气候变化领域集成服务门户(CCPortal): Modeling Complex Species-Environment Relationships Through Spatially-Varying Coefficient Occupancy Models

CCPortal

DOI	10.1007/s13253-023-00595-6
	Modeling Complex Species-Environment Relationships Through Spatially-Varying Coefficient Occupancy Models
	Doser, Jeffrey W.; Finley, Andrew O.; Saunders, Sarah P.; Kery, Marc; Weed, Aaron S.; Zipkin, Elise F.
发表日期	2024
ISSN	1085-7117
EISSN	1537-2693
英文摘要	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 P & oacute;lya-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 USA. 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.
英文关键词	Bayesian; Species distribution model; Wildlife; Monitoring; Nonstationarity
语种	英语
WOS研究方向	Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biology ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:001144997300001
来源期刊	JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298779
作者单位	Michigan State University; Michigan State University; Michigan State University; Michigan State University; Swiss Ornithological Institute; United States Department of the Interior
推荐引用方式 GB/T 7714	Doser, Jeffrey W.,Finley, Andrew O.,Saunders, Sarah P.,et al. Modeling Complex Species-Environment Relationships Through Spatially-Varying Coefficient Occupancy Models[J],2024.
APA	Doser, Jeffrey W.,Finley, Andrew O.,Saunders, Sarah P.,Kery, Marc,Weed, Aaron S.,&Zipkin, Elise F..(2024).Modeling Complex Species-Environment Relationships Through Spatially-Varying Coefficient Occupancy Models.JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS.
MLA	Doser, Jeffrey W.,et al."Modeling Complex Species-Environment Relationships Through Spatially-Varying Coefficient Occupancy Models".JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS (2024).