Predicting the distribution of ground beetle species (Coleoptera, Carabidae) in Britain using land cover variables.
Predictions of plant and animal species distributions are important for conservation and for the assessment of large-scale ecosystem change. Land cover data are becoming more widely available for use in land management and conservation. We use a logistic regression modelling approach to investigate the utility of these data for modelling. The relationship between the distribution of 137 British ground beetles species and land cover was investigated using data from 1,687 10 km national grid squares. Land cover data were simplified using ordination and the axes used as predictors in logistic regression with presence absence data for individual beetle species as response variables. Significant regression models were generated for all species with first and second axis scores. The amounts of variation explained by models were generally low, but predictions derived from models generally matched the known distributions of the species in Britain. Species with coastal preferences were poorly modelled and predicted to occur throughout lowland Britain whilst a number of species occurring in southern Britain were predicted to occur into Scotland. A validation exercise comparing model predictions with new data from a survey of 59 10 km(2) produced mixed results with the distribution of grassland species being better predicted than riverine species. Jack-knifing was used to assess the robustness of models for four species which differed in their apparent responses to the land cover variables. Methods for improving the predictive power of these models and their potential for use in assessing the impact of global climate change are discussed.
Eyre
M D
MD
Centre for Life Sciences Modelling, School of Biology, The University, Newcastle upon Tyne NE1 7RU, UK. m.d.eyre@ncl.ac.uk
Rushton
S P
SP
Luff
M L
ML
Telfer
M G
MG
D000818:Animals; D044822:Biodiversity; D001517:Coleoptera / Q000254:growth & development; D003710:Demography; D004777:Environment; D005544:Forecasting; D016015:Logistic Models; D008962:Models, Theoretical; D006113:United Kingdom