Louis W. Botsford and John G. Brittnacher
Department of Wildlife and Fisheries Biology
University of California
Davis, California 95616
Population modeling often requires prior evaluation of potential environmental influences on the population. A common approach is to attempt to detect covariability between time series of population and environmental data by computing correlations. Problems associated with that approach include the effects of intraseries correlation on significance of computed correlations and the fact that the available population series is often not the variable affected by the environment. We discuss methods for dealing with intraseries correlation and a method for obtaining a recruitment series from an abundance series. These are demonstrated in examples from our work on the influence of environment on reproduction in populations of California quail. We have detected an influence of winter precipitation on recruitment in semi-arid regions of California. Ongoing work focuses on: (1) detection of density-dependence and (2) how remotely forced spatial variability in the environment influences quail populations.
in Wildife 2001 edited by McCulloch and Barrett, 1992