| Issue |
BIO Web Conf.
Volume 142, 2024
2024 International Symposium on Agricultural Engineering and Biology (ISAEB 2024)
|
|
|---|---|---|
| Article Number | 02005 | |
| Number of page(s) | 5 | |
| Section | Agricultural Natural Systems and Crop Growth Research | |
| DOI | https://doi.org/10.1051/bioconf/202414202005 | |
| Published online | 21 November 2024 | |
Comparative study on habitat of Moschus berezovskii and Elaphodus cephalophus in Lixian County based on MaxEnt
1 College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, China
2 Forestry and grassland Bureau of Lixian County, Sichuan, China
* Corresponding author: kwl@swfu.edu.cn
In order to understand the spatial distribution pattern and habitat utilization characteristics of Moschus berezovskii and Elaphodus cephalophus in Lixian County, and provide scientific theoretical supports for protecting these two types of small animals, this study used geographical distribution points and environmental factor variables to construct a MaxEnt model to compare and analyse the habitat suitability of these two species of Artiodactyls animals. The results includes: (1) the constructed MaxEnt prediction models for the habitats of both species achieved an average AUC of 0.949 and 0.974 in the test set respectively, which can meet the needs of their habitat suitability analysis; (2) Elevation, variation coefficient of precipitation (bio15), mean diurnal range (Bio2), isothermality (bio3), annual mean temperature (Bio1), temperature annual range (bio17) are the six main environmental factors influencing the habitat suitability of Moschus berezovskii and Elaphodus cephalophus; (3) The habitat selection and utilization of the two species are very similar. This study implicating that the Moschus berezovskii and Elaphodus cephalophus are cospecies in Lixian County, and can be protected by using an uniform strategy.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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