Spatially explicit models of seasonal habitat for greater sage‐grouse at broad spatial scales: Informing areas for management in Nevada and northeastern California

Abstract

Defining boundaries of species' habitat across broad spatial scales is often necessary for management decisions, and yet challenging for species that demonstrate differential variation in seasonal habitat use. Spatially explicit indices that incorporate temporal shifts in selection can help overcome such challenges, especially for species of high conservation concern. Greater sage-grouse Centrocercus urophasianus (hereafter, sage-grouse), a sagebrush obligate species inhabiting the American West, represents an important case study because sage-grouse exhibit seasonal habitat patterns, populations are declining in most portions of their range and are central to contemporary national land use policies. Here, we modeled spatiotemporal selection patterns for telemetered sage-grouse across multiple study sites (1,084 sage-grouse; 30,690 locations) in the Great Basin. We developed broad-scale spatially explicit habitat indices that elucidated space use patterns (spring, summer/fall, and winter) and accounted for regional climatic variation using previously published hydrographic boundaries. We then evaluated differences in selection/avoidance of each habitat characteristic between seasons and hydrographic regions. Most notably, sage-grouse consistently selected areas dominated by sagebrush with few or no conifers but varied in type of sagebrush selected by season and region. Spatiotemporal variation was most apparent based on availability of water resources and herbaceous cover, where sage-grouse strongly selected upland natural springs in xeric regions but selected larger wet meadows in mesic regions. Additionally, during the breeding period in spring, herbaceous cover was selected strongly in the mesic regions. Lastly, we expanded upon an existing joint-index framework by combining seasonal habitat indices with a probabilistic index of sage-grouse abundance and space use to produce habitat maps useful for sage-grouse management. These products can serve as Additional supporting information may be found online in the Supporting Information section. How to cite this article: Coates PS, Brussee BE, Ricca MA, et al. Spatially explicit models of seasonal habitat for greater sage-grouse at broad spatial scales: Informing areas for management in Nevada and northeastern California. Ecol

Sage‐Grouse Population Dynamics are Adversely Affected by Overabundant Feral Horses

“…Importantly, in Nevada and northeastern California, increasing horse abundance represents a poorly quantified yet likely additional threat to sage‐grouse populations because areas occupied by horses comprise ≥4,498,534 ha of sage‐grouse habitat (Fig. 1A), of which 1,648,807 ha is considered priority habitat (31%) from recent sage‐grouse habitat mapping (Coates et al 2016 a , 2020 a ).…”

Nesting, brood rearing, and summer habitat selection by translocated greater sage‐grouse in North Dakota, USA

“…We tested for correlation using Pearson's correlation test with an | r | > .7 threshold for location data (Hosmer & Lemeshow, 2013). We removed slope, due to its collinear relationship with ruggedness and elevation; the latter variables were retained due to their importance to sage‐grouse habitat selection patterns in other regions (e.g., Coates, Brussee, et al., 2016; Coates, Casazza, et al., 2016; Coates et al., 2020). No other significant correlations occurred among the variables we considered.…”

Integrating Ecosystem Resilience and Resistance Into Decision Support Tools for Multi-Scale Population Management of a Sagebrush Indicator Species

“…Site-level implementation of management is also a key component of the Framework (Chambers et al, 2017;Crist et al, 2019), and direct application of mid-scale models may be too coarse in some cases to inform the most effective targeting of treatments given within site heterogeneity. In our examples, substantial variation in habitat selection within areas identified at the mid-scale can occur given inter-site differences in the availability of resources required by sage-grouse (Coates et al, 2019), and how that availability changes subsequent to disturbance and management carried out at a finer grain within the local scale. Here, we describe recently developed decision-support tools for conifer treatment and fire restoration that downscale mid-scale models, or leverage existing and extensive site-specific models, and apply simulated changes to land cover or habitat characteristics and concomitant quantified improvement in habitat quality to sage-grouse across candidate treatment sites while implicitly or explicitly considering underlying R&R. Such tools are also especially helpful when disturbance is widespread across midscale identified areas, but limited resources are available for uniform implementation of restoration treatments that are intensive and costly.…”

“…Fish and Wildlife Service, 2013) because it more directly accounts for habitat features selected by breeding sagegrouse in areas with abundant populations as determined by counts of sage-grouse at traditional breeding leks that are widely used to assess sage-grouse population trends (WAFWA, 2015). This property builds on the hierarchical approach of Coates et al (2016bCoates et al ( , 2019, which facilitates more precise prioritization of highly suitable habitats where sage-grouse are known to occur, while still accounting for unoccupied habitats of varying quality that may provide connectivity or other non-breeding life history needs. The binning of sagebrush R&R and sagegrouse population index layers into 3 respective classes each (i.e., high, moderate, and low) yields a 3 x 3 "sage-grouse habitat resilience and resistance matrix" that provides a highly tractable means for triaging management decisions relative to primary disturbance threats (e.g., conifer expansion, wildfire, invasive species) transcending broad to mid to local spatial scales across the species range (Chambers et al, 2016(Chambers et al, , 2017(Chambers et al, , 2019a.…”

“…However, telemetrybased models can better account for specific resources selected differently among seasons by individual grouse to fulfill specific life-history needs such as nesting, brood rearing, overwintering, and movement corridors (Chambers et al, 2017). Compared to the SGCA-based overlay, a composite index derived by intersecting spatially explicit models of: (1) habitat selection informed by >44,000 locations from >1,700 telemetered sagegrouse that explicitly account for seasonal and regional climatic variation; and (2) a lek-based probabilistic index of abundance and space use (Coates et al, 2019) can provide finer scale depictions of predicted suitable habitat in areas likely occupied by sage-grouse ( Figure 5). Subsequent intersections with R&R and recovered and non-recovered CBA layer using the same FIGURE 3 | Example spatially explicit strategies for prioritizing wildfire management in the Great Basin based on geospatial intersections of fire probability (Short et al, 2016), wildfire impacted areas (Coates et al, 2016a), soil regime-based resilience and resistance indices (RandR, Maestas et al, 2016), and sage-grouse concentration areas (SGCA, Coates et al, 2016a).…”