Format

Send to

Choose Destination
Environ Monit Assess. 2018 Dec 5;191(1):4. doi: 10.1007/s10661-018-7130-4.

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

Author information

1
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou, 221116, Jiangsu, China. liangliang198119@163.com.
2
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210008, Jiangsu, China. liangliang198119@163.com.
3
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou, 221116, Jiangsu, China.
4
College of Biology and Environment Science, Jishou University, Jishou, 416000, Hunan, China. zxliu1965@163.com.

Abstract

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.

KEYWORDS:

Habitat selection; Logistic regression model; Rhinolophus sinicus; Spatial distribution prediction

PMID:
30519741
DOI:
10.1007/s10661-018-7130-4
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center