Harbert Robert S, Nixon Kevin C
Section of Plant Biology, 412 Mann Library, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853, USA L.H. Bailey Hortorium, Cornell University, Ithaca, New York 14853, USA.
Am J Bot. 2015 Aug;102(8):1277-89. doi: 10.3732/ajb.1400500. Epub 2015 Aug 5.
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Plant distributions have long been understood to be correlated with the environmental conditions to which species are adapted. Climate is one of the major components driving species distributions. Therefore, it is expected that the plants coexisting in a community are reflective of the local environment, particularly climate.•
Presented here is a method for the estimation of climate from local plant species coexistence data. The method, Climate Reconstruction Analysis using Coexistence Likelihood Estimation (CRACLE), is a likelihood-based method that employs specimen collection data at a global scale for the inference of species climate tolerance. CRACLE calculates the maximum joint likelihood of coexistence given individual species climate tolerance characterization to estimate the expected climate.•
Plant distribution data for more than 4000 species were used to show that this method accurately infers expected climate profiles for 165 sites with diverse climatic conditions. Estimates differ from the WorldClim global climate model by less than 1.5°C on average for mean annual temperature and less than ∼250 mm for mean annual precipitation. This is a significant improvement upon other plant-based climate-proxy methods.•
CRACLE validates long hypothesized interactions between climate and local associations of plant species. Furthermore, CRACLE successfully estimates climate that is consistent with the widely used WorldClim model and therefore may be applied to the quantitative estimation of paleoclimate in future studies.
• 研究前提：长期以来人们都知道植物分布与物种所适应的环境条件相关。气候是驱动物种分布的主要因素之一。因此，可以预期在一个群落中共存的植物反映了当地环境，尤其是气候。
• 方法：本文介绍了一种根据当地植物物种共存数据估算气候的方法。该方法，即使用共存似然估计的气候重建分析（CRACLE），是一种基于似然的方法，它利用全球范围内的标本采集数据来推断物种的气候耐受性。CRACLE通过给定单个物种的气候耐受性特征来计算共存的最大联合似然，以估计预期气候。
• 关键结果：利用4000多种植物的分布数据表明，该方法能准确推断出165个具有不同气候条件地点的预期气候概况。对于年平均温度，估计值与世界气候全球气候模型的差异平均不到1.5°C，对于年平均降水量，差异不到约250毫米。这比其他基于植物的气候代理方法有显著改进。
• 结论：CRACLE验证了长期以来关于气候与当地植物物种关联之间相互作用的假设。此外，CRACLE成功估算出与广泛使用的世界气候模型一致的气候，因此可应用于未来古气候的定量估计研究。
