Comparison of Estimators for Monitoring Long-Term Population Trends in Deer Mice, Peromyscus maniculatus, on the California Channel Islands

Capture-recapture methods are commonly used to estimate abundance and density of wild animal populations. Although a variety of sophisticated analytical techniques are available to evaluate capture-recapture data, vertebrate monitoring programs often lack the resources (e.g., time, personnel, and/or analytical expertise) to apply these methods. As an alternative, simple population indices, such as counts of unique individuals, may provide sufficient information to detect meaningful changes in population size. In this study we investigated whether a population index, easily generated from mark-recapture data under all conditions, might be used to provide valid ecological information for managers interested in long-term population trends of deer mice (Peromyscus maniculatus) on the California Channel Islands. In practice, determining the efficacy of estimating abundance from mark-recapture data and indices using empirical data (as opposed to simulated data) is difficult given the scarcity of long-term data sets that describe real populations. Using mark-recapture data that span 18 years (n = 122 trapping events, >12,000 marked individuals) for deer mice on 2 of the islands, we compared density estimates obtained from several commonly used mark-recapture models and also compared these estimates to index counts. Populations of island deer mice are extremely dynamic; estimated densities over the data period varied from 0 to >1200 mice/ha. Density estimates from models in program CAPTURE and program DENSITY, as well as from model-averaged Huggins models, were strongly correlated with each other and with the density index. Densities calculated by the models and the index showed similar patterns of population variation and trend over time for all 5 sites. For long-term population monitoring and assessment of population trends in deer mice, our findings suggest that the use of a simple index may provide adequate understanding of ecologically relevant population changes, though data collection methods that allow for more detailed analyses using advanced modeling techniques should be maintained.