Habitat selection and prediction of the spatial distribution of the Chinese horseshoe bat (R. sinicus) in the Wuling Mountains

  • Liang LiangEmail author
  • Xiang Luo
  • Zhixiao LiuEmail author
  • Jiahui Wang
  • Ting Huang
  • Erzhu Li


Habitat selection by the Chinese horseshoe bat (Rhinolophus sinicus) in the Wuling Mountains was studied in this paper. Global positioning system (GPS), remote sensing (RS) and geographic information system (GIS) technologies were used to obtain ground survey data and analyse the habitat factors driving the distribution of R. sinicus. Based on these basic data, a binary logistic regression method was used to establish habitat selection models of R. sinicus. Then, the corrected Akaike information criterion (AICC) was used to screen an optimal model, and the Hosmer-Lemeshow test indicated that the optimal model has suitable goodness of fit. Finally, the optimal model was used to predict the spatial distribution of R. sinicus in the Wuling Mountains. Verification analysis showed that the overall accuracy of the model was 72.7% and that the area under the curve (AUC) value was 0.947, which indicated that the model was effective for predicting suitable habitat for R. sinicus. The model results also showed that the main factors that influenced habitat selection were slope, annual mean temperature and distances from roads, rivers and residential land. R. sinicus preferred areas far from roads and residential land and areas near rivers. Generally, higher values of slope and annual mean temperature were associated with a greater likelihood of R. sinicus presence. Therefore, the protection of the water bodies surrounding R. sinicus habitats and fully addressing the impacts of human activities on R. sinicus habitats are recommended to protect the survival and reproduction of the population.


Habitat selection Spatial distribution prediction Rhinolophus sinicus Logistic regression model 



The authors particularly thank NASA for providing the basic data (including the Landsat series data and ASTER GDEM data) and the NGCC for providing the GlobeLand30 data.

Author contributions

L. Liang and Z. X. Liu proposed the main idea and offered guidance to complete the work. L. Liang and X. Luo wrote the paper. Z. X. Liu provided ground survey data. X. Luo, J. H. Wang, T. Huang and E. Z. Li carried out the data processing and analysis.


This research was supported by the National Natural Science Foundation of China (No. 31560130 and No. 41401473), the Natural Science Foundation of Jiangsu Province (BK20181474), the Open Fund of State Key Laboratory of Remote Sensing Science (OFSLRSS201804), the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and the 2018 Jiangsu Province Graduate Research and Innovation Project (KYCX18_2157).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Geography, Geomatics and PlanningJiangsu Normal UniversityXuzhouChina
  2. 2.School of Geographic and Oceanographic SciencesNanjing UniversityNanjingChina
  3. 3.College of Biology and Environment ScienceJishou UniversityJishouChina

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