Aim In the face of global change, understanding causes of range limits are one of the most pressing needs in biogeography and ecology. A prevailing hypothesis is that abiotic stress forms cold (upper latitude/altitude) limits, whereas biotic interactions create warm (lower) limits. A new framework – Interactive Range‐Limit Theory (iRLT) – asserts that positive biotic factors such as food availability can ameliorate abiotic stress along cold edges, whereas abiotic stress can have a positive effect and mediate biotic interactions (e.g., competition) along warm limits. Location Northeastern United States Taxon Carnivora Methods We evaluated two hypotheses of iRLT using occupancy and structural equation modeling (SEM) frameworks based on data collected over a 6‐year period (2014–2019) of six carnivore species across a broad latitudinal (42.8–45.3°N) and altitudinal (3–1451 m) gradient. Results We found that snow directly limits populations, but prey or habitat availability can influence range dynamics along cold edges. For example, bobcats (Lynx rufus) and coyotes (Canis latrans) were limited by deep snow and long winters, but the availability of prey had a strong positive effect. Conversely, snow had a strong positive effect on the warm limits of Canada lynx (Lynx canadensis), countering the negative effect of competition with the phylogenetically similar bobcat and with coyotes, highlighting how climate mediates competition between species. Main conclusions We used an integrated dataset that included competitors and prey species collected at the same spatial and temporal scale. As such, this design, along with a causal modeling framework (SEM), allowed us to evaluate community‐wide hypotheses at macroecological scales and identify coarse‐scale drivers of species' range limits. Our study supports iRLT and underscores the need to consider direct and indirect mechanisms for studying range dynamics and species' responses to global change.

High-elevation forests that contain mature, closed canopy stands are considered important habitat for American martens (Martes americana (Turton, 1806)) in the northeastern United States. To investigate this hypothesis, we monitored 15 radio-collared martens over a 2-year period and measured spatial use, as well as second- and third-order resource selection, from 33 seasonal home ranges and 889 telemetry locations. The population was composed primarily of adults that had small home-range size with average seasonal fidelity. During leaf-off seasons, martens selected against regenerating forest at both scales and selected for mixedwood and softwood forests and areas with rugged terrain within home ranges. Second-order selection was less pronounced during leaf-on seasons, yet martens exhibited greater selection for hardwood forest and areas with rugged terrain within home ranges. Home-range size was correlated positively with the amount of regenerating forest and body-condition index scores were lower during winter, indicating that these spatial and temporal attributes were influential. Although martens utilized low-elevation forest with extensive timber harvesting, contiguous, mature, and rugged high-elevation forest was used preferentially during winter. Land managers should minimize disturbance of montane ecosystems to ensure population viability for martens and other boreal forest species along distributional edges.

With the accelerating pace of global change, it is imperative that we obtain rapid inventories of the status and distribution of wildlife for ecological inferences and conservation planning. To address this challenge, we launched the SNAPSHOT USA project, a collaborative survey of terrestrial wildlife populations using camera traps across the United States. For our first annual survey, we compiled data across all 50 states during a 14‐week period (17 August–24 November of 2019). We sampled wildlife at 1,509 camera trap sites from 110 camera trap arrays covering 12 different ecoregions across four development zones. This effort resulted in 166,036 unique detections of 83 species of mammals and 17 species of birds. All images were processed through the Smithsonian’s eMammal camera trap data repository and included an expert review phase to ensure taxonomic accuracy of data, resulting in each picture being reviewed at least twice. The results represent a timely and standardized camera trap survey of the United States. All of the 2019 survey data are made available herein. We are currently repeating surveys in fall 2020, opening up the opportunity to other institutions and cooperators to expand coverage of all the urban–wild gradients and ecophysiographic regions of the country. Future data will be available as the database is updated at eMammal.si.edu/snapshot‐usa, as will future data paper submissions. These data will be useful for local and macroecological research including the examination of community assembly, effects of environmental and anthropogenic landscape variables, effects of fragmentation and extinction debt dynamics, as well as species‐specific population dynamics and conservation action plans. There are no copyright restrictions; please cite this paper when using the data for publication.

The use of remote cameras is widespread in wildlife ecology, yet few examples exist of their utility for collecting environmental data. We used a novel camera trap method to evaluate the accuracy of gridded snow data in a mountainous region of the northeastern US. We were specifically interested in assessing (1) how snow depth observations from remote cameras compare with gridded climate data, (2) the sources of error associated with the gridded data and (3) the influence of spatial sampling on bias. We compared daily observations recorded by remote cameras with Snow Data Assimilation System (SNODAS) gridded predictions using data from three winters (2014)(2015)(2016). Snow depth observations were correlated with SNODAS predictions for sites (R 2 = 0.20) and regions (R 2 = 0.16), yet we detected factors associated with SNODAS bias at both scales. Specifically, SNODAS underpredicted depths at high elevations, at sites with higher solar radiation, and within conifer-dominated forest. Depths were most underpredicted at highest elevations, up to 44 and 26 cm on average at the site and region scales, respectively. Bias was greatest when predictions were lowest, occasionally predicting snow absence when depths were >100 cm at camera sites. We also detected breakdowns in accuracy when certain environmental conditions varied within the 1 km 2 SNODAS grid cells. For example, underprediction was greatest when the solar radiation values of camera stations increased relative to the mean of the SNODAS grid cells. This relationship was most prominent in mountainous regions, suggesting that factors which influence solar radiation (e.g. topographic complexity) contribute to SNODAS inaccuracy. We caution using gridded snow data for ecological studies when bias is unknown. We suggest increased sampling to adjust for errors associated with gridded data products that arise from factors, such as forest cover and topographic variability. Increasing resolution and accuracy of climate data will improve predictions of species' responses to climate change.

Remote telemetry data loggers are commonly used for monitoring wildlife species. Although remote telemetry data loggers provide reliable microhabitat use data, few studies have used them to evaluate landscape‐scale, temporal, and spatial habitat use. We installed 3 data loggers along a mountain ridgeline that was being developed for a commercial wind farm in northern New Hampshire, USA, to monitor use of a high‐elevation forest by American martens (Martes americana). We tested 1) the efficacy of data loggers to record presence–absence and index space use in a 6.75‐km2 area, validating marten locations using radiotelemetry and camera traps; and 2) whether there were diel and seasonal biases of data logger detections. As a case study, we evaluated temporal and spatial habitat‐use hypotheses with respect to variations in vegetation cover (leaf‐on or leaf‐off) and astronomical seasons using data from 11 martens monitored for nearly 2 years (6 Feb 2011–23 Dec 2012). Data loggers recorded presence–absence of radiocollared martens across 80% (240 of 299) of the trials within the study area with few false positives (4%; 19 of 494 trials). Detection probability and spatial use were most influenced by elevation, distance to data loggers, and line‐of‐sight view. We did not detect a seasonal bias but data loggers recorded fewer nocturnal detections. We recorded 118,120 detections of radiocollared martens, most of which occurred during leaf‐off seasons (87%; 102,931). Spatial use declined significantly (β = −0.54, P = 0.04) during the first leaf‐on season, corresponding with the construction phase of the wind farm project, and remained lower throughout the study. Diel use was explained by an astronomical calendar with greater nocturnal use during autumn and winter. Our results show that remote telemetry data loggers provided accurate spatiotemporal data for landscape‐scale habitat monitoring, overcoming many of the problems with telemetry data—such as limited sampling period and bias—that can compromise measurements of space use. We suggest incorporating remote telemetry data loggers for space use and disturbance studies of wildlife species. © 2016 The Wildlife Society.