只有存在数据的物种分布模型的新值选择方法:通过值回归提取P/E曲线的突变点
Boyang Yu1, Wenyu Dai1, Siqing Li1
1Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science Nanjing University Nanjing China.
Ecology and evolution
|April 4, 2024
概括
一种新的方法,Boyce-Threshold量子回归 (BTQR),客观地选择物种分布模型的值,而不需要伪缺席. 这种方法使用预测到预期的曲线.
科学领域:
- 生态生态学 生态生态学
- 计算生物学 计算生物学
- 环境科学 环境科学
背景情况:
- 门选择对于物种分布模型 (SDM) 至关重要,但目前仅存在数据的方法是主观的或需要未经验证的伪缺席.
- 现有的方法通常依赖于研究人员的决定或数据,这些数据不能直接代表物种的存在.
研究的目的:
- 为物种分布模型引入一个客观值选择方法,Boyce值量子回归 (BTQR).
- 消除对虚假缺席数据和主观研究人员在值确定中的投入的需求.
- 通过使用各种数据集,验证BTQR与传统方法的有效性.
主要方法:
- 拟议的博伊斯值量子回归 (BTQR) 方法用于客观值选择.
- 在预测到预期 (P/E) 曲线中确定突变点作为值识别的关键特征,由源沉积理论提供信息.
- 使用值回归来准确地确定P/E曲线上的突变点.
主要成果:
- 在不需要伪缺席数据的情况下,BTQR成功地客观地确定了值.
- 与九种传统的门选择方法相比,比较实验表明BTQR的精度,适用性和一致性优越.
- 在六种不同的物种分布模型中,使用四种虚拟物种和一种真实物种进行了验证.
结论:
- 在物种分布建模中,BTQR为值选择提供了一个强大的客观替代方案.
- 该方法依赖于固有的曲线特征 (突变点) 使其具有广泛的适用性和一致性.
- 在生态应用中,BTQR提高了二进制物种分布输出的可靠性.
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