PARENT SESSION
Oral Session # 94: Pathogens, Toxins, and Disease III.
Presiding: C Duffie
Friday, August 8. 8:30 AM to 12:00 PM, SITCC Meeting Room 200.

Exposing population viability analysis to disease.

McCallum, Hamish^*,1, Gerber, Leah², Lafferty, Kevin³, Dobson, Andrew⁴, ¹ University of Queensland, Brisbane, Queensland, Australia² Arizona State University, Tempe, Arizona³ USGS, Santa Barbara, California⁴ Princeton University, Princeton, New Jersey

ABSTRACT- Despite the importance of pathogens in natural populations, little attention has been given to host-pathogen dynamics in population viability analysis. The usual advice is to treat epidemics as "catastrophes", which occur with a low probability and kill some random proportion of the current population. We present a modeling framework for testing hypotheses about the role of disease in extinction risk analyses. We ask: (1) how disease affects variability in abundance and thus, population viability and (2) whether viability estimates of real populations suffering from disease require explicit modeling. As would be expected, we found that the presence of a lethal disease decreased the median survival time of simulated populations. Treating disease as a catastrophe in PVA leads to several erroneous conclusions. A key feature of epidemics is the existence of a host population threshold for disease introduction. This threshold means that the effect of epidemics is both density-dependent and overcompensatory. Modeling epidemics as increased stochastic mortality (catastrophe) grossly overestimated the probability of extinction, because of the density dependent effects of the threshold. Another effect of disease was to change the predicted relationship between population growth rate and median time to extinction. Populations with high growth rates that would normally be in little danger of extinction were more likely to exceed the host-density threshold and suffer virulent epidemics. These results suggest that, when confronted with disease, populations with high rates of increase could be more susceptible to extinction than populations with lower rates of increase. If potential management actions involve manipulating pathogens, then it will be important to model disease explicitly.

Key words: population viability analysis, disease ecology, modeling, conservation