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用气候、接近度和系统发育推断人口统计学:谨慎使用该方法。

Extrapolating demography with climate, proximity and phylogeny: approach with caution.

机构信息

School of Biological Sciences, Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, Qld., 4072, Australia.

Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield, UK.

出版信息

Ecol Lett. 2016 Dec;19(12):1429-1438. doi: 10.1111/ele.12691. Epub 2016 Oct 28.

Abstract

Plant population responses are key to understanding the effects of threats such as climate change and invasions. However, we lack demographic data for most species, and the data we have are often geographically aggregated. We determined to what extent existing data can be extrapolated to predict population performance across larger sets of species and spatial areas. We used 550 matrix models, across 210 species, sourced from the COMPADRE Plant Matrix Database, to model how climate, geographic proximity and phylogeny predicted population performance. Models including only geographic proximity and phylogeny explained 5-40% of the variation in four key metrics of population performance. However, there was poor extrapolation between species and extrapolation was limited to geographic scales smaller than those at which landscape scale threats typically occur. Thus, demographic information should only be extrapolated with caution. Capturing demography at scales relevant to landscape level threats will require more geographically extensive sampling.

摘要

植物种群的反应是理解气候变化和入侵等威胁影响的关键。然而,我们缺乏大多数物种的人口统计数据,而且我们拥有的数据通常在地理上是聚合的。我们确定了现有数据在多大程度上可以外推,以预测更大的物种和空间范围内的种群表现。我们使用了来自 COMPADRE 植物矩阵数据库的 550 个矩阵模型,涉及 210 个物种,来模拟气候、地理接近度和系统发育如何预测种群表现。仅包括地理接近度和系统发育的模型解释了四个关键种群表现指标的 5-40%的变化。然而,在物种之间的外推效果很差,外推仅限于比景观尺度威胁通常发生的地理尺度小的范围。因此,人口统计信息的外推应该谨慎。在与景观水平威胁相关的尺度上获取人口统计数据将需要更广泛的地理采样。

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