Strategic decision support for long-term conservation management planning

Abelson, Eric S.; Reynolds, Keith M.; Manley, Patricia N.; Paplanus, Steven

doi:10.1016/j.foreco.2021.119533

Cited by 10 publications

(11 citation statements)

References 19 publications

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“…The 16 individual attributes were in turn represented by 1 or more metrics of condition. The number of metrics selected to represent each attribute varied based on the complexity of the attribute (see Saunders 2002, Saunders and, with those associated with environmental quality having the largest suite of metrics, and those associated with community values and operations typically being represented by single metrics (Appendices 1-3; see Abelson et al 2021 for detailed methods). For example, the quality water topic was represented by 2 metrics (phosphorus load and fine sediment), whereas biodiversity conservation consisted of 13 metrics including 3 focal species of interest, 6 functional species groups, and 4 measures of species diversity (Appendices 2 and 3).…”

Section: Fire Suppression Onlymentioning

confidence: 99%

“…Subject-matter experts (Table 2) identified data that were necessary to evaluate attributes and determined metric values that corresponded with poor to optimal conditions (Appendices 1 and 4). These values were used in the DST to evaluate forest conditions, propagating from the bottom of the hierarchy (attributes) to the top (resilience), for each incoming piece of data (Appendix 5) following methods described in Abelson et al 2021.…”

Section: Fire Suppression Onlymentioning

confidence: 99%

“…The DST we used was developed as detailed in Abelson et al 2021. We substantively expanded the DST model presented in Abelson et al 2021 beyond the environmental quality focal area to also include community values and operational focal areas (Fig. 2).…”

Section: Decision Support Tool Developmentmentioning

confidence: 99%

“…2; Appendix 1). Data resulting from LANDIS-II modeling (Appendix 5) were either processed (e.g., calculating percent of landscape from hectares) and entered directly into the MCDM (Appendix 1) or were pre-processed by NetWeaver logic modeling software (Appendices 2-4; Saunders 2002, Saunders and before inclusion in the MCDM (Abelson et al 2021). We used the Criterium DecisionPlus software (CDP; Murphy 2014) to develop our MCDM and calculate scenario performance scores (Appendix 1; .…”

Section: Decision Support Tool Developmentmentioning

confidence: 99%

“…Using moderated discussion groups, we convened a group of 24 stakeholders to derive pairwise weighting of the 16 attributes (Fig. 2) in our DST (Abelson et al 2021); ultimately, the weight of an observed value for each attribute indicates the degree to which that value contributes to the model's overall performance score (Fig. 2).…”

Section: Decision Support Tool Developmentmentioning

confidence: 99%

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Evaluating pathways to social and ecological landscape resilience

Abelson¹,

Reynolds²,

White³

et al. 2022

E&S

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Rapid environmental changes challenge the resilience of wildlands. The western portion of the Lake Tahoe Basin in California is an important ecological and cultural hotspot that is at risk of degradation from current and future environmental pressures. Historical uses, fire suppression, and a changing climate have created forest landscape conditions at risk of drought stress, destructive fire, and loss of habitat diversity. We prospectively modeled forest landscape conditions for a period of 100 years to evaluate the efficacy of 5 unique management scenarios in achieving desired landscape conditions. Management scenarios ranged from no management other than fire suppression to applying treatments consistent with historical fire frequencies and extent (i.e., regular and broadscale biomass reduction). We developed a decision support tool to evaluate environmental and social outcomes within a single framework to provide a transparent set of costs and benefits. Results illuminated underlying mechanisms of forest resilience and provided actionable guidance to decision makers. Sixteen attributes were assessed in the model after assigning weights to each. We found that removing forest biomass across the landscape, particularly when accomplished using extensive fire-based removal techniques, led to highly favorable conditions for environmental quality and promoted overall landscape resilience. Environmental conditions resulting from extensive fire-based biomass removal also had nominal variation over time, in contrast with strategies that had less extensive and/or used physical removal techniques (e.g., mechanical thinning). Our analysis provides a transparent approach to assess large datasets with complex and interacting variables. Ultimately, we aim to provide insights into the complexities of maintaining optimal conditions and managing landscapes to promote ecosystem resilience in a changing world.

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Section: Fire Suppression Onlymentioning

confidence: 99%

Section: Fire Suppression Onlymentioning

confidence: 99%

Section: Decision Support Tool Developmentmentioning

confidence: 99%

Section: Decision Support Tool Developmentmentioning

confidence: 99%

Section: Decision Support Tool Developmentmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating pathways to social and ecological landscape resilience

Abelson¹,

Reynolds²,

White³

et al. 2022

E&S

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Assessing impacts of sulfur deposition on aquatic ecosystems: A decision support system for the Southern Appalachians

et al. 2023

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With climate change and ongoing impacts from human development and resource extraction, US federal land management agencies are acutely concerned with managing for healthy aquatic ecosystems in the Southern Appalachian Mountain (SAM) Region. Here, we describe development of a spatial decision support application to assess the biological and ecological impacts of atmospheric S and N deposition on aquatic ecosystems of the region. We first summarize foundational published work to predict continuous maps of surface water acid neutralizing capacity (ANC) and soil base cation weathering (BCw). We use the predicted ANC and BCw maps to estimate steady‐state critical loads (CLs) of atmospheric S and N deposition. We included estimated CLs of atmospheric N to get a complete picture of CLs and potential exceedances. We then present a logic‐based decision support model for assessing effects of S and N deposition based on statistically modeled stream ANC and CL exceedance. The model is easily modified for continuous monitoring of CL exceedance patterns as new S and N deposition and ANC data become available. We present mapped model results for the SAM study area and an important subset of the region, the Great Smoky Mountains National Park. ANC modeling results revealed that predicted acid sensitivity was spatially variable, with areas of relatively low stream ANC (<50 μeq · L−1) and soil BCw (<50 meq · m−2 · year−1) predominantly found in certain critical areas. Within the Great Smoky Mountains National Park, evidence for S CL exceedance based on an ANC criterion of 50 μeq · L−1 was strong at locations where ambient S deposition was at least two times the CL. We also predicted likely impacts of CL exceedances on aquatic insect species richness and native fish abundance. Responses for insect species richness and fish impact showed variability similar to CL exceedance, with increasing impact positively correlated with elevation. Finally, we discuss ways that the decision support system can be used to prioritize management across the region.

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