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Linking Chronic Wasting Disease To Mule Deer Movement Scales: A Hierarchical Bayesian Approach
Author(s) -
Farnsworth Matthew L.,
Hoeting Jennifer A.,
Hobbs N. Thompson,
Miller Michael W.
Publication year - 2006
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1890/1051-0761(2006)016[1026:lcwdtm]2.0.co;2
Subject(s) - chronic wasting disease , odocoileus , spatial ecology , temporal scales , ecology , bayesian probability , spatial distribution , spatial analysis , scale (ratio) , habitat , bayesian hierarchical modeling , mixing (physics) , geography , biology , bayes' theorem , cartography , disease , statistics , mathematics , medicine , prion protein , physics , remote sensing , pathology , quantum mechanics , scrapie
Observed spatial patterns in natural systems may result from processes acting across multiple spatial and temporal scales. Although spatially explicit data on processes that generate ecological patterns, such as the distribution of disease over a landscape, are frequently unavailable, information about the scales over which processes operate can be used to understand the link between pattern and process. Our goal was to identify scales of mule deer ( Odocoileus hemionus ) movement and mixing that exerted the greatest influence on the spatial pattern of chronic wasting disease (CWD) in northcentral Colorado, USA. We hypothesized that three scales of mixing (individual, winter subpopulation, or summer subpopulation) might control spatial variation in disease prevalence. We developed a fully Bayesian hierarchical model to compare the strength of evidence for each mixing scale. We found strong evidence that the finest mixing scale corresponded best to the spatial distribution of CWD infection. There was also evidence that land ownership and habitat use play a role in exacerbating the disease, along with the known effects of sex and age. Our analysis demonstrates how information on the scales of spatial processes that generate observed patterns can be used to gain insight when process data are sparse or unavailable.