Scaling Relations in Experimental Ecology (Complexity in Ecological Systems (Paperback))
Will the failure to consider external factors affect one's ability to extrapolate information across temporal and spatial scales? This volume provides a compilation from a broad range of ecologists with extensive experimental research experience that addresses these and other questions of scaling relations. Figures Tables Contributors Preface I. Background 1.
Michael Kemp, John E. Petersen, Robert H. Gardner II. Scaling Theory 2. Wiens 3. Allen 4. Schneider III.
Microbiome analysis in r
Scaling Mesocosms to Nature 5. Pace 6. Houde 8. Frost, Robert E.
Ulanowicz, Steve C. Blomenshine, Timothy F. Allen King, Robert H. Gardner, Colleen A. Hatfield, Shahid Naeem, John E. A few papers have recently begun to shift the focus to large-scale commons Cox b , Epstein et al. Perhaps the most interesting trend is the extensive use of secondary data. This may be occurring for numerous reasons. Many authors are simply reanalyzing existing data, using the SESF as a conceptual tool to reframe, restructure, or integrate existing data for new analysis.
This also suggests that many scholars are revisiting existing case studies to provide a new conceptual lens. The combination of primary and secondary data is common and is likely a result of the difficulties in collecting sufficient primary data on all the relevant second-tier variables in a case study. If scholars are returning to previous case studies, it is likely that previous data exist. However, very few studies are looking at temporal changes within cases, where there is room for future research.
In addition, metaanalysis studies are using secondary data as well as many comparative analysis studies. Nonetheless it is evident that many scholars find it difficult to design empirical research approaches using the SESF from scratch. There are substantial methodological challenges with applying the SESF to a new case study, such as the meaning of a tiered framework, familiarity with collective action literature, understanding diagnostic methodologies, as well as analyzing nested social and ecological systems in an integrated way, as well as how outcome variables relate to other variables in the framework Hinkel et al.
These likely explain why relatively few articles use primary data. Primary empirical data collection guided by the SESF involves considerable methodological attention to detail, particularly for the design and implementation of empirical data collection. Studies that reanalyze existing data do not have this difficulty to the same extent with data collection, but have many substantial challenges with understanding the data collection methods of previous studies, data formatting, and analysis. A main challenge with secondary data is that it typically involves some sort of data coding procedure Ratajczyk et al.
This might explain why the framework is a useful conceptual tool but is less applied empirically due to a lack of methodological knowledge or guidance on how to do so. Many have argued for the need to modify variables in the SESF, given new empirical analysis of more diverse cases. For example, numerous articles have suggested modifications to include more biophysical variables e. This review confirms that this bias exists. This is most likely due to the development and almost exclusive use of the framework by social scientists.
However, when suggesting modifications, a key question needs to be asked in relation to epistemological congruence i. Below I discuss whether this is important or not. The framework does have a history that justified the inclusion of variables into a theoretical framework because they were shown to influence collective action. This is not inherently problematic; it seems likely that the SESF may take numerous developmental trajectories as it becomes useful for different purposes. However, difficulties and confusion in the literature may arise when explicit distinctions are not made between differing goals across the many papers that are applying the framework.
For example, are variable modifications being suggested because they have been shown to influence collective action i. This issue arises due to a problem in the logic of how the SESF should continue developing i. It is clear that a large majority of research using the framework engages with collective action theories. However, it is also clear that many studies do not focus on collective action, and that knowledge on collective action theory is not necessary for the SESF to be a useful research tool in the general SES literature.
Social-ecological systems framework literature suggests that the framework is useful for characterizing a system as a SES, and for diagnosing general challenges for sustainability. These applications have shown that an analysis with the SESF does not have to be related to the collective action theory roots of the framework. Nonetheless, there are also clear benefits of having a malleable framework, as envisioned by Ostrom.
From the argument above, it becomes clear that the SESF does not provide a list of all relevant intrinsic variables and interactions in a social or ecological system i. Certainly there would be more variables if there were no limitations for adding variables based on theoretical inclusion criteria. In contrast, from a social science perspective, it may be argued that all variables likely affect collective action processes in some way, or it would at least be difficult be parse out that a variable is not influential in an observational study nearly all applications of the SESF , and that the argument for including new variables may be leveraged more on the degree of observable or explicit influence and the degree of empirical support across studies.
However, it is also evident in the literature that many variable modifications are not being suggested with explicit justification as to the relevance of new or modified variables insofar as they may have a causal claim associated with them for how they affect collective action processes. Perhaps broader theoretical inclusion criteria, beyond collective action processes, could be related to a more general SES theory. Variables would then be included if causal interactive effects can be shown between new and existing variables that interdependently influence joint social-ecological outcomes more generally.
This would broaden the theoretical scope of inclusion criteria, but would alter the historically consistent development of the framework thus far. This debate should find roots in future research. This raises a second point. This history has implications for how we view a SES with the framework and how we interpret the concept of sustainability. What is worth knowing about a SES, from an Ostromian perspective, is how different parts of the system influence cooperation and resource-use behavior through the development of institutions for commons governance. Sustainability, from this perspective, is arguably the development and maintenance of contextually appropriate institutions that can enable actors to cooperate and use resources in a way that allows for the long-term and equitable availability of those common resources.
Certainly the broader concept of sustainability is not limited to this view, but it must be recognized that this creates a refined and in some ways path-dependent discourse on sustainability. This leads to a critical reflection on the discourse that the SESF has created with its terminology. Nonetheless, this terminology has created an anthropocentric discourse on how the SESF portrays the biophysical environment.
Arguably the SESF in large part portrays the biophysical environment through a lens of economic and institutional utility. These are the most obvious examples at the first-tier level, but many other second-tier variables in the framework reflect a similar discursive lens, and it is worth acknowledging how this discourse shapes a certain social-ecological worldview. In a separate but related terminological discussion, reference to and application of the SESF requires the use of certain practical terminology. The variables of the framework are referred to with a large variety of terms including: variables, tiers, components, processes, indicators, dimensions, concepts, interactions, elements, attributes, and system dynamics, among others.
Although inconsistent terminology when referring to the first- and second-tier variables is not inherently problematic, it may create confusion or a lack of clarity in the literature and in the interpretation of findings, particularly confusion between variables and indicators. This may stem from the lack of clarity and clear definitions for many of the second-tier variables.
Some are well-defined and nuanced whereas others represent broader concepts that often need further refinement or defining in the context. Not all of the second-tier variables are created equal in this way and may require modification as the framework evolves. Many articles have suggested variable modifications see Table 5. This is an inevitable progression as more empirical analysis emerges. However, reflection is warranted on whether suggested variable modifications are actually new variables i.
Also, what the level of generalizability of suggested modifications is in relation to other cases and sectors. It is evident that separate frameworks are likely to evolve for use in specific sectors because many relevant variables in specific sectors may not be generalizable Fig. The role of some variables is likely to be unique to certain sectors. However, the relationship between potential specialized frameworks for specific sectors and a general framework cannot be made a priori. This will depend on the degree of empirical support for the specialized framework and the ability to compare data across cases with a sector, and then between sectors Fig.
As discussed above, one of the methodological difficulties is that there are no rules or guidelines for variable modifications. Frey and Cox suggest the use of a consistent ontological logic for adding new variables i. Having an ontological logic would certainly create consistency, but it does not address the theoretical inclusion criteria problem. Second, it is important to recognize that indicators used to measure second-tier variables are not necessarily nested subconcepts that warrant inclusion into the framework.
Many articles do not make this distinction. For example, Partelow and Boda suggest a substantially modified framework that is specific to lobster fisheries but they do not make a clear distinction between what modifications are nested subconcepts of potentially new variables, and which are indicators for simply measuring the parent variable.
They also do not follow a clear ontological logic. The review in this article supports conclusions from Thiel et al. Future research and discussion could focus on this issue. For further guidance on logical criteria for expanding the SESF in a cohesive way, recommendations are provided by Frey and Cox as a starting point. These include developing tiers and variables with meaningful relationships, restrictions, or instances between classes i. In addition, guidelines for creating classes and subclasses with meaningful relationships between them may include rules such as do not create singular subvariables, too many subvariables, and creating similar or reciprocal classes with related relationships to the parent variable.
In reflecting on methodological challenges outlined above, four aspects are useful to consider when suggesting modifications to the framework in the future. These variables arguably have the strongest theoretical link to institutional change and collective action theories. However, they are also some of the least focused on second-tier variables despite their central placement Fig. This may be related to a lack of knowledge about their theoretical origin as the framework has gained a wider audience. This could be viewed as a process of building a general theory of SES interactions similar to how property rights and biophysical traits are often interpreted as interacting bundles or commonly associated system characteristics with repeating patterns of variable interactions and outcomes.
For example, a social-ecological trap Boonstra and De Boer may be a common archetype or bundle of interacting variables with certain values that could be identified with the SESF variables e. Thus, a social-ecological trap could be an example of an archetype of interactions that is part of a general SES theory using the frameworks variables. The most important interactions shaping SES outcomes not referring to the Interactions I variables, but general system interactions to be analyzed may be among variables between tiers rather than within tiers of the framework.
There are no general methods, guidelines, or procedures for applying the SESF, although numerous articles have provided conceptual guidance e. However, there is lack of reflection between the different papers that make explicit suggestions regarding the benefits and challenges of different methods. There is no right or wrong way to apply the framework. The variables can be defined, modified, and measured, as needed, in different contexts Ostrom , Furthermore, multiple data collection and analysis methods are often used.
The discussion below highlights the lessons and reflections learned across the literature and from experience applying the framework. These gaps are not unique to the framework, they relate to general scientific methodologies more broadly, but are explicitly applicable and relevant for applying the SESF. If common definitions of variables and concepts are not used across cases, additional layers of abstraction will hinder the ability for synthesis and comparison.
However, there is a trade-off here between specificity and generalizability, as it is often necessary to define variables differently across contexts. For example, the concept of social capital A6 is not well defined and can vary in meaning across contexts. Social capital may refer to the structure, connectedness, and types of exchanges in a social network Pretty , Borgatti et al. Definitions can dictate what will be measured and the theoretical conclusions drawn from that data about the role of that variable in a system. Many other variables in the framework create similar challenges because they are defined and measured differently, compromising the ability for comparison if definitions are not transparent to readers or those conducting synthesis research.
The variable-indicator gap refers to which indicators are selected to empirically measure or code variables. Many variables are broad concepts that are not directly measurable or easily defined, such as socioeconomic attributes A2 , norms, trust and social capital A6 , resource unit value RU4 , equilibrium properties RS6 , predictability of system dynamics RS7 , and outcomes O1; O2; O3. Context-specific indicators are often needed to measure these variables, or at least to understand a variable in context.
Two studies may examine the same variable with the same definition, but they may select different indicators to measure them. This creates a degree of abstraction for comparative research. For example, indicators to measure actor location A4 could be the distance between the home of an actor to the place where they access the resource system RS or resource units RU , or, it could be the distance from the home to other actors or community meeting places where collective decisions are taken.
The measurement gap refers to how variables or indicators are actually measured or coded. It is evident that two studies can examine the same variable, use a common definition and indicator, but still measure the variable in a different way. For example, economic value RU4 may be defined as the market value of the resource unit, and both studies use the indicator of price per kilogram. One study may employ qualitative methods, asking individual actors e. A second study may collect quantitative data on fish sales from fish markets to establish price averages over the last 6 months.
The studies may draw different conclusions on the economic value of the resource and the role that market variability has on system dynamics. The data transformation gap refers to how raw data are transformed into usable or presentable data in an analysis, graphic or written text form. Or, how published data are recorded or transformed from literature review or metaanalysis for additional analysis.
Transforming data into different structures e.
ISBN 13: 9780231114998
Many different data types have been used to analyze the variables and their interactions in the SESF. Data transformation can enhance comparability but also compromises meaning and context. For example, raw qualitative interview data may be coded, synthesized, and transformed into ordinal data e. This problem occurs in both qualitative and quantitative research. Different studies will inevitably use different transformation methods, stressing the need for transparency. Much of the above discussion provides insights into future research considerations given the trends in the literature.
Ostrom , argued that the SESF could provide numerous benefits for scholars, including 1 a general framework that could be adapted and applied to diverse cases, 2 a core set of variables and a common language to better enable comparison and communication, as well as 3 a diagnostic tool, potentially enabling new theories to be developed through analysis of interlinkages between variables and outcomes.
Each is briefly discussed below as to how future research may be able to make progress toward achieving them. The framework can be tailored to context by modifying the definitions of variables, indicators to measure them, data collection, and analysis methods. As a result, the framework can be, and has been, applied to a wide variety of cases. This is arguably its strong point. However, it is also clear from this study that applying the framework has led to many suggested modifications to variables.
Some articles suggest more generalizable modifications McGinnis and Ostrom , some for use in specific sectors Table 5. It appears that the general framework will evolve, but specific frameworks will also evolve for use in specific sectors e. Figure 3 conceptualizes this potential future research process. An intermediary step may be the development of sector-specific frameworks e. Sector-specific frameworks, which would add, develop, and define new or existing variables of the framework within the scope of a sector e.
Sector-specific frameworks could help avoid confusion between the many diverse studies that apply the framework and allow more robust comparison between similar cases before attempting more abstract comparisons between cases where the social and ecological conditions may be less similar. Degrees of generalizability could be assessed between similar cases within sectors before abstracting their potential generalizability to the general framework. Overlapping commonalities could then more robustly inform a general framework which would remain the pillar for collective action theory across contexts.
The literature applying the SESF is heterogeneous, and it is unclear the extent to which the empirical data can be compared across cases in a meaningful way without substantial recoding, transforming, or simplifying heterogeneous data. Without general but clear guidelines, a metaanalysis of empirical case studies would currently be a monumental effort to overcome methodological blind spots and integrate data, and with current SESF studies, provide largely unreliable data given the high degrees of heterogeneity in data collection methods, context, and system scales examined in the literature.
A few studies have been successful with large comparative studies, but they have largely relied on highly systematized primary data collection on common variables controlled by the authors e. Either way, successful comparative studies are made easier when the data available were collected with the intention to be compared. However, many individual case studies are not designed to be compared with other cases within or between sectors, and efforts to do so without methodological transparency would likely draw highly abstracted conclusions about the empirical studies being examined.
Databases are a promising way forward for enabling comparison, where the authors of individual studies format their data themselves into shared digital repositories. This eliminates data abstraction barriers by nonauthors but also requires incentives for authors to contribute to common databases, which is a provision of the public goods collective action dilemma itself.
Many of the databases presented in Table 2 are attempting to facilitate this, but their success requires largely voluntary contributions, which are encouraged by those facilitating them and recommended for scholars using the framework to engage with. The SESF is not a theory-neutral tool. Historically, the inclusion criteria for variables were based on their influence on collective action in small-scale CPR systems. However, the generalizability of these variables seems to be broad in scope, with numerous studies using the variables to generally characterize SES or to develop other closely related theory on natural resource governance Cox et al.
This has not yet been fully explored in the literature, and it remains unclear, although still promising, that the SESF can aid the process of theory development for general SES research. Perhaps future research can further explore further uses for the framework, particularly its potential to contribute to building general theories of social-ecological interactions by identifying typologies or archetypes of social-ecological interactions Alessa et al.
Integrating the framework with other conceptual and theoretical frameworks may expand its usefulness for contributing to other theories and frameworks in associated fields such as ecosystem services, sustainability science, the Coupled Infrastructure Systems framework, and resilience theory Binder et al. This would somewhat remove the theoretical history with collective action theory in parts of the literature engaging with the framework. However, there is also recognition that collective action theory is nested within broader concepts of SES and sustainability, both of which are likely to evolve.
Many thanks to the editors and three anonymous reviewers for constructive and thoughtful comments that have improved the manuscript. Any remaining errors or inconsistencies are my own. Additional thanks to Vigneshwaran Soundararajan for assistance. Abson, D.
Scaling Relations in Experimental Ecology (Complexity in Ecological Systems (Paperback))
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