Insights for managers from modeling species interactions across multiple scales in an idealized landscape

Schoon, Michael; Baggio, Jacopo A.; Salau, Kehinde R.; Janssen, Marco A.

doi:10.1016/j.envsoft.2013.12.010

Cited by 14 publications

(10 citation statements)

References 39 publications

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“…4A and details in SI Appendix). Future work might create a dataset where habitat quality indicators are represented as different ecological edge values in an ecological network as done in some theoretical modeling studies (30,75). Additional advances should also incorporate multiple social and multiple ecological network relationships (58,(76)(77)(78)(79) into the analysis of scale mismatch.…”

Section: Discussionmentioning

confidence: 99%

“…Analyzing scale mismatch with SENA is relatively new, and previous research largely focuses on single-governance levels (e.g., local municipalities) (10,12,28,30,31). Although a necessary first step to understand social-ecological systems as networks, single-level approaches fail to represent the reality of most natural resource governance, which unfolds at local, regional, and larger levels (32,33).…”

Section: Significancementioning

confidence: 99%

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Social–ecological network analysis of scale mismatches in estuary watershed restoration

Sayles

Baggio

2017

Proc. Natl. Acad. Sci. U.S.A.

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173

116

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Resource management boundaries seldom align with environmental systems, which can lead to social and ecological problems. Mapping and analyzing how resource management organizations in different areas collaborate can provide vital information to help overcome such misalignment. Few quantitative approaches exist, however, to analyze social collaborations alongside environmental patterns, especially among local and regional organizations (i.e., in multilevel governance settings). This paper develops and applies such an approach using social-ecological network analysis (SENA), which considers relationships among and between social and ecological units. The framework and methods are shown using an estuary restoration case from Puget Sound, United States. Collaboration patterns and quality are analyzed among local and regional organizations working in hydrologically connected areas. These patterns are correlated with restoration practitioners' assessments of the productivity of their collaborations to inform network theories for natural resource governance. The SENA is also combined with existing ecological data to jointly consider social and ecological restoration concerns. Results show potentially problematic areas in nearshore environments, where collaboration networks measured by density (percentage of possible network connections) and productivity are weakest. Many areas also have high centralization (a few nodes hold the network together), making network cohesion dependent on key organizations. Although centralization and productivity are inversely related, no clear relationship between density and productivity is observed. This research can help practitioners to identify where governance capacity needs strengthening and jointly consider social and ecological concerns. It advances SENA by developing a multilevel approach to assess social-ecological (or social-environmental) misalignments, also known as scale mismatches.social-ecological fit | environmental governance | multilevel governance | social-ecological networks | environmental restoration planning M ore than a century ago, John Wesley Powell, second director of the US Geological Survey, advised politicians to align political borders with watersheds for successful resource management. His advice was ignored but continues to resonate (1). Spatial-scale mismatch, where the boundaries of governing organizations do not align with the environmental systems that they govern, often leads to failed or inefficient resource management (2-5).* For example, a small municipality cannot regulate upstream land use outside of its jurisdiction to protect water quality (7). Regional fisheries management may not respond to local stock variations or local fishermen's needs (2, 8).Organizations, both public and private, can overcome scale mismatches through collaboration and coordination (9-13). This network approach to governing is not without challenges but is often preferable to rescaling existing sociopolitical and jurisdictional boundaries, which might undermine other g...

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Section: Discussionmentioning

confidence: 99%

Section: Significancementioning

confidence: 99%

Social–ecological network analysis of scale mismatches in estuary watershed restoration

Sayles

Baggio

2017

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

173

116

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“…Connectivity as the structure and strength of information or resource flows in networks is highly context dependent though (Dakos et al 2015). In general, intermediate levels of connectivity are often considered better than the extremes of very high or very low levels (Salau et al 2012, Schoon et al 2014, Dakos et al 2015.…”

Section: Clustering Coefficientmentioning

confidence: 99%

Resilience to climate change in a cross-scale tourism governance context: a combined quantitative-qualitative network analysis

Luthe¹,

Wyss²

2016

E&S

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Resilience to climate change in a cross-scale tourism governance context: a combined quantitativequalitative network analysis. Ecology and Society 21 (1) ABSTRACT. Social systems in mountain regions are exposed to a number of disturbances, such as climate change. Calls for conceptual and practical approaches on how to address climate change have been taken up in the literature. The resilience concept as a comprehensive theory-driven approach to address climate change has only recently increased in importance. Limited research has been undertaken concerning tourism and resilience from a network governance point of view. We analyze tourism supply chain networks with regard to resilience to climate change at the municipal governance scale of three Alpine villages. We compare these with a planned destination management organization (DMO) as a governance entity of the same three municipalities on the regional scale. Network measures are analyzed via a quantitative social network analysis (SNA) focusing on resilience from a tourism governance point of view. Results indicate higher resilience of the regional DMO because of a more flexible and diverse governance structure, more centralized steering of fast collective action, and improved innovative capacity, because of higher modularity and better core-periphery integration.Interpretations of quantitative results have been qualitatively validated by interviews and a workshop. We conclude that adaptation of tourism-dependent municipalities to gradual climate change should be dealt with at a regional governance scale and adaptation to sudden changes at a municipal scale. Overall, DMO building at a regional scale may enhance the resilience of tourism destinations, if the municipalities are well integrated.

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“…Agent-based modelling is particularly well suited to modelling SES as it allows representation of individual system elements and interactions between them at the relevant spatio-temporal scales (Filatova et al, 2013;Hare & Deadman, 2004); thus, also feedbacks between these elements can be captured. In particular, ABMs have been used for modelling land use since they enable incorporation of human decision-making on the environment in a mechanistic and spatially explicit way (Matthews & Selman, 2006;Schoon, Baggio, Salau, & Janssen, 2014). The prediction of future land use changes is especially important in the context of the effects of land use, e.g., on provision of ecosystem services and on impacts on wildlife (Matthews, Gilbert, Roach, Polhill, & Gotts, 2007).…”

Section: Introductionmentioning

confidence: 99%

Applying a biocomplexity approach to modelling farmer decision‐making and land use impacts on wildlife

Malawska

Topping

2017

Journal of Applied Ecology

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The biocomplexity approach refers to a fully integrated social‐ecological systems (SES) simulation that represents bidirectional feedbacks between social and ecological components. This method is essential to accurately assess impacts of economy and policy on SES such as agroecosystems, where feedbacks between the drivers and impacts of cropping changes need to be simulated. Here we exemplify the biocomplexity approach using energy maize, which is becoming an important source of bioenergy in Europe, and thus, might cause a significant change in land use with knock on‐effects for wildlife. The integrated simulation tool consisted of a farmer decision‐making agent‐based model fully coupled to the Animal Landscape and Man Simulatin System (ALMaSS), an agent‐based simulation system for predicting impacts of land use on a range of Danish wildlife species: the brown hare (Lepus europaeus), the grey partridge (Perdix perdix), the skylark (Alauda arvensis), a carabid beetle (Bembidion lampros), a linyphiid spider (Erigone atra) and the field vole (Microtus agrestis). This was used to assess the impacts of increasing demand on energy maize on the six animal species. Two types of experimental scenarios were evaluated, with and without feedback between the social and ecological system. The assessment of species responses was based on changes in population size, abundance and occupancy. The response to the cultivation of energy maize was negative for three vertebrate species (skylark, hare and field vole) and positive for partridge and the two invertebrate species. The feedback scenarios showed that the incorporation of information from ecological system to the farmer decision‐making affected both a trend in area cultivated with energy maize as well as the animal responses. Synthesis and applications. Fully coupling agent‐based decision‐making and environmental simulation allows a detailed representation and integration of both social and ecological components of agricultural systems at proper spatial and temporal scales as well as of dynamic feedbacks between the two systems. By employing easy to interpret measures of changes in abundance and spatial occupancy of animal species, the simulation results could inform and simplify decision‐making on expected impacts of economy and policy regulations on wildlife.

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Insights for managers from modeling species interactions across multiple scales in an idealized landscape

Cited by 14 publications

References 39 publications

Social–ecological network analysis of scale mismatches in estuary watershed restoration

Social–ecological network analysis of scale mismatches in estuary watershed restoration

Resilience to climate change in a cross-scale tourism governance context: a combined quantitative-qualitative network analysis

Applying a biocomplexity approach to modelling farmer decision‐making and land use impacts on wildlife

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