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The forage maturation hypothesis (FMH) states that energy intake for ungulates is maximised when forage biomass is at intermediate levels. Nevertheless, metabolic allometry and different digestive systems suggest that resource selection should vary across ungulate species. By combining GPS relocations with remotely sensed data on forage characteristics and surface water, we quantified the effect of body size and digestive system in determining movements of 30 populations of hindgut fermenters (equids) and ruminants across biomes. Selection for intermediate forage biomass was negatively related to body size, regardless of digestive system. Selection for proximity to surface water was stronger for equids relative to ruminants, regardless of body size. To be more generalisable, we suggest that the FMH explicitly incorporate contingencies in body size and digestive system, with small‐bodied ruminants selecting more strongly for potential energy intake, and hindgut fermenters selecting more strongly for surface water.

Feral burros (Equus asinus) and horses (E. ferus caballus) inhabiting public land in the western United States are intended to be managed at population levels established to promote a thriving, natural ecological balance. Double‐observer sightability (MDS) models, which use detection records from multiple observers and sighting covariates, perform well for estimating feral horse abundances, but their effectiveness for use in burro populations is less understood. These MDS models help minimize detection bias, yet bias can be further reduced with models that account for unmodeled variation, or residual heterogeneity, in detection probability. In populations containing radio‐marked individuals, residual heterogeneity can be estimated with MDS models by including a covariate that corresponds to the marked status of a group (MH models). Another approach is to use information from detections missed by both observers to account for the characteristics that make groups more or less likely to be detected, or recaptured, by the second observer (MR models). We used aerial survey data from 3 burro populations (Sinbad Herd Management Area, UT [2016–2018], Lake Pleasant Herd Management Area, AZ [2017], and Fort Irwin National Training Center, CA [2016–2017]) to develop MDS models applicable for feral burros in the southwestern United States. Our objectives were to quantify precision and bias of standard MDS surveys for feral burros and to examine which model type for incorporating residual heterogeneity (MH or MR) would result in the least‐biased estimates of burro populations relative to the minimum number known alive (MNKA) within the Sinbad Herd Management Area. Standard MDS model estimates achieved a mean coefficient of variation of 0.08, while underestimating MNKA by an average of 27.1%. Accounting for residual heterogeneity through recapture probability in MR models resulted in estimates closer to MNKA than MH models (9.5% vs. 16.5% less than MNKA). Our results indicate that MDS models can achieve precise enough estimates to monitor feral burro populations, but they routinely produce negatively biased estimates. We encourage the use of radio‐collars to reduce bias in future burro surveys by accounting for residual heterogeneity through MR models.

Imperiled species recovery is a high‐stakes endeavor where uncertainty surrounding effectiveness of conservation actions can be an impediment to implementation at necessary scales, especially where habitat restoration is required. Gunnison sage‐grouse (Centrocercus minimus) represents one such species in need of large‐scale habitat restoration. It is a federally threatened sagebrush (Artemisia spp.) obligate bird with a limited range in Colorado and Utah. Threats to recovery of Gunnison sage‐grouse include conifer expansion into sagebrush along with additional habitat loss and degradation attributed to human development and agricultural conversion. Recovery of Gunnison sage‐grouse and other sensitive species can be aided by spatial tools that forecast plausible outcomes of conservation actions. We illustrate this by using a novel framework for predicting outcomes of proactive tree removal and subsequent sagebrush restoration for the Gunnison sage‐grouse. To assess threats on Gunnison sage‐grouse lek presence, we developed a spatially explicit breeding habitat model to compare active lek and random pseudo‐absence locations from 2015. Models identified land cover, climatic, and abiotic variables at landscape‐level scales (0.56 and 4 km) most important for predicting breeding habitat. Our model correctly differentiated between lek and pseudo‐absence locations 94% of the time. All but one of the active leks (n = 94) were in areas with >0.65 probability of lek occurrence. Using this probability value as a threshold, we predicted 15% of the current grouse range as high‐quality breeding habitat. Simulated removal of trees in areas with ≤30% tree canopy cover (0.56‐km scale) increased extent of high‐quality habitat fourfold (59%). Hypothetical restoration of sagebrush cover in the same areas increased habitat quality an additional 11%. Our breeding habitat model indicated that targeted tree removal and sagebrush restoration have potential to improve Gunnison sage‐grouse breeding habitat. While our habitat treatment scenarios were not meant to be prescriptive, they highlight that considerable uplift in Gunnison sage‐grouse breeding habitat may be possible across much of its range with cooperation from multiple stakeholders and illustrates the utility of this approach for predicting biological return on investment.

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