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Operationalizing the social-ecological systems framework to assess sustainability

Operationalizing the social-ecological systems framework to assess sustainability Operationalizing the social-ecological systems framework to assess sustainability a,b,1 c,1 c a,b,d a,b,e,2 Heather M. Leslie , Xavier Basurto , Mateja Nenadovic , Leila Sievanen , Kyle C. Cavanaugh , f g,3 h f,4 f,g Juan José Cota-Nieto , Brad E. Erisman , Elena Finkbeiner , Gustavo Hinojosa-Arango , Marcia Moreno-Báez , b,i a,b,j f a,b,5 Sriniketh Nagavarapu , Sheila M. W. Reddy , Alexandra Sánchez-Rodríguez , Katherine Siegel , k l g José Juan Ulibarria-Valenzuela , Amy Hudson Weaver , and Octavio Aburto-Oropeza a b Department of Ecology and Evolutionary Biology and Institute at Brown for Environment and Society, Brown University, Providence, RI 02912; c d Duke University Marine Laboratory, Nicholas School of the Environment, Duke University, Beaufort, NC 28516; Joint Institute for Marine and Atmospheric Research/Pacific Islands Fisheries Science Center, Honolulu, HI 96818; Smithsonian Environmental Research Center, Smithsonian Institution, Edgewater, f g MD 21037; Centro para la Biodiversidad Marina y la Conservación A.C., La Paz, BCS, 23090 Mexico; Scripps Institution of Oceanography, Marine Biology Research Division, University of California, San Diego, La Jolla, CA 92093-0202; Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950; i j k Department of Economics, Brown University, Providence, RI 02912; Central Science Division, The Nature Conservancy, Durham, NC 27701; Fondo para la Protección de los Recursos Marinos, La Paz, BCS, 23090 Mexico; and Sociedad de Historia Natural Niparaja A.C., La Paz, BCS, 23020 Mexico Edited by Bonnie J. McCay, Rutgers, The State University of New Jersey, New Brunswick, New Brunswick, NJ, and approved April 2, 2015 (received for review August 22, 2014) Environmental governance is more effective when the scales of temporal and spatial scales (SI Appendix,Fig.S1). To operationalize ecological processes are well matched with the human institutions the framework, we translated these four dimensions into quantita- charged with managing human–environment interactions. The tive, theoretically derived measures of factors known to contribute social-ecological systems (SESs) framework provides guidance on to sustainable resource use (see following and SI Appendix for a how to assess the social and ecological dimensions that contribute detailed description of the materials and methods). to sustainable resource use and management, but rarely if ever We used the SES framework to assess the spatial variation in has been operationalized for multiple localities in a spatially ex- the potential for social-ecological sustainability of small-scale fish- plicit, quantitative manner. Here, we use the case of small-scale eries in the Mexican state of Baja California Sur (BCS). We focus fisheries in Baja California Sur, Mexico, to identify distinct SES re- on small-scale, coastal fisheries because of their importance to gions and test key aspects of coupled SESs theory. Regions that human communities for both income and food security (7), as well exhibit greater potential for social-ecological sustainability in one as the effects fisheries have on marine populations and ecosystem dimension do not necessarily exhibit it in others, highlighting the importance of integrative, coupled system analyses when imple- Significance menting spatial planning and other ecosystem-based strategies. Meeting human needs while sustaining ecosystems and the coupled natural and human systems marine governance small-scale | | | benefits they provide is a global challenge. Coastal marine fisheries conservation science systems present a particularly important case, given that >50% of the world’s population lives within 100 km of the coast and central challenge facing humanity is how to achieve sus- fisheries are the primary source of protein for >1 billion people Atainable outcomes that benefit both people and nature (1). worldwide. Our integrative analysis here yields an under- Using a social-ecological systems (SESs) approach in the gen- standing of the sustainability of coupled social-ecological sys- eration of knowledge and the formulation of sustainable gover- tems that is quite distinct from that provided by either the nance solutions is critical, as it explicitly recognizes the connections biophysical or the social sciences alone and that illustrates the and feedbacks linking human and natural systems. Understanding feasibility and value of operationalizing the social-ecological how the potential for social-ecological sustainability varies with systems framework for comparative analyses of coupled sys- context is vital to solving this dilemma (2, 3). A highly visible tems, particularly in data-poor and developing nation settings. conceptual tool, the SESs framework (4) offers the potential to address this scientific and societal challenge, but operationali- Author contributions: H.M.L., X.B., M.N., and L.S. designed research; H.M.L., X.B., M.N., L.S., G.H.-A., S.M.W.R., K.S., A.H.W., and O.A.-O. conceived of the study; H.M.L., X.B., M.N., zation has been elusive. In this study, we demonstrate how the L.S., K.C.C., J.J.C.-N., E.F., G.H.-A., S.M.W.R., K.S., J.J.U.-V., A.H.W., and O.A.-O. collected framework can be applied in a new way to identify opportuni- the data; H.M.L., X.B., M.N., L.S., K.C.C., J.J.C.-N., B.E.E., E.F., G.H.-A., M.M.-B., S.N., S.M.W.R., ties and tradeoffs in managing for the sustainability of coupled A.S.-R., K.S., J.J.U.-V., A.H.W., and O.A.-O. performed research; H.M.L., X.B., M.N., and L.S. SESs (5, 6). contributed new reagents/analytic tools; H.M.L., X.B., M.N., L.S., K.C.C., and M.M.-B. analyzed data; and H.M.L., X.B., M.N., and L.S. wrote the paper. The SES framework enables the integration of data from di- The authors declare no conflict of interest. verse natural and social science disciplines, and thus provides a This article is a PNAS Direct Submission. theoretically grounded means of testing hypotheses about the dy- Freely available online through the PNAS open access option. namics and implications of social-ecological interactions (see SI Appendix for further discussion). At its broadest level, the SES To whom correspondence may be addressed. Email: heather.m.leslie@gmail.com or xavier.basurto@duke.edu. framework describes the four essential dimensions, or first-tier Present address: Department of Geography, University of California, Los Angeles, variables, of a SES (Table 1, after ref. 4). Actors within and CA 90095. outside government operate within a Governance System char- Present address: Marine Science Institute, The University of Texas at Austin, Port Aransas, acterized by formal and informal rules at one or more identifi- TX 78373. able geographic scales. Resource Units inhabit and interact with Present address: Cátedra Consejo Nacional de Ciencia y Tecnología, Centro Interdisciplinario a broader Resource System that is characterized by particular de Investigación para el Desarrollo Integral Regional Unidad Oaxaca, Instituto Politécnico ecosystem types and biophysical processes, also at one or more Nacional, Oaxaca, 71236, Mexico. geographic scales. Interactions among these four dimensions are 5 Present address: Sustainable Fisheries Group, Marine Science Institute, University of Cal- mediated by the broader social, economic, and political settings ifornia, Santa Barbara, CA 93106. and related ecosystems within which the SES is embedded. To- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. gether, these dynamics lead to diverse outcomes at particular 1073/pnas.1414640112/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1414640112 PNAS | May 12, 2015 | vol. 112 | no. 19 | 5979–5984 SUSTAINABILITY SCIENCE Table 1. SES variables analyzed for BCS’s small-scale fisheries To operationalize the SES framework for our focal system, we identified 13 variables that have been linked to the likelihood of Variable Weight* the emergence of locally appropriate governance of SESs, and Dimension 1: Governance System 1.00 small-scale fisheries SESs in particular (19). These 13 variables 1. Operational and collective-choice rules 0.50 were nested underneath the four dimensions introduced earlier 2. Territorial use privileges 0.25 (Table 1). We then identified indicators for each of the 13 variables 3. Fishing licenses 0.25 and quantified them on the basis of primary data (Table 2). Once Dimension 2: Actors 1.00 we calculated indicators for all 13 variables on a common scale 4. Diversity of relevant actors 0.20 and then created composite, quantitative measures of each of 5. Number of relevant actors 0.20 the four SES dimensions (i.e., first-tier variables), we were able 6. Migration 0.20 to test our hypotheses of social-ecological alignment, within- 7. Isolation 0.20 domain correlation, and spatial variation in the potential for 8. Livelihood diversity potential 0.20 social-ecological sustainability. Fig. 2 provides a visualization Dimension 3: Resource Units 1.00 of our methods. 9. Diversity of targeted taxa 0.50 Contrary to the first hypothesis, we found few consistent positive 10. Per capita revenue 0.50 relationships between the social and ecological dimensions related Dimension 4: Resource System 1.00 to the potential for sustainable resource use (Fig. 3 and SI Ap- 11. System productivity 0.33 pendix, Results). Among the a priori tests we conducted regarding 12. System size 0.33 the first-tier SES variables, only one pair exhibited the predicted 13. System predictability 0.33 relationship. Regions characterized by high Governance System scores also had high Resource Units scores (Fig. 3; linear re- *Weight refers to the weight given to each lower-tier variable (numbered gression: R = 0.33; F = 4.86; P = 0.05). This association was 1–13), when used to calculate the four first-tier variables (i.e., Dimensions). 1, 10 See Materials and Methods and SI Appendix, section 2 for details. particularly evident for regions with the highest and lowest sets of scores: Pacífico Norte and Todos Santos and Cabo San Lucas, Gulf of Ulloa and East Cape, respectively (Fig. 4). health (8). We define the potential for social-ecological sustain- ability as the likelihood that human and nonhuman components of the focal coupled SES will be maintained so as to meet the needs of both people and nature, now and in the future (3, 4). Previous work has highlighted that the matching of ecological and institutional scales increases the likelihood for sustainable governance of fisheries and other common pool resources (e.g., refs. 6, 9, and 10). However, contemporary environmental gov- ernance regimes often neglect the array of social, institutional, and ecological factors known to be vital to develop potential for social-ecological sustainability (e.g., refs. 4 and 11–13). We hy- pothesized that those regions of BCS with greater potential for social-ecological sustainability in the ecological dimensions (i.e., fish populations and the marine ecosystems they are part of) would exhibit greater potential in the social dimensions (i.e., fishers and the institutions that govern fishers’ interactions with BCS’ marine ecosystems) (SI Appendix, Fig. S1). We also hy- pothesized that measures of the two social system dimensions (Actors and Governance System) would be positively correlated, as would the measures of the two ecosystem dimensions (Re- source System and Resource Units), given the linkages within the social and ecological domains (SI Appendix, Fig. S1). Finally, we predicted that we would observe substantial spatial variation in the potential for social-ecological sustainability. Results To test these three hypotheses, we first mapped regions of major small-scale fishing activity in BCS. These regions, hereafter re- ferred to as SES regions (Fig. 1), were derived using data from the peer-reviewed literature, government environmental and economic data, and expert knowledge from fishers, resource management and conservation practitioners, and researchers (see SI Appendix for details). This map was essential for the translation of the SES framework and hypothesis tests we report here. If, instead, we had relied on a political map, delineating the five municipalities (SI Appendix, Fig. S2), we would have been unable to incorporate well-known differences in biogeography and human population Fig. 1. The 12 SES regions identified for BCS, Mexico, based on the extent of density between the Gulf and Pacific coasts of BCS. Relatedly, a small-scale fishing activity by members of fishing communities throughout map based primarily on environmental factors (e.g., refs. 14 and the state. The hatched areas indicate overlaps between adjacent regions; 15) would not have captured documented variation in adaptive that is, where fishers from different regions report using the same areas. capacity, local institutions, and market dynamics related to small- Fishing occurs on the Pacific coast between regions 4 and 5, but existing scale fisheries (16–18). information did not yield a distinct SES region. 5980 | www.pnas.org/cgi/doi/10.1073/pnas.1414640112 Leslie et al. Table 2. Representative data used to calculate the scores for the four first-tier SES variables Index, Total number Taxa Per capita Mean chlorophyll a CV (coefficient of −3 SES region local rules of fishers reported revenue, $USD (chl a), μg·m variation), mean chl a 1. Guerrero Negro 0.42 293 39 12,434 2.73 20.20 2. Pacífico Norte 1.00 1,083 73 15,123 1.76 57.86 3. Gulf of Ulloa 0.25 614 85 14,467 2.64 68.76 4. Magadalena Bay 0.25 1,283 90 15,060 2.19 50.20 5. Todos Santos 1.00 77 59 22,243 1.13 109.12 6. Cabo San Lucas 0.25 81 9 2,337 0.84 76.04 7. East Cape 0.25 247 36 2,641 0.88 65.28 8. La Paz 0.25 974 55 986 1.22 58.51 9. El Corredor 0.25 102 52 8,320 1.15 59.16 10. Loreto 0.58 152 44 5,220 1.43 57.25 11. Mulegé 0.58 126 54 6,750 2.31 46.51 12. Santa Rosalía 0.25 523 67 9,803 1.50 45.54 The full dataset can be found in SI Appendix, Table S2. Neither the two first-tier social system variables (Actors and to quite divergent conclusions. Consider, for example, Magda- Governance System) nor the two first-tier ecosystem variables lena Bay, where fishers report the most taxon-rich catches of the (Resource Units and Resource System) were associated, con- 12 SES regions. Previous theory and empirical work suggest that trary to our second hypothesis (Fig. 3; P > 0.10). However, these such ecological diversity should buffer the coupled SES from analyses revealed the dimensions within which the potential for disturbances and confer resilience in the face of environmental sustainable resource use and governance is particularly high or and institutional changes (20). However, although Magdalena low, which could inform future capacity-building efforts and other Bay had a very high Resource Units score, its Actors score was among the lowest. So, depending on which type of data one musters policy and management interventions. For example, although El regarding the potential for sustainable fisheries, Magdalena Corredor’s Governance System score was the lowest of the 12 re- Baycouldbescoredaseitherwell-endowedorquite weak. gions, its Actors score was almost as high as that of Pacífico Norte, Perhaps more important, this result suggests that in the Actors indicating that El Corredor already exhibits substantial potential for sustainable resource use in the latter dimension (Fig. 4 and SI dimension, there is opportunity to build management capacity Appendix, Table S4). (e.g., by increasing the ratio of permitted to illegal fishers), Finally, the potential for social-ecological sustainability varied whereas in the Resource Units dimension, it may be more substantially among the SES regions, as predicted (Fig. 4). As important to maintain existing management capacity (e.g., by reported earlier, regions that scored high in one dimension (i.e., creating institutions to help ensure continued diversity of targeted taxa). These scores are consistent with our personal one first-tier variable) did not necessarily score high in all four experiences in this particular region, where the sheer number dimensions. Magdalena Bay and Gulf of Ulloa, for example, had of fishers, including many unpermitted fishers, and the di- Resource Units scores close to 1 (SI Appendix, Table S4), yet the versity of gear types and interests they represent contribute to Actors scores for these two regions were both less than the significant social conflict. median. Cabo San Lucas, East Cape, La Paz, and Loreto had some of the lowest scores overall (linear contrast of these four regions vs. the other eight, following ANOVA of scores by re- gion: F = 13.08; P = 0.001). Principal components analysis 1, 36 provided another means of visualizing spatial variation among the regions, suggesting there are multiple paths to achieving sustainability (SI Appendix, Fig. S3). The results of the first-tier variable analyses were also reflected in the primary data (Table 2 and SI Appendix, Table S2). Together, these analyses illustrate substantial spatial heterogeneity in the potential for sustainable resource use related to small-scale fisheries in BCS and also elucidate how this variation is created by a combination of social and ecological factors (SI Appendix, Figs. S4–S8). Discussion Our approach illustrates how diverse qualitative and quantita- tive datasets can be integrated in a robust and spatially explicit manner to describe multiple SESs and to test related theory. Analyses of the theoretically grounded measures we created (Table 1 and SI Appendix,TablesS2–S4) revealed that regions that are strong in one dimension are not necessarily strong in the other three (Figs. 3 and 4 and SI Appendix,Table S3). Moreover, variation in the potential for social-ecological sus- tainability exists at a finer spatial scale than that at which the state currently regulates small-scale fisheries (as described in detail in the SI Appendix). Fig. 2. Steps to translate the SES framework into quantitative measures of Our translation of the SES framework also highlights how the potential for social-ecological sustainability, with references to the rel- assessments based on solely biophysical or social data may lead evant SI Appendix sections. Leslie et al. PNAS | May 12, 2015 | vol. 112 | no. 19 | 5981 SUSTAINABILITY SCIENCE hundreds, of species, which vary in their life histories, economic value, and many other important characteristics. We managed this complexity by scaling up our analysis to the level of major fishing areas, which represented fishers’ use of ocean space to catch many species over the entire year (Fig. 1). Nonetheless, this level of analysis obscures some valuable information. Similarly, although we can think of biogeographic, oceano- graphic, and human history as exogenous drivers of the dynamics of a given SES region, on long time scales, they are not static. Global climate change continues to alter the biophysical template on which social-ecological interactions play out; for example, by changing water and air temperatures and the frequency and magnitude of precipitation and coastal storms. Sociopolitical dynamics enter the SES framework both as context and in relationships between attributes internal to the coupled system (25). Evolving sociopolitical dynamics from local to national scales and their interactions with narco-trafficking and other multinational influences shape the opportunities and constraints facing BCS’ small-scale fishers and their decisions about how, where, and when to fish (as in ref. 26). Our analyses inform four types of management strategies, focused on each of the four dimensions (or first-tier SES vari- ables). Interventions focused on improving existing institutional arrangements are most likely to strengthen the Governance System dimension, whereas those focused on improving re- Fig. 3. Scatterplot of the relationships among all four SES dimensions or lationships among stakeholders will aid in building capacity in first-tier variables demonstrates the heterogeneity, both among the four the Actors dimension. Similarly, we anticipate that strategies dimensions and among regions, in the potential for sustainable resource use focused on maintaining or improving ecosystem health would and management in BCS, Mexico. Values range from 0 to 1, where a larger benefit the Resource System dimension, and that interventions value is associated with a greater probability that fisheries will be sustain- focused on improving the status of populations of the target ably managed. Details regarding the quantification of these dimensions and species would build capacity in the Resource Units dimension. underlying data and theory can be found in the SI Appendix, Sub-Appendix D. Such tailored strategies will likely reduce the costs associated The solid lines are fit with simple linear regression models, and the shading refers to the 95% confidence intervals. Only one model (marked with an asterisk) was with blueprint, or one-size-fits-all, types of policies. Given the significant at α = 0.05. physical isolation and consequent high reliance on marine re- sources in the Pacífico Norte and El Corredor regions, for example, especially when contrasted with the highly populated southern re- These results demonstrate how integrative, interdisciplinary gions, a blueprint approach to fisheries management and MPA research that includes both qualitative and quantitative data may implementation will not serve either the human communities or be synthesized to yield a richer understanding of coupled SESs in the marine ecosystems of BCS well. Instead, we advocate for more particular places. Many of these data have never before been strategic approaches, targeted to the needs and strengths of specific mapped at this scale, and yet such fine-scale information could regions. However, it also is important to acknowledge that gover- help inform implementation of ecosystem-based fisheries man- nance approaches tailored to address problems in one dimension agement, marine spatial planning, and related strategies. The or region could trigger unintended consequences in other di- variation among the SES regions suggests that certain marine mensions or regions if issues are not addressed holistically. Ap- management strategies, implemented at particular geographic plying integrative, place-based understanding of SESs in this and scales, are likely to be more effective in some places than others other geographies will enable sustainability science to more fully (see also refs. 21 and 22). inform sustainability practice. Mapping is necessarily a political project in which local actors must be involved to negotiate boundaries in ways that are more Materials and Methods likely to lead to just outcomes (23, 24). Our purpose here is not SES Mapping. To map the SES regions, we began by listing all the small-scale to portray the SES regions as definitive boundaries but, rather, to fishing communities along the coast of BCS, with reference to ref. 27. We then identified distinct clusters among them based on four primary factors: encourage finer-scale, spatially explicit governance of SESs, in biophysical context, including coastal topography, habitats, and species which local stakeholders participate in the necessary refinement distributions; historical and contemporary coastal land and marine resource of social-ecological management boundaries. Stakeholder engage- use; municipal and state political boundaries; and the concentrations and ment will require and also provide an impetus to create finer- movement of fishers and fisheries products. Further detail on these factors resolution spatial data at the level of individual fishing com- and the current fisheries management regime can be found in the SI Ap- munities. To our knowledge, these are not yet available for pendix, Sub-Appendix A. most of BCS and other coastal marine SESs around the world. Based on these first two steps, we drew an initial map of major fishing To extend our approach and to evaluate spatial heterogeneity areas of BCS. This initial map informed a series of unstructured interviews in the actual interactions and outcomesassociatedwithsmall- with key informants about the scale and nature of small-scale fisheries ac- tivities throughout BCS. Fourth, following refinement of the maps based on scale fisheries will require substantially more and different the results of the interviews, we created a survey instrument to elicit stan- types of data than those currently available. dardized area-specific expert knowledge of the human and environmental The diversity of species and fishing practices that are inherently dimensions of BCS’ small-scale fisheries from fisheries managers and con- part of small-scale fisheries like BCS’ present other challenges for servation and community development practitioners. The survey was dis- coupled systems analyses, and for the SES framework specifically. tributed to 15 individuals and is included as SI Appendix, Sub-Appendix B. By design, the framework focuses on interactions between resource After analysis of the survey results, the SES regions were amended as users and other actors regarding a specific resource in the context needed. Further details on the mapping protocol can be found in of a particular SES. However, small-scale fishers target tens, if not SI Appendix, Materials and Methods, along with detailed descriptions of 5982 | www.pnas.org/cgi/doi/10.1073/pnas.1414640112 Leslie et al. Fig. 4. Scores for the (A) Governance System, (B) Actors, (C) Resource Units, and (D) Resource System dimensions (i.e., first-tier variables) vary among the SES regions, illustrating spatial heterogeneity in the potential for sustainable resource use and management in BCS, Mexico. See Fig. 1 for the names of each region. Values range from 0 to 1, where a larger value is associated with a greater probability that fisheries will be sustainably managed. Details regarding the quantification of these dimensions and underlying data and theory can be found in the SI Appendix, Sub-Appendix D. each region (SI Appendix, Sub-Appendix C). The SES regions map and all relevant theory regarding how each variable has been associated with a SES’s other maps were produced using ESRI ArcGIS 10.1. potential for sustainable resource governance. Operationalizing the Framework. Once we had an appropriate map with which Variable Weighting. Each of the four dimensions, Governance System, Actors, to test our hypotheses, we translated the SES framework from a conceptual Resource Units, and Resource System, has a cumulative weight score of one model into a quantifiable set of indicators, linked with each of the four SES (Table 1). The relative contribution of each of the lower-tier variables to this dimensions or first-tier variables. The 13 lower-tier variable selection process weight depends on the total number of such variables analyzed under a was driven by our knowledge of the BCS SESs, review of the scholarly literature, particular dimension, or first-tier variable. For example, the dimension and theoretical underpinnings of our work, including the governance of Actors is composed of five lower-tier variables, each of which is weighted common-pool resources and the relationships between biodiversity and eco- 0.20 for an overall score of 1.00 (Table 1 and SI Appendix,Table S1 and SI system functioning. Fig. 2 provides a step-by-step visualization of the full methods. Appendix, Sub-Appendix D). For the variables that have more than one indicator (SI Appendix, Table S2 and SI Appendix, Sub-Appendix D), scores Variable Selection and Ranking of the Indicators. A varying number of second, were averaged before being weighted. third, and fourth-tier variables are nested underneath each of the four di- mensions (Governance System, Actors, Resource System, and Resource Units); in Data and Analyses. The data can be found in SI Appendix, Tables S2–S4.All total, there are 13 second-, third-, and fourth-tier variables (Table 1 and SI statistical tests were performed using JMP 11 (SAS Institute) or SPSS 22.0 (SPSS Appendix, Table S1, after refs. 4 and 19). Importantly, all 13 variables, re- Statistics Inc.). We used standard statistical approaches (i.e., linear regression, gardless of whether the primary data used to develop the indicator were analysis of variance, and principal component analysis models) to explore the qualitative or quantitative, were normalized to a scale of 0–1, so they could be primary data used to develop the indicators for the SES variables and test a combined and compared. For those variables for which the primary data were priori hypotheses regarding the first-tier SES variables. Before analysis, the continuous, such as total fisheries biomass, the values for each SES region were primary data and the calculated variables were plotted to investigate their fit calculated on the basis of the quantile distribution of the original data. For to statistical assumptions; for example, normality. When necessary, data were those variables for which the primary data were qualitative (e.g., presence/ transformed. The details of these models are described in SI Appendix, Results. absence), they were translated into categorical 0/1 variables. These data and the resulting rankings are presented in SI Appendix,Tables S2–S4. SI Appendix, Sub-Appendix D includes the description of all 13 SES variables ACKNOWLEDGMENTS. We appreciate the thoughtful and constructive com- ments provided by S. Levin, M. Ballestreros, and two anonymous reviewers on and the related indicators. The description includes each variable’s name, po- earlier versions of this manuscript, and we thank our community partners, who sition in the original SES framework (4), definition, theoretical importance, a have contributed to our individual and collaborative activities in the region. brief description of the indicator or indicators associated with the variable, and Funding was provided by the US National Science Foundation (GEO 1114964, the ranking system for each indicator. Where appropriate, we include the to H.M.L., S.N., S.M.W.R., and O.A.-O.), The David and Lucile Packard Foundation quantile distribution of the original data. For each variable, we identified one or (H.M.L., O.A.-O., B.E.E., and M.M.-B.), Brown University’s Environmental Change more indicators that enabled either qualitative or quantitative comparison Initiative (H.M.L., L.S., S.N., and S.M.W.R.), Voss Environmental Fellowship Pro- among the 12 SES regions. 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McCay BJ, Weisman W, Creed C (2011) Coping with environmental change: Systemic responses and the roles of property and community in three fisheries. World Fisheries: A cias Marinas del Instituto Politécnico Nacional CICIMAR - IPN, La Paz, Mexico). 5984 | www.pnas.org/cgi/doi/10.1073/pnas.1414640112 Leslie et al. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings of the National Academy of Sciences of the United States of America Pubmed Central

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Abstract

Operationalizing the social-ecological systems framework to assess sustainability a,b,1 c,1 c a,b,d a,b,e,2 Heather M. Leslie , Xavier Basurto , Mateja Nenadovic , Leila Sievanen , Kyle C. Cavanaugh , f g,3 h f,4 f,g Juan José Cota-Nieto , Brad E. Erisman , Elena Finkbeiner , Gustavo Hinojosa-Arango , Marcia Moreno-Báez , b,i a,b,j f a,b,5 Sriniketh Nagavarapu , Sheila M. W. Reddy , Alexandra Sánchez-Rodríguez , Katherine Siegel , k l g José Juan Ulibarria-Valenzuela , Amy Hudson Weaver , and Octavio Aburto-Oropeza a b Department of Ecology and Evolutionary Biology and Institute at Brown for Environment and Society, Brown University, Providence, RI 02912; c d Duke University Marine Laboratory, Nicholas School of the Environment, Duke University, Beaufort, NC 28516; Joint Institute for Marine and Atmospheric Research/Pacific Islands Fisheries Science Center, Honolulu, HI 96818; Smithsonian Environmental Research Center, Smithsonian Institution, Edgewater, f g MD 21037; Centro para la Biodiversidad Marina y la Conservación A.C., La Paz, BCS, 23090 Mexico; Scripps Institution of Oceanography, Marine Biology Research Division, University of California, San Diego, La Jolla, CA 92093-0202; Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950; i j k Department of Economics, Brown University, Providence, RI 02912; Central Science Division, The Nature Conservancy, Durham, NC 27701; Fondo para la Protección de los Recursos Marinos, La Paz, BCS, 23090 Mexico; and Sociedad de Historia Natural Niparaja A.C., La Paz, BCS, 23020 Mexico Edited by Bonnie J. McCay, Rutgers, The State University of New Jersey, New Brunswick, New Brunswick, NJ, and approved April 2, 2015 (received for review August 22, 2014) Environmental governance is more effective when the scales of temporal and spatial scales (SI Appendix,Fig.S1). To operationalize ecological processes are well matched with the human institutions the framework, we translated these four dimensions into quantita- charged with managing human–environment interactions. The tive, theoretically derived measures of factors known to contribute social-ecological systems (SESs) framework provides guidance on to sustainable resource use (see following and SI Appendix for a how to assess the social and ecological dimensions that contribute detailed description of the materials and methods). to sustainable resource use and management, but rarely if ever We used the SES framework to assess the spatial variation in has been operationalized for multiple localities in a spatially ex- the potential for social-ecological sustainability of small-scale fish- plicit, quantitative manner. Here, we use the case of small-scale eries in the Mexican state of Baja California Sur (BCS). We focus fisheries in Baja California Sur, Mexico, to identify distinct SES re- on small-scale, coastal fisheries because of their importance to gions and test key aspects of coupled SESs theory. Regions that human communities for both income and food security (7), as well exhibit greater potential for social-ecological sustainability in one as the effects fisheries have on marine populations and ecosystem dimension do not necessarily exhibit it in others, highlighting the importance of integrative, coupled system analyses when imple- Significance menting spatial planning and other ecosystem-based strategies. Meeting human needs while sustaining ecosystems and the coupled natural and human systems marine governance small-scale | | | benefits they provide is a global challenge. Coastal marine fisheries conservation science systems present a particularly important case, given that >50% of the world’s population lives within 100 km of the coast and central challenge facing humanity is how to achieve sus- fisheries are the primary source of protein for >1 billion people Atainable outcomes that benefit both people and nature (1). worldwide. Our integrative analysis here yields an under- Using a social-ecological systems (SESs) approach in the gen- standing of the sustainability of coupled social-ecological sys- eration of knowledge and the formulation of sustainable gover- tems that is quite distinct from that provided by either the nance solutions is critical, as it explicitly recognizes the connections biophysical or the social sciences alone and that illustrates the and feedbacks linking human and natural systems. Understanding feasibility and value of operationalizing the social-ecological how the potential for social-ecological sustainability varies with systems framework for comparative analyses of coupled sys- context is vital to solving this dilemma (2, 3). A highly visible tems, particularly in data-poor and developing nation settings. conceptual tool, the SESs framework (4) offers the potential to address this scientific and societal challenge, but operationali- Author contributions: H.M.L., X.B., M.N., and L.S. designed research; H.M.L., X.B., M.N., L.S., G.H.-A., S.M.W.R., K.S., A.H.W., and O.A.-O. conceived of the study; H.M.L., X.B., M.N., zation has been elusive. In this study, we demonstrate how the L.S., K.C.C., J.J.C.-N., E.F., G.H.-A., S.M.W.R., K.S., J.J.U.-V., A.H.W., and O.A.-O. collected framework can be applied in a new way to identify opportuni- the data; H.M.L., X.B., M.N., L.S., K.C.C., J.J.C.-N., B.E.E., E.F., G.H.-A., M.M.-B., S.N., S.M.W.R., ties and tradeoffs in managing for the sustainability of coupled A.S.-R., K.S., J.J.U.-V., A.H.W., and O.A.-O. performed research; H.M.L., X.B., M.N., and L.S. SESs (5, 6). contributed new reagents/analytic tools; H.M.L., X.B., M.N., L.S., K.C.C., and M.M.-B. analyzed data; and H.M.L., X.B., M.N., and L.S. wrote the paper. The SES framework enables the integration of data from di- The authors declare no conflict of interest. verse natural and social science disciplines, and thus provides a This article is a PNAS Direct Submission. theoretically grounded means of testing hypotheses about the dy- Freely available online through the PNAS open access option. namics and implications of social-ecological interactions (see SI Appendix for further discussion). At its broadest level, the SES To whom correspondence may be addressed. Email: heather.m.leslie@gmail.com or xavier.basurto@duke.edu. framework describes the four essential dimensions, or first-tier Present address: Department of Geography, University of California, Los Angeles, variables, of a SES (Table 1, after ref. 4). Actors within and CA 90095. outside government operate within a Governance System char- Present address: Marine Science Institute, The University of Texas at Austin, Port Aransas, acterized by formal and informal rules at one or more identifi- TX 78373. able geographic scales. Resource Units inhabit and interact with Present address: Cátedra Consejo Nacional de Ciencia y Tecnología, Centro Interdisciplinario a broader Resource System that is characterized by particular de Investigación para el Desarrollo Integral Regional Unidad Oaxaca, Instituto Politécnico ecosystem types and biophysical processes, also at one or more Nacional, Oaxaca, 71236, Mexico. geographic scales. Interactions among these four dimensions are 5 Present address: Sustainable Fisheries Group, Marine Science Institute, University of Cal- mediated by the broader social, economic, and political settings ifornia, Santa Barbara, CA 93106. and related ecosystems within which the SES is embedded. To- This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. gether, these dynamics lead to diverse outcomes at particular 1073/pnas.1414640112/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1414640112 PNAS | May 12, 2015 | vol. 112 | no. 19 | 5979–5984 SUSTAINABILITY SCIENCE Table 1. SES variables analyzed for BCS’s small-scale fisheries To operationalize the SES framework for our focal system, we identified 13 variables that have been linked to the likelihood of Variable Weight* the emergence of locally appropriate governance of SESs, and Dimension 1: Governance System 1.00 small-scale fisheries SESs in particular (19). These 13 variables 1. Operational and collective-choice rules 0.50 were nested underneath the four dimensions introduced earlier 2. Territorial use privileges 0.25 (Table 1). We then identified indicators for each of the 13 variables 3. Fishing licenses 0.25 and quantified them on the basis of primary data (Table 2). Once Dimension 2: Actors 1.00 we calculated indicators for all 13 variables on a common scale 4. Diversity of relevant actors 0.20 and then created composite, quantitative measures of each of 5. Number of relevant actors 0.20 the four SES dimensions (i.e., first-tier variables), we were able 6. Migration 0.20 to test our hypotheses of social-ecological alignment, within- 7. Isolation 0.20 domain correlation, and spatial variation in the potential for 8. Livelihood diversity potential 0.20 social-ecological sustainability. Fig. 2 provides a visualization Dimension 3: Resource Units 1.00 of our methods. 9. Diversity of targeted taxa 0.50 Contrary to the first hypothesis, we found few consistent positive 10. Per capita revenue 0.50 relationships between the social and ecological dimensions related Dimension 4: Resource System 1.00 to the potential for sustainable resource use (Fig. 3 and SI Ap- 11. System productivity 0.33 pendix, Results). Among the a priori tests we conducted regarding 12. System size 0.33 the first-tier SES variables, only one pair exhibited the predicted 13. System predictability 0.33 relationship. Regions characterized by high Governance System scores also had high Resource Units scores (Fig. 3; linear re- *Weight refers to the weight given to each lower-tier variable (numbered gression: R = 0.33; F = 4.86; P = 0.05). This association was 1–13), when used to calculate the four first-tier variables (i.e., Dimensions). 1, 10 See Materials and Methods and SI Appendix, section 2 for details. particularly evident for regions with the highest and lowest sets of scores: Pacífico Norte and Todos Santos and Cabo San Lucas, Gulf of Ulloa and East Cape, respectively (Fig. 4). health (8). We define the potential for social-ecological sustain- ability as the likelihood that human and nonhuman components of the focal coupled SES will be maintained so as to meet the needs of both people and nature, now and in the future (3, 4). Previous work has highlighted that the matching of ecological and institutional scales increases the likelihood for sustainable governance of fisheries and other common pool resources (e.g., refs. 6, 9, and 10). However, contemporary environmental gov- ernance regimes often neglect the array of social, institutional, and ecological factors known to be vital to develop potential for social-ecological sustainability (e.g., refs. 4 and 11–13). We hy- pothesized that those regions of BCS with greater potential for social-ecological sustainability in the ecological dimensions (i.e., fish populations and the marine ecosystems they are part of) would exhibit greater potential in the social dimensions (i.e., fishers and the institutions that govern fishers’ interactions with BCS’ marine ecosystems) (SI Appendix, Fig. S1). We also hy- pothesized that measures of the two social system dimensions (Actors and Governance System) would be positively correlated, as would the measures of the two ecosystem dimensions (Re- source System and Resource Units), given the linkages within the social and ecological domains (SI Appendix, Fig. S1). Finally, we predicted that we would observe substantial spatial variation in the potential for social-ecological sustainability. Results To test these three hypotheses, we first mapped regions of major small-scale fishing activity in BCS. These regions, hereafter re- ferred to as SES regions (Fig. 1), were derived using data from the peer-reviewed literature, government environmental and economic data, and expert knowledge from fishers, resource management and conservation practitioners, and researchers (see SI Appendix for details). This map was essential for the translation of the SES framework and hypothesis tests we report here. If, instead, we had relied on a political map, delineating the five municipalities (SI Appendix, Fig. S2), we would have been unable to incorporate well-known differences in biogeography and human population Fig. 1. The 12 SES regions identified for BCS, Mexico, based on the extent of density between the Gulf and Pacific coasts of BCS. Relatedly, a small-scale fishing activity by members of fishing communities throughout map based primarily on environmental factors (e.g., refs. 14 and the state. The hatched areas indicate overlaps between adjacent regions; 15) would not have captured documented variation in adaptive that is, where fishers from different regions report using the same areas. capacity, local institutions, and market dynamics related to small- Fishing occurs on the Pacific coast between regions 4 and 5, but existing scale fisheries (16–18). information did not yield a distinct SES region. 5980 | www.pnas.org/cgi/doi/10.1073/pnas.1414640112 Leslie et al. Table 2. Representative data used to calculate the scores for the four first-tier SES variables Index, Total number Taxa Per capita Mean chlorophyll a CV (coefficient of −3 SES region local rules of fishers reported revenue, $USD (chl a), μg·m variation), mean chl a 1. Guerrero Negro 0.42 293 39 12,434 2.73 20.20 2. Pacífico Norte 1.00 1,083 73 15,123 1.76 57.86 3. Gulf of Ulloa 0.25 614 85 14,467 2.64 68.76 4. Magadalena Bay 0.25 1,283 90 15,060 2.19 50.20 5. Todos Santos 1.00 77 59 22,243 1.13 109.12 6. Cabo San Lucas 0.25 81 9 2,337 0.84 76.04 7. East Cape 0.25 247 36 2,641 0.88 65.28 8. La Paz 0.25 974 55 986 1.22 58.51 9. El Corredor 0.25 102 52 8,320 1.15 59.16 10. Loreto 0.58 152 44 5,220 1.43 57.25 11. Mulegé 0.58 126 54 6,750 2.31 46.51 12. Santa Rosalía 0.25 523 67 9,803 1.50 45.54 The full dataset can be found in SI Appendix, Table S2. Neither the two first-tier social system variables (Actors and to quite divergent conclusions. Consider, for example, Magda- Governance System) nor the two first-tier ecosystem variables lena Bay, where fishers report the most taxon-rich catches of the (Resource Units and Resource System) were associated, con- 12 SES regions. Previous theory and empirical work suggest that trary to our second hypothesis (Fig. 3; P > 0.10). However, these such ecological diversity should buffer the coupled SES from analyses revealed the dimensions within which the potential for disturbances and confer resilience in the face of environmental sustainable resource use and governance is particularly high or and institutional changes (20). However, although Magdalena low, which could inform future capacity-building efforts and other Bay had a very high Resource Units score, its Actors score was among the lowest. So, depending on which type of data one musters policy and management interventions. For example, although El regarding the potential for sustainable fisheries, Magdalena Corredor’s Governance System score was the lowest of the 12 re- Baycouldbescoredaseitherwell-endowedorquite weak. gions, its Actors score was almost as high as that of Pacífico Norte, Perhaps more important, this result suggests that in the Actors indicating that El Corredor already exhibits substantial potential for sustainable resource use in the latter dimension (Fig. 4 and SI dimension, there is opportunity to build management capacity Appendix, Table S4). (e.g., by increasing the ratio of permitted to illegal fishers), Finally, the potential for social-ecological sustainability varied whereas in the Resource Units dimension, it may be more substantially among the SES regions, as predicted (Fig. 4). As important to maintain existing management capacity (e.g., by reported earlier, regions that scored high in one dimension (i.e., creating institutions to help ensure continued diversity of targeted taxa). These scores are consistent with our personal one first-tier variable) did not necessarily score high in all four experiences in this particular region, where the sheer number dimensions. Magdalena Bay and Gulf of Ulloa, for example, had of fishers, including many unpermitted fishers, and the di- Resource Units scores close to 1 (SI Appendix, Table S4), yet the versity of gear types and interests they represent contribute to Actors scores for these two regions were both less than the significant social conflict. median. Cabo San Lucas, East Cape, La Paz, and Loreto had some of the lowest scores overall (linear contrast of these four regions vs. the other eight, following ANOVA of scores by re- gion: F = 13.08; P = 0.001). Principal components analysis 1, 36 provided another means of visualizing spatial variation among the regions, suggesting there are multiple paths to achieving sustainability (SI Appendix, Fig. S3). The results of the first-tier variable analyses were also reflected in the primary data (Table 2 and SI Appendix, Table S2). Together, these analyses illustrate substantial spatial heterogeneity in the potential for sustainable resource use related to small-scale fisheries in BCS and also elucidate how this variation is created by a combination of social and ecological factors (SI Appendix, Figs. S4–S8). Discussion Our approach illustrates how diverse qualitative and quantita- tive datasets can be integrated in a robust and spatially explicit manner to describe multiple SESs and to test related theory. Analyses of the theoretically grounded measures we created (Table 1 and SI Appendix,TablesS2–S4) revealed that regions that are strong in one dimension are not necessarily strong in the other three (Figs. 3 and 4 and SI Appendix,Table S3). Moreover, variation in the potential for social-ecological sus- tainability exists at a finer spatial scale than that at which the state currently regulates small-scale fisheries (as described in detail in the SI Appendix). Fig. 2. Steps to translate the SES framework into quantitative measures of Our translation of the SES framework also highlights how the potential for social-ecological sustainability, with references to the rel- assessments based on solely biophysical or social data may lead evant SI Appendix sections. Leslie et al. PNAS | May 12, 2015 | vol. 112 | no. 19 | 5981 SUSTAINABILITY SCIENCE hundreds, of species, which vary in their life histories, economic value, and many other important characteristics. We managed this complexity by scaling up our analysis to the level of major fishing areas, which represented fishers’ use of ocean space to catch many species over the entire year (Fig. 1). Nonetheless, this level of analysis obscures some valuable information. Similarly, although we can think of biogeographic, oceano- graphic, and human history as exogenous drivers of the dynamics of a given SES region, on long time scales, they are not static. Global climate change continues to alter the biophysical template on which social-ecological interactions play out; for example, by changing water and air temperatures and the frequency and magnitude of precipitation and coastal storms. Sociopolitical dynamics enter the SES framework both as context and in relationships between attributes internal to the coupled system (25). Evolving sociopolitical dynamics from local to national scales and their interactions with narco-trafficking and other multinational influences shape the opportunities and constraints facing BCS’ small-scale fishers and their decisions about how, where, and when to fish (as in ref. 26). Our analyses inform four types of management strategies, focused on each of the four dimensions (or first-tier SES vari- ables). Interventions focused on improving existing institutional arrangements are most likely to strengthen the Governance System dimension, whereas those focused on improving re- Fig. 3. Scatterplot of the relationships among all four SES dimensions or lationships among stakeholders will aid in building capacity in first-tier variables demonstrates the heterogeneity, both among the four the Actors dimension. Similarly, we anticipate that strategies dimensions and among regions, in the potential for sustainable resource use focused on maintaining or improving ecosystem health would and management in BCS, Mexico. Values range from 0 to 1, where a larger benefit the Resource System dimension, and that interventions value is associated with a greater probability that fisheries will be sustain- focused on improving the status of populations of the target ably managed. Details regarding the quantification of these dimensions and species would build capacity in the Resource Units dimension. underlying data and theory can be found in the SI Appendix, Sub-Appendix D. Such tailored strategies will likely reduce the costs associated The solid lines are fit with simple linear regression models, and the shading refers to the 95% confidence intervals. Only one model (marked with an asterisk) was with blueprint, or one-size-fits-all, types of policies. Given the significant at α = 0.05. physical isolation and consequent high reliance on marine re- sources in the Pacífico Norte and El Corredor regions, for example, especially when contrasted with the highly populated southern re- These results demonstrate how integrative, interdisciplinary gions, a blueprint approach to fisheries management and MPA research that includes both qualitative and quantitative data may implementation will not serve either the human communities or be synthesized to yield a richer understanding of coupled SESs in the marine ecosystems of BCS well. Instead, we advocate for more particular places. Many of these data have never before been strategic approaches, targeted to the needs and strengths of specific mapped at this scale, and yet such fine-scale information could regions. However, it also is important to acknowledge that gover- help inform implementation of ecosystem-based fisheries man- nance approaches tailored to address problems in one dimension agement, marine spatial planning, and related strategies. The or region could trigger unintended consequences in other di- variation among the SES regions suggests that certain marine mensions or regions if issues are not addressed holistically. Ap- management strategies, implemented at particular geographic plying integrative, place-based understanding of SESs in this and scales, are likely to be more effective in some places than others other geographies will enable sustainability science to more fully (see also refs. 21 and 22). inform sustainability practice. Mapping is necessarily a political project in which local actors must be involved to negotiate boundaries in ways that are more Materials and Methods likely to lead to just outcomes (23, 24). Our purpose here is not SES Mapping. To map the SES regions, we began by listing all the small-scale to portray the SES regions as definitive boundaries but, rather, to fishing communities along the coast of BCS, with reference to ref. 27. We then identified distinct clusters among them based on four primary factors: encourage finer-scale, spatially explicit governance of SESs, in biophysical context, including coastal topography, habitats, and species which local stakeholders participate in the necessary refinement distributions; historical and contemporary coastal land and marine resource of social-ecological management boundaries. Stakeholder engage- use; municipal and state political boundaries; and the concentrations and ment will require and also provide an impetus to create finer- movement of fishers and fisheries products. Further detail on these factors resolution spatial data at the level of individual fishing com- and the current fisheries management regime can be found in the SI Ap- munities. To our knowledge, these are not yet available for pendix, Sub-Appendix A. most of BCS and other coastal marine SESs around the world. Based on these first two steps, we drew an initial map of major fishing To extend our approach and to evaluate spatial heterogeneity areas of BCS. This initial map informed a series of unstructured interviews in the actual interactions and outcomesassociatedwithsmall- with key informants about the scale and nature of small-scale fisheries ac- tivities throughout BCS. Fourth, following refinement of the maps based on scale fisheries will require substantially more and different the results of the interviews, we created a survey instrument to elicit stan- types of data than those currently available. dardized area-specific expert knowledge of the human and environmental The diversity of species and fishing practices that are inherently dimensions of BCS’ small-scale fisheries from fisheries managers and con- part of small-scale fisheries like BCS’ present other challenges for servation and community development practitioners. The survey was dis- coupled systems analyses, and for the SES framework specifically. tributed to 15 individuals and is included as SI Appendix, Sub-Appendix B. By design, the framework focuses on interactions between resource After analysis of the survey results, the SES regions were amended as users and other actors regarding a specific resource in the context needed. Further details on the mapping protocol can be found in of a particular SES. However, small-scale fishers target tens, if not SI Appendix, Materials and Methods, along with detailed descriptions of 5982 | www.pnas.org/cgi/doi/10.1073/pnas.1414640112 Leslie et al. Fig. 4. Scores for the (A) Governance System, (B) Actors, (C) Resource Units, and (D) Resource System dimensions (i.e., first-tier variables) vary among the SES regions, illustrating spatial heterogeneity in the potential for sustainable resource use and management in BCS, Mexico. See Fig. 1 for the names of each region. Values range from 0 to 1, where a larger value is associated with a greater probability that fisheries will be sustainably managed. Details regarding the quantification of these dimensions and underlying data and theory can be found in the SI Appendix, Sub-Appendix D. each region (SI Appendix, Sub-Appendix C). The SES regions map and all relevant theory regarding how each variable has been associated with a SES’s other maps were produced using ESRI ArcGIS 10.1. potential for sustainable resource governance. Operationalizing the Framework. Once we had an appropriate map with which Variable Weighting. Each of the four dimensions, Governance System, Actors, to test our hypotheses, we translated the SES framework from a conceptual Resource Units, and Resource System, has a cumulative weight score of one model into a quantifiable set of indicators, linked with each of the four SES (Table 1). The relative contribution of each of the lower-tier variables to this dimensions or first-tier variables. The 13 lower-tier variable selection process weight depends on the total number of such variables analyzed under a was driven by our knowledge of the BCS SESs, review of the scholarly literature, particular dimension, or first-tier variable. For example, the dimension and theoretical underpinnings of our work, including the governance of Actors is composed of five lower-tier variables, each of which is weighted common-pool resources and the relationships between biodiversity and eco- 0.20 for an overall score of 1.00 (Table 1 and SI Appendix,Table S1 and SI system functioning. Fig. 2 provides a step-by-step visualization of the full methods. Appendix, Sub-Appendix D). For the variables that have more than one indicator (SI Appendix, Table S2 and SI Appendix, Sub-Appendix D), scores Variable Selection and Ranking of the Indicators. A varying number of second, were averaged before being weighted. third, and fourth-tier variables are nested underneath each of the four di- mensions (Governance System, Actors, Resource System, and Resource Units); in Data and Analyses. The data can be found in SI Appendix, Tables S2–S4.All total, there are 13 second-, third-, and fourth-tier variables (Table 1 and SI statistical tests were performed using JMP 11 (SAS Institute) or SPSS 22.0 (SPSS Appendix, Table S1, after refs. 4 and 19). Importantly, all 13 variables, re- Statistics Inc.). We used standard statistical approaches (i.e., linear regression, gardless of whether the primary data used to develop the indicator were analysis of variance, and principal component analysis models) to explore the qualitative or quantitative, were normalized to a scale of 0–1, so they could be primary data used to develop the indicators for the SES variables and test a combined and compared. For those variables for which the primary data were priori hypotheses regarding the first-tier SES variables. Before analysis, the continuous, such as total fisheries biomass, the values for each SES region were primary data and the calculated variables were plotted to investigate their fit calculated on the basis of the quantile distribution of the original data. For to statistical assumptions; for example, normality. When necessary, data were those variables for which the primary data were qualitative (e.g., presence/ transformed. The details of these models are described in SI Appendix, Results. absence), they were translated into categorical 0/1 variables. These data and the resulting rankings are presented in SI Appendix,Tables S2–S4. SI Appendix, Sub-Appendix D includes the description of all 13 SES variables ACKNOWLEDGMENTS. We appreciate the thoughtful and constructive com- ments provided by S. Levin, M. Ballestreros, and two anonymous reviewers on and the related indicators. The description includes each variable’s name, po- earlier versions of this manuscript, and we thank our community partners, who sition in the original SES framework (4), definition, theoretical importance, a have contributed to our individual and collaborative activities in the region. brief description of the indicator or indicators associated with the variable, and Funding was provided by the US National Science Foundation (GEO 1114964, the ranking system for each indicator. Where appropriate, we include the to H.M.L., S.N., S.M.W.R., and O.A.-O.), The David and Lucile Packard Foundation quantile distribution of the original data. For each variable, we identified one or (H.M.L., O.A.-O., B.E.E., and M.M.-B.), Brown University’s Environmental Change more indicators that enabled either qualitative or quantitative comparison Initiative (H.M.L., L.S., S.N., and S.M.W.R.), Voss Environmental Fellowship Pro- among the 12 SES regions. Together, these complementary indicators captured gram (K.S.), The Walton Family Foundation (X.B., O.A.-O., B.E.E., M.M.-B., and multiple dimensions of a variable. We developed the ranking system from M.N.), and the World Wildlife Fund Fuller Fellowship Program (M.N.). 1. Kareiva P, Marvier M (2012) What is conservation science? Bioscience 62(11):962–969. 3. Levin SA, Clark WC (2010) Toward a Science of Sustainability. Report from Toward a 2. Liu J, et al. (2007) Complexity of coupled human and natural systems. Science Science of Sustainability Conference, Airlie Center, Warrenton, VA, November 29, 317(5844):1513–1516. 2009–December 2, 2009. Center for International Development Working Papers 196. Leslie et al. PNAS | May 12, 2015 | vol. 112 | no. 19 | 5983 SUSTAINABILITY SCIENCE John F. Kennedy School of Government, Harvard University. Available at www.hks. 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