Resource selection functions (RSF) and associated maps are often used by managers to guide conservation actions (Crawford et al., 2020; Pratt and Beck, 2021; Saher et al., 2022). However it is important to move beyond designating important habitat solely based on species occupancy or use. Incorporating demographic measures such as reproductive success will provide increased power and detail for ranking habitat for management priority, particularly across multiple life stages and large spatial extents (Gibson et al., 2016; Pratt and Beck, 2021; Stephens et al., 2015). We provide a quantitative approach to differentiate productive habitats supporting high selection and survival from areas of maladaptive selection where selection and survival are misaligned at large spatial scales. References cited Crawford, B. A., Maerz, J. C., and Moore, C. T. (2020). Expert-informed habitat suitability analysis for at-risk species assessment and conservation planning. Journal of Fish and Wildlife Management, 11(1), 130-150. https://doi.org/10.3996/092019-JFWM-075 Gibson, D., Blomberg, E. J., Atamian, M. T., and Sedinger, J. S. (2016). Nesting habitat selection influences nest and early offspring survival in Greater Sage-Grouse. The Condor: Ornithological Applications, 118(4), 689-702. https://doi.org/10.1650/CONDOR-16-62.1 Pratt, A. C., and Beck, J. L. (2021). Do greater sage-grouse exhibit maladaptive habitat selection? Ecosphere, 12(3), e03354. https://doi.org/10.1002/ecs2.3354 Saher, D. J., O’Donnell, M. S., Aldridge, C. L., and Heinrichs, J. A. (2022). Balancing model generality and specificity in management-focused habitat selection models for Gunnison sage-grouse. Global Ecology and Conservation, 35, e01935. https://doi.org/10.1016/j.gecco.2021.e01935 Stephens, P. A., Pettorelli, N., Barlow, J., Whittingham, M. J., and Cadotte, M. W. (2015). Management by proxy? The use of indices in applied ecology. Journal of Applied Ecology, 52(1), 1-6.

Resource selection functions (RSF) and associated maps are often used by managers to guide conservation actions (Crawford et al., 2020; Pratt and Beck, 2021; Saher et al., 2022). However it is important to move beyond designating important habitat solely based on species occupancy or use. Incorporating demographic measures such as reproductive success will provide increased power and detail for ranking habitat for management priority, particularly across multiple life stages and large spatial extents (Gibson et al., 2016; Pratt and Beck, 2021; Stephens et al., 2015). We provide a quantitative approach to differentiate productive habitats supporting high selection and survival from areas of maladaptive selection where selection and survival are misaligned at large spatial scales. References cited Crawford, B. A., Maerz, J. C., and Moore, C. T. (2020). Expert-informed habitat suitability analysis for at-risk species assessment and conservation planning. Journal of Fish and Wildlife Management, 11(1), 130-150. https://doi.org/10.3996/092019-JFWM-075 Gibson, D., Blomberg, E. J., Atamian, M. T., and Sedinger, J. S. (2016). Nesting habitat selection influences nest and early offspring survival in Greater Sage-Grouse. The Condor: Ornithological Applications, 118(4), 689-702. https://doi.org/10.1650/CONDOR-16-62.1 Pratt, A. C., and Beck, J. L. (2021). Do greater sage-grouse exhibit maladaptive habitat selection? Ecosphere, 12(3), e03354. https://doi.org/10.1002/ecs2.3354 Saher, D. J., O’Donnell, M. S., Aldridge, C. L., and Heinrichs, J. A. (2022). Balancing model generality and specificity in management-focused habitat selection models for Gunnison sage-grouse. Global Ecology and Conservation, 35, e01935. https://doi.org/10.1016/j.gecco.2021.e01935 Stephens, P. A., Pettorelli, N., Barlow, J., Whittingham, M. J., and Cadotte, M. W. (2015). Management by proxy? The use of indices in applied ecology. Journal of Applied Ecology, 52(1), 1-6.

These data are the input tables for the habitat selection and survival models. The tables are the result of extracting values from rasters to both 'used' and 'available' locations; 'used' refers to an observation of a sage-grouse nesting or brood rearing, 'available' is a randomly-generated location proximal to a paired 'used' location. For these locations, we extract values from multiple rasters expressing landscape characteristics such as landcover (such as sagebrush, annual grass, or shrubs, expressed as a percentage), height of sagebrush, distance to water features, distance to anthropogenic features, and topographic transformations (such as slope, heat load index, and roughness). There are also some values in the table that are not the result of value extraction, but rather field-collection such as date, fate, and age (in days) of the nest or brood. Ultimately, the locations were removed from these tables as sage-grouse are considered to be a sensitive species.

These data are the input tables for the habitat selection and survival models. The tables are the result of extracting values from rasters to both 'used' and 'available' locations; 'used' refers to an observation of a sage-grouse nesting or brood rearing, 'available' is a randomly-generated location proximal to a paired 'used' location. For these locations, we extract values from multiple rasters expressing landscape characteristics such as landcover (such as sagebrush, annual grass, or shrubs, expressed as a percentage), height of sagebrush, distance to water features, distance to anthropogenic features, and topographic transformations (such as slope, heat load index, and roughness). There are also some values in the table that are not the result of value extraction, but rather field-collection such as date, fate, and age (in days) of the nest or brood. Ultimately, the locations were removed from these tables as sage-grouse are considered to be a sensitive species.

Map of cumulative 38-day nest survival predicted from a Bayesian hierarchical shared frailty model of sage-grouse nest fates. The midpoint of coefficient conditional posterior distributions of 38-day nest survival were used for prediction at each 30 meter pixel across the landscape.

Map of cumulative 38-day nest survival predicted from a Bayesian hierarchical shared frailty model of sage-grouse nest fates. The midpoint of coefficient conditional posterior distributions of 38-day nest survival were used for prediction at each 30 meter pixel across the landscape.

We used movement and demographic data to simultaneously evaluate habitat selection by sage-grouse across multiple seasons, and measures of survival during key reproductive life stages (nesting and brood-rearing) to identify priority habitat by linking resource selection to demographic performance. We calculated and mapped composite selection and survival indices across the Bi-State Distinct Population Segment (DPS) to differentiate productive habitat that supported high selection and survival compared to areas of maladaptive selection where selection and survival were misaligned. We then reclassified the indices into categorical rasters representing different classes of selection (high, moderate, low, non-habitat) and survival (high, moderate, low, and very low).

We used movement and demographic data to simultaneously evaluate habitat selection by sage-grouse across multiple seasons, and measures of survival during key reproductive life stages (nesting and brood-rearing) to identify priority habitat by linking resource selection to demographic performance. We calculated and mapped composite selection and survival indices across the Bi-State Distinct Population Segment (DPS) to differentiate productive habitat that supported high selection and survival compared to areas of maladaptive selection where selection and survival were misaligned. We then reclassified the indices into categorical rasters representing different classes of selection (high, moderate, low, non-habitat) and survival (high, moderate, low, and very low).

We used movement and demographic data to simultaneously evaluate habitat selection by sage-grouse across multiple seasons, and measures of survival during key reproductive life stages (nesting and brood-rearing) to identify priority habitat by linking resource selection to demographic performance. We calculated and mapped composite selection and survival indices across the Bi-State Distinct Population Segment (DPS) to differentiate productive habitat that supported high selection and survival compared to areas of maladaptive selection where selection and survival were misaligned.

We used movement and demographic data to simultaneously evaluate habitat selection by sage-grouse across multiple seasons, and measures of survival during key reproductive life stages (nesting and brood-rearing) to identify priority habitat by linking resource selection to demographic performance. We calculated and mapped composite selection and survival indices across the Bi-State Distinct Population Segment (DPS) to differentiate productive habitat that supported high selection and survival compared to areas of maladaptive selection where selection and survival were misaligned.