Individual-based modeling as a decision tool for the conservation of the endangered huemul deer (Hippocamelus bisulcus) in southern Chile

One of the greatest challenges for conservation biology is providing solutions for endangered species in modern landscapes, usually with deficient biological information on how species respond to landscape disturbances. These limitations are severe in developing countries where the lack of resources restricts the potential for basic ecological research. One way in which this limitation has been mitigated is with the use of individual-based spatially explicit population models (SEPMs). We developed a SEPM for the endangered huemul deer (Hippocamelus bisulcus) of southern Chile. The goal was to project the population trajectories under three different development scenarios (present conditions, increased livestock density and hydroelectric dams) in southern Chile, identifying key demographic variables associated to landscape features. The model simulated weekly movements, age and general status of all individuals in the population and the landscape in which they lived during a 40-year period. Age-dependent and landscape-related mortality probabilities were applied. Four population sizes and three spatial aggregation patterns were used as initial conditions. Although the model was very sensitive to age-dependent mortality rate, individual landscape perception scale and initial population size and location, the general trend was that of a positive population growth. Livestock and dam scenarios had minimal impacts on population dynamics. Most simulations starting with 100 individuals resulted in extinctions and disaggregated initial location of individuals produced slow rates of population growth, suggesting the existence of a population viability threshold. Our results suggest that the huemul population in the Aysén region has the potential to recover only if limiting factors are kept under control and they highlight the urgency of conducting studies aimed at estimating total population size and distribution together with survival data to improve the conservation plans for this endangered species.