Analysis of regional species distribution models based on radio‐telemetry datasets from multiple small‐scale studies

Abstract

The identification of core habitat areas and resulting prediction maps are vital tools for land managers. Often, agencies have large datasets from multiple studies over time that could be combined for a more informed and complete picture of a species. Colorado Parks and Wildlife has a large database for greater sage‐grouse (Centrocercus urophasianus) including 11 radio‐telemetry studies completed over 12 years (1997–2008) across northwestern Colorado. We divided the 49,470‐km2 study area into 1‐km2 grids with the number of sage‐grouse locations in each grid cell that contained at least 1 location counted as the response variable. We used a generalized linear mixed model (GLMM) using land cover variables as fixed effects and individual birds and populations as random effects to predict greater sage‐grouse location counts during breeding, summer, and winter seasons. The mixed effects model enabled us to model correlations that may exist in grouped data (e.g., correlations among individuals and populations). We found only individual groupings accounted for variation in the summer and breeding seasons, but not the winter season. The breeding and summer seasonal models predicted sage‐grouse presence in the currently delineated populations for Colorado, but we found little evidence supporting a winter season model. According to our models, about 50% of the study area in Colorado is considered highly or moderately suitable habitat in both the breeding and summer seasons. As oil and gas development and other landscape changes occur in this portion of Colorado, knowledge of where management actions can be accomplished or possible restoration can occur becomes more critical. These seasonal models provide data‐driven, distribution maps that managers and biologists can use for identification and exploration when investigating greater sage‐grouse issues across the Colorado range. Using historic data for future decisions on species management while accounting for issues found from combining datasets allows land managers the flexibility to use all information available. © 2013 The Wildlife Society.

Integrating spatially explicit indices of abundance and habitat quality: an applied example for greater sage‐grouse management

“…Only recently a few studies have relied on large-scale collaborative efforts to develop detailed habitat selection models using multiple telemetry data sets across relatively large spatial extents (e.g. Rice et al 2013;Fedy et al 2014). In our study, HSIs were derived from such high resolution data across multiple local sites and then scaled up by averaging predicted values across the region-wide extents.…”

Mapping and prioritizing seasonal habitats for greater sage‐grouse in Northwestern Colorado

“…Recently, state wildlife agencies have identified and mapped portions of sage‐grouse range as core/priority or general habitat with different recommended levels of protection (Colorado Greater Sage‐grouse Steering Committee [CGSSC] ; Bureau of Land Management , b ; Wyoming Governor's Office ). These maps have been developed using a variety of different data sources across states, including state and regional species distribution models (Rice et al ). However, patterns and scales of habitat selection and habitat availability vary among populations across sage‐grouse range (Coates et al , Fedy et al ), so layers developed at a state or regional extent may not always correctly reflect important seasonal habitats for local populations.…”

“…Resource selection studies often rank different areas of sage‐grouse habitat as high, medium, or low priority based on statistical features of layers showing relative probability of selection (e.g., quantile breaks, no. SDs from the mean, equal‐interval bins, geometric bins; Dzialak et al , Aldridge et al , Rice et al , Coates et al , Smith et al ). However, a more intuitive and defensible approach would designate habitat based on quantitative, biological goals for the population set by managers or by policy.…”

Seasonal Habitat Use by Greater Sage-Grouse (Centrocercus urophasianus) on a Landscape with Low Density Oil and Gas Development

“…In addition, we removed aspen ( Populus tremuloides ) and forest because models did not converge due to the lack of these variables in the presence buffers in all seasons. Thus, the vegetation categories we used in model development were irrigated agriculture, sagebrush, grassland, sagebrush/grassland, and riparian, which have all been shown to be important predictors of GRSG habitat in previous studies (S1 Appendix; [15, 19, 26–28]). We also updated the irrigated agriculture vegetation class from the basinwide layer using the Colorado Division of Water Resources District 47 irrigated agriculture shapefile from 2011 [29].…”