综合社区占用模型：使用多个数据源评估发生和biodiversity动态的框架
Integrated community occupancy models: A framework to assess occurrence and biodiversity dynamics using multiple data sources
摘要：
由于全球变化，野生动物种群和群落的发生和分布正在发生变化。为了评估这些变化是否对生物多样性进程产生负面影响，监测环境变量对整个社区的状况、趋势和影响至关重要。然而，同时对多个物种的动态进行建模可能需要大量不同的数据，并且很少有建模方法可以同时提供物种和群落水平的推断。我们提出了一个“综合社区占用模型”（ESTA），统一的原则，数据集成和分层社区建模在一个单一的框架，提供推论的物种特异性和社区发生动态使用多个数据源。我们使用模拟来比较的层次结构的社区占用模型和单物种综合分布模型。然后，我们使用三个独立的数据源，应用我们的模型评估2010-2018年美国东北部白山国家森林中落叶采集鸟类的发生和生物多样性动态。模拟结果显示，与单一数据源模型相比，将多个数据源集成到数据库中可以提高物种和群落水平推断的精度和准确性，尽管集成的好处取决于数据源质量（例如，复制数量）。与单物种模型相比，该模型产生了更精确的物种水平估计。在我们的案例研究中，与单物种模型和仅使用三个数据源的子集的模型相比，该模型具有最高的样本外预测性能。生物多样性指数提供了一个有吸引力的方法来估计物种和生物多样性动态，这是额外的宝贵通知管理目标的个别物种和更广泛的社区。
英文摘要：
The occurrence and distributions of wildlife populations and communities are shifting as a result of global changes. To evaluate whether these shifts are negatively impacting biodiversity processes, it is critical to monitor the status, trends, and effects of environmental variables on entire communities. However, modeling the dynamics of multiple species simultaneously can require large amounts of diverse data, and few modeling approaches exist to simultaneously provide species and community level inferences. We present an "integrated community occupancy model" (ICOM) that unites principles of data integration and hierarchical community modeling in a single framework to provide inferences on species-specific and community occurrence dynamics using multiple data sources. We use simulations to compare the ICOM to previously developed hierarchical community occupancy models and single species integrated distribution models. We then apply our model to assess the occurrence and biodiversity dynamics of foliage-gleaning birds in the White Mountain National Forest in the northeastern USA from 2010-2018 using three independent data sources. Simulations reveal that integrating multiple data sources in the ICOM increased precision and accuracy of species and community level inferences compared to single data source models, although benefits of integration were dependent on data source quality (e.g., amount of replication). Compared to single species models, the ICOM yielded more precise species-level estimates. Within our case study, the ICOM had the highest out-of-sample predictive performance compared to single species models and models that used only a subset of the three data sources. The ICOM offers an attractive approach to estimate species and biodiversity dynamics, which is additionally valuable to inform management objectives of both individual species and their broader communities.
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