Modeling parasitism rate and parasitism risk: An illustration using a colonially nesting songbird, the red-winged blackbird Agelaius phoeniceus

Authors

Max Post van der Burg, University of Nebraska-Lincoln,Follow
Larkin A. Powell, University of Nebraska-LincolnFollow
Andrew J. Tyre, University of Nebraska-LincolnFollow

Date of this Version

5-2009

Citation

Journal of Avian Biology 40:3 (May 2009), pp. 263–270; doi: 10.1111/j.1600-048X.2008.04460.x

Comments

Copyright © 2009 Max Post van der Burg, Larkin A. Powell and Andrew J. Tyre. Journal compilation © 2009 Journal of Avian Biology; published by Wiley-Blackwell. Used by permission.

Abstract

Ornithologists interested in the drivers of nest success and brood parasitism benefit from the development of new analytical approaches. One example is the development of so-called “log exposure” models for analyzing nest success. However, analyses of brood parasitism data have not kept pace with developments in nest success analyses. The standard approach uses logistic regression which does not account for multiple parasitism events, nor does it prevent bias from using observed proportions of parasitized nests. Likewise, logistic regression analyses do not capture fine scale temporal variation in parasitism. At first glance, it might be tempting to apply log exposure models to parasitism data, but the process of parasitism is inherently different from the process of nest predation. We modeled daily parasitism rate as a Poisson process, which allowed us to correct potential biases in parasitism rate. We were also able to use our estimated parasitism rate to model parasitism risk as the probability of one or more parasitism events. We applied this model to red-winged blackbird Agelaius phoeniceus nesting colonies subject to parasitism by brown-headed cowbirds Molothrus ater. Our approach allowed us to model parasitism using a wider rage of covariates, especially functions of time. We found strong support for models combining temporal fluctuations in parasitism rate and nest-site characteristics. Similarly, we found that our annual predicted parasitism risk was lower on average than the risk estimated from observed parasitism levels. Our approach improves upon traditional logistic regression analyses and opens the door for more mechanistic modeling of the process of parasitism.