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

Habitat‐based statistical models for predicting the spatial distribution of butterflies and... 1. Most species’ surveys and biodiversity inventories are limited by time and money. Therefore, it would be extremely useful to develop predictive models of animal distributions based on habitat, and to use these models to estimate 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 relative density of each species in 16 major habitat types in a 35‐km2 area of fragmented landscape in north‐west Wales. For the second set of data, the area was divided into 140 cells using a 500‐m grid, and the extent of each habitat and the presence or absence of each butterfly and moth species was 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 square. The resultant models were used to predict butterfly distributions and occupancy at a range of spatial scales. 4. Using a jack‐knife procedure, our models successfully reclassified the presence or absence of species in a high percentage of grid squares (mean 83% agreement). There were highly significant relationships between the modelled probability of species occurring at regional and local scales and the number of grid squares occupied at those scales. 5. We conclude that basic habitat data can be used to predict insect distributions and relative densities reasonably well within a fragmented landscape. It remains to be seen how accurate these predictions will be over a wider area. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Ecology Wiley

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

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Abstract

1. Most species’ surveys and biodiversity inventories are limited by time and money. Therefore, it would be extremely useful to develop predictive models of animal distributions based on habitat, and to use these models to estimate 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 relative density of each species in 16 major habitat types in a 35‐km2 area of fragmented landscape in north‐west Wales. For the second set of data, the area was divided into 140 cells using a 500‐m grid, and the extent of each habitat and the presence or absence of each butterfly and moth species was 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 square. The resultant models were used to predict butterfly distributions and occupancy at a range of spatial scales. 4. Using a jack‐knife procedure, our models successfully reclassified the presence or absence of species in a high percentage of grid squares (mean 83% agreement). There were highly significant relationships between the modelled probability of species occurring at regional and local scales and the number of grid squares occupied at those scales. 5. We conclude that basic habitat data can be used to predict insect distributions and relative densities reasonably well within a fragmented landscape. It remains to be seen how accurate these predictions will be over a wider area.

Journal

Journal of Applied EcologyWiley

Published: Sep 1, 2000

References

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