通过测量和传达不确定性来改善物种分布预测:一种入侵物种案例研究
Shyam M Thomas1, Michael R Verhoeven1, Jake R Walsh1
1Department of Fisheries, Wildlife and Conservation Biology and Minnesota Aquatic Invasive Species Research Center, University of Minnesota, Saint Paul, Minnesota, USA.
Ecology
|April 13, 2024
概括
使用物种分布模型 (SDM) 预测入侵物种风险至关重要. 对于非模拟条件,不确定性很高,需要方法来量化模型变异性,以便更好地进行侵入性巨菌管理.
科学领域:
- 生态生态学 生态生态学
- 气候变化生物学 气候变化生物学
- 侵袭性物种管理 侵袭性物种管理
背景情况:
- 预测气候变化下的入侵物种风险对于有效管理至关重要.
- 物种分布模型 (SDM) 是关键的工具,但预测可能是不确定的,特别是当推断到新的环境条件时.
研究的目的:
- 评估不同功能形式和气候模型对入侵性巨型植物Myriophyllum spicatum预计息地适宜性的影响.
- 量化SDM预测中的不确定性,特别是非模拟的未来气候条件.
主要方法:
- 在未来的气候场景下利用高分辨率的湖水温度预测.
- 采用了五种全球循环模型和三种统计模型,具有不同的物种温度反应功能.
- 在模拟和非模拟热域中分析了Myriophyllum spicatum的息地适宜性变化.
主要成果:
- 在未来的气候条件下,湖泊适合M. spicatum的总体预测增加.
- 湖泊之间的适合性变化大小和方向的显著变化.
- 在经历非模拟温度条件的湖泊中观察到的最高预测不确定性.
结论:
- 整合多样化的SDM预测和明确量化不确定性对于强大的入侵物种预测至关重要.
- 了解不同气候和模型场景中的不确定性可以改善入侵物种管理策略.
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