Obtaining unbiased estimates of wildlife distribution and abundance is an important objective in research and management. Occupancy and N‐mixture abundance models, which correct for imperfect detection, are commonly used for this purpose. Fitting these models in a Bayesian framework has advantages but doing so can be challenging and time‐consuming for many researchers. We developed an R package, ubms, which provides an easy‐to‐use, formula‐based interface for fitting occupancy, N‐mixture abundance and other models in a Bayesian framework using Stan. The package also provides tools for visualizing parameter effects, calculating residuals, assessing goodness‐of‐fit and comparing models. We demonstrate the use of ubms by fitting an N‐mixture model to ruffed grouse Bonasa umbellus count data from drumming surveys conducted at roadside points sampled on five occasions annually during 2013–2015. To demonstrate the functionality of ubms, we used survey site as a random effect, and occasion date and per cent aspen cover at each site as covariates of detection and abundance respectively. The top‐ranked model included a positive effect of per cent aspen on grouse abundance. ubms has the potential to greatly increase the range of users who will be able to rigorously assess species distribution and abundance while correcting for imperfect detection in a Bayesian framework.

Predation is the dominant source of mortality for white‐tailed deer (Odocoileus virginianus) <6 months old throughout North America. Yet, few white‐tailed deer fawn survival studies have occurred in areas with 4 predator species or have considered concurrent densities of deer and predator species. We monitored survival and cause‐specific mortality from birth to 6 months for 100 neonatal fawns during 2013–2015 in the Upper Peninsula of Michigan, USA, while simultaneously estimating population densities of deer, American black bear (Ursus americanus), coyote (Canis latrans), bobcat (Lynx rufus), and gray wolf (Canis lupus). We estimated fawn predation risk in response to sex, birth mass, and date of birth. Six‐month fawn survival pooled among years was 36%, and fawn mortality risk was not related to birth mass, date of birth, or sex. Estimated mean annual deer and predator densities were 334 fawns/100 km2, 25.9 black bear/100 km2, 23.8 coyotes/100 km2, 3.8 bobcat/100 km2, and 2.8 wolves/100 km2. Despite lower estimated per‐individual kill rates, coyotes and black bears were the leading sources of fawn mortality because they had greater densities relative to bobcats and wolves. Our results indicate that the presence of more predator species in a system is not entirely additive in its effect on fawn survival. © The Wildlife Society, 2019