Influences of potential oil and gas development and future climate on Sage‐grouse declines and redistribution

Abstract

Multiple environmental stressors impact wildlife populations, but we often know little about their cumulative and combined influences on population outcomes. We generally know more about past effects than potential future impacts, and direct influences such as changes of habitat footprints than indirect, long‐term responses in behavior, distribution, or abundance. Yet, an understanding of all these components is needed to plan for future landscapes that include human activities and wildlife. We developed a case study to assess how spatially explicit individual‐based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater Sage‐grouse in southwest Wyoming, USA, we projected oil and gas development footprints and climate‐induced vegetation changes 50 years into the future. Using a time‐series of planned oil and gas development and predicted climate‐induced changes in vegetation, we recalculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long‐term Sage‐grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual‐based modeling offered a useful means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. The inclusion of movement and demographic responses to oil and gas infrastructure resulted in substantive changes in distribution and abundance when cumulated over several decades and throughout the regional population. When combined, additive development and climate‐induced vegetation changes reduced abundance by up to half of the original size. In our example, the consideration of only a single population stressor the final possible population size by as much as 50%. Multiple stressors and their cumulative impacts need to be broadly considered through space and time to avoid underestimating the impacts of multiple gradual changes and overestimating the ability of populations to withstand change.

Maximizing the effectiveness of national commitments to protected area expansion for conserving biodiversity and ecosystem carbon under climate change

“…In place of such statistical niche models, a variety of more complex mechanistic models of species range shifts have been developed. Some incorporate simple dispersal kernels while others are full spatially explicit population models (Heinrichs et al, 2019; Miller & McGill, 2018; Phillips et al, 2008).…”

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“…Spatial individual-based models such as HexSim (Schumaker and Brookes 2018) create connectivity outputs that are similar to those produced by circuitbased analyses but include greater biological realism by considering dispersal behavior and population demography and dynamics. The mechanistic simulations created by such models link the behavior and fates of individuals to their locations, supporting evaluation of the effects of environmental change on movement, population sizes, distributions, and genetic diversity (e.g., Heinrichs et al 2019aHeinrichs et al , 2019bNogeire-McRae et al 2019). Accordingly, the results of these models can project the population-level effects of barriers to movement and alternative reserve designs and be applied to examine the effects of compensatory mitigation (Barbosa et al 2019).…”

Development of ecologically meaningful, multiple-species conservation strategies under the California and U.S. Endangered Species Acts

“…Spatial individual-based models such as HexSim (Schumaker and Brookes 2018) create connectivity outputs that are similar to those produced by circuitbased analyses but include greater biological realism by considering dispersal behavior and population demography and dynamics. The mechanistic simulations created by such models link the behavior and fates of individuals to their locations, supporting evaluation of the effects of environmental change on movement, population sizes, distributions, and genetic diversity (e.g., Heinrichs et al 2019aHeinrichs et al , 2019bNogeire-McRae et al 2019). Accordingly, the results of these models can project the population-level effects of barriers to movement and alternative reserve designs and be applied to examine the effects of compensatory mitigation (Barbosa et al 2019).…”