Abstract
Urban forest management is a multistakeholder, multi-objective situation whereby a surfeit of synergistic or competing goals may exist. Greater research and applied guidance for what works in which urban forest contexts could help improve urban tree and forest outcomes. The challenge in conducting research of this nature is systematic definitions of “what works” and “which contexts” across multidimensional, polycentric urban forest social-ecological systems. This paper presents a comprehensive framework for studying the complexities in urban forest systems (synthesized from numerous other frameworks in the field) that could be used to generate context-specific insights into urban forest management and dynamics. The logic of using frameworks and specific frameworks that already exist within the field are reviewed. Then, I present the urban forest social-ecological system (UFSES) framework. The UFSES framework specifies 5 first-tier factors: the Characteristics of Trees in the Urban Forest (T); the Surrounding Growing Environment (E); Management & Institutions (M); and Characteristics of the Human Community (H); which influence Urban Forest Outcomes (O). A detailed set of second-tier variables nested within these factors are presented in tables at the end of the paper. The framework can foster holistic systems thinking in a systematic yet flexible way; provide a working draft of a common language for thinking about and studying urban forest systems; and enable comparative case research.
Introduction
Urban forests are complex systems. Despite their relatively simple appearances—all trees and forest resources in and around the cities, towns, and communities where people live, work, and play (Konijnendijk et al. 2006; Vogt 2020a)—the characteristics of the ecological and human elements in urban forests range widely: from a large well-cared-for tree in a suburban front yard, to a relatively neglected tree in a small tree pit in front of a vacant commercial building; from a patch of woodlands adjacent to a paved trail in a public nature preserve managed intensively to remove invasive species, to a patch of minimally managed invasive woody shrubs and trees along a railroad corridor, to a patch of fruit trees in a vacant lot being used as a food garden by the local neighborhood residents; from a palm tree in the center of a golf course in a coastal tropical city, to a sprawling sycamore in a public plaza paved with cobblestones; from a diverse cohort of newly planted trees on the shoulder next to a recently upgraded highway exit ramp, to an allée of a single species that have been growing nearly a century along a residential street in the old city center; trees in public spaces and on private property; trees intentionally transplanted and those grown spontaneously from seed dispersed by wind or animal or muddy shoes; growing in the isolation of a sea of grey infrastructure and in patches of greenspace. The urban forest is highly varied not only in appearance but in how people in urban areas manage, mismanage, and interact with trees.
Across urban forestry and urban greening scholarship, there are lots of ways of studying and thinking about urban forest complexity (Table 1 defines key concepts that appear in italics throughout the paper). Many have effectively argued that urban trees and forests are social-ecological systems (SESs)(Oleyar et al. 2008; Mincey et al. 2013; Mincey and Vogt 2014; Vogt et al. 2015d; Fischer 2018; Roman et al. 2018; Johnson et al. 2019; Schmitt-Harsh and Mincey 2020; Vogt 2020b; Johnson et al. 2021; Roman et al. 2021b; Tzoulas et al. 2021; Lorenzo 2024). In SESs (sometimes also called “coupled human and natural systems”), the interaction of human and environmental elements is inextricably linked, resulting in complex systems dynamics (Liu et al. 2007). Features of complex systems (Liu et al. 2007) include feedbacks (e.g., environmental injustices in current street tree assemblages resulting from race-based housing policies in US cities) (Burghardt et al. 2022); nonlinearity (e.g., in urban tree mortality curves through time)(Roman and Scatena 2011; Hilbert et al. 2019); legacy effects (e.g., of biophysical and socio-historical patterns in land use over time)(Roman et al. 2018; Johnson et al. 2021); indirect effects and unintended consequences (e.g., urban forest ecosystem disservices such as the reaction of residents to storm damage to and by trees)(Conway and Yip 2016; Roman et al. 2021b); vulnerability and resilience (e.g., as connected to climate change)(Brandt et al. 2016; Steenberg et al. 2017); or socio-economic indicators of resilience (Huff et al. 2020; Landry et al. 2020); and more. Complex systems also exhibit hierarchies—nested-ness or couplings across levels—wherein the dynamics at one level influence and are influenced by the dynamics at higher and lower levels (Holling 1992; Holling 2001; Cash et al. 2006; Liu et al. 2007; Ostrom 2010; Unnikrishnan et al. 2023). In urban forests, this can manifest as the dynamics of single trees, private backyards, and urban forest patches influencing landscape-level ecological connectivity across large urban agglomerations (Rudd et al. 2002; Pirnat and Hladnik 2016; Johnson et al. 2021). The nested yet decentralized complex nature of urban forest management has been described as polycentricity (Mincey et al. 2013; Ordóñez 2019; Ordóñez et al. 2020; Tzoulas et al. 2021) or mosaic governance (Buijs et al. 2016; Buijs et al. 2019). But no matter the term used, the multidimensional layering of actors (i.e., organizations or individuals, sometimes called “stakeholders”)(cf., Reed et al. 2024), decision-making venues, and discourses (i.e., narratives)(cf., Lawrence et al. 2013) with biophysical conditions and human communities all influence urban forest outcomes.
Definitionsa of key concepts for understanding complexity in SESs, with examples from urban forestry. These words appear in italics when first used in the main text. UFSES (urban forest social-ecological system); SES (social-ecological system).
Amidst such complexity, urban forest management—defined by Ordóñez et al. (2020) as “the strategic decisions, often guided by institutional mandates or top-down policies, such as the canopy-cover or tree diversity goals set by local governments; as well as operational decisions, or those involving day-to-day issues, such as where and how to protect, retain, or plant specific trees”—is a multistakeholder, multiobjective situation whereby a surfeit of synergistic or competing goals may exist. Urban forest management and the companion field of arboriculture abound with best management practices (BMPs), licenses, voluntary certifications, and standards, all of which attempt to standardized management practices to ensure a high quality of tree care and a correspondingly high quality urban forest. Some of these are legally mandated in certain circumstances (e.g., included as contract specifications for tree work) or jurisdictions and improve worker safety and tree health alike, such as the pesticide applicators’ license required for application of restricted use pesticides to treat some tree pests and diseases in the United States pursuant to 40 CFR §152.160 - §152.175. Voluntary standards include the ANSI A300 Tree Care Standards, published by the American National Standards Institute (ANSI) and the Tree Care Industry Association (American National Standards Institute 2023). Adherence to these standards is sometimes mandated in tree care contracted by municipalities to the private sector (Hauer and Peterson 2016a). The International Society of Arboriculture (ISA) also maintains an updated series of BMP guides for planting (Watson 2014), pruning (Lilly et al. 2019), and other kinds of tree care as well as multitree management such as integrated pest management (Wiseman and Raupp 2016) and tree inventory (Bond 2013). And the US Forest Service occasionally publishes relevant General Technical Reports containing guidelines for particular activities in urban forestry (e.g., tree monitoring)(Roman et al. 2020; van Doorn et al. 2020).
However, BMPs are insufficiently context specific, and goals are overly simplistic. One-size-fits-all fails to capture the complexities described above. Management practices geared towards achieving optimal tree and urban forest quality and benefits in one place, time, or circumstance may not work for another. Existing standards, BMPs, and guides either focus solely on how to best perform maintenance practices as related to tree biology and physiology (e.g., pruning) or provide a kind of menu of flexible options that are adaptable to local contexts without making adequate recommendations about which kinds of contexts are most appropriate for particular methods (e.g., Urban Tree Monitoring resource and field guide produced by the US Forest Service)(Roman et al. 2020; van Doorn et al. 2020). For instance, it is undeniable that proper planting encourages root growth and ensures that trees become established and thrive in the landscape. Yet BMPs for planting (Watson 2014) do not account for differences in the growing environment (e.g., planting in an open park setting versus a small tree-pit) or variations in after-planting care and maintenance, although both of these factors interact with at-planting decisions and influence tree establishment (cf., the interaction of planting season with watering method [Vogt et al. 2015d] and the interactions between nursery container and irrigation/no irrigation [Gilman 2001]). Similarly, for pruning, BMPs focus on minimizing harm to the tree (reducing opportunity for wounding and decay through appropriate branch collar pruning cuts)(Lilly et al. 2019) while improving tree structure and stability (e.g., maintaining a strong central leader or trunk); however, there are many kinds of pruning that are more aesthetic and cultural than biological and these are scarcely researched (Clark and Matheny 2010) and do not appear in BMP guidance or ANSI tree pruning standards. Further, the aforementioned voluntary standards are largely applicable to single tree management, that is, to arboriculture, while BMPs for managing entire populations of trees in the urban forest largely do not exist (cf., Vogt et al. 2015c)(see also Appendix B). With acknowledgement to the fact that that BMPs are useful practical guides and not intended to be applicable for every circumstance—indeed, in the preface to the second edition of the Tree Planting BMP, Watson and Himelick (2005) wrote, “The authors recognize that trees are unique living organisms and not all planting practices will work for all species in all situations”—greater research and applied guidance for what works when, where, and how, that is in which urban forest contexts, could help improve urban tree and forest outcomes. The challenge in conducting research of this nature is systematic definitions of “what works” and “which contexts.”
Compounding the aforementioned complexity of urban forest systems is that research—and education (Vogt et al. 2016)—happens within and across many disparate fields of study, including (an incomplete list in alphabetical order): arboriculture, biology, botany, ecology, environmental science and studies, forestry, geography and geographic information science (GIS), horticulture, landscape architecture, natural resources, public health/epidemiology, public policy/public affairs, sociology, and urban planning. Such multidisciplinarity has strengths. The inclusion of this many disciplines demonstrates the broad salience of urban forestry and urban greening issues. However, it can result in a disciplinary “cross-talk” problem (Vogt 2018) wherein different fields utilize potentially vastly different methods and vocabularies to conduct research (e.g., Eisenman et al. 2019).
In this paper, I present a comprehensive framework for studying the complexities in urban forest systems that could help harmonize vocabularies and be used to generate context-specific insights into urban forest management and dynamics. This paper proceeds as follows. The first section, “Why Frameworks,” defines frameworks and discusses their utility to research. The second section, “Existing Frameworks,” summarizes some select frameworks from within the field of urban forestry. In the third section, I present the urban forest social-ecological system (UFSES) framework, including methods of its evolution and development. To conclude, I discuss the potential for use and evolution of the framework for urban forest research and practice.
Why Frameworks
Frameworks help us organize our understanding of a complex system with many parts. Good frameworks gather insights from theory, research, and practice, helping organize scientific inquiry and real-world applications alike to ensure all aspects of a system are considered. As I draw heavily on the work of Nobel Prize-winning economist, political scientist, and natural resource management scholar Elinor Ostrom, it is worth quoting her at length here about the utility of frameworks. She usefully distinguishes a “framework” from the often-conflated terms “theory” and “model.” Frameworks, theories, and models are best understood, she writes:
…in a nested manner to range from the most general to the most precise set of assumptions made by a scholar.‖[A]framework is intended to contain the most general set of variables.…It provides a metatheoretical language to enable scholars to discuss any particular theory or to compare theories. A specific theory is used by an analyst to specify which working parts of a framework are considered useful to explain diverse outcomes and how they relate to one another.…Models make precise assumptions about a limited number of variables in a theory that scholars use to examine the formal consequences of these specific assumptions about the motivation of actors and the structure of the situation they face. (Ostrom 2010)
Frameworks encourage use of a common language where scholars of different disciplinary backgrounds are conducting related lines of research from different perspectives. “Without a common framework to organize findings,” Ostrom writes elsewhere, “isolated knowledge cannot accumulate” (Ostrom 2009).
A framework is helpful for both descriptive and diagnostic purposes. From a descriptive or exploratory research standpoint, a framework provides a set of parameters or variables that can be used to describe a system of interest intentionally and holistically, including examining interactions between system components. Without such a systems view, one may miss important drivers of system function and dynamics, and, especially when changing or improving system outcomes is desired, any management efforts risk indirect effects with unintended consequences (Meadows 2008; Ostrom 2009). If only one particular system or case is described, this is a case study. Multiple case studies examined through the lens of a single framework with a common set of variables evaluated for each case then allows for systematic comparison across cases and enables scholars to move beyond single-case research to see broader patterns (Onghena et al. 2019). This accumulation of systematic cases is the kind of research for which frameworks were useful to Ostrom and colleagues (see also Appendix A).
Frameworks are also useful diagnostically (Ostrom and Cox 2010). The logic of the diagnostic approach is that a detailed understanding of a system—and in particular the underlying structure and configuration of the parts and interactions of parts, or “architecture of a system” (Meadows 2008)—enables diagnosis of the root causes and application of the best possible solution to problems within the system (Ostrom and Cox 2010). In natural resource commons scholarship and practice, it is now widely acknowledged that there is no one single “best” approach to all management scenarios—there is no “panacea” (Ostrom 2007; Ostrom and Cox 2010). Applications of one-size-fits-all best management practices will not ensure sustainable commons management. Thus, a thorough accounting of a system helps scholars and managers intervene with prescriptions that have the best chance at achieving desired system outcomes (Ostrom 2007; Meadows 2008; Ostrom and Cox 2010). This is analogous to the idea of transferability, whereby the results of research conducted under particular circumstances or in a particular place are not generally applicable across all other cases but instead are transferable to similar cases (Tashakkori et al. 2020). The key factor in successful diagnosis and transferability of research findings is a sufficiently detailed understanding of the system at hand—its components, interactions, observed outcomes, dynamics over time, and so on—so that others may thereby judge when and where future cases are sufficiently similar as to warrant application of the insights from the first case. Frameworks are a good way to systematically organize our understanding of complex systems and assess transferability of findings.
Existing Frameworks for Understanding Urban Forests
Frameworks have been widely used to study systems of all kinds, perhaps especially systems with both human and natural components; see, for example, the review of frameworks by Binder et al. (2013). Within the field of urban forestry and urban greening, a fair number of frameworks exist. Thirteen frameworks, all but one (Clark et al. 1997) published since 2013, were synthesized for the UFSES framework presented in this paper. Table 2 describes each of the frameworks in brief. Summary insights from these frameworks include:
Most treat human and biophysical components as subsystems that interact with each other (Clark et al. 1997; Kenney et al. 2011; Vogt and Fischer 2014; Vogt et al. 2015d; Roman et al. 2018; Hilbert et al. 2019; Vogt 2020b; Johnson et al. 2021).
A few frameworks focus exclusively on the more human-centric aspects of governance and decisionmaking (Lawrence et al. 2013; Mincey et al. 2013; Vogt et al. 2015c; Ordóñez et al. 2020).
Some frameworks emphasize the response of the urban forest and people in it to general disturbances (Steenberg et al. 2017; Roman et al. 2018) or specifically to climate change (Brandt et al. 2016).
Some frameworks focus on particular kinds of urban forest outcomes, such as the mortality of individual trees (Hilbert et al. 2019), persistence of urban forest patches (Johnson et al. 2021), or the robustness of urban forestry institutions and policy arrangements (Mincey et al. 2013).
A few frameworks are dynamic and make explicit the element of time or “legacies” (Roman et al. 2018; Vogt 2020b; Johnson et al. 2021).
Many frameworks make use of nested categories into which to group variables (Clark et al. 1997; Kenney et al. 2011; Lawrence et al. 2013; Vogt and Fischer 2014; Vogt et al. 2015d; Roman et al. 2018; Hilbert et al. 2019; Ordóñez et al. 2020; Vogt 2020b; Johnson et al. 2021; Tzoulas et al. 2021).
Some further clarify (usually with arrows) which variables or categories of variables interact with one another (Mincey et al. 2013; Vogt and Fischer 2014; Steenberg et al. 2017; Roman et al. 2018; Hilbert et al. 2019; Vogt 2020b; Johnson et al. 2021; Tzoulas et al. 2021).
Just half of frameworks are explicit in how they attempt to link the function and interaction of system components to observed urban forest outcomes (Mincey et al. 2013; Vogt and Fischer 2014; Vogt et al. 2015d; Steenberg et al. 2017; Roman et al. 2018; Hilbert et al. 2019; Vogt 2020b; Johnson et al. 2021).
Models range from the relatively simple (Vogt and Fischer 2014; Vogt et al. 2015c; Steenberg et al. 2017) to the highly complex (Vogt 2020b; Tzoulas et al. 2021) and from those with an applied focus (Clark et al. 1997; Kenney et al. 2011; Vogt et al. 2015c; Brandt et al. 2016) to the more theoretical and analytical in nature (Lawrence et al. 2013; Mincey et al. 2013), with 2 purporting to be useful to both theory and practice (Clark et al. 1997; Tzoulas et al. 2021).
Select frameworks within the field of urban forestry and urban greening. Note that this is not an exhaustive list of frameworks proposed or used in the urban forestry and urban greening field, just those that were influential and useful in the development of the comprehensive framework proposed in this paper. A collection of the visuals (tables and figures) containing the details of these frameworks excerpted from the original papers along with annotations for how the specific components of each framework aligns with the comprehensive UFSES framework presented in this paper is available by request from the author. The “Cit. key” lettering system in the first column of this table gives each framework a shorthand name and appears in the “Other UF framework” column of Table 3 through Table 9 in this paper. Asterisks next to the framework name indicate which informed the evolution of the UFSES perspective and which influenced earlier versions of the UFSES framework. UFSES (urban forest social-ecological system); Cit. key (citation key); UF (urban forest); SES (social-ecological system).
Overall, the frameworks have found varying degrees of utility in research and practice (see Citations column in Table 2). The earliest, most widely cited, and most utilized by practitioners is the Clark et al. (1997) framework (see also Appendix B). There are additional frameworks related to UFSESs that have not been explicitly or systematically incorporated into the UFSES framework presented in the main article text (see also Appendix C, which lists the citations to these additional frameworks, acknowledging the breadth of topics for which frameworks have been proposed; since these additional references were not integrated explicitly and systematically into the UFSES framework presented below, they are excluded from Table 2).
A Comprehensive Framework
Evolution of Urban Forest SES Thinking—From a Perspective to a Framework
The purpose of this paper is to propose a framework that comprehensively explores and defines urban forests as SESs with nested interacting human and biophysical components.A chronology of the development of this framework is helpful to understanding the methods that led to Figure 1 and Figure 2 and Table 3 through Table 9 in this paper. The first version of the urban forest SES perspective was published while I was in graduate school conducting research on recently planted trees with fellow Ph.D. student Sarah Mincey under the guidance of our advisor Dr. Burney Fischer with the Bloomington Urban Forestry Research Group (BUFRG) at the Ostrom Workshop at Indiana University. The BUFRG urban forest SES perspective (SES1 in Table 2)(Vogt and Fischer 2014) was an explicit combination of the Ostrom SES (Ostrom 2009)(see also Appendix A), Clark et al. (1997)(see also Appendix B), and an understanding of tree physiology (Kozlowski and Pallardy 1997). The SES1 perspective was applied quantitatively to data collected on trees planted by Keep Indianapolis Beautiful, Inc., revealing that the full complex SES variables predicted tree outcomes (survival and growth) better than models that excluded any of the SES components (Vogt et al. 2015d). A second, more detailed iteration of the framework was published as a draft UFSES framework in an encyclopedia entry on “Urban forests as social-ecological systems” (SES2 in Table 2)(Vogt 2020b). This version 2 integrated some additional insights from frameworks published in the urban forestry literature (Brandt et al. 2016; Steenberg et al. 2017) as well as some literature reviews and synthesis research of which I was a part (Vogt et al. 2015c; Roman et al. 2018; Hilbert et al. 2019). Most recently, my students and I at the Lab for Urban Forestry in the Anthropocene (LUFA) at DePaul University have applied the UFSES framework to collaborative urban forestry in Northwest Indiana using mixed qualitative and quantitative methods (e.g., Lorenzo 2024). This research has reinforced the idea that planted tree survival and condition outcomes are best explained by the totality of SES factors rather than any component in isolation.
A simple version of the urban forest social-ecological systems (UFSES) framework.
The expanded urban forest social-ecological systems (UFSES) framework. See main text for a full verbal explanation. An earlier version of this visual was published in Vogt (2020b). See Tables 3 through 9 in this paper for an expanded list of second-, third-, and fourth-tier variables and citations to the literature that informed the development of the framework.
System Definition (SYS). Endogenous second-tier variables defining the system of interest in the UFSES framework. Abbreviations in the “Other UF frameworks” column correspond to shorthand citation keys for frameworks described in Table 2. Notes for Table 3 through Table 9 appear in Appendix D. SYS (system definition); UFSES (urban forest social-ecological system); UF (urban forest).
The evolution of the comprehensive UFSES framework reflects the development of my own thinking on urban forests, including a synthesis of the above frameworks with my experiences. It is the result of more than a decade of thinking and learning about the urban forest in the classroom, through fieldwork, and at conferences and via webinars; reading about the urban forest in journal articles, technical reports, newspapers, and books; teaching the urban forest to students, community groups, and my young children; researching the urban forest using applied, transdisciplinary, empirical mixed methods with partner organizations (especially with urban forestry nonprofit organizations and collaborative initiatives); and above all doing urban forestry through participation in TreeKeeping with Chicago-area greening nonprofit Openlands, the Northwest Indiana CommuniTree initiative steering committee, and Chicago’s Urban Forestry Advisory Board. All the while, fellow researchers, professionals, students, and community members have been my sounding boards. With each new experience, I integrated information into a new iteration of the framework. What I present here is the version current to the publication of this journal issue; I expect the framework to continue to evolve through use and critique (see especially the section at the end of this paper, “A Working Draft…”).
The Urban Forest Social-Ecological Systems (UFSES) Framework
The UFSES framework presented here—elaborated fully in Figure 1 and Figure 2 and Table 3 through Table 9—specifies 5 first-tier factors (Italicized in Title Case throughout): the (1) Characteristics of Trees in the Urban Forest (T)(Table 5) and the (2) Surrounding Growing Environment (E)(Table 6) that together form the Biophysical Subsystem, plus the (3) Management & Institutions (IM)(Table 7) and (4) Characteristics of the Human Community (H) (Table 8) that form the Human Subsystem, which influence (5) Urban Forest Outcomes (O)(Table 4). These 5, endogenous first-tier factors (the colored boxes in the center of Figure 1 and Figure 2) interact with one another within the core urban forest system of interest (e.g., a particular city, a single neighborhood)(Table 3) but are nested within broader systems exogenous to the urban forest system of interest and influenced by environmental context and larger social, economic, and political systems.
Urban Forest Outcomes (O). Second-, third-, and fourth-tier variables relating to (O), one of the five core first-tier factors in the UFSES framework. Abbreviations in the “Other UF frameworks” column correspond to shorthand citation keys for frameworks described in Table 2. Notes for Table 3 through Table 9 appear in Appendix D. UFSES (urban forest social-ecological system); UF (urban forest); DBH (diameter and breast height); RGR (relative growth rate).
Characteristics of Trees in the Urban Forest (T). Second-, third-, and fourth-tier variables relating to (T), one of the five core first-tier factors in the UFSES framework. Abbreviations in the “Other UF frameworks” column correspond to shorthand citation keys for frameworks described in Table 2. Notes for Table 3 through Table 9 appear in Appendix D. UFSES (urban forest social-ecological system); UF (urban forest); DBH (diameter at breast height).
Surrounding Growing Environment (E). Second-, third-, and fourth-tier variables relating to (E), one of the five core first-tier factors in the UFSES framework. Abbreviations in the “Other UF frameworks” column correspond to shorthand citation keys for frameworks described in Table 2. Notes for Table 3 through Table 9 appear in Appendix D. UFSES (urban forest social-ecological system); UF (urban forest); SOM (soil organic matter); CEC (cation exchange capacity).
Management & Institutions (M). Second-, third-, and fourth-tier variables relating to (M), one of the five core first-tier factors in the UFSES framework. Abbreviations in the “Other UF frameworks” column correspond to shorthand citation keys for frameworks described in Table 2. Notes for Table 3 through Table 9 appear in Appendix D. UFSES (urban forest social-ecological system); UF (urban forest); NLCD (National Land Cover Database).
Characteristics of the Human Community (H). Second-, third-, and fourth-tier variables relating to (H), one of the five core first-tier factors in the UFSES framework. Abbreviations in the “Other UF frameworks” column correspond to shorthand citation keys for frameworks described in Table 2. Notes for Table 3 through Table 9 appear in Appendix D. UFSES (urban forest social-ecological system); UF (urban forest).
First-tier factors are decomposable into second-tier variables, which can be defined and clarified when operationalized for measurement in research or practice (Figure 2)(I borrow the language of first- and second-tier variables and the numbering convention utilized by the original Ostrom SES framework [Ostrom 2009] and subsequent updates [Ostrom and Cox 2010; Epstein et al. 2013; Cox 2014; McGinnis and Ostrom 2014; Vogt et al. 2015b; Cole et al. 2019; Cox et al. 2020; Unnikrishnan et al. 2023].) When a high degree of system detail is desired and data is available, these second-tier variables can be expanded into nested sets of third- or even fourth-tier variables. For example, second-tier variable M3. Proactive management activities comprises 10 identifiable third-tier variables, including M3-2. Tree preservation planning, which in turn includes 3 fourth-tier variables related to the kinds of tree preservation planning that might be engaged in: M3-2a. Tree preservation planning (during new construction/development); M3-2b. Tree preservation planning (during redevelopment/renovations); and M3-2c. Tree preservation planning (during municipal capital improvements). Table 3 through Table 9 provide a working first draft of a list of second-, third-, and some fourth-tier variables for the UFSES framework: a common set of definitions that can be used as a sort of menu of possibilities for operationalizing or measuring variables in the framework. These tables also include citations to the UF frameworks synthesized in Table 2, and Appendix C includes other relevant literature that informs the logic of each variable.
Exogenous Factors. Second-tier variables relating to the exogenous factors in the broader environmental and social-economic-political systems of which urban forests are a part. Abbreviations in the “Other UF frameworks” column correspond to shorthand citation keys for frameworks described in Table 2. Notes for Table 3 through Table 9 appear in Appendix D. UFSES (urban forest social-ecological system); UF (urban forest).
There are multiple ways to operationalize many of the variables. Table 3 through Table 9 contain some suggestions. For example, second-tier variable T1. Tree taxonomy might be operationalized as tree genus in one study for which genus is a sufficient level of detail, while another study might require tree taxonomy information at the level of species or even cultivar/variety, and still others may only require information about tree stature at maturity or growth habit. Some variables in the framework will require greater elaboration and specification than others. What specification is necessary may be influenced by the particular system of interest (SYS1), the system boundaries (SYS2), and/or the time period under study (TIME-X) (Table 3). For instance, the Characteristics of Trees in the Urban Forest (T) are relatively well-defined by tree biology and physiology (Kozlowski and Pallardy 1997; Pallardy 2008), and the same set of tree-related variables are arguably always important to urban forest outcomes, no matter what the system of interest is. More simply: the biology of trees is constant, even while the interaction of tree biology with other relevant variables may change across and within a system. On the other hand, which of the second- and third-tier variables within the Surrounding Growing Environment (E) and Characteristics of the Human Community (H) factors are most relevant to outcomes might differ greatly depending on the variability in these elements of the system as well as by cultural factors. (Still, there may be good reasons to nonetheless specify as many as possible of the variables in the full UFSES framework if the purpose of analysis is to examine comparative cases; see section “A Research Agenda…” below.)
Across different geographies or urban forestry programs there may be a range of differences for variables within the Management & Institutions (M) factor. For the UFSES framework, I draw a definition of institutions from the field of political science and new institutional economics (Ostrom 2005a) and as applied to urban forestry by Mincey et al. (2013): “rules, norms, and strategies” that structure the interactions of individuals and groups and between people and our environments. Political scientists have also referred to institutions as the “rules-in-use” (i.e., the updated Ostrom SES framework presented in McGinnis and Ostrom [2014]), analogous to what urban forest governance scholars using a variation of the “policy arrangement approach” have called “rules of the game” (Buijs et al. 2016; Buijs et al. 2019; Ordóñez et al. 2020; Konijnendijk et al. 2021; Geron et al. 2023). In colloquial terms, institutions are analogous to management, broadly defined, which is why I couple these terms in the UFSES framework. Whatever their formal title, there are a relatively large array of possible institutions/rules-in-use for urban forests, including operational level maintenance activities, data/record keeping practices, means of proactive and reactive management, norms, property rights, and legal institutions (Table 7). There are also more complicated, nested levels of institutions, such as “collectivechoice” level institutions (Ostrom 2005b), which refer to rules and strategies about operations maintenance, record keeping, and proactive management activities. Collective-choice institutions, in other words, include who and how it is decided that, for instance, tree maintenance is conducted at a particular intensity or frequency (i.e., a 5-year pruning cycle) as well as the monitoring and enforcement of operational level maintenance activities (e.g., who enforces a warranty guarantee on contractor plantings of municipal street trees). A large and complex urban forest system of interest may have a “thick” set of institutions, where all of the second- and third-tier variables in Table 7 require specification; or, in a case of a relatively unmanaged urban forest where only norms govern interactions between people and trees, these “thin” institutions may require little unpacking beyond a few statements describing these informal interactions.
Complex Urban Forest Outcomes and System Endogeneity
Two key features of this UFSES framework are, first, a focus on outcomes of the urban forest, which leads to, second, an acknowledgement of system endogeneity . One goal of the UFSES framework is to help elucidate how the confluence of parts within the urban forest system are linked to the complex, nuanced, multifaceted outcomes observed across urban forest systems. Like the specification of the second- and third-tier variables of interest, what outcomes are most relevant is stakeholder-defined and can vary case to case. Outcomes can include urban forest structure (O1. Urban forest composition, O2. Individual tree structure, O3. Urban forest structure) and ecosystem services (O4. Urban forest function and benefits [& monetary value]), but the UFSES framework enables research to go beyond the archetypical “trees are good” approach (Roman et al. 2021b). More sophisticated outcomes are included in the UFSES framework (Table 4) including O5. Urban forest ecosystem disservices & costs (including private costs such as O5-1. Infrastructure interference costs [private cost] and O5-2. Liability costs as well as public costs such as O5-3. Opportunity costs and O5-4. True ecosystem disservices [externality-related costs]). The balance of ecosystem services and disservices is also included as an outcome metric in the framework (O7. Urban forest multifunctionality), including both positive and negative synergies and tradeoffs (O7-2, O7-3, O7-4)(Roman et al. 2021b). Overall metrics of urban forest performance through time are also included: O6. Urban forest sustainability & persistence; O8-1. Social-ecological resilience; O8-2. Institutional robustness; O8-3. Urban forest adaptive capacity; and O8-4. Urban forest vulnerability (Holling 1973; Turner et al. 2003; Anderies et al. 2004; Mincey et al. 2013; Steenberg et al. 2017; Huff et al. 2020).
How outcomes interact with the endogeneous components of the framework—the 5 central boxes in Figure 2—and illustrate the polycentricity of the system of interest within larger systems is a key insight here. In any system, components that are endogenous to the system are those that influence and are influenced by the other system components. In Figure 2, these interactions are indicated with bidirectional arrows. Outcomes are endogenous to the system. Urban forest structure, sustainability, multifunctionality, resilience, and so on are not only results of the other 4 main factors (including second- and third-tier variables) but also influence these factors. Exogenous elements of the framework are those that are relatively external to the core system function at this level of analysis; they have largely one-way interactions with the first- and second-tier factors in the system. Exogenous elements include Earth system processes like overall climate patterns for a region but also regional and national governance structures, economic systems, etc.
An example is illustrative. For instance, consider the case of invasion of the urban forest by an invasive pest such as the emerald ash borer (Agrilus planipennis) (EAB) and its impact on the urban forest. The pest itself is an exogenous factor. In the language of the UFSES framework, EAB is a pulse disturbance (Table 9) since onset can be sudden, particularly if pest monitoring is insufficient or in the case of a new unexpected pest. EAB influences the health and condition (T4. Canopy condition) of ash trees (Fraxinus spp.)(T1. Tree taxonomy) in the urban forest, and trees that are in closer proximity to one another (E3. Proximity to other features in the urban environment) will be more swiftly impacted by the pest and facilitate spread. Eventually, if left untreated, EAB will cause the mortality of some or even all trees (O2-3. Tree survival/mortality), impacting the species mix of remaining trees (O1. Urban forest composition). Many human factors influence whether any trees are treated for EAB, including if the community has data on the presence of and location of ash trees (M2. Data/record keeping practices), if they have a plan for how to deal with pests generally (M3. Proactive management activities), the presence and advocacy of stakeholder groups (H6. List of stakeholders), and the available budget of any groups who might be paying for treatment (H9-3. Municipal budget/fiscal resources and H10-3. Nonprofit budget/fiscal resources), to name a few of many potentially relevant variables. The species composition—both the initial as well as the change due to EAB losses—in turn impacts many operational-level choices surrounding EAB treatment (e.g., M1-1. Type of maintenance [namely, removal versus a multiyear commitment to pesticide application]; M1-2. Party performing the maintenance; M1-4. Frequency; M1-8. Maintenance costs; etc.), which are also influenced by other endogenous features of the system (e.g., H13. Community adaptive capacity & learning [or lack of learning from past pest infestations]). For an urban forest facing imminent infestation by EAB (or a future EAB-like pest attacking primarily a single genus of trees), if managers had access to a set of research-based case studies searchable by system characteristics such as those identified above, they might be able to more easily make decisions that effectively preserve as many high quality trees as possible while reducing pest management costs to financially tenable levels.
Systems Boundaries: Jurisdictional, Geographical, and Temporal Considerations
What is endogenous versus exogenous to the system is defined by system boundaries and level of analysis (Table 3). What is endogenous at one level of analysis may be exogenous at another level. To extend the example above, municipal-level decisions of whether and how to treat public trees for EAB that are endogenous at the level of the entire city may be better thought of as exogenous if the level of analysis of interest is a single park governed independently by a park advisory council or conservancy (e.g., the case of New York City’s Central Park Conservancy). Thus, an important consideration in applying the UFSES framework—whether as a thought experiment or to any particular system one may be researching, managing, or of which we are a part—is answering the question, what are the system’s boundaries?
Boundaries can refer to the physical or analytical bounds of a system and are helpfully conceptualized as a combination of the scale and level of analysis. In a seminal article from the field of natural resource management, Cash et al. (2006) articulated the critical importance of scales and levels for enacting effective governance of human-environment systems. Scales, according to these authors, are the “spatial, temporal, quantitative, or analytical dimensions used to measure and study any phenomenon,” while levels refer to the “different positions on a scale” (Cash et al. 2006).
For UFSESs, defining the levels of operation within jurisdictional, geographical, analytical, and temporal scales are important. Jurisdiction refers to the entity or entities possessing legal authority over a particular kind of situation or geography. For urban forests, we might consider jurisdictional boundaries (SYS2-2), such as a local municipality, and further within those bounds only the public trees over which a municipality may have management authority. Boundaries existing in physical space (SYS2-1. Geographic boundaries) may overlap jurisdictional boundaries (e.g., a line can be drawn on a map demarcating city limits for a municipality), or they may be more ecological in nature (e.g., a watershed boundary may not align with municipal jurisdictional boundary). We may also consider the relevant analytical scale for a system as urban forest type (SYS1-3), a qualitative classification describing a collection of trees or forested spaces within an urban area at the intersection of growing environment, management regime, and/or land ownership. Urban forest type may refer to street trees, park trees (manicured versus naturalized parks might be considered together or separate depending on the system), residential/yard trees, and closed-canopy settings such as urban forest patches (cf., Johnson et al. 2021; Freeman-Day and Fischer 2022). Urban forest type may overlap with jurisdictional boundaries (e.g., street trees may all be managed by a municipal authority) or not (e.g., trees along different road classes may be managed by separate governmental authorities, such as highway right-of-way trees versus those along surface roads). Some urban forest types may resemble one another (e.g., trees and forested areas on a college or university campus may be treated like a manicured public park accessible to and used by the campus community and the general public, while trees in a similar park-like setting of a secure industrial facility may not be accessed by people at all).
Temporal aspects similarly can have an impact on the understanding of how human and biophysical factors in the UFSES impact outcomes. Temporal boundaries refer to the time period of interest for understanding urban forest system outcomes. Analyzing a particular urban forest or part of an urban forest at a single point in time provides a “snapshot” of the current state of the system and captures relationships between system components at a particular moment. But snapshot analysis cannot tell us how a system might change through time; monitoring and longitudinal studies are necessary for that (van Doorn et al. 2020). Further, existing best management practices often reflect snapshot-style management: for instance, what the best optimal pruning for a single street tree or group of trees in a given year is may look quite different for trees managed on 5-year pruning/inspection cycles versus those on a 10-year cycle versus those in a municipality that only prunes trees by resident request. Operationalizing the second- and third-tier variables in the UFSES framework at multiple points in time could elucidate how urban forest outcomes change through time, how the core components and variables within the UFSES interact to produce outcomes, and whether these interactions change in quality, direction, or magnitude through time. Both when in time and over what time period is important. How and which characteristics of trees and the surrounding environment are connected to urban forest change, for instance, may be different if change is measured as growth at the level of the individual tree (O2-1. Tree growth) during the period of establishment less than 5 years after trees are planted (e.g., Vogt et al. 2015d), rather than if urban forest change is measured as an increase or decrease in urban tree canopy cover (O6-1. Change in canopy cover) over a span of decades (e.g., Roman et al. 2021a). A better understanding of how trees with different characteristics in different growing environments surrounded by different communities of people respond over time to different kinds of management (as well as an understanding of how management decisions are made) could powerfully aid urban foresters and communities in making more holistic and context-informed decisions as they manage their trees.
Why these kinds of analytical boundaries matter becomes clear if we consider the level of variability within second- or third-tier variables for a particular system. To operationalize the UFSES framework requires making determinations about which second- and third-tier variables might be most relevant for a system of interest. A comparison of 2 hypothetical scenarios is instructive. In Subdivision A, the only trees planted are public street trees between the street and the sidewalk which fall under the management jurisdiction of a municipal authority, such as a Division of Forestry within a Department of Public Works, who manages all street trees the same way (e.g., 5-year pruning cycle; trees planted within the past 3 years are watered only during summer droughts). In Subdivision B, the street trees in boulevards along 2 major through-roads are managed by the Division of Forestry, but there are also trees in front and backyards and in cul-de-sacs on the public right-of-way that are the responsibility of the residents/homeowners, as well as trees recently planted around a retention pond between the subdivision and the county highway that are the responsibility of a homeowners’ association (HOA) that collects dues from homeowners and contracts out care of this semipublic space to a local landscaping firm; the stipulation for the collection of dues as well as who manages which trees are specified in a codes, covenants, and restrictions (CCR) attached to the deeds for all homes in membership of the HOA. Now, for both cases, if the question of interest is strictly municipally managed trees—a jurisdictionally bounded inquiry—one need only consider second-tier variables relating to M1. Operational level maintenance activities as the only trees of import are street trees planted in the public right-of-way and managed uniformly by the Division of Forestry. If, however, one is more broadly concerned with the canopy cover and provision of tree benefits across entire subdivisions—a geographically bounded inquiry—one should certainly consider variations in maintenance and property rights. In the more complicated case of Subdivision B, the legal responsibility for tree care and management varies quite a bit across all the trees planted in the new development, and therefore M8. Property rights regimes (who owns and manages what lands) and M9/MX. Legal institutions (such as the CCR agreement) are likely relevant to explaining patterns of observed canopy cover across the entire subdivision. The UFSES framework should be flexible in its operationalization so as to allow application to systems with different boundaries.
A Research Agenda and Database for Urban Forest Social-Ecological Systems
I hope the UFSES framework will be taken up by other researchers, adapted to their use, and through those efforts we can accumulate holistic knowledge and develop context-specific insights for management and practice of UFSESs as complex systems. There are 3 major benefits of research guided by the UFSES framework presented above. First, the framework can foster holistic, systems thinking in a systematic yet flexible way. Second, in doing so, it can provide a working draft of a common language for thinking about and studying urban forest systems. And third, the framework systematically applied can enable comparative case research with the goal of developing applied urban forest management insights.
Systematic Yet Flexible Systems Thinking
Operationalizing the framework is by necessity a unique operation for each UFSES, for each case. For any particular case—geography/cultural context, jurisdiction, research question, or practical application— there may be different second- and third-tier variables that are most relevant to that case, or the same secondtier variable may be operationalized slightly differently (e.g., measuring T3. Current tree size as DBH versus caliper). The systematic listing of variables in Table 3 through Table 9 requires one to consider each in the context of their system, documenting information that while not appearing germane to a case at first blush might be revealed as important when viewed in confluence with other variables or in comparison with other cases. In this way, the framework can be a starting point for building out research on how social-ecological elements of a system interact to produce urban forest outcomes. The detailed specification of a compendium of cases according to the UFSES could elucidate those system components that might be most relevant to the particular outcomes and dynamics of interest for any given research question, policy proposal, or application to practice.
A Working Draft of a Common Language
There are trade-offs in developing a framework that is both systematic and flexible. Flexibility allows for application across a diverse range of cases, yet the common language utilized in the framework needs to encourage accumulation of sufficiently comparable data so as to allow for comparison of cases and transferability of insights gained. “Language matters,” Reed et al. (2024) write in a recent critique of the use of the term “stakeholder” across transdisciplinary research contexts. They continue: “The specific words we use represent knowledge, construct concepts and convey meaning and are, therefore, central to how we relate, communicate, engage, and even conceptualise the world.” I agree. For this reason, I’ve provided a downloadable Microsoft Excel/Google Sheets version of all the second-through fourth-tier variables that appear in Table 3 through Table 9 at the following URL: https://tinyurl.com/UFSES. This Google Sheet serves as a working version of a common lexicon for multi-, inter-, and transdisciplinary studies of UFSES using the framework. The framework is still at its most flexible; I have not specified detailed metrics or precise measurement methods for any of the second-, third-, or fourth-tier variables in Table 3 through Table 9, though I have mentioned in the table notes (Appendix D) existing standardized measurement guides where available (e.g., the Urban Tree Monitoring guides of the US Forest Service)(Roman et al. 2020; van Doorn et al. 2020). Further synthesis research and discussions among users of the UFSES framework and with urban forestry and arboriculture practitioners can and should refine these definitions and provide linkages to common procedures and methods to fit the plethora of urban forest systems and management scenarios.
Standardization of definitions in a common language is a challenge often mentioned in urban forestry and adjacent fields; two separate recent reviews connecting the three literatures around urban ecosystem services (UES), green infrastructure (GI), and the newest term “nature-based solutions” (NBS) both came to similar conclusions: that although the three fields had different disciplinary origins, they have intersected and begun to learn from one another (Escobedo et al. 2019; Fang et al. 2023). Escobedo et al. (2019) concluded, “Although language and metaphors will evolve, care is especially necessary in clearly defining these metaphors as this can affect research (e.g., meta-analyses and reviews based on inconsistent definitions) as well as community and policy uptake of these metaphors.” Although these authors were speaking specifically about the intersection of UES, GI, and NBS language, the sentiment regarding clear definitions and common terms resounds for all of urban forest and urban greening research and practice.
Comparable Case Research
As cases that utilize the framework amass, one way of ensuring that “knowledge accumulates” (Ostrom 2009) is to create a searchable database of urban forest systems replete with information about the System Definition (SYS), Characteristics of Trees in the Urban Forest (T), Surrounding Growing Environment (E), Management & Institutions (M), and Characteristics of the Human Community (H), as well as Urban Forest Outcomes (O). In this manner, researchers might then utilize aggregate data on the cases in qualitative or even statistical analyses that generate larger insights. Once the database is sufficiently developed, practitioners in search of insights for practice could search for a case sufficiently similar to their own in community characteristics or management practices and view observed outcomes and system characteristics for the comparative case. The Holy Grail of a database of context-specific best management practices is likely a long way off, but comparable case research using this systematic framework would be a first step.
Such an approach would not be unique to urban forestry. Other research areas have been examined by the consistent gathering of particular kinds of data that is then entered into a common database or repository. The Ostrom SES framework (see also Appendix A) and its precursor, the institutional analysis and development framework (e.g., Ostrom 1986), have been applied to the comprehensive study of factors influencing successful and sustainable management across a wide variety of natural resource “commons” including fisheries, irrigation systems, and communally managed forests (Partelow 2018; Cox et al. 2021). The current generation of SES scholars studying these systems have developed the Social-Ecological Systems Meta-Analysis Database project (SESMAD 2014). SESMAD is a set of relational databases resulting in a “repository of variables,” 177 in total, connected to environmental governance across a wide variety of common-pool resources, studied across many academic disciplines, though notably urban cases are largely absent from SESMAD.
To my knowledge, no similar approach has been promoted or utilized for urban forests to date. The closest is the “case studies” collected in the Vibrant Cities Lab website (Vibrant Cities Lab 2023) hosted by the US Forest Service and American Forests. The narrative examples on this website are presented in a common format and through the lens of the USFS’s “Urban Forestry Toolkit.” While these cases are not searchable or analyzable as-is by researchers looking to generate insights via statistical or comparative case research, the Vibrant Cities Lab stories would be a good starting point to search for urban forest systems to include in a database organized along the lines of the UFSES framework.
Developing and maintaining such a database is not without its challenges. Among other challenges, Cox et al. (2021) identified the issue of trade-offs between “case-based relevancy and generalizability” that I also experienced in the development of Table 3 through Table 9 in this paper. Based on their experiences with SESMAD, these authors identify a “checklist of issues” researchers should consider when developing such “broadly comparative research projects” (Cox et al. 2021). This may be a good checklist to consult if the UFSES framework is developed into a database of cases. The benefits and drawbacks of a UFSES database and management thereof would be a laudable task for a collaborative group of scholars and practitioners to take on, perhaps through a large research grant, the support of a professional society such as the International Society of Arboriculture, or support from a foundation or synthesis research outfit, similar to the now-sunsetting National Socio-Environmental Synthesis Center, SESYNC, which supported collaborative transdisciplinary, team-based research including the 2016 “Growing the urban forest” workshop (Roman et al. 2015) that led to the publication by Roman et al. (2018).
Conclusion
Context matters. Ecologists know this. Foresters know this. Sociologists and anthropologists and geographers know this. Urban forestry research is getting more holistic and less case-study oriented. However, the body of evidence accumulating in the field at present is inconsistent in which parts of urban forest context are examined and how. Many literature reviews in the past decade across a variety of topic areas have noted such. In a review of research on municipal urban forest decision making: “knowledge of how municipal managers take decisions and what influences their decisions is dispersed across disparate bodies of knowledge and case studies” (Ordóñez et al. 2019). In a review of urban forest air quality benefits and asthma: “The substantial differences in how epidemiology and natural science study and draw conclusions relating to air quality and urban vegetation exhibit ‘disciplinary crosstalk’—poor communication, unconscious misunderstandings, and inconsistent use of terms and literature between disciplines” (Eisenman et al. 2019). In a review of urban forest resilience: “there is not yet consensus on what makes the urban forest more resilient, nor the degree to which urban forests improve overall resilience in cities” (Huff et al. 2020). In a review on urban tree planting and social equity: “we have attempted to highlight some practical recommendations.. .to help identify best practices for equitable tree planting,” while simultaneously noting that “some of these successful strategies might be difficult to implement in certain contexts”, yet the literature is unable to provide any insights about said context (Myers et al. 2023). Other literatures make the clarion call for databases and a common language. In a review of urban tree mortality: “for both research and practice, researchers, arborists, and urban forest practitioners should explicitly define mortality, survival, and the procedures used to measure and calculate each.. .[and] not only should definitions be clear, procedures should be standardized” (Hilbert et al. 2019). In a review on tree maintenance costs: “Large, long-term data sets, with lots of variables, [few of which exist] are one way to begin to understand more fully the marginal causal impact of different levels and combinations of maintenance activities on tree and urban forests outcomes, benefits, and costs” (Vogt et al. 2015c). Case study research has also identified the importance of context: Bigelow et al. (2024) conclude their Philadelphia street tree survival by stating, “comparative mortality studies are need across different regions and countries with distinct ecological, socioeconomic, and political contexts.”
The UFSES framework as put forth in this article could ensure that teams of scholars more fully and systematically examine our study systems, initially enabling greater transferability of findings and eventually yielding accumulation of systematic case studies and more sophisticated practical insights to go beyond panacea-like BMPs to fully adaptive management that will stand up to the complexities and polycentricities of modern urban forests.
Conflicts of Interest
The author reported no conflicts of interest.
Acknowledgements
I am grateful to the many urban forestry colleagues—academic researchers and practitioners—with whom I have had discussions over the years that have inevitably shaped my views and the development of the framework, both explicitly and implicitly. I am particularly grateful to the work by Burney Fischer and Sarah Mincey with the Bloomington Urban Forestry Research Group and the Ostrom Workshop at Indiana University, with whom I developed earlier versions of this framework (Mincey et al. 2013; Vogt and Fischer 2014; Vogt et al. 2015d). Burney Fischer especially provided helpful comments on Figure 2 and Table 3 through Table 9 in this paper. This work was supported by a sabbatical funded by the University Research Council of DePaul University.
Literature Cited
Literature Cited
Appendix A. The Ostrom Social-Ecological Systems (SES) Framework for Studying the Commons
Elinor Ostrom wrote extensively about the use of frameworks specifically for studying common pool resources. Common pool resources are resource systems that “are sufficiently large that it is difficult, but not impossible, to define recognized users and exclude other users altogether” and “each person’s use of such resources subtracts benefits that others might enjoy” (Ostrom 2008). Common pool resource systems include not only communally managed fisheries, forests, irrigation systems, but also knowledge commons and even parts of the internet. Common pool resources are frequently also social-ecological systems. Based on several decades of studying the commons with colleagues, Ostrom developed the social-ecological systems (SES)framework in order to “clarify the structure of an SES so we understand the niche involved and how a particular solution may help to improve outcomes or make them worse” (Ostrom 2007). The framework (Figure S1) distinguished 4 core subsystems—Resource Units (RU), Resource Systems (RS), Governance Systems (GS), and Actors (A)(called Users in previous iterations of the framework)— which have Interactions (I) that produce Outcomes (O)(Ostrom 2007; Ostrom 2009; McGinnis and Ostrom 2014; Partelow 2018). These major “first tier” components act as categories of variables in which second-tier variables can be elucidated for further system definition and study; Ostrom (2007) called this the “decomposable” structure of a system. The endogenous interactions of the first- and second-tier variables in the SES are influenced by 2 exogenous subsystems: Social, Economic, and Political Settings (S), and Related Ecosystems (ECO). The SES framework was subsequently adapted and amended by other scholars (Ostrom and Cox 2010; Epstein et al. 2013; Cox 2014; McGinnis and Ostrom 2014; Vogt et al. 2015b; Cole et al. 2019; Cox et al. 2020; Unnikrishnan et al. 2023).
The Ostrom social-ecological systems (SES) framework. Used with permission from McGinnis and Ostrom (2014).
A very few scholars have applied the Ostrom SES framework directly to the analysis of urban ecosystems, including urban water systems (Perrotti et al. 2020; O’Connor and Levin 2023) and land use change and development (Deslatte et al. 2022). Nagendra and Ostrom (2014) applied the Ostrom SES framework to examine urban lakes as social-ecological commons in Bangalore, India. The only explicit application of the Ostrom SES to urban forests is in Schmitt-Harsh and Mincey (2020), who applied the framework to compare residential urban forest structure for homeowners’ associations versus neighborhood associations. My own previous research (Vogt et al. 2015d; Lorenzo 2024) has applied an urban forest SES perspective based on the Ostrom SES, though not explicitly made use of the framework itself.
Appendix B. A “Model of Urban Forest Sustainability”—the Clark et al. (1997) Framework
The most widely cited framework within the field of urban forestry—and quite possibly the closest to an urban forest BMP—is the “model of urban forest sustainability” presented by Clark et al. (1997). Although they use the word “model” in their paper, what they present is better described as a “framework” as I use the term in this paper. The central tenet put forth by these authors is that a “sustainable urban forest”—defined as “The naturally occurring and planted trees in cities which are managed to provide the inhabitants with a continuing level of economic, social, environmental and ecological benefits today and into the future”—requires 3 components: a healthy “vegetation resource” (i.e., the trees, forests, and surrounding ecosystem); a supportive community; and adequate management (Clark et al. 1997). Clark et al. (1997) go on to specify a set of “criteria” for each of the 3 components. For instance, within the vegetation resource, they identify criteria that communities should meet for canopy cover (“achieve climate-appropriate tree cover, community-wide”), age distribution (“provide for uneven age distribution”), species mix (“provide for species diversity”), and native vegetation (“preserve and manage regional biodiversity”)(Clark et al. 1997). The community and management components have their own criteria that the framework purports are crucial to meet for a sustainable urban forest to persist.
The urban forest sustainability framework has been used in practice as a standard to which to compare municipal forest management. Clark and Matheny (1998) applied their own framework to 25 US cities via use of open- and closed-ended questions on a survey. Kenney et al. (2011) operationalized the Clark et al. (1997) framework by further detailing and clarifying numeric measurements or “indicators” for levels of “low,” “moderate,” “good,” and “optimal” performance in order to more systematically evaluate the performance of a particular community’s urban forest. For instance, for the “relative canopy cover” criteria, optimal performance is measured as canopy cover equaling “75%-100% of the potential,” while the lowest performance level is indicated by existing cover of only “0-25% of the potential” (Kenney et al. 2011). In 2016, Michael Leff of the USDA Forest Service and The Davey Institute (a research division of The Davey Tree Company) developed a detailed guide for evaluating a community’s urban forest based on the Kenney et al. (2011) indicators approach and then translating the results into a plan for goal-setting and future urban forest management (Leff 2016). The Leff guide has been utilized by consultants, municipalities, and others as a practical approach to evaluate and manage urban forests (e.g., as an evaluative tool informing the 2023 City of Chicago Urban Forest Management Plan)(City of Chicago 2023).
Appendix C. Frameworks Related to Urban Forest Social-Ecological Systems That Have Not Been Explicitly and Systematically Incorporated into the UFSES Framework Presented in the Main Article Text
There are additional frameworks (sometimes called “conceptual models”) proposed in a number of fields. Examples are included below. This is not an exhaustive list but rather meant to acknowledge the breadth of topics for which frameworks have been proposed.
Additional frameworks from the field of urban forestry and urban greening:
Urban forest ecosystem services (Dobbs et al. 2011; Cortinovis and Geneletti 2019)
Civic ecology education (Tidball and Krasny 2010)
Urban forest stewardship (Moskell and Allred 2013)
Governance of informal urban greenspace (Stanford et al. 2022)
Biogeochemical drivers of land-use change (Li et al. 2023)
Governance assessment of nature-based solutions (van der Jagt et al. 2023)
Additional frameworks from related fields:
Urban ecology:
Classic frameworks (Pickett et al. 1997; Alberti 2008)
Newer frameworks (Pickett et al. 2017; Andersson et al. 2021)
Urban conservation and restoration (Klaus and Kiehl 2021)
Frameworks” that would be better classified as research approaches:
The Stewardship Mapping and Assessment Project (STEW-MAP)(Svendsen et al. 2016)
Annex (Frameworks Excluded From the Comprehensive UFSES Framework Described in the Main Text)
Appendix D. Notes for Table 3 Through Table 9
1. Other authors have attempted typologies for urban forest or related systems. Adams and Lindsey (2009) provided a classification system for greenspace patches as remnant habitat patches, successional habitat patches, and managed habitat patches. Vogt (2020b) uses a gradient-based classification to identify urban forest types based on management intensity and degree of urbanization and identifies the following: well-maintained street trees, yard trees, manicured parks, urban forest natural areas, vacant lots and brownfields, and neglected street trees.
2. The language on scales and levels within scales is in the style of Cash et al. (2006).
3. Some kinds of boundaries—such as only publicly managed trees in a system of interest—would not be truly a holistic look at the system but may be a boundary that is useful for management-purposes for a particular stakeholder, such as a municipal forestry department. However, even then, it is arguably useful to still consider all trees within the boundary of a municipality as the system of interest, as dynamics of trees on private property arguably influence public trees (e.g., prevalence of pests that don’t pay attention to human property rights).
4. Biodiversity metrics like Shannon’s diversity index could be used, although these are less commonly used in urban forestry than in ecology. However, see Galle et al. (2021) for a comparison of the diversity of street tree assemblages internationally using the Shannon diversity index.
5. Individual tree outcomes such as growth or condition might not be particularly useful metrics on their own for larger systems of interest (the neighborhood-level and above) and might be better accompanied by larger, systems-level outcomes.
6. Basal area may not be particularly meaningful to urban forest systems, except perhaps to urban forest patches. Basal area may be more meaningful if any part of the urban forest is managed as timbered forestland (in rare cases).
7. The definition of ecosystem services is from Roman et al. (2021b), the “benefits that people derive from functioning ecosystems,” based on Costanza et al. (1997).
8. The Ostrom SES framework (Ostrom 2009; McGinnis and Ostrom 2014) also considers “Externalities to other SESs” as one of their Outcomes.
9. Categories of ecosystem disservices costs after “Types of costs associated with urban forests” appearing in Table 2 in Vogt (2020b).
10. For examples of how aerial photographs and historical data have been used to analyze urban forest sustainability see Roman et al. (2017), Roman et al. (2021a), and Freeman-Day and Fischer (2022).
11. Definitions of annual mortality rate and survivorship from Roman et al. (2016); this report is a good authority on how to collect data that enables one to measure and calculate tree mortality and survival metrics.
12. The logic of categorizing not just O4. Urban forest function and benefits (& monetary value) and O5. Urban forest ecosystem disservices & costs, but also positive synergies, negative synergies, and tradeoffs after Roman et al. (2021b).
13. See also the review of the literature on urban forestry resilience by Huff et al. (2020).
14. Robustness in the literature is usually reserved for social characteristics/dynamics of systems, while resilience generally refers to ecological or social-ecological functions/behaviors of systems (Mincey et al. 2013; Huff et al. 2020).
15. Steenberg et al. (2017) note that adaptive capacity and resilience are sometimes conflated and considered the same thing. They are included separately here with unique definitions so as to allow for separation of these as system outcomes if desired.
16. Adaptive capacity of communities is included as H13. Community adaptive capacity & learning, which refers to social adaptive capacity rather than whole-system adaptive capacity.
17. See the extensive discussion of vulnerability, resilience/adaptive capacity, and related concepts of exposure (“the magnitude, frequency, duration, and spatial extent of stressors and disturbances that affect a system”) and sensitivity (“relative level of response by a system to stressors or disturbances”) in Steenberg et al. (2017). In the framework for urban forest vulnerability, which draws extensively on the general framework for sustainability and vulnerability by Turner et al. (2003), Steenberg et al. (2017) present a long list of “potential indicators” of urban forest exposure, sensitivity, and adaptive capacity, most of which have been captured elsewhere in this UFSES framework.
18. The US Forest Service has published a pair of General Technical Reports on Urban Tree Monitoring inclusive of a Resource Guide (van Doorn et al. 2020) and Field Guide (Roman et al. 2020), which together contain detailed guidance and recommendations on how to measure many of the second-tier variables related to Characteristics of Trees in the Urban Forest (T).
19. Others have researched the importance of at-planting characteristics (e.g., Gilman and Beeson 1996; Gilman 2001; Watson 2005). See also Vogt et al. (2015d) on the importance of keeping track of at-planting data.
20. The list of potentially relevant soil variables in E6-1, E6-2, and E6-3 are borrowed from the “conceptual model for the effects of organic materials on soil quality, tree health and environmental health” presented by Scharenbroch (2009) as resulting from a meta-analysis and literature review, although this was not included as one of the core frameworks synthesized for the UFSES framework.
21. Ordóñez et al. (2019) comments on the necessity of distinguishing operational from management activities: “Future research aimed at gaining insights on urban forest governance and decision-making from the perspective of municipal managers will benefit from distinguishing operational capacities (i.e., budgets, personnel) from management processes (i.e., coordination), and focus on how municipal managers understand, facilitate, and find support in management processes” (Ordóñez et al. 2019).
22. As reference, see the summary of existing standardized assessment programs (Tree City USA, Tree Cities of the World, Society of Municipal Arborists, Urban Forest Sustainability and Management Audit, and Urban Salvaged and Reclaimed Wood) by Kadam and Dwiveldi (2021).
23. Excludes 311-based reactive tree maintenance activities (e.g., a resident request for removal of a dead tree), which fall under M1. Operational level maintenance activities.
24. Levels of institutions (operational, collective-choice, constitutional-choice, meta-constitutional) are after Ostrom (2005a): “All rules are nested in another set of rules that define how the first set of rules can be changed.”
25. The 7 types of rules are specified further in the original institutional analysis and development (IAD) framework; see also Ostrom (2005a), plus the application to urban forests by Mincey et al. (2013).
26. As clarified in McGinnis and Ostrom (2014): “...property-rights systems are not rules. Instead, property-rights systems define relations among people in relation to things, and specify both duties and obligations.”
27. Legal institutions may exist as part of the collective-choice (M5), constitutional-choice (M6), and metaconstitutional choice (M7) levels depending on the particular urban forest under examination and can be given appropriate numbering (e.g., an ordinance establishing a tree advisory board formally tasked with undertaking urban forest strategic/master planning might be numbered M5-2, but if that ordinance established a pro-forma tree board with no legal authority it might be numbered M9-1).
28. The original Ostrom SES framework (Ostrom 2009)(see also Appendix A) put norms inside the Actors (A) category (equivalent to the UFSES framework’s Characteristics of the Human Community [H] first-tier factor), while more recent updates (McGinnis and Ostrom 2014; Cole et al. 2019) put norms in the Governance Systems (GS) first-tier category (the UFSES framework’s Management and Institutions [M]). However, if for a particular application of UFSES framework norms/behaviors aren’t shared across stakeholder groups, these could be separated out and specified within H9-X, H10-X, or H11-X.
29. What socio-demographic characteristics are most interesting or relevant to a particular system of interest depends on the human communities being examined, the boundaries of the system, as well as the level of variability in socio-demographics within and across study areas. Some of the most common socio-demographic metrics used in urban forestry studies are listed in H2. Socio-demographics.
30. If demographics are relevant for the particular community(ies) of interest, the distribution of age ranges by gender could be examined together using the “population pyramid” graph used by demographers.
31. A wide variety of historical legacies might impact observed urban forest outcomes, and what kinds of legacies are important to one place might not be important to another. See the recent review paper by Roman et al. (2018) as well as case studies of legacies by Roman et al. (2017), Roman et al. (2021a), and Freeman-Day and Fischer (2022). Legacies also appear in the Ostrom SES framework (see also Appendix A) in their Actors (A) first-tier factor (Ostrom 2009; McGinnis and Ostrom 2014).
32. Reed et al. (2024) recently wrote an extensive commentary critiquing transdisciplinary sustainability research’s use of the term “stakeholder.” These authors identify the “stakeholder paradigm” as problematic because of the origins of the term in the financial sector and corporate context, especially as related to the colonial harm perpetuated on indigenous communities. In their view, the term stakeholder “reduces the relationships between people and place to financial or economic transactions that ignore the cultural and spiritual significance of the land and the non-human species to which people are inherently connected” and “ignores power imbalances and histories of colonisation and dispossession” (Reed et al. 2024). These critiques are valid; however, as the term “stakeholder” is commonly used within urban forestry research and practitioner, the UFSES framework will use this term for now until a more ethically acceptable term becomes more broadly used. Reed et al. (2024) suggest “co-develop[ing] vocabularies” with all relevant parties as part of the research and engagement process.
33. The knowledge-action systems approach by Muñoz-Erickson (2014), which seeks to identify the connections between social networks and how knowledge translates into decision making for urban environmental governance, provides a good example of the kinds of metrics related to stakeholder networks that might be used for M6-2 in the UFSES framework.
34. For municipal forestry in the United States, the 2014 municipal forestry census project examined the many dimensions of municipal tree care and management activities; see the full report with results (Hauer and Peterson 2016a) as well as the Arborist News series with summaries of particular aspects of municipal tree management, including human resource capacities (Hauer and Peterson 2016b; Peterson and Hauer 2016) and budgeting and financing (Hauer and Peterson 2016c; Johnson et al. 2016).
35. For a discussion of the impact of nonprofit mission on urban greening nonprofit activities, see Nguyen et al. (2017).
36. See also Urban Forest Outcomes (O) variable O8-3. Urban forest adaptive capacity, which refers to whole-system adaptive capacity, as well as Notes 15 and 16 above.
37. Earth system processes are included after the planetary boundaries framework (Rockström et al. 2009; Steffen et al. 2015; Richardson et al. 2023). Similar language is used in the external ecological factors of the naturebased solutions conceptual model by Tzoulas et al. (2021).
38. See also Cash et al. (2006) and Holling (2001) regarding the nested-ness of ecological systems.
39. Disturbance language borrowed from the field of ecology; e.g., Walker and Willig (1999), also used in Steenberg et al. (2017).
40. “Ecological rules” idea borrowed from the Epstein et al. (2013) and Vogt et al. (2015b) updates of the Ostrom SES framework.
41. Human legacies also appear as endogenous factors within the Characteristics of the Human Community (H) as H3. Legacies of historical periods. Whether legacies should be considered as endogenous to the system (H3) or exogenous (SOC) depends on the boundaries of the system of interest, including the time period under examination. See Roman et al. (2018).
42. The term polycentric can be defined as “many centers of decision-making” after Ostrom et al. (1961) and has been applied to urban forest systems by Mincey et al. (2013) and Ordóñez (2019), among others.
- © 2024 International Society of Arboriculture
Jump to section
- Article
- Abstract
- Introduction
- Why Frameworks
- Existing Frameworks for Understanding Urban Forests
- A Comprehensive Framework
- Systems Boundaries: Jurisdictional, Geographical, and Temporal Considerations
- A Research Agenda and Database for Urban Forest Social-Ecological Systems
- Conclusion
- Conflicts of Interest
- Acknowledgements
- Literature Cited
- Appendix A. The Ostrom Social-Ecological Systems (SES) Framework for Studying the Commons
- Appendix B. A “Model of Urban Forest Sustainability”—the Clark et al. (1997) Framework
- Appendix C. Frameworks Related to Urban Forest Social-Ecological Systems That Have Not Been Explicitly and Systematically Incorporated into the UFSES Framework Presented in the Main Article Text
- Annex (Frameworks Excluded From the Comprehensive UFSES Framework Described in the Main Text)
- Appendix D. Notes for Table 3 Through Table 9
- Figures & Data
- Info & Metrics
- References
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