Habitat-based statistical models for predicting the spatial distribution of butterflies and day-flying moths in a fragmented landscape

1. Most species' surveys and biodiversity inventories are limited by time a nd money. Therefore, it would be extremely useful to develop predictive mod els of animal distributions based on habitat, and to use these models to es timate species' densities and range sizes in poorly sampled regions. 2. In this study, two sets of data were collected. The first set consisted of over 2000 butterfly transect counts, which were used to determine the re lative density of each species in 16 major habitat types in a 35-km(2) area of fragmented landscape in north-west Wales. For the second set of data, t he area was divided into 140 cells using a 500-m grid, and the extent of ea ch habitat and the presence or absence of each butterfly and moth species w as determined for each cell. 3. Logistic regression was used to model the relationship between species' distribution and predicted density, based on habitat extent, in each grid s quare. The resultant models were used to predict butterfly distributions an d occupancy at a range of spatial scales. 4. Using a jack-knife procedure, our models successfully reclassified the p resence or absence of species in a high percentage of grid squares (mean 83 % agreement). There were highly significant relationships between the model led probability of species occurring at regional and local scales and the n umber of grid squares occupied at those scales. 5. We conclude that basic habitat data can be used to predict insect distri butions and relative densities reasonably well within a fragmented landscap e. It remains to be seen how accurate these predictions will be over a wide r area.