Strategic decision support for long-term conservation management planning

• Published in: Forest ecology and management Vol. 497; p. 119533
• Main Authors: Abelson, Eric S., Reynolds, Keith M., Manley, Patricia, Paplanus, Steven
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2021
• Subjects: Forest ecology; Landscape resilience; Logic modeling; Multi-criteria decision analysis; Scenario planning; Spatial decision support; Wildlife ecology; Wildlife ecology; Spatial decision support; Logic modeling; Forest ecology; Scenario planning; Landscape resilience; Multi-criteria decision analysis
• ISSN: 0378-1127; 1872-7042
• DOI: 10.1016/j.foreco.2021.119533

•Understanding forest response to perturbations over long time spans is important.•Designed a decision support tool (DST) to assess five modeled forests over 100 years.•The five modeled forests reflected management developed to explore ecosystem resilience.•DST design yielded a transparent framework to assess ecological variables/perspectives.•We found widescale biomass removal resulted in more favorable ecological outcomes. Forward thinking conservation-planning can benefit from modeling future landscapes that result from multiple alternative management scenarios. However, long-term landscape modeling and downstream analyses of modeling results can lead to massive amounts of data that are difficult to assemble, analyze, and to report findings in a way that is easily accessible to decision makers. In this study, we developed a decision support process to evaluate modeled forest conditions resulting from five management scenarios, across 100 years in California’s Lake Tahoe basin; to this end we drew upon a large and complex hierarchical dataset intended to evaluate landscape resilience. Trajectories of landscape characteristics used to inform an analysis of landscape resilience were modeled with the spatially explicit LANDIS-II vegetation simulator. Downstream modeling outputs of additional landscape characteristics were derived from the LANDIS-II outputs (e.g., wildlife conditions, water quality, effects of fire). The later modeling processes resulted in the generation of massive data sets with high dimensionality of landscape characteristics at both high spatial and temporal resolution. Ultimately, our analysis distilled hundreds of data inputs into performance trajectories for the five modeled management scenarios over a 100-year time horizon. We then evaluated each management scenario based on inter-year variability, and absolute and relative performance. We found that management scenarios with a greater emphasis on proactive biomass reduction outperformed management approaches with minimal biomass reduction. These results, and the process that led to them, provided decision makers with insight into forest dynamics based on a rational, transparent, and repeatable decision support processes.