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The authors present findings from two landscape epidemiology studies of chronic wasting disease (CWD) in northern Colorado mule deer (Odocoileus hemionus). First, the effects of human land use on disease prevalence were explored by formulating a set of models estimating CWD prevalence in relation to differences in human land use, sex and geographic location. Prevalence was higher in developed areas and among male deer suggesting that anthropogenic influences (changes in land use), differences in exposure risk between sexes and landscape-scaled heterogeneity are associated with CWD prevalence. The second study focused on identifying scales of mule deer movement and mixing that had the greatest influence on the spatial pattern of CWD in north-central Colorado. The authors hypothesised that three scales of mixing - individual, winter subpopulation and summer subpopulation - might control spatial variation in disease prevalence. A fully Bayesian hierarchical model was developed to compare the strength of evidence for each mixing scale. Strong evidence was found indicating that the finest mixing scale corresponded best to the observed spatial distribution of CWD prevalence. This analysis demonstrates how information on the scales of spatial processes that generate observed patterns can be used to gain insight into the epidemiology of wildlife diseases when process data are sparse or unavailable.

From introduction: "I agree with Rothley and others that these cracks are potentially problematic for landscape connectivity analyses, and I join them in cautioning conservation scientists to create valid models when generating spatial data inputs to cost-weighted analyses. However, I disagree as to the cause and the ubiquity of these cracks. I also provide a few additional procedures that will eliminate potential problems resulting from the misspecification of resistance surfaces and minimize possible additional artifacts arising from any correction processes. This note is intended to continue the important discussion about developing useful methods for computing functional landscape connectivity."

"Planning activities by local government often seek to identify areas of land that offer particularly high value for conserving biotic resources. Because private land is being developed rapidly, there is heightened concern about identifying these areas. Although general principles on setting priorities for habitat protection are emerging, substantial ambiguity remains about how to implement these principles. Here, we offer a general modeling framework for evaluating how planning alternatives could affect Critical Habitat. The framework contains four components: stakeholder involvement, spatial modeling of Critical Habitat and development patterns, analysis of alternative scenarios, and evaluation and monitoring. We illustrate this approach using a case study from Summit County, Colorado, USA."