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

Peter S. Coates; Michael L. Casazza; Mark A. Ricca; Brianne E. Brussee; Erik J. Blomberg; K. Benjamin Gustafson; Cory T. Overton; Dawn M. Davis; Lara E. Niell; Shawn P. Espinosa; Scott C. Gardner; David J. Delehanty

doi:10.1111/1365-2664.12558

USGS · 70159410

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

Peter S. Coates·Michael L. Casazza·Mark A. Ricca·Brianne E. Brussee·Erik J. Blomberg·K. Benjamin Gustafson·Cory T. Overton·Dawn M. Davis·Lara E. Niell·Shawn P. Espinosa·Scott C. Gardner·David J. Delehanty

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Abstract

Predictive species distributional models are a cornerstone of wildlife conservation planning. Constructing such models requires robust underpinning science that integrates formerly disparate data types to achieve effective species management. Greater sage-grouse Centrocercus urophasianus , hereafter “sage-grouse” populations are declining throughout sagebrush-steppe ecosystems in North America, particularly within the Great Basin, which heightens the need for novel management tools that maximize use of available information. Herein, we improve upon existing species distribution models by combining information about sage-grouse habitat quality, distribution, and abundance from multiple data sources. To measure habitat, we created spatially explicit maps depicting habitat selection indices (HSI) informed by > 35 500 independent telemetry locations from > 1600 sage-grouse collected over 15 years across much of the Great Basin. These indices were derived from models that accounted for selection at different spatial scales and seasons. A region-wide HSI was calculated using the HSI surfaces modelled for 12 independent subregions and then demarcated into distinct habitat quality classes. We also employed a novel index to describe landscape patterns of sage-grouse abundance and space use (AUI). The AUI is a probabilistic composite of: (i) breeding density patterns based on the spatial configuration of breeding leks and associated trends in male attendance; and (ii) year-round patterns of space use indexed by the decreasing probability of use with increasing distance to leks. The continuous AUI surface was then reclassified into two classes representing high and low/no use and abundance. Synthesis and application s. Using the example of sage-grouse, we demonstrate how the joint application of indices of habitat selection, abundance, and space use derived from multiple data sources yields a composite map that can guide effective allocation of management intensity across multiple spatial scales. As applied to sage-grouse, the composite map identifies spatially explicit management categories within sagebrush steppe that are most critical to sustaining sage-grouse populations as well as those areas where changes in land use would likely have minimal impact. Importantly, collaborative efforts among stakeholders guide which intersections of habitat selection indices and abundance and space use classes are used to define management categories. Because sage-grouse are an umbrella species, our joint-index modelling approach can help target effective conservation for other sagebrush obligate species, and can be readily applied to species in other ecosystems with similar life histories, such as central-placed breeding.

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Journal of Applied Ecology Article

Source-reported bounding extent: 36.949891786813296° to 42.00848901572399° latitude; -121.805419921875° to -114.0380859375° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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Peter S. Coates, Michael L. Casazza, Mark A. Ricca, Brianne E. Brussee, Erik J. Blomberg, K. Benjamin Gustafson, Cory T. Overton, Dawn M. Davis, Lara E. Niell, Shawn P. Espinosa, Scott C. Gardner, David J. Delehanty. 2015-11-27. Integrating spatially explicit indices of abundance and habitat quality: an applied example for greater sage-grouse management. https://doi.org/10.1111/1365-2664.12558

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