Modelling invasive alien species distributions from digital biodiversity atlases. Model upscaling as a means of reconciling data at different scales
Article first published online: 10 APR 2012
© 2012 Blackwell Publishing Ltd
Diversity and Distributions
Volume 18, Issue 12, pages 1177–1189, December 2012
How to Cite
Marcer, A., Pino, J., Pons, X. and Brotons, L. (2012), Modelling invasive alien species distributions from digital biodiversity atlases. Model upscaling as a means of reconciling data at different scales. Diversity and Distributions, 18: 1177–1189. doi: 10.1111/j.1472-4642.2012.00911.x
- Issue published online: 6 NOV 2012
- Article first published online: 10 APR 2012
- CONSOLIDER-MONTES. Grant Number: CSD2008-00040
- Ministerio de Ciencia e Innovación of the Spanish government. Grant Number: FP7-226852
- EU-project SCALES
- Biodiversity databases;
- cross-scale validation;
- invasive alien species;
- species distribution models
There is a wealth of information on species occurrences in biodiversity data banks, albeit presence-only, biased and scarce at fine resolutions. Moreover, fine-resolution species maps are required in biodiversity conservation. New techniques for dealing with this kind of data have been reported to perform well. These fine-resolution maps would be more robust if they could explain data at coarser resolutions at which species distributions are well represented. We present a new methodology for testing this hypothesis and apply it to invasive alien species (IAS).
We used species presence records from the Biodiversity data bank of Catalonia to model the distribution of ten IAS which, according to some recent studies, achieve their maximum distribution in the study area. To overcome problems inherent with the data, we prepared different correction treatments: three for dealing with bias and five for autocorrelation. We used the MaxEnt algorithm to generate models at 1-km resolution for each species and treatment. Acceptable models were upscaled to 10 km and validated against independent 10 km occurrence data.
Of a total of 150 models, 20 gave acceptable results at 1-km resolution and 12 passed the cross-scale validation test. No apparent pattern emerged, which could serve as a guide on modelling. Only four species gave models that also explained the distribution at the coarser scale.
Although some techniques may apparently deliver good distribution maps for species with scarce and biased data, they need to be taken with caution. When good independent data at a coarser scale are available, cross-scale validation can help to produce more reliable and robust maps. When no independent data are available for validation, however, new data gathering field surveys may be the only option if reliable fine-scale resolution maps are needed.