaly (see Supplementary Table 2 for details). We assigned zone-specific weights to these layers following a consensus approach based on the collective field knowledge of the assessors. The weights assigned to the threats varied across zones, reflecting the relative severity of impacts in each zone (see Supplementary Table 3 for zone-wise threat ranks). We chose the ABF cell removal rule again so that locations under more severe threat and those under multiple threats were ranked high. This ranking thus combines information on the presence and intensity of threats across space.

In addition to the four sets of theme-wise analyses described above, we also undertook two composite analyses. First, we used the outputs from the theme-wise analyses of Habitats, Ecosystem Services and Biodiversity. All input layers were assigned equal weights and the ABF cell removal rule was specified. Second, we used the aforementioned three layers along with the rank output from the Threats analysis. In this case, we assigned equal positive weights to Habitats, Ecosystem Services and Biodiversity and an equal but negative weight to Threats (that is, 1, 1, 1 and −1; see ref. 69 for the justification of using equal weights for input attributes). The first analysis thus gave us an assessment of conservation importance, while the second incorporated feasibility. To assess the sensitivity of our weighting scheme, we performed similar composite analyses to consider alternate scenarios in which (1) the Threats layer was assigned a weight of +1, such that areas with high human impacts are also prioritized rather than penalized, and (2) the Threats layer was weighted −1, but the Habitats, Ecosystem Services and Biodiversity were each iteratively assigned higher weights than the other two, that is, 1, 0.5 and 0.5. These outputs are presented in Supplementary Figs. 2–5 .

Alignment with global and national policy

We collated results across zones post-prioritization to demarcate the top 30% priority areas in the country so as to align our results with the Post-2020 Global Biodiversity Framework targets. The areas were selected on the basis of the pixel ranks in each zone; these covered 30% of the area in every zone and therefore collectively included 30% of the country (excluding the coasts and islands). This approach ensured representation of unique features from each zone, encompassing a more diverse set of habitats, ecosystem services, biodiversity and associated threats. We then overlaid administrative units (districts) and calculated the proportion of each district covered by pixels with the top 30% ranks. We chose two sets of districts: (1) those in the top 25% quantile, which we term ‘high priority’ districts, and (2) those in the subsequent 25% quantile, which we term ‘potential priority’ districts. We juxtaposed these results with districts earmarked by the Government of India’s flagship Aspirational Districts Programme for economic development. India’s Aspirational Districts Programme was launched in 2018 by the Government of India through the think tank, the NITI Aayog. The NITI Aayog replaced the erstwhile Planning Commission with the aim of achieving sustainable development goals for the country. The programme has earmarked over 100 of India’s most economically backward districts as ‘aspirational’ districts to help reduce regional imbalances in development. The programme aims to reduce disparity across regions and improve baseline rankings of district-level development using real-time data on 49 indicators across five thematic sectors, namely health and nutrition, education, infrastructure, financial inclusion and skill development. Of particular relevance to our assessment is the programme’s thrust towards increasing road connectivity and intensification of agriculture 70 (additional details are available at www.niti.gov.in/aspirational-districts-programme ). Our maps were generated with the goal of directly offering prescriptive management actions for the governments of the corresponding administrative units.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All the analyses were based on open source datasets; details are provided in Supplementary Table 2 . The input data used in the analyses can be accessed from https://doi.org/10.6084/m9.figshare.21678518 .

Change history

20 february 2023.

A Correction to this paper has been published: https://doi.org/10.1038/s41893-023-01091-y

Levett, R. Sustainability indicators—integrating quality of life and environmental protection. J. R. Stat. Soc. A 161 , 291–302 (1998).

Article   Google Scholar  

Harrison, P. A. Ecosystem services and biodiversity conservation: an introduction to the RUBICODE project. Biodivers. Conserv. 19 , 2767–2772 (2010).

Otero, I. et al. Biodiversity policy beyond economic growth. Conserv. Lett. 13 , e12713 (2020).

Seppelt, R., Lautenbach, S. & Volk, M. Identifying trade-offs between ecosystem services, land use, and biodiversity: a plea for combining scenario analysis and optimization on different spatial scales. Curr. Opin. Environ. Sustain. 5 , 458–463 (2013).

Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019); accessed from https://ipbes.net/document-library-categories

Dinerstein, E. et al. A “Global Safety Net” to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6 , eabb2824 (2020).

Ostrom, E. A general framework for analyzing sustainability of social-ecological systems. Science 325 , 419–422 (2009).

Article   CAS   Google Scholar  

Bennett, E. M. et al. Linking biodiversity, ecosystem services, and human well-being: three challenges for designing research for sustainability. Curr. Opin. Environ. Sustain. 14 , 76–85 (2015).

Haines-Young, R & Potschin, M. in Ecosystem Ecology: A New Synthesis (eds Raffaelli, D. & Frid, C.) 110–139 (Cambridge Univ. Press, 2010).

Google Scholar  

Tallis, H. M. & Kareiva, P. Shaping global environmental decisions using socio-ecological models. Trends Ecol. Evol. 21 , 562–568 (2006).

Steffen, W. et al. Trajectories of the Earth System in the Anthropocene. Proc. Natl Acad. Sci. USA 115 , 8252–8259 (2018).

Wilson, K. A. et al. Conserving biodiversity efficiently: what to do, where, and when. PLoS Biol. 5 , e223 (2007).

Moilanen, A. et al. Prioritizing multiple-use landscapes for conservation: methods for large multi-species planning problems. Proc. R. Soc. B 272 , 1885–1891 (2005).

Moilanen, A. et al. Balancing alternative land uses in conservation prioritization. Ecol. Appl. 21 , 1419–1426 (2011).

Kremen, C. et al. Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools. Science 320 , 222–226 (2008).

Pressey, R. L., Cabeza, M., Watts, M. E., Cowling, R. M. & Wilson, K. A. Conservation planning in a changing world. Trends Ecol. Evol. 22 , 583–592 (2007).

Sayer, J. et al. Ten principles for a landscape approach to reconciling agriculture, conservation, and other competing land uses. Proc. Natl Acad. Sci. USA 110 , 8349–8356 (2013).

Watts, M. E. et al. Marxan with Zones: software for optimal conservation based land- and sea-use zoning. Environ. Model. Softw. 24 , 1513–1521 (2009).

Arkema, K. K. et al. Embedding ecosystem services in coastal planning leads to better outcomes for people and nature. Proc. Natl Acad. Sci. USA` 112 , 7390–7395 (2015).

Wyborn, C. & Evans, M. C. Conservation needs to break free from global priority mapping. Nat. Ecol. Evol. 5 , 1322–1324 (2021).

Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110 , e2602–e2610 (2013).

Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114 , 7641–7646 (2017).

Silveira, F. A. et al. Biome Awareness Disparity is BAD for tropical ecosystem conservation and restoration. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.14060 (2021).

Bond, W. J. & Parr, C. L. Beyond the forest edge: ecology, diversity and conservation of the grassy biomes. Biol. Conserv. 143 , 2395–2404 (2010).

Veach, V., Di Minin, E., Pouzols, F. M. & Moilanen, A. Species richness as criterion for global conservation area placement leads to large losses in coverage of biodiversity. Divers. Distrib. 23 , 715–726 (2017).

Venter, O. et al. Targeting global protected area expansion for imperiled biodiversity. PLoS Biol. 12 , e1001891 (2014).

Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. 3 , e1600821 (2017).

Jung, M. et al. Areas of global importance for conserving terrestrial biodiversity, carbon and water. Nat. Ecol. Evol. 5 , 1499–1509 (2021).

First Draft of the Post-2020 Global Biodiversity Framework (CBD, 2021); accessed from www.cbd.int/conferences/post2020

Westgate, M. J., Barton, P. S., Lane, P. W. & Lindenmayer, D. B. Global meta-analysis reveals low consistency of biodiversity congruence relationships. Nat. Commun. 5 , 3899 (2014).

Cadotte, M. W. & Tucker, C. M. Difficult decisions: strategies for conservation prioritization when taxonomic, phylogenetic and functional diversity are not spatially congruent. Biol. Conserv. 225 , 128–133 (2018).

Madhusudhan, M. D. & Vanak, A. T. (2022). Mapping the distribution and extent of India’s semi-arid open natural ecosystems. Journal of Biogeography 00 , 1–11; https://doi.org/10.1111/jbi.14471

Wastelands Atlas of India 2019 (Department of Land Resources, Ministry of Rural Development and the National Remote Sensing Centre, Indian Space Research Organisation, Department of Space, Government of India, 2019); www.dolr.gov.in/documents/wasteland-atlas-of-india

Krishnaswamy, J., John, R. & Joseph, S. Consistent response of vegetation dynamics to recent climate change in tropical mountain regions. Glob. Change Biol. 20 , 203–215 (2014).

Parida, B. R., Pandey, A. C. & Patel, N. R. Greening and browning trends of vegetation in India and their responses to climatic and non-climatic drivers. Climate 8 , 92 (2020).

Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1 , 14–27 (2020).

Martin, D. A. et al. Land-use trajectories for sustainable land system transformations: identifying leverage points in a global biodiversity hotspot. Proc. Natl Acad. Sci. USA 119 , e2107747119 (2022).

Pandit, M. K. & Grumbine, R. E. Potential effects of ongoing and proposed hydropower development on terrestrial biological diversity in the Indian Himalaya. Conserv. Biol. 26 , 1061–1071 (2012).

Nayak, R. et al. Bits and pieces: forest fragmentation by linear intrusions in India. Land Use Policy 99 , 104619 (2020).

Srinivasan, U. et al. Oil palm cultivation can be expanded while sparing biodiversity in India. Nat. Food 2 , 442–447 (2021).

Vasudev, D., Goswami, V. R., Srinivas, N., Syiem, B. L. N. & Sarma, A. Identifying important connectivity areas for the wide‐ranging Asian elephant across conservation landscapes of Northeast India. Divers. Distrib. 27 , 2510–2526 (2021).

Goswami, V. R., Vasudev, D., Joshi, B., Hait, P. & Sharma, P. Coupled effects of climatic forcing and the human footprint on wildlife movement and space use in a dynamic floodplain landscape. Sci. Total Environ. 758 , 144000 (2021).

Rodrigues, R. G., Srivathsa, A. & Vasudev, D. Dog in the matrix: envisioning countrywide connectivity conservation for an endangered carnivore. J. Appl. Ecol. 59 , 223–237 (2022).

Ghosh-Harihar, M. et al. Protected areas and biodiversity conservation in India. Biol. Conserv. 237 , 114–124 (2019).

Hesselbarth, M. H., Sciaini, M., With, K. A., Wiegand, K. & Nowosad, J. landscapemetrics: an open‐source R tool to calculate landscape metrics. Ecography 42 , 1648–1657 (2019).

Brennan, A. et al. Functional connectivity of the world’s protected areas. Science 376 , 1101–1104 (2022).

Alves-Pinto, H. et al. Opportunities and challenges of other effective area-based conservation measures (OECMs) for biodiversity conservation. Perspect. Ecol. Conserv. 19 , 115–120 (2021).

Joshi, A. A., Sankaran, M. & Ratnam, J. ‘Foresting’ the grassland: historical management legacies in forest-grassland mosaics in southern India, and lessons for the conservation of tropical grassy biomes. Biol. Conserv. 224 , 144–152 (2018).

Chisholm, R. A. Trade-offs between ecosystem services: water and carbon in a biodiversity hotspot. Ecol. Econ. 69 , 1973–1987 (2010).

Clark, B., DeFries, R. & Krishnaswamy, J. India’s commitments to increase tree and forest cover: consequences for water supply and agriculture production within the Central Indian Highlands. Water 13 , 959 (2021).

Paul, S., Ghosh, S., Rajendran, K. & Murtugudde, R. Moisture supply from the Western Ghats forests to water deficit east coast of India. Geophys. Res. Lett. 45 , 4337–4344 (2018).

Almond, R. E. A, Grooten, M., Juffe Bignoli, D. & Petersen, T. (eds) Living Planet Report 2022—Building a Nature-Positive Society (WWF, 2022).

Srivathsa, A. et al. Opportunities for prioritizing and expanding conservation enterprise in India using a guild of carnivores as flagships. Environ. Res. Lett. 15 , 064009 (2020).

Vira, B. et al., Negotiating trade-offs: choices about ecosystem services for poverty alleviation. Econ. Polit. Wkly 67–75 (2012).

Ravindranath, N. H. & Murthy, I. K. Greening India mission. Curr. Sci. 99 , 444–449 (2010).

Fedele, G., Donatti, C. I., Bornacelly, I. & Hole, D. G. Nature-dependent people: mapping human direct use of nature for basic needs across the tropics. Glob. Environ. Change 71 , 102368 (2021).

Strassburg, B. B. et al. Global priority areas for ecosystem restoration. Nature 586 , 724–729 (2020).

Belote, R. T. et al. Beyond priority pixels: delineating and evaluating landscapes for conservation in the contiguous United States. Landsc. Urban Plan. 209 , 104059 (2021).

Bawa, K. S. et al. Securing biodiversity, securing our future: a national mission on biodiversity and human well-being for India. Biol. Conserv. 253 , 108867 (2021).

Rodgers, W. A. & Panwar, H. S. Planning a Wildlife Protected Area Network in India. Vol. 1. A Report (Wildlife Institute of India, 1988).

Watts, M., Klein, C. J., Tulloch, V. J., Carvalho, S. B. & Possingham, H. P. Software for prioritizing conservation actions based on probabilistic information. Conserv. Biol. 35 , 1299–1308 (2021).

Moilanen, A. et al. Zonation: spatial conservation planning methods and software. Version 4. User Manual. C-BIG ; https://core.ac.uk/download/pdf/33733621.pdf (2014).

Sierra-Altamiranda, A. et al. Spatial conservation planning under uncertainty using modern portfolio theory and Nash bargaining solution. Ecol. Model. 423 , 109016 (2020).

Silvestro, D., Goria, S., Sterner, T. & Antonelli, A. Improving biodiversity protection through artificial intelligence. Nat. Sustain. 5 , 415–424 (2022).

Delavenne, J. et al. Systematic conservation planning in the eastern English Channel: comparing the Marxan and Zonation decision-support tools. ICES J. Mar. Sci. 69 , 75–83 (2012).

Roy, P. S. et al. Development of decadal (1985–1995–2005) land use and land cover database for India. Remote Sens. 7 , 2401–2430 (2015).

Champion, H. G. & Seth, S. K. A Revised Survey of the Forest Types of India (Government of India, 1968).

BirdLife International World Database of Key Biodiversity Areas (KBA Partnership, version March 2021); accessed from www.keybiodiversityareas.org/kba-data/request

Koschke, L., Fürst, C., Frank, S. & Makeschin, F. A multi-criteria approach for an integrated land-cover-based assessment of ecosystem services provision to support landscape planning. Ecol. Indic. 21 , 54–66 (2012).

Sarkar, T., Mishra, M. & Singh, R. B. in Regional Development Planning and Practice (eds Mishra, M. et al.) 205–232 (Springer, 2022).

Download references

Acknowledgements

This work was catalysed and supported by the Office of the Principal Scientific Adviser to the Government of India as part of the National Mission for Biodiversity and Human Well-Being. A.S. was supported by the Department of Science and Technology–Government of India’s Innovation in Science Pursuit for Inspired Research Faculty Award. M.S. was supported by the Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India (SERB DIA/2018/000038). We are grateful to J. Zacharias, V. Athreya, P. Bindra, A. Harihar, U. Ganguly, A. Kumar, M. Manuel, S. Babu, A. Kshettry, S. Nijhawan, M. Muralidharan, N. Namboothiri, Y. Jhala, K. S. Subin, S. Datta, M. Sen, S. Madhulkar, T. Thekaekara, V. Vasudevan, I. Anwardeen, S. Sahu, S. Reddy, V. Varadhan, K. T. Subramaniam, C. Madegowda, C. Meenakshi, K. Karanth, P. G. Krishnan, T. Dash, A. Chanchani and A. Bijoor for partaking in discussions on landscape-scale conservation in India. We thank K. Bawa and R. Chellam for their guidance, R. G. Rodrigues, A. Samrat and N. Srinivas for assistance with data processing and compilation, and the National Centre for Biological Sciences–TIFR and Ashoka Trust for Research in Ecology and the Environment (part of the Biodiversity Collaborative) for facilitating this study. The authors received no specific funding for this work.

Author information

These authors contributed equally: Arjun Srivathsa, Divya Vasudev, Tanaya Nair, Jagdish Krishnaswamy, Uma Ramakrishnan.

Authors and Affiliations

National Centre for Biological Science, TIFR, Bengaluru, India

Arjun Srivathsa, Tanaya Nair, Mahesh Sankaran & Uma Ramakrishnan

Wildlife Conservation Society-India, Bengaluru, India

Arjun Srivathsa

Conservation Initiatives, Guwahati, India

Divya Vasudev & Varun R. Goswami

Division of Biosciences, University College London, London, UK

Tanaya Nair

Departments of Biology and Environmental Studies, Macalester College, Saint Paul, MN, USA

Stotra Chakrabarti

World Wildlife Fund, Delhi, India

Pranav Chanchani, Arpit Deomurari, Dipankar Ghose & Prachi Thatte

Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA

Ruth DeFries

Amity Institute of Forestry and Wildlife, Amity University, Noida, India

Arpit Deomurari

Wildlife Institute of India, Dehradun, India

Sutirtha Dutta

Foundation for Ecological Research, Advocacy and Learning, Bengaluru, India

Rajat Nayak & Srinivas Vaidyanathan

Network for Conserving Central India, Gurgaon, India

Amrita Neelakantan

World Resources Institute, New Delhi, India

Madhu Verma

School of Environment and Sustainability, Indian Institute for Human Settlements, Bengaluru, India

Jagdish Krishnaswamy

Ashoka Trust for Research in Ecology and the Environment, Bengaluru, India

Biodiversity Collaborative, Bengaluru, India

Jagdish Krishnaswamy, Mahesh Sankaran & Uma Ramakrishnan

You can also search for this author in PubMed   Google Scholar

Contributions

All the authors were involved in the conceptualization of the study. A.S., D.V. and T.N. led the analysis. A.S., D.V., T.N., A.D., R.N. and S.V. processed and analysed the data. A.S., D.V., V.R.G., A.N., J.K. and U.R. prepared the first draft. All authors provided critical feedback and approved the final version of the manuscript.

Corresponding authors

Correspondence to Arjun Srivathsa or Uma Ramakrishnan .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Sustainability thanks Daniele Silvestro, Kayo Murakami and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information.

Supplementary Tables 1–3 and Figs. 1–7.

Reporting Summary

Rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Srivathsa, A., Vasudev, D., Nair, T. et al. Prioritizing India’s landscapes for biodiversity, ecosystem services and human well-being. Nat Sustain 6 , 568–577 (2023). https://doi.org/10.1038/s41893-023-01063-2

Download citation

Received : 25 July 2022

Accepted : 12 January 2023

Published : 06 February 2023

Issue Date : May 2023

DOI : https://doi.org/10.1038/s41893-023-01063-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Integrating patch stability and network connectivity to optimize ecological security pattern.

Landscape Ecology (2024)

India’s biogeochemical capacity to attain food security and remediate climate

• Ishfaq Ahmad Mir
• Thomas J. F. Goreau
• James Jerden

Environmental Geochemistry and Health (2024)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.