ecological systems theory literature review

Framing Childhood Resilience Through Bronfenbrenner’s Ecological Systems Theory: A Discussion Paper

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Bronfenbrenner’s ecological systems theory (1979) conceptualises children’s development as a process of bi-directional and reciprocal relationships between a developing individual and those in surrounding environments, including teachers, parents, mass media and neighbouring communities. Using Bronfenbrenner’s ecological systems theory, this paper will argue that resilience can be taught during childhood, from the complex social interactions that children have with parents to the interactions they have in school. First, there will be a focus on how resilience emerges from children’s individual personality traits and emotional intelligence. Bi-directional and reciprocal relationships will be addressed by focusing on the effects of parental abandonment on children’s attachment styles, as well as parent-focused interventions. Following this, the role of teachers and school-based interventions (SBIs) will be explored as sources for bolstering resilience among children. Alternative perspectives on resilience pathways, including meaning-oriented approaches and those that recognise the impact of broader influences beyond the microsystem (e.g., culture and media), will also be addressed in this paper. Finally, implications of resilience research for play-based approaches and educational psychologists will be discussed.

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Ecological Systems Theory

Ecological Systems Theory (EST), also known as human ecology, is an ecological/ system framework developed in 1979 by Urie Bronfenbrenner (Harkonen, 2007). Harkonen notes that this theory was influenced by Vygotsky’s socio-cultural theory and Lewin’s behaviorism theory. Bronfenbrenner’s research focused on the impact of social interaction on child development. Bronfenbrenner believed that a person’s development was influenced by everything in the surrounding environment and social interactions within it. EST emphasizes that children are shaped by their interaction with others and the context. The theory has four complex layers called systems, commonly used in research. At first, ecological theory was most used in psychological research; however, several studies have used it in other fields such as law, business, management, teaching and learning, and education.

Previous Studies

EST has been used in many different fields, however, commonly, it is used in health and psychology, especially in child development (e.g., Heather, 2016; Esolage, 2014; Matinello, 2020). For instance, Walker et al. (2019) used an EST framework to examine risk factors for overweight and obese children with disabilities. The study focused on how layers of an ecological system or environment can negatively affect children with special needs in terms of weight and obesity. They found that microsystem such as school, family home, and extracurricular activities can impact overall health through physical activities and food selectivity. Furthermore, the second layer, mesosystem (e.g., family dynamic and parental employment), also can lead to an increase in children’s weight because of a lack of money to buy nutritious food. In addition, children may be socially isolated and excluded in ways that cause stress, and their parents might use food to reinforce or comfort them. The third layer the study adopted was the macrosystem. For example, some cultures discriminate against children with disabilities so that they face more difficulty gaining access to health services.

In the field of language teaching, Mohammadabadi et al. (2019) researched factors influencing language teaching cognition. They used an ecological framework to explore the factors influencing language teachers at different levels. They adopted the four systems from Bronfenbrenner’s theory for studying the issue. This study found that the ecological systems affect language teaching.  For example, the microsystem included a direct influence on teachers’ immediate surroundings, such as facilities, emotional mood, teachers’ job satisfaction, and linguistic proficiency. The mesosystem defined interconnections between teachers’ collaboration and their prior learning experience. The exosystem included the teaching program and curriculum and teachers’ evaluation criteria, while the macrosystem addressed the government’s rules, culture, and religious beliefs. In other words, researchers use EST to guide the design of their studies and to interpret the results.

Model of EST

Concepts, Constructs, and Propositions

The four systems that Brofenbrenner proposed are constructed by roles, norm and rules (see Figure 1). The first system is the microsystem. The microsystem as the innermost system is defined as the most proximal setting in which a person is situated or where children directly interact face to face with others. This system includes the home and child-care (e.g., parents, teacher, and peers). The second is the mesosystem. The mesosystem is an interaction among two or more microsystems where children actively participate in a new setting; for instance, the relationship between the family and school teachers. The third is the exosystem. This system does not directly influence children, but it can affect the microsystem. The effect is indirect. However, it still may positively or negatively affect children’s development through the parent’s workplace, the neighborhood, and financial difficulties. The outermost system is the macrosystem. Like the exosystem, the macrosystem does not influence children directly; however, it can impact all the systems such as economic, social, and political systems. The influence of the macrosystem is reflected in how other systems, such as family, schools, and the neighborhood, function (Kitchen et al., 2019). These four systems construct the EST which considers their influences on child or human development.

Bronfenbrenner (cited in Harkonen, 2007) noted that those environments (contexts) could influence children’s development constructively or destructively. As the proposition, the system influences children or human development in many aspects, such as how they act and interact, their physical maturity, personal characteristics, health and growth, behavior, leadership skills, and others. At the end of the ecological system improvement phase, Bronfenbrenner also added time (the chronosystem) that focuses on socio-history or events associated with time (Schunk, 2016). In summary, the views of this ecological paradigm is that environment, social interaction, and time play essential roles in human development.

Using the Model

There are many possible ways to use the model as teachers and parents. For teaching purposes, teachers can use the model to create personalized learning experiences for students. The systems support teachers and school administrators to develop school environments that are suitable to students’ needs, characteristics, culture, and family background (Taylor & Gebre, 2016). Because the model focuses on the context (Schunk, 2016), teachers and school administration can use the model to increase students’ academic achievement and education attainment by involving parents and observing other contextual factors (e.g., students’ peers, extra-curricular activities, and neighbor) that may help or inhibit their learning.

Furthermore, the EST model can support parents to educate and guide their children. It can prompt parents to assist their children in choosing their friends and finding good neighborhoods and schools. Additionally, they can build close connections to teachers, so they know their children’ skills and abilities. By involving themselves in schools, parents can positively influence their children’s educational context (Hoover & Sandler, 1997).

For research purposes, researchers can test and modify or refine the EST proposition, or they can find additional ways to measure it. Researchers also can develop questionnaires from the components or concepts and construct of EST. Additionally, the four levels of EST can be used by researchers to frame qualitative, quantitative, and mixed research (Onwuegbuzie, et.al., 2013).

At first, EST was used in children’s development studies to describe their development in their early stages influenced by the person, social, and political systems. Currently, EST is broadly applied in many fields. Schools or educational institutions can use EST to improve students’ achievement and well-being. Interaction between the family, parents, teachers, community, and political system will determine students’ development outcomes.

Esolage, D. L. (2014). Ecological theory: Preventing youth bullying, aggression, and victimization.  Theory into Practice. 53 , 257–264.

Harkonen, U. (2007, October 17). The Bronfenbenner ecological system theory of human development. Scientific Articles of V International Conference PERSON.COLOR.NATURE.MUSIC , Daugavpils University, Latvia, 1 – 17.

Heather, M.F. (2016). An ecological approach to understanding delinquency of youth in foster care . Deviant Behavior, 37 (2), 139 – 150.

Hoover-Dempsey, K. V., & Sandler, H. M. (1997). Why do parents become involved in their children’s education? Review of Educational Research , 67(1), 3–42. https://doi.org/10.3102/00346543067001003

Kitchen, J. A, (list all authors in reference list) (2019). Advancing the use of ecological system theory in college students research: The ecological system interview tool.  Journal of College Students Development, 60  (4), 381-400.

Martinello, E. (2020). Applying the ecological system theory to better understanding and prevent child sexual abuse.  Sexuality and Culture, 24 , 326-344

Mohammadabadi, A., Ketabi, S., & Nejadansari, D. (2019). Factor influencing language teaching cognition.  Studies in Second Language Learning and Teaching. 9 (4), 657 – 680.

Onwuegbuzie, A.J., Collins, K.M.T., & Frels, R.K. (2013). Foreword. International Journal of Multiple Research Approaches, 7 (1), 2-8.

Schunk, D. H. (2016). Learning theory: An educational perspective .  Pearson.

Taylor, R. D., & Gebre, A. (2016). Teacher–student relationships and personalized learning: Implications of person and contextual variables. In M. Murphy, S. Redding, & J. Twyman (Eds.), Handbook on personalized learning for states, districts, and schools (pp. 205–220). Temple University, Center on Innovations in Learning.

Walker, M., Nixon, S., Haines. J., & McPherson, A.C. (2019). Examining risk factors for overweight and obesity in children with disabilities: A commentary on Bronfenbrenner’s ecological system framework. Developmental Neurorehabilitation, 22 (5), 359 – 364.

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Bronfenbrenner’s Ecological Systems Theory

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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.

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The Social Ecology of Childhood and Early Life Adversity

Marcela lopez.

1 Pain/Stress Neurobiology Lab, Maternal & Child Health Research Institute, Stanford University School of Medicine

Monica O. Ruiz

2 Department of Pediatrics, Stanford University School of Medicine, Stanford, CA

Cynthia R. Rovnaghi

Grace k-y. tam, jitka hiscox.

3 Department of Civil Engineering, Stanford School of Engineering, Stanford, CA

Ian H. Gotlib

4 Department of Psychology, Stanford University School of Humanities & Sciences, Stanford, CA

Donald A. Barr

5 Stanford University Graduate School of Education, Stanford, CA

Victor G. Carrion

6 Department of Psychiatry (Child and Adolescent Psychiatry), Clinical & Translational Neurosciences Incubator, Stanford University School of Medicine, Stanford, CA

Kanwaljeet J. S. Anand

Author Contributions:

An increasing prevalence of early childhood adversity has reached epidemic proportions, creating a public health crisis. Rather than focusing only on adverse childhood experiences (ACEs) as the main lens for understanding early childhood experiences, detailed assessments of a child’s social ecology are required to assess ‘early life adversity’. These should also include the role of positive experiences, social relationships, and resilience-promoting factors. Comprehensive assessments of a child’s physical and social ecology not only require parent/caregiver surveys and clinical observations, but also include measurements of the child’s physiology using biomarkers. We identify cortisol as a stress biomarker and posit that hair cortisol concentrations represent a summative and chronological record of children’s exposure to adverse experiences and other contextual stressors. Future research should use a social ecological approach to investigate the robust interactions among adverse conditions, protective factors, genetic and epigenetic influences, environmental exposures, and social policy, within the context of a child’s developmental stages. These contribute to their physical health, psychiatric conditions, cognitive/executive, social, and psychological functions, lifestyle choices, and socioeconomic outcomes. Such studies must inform preventive measures, therapeutic interventions, advocacy efforts, social policy changes, and public awareness campaigns to address early life adversities and their enduring effects on human potential.

The social ecology of childhood includes positive and negative experiences, providing children with a socio-biological framework to meet age-specific developmental goals. Disruptions in this ecology, including frequent low-grade stressors (insecurity, inattention), marked variability (life changes), and trauma (abuse/neglect), can have deleterious effects on children’s health and wellbeing that may continue into adulthood ( 1 , 2 ). Researchers studying the lifelong effects of a child’s social ecology have focused primarily on major adverse events. Metrics like the Adverse Childhood Experiences (ACEs) questionnaire are administered in public health efforts to evaluate, understand, and prevent the health outcomes associated with childhood trauma( 3 , 4 ). Beyond the ACEs, however, preventable sources of early life stress may include food and housing insecurity, bullying, discrimination, inattentive parenting, or family separations. Clinicians do not routinely screen for trauma or the child’s social ecology, partly due to the lack of validated, objective metrics that can be assessed longitudinally.

We review the current discourse on the social ecology of early childhood in relation to child, adolescent, and adult health outcomes, summarize previous social ecology theories, and compare quantitative metrics. We argue that the practice of using ACEs as a method for understanding early life experiences paints a two-dimensional picture of the many interacting factors that comprise a growing child’s multi-dimensional environment. We review the underlying physiology of neuroendocrine stress responses and further contend that biomarkers, such as hair cortisol concentrations (HCC), may provide critical insights into the relations among early adversity, stress, hypothalamic-pituitary-adrenal (HPA)-axis regulation, and subsequent health outcomes.

Social Ecology of Childhood: A Historical Perspective

French philosopher Jean-Jacques Rousseau (1712-1778) first proposed that early childhood experiences establish adult behaviors. Lev Vygotsky (1896-1934) from Moscow proposed the role of social and cultural factors in his theory of speech development, described in his book Thought and Language (1934). This work influenced many, including Jean Piaget (1896-1980), to propose theories of cognitive development in early childhood. Thomas and Znaniecki established a life-course perspective through their longitudinal studies (1918-1920) of Polish peasants in Europe and America( 5 ). Across the 20 th century( 6 – 10 ), early childhood experiences were associated with cognitive, behavioral, social, and psychological outcomes, including the influences of family size and socioeconomic status( 9 ), kindergarten enrollment( 11 , 12 ), and social class( 8 ).

These factors were integrated into the Ecological Systems Theory by Urie Bronfenbrenner (1979), a Russian-American psychologist. Bronfenbrenner conceptualized that human development is shaped by complex relationships between individuals and their environments( 13 ). He argued that contemporary understanding of human development had failed to consider interactive, layered systems within a child’s environment( 14 ). These limitations led him to develop his Ecological Systems model.

Bronfenbrenner’s model depicts four systems – the microsystem, mesosystem, exosystem, and macrosystem – embedded in a chronosystem representing the era in which an individual grows up ( Figure 1 ). The microsystem comprises of interactions, roles and relationships within the home, child-care centers, or playgrounds( 13 ). The interplay among different microsystems is the mesosystem ( 13 ). The exosystem consists of extrinsic environments that affect the child indirectly (where the child is not an active participant), like the parents’ work environments, sibling’s school, or local government( 13 ). Lastly, the macrosystem encompasses greater societal characteristics, such as norms, customs, beliefs, and political structures. Bronfenbrenner’s model serves as a useful tool for exploring, categorizing, and interpreting different facets of children’s environments and experiences. It identifies a plethora of micro- and macro-level characteristics and encourages us to consider factors that impact a child’s life outside their insular family unit. This model presented a major breakthrough in theorizing the complicated structures of multicultural/multiethnic societies, and allowed us to organize complex, hierarchical systems within a Person, Process, Context, and Time (PPCT) framework( 15 ) to address issues at the core of programs and policies targeting children at the family and community level.

Bronfenbrenner’s Ecological Systems Theory presented a breakthrough model for theorizing how the complex, hierarchically organized systems in societies can interact with a child’s life, with a rich interplay between systems leading to the variable or opposing effects on early life adversity (ELA).

Other conceptual models have since been developed to assess the relationship between children’s broader social contexts and their health. In his 1992 book, The Strategies of Preventive Medicine , Geoffrey Rose stated that “the primary determinants of disease are mainly economic and social, and therefore its remedies must also be economic and social”( 16 ). His colleagues, Michael Marmot and Richard Wilkinson, as part of a World Health Organization initiative, expanded on his work to identify the social and economic characteristics which significantly influenced individuals’ well-being and life expectancy, and referred to these as the Social Determinants of Health( 17 ). They focused on poverty, drug addiction, working conditions, unemployment status, access to food, social support, and transportation infrastructure. Other determinants identified since then include social organization, race/ethnicity, gender, immigrant status, neighborhood and housing characteristics( 17 ).

The Life Course Theory emphasizes the timing and temporal context of lived experiences and how they can impact an individuals’ development and wellbeing( 18 ). In response to the “ notion that changing lives alter developmental trajectories ”( 18 ), Glen H. Elder proposed the four principles of Life Course Theory in 1998 as: ( 1 ) “ the life course of individuals is embedded in and shaped by historical times and places they experience over their lifetime ”; ( 2 ) “ the developmental impact of a succession of life transitions or events ”; ( 3 ) “ lives are lived independently, and social and historical influences are expressed through this network of shared relationships ”; and ( 4 ) “ individuals construct their own life course through the choices and actions they take within the opportunities and constraints of history and social circumstances ”( 18 ).

Epidemiologist Nancy Krieger proposed the concept of “embodiment” in 2005, which she defined as “ referring to how we literally incorporate biologically, the material and social world in which we live, from conception to death ,” arguing that human biology could not be understood without “ knowledge of history and individual and societal ways of living ”( 19 ). Through this lens, human interactions “become” human biology. Anthropologist Clarence Gravlee applied this concept to explain how and why racialized experiences and social constructs can negatively impact the health of racial and ethnic minorities in the U.S.( 20 ).

Despite widespread acceptance of these theoretical constructs, most studies focus solely on adversities within the home, testing their associations with physical( 21 – 24 ) and mental health outcomes( 25 ). Many authors use the term early life stress (ELS) to link adverse experiences in a child’s life with negative health outcomes( 1 , 2 , 26 – 28 ); other scholars refer to this phenomenon as “toxic stress”( 29 , 30 ), with no consensus on the nomenclature used to describe relationships between childhood adversity and potential health outcomes. While the ‘stress’ caused by adversity may explain many long-term consequences, ‘stress’ is not the operative factor for all observed outcomes( 1 , 26 ). Instead, we prefer early life adversity (ELA) as a holistic term, including family functions, socioeconomic factors, social supports, neighborhood characteristics, and other factors, more suited for linking early adversities with long-term outcomes. Several measures have been developed to study ELA, with most relying on adult retrospective recall.

Measures of Early Life Adversity

Several inventories, systematically reviewed by Vanaelst et al.( 31 ), assess the frequency of adverse childhood events( 31 ) ( Table 1 ). These were adapted from existing stress questionnaires and modified to inquire about major life events, chronic environmental strains (family, school, relationships, health), and other childhood-related stressors( 31 – 33 ). A cumulative risk approach was first proposed by Holmes and Rahe in their Social Readjustment Rating Scale ( 34 ), then applied to child adversities by Rutter( 35 ), and subsequently followed in other studies( 36 , 37 ). This approach rests on the scientific premise that challenges in one domain are easier to negotiate than challenges in multiple domains. It was simple to use, easy to understand, generated strong statistical associations to engage non-academic stakeholders( 38 ), accounted for the co-occurrence of childhood adversities( 39 ), and helped to identify people at highest risk for poor outcomes( 24 ).

Early Life Adversity Screening Tools

Against this backdrop, Vincent Felitti decided to focus on a specific set of ACEs. Felitti observed that dropouts from an adult obesity program had experienced adverse events as children or youth( 40 ). Detailed patient interviews revealed that childhood abuse was common and predated their obesity; thus, obesity was a self-protective solution to prior adverse experiences and not their primary problem. With Robert Anda and others, Felitti designed the ACEs Study, which surveyed 9,508 adults about ten adverse experiences( 32 , 41 – 43 ). Compared to individuals with no ACEs, persons exposed to four or more ACEs had 4- to 12-fold higher risks for drug abuse, alcoholism, depression, and suicide, 2- to 4-fold increased risks for smoking, poor health, multiple sexual partners, and sexually transmitted diseases, and 1.4- to 1.6-fold increased risks for physical inactivity and obesity( 38 , 40 ). ACEs also showed linear relations with heart disease, cancer, lung disease, fractures, liver disease, and multiple health outcomes.

By summing a fixed number of ACEs, Felitti and others created a quantitative method for estimating childhood adversities( 38 , 40 ). Their work stimulated research, social policy, and public health measures to combat the increasing prevalence of ACEs, and extended the movement for trauma-informed care into the pediatric age groups( 33 ).

Prevalence of Adverse Childhood Experiences

The increasing prevalence of ACEs is a major public health concern( 31 , 32 , 44 , 45 ). In the ACEs study, 63.5% of adults recalled at least one ACE and 12% recalled 4 or more ACEs( 46 ). Subsequent studies, not limited to adult respondents, reported higher prevalence rates of 67%-98%( 47 – 49 ). Preschool children are at greatest risk for child abuse and neglect( 50 ), or domestic violence( 40 , 51 ), but cannot report these experiences due to limited behavioral or verbal expressions( 40 ). ACEs in early childhood remain underreported and underestimated( 30 , 39 , 50 , 52 ).

The U.S. Children’s Bureau reported that 678,000 children suffered abuse and neglect in 2018, with a crude prevalence rate of 9.2 per 1000 children. Of these, 60.8% were neglected, 10.7% physically abused, 7.0% sexually abused, and 15.5% suffered two or more types of abuse( 53 ). Although caregivers often minimize or fail to report the maltreatment of preverbal children( 54 ), children under 1 year of age had the highest rates of abuse (26.7 per 1000 children). In 2018, 1,770 children died of abuse/neglect (case fatality rate 2.39 per 100,000 children), with the highest case fatality rates in infants below 1-year (case fatality rate 22.8/100,000 children)( 53 ). Cumulative exposures have multi-layered effects on child development, with a “mediated net of adversity” that simultaneously augments their risk across cognitive, quality of life, social, economic, psychiatric, and physical health outcomes( 55 ).

Health Implications of Adverse Childhood Experiences

A systematic review of pediatric health outcomes associated with ACEs found prospective evidence for impaired physical growth and cognitive development, higher risks for childhood obesity, asthma, infections, non-febrile illnesses, disordered sleep, delayed menarche, and non-specific somatic complaints( 56 ). These outcomes depended on the ACE characteristics, age of occurrence, and specific types of exposures. For example, prospective studies showed that parental discord or violence were associated with obesity in childhood( 57 , 58 ), whereas prospective studies showed that physical or sexual abuse were associated with youth obesity( 59 – 61 ). From prospective data, Brown et al. clustered the specific ACEs that led to specific risks, to form an ACEs-directed tree for identifying health outcomes( 41 ). For each additional ACE, children were 29-44% more likely to have complex health problems, with multiple needs across developmental, physical, and mental health( 41 ).

Children aged 2-5 years exposed to caregiver mental illness were most likely (56-57%) to have complex health concerns, with the additive effects of other risk factors( 41 ). A significantly higher prevalence of four or more ACEs was found in children with multiple unexplained chronic symptoms in six functional domains (executive dysfunction, sleep disturbances, autonomic dysregulation, somatic complaints, digestive symptoms, emotional dysregulation) compared to matched controls (88% vs. 33%)( 62 ); suggesting a syndrome of nervous system dysregulation in these children, much like that seen in Gulf War veterans( 63 ).

Retrospective studies based on adult recall linked ACEs with an increased vulnerability to chronic non-communicable diseases, substance abuse, sexual risk-taking behaviors( 52 , 64 – 69 ), suicide, domestic violence( 66 , 70 – 73 ), and worse physical and mental health( 44 , 74 – 77 ). From 24,000 adults in the World Mental Health Surveys, retrospective data on childhood adversities doubled the risk of adult psychotic episodes, accounting for 31% of psychotic episodes globally( 78 ). Sexual abuse, physical abuse, and parent criminality had the strongest associations with later psychotic episodes( 78 ).

A meta-analysis of adult health outcomes following four or more ACEs found increased risks for all 23 health and social outcomes, with weak associations for physical inactivity, weight gain, and diabetes; moderate associations for smoking, heavy alcohol use, poor self-rated health, cancer, heart, lung, and digestive diseases; stronger associations for sexual risk-taking, mental ill health, problematic alcohol use, and decreased life satisfaction; and the strongest associations for drug abuse, interpersonal violence, and suicide( 79 ) ( Table 2 ). Thus, ACEs not only contribute to global burdens of adult disease, but their strongest associations with drug abuse, domestic violence, and suicide may directly inflict ACEs onto the next generation( 80 – 82 ).

Outcomes following exposure ≧4 to Adverse Childhood Experiences

Pooled Odds Ratios (ORs) from random effects meta-analyses.

(Modified with permission from: Hughes, et al., Lancet Public Health 2017, 2: e356-e366 (ref. 64 ))

Genetic and Epigenetic Changes:

These intergenerational effects are accentuated via altered gene expression through conserved transcriptional responses to adversity( 83 ), coupled with epigenetic changes such as telomere shortening, reduced stem cell populations, elevated methylation and nitration states among genes in the stress-responsive, inflammation, or other pathways( 84 – 87 ). Stress-associated epigenetic changes contribute to aberrant neuronal plasticity ( 88 ), affect disorders ( 88 ), post-traumatic stress disorder, alcohol use disorder ( 89 ) and depression ( 90 – 93 ), transmitting their physical and mental health risks to future generations( 79 , 94 , 95 ). Mechanisms of stress-associated epigenetic changes may involve DNA methylation or histone acetylation( 90 , 92 , 96 2015, 97 ), changes in mitochondrial DNA copy number and mitochondrial dynamics( 97 ), and microRNAs which are transported via exosomes or binding proteins( 98 ) to regulate the signaling pathways for gene silencing, cellular differentiation, autophagy, and apoptosis( 99 ).

From a systematic review of epigenetic changes in HPA-axis genes, Argentieri et al. found prospective evidence for methylation of HSD11beta2 with hypertension, NR3C1 with small cell lung cancer and breast cancer, FKBP5 and NR3C1 with PTSD, as well as plausible associations of FKBP5 methylation with Alzheimer’s Disease( 84 ). In particular, the glucocorticoid nuclear receptor gene NR3C1 undergoes methylation in varying gene regions from different social and environmental exposures, associated with different mental health outcomes( 84 ).

Focusing solely on PTSD-associated genetic changes, Blacker et al. found 3989 genes upregulated and 3 genes downregulated from 4 GWAS studies in PTSD patients( 85 ). Among the differentially methylated genes, DOCK2 (dedicator of cytokinesis 2) and MAN2C1 (α-mannosidase) were associated with immune system dysregulation in PTSD subjects( 85 ). Urban African-American males with PTSD showed increased global DNA methylation and differential DNA methylation in several genes: decreased in TPR (nuclear membrane trafficking) and ANXA2 genes (calcium-regulated membrane-binding protein), increased in CLEC9A (activation receptor on myeloid cells), ACP5 (leukemia-associated glycoprotein), and TLR8 genes (innate immunity activation)( 100 ). In African-American women with PTSD, this study found a higher methylation of the histone deacetylase 4 gene (HDAC4)( 100 ). A systematic review of stress-associated epigenetic changes and depression found differential methylation of NRC31, SLCA4, BDNF, FKBP5, SKA2, OXTR, LINGO3, POU3F1 and ITGB1, associated with altered glucocorticoid signaling (NR3C1, FKBP5), serotonergic signaling (SLC6A4), and neurotrophin genes (BDNF)( 87 ). Another systematic review confirmed that ELS-triggered epigenomic modulation of NR3C1 was correlated with major depressive disorder( 101 ).

Childhood socioeconomic deprivation and ACEs can lead to adult diseases by increasing their inflammatory burden via multiple genetic factors, including single nucleotide polymorphisms, and epigenetic factors, including nuclear factor-kappaB (NFκB)-mediated gene methylation and histone acetylation. These changes increase expression of pro-inflammatory cytokines, reactive oxygen species, reactive nitrogen species and induce several microRNAs (miR-155, miR-181b-1, miR-146a), with widespread effects on the immune system( 86 ). ELA also alters HPA-axis reactivity in adulthood by (i) genetic factors, such as glucocorticoid receptor polymorphisms; (ii) epigenetic factors altering glucocorticoid receptor function, including methylation of NR3C1, FKBP5, and HSD11beta2; (iii) chronic inflammation due to chronic nitrosative and oxidative stress; and (iv) brain mitochondrial DNA copy number and transcription, with altered mitochondrial dynamics, structure, and function in adulthood( 86 ).

Limitations of the ACEs Score

Despite the known effects of ACEs on genetic/epigenetic changes and long-term health outcomes, it is short-sighted to focus only on ACEs for clinical decisions related to ELA. Newer frameworks must include factors ignored by ACEs scores, including (a) the age of onset and offset; (b) severity of trauma ; (c) frequency of traumatic events; (d) periodicity of trauma within specific developmental periods; (e) concurrence of traumatic events; and (f) multiplicity of events across childhood( 102 ). Thus, popular use of the ACEs score as a proxy for toxic stress appears grossly inadequate.

The American Academy of Pediatrics defines toxic stress “as the excessive or prolonged activation of physiologic stress response systems in the absence of the buffering protection afforded by stable, responsive relationships”( 29 , 30 ). However, toxic stress depends on the child’s complete social ecology, including multiple variabilities in their adverse experiences, environmental conditions, and protective factors( 1 , 33 , 103 , 104 ). Lacey et al. argued that because all ACEs do not carry the same emotional weight or elicit similar distress levels , binary “yes/no” responses cannot represent their impact on the child( 46 ). Lack of consistency in defining ACEs also makes it difficult to compare childhood adversities across different studies( 46 ); further limited by the lack of self-report, absence of protective factors, and dependence on caregiver report( 31 , 46 ). Caregivers may be more inclined to report their child’s behaviors as “problematic” than to divulge personal difficulties, family dynamics, or household dysfunctions( 31 ).

The ACEs score originated as an epidemiological research tool based on adult interpretations of their childhood experiences, but has since been extrapolated to clinical settings( 105 , 106 ). California launched a public health initiative in 2020 to screen children for ACEs in all outpatient visits( 45 ). However, there is limited practical experience of ACEs screening in the clinic, limited resources to address the identified ACEs, and nominal evidence-based algorithms for managing children with multiple ACEs( 31 , 46 ). If clinic-screened ACEs do not relate to recent trauma and the patient appreas asymptomatic, the next steps remain unclear( 42 , 45 ). Potential outcomes of this policy may include unnecessary referrals to Child Protective Services or pediatric subspecialists( 32 , 45 ). The inconsistent description of ACEs in different inventories highlights the broader point that there is no consensus on how to define childhood adversity or grade its intensity( 46 ). This has serious implications for how the ACEs questionnaire is used outside of epidemiology, especially to inform clinical, social, or policy interventions.

Other Factors in the Social Ecology of Childhood

ELA incorporates broader features beyond the individual experiences identified as ACEs( 107 ). For instance, the association between ACEs and child health was strengthened when researchers also accounted for interpersonal victimization (community violence, property crime, bullying), highlighting the cumulative harm from different forms of trauma( 70 ). ELA can be attributed to factors within all ecological systems affecting individuals, families, communities, or broader societies( 52 , 108 ). The rich interplay between these systems must be emphasized, since significant ecological factors are not “stand-alone” but can alter multiple systems at once.

Individual factors:

Effects of childhood adversity typically emphasize the unidirectional effect of negative experiences on child development, disregarding individual demographics or personality factors. Substantial theoretical work on child development highlights the transactional and dynamic interplay between individuals and their environment( 109 ). Sameroff and Chandler consider developmental outcomes to be a function of such transactions, which exert continual effects on one another( 110 ). Similarly, individuals function as active and self-regulating entitities, changing dynamically with the environment and also changing their environment( 111 ). Thus, explanations for emerging health outcomes must account for mutual interactions between individual children and their environmental inputs( 109 ).

Household Factors:

Family environments, characterized by overt conflict, neglect, passive aggression, or unaffectionate interaction styles( 112 ) are associated with a broad range of mental and physical health disorders( 40 , 113 ). Parental traumatic experiences and environments can affect the quality of parenting and child development( 113 ). Maternal depression and trauma are associated with increased rates of insecure attachment in children( 114 – 117 ), related to decreased maternal responsiveness and affective availability( 114 , 118 , 119 ).

Sustained economic problems affect children directly by limiting material resources and indirectly through parental distress, which undermines the parents’ capacity for supportive and consistent parenting( 120 ) , ( 121 ). For example, fathers facing financial losses became more irritable, tense, and explosive, with punitive, rejecting, and inconsistent disciplining behaviors, associated with emotional difficulties in their children( 121 – 123 ).

Community Factors:

Neighborhood deprivation negatively impacts mental and physical health lasting into adulthood( 124 ), likely related to telomere shortening( 125 , 126 ), altered cortisol regulation( 126 ), increased inflammation( 127 ), and differential DNA methylation( 128 ). Children who grow up in communities with higher rates of violence, crime, and noise may suffer from increased stress and lasting trauma( 129 – 131 ). Poor local infrastructure can also affect access to resources such as food and healthcare which can exacerbate health issues( 131 ).

Broader Societal Factors:

Negative societal attitudes and biases, like racial discrimination or segregation, pervade all aspects of a child’s ecology and persist over time; therefore evaluating these factors is particularly important for long-term health outcomes in children of color( 132 – 134 ). Perceived racial discrimination and stereotype threat can trigger stress responses and can affect cognitive processes and academic performance( 135 ). For example, greater perceived discrimination was associated with greater cortisol output in Mexican–American youth( 136 ). Childhood exposures to interpersonal racial discrimination and structural racism stemming from media, schools, law enforcement, government policies, and other cultural stressors also lead to psychological distress and changes in allostatic load for racial minorities in the U.S.( 133 , 134 ). While negative inputs clearly affect the developing brain, positive inputs and protective factors, such as social buffering or individual resilience play equally important roles( 109 , 137 )( Figure 2 ).

Adverse and protective factors in a child’s life are organized by Bronfenbrenner’s ecological systems model. Governmental, socioeconomic and cultural factors in the macrosystem may steer the child’s exosystem either towards adversity or adaptation. ELA (red box/arrows) and adaptation (green box/arrows) may work in tandem to build a child’s resilience, support education, income adequacy, health equity, and access to basic social services. The mesosystem forms an interface between the exosytem and the family unit with variable effects on the child’s milieu. In the microsystem, children are exposed to ELA or pro-social affiliations that affect their developmental, cognitive, behavioral, and health outcomes.

Protective Factors in the Child’s Social Ecology

ELA research must account for the factors that temper adversity, including support, temperament, resilience, and adaptation. For example, the Risky Families questionnaire includes supportive factors (e.g., parental love and support, household dynamics) and ACEs( 138 ). Although stress biology is highly susceptible to early experiences, it is just as malleable to supportive and protective factors( 139 , 140 ). We discuss the role of positive experiences, social relationships, and resilience factors that help children cope with adversity.

Positive Experiences:

Greater emphasis on positive and supportive experiences, fundamental to developing healthy brain architectures and buffering children against the effects of contextual stressors( 141 , 142 ), would complement existing data on the health consequences of ELA. A validated method to assess positive/protective experiences in ELA is the Benevolent Childhood Experiences scale( 143 ).

The Healthy Outcomes from Positive Experiences (HOPE) framework led by Sege and colleagues focuses on promoting positive childhood experiences to prevent or mitigate the effects of ELA. HOPE creates a strong foundation for learning, productive behavior, physical, and mental health( 144 ). Given that young children experience their world through their relationships with parents and other caregivers, positive childhood experiences that engage the child, the parent, and the parent-child relationship are essential( 141 , 142 ). In Wisconsin, positive childhood experiences were associated with dose-dependent reductions in the adult mental health and relational health impairments resulting from ACE exposures( 145 ).

HOPE identifies 4 broad categories of positive experiences and their effects on child development. ( 1 ) Sustained supportive relationships are associated with better physical and mental health, fewer behavior problems, higher educational achievement, more productive employment, and less involvement with social services and criminal justice systems( 141 ). ( 2 ) Growing and learning in safe, stable environments are important for children’s physical, emotional, social, cognitive development, and behavioral health, conferring lifelong benefits( 141 , 146 ). ( 3 ) Opportunities for constructive social engagement and connectedness can promote secure attachment, belonging, personal value, and positive regard( 141 , 147 , 148 ). ( 4 ) Social and emotional competencies cultivate self-awareness and confidence, laying the foundation for learning and problem-solving, identity development, communication skills, and secure personal relationships( 141 ).

Social Relationships:

John Bowlby observed that children separated from their mothers showed intense distress and later maladjustments. In the Attachment Theory , he posited that uninterrupted, secure maternal-infant bonding was evolutionarily adaptive( 149 ). Beginning with maternal-infant bonding, the layering of nurturing, supportive relationships throughout child development enriches self-perception, self-image, and coping skills. Positive social relationships also reduce pain ratings, HPA-axis reactivity, and aberrant brain activation( 150 – 154 ). Perceived social support from friends (not family members) was associated with fewer trauma symptoms in adult survivors of childhood maltreatment( 155 ). Culture-related protective factors can also be leveraged to overcome ELA and promote normal development( 156 ). Thus, social connections with family and non-family members may protect against stress responses to adversity across the lifespan.

Resilience:

Resilience science grew out of concerted efforts to understand, prevent, and treat mental health problems( 157 ). Scientists observed that some children adapted remarkably well despite high levels of adversity. Resilience generally refers to the capacity of any system to recover from exposure to stressors or adversity; it is a mirror image of vulnerability, with processes and capacities common to both( 158 – 160 ). Feldman argues that the construct of resilience involves systems and processes that tune the brain to its social ecology and adapt to its hardships( 161 ). In traumatized children, Happer et al. found stronger evidence for resilience as a process, partial support for resilience as an outcome, but none for resilience as a trait( 162 ).

While resilience research is summarized elsewhere( 160 , 161 , 163 , 164 ), an emerging list of resilience factors in children is featured in Table 3 ( 160 ). Resilience science distinguishes between protective and promotive factors; protective factors have greater effects in the context of adversity, but promotive factors improve outcomes more broadly( 160 , 165 , 166 ).

Resilience-Associated Factors in the Child’s Social Ecology

Adapted from Table 2 in Masten and Barnes 2018 .

Early life adversities, particularly in the absence of protective factors, can trigger a set of emotional responses, metabolic adjustments, physical/behavioral responses, and immune changes contributing to allostasis through the “fight or flight or freeze response” . Many stress responses are regulated through the neuroendocrine system, studied most extensively for the HPA axis.

Neuroendocrine Regulation of Stress Responses

Stress activates the neuroendocrine system, resulting in cortisol and catecholamine release( 31 , 44 ). The stress response evolves through two phases: the first is dominated by catecholamine release, and the second by cortisol. Simultaneous activation of the salience neuronal network and deactivation of the executive control network mediate the first phase( 44 ). The salience network includes the anterior insula, amygdala, hippocampus, striatum, medial prefrontal and anterior cingulate cortices; it integrates cognitive processes for responding to threats, with swift actions to promote survival( 44 , 167 ). The executive control network includes prefrontal and parietal cortices to mediate working memory, impulse control, and emotional regulation( 44 , 167 ). The second phase mediates recovery from stress responses by deactivating the salience network and re-engaging executive control. Such restoration of homeostasis after stress is termed the “adaptive stress response”( 44 ).

Emotional stimuli can activate salience network activity at lower thresholds in the “maladaptive stress response,” resulting in conditioned hyperarousal( 44 ). Allostasis, the HPA-axis adaptation to stress, is maintained in maladaptive stress responses, although resulting in somewhat delayed homeostasis( 31 , 44 , 167 ). Allostatic load results from the repetitive activation of HPA mechanisms attempting to restore homeostasis without returning to baseline( 44 ). Excessive HPA activation causes allostatic components to be unbalanced, leading to architectural and functional changes in the salience and executive control networks( 31 , 43 , 167 , 168 ). Indeed, higher bedtime cortisol levels predicted the reduced prefrontal cortex volumes in traumatized adolescents( 169 ). Chronic adversities overload the neuroendocrine system’s capacity to maintain homeostasis and, especially during periods of heightened neuroplasticity (from prenancy to early childhood), affect crucial aspects of brain development implicated in cognition, self-regulation, physical and mental health( 41 , 43 , 44 , 167 ).

The HPA axis and executive functions mature by age 4-6 years( 170 – 172 ), and a normally functioning HPA axis limits cortisol exposures through negative feedback loops to the anterior pituitary and hypothalamus. These negative feedback loops become ineffective in children with HPA-axis dysregulation( 173 ). Thus, toxic stress may lead to hyper- or hypo-responsivity of the HPA axis, with failed adaptation and eventual exhaustion( 174 ) ( Figure 3 ). HPA-axis dysregulation manifests as emotional problems in preschool children such as internalizing and externalizing behaviors( 175 – 179 ). Considering the harmful manifestations of HPA-axis dysregulation in children and vulnerability of their immature HPA-axis, it is critical that we establish biomarkers for screening preschool children.

Representative patterns of adaptive (green) and dysregulated (red) HPA-axis responses. In the perinatal phase, the fetal brain may be exposed to maternal cortisol levels resulting from prenatal stress, usually associated with dampening of the infant’s HPA-axis postnatally, often lasting into infancy and early childhood. Exposures to ELA/stress then manifest as hyperactive responses to acute stress, which, if prolonged or repetitive, can lead to chronically dysregulated diurnal rhythms and HPA-axis exhaustion.

Cortisol as a Biomarker of Early Life Adversity

Long-term consequences of ELA are mediated through the neuroendocrine system, with downstream effects on neuroimmune, neuroenteric, and cardiometabolic regulation( 43 , 50 ). Measuring stress biomarkers could overcome the inherent limitations of subjective questionnaires and difficulties of implementing the ACEs checklist in children( 44 ). Cortisol, the end-product of HPA-axis activation, regulates the HPA axis through negative feedback loops, activates the autonomic nervous system, alters intermediary metabolism, modulates physiological and immune responses, and contributes to the memory and learning from traumatic experiences( 180 , 181 ). Therefore, cortisol is an important biomarker for ELA( 182 ).

Plasma, salivary, or urinary cortisol levels reflect acute stress reactivity but cannot assess chronic stress because of its diurnal cycles, high state reactivity, pulsatile secretion patterns, and robust changes across age, sex, reproductive cycles, and food intake( 183 – 185 ). A systematic review concluded that HCC represents a measure recent stress, but it included studies from 16 species, which only collected cross-sectional data( 186 ). Measuring acute cortisol responses has significant limitations; repeated sampling over prolonged periods is time-consuming, expensive, and subject to non-compliance. Blood sampling is painful, difficult in children, requires trained staff and stringent laboratory conditions. Salivary sampling is inexpensive and less invasive( 31 , 167 ), but limited by inconsistent collection methods and food-related variability( 31 , 167 , 183 ). Urine sampling from children is challenging, with low participant compliance, sample refrigeration, and urinary metabolites interfere with cortisol measurements( 31 ). In contrast, hair sampling is non-invasive, independent of diurnal cycles, stored at room temperature, and provides chronologically distinct data for cortisol activity up to 6 months( 187 – 189 ).

Emerging research suggests that human hair follicles are neuroendocrine organs that index physiological stress responses( 190 , 191 ). Hair grows about 1 centimeter per month( 192 ) and incorporates the circulating free cortisol( 193 , 194 ), although the underlying mechanisms remain unknown( 195 , 196 ). Russell et al. proposed that free cortisol from the follicular vasculature passively diffuses into the hair shaft, or the hair follicle, sweat, and sebaceous glands may secrete and deposit cortisol into the hair shaft( 196 , 197 ). Like hemoglobin A 1c for blood glucose, hair cortisol concentrations (HCC) summate the cortisol release over time( 198 – 200 ). Earlier concerns about hair washing( 201 , 202 ) and HCC contamination from cortisol secreted by sebaceous or sweat glands have been refuted( 203 , 204 ). HCC show high test-retest reliability, were validated against serum, salivary, and urine cortisol, and are widely accepted as measures of chronic stress in adults( 200 , 205 ) and children( 193 , 199 , 206 ).

Effects of sex, age, and race:

Previous studies reported higher HCC in boys than in girls( 201 , 207 ). However, current data show no sex differences among preschool children( 28 , 193 ), higher HCC in pre-pubertal boys than girls, and no differences after puberty( 208 ). Variations of HCC with age are unclear, with most studies showing age-related decreases in preschool years( 28 , 209 , 210 ). Racialized experiences and structural racial discrimination may contribute to the higher HCC in African-American children compared to children from other races( 28 , 211 ).

Effects of prenatal and postnatal environments:

Higher HCC in 1-year-old infants were associated with maternal parenting stress, depression, and psychological distress( 211 ). Prenatal traumatic events were significantly associated with their child’s HCC at age 3 and 4 years, even after adjustments for known mediators like postpartum depression, parenting stress, psychological distress, child abuse potential; as well as preterm birth or body mass index (BMI)( 212 ).

Other studies found higher HCC in newborns following neonatal intensive care( 213 ), children with early trauma( 214 , 215 ), and children with high fearfulness ratings upon school entry( 206 ). In 6-7 year-olds, low HCC values suggestive of HPA-axis dysregulation were associated with exposures to frequent neonatal pain( 216 ), or harsh parenting( 217 ). Although perinatal adversities may alter long-term HPA-axis regulation into the school-age period, the most prominent postnatal influences on HPA activity result from poverty and early deprivation( 210 , 218 , 219 ).

Effects of socioeconomic adversity:

Children raised in poverty are often exposed to chronic stress, either directly (from food, housing, energy insecurity( 220 ), bullying( 221 , 222 ), or neighborhood violence( 126 )) or indirectly via parental stress( 223 ). Higher HCC were associated with lower parental education( 224 ), lower family income, more household members, single-parent households( 201 ), and deprived neighborhoods( 219 ). Similar associations between ELA and chronic stress( 225 – 228 ) may result from insensitive or rigid parenting( 217 ), parenting stress( 211 , 212 ), neighborhood effects( 126 , 219 ), and other poverty-related factors( 229 – 231 ). To understand the importance of these differences, we explore the implications of HCC as a chronic stress marker and subsequent health outcomes.

Hair Cortisol Concentrations: Implications for Health

Epidemiologic studies have established links between chronic stress, HPA-axis dysregulation, and subsequent physical and mental health outcomes( 27 , 232 ), but only a few of these studies have included HCC as a biomarker for chronic stress ( 189 , 193 ).

Higher HCC in preschool children were associated with impaired social-emotional development and increased risks for developmental delay( 28 , 211 ). In 6-8 year-old children, increased HCC were associated with higher BMI in girls and somatic complaints in boys( 207 ). In older children, increased HCC were associated with higher BMI( 208 ), other measures of obesity( 233 – 236 ), and vulnerability to common childhood illnesses( 237 ), even after controlling for factors such as race, age, gestational age, and birth weight. HCC were reduced in children with asthma( 238 ), possibly from HPA axis suppression due to inhaled corticosteroids( 239 – 241 ). Higher HCC also occurred in children with epilepsy( 242 ) and girls with anorexia nervosa( 243 ), but no differences were found in children with anxiety( 244 ) or depression( 215 , 244 ) as compared to controls.

In adults, HCC was increased in major depression, decreased in general anxiety disorder, whereas HCC changes in PTSD depended on the type of traumatic experience and elapsed time since trauma( 245 , 246 ). Increased HCC was used as a biomarker for stratifying cardiovascular risk and linked to obesity, hypertension, diabetes, metabolic syndrome and cardiovascular disease( 245 , 247 ). In the survivors of physical and sexual abuse, higher HCC during pregnancy were associated with preterm labor( 248 – 250 ).

Since HCC has been correlated with physical and mental illnesses in children and adults, it can be used to probe the connections between ELA, HPA-axis activity, and health outcomes. HCC may also provide unique insights into the physiological ramifications of adversities located and perpetuated in a child’s social ecology.

Current Knowledge Gaps and Future Directions

Significant gaps in our knowledge of ELA must be addressed to understand relationships between ELA and health outcomes. Research using subjective and objective methods should assess community and societal factors alongside with household conditions and parental factors, complemented concurrently by biomarkers.

The ACEs questionnaire was created using patients’ recollection of childhood experiences and correlated with subsequent health conditions. However, the equivalence between adult recollections of ACEs and caregivers’ responses on behalf of their child’s current lived experiences remains undetermined. Caregivers may be unreliable historians of their young child’s experiences, with significant differences between their and the child’s perceptions. Additionally, serial ACEs screening in children does not help us to understand how to prevent or treat ACEs, and potentially reinforces the negative emotions that children have of their experiences.

Historically, the relations among ELA, ELS, and health were studied using lab stress tests, sleep studies, neuroimaging, anthropometrics, epigenetic markers, or galvanic skin responses( 251 , 252 ). This research included small sample sizes, failed to account for developmental differences, and inconsistently sampled age, sex, and racial/ethnic subgroups. Large, population-based studies can overcome these weaknesses using less invasive and less expensive means for recording ELA/ELS, child-centered measurements of stress responses, recording protective/supportive factors, and web-based data entry to minimize costs and increase compliance. Monitoring vital signs for ELA assessments may be less useful if these measures are temporally separated from the adverse experiences. Researchers should consider real-time measures of chronic stress through wearables to index the impact of ELA on health.

ELA alters gene expression through conserved transcriptional responses to adversity (CTRA)( 83 ) contributing to aberrant neuronal plasticity, affect disorders, PTSD, depression and substance abuse( 88 – 93 ). Mechanisms of stress-associated epigenetic changes( 86 , 92 , 253 , 254 ), mitochondrial DNA copy number, telomere shortening( 255 ), and secreted microRNAs( 98 , 99 ) must be investigated in children and adolescents, while also examining the reversibility of these epigenetic modifications and their contributions to later health outcomes.

Social interactions with attentive caregivers reduce infant stress responses and facilitate development( 256 ). Nurturing experiences like “kangaroo care” can reduce neurodevelopmental risks in preterm infants( 257 , 258 ). Secure attachments and friendships across the lifespan play protective roles in cognitive function, physical health, and emotional self-regulation( 259 ). Parent-child involvement in mindfulness-based, mind-body approaches can reduce stress and enhance recovery( 260 ). We encourage researchers to explore the underlying biological mechanisms for social buffering, positive experiences, and other protective/supportive factors.

Screening for ELA without concurrent efforts to abolish the social injustices that promote such adversities is futile. Individual screening cannot, and should not, replace efforts to address the root causes of health inequity, including poverty, lack of healthcare, community violence, racism, and gender-based discrimination. Researchers should work alongside clinicians, politicians, educators, social workers, and community members to develop intervention programs that promote resilience in children and to deconstruct the societal and legal infrastructures that perpetuate systemic inequities. We recommend use of biomarkers such as HCC to supplement existing research efforts and public health interventions as a quantitative, biological marker, firstly, to enhance our understanding of the underlying pathophysiology that mediates the association of ELA with poor health outcomes and secondly, to improve evaluations of the impact of preventive or therapeutic interventions on their intended beneficiaries (i.e., children) in the community.

Conclusions

Research to ensure that ELA can be assessed in the context of a child’s social ecology, not just their ACEs score, is urgently needed. ELA and ELS increase the child’s vulnerability to short-term effects on behaviors, emotions, lifestyle choices, and relationships; with long-term effects on their physical health, psychiatric, social, and economic outcomes. Positive experiences and protective factors must also be considered when investigating the long-term consequences of ELA. Cumulative knowledge from these studies can then guide practical interventions for improving childhood ecologies to decrease ELA and improve health outcomes.

Significant knowledge gaps need to be filled through research in this area. Objective biomarkers for ELA/ELS and protective factors should be validated and used to probe the social ecology of childhood. Intergenerational effects of ELA through epigenetic changes associated with increased vulnerability or resilience must be identified and incorporated into therapeutic trials. Novel approaches for studying the child’s social ecology, possibly from wearables, other real-time measures, or biomarkers, will supplement the parent/caregiver surveys and clinic-based observations. This will inform the development of screening programs, investigations of the underlying mechanisms, and the interventions designed to address the short- and long-term outcomes of ELA across the lifespan. Well-designed trials are essential to establish a scientific framework for proposed preventive measures, therapeutic interventions, social policy changes, or public awareness campaigns. Lack of sufficient investment in investigating and/or addressing the pervasive, pernicious effects of ELA will only escalate its prevalence and long-term consequences for future generations, thereby trapping at-risk families, communities, and neighborhoods into further early life adversities and reduced human potential.

• Current research does not support the practice of using adverse childhood experiences (ACEs) as the main lens for understanding early childhood experiences.
• The social ecology of early childhood provides a contextual framework for evaluating the long-term health consequences of early life adversity.
• Comprehensive assessments reinforced with physiological measures and/or selected biomarkers, such as hair cortisol concentrations to assess early life stress, may provide critical insights into the relationships between early adversity, stress axis regulation, and subsequent health outcomes.

Acknowledgments

Financial Support : Grants from the NIH/National Institute for Drug Abuse (R41 DA046983, P.I. Anand), NIH/ Eunice Kennedy Shriver National Institute for Child Health & Human Development (R01 HD099296, P.I. Anand; R21 HD090493, P.I. Gotlib); and NIH/National Institute of Mental Health (R37 MH101495, P.I. Gotlib). Study sponsors had no role in the design and conduct of the study; the collection, management, analysis, or interpretation of the data; the preparation, review, approval, or decision to publish this manuscript. Authors received no honoraria, grants, or other payments for writing this manuscript.

Disclosure statement : Authors received no honoraria, grants, or other payments for writing this manuscript. In addition, the authors report no relevant financial relationships and no conflicts of interest.

Ethics Statement : No patients were studied, IRB approval was not required, and patient consent was not obtained for writing this review article.

Different uses of Bronfenbrenner’s ecological theory in public mental health research: what is their value for guiding public mental health policy and practice?

• Original Article
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• Published: 14 March 2018
• Volume 16 , pages 414–433, ( 2018 )

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• Malin Eriksson 1 ,
• Mehdi Ghazinour 2 &
• Anne Hammarström 3  

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Bronfenbrenner’s ecological theory is appealing as a conceptual tool for guiding public mental health interventions. However, his theory underwent significant changes since its first inception during the late 1970s until his death in 2005, due to which the implications that can be drawn might differ depending on what concepts (i.e. early or later) of the theory is utilized. The aim of this paper was to examine how different concepts of Bronfenbrenner’s theory have been utilized in (public) mental health research, and to analyse the value of these different uses for guiding public mental health policy and practice. A systematic search for articles that have utilized concepts of Bronfenbrenner’s theory within the field of mental health resulted in a review of 16 published papers. We found that one set of papers ( N  = 10) used the early concepts of ecological systems without investigating interactions between these systems, while another set of papers used the concepts of ecological systems by also investigating interactions within and between these systems ( N  = 4). Another limited set of papers ( N  = 2) utilized the later concepts of proximal processes and the PPCT model. Our results show that studies using Bronfenbrenner’s ecological system concepts by clearly considering interactions between and within these systems can result in recommendations that are most useful for guiding public mental health policy and practice.

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Introduction

Mental health is an integral part of health, defined as “a state of well-being in which an individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and is able to make a contribution to his or her community” (WHO 2014 ). Thus, mental health is more than just the absence of mental disorders or disabilities but a fundamental for good quality of life (WHO 2012 ). Mental illness is a growing global public health problem. The burden of mental and substance use disorders increased by 37.6% between 1990 and 2010 (Whiteford et al. 2013 ). In 2010, mental and substance abuse disorder accounted for 7.4% of disability-adjusted life years (DALYs) worldwide, not the least caused by depressive and anxiety disorders (Whiteford et al. 2013 ). Depression alone accounts for 4.3% of the global burden of disease and is among the largest single causes of disability worldwide, particularly for women (WHO 2013 ). A review of the global burden of mental disorders (Kessler et al. 2007 ), based on data from the WHO mental health survey in 28 countries around the globe, concludes that mental disorders commonly occur in the general population worldwide, often making a debut at an early age, and are often associated with significant adverse costs to society. Since many mental disorders begin in childhood and adolescence (Kessler et al. 2007 ), early detection and interventions are needed. Given the magnitude of mental health problems worldwide, improvements in population health are only possible if countries make prevention of mental health disorders a public health priority (Whiteford et al. 2013 ).

Determinants of mental health and illness include individual, social and societal factors, and their interaction with each other (Sturgeon 2007 ). Thus, mental health needs to be understood from biological, psychological as well as sociocultural perspectives (Kendler 2008 ), and in order to prevent mental illness and promote mental health, there is a need to simultaneously target several multilayered factors (WHO 2012 ). Consequently, a broad public health perspective is needed to promote mental health and prevent mental illness (WHO 2005 ). Public mental health promotion focuses on the social determinants of health in order to strive for positive mental health for all (Jané-Llopis et al. 2005 ). The need for a holistic approach in (mental) health promotion and intervention has been underlined in several international health documents, not the least in the Alma Ata Declaration (WHO 1978 ), the Ottawa Charter (WHO 1986 ) and later by the WHO Commission on the Social Determinants of Health (CSDH, WHO 2008 ). However, in order to clearly understand and act upon these multilayered and interacting social and biological processes that determine mental health, theory is crucial. Theory offers understandings of the causal pathways between various factors and health and disease, and can thus guide the planning and design of public mental health interventions. Despite this, the use of theory in epidemiology and public health research and interventions is still quite sparse (Krieger 2001 ). Further, despite the renewed interest in the social determinants of health, the dominant theories in epidemiology and public health have so far mainly been biomedical or lifestyle oriented, implying a focus on individual-level exposures, behaviours and interventions (Krieger 2014 ). There is clearly a need for theories embracing the complex and multifaceted pathways in mental health, in order to be useful for guiding public mental health policy and practice.

An ecological approach to public mental health

An ecological perspective offers a way to simultaneously emphasize both individual and contextual systems and the interdependent relations between these two systems, and thus offers a variety of conceptual and methodological tools for organizing and evaluating health-promotion interventions (Stokols 1996 ). From a public (mental) health perspective, ecological thinking is appealing since it encompasses several contexts in a very broad sense, including trends such as globalization, urbanization and environmental change, together with (but not solely focusing on) attributes and behaviours of individuals—all relevant aspects for understanding and determining public health (McLaren and Hawe 2004 ). Ecological theories emanate from many disciplines, but health research has mainly been influenced by psychology, including community and developmental psychology (Richard et al. 2011 ). The developmental psychologist, Urie Bronfenbrenner, stands out as one of the most influential contributors to ecological thinking in health research. Influenced by his mentor, Kurt Lewin, Bronfenbrenner ( 1977 ) started to develop his ecological theory as a new theoretical perspective for understanding human development. His theory underwent significant changes since its first inception during the late 1970s, as he constantly revised the theory until his death in 2005. Even though Bronfenbrenner developed his theory to understand human development, it has been extensively applied in many other fields including health research (see e.g. Richard et al. 2011 ; Grzywacz and Fuqua 2000 ).

The evolution of Bronfenbrenner’s theory has been described in different phases (Rosa and Tudge 2013 ): from an ecological approach to human development during the initial phase (1973–1979), followed by a stronger emphasis on the role of the individual and developmental processes during 1980–1993. Finally, in the last phase (1993–2006), the Process–Person–Context–Time model (PPCT) was developed and described as the most appropriate research design for the theory. This development of Bronfenbrenner’s theory has, however, been neglected in most studies. Tudge et al. ( 2009 ) examined 25 papers, all explicitly claiming to be based on Bronfenbrenner’s theory and published in 2001 or later, and found that only four of these studies built on the latest form (PPCT) of the theory. In this paper, we use the term “Bronfenbrenner’s theory” when referring to any of the versions of his theory, and elsewhere we specify what version or concepts we refer to.

Bronfenbrenner’s theory is clearly appealing as a conceptual tool for guiding interventions within the field of public mental health. However, the implications that can be drawn for public mental health policy and practice might differ depending on what concepts (i.e. early or later) of the theory are utilized, and how these concepts are applied. Therefore, the aim of this paper was to examine how different concepts of Bronfenbrenner’s theory have been utilized in (public) mental health research, and to analyse the value of these different uses for guiding public mental health policy and practice.

This implies that we do not intend to judge what version of the theory is the most correct to use, but rather to assess the value of using different concepts of the theory for guiding public mental health interventions. Even though Bronfenbrenner himself acknowledged the latest form of his theory as the most appropriate (Bronfenbrenner and Evans 2000 ), we adhere to a pragmatic view of knowledge and theory. In line with Bryant ( 2009 ), we believe that “knowledge exists in the form of statements or theories which are best seen as instruments or tools; coping mechanisms, not once-and-for-all-time truths. … Rather knowledge [or theory, our note] is a web or a network of statements rather than an edifice, and the value of any form of knowledge [or theory, our note] is its usefulness and applicability which may be constrained in terms of time and place and user” (Bryant 2009 , pp. 4–5).

Thus, we believe that even use of earlier concepts from Bronfenbrenner’s ecological theory might potentially be useful for guiding public mental health interventions.

Methodological approach

Our overall research approach was theoretical in that we examined how different concepts (i.e. earlier versus later) of Bronfenbrenner’s theory have been used within the public mental health field and analysed the value of these uses for guiding public mental health policy and practice. The study was conducted in several distinct steps. Initially, we systematically read through a selection of Bronfenbrenner’s key publications (starting with earlier publications and stepwise continuing with later publications) in order to get a good overview and understanding of how his theory evolved and developed over time. Next, we identified key concepts and basic assumptions in the early and later versions of his theory that could be contrasted and compared with regard to mental health.

After that, we systematically searched for published articles that have utilized Bronfenbrenner’s theory within the field of mental health. This search was conducted to identify illustrative examples of how different concepts of his theory have been applied in mental health research. We searched for articles in the database Web of Sciences, using the following search terms: “Bronfenbrenner” AND “mental health” (topic, all years until November 9, 2015). This search resulted in 34 articles.

These 34 articles were briefly read through to assess their relevance for the purpose of our study. Our criterion for selecting articles for further review was that it should be possible to identify from the article what concepts of Bronfenbrenner’s theory were utilized (i.e. earlier or later concepts), even if not specifically stated by the authors. We made an independent assessment of what concepts of the theory were utilized in each paper, beyond the references used by the authors themselves. In some cases, the authors had referred, for example, to Bronfenbrenner’s later texts, without using concepts of later versions of the theory. Other inclusion criteria were that the concepts used should have been clearly described/defined and applied in the study (as opposed to only discussed in relation to results). Further, some kind of mental health indicator ought to have been used as an “outcome variable”. Articles that did not fulfil these criteria were excluded from further analysis, including purely methodological and/or theoretical papers. In this way, 15 of the 34 papers were selected for further analysis. In addition, another relevant article was found in the database Pub-Med, using “Bronfenbrenner” as the search term (all fields, until 9 November 2015).

In total, 16 relevant articles were identified, and these papers were used as a basis for analysing the value of using different concepts of Bronfenbrenner’s theory for guiding public mental health policy and practice. The selected articles were summarized by content in Appendix 1. They are included in the list of references (indicated by *) and are cited below.

Analysis of selected articles

All 16 papers were read and reread thoroughly in order to identify how concepts of Bronfenbrenner’s theory were utilized; the aims of using his theory within the field of mental health; study design; ecological concepts used; main findings with regard to mental health; conclusions drawn and implications for public mental health interventions (see Appendix 1 for a summary of this review). This review was then used as a basis for analysing the overall strengths and limitations of using different concepts of the theory with regard to guiding public mental health interventions.

Results and reflections

This section is structured in three parts: first, we briefly present the development of Bronfenbrenner’s theory over time and compare the analytical focus between different conceptual versions of his theory, with regard to mental health. Next, we present a summary of how various concepts of Bronfenbrenner’s theory have been applied in mental health research, and finally we discuss the value of these different uses of the theory for guiding public mental health interventions.

Key concepts and basic assumptions in early and later versions of Bronfenbrenner’s theory

In this section, we give a brief overview of the development of Bronfenbrenner’s theory during the period 1973–2006, mainly based on the three phases proposed by Rosa and Tudge ( 2013 ).

Phase 1 (1973–1979)—an ecological approach to human development

During the 1970s, Bronfenbrenner named his emerging theory an “ecological model of human development” (Rosa and Tudge 2013 ). Ecology was defined as a fit between the individual and his/her environment. In order to develop, and not only survive, the fit between the individual and its environment must be even closer (Bronfenbrenner 1975 ). In this earliest stage of the theory, Bronfenbrenner described the ecological environment as composed of systems at four different levels. The microsystem contains relations between the individual and the immediate environment surrounding the individual, such as the home, school and workplace (Bronfenbrenner 1977 ). The mesosystem comprises interrelations between major settings containing an individual, such as relations between home and school, home and peer-groups, etc. (Bronfenbrenner 1977 ). The exosystem embraces social structures—major institutions of the society—such as the world of work, the mass media and public agencies. These social structures do not themselves contain the developing person but impinge upon the immediate settings in which that person is found, and as such influence what is going on in these settings (Bronfenbrenner 1977 ). The macrosystem consists of the blueprints of a particular society such as laws and regulations but also unprinted rules and norms (Bronfenbrenner 1978 ). Analysing the composition of these ecological systems as well as interactions between and within these systems and individual factors was regarded as crucial in order to understand and explain a developmental outcome. The requirement for ecological research was to include at least two different ecological systems in the analysis to understand a particular developmental outcome (Bronfenbrenner 1975 ). In addition, Bronfenbrenner also emphasized ecological transitions in his early texts, i.e. shifts from one ecological context to another that every person undergoes throughout life (Bronfenbrenner 1979 ), such as starting school, getting a sibling, marriage, divorce, getting a new teacher, moving, etc. Investigating the characteristics, qualities and impact of the ecological transitions an individual goes through was also proposed by Bronfenbrenner ( 1978 ) as an important part of ecological research.

Phase 2 (1980–mid-1990s)—adding biology and chronosystem into the ecological framework

During this period, Bronfenbrenner further developed ideas about how individual characteristics interplay with context. In a paper from 1994 about the relation between nature and nurture, Bronfenbrenner and Ceci state that genetic material is not finished traits, but interacts with environmental experiences in determining developmental outcomes. According to them (Bronfenbrenner and Ceci 1994 ) human development involves interaction between the biological and psychological person and his/her environments, and the realization of human potential requires an intervening mechanism that connects the inner with the outer in a two-way process occurring over time.

During this phase, Bronfenbrenner put more emphasis on the close and reciprocal face-to-face interactions with the child’s immediate environment (Bronfenbrenner and Ceci 1994 ). This was later referred to as “proximal processes”—a concept that was fully developed in phase 3 (see below). During this phase, Bronfenbrenner also developed his thinking about time by adding “chronosystems” to his ecological model. Although Bronfenbrenner mentioned time already in his book from 1979, the concept of chronosystem was not added until this second phase. By adding chronosystems, Bronfenbrenner wanted to take into account changes over time, not only within the person but also in the environments in which that person is found, to investigate how these changes may affect a person’s developmental outcomes (Bronfenbrenner 1986 ). This could entail investigating how changes in a parent’s work status (part-time, full-time, etc.) over time during a child’s school ages could affect patterns of parent–child communication, and how these patterns in turn could influence the child’s achievement and social behaviour in school (Bronfenbrenner 1986 ).

Phase 3 (mid-1990s–2006)—a Process–Person–Context–Time (PPCT) model

During this final phase, Bronfenbrenner finalized his theory by developing his thinking about “proximal processes”, now referred to as the “engine of development”. Proximal processes involved reciprocal interaction between the developing individual and other (significant) persons, objects and symbols in his/her immediate environment, and these processes could involve activities between parents and child and child and child, such as playing, reading and learning new skills (Bronfenbrenner 1995 ). Proximal processes were viewed as the most powerful predictor of human development and Bronfenbrenner wanted to show how individual characteristics, together with aspects of the environment, influence proximal processes (Rosa and Tudge 2013 ). In specifying the nature, operation and developmental effects of proximal processes, Bronfenbrenner “re-conceptualized” the microsystem. According to him, proximal processes operate within microsystems and involve interaction with three features of the immediate environment: persons, objects and symbols. Persons were further referred to as “significant others” by adopting Mead’s terminology (Bronfenbrenner 1995 ). In further trying to rule out why different developmental outcomes vary between individuals, Bronfenbrenner and his colleagues (Bronfenbrenner and Ceci 1994 ; Bronfenbrenner 1995 ; Bronfenbrenner and Evans 2000 ) developed this hypothesis into a Process–Person–Context–Time model (PPCT), and the model was developed to guide how bioecological research best could be conducted (Rosa and Tudge 2013 ). Considering Process would imply assessment of regularly occurring activities and interactions with significant persons, objects and symbols in the developing individual’s lives. Accounting for Person would require analysing how individual characteristics influence proximal processes, such as assessing how age, gender, temperament, intelligence, etc. influence these activities and interactions. Context was described as involving four interrelated systems: microsystem (the immediate environment where the developing person engages in activities and interactions, i.e. where proximal processes occur), mesosystem (interrelations among several microsystems in which that person is situated), exosystems (contexts having an indirect influence on the person) and finally, macrosystem (contexts with a shared belief system). Adding Context could thus imply evaluating the influences of different exosystems (such as parent’s work or the mass media) and/or different macrosystems (such as values within cultural groups) on the proximal processes of interest. Finally, considering aspects of Time would ideally require a longitudinal study with at least two measurement points taking into account the current point of historical time (Tudge et al. 2009 ). Bronfenbrenner never implied that all four elements have to be included in every study, but underlined that studies involving the PPCT model should focus on proximal processes, showing how they are influenced both by characteristics of the developing individual and by the context in which they occur (Tudge et al. 2009 ).

Table  1 shows that the core of analysis of mental health studies applying the earliest concepts (developed in phase 1) of Bronfenbrenner’s theory would be to examine how mental health is determined by mutual influence between individual factors and the ecological systems surrounding an individual/group, as well as interactions between and within these ecological systems. Further, mental health studies applying later concepts (from phase 2) of Bronfenbrenner’s theory would also add chronosystem to the ecology. Finally, studies using the most mature concepts of the theory (developed in phase 3) would focus on proximal processes and applying the PPCT model. As Table  1 shows, it is also clear that the earlier phase of the theory put more emphasis on context, while the later phases put more emphasis on the closer environment.

Different uses of Bronfenbrenner’s theory in mental health research

From the 16 reviewed articles, we were unable to identify articles that could be regarded as “purely” using concepts from just one of the identified phases of the theory, as outlined by Rosa and Tudge ( 2013 ). This probably reflects a general unawareness of how Bronfenbrenner’s theory developed over time, a fact also noted by others (Tudge et al. 2009 ; 2016 ). Instead, we found three main ways of using concepts from Bronfenbrenner’s theory within our 16 reviewed papers. One set of papers ( N  = 10) used the concepts of ecological system (of which five also included chronosystem) without investigating interactions between these systems, while another set of papers used the concepts of ecological systems by also investigating interactions within and between these systems ( N  = 4). Another limited set of papers ( N  = 2) utilized the later concepts of proximal processes and the PPCT model. Two of the reviewed articles (Mutumba and Harper 2015 ; Romano et al. 2015 ) used concepts of Bronfenbrenner’s theory (at least partly) in conjunction with other theoretical frameworks, while the others were based solely on concepts from Bronfenbrenner’s theory. Table  2 summarizes how the theory has been utilized within these three identified groups of articles with regard to the purpose of using Bronfenbrenner’s theory; study designs; concepts utilized; main results; implications for public mental health policy and interventions; and strengths and weaknesses for guiding public mental health policy and practice.

Studies utilizing ecological systems concepts without considering interactions between and within ecological systems

Table  2 illustrates that ten out of 16 reviewed articles utilized ecological systems concepts without clearly considering interactions within and between these different ecological systems. This implies that the majority of our reviewed articles utilize Bronfenbrenner’s theory in a way that was never intended by Bronfenbrenner himself, since even in his earliest writings he underlined the importance of considering interactions within and between ecological systems (Bronfenbrenner 1975 ). These ten studies have in common that they aim to go beyond individual risk factors for understanding various mental health outcomes, since previous studies have mainly focused on personal characteristics without considering the larger surrounding environments. Thus, these studies use concepts of Bronfenbrenner’s ecological theory for identifying factors at different ecological levels that can explain the development of mental health outcomes in general (Pilgrim and Blum 2012 ; Aston 2014 ), but also more specific mental health related outcomes such as parenting capacity (Grant and Guerin 2014 ), bullying and peer victimization in schools (Hong and Espelage 2012 ; Huang et al. 2013 ; Upton Patton et al. 2013 ), school shootings (Hong et al. 2010 ), and sexual assaults (Campbell et al. 2009 ).

The concepts used in these studies are naturally different ecological systems (micro, meso, exo, macro, and chrono) as well as various individual factors. Consequently, the results from these studies end up identifying factors at different ecological levels that are positively and/or negatively associated with the particular mental health outcome in focus. Further, even if the need to consider interactions between and within ecological systems in order to understand mental health outcomes is pointed out in (some of) these studies, this is not explicitly done in the analyses. As an example, Hong and Espelage ( 2012 ) in their literature review identified risk factors at all ecological levels associated with bullying and peer victimization in school, but did not really consider interactions between these different systems beyond bringing up the fact that the associations between parent–youth relationships and bullying may differ for boys and girls. Likewise, Campbell et al. ( 2009 ) point out that the “next step” of developing a model of rape recovery would be to examine interactions across different levels of the social ecology, in order to get a comprehensive understanding. They (Campbell et al. 2009 ) further discuss that the mixed results found in their review regarding the influence of individual characteristics and assault characteristics on the mental health effects of sexual assaults probably are due to unexplored cross-level interactions.

The policy implications that can be drawn from these studies are consequently quite unspecific. When discussing the policy implications from their review of factors associated with school bullying and peer victimization in the People’s Republic of China, Huang et al. ( 2013 ) end up in general recommendations such as the need for (1) considering individual factors (age and gender) by targeting younger children and boys in particular (since these groups are more prone to engage in bullying); (2) setting up parent education for abusive parents ( micro level ); and (3) restricting children’s exposure to media violence ( exo level ). Similarly, Hong et al. ( 2010 ), when discussing the policy implications of how to prevent school shootings, end up with unspecific recommendations such as the need for skill-building programmes for parents and youths on communication and conflict resolution ( micro level ); setting up of arenas where parents and teachers can meet ( meso level ); provision of educational material about the detrimental effects of exposure to media violence ( exo level ); implementing school programmes that address gun violence in school ( macro level ); and educating governments about the relation between social conditions and negative outcomes among immigrants ( chrono level ). Likewise, Yakushko and Chronister ( 2005 ) outline various counselling strategies and interventions at different ecological levels for immigrant women in the US. They suggest the importance of the counsellor valuing immigrant women’s cultural experiences (individual level); assessing changes in women’s family structure (micro level); strengthening existing support networks (meso and exo levels); and informing about laws that prohibit discrimination (macro level). Although these recommendations are relevant and valid, one might assume that these recommendations could have been brought up even without using an ecological theoretical framework. Likewise, Mutumba and Harper ( 2015 ) use an ecological framework to identify the risk and protective factors for mental health diseases for sexual minority youth at different ecological levels. However, in their recommendations for treatment and support, they end up in very broad recommendation such as “developing and enforcing child protection systems”, without even linking these recommendations to the ecological levels where they “belong”.

Thus, even though these studies bring up general suggestions for how to move beyond individual factors to also intervene in the social environment, they do not give any detailed advice on how to prevent a specific mental health outcome for a particular target group. One exception though is Pilgrim and Blum’s ( 2012 ) study about the risk and protective factors for adolescents’ mental and physical health in the English-speaking Caribbean. They identified that girls are more likely to experience internalizing problems, while boys are more likely to have externalizing problems. Therefore, interventions focusing on skills training for emotional regulations, coping skills for managing stress and dietary behaviour may be especially beneficial for girls, while policies advocating for reduced youth access to drugs and weapons and programmes focusing on conflict resolution skills may be especially beneficial for boys. However, beyond this example, studies utilizing early concepts of Bronfenbrenner’s theory without considering interactions between and within ecological systems tend just to include many factors at various levels in a mental health-risk model, without being able to rule out the complex interactions between these factors. These kind of results easily lead to the conclusion that “everything affects everything”, which is not very helpful for health policy and planning (Grzywacz and Fuqua 2000 ).

Studies utilizing ecological systems concepts by considering interactions within and between systems

Four of the reviewed articles used a more multifaceted ecological analysis by taking into account interactions within and between ecological systems. Beyond identifying factors within different ecological systems (micro, meso, exo and macro) associated with various health outcomes for different groups of people (e.g. based on gender, age, etc.), these articles also aim to analyse interactions between risk factors at different levels and if and how risk factors act in a cumulative manner. Thus, these studies move beyond focusing on isolated variables and contribute to an understanding of the complex interactions between various risk and/or protective factors and their effect on mental health outcomes for different groups of people.

When analysing risk factors for problem behaviour among English and Indian children living in London, Atzaba-Poria et al. ( 2004 ) not only identified risk factors at different ecological levels, but also analysed how much of the risk could be attributed to each of the different ecological levels, as well as cumulative risks of various exposures. They found that regardless of the specific type of risk, the more accumulated risks children experienced, the higher the levels of total problem behaviour. They were also able to detect how different kinds of accumulated risks (emanating from the micro, meso or exo level or individual factors) were associated with different behavioural problems (aggressive behaviour versus anxiety and depression). Likewise, Behnke et al. ( 2011 ) were able to detect how the association between factors at different ecological levels and depressive symptoms differed for girls and boys. Equally, Romano et al. ( 2015 ), in their review of the complex relationship between childhood maltreatment and later academic achievement and mental health, found that the negative consequences of childhood maltreatment seemed to be greater for boys than girls. They also found that some forms of maltreatment (early in life, multiple, neglect) seemed to be especially harmful for academic achievements. Further, McDaniel et al. ( 2012 ) explored interactions between micro and meso level interactions and found that blogging (meso level interactions) positively influenced family relations (micro level interactions) which in turn had a positive effect on maternal well-being. Thus, the positive effects of the mesosystem went through interactions with the microsystem.

The results of these studies show how the influence of different risk factors may vary for different groups and depending on the mental health outcome in focus. Thus, the recommendations for interventions that can be drawn from these studies are in general more specific. One clear example is the study by Atzaba-Poria et al. ( 2004 ). They found that interventions within the microsystem were needed in order to prevent aggressive behaviours among children, while interventions in the exosystems (peer and parental relations) were needed in order to prevent anxious and depressive behaviours among children. Behnke et al.’s study ( 2011 ) further suggests that interventions targeting adolescents’ self-esteem and depressive symptoms need to be tailored differently for boys and girls; targeting neighbourhood factors might have to be especially tailored to meet the needs of boys while targeting societal discrimination has to specifically address the needs of girls. Finally, the review by Romano et al. ( 2015 ) suggests that some forms of child maltreatment—neglect, early and multiple—might be especially important to detect and intervene against in order to promote later academic achievement and mental health. These recommendations can thus be used for tailoring interventions for the specific target group and outcome in focus. Consequently, studies using Bronfenbrenner’s ecological system concepts by clearly considering interactions between and within these systems can result in recommendations that are most useful for guiding public mental health policy and practice. However, even if these recommendations might be specific, one needs to acknowledge that the recommendations might not be too easy to implement in practice since they require quite complex societal interventions.

Studies applying later concepts of the theory

We identified only two studies that have utilized the later concepts of Bronfenbrenner’s theory. Our review suggests, in line with others (Tudge et al. 2009 ; Tudge et al. 2016 ), that the later version of Bronfenbrenner’s theory is still less utilized in research, including the field of public mental health. Liem et al. ( 2010 ) used longitudinal data from a random sample of young people in Boston, USA, to explore differences in mental health outcomes (depressive symptoms, life satisfaction) between high school dropouts and graduates, while Williams and Nelson-Gardell ( 2012 ) used data from the US National Survey of Child and Adolescent Well-Being to examine factors predicting resilience in sexually abused adolescents. Both these studies used all or some elements from the PPCT model to analyse factors positively and negatively associated with mental health outcomes for different population groups. In these studies, proximal factors are given more “weight” for understanding mental health outcomes, although especially Williams and Nelson-Gardell ( 2012 ) also considered some more distal factors (family SES) that proved to be of equal importance in predicting clinical symptoms in sexually abused adolescents.

Both these studies found that peer and family support, in combination with an individual’s capacity to accept and utilize these resources, is critical for protecting individuals against poor mental health. Thus, these studies underline the importance of a close supporting surrounding environment, and the policy recommendations, therefore, suggest interventions to support and strengthen the parent, peer and child relations. Williams and Nelson-Gardell ( 2012 ) conclude that in order to promote resilience in sexually abused adolescents, interventions focusing on caregiver support and school engagement (proximal processes) or addressing caregiver education or economic assistance (contextual factors) will be the most effective and beneficial.

In summary, these studies give quite detailed guidance on (proximal) factors influencing the particular mental health outcomes in focus. However, given the weights on factors in the immediate, close environment, the recommendations that can be drawn from these studies focus mainly on interventions in the close and immediate environment, while somewhat downgrading actions are needed in the wider environment.

Conclusion—different uses of Bronfenbrenner’s theory; what is their value for guiding public mental health policy and practice?

In summary, our study shows how the majority of mental health studies utilizing Bronfenbrenner’s theory seem to use the early developed ecological system concepts without considering interactions within and between these systems. We do not believe that our review covers all studies within the field of public mental health that utilize Bronfenbrenner’s theory. Still, it is striking that the vast majority of the identified articles use concepts of the theory in a way that was never intended by Bronfenbrenner himself. This finding supports Tudge et al.’s ( 2009 ) conclusion that one common misuse of early versions of Bronfenbrenner’s theory is that it is used to map out contextual and individual factors contributing to an outcome while not analysing mutual interactions between the individual and the context, which was the explicit intention even with the initial version of the theory. Above, we claimed a “pragmatic view of theory”, implying that concepts of a theory could be potentially useful (within a specific context) even if used in a way that was never intended. However, our results show that the recommendations for public mental health policy and practice that can be drawn from these studies are not very useful in that they are too broad and unspecific for suggesting what needs to be done for whom in order to influence a particular mental health outcome. As Stokols ( 1996 , p. 288) puts it, “overly inclusive models are not likely to assist researchers in targeting selected variables for study, or clinicians and policy-makers in determining where, when, and how to intervene”. Thus, we propose that using early concepts of Bronfenbrenner’s theory without considering interactions within and between different ecological systems might be a less valuable use of the theory within the field of public mental health.

In contrast, our analysis shows that studies utilizing Bronfenbrenner’s ecological system concepts, by clearly considering interactions within and between different ecological systems, can come up with most useful recommendations for public mental health promotion and interventions. These kinds of studies have the potential to rule out the “specific circumstances (e.g. intrapersonal, physical environmental, organizational, cultural) that account for the occurrence and prevalence of particular health problems, and a corresponding analysis of the contextual factors that are likely to influence the effectiveness of health-promotive interventions designed to reduce those problems” (Stokols 1996 , p. 288). These kinds of recommendations may suggest what works for whom to prevent a particular mental health outcome. Therefore, we conclude that studies using early concepts of Bronfenbrenner’s theory, by considering interactions within and between different ecological systems, can come up with valuable results for guiding public mental health interventions. This use of the theory offers a way to simultaneously focus on intrapersonal and environmental factors and the dynamic interplay between these factors in determining mental health. This way of using early concepts of the theory therefore corresponds very well to the ecological “needs” within public (mental) health for understanding the complexity of public health problems, including social inequality in health and the effects of place on health (McLaren and Hawe 2004 ). In addition, using concepts of Bronfenbrenner’s theory in this way is well in line with a life course and social determinants of mental health perspective that emphasizes how mental health is shaped not only by individual factors but to a great extent by the social, economical and physical environments in which people live throughout their lives (WHO and Calouste Gulbenkian Foundation 2014 ).

We found only two mental health studies that had utilized the later concepts of proximal processes and the PPCT model of Bronfenbrenner’s theory. This is despite the fact that these concepts were stated to be the most appropriate use of his theory (Bronfenbrenner and Evans 2000 ). The lack of studies utilizing these concepts might be due to the fact that this version of the theory is less known and spread in the scientific community, as indicated by Tudge et al. ( 2009 ). Alternatively, there may be a considered decision not to use these later concepts, given their main focus on proximal processes at the expense of environmental factors. Our analysis show that these final concepts do not obviously fit a public health and social determinants of mental health perspective, but might be more suitable within other fields such as psychotherapy where person-centred theories are the most appropriate to understand the structure and development of personality, taking into account dimensions of both temperament and character. The PPCT model is well in line with the ideas of Cloninger et al. ( 1993 ), who describe four dimensions of temperament: novelty seeking, harm avoidance, reward dependence, and persistence, which are independently heritable and manifest early in life. Cloninger et al. ( 1993 ) additionally describe three dimensions of character that mature in adulthood and influence personal and social effectiveness by insight learning about self-concepts. Self-concepts vary according to the extent to which a person identifies the self as (1) an autonomous individual, (2) an integral part of humanity, and (3) an integral part of the universe as a whole. Consequently, our study suggests that within the field of public mental health research and practice, the later concepts of Bronfenbrenner’s theory might not be the most useful. The final version of his theory, with its emphasis on proximal processes and the immediate environment, lacks a clear focus on how the social, economic and cultural environments that people are exposed to influence mental health. The policy implications that can be drawn from the PPCT model thus focus much more on the individual and consequently lean towards individual health promotion models, with an emphasis on changing individual health behaviour without considering the social and organizational context. These models have previously been extensively used in health promotion but have been criticized, not least for their “victim-blaming” ideology (McLeroy et al. 1988 ; Baum 2008 ). We should, however, remember that Bronfenbrenner was a developmental psychologist - a knowledge field with a clear focus on human growth and development in relation to age. Therefore, the latest concepts of Bronfenbrenner’s theory could be seen as a return from a macro level perspective to a more individual-directed perspective where most developmental psychologists operate. In addition, one must also acknowledge that we were able to find very few articles that had tried to utilize these later, most mature concepts of Bronfenbrenner’s theory. One of our two identified articles (Williams and Nelson-Gardell 2012 ) was also brought up in a recent re-evaluation of the uses of Bronfenbrenner’s theory (Tudge et al. 2016 ) where it was evaluated as a study utilizing variables related to the PPCT, but without really testing the theory. The lack of illustrative examples of studies using the PPCT model limits a “fair” assessment of the value of using these concepts to guide public mental health interventions. Indeed, assessing the “influence of individual and contextual characteristics, through their influence on proximal processes” (Bronfenbrenner 1995 ), might be an appealing approach also in public mental health research. We believe that further development of an ecological approach in public mental health research would benefit from exploring proximal processes, operating on a more collective level, beyond Bronfenbrenner’s more individually focused approach. Finally, even if we conclude that the PPCT model might not be the most useful version of Bronfenbrenner’s theory within the field of public (mental) health, we do not claim that individual factors do not matter. In fact, equally important for public mental health policy and practice is to consider the variety of personal attributes such as psychological disposition and behavioural patterns that influence mental health (Stokols 1996 ). An ecological perspective that can “integrate the community wide, preventive strategies of public health and epidemiology with the individual-level, therapeutic and curative strategies of medicine” (Stokols 1996 , p. 286) is needed within public mental health. This dual focus both on the surrounding environment and on personal attributes for explaining and promoting mental health can be achieved by utilizing early concepts of Bronfenbrenner’s ecological theory, as long as interactions between and within ecological systems and individual factors are thoroughly investigated and considered.

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Eriksson, M., Ghazinour, M. & Hammarström, A. Different uses of Bronfenbrenner’s ecological theory in public mental health research: what is their value for guiding public mental health policy and practice?. Soc Theory Health 16 , 414–433 (2018). https://doi.org/10.1057/s41285-018-0065-6

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Journal of Entrepreneurship in Emerging Economies

ISSN : 2053-4604

Article publication date: 20 April 2022

Issue publication date: 20 November 2023

The research effort on entrepreneurship education has been mainly for the higher education settings and on the individual level of analysis. On the contrary, this research urges scholars to expedite attention to the secondary education settings, especially in the emerging economies in Asia and Africa. This paper aims to reveal the existing landscape of literature development on the topic and promote ecological approaches of constructing entrepreneurship education programs in schools. It advocates the “incubator” role of schools for students and the necessity of establishing socially embedded entrepreneurship education as the playground for future entrepreneurs.

Design/methodology/approach

This study followed the systematic literature review as its research design. It obtained 1,555 publications from six academic databases and 60 more publications from expert consulting and backward snowballing technique. Data screening resulted in a total of 101 relevant publications with the upper secondary education as their research context. The qualitative integrative synthesis method was then applied to integrate research evidence to the five circles of systems according to Urie Bronfenbrenner’s ecological systems theory.

This study contributes to the entrepreneurship education and youth career development literature, especially in the developing countries. Results discovered that entrepreneurship education programs, when interacting with ecological systems, resulted in training success. The most frequently studied systems were microsystems; here, there was a dominant focus on program-level reporting and analyzing. There was less focus on other systems such as mesosystems, exosystems or macrosystems. Moreover, only one study was associated with chronosystems, suggesting a significant research gap regarding the longitudinal studies. However, this review validated the different approaches to delivering entrepreneurship education in emerging and developed economies.

Research limitations/implications

One limitation of this research lies in the methodology. The inclusion criteria limited the studies to the context of upper secondary education and excluded those of secondary education in general. The sampling method limited the power of this research to analyze and discuss policy-level studies because policies most likely embrace the whole secondary education level as its target. Another limitation is associated with the lack of experimental studies in assessing the comparative advantages of following the ecological approach when constructing entrepreneurship education. It, therefore, remains an undiscussed matter within this study regarding whether following the ecological approach means empirically a better educational choice or not.

Practical implications

This study discusses the implications for policymakers, especially in emerging economies, and suggests that awareness, attention and funding are needed to empower youth entrepreneurship education from an ecological systems view.

Originality/value

To the best of the authors’ knowledge, this research is one of few studies that use the ecological systems theory in the context of entrepreneurship education with the purpose of focusing on environment-level analysis instead of individual-level analysis. Through the systematic literature review, this study proposes an ecological approach to comprehend, guide, evaluate and improve the design and implementation of entrepreneurship education programs in schools based on well-articulated research evidence. The research can inform both researchers and educators by offering a holistic perspective to observe and evaluate entrepreneurship education programs and their levels of social connectedness.

• Systematic literature review
• Entrepreneurship education
• Ecological systems theory
• Career choices
• Upper secondary education
• Adolescents

Acknowledgements

The authors have no conflicts of interest to disclose. This work was supported by the JSPS Postdoctoral Fellowships for Research in Japan (Grant number: P19779); the Grants-in-Aid for Scientific Research (Grant number: 19F19779); the New Teacher Startup Support Funding at the Toyohashi University of Technology.

Lin, J. , Qin, J. , Lyons, T. , Nakajima, H. , Kawakatsu, S. and Sekiguchi, T. (2023), "The ecological approach to construct entrepreneurship education: a systematic literature review", Journal of Entrepreneurship in Emerging Economies , Vol. 15 No. 6, pp. 1333-1353. https://doi.org/10.1108/JEEE-12-2021-0455

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A methodological guide for applying the social-ecological system (SES) framework: a review of quantitative approaches

• Stefan Partelow Stefan Partelow Leibniz Centre for Tropical Marine Research (ZMT), Bremen, Germany; Jacobs University, Bremen, Germany

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The social-ecological systems framework (SESF) remains one of the most highly cited and empirically applied conceptual frameworks for diagnosing social-ecological systems (Ostrom, 2007, 2009, McGinnis and Ostrom 2014). Notably, the SESF does not have a methodological guide or a standardized set of procedures to empirically apply it. This is to some extent by design, to allow flexibility in how methods are adapted to diverse contexts (McGinnis and Ostrom 2014). However, this has led to highly heterogeneous applications and challenges in designing a coherent set of data collection and analysis methods across cases.

A main challenge is that methodology is a general term, which actually refers to a set of stepwise specific procedures which can include study design, conceptualization of variables and indictors for data collection, empirical or secondary data collection, data processing and cleaning, data analysis, as well as data visualization, communication, and sharing. Although the SESF provides a uniform set of variables, it does not indicate any of the other necessary steps for a robust scientific study. Applying the SESF is not a method itself, but it is arguably a theory-derived conceptual guide for focusing the methods a researcher does choose on a set of variables that have previous empirical support in shaping commons, institutional development and change, and/or collective action outcome. Thus, scholars are forced to either mirror previous studies or develop their own procedures, leaving heterogeneous applications that enable contextually tailored approaches but hinder comparability across studies.

The focus of this study is to explicitly synthesize the methods applied in SESF studies by systematically reviewing published quantitative applications of the SESF and to develop a methodological guide for the framework’s continued application while highlighting the challenges in current literature. A guide is useful so that scholars can map their methodological choices more transparently, sparking reflections for their own study designs and better enabling the systematic communication of study methodological decisions to others. To apply the SES framework, a series of methodological steps are needed. These steps have been referred to by Partelow (2018) as methodological gaps, because if they are not explicitly defined by authors, they can lead to a lack of transparency for future comparability and interpretability by other scholars. The methodological gaps include: the (1) variable definition gap, (2) variable to indicator gap, (3) the measurement gap, (4) the data transformation gap.

Focusing on methodologies is important for two reasons. First, synthesis research to build theoretical insights across SES applications has been a challenge because the full spectrum of methodological designs and concept definitions are often not fully published or are simply too heterogeneous for making contextually meaningful comparisons (Thiel et al. 2015, Partelow 2018, Cumming et al. 2020, Cox et al. 2021). For example, Villamayor-Tomas et al. (2020) found that the majority of reviewed models from 30 SESF studies were lacking detail regarding what methods or approaches were used to identify the relationships between variables that the authors were presenting. Second, the SESF itself does not provide any explanation of the factors or causal relationships that are shaping the observed SES problem or phenomena. The framework only provides a common vocabulary and a diagnostic conceptual organization of 1st-tier component interactions, not a procedure regarding how or which methods should be applied with the SESF to investigate these factors.

The methodological guide proposed from this review is applicable, in our view, to all future applications of the framework, both quantitative and qualitative. Nonetheless, quantitative methods were used as the basis for the review because they typically follow systematic procedures for data collection and analysis through the discipline of statistics, which in the data collection phase, translates empirical observations into comparable sets of numbers that can be analyzed with standardized analytical techniques. Specifically defined indicators and variables are needed for quantification along with specific steps to appropriately transform and analyze data, in contrast to qualitative studies, in which reproducibility and generalizable measurement may not be possible or is not the goal of the research. Reproducible criteria for how variables are measured in qualitative studies is by nature more difficult because a primary objective in many qualitative contexts is the rich analysis of data, contexts, and processes not easily reduced to individual variables (Queirós et al. 2017) and often focused on broader knowledge transferability than specific data comparability (Guba 1981).

Previous studies have outlined sets of questions or procedures for applying the framework more specifically, such as for conceptualizing and defining the case SES and action situation (Hinkel et al. 2014, 2015, Partelow 2016). However, there is no systematic or procedural guide with a focus on outlining different methodological strategies and choices. As such, this review aims to make two major contributions. First, to review current applications of the SESF to compile a multi-step guide of methodological steps for applying the SESF framework. Second, to use these results as a base for constructively analyzing current trends, inconsistencies, and challenges in applying the framework to date and to highlight needed methodological advancements and paths forward in SESF research. Through a systematic review of SESF methodologies, we explored the methodological heterogeneity and gaps across the literature and discuss how this heterogeneity can lead to ambiguity for synthesis work. Combined with feedback from a survey regarding ongoing SESF challenges from 22 co-authors of publications included in this review, we identified methodological strategies at each step of study design, data collection, and analysis and then we provide a synthetic methodological guide to inform future applications, while also positing critical reflections on the limitations of current approaches.

FRAMEWORK AND REVIEW METHODOLOGY

Social-ecological systems framework.

The SESF was developed to conduct institutional analyses on natural resource systems and diagnose related collective action challenges. The core of the framework provides a decomposable list of variables situated around an “ action situation ” in which actors make decisions and actions based on the available information within their positions, which enables researchers to structure diagnostic inquiry and compare findings. Although most empirical applications of the SESF have established some theoretical ties to the study of the commons and collective action (Partelow 2018), the SESF was conceived and gained traction as a useful tool for the broader characterization and analysis of SES sustainability (Ostrom 2009) and as a “ theory-neutral ” framework that can be used with other theories or to build new theories (McGinnis and Ostrom 2014, Cox et al. 2016). For a more complete history of the SESF and its connection to the institutional analysis and design (IAD) framework, see its foundational publications (Ostrom, 2007, 2009, McGinnis and Ostrom 2014) as well as previous syntheses and reviews (Thiel et al. 2015, Partelow 2018, 2019).

The SESF is divided into several 1st-tier components representing social and ecological as well as external factors and system interactions and outcomes, each divided into multiple 2nd-tier variables (Ostrom 2009; Table 1). By breaking down an SES into a set of decomposable, nested, and generalizable concepts, the SESF aims to achieve a dual purpose, (1) facilitating an understanding of the specific and contextual factors influencing SES outcomes at a fine local scale and (2) also sharing a common general vocabulary of variables to facilitate the identification of commonalities across cases to build policy recommendations and theory at varying levels of generalizability (Basurto and Ostrom 2009, Ostrom and Cox 2010).

Although the existing literature suggests that the SESF is being successfully applied as a contextually adaptable tool for local SES case analysis, synthetic analysis remains a critical challenge, and the goal of comparability across studies has arguably not been fully realized (Partelow 2018). Scholars applying the SESF have been innovative and exploratory in how their data are collected, analyzed, and reused, leading to methodological pluralism, heterogeneity, and often ambiguity in how the SESF is or should be applied, such as the lack of clarity in how case-relevant variables should be selected and measured (Partelow 2018), as well as difficulties with ambiguous or abstract variable definitions (Hinkel et al. 2014, Thiel et al. 2015). Existing SES and commons database synthesis efforts exist but are made more difficult by the broad range of methodological approaches and inconsistencies with how the framework is applied and variables measured (Cox et al. 2020, 2021). Recent synthesis work of the SESF has noted challenges including both the lack and heterogeneity of information on variable relationships and causal inferences across publications, limiting analysis to only the co-occurrence of variables across SESF studies (Villamayor-Tomas et al. 2020). Social-ecological systems framework applications are taking different approaches to selecting, justifying, measuring, and analyzing SESF variables and lack precision in concepts and measurements (Cumming et al. 2020). We therefore identify methodological inconsistencies in applying the SESF as one major ongoing hurdle to comparable and synthetic SES research, and thus the primary focus of our review.

This study applied systematic review methods to peer-reviewed literature collected from SCOPUS, Web of Science Core Collection, and Google Scholar between August to September 2020 (with a follow-up search in January 2021) to identify any literature applying the SESF with some degree of quantitative data analysis (Appendix 1, Fig. A1.1). The initial SCOPUS and Web of Science title/abstract search used search terms (TITLE-ABSTRACT ("social-ecological system* framework" OR “ social ecological system* framework ” ) OR "SES framework") OR TITLE-ABSTRACT ("social-ecological system*" AND "framework" AND Ostrom")) OR TITLE-ABSTRACT ("social-ecological system*" AND "SESF")) and a follow-up search with Google Scholar to identify any additional publications, which after removing duplicates resulted in an initial set of 330 peer-reviewed publications. Because a key focus of this review is on the heterogeneity of explicit methodological procedures and variable measurements affecting generalizability, comparability, and reproducibility of results, we chose to focus on completely or mixed-methods quantitative applications of the SESF, which are more likely to face limitations in these regards. These criteria included all publications that applied the SESF and analyzed any amount of quantitative raw or transformed data. Publications with any ambiguities with regard to these criteria were discussed between co-authors to reach consensus on inclusion in the review. A title/abstract scan removed all publications not applying the SESF, followed by a full-text review to identify those applying a quantitative analysis, which identified 46 publications. A follow-up search in January 2021 identified 4 additional publications and 1 additional publication was identified during peer-review, resulting in a total of 51 publications for final review. Each article was evaluated using a standardized coding form that was pre-tested by the authors for consistency. The review followed two guiding questions: (1) How is the SESF being applied with quantitative/mixed-methods quantitative approaches (sectors, research aims, and analytical methods)? (2) How are the 2nd-tier SESF variables being applied (variable selection criteria, data collection, measurable indicator selection criteria)?

To answer these questions, we coded the following data from each publication: purpose for applying the SESF, focal SES analyzed, data analysis methods, challenges in applying the SESF, 2nd-tier variable selection and inclusion criteria, measurable indicator selection, data collection methods, and data type. We make an important distinction between “ variables, ” or the generally defined 2nd-tier concepts of the SESF, and “ indicators ” referring to how the variables are actually measured. Any ambiguities during the coding and evaluation process were flagged and discussed between co-authors to reach consensus. Initial coding was completed in February 2021. To gather more explicit reflections from researchers regarding SESF methodological challenges, critiques, and reflections, a researcher survey was also conducted. The survey questionnaire was distributed to all corresponding authors of the reviewed publications starting in February 2021 and consisted of Likert scale and full-text response questions about their experiences with the SESF. The full list of reviewed publications can be found in Appendix 2, 2nd-tier SESF variable indicators from reviewed publications in Appendix 3, and the evaluation forms, procedure, and author survey questionnaire in Appendix 4. The guide steps were developed based on gaps and trends in the SESF literature, in particular the previously noted methodological gaps in the SESF (Partelow 2018) and were further iterated based on the results of the review, researcher survey, experiences in planning our own research with the SESF, and on-going discussions between novice and experienced SESF researchers in our working group.

A multi-step methodological guide for applying the social-ecological systems (SES) framework

Our findings indicate that researchers applying the SESF make a series of methodological choices that can be organized into a multi-step guide that includes all the aggregated choice options across studies at each step. We present this as a 10-step methodological guide and decision tree (Fig. 1). The steps are arranged in what we identified as a generally logical order, but the specific order of operations is likely to vary based on specific research aims. The branches within the decision tree for each numbered step are not all-encompassing, but instead represent, for each step, the categories that were identified and coded in the reviewed SESF publications, with a handful of potential additional categories identified by the authors. A total of 22 complete responses to the SESF researcher survey were received from co-authors of the 51 reviewed publications. Likert-scale survey responses are presented in Figure 2, and Appendix 1 (Table A1.1) summarizes categories of responses to the short answer survey questions.

(1) What is the primary purpose for applying the SESF?

The SESF is generally positioned as a tool to guide diagnostic SES inquiry, but how it is actually applied varies substantially. One application may develop theoretically derived hypotheses on how 2nd-tier variables are linked to collective action and self-organization in a case (e.g., Klümper and Theesfeld 2017, Su et al. 2020). Others might take an inductive approach, using the SESF to code and compare local perceptions of the SES (e.g., Ziegler et al. 2019, Partelow et al. 2021), or use the SESF basis to develop a model of individual actor behavior in an SES (e.g., Cenek and Franklin 2017, Lindkvist et al. 2017).

Most respondents to the researcher survey stated that it was clear how to apply the SESF to their research, how to use the SESF to support theory building and testing, and how to identify relevant variables for a given case. The SESF was typically chosen by respondents because of its clear and coherent organizational structure and comprehensive coverage of a wide range of social and ecological dimensions, however, nearly a third (n = 7) of respondents chose the SESF at least in part due to its origins in the study of the commons and collective action theory. In our synthetic review, we broadly categorized the purpose for applying the framework as extracted from introduction and methods sections of reviewed publications. Although most studies incorporate multiple objectives, the majority of reviewed publications applied the framework with the primary aim of predicting explanatory social-ecological drivers of (typically a small number of) measured dependent variables representing SES outcomes (e.g., Fujitani et al. 2020, Okumu and Muchapondwa 2020; n = 31). The remaining publications were divided between characterization of SESs through descriptive or diagnostic measurements of the important variables (e.g., Leslie et al. 2015, Rocha et al. 2020; n = 10), testing or projecting potential future SES scenarios through simulations or models of system behavior (e.g., Baur and Binder 2015, Cenek and Franklin 2017; n = 5), or social learning aimed at understanding or better integrating local SES user knowledge and perspectives (e.g., Delgado-Serrano et al. 2015, Oviedo and Bursztyn 2016; n = 5). This broader purpose or goal in applying the SESF informs a wide heterogeneity of methodological decisions and considerations leading to the final study outcome.

(2) Is inter- or transdisciplinary research needed to appropriately conduct the study?

Research with the SESF often requires the integration of concepts and data from a wide array of disciplines. Researchers must consider whether adequately analyzing, describing, or diagnosing an SES may require the integration of diverse knowledge types and formats. This integration can take place across multiple dimensions, levels, and scales (Guerrero et al. 2018). Common criticisms of the SESF, for instance, note that the framework itself developed from disciplinary roots in the social sciences, and it is lacking an equivalent depth of consideration of ecological processes and theories (Epstein et al. 2013, Vogt et al. 2015). Our review found that ecological variables are underrepresented compared to social variables in SESF studies (Table 2), and SESF researchers are also more likely to rely on secondary data for ecological variables than for social variables (Fig. 3).

Integration of different scientific disciplinary expertise (interdisciplinary; Hicks et al. 2010, Bennett et al. 2016) or of scientific and non-scientific expertise (transdisciplinary; Caniglia et al. 2021, Lam et al. 2021) can influence how and to what extent all social and biophysical components and dynamics of the SES are investigated, as well as for whom the study outcomes are relevant and meaningful (Guerrero et al. 2018). Many reviewed publications included stakeholders in the research through household surveys or interviews, but only 12 studies were identified that actually integrated stakeholders into the study co-design process, either by influencing the research questions or objectives, or by playing a direct role in the selection and evaluation of relevant SESF variables. Including relevant non-scientific stakeholders at multiple stages in the research can increase knowledge exchange and research influence (Reyers et al. 2015) and the SESF has been demonstrated as a tool to enhance communication between actors in SES governance (Gurney et al. 2019, Partelow et al. 2019). Reflecting on the appropriate type and level of integration should be an important early methodological consideration in SESF research design.

(3) What is the focal SES(s) of analysis and factors determining its boundaries?

Defining the SES and its boundaries is essential for determining how the individual variables are analyzed in relation to what the internal and external influences on those variables are. The focal sector will also determine the degree to which the analysis could be compared to another study or the practical implications of the findings. Most studies are still applying the SESF to classic common pool resource problems (van Laerhoven et al. 2020) in sectors such as forestry and fisheries (Appendix 1, Table A1.2), providing a larger library of sector-specific comparable studies and variables for authors studying these SESs to reference in designing their own research. The SESF is place-based in design, and researchers should also consider what is within the study system and what is external to its context, and this justification should be established based on the research objectives. For example, SESs often have fuzzy social and ecological boundaries that are not easily delineated and often do not align with each other, and how a researcher bounds the system in their study can have implications for the study findings. The focal SESs in the reviewed literature were described or analyzed with boundaries based on social (n = 29), ecological (n = 8), or mixed or fuzzy factors (n = 12; Appendix 1, Table A1.2). A study might have increased clarity or relevance to policymakers by bounding their analysis by administrative borders but fail to adequately capture important ecological processes not conforming to these social boundaries. We have included defining scope and SES boundary clarification as a key step in our guide because of its methodological implications for the rest of the study, but direct researchers to an existing detailed procedure for conceptualizing and defining the focal SES and institutional action situation of analysis (Hinkel et al. 2015).

(4) What are the primary unit(s) of analysis, number of units, and scales of analysis?

Who or what does the study hope to specifically inform? What is the best spatial fit for the SES phenomena being studied? Although most SESF studies are situated within the case context of one or more SESs, actual units of analysis might range from individual aquaculture ponds (Partelow et al. 2018) to residential neighborhoods (Schmitt-Harsh and Mincey 2020) to administrative provinces (Dressel et al. 2018). The selection of unit of analysis, including number of units compared and spatial and temporal levels of analysis, all impact the granularity and types of generalizations that can be made by the study findings and may also reflect certain practical considerations in terms of data collection. We coded units of analysis at the individual (e.g., individual survey respondent), local (e.g., community), or regional (e.g., geographic region or administrative level encompassing multiple communities or governance units) spatial level. Local and individual units were the most common, followed by regional units ranging from political districts (Dressel et al. 2018, Rocha et al. 2020) to large social-ecological regions (Leslie et al. 2015; Table 3). We categorized studies comparing 30 or more units as large-N, following the central limit theorem (with some studies comparing multiple units of analysis). Large-N comparisons of individual or local units were the most common in the reviewed literature, with only two large-N studies comparing regional units. Additionally, although we identified eight publications analyzing cases across multiple countries, only three cross-national studies collected empirical data (including two studies from the same project: Aaron MacNeil and Cinner 2013; Cinner et al. 2012), with the rest reliant entirely on existing secondary data sources. Although our review focused primarily on coding the number and spatial level of units of analysis, we also emphasize the importance of a wide range of critical scales or dimensions for SES analysis. See Glaser and Glaeser 2014 for further reflections on these dimensions.

(5) Which 2nd-tier SESF variables are being examined and what are the inclusion or exclusion criteria?

No empirical studies examine all of the 2nd-tier variables in the framework. Clearly communicating which 2nd-tier variables were selected, and why or why not, improves understandability and comparability. Ambiguities regarding interpreting, selecting, and defining relevant 2nd-tier variables for a given case were the most frequently reported negative aspect of applying the SESF in our survey. Respondents noted the subjectivity in how variables can be defined, allowing for great flexibility but diminishing comparability. Challenges also exist with interpreting whether high or low “ states ” of a variable may lead to favorable or unfavorable outcomes (e.g., variable hypotheses). Of the 51 reviewed publications, 26 provided clear documentation of all 2nd-tier variables being examined (Fig. 4). The remaining 25 publications were excluded from 2nd-tier variable and indicator analysis because they were either opting not to apply the 2nd-tier variables or lacked clarity regarding which (if any) 2nd-tier variables were being examined. For example, some studies were merging parts of the SESF with other conceptual frameworks, and others provided only a list of indicators categorized by the 1st-tier components, without conclusive indication of which (if any) 2nd-tier variables they aligned with. In some studies, there was a purposive decision to not to apply the 2nd-tier variables by study authors, such as in modeling approaches focused on individual unit behavior within the SES rather than broader SES components. However, in many studies the reasoning was unclear. Some of the 25 excluded publications included alternative 2nd-tier variable definitions or numbering schemes without specifying if these alterations were intended to be interpreted as unmodified, modified, or entirely new 2nd-tier variables (Roquetti et al. 2017, Okumu and Muchapondwa 2020). Modifications to the framework, including adding variables, should be justified while noting the theoretical inclusion criteria that the included variables were based on (Frey and Cox 2015, Partelow 2018). Because journal word counts are often a limiting factor, authors might consider including a clearly formatted 2nd-tier variable appendix as supplementary material (Leslie et al. 2015, Foster and Hope 2016, Dressel et al. 2018, Osuka et al. 2020).

Each study selects this subset of variables based on criteria such as expected relevance to the study. Was a variable excluded because it was not empirically meaningful for the case, because it was potentially relevant but not easily empirically measurable, or because it was not in the authors’ interest to examine it? Was a variable included because the authors have formulated a clear hypothesis for its case relevance or because an abundance of secondary data are readily available to measure it? In the reviewed publications, existing literature and theory was the most common reported criteria, followed by local SES actor expert knowledge, as well as data availability and scarcity influencing variable selection (Table 2). Most studies reported only a general list of inclusion/exclusion criteria (e.g., “ our variables were selected based on literature review and expert knowledge ” ), rather than specific criteria for every included variable in either the main text or supplementary material. Additionally, in five studies we could find no basis for why the selected variables were chosen. Clearly formulated hypotheses for why each included variable was relevant to a case were only identified in eight studies (Leslie et al. 2015, Foster and Hope 2016, Dressel et al. 2018, Partelow et al. 2018, Haider et al. 2019, Rana and Miller 2019a, Osuka et al. 2020, Rocha et al. 2020). Inclusion and exclusion criteria are not always clear-cut and might be based on multiple theoretical, methodological, or logistical aspects. Particularly for quantitative approaches, 2nd-tier variable inclusion and exclusion is likely to also be influenced by statistical factors. In many cases, adding additional variables may need to be weighed against the potential loss of statistical power that this may entail. Similarly, some otherwise relevant variables might be omitted from a study because preliminary data exploration shows high multi-collinearity in their measurements (e.g., Gurney et al. 2016). Documenting not only inclusion criteria but also exclusion criteria should be strongly considered by authors, particularly when 2nd-tier variables may have been omitted for reasons beyond solely a lack of case relevance.

(6) How are selected 2nd-tier variables being measured?

Can the variable be directly measured empirically, given the study design and data collection method? Most of the 2nd-tier variables are concepts and are not directly measurable (at least quantitatively) without specifying one or multiple indicators to represent the concept empirically or to specify its empirical meaning, thus these indicators often form the true unit of comparison in many SESF studies. Even if studies examine the same 2nd-tier variable, they likely select different indicators to specify and measure them. In such cases, what indicators are selected, how many, and why should be considered. Almost half (n = 10) of survey respondents disagreed that it was clear how to identify relevant measurable indicators, and respondents also noted subjectivity and inconsistencies regarding where a given indicator might be coded into the SESF. Our findings suggest heterogeneous and context-dependent indicator selection decisions, with most publications collecting indicators from a wide range of sources and data types. Examples of this indicator diversity for variables RS5 and A2 are shown in Table 4. Study-specific interpretations of 2nd-tier variables and related choice of measurable indicators were highly varied, and reviewed publications were inconsistent in documenting which measurable indicators were applied. Because existing SESF case studies are likely to be an important resource and reference point when identifying appropriate measurable indicators, specificity in documentation of this step when publishing SESF research is critical to improve interpretability and comparability of findings. A selection of all 2nd-tier variable indicators that could be clearly identified in our synthesis can be found in Appendix 3.

(7) What data collection methods are used for the selected indicators?

Social-ecological systems framework studies are likely to rely on a range of different data collection methods and both primary and secondary sources in collecting data for a heterogeneous range of variables in often data-scarce contexts, and researchers should carefully consider the implications for their study design and analysis. Primary data collection ensures complete researcher control over how variables and indicators are measured but is often not feasible across a wide and mixed range of variables. Secondary data collection is often more feasible but may have issues of ambiguity regarding the data quality and clarity of data collection and measurement. Almost all primary data are being collected via social science methods such as questionnaires, interviews, and focus groups (Table 5). Across the 26 studies with clearly articulated 2nd-tier variable selections, primary ecological or biophysical survey data were collected to measure only 9 indicators. Overall, primary data collection is more common than reliance on secondary data. Comparing data collection methods by 1st-tier SESF components suggests that researchers using the SESF are collecting a higher proportion of their social variable data from primary sources compared to their ecological variable data (Fig. 3). However, this trend is highly heterogeneous at the 2nd-tier level (Fig. 4). Thirteen studies relied only on primary data, 20 studies on only secondary data, and 15 studies collected data from a mixture of primary and secondary sources. Our findings indicate that data collection methods across the reviewed literature are wide-ranging with most individual studies applying multiple data collection methods and mixed data types.

(8) What type of data is measured for the selected indicators?

Heterogeneity in data sources and collection methods in SESF studies is likely to result in a range of data types or formats. Schmitt-Harsh and Mincey 2020, for example, combined continuous quantitative indicators calculated from GIS data with ordinal indicators from a multiple-choice survey and binary presence/absence classifications of residential properties. Measuring indicators with a range of mixed data types (e.g., continuous, ordinal, categorical) might facilitate the inclusion of more SESF variables but limits the types of statistical analyses available or requires extensive data processing and transformation. Documentation regarding which indicators were data transformed for analysis was not consistent enough across publications to evaluate in full, however min-max normalization was the most frequent transformation identified. The type or format of the collected data can also add a further layer of abstraction to interpreting or comparing SESF variables in a given study and should be made transparent. For example, two studies seemingly defining the same indicator, e.g. "Kilograms of fish catch," may measure it in different ways, such as from a numeric value (e.g., 37 kg) to a qualitative ordinal scale (e.g., below average, average, above average). These differences in measurement may lead to notable differences in interpretation.

(9) What data analysis methods are being applied?

Data analysis methods broadly encompass the techniques for collection and analysis of data to draw insights. Because the SESF is to an extent only a selection of potentially relevant variables, it can be applied to any number of analysis methods that are determined by the research objectives. The choice of analysis method influences (or is influenced by) overall study design, sample sizes, variable selection, data collection, as well as the inferences that can be made regarding the SESF variables being evaluated and external validity of the study findings. In some regard then, the choice of analysis method encompasses all the previous steps in this methodological guide. We coded the data analysis methods used in the reviewed literature into 11 general categories, provided in Table 6, including potential advantages and disadvantages that researchers might have to weigh with each approach, as well as example studies that exemplify each category.

Studies generally applied multiple analysis methods, but the most frequently coded approach included explanatory/dependent variable analyses (n = 31). Fourteen studies focused on characterizing one or multiple SESs through descriptive or comparative assessments of SESF variables rather than explicitly analyzing causal mechanisms or dependent variables. We further differentiated these SES characterization studies into “ descriptive ” characterization studies (n = 7), which assess and compare variable measures without a normative value judgement, and “ evaluative ” characterization studies (n = 7), which provide a normative score (such as from 0-1), alongside supporting theory or literature, for how high or low measures for each variable relate to the evaluative criteria, e.g., potential for sustainability or collective action. Twelve studies utilized modeling and simulation-based analyses (n = 12) to investigate SES structure and behavior, including agent-based and system dynamics models. Seven studies used participatory modeling and evaluation techniques, exploring local expert knowledge and perceptions of the SES as a key source of scientific insight in what are often otherwise data-scarce SES contexts. An additional seven publications applied meta-analyses of the published literature or other existing aggregated case databases. Notably, only one of these studies specifically synthesized empirical SESF literature (Villamayor-Tomas et al. 2020), while the rest used the SESF as a coding tool for existing aggregated cross-case data. We labeled another category as mixed-conceptual (n = 6), representing studies that drew from other conceptual or theoretical frameworks, typically adapting only certain components, or heavily modified versions, of the SESF. Although the results of such studies may be less directly comparable to other SESF applications, they represent one way in which the SESF is being adapted to explore new theoretical insights and lines of inquiry beyond its original design.

(10) Is study SES data publicly available?

Data transparency, including data sharing as well as other contextual information such as how the data were generated or limitations regarding the data, is a critical component of creating more comparable SES knowledge. Eight of the reviewed publications identified an available data source, evaluated by the criteria of whether the publication, journal page, or linked supplementary material explicitly identified a publicly available source for the study data. Although the majority of survey respondents agreed that using the SESF made it more likely that their empirical data can be compared with other SESF studies, this question also had the largest number of neutral responses (7) of all of the questions. Response comments noted the diversity of SES case contexts and uniqueness of each case as challenges. Supplementary publication materials, synthetic databases, and open-source repositories are examples of useful strategies for increasing comparability across heterogeneous SES studies. Several databases have been developed in an attempt to facilitate data synthesis and comparison across SES cases, such as the Dartmouth SESMAD project (Cox et al. 2020; https://sesmad.dartmouth.edu/ ), SES Library ( https://seslibrary.asu.edu/ ), and more context specific databases such as the International Forestry Resources and Institutions (IFRI; http://ifri.forgov.org ) and Nepal Irrigation Institutions and Systems (NIIS; https://ulrichfrey.eu/en/niis/ ). How well a given case dataset “ fits ” to the content structure of these databases may vary depending on how the SESF was applied for a given study. Open-source data repositories provide more flexibility for authors regarding how or in what format they share their SES case data but may be less immediately comparable to other cases.

The SESF partly aims to provide a common language of variables to coordinate and compare findings, while simultaneously allowing for adaptability by not specifying which variables or methods should be applied to case-specific contexts (McGinnis and Ostrom 2014). It has become increasingly clear that there is a tension between these two goals (Thiel et al. 2015, Partelow 2018). The contextual adaptability of the SESF has been empirically demonstrated (Partelow 2018) and is arguably its core strength, but so far there has been little progress in building synthetic and cumulative SES knowledge from across empirical SESF cases (Schlager and Cox 2018, Villamayor-Tomas et al. 2020). Social-ecological systems frameworks’ study comparability has been challenged by inconsistent applications, interpretations, definitions, and measures (Cumming et al. 2020), which may be exacerbated by the lack of clear procedures or guidance for how to actually apply the SESF (Partelow 2018). Our methodological guide attempts to address this by providing a set of steps or decisions that encourage researchers to critically reflect upon and provide transparency regarding these methodological decisions, which can improve both contextualized study designs while enabling cross-study comparability without limiting flexibility. In the following sections, we discuss the above trends and gaps in the reviewed literature and reflect on how they have influenced our presentation of the guide, which emphasizes transparency over rigid procedure. Transparency emerged as the key issue during the review and coding process when we noted inconsistencies in documenting what we viewed as key methodological decisions in applying the SESF.

Methods used in the SESF literature are highly heterogeneous

Quantitative applications of the SESF are highly heterogeneous. Two non-mutually exclusive perspectives can be considered. The SESF applications generally require interdisciplinary knowledge to operationalize the many variables, i.e., variable selection, data collection, data transformation, analysis, etc. The framework is also applied to understand different contextual problems. Thus, researchers will choose different methodological strategies because there is no current guide or template. More applications may be needed until a reasonable saturation point of studies applying similar methods can be meaningfully compared within contexts.

Using quantitative data is typically employed to facilitate hypothesis testing, prediction, and forecasting. The majority of reviewed publications relied heavily on explanatory/outcome variable analysis methods such as linear and logistic regression techniques. However, several publications in this review noted the limitation of these methods in narrowing analyses of SESs to a series of linear pairwise relationships that often involve investigating the explanatory power of a wide range of social-ecological indicators on only a single or small number of dependent variables representing overall outcomes. Development of more experimental methods and large time-scale studies are needed to advance research into SES causal mechanisms (Table 6; Cumming et al. 2020). Methodological transparency is critically important when making theoretical jumps to generalizability, necessitating clarity and transparency regarding the causal inferences and variable relationships being reported (Villamayor-Tomas et al. 2020).

Social-ecological systems research and the SESF itself draw heavily from complex systems theory, conceptualizing SESs as components with a high degree of interaction or connections, forming a network with often nonlinear, dynamic, and emergent properties (Berkes et al. 2003, Ostrom 2009, Preiser et al. 2018). Despite this, previous critical reflections have identified a lack of SES research that empirically applies these concepts of complexity, such as modeling approaches that explore the connections, dynamics, and feedback effects within SESs rather than simply analyses of pairwise relations between variables (Pulver et al. 2018, Cumming et al. 2020, Gomez-Santiz et al. 2021). To be certain, the often data-scarce and open nature of many SES contexts can obscure attempts to explore the interdependent and interactive effects in more detail, and the SESF’s focus on variables rather than connections adds further ambiguity as to how researchers should conceptualize an SES (Pulver et al. 2018). Still, if we accept that complex systems have emergent properties, then it is clear that our SES methodological toolkit needs to explore ways to expand beyond sums of variable-outcome interactions and into methods that focus on capturing, rather than reducing, complexity. Several publications in our review explore promising analytical techniques in these directions, including agent-based modeling to test the emergent properties of individual actor and resource unit behavior on SES outcomes (Cenek and Franklin 2017, Lindkvist et al. 2017), supervised and unsupervised machine learning to analyze policy impacts on SESs (Rana and Miller 2019b) and assess spatial SES archetypes (Rocha et al. 2020), and system dynamics modeling to simulate SES dynamics under various scenarios (Baur and Binder 2015).

Integrative participatory methods, those which involve local actors in knowledge co-production and study design, are some of the most promising and feasible approaches for improving our understanding of SES complexity in information-scarce contexts. They can further lead to better forecasting and scenario building that inform policy and actionable change because of the embedded nature of knowledge creation and learning with those actors directly involved in social-ecological change processes (Eelderink et al. 2020, Caniglia et al. 2021). Notable approaches from our review include participatory fuzzy cognitive mapping to create SES dynamics models based on stakeholder knowledge (Ziegler et al. 2019) and prospective structural analysis to support SES scenario building (Delgado-Serrano et al. 2015). Such strategically designed integration may come at the cost of time and resources and may require a shared learning process to integrate differing knowledge systems and epistemologies (e.g., transdisciplinarity; Tengö et al. 2014, Norström et al. 2020). Nonetheless, it can promote stakeholder ownership and local study relevance while providing scientists with improved knowledge of important social and ecological components and processes within the SES (Reed et al. 2014, Fischer et al. 2015, Guerrero et al. 2018).

In calling for more transdisciplinary SES research, it is pertinent to consider the tension between case specificity and the need for comparability. This is because transdisciplinary and other knowledge co-production methods have been more often associated with case-specific research than that designed to allow generalizability across multiple cases. However, recent literature demonstrates that knowledge co-production approaches are increasingly being applied with decision makers working across multiple regions or even countries (Gurney et al. 2019). We do not view the need for broadly comparable SES research as being diametrically opposed to case-focused and problem-driven or action-oriented research. Although empirical applications are growing, published SESF research is still relatively scarce, and the sample becomes smaller still when subdivided into more granular categories such as methodological approach or sector (Appendix 1, Table A1.2; Partelow 2018). Although recent literature rightfully pushes for SES research to move beyond the exploration and into theory development (Cumming et al. 2020, Cox et al. 2021), we particularly emphasize the need for more (and more diverse) empirical SESF applications to identify patterns of both more broadly comparable, as well as more context specific, SES variables and interactions across cases. In their post-Ostrom agenda, Cumming et al. 2020 charted a path forward for theory-oriented SES research via “ middle-range ” theory development in which building explanations of highly complex SES phenomena might entail building partial theories with a bounded or contextual applicability rather than one all-encompassing SES theory. More highly detailed case-specific SES studies play an important building block in developing new hypotheses and theories to test (Guerrero et al. 2018), and “ filling out ” the SESF literature with more wide-ranging cases is needed for these bounded explanations to emerge. This will likely lead to not only bounded theories but also more bounded SES frameworks covering a more specific and comparable range of contexts, such as SES frameworks for specific resource sectors (Partelow 2018), governance arrangements, or geographic or social-cultural contexts.

The SES literature has made note of a number of gaps that limit the accumulation of knowledge from individual case studies to broader theoretical generalizations (Cox et al. 2021). Both syntheses of diverse case studies and large-scale comparative research projects are key for enabling empirically robust theory building, but current SESF literature struggles to do both (Partelow 2018). Additionally, although we identified 21 large-N comparative studies, most units of analysis were at the individual or local level (rather than, e.g., comparisons of multiple SES cases) and sampled within a limited spatial context (e.g., within one district), likely reducing the external validity beyond that context (Poteete et al. 2010). Only two reviewed studies applied large-N analyses to regional units of analysis, which has been identified as a critical and under-represented focal level of SES analysis (Rounsevell et al. 2012, Glaser and Glaeser 2014), suggesting that researchers are facing a challenge in creating broadly comparative SES research at larger spatial levels. To some extent this may reflect a collective action problem in scientific research itself, in which the collective goal of large-scale SES research may be offset by costs of coordination and collaboration, incentivizing smaller projects at the individual level (Cox et al. 2021). However, it also reflects trade-offs in study design between comparability and case-specificity, in which comparing a wider and more diverse range of SES contexts may necessitate measuring a more general list of broadly relevant variables, risking overgeneralization or missing key variables that are highly relevant but not to all cases (Gurney et al. 2019). Because the SESF itself is decomposable into multiple levels of generalization, one approach for large-N SES analyses is to compare a range of broad, universally relevant 2nd-tier variables across all SES cases, while also including more bounded and decomposed (e.g., 3rd-tier variables), which might be highly influential but only within a subset of cases (Gurney et al. 2019). Still, these approaches are likely to have high resource and coordination costs, suggesting the need for continued synthetic analysis of case-specific SESF research. Several reviewed studies synthesized secondary case databases to assess patterns across multiple SESs, however only one specifically synthesized patterns across existing empirical SESF studies, and this meta-analysis noted challenges regarding methodological transparency that limited the level of detail for case comparison (Villamayor-Tomas et al. 2020). It is evident from these patterns in the literature that further attention to methodological transparency and documentation in SESF studies is needed.

Methodological transparency issues: two main challenges

We identified continued ambiguity regarding 2nd-tier variable and measurable indicator selection as perhaps one of the most critical methodological challenges facing between-study SESF comparability and middle-range theory development. Methodological transparency is a broader academic challenge but should not necessarily be attributed to carelessness or negligence. A variety of reasons exist, ranging from scientific publishing standards regarding short and concise methods, journal word counts and formatting requirements, and procedural doubt or the “ fear ” of showing too much. Or, publications may simply have enough documentation to support the findings being presented, only lacking in certain explicit details at the meta-analytical level. Furthermore, many SESF publications are interdisciplinary, and methodological assumptions regarded as common knowledge in one field or discipline may need to be explained to scholars in another field in interdisciplinary journals. Regardless, we encourage SESF researchers to be as transparent as possible regarding the methodological steps we have outlined, such as making full use of supplementary materials to share these extra layers of methodological procedure (i.e., the choices at each step of the guide). Below we reflect on two specific transparency challenges identified in this review:

Transparency challenge 1: which 2nd-tier variables are being applied and why?

The SESF 2nd-tier variables lack clarity in how to conceptualize and measure them for a given case, and many researchers are finding it difficult and subjective to link their case SES data to the generalized concepts, which are the SESF 2nd-tier variables. Although the majority of surveyed authors stated that they understood how to identify relevant variables for a case, both publications and survey respondents noted recurring challenges regarding how to conceptualize or define the 2nd-tier variables within their specific case context, or how to categorize existing empirical and secondary data to specific variables. Importantly, the variable selection criteria in many studies is often unclear, which hinders learning in the research community, interpretability, and cross-case comparisons. One critical building block to SESF research is identifying which 2nd-tier variables are relevant or generalizable across specific SES contexts (McGinnis and Ostrom 2014). However, it is often unclear if the inclusion or exclusion of variables is deductive and theory driven (e.g., hypothesis-based), inductive (e.g., participatory evaluation), or because available secondary data aligns with particular variables. It could also be that certain variables are relevant across a larger number of cases, or that they are less abstract and easier to conceptualize and measure than others. Criteria for variable modifications including the inclusion of new variables are also often unclear and lacking justification (Partelow 2018). We argue that although there is no specifically right or wrong approach to applying the SESF variables, it is clear from our review that the lack of consistency and transparency limits both the ability to compare and contrast study findings with others.

Transparency challenge 2: how are 2nd-tier variables being measured?

To quantitatively measure abstract concepts, such as many of the 2nd-tier SESF variables, one or more empirically measurable indicators are required. Nearly all the variables could have many different possible indicators, such as RS5 - System productivity, in which indicators range from coastal chlorophyll levels, to kilograms of production of a resource unit, to average park visitation (Table 4). The context of those indicators presumably matters in each case, and the role that each plays in the case when abstracted to the broader concept of “ system productivity ” , may not mean the same thing outside of those contexts. Even indicators that appear similar on the surface may be representing different conceptual phenomena in the SES, such as A1, i.e., number of actors; different studies measure the number of relevant actors in terms of a raw population value, or as population density in a given spatial unit, or as a ratio of another population. Each measure informs us about the same concept in ways that might confer different insights or highlight different phenomena. Most surveyed researchers found it unclear how to select appropriate measurable indicators for the variables in their research (Fig. 2) and documentation of indicator selection was inconsistent in the reviewed literature. Indeed, indicator selection is an often messy process driven by data availability and feasibility. Numerous publications noted challenges in data scarcity (Budiharta et al. 2016, Lindkvist et al. 2017, Filbee-Dexter et al. 2018, Rana and Miller 2019b, Rocha et al. 2020), and studies are often relying on a wide range of primary and secondary sources to collect indicator data (Table 5), which may vary in structure, comprehensiveness, feasibility, and quality (Neumann and Graeff 2015). As such, research with the SESF is often by practical necessity relying on incomplete or low-quality data sources or using certain available data as proxies for other indicators. Transparency regarding how these decisions were made will help future researchers learn how to deal with those issues and enhance the interpretability of study findings.

Standardizing SES indicators is not a feasible or arguably desirable approach given the range of case contexts and research objectives across individual SESF studies. We rather encourage continued empirical applications so that patterns of context specific indicator measures may emerge, even when generalizability is not the core objective. Increased transparency regarding SESF variable and empirical indicator selection can aid in this cumulative accumulation of knowledge. As existing SESF studies are one of the most important references for researchers operationalizing the SESF variables in their work, we further suggest the development of a more comprehensive and accessible database of SESF variables and measurable indicators, such as the wiki-type format proposed by Cox et al. 2021 as an important path forward.

Applying the multi-step methodological guide to the SESF

This review builds on the methodological gaps identified by Partelow 2018, by providing a full methodological guide to the SESF. We see this guide as being supplemental to existing SESF guides in the literature, including guides for conceptualizing a case SES and related institutional and collective action challenges (Hinkel et al. 2015), for characterizing an SES at the local level (Delgado-Serrano and Ramos 2015), and for coevolving SESF research with sustainability science (Partelow 2016).

Our guide should be considered a multi-step, rather than step-by-step, procedure. We recognize that different research goals and researcher interests will align with different methodological trajectories. For example, a theory-driven researcher might first select the 2nd-tier variables and the hypotheses they expect to be important for collective action in their case SES, after which they might identify a set of measurable indicators, whereas another researcher applying a more inductive approach might apply participatory modeling methods to identify important SES factors and only in the analysis stage code these to the SESF variables. We see this flexibility as a strength of the framework, and although we present our methodological steps in what we interpret as a broadly logical order, we encourage researchers using this guide to answer these questions in the order that makes sense for their own research. The steps of this guide may best be interpreted as key “ decision points ” and questions that a researcher should be able to answer and clearly document with the long-term goal of building and improving comparable research with the SESF.

Although this guide was specifically developed around a review of quantitative applications of the SESF, we believe it is applicable to all future applications of the framework including qualitative approaches, and it may be able to inform SES studies beyond the SESF. Both quantitative and qualitative studies are critical for progressing the field. For example, descriptive SESF analyses have been found to often include case descriptions of a large range of variables that are then ignored in explanations of case outcomes, leading to confusion about which variables are actually relevant (Villamayor-Tomas et al. 2020). This also warrants some reflection by researchers on the anticipated level of generalizability of the research, where, in many cases, a more in-depth case study may simply be less focused on generalizability in lieu of a richer descriptive analysis of a specific context. Still, clear and formal narrative summaries answering the questions in this guide (even simple visual diagrams of the variable relationships identified, as suggested by Villamayor-Tomas et al. 2020) could improve generalizability and accessibility of SES findings for synthetic analysis even in cases where creating generalizable findings is not a priority, without compromising the depth of the overall analysis. Our guide was developed with an understanding of this current state of the SESF literature, and we expect more context-specific and potentially more standardized procedures to eventually develop based out of these more specialized versions of the SESF, similar to existing SESF modifications for marine aquaculture (Johnson et al. 2019), lobster and benthic small-scale fisheries (Basurto et al. 2013, Partelow and Boda 2015), urban stormwater management (Flynn and Davidson 2016) and food systems research (Marshall 2015).

Our review analyzed the step-by-step decisions scholars have made when applying the SESF with quantitative methods. With this review data, we have developed a multi-step methodological guide for new applications of the SESF, while also examining current trends and discussing challenges. Our guide and discussion aim to promote methodological transparency as the basis for enhancing comparability across publications and making diagnostic place-based research more meaningfully tailored to context. Still, our review found that researchers are finding it unclear how to apply the SESF to create comparable research, particularly in the areas of variable and indicator selection, and the methodological decisions being made within studies are often ambiguous. Although we noted a high degree of methodological heterogeneity in quantitative SESF applications, analyses are still skewed toward certain methods and case sectors. We call for more empirical applications of the SESF and encourage both methodological plurality and case diversity, alongside enhanced methodological transparency. In doing so, comparability and synthesis can emerge across varying methodological, theoretical, sector-specific, and other dimensions. We argue that this can move our understanding of SESs as complex adaptive systems forward and help resolve tensions between the need for contextual adaptability and the need for comparison.

RESPONSES TO THIS ARTICLE

ACKNOWLEDGMENTS

This project was made possible through funding by the German Ministry of Research and Education (BMBF) under the project COMPASS: Comparing Aquaculture System Sustainability (grant number 031B0785). We are thankful to the editors and anonymous reviewers for their detailed and insightful comments.

DATA AVAILABILITY

The data that support the findings of this study are publicly available at https://figshare.com/s/e81b2ff83543c5bb0aac . The 51 publications evaluated for this review are listed in Appendix 2. Code sharing is not applicable to this article because results are descriptive summaries.

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Figures, Tables, & Appendices ×

Fig. 1. A methodological guide for applying the SESF. All decision tree branches for each step represent “and/or” considerations. Categories were coded based on the reviewed publications. † denotes categories which were not coded from the reviewed publications, but which we identify as additional potential considerations for that step.

Fig. 2. Summary of Likert-scale responses to social-ecological systems framework (SESF) researcher survey. n = 23 responses.

Fig. 3. Sankey flow diagrams summarizing how coded SESF variable indicators (categorized by the four most frequently applied SESF 1st-tier components) in the reviewed literature are associated with data collection type (left) and data type (right).

Fig. 4. 2nd-tier variable frequency and indicator data source (n = 26 publications which clearly documented which 2nd-tier variables were examined).

Table 1. 1st- and 2nd-tier variables of the SESF. Adapted from McGinnis and Ostrom (2014).

Table 2. 2nd-tier variable frequency by 1st-tier component category (n = 26 publications), and general variable selection criteria (n = 51 publications). note: sesf = social-ecological systems framework, ses = social-ecological system., table 3. spatial level of units of analysis vs. number of units being compared. some studies contain multiple units of analysis (e.g., households and communities)., table 4. indicators for two of the most frequently applied 2nd-tier variables, rs5 and a2, extracted from reviewed publications. multiple indicators separated by commas., table 5. data collection methods and data measurement type for social-ecological systems framework (sesf) 2nd-tier variable indicators. derived from n = 26 publications in which the examined 2nd-tier variables could be clearly identified., table 6. study design and quantitative data analysis methods. because many studies apply multiple analytical methods, the sum of number of publications across categories is greater than 51. note: ses = social-ecological systems, sesf = social-ecological systems framework..

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