DOI: 10.5061/dryad.jq2bvq8fb
title: Data for: A new threshold selection method for species distribution models with presence-only data: extracting the mutation point of the P/E curve by threshold regression

description: 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.
description: 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.
description: # The distribution data of giant pandas recorded in the fourth survey on
 Giant Panda Includes 910 presence records and the suitable habitat map of
 giant pandas. ## Description of the Data and file structure The dataset is
 organized into two files: *'Panda_Presence_data.xls' the
 presence records of giant pandas. the location of presence data is
 represented by latitude and longitude. The precision of coordinate data is
 0.1 decimal degrees, which is not able to have potential negative impacts
 on species. *'Suitable_habitat.tif' the suitable habitat map of
 giant pandas, where Areas assigned a value of 1 are suitable habitats. ##
 Sharing/access Information Data was derived from the following sources:
 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.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.