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Mule deer (Odocoileus hemionus) survival and population growth in north‐central New Mexico, USA, was previously reported to be limited by nutritional constraints due to poor forage conditions in degraded habitats. Management recommendations suggested thinning of pinyon–juniper to improve habitat quality for mule deer. To evaluate the influence of these vegetation treatments, we monitored habitat selection by 48 adult female mule deer from 2011 to 2013 in a population previously reported to be nutritionally limited. Monitoring occurred 1–4 years after completion of treatments that were intended to improve forage conditions, including mechanical reduction of pinyon pine (Pinus edulis) and juniper (Juniperus spp.) density and senescent brush (Quercus gambelii–Cercocarpus montanus) cover. During the summer season, deer selected recently treated areas, but odds ratios decreased with treatment age. However, during winter, deer avoided more recently treated areas and selected thinned areas >4 years old. Deer selected mixed oak (Quercus spp.) and pinyon–juniper savanna vegetation cover types with a moderately open canopy and ponderosa pine (Pinus ponderosa) forests while avoiding grasslands and montane shrublands across all seasons. Deer selected areas closer to water and developed areas, northeast aspects, on gentle slopes, and at lower elevations. Creating a savanna‐like cover type may elicit a positive deer response as a result of their strong avoidance of dense, closed canopy pinyon–juniper woodlands. © 2020 The Wildlife Society. We evaluated the influence of pinyon–juniper and mixed oak vegetation reduction treatments on mule deer habitat selection. During summer, deer selected recently treated areas, but odds ratios decreased with treatment age. However, during winter, deer avoided more recently treated areas and selected thinned areas >4 years old. Deer selected for a savanna‐like cover types while showing strong avoidance of dense, closed canopy pinyon–juniper woodlands. [ABSTRACT FROM AUTHOR]

ABSTRACT The increasing use of Global Positioning System (GPS) collars in habitat selection studies provides large numbers of precise location data points with reduced field effort. However, inclusion of activity sensors in many GPS collars also grants the potential to remotely estimate behavioral state. Thus, only using GPS collars to collect location data belies their full capabilities. Coupling behavioral state with location data would allow researchers and managers to refine habitat selection models by using diel behavioral state changes to partition fine-scale temporal shifts in habitat selection. We tested the capability of relatively unsophisticated GPS-collar activity sensors to estimate behavior throughout diel periods using free-ranging female elk ( Cervus canadensis) in the Jemez Mountains of north-central New Mexico, USA, 2013-2014. Collars recorded cumulative number of movements (hits) per 15-min recording period immediately preceding GPS fixes at 0000, 0600, 1200, and 1800 hr. We measured diel behavioral patterns of focal elk, categorizing active (i.e., foraging, traveling, vigilant, grooming) and inactive (i.e., resting) states. Active behaviors (foraging, traveling) produced more average hits (0.87 ± 0.69 hits/min, 4.0 ± 2.2 hits/min, respectively; 95% CI) and inactive (resting) behavior fewer hits (−1.1 ± 0.61 95% CI). We differentiated active and inactive behavioral states with a bootstrapped threshold of 5.9 ± 3.9 hits/15-min recording period. Mean cumulative activity-sensor hits corresponded with observed diel behavioral patterns: hits increased during crepuscular (0600, 1800 hr) observations when elk were most active (0000-0600 hr: d = 0.19; 1200-1800 hr: d = 0.64) and decreased during midday and night (0000 hr, 1200 hr) when elk were least active (1800-0000 hr: d = −0.39; 0600-1200 hr: d = −0.43). Even using relatively unsophisticated GPS-collar activity sensors, managers can remotely estimate behavioral states, approximate diel behavioral patterns, and potentially complement location data in developing habitat selection models. © 2016 The Wildlife Society. [ABSTRACT FROM AUTHOR]