The rapid pace of wind‐energy development has increased stakeholder concerns regarding the potential effects on wildlife. Locations targeted for wind‐energy development frequently overlap prairie grouse and greater sage‐grouse (Centrocercus urophasianus) habitats. Research suggests that anthropogenic developments may have negative effects on these species. There is, however, no information published regarding the effect of wind‐energy development on Columbian sharp‐tailed grouse (Tympanuchus phasianellus columbianus), a subspecies that has twice been petitioned for Endangered Species Act protection. To address this need, from 2014 to 2015 we studied Columbian sharp‐tailed grouse nesting ecology across restored grasslands in eastern Idaho, USA, where a 215‐turbine wind‐energy complex had been developed. We monitored 147 nests from 135 females captured at leks 0.1–13.8 km from wind turbines. We used an information‐theoretic approach to evaluate the influence of wind‐energy infrastructure and habitat characteristics on nest‐site selection and daily nest survival. We did not detect any influence of wind‐energy infrastructure on nest‐site selection or nest survival. Nest‐site selection and daily nest survival were influenced by vegetation structure and composition measured at 2 spatial scales. Females selected nest sites with more restored grassland containing >30% forb cover within the nesting core‐use area (i.e., 60 ha around the nest) and exhibited a functional response to the availability of that land cover type. Daily nest survival was best predicted by visual obstruction at the nest site and the amount of restored grassland containing >30% forb cover within the nesting core‐use area. We recommend wildlife managers continue to implement management practices that will provide bunchgrass‐dominated grasslands with >30% forb cover in restored grasslands (e.g., Conservation Reserve Program fields) within Columbian sharp‐tailed grouse range. © The Wildlife Society, 2019

Monitoring the abundance of rare carnivores is a daunting task for wildlife biologists. Many carnivore populations persist at relatively low densities, public interest is high, and the need for population estimates is great. Recent advances in trail camera technology provide an unprecedented opportunity for biologists to monitor rare species economically. Few studies, however, have conducted rigorous analyses of our ability to estimate abundance of lowdensity carnivores with cameras. We used motion-triggered trail cameras and a space-to-event model to estimate gray wolf (Canis lupus) abundance across three study areas in Idaho, USA, 2016-2018. We compared abundance estimates between cameras and noninvasive genetic sampling that had been extensively tested in our study areas. Estimates of mean wolf abundance from camera and genetic surveys were within 22% of one another and 95% CIs overlapped in 2 of the 3 years. A single camera with many detections appeared to bias camera estimates high in 2018. A subsequent bootstrapping procedure produced a population estimate from cameras equal to that derived from genetic sampling, however. Camera surveys were less than half the cost of genetic surveys once initial camera purchases were made. Our results suggest that cameras can be a viable method for estimating wolf abundance across broad landscapes (>10,000 km 2 ).

For wildlife inhabiting snowy environments, snow properties such as onset date, depth, strength, and distribution can influence many aspects of ecology, including movement, community dynamics, energy expenditure, and forage accessibility. As a result, snow plays a considerable role in individual fitness and ultimately population dynamics, and its evaluation is, therefore, important for comprehensive understanding of ecosystem processes in regions experiencing snow. Such understanding, and particularly study of how wildlife–snow relationships may be changing, grows more urgent as winter processes become less predictable and often more extreme under global climate change. However, studying and monitoring wildlife–snow relationships continue to be challenging because characterizing snow, an inherently complex and constantly changing environmental feature, and identifying, accessing, and applying relevant snow information at appropriate spatial and temporal scales, often require a detailed understanding of physical snow science and technologies that typically lie outside the expertise of wildlife researchers and managers. We argue that thoroughly assessing the role of snow in wildlife ecology requires substantive collaboration between researchers with expertise in each of these two fields, leveraging the discipline‐specific knowledge brought by both wildlife and snow professionals. To facilitate this collaboration and encourage more effective exploration of wildlife–snow questions, we provide a five‐step protocol: (1) identify relevant snow property information; (2) specify spatial, temporal, and informational requirements; (3) build the necessary datasets; (4) implement quality control procedures; and (5) incorporate snow information into wildlife analyses. Additionally, we explore the types of snow information that can be used within this collaborative framework. We illustrate, in the context of two examples, field observations, remote‐sensing datasets, and four example modeling tools that simulate spatiotemporal snow property distributions and, in some cases, evolutions. For each type of snow data, we highlight the collaborative opportunities for wildlife and snow professionals when designing snow data collection efforts, processing snow remote sensing products, producing tailored snow datasets, and applying the resulting snow information in wildlife analyses. We seek to provide a clear path for wildlife professionals to address wildlife–snow questions and improve ecological inference by integrating the best available snow science through collaboration with snow professionals.

Many land‐trust organizations attempt to preserve habitat that will benefit specific wildlife species or suites of species. With limited resources available, these organizations need tools to prioritize preservation efforts. One such organization, the Kiawah Island Natural Habitat Conservancy (KINHC), is attempting to preserve wildlife habitat in the face of ever‐increasing property values and development pressure on Kiawah Island, South Carolina, USA. We modified an existing bobcat (Lynx rufus) habitat suitability index model, which focuses on suitability of habitats for food, by including components for concealment cover and den habitat. We developed a windows‐based computer program that calculates modified habitat suitability index (MHSI) values that can easily be imported into a Geographic Information System for display in map form, allowing for frequent reevaluation of site‐specific habitat suitability as land‐cover patterns change. We used locations collected from radiocollared bobcats to assess validity of the food and cover components of the MHSI. Bobcats used areas identified as highly suitable for food more than expected during nocturnal time periods (G52 = 640.9, P < 0.001) and areas identified as highly suitable for cover more than expected during diurnal time periods (G37 = 1,194.0, P < 0.001). Our approach for evaluating bobcat habitat suitability will allow KINHC to identify parcels that likely provide the greatest ecological benefit to bobcats and their associated wildlife community. Our approach could be altered to consider habitat requirements of other species, or multiple species, at virtually any location for which fine‐scale land‐cover data are available.

Investigators rely on brood surveys to estimate annual fecundity of game birds. However, investigators often do not account for factors that influence brood detection probability nor rarely document how much females and their broods are disturbed (flush rates) during surveys, which could lead to biased survival estimates. We used 45 radio‐tagged female Greater Sage‐Grouse (Centrocercus urophasianus) with broods to compare detection probabilities and document disturbance among four survey methods to allow future investigators to select the method that best meets their objectives. These methods included daytime flush, daytime visual, nocturnal spotlight, and fecal surveys at nocturnal roost sites, with the latter being a novel method. We used Cormack–Jolly–Seber (CJS) models to compare detection probability and daily survival estimates for visual and fecal surveys of broods 0–47 d post‐hatch and a double‐survey approach to compare detection probabilities among flush, fecal, and spotlight surveys ~42 d post‐hatch when investigators often determine brood fate. From CJS models, detection probability for visual surveys increased with brood age (0.618–0.881), whereas detection probability for fecal surveys did not (0.748). Daily survival probability estimates increased with brood age and differed annually based on fecal surveys (2016: 0.978–1.000 and 2017: 0.839–0.998). We detected age‐specific daily survival probability with visual surveys (0.956–0.997), but not annual differences. Based on the double‐survey approach, detection probability was high (0.857–1.000) for all methods. We flushed ~310–750% fewer females and broods during fecal and spotlight surveys than during both types of daytime surveys. Our results highlight the need to account for detection probabilities among methods and document disturbance to hens and broods that can help investigators design surveys to minimize impacts to birds. Furthermore, our result suggest that actions to improve brood survival during the first week post‐hatch may improve local recruitment.