globalchange  > 气候减缓与适应
DOI: 10.1111/ele.12763
Scopus记录号: 2-s2.0-85017389523
论文题名:
When mechanism matters: Bayesian forecasting using models of ecological diffusion
作者: Hefley T.J.; Hooten M.B.; Russell R.E.; Walsh D.P.; Powell J.A.
刊名: Ecology Letters
ISSN: 1461023X
EISSN: 1461-0248
出版年: 2017
卷: 20, 期:5
起始页码: 640
结束页码: 650
语种: 英语
英文关键词: Agent-based model ; Bayesian analysis ; boosted regression trees ; dispersal ; generalised additive model ; invasion ; partial differential equation ; prediction ; spatial confounding
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
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
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/107626
Appears in Collections:气候减缓与适应

Files in This Item:

There are no files associated with this item.


作者单位: Department of Statistics, Kansas State University, 205 Dickens Hall, 1116 Mid-Campus Drive North, Manhattan, KS, 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, United States; U.S. Geological Survey, National Wildlife Health Center, 6006 Schroeder Road, Madison, WI, United States; Department of Mathematics and Statistics, Utah State University, 3900 Old Main Hill, Logan, UT, United States

Recommended Citation:
Hefley T.J.,Hooten M.B.,Russell R.E.,et al. When mechanism matters: Bayesian forecasting using models of ecological diffusion[J]. Ecology Letters,2017-01-01,20(5)
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Hefley T.J.]'s Articles
[Hooten M.B.]'s Articles
[Russell R.E.]'s Articles
百度学术
Similar articles in Baidu Scholar
[Hefley T.J.]'s Articles
[Hooten M.B.]'s Articles
[Russell R.E.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Hefley T.J.]‘s Articles
[Hooten M.B.]‘s Articles
[Russell R.E.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.