Dynamic spatiotemporal modeling of a habitat‐defining plant species to support wildlife management at regional scales

Abstract

AbstractSagebrush (Artemisia spp.) ecosystems provide critical habitat for the Greater sage‐grouse (Centrocercus urophasianus), a species of conservation concern. Thus, future loss of sagebrush habitat because of land use change and global climate change is of concern. Here, we use a dynamic additive spatiotemporal model to estimate the effects of climate on sagebrush cover dynamics at 32 sage‐grouse management (core) areas in Wyoming. We use the fitted models to quantify the sensitivity of each management area to precipitation and temperature, and to make probabilistic projections of sagebrush cover from present to 2100 under three climate change scenarios. Global circulation models predict an increase in temperature and no change in precipitation for Wyoming. Sensitivity to climate varied among management areas, but the most common response (70% of management areas) was a positive effect of temperature on sagebrush performance. The combination of positive sensitivity to temperature and the predicted increase in temperature under all climate change scenarios resulted in projections of increased sagebrush cover for most management areas. We characterized management areas as “optimal” or “suboptimal” based on the percentage of grid cells in each management area with sagebrush cover exceeding a nesting habitat target value. Only 18% of management areas are projected to switch from being currently optimal to suboptimal in the future. Thirty‐five percent of management areas are projected to switch from being suboptimal to optimal. The most common outcome (47%) was for currently suboptimal management areas to remain suboptimal, even though average cover tended to increase in those areas. The direct effects of climate change appear to favor sagebrush performance in the future for most sage‐grouse core areas in Wyoming. Our approach is broadly applicable to quantitative climate change assessments where remotely sensed estimates of habitat‐defining vegetation are available.