THE EFFECT OF DENSITY-DEPENDENCE AND WEATHER ON POPULATION-SIZE OF A POLYVOLTINE SPECIES

The identification of what factors determine the population dynamics o f polyvoltine species has been a difficult problem in ecology because population dynamics can contain intra- and interannual variability, an d because the time scale at which factors affect the population is oft en unknown. We created a comprehensive population model to determine h ow density dependence (linear, nonlinear, and time-delayed) and weathe r affected the rate of population growth of white-footed mice (Peromys cus leucopus) in an isolated woodlot. We studied this nonoutbreak, pol yvoltine species using a 257-mo data set spanning 23 yr, which incorpo rated both detailed intra-annual and long-term dynamics, and we used t his model to forecast future population size. We then evaluated whethe r 3-yr spans of monthly data or a 22-yr span of annual data were bette r able to identify the key determinants that drive population dynamics , and we identified which data type created more accurate forecasts. T he 257-mo comprehensive model determined that the intra-annual cycle w as caused by seasonally varying intrinsic growth rates and density dep endence on a 1-2 mo scale and indicated that peak population size in o ne year did not affect the population in the subsequent year. Interann ual variability in peak and trough density was caused by the effect of weather on monthly rate of growth with a 0-2 mo time delay, with the exception of two droughts. These droughts negatively affected the popu lation for 9 mo; the effects were probably mediated through reduced se ed crop. This model explained 81% of the variability in density. Becau se weather determined interannual variability in density, forecasts th at did not use known weather data during the forecast period were poor . When weather data were used, forecasts were accurate within 1-3 anim als (10%) of observed densities up to 8 mo in the future but were inac curate beyond 8 mo. We found that shortterm monthly data detected more factors affecting the population and created more accurate forecasts than long-term annual data, because all factors affecting the populati on (except droughts) occurred on a monthly scale. The annual model did not detect any weather effects except droughts and detected annual de nsity dependence, which represents time-delayed density dependence in polyvoltine species. We argue that this annual relationship is spuriou s and caused by studying this polyvoltine species on an inappropriate time scale. Our work suggests that the time scale of the analysis may affect the conclusions drawn about which types of factors determine po pulation size and with what time lag. It also suggests that, even when population fluctuations can be explained, accurately predicting futur e densities may be impossible when fluctuations are driven by weather.