Species distribution models (SDMs) can help to describe potential occurrence areas and habitat requirements of a species. These data represent key information in ecology and conservation, particularly for rare or endangered species. Presence absence (PA) and presence only (PO) records of European Turtle Doves Streptopelia turtur in Germany were ... used to run SDMs, whilst climate and land coverage variables provided environmental information. GLM (Generalised Linear model), GBM (Generalised Boosted model), CTA (Classification Tree analysis), SRE (Surface Range Envelope) and RF (Random Forests) algorithms were run with both datasets. Best model quality was obtained with PO in the RF algorithm (AUC 0.83). PA and PO probability maps differed substantially, but both excluded mountainous regions as potential occurrence areas. However, PO probability maps were more discriminatory and highlighted a possible distribution of Turtle Doves near Saarbrucken, west of Dusseldorf, in the Black Forest lowlands and Lusatia. Mainly, the climate variables ´minimum temperature in January´ and ´precipitation of the warmest quarter´ shaped these results, but variables like soil type or agricultural management strategy could improve future SDMs to specify local habitat requirements and develop habitat management strategies. Eventually, the study demonstrated the utility of PO data in SDMs, particularly for scarce species.