Macroclimate data overestimate range shifts of plants in response to climate change
Macroclimate data overestimate range shifts of plants in response to climate change
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DOI：
10.1038/s41558-023-01650-3
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发表时间：
2023-04-24
影响因子：
30.7
通讯作者：
Early, Regan
中科院分区：
文献类型：
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作者：
Maclean, Ilya M. D.;Early, Regan
目前的保护政策已经形成了预期，对许多物种来说，具有合适气候的地方将位于它们目前的范围之外，因此导致了许多保护的预测。在这里，我们表明，范围变化的幅度往往被高估的气候数据不反映微气候条件下，许多生物体的经验。我们使用宏观和小气候数据对244种荒原和草原植物分类群的历史（1977-1995年）分布进行了建模，并将这些分布预测到当今（2003-2021年）。而大气候模型预测的主要范围的变化（中位数14公里的移动），小气候模型预测本地化的变化，一般不到1公里，到有利的小气候，更密切地匹配观察到的模式的建立和灭绝。因此，在物种现有的地理范围内改善对避难种群的保护，对于生活在暴露于阳光下的环境中的物种来说，可能比辅助迁移和保护区网络的大修更有效。作者使用宏观和微观气候数据模拟了草地和石南地植物的历史和当前分布。虽然宏观气候模型预测需要大幅度的变化（中位数为14公里），但微观气候模型预测的变化要小得多，更接近于观察到的模式。
Current conservation policy has been shaped by the expectation that, for many species, places with suitable climate will lie outside their current range, thus leading to predictions of numerous extinctions. Here we show that the magnitude of range shifts is often overestimated as climate data used do not reflect the microclimatic conditions that many organisms experience. We model the historic (1977-1995) distributions of 244 heathland and grassland plant taxa using both macro- and microclimate data and project these distributions to present day (2003-2021). Whereas macroclimate models predicted major range shifts (median 14 km shift), microclimate models predicted localized shifts, generally of less than 1 km, into favourable microclimates that more closely match observed patterns of establishment and extirpation. Thus, improving protection of refugial populations within species' existing geographic range may, for species living in environments exposed to sunlight, be more effective than assisted translocations and overhaul of protected area networks.The authors model historic and current distributions of grassland and heathland plants using both macro- and microclimate data. While macroclimate models predict the need for major range shifts (14 km median), microclimate models predict much smaller shifts that more closely match observed patterns.