Bayesian methods for comparing species physiological and ecological response curves
Bayesian methods for comparing species physiological and ecological response curves
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DOI：
10.1016/j.ecoinf.2016.03.001
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发表时间：
2016-07-01
影响因子：
5.1
通讯作者：
Robinson, Sharon A.
中科院分区：
文献类型：
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作者：
Ashcroft, Michael B.;Casanova-Katny, Angelica;Robinson, Sharon A.
许多生态学问题需要关于物种沿环境梯度的最佳条件或临界极限的信息。这些属性可以用来回答关于生态位划分、物种共存和生态位保守性的问题。然而，当现有的方法没有量化属性中的不确定性或依赖于关于物种对环境梯度的反应的形状的假设时，这些比较是不令人信服的。这项研究的目的是开发一个模型来量化物种响应曲线属性中的不确定性，并允许对它们进行实质性差异测试，而不对响应的形状做出假设。我们开发了一个模型，该模型使用贝叶斯惩罚样条法来产生和比较任意两个给定物种的反应曲线。这些样条线允许数据确定响应曲线的形状，而不是做出先验假设。模型是使用R的R2OpenBUGS程序包实现的，该程序包使用马尔可夫链蒙特卡罗模拟来重复地将替代响应曲线与数据进行拟合。由于每次迭代产生一条不同的曲线，该曲线在最优、生态位宽度和限制方面各不相同，该模型估计了这些属性中每一个属性的不确定性以及两条曲线不同的概率。这些模型是用来自南极洲的两个苔藓数据集进行测试的。这两个数据集都具有高度的分散性，这是生态学研究的典型特征。这种噪声导致物种响应曲线的最佳值和极限值存在相当大的不确定性，但发现了实质性的差异。在强光条件下，高山五味子的光合作用最适温度低于高山五味子。我们的研究强调了考虑物种响应曲线的生理最优和其他属性的不确定性的重要性。我们发现，在处理嘈杂的生态数据时，7.5摄氏度的最优值的明显差异不一定是实质性的，在比较不同物种的曲线时，有必要考虑属性的不确定性。该模型可以提高生态位划分、物种共存和生态位保守性研究的稳健性。(C)2016爱思唯尔B.V.保留所有权利。
Many ecological questions require information on species' optimal conditions or critical limits along environmental gradients. These attributes can be compared to answer questions on niche partitioning, species coexistence and niche conservatism. However, these comparisons are unconvincing when existing methods do not quantify the uncertainty in the attributes or rely on assumptions about the shape of species' responses to the environmental gradient. The aim of this study was to develop a model to quantify the uncertainty in the attributes of species response curves and allow them to be tested for substantive differences without making assumptions about the shape of the responses. We developed a model that used Bayesian penalised splines to produce and compare response curves for any two given species. These splines allow the data to determine the shape of the response curves rather than making a priori assumptions. The models were implemented using the R2OpenBUGS package for R, which uses Markov Chain Monte Carlo simulation to repetitively fit alternative response curves to the data. As each iteration produces a different curve that varies in optima, niche breadth and limits, the model estimates the uncertainty in each of these attributes and the probability that the two curves are different. The models were tested using two datasets of mosses from Antarctica. Both datasets had a high degree of scatter, which is typical of ecological research. This noise resulted in considerable uncertainty in the optima and limits of species response curves, but substantive differences were found. Schistidium antarctici was found to inhabit wetter habitats than Ceratodon purpureus, and Polytrichastrum alpinum had a lower optimal temperature for photosynthesis than Chorisodontium aciphyllum under high light conditions. Our study highlights the importance of considering uncertainty in physiological optima and other attributes of species response curves. We found that apparent differences in optima of 7.5 degrees C were not necessarily substantive when dealing with noisy ecological data, and it is necessary to consider the uncertainty in attributes when comparing the curves for different species. The model introduced here could increase the robustness of research on niche partitioning, species coexistence and niche conservatism. (C) 2016 Elsevier B.V. All rights reserved.