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评估物种分布模型的时间预测与古动物学记录对比

Ignacio A Lazagabaster^1,2, Chris D Thomas³, Juliet V Spedding⁴

¹Department of Evolution, Ecology and Behaviour University of Liverpool Liverpool UK.

Ecology and evolution

|October 24, 2024

概括

很少测试物种分布模型 (SDM) 的长期准确性. 古动物学数据显示,最适合当前物种分布的模型可能无法准确预测过去的分布,这凸显了古生物学的必要性.

科学领域:

古生态学古生态学
生态建模生态建模
气候变化生物学气候变化生物学

背景情况:

物种分布模型 (SDM) 对于预测物种对气候变化的反应至关重要.
目前的SDM评估主要侧重于模型适合当今的分布.
在环境变化下对SDM的长期绩效评估仍未得到充分探索.

研究的目的:

通过使用古动物学记录,独立评估SDM的预测准确度.
将传统模型评估指标与用于历史分布后预测的新型"古文计"进行比较.
调查输入数据和建模算法的对SDM性能的影响.

主要方法:

使用了Holocene存在/缺席记录的Hardebeest (Alcelaphus buselaphus) 的记录.
将各种SDM算法应用于事件数据集 (点数据,当前范围地图,"自然"范围地图).
传统指标 (TSS_c,AUC_c) 与古测量 (TSS_p,AUC_p,Boyce_p,F2-score_p,Sorensen_p) 的比较.

主要成果:

发现常规度量与古度量之间存在弱相关性.
确定在当前分布中表现出色的模型可能无法可靠地预测历史分布.
证明输入数据选择和算法显著影响SDM预测和性能.

结论:

倡导 SDM 绩效的古测量评估,特别是专注于存在预测的指标 (F2-score,Sorensen).
强调古动物记录中缺席数据的潜在不可靠性.
建议整合古纪录以更好地了解对物种分布随时间的环境约束.

关键词:

整个全新纪 (Holocene) 的时间.SDM SDM SDM SDM SDM SDM SDM SDM SDM SDM SDM SDM SDM SDM SDM SDM SDM SDM 准确度准确度准确度准确度准确度哈德贝斯特是最坚强的人.模型评价模型评价过去的过去,过去的过去.灵敏度灵敏度灵敏度灵敏度灵敏度真正的技能统计数据统计数据

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