Joint species distribution models with imperfect detection for high‐dimensional spatial data
Joint species distribution models with imperfect detection for high‐dimensional spatial data
复制标题
高维空间数据不完善检测的联合物种分布模型
DOI：
10.1002/ecy.4137
复制
发表时间：
2023
期刊：
影响因子：
4.8
通讯作者：
Banerjee, Sudipto
中科院分区：
文献类型：
--
作者：
Doser, Jeffrey W.;Finley, Andrew O.;Banerjee, Sudipto
确定物种和群落的空间分布是生态和保护工作的一项关键任务。联合物种分布模型是群落生态学中的基本工具，它使用多物种检测-未检测数据来估计物种分布和生物多样性指标。由于物种之间的残余相关性、不完善的检测和空间自相关，此类数据的分析变得复杂。虽然存在许多方法来适应这些复杂性，但文献中很少有同时解决和探索所有三种复杂性的例子。在这里，我们开发了一个空间因素多物种占用模型，以明确解释物种相关性、不完善检测和空间自相关。所提出的模型使用空间因子降维方法和最近邻高斯过程来确保具有大量物种（例如，> 100）和空间位置（例如，100,000）的数据集的计算效率。我们将所提出的模型性能与五个替代模型进行了比较，每个模型都解决了三种复杂性的子集。我们在 spOccupancy 软件中实现了提议的模型和替代模型，旨在通过可访问的、文档齐全的开源 R 包来促进应用程序。通过模拟，我们发现忽略存在的三种复杂性会导致模型预测性能较差，而未能考虑一种或多种复杂性的影响将取决于给定研究的目标。通过对美国大陆 98 种鸟类的案例研究，空间因素多物种占用模型在替代模型中具有最高的预测性能。我们提出的框架及其实现 inspOccupancy 可以作为一种用户友好的工具来了解物种分布和生物多样性的空间变化，同时解决多物种检测-非检测数据中常见的复杂性。
Determining the spatial distributions of species and communities is a key task in ecology and conservation efforts. Joint species distribution models are a fundamental tool in community ecology that use multi‐species detection–nondetection data to estimate species distributions and biodiversity metrics. The analysis of such data is complicated by residual correlations between species, imperfect detection, and spatial autocorrelation. While many methods exist to accommodate each of these complexities, there are few examples in the literature that address and explore all three complexities simultaneously. Here we developed a spatial factor multi‐species occupancy model to explicitly account for species correlations, imperfect detection, and spatial autocorrelation. The proposed model uses a spatial factor dimension reduction approach and Nearest Neighbor Gaussian Processes to ensure computational efficiency for data sets with both a large number of species (e.g., >100) and spatial locations (e.g., 100,000). We compared the proposed model performance to five alternative models, each addressing a subset of the three complexities. We implemented the proposed and alternative models in thespOccupancysoftware, designed to facilitate application via an accessible, well documented, and open‐source R package. Using simulations, we found that ignoring the three complexities when present leads to inferior model predictive performance, and the impacts of failing to account for one or more complexities will depend on the objectives of a given study. Using a case study on 98 bird species across the continental US, the spatial factor multi‐species occupancy model had the highest predictive performance among the alternative models. Our proposed framework, together with its implementation inspOccupancy, serves as a user‐friendly tool to understand spatial variation in species distributions and biodiversity while addressing common complexities in multi‐species detection–nondetection data.