Endangered Huemul Deer Hippocamelus Bisulcus Research Articles

Predicting the potential distribution of the Endangered huemul deer Hippocamelus bisulcus in North Patagonia

AbstractHabitat loss is one of the main threats to wildlife, particularly large mammals. Estimating the potential distribution of threatened species to guide surveys and conservation is crucial, primarily because such species tend to exist in small fragmented populations. The Endangered huemul deer Hippocamelus bisulcus is endemic to the southern Andes of Chile and Argentina. Although the species occurs in the Valdivian Ecoregion, a hotspot for biodiversity, we have no information on its occupancy and potential distribution in this region. We built and compared species distribution models for huemul using the maximum entropy approach, using 258 presence records and sets of bioclimatic and geographical variables as predictors, with the objective of assessing the potential distribution of the species in the Valdivian Ecoregion. Annual temperature range and summer precipitation were the predictive variables with the greatest influence in the best-fitting model. Approximately 12,360 km2 of the study area was identified as suitable habitat for the huemul, of which 30% is included in the national protected area systems of Chile and Argentina. The map of potential distribution produced by our model will facilitate prioritization of future survey efforts in other remote and unexplored areas in which huemul have not been recorded since the 1980s but where there is a high probability of their occurrence.

Incorporating preferential prey selection and stochastic predation into population viability analysis for rare prey species

There is increasing evidence that predation can cause the decline and extinction of small populations of prey, and that stochastic predation resulting from variation in prey selection by individual predators can have significant consequences for population persistence. Modelling approaches that ignore variation in prey selection exhibited by individual predators may inaccurately predict the effect of predation on prey populations, especially over longer time scales. We assess the impacts of variation in prey selection by building PVA models for endangered huemul deer Hippocamelus bisulcus that sequentially include and exclude observed stochasticity in predation among individual pumas Puma concolor. Our results indicated that huemul are at risk of extinction in all scenarios modelled, although the immediacy of this risk differed based on model structure and time period considered. Specifically, modelling predation as a random effect based on an interrupted Poisson process rather than as a directional and continuous change in survival rates, resulted in significantly longer estimates of time to extinction independent of the assumed intensity of predation. Our results highlight the importance of determining whether specialist predators are driving predation on rare prey, and when they are, incorporating said stochastic predation when attempting to predict persistence probabilities of rare prey using PVA models. Since results of PVA models are commonly used to develop conservation strategies, we advocate for the inclusion of stochastic predation in future PVA models where warranted to more accurately inform strategies for the conservation of rare prey and their predators.

Learning to count: adapting population monitoring for Endangered huemul deer Hippocamelus bisulcus to meet conservation objectives

AbstractConsiderable efforts have been invested in recent years to improve methods for both data collection and analyses required for population monitoring. Where historical or current estimates of population size are not adjusted for detection probabilities they may be too inaccurate to provide meaningful estimates of trends and thus monitoring methods need to be adapted. Here, we use data from the Endangered huemul deer Hippocamelus bisulcus to outline a framework to develop accurate robust estimates of detection probabilities that can be incorporated into new surveys in a cost-effective way and applied to existing survey data sets. In particular, by retroactively estimating detection probabilities for surveys of huemul, we show that current survey methods for huemul are inadequate to determine population trends reliably. Based on these results we propose a new monitoring method for the huemul and discuss the importance of estimating accuracies of historical survey data to ensure that changes in the abundance of the species reflect real population trends and are not an artefact of variation over time in the accuracy of survey data.