Predicting immigration of two species in contrasting landscapes: effects of scale, patch size and isolation

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Abstract
Migration is a key process for spatially structured populations. We examined how a variety of patch based metrics commonly used to predict the number of immigrants to a habitat patch performed based on data from three different years, in two distinct insect systems. The first system was an herbivorous beetle inhabiting patches of its host plant within a ‘typical’ patch network. In this system there were numerous patches located relatively close to one another, given the beetle's dispersal ability. The second system consisted of a butterfly inhabiting a series of 17 subalpine meadows. Here, the patches were arranged in a linear fashion and were more distant from each other. Overall, we found that the best models incorporating aspects of patch size and/or isolation explained a large (30–40%) amount of deviance in immigration, but there were considerable differences between the systems. For the first system, we found that metrics including the size of the target patch explained the highest proportion of deviance in immigrant numbers, while metrics based only on interpatch distances explained very little deviance. The situation was reversed for the second system. Metrics including the size of the target patch explained little deviance, while metrics based on the distance between patches explained the bulk of deviance in the number of immigrants. The results of our study show that the effects of patch size and isolation on the number of immigrants are highly important, but dependent on spatial scale, the organism studied, and how it responds to the spatial arrangement of patches. Correspondingly, there will be no single generalized metric to predict immigration for all cases. Given the dependency of the results on the system studied, we recommend that future studies provide explicit data on habitat areas and dispersal distance relative to interpatch distance to allow for meaningful comparison among organisms and systems.