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 ).

Bias introduced by detection errors is a well‐documented issue for abundance and occupancy estimates of wildlife. Detection errors bias estimates of detection and abundance or occupancy in positive and negative directions, which can produce misleading results. There have been considerable design‐ and model‐based methods to address false‐negative errors, or missed detections. However, false‐positive errors, or detections of individuals that are absent but counted as present because of misidentifications or double counts, are often assumed to not occur in ecological studies. The dependent double‐observer survey method is a design‐based approach speculated to reduce false positives because observations have the ability to be confirmed by two observers. However, whether this method reduces false positives compared to single‐observer methods has not been empirically tested. We used prairie songbirds as a model system to test if a dependent double‐observer method reduced false positives compared to a single‐observer method. We used vocalizations of ten species to create auditory simulations and used naive and expert observers to survey these simulations using single‐observer and dependent double‐observer methods. False‐positive rates were significantly lower using the dependent double‐observer survey method in both observer groups. Expert observers reported a 3.2% false‐positive rate in dependent double‐observer surveys and a 9.5% false‐positive rate in single‐observer surveys, while naive observers reported a 39.1% false‐positive rate in dependent double‐observer surveys and a 49.1% false‐positive rate in single‐observer surveys. Misidentification errors arose in all survey scenarios and almost all species combinations. However, expert observers using the dependent double‐observer method performed significantly better than other survey scenarios. Given the use of double‐observer methods and the accumulating evidence that false positives occur in many survey methods across different taxa, this study is an important step forward in acknowledging and addressing false positives.

Knowledge of snow cover distribution and disappearance dates over a wide range of scales is imperative for understanding hydrological dynamics and for habitat management of wildlife species that rely on snow cover. Identification of snow refugia, or places with relatively late snow disappearance dates compared to surrounding areas, is especially important as climate change alters snow cover timing and duration. The purpose of this study was to increase understanding of snow refugia in complex terrain spanning the rain-snow transition zone at fine spatial and temporal scales. To accomplish this objective, we used remote cameras to provide relatively high temporal and spatial resolution measurements on snowpack conditions. We built linear models to relate snow disappearance dates (SDDs) at the monitoring sites to topoclimatic and canopy cover metrics. One model to quantify SDDs included elevation, aspect, and an interaction between canopy cover and cold-air pooling potential. High-elevation, north-facing sites in cold-air pools had the latest SDDs, but isolated lower-elevation points also exhibited relatively late potential SDDs. Importantly, canopy cover had a much stronger effect on SDDs in cold-air pools than in non-cold-air pools, indicating that best practices in forest management for snow refugia could vary across microtopography. A second model that included in situ hydroclimate observations (DJF temperature and March 1 snow depth) indicated that March 1 snow depth had little impact on SDD at the coldest winter temperatures, and that DJF temperatures had a stronger effect on SDD at lower snow depths, implying that the relative importance of snowfall and temperature could vary across hydroclimatic contexts in their impact on snow refugia. This new understanding of factors influencing snow refugia can guide forest management actions to increase snow retention and inform management of snow-dependent wildlife species in complex terrain.

Remote cameras are used to study demographics, ecological processes, and behavior of wildlife populations. Cameras have also been used to measure snow depth with physical snow stakes. However, concerns that physical instruments at camera sites may influence animal behavior limit installation of instruments to facilitate collecting such data. Given that snow depth data are inherently contained within images, potential insights that could be made using these data are lost. To facilitate camera‐based snow depth observations without additional equipment installation, we developed a method implemented in an R package called edger to superimpose virtual measurement devices onto images. The virtual snow stakes can be used to derive snow depth measurements. We validated the method for snow depth estimation using camera data from Latah County, Idaho, USA in winter 2020–2021. Mean bias error between the virtual snow stake and a physical snow stake was 5.8 cm; the mean absolute bias error was 8.8 cm. The mean Nash Sutcliffe Efficiency score comparing the fit of the 2 sets of measurements within each camera was 0.748, indicating good agreement. The edger package provides researchers with a means to take critical measurements for ecological studies without the use of physical objects that could alter animal behavior, and snow data at finer scales can complement other snow data sources that have coarser spatial and temporal resolution.