Journal Article

Weighing Risk Factors Associated With Bee Colony Collapse Disorder by Classification and Regression Tree Analysis

Dennis vanEngelsdorp, Niko Speybroeck, Jay D. Evans, Bach Kim Nguyen, Chris Mullin, Maryann Frazier, Jim Frazier, Diana Cox-Foster, Yanping Chen, David R. Tarpy, Eric Haubruge, Jeffrey S. Pettis and Claude Saegerman

in Journal of Economic Entomology

Volume 103, issue 5, pages 1517-1523
Published in print October 2010 | ISSN: 0022-0493
Published online September 2014 | e-ISSN: 1938-291X | DOI:
Weighing Risk Factors Associated With Bee Colony Collapse Disorder by Classification and Regression Tree Analysis

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Colony collapse disorder (CCD), a syndrome whose defining trait is the rapid loss of adult worker honey bees, Apis mellifera L., is thought to be responsible for a minority of the large overwintering losses experienced by U.S. beekeepers since the winter 2006-2007. Using the same data set developed to perform a monofactorial analysis (PloS ONE 4: e6481, 2009), we conducted a classification and regression tree (CART) analysis in an attempt to better understand the relative importance and interrelations among different risk variables in explaining CCD. Fifty-five exploratory variables were used to construct two CART models: one model with and one model without a cost of misclassifying a CCD-diagnosed colony as a non-CCD colony. The resulting model tree that permitted for misclassification had a sensitivity and specificity of 85 and 74%, respectively. Although factors measuring colony stress (e.g., adult bee physiological measures, such as fluctuating asymmetry or mass of head) were important discriminating values, six of the 19 variables having the greatest discriminatory value were pesticide levels in different hive matrices. Notably, coumaphos levels in brood (a miticide commonly used by beekeepers) had the highest discriminatory value and were highest in control (healthy) colonies. Our CART analysis provides evidence that CCD is probably the result of several factors acting in concert, making afflicted colonies more susceptible to disease. This analysis highlights several areas that warrant further attention, including the effect of sublethal pesticide exposure on pathogen prevalence and the role of variability in bee tolerance to pesticides on colony survivorship.

Keywords: colony collapse disorder; epidemiology; classification and regression tree analysis; pathogens; Apis mellifera

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