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首页> 外文期刊>Global change biology >Climate change and plant distribution: local models predict high-elevation persistence
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Climate change and plant distribution: local models predict high-elevation persistence

机译:气候变化与植物分布:当地模型预测高海拔持续性

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摘要

Mountain ecosystems will likely be affected by global warming during the 21st century, with substantial biodiversity loss predicted by species distribution models (SDMs). Depending on the geographic extent, elevation range, and spatial resolution of data used in making these models, different rates of habitat loss have been predicted, with associated risk of species extinction. Few coordinated across-scale comparisons have been made using data of different resolutions and geographic extents. Here, we assess whether climate change-induced habitat losses predicted at the European scale (10 x 10' grid cells) are also predicted from local-scale data and modeling (25 m x 25 m grid cells) in two regions of the Swiss Alps. We show that local-scale models predict persistence of suitable habitats in up to 100% of species that were predicted by a European-scale model to lose all their suitable habitats in the area. Proportion of habitat loss depends on climate change scenario and study area. We find good agreement between the mismatch in predictions between scales and the fine-grain elevation range within 10 x 10' cells. The greatest prediction discrepancy for alpine species occurs in the area with the largest nival zone. Our results suggest elevation range as the main driver for the observed prediction discrepancies. Local-scale projections may better reflect the possibility for species to track their climatic requirement toward higher elevations.
机译:山区生态系统很可能会受到21世纪全球变暖的影响,物种分布模型(SDM)预测生物多样性将大量减少。根据用于制作这些模型的数据的地理范围,海拔范围和空间分辨率,已经预测了不同的生境丧失速率,并伴有物种灭绝的风险。使用不同分辨率和地理范围的数据很少进行跨尺度的比较。在这里,我们评估是否还根据瑞士阿尔卑斯山两个地区的局部规模数据和模型(25 m x 25 m网格),以欧洲规模(10 x 10'网格)预测的气候变化引起的栖息地损失。我们表明,地方尺度模型预测了欧洲尺度模型所预测的多达100%的物种的合适栖息地的持久性,该物种将失去该地区所有合适的栖息地。生境丧失的比例取决于气候变化情景和研究区域。我们发现尺度之间的预测不匹配与10 x 10'单元内的细颗粒高程范围之间存在良好的一致性。对于高山物种而言,最大的预测差异出现在最大的雪域区。我们的结果表明海拔范围是观察到的预测差异的主要驱动因素。局部尺度的投影可能更好地反映物种向更高海拔跟踪其气候需求的可能性。

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