Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real-world applications and model assessments. Prevalent threshold selection methods for presence-only data require unproven pseudo-absence data or subjective researchers’ decisions. This study proposes a new method, Boyce-Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo-absence data. We summarize that the mutation point is a typical shape feature of the predicted-to-expected (P/E) curve after reviewing relevant articles. Analysis based on source-sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point.

To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods.

The data in this dataset comes from a published book: Jiang, C., Wang, H. & Gu, X. 2015. Giant Pandas in Sichuan: Report of the Fourth Giant Panda Survey in Sichuan Beijing, Sichuan Science and Technology Press.We digitised the accompanying maps provided in this book using the geographic alignment tool of Arcgis to obtain 910 occurrence data and the distribution of suitable habitat for giant pandas.Considering that the data used in this study was already publicly released in 2015,  we believe that uploading this data will not have any potential negative impact on the conservation of giant pandas.