A multi-state occupancy model to non-invasively monitor visible signs of wildlife health with camera traps that accounts for image quality.

Camera traps are an increasingly popular tool to monitor wildlife distributions. However, traditional analytical approaches to camera trap data are difficult to apply to visible wildlife characteristics in single images, such as infection status. Several parasites produce visible signs of infection that could be sampled via camera traps. Sarcoptic mange Sarcoptes scabiei is an ideal disease to study using cameras because it results in visible hair loss and affects a broad host range. Here, we developed a multi-state occupancy model to estimate the occurrence of mange in coyotes Canis latrans across an urban gradient. This model incorporates a secondary detection function for apparent by-image infection status to provide detection-corrected estimates of mange occurrence. We analysed a multi-year camera trap dataset in Chicago, Illinois, United States, to test whether the apparent occurrence of sarcoptic mange in coyotes Canis latrans increases with urbanization or varies through time. We documented visible signs consistent with current or recovering mange infection and variables we hypothesized would improve mange detection: The proportion of the coyote in frame, image blur and whether the image was in colour. We were more likely to detect coyotes with mange in images that were less blurry, in colour, and if a greater proportion of the coyote was visible. Mangy coyote occupancy was significantly higher in urban developed areas with low housing density and higher canopy cover whereas coyote occupancy, mangy or otherwise, decreased with urbanization. By incorporating image quality into our by-image detection function, we provide a robust method to non-invasively survey visible aspects of wildlife health with camera traps. Apparently mangy coyotes were associated with low-density forested neighbourhoods, which may offer vegetated areas while containing sources of anthropogenic resources. This association may contribute to human-wildlife conflict and reinforces posited relationships between infection risk and habitat use. More generally, our model could provide detection-corrected occupancy estimates of visible characteristics that vary by image such as body condition or injuries.

Uncovering challenges to sustainability of AZA Animal Programs by evaluating the outcomes of breeding and transfer recommendations with PMCTrack.

The Association of Zoos and Aquariums (AZA) cooperatively manages Species Survival Plans® to create demographically and genetically viable populations. SSPs issue animal-specific recommendations to participating institutions via Breeding and Transfer Plans (BTPs). Fulfillment of recommendations is a crucial step in maintaining viable populations, but there have been no comprehensive evaluations of the system. Using PMCTrack, a database of over 110,000 breeding and transfer recommendations issued from over 200 SSPs from 1999 to 2013, we analyzed fulfillment of recommendations. Action-based recommendations had lower rates: Breed With recommendations were fulfilled at a rate of 20.0% before the next BTP, Send To at 56.8%, Do Not Breed at 95.7%, and Hold at 92.9%. We used generalized (logistic) mixed-effects model regressions to evaluate the impact of biological, management, and programmatic factors on fulfillment rates. For all recommendation types, there was significant variation in fulfillment among Programs and within Programs among BTPs. Out of 80 potential hypothesized fixed effects (20 factors * 4 recommendation types), only 20 effects (25%) were statistically significant. Rates were strongly influenced by temporal variables, generally increasing over time and as programs get older. Notably, the amount of time between BTPs was the only variable that significantly predicted fulfillment of all recommendation types. Many additional factors at the individual animal and institutional level may effect fulfillment rates and should be the subject of future analysis. Our results illustrate the utility of PMCTrack and indicate that improvements to the viability of SSPs will need to address multiple biological and management challenges.

Demographic stochasticity reduces the synchronizing effect of dispersal in predator-prey metapopulations.

Dispersal may affect predator-prey metapopulations by rescuing local sink populations from extinction or by synchronizing population dynamics across the metapopulation, increasing the risk of regional extinction. Dispersal is likely influenced by demographic stochasticity, however, particularly because dispersal rates are often very low in metapopulations. Yet the effects of demographic stochasticity on predator-prey metapopulations are not well known. To that end, I constructed three models of a two-patch predator-prey system. The models constitute a hierarchy of complexity, allowing direct comparisons. Two models included demographic stochasticity (pure jump process [PJP] and stochastic differential equations [SDE]), and the third was deterministic (ordinary differential equations [ODE]). One stochastic model (PJP) treated population sizes as discrete, while the other (SDE) allowed population sizes to change continuously. Both stochastic models only produced synchronized predator-prey dynamics when dispersal was high for both trophic levels. Frequent dispersal by only predators or prey in the PJP and SDE spatially decoupled the trophic interaction, reducing synchrony of the non-dispersive species. Conversely, the ODE generated synchronized predator-prey dynamics across all dispersal rates, except when initial conditions produced anti-phase transients. These results indicate that demographic stochasticity strongly reduces the synchronizing effect of dispersal, which is ironic because demographic stochasticity is often invoked post hoc as a driver of extinctions in synchronized metapopulations.

Evaluating probabilistic ecological forecasts.

Probabilistic near-term forecasting facilitates evaluation of model predictions against observations and is of pressing need in ecology to inform environmental decision-making and effect societal change. Despite this imperative, many ecologists are unfamiliar with the widely used tools for evaluating probabilistic forecasts developed in other fields. We address this gap by reviewing the literature on probabilistic forecast evaluation from diverse fields including climatology, economics, and epidemiology. We present established practices for selecting evaluation data (end-sample hold out), graphical forecast evaluation (times-series plots with uncertainty, probability integral transform plots), quantitative evaluation using scoring rules (log, quadratic, spherical, and ranked probability scores), and comparing scores across models (skill score, Diebold-Mariano test). We cover common approaches, highlight mathematical concepts to follow, and note decision points to allow application of general principles to specific forecasting endeavors. We illustrate these approaches with an application to a long-term rodent population time series currently used for ecological forecasting and discuss how ecology can continue to learn from and drive the cross-disciplinary field of forecasting science.