• Bioclimatic envelope models;
  • biotic interactions;
  • bird atlas;
  • climate;
  • distribution;
  • land cover;
  • model validation;
  • species distribution modelling


Aim  The role of biotic interactions in influencing species distributions at macro-scales remains poorly understood. Here we test whether predictions of distributions for four boreal owl species at two macro-scales (10 × 10 km and 40 × 40 km grid resolutions) are improved by incorporating interactions with woodpeckers into climate envelope models.

Location  Finland, northern Europe.

Methods  Distribution data for four owl and six woodpecker species, along with data for six land cover and three climatic variables, were collated from 2861 10 × 10 km grid cells. Generalized additive models were calibrated using a 50% random sample of the species data from western Finland, and by repeating this procedure 20 times for each of the four owl species. Models were fitted using three sets of explanatory variables: (1) climate only; (2) climate and land cover; and (3) climate, land cover and two woodpecker interaction variables. Models were evaluated using three approaches: (1) examination of explained deviance; (2) four-fold cross-validation using the model calibration data; and (3) comparison of predicted and observed values for independent grid cells in eastern Finland. The model accuracy for approaches (2) and (3) was measured using the area under the curve of a receiver operating characteristic plot.

Results  At 10-km resolution, inclusion of the distribution of woodpeckers as a predictor variable significantly improved the explanatory power, cross-validation statistics and the predictive accuracy of the models. Inclusion of land cover led to similar improvements at 10-km resolution, although these improvements were less apparent at 40-km resolution for both land cover and biotic interactions.

Main conclusions  Predictions of species distributions at macro-scales may be significantly improved by incorporating biotic interactions and land cover variables into models. Our results are important for models used to predict the impacts of climate change, and emphasize the need for comprehensive evaluation of the reliability of species–climate impact models.