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Extinction risk assessment of a Patagonian ungulate using population dynamics models under climate change scenarios

Summary/Abstract

Climate change affects population cycles of several species, threatening biodiversity. However, there are few long-term studies on species with conservation issues and restricted distributions. Huemul is a deer endemic to the southern Andes in South America and it is considered endangered mostly due to a 50% reduction of its distribution over the last 500 years. To assess environmental variables potentially affecting huemul population viability and the impact of climate change, we developed population dynamics models. We used a 14-year survey data from Bernardo O’Higgins National Park, coastal Chilean Patagonia. We used Ricker models considering winter and spring temperatures and precipitation as variables influencing huemul population dynamics. We used the Bayesian information criterion (BIC) to select models with the greatest predictive power. The two best models (ΔBIC < 2) included winter temperature and density-dependence population growth drivers. The best model considered a lateral effect, where winter temperature influences carrying capacity and the second best a vertical effect with winter temperature influencing Rmax and carrying capacity. Population viability was evaluated using those models, projecting them over a 100-year period: (a) under current conditions and (b) under conditions estimated by Global Climate Models for 2050 and 2070. The extinction risk and quasi-extinction were estimated for this population considering two critical huemul abundance levels (15 and 30 individuals) for persistence. The population is currently in a quasi-extinction process, with extinction probabilities increasing with climate change. These results are crucial for conservation of species like huemul that have low densities and are threatened by climate change.

View Resource
July 2020
Carlos Riquelme, Sergio A. Estay, Rafael Contreras, Paulo Corti
International Journal of Biometeorology
Peer-reviewed Study
Impact Attribution → Species Impacts

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