基于最大熵和随机森林算法的山地和低地Anoa生境潜力空间分析

WORLD Pub Date : 2023-10-03 DOI:10.3390/world4040041
Diah Ardiani, Lalu Muhamad Jaelani, Septianto Aldiansyah, Mangapul Parlindungan Tambunan, Mochamad Indrawan, Andri A. Wibowo
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引用次数: 0

摘要

Anoa是苏拉威西岛特有的一种野生动物,看起来像一头小母牛。在世界自然保护联盟的红色名录中,Anoa被列为易灭绝物种。野蛙有低地野蛙(Bubalus depressicornis)和山地野蛙(Bubalus quarlesi)两种。采用最大熵(MaxEnt)机器学习算法和随机森林(RF)方法对Anoa物种的潜在栖息地模型进行了比较。该建模使用了8个环境变量。其中,基于潜在栖息地建模结果,RF 75:25模型是最佳算法,变量贡献最大,湿度为82.444%,潜在面积为苏拉威西岛的5%,曲线下面积(AUC)为0.987。同时,最佳的生境潜力模型为RF 70:30算法,其变量贡献最高,种群占苏拉威西岛的88.891%,潜在面积占苏拉威西岛的36%,AUC为0.967。这表明,Anoa灭绝是非常敏感的湿度和人口水平的存在。
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Spatial Analysis of Mountain and Lowland Anoa Habitat Potential Using the Maximum Entropy and Random Forest Algorithm
The Anoa is a wild animal endemic to Sulawesi that looks like a small cow. Anoa are categorized as vulnerable to extinction on the IUCN red list. There are two species of Anoa, namely Lowland Anoa (Bubalus depressicornis) and Mountain Anoa (Bubalus quarlesi). In this study, a comparison of potential habitat models for Anoa species was conducted using Machine Learning algorithms with the Maximum Entropy (MaxEnt) and Random Forest (RF) methods. This modeling uses eight environmental variables. Where based on the results of Bubalus quarlesi potential habitat modeling, the RF 75:25 model is the best algorithm with the highest variable contribution, namely humidity of 82.444% and a potential area of 5% of Sulawesi Island, with an Area Under Curve (AUC) of 0.987. Meanwhile, the best Bubalus depressicornis habitat potential model is the RF 70:30 algorithm, with the highest variable contribution, namely population of 88.891% and potential area of 36% of Sulawesi Island, with AUC 0.967. This indicates that Anoa extinction is very sensitive to the presence of humidity and human population levels.
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