Does seasonal fine-tuning of climatic variables improve the performance of bioclimatic envelope models for migratory birds?
Article first published online: 15 AUG 2006
Diversity and Distributions
Volume 12, Issue 5, pages 502–510, September 2006
How to Cite
Heikkinen, R. K., Luoto, M. and Virkkala, R. (2006), Does seasonal fine-tuning of climatic variables improve the performance of bioclimatic envelope models for migratory birds?. Diversity and Distributions, 12: 502–510. doi: 10.1111/j.1366-9516.2006.00284.x
- Issue published online: 15 AUG 2006
- Article first published online: 15 AUG 2006
- Bird atlas;
- boreal regions;
- model accuracy;
- species-climate model
We examined the influence of ‘seasonal fine-tuning’ of climatic variables on the performance of bioclimatic envelope models of migrating birds. Using climate data and national bird atlas data from a 10 × 10 km uniform grid system in Finland, we tested whether the replacement of one ‘baseline’ set of variables including summer (June–August) temperature and precipitation variables with climate variables tailored (‘fine-tuned’) for each species individually improved the bird-climate models. The fine-tuning was conducted on the basis of time of arrival and early breeding of the species. Two generalized additive models (GAMs) were constructed for each of the 63 bird species studied, employing (1) the baseline climate variables and (2) the fine-tuned climate variables. Model performance was measured as explanatory power (deviance change) and predictive power (area under the curve; AUC) statistics derived from cross-validation. Fine-tuned climate variables provided, in many cases, statistically significantly improved model performance compared to using the same baseline set of variables for all the species. Model improvements mainly concerned bird species arriving and starting their breeding in May–June. We conclude that the use of the fine-tuned climate variables tailored for each species individually on the basis of their arrival and critical breeding periods can provide important benefits for bioclimatic modelling.