High resolution species distribution models of two nesting water bird species: a study of transferability and predictive performance
Species distribution modelling is increasingly used in ecological studies and is particularly useful in conservation planning. Models are, however, typically created with a coarse resolution, although conservation planning often requires a high resolution. In this study we created high resolution mo...
| Published in: | Landscape Ecology |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
2012
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| Subjects: | |
| Online Access: | https://research.abo.fi/en/publications/31bcd375-8d33-4fda-868d-ecfa67adc528 https://doi.org/10.1007/s10980-012-9705-8 |
| Summary: | Species distribution modelling is increasingly used in ecological studies and is particularly useful in conservation planning. Models are, however, typically created with a coarse resolution, although conservation planning often requires a high resolution. In this study we created high resolution models and explored central aspects of the modelling procedure; transferability and predictive performance of the models. We created models for two breeding water bird species, common eider Somateria mollissima and herring gull Larus argentatus, based on data from two regions in the Finnish archipelago (234 islands). We used seven variables which we considered as potential predictors of nest site location: distance to forest, distance to rock and distance to low vegetation, exposure, elevation, slope and curvature of the land surface. We tested the predictive ability of the models crosswise between the areas by using area under the receiver operating characteristic curve. The models were transferable between our study areas and the predictive performance varied from fair to excellent. The most important predictors overall were exposure and distance to forest. More general models, with higher regularization values in the Maxent software, had better transferability regarding predictive performance. However, when we fitted a model based on 60% of the data from both regions and evaluated the model on the remaining 40%, the most complex model had the highest accuracy. Extrapolation of SDMs, evaluated on data from the same region, should therefore always be done with caution as the most accurate model might not have the best transferability if it is not general enough. |
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