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David J Delehanty

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Integrating Spatially Explicit Indices of Abundance and Habitat Quality: An Applied Example for Greater Sage-grouse Management
This USGS data release represents geospatial data for the sage-grouse habitat mapping project. This study provides timely and highly useful information about greater sage-grouse over a large area of the Great Basin. USGS researchers and their colleagues created a template for combining landscape-scale occurrence or abundance data with habitat selection data in order to identify areas most critical to sustaining populations of species of conservation concern. The template also identifies those areas where land use changes have minimal impact. To inform greater sage-grouse conservation planning, the researchers developed greater sage-grouse habitat management categories based on habitat selection indices (HSI) and...
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Spatially-Explicit Predictive Maps of Greater Sage-Grouse Brood Selection Integrated with Brood Survival in Nevada and Northeastern California, USA
We used a hierarchical Bayesian modeling framework to estimate resource selection functions and survival for early and late brood-rearing stages of sage-grouse in relation to a broad suite of habitat characteristics evaluated at multiple spatial scales within the Great Basin from 2009 to 2019. Sage-grouse selected for greater perennial grass cover, higher relative elevations, and areas closer to springs and wet meadows during both early and late brood-rearing. Terrain characteristics, including heat load and aspect, were important in survival models, as was variation in shrub height. We also found strong evidence for higher survival for both early and late broods within previously burned areas, but survival within...
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Ranked habitat classes for sage-grouse brood-rearing productivity at each 90 m pixel. Habitat classes represent areas where high brood selection and high brood survival intersected, whereas the lowest ranks represent areas where high brood habitat selection intersected with the low brood survival. Hierarchical models of brood selection and survival were fit to landscape covariates within a Bayesian modeling framework in Nevada and California from 2009 - 2017 to develop spatially explicit information about brood habitat selection and survival.
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