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).
| Concept | Definition | Example or application to urban forest systems |
|---|---|---|
| Case study | “An in-depth study of a single unit (a relatively bounded phenomenon) where the scholar’s aim is to elucidate features of a larger class of similar phenomena” (Gerring 2004). Insights generated from case studies are transferable to similar cases, but not generalizable. | Most urban forest research that utilizes data from a single city is case study research, either explicitly (e.g., Mincey and Vogt 2014; Conway and Yip 2016; Schmitt-Harsh and Mincey 2020; Vogt and Abood 2021; Freeman-Day and Fischer 2022; Geron et al. 2023) or implicitly (e.g., Burghardt et al. 2022; Bigelow et al. 2024). Multicity studies or meta-analyses are relatively rare, although see the recent multicity studies by Esperon-Rodriguez et al. (2022) and Chambers-Ostler et al. (2024). |
| Complexity | “An interconnected network of components that cannot be described by a few rules; generally manifest in structure, order and functioning emerging from the interactions among diverse parts” (Levin 2000). | Why a particular tree or collection of trees exists in a particular urban place cannot be accounted for by a simple explanation but rather is a function of a large set of interacting components (social, ecological, economic, historical, etc.) through time. |
| Endogenous, endogeneity | Features or components of the system (performance/function, dynamics, trends, etc.) arising from within the system. At a given level of analysis, endogenous variables influence and are influenced by other aspects of the system (cf., Levin 2000). | Endogenous factors that influence urban forest outcomes are numerous, including everything from the biophysical characteristics of tree species and growing environments to management decisions and the wealth and resources of human communities. (See also Table 3 through Table 8.) |
| Exogenous, exogeneity | Features or components of the system that arise from outside of the system. At a given level of analysis, exogenous variables influence but are not influenced by endogenous system components (or the relative influence of endogenous on exogenous system components is relatively small)(cf., Levin 2000). | Exogenous factors to the UFSES include Earth system processes, such as climate change, and also national-level politics that influence funding, such as the Inflation Reduction Act of 2022 which provided funding for urban and community forestry in the United States (USDA 2023). (See also Table 9.) |
| Feedbacks | Linkages between components of a system that ultimately connect back and influence one another, also a “chain of influence” (Levin 2000). | A decline in tree canopy cover in a residential neighborhood precipitated by a pest may influence and be accompanied by overall disinvestment in that neighborhood, leading to further neglect of trees due to lack of resources to care for the urban forest (through planting trees, pest management, etc.), leading to a further decline in canopy cover. |
| Hierarchies, hierarchical organization | The nested-ness or couplings across levels. “Systems organized in such a way as to create a larger system. Subsystems within systems” (Meadows 2008). | For example, trees nested within urban forest patches, nested within a neighborhood, nested within a municipality, nested within a larger metropolitan area, etc. |
| Indirect effects | Effects of one system component on another through an indirect series of interactions (where a direct effect is the direct impact of one component on another); may be difficult to predict and thus unanticipated. Sometimes indirect effects are also unintended or undesirable consequences. Indirect effects that are desirable may be conceived of as synergies. | Planting trees in an underserved area of the city with low tree canopy cover without consulting neighborhood residents may have the intended effect of increasing canopy and access to the benefits of trees, but it may also have the indirect effect of gentrifying the neighborhood and actually result in excluding (now priced-out, former) residents from access to the benefits of these planted trees (e.g., Donovan et al. 2021; Wolf-Jacobs et al. 2023). |
| Institutions | The “rules, norms, and strategies” that structure the interactions of individuals and groups and between people and our environments (Ostrom 2005a; Mincey et al. 2013). | All kinds of planning management practices from pruning to tree inventorying to urban forest master planning can be considered institutions. Norms of individuals regarding trees, such as the misconception that trees cause sewer pipe damage, are institutions also. (See also Table 7.) |
| Legacy effects | The “cumulative and evolving impacts of past interactions in CHANS [coupled human-natural systems] on current and future conditions” (Liu et al. 2007). | Current tree species composition in a given city is influenced by past pest and disease exposure, the human decisions to treat or not treat when infestations occur, historical trends in tree species popularity, economic trends influencing the financial ability of the municipality to replace and replant public trees lost during an infestation, etc. (Roman et al. 2018). |
| Levels | The “different positions on a scale” (Cash et al. 2006). See also the definition of Scales in this table. As-in level of analysis. | Urban forest dynamics might be examined at the level of the individual property owner or parcel or at the level of the entire city, for example. |
| Longitudinal data, longitudinal studies | Repeated observations of the same units (trees, people, cities, urban forests) through time. | Street tree inventories may generate longitudinal data if they inventory the same trees at multiple points in time and connect the observations of the same tree across multiple years using unique identifiers or other methods; see Roman et al. (2020) regarding tree monitoring methods. |
| Nonlinearity, nonlinear relationship | “A relationship between two elements in a system where the cause does not produce a proportional (straight-line) effect” (Meadows 2008). | The relationship between tree diameter and the many functions and benefits of trees (e.g., those associated with leaf surface area, such as air pollution removal and evapotranspiration) is nonlinear as trees grow. Tree mortality rates are nonlinearly related to tree age/size. |
| Polycentricity | “Many centers of decision making which are formally independent of each other”; decision making entities may “function independently, or instead constitute an interdependent system of relations” where they “take each other into account in competitive relationships, enter into various contractual and cooperative undertakings or have recourse to central mechanisms to resolve conflicts”; polycentric systems may or may not yield “consistent and predictable patterns of interacting behavior” (Ostrom et al. 1961). | Trees in a metropolitan area may be influenced by decisions made by the local city and county governments; regional planning agencies; water, gas, and electric utilities; nonprofit greening organizations; institutional landowners such as schools or hospitals; private-sector arborists and landscaping companies; individual urban residents; and many others. |
| Resilience | The “capacity of a system to absorb disturbance and re-organize while undergoing change so as to still retain essentially the same function, structure, identity and feedbacks” (Folke 2006); that is, a system’s ability to “still maintain the same relationships between populations or state variables” despite perturbation (Holling 1973). | A resilient urban forest is one that can withstand some kind of disturbance and still maintain its function as an urban forest and producer of ecological, social, and economic benefits. (See also Table 4, O8-1. Social-ecological resilience). |
| Scales | The “spatial, temporal, quantitative, or analytical dimensions used to measure and study any phenomenon” (Cash et al. 2006). | Scales relevant to urban forests include the jurisdictional (e.g., municipality), spatial/geographic (e.g., watershed), temporal (e.g., 5-year planning horizon), analytical (e.g., social, political, economic), etc. |
| Transferability | The extension of research findings from one system to another system that shares similarities to the study system; “the degree to which an inference about a particular sending context fits or generalizes to a particular receiving context” (Tashakkori et al. 2020). | Findings about planted street tree survival rates in one city may be transferable or applicable to planted street trees in another city if the planting and management contexts are sufficiently similar. For instance, street trees planted in tree pits in Philadelphia may be transferable to street trees in tree pits in New York City; however, street tree research in the Northeastern United States is likely not transferable to understanding the dynamics of trees planted in urban-adjacent golf courses in Miami. |
| Vulnerability | The “…degree to which a system, subsystem, or system component is likely to experience harm due to exposure to a hazard, either a perturbation or a stress/stressor” (Turner et al. 2003; Steenberg et al. 2017). | An urban forest with low species diversity may be particularly vulnerable and experience long-lasting damage to a pest infestation affecting a particularly abundant tree species. (See also Table 4, O8-4. Urban forest vulnerability). |
↵a Definitions in this table draw substantially from those in two classic texts on complexity and systems thinking (Levin 2000; Meadows 2008).