卷积神经网络的表现优于其他仅存在的物种分布建模算法
Akash Anand1, Benjamin Deneu2, Volker C Radeloff1
1SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin, Madison, WI 53706.
概括
卷积神经网络 (CNN) 通过捕捉空间背景来改善物种分布建模,优于预测物种范围的传统方法,特别是在罕见物种中. 数据增强进一步提高了CNN在生物多样性建模中的准确性.
科学领域:
- 生态生态学 生态生态学
- 生物多样性建模模型
- 保护生物学 保护生物学
背景情况:
- 物种分布模型 (SDM) 对于理解物种的发生和预测分布至关重要.
- 大多数SDM都无法捕捉影响物种的空间异质性和景观模式,因为关系在空间范围内是等级化的.
- 卷积神经网络 (CNN) 可以处理空间上下文,包括异质性,模式和多层次关系.
研究的目的:
- 评估CNN是否在使用仅存在数据预测物种分布方面优于传统的SDM算法.
- 为了比较CNN的表现与广泛使用的算法,如Maxent和合奏模型.
- 评估数据增强对CNN业绩的影响,特别是对数据有限的物种.
主要方法:
- 通过仅使用存在数据,在不同地区和种群中建模了225种物种.
- 将CNN与马克森特和合奏模型进行了比较.
- 评估了CNN中数据增强的效率,以减轻对有限训练数据的敏感性.
主要成果:
- CNNs 始终超过其他 SDM 算法.
- 与整体模型 (0.74和0.61) 相比,使用增强数据的CNN实现了更高的中位数AUC_ROC (0.77) 和AUC_PRG (0.78).
- 对于罕见的物种 (<30个事件),CNN与增强保持高性能 (AUC_ROC = 0.75),超过组合模型 (AUC_ROC = 0.68).
结论:
- CNN有效地结合了多尺度的空间复杂性,提高了物种分布建模中的预测准确性.
- CNNs,特别是数据增强,为模拟罕见和数据有限的物种提供了显著的优势.
- 生物多样性CNN显示了改变生物多样性建模的潜力,为保护提供了更具空间明确性和生态意义的利基代表.
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