Implications of empirical data quality to metapopulation model parameter estimation and application

Parameter estimation is a critical step in the use of any metapopulation model for predictive purposes. Typically metapopulation studies assume that empirical data are of good quality and any errors are so insignificant that they can be ignored. However, three types of errors occur commonly in metapopulation data sets. First, patch areas can be mis-estimated. Second, unknown habitat patches may be located within or around the study area. Third, there may be false zeros in the data set, that is, some patches were observed to be empty while there truly was a population in the patch. This study investigates biases induced into metapopulation model parameter estimates by these three types of errors. It was found that mis-estimated areas influence the scaling of extinction risk with patch area; extinction probabilities for large patches become overestimated. Missing patches cause overestimation of migration distances and colonization ability of the species. False zeros can affect very strongly all model components, the extinction risk in large patches, intrinsic extinction rates in general, migration distances and colonization ability may become all overestimated. Biases in parameter estimates translate into corresponding biases in model predictions, which are serious particularly if metapopulation persistence becomes overestimated. This happens for example when the migration capability of the species is overestimated. Awareness of these biases helps in understanding seemingly anomalous parameter estimation results. There are also implications for field work: it may be reasonable to allocate effort so that serious errors in data are minimized. It is particularly important to avoid observing false zeros for large and/or isolated patches.