RESAMPLING TESTS FOR METAANALYSIS OF ECOLOGICAL DATA

Meta-analysis is a statistical technique that allows one to combine th e results from multiple studies to glean inferences on the overall imp ortance of various phenomena. This method can prove to be more informa tive than common ''vote counting,'' in which the number of significant results is compared to the number with nonsignificant results to dete rmine whether the phenomenon of interest is globally important. While the use of metaanalysis is widespread in medicine and the social scien ces, only recently has it been applied to ecological questions. We com pared the results of parametric confidence limits and homogeneity stat istics commonly obtained through meta-analysis to those obtained from resampling methods to ascertain the robustness of standard meta-analyt ic techniques. We found that confidence limits based on bootstrapping methods were wider than standard confidence limits, implying that resa mpling estimates are more conservative. In addition, we found that sig nificance tests based on homogeneity statistics differed occasionally from results of randomization tests, implying that inferences based so lely on chi-square significance tests may lead to erroneous conclusion s. We conclude that resampling methods should be incorporated in meta- analysis studies, to ensure proper evaluation of main effects in ecolo gical studies.