Explaining variation in tropical plant community composition: influence of environmental and spatial data quality
Explaining variation in tropical plant community composition: influence of environmental and spatial data quality
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DOI：
10.1007/s00442-007-0923-8
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发表时间：
2008-03-01
期刊：
影响因子：
2.7
通讯作者：
Olivas, Paulo C.
中科院分区：
文献类型：
--
作者：
Jones, Mirkka M.;Tuomisto, Hanna;Olivas, Paulo C.
相对于其他空间过程，植物群落组成的变化(β多样性)在多大程度上可以从环境变化中预测出来，目前是一个相当感兴趣的问题。我们在哥斯达黎加热带雨林的蕨类植物(1045个样地，127个物种)中解决了这个问题。我们还测试了数据质量对结果的影响，这在以前的研究中基本上被忽视了。为此，我们比较了两个可供选择的空间模型[多项式与相邻矩阵的主坐标(PCNM)]和十个可供选择的环境模型(所有可用的环境变量与四个子集，以及包括它们的多项式与非)。在环境数据类型中，土壤化学对蕨类植物群落变化的解释作用最大，其次是地形、土壤类型和森林结构。当包括环境变量的多项式时，环境解释差异适度增加。当使用多尺度PCNM空间模型而不是传统的大尺度多项式空间模型时，空间解释的差异显著增加。在对样点数和解释变量进行校正后，最佳模型组合(PCNM空间模型和包括多项式的全环境模型)可以解释32%的蕨类植物群落变异。总体而言，环境控制贝塔多样性的证据很充分，检测到的主要植物区系梯度与研究涵盖的所有尺度(约100-2000米)的环境变化相关。然而，根据模型选择的不同，总的解释差异超过四倍，空间和环境的明显相对重要性可能会颠倒。因此，我们主张更广泛地认识数据质量对分析结果的影响。对空间和环境过程对物种分布和贝塔多样性的相对贡献的一般理解需要将方法学上的人工制品与真实的生态差异分开。
The degree to which variation in plant community composition (beta-diversity) is predictable from environmental variation, relative to other spatial processes, is of considerable current interest. We addressed this question in Costa Rican rain forest pteridophytes (1,045 plots, 127 species). We also tested the effect of data quality on the results, which has largely been overlooked in earlier studies. To do so, we compared two alternative spatial models [polynomial vs. principal coordinates of neighbour matrices (PCNM)] and ten alternative environmental models (all available environmental variables vs. four subsets, and including their polynomials vs. not). Of the environmental data types, soil chemistry contributed most to explaining pteridophyte community variation, followed in decreasing order of contribution by topography, soil type and forest structure. Environmentally explained variation increased moderately when polynomials of the environmental variables were included. Spatially explained variation increased substantially when the multi-scale PCNM spatial model was used instead of the traditional, broad-scale polynomial spatial model. The best model combination (PCNM spatial model and full environmental model including polynomials) explained 32% of pteridophyte community variation, after correcting for the number of sampling sites and explanatory variables. Overall evidence for environmental control of beta-diversity was strong, and the main floristic gradients detected were correlated with environmental variation at all scales encompassed by the study (c. 100-2,000 m). Depending on model choice, however, total explained variation differed more than fourfold, and the apparent relative importance of space and environment could be reversed. Therefore, we advocate a broader recognition of the impacts that data quality has on analysis results. A general understanding of the relative contributions of spatial and environmental processes to species distributions and beta-diversity requires that methodological artefacts are separated from real ecological differences.