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.

中文翻译：

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使用气候变化情景下的种群动态模型对巴塔哥尼亚有蹄类动物进行灭绝风险评估

气候变化影响多个物种的种群周期，威胁生物多样性。然而，关于存在保护问题和分布受限的物种的长期研究很少。Huemul 是南美洲安第斯山脉南部特有的一种鹿，它被认为是濒临灭绝的主要是因为它的分布在过去 500 年里减少了 50%。为了评估可能影响 Humul 种群生存能力和气候变化影响的环境变量，我们开发了种群动态模型。我们使用了来自智利巴塔哥尼亚沿海贝尔纳多奥希金斯国家公园的 14 年调查数据。我们使用 Ricker 模型，将冬季和春季的温度和降水作为影响 Humul 种群动态的变量。我们使用贝叶斯信息准则 (BIC) 来选择具有最大预测能力的模型。两个最佳模型 (ΔBIC < 2) 包括冬季温度和密度依赖性人口增长驱动因素。最好的模型考虑了横向效应，其中冬季温度影响承载能力，其次是垂直效应，冬季温度影响 Rmax 和承载能力。使用这些模型评估了人口生存能力，并在 100 年期间对它们进行了预测：(a) 在当前条件下和 (b) 在全球气候模型估计的 2050 年和 2070 年条件下。为此估计了灭绝风险和准灭绝种群考虑两个关键的 Humul 丰度水平（15 和 30 个人）的持久性。人口目前处于准灭绝过程中，随着气候变化，灭绝概率增加。