空间Maxent:适应物种分布建模的空间数据
Lisa Bald1, Jannis Gottwald1, Dirk Zeuss1
1Department of Geography, Environmental Informatics Philipps-University Marburg Marburg Germany.
Ecology and evolution
|October 26, 2023
概括
新的空间Maxent软件通过计算空间数据结构来改进物种分布模型,从而为生物多样性保护做出更可靠的预测. 在大多数情况下,它的性能优于传统的方法.
科学领域:
- 生态建模 生态建模
- 生物多样性信息学 生物多样性信息学
- 计算生物学是一种计算生物学.
背景情况:
- 传统的物种分布模型往往缺乏预测准确性,因为空间数据结构没有被解决.
- 在模型训练过程中过度装配会损害对独立的,空间分离的数据的预测性能.
- 可靠的物种分布模型对于有效的生物多样性保护战略至关重要.
研究的目的:
- 介绍spatialMaxent,这是一个新的软件,将高级空间建模与Maxent集成在一起.
- 通过解决空间依赖和过度适应,增强物种分布建模.
- 为生态研究和保护实践提供一个用户友好的工具.
主要方法:
- spatialMaxent包含空间交叉验证,用于变量选择,特征选择和规则化乘数调整.
- 该软件明确考虑了训练数据中的空间依赖的影响,以减轻过度拟合.
- 绩效是使用一个大型,多样化的数据集 (NCEAS) 来评估的,包括全球200多种物种.
主要成果:
- 在80%的评估案例中,spatialMaxent与传统的Maxent和非空间调整模型相比表现出更高的性能.
- 实施的空间调整策略显著提高了模型可靠性和预测能力.
- 该软件在各种物种和地理区域中被证明是有效的.
结论:
- spatialMaxent通过有效处理空间数据结构,为物种分布建模提供了显著的进步.
- 它的用户友好性使得广泛的用户可以访问它,包括研究人员和保护从业人员.
- 该工具具有强大的潜力,可以通过改进的生态预测来帮助应对关键的生物多样性保护挑战.
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