Estimating large carnivore populations at global scale based on spatial predictions of density and distribution – Application to the jaguar (Panthera onca)

Broad scale population estimates of declining species are desired for conservation efforts. However, for many secretive species including large carnivores, such estimates are often difficult. Based on published density estimates obtained through camera trapping, presence/absence data, and globally available predictive variables derived from satellite imagery, we modelled density and occurrence of a large carnivore, the jaguar, across the species’ entire range. We then combined these models in a hierarchical framework to estimate the total population. Our models indicate that potential jaguar density is best predicted by measures of primary productivity, with the highest densities in the most productive tropical habitats and a clear declining gradient with distance from the equator. Jaguar distribution, in contrast, is determined by the combined effects of human impacts and environmental factors: probability of jaguar occurrence increased with forest cover, mean temperature, and annual precipitation and declined with increases in human foot print index and human density. Probability of occurrence was also significantly higher for protected areas than outside of them. We estimated the world’s jaguar population at 173,000 (95% CI: 138,000–208,000) individuals, mostly concentrated in the Amazon Basin; elsewhere, populations tend to be small and fragmented. The high number of jaguars results from the large total area still occupied (almost 9 million km2) and low human densities (2) coinciding with high primary productivity in the core area of jaguar range. Our results show the importance of protected areas for jaguar persistence. We conclude that combining modelling of density and distribution can reveal ecological patterns and processes at global scales, can provide robust estimates for use in species assessments, and can guide broad-scale conservation actions.保护工作亟需针对衰退物种种群的大范围估算数据。然而，对于包括大型食肉动物在内的诸多隐秘物种而言，这类估算往往难度颇高。本研究基于通过红外相机陷阱（camera trapping）获取的已发表种群密度估算结果、存在/缺失数据，以及源自卫星影像（satellite imagery）的全球可获取预测变量，对该大型食肉动物——美洲豹（jaguar）在其整个分布范围内的种群密度与发生概率进行了建模。随后我们在层级框架内整合这些模型，以估算其总种群数量。我们的模型显示，美洲豹的潜在种群密度最佳预测因子为初级生产力（primary productivity）相关指标：在生产力最高的热带生境中密度最高，且随距赤道距离增加呈现显著的下降梯度。与之相对，美洲豹的分布由人类活动影响与环境因子的共同作用决定：其发生概率随森林覆盖率、平均气温与年降水量的升高而增加，随人类足迹指数（human footprint index）及人口密度的提升而降低。保护区内的物种发生概率亦显著高于非保护区。我们估算全球美洲豹种群数量约为173000只（95%置信区间：138000~208000只），种群主要集中于亚马孙盆地（Amazon Basin）；其余区域的种群往往规模较小且呈破碎化分布。美洲豹种群规模庞大的原因在于其现存总分布面积广阔（近900万平方千米），且在其分布核心区，低人口密度与高初级生产力相契合。本研究结果凸显了保护区对于美洲豹存续的重要性。综上，整合种群密度与分布建模，能够揭示全球尺度下的生态格局与过程，可为物种评估提供可靠的估算结果，并可为大范围保护行动提供指导。