The effects of climate change on biodiversity have emerged as a dominant theme in conservation biology, possibly eclipsing concern over habitat loss in recent years. The extent to which this shifting focus has tracked the most eminent threats to biodiversity is not well documented. We investigated the mechanisms driving shifts in the southern range boundary of a forest and snow cover specialist, the snowshoe hare, to explore how its range boundary has responded to shifting rates of climate and land cover change over time. We found that although both forest and snow cover contributed to the historical range boundary, the current duration of snow cover best explains the most recent northward shift, while forest cover has declined in relative importance. In this respect, the southern range boundary of snowshoe hares has mirrored the focus of conservation research; first habitat loss and fragmentation was the stronger environmental constraint, but climate change has now become the main threat. Projections of future range shifts show that climate change, and associated snow cover loss, will continue to be the major driver of this species' range loss into the future.

Species' population dynamics are tied to neonatal survival. White-tailed deer (Odocoileus virginianus) fawn survival varies according to spatially explicit patterns of natural (e.g., starvation, predation) and human-caused mortalities (e.g., vehicle collision). Our objective was to compare fawn survival under different, though representative, ecological conditions in Wisconsin USA. We identified 2 ecologically distinct study areas: the northern forest (NF) and the eastern farmland (EF). Beginning in May (2011-2013), we fitted fawns in both areas with radio-collars and tracked their survival daily until 31 August of the capture year. We obtained daily weather data for each study area to model weather effects on survival. We captured 89 (NF), and 139 (EF) fawns, and observed 42 (NF) and 43 (EF) mortalities. Predation mortality was higher than other mortality causes in the NF, and mortality due to natural causes other than predation was higher for fawns in the EF. Female fawns had higher survival than males, and fawns in 2011 in the NF had lower survival than fawns in 2012 or 2013. During the first 40 days of life, occurrence of precipitation associated with a threefold increase in daily hazard of death in the EF, but effects of daily low temperatures were trivial. In the NF, precipitation had little effect, but a decrease in daily low temperature by 0.568C increased the daily hazard of mortality by 5%. Because risks facing fawns vary with ecological context, understanding specific factors that affect fawn survival is important for predicting local outcomes of white-tailed deer management.

Fates of individuals outfitted with radiotransmitters commonly are used for estimating survival rates in populations of large animals that are hunted. Despite precautions, this practice may be subject to complex biases associated with hunter reaction to presence of radiotransmitters. To assess this potential bias we conducted an experiment using artificial deer (i.e., decoys) to measure hunters' abilities to see deer and determine if deer seen were wearing radiocollars. We used logistic regression to quantify probabilities that seeing deer and subsequently seeing radiocollars might be influenced by distance, percent visual obstruction, body orientation, hunter experience, and antler characteristics of deer. Additionally, we evaluated how experience and antler characteristics of deer might influence a hunter's decision to harvest a radiocollared deer. We found that 25.8% of the potentially observable collared deer (n = 663) were subsequently observed by hunters. Odds of observing deer and radiocollars increased 95% and 230%, respectively, for each additional log(yr) of hunting experience. Willingness to harvest radiocollared deer increased 89% for each additional log(yr) of hunting experience and 144% for large‐antlered deer relative to antlerless deer. When hunting is an important source of mortality, analysts need to understand how potential biases associated with observing deer are associated with hunters' reactions to and subsequent decisions to harvest radiocollared animals. Our study suggested that presence of radiocollars may influence a deer's potential risk of being harvested and in turn bias telemetry‐based estimates of survival, given that hunting mortality is the largest component of total mortality in hunted deer populations. Collar‐based telemetry is used nearly universally by wildlife managers and researchers throughout North America and elsewhere to estimate and monitor the survival of big game populations that are managed through hunting. Our findings demonstrate that these estimates are likely subject to complex and systematic biases that managers should consider when evaluating future population‐level effects of managed hunting. © 2011 The Wildlife Society

Measurement or observation error is common in ecological data: as citizen scientists and automated algorithms play larger roles processing growing volumes of data to address problems at large scales, concerns about data quality and strategies for improving it have received greater focus. However, practical guidance pertaining to fundamental data quality questions for data users or managers—how accurate do data need to be and what is the best or most efficient way to improve it?—remains limited. We present a generalizable framework for evaluating data quality and identifying remediation practices, and demonstrate the framework using trail camera images classified using crowdsourcing to determine acceptable rates of misclassification and identify optimal remediation strategies for analysis using occupancy models. We used expert validation to estimate baseline classification accuracy and simulation to determine the sensitivity of two occupancy estimators (standard and false‐positive extensions) to different empirical misclassification rates. We used regression techniques to identify important predictors of misclassification and prioritize remediation strategies. More than 93% of images were accurately classified, but simulation results suggested that most species were not identified accurately enough to permit distribution estimation at our predefined threshold for accuracy (<5% absolute bias). A model developed to screen incorrect classifications predicted misclassified images with >97% accuracy: enough to meet our accuracy threshold. Occupancy models that accounted for false‐positive error provided even more accurate inference even at high rates of misclassification (30%). As simulation suggested occupancy models were less sensitive to additional false‐negative error, screening models or fitting occupancy models accounting for false‐positive error emerged as efficient data remediation solutions. Combining simulation‐based sensitivity analysis with empirical estimation of baseline error and its variability allows users and managers of potentially error‐prone data to identify and fix problematic data more efficiently. It may be particularly helpful for “big data” efforts dependent upon citizen scientists or automated classification algorithms with many downstream users, but given the ubiquity of observation or measurement error, even conventional studies may benefit from focusing more attention upon data quality.

Developing conservation strategies for threatened species increasingly requires understanding vulnerabilities to climate change, in terms of both demographic sensitivities to climatic and other environmental factors, and exposure to variability in those factors over time and space. We conducted a range-wide, spatially explicit climate change vulnerability assessment for Eastern Massasauga (Sistrurus catenatus), a declining endemic species in a region showing strong environmental change. Using active season and winter adult survival estimates derived from 17 data sets throughout the species' range, we identified demographic sensitivities to winter drought, maximum precipitation during the summer, and the proportion of the surrounding landscape dominated by agricultural and urban land cover. Each of these factors was negatively associated with active season adult survival rates in binomial generalized linear models. We then used these relationships to back-cast adult survival with dynamic climate variables from 1950 to 2008 using spatially explicit demographic models. Demographic models for 189 population locations predicted known extant and extirpated populations well (AUC = 0.75), and models based on climate and land cover variables were superior to models incorporating either of those effects independently. These results suggest that increasing frequencies and severities of extreme events, including drought and flooding, have been important drivers of the long-term spatiotemporal variation in a demographic rate. We provide evidence that this variation reflects nonadaptive sensitivity to climatic stressors, which are contributing to long-term demographic decline and range contraction for a species of high-conservation concern. Range-wide demographic modeling facilitated an understanding of spatial shifts in climatic suitability and exposure, allowing the identification of important climate refugia for a dispersal-limited species. Climate change vulnerability assessment provides a framework for linking demographic and distributional dynamics to environmental change, and can thereby provide unique information for conservation planning and management.