Occupancy models are frequently used by ecologists to quantify spatial variation in species distributions while accounting for observational biases in the collection of detection-nondetection data. However, the common assumption that a single set of regression coefficients can adequately explain species-environment relationships is often unrealistic, especially across large spatial domains. Here we develop single-species (i.e., univariate) and multi-species (i.e., multivariate) spatially-varying coefficient (SVC) occupancy models to account for spatially-varying species-environment relationships. We employ Nearest Neighbor Gaussian Processes and Polya-Gamma data augmentation in a hierarchical Bayesian framework to yield computationally efficient Gibbs samplers, which we implement in the spOccupancy R package. For multi-species models, we use spatial factor dimension reduction to efficiently model datasets with large numbers of species (e.g., > 10). The hierarchical Bayesian framework readily enables generation of posterior predictive maps of the SVCs, with fully propagated uncertainty. We apply our SVC models to quantify spatial variability in the relationships between maximum breeding season temperature and occurrence probability of 21 grassland bird species across the U.S. Jointly modeling species generally outperformed single-species models, which all revealed substantial spatial variability in species occurrence relationships with maximum temperatures. Our models are particularly relevant for quantifying species-environment relationships using detection-nondetection data from large-scale monitoring programs, which are becoming increasingly prevalent for answering macroscale ecological questions regarding wildlife responses to global change.

翻译：占有模型常被生态学家用于量化物种分布的空间变异，同时校正检测-未检测数据收集中存在的观测偏差。然而，假设单一组回归系数能充分解释物种-环境关系的常见假设往往不切实际，尤其是在大空间尺度上。本文开发了单物种（即单变量）和多物种（即多变量）空间变系数（SVC）占有模型，以刻画空间变化的物种-环境关系。我们在分层贝叶斯框架中采用最近邻高斯过程和波利亚-伽马数据增广技术，构建了计算高效的吉布斯采样器，并将其实现在spOccupancy R包中。对于多物种模型，我们利用空间因子降维技术高效建模大数据集（例如超过10个物种）。该分层贝叶斯框架能便捷生成具有完全传播不确定性的SVC后验预测图。我们将SVC模型应用于量化美国21种草原鸟类在最大繁殖期温度与其出现概率之间关系的空间变异性。联合多物种建模整体优于单物种模型，所有模型均揭示了物种出现与最高温度关系的显著空间变异。本模型特别适用于基于大规模监测项目（此类项目在回答全球变化下野生动物响应的宏观生态问题中日益普遍）的检测-未检测数据来量化物种-环境关系。