ABSTRACT. The emerging discipline of sustainability science is focused explicitly on the dynamic interactions between nature and society and is committed to research that spans multiple scales and can support transitions toward greater sustainability. Because a growing body of place-based social-ecological sustainability research (PBSESR) has emerged in recent decades, there is a growing need to understand better how to maximize the effectiveness of this work. The Programme on Ecosystem Change and Society (PECS) provides a unique opportunity for synthesizing insights gained from this research community on key features that may contribute to the relative success of PBSESR. We surveyed the leaders of PECS-affiliated projects using a combination of open, closed, and semistructured questions to identify which features of a research project are perceived to contribute to successful research design and implementation. We assessed six types of research features: problem orientation, research team, and contextual, conceptual, methodological, and evaluative features. We examined the desirable and undesirable aspects of each feature, the enabling factors and obstacles associated with project implementation, and asked respondents to assess the performance of their own projects in relation to these features. Responses were obtained from 25 projects working in 42 social-ecological study cases within 25 countries. Factors that contribute to the overall success of PBSESR included: explicitly addressing integrated social-ecological systems; a focus on solutionand transformation-oriented research; adaptation of studies to their local context; trusted, long-term, and frequent engagement with stakeholders and partners; and an early definition of the purpose and scope of research. Factors that hindered the success of PBSESR included: the complexities inherent to social-ecological systems, the imposition of particular epistemologies and methods on the wider research group, the need for long periods of time to initiate and conduct this kind of research, and power asymmetries both within the research team and among stakeholders. In the self-assessment exercise, performance relating to team and context-related features was ranked higher than performance relating to methodological, evaluation, and problem orientation features. We discuss how these insights are relevant for balancing place-based and global perspectives in sustainability science, fostering more rapid progress toward inter-and transdisciplinary integration, redefining and measuring the success of PBSESR, and facing the challenges of academic and research funding institutions. These results highlight the valuable opportunity that the PECS community provides in helping build a community of practice for PBSESR.
This study compares local-level socioeconomic variables interpolated with three different methods: 1) Thiessen polygons, 2) Inverse distance weighting, and 3) Areas of influence based on cost of distance. The main objective was to determine the interpolation technique capable of generating the most efficient variable to explain the distribution of deforestation through two statistical approaches: generalized linear models and hierarchical partition. The study was conducted in two regions of western Mexico: Coyuquilla River watershed, and the Sierra de Manantlan Biosphere Reserve (SMBR). For SMBR it was found that the Thiessen polygons and areas of influence were the techniques that interpolated variables with greatest explanatory power for the deforestation process, in Coyuquilla it was inverse distance weighting. These differences are related to the distribution and the spatial correlation of the values of the variables.
Abandonment of agricultural lands promotes the global expansion of secondary forests, which are critical for preserving biodiversity and ecosystem functions and services. Such roles largely depend, however, on two essential successional attributes, trajectory and recovery rate, which are expected to depend on landscape-scale forest cover in nonlinear ways. Using a multi-scale approach and a large vegetation dataset (843 plots, 3511 tree species) from 22 secondary forest chronosequences distributed across the Neotropics, we show that successional trajectories of woody plant species richness, stem density and basal area are less predictable in landscapes (4 km radius) with intermediate (40–60%) forest cover than in landscapes with high (greater than 60%) forest cover. This supports theory suggesting that high spatial and environmental heterogeneity in intermediately deforested landscapes can increase the variation of key ecological factors for forest recovery (e.g. seed dispersal and seedling recruitment), increasing the uncertainty of successional trajectories. Regarding the recovery rate, only species richness is positively related to forest cover in relatively small (1 km radius) landscapes. These findings highlight the importance of using a spatially explicit landscape approach in restoration initiatives and suggest that these initiatives can be more effective in more forested landscapes, especially if implemented across spatial extents of 1–4 km radius.
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