Document Type

Article

Journal/Book Title/Conference

Ecological Applications

Volume

17

Issue

1

Publisher

Ecological Society of America

Publication Date

1-1-2007

First Page

140

Last Page

153

Abstract

Advances in acquiring and analyzing the spatial attributes of data have greatlyenhanced the potential utility of wildlife disease surveillance data for addressing problems ofecological or economic importance. We present an approach for using wildlife diseasesurveillance data to identify areas for (or of ) intervention, to spatially delineate pairedtreatment and control areas, and then to analyze these nonrandomly selected sites in a meta-analysis framework via before–after–control–impact (BACI) estimates of effect size. We applythese methods to evaluate the effectiveness of attempts to reduce chronic wasting disease(CWD) prevalence through intensive localized culling of mule deer (Odocoileus hemionus)innorth-central Colorado, USA. Areas where surveillance data revealed high prevalence or caseclusters were targeted by state wildlife management agency personnel for focal scale (onaverage ,17 km2) culling, primarily via agency sharpshooters. Each area of sustained cullingthat we could also identify as unique by cluster analysis was considered a potential treatmentarea. Treatment areas, along with spatially paired control areas that we constructed post hocin a case-control design (collectively called ‘‘management evaluation sites’’), were thendelineated using home range estimators. Using meta-BACI analysis of CWD prevalence datafor all management evaluation sites, the mean effect size (change of prevalence on treatmentareas minus change in prevalence on their paired control areas) was 0.03 (SE ¼ 0.03); meaneffect size on treatment areas was not greater than on paired control areas. Excluding cullsamples from prevalence estimates or allowing for an equal or greater two-year lag in systemresponses to management did not change this outcome. We concluded that managementbenefits were not evident, although whether this represented true ineffectiveness or was a resultof lack of data or insufficient duration of treatment could not be discerned. Based on ourobservations, we offer recommendations for designing a management experiment with 80%power to detect a 0.10 drop in prevalence over a 6–12-year period.

Download

Share

COinS