Summary Species occurrence is influenced by environmental conditions and the presence of other species. Current approaches for multispecies occupancy modelling are practically limited to two interacting species and often require the assumption of asymmetric interactions. We propose a multispecies occupancy model that can accommodate two or more interacting species. We generalize the single‐species occupancy model to two or more interacting species by assuming the latent occupancy state is a multivariate Bernoulli random variable. We propose modelling the probability of each potential latent occupancy state with both a multinomial logit and a multinomial probit model and present details of a Gibbs sampler for the latter. As an example, we model co‐occurrence probabilities of bobcat (Lynx rufus), coyote (Canis latrans), grey fox (Urocyon cinereoargenteus) and red fox (Vulpes vulpes) as a function of human disturbance variables throughout 6 Mid‐Atlantic states in the eastern United States. We found evidence for pairwise interactions among most species, and the probability of some pairs of species occupying the same site varied along environmental gradients; for example, occupancy probabilities of coyote and grey fox were independent at sites with little human disturbance, but these two species were more likely to occur together at sites with high human disturbance. Ecological communities are composed of multiple interacting species. Our proposed method improves our ability to draw inference from such communities by permitting modelling of detection/non‐detection data from an arbitrary number of species, without assuming asymmetric interactions. Additionally, our proposed method permits modelling the probability two or more species occur together as a function of environmental variables. These advancements represent an important improvement in our ability to draw community‐level inference from multiple interacting species that are subject to imperfect detection.

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Mule deer and black‐tailed deer Odocoileus hemionus have exhibited marked population fluctuations throughout their range over the past century. The relative contributions of predation, forage availability and weather to observed population changes remain unclear and controversial. We reviewed 48 studies on Odocoileus hemionus survival and predation from the past 30 years and quantified age‐specific vital rates, population growth rates (λ) and causes of mortality. We also evaluated the effect of environmental variables on variation in vital rates and the contribution of age‐specific survival to population growth. Age‐specific survival (ϕ) was the most frequently studied population parameter. Odocoileus hemionus have lower and more variable fawn survival than other ungulate species (ϕsummer = 0.44, CV = 0.42; ϕannual = 0.29, CV = 0.67). Adult female survival conversely appeared to be high and stable throughout the geographical range of the species (ϕannual = 0.84, CV = 0.06). Observed low fawn survival appears to be compensated for by high fecundity rates. Predation was the primary proximate cause of mortality for all age classes, and was an important source of summer fawn mortality and of mortality in multi‐prey, multi‐predator systems. However, predator removal studies suggest that predation is compensatory, particularly at high deer densities, and that nutrition and weather shape population dynamics. We propose three models to explain local population dynamics of Odocoileus hemionus: (i) populations are limited by forage availability and weather; (ii) adult females are limited by forage availability, fawns are limited by forage availability and predation, and population growth is constrained by fecundity and fawn predation; and (iii) large changes in the abundance of predators or alternative prey change predation risk and destabilize population dynamics. Future research should be focused on: the effects of age‐specific survival on population growth; possible interactions between predation, forage availability and weather; and the importance of multiple predator and prey species in shaping the population dynamics of Odocoileus hemionus.

Camera trapping has become an increasingly widespread tool for wildlife ecologists, with large numbers of studies relying on photo capture rates or presence/absence information. It is increasingly clear that camera placement can directly impact this kind of data, yet these biases are poorly understood. We used a paired camera design to investigate the effect of small-scale habitat features on species richness estimates, and capture rate and detection probability of several mammal species in the Shenandoah Valley of Virginia, USA. Cameras were deployed at either log features or on game trails with a paired camera at a nearby random location. Overall capture rates were significantly higher at trail and log cameras compared to their paired random cameras, and some species showed capture rates as much as 9.7 times greater at feature-based cameras. We recorded more species at both log (17) and trail features (15) than at their paired control cameras (13 and 12 species, respectively), yet richness estimates were indistinguishable after 659 and 385 camera nights of survey effort, respectively. We detected significant increases (ranging from 11–33%) in detection probability for five species resulting from the presence of game trails. For six species detection probability was also influenced by the presence of a log feature. This bias was most pronounced for the three rodents investigated, where in all cases detection probability was substantially higher (24.9–38.2%) at log cameras. Our results indicate that small-scale factors, including the presence of game trails and other features, can have significant impacts on species detection when camera traps are employed. Significant biases may result if the presence and quality of these features are not documented and either incorporated into analytical procedures, or controlled for in study design.