Protected areas as social-ecological systems: perspectives from resilience and social-ecological systems theory

Conservation biology and applied ecology increasingly recognize that natural resource management is both an outcome and a driver of social, economic, and ecological dynamics. Protected areas offer a fundamental approach to conserving ecosystems, but they are also social-ecological systems whose ecological management and sustainability are heavily influenced by people. This editorial, and the papers in the invited feature that it introduces, discuss three emerging themes in social-ecological systems approaches to understanding protected areas: (1) the resilience and sustainability of protected areas, including analyses of their internal dynamics, their effectiveness, and the resilience of the landscapes within which they occur; (2) the relevance of spatial context and scale for protected areas, including such factors as geographic connectivity, context, exchanges between protected areas and their surrounding landscapes, and scale dependency in the provision of ecosystem services; and (3) efforts to reframe what protected areas are and how they both define and are defined by the relationships of people and nature. These emerging themes have the potential to transform management and policy approaches for protected areas and have important implications for conservation, in both theory and practice

Formal Modeling of Social-Ecological Systems

International audienceThe success of Integrated Assessment and Modeling of social-ecological systems requires a framework allowing the members of such a process to share and gather their respective knowledge about the system under consideration and to get confidence into the reliability of the software that implements the system's model they have produced. To this end, this paper presents an Entity-Process meta-model of SESs and outlines its use

Spatial resilience in social-ecological systems

Spatial Resilience is a new and exciting area of interdisciplinary research. It focuses on the influence of spatial variation - including such things as spatial location, context, connectivity, and dispersal - on the resilience of complex systems, and on the roles that resilience and self-organization play in generating spatial variation. Professor Cumming provides a readable introduction and a first comprehensive synthesis covering the core concepts and applications of spatial resilience to the study of social-ecological systems. The book follows a trajectory from concepts through models, methods, and case study analysis before revisiting the central problems in the further conceptual development of the field. In the process, the author ranges from the movements of lions in northern Zimbabwe to the urban jungles of Europe, and from the collapse of past societies to the social impacts of modern conflict. The many case studies and examples discussed in the book show how the concept of spatial resilience can generate valuable insights into the spatial dynamics of social-ecological systems and contribute to solving some of the most pressing problems of our time. Although it has been written primarily for students, this book will provide fascinating reading for interdisciplinary scientists at all career stages as well as for the interested public. In this engagingly crafted book Graeme Cumming provides a novel, and I believe important, synthesis of spatial aspects of the resilience of coupled ecological and social systems

Microeconomic adaptation in social-ecological systems

Henry Bartelet explored the socio-economic resilience of dive tourism on the Great Barrier Reef and other Pacific communities that depend economically on coral reefs. He identified complex and dynamic relationships between adaptive capacity, adaptive responses, and adaption outcomes. His thesis extends and refines existing and emerging climate change adaptation theories

Implications of external validity for research on polycentric and complex adaptive systems

Much recent research has examined the implications of policy analysis for complex adaptive social-ecological systems. System complexity comes from both the natural environment as well as complex institutional arrangements that humans use to manage and regulate such systems. Such research has systematically investigated how the interaction of a host of variables relate to some evaluation criteria. Many scholars argue that a deep understanding of the social-ecological systems, however, comes at the expense of externally valid inferences to other systems. In this paper I argue that having a nuanced understanding of the social-ecological system actually helps one to understand which types of policy domains an analysis might be generalized. --Complex Adaptive Systems,External Validity,Polycentric Systems

Toward a typology for social-ecological systems

Characterizing and understanding social-ecological systems (SESs) is increasingly necessary to answer questions about the development of sustainable human settlements. To date, much of the literature on SES analysis has focused on "neat" systems involving a single type of resource, a group of users, and a governance system. While these studies provide valuable and specific insights, they are of limited use for application to "messy" SESs that encompass the totality of human settlements, including social organization and technologies that result in the movement of materials, energy, water, and people. These considerations, in turn, create distribution systems that lead to different types of SESs. In messy SESs the concept of resilience, or the ability of a system to withstand perturbation while maintaining function, is further evolved to posit that different settlements will require different approaches to foster resilience. This article introduces a typology for refining SESs to improve short- and long-term adaptive strategies in developing human settlements

Understanding Social–Ecological Systems using Loop Analysis

The sustainable management of social–ecological systems (SESs) requires that we understand the complex structure of relationships and feedbacks among ecosystem components and socioeconomic entities. Therefore, the construction and analysis of models integrating ecological and human actors is crucial for describing the functioning of SESs, and qualitative modeling represents an ideal tool since it allows studying dependencies among variables of diverse types. In particular, the qualitative technique of loop analysis yields predictions about how a system’s variables respond to stress factors. Different interaction types, scarce information about functional relationships among variables, and uncertainties in the values of the parameters are the rule rather than exceptions when studying SESs. Accordingly, loop analysis seems to be perfectly suitable to investigate them. Here, we introduce the key aspects of loop analysis, discuss its applications to SESs, and suggest it enables making the first steps toward the integration of the three dimensions of sustainability

Visualization of causation in social-ecological systems

In social-ecological systems (SES), where social and ecological processes are intertwined, phenomena are usually complex and involve multiple interdependent causes. Figuring out causal relationships is thus challenging but needed to better understand and then affect or manage such systems. One important and widely used tool to identify and communicate causal relationships is visualization. Here, we present several common visualization types: diagrams of objects and arrows, X-Y plots, and X-Y-Z plots, and discuss them in view of the particular challenges of visualizing causation in complex systems such as SES. We use a simple demonstration model to create and compare exemplary visualizations and add more elaborate examples from the literature. This highlights implicit strengths and limitations of widely used visualization types and facilitates adequate choices when visualizing causation in SES. Thereupon, we recommend further suitable ways to account for complex causation, such as figures with multiple panels, or merging different visualization types in one figure. This provides caveats against oversimplifications. Yet, any single figure can rarely capture all relevant causal relationships in an SES. We therefore need to focus on specific questions, phenomena, or subsystems, and often also on specific causes and effects that shall be visualized. Our recommendations allow for selecting and combining visualizations such that they complement each other, support comprehensive understanding, and do justice to the existing complexity in SES. This lets visualizations realize their potential and play an important role in identifying and communicating causation.Peer reviewe