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以数据为中心的物种分布建模:在对入侵性欧洲青咬伤的案例研究中,建模器决策的影响

Sara E Hansen¹, Michael J Monfils², Rachel A Hackett²

¹Central Michigan University 2401 Biosciences Building Mount Pleasant 48858 Michigan USA.

Applications in plant sciences

|June 24, 2024

概括

数据选择显著影响物种分布模型 (SDM). 仔细的数据处理和探索对于准确的生态解释和可重复的保护决策至关重要.

科学领域:

生态生态学生态生态学
保护生物学保护生物学
生物地理学是生物地理学.

背景情况:

物种分布模型 (SDM) 是保护规划的重要工具.
SDM的准确性和可解释性在很大程度上取决于数据质量和处理方法.
了解数据选择如何影响模型结果对于可靠的生态应用至关重要.

研究的目的:

调查数据选择和处理决策对SDM的影响.
评估不同数据特征如何影响模型性能和变量重要性.
提供关于SDM开发中数据处理最佳实践的指导.

主要方法:

用不同的数据集开发了入侵植物欧洲青虫的SDM.
测试了排除缺失和相关数据的影响.
评估数据规模,来源和类型 (存在-背景与存在-缺失) 对模型结果的影响.

主要成果:

除了缺失的/相关的数据和数据规模/类型的选择,显著改变了模型性能指标.
数据来源和类型影响了关键环境驱动因素的识别和息地适宜性预测.
模型的结果根据特定的数据处理决策而有很大差异.

结论:

数据的选择和处理对SDM结果及其生物解释产生了关键的影响.
强调数据探索和透明协议可以提高SDM的可重复性和可靠性.
有信息的以数据为中心的方法对于强大的生态建模和保护策略是必要的.

关键词:

这种水虫叫Hydrocharis morsus-ranae.侵袭性水生植物侵袭性水生植物数据中心的数据中心机器学习是机器学习.自然历史收藏,自然历史收藏.开放数据是开放的数据.可重复的研究可重复性研究种类分布模型的物种分布模型.

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Frequency-dependent Selection

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Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.