Roles of human disturbance, precipitation, and a pathogen on the survival and reproductive probabilities of deer mice.

Climate change, human disturbance, and disease can have large impacts on the dynamics of a species by affecting the likelihood of survival and reproduction of individuals. We investigated the roles of precipitation, off-road vehicle (ORV) alteration of habitat, and infection with Sin Nombre virus on the survival and reproductive probabilities of deer mice (Peromyscus maniculatus). We used generalized linear mixed models to estimate the effects of these factors and their interactions by fitting capture-recapture data collected seasonally from 2002 to 2007 at 17 sites in the Great Basin Desert of central Utah, USA. During periods with high precipitation, we found no difference in survival and reproductive probabilities between seasons, but during drier periods, we found a reduction of overwinter survival and fall reproductive activity. Precipitation also interacted with disturbance to affect survival probabilities and female reproduction; in periods with low precipitation, deer mice on highly disturbed sites had extremely low survival probabilities and low reproductive probabilities of females compared to those of individuals from low-disturbance sites. However, high precipitation ameliorated the effect of disturbance on both parameters. Deer mice from sites with high impact of ORV disturbance also had low survival over summer. Additionally, male reproductive probabilities were diminished on highly disturbed sites in both seasons; in contrast, they were reduced only in the fall on low-disturbance sites. Density had an overall negative effect on survival and reproductive probabilities of deer mice. For females, the negative effect on reproductive activity was amplified in highly disturbed sites. We found no effect of hantavirus infection on survival probabilities of deer mice. Overall, this study revealed complexity in the determinants of deer mouse survival and reproduction given by the effects of a number of significant interactions among explanatory variables. Thus, factors that may not appear to have a strong effect when investigated alone can still be influential by modulating the effect of a different factor.