Short communicationWhich breeding bird categories should we use in models of species distribution?

- Ambiguous breeding categories influence species distribution models performance.
- Model validation is more influenced by ambiguous data than model calibration.
- Breeding category should be used to select high quality data to validate the models.

Studies often use breeding bird atlases to assess species' habitat requirements or to estimate species' potential distribution under environmental changes. In breeding bird atlases, one of the attributes recorded for each grid square is evidence of breeding. The attribute represent probability of breeding (confirmed, probable, possible) categorized according to breeding behaviour. However, the majority of studies often make arbitrary decisions on which category to use. This may have severe consequences for results. This study evaluated whether models' discrimination ability change by inclusion of ambiguous breeding categories (probable, possible). We compared models' predictions for distribution of nine wetland birds derived from Atlas of the breeding distribution of birds in the Czech Republic. For each species, we developed generalized linear models using combinations of the breeding categories as input to model calibration and validation. Our results show that the discrimination ability (AUC) decreased in most cases when all breeding categories were uncritically used in calibration and validation process. On the other hand, however, inclusion of probable and possible breeding categories to model calibration did not affect models' abilities to predict confirmed presences and absences. This implies that inclusion of ambiguous breeding categories has more serious impact on models' performance when added to validation than to calibration data set. We advocate for more rigorous use of different breeding categories and emphasize that widely used atlases from citizen science programmes offer more than simple occurrence data. Additional attributes (e.g. breeding category, sampling effort) should be used to select high quality data to validate the models.