Abstract
Wildlife trade represents a major threat to endangered-species populations, especially in Southeast Asia, where trade continues at high levels despite increased efforts to control illegal activities. To identify management strategies that better mitigate the threat of this trade, research must address knowledge gaps about the complexity of established trade networks. This requires a comprehensive and interdisciplinary approach that integrates biological, anthropological, socioeconomic, and other kinds of data and involves multiple stakeholders across sectors. We present here an interdisciplinary research framework for developing such an approach. Our integrative framework, based on the social–ecological systems framework by Ostrom, can be used to explore and untangle complex wildlife trade dynamics across scales and test hypotheses derived from different disciplines to provide robust recommendations for trade management. We also discuss the need for developing databases for trade-targeted species and outline steps to build and strengthen technical and interdisciplinary capacity to support the integrative framework.
Overexploitation of wildlife to supply domestic and international trade is a global threat to biodiversity conservation goals. In particular, it has been recognized as the single largest threat to biodiversity in many Southeast Asian countries, where increasing wealth and demand for wildlife products correspond with low levels of enforcement (TRAFFIC 2008, Nijman 2010, Bennett 2011).
A large volume of wildlife is traded internationally: Each year, consumers in China, Europe, Japan, and the United States purchase billions of dollars’ worth of wildlife products from Southeast Asia (Nijman 2010). However, wildlife is also traded locally or nationally, and hunting for subsistence and traditional medicines are long-established traditions in Southeast Asia and also provide sources of income (e.g., Nekaris et al. 2010). Reductionist management of wildlife trade impedes not only goals related to environmental sustainability but also goals related to health, poverty, and hunger (TRAFFIC 2008). On the other hand, wildlife trade has significant negative implications and is known to synergize with other threats to biodiversity: Hunting and trade increase as access to forests increases through other mining or extraction efforts (e.g., Suarez et al. 2009), and zoonotic viruses are often associated with illegally imported wildlife products (Greatorex et al. 2016). Wildlife trade has also been connected with conflict and national-security issues (Douglas and Alie 2014).
Despite increased media attention to the problem, collaborative actions on the ground (e.g., the establishment of the ASEAN Wildlife Enforcement Network in 2004), and international commitments to tackle illegal wildlife trade (e.g., the London Declaration and the Kasane and Hanoi Statements), wildlife trafficking continues at high levels (Hanoi Statement 2016, UNODC 2016). Many researchers and officials agree that existing regulatory top-down or “command and control” policies are failing in this region and that changes are necessary to work toward sustainable resource use (CITES Vietnam 2008, TRAFFIC 2008). Bennett (2011) has argued that regulatory-based interventions remain the best approach because the greatest driver of trade is wildlife demand from wealthy consumers in East Asia; in other words, the major problem is not the type of intervention but rather the gaps in capacity and resources to enforce existing regulations. However, the ubiquity of the trade makes it impractical to govern using regulatory-based interventions alone (CITES Vietnam 2008, TRAFFIC 2008); to work more effectively toward sustainability, research must assess how feasible other nonregulatory incentives and interventions might be, including market-based instruments (Jepson and Ladle 2009), which some argue may have a greater chance of being equitable and effective. However, bold supply-side strategies, including regulated trade, ranching, and wildlife farming, remain hotly debated regarding their application both in Southeast Asia (Drury 2009, Brooks et al. 2010) and at a global scale (Phelps et al. 2014).
Behind each of these arguments are assumptions about the specific set of drivers of wildlife trade in Southeast Asia. In Africa and in the Neotropics, several studies have shown how wild-meat overexploitation is driven by poverty (e.g., Brashares et al. 2011, Wittemyer 2011), a lack of alternatives to wild protein (Foerster et al. 2011), conflicts and displacement (e.g., Wittemyer 2011), and the implementation and choice of enforcement measures and policies (e.g., Nyaki et al. 2014). This is not to say that the drivers of overexploitation in these regions have been completely clarified; recent work highlights how assumptions and limited contexts regarding definitions of poverty have restricted researchers’ understandings of the motivations for illegal hunting (Duffy et al. 2016).
To avoid the development of management efforts and intervention measures in Southeast Asia on assumptions based on thin evidence (Nadal and Aguayo 2014), several knowledge gaps should be addressed, especially those characterizing the highly variable and complex wildlife trade chains and socioeconomic drivers of trade (TRAFFIC 2008, Lee et al. 2014). In part, the complexity of wildlife trade in Southeast Asia stems from traditional uses and cultural values relating to wildlife and wildlife products (Donovan 2004). For example, some studies indicate that wealth and social status appear to be stronger drivers of wildlife trade in Southeast Asia than poverty, with urban consumers driving demand for wildlife products more than local subsistence in some cases (e.g., Drury 2011, Sandalj et al. 2016). However, other studies show that most wildlife is still traded locally in rural regions (e.g., Nijman 2010), meaning that further complexity may not be addressed by research that focuses solely on urban consumers. There are diverse actors with multiple cultural backgrounds along trade chains in Southeast Asia, and their actions are likely shaped by factors that vary from site to site, including financial gain, social esteem, cultural identity, and customs, among others (Nekaris et al. 2010, MacMillan and Nguyen 2014). Thus, different policy incentives and interventions may be effective at different points along the trade chain and in different locales, and research aimed at informing wildlife trade management should take into account the possibility of spatial and cultural heterogeneity in potential trade drivers at multiple scales.
We argue here that interdisciplinary research approaches that integrate socioeconomic, anthropological, psychological, governance, and biological data across multiple scales are necessary to understand the characteristics of wildlife trade dynamics and effects. Others have made similar arguments about how to improve research approaches for studying the links between poverty and illegal wildlife hunting (Duffy et al. 2016) and for studying “conservation crime” more broadly (Gibbs et al. 2010, Gore 2011). These authors note the importance of studying the structural contexts of hunting holistically, questioning assumptions about key variables and concepts, and capturing historical social, economic, and political contexts (Duffy et al. 2016). The need for interdisciplinary, holistic research approaches is of particular importance given the complex cultural, political, economic, and social contexts of Southeast Asia (McElwee 2004). However, what is needed to operationalize this idea?
Recent papers have articulated the utility of a social–ecological systems framework to study the sustainability of hunting for meat at local-site scales (van Vliet et al. 2015). Here, we describe a social–ecological systems framework to design an interdisciplinary research approach to study the illicit wildlife trade regionally and across scales in Southeast Asia. A common analytical framework that can be applied and understood across disciplines is especially necessary in cases such as the Southeast Asian wildlife trade, in which knowledge and theories from different disciplines are required to understand dynamic trade systems. The framework we present here is designed to guide the holistic study of complex wildlife trade systems, including to explore variables, question assumptions, and design interdisciplinary research questions. We focus on Southeast Asia because (a) there is an established need to fill knowledge gaps about wildlife trade in this region and (b) this region exhibits the complex cross-scale dynamics that can illustrate why an interdisciplinary research approach is so crucial to guide research on wildlife trade. However, our framework is flexible enough to be applied in other regions as well.
We discuss the steps taken and planned toward developing an interdisciplinary analysis of the pattern, scale, and drivers of trade in key targeted species in Indochina and Vietnam, a hotspot for the Southeast Asian trade network, as examples of how to implement the framework. We highlight examples of cases in which researchers might draw the wrong conclusions without a framework or rigorous integration of methods and data from different disciplines, which would lead to the misdirection of wildlife trade management efforts. We also argue for building and strengthening technical and interdisciplinary capacity to implement the approach, including the enlistment of DNA barcoding and the integration of social-science approaches.
An interdisciplinary research framework
The dynamic complexity of wildlife trade in Southeast Asia highlights the need for an interdisciplinary research framework to guide academic study of the trade and inform management decisions. An ideal framework would enable systems-level conceptualizations, or systems thinking, to identify and analyze the links among complex system elements. Emphasizing interrelationships, feedback loops, nonlinearities, and time delays, among other systems principles, promotes iterative analyses of a system's dynamic connections and interactions toward a better understanding of the whole system, thus informing an understanding of its components (Sterling et al. 2010).
The need for dynamic models and frameworks to understand complex systems has been established (Costanza et al. 1993), as has the need to integrate knowledge and theories across disciplines for effective biodiversity management (Watzold et al. 2006). Elinor Ostrom's social–ecological systems (SES) framework, which organizes multiple diverse variables across scales, represented a leap forward toward an interdisciplinary framework for empirical studies (Ostrom 2009). The SES framework enables the formal exploration of dynamic, nonlinear links and interactions among variables across scales to better understand system outcomes. Being theory neutral, a SES framework can also facilitate the exploration of variables that might be derived theoretically from different disciplines and of how assessments based only on biological or social data alone may lead to divergent interpretations of the system (Schlüter et al. 2014, Leslie et al. 2015).
Our interdisciplinary conceptual-framework (figure 1) groups variables derived from different disciplines within system components, or first-tier variables, as in Ostrom's SES framework (McGinnis and Ostrom 2014, Ostrom 2009). Second-tier variables or attributes facilitate analysis of wildlife trade in Southeast Asia. To characterize resource units and resource systems, variables stem from biological data and models at varying scales. This includes, for example, the genetic and morphological diversity as well as the population status and distribution of resource units (figure 1). Anthropological, psychological, governance, and economic data and models inform our understanding of the behavior and decisions of actors, as well as the nature of relevant governance systems. Variables could include, for example, the economic and cultural values actors place on traded species, the social networks of actors (in different hierarchical groupings, as has been noted by McGinnis and Ostrom 2014), access to education and technology, laws and knowledge of laws, and infrastructure related to enforcement and transportation. We describe the analysis of process relationships, interactions, and outcomes to operationalize the framework into mathematical model(s) in the next section. Related ecosystems and additional social, economic, and political settings are considered external to the focal SES analyzed but are relevant to a broader context (figure 1).
An interdisciplinary conceptual framework identifying relevant components or first-tier variables (quarters of the circle, labeled on the outside of the circle), subcomponents (inside the circle), and second-tier variables (below) to analyze wildlife trade with a focus on Southeast Asia, modeled after Ostrom (2009)’s social–ecological systems framework.
Iterative exploration across disciplines
Our integrated framework enables exploration of variables and data sets that combine the biological information of trade-targeted populations with other information on how people engage in the trade of these populations at multiple scales. The selection and outlining of second-tier variables and attributes can be helpful to explore the dynamics and interconnections of a system across scales, to test assumptions about the drivers of wildlife trade, and to test combined policy interventions to identify points and locations in the trade chain where interventions are likely to have the greatest impact. We explore below some examples of how integration and exploration of different disciplinary approaches and technologies illuminate the complexity of wildlife trade in Southeast Asia.
Biology
Two major problems for law enforcement and the study of taxa in the trade are species identification and product provenance, often because products have been processed before being sold in markets. Sophisticated techniques can help in assigning species and in identifying wildlife products (Ogden et al. 2009, Alacs et al. 2010). DNA barcoding, for instance, has been used successfully over several years for many different groups of plants and animals (Hebert et al. 2003, Dawnay et al. 2007, Eaton et al. 2010). This technique has not been applied widely in Southeast Asia because of limited access to molecular laboratories. The situation is expected to change quickly because DNA amplification and sequencing have become more accessible and inexpensive. Indeed, a growing number of wildlife trade studies in Asia employ DNA to investigate species under threat (e.g., Chen et al. 2015, Zhang et al. 2015). By combining DNA barcoding with morphological, anthropological, and socioeconomic data, researchers can clarify the patterns, scales, and drivers of wildlife trade; determine hotspots of trade activities; and identify the taxa under critical pressure.
Anthropology
Noneconomic social and cultural elements are often neglected in studies that integrate ecological and economic factors for decision-making (Fagerholm et al. 2012), and social, cultural, and political contexts can play significant roles in supporting or preventing wildlife trade (figure 1). Strong governance, regulations, and enforcement of regulations, for instance, can vary across regions, as can cultural norms regarding trade. A better understanding of why and when individual actors participate in trade—what social and cultural forces and norms drive hunting, subsistence uses, and market activities, such as valuing of rarity or connection to identity—is crucial to sustainable management and situated governance. For example, slow lorises (genus Nycticebus) seem to be subject to opportunistic or incidental exploitation for uses that vary widely from meat to medicine to pets depending on both the ethnic group and the region under study (Thach et al. in press). Also, methods such as social-network analysis, developed first in sociology but used in anthropology and other fields, can be applied to explore the importance of social networks as drivers and operators of wildlife trade chains (Freeman 2004).
Economics
Traditional economic models are anthropocentric and analyze human uses of ecosystems for production and consumption activities. In contrast, over the past two decades, there has been a spate of interest in bioeconomic modeling, an exercise that employs both economic and biophysical components, largely through the application of econometrics. Although econometric models have been applied to the bushmeat trade in Africa (e.g., Skonhoft 1998, Fischer et al. 2011), analyses of the Asian wildlife trade to date have been limited to characterizing trade chains or quantification of species, consumption patterns, and profits in trade at particular locations (e.g., Sandalj et al. 2016, Shairp et al. 2016); very few studies have used models to illustrate broader or more complex trade dynamics across sites.
Although such interdisciplinary empirical analyses constitute a step in the right direction, successful integration of ecological phenomena remains a major challenge because of sharp differences in disciplinary foci, mindsets, and vocabulary. This is beginning to change, however, with the advent of SES models. An important element of SES frameworks that has not been examined in previous studies is how to operationally connect data, hypotheses, and questions from different disciplines. The interdisciplinary SES framework that we outline in figure 1 can also be used to iteratively investigate different assumptions supported by data from any discipline included in the framework and to pinpoint key interactions and outcomes of the system. For example, if there is initial evidence from interview-derived data suggesting that trade products are transported from southern to northern Vietnam, it can be articulated as a preliminary hypothesis. This hypothesis can be further supported using patterns inferred from forensic DNA barcoding of trade confiscations or another biological approach (figure 2). Other types of data (e.g., transportation information) can be used to further triangulate the nature of interactions and outcomes along this dimension of the system.
Iterative investigation of different research questions, assumptions, and hypotheses can be supported by data from different disciplines included in the social–ecological systems framework. The arrows represent exit from investigation after sufficient support for a hypothesis toward an understanding of the interactions and outcomes of the system (after DeSalle et al. 2005).
Operationalizing the framework
SES models developed from the perspective of a single discipline, such as resource economics, applied ecology, or fisheries science, tend to oversimplify either the ecological or the social domain and often fall short in exploring and explaining the social–ecological feedback loops that drive the development of the coupled SES (Schlüter et al. 2014). In particular, the ecology component of the SES framework has been underdeveloped; however, policy recommendations are more likely to stem from SES research that includes both ecological and social variables (Rissman et al. 2017). Under an interdisciplinary SES framework, as is proposed in figure 1, process- and pattern-oriented submodels or component models for different system components or variables (e.g., econometric dichotomous choice models to predict actor behavior in relation to hunting) can be bundled into a suite of models using knowledge and theories from diverse disciplines to further explore interactions and outcomes at various scales (figure 1).
Challenges to the operationalization of SES frameworks via quantitative or semiquantitative models (e.g., process-oriented, decision-making, general equilibrium, general algebraic systems, dynamic systems, fuzzy-logic cognitive mapping, or input–output models) include (a) parameterization of dynamic processes to account for scale, as well as cultural, biological, and economic change, and (b) integrating spatially explicit variables with other factors. The recent operationalization of SES frameworks into models has accounted for dynamic scales by distinguishing between different levels of aggregation, such as individual actors versus groups of actors, as well as individual resource units versus populations of resource units (Hinkel et al. 2014). Others have clarified hierarchies of process relationships and interactions among components through influence diagramming and the top-down unpacking of process relationships until changes in all relevant variables are explained (Schlüter et al. 2014). Making explicit the underlying assumptions of component and systems models will be particularly important to integrate effectively across inputs and assumptions that stem from the conceptual backgrounds of different disciplines (Schlüter et al. 2014).
Rather than prescribe a particular modeling or analysis approach to implement our framework, analysis should be tailored to the specific data-driven requirements of a given research question and the associated considerations of statistical assumptions and power and should remain open to qualitative and thematic approaches. For example, below, we describe our analysis under development for key trade-targeted species in Indochina, where we are integrating regression approaches with qualitative analysis. Simply put, qualitative research can support internal validity (sensu Drury et al. 2011), meaning that data represent the phenomenon under study as a complement to the external validity of quantitative data.
Key trade-targeted species
The Southeast Asian wildlife trade involves a huge diversity of plants and animals. Although we focus here on animal groups, plants are also heavily traded in Southeast Asia but possibly at different scales and influenced by different socioeconomic drivers (e.g., orchids and cycads; CITES Vietnam 2008). In terms of taxonomic groups, turtles, pangolins, and snakes have been most traded internationally. Other heavily traded groups include civet, muntjac, bear, primate, sambar, otter, and serow (Nguyen 2008, TRAFFIC and WCS 2004). The abundance of many trade-targeted animal species in Southeast Asia has declined severely over the past decade. However, in some cases, local extinctions may be linked to wildlife trade by little more than assumed association because key information on distribution, taxonomy, and population status is lacking for many trade-targeted species, especially in Vietnam and Indochina (CITES Vietnam 2008, Nguyen 2008, TRAFFIC 2008, Benitez-López et al. 2017). Guidance from our SES framework might help to prioritize data collection. For example, a great deal of trade data, such as the number of seizures, specimens, and species in the trade at both regional and local scales, are available, but data on the population status of traded species are generally limited. The latter should therefore be a focus of future programs to help link trade to the local extinction of species and/or populations.
Research that narrows to a suite of focal species could serve to investigate multiple scales of trade within a hierarchical SES research framework. Candidate species groups should be those for which wildlife trade is their major threat and that are subject to local, regional, or international demand, advancing our understanding of the multiple scales of wildlife trade in the region. In addition, data on their taxonomy, genetic patterns, distribution, and level of exploitation should be available for hypothesis testing within the framework. In our work, we have identified four focal groups of trade-targeted species for which sufficient data are available as per the categories above but that differ in their relative prevalence in trade across scales to test hypotheses about scales and drivers of wildlife trade in Vietnam or Indochina: turtles, muntjacs, pangolins, and slow lorises.
With the exception of some of the turtles and muntjac species, these groups are overall widespread and taxonomically diverse. However, a majority of species in the two aforementioned groups is traded in two vastly different networks. Although most turtle species in mainland Southeast Asia are hunted for export, muntjacs are often consumed domestically (TRAFFIC and WCS 2004, Nguyen 2008). Trade-targeted species, such as turtles and pangolins, are under immense pressures from rising demands of the international trade. As turtle and pangolin populations decline, their value in the trade is increasing at rates greater than inflation (Newton et al. 2008), exhibiting an anthropogenic allee effect, in which the extinction of rare species is influenced by human value attributed to rarity (Courchamp et al. 2006). Because of the nature of turtle and pangolin trade, namely their rarity and high price, all pangolins and turtles caught in local villages are sold to traders for sale in urban or international markets. Therefore, data on most turtle and pangolin trade may not be able to tell us very much about local scales of trade. On the other hand, muntjacs are suitable for studies documenting the nature of the trade at the local scale because of their domestic consumption.
Slow lorises (genus Nycticebus) are small, nocturnal primates. Slow lorises are widespread; have naturally low densities; and are in high demand for traditional medicines, as pets, and for food and are also traded internationally for these purposes (Nekaris et al. 2010). Despite all slow lorises having protected status across their range, enforcement of this status remains quite neglected compared with that for other higher-profile animals (Beyle et al. 2014). Importantly, traded species are not necessarily traded in isolation; a targeted species may be opportunistically, incidentally, or accidentally exploited when hunters are looking for more common or other species (Branch et al. 2013). Because of their natural low densities (rarity), slow lorises seem to be subject to opportunistic or incidental exploitation, depending on the area under study (Thach et al. in press). More research needs to be done both across and within species, in the latter case focusing on how the same species could be exploited in different ways at different scales and by different people. Data on highly targeted species alone may not be able to tell us very much about other species, and it might be quite difficult to get unbiased information from hunters and traders on the targeted species.
In our preliminary analysis on slow loris trade, iterative exploration including qualitative methods and analysis of key informant interviews was essential to understanding why people engage in trade and also to accurately characterize trade pathways. We have used genetic information to identify a pygmy slow loris confiscated by Vietnamese authorities in northern Vietnam as originating from southern Vietnam, supporting a trend of trafficking from southern Vietnam to northern Vietnam. Our interviews with key informants confirm this trend but also suggest some movement from central to southern Vietnam (Thach et al. in press). However, none of our confiscated samples from southern Vietnam show genetic provenance from northern or central Vietnam. Together, our data sets, collected and integrated within our framework, show a more complicated spatial pattern of trade than would be inferred by using only one method and have inspired a next set of iterative research questions that could be answered by integrating new sources of data within our integrative framework, such as transportation information: Is trade more frequent in one direction than the other? Are prices and uses different in trade going different directions? Where and how do intermediaries sort pricing and routes? Interventions that might be informed by the outcomes of this research might include activities targeted at sorting points that are tailored to the particular drivers in each direction, which may differ.
Building and strengthening technical and interdisciplinary capacity for integrative approaches
A key challenge in this effort will be building the capacity for effective interdisciplinary teams of researchers enabled to operationalize the iterative exploration of wildlife trade within a SES framework. Our framework originated in the lead authors’ backgrounds in evolutionary biology, and to complete our framework development, we built a team that includes anthropologists and economists. In addition, integration and increased collaboration with legal, anticorruption, and governance research fields will be essential to bring the framework to action at multiple scales (figure 1; e.g., Gibbs et al. 2010, Gore 2011). Interdisciplinary and international teams must negotiate conceptual differences; theories of knowledge; research ethics requirements; power dynamics; disciplinary prejudices; and challenges in communication, infrastructure, and logistics. Effective interdisciplinary collaborations require a great deal of work to implement, including team leadership that is committed to true conceptual integration among carefully selected team members who collaborate toward a cocreated research question (Black and Copsey 2014, Pooley et al. 2014).
Although the integrative framework requires well-rounded research capabilities, we highlight here the need for strengthening key areas, which, we argue, currently fall short of the standard for quality interdisciplinary research, especially in our region of focus. As a key component of the integrative framework developed here, DNA forensic and barcoding tools should be made widely available to facilitate wildlife trade management and hypothesis testing for a better understanding of the critical parameters—such as the scale, driver, and pattern—of the complex conservation threat (UNODC 2016). Recent advances in molecular technologies have led to a rapid increase in the application of DNA barcoding and other assignment tools to wildlife trade (e.g., Eaton et al. 2010, Chen et al. 2015, Zhang et al. 2015). Recognizing the advantages of the method, Pakistan became the first country in Asia to adopt DNA barcoding as a technique to curb illegal wildlife trade (Shahid 2015). Other countries are also considering the use of DNA analyses as an official wildlife enforcement tool (TRAFFIC 2015).
Currently, however, infrastructure, such as comprehensive DNA databases, is not ready to support comprehensive use of the technique. GenBank data are not well curated, and many available sequences are missing key information, such as locality. The few available curated databases, such as DNA Surveillance and DNABUSHMEAT, only cover specific taxonomic groups, most often mammals (Ross et al. 2003, Gaubert et al. 2015). For other lesser-known but widely traded vertebrates, such as turtles and other reptile species, resources have not been developed. To better control the wildlife trade, it is crucial to develop such a DNA database for range countries in Southeast Asia. As a first step, the database should have representatives of all vertebrates protected under the law in the countries for wildlife trade enforcement. DNA barcoding regions for the database could include the mitochondrial genes, cytochrome b or the cytochrome c oxidase subunit 1 (COI), because they are the markers of choice in wildlife forensic science (Alacs et al. 2010).
Other species that are currently not protected under law but have been heavily traded should also be included in the database in the likelihood that these species will be regulated in the future. More importantly, multiple sequences from geographically isolated populations of the targeted species should be incorporated into the database to help determine trade patterns, hotspots of trade activities, or populations under a high level of harvesting pressure. Recent studies demonstrate that such a fine-scale population assignment is particularly informative for wildlife trade management (e.g., Zhang et al. 2015), especially to trace geographic provenance and provide detail to supplement data from other disciplines within our integrative framework.
There are many challenges in developing a database for wildlife trade enforcement and research within the interdisciplinary framework. Its development requires collaboration between a wide variety of research institutions, including natural-history museums, nongovernment organizations, universities, and other research institutes from different countries in the region. This involves establishing data-sharing mechanisms among participating organizations. In addition, because funding for biodiversity research is in serious shortage (Amato and DeSalle 2012), this could prove a daunting task without support from governments and international funding agencies.
Conclusions
In summary, we believe that complex conservation problems merit interdisciplinary frameworks such as the one we have developed and described here. Our framework will allow researchers to test assumptions about how different aspects of a system interact and where there are nonlinearities in feedback loops across scales and dimensions. Our approach is intended to guide the holistic study of complex wildlife trade systems rather than to prescribe specific policy actions, which should be assessed by policymakers and managers in specific sociopolitical contexts on the basis of new information produced under the framework. However, the ideas put forward in our framework also relate to broader discussions in conservation aimed at intervention design and planning. The Open Standards (OS) for the Practice of Conservation (CMP 2013) is a tool to facilitate adaptive management in planning, implementing, and monitoring conservation initiatives. The OS fosters transparency by making explicit assumed causal relationships between strategies and anticipated outcomes (Schwartz et al. 2012). Our framework could be helpful to inform the process by which teams come to and question their stated assumptions during the process of formulation and help to promote systems analyses of problems. In the future, we expect to collaborate with more enforcement-focused organizations, such as the Society for Wildlife Forensics, to bridge holistic understanding guided by the framework to specific enforcement outcomes that avoid one-size-fits-all solutions and pinpoint where to invest effort to address problems such as wildlife trade in Southeast Asia.
Capturing the complexity of cross-scale interactions in a wildlife trade system does not mean that management needs to be so complicated and convoluted that it will no longer be feasible; different management strategies can be tailored to focus on different dimensions of the social–ecological system, keeping in mind how they influence and are influenced by other aspects of the system (Sterling et al. 2010). This systems perspective helps to direct initial questions toward those that are tractable and appropriate and away from fixes that fail. Strategic management approaches can be targeted to the needs and strengths of specific regions or scales; for example, if variables related to local actors are found to be the most important drivers of trade at the local scale, interventions can focus on improving relationships among relevant stakeholders. If variables related to governance are found to be the most important drivers at the national scale, interventions may focus on improving institutional arrangements (Leslie et al. 2015). Capacity development and database development and sharing will be key to acting on recommendations derived from the framework analysis in order to address the critical issue of wildlife trade in Southeast Asia.
Acknowledgments
We are grateful to the three anonymous reviewers for their comments on previous versions of this manuscript. We also thank Rob DeSalle for inspiration, Nadav Gazit for graphic design, and collaborators and student research assistants on the ongoing larger study: Marina Kenyon, Badrul Zain, Khoi Nguyen, Huan X. Nguyen, Thang T. Nguyen, Thong V. Pham, Thanh V. Nguyen, Ha Duong, Giang Cao, Elora Lopez, Daniel Veronese, Ngoc Vu, Anna Panariello, and Bonnie Yates from the USFWS Forensic Laboratory. We also thank Lien T. Tran, Hiep M. Nguyen, Pam McElwee, Christian Roos, Anna Nekaris, Ha M. Nguyen, Thang V. Hoang, Duc M. Hoang, and Tilo Nadler for support, advice, and discussion.
Funding statement
This material is based on work supported by US National Science Foundation Grant no. CHE-1313908, the Center for Biodiversity and Conservation at the American Museum of Natural History, the Margot Marsh Biodiversity Foundation, the Eppley Foundation for Research, the Disney Worldwide Conservation Fund, the Critical Ecosystem Partnership Fund, the Turtle Conservation Fund, the NAGAO Natural Environment Foundation, and USAID-PEER Science Project no. 3–149. The authors’ views expressed in this publication do not necessarily reflect the views of the US Agency for International Development or the US Government.
