A test for density dependence in time-series data allowing for weather effects is presented. The test is based on a discrete time autoregressive model for changes in population density with a covariate for the effects of weather. The distribution of the test statistic on the null hypothesis of density independence is obtained by parametric bootstrapping. A computer simulation exercise is used to demonstrate the gain in statistical power by allowing for weather effects. Application of the method to time-series data on three species of butterflies and two species of songbirds showed stronger evidence of density dependence than two standard tests.
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