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DOI	10.1111/ele.12763
	When mechanism matters: Bayesian forecasting using models of ecological diffusion
	Hefley T.J.; Hooten M.B.; Russell R.E.; Walsh D.P.; Powell J.A.
发表日期	2017
ISSN	1461-023X
EISSN	1461-0248
卷号	20 期号:5
英文摘要	Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting. © 2017 John Wiley & Sons Ltd/CNRS
英文关键词	Agent-based model; Bayesian analysis; boosted regression trees; dispersal; generalised additive model; invasion; partial differential equation; prediction; spatial confounding
学科领域	Odocoileus virginianus; animal; Bayes theorem; deer; female; forecasting; male; prevalence; theoretical model; Wasting Disease, Chronic; Wisconsin; Animals; Bayes Theorem; Deer; Female; Forecasting; Male; Models, Theoretical; Prevalence; Wasting Disease, Chronic; Wisconsin
语种	英语
scopus关键词	Odocoileus virginianus; animal; Bayes theorem; deer; female; forecasting; male; prevalence; theoretical model; Wasting Disease, Chronic; Wisconsin; Animals; Bayes Theorem; Deer; Female; Forecasting; Male; Models, Theoretical; Prevalence; Wasting Disease, Chronic; Wisconsin
来源期刊	Ecology Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/118389
作者单位	Department of Statistics, Kansas State University, 205 Dickens Hall, 1116 Mid-Campus Drive North, Manhattan, KS 66506, United States; U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Department of Fish, Wildlife, and Conservation Biology, Department of Statistics, Colorado State University, 1484 Campus Delivery, Fort Collins, CO 80523, United States; U.S. Geological Survey, National Wildlife Health Center, 6006 Schroeder Road, Madison, WI 53711, United States; Department of Mathematics and Statistics, Utah State University, 3900 Old Main Hill, Logan, UT 84322, United States
推荐引用方式 GB/T 7714	Hefley T.J.,Hooten M.B.,Russell R.E.,et al. When mechanism matters: Bayesian forecasting using models of ecological diffusion[J],2017,20(5).
APA	Hefley T.J.,Hooten M.B.,Russell R.E.,Walsh D.P.,&Powell J.A..(2017).When mechanism matters: Bayesian forecasting using models of ecological diffusion.Ecology Letters,20(5).
MLA	Hefley T.J.,et al."When mechanism matters: Bayesian forecasting using models of ecological diffusion".Ecology Letters 20.5(2017).