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A Comprehensive Guide to the Bronfenbrenner Ecological Model

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

d3sign/Moment/Getty images

• Five Ecological Systems
• Interactions

Bronfenbrenner's ecological model is a framework that can be utilized to understand the complex systems that influence human development . In particular, this model emphasizes the importance of environmental factors and social influences in shaping development and behavior.

The model takes a holistic approach, suggesting that child development involves a dynamic interaction between environment, societal, biological, and psychological factors. In Bronfenbrenner's model, there is a reciprocal interplay between the individual and the various levels of influence that affect development.   

Introduction to the Bronfenbrenner Ecological Model

The theory suggests that a child's development is affected by the different environments that they encounter during their life, including biological, interpersonal, societal, and cultural factors.

What Does Bronfenbrenner's Ecological Model Describe?

This model describes the interactions between individuals and their environments and how these complex relationships affect development over time. According to this model, many interconnected systems make up a person's environment that all interact to influence and shape how people grow and respond.

The factors that influence development include a person's immediate setting and the broader culture in which they live.

The theory stresses the interdependency and interaction between people and their environments. Bronfenbrenner suggested that more nurturing and encouraging environments led to better developmental outcomes.

History and Development of the Model

This model, also known as the ecological systems theory, was introduced by Russian-American psychologist Urie Bronfenbrenner. Bronfenbrenner was born in Russia and immigrated to the United States when he was six. His early experiences shaped his ideas about how children adjust to new environments and how factors such as environment, language, and culture can play a part in how children learn and grow.

Bronfenbrenner earned his PhD in developmental psychology from the University of Michigan in 1942. He began developing his influential theory during the 1970s and presented his ideas in his 1979 book "The Ecology of Human Development: Experiments by Nature and Design." The book elaborated the key aspects of his theory.

Over time, Bronfenbrenner continued refining his ideas. In addition to emphasizing the importance of understanding how humans develop within their environmental contexts, he also stressed that this influence is bidirectional; humans also actively shape their surroundings.

Ecological systems theory has gained widespread acceptance, significantly influencing developmental psychology and related disciplines. The theory has also been applied in many different contexts, including family therapy , education, political policy, and social work .

Bronfenbrenner died in 2005, but his theory continues to profoundly influence our understanding of the dynamic interactions that affect how humans develop and change during childhood and throughout their lives.

Five Ecological Systems in Bronfenbrenner’s Model

Bronfenbrenner's theory is organized into a series of five nested systems or levels. The five main elements of Bronfenbrenner’s theory are the microsystem, mesosystem, exosystem, macrosystem, and chronosystem.

You can visualize the framework by imagining the individual at the center of a circle, surrounded by five concentric rings starting with the first circle (the microsystem) and expanding outward to the outermost circle (the chronosystem).

Microsystem

The microsystem is the innermost level, composed of an individual's immediate environment. It includes the people the person interacts with daily, including their family members, friends, classmates, teachers, and others.

The microsystem has the most direct, immediate impact on the individual.

The relationships and interactions within the microsystem are also bidirectional; people are influenced by their close contacts, but they also affect the people and environments around them. Because of these relationships' close, direct nature, they have a powerful effect on shaping an individual’s development and behavior.

Personal characteristics, including mental abilities, physical attributes, temperament, and personality , also impact a person's development.

A proposed update to Bronfenbrenner's theory suggests two types of microsystems: physical and virtual. Given the importance of digital influences on young people today, it is essential to recognize how virtual environments may influence child development.

The microsystem accounts for the experiences that directly involve and affect the individual and shape their behavior, learning, values, and beliefs.

The mesosystem is the next level of the model, comprised of all the relationships and interactions between the microsystems. Examples of mesosystems in a child’s life include the interactions between their family and school or between their friends and family. 

Like the microsystem, the mesosystem has a direct effect on the individual.

The different microsystems are connected at this level. This means that changes in one microsystem can then impact other microsystems. 

In other words, how these elements interact can influence how a child develops. For example, a child's family and school interaction can impact learning and academic performance. 

The exosystem refers to environments in which the individual is not an active participant but still impacts development. This level encompasses the social context in which a person lives and other aspects of the environment, including government policies, social services, community resources, and mass media.

The individual does not have direct contact with these influences, but they still shape how a child develops.

For example, government policies and community resources impact a child's access to healthcare, quality child care, and education. 

Macrosystem

The macrosystem involves the broader society and cultural forces that contribute to individual development. Important components of this level of Bronfenbrenner's theory include values, social norms, customs, traditions, ideology, and cultural beliefs.

These cultural beliefs are often shared by groups of people with a similar history or identity. Such beliefs can also shift over time. Such beliefs can also vary based on geographic location and socioeconomic status.

Chronosystem

The chronosystem is the outermost level of the model, accounting for the role that time plays in influencing individual development. This includes personal experiences that occur over the course of life, the various life transitions that people experience, historical events, and societal changes.

Challenges and transitions that can affect development, including the birth of siblings, moving to a new place, parental divorce, and the death of family members, can affect the family's dynamic or structure.

The model recognizes that environments are not static; they change over time, and these changes can have a significant effect on how people develop.

Interactions Among the Systems: A Dynamic View

The interactions between different systems in Bronfenbrenner's theory interact in intricate, bidirectional ways. The changes in one level can have a resounding impact on the other levels. 

Examples of Bronfenbrenner's Ecological Model

You can better understand the different levels of Bronfenbrenner’s model by looking at examples of influences at each level:

Each system within the model interacts with other systems in complex ways. A child's family (microsystem), for example, can impact how they interact with others at school (microsystem). The relationship between these microsystems (the mesosystem), can then impact a child's behavior and academic success.

These systems don't just interact with the levels that proceed or follow them. And interactions that occur at one level can have cascading effects on other levels of influence

For example, workplace stress can impact how parents interact with their children at home. And economic changes that occur in a society (chronosystem) can influence the type of resources that are available in communities (exosystem), which can then play a role in the dynamics within individual families (microsystem).

By examining these influences more closely, we can gain a better appreciation of the dynamic interactions and interdependencies between the different levels of Bronfenbrenner's theory.

The Relevance of the Model Today

Bronfenbrenner's theory significantly impacted how researchers, psychologists, and educators view human development.  The ecological model continues to inform our understanding of how children develop and how different aspects of their environment may positively or negatively impact their growth. 

The framework’s holistic approach emphasizes the need to understand all aspects of a person's environment to appreciate the complex, interrelated factors that influence their development.

Some of the ways in which Bronfenbrenner's model has influenced our understanding of human development include:

The theory has been applied extensively within the field of education to help design effective learning environments that emphasize the classroom experience and focus on the influence of families, communities, societies, and the broader culture.

The early childhood education program, Head Start, is an example of an intervention informed by Bronfenbrenner's model. First introduced in 1965, Urie Bronfennbrenner served as a government advisor for the development of the program. The program takes a holistic approach and supports infants, toddlers, and preschoolers to promote school readiness.

Research suggests the program has numerous benefits, including the long-term effects of increased high school completion, college enrollment, and college completion.

Mental Health Care

The ecological model also plays a role in informing mental health care. Mental health treatments that take a holistic approach often lead to better outcomes. And looking at the community, societal, and cultural influences that affect a person's development and well-being can help mental health professionals understand the issues people face.

The framework has also affected approaches to mental health, both in terms of treatment and public policy. For example, it has contributed to the development of the ecological approach to counseling , which focuses on understanding personal and environmental factors when treating mental health issues.

Cultural Sensitivity

Because the model stresses how cultural factors can influence development, it can support greater cultural sensitivity among therapists , educators, and others.

Understanding ecological factors, for example, can produce greater cultural competency among therapists who work with diverse populations.

Bronfenbrenner's ecological model offers a comprehensive framework for understanding the many factors that affect development. In addiction to describing the different levels of influence, the ecological model also describes the dynamic interaction that occurs between the different levels, from the direct relationships at the microsystem level through the broader societal, cultural, and temporal factors that play a role.

Understanding these influences and their complex connections is important. By doing so, parents, educators, social program developers, and policy makers can gain greater insight and create supportive interventions that foster healthy development.

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By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Ecological Model and Dynamic Systems: Understanding Human Development

This essay about Bronfenbrenner’s Ecological Model and Dynamic System Theory elucidates the intricate interplay between environmental systems and individual development. Through Bronfenbrenner’s framework, which encompasses nested ecosystems from the micro to the macrosystem, and Dynamic System Theory’s emphasis on self-organization and feedback loops, a comprehensive understanding of human development emerges. The text illustrates how familial, social, cultural, and temporal factors shape developmental trajectories, highlighting the significance of interconnected influences. By integrating these theories, the essay unveils a holistic perspective on human development, emphasizing the dynamic nature of interactions between individuals and their environment. This synthesis enriches our comprehension of development and underscores the importance of holistic interventions to support positive outcomes.

How it works

Delving into the intricacies of human development unveils a tapestry woven from a myriad of threads, blending individual attributes with the multifaceted layers of the surrounding environment. Among the array of theories illuminating this complex phenomenon, Bronfenbrenner’s Ecological Model and Dynamic System Theory emerge as beacons guiding our understanding towards a holistic comprehension of human development.

Urie Bronfenbrenner, a luminary in developmental psychology, crafted the Ecological Model as a conceptual map delineating the interconnectedness of human experiences within environmental systems.

At its essence, Bronfenbrenner’s theory paints a portrait of nested ecosystems, spanning from the microsystem—the immediate familial and social milieu—to the macrosystem, encompassing cultural and societal influences. These systems, interlaced with the mesosystem and exosystem, encapsulate the myriad contexts shaping human development, each exerting its unique sway on an individual’s growth trajectory.

In the intimate realm of the microsystem, familial dynamics, peer interactions, and educational settings converge to sculpt the landscape of daily experiences. Here, the tender tendrils of influence intertwine, shaping beliefs, attitudes, and interpersonal relationships. For instance, the nurturing cocoon of family bonds and the formative crucible of peer interactions play pivotal roles in molding cognitive schemas and emotional resilience.

Venturing beyond the immediate horizon, the mesosystem unfurls, weaving together the disparate strands of microsystems into a cohesive tapestry of developmental influences. It is within this realm of interconnectedness that the synergy between familial and educational spheres, for instance, catalyzes cognitive development and socialization. Conversely, discordant notes in the mesosystem symphony may herald challenges in navigating the developmental journey.

As the concentric circles expand, the exosystem beckons—a realm where indirect influences cast their shadow upon the developmental landscape. Here, the tendrils of societal structures, economic dynamics, and community resources intertwine, shaping the contours of developmental opportunities and constraints. From the ripple effects of parental employment policies to the reverberations of community resources, the exosystem casts a far-reaching shadow upon individual development.

Eclipsing the micro and exo realms, the macrosystem looms large—a vast expanse encompassing cultural mores, societal norms, and ideological undercurrents. Embedded within this intricate web of cultural influences lie the blueprints of gender roles, educational paradigms, and ethnic identities—shaping the developmental trajectory through subtle yet profound nudges.

Moreover, Bronfenbrenner’s Ecological Model embraces the temporal dimension through the chronosystem—a dynamic canvas upon which historical events, life transitions, and socio-cultural shifts unfold. From the epochal waves of technological revolutions to the ebbs and flows of socio-political landscapes, the chronosystem paints a vivid tableau of temporal flux, etching its imprint upon the developmental narrative.

Complementing Bronfenbrenner’s framework, Dynamic System Theory adds a dynamic hue to the developmental canvas, illuminating the ever-evolving interplay between individuals and their environment. Embracing the ethos of self-organization, this theory unveils the emergent patterns and behaviors forged through the crucible of environmental interactions.

Central to Dynamic System Theory are the intricate feedback loops—engines propelling the perpetual dance between individuals and their milieu. Through these feedback loops, the echoes of environmental influences reverberate, shaping developmental trajectories and catalyzing emergent phenomena.

Furthermore, Dynamic System Theory unveils the enigmatic allure of attractors and bifurcation points—heralding the threshold moments where developmental trajectories diverge or converge. These pivotal junctures, akin to cosmic crossroads, beckon the flux of change, steering the developmental odyssey towards new horizons.

Integrating Bronfenbrenner’s Ecological Model with Dynamic System Theory unveils a kaleidoscopic vista of human development—one where the intricate tapestry of environmental influences intertwines with the dynamic currents of individual agency and adaptation. This synergistic alliance illuminates the nuanced interplay between context and process, enriching our understanding of human development and paving the path towards holistic interventions fostering positive developmental outcomes.

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Bronfenbrenner’s Ecological Systems Theory

Olivia Guy-Evans, MSc

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On This Page:

Bronfenbrenner’s ecological systems theory posits that an individual’s development is influenced by a series of interconnected environmental systems, ranging from the immediate surroundings (e.g., family) to broad societal structures (e.g., culture).

These systems include the microsystem, mesosystem, exosystem, macrosystem, and chronosystem, each representing different levels of environmental influences on an individual’s growth and behavior.

Key Takeaways

• Bronfenbrenner’s ecological systems theory views child development as a complex system of relationships affected by multiple levels of the surrounding environment, from immediate family and school settings to broad cultural values, laws, and customs.
• To study a child’s development, we must look at the child and their immediate environment and the interaction of the larger environment.
• Bronfenbrenner divided the person’s environment into five different systems: the microsystem, the mesosystem, the exosystem, the macrosystem, and the chronosystem.
• The microsystem is the most influential level of the ecological systems theory. This is the most immediate environmental setting containing the developing child, such as family and school.
• Bronfenbrenner’s ecological systems theory has implications for educational practice.

The Five Ecological Systems

Bronfenbrenner (1977) suggested that the child’s environment is a nested arrangement of structures, each contained within the next. He organized them in order of how much of an impact they have on a child.

He named these structures the microsystem, mesosystem, exosystem, macrosystem and the chronosystem.

Because the five systems are interrelated, the influence of one system on a child’s development depends on its relationship with the others.

1. The Microsystem

The microsystem is the first level of Bronfenbrenner’s theory and is the things that have direct contact with the child in their immediate environment.

It includes the child’s most immediate relationships and environments. For example, a child’s parents, siblings, classmates, teachers, and neighbors would be part of their microsystem.

Relationships in a microsystem are bi-directional, meaning other people can influence the child in their environment and change other people’s beliefs and actions. The interactions the child has with these people and environments directly impact development.

For instance, supportive parents who read to their child and provide educational activities may positively influence cognitive and language skills. Or children with friends who bully them at school might develop self-esteem issues. The child is not just a passive recipient but an active contributor in these bidirectional interactions.

2. The Mesosystem

The mesosystem is where a person’s individual microsystems do not function independently but are interconnected and assert influence upon one another.

The mesosystem involves interactions between different microsystems in the child’s life. For example, open communication between a child’s parents and teachers provides consistency across both environments.

However, conflict between these microsystems, like parents and teachers blaming each other for a child’s poor grades, creates tension that negatively impacts the child.

The mesosystem can also involve interactions between peers and family. If a child’s friends use drugs, this may introduce substance use into the family microsystem. Or if siblings do not get along, this can spill over to peer relationships.

3. The Exosystem

The exosystem is a component of the ecological systems theory developed by Urie Bronfenbrenner in the 1970s.

It incorporates other formal and informal social structures. While not directly interacting with the child, the exosystem still influences the microsystems. 

For instance, a parent’s stressful job and work schedule affects their availability, resources, and mood at home with their child. Local school board decisions about funding and programs impact the quality of education the child receives.

Even broader influences like government policies, mass media, and community resources shape the child’s microsystems.

For example, cuts to arts funding at school could limit a child’s exposure to music and art enrichment. Or a library bond could improve educational resources in the child’s community. The child does not directly interact with these structures, but they shape their microsystems.

4. The Macrosystem

The macrosystem focuses on how cultural elements affect a child’s development, consisting of cultural ideologies, attitudes, and social conditions that children are immersed in.

The macrosystem differs from the previous ecosystems as it does not refer to the specific environments of one developing child but the already established society and culture in which the child is developing.

Beliefs about gender roles, individualism, family structures, and social issues establish norms and values that permeate a child’s microsystems. For example, boys raised in patriarchal cultures might be socialized to assume domineering masculine roles.

Socioeconomic status also exerts macro-level influence – children from affluent families will likely have more educational advantages versus children raised in poverty.

Even within a common macrosystem, interpretations of norms differ – not all families from the same culture hold the same values or norms.

5. The Chronosystem

The fifth and final level of Bronfenbrenner’s ecological systems theory is known as the chronosystem.

The chronosystem relates to shifts and transitions over the child’s lifetime. These environmental changes can be predicted, like starting school, or unpredicted, like parental divorce or changing schools when parents relocate for work, which may cause stress.

Historical events also fall within the chronosystem, like how growing up during a recession may limit family resources or growing up during war versus peacetime also fall in this system.

As children get older and enter new environments, both physical and cognitive changes interact with shifting social expectations. For example, the challenges of puberty combined with transition to middle school impact self-esteem and academic performance.

Aging itself interacts with shifting social expectations over the lifespan within the chronosystem.

How children respond to expected and unexpected life transitions depends on the support of their ecological systems.

The Bioecological Model

It is important to note that Bronfenbrenner (1994) later revised his theory and instead named it the ‘Bioecological model’.

Bronfenbrenner became more concerned with the proximal development processes, meaning the enduring and persistent forms of interaction in the immediate environment.

His focus shifted from environmental influences to developmental processes individuals experience over time.

‘…development takes place through the process of progressively more complex reciprocal interactions between an active, evolving biopsychological human organism and the persons, objects, and symbols in its immediate external environment.’ (Bronfenbrenner, 1995).

Bronfenbrenner also suggested that to understand the effect of these proximal processes on development, we have to focus on the person, context, and developmental outcome, as these processes vary and affect people differently (Bronfenbrenner & Evans, 2000).

While his original ecological systems theory emphasized the role of environmental systems, his later bioecological model focused more closely on micro-level interactions.

The bioecological shift highlighted reciprocal processes between the actively evolving individual and their immediate settings. This represented an evolution in Bronfenbrenner’s thinking toward a more dynamic developmental process view.

However, the bioecological model still acknowledged the broader environmental systems from his original theory as an important contextual influence on proximal processes.

The bioecological focus on evolving person-environment interactions built upon the foundation of his ecological systems theory while bringing developmental processes to the forefront.

Classroom Application

The Ecological Systems Theory has been used to link psychological and educational theory to early educational curriculums and practice. The developing child is at the center of the theory, and all that occurs within and between the five ecological systems are done to benefit the child in the classroom.

• According to the theory, teachers and parents should maintain good communication with each other and work together to benefit the child and strengthen the development of the ecological systems in educational practice.
• Teachers should also be understanding of the situations their student’s families may be experiencing, including social and economic factors that are part of the various systems.
• According to the theory, if parents and teachers have a good relationship, this should positively shape the child’s development.
• Likewise, the child must be active in their learning, both academically and socially. They must collaborate with their peers and participate in meaningful learning experiences to enable positive development (Evans, 2012).

There are lots of studies that have investigated the effects of the school environment on students. Below are some examples:

Lippard, LA Paro, Rouse, and Crosby (2017) conducted a study to test Bronfenbrenner’s theory. They investigated the teacher-child relationships through teacher reports and classroom observations.

They found that these relationships were significantly related to children’s academic achievement and classroom behavior, suggesting that these relationships are important for children’s development and supports the Ecological Systems Theory.

Wilson et al. (2002) found that creating a positive school environment through a school ethos valuing diversity has a positive effect on students’ relationships within the school. Incorporating this kind of school ethos influences those within the developing child’s ecological systems.

Langford et al. (2014) found that whole-school approaches to the health curriculum can positively improve educational achievement and student well-being. Thus, the development of the students is being affected by the microsystems.

Critical Evaluation

Bronfenbrenner’s model quickly became very appealing and accepted as a useful framework for psychologists, sociologists, and teachers studying child development.

The Ecological Systems Theory provides a holistic approach that is inclusive of all the systems children and their families are involved in, accurately reflecting the dynamic nature of actual family relationships (Hayes & O’Toole, 2017).

Paat (2013) considers how Bronfenbrenner’s theory is useful when it comes to the development of immigrant children. They suggest that immigrant children’s experiences in the various ecological systems are likely to be shaped by their cultural differences. Understanding these children’s ecology can aid in strengthening social work service delivery for these children.

Limitations

A limitation of the Ecological Systems Theory is that there is limited research examining the mesosystems, mainly the interactions between neighborhoods and the family of the child (Leventhal & Brooks-Gunn, 2000). Therefore, the extent to which these systems can shape child development is unclear.

Another limitation of Bronfenbrenner’s theory is that it is difficult to empirically test the theory. The studies investigating the ecological systems may establish an effect, but they cannot establish whether the systems directly cause such effects.

Furthermore, this theory can lead to assumptions that those who do not have strong and positive ecological systems lack in development. Whilst this may be true in some cases, many people can still develop into well-rounded individuals without positive influences from their ecological systems.

For instance, it is not true to say that all people who grow up in poverty-stricken areas of the world will develop negatively. Similarly, if a child’s teachers and parents do not get along, some children may not experience any negative effects if it does not concern them.

As a result, people need to avoid making broad assumptions about individuals using this theory.

How Relevant is Bronfenbrenner’s Theory in the 21st Century?

The world has greatly changed since this theory was introduced, so it’s important to consider whether Bronfenbrenner’s theory is still relevant today. 

Kelly and Coughlan (2019) used constructivist grounded theory analysis to develop a theoretical framework for youth mental health recovery and found that there were many links to Bronfenbrenner’s ecological systems theory in their own more recent theory.

Their theory suggested that the components of mental health recovery are embedded in the ‘ecological context of influential relationships,’ which fits in with Bronfenbrenner’s theory that the ecological systems of the young person, such as peers, family, and school, all help mental health development.

We should also consider whether Bronfenbrenner’s theory fits in with advanced technological advancements in the 21st century. It could be that the ecological systems are still valid but may expand over time to include new modern developments.

The exosystem of a child, for instance, could be expanded to consider influences from social media, video gaming, and other modern-day interactions within the ecological system.

Neo-ecological theory

Navarro & Tudge (2022) proposed the neo-ecological theory, an adaptation of the bioecological theory. Below are their main ideas for updating Bronfenbrenner’s theory to the technological age:

• Virtual microsystems should be added as a new type of microsystem to account for online interactions and activities. Virtual microsystems have unique features compared to physical microsystems, like availability, publicness, and asychnronicity.
• The macrosystem (cultural beliefs, values) is an important influence, as digital technology has enabled youth to participate more in creating youth culture and norms.
• Proximal processes, the engines of development, can now happen through complex interactions with both people and objects/symbols online. So, proximal processes in virtual microsystems need to be considered.

Urie Bronfenbrenner was born in Moscow, Russia, in 1917 and experienced turmoil in his home country as a child before immigrating to the United States at age 6.

Witnessing the difficulties faced by children during the unrest and rapid social change in Russia shaped his ideas about how environmental factors can influence child development.

Bronfenbrenner went on to earn a Ph.D. in developmental psychology from the University of Michigan in 1942.

At the time, most child psychology research involved lab experiments with children briefly interacting with strangers.

Bronfenbrenner criticized this approach as lacking ecological validity compared to real-world settings where children live and grow. For example, he cited Mary Ainsworth’s 1970 “Strange Situation” study , which observed infants with caregivers in a laboratory.

Bronfenbrenner argued that these unilateral lab studies failed to account for reciprocal influence between variables or the impact of broader environmental forces.

His work challenged the prevailing views by proposing that multiple aspects of a child’s life interact to influence development.

In the 1970s, drawing on foundations from theories by Vygotsky, Bandura, and others acknowledging environmental impact, Bronfenbrenner articulated his groundbreaking Ecological Systems Theory.

This framework mapped children’s development across layered environmental systems ranging from immediate settings like family to broad cultural values and historical context.

Bronfenbrenner’s ecological perspective represented a major shift in developmental psychology by emphasizing the role of environmental systems and broader social structures in human development.

The theory sparked enduring influence across many fields, including psychology, education, and social policy.

Frequently Asked Questions

What is the main contribution of bronfenbrenner’s theory.

The Ecological Systems Theory has contributed to our understanding that multiple levels influence an individual’s development rather than just individual traits or characteristics.

Bronfenbrenner contributed to the understanding that parent-child relationships do not occur in a vacuum but are embedded in larger structures.

Ultimately, this theory has contributed to a more holistic understanding of human development, and has influenced fields such as psychology, sociology, and education.

What could happen if a child’s microsystem breaks down?

If a child experiences conflict or neglect within their family, or bullying or rejection by their peers, their microsystem may break down. This can lead to a range of negative outcomes, such as decreased academic achievement, social isolation, and mental health issues.

Additionally, if the microsystem is not providing the necessary support and resources for the child’s development, it can hinder their ability to thrive and reach their full potential.

How can the Ecological System’s Theory explain peer pressure?

The ecological systems theory explains peer pressure as a result of the microsystem (immediate environment) and mesosystem (connections between environments) levels.

Peers provide a sense of belonging and validation in the microsystem, and when they engage in certain behaviors or hold certain beliefs, they may exert pressure on the child to conform. The mesosystem can also influence peer pressure, as conflicting messages and expectations from different environments can create pressure to conform.

Bronfenbrenner, U. (1974). Developmental research, public policy, and the ecology of childhood . Child development, 45 (1), 1-5.

Bronfenbrenner, U. (1977). Toward an experimental ecology of human development . American psychologist, 32 (7), 513.

Bronfenbrenner, U. (1995). Developmental ecology through space and time: A future perspective .

Bronfenbrenner, U., & Evans, G. W. (2000). Developmental science in the 21st century: Emerging questions, theoretical models, research designs and empirical findings . Social development, 9 (1), 115-125.

Bronfenbrenner, U., & Ceci, S. J. (1994). Nature-nurture reconceptualised: A bio-ecological model . Psychological Review, 10 (4), 568–586.

Hayes, N., O’Toole, L., & Halpenny, A. M. (2017). Introducing Bronfenbrenner: A guide for practitioners and students in early years education . Taylor & Francis.

Kelly, M., & Coughlan, B. (2019). A theory of youth mental health recovery from a parental perspective . Child and Adolescent Mental Health, 24 (2), 161-169.

Langford, R., Bonell, C. P., Jones, H. E., Pouliou, T., Murphy, S. M., Waters, E., Komro, A. A., Gibbs, L. F., Magnus, D. & Campbell, R. (2014). The WHO Health Promoting School framework for improving the health and well‐being of students and their academic achievement . Cochrane database of systematic reviews, (4) .

Leventhal, T., & Brooks-Gunn, J. (2000). The neighborhoods they live in: the effects of neighborhood residence on child and adolescent outcomes . Psychological Bulletin, 126 (2), 309.

Lippard, C. N., La Paro, K. M., Rouse, H. L., & Crosby, D. A. (2018, February). A closer look at teacher–child relationships and classroom emotional context in preschool . In Child & Youth Care Forum 47 (1), 1-21.

Navarro, J. L., & Tudge, J. R. (2022). Technologizing Bronfenbrenner: neo-ecological theory.  Current Psychology , 1-17.

Paat, Y. F. (2013). Working with immigrant children and their families: An application of Bronfenbrenner’s ecological systems theory . Journal of Human Behavior in the Social Environment, 23 (8), 954-966.

Rhodes, S. (2013).  Bronfenbrenner’s Ecological Theory  [PDF]. Retrieved from http://uoit.blackboard.com

Wilson, P., Atkinson, M., Hornby, G., Thompson, M., Cooper, M., Hooper, C. M., & Southall, A. (2002). Young minds in our schools-a guide for teachers and others working in schools . Year: YoungMinds (Jan 2004).

Further Information

Bronfenbrenner, U. (1974). Developmental research, public policy, and the ecology of childhood. Child Development, 45.

Bronfenbrenner’s Bioecological System Theory Essay

Introduction, microsystem, macrosystem.

Bronfenbrenner’s bioecological systems theory postulates that human development is the sum of factors of bioecological systems that are in an environment that one lives. The theory elucidates how bioecological systems influence human development throughout one’s lifespan, as it is extensively applicable in developmental psychology. Developmental psychology majorly entails the study of children’s behavior under strange circumstances and their interaction with adults. The theory views human development in the context of relationships that exist in bioecological systems of one’s environment. Bronfenbrenner (1994) argues that, human development occurs progressively through complex and reciprocal interactions between an individual and people, and objects and symbols that are in a given immediate environment (p.37). For interactions to be effective, they must be enduring and should occur in the immediate environment to form proximal processes that significantly influence human development. The proximal process exists in bioecological systems made of five spheres, namely microsystem, mesosystem, exosystem, macrosystem, and chronosystem. This essay describes four spheres of bioecological systems viz. microsystem, mesosystem, exosystem, and macrosystem, and analyzes the past and present biopsychosocial factors that influence human development.

Microsystem is the closest bioecological environment that directly influences human development. Microsystem consists of structures such as family, childcare, neighborhood, school, and workplace, which mainly form part of immediate bioecological environment. In microsystem, an individual experience regular interactions through relationships, routine activities, and social roles that elicit progressive and sustained interactions, which bring about human development. According to Bronfenbrenner (1994), proximal processes operate optimally in microsystem because it forms an immediate environment that elicit and sustain human development (p.39). Under microsystem level, family is a dominant structure that does not only influence child development but also development in adults. At microsystem level, relationships have a reciprocal influence that shape development of individuals in a given social structure. For instance, parents have the capacity to influence beliefs, behavior, and values of a child, and vice versa. Bioecological systems theory states that, reciprocal interactions are strongest at microsystem level, and they have the greatest impact on human development due to the proximity of bioecological factors.

Family, as a social structure, significantly influenced my development during childhood because family members advised me on how to go about in life and become a successful person. For example, my mother loved me immensely in that she used to advise me regularly on how to have a decent discipline and work hard in my studies. Since I perceived that she loved me and wanted the best of me, I became determined not to let my mother down and thus I obeyed her advices to the letter. Then, I became an exceptionally courteous and industrious student in my class, which earned me warm reputation not only at school but also at home. Our relationship with my mother strengthened to the extent that, she would not deny me anything that I asked and on my part, I was so afraid to do anything that would disgrace her. Thus, reciprocal interaction between my mother and I significantly influenced my beliefs, values and behavior.

Present interaction with my spouse has tremendously influenced my social skills since I have learned that different individuals have diverse beliefs, values, and behaviors that complicate formation of relationships. When I first met my spouse, we differed in most aspects of social interest, but with time, through effective interactions, we managed to make numerous compromises to accommodate our differences. From experiences of disagreements, I learned that an individual is an entity with unique values, beliefs, and behavior that need tolerance for a healthy relationship that would stand the test to develop. Thus, my interaction with my spouse has shaped my perception of individuals as unique members of society who have different interests and, therefore, they need tolerance and forbearance from their interacting partners.

Mesosystem comprises interaction of various microsystems that are in bioecological environment where one lives. For instance, interaction of microsystem structures such as family, childcare, neighborhood, school and workplace, determines overall human development in the society. Mesosystem has increased societal forces that influence human development, unlike microsystem that only depends on individual interaction. Johnson (2008) argues that, interaction between family and school is particularly crucial in shaping the development of elementary school pupils because it provides a platform for teachers and parents to interact effectively in educating the pupils (p.3). Therefore, it implies that interactions of microsystems enhance concerted efforts of societal forces that are crucial in shaping human development. Thus, the more the interacting microsystems, the significant are the societal forces that influence an individual.

Family and school are social structures that significantly influenced my development during my childhood. Both structures influenced my behavior because they taught me to be a hardworking and discipline student so that I could achieve extraordinary dreams. For example, during my childhood, my mother and my teacher were friends, for they interacted more often. Since my mother wanted the best out of me, she constantly consulted the teacher to hear about my progress and in turn sought advices on how to enhance my academic performance. With time, I realized that my teacher cared so much like my mother in that she would always ensure that I have done my assignments and encouraged me to work hard lest I disgraced my mother. Hence, relationships between my mother and my teacher compelled me to work hard in my studies because I had no way of evading my duties because both school and family constantly monitored my progress.

Currently, interaction between my mother and my spouse has significantly shaped relationships in my family. Before I got married, my mother has been advising me on how to become a responsible father in a family so that when time comes I assume my responsibility well. Throughout my life, I have liked the way my mother treated us as a family, and I terribly longed to marry a spouse with qualities that resembled those of my mother. At first, we differed on many issues with my spouse, but when she interacted with my mother, she changed appreciably and we lived happily. Current interaction of my mother and my spouse has saved my family a fantastic deal of conflicts that usually did arise due to poor relationships.

Exosystem consists of interaction of diverse microsystems with at least one social structure that has indirect influence on an individual. At exosystem level, social structures that do not exist in microsystem sphere of an individual have indirect influence on human development, for they contribute to direct influences from immediate social structures. For example, interaction of family and parent’s workplace or school and neighborhood influence development of children in the society. Boyd, Bee, and Johnson (2008) argue that, although children in the family may not have direct contact with social structures workplace and neighborhood, they experience both negative and positive impacts from remote interactions that influence their own microsystem (p.52). Three microsystems, family, school and peer group, which form part of exosystem, indirectly affect development of children in the society.

During my childhood, my parents used to spent a considerable deal of time in their workplaces leaving us alone as children to stay alone. My father would come home rarely, for he worked in a different state from where we lived. Although my mother worked within the state where we lived, she would usually leave early in the morning and arrive late in the evening. Thus, their constant absence in the family made me take responsibility of taking care of my siblings as I learned that my parents were busy working hard in their respective workplaces so that they could provide for us. Therefore, interaction of our family with workplaces through my parents taught me to take responsibility in the family, which has made me develop leadership qualities.

Currently, since children are susceptible to various diseases, I have been taking my children to hospital for treatment and medical checkup quite often. Since my family interacts with hospital quite often, I have been able to learn a lot from Canadian health care system regarding prevention, treatment, and management of common infections that affect children and other family members, as well. If it were not for my children, I would not have bothered to learn health issues that affect families; thus, my children interaction with hospital gave me an insight of not only Canadian health care system but understanding of general human health.

Macrosystem is a complex of social structures such as microsystem, mesosystem, and exosystem, which are under the influence of customs, norms, values, and laws that govern societal culture. According to Johnson (2008), macrosystem is the outermost sphere that has a cascading effect on development of children through interaction of various spheres, which consequently determines values, beliefs, norms, customs and laws that influence children’s microsystem (p.3). Biopsychosocial factors that exist in the community, society, and culture interact with diverse microsystems, mesosystems, and exosystems, thus forming a complex of macrosystem, which entirely determines human development in the society. It means that macrosystem is the blueprint of societal culture since it consists of diverse beliefs, values, norms, laws, and customs that dominate society and thus significantly influence human development.

During my childhood, Canadian customs and values significantly influenced me to adopt British and French culture since I attended a school, which had both British and French students. History shows that Canadian culture emanated from interaction of British and French culture; therefore, it enabled me to interact effectively with other students while at school. Since Canadian culture had elements of British and French culture, I developed interests in learning music and literature, which enabled me to adopt and develop their culture during my childhood. Hence, Canadian customs and values made me appreciate and learn other cultures at school for I perceived that we had common elements in our different cultures.

Currently, government policies have dictated my career development as a nurse. Government polices stipulate that I must undergo a recommendable nursing course for me to qualify and obtain practicing license. Furthermore, government polices do not only dictate that I must have certain qualification, but also expect that I must comply with nursing codes of ethics so that I can practice nursing. Hence, government policies have influenced my nursing course, schooling years and ultimately my career development. For one to qualify as a nurse, it depends on compliance with government policies and laws that govern nursing profession. In my case, since government regard nurses by paying them well, I opted to choose nursing as my career.

Bronfenbrenner’s bioecological systems theory has taught me that human development occurs due to interplay of many factors in bioecological environment, which act in hierarchical levels of life; microsystem, mesosystem, exosystem and macrosystem. These hierarchical levels of systems have proximal processes that directly or indirectly affect human development in a complex society. As a nurse, I have learned that educating people on health issues requires one to target the microsystem, mesosystem, exosystem, and macrosystem spheres to have comprehensive impact on population.

Boyd, D., Bee, H., & Johnson, P. (2008). Lifespan Development, Third Canadian Edition. Canada: Pearson Education.

Bronfenbrenner, U. (1994). Ecological Models of Human Development. In International Encyclopedia of Education , 3, 2nd. Ed. Oxford: Elsevier.

Reprinted in: Guavain, M., & Cole, M. (Eds.). (1993). Readings on the Development of Children (2nd Ed.) New York: Freeman.

Johnson, E. (2008). Ecological Systems and Complexity Theory: Toward an Alternative Model of Accountability in Education. An International Journal of Complexity and Education , 6, 1-10.

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1. IvyPanda . "Bronfenbrenner's Bioecological System Theory." March 28, 2022. https://ivypanda.com/essays/bronfenbrenners-bioecological-system-theory/.

Bibliography

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What is Bronfenbrenner’s main theory?

Urie Bronfenbrenner’s main idea was simple: each individual is affected by a set of external factors, which are in turn affected by external factors, which are in turn affected by… You can see where this is going.

Bronfenbrenner’s original diagram of concentric circles was called “the Ecological Systems Theory,” and revolved—quite literally—around the child, with influences such as parents, peers, teachers, community members, and the greater societal values and views. However, we can apply this American psychologist’s socio-ecological framework to any person, object, or idea by placing it in the center of the diagram.

What are the 5 systems of Bronfenbrenner’s theory?

Bronfenbrenner’s theory places a central figure inside five circles known as “systems.” From smallest to largest, these circles represent:

- The microsystem – The microsystem is the immediate environment. To give an example that stays true to Bronfenbrenner’s original theory, a child’s microsystem would include their parents, family members, peers, and teachers.

- The mesosystem – The mesosystem describes the way multiple microsystems interact. So, what is an example of Bronfenbrenner’s mesosystem? Following the example above, this could be the relationship between the child’s parents and teachers.

- The exosystem – Indirect connections make up the exosystem. Examples could include the neighborhood, the social environment, the child’s parent’s friends, and other groups with a direct impact on the child’s direct relationships.

- The macrosystem – The macrosystem is society at large, representing current cultural values and norms.

- The chronosystem – The fifth and final circle denotes the way things change over time.

How do I fill out a Bronfenbrenner diagram?

Most of Bronfenbrenner’s ecological model examples follow the same approach: start by placing your concept of interest in the center, then expand outward, identifying and adding new elements to each circle.

With that in mind, the easiest way to fill out a Bronfenbrenner diagram is to start with the circles already in place. With pre-made social-ecological model examples from FigJam, you can easily rearrange your ideas and add your own flair with colored markers, icons, stamps, widgets, and so much more.

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Bronfenbrenner's Ecological Systems Theory

Cite this chapter

• Jonathan R. H. Tudge 5   na1 ,
• Elisa A. Merçon-Vargas 5   na1 &
• Ayse Payir 6   na1  

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One goal in this chapter is to show how Urie Bronfenbrenner’s theory developed over the course of his lifetime, focusing partly not only on the changes that occurred over the three distinct phases of its development (see Rosa & Tudge, 2013) but also on what remained largely the same. Specifically, it is important to recognize that the construct of ecology—the interdependence of individual and context—was central in each phase. This interdependence is relevant to a second goal—showing that Bronfenbrenner’s theory fits within what Pepper (1942) termed the contextualist paradigm. Given that Bronfenbrenner has been largely treated as a mechanist by many who “misuse” his theory (Tudge et al., 2009), it is important to make clear the distinction between the two. A third goal is to show how the theory can be used effectively, as well as consider some of the theory’s limitations and how it has been built upon.

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We are drawing primarily on this 1998 chapter, but their 2006 chapter is identical apart from the addition of pages from Bronfenbrenner (2001), and the same points we will make about the third and final phase of the theory could be drawn from almost any of Bronfenbrenner’s writings from 1994 onward (Rosa & Tudge, 2013 ).

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Elisa A. Merçon-Vargas and Ayse Payir contributed equally with all other contributors.

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Department of Human Development and Family Studies, School of Health and Human Sciences, University of North Carolina at Greensboro, Greensboro, NC, USA

Jonathan R. H. Tudge & Elisa A. Merçon-Vargas

Wheelock College of Education and Human Development, Boston University, Boston, MA, USA

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Tudge, J.R.H., Merçon-Vargas, E.A., Payir, A. (2022). Urie Bronfenbrenner’s Bioecological Theory: Its Development, Core Concepts, and Critical Issues. In: Adamsons, K., Few-Demo, A.L., Proulx, C., Roy, K. (eds) Sourcebook of Family Theories and Methodologies. Springer, Cham. https://doi.org/10.1007/978-3-030-92002-9_16

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What is Bronfenbrenner’s Ecological Systems Theory?

American psychologist Urie Bronfenbrenner formulated the Ecological Systems Theory to explain how social environments affect children’s development. This theory emphasizes the importance of studying children in multiple environments, known as ecological systems, in the attempt to understand their development.

According to Bronfenbrenner’s ecological systems theory, children typically find themselves enmeshed in various ecosystems, from the most intimate home ecological system to the larger school system, and then to the most expansive system which includes society and culture. Each of these ecological systems inevitably interact with and influence each other in all aspects of the children’s lives.

Bronfenbrenner’s ecological model organizes contexts of development into five nested levels of external influence: Microsystem , Mesosystem , Ecosystem , Macrosystem , and Chronosystem . These levels are categorized from the most intimate level to the broadest.

The Microsystem

The Bronfenbrenner theory suggests that the microsystem is the smallest and most immediate environment in which children live. As such, the microsystem comprises the home, school or daycare, peer group and community environment of the children.

Interactions within the microsystem typically involve personal relationships with family members, classmates, teachers and caregivers. How these groups or individuals interact with the children will affect how they develop. More nurturing and supportive interactions and relationships will likely to foster a better environment for development.

Bronfenbrenner proposed that many of these interactions are bi-directional: how children react to people in their microsystem will also affect how these people treat the children in return.

For example, a little boy playing alone in a room. This little boy suddenly bursts out crying for no apparent reason. His mother, who is making lunch in the kitchen, hears the boy crying. She comes into the room, picks the little boy up, and carries him to the living room.

In the above example, the little boy initiated the interaction (crying), and his mother responded. In a way, the little boy influenced his mother’s behavior.

One of the most significant findings that Urie Bronfenbrenner unearthed in his study of ecological systems is that it is possible for siblings who find themselves in the same ecological system to experience very different environments.

Therefore, given two siblings experiencing the same microsystem, it is not impossible for the development of them to progress in different manners. Each child’s particular personality traits, such as temperament, which is influenced by unique genetic and biological factors, ultimately have a hand in how he/she is treated by others.

The Mesosystem

The mesosystem encompasses the interaction of the different microsystems which children find themselves in. It is, in essence, a system of microsystems and as such, involves linkages between home and school, between peer group and family, and between family and community.

According to Bronfenbrenner’s ecological theory, if a child’s parents are actively involved in the friendships of their child, for example they invite their child’s friends over to their house from time to time and spend time with them, then the child’s development is affected positively through harmony and like-mindedness.

However, if the child’s parents dislike their child’s peers and openly criticize them, the child will experience disequilibrium and conflicting emotions, which will likely lead to negative development.

The Exosystem

The exosystem in Bronfenbrenner’s ecological model pertains to the linkages that may exist between two or more settings, one of which may not contain the developing children but affect them indirectly nonetheless.

Based on Bronfenbrenner’s findings, people and places that children may not directly interact with may still have an impact on their lives. Such places and people may include the parents’ workplaces, extended family members, and the neighborhood the children live in.

For example, a father who is continually passed up for promotion by an indifferent boss at the workplace may take it out on his children and mistreat them at home. This will have a negative impact on the child’s development.

The Macrosystem

The macrosystem in Bronfenbrenner’s ecological model is the largest and most distant collection of people and places to the children that still have significant influences on them. This ecological system is composed of the children’s cultural patterns and values, specifically their dominant beliefs and ideas, as well as political and economic systems.

For example, children in war-torn areas will experience a different kind of development than children in a peaceful environment.

The Chronosystem

The chronosystem adds the useful dimension of time to Bronfenbrenner’s ecological systems theory. It demonstrates the influence of both change and constancy in the children’s environments. The chronosystem may include a change in family structure, address, parents’ employment status, as well as immense society changes such as economic cycles and wars.

Application of Bronfenbrenner’s Ecological Systems Theory

Through the various ecological systems, Bronfenbrenner’s theory demonstrates the diversity of interrelated influences on child development. Awareness of the contexts that children are in can sensitize us to variations in the way children may act in different settings.

For example, a child who frequently bullies smaller children at school may portray the role of a terrified victim at home. Due to these variations, adults who are concerned with the care of a particular child should pay close attention to his/her behavior in different settings, as well as to the quality and type of connections that exist between these settings.

How to cite this post: What is Bronfenbrenner’s Ecological Systems Theory?. (2019, May 3). The Psychology Notes Headquarters. https://www.psychologynoteshq.com/bronfenbrenner-ecological-theory/

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• Lev Vygotsky’s Sociocultural Theory of Cognitive Development
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Categories: Developmental Psychology

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Very useful overview of Bronfenbrenner, thank you

You’re very welcome.

How would I cite this information APA format?

Please refer to this website: http://www.bibme.org/citation-guide/apa/website

Hope this helps, A

What is the date of this pulblication

The post was originally published in November 2013.

Very educative indeed…

when was this article published and by who, need it for reference purposes

This does not help. Who is the author of these words?

The articles and diagrams on this website are written/created by our team of writers. If you use the content on this website, you MUST provide appropriate credit. Please see here on how to cite the content on this website.

Hi, just trying to find out who wrote this article (Author)?

Can you explain the important of studying bronfenbrenner ecology tk an ecd teacher

Use the site as the author e.g. for in-text citations (The Psychology Notes Headquarters, 2011). Seeing as this information isn’t peer-reviewed I probably wouldn’t cite it as anyone could have written it.

Good advice!

I have a couple of questions: – who is the author of this article? – I note you list copyright on the diagram demonstrating the model. Is this your original work? What Bronfenbrenner source did you use to create it?

Good morning,

I have to submit a task on Bronfenbrenner and appreciate your notes – it really helps. May I know who the author is for proper reference? Or will it be Psychology Notes HQ?

Who are the authors of these articles? The articles and diagrams on this website are written/created by our team of writers. If you use the content on this website, you MUST provide appropriate credit. Please see below on how to cite the content on this website.

How to cite the content on this website? If you use the content on this website in your work, you MUST cite this website as your source. Here’s how to do so in APA format .

Hope this helps.

good afternoon

whoah this blog is excellent i like studying your posts. Keep up the good work! You realize, a lot of persons are hunting round for this info, you can help them greatly.

Loved this clear and informative blog post! This theory seems so great that it makes me wonder what the cons or problems with using this theory? I’m not trying to be negative, just well informed. Thanks!

how do I cite this?

Does this apply to adults as well?

who is the author of this aritcle so i can properply cite this in my essay!?

Here’s how to cite the content on this website: https://www.psychologynoteshq.com/contactu/

This is not peer-reviewed, so probably just “Retrieved from” would be better. I hate that its not as it explains things in much simpler terms.

You can google for apa format citations for ur referal hehe

Can i get the cite of this information.

Informative. This helped me a lot. Thanks.

Thank you so much!

Thank you this is very helpful

Very well defined article with examples

Are there four or five systems in Bronfenbrenner’s ecological theory of development? Some resources include chronosystem, but some exclude.

Hello, Can I please use this diagram for my honour’s thesis literature review? Thank you, Luella

your article is presented with the help of a systematic approach.

Useful content 👌 👍

In 1979 is when he first found about this information ?

Excellent article and diagram. May I use the diagram for my dissertation? Is this the correct process for requesting permission? Thank you.

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2.17: Assignment- Bioecological Model Journal

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• Lumen Learning

STEP 1 : Think of yourself at a particular time in your childhood (e.g., age 10). Use the following prompts to help you write a journal entry about your childhood experiences as seen through Urie Bronfenbrenner’s bioecological model. Write you answers as a personal reflection paper, in paragraph form, between 400-600 words.

Microsystem

• your parents:
• your siblings:
• your peers:
• your school and teacher:
• how your parents interacted with your school and helped with schoolwork:
• how your parents interacted with your peers:
• how your community interacted with your family/peers:
• how your religious background influenced your family:
• your parents’ jobs and socioeconomic status:
• how your family explored or interacted with the world beyond your community (e.g., vacations, travel sports, mission trips, etc.):
• popular media—television, music, movies, social media:
• any interactions with social services:
• the economic condition of your community:
• the history and values of your community:

Macrosystem

• what was going on in the world at the time (e.g., Hurricane Katrina, who was president, etc.):
• technological advancements:
• national or international cultural values (e.g., racial diversity, gender equality, etc.):

Chronosystem

• major life transitions (such as the birth or death of a sibling):
• major world events that changed history at that time (e.g., terrorist attacks, presidential elections, wars, etc.):
• more gradual historical changes (the history of transgender people in the United States or the change in the number of women in the workplace):

STEP 2 : Submit your paper.

Contributors and Attributions

• Authored by : Nancee Ott. License : CC BY: Attribution
• Modification, adaptation, and original content. Authored by : Sonja Ann Miller for Lumen Learning. Provided by : Lumen Learning. License : CC BY: Attribution

Essay about Bronfenbrenner’s Ecological Systems Theory

A child’s development is affected by their social relationships and the world around them. The ecological systems theory introduced by Urie Brofenbrenner (1979) focuses on the development of a person within the ecological environment, outlining and explaining the complex relationship and exchange between the infant, the family and society, and how these exchanges impact upon child development.

Bronfenbrenner challenges previous understandings on how children develop and within his model, identifies a hierarchy of influence levels that impact on child development including the Microsystem, the Mesosystem, the Exosystem and the Macrosystem. This essay provides an introduction to and explanatory on Brofenbrenners theory whilst referencing the author’s own childhood development in the context of opportunities and risks. “A child’s well-being is an essential foundation for early learning, and all subsequent learning” (NCCA 2004).

Development occurs through the process of progressively more complex exchanges between a child and its environment, with Bronfenbrenner describing the ecological environment as a “set of nested structures, each inside the next like a set of Russian dolls” (Bronfenbrenner 1979). The ecological theory explains that an individual will encounter different environments throughout their lifespan, and that it is the interrelationship between the child and the environment that may influence their behavior to varying degrees.

An example of this is a child’s parents affecting their beliefs and behaviours whilst at the same time, the child affecting the parents’ in return. As such, each child’s ecological model is unique and has different environmental influences. The first system within the theory is the Microsystem. This is widely considered the most influential level of the Ecological Systems Theory and is the setting in which an individual lives and where most of their direct interactions occur. As the child ages, the Microsystem becomes more complex and involves a greater number of people such as childcare centers or pre-school.

The Microsystem comprises ‘a pattern of activities, roles and interpersonal relations experienced by the developing person in a given setting with particular physical and material characteristics’ (Bronfenbrenner 1979). Family, peers and school are all examples of the type of interactions that populate this system. Bronfenbrenner’s theory explains that an individual is not a passive recipient of experiences in these settings, with relationships being bidirectional. A child’s interactions with them determine what is possible and what is not.

Their responses to the environment they create, personal preferences and genetics dictate the possibilities of what a child might become as “microsystems evolve and develop much as adolescents themselves do from forces within and without” (Garbarino 1985). The importance of a baby’s attachments to their parents (mothers and fathers) has long been acknowledged (Bowlby, 1988), with the experience young babies have of forming relationships crucial in that it can influence all future relationships (Perry, 1995; Karr-Morse and Wiley, 1997).

As adoptive children may experience difficulties with behavioral and emotional control, the establishment of positive family relationships can be challenging (Brodzinsky & Pinderhughes 2002). Parental responses are linked to their own experiences from childhood and can determine the quality of current parent-child relationships and parenting styles (Howard, 2011; Newland, Freeman, & Coyle, 2011). Garbarino states that to develop a sense of self”adolescents need warm, responsive and active ‘partners”(Garbarino 1985).

As an adopted child who was emotionally reactive to the adoptive process, and having been placed in a family who were emotionally unable or unwilling, due to limited experience and understanding, to interact in a way that fostered a positive parent rapport and therefore develop a healthy relationship, this had a negative affect on my development as it lead to increased emotional unresponsiveness in my broader relationships, and negative self-evaluation (Garbarino 1985).

However, the development of independence from my family structure in response to the situation, led to an increase in my resilience which was developmentally positive as “resilient children are better equipped to resist stress and adversity, cope with change and uncertainty, and to recover faster and more completed from traumatic event or episodes” (Newman and Blackburn (2002). The next level of the ecological theory is the Mesosystem.

The Mesosystem consists of the interactions between the different parts of a child’s Microsystem, and therefore essentially represents the connections between the Microsystems. Keenan and Evans (2009) state “one could think about the mesosystem as the connections which bring together the different contexts in which a child develops”. Therefore, whilst the “proximal processes within the family are considered within ecological theory to be the primary mechanism of development, links between contexts in which the child participates also affect development trajectories” (Schweiger & O’Brien 2005).

The examination of the Mesosystem can be viewed as important to the understanding of family relationships, as a child’s experience in other contexts away from the family structure can alter their perceptions and ultimately influence the way that they interact with their parent and siblings (Schweiger & O’Brien 2005). A positive effect a Mesosystem can have on a child can be seen through the opportunities it creates to provide social support and consistency in its daily activities.

My adoptive father was a sergeant in the army. A common way for army families to bond and socialise when the regiment was on base outside of training periods, was for communal barbeques and parties to be held. This allowed me to come into contact with different mesosystems in new settings, and showed then when together, my adopted parents were united in raising me. However, Mesosystems also have the potential to cause stress for the child.

As an adopted child who has had access to and contact with my biological family, including siblings and other relatives, these interactions have been difficult and have affected my relationship with my adoptive parents even though they did not actively participate in the interactions. An example of this is the abandonment feelings that surface when interacting with my biological family and the expressions of anger and resentment that impacts on my adoptive parents through my negative behavior and emotional state which was sometimes directed at them.

This is the direct result of two microsystems coming together, and my feelings of being placed in a situation where | felt I had to play multiple roles at once. Beyond the Microsystem and Mesosystem, Bronfenbrenners system is expanded to include environmental factors that are less direct in a child’s life. The Exosystem is a setting that does not involve the child as an active participant, but structures existing within it can be see to indirectly impact upon them. As an adopted child, it is arguable that these outer systems are more important and influential than the microsystem.

The affects on an adopted child can be seen in greater detail through social services interventions in their life. Adoption is the choice of an individual/s to parent children who are biologically unrelated to them. The system of social services is engaged when establishing a legal parent-child relationship, and the ecological theory highlights the importance of the experiences between social workers and therapists in terms of how the experiences might affect the child (Schweiger & O’Brien 2005).

My adoptive parents’ experiences with social services were quite negative and challenging. When choosing to foster additional children this caused them to relive previous experiences and emotions and caused tension in the household. As a result this caused negative affects between me and my adopted parents as my views as a child hearing various comments and witnessing their behavior both pre and post social services meetings was that the adoption and fostering processes was a burden, and that I as the central factor was the cause of their problems.

It has also impacted on my beliefs and attitudes towards adoption and fostering as a whole, and my beliefs and attitudes towards them. The Macrosystem includes the belief systems or ideologies that inform cultures or sub-cultures. It is the overall culture that the child is involved in, and can include Australian culture. The ecological systems theory “emphasizes the impact that the wider society has on how families function and view themselves” (Schweiger & O’Brien 2005).

Traditional family preservation views and the stigma that is arguably still attached to the concept of adoption are all pressures and messages from the outer systems that influence a child’s own perception on who they are and what there identity is. All the levels in Bronfenbrenners Ecological Systems Theory play an important roll in the wellbeing of children and families. In concluding I have evidenced the complexity of Bronfenbrenners Ecological theory, whilst highlighting that

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7 Exosystem Examples & An Easy Definition (Bronfenbrenner)

Exosystem examples include a parent’s workplace, mass media, school policy, social support systems, family friends, and local government policy settings.

We can define the exosystem as any setting in which a child is not directly involved yet still which still influences them. For example, a child is not involved in setting school policy on school uniforms, and yet the policy will directly impact what the child wears.

The exosystem is the third level in Urie Bronfenbrenner’s 5-tiered model of child development called the ecological systems theory .

Exosystem Definition

“Exos” in Greek means outside or external. The exosystem thus comprises institutions and influences that are external to the individual, and yet exert a decisive influence on his/her psycho-social development.  

Unlike a microsystem, the institutions that make up the exosystem are not necessarily ones with which an individual has a close, personal or intimate relationship.

At the same time, while the ecosystem is external to the child’s immediate environment, it is not so far removed from the child that it should be conceived of as a distant or abstract influence.

Changes within an exosystem (such as your father joining a golf club) may directly impact you (he may spend less time with you) .

1. A Parent’s Occupation

The occupation of a child’s parent, or the changes in the parents’ occupation, are factors not directly related to the child and yet they have a major influence in shaping their selves.

For instance, if one of the parents has a job that requires frequent moving between cities, such as being in the Armed Forces, the child might be required to frequently adjust to new surroundings. 

This can have multifaceted consequences on a child’s development.

Studies have shown that frequent family shifts negatively affect a child’s academic performance and that this negative impact continues throughout the academic year (Adam,2004).

 This may in turn affect the child’s self-esteem leading to negative interference with his/her social relations with their peers. 

Thus, the exosystem of parental occupation influences the family-school-peer group mesosystem which in turn affects the individual.

2. Mass Media

The mass media exosystem acts in subtle yet pervasive ways to shape the lives of children.

Children passively pick up ways of behaving from images they see or hear being conveyed through mass media channels such as TV, cinema, or music. 

As an example, the negative depictions of LGBTQ characters in popular culture until the late 90s created an exosystem that has been linked by researchers to instances of increased bullying among school children (Hong & Garbarino, 2012).

For instance, Silence of the Lambs (1991), widely considered one of the greatest and most influential Hollywood films of all time, was criticized for depicting its primary antagonist, Buffalo Bill, as another one “in a long line of homophobic representations”(Phillips, 1998).

The film, according to critics, made no attempt to understand the trans or LGBTQ community and very crassly pathologized the community (Griffin, 2019).

Similarly, as late as 2000, other popular and critically acclaimed films such as Boys Don’t Cry tended to depict LGBTQ characters as living closeted, tragic, and doomed lives.

Such influences from the exosystem can easily shape the minds and behavior of children, resulting in consequences such as bullying in the classroom or the playground.

3. Teacher Training Institutes

The kind of training teachers have had in turn impacts the kind of education that children under their care receive.

As an example, consider two schools – one in which the teachers have had quality education and been trained in the proper teaching methodologies, and another one in which the education and training of the teachers only meets the bare minimum criteria.            

It is evident that there would be a great difference in the quality of education imparted to the students in the two cases.

Thus, the exosystem of teacher training reaches down and influences the microsystem of the school.

4. Specialized Policy Framework

Specific government policies can impact certain mesosystems that have an influence on child development.

For instance, in the United States, the Welfare Reform Act of 1996 encouraged low-income mothers on welfare to seek employment.

This in turn required that the mothers trust their children to childcare institutions while they worked.

This change in policy fundamentally altered the environment for a generation of low-income children born to single mothers, who were now being brought up increasingly in childcare facilities rather than at home (Marshall, 2004).

On the other hand, several single mothers who were unable or unwilling to find employment were forced to return to abusive partners or live with friends. Some were also forced to be constantly on the move for food and shelter, impacting the developmental environment of their children (DeParle, 2012).

Thus, a change in the policy exosystem had a profound impact on the family-child care mesosystem, and the family microsystem and eventually, the individual. 

5. Special Interest Organizations

Special interest organizations are often the result of microsystem level events whose impact percolates up several layers to act as an exosystem. 

Take for instance an organization such as Mothers Against Drunk Driving (MADD) . The organization was formed in 1980 after a mother in California lost her 13- year old daughter to an accident involving a car being driven by a drunk driver.

The organization quickly gained a large following as it was joined and supported by numerous mothers who had been through similar tragedies.

This led to sustained and successful advocacy towards stricter drunk driving rules as well as providing support to those affected by incidents of drunk driving.

The overall result was a reduction in incidents of children and teenagers being victims of drunk driving, and thus a safer environment for children (Newman & Newman, 2020).

In this way the organization acts as an exosystem impacting the lives of children even though it is not in direct contact with them. 

We see in this case the classic two-way interaction between the systems as hypothesized by Bronfenbrenner.

The individual and the family microsystem influence the exosystem by creating an advocacy organization and influencing policy.

This exosystem in turn reaches down and influences the microsystem and the individual by delivering favorable outcomes as a result of policy changes. 

6. Local Government

The local government in a child’s community has a significant impact on a child, even though the child has no contact with it.

An example is the local policy on public transit. If the local transit policy enables free transport for children and parents, then the child will have a lot more of their local area opened-up for them to explore, which can progress their development.

Similarly, the local government’s policy on greenspaces can impact a child’s experience. If they open up more greenspaces for children to access, then the child will have more access to play spaces which can help with their physical development.

7. Parents’ Friends

While a child may never interact with the friends of their parents, they are nonetheless impacted by them.

For example, mothers who get together in a mother’s group may discuss parenting strategies. The strategies discussed amongst those friends will, in turn, impact how the mother parents her own children.

What is Bronfenbrenner’s Ecological Systems Theory?

Each level in the ecological systems model is represented by a circle, with the entire model taking the appearance of a series of 5 concentric circles moving outwards, having the child at its center.

The levels are:

• The  Microsystem (see examples)
• The Mesosystem (see examples)
• The Exosystem
• The  Macrosystem (see examples)
• The  Chronosystem (see examples)

The closer a circle or level is to the child, the greater is the degree of immediate influence it has on the development of the individual.

Further, Bronfenbremmer’s ecological perspective proposed that the interaction between the individual and his/her ecology is a two-way process, with the individual both being shaped and in turn shaping the environment around him/her.

Bronfenbrenner’s ecological system model is a comprehensive model of human development which proposes that the development of an individual is the result of not just biological factors alone but rather the result of the entire ecosystem of institutions, norms, interactions, and events that surround an individual.

Furthermore, Bronfenbrenner demonstrated that this ecosystem includes not just elements that are in direct contact with the individual ( represented by the microsystem and mesosystem in Bronfenbrenner’s model) but also elements that are external to the individual’s immediate environment. The exosystem represents all such elements that though not intimately connected to the individual, exert a determining influence on their psycho-social development. 

Adam, E. K. (2004). Beyond quality: Parental and residential stability and children’s adjustment. Current Directions in Psychological Science , 13, 210-213. doi: https://doi.org/10.1111%2Fj.0963-7214.2004.00310.x

DeParle, J. (2012) Welfare limits left poor adrift as recession hit New York Times . Retrieved from: https://www.nytimes.com/2012/04/08/us/welfare-limits-left-poor-adrift-as-recession-hit.html  

Griffin, A. (2019) Before we knew better: Silence of the Lambs is a win for women—but fails LGBTQ culture Quartz https://qz.com/quartzy/1566136/silence-of-the-lambs-is-a-win-for-women-but-fails-lgbtq-culture/  

Hong, J.S., & Garbarino, J. (2012) Risk and protective factors for homophobic bullying in schools: An Application of the Social–Ecological Framework. Educational  Psychology Review 24, 271–285. https://doi.org/10.1007/s10648-012-9194-y  

Marshall, N.L. (2004) The quality of early child care and children’s development. Current Directions in Psychological Science , 13(4), 165-168. doi: https://doi.org/10.1111%2Fj.0963-7214.2004.00299.x .

Newman, B.M. & Newman, P.R. (2020) Ecological theories In B.M. Newman & P.R. Newman (Eds.) Theories of Adolescent Development, (pp. 313-335) Elsevier.

Phillips, K. R. (1998). Unmasking Buffalo Bill: Interpretive controversy and “The Silence of the Lambs.” Rhetoric Society Quarterly , 28 (3), 33–47. http://www.jstor.org/stable/3886379

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Article Contents

The conceptual model, the role of quantitative models in ecological research, when should a quantitative model be developed, building quantitative ecological models, nuts and bolts of assembling a quantitative ecological model, deterministic or stochastic, a way forward, acknowledgments, references cited, common pitfalls and potential solutions, decisions about model implementations.

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An Introduction to the Practice of Ecological Modeling

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Leland J. Jackson, Anett S. Trebitz, Kathryn L. Cottingham, An Introduction to the Practice of Ecological Modeling, BioScience , Volume 50, Issue 8, August 2000, Pages 694–706, https://doi.org/10.1641/0006-3568(2000)050[0694:AITTPO]2.0.CO;2

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Modeling has become an important tool in the study of ecological systems, as a scan of the table of contents of any major ecological journal makes abundantly clear. A number of books have recently been published that provide excellent advice on model construction, building, and use (e.g., Gotelli 1995 , Gurney and Nisbet 1998 , Roughgarden 1998 ) and add to the classic literature on modeling ecological systems and their dynamics (e.g., Maynard Smith 1974 , Nisbet and Gurney 1982 ). Unfortunately, however, littleany—of this growing literature on ecological modeling addresses the motivation to model and the initial stages of the modeling process, information that beginning students would find useful.

Fast computers and graphical software packages have removed much of the drudgery of creating models with a programming language and opened new avenues of model construction, use, and even misuse. There are many reasons why a student might want to consider modeling as a component of his or her education. Models provide an opportunity to explore ideas regarding ecological systems that it may not be possible to field-test for logistical, political, or financial reasons. Often, learning occurs from apparently strange results and unexpected surprises. The process of formulating an ecological model is extremely helpful for organizing one's thinking, bringing hidden assumptions to light, and identifying data needs. More and more, students want to “do something” with modeling but are not sure how to get started.

The goals of this article are to outline issues concerning the value of ecological models and some possible motivations for modeling, and to provide an entry point to the established modeling literature so that those who are beginning to think about using models in their research can integrate modeling usefully. We therefore envision the typical reader to be an advanced undergraduate, a beginning graduate student, or a new modeler. We first consider some of the values of models and the motivation for modeling. We then discuss the steps involved in developing a model from an initial idea to something that is implemented on a computer, outlining some of the decisions that must be made along the way. Many excellent texts and journal articles deal with the technical details of models and model construction; we do not attempt to replace this literature, but rather try to make the reader aware of the issues that must be considered and point to some of the sources we have found particularly useful.

We begin with the assumption that the reader has decided that he or she would like to “do something” with modeling as part of his or her research (Figure 1) . It is important to recognize the difference between models and the modeling process. A model is a representation of a particular thing, idea, or condition. Models can be as simple as a verbal statement about a subject or two boxes connected by an arrow to represent some relationship. Alternatively, models can be extremely complex and detailed, such as a mathematical description of the pathways of nitrogen transformations within ecosystems. The modeling process is the series of steps taken to convert an idea first into a conceptual model and then into a quantitative model. Because part of what ecologists do is revise hypotheses and collect new data, the model and the view of nature that it represents often undergo many changes from the initial conception to what is deemed the final product.

The discussion that follows is organized to consider issues in a sequence similar to what a new modeler would encounter. Because individuals' backgrounds differ, the sequence is not fixed. We map one possible route through the sorts of decisions that will most likely need to be considered; this course is derived from our individual experiences plus the collective knowledge of our reviewers. We begin with conceptual models because many people, even self-labeled nonmodelers, formulate conceptual models.

The development of a conceptual model can be an integral part of designing and carrying out any research project. Conceptual models are generally written as diagrams with boxes and arrows, thereby providing a compact, visual statement of a research problem that helps determine the questions to ask and the part of the system to study. The boxes represent state variables , which describe the state or condition of the ecosystem components. The arrows illustrate relationships among state variables, such as the movement of materials and energy (called flows ) or ecological interactions (e.g., competition). Shoemaker (1977) provides an excellent discussion about how to develop conceptual models.

The development of a conceptual model is an iterative process. The skeleton of a conceptual model begins to take shape when a general research question is formulated. For example, suppose the goal of a research project is to determine the relationship between different strategies for stocking exotic salmon in the Great Lakes and the concentrations of potentially toxic contaminants in the salmon and their alewife prey. The initial conceptual model might consist of two linked boxes labeled “alewife” and “chinook salmon,” with an additional arrow labeled “stocking” pointing to the salmon's box (Figure 2a) . We have chosen to place two-way arrows between the boxes to reflect the flow of biomass and contaminants from alewife to salmon and the effect of salmon on the alewife; an alternative model might have used only one arrow, since the flow of material between boxes is the result of predation by salmon on alewife. Details would then be added to the conceptual model based on the answers to questions such as, Are there other important species besides alewife and chinook salmon? What mechanistic processes should be included? What environmental factors influence each species? What currency should be used to describe compartment interactions (e.g., elements, biomass, individuals, energy)?

After making refinements driven by such questions, the conceptual model might have alewife, chinook salmon, rainbow smelt, and lake trout (Figure 2b) , although the research interest might still be with the original two species. The next round of refinements to the conceptual model might be based on available data or consultation with ecologists who have studied the interactions of the four species shown in Figure 2b . For example, if contaminant concentrations are a function of prey body size, and if predators seek certain size classes of prey, then size structure might be added to the model to more accurately reflect these ecological features and to better simulate contaminant intake by predators (Figure 2c) . Depending on the nature of the research question, the addition of size structure might be made for just the alewife and chinook salmon. This simple example assumes that there are changes only in the state variables, but there could also be changes in the relationships among the state variables.

In general, a parsimonious approach is best for creating an appropriate conceptual model. The model should strike a balance between incorporating enough detail to capture the necessary ecological structure and processes and being simple enough to be useful in generating hypotheses and organizing one's thoughts. Creating a good conceptual model forces an ecologist to formulate hypotheses, determine what data are available and what data are needed, and assess the degree of understanding about key components of the system. Because outside viewpoints and questions often force clarification of biases and assumptions, discussing the evolving conceptual model with colleagues can be helpful. Group construction of a conceptual model can also be a useful consensus-building tool in collaborative research ( Walters 1986 , Carpenter 1992 ). Conceptual models should therefore be included in dissertation and grant proposals, especially in the early stages of project development. Revisions of the initial conceptual model then become focal points for discussion in subsequent meetings of the dissertation committee or research planning group.

A quantitative model is a set of mathematical expressions for which coefficients and data have been attached to the boxes and arrows of conceptual models; with those coefficients and data in place, predictions can be made for the value of state variables under particular circumstances. Ecologists use quantitative models for various purposes, including explaining existing data, formulating predictions, and guiding research. Simple quantitative models can be solved with pencil and paper (see mathematical ecology textbooks such as Pielou 1977 , Hallam and Levin 1986 , and Edelstein-Keshet 1988 ), but most ecological models are now implemented on a computer.

Quantitative ecological models can guide research in a number of ways. Constructing a quantitative model and running simulations may help in the design of experiments ( Carpenter 1989 , Hilborn and Mangel 1997 ), for example, to evaluate experimental power for different hypothesized effect sizes. Sensitivity analysis of a quantitative model can reveal which processes and coefficients have the most influence on observed results and therefore suggest how to prioritize sampling efforts. Quantitative models can even be used to generate “surrogate” data on which to test potential environmental indicators or evaluate potential sampling schemes. Most important, quantitative models translate ecological hypotheses into predictions that can be evaluated in light of existing or new data.

Ecologists often use quantitative models to formulate predictions about the systems they study. Some predictive models are empirical, meaning that they represent relationships determined strictly by data. Because empirical models are not based on a knowledge of underlying mechanisms, they are most useful within the bounds of the data with which they are developed ( Weiner 1995 ). A well-known empirical model from aquatic ecology predicts the level of summer chlorophyll from spring total phosphorus ( Dillon and Rigler 1974 ). Other predictive models are more mechanistic, based on hypotheses about the particular ecological processes that cause an observed pattern. The incorporation of key ecological features, such as size-selective predation and increasing contaminant concentrations with increasing prey body size (to use an example similar to that in Figure 2 ), leads to the prediction of a tradeoff between decreasing concentrations of PCBs in salmon and the probability of survival of salmon prey (Figure 3; Jackson 1997 ). In the absence of these mechanistic ecological details, lower contaminant concentrations are predicted in predators ( Jackson 1996a , 1996b ).

Predictive models can become quite complex, especially when their forecasts are used as the basis for resource management and policy decisions. Examples include global climate models, fisheries management models for setting catch and harvest quotas, watershed management models for nutrient control strategies, and risk assessment models for environmental engineering. Often, these complex predictive models are used to generate predictions for scenarios for which actual tests are difficult or impossible to run for ecological, social, or economic reasons.

Like a conceptual model, a quantitative model is rarely an end in itself. Often learning results from considering a changing suite of several quantitative models, or several formulations of processes within a particular model ( Pascual et al. 1997 ). The assessment of different models and processes allows an evaluation of the assumptions specific to those formulations and processes. In this context, it is useful to remember that models are only tools and not reality, and there is no “correct” model.

Models should follow from specific research questions rather than questions following from models. Thus, the decision to build a quantitative model from a conceptual model should occur only after a clear, focused research question has been distilled from initial ideas. A full-scale quantitative model should be created only when each of the following questions can be answered with a yes:

Will a quantitative model add to the scientific content of the study?

Is there sufficient motivation to devote the necessary time to develop a quantitative model?

Will the investment in modeling enhance the quality of knowledge produced?

There are clear advantages to the incorporation of quantitative modeling in a research program. We have already touched on some of these benefits, such as formulating predictions and identifying data needs or knowledge gaps. Models are also useful for organizing one's thinking about a problem. Once a conceptual model is converted to a quantitative model and used, new questions may arise as a result of interesting and unexpected results. However, the time it takes to build a useful quantitative model should not be underestimated. Model building becomes easier with practice, but modelers should expect to spend several weeks or months constructing, parameterizing, testing, and running a modestly complex model. (The time spent depends to some degree on the software used, which is discussed more below.)

Once an ecologist has decided to build a quantitative model, how should he or she choose the type of model to build? Some general classes of models used in ecology include energy and mass balance models (e.g., Hewett 1989 ), population genetics models (e.g., Roughgarden 1979 ), optimization and game theory models (e.g., Mangel and Clark 1988 ), individual-based population models (e.g., DeAngelis and Gross 1992 ), size- or age-structured population models (e.g., Caswell 1989 ), community and ecosystem models (e.g., Scavia and Robertson 1980 ), and landscape models (e.g., Baker 1989 ). Because the degree of detail varies widely within these broad categorizations (Table 1) , we recommend reading papers that discuss the merits of various modeling approaches (e.g., Levins 1966 , DeAngelis and Waterhouse 1987 , DeAngelis 1988 ). An overview of model types and formulations can also be obtained from a survey course in mathematical modeling, and we strongly recommend taking such a course as soon as the idea to “do something” with models arises. The specific types of models being considered may suggest further course work. For example, differential equations are used in many models, matrix algebra underlies size- and age-structured models, and geographical information systems (GIS) are needed to work with many spatial and metapopulation models.

The choice of model type and detail will depend on the system studied, the questions asked, and the data available. Quantitative models can quickly become complex and clear problem definition is essential to keeping the model focused. A good conceptual model is invaluable for deciding what ecological detail to include and what to ignore. For example, suppose an ecologist is studying two forest stands: One stand is intact, whereas a presumedly important seed disperser has been removed from the other. Has the removal of the seed-dispersing animal caused any changes in the population of a particular tree species in the experimental stand? There are several ways in which quantitative modeling can be used to address this question. A simple age-structured model (e.g., Caswell 1989 ) of the tree population may be useful if the ecologist wants to look for changes in age structure. Alternatively, a spatially explicit model might be needed if the ecologist wants to explore differences in spatial pattern. If the ultimate goal is to test the findings from the quantitative model in the field, then the model that is developed will dictate the types of data that will need to be collected from the two forest stands.

Once the general type of quantitative model has been chosen, the ecologist must determine the appropriate level of abstraction for the model. Consulting papers on the value of simple ( Fagerström 1987 , Scheffer and Beets 1993 ) versus complex ( Logan 1994 ) models may help guide this decision. Good models never include all possible compartments and interactions ( Fagerström 1987 , Starfield 1997 ), and the complexity of a model depends very much on the purpose and question addressed by the model. There are tradeoffs between the generality of a model and its practical utility for a particular situation ( Levins 1966 ). A highly abstract model with few parameters may be best to test general ecological hypotheses. However, for specific questions, such as whether changes in fire frequency have affected the spatial pattern of a species, a detailed spatial model coupled to GIS data may be required. Thus, a model's structure should be consistent with both the question(s) asked and the measurements made ( Costanza and Sklar 1985 , Ludwig and Walters 1985 , DeAngelis et al. 1990 ). Data for many populations are collected by size or developmental stage or at fixed time intervals, leading naturally to models with size or stage structure and certain time steps (see the box on page 700 for more on time steps). With too little detail in the model, the mechanisms driving the response of interest may not be captured. On the other hand, too much detail makes a model difficult to parameterize (determine coefficients for equations) and to validate ( Beck 1983 , Ludwig and Walters 1985 , DeAngelis et al. 1990 ). An active area of research therefore considers how to reduce model complexity while retaining essential system behavior ( Rastetter et al. 1992 , Cale 1995 ).

Once the decision to build a quantitative model has been made, and issues of model complexity and structure have been dealt with, it is necessary to develop algebraic formulations (equations) for model processes, to establish means for solving them, and to choose parameters for each equation before implementing the model on a computer. Thinking about these issues in advance may save a modeler from having to go back and redevelop portions of the model.

The importance of keeping good notes

The litmus test for a model description is that a relatively experienced modeler must be able to reproduce the model and its output, just as experiments should be capable of being replicated. Therefore, it is important to document decisions about equation forms, parameter values, and computational details, as well as any sources of information used to make these decisions. Good notes taken during model building will save hours combing the literature to rediscover the source of assumptions or parameter values.

Choosing equations

One of the initial steps in converting a conceptual model to a quantitative model involves quantifying the arrows between the state variables. This process actually involves two steps: choosing appropriate equations and determining the parameters for those equations. Equations represent mathematically the interactions among or transfers of energy or materials between state variables in a model. For example, equations 1 , 2 , and 3 represented different (hypothesized) ways to describe the process of predator consumption. Parameters are constants in the equations that make the algebraic expressions correspond to actual data.

Equations appropriate to a particular situation may be available in the literature. Certain constructs (e.g., feeding relationships, energetic equations) are common to many ecological models, although they may need to be reparameterized for different systems. Many relationships can be found in modeling textbooks, including Models in Ecology ( Maynard Smith 1974 ), Ecological Implications of Body Size ( Peters 1983 ), Handbook of Ecological Parameters and Ecotoxicology ( Jorgensen et al. 1991 ), Dynamics of Nutrient Cycling and Food Webs ( DeAngelis 1992 ), A Primer of Ecology ( Gotelli 1995 ), and Primer of Ecological Theory ( Roughgarden 1998 ). First principles (i.e., physical laws) can also provide useful relationships. Mathematically important differences among alternative formulations may or may not be important for a particular situation. If the particular form of an equation is of concern, the effects of each formulation on model results can be explored as part of the modeling exercise.

Computational issues associated with equations

Difference equations are simply solved by recursion; that is, later predictions depend on earlier predictions. Differential equations describe continuous processes, but must nevertheless be solved in discrete time steps on a computer. The two principal methods used to solve differential equations are the Euler and the Runge-Kutta methods. The Euler method steps through the differential equation as if it were a difference equation by using information at the beginning of each time interval to calculate values at the next time interval. The Euler method can be unstable when the interval between solutions (the step size) is small, because rapid accumulation of errors prevents convergence on the real solution. The Euler method may also be unstable at large step sizes because small changes in rates and local maxima and minima in the solution may be missed, which can be particularly problematic if the differential equations are nonlinear ( Press et al. 1992 ). Runge-Kutta algorithms also start with the information at the beginning of a time interval but then sample the solution at several points between the beginning and end of the interval. For most differential equation models, the Runge-Kutta is more accurate than the Euler method, and fourth-order Runge-Kutta is particularly recommended ( Press et al. 1992 ). Graphical and algebraic explanations of the Euler and Runge-Kutta algorithms appear in Press et al. (1992 ) and in textbooks on numeric methods in computing (e.g., Atkinson 1989 ). Variable step-size methods can be used to find the optimum balance between accuracy and computational speed by using small step sizes when variables are rapidly changing and long step sizes when variables are changing slowly.

A deterministic model has no random components; for the same initial conditions and time period projected, it always gives the same result. In contrast, a stochastic model incorporates at least one random factor, and thus the results are different every time the model is run. One type of stochastic model assumes that the values of some or all parameters vary through time or across individuals and are therefore described by probability distributions. Each time the model is run, the parameter values are drawn from their specified probability distributions. Other stochastic models add random errors following each calculation to simulate the effects of environmental variability. One reason to add stochasticity is to produce realistic variability in the trajectories of the state variables through time, either because the variance as well as the average value is of interest or because the effect of variability in one state variable on another state variable is of interest. Model results might be cast in terms of probabilities—for example, as the percentage of simulations in which a certain outcome (such as a catastrophic population crash) was attained. A stochastic model is not necessarily more “correct” than a deterministic model, and it is more work to create. It does provide additional information, but whether this information is of value depends on the purpose of the model. We recommend Nisbet and Gurney (1982) as the starting point for an introduction to deterministic and stochastic models.

Selecting modeling software

Implementation of a quantitative model on a computer requires the modeler (or the computer program) to keep track of many details. Some of these details, while necessary for the model to run, are irrelevant to the model predictions (e.g., allocating computer memory for arrays and matrices, creating a user interface, and writing output). Other details, such as how variables are initialized, how random numbers are generated, the order in which equations are solved, and the algorithm (computer instructions) used for solving them, do affect the predictions. We discuss some of these details further in the boxes on FPAGE 697 and 699.

The computer software selected should be determined by the degree to which the modeler wishes to control these details. At one extreme are general programming languages (e.g., C, Basic, Fortran, Pascal) that allow the modeler complete control over the model construction but also require the modeler to handle all of the sometimes tedious details. Model building gets easier with practice and by reusing bits of previously generated code, but it can still be quite time-consuming even for relatively experienced programmers. Prewritten routines for random numbers, matrix algebra, and other algorithms are available for most programming languages, reducing the need to reinvent some wheels (e.g., Numerical Recipes; Press et al. 1992 ). If this option is chosen, coursework in at least one programming language might be helpful; general programming concepts and skills translate across languages.

At the other extreme are graphical programs (e.g., STELLA, SimuLink, ModelMaker) that allow the user to create the computer program (the model) by choosing icons from a menu while the software handles the details. Models can be constructed quickly, but there are limits on what can be built and the implementation details are often hidden from the user. This final point is a significant weakness of graphical modeling packages, and we therefore tend to create our own models using programming languages. However, intelligent use of modeling packages can permit incorporation of modeling into a study with far less effort than building a model from scratch.

Between these two extremes are programming packages that include functions to handle many of the details but still leave some control to the modeler (e.g., Matlab; see Roughgarden 1998 ) and spreadsheets (e.g., Excel; see Weldon 1999 ). This intermediate approach may appeal to those who want to know how equations are being solved without becoming mired in the syntax of a programming language.

Parameter estimation and model calibration

Parameter estimation is the process of finding parameter values for each equation in the quantitative model. The source of parameter values depends on how the model is going to be used. If the model is being developed to explore the consequences of different parameter values, then the model will be run for a wide range of different parameters without reference to particular ecological systems. However, if a model is being developed to predict behavior in a particular system, then usually a single (mean) value will be chosen for each parameter. In this case, parameter values are estimated by fitting equations to the data from the system, or perhaps from data available in the literature. Sometimes data are not available, in which case a modeler might estimate parameters by an iterative process of matching model output to observed system behavior. This latter practice is referred to as tuning (calibration) by direct search, and the parameters are altered until the model produces a reasonable fit with observations of the state variables. Tuning can be done systematically or by trial and error. Either way, keeping good notes is essential. Parameters determined by direct search are best viewed as hypotheses to be tested as data become available.

When parameters are estimated from observed data, the modeler seeks the parameters that lead to the best fit between an equation and the observed data (e.g., Hilborn and Mangel 1997 ). The least-squares criterion and maximum likelihood estimation are the two most commonly employed methods for this kind of parameter estimation. Least-squares estimates of parameters minimize the value of the squared deviations between the simulated and observed data; these estimates can be used for just about any deterministic component of a model for which distributions are near normal and variance is constant throughout the range of an independent variable ( Brown and Rothery 1993 ). However, for models that are nonlinear in the parameters, least squares may produce biased parameter estimates; for these models, maximum likelihood may yield better parameter estimates. Maximum likelihood algorithms determine the parameter values that maximize the probability that the observations would have occurred if the parameters were correct ( Hilborn and Walters 1992 ). Unlike least squares, maximum likelihood does not require that error terms be normally distributed ( Hilborn and Mangel 1997 ). It is beyond the scope of this article to review parameter estimation techniques, but useful information on that subject can be found in Draper and Smith (1981) , Hilborn and Walters (1992) , and Hilborn and Mangel (1997) .

Debugging, sensitivity analysis, and validation

Once a quantitative model is assembled, it must be tested to ensure that it is functioning properly; that process is called “debugging.” We recommend that the equations be calculated by hand to ensure that the code is performing as it should—that is, arrays and matrices are properly indexed, equations are properly calculated, and so forth. Each module or subroutine of a model developed with a programming language should be tested separately before the completed model is run. Output should be tabulated, state variables graphed, and intermediate parameter and rate values monitored to ensure that they are realistic during simulations. One also should check that the model behaves as expected in situations for which the analytical solution is known.

Sensitivity analysis explores whether the conclusions would change if the parameters, initial values, or equations were different. Consequently, sensitivity analyses can be used to guide further research (for example, to identify those parameters that would be worth the investment of additional field measurements or experiments), to corroborate the model, and to improve parameter estimates. There are three basic approaches to sensitivity analysis: varying parameter values one at a time, systematic sampling, and random sampling ( Hamby 1994 ). Swartzman and Kaluzny (1987) provide an excellent discussion of the advantages and disadvantages of each of these approaches. The simplest sensitivity analysis examines the effect of each parameter on model dynamics individually ( Bartell et al. 1986 ). The model is typically deemed sensitive to a particular parameter if changing that parameter's value by 10% leads to more than a 10% change in the output from the baseline scenario. Because analysis of one parameter at a time will not identify sensitive interactions among parameters, it may also be worthwhile to explore the effects of variation in two or more parameters at the same time using either systematic or random sampling ( Swartzman and Kaluzny 1987 ). When many parameters may interact, random sampling may be the best approach. Random sampling is most often done with Monte Carlo techniques (e.g., Swartzman and Kaluzny 1987 , Bartell et al. 1988 ), whereby, during each of perhaps 1000 model runs, a value for each parameter is “sampled” from a range or probability distribution. Model runs then undergo partial correlation analyses, which yield estimates of the contribution of each parameter to the overall variance in the output. Parameters with high partial correlations have the most influence on results.

In addition to doing a sensitivity analysis on parameter values, the model should be checked for sensitivity to initial conditions and equations. For example, the model can be initialized with different species ratios or size structures to find out whether output is driven by these choices. Model sensitivity to alternative equations for relationships among state variables can also be checked by rerunning the model with different equations and seeing whether the conclusions change.

Once a model works, the modeler may need to ask whether it sufficiently resembles reality, but whether that question can be answered at all is a matter of considerable philosophical debate ( Mankin et al. 1975 , Oreskes et al. 1994 , Rastetter 1996 , Rykiel 1996 ). Nevertheless, at some point the researcher must decide that the model is good enough and no more tinkering is necessary. For many system-specific ecological models, this decision is made based on comparisons of simulated data with field or experimental data. If the simulated data are sufficiently similar to the observed data, then the model is judged to be validated or corroborated, and simulations with the model proceed. If the simulated data do not match the observed data, then further work is necessary. Objective criteria for model validation include the standard error of model predictions and the proportion of variance explained by the model ( Caswell 1976 , Power 1993 ). It is preferable to have independent data for model corroboration and calibration, although in practice independent data are often hard to find, particularly for whole ecosystems.

Modeling offers exciting possibilities for the exploration of ideas that are not easily pursued through field experimentation or laboratory studies. Ecologists, for example, use models to simulate the systems they study and to investigate general theories of the way those systems operate. Moreover, simulation of systems with models helps identify data needs and knowledge gaps.

Many research programs can benefit from the integration and development of conceptual and quantitative models. The process of creating a conceptual model begins with a question; from there, the researcher formulates hypotheses, evaluates available and needed data, and assesses the degree of understanding of the system under consideration. Then the conceptual model is converted to a quantitative model; that process is iterative, evolving as new data and ideas are discovered.

We cannot possibly cover every aspect of ecological modeling—which is both a skill and a process—in one short article. We do hope, however, that we have successfully raised the issues that a beginning modeler must consider, provided an entry point to the modeling literature, and discussed the role of modeling in ecological research.

We thank Steve Carpenter for numerous suggestions during the writing of the manuscript. We are grateful to many people at the Center for Limnology, University of Wisconsin–Madison, for support during our model building years there (especially David Christensen, Xi He, Daniel Schindler, Craig Stow, and Rusty Wright). We thank Steve Carpenter, George Gertner, Lloyd Goldwasser, Bruce Kendall, Russell Kreis, Bill Nelson, John Nichols, Daniel Schindler, and, in particular, Rebecca Chasan, Wayne Getz, and an anonymous reviewer for their thoughtful reviews of the manuscript. L. J. J.'s research with simulation models was funded by the Natural Sciences and Engineering Research Council of Canada and by the Wisconsin Sea Grant Institute under grants from the National Sea Grant College Program, National Oceanic and Atmospheric Administration, US Department of Commerce, and from the State of Wisconsin (Federal grant NA90AA-D-SG469, project R/MW-41). K. L. C.'s initial research with simulation models was funded by a predoctoral fellowship from the National Science Foundation. K. L. C. also thanks the National Center for Ecological Analysis and Synthesis, which is funded by NSF (DEB94-21535); the University of California at Santa Barbara; and the State of California for financial and logistical support while preparing this paper for publication.

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This troubleshooting box outlines some common mistakes made during model construction. It is not an exhaustive list. We hope that the novice modeler will profit from our experience in solving these problems, which arise largely from writing one's own code in a programming language.

Pay careful attention to units, scaling, and conversions. For example, translating prey eaten by one trophic level (units of mass) to a mortality rate for another (numbers) requires a conversion and change of units. We go through our equations and write the dimensions and units to ensure that we are making appropriate conversions. Units and dimensions for empirically derived relationships tend to be built into regression parameters (e.g., ungulate biomass [kg] derived from grass productivity [g · m −2 · d −1 of carbon]). Problems often arise when different state variables operate on different spatial scales, which is sometimes less obvious than when the variabes operate on different time scales. Fish, for example, occupy a volume (g · m −3 ) but may eat benthic invertebrates that occupy a surface (g · m −2 ), requiring rescaling when computing trophic transfers. Apparent conversion problems can also be caused by failure to properly share variables among subroutines.

Be careful with time steps and model stability, especially for models with differential equations. The modeler typically must choose a single step size (e.g., hourly, daily, monthly, yearly) over which to have the algorithm solve the equations, even though the time step appropriate for evaluating one process (e.g., hourly nutrient uptake by phytoplankton) may not be appropriate for evaluating another (e.g., annual growth of fishes). Equations whose dynamics suffer when independent variables change on widely disparate time scales are known as “stiff” equations. Problems often occur because small roundoff or truncation errors in one variable lead to enormously inflated errors in another; such problems can be diagnosed by evaluating output variables at a variety of step sizes. An alternative approach to manually manipulating step size is to use an algorithm with an adaptive step size ( Press et al. 1992 ), which gives smoother dynamics but takes more work to program. One can also explicitly divide the model into “fast” and “slow” components and then update the fast components much more frequently than the slow components.

Pay attention to setting and resetting values. Arrays and matrices are a common source of computer bugs, thus warranting extra attention to their dimensioning, initializing, and indexing. We assign values to parameters before they are used rather than relying on the software to initialize them. We also check that parameters and initial conditions obtained from an input file are properly read and assigned. After the lapse of important time periods, we check that variables have been zeroed or renewed as appropriate. For example, in a model in which seed germination for a plant proceeds only when certain environmental conditions are met, the value for seedlings should be set to zero each time germination fails rather than (unintentionally) taking the value from the previous year. Similarly, when all individuals in a particular size or age class die or are eaten, the variables tracking their characteristics must be properly reset to prevent carryover effects when a new cohort arrives. Populations modeled with real numbers will approach but not equal zero when subjected to a constant mortality rate, and should be set to zero after some minimum population size is attained. Inspecting graphs of state variables will elucidate what is happening.

Test random number generators before using them. Random number generators vary in quality and should be tested before use. A statistics package can be used to analyze the results of 10,000 or so sequential random numbers to ensure that the mean, standard deviation, and distribution are as specified and the shape is as expected. If qualitatively different results occur when initializing the random number generator at the beginning of the program versus the beginning of each replicate, we look for another random number generator. We recommend reading Press et al.'s (1992 ) discussion of random-number generating algorithms. One way to keep random numbers the same from run to run, which is useful when developing or debugging a model, is to start each simulation with the same “seed” (the initial number from which the random numbers are generated). When the time comes to use different seeds, the computer's clock can be used for the seed value.

Issues concerning how numbers are stored and updated, how calculations are sequenced, and how inputs and outputs are made may seem unimportant to the novice modeler, but our experience is that computational details merit attention early in the modeling process because they can have substantial implications for model use and behavior.

The nature of inputs and outputs determines how easily a model is used and analyzed. If inputs are part of the model code, the model must be recompiled (translated from text into instructions the computer executes) each time the inputs are changed. If inputs are read in as a separate file (which takes more work to program), the model can be run many times with different inputs without recompiling. It is worth formatting output with the planned analysis in mind—select formats amenable to processing with statistical or graphics software. Excessive output slows the simulation time, but representative subsets of intermediate calculations should be inspected to ensure that everything is reasonable.

The sequence in which events proceed can affect results. Events that happen simultaneously in nature must occur in sequence in computer models. For example, if the organism or size class that is first in numerical order in a vector of state variables is always the first for which foraging is evaluated, it may unintentionally be the one that gets the most food!

Separating old from new values allows sequential calculations of simultaneous events to proceed correctly. Newly calculated values should be assigned to temporary variables so that subsequent calculations are not based on a mixture of old and new state variables. The value of the state variables should be updated with the values in the temporary variables only after all calculations have been completed for that time step.

Decide whether to model populations as whole or real numbers. Neither choice is perfect. Using real numbers gives fractions of individuals, whereas using integers presents stochasticity and rounding problems. For example, if the number of survivors is calculated by multiplying the survival rate by the number of starting individuals and then rounding to the nearest integer, then a single individual with a survival rate of 0.8 will live forever! It would be better to use 0.8 as a probability and then do the equivalent of flipping a coin—that is, draw a random number.

Decide how many stability checks and assurances to build into a model. The inherent mathematical and architectural constraints of computers can lead to unexpected model behavior ( Acton 1996 ). It is important to anticipate both mathematically illegal operations (e.g., division by zero) that would cause the simulation to crash and circumstances that would cause the simulation to become invalid. For example, it might be appropriate to stop the simulation if one species in a multispecies model goes extinct, to build in a means for its reestablishment if it goes extinct, or to build in a refuge or alternate food supply so that extinction is prevented. These types of stability guarantees should be used prudently. Excessive stabilizing components can hide programming errors or even dominate model dynamics; on the other hand, if used sparingly, they can prevent the frustration of having a long simulation rendered useless by a circumstance for which a stability check could easily have been programmed.

Table 1. Ecological models for representing populations

Figure 1. Flow chart summarizing the process of creating an ecological simulation model. The model building process distills current knowledge into a conceptual framework, which forms the scaffolding for the model's construction. A number of steps involve iterations or refinements that follow from consulting data, experienced modelers, or other ecologists. Once there is output from the model, the original idea or state of knowledge may be modified and additional model refinements, data collection or experiments might be planned. Benefits of the modeling process include eliminating alternatives, identifying gaps in knowledge, identifying testable hypotheses, and indicating avenues for additional experimentation and data collection

Figure 2. Example of the iterative nature of building a conceptual model from an initial idea. The first iteration (a) describes a simple relationship between one predator and prey. One arrow identifies biomass and contaminants as the material flowing from alewife to chinook salmon, and the other arrow identifies predation as an important ecological process structuring the alewife population. In this example, interest is in how the rate at which salmon are stocked affects the relationship between salmon and alewife. Additional information at the second iteration might indicate that the dynamics of the salmon and alewife (a) are also affected by rainbow smelt and lake trout, which are subsequently incorporated into the conceptual model (b). Finally, information on contaminant concentrations as a function of body size and more detail on predator preference of prey might indicate that age or size structure should be included (c). Depending on the goal of the modeling exercise, detailed age structure might be examined for the original two species of interest. In b and c, the double-headed arrows indicate state variables that directly interact. In c, the wide gray arrows represent the movement of fish to older age classes. Box labels represent the age of fish; YOY are young-of-year. Two quantitative models might be constructed: one for conceptual model b and one for conceptual model c

Figure 3. PCB concentrations (solid line) of age class 4+ chinook salmon and the probability of an alewife population crash (dashed line) for chinook salmon stocking rates and a Shepherd stock-recruitment relationship. PCB concentrations are the result of 200 model runs to year 2015, at each stocking rate, based on bootstrapped estimates of the Shepherd stock-recruitment relationship from 14 years of data for Lake Ontario. The arrow indicates 1994 stocking rates. The dotted line around the chinook salmon PCB concentrations represents +/− 2 SE

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The Bioecological Model

Exploring how children’s relationships and environment interact to help them thrive., what is the bioecological model.

The bioecological model is based on the idea that the relationships children have with parents and caregivers impacts their development – and that these relationships are affected by their work, school, and community settings, which are in turn affected by broader social, cultural, and policy conditions. These many layers of relationships and environments interact with each other – ultimately influencing how children develop and become resilient. This theory of human development was originally proposed by Urie Bronfenbrenner and Stephen J. Ceci in 1994.

How we use the model at CCFW

The “bioecological” or “whole child” model sits at the core of our approach. We recognize the roles that individuals, families, communities and society play in influencing children’s development, and engage the whole community to support their resilience. Grounded in research, our programs share  social-emotional skills, mindfulness, and compassion practices with parents, caregivers, educators, and practitioners – to support well-being for all.

Child & Youth Well-Being and Resilience

Through our research, we examine interdependent systems that support child and youth well-being, including mental and physical health, social and emotional adjustment, physiological stress responses and academic achievement. 

Parent & Family Well-Being

We know that parents and families play a critical role in supporting children’s social, emotional and behavioral well-being, and their ability to develop resilience. We support these relationships by studying and sharing effective parenting practices based in mindfulness and self-compassion.  

Supportive school, work and family environments

We’ve observed how families become resilient when they can draw on support from extended family, teachers, employers and care providers. We engage this network of supportive individuals and systems by sharing evidence-based trainings, workshops and tools.

Neighborhoods and communities

Through our research, we examine the effects of different kinds of adversities (such as social inequity, crime, economic status, and pollution) on neighborhoods and communities, and how these factors impact children’s well-being. Our findings are translated into culturally-informed programs and resources co-created with communities that experience inequity and adversity.

Social, economic and cultural contexts and policies

To thrive, all children and families need stable housing, food security, economic opportunity, freedom from violence and hate, health and mental health care, high-quality child-care and education. While our work focuses on supporting the social-emotional well-being of children and the adults in their lives, we situate our work in a recognition of and advocacy for safe, stable, nurturing relationships and contexts. We aim to inform policy by responsibly sharing current research in a variety of ways and convening “Research to Real World” forums that align policymakers, philanthropists, and practitioners around shared evidence-based efforts.