For species of conservation concern and human–wildlife conflict, it is imperative that spatial population data be available to design adaptive-management strategies and be prepared to meet challenges such as land use and climate change, disease outbreaks, and invasive species spread. This can be difficult, perhaps impossible, if spatially explicit wildlife data are not available. Low-resolution areal counts, however, are common in wildlife monitoring, that is, the number of animals reported for a region, usually corresponding to administrative subdivisions, for example, region, province, county, departments, or cantons. Bayesian areal disaggregation regression is a solution to exploit areal counts and provide conservation biologists with high-resolution species distribution predictive models. This method originated in epidemiology but lacks experimentation in ecology. It provides a plethora of applications to change the way we collect and analyze data for wildlife populations. Based on high-resolution environmental rasters, the disaggregation method disaggregates the number of individuals observed in a region and distributes them at the pixel level (e.g., 5 × 5 km or finer resolution), thereby converting low-resolution data into a high-resolution distribution and indices of relative density. In our demonstrative study, we disaggregated areal count data from hunting bag returns to disentangle the changing distribution and population dynamics of three deer species (red, sika, and fallow) in Ireland from 2000 to 2018. We show an application of the Bayesian areal disaggregation regression method and document marked increases in relative population density and extensive range expansion for each of the three deer species across Ireland. We challenged our disaggregated model predictions by correlating them with independent deer surveys carried out in field sites and alternative deer distribution models built using presence-only and presence–absence data. Finding a high correlation with both independent data sets, we highlighted the ability of Bayesian areal disaggregation regression to accurately capture fine-scale spatial patterns of animal distribution. This study uncovers new scenarios for wildlife managers and conservation biologists to reliably use regional count data disregarded so far in species distribution modeling. Thus, it represents a step forward in our ability to monitor wildlife population and meet challenges in our changing world.

中文翻译：

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贝叶斯面积分解回归利用低分辨率数据预测野生动物分布和相对密度

对于受保护问题和人类与野生动物冲突的物种，必须提供空间种群数据来设计适应性管理策略，并准备好应对土地利用和气候变化、疾病​​爆发和入侵物种传播等挑战。如果没有空间明确的野生动物数据，这可能很困难，甚至不可能。然而，低分辨率面积计数在野生动物监测中很常见，即报告一个地区的动物数量，通常对应于行政分区，例如地区、省、县、部门或州。贝叶斯面积分解回归是一种利用面积计数并为保护生物学家提供高分辨率物种分布预测模型的解决方案。这种方法起源于流行病学，但缺乏生态学的实验。它提供了大量的应用程序来改变我们收集和分析野生动物种群数据的方式。分解方法以高分辨率环境栅格为基础，将某个区域观测到的个体数量进行分解，并按像素级别（如5×5 km或更高分辨率）进行分布，从而将低分辨率数据转换为高分辨率数据。分布和相对密度指数。在我们的示范性研究中，我们对狩猎袋返回的面积计数数据进行了分类，以理清 2000 年至 2018 年爱尔兰三种鹿（红鹿、梅花鹿和休耕鹿）的分布变化和种群动态。我们展示了贝叶斯面积分类的应用回归方法和文件显着增加了爱尔兰三种鹿品种的相对种群密度和广泛的活动范围。我们通过将分类模型预测与在现场进行的独立鹿调查以及使用仅存在数据和存在-不存在数据构建的替代鹿分布模型相关联来挑战我们的分类模型预测。通过发现与两个独立数据集的高度相关性，我们强调了贝叶斯区域分解回归准确捕获动物分布的精细尺度空间模式的能力。这项研究为野生动物管理者和保护生物学家揭示了新的场景，以可靠地使用迄今为止在物种分布模型中被忽视的区域计数数据。因此，它代表了我们监测野生动物种群和应对不断变化的世界挑战的能力向前迈出了一步。