Determining spatial distributions of species and communities are key objectives of ecology and conservation. Joint species distribution models use multi-species detection-nondetection data to estimate species and community distributions. The analysis of such data is complicated by residual correlations between species, imperfect detection, and spatial autocorrelation. While 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 compare the proposed model performance to five candidate models, each addressing a subset of the three complexities. We implemented the proposed and competing models in the spOccupancy software, designed to facilitate application via an accessible, well-documented, and open-source R package. Using simulations, we found ignoring the three complexities when present leads to inferior model predictive performance. 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 candidate models. Our proposed framework, together with its implementation in spOccupancy, serves as a user-friendly tool to understand spatial variation in species distributions and biodiversity metrics while addressing common complexities in multi-species detection-nondetection data.
翻译:确定物种和群落的空间分布是生态和养护的关键目标。联合物种分布模型使用多物种检测-非检测数据来估计物种和群落分布。这些数据的分析由于物种之间的剩余关联、不完善的检测和空间自动关系而变得复杂。虽然存在适应这些复杂情况的方法,但在文献中很少有同时讨论和探讨所有三个复杂情况的例子。我们在这里开发了一个多物种空间系数占用模型,以明确说明物种的关联性、不完善的检测和空间自动化关系。拟议模型使用多物种检测-不探测-检测-检测数据来估计物种和群落分布。拟议模型使用空间要素减少方法和近邻-高比斯进程来确保数据集的计算效率,同时利用大量物种(例如, > 100 000)和空间方位(例如,100 000)和空间方位之间的剩余关系。我们将拟议模型的性能与五个候选模型进行了比较,我们用Speopprecial 软件应用了拟议和竞争模型,目的是通过无障碍、有记录和开源的R组合来便利应用应用。我们用模拟的检测模型,在使用98个标准模型时,我们利用了当前标准模型的精确度数据,我们用了一个多层模型来解释。我们用一个模型来理解的模型来解释。