Authors

Kaliontzopoulou A, Brito JC , Carretero MA, Larbes S, Harris DJ

Abstract

Species distribution modelling (SDM) is a powerful tool to investigate various biological questions with a spatial component, but is also sensitive to presence-data characteristics, particularly data precision and clustering. Here, we investigate the effect of these two factors on SDM using Maxent as the modelling technique and wall lizards (genus Podarcis Wagler, 1830) from North Africa as a model system. Podarcis are not ubiquitous in Africa as they are in Europe, but their ecological and distributional characteristics in this area are poorly known. Our results show that the most important environmental factors related to the distribution of this genus in North Africa are humidity, habitat type, and temperature. The areas of potential distribution predicted by models based on data sets with different precision and clustering characteristics show high relatedness to coastal areas and mountain ranges and extend to areas were presence records for these lizards are lacking. Our comparison of models based on different data sets indicates that finer scale models, even if based on fewer presence locations, outperform coarser scale ones. Data clustering does not have a negative effect on model performance, but is rather overcome by sample-size effects. Similar approaches may be of general application to other stenoic species for which available locations are scarce in comparison with the extension of the study area.

 

Journal:  Canadian Journal of Zoology

DOI: 10.1139/Z08-078