How tomodel species responses along ecological gradients - Huisman-Olff-Fresco models revisited
How tomodel species responses along ecological gradients - Huisman-Olff-Fresco models revisited
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DOI：
10.1111/jvs.12050
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发表时间：
2013-11-01
影响因子：
2.8
通讯作者：
Oksanen, Jari
中科院分区：
文献类型：
--
作者：
Jansen, Florian;Oksanen, Jari
在物种响应建模中，分层逻辑回归框架能否在统计推断方面与GAM竞争？双峰形状对模拟沿着生态梯度的物种响应有用吗？在分层逻辑回归建模[也称为Huisman，Olff，Fresco（霍夫）模型]中，使用统计信息标准从一组预定模型中选择最佳模型，即模型拟合数据和模型简单性之间的平衡。我们扩展了经典的五个模型类型与两个双峰形状。我们改进了模型优化过程，以抑制不切实际的陡坡和突变。模型选择的稳定性是通过自举来保证的。该框架进行了测试的数据集547植被地块的耕地测量土壤pH值（KCL）。用人工数据集测试了再现已知形状的能力。形状参数，如生态位宽度和范围，斜率（营业额）和物种最佳，可以计算出的模型，并用于进一步的分析。该模型框架与先进的绘图功能包括在包eHOF的统计软件环境R. ResultsBased的AIC，66 131种建模与模型拟合和模型复杂性之间的一个更好的妥协的逻辑回归模型相比，GAM自动平滑的选择。在模型框架内，17个物种（13%）最好的建模与新的双峰类型之一。与已知形状的人工数据的测试揭示了良好的可靠性eHOF模型的单峰响应在均匀的信息领域，但增加不确定性，如果采样是不均匀的，或者如果只有一部分的响应被覆盖在所观察到的梯度range.ConclusionsHierarchical logistic回归模型提供了一种灵活的方法来有效地适应物种的响应数据。他们提出了一个健全的理论背景的生态解释。扩展的霍夫模型，这里被判断为一个有效的工具，单变量物种响应建模。
QuestionsIn species response modelling, can a hierarchical logistic regression framework compete against GAM in terms of statistical inference? Are bimodal shapes useful to model species responses along ecological gradients?LocationGermany.MethodsIn hierarchical logistic regression modelling [also known as Huisman, Olff, Fresco (HOF) models], the best model is chosen from a set of predetermined models using statistical information criteria, i.e. a balance between model fit to the data and simplicity of the model. We extended the classical five model types with two bimodal shapes. We improved the model optimization process to inhibit unrealistically steep slopes and abrupt changes. The stability of model choices is safeguarded through bootstrapping. The framework was tested on a data set of 547 vegetation plots of arable land with measured soil pH(KCL). The ability to reproduce known shapes was tested with artificial data sets. Shape parameters, e.g. niche width and range, slope (turnover) and species optima, can be calculated from the models and used for further analyses. The model framework together with advanced plot functions is included in the package eHOF for the statistical software environment R.ResultsBased on the AIC, 66 out of 131 species are modelled with a better compromise between model fit and model complexity by one of the logistic regression models as compared to GAM with automatic smoother selection. Within the model framework, 17 species (13%) are best modelled with one of the new bimodal types. The test with artificial data of known shape reveals good reliability of eHOF models for unimodal responses in areas with homogeneous information, but increasing uncertainty if the sampling is uneven or if only a part of the response is covered within the observed gradient range.ConclusionsHierarchical logistic regression models offer a flexible way to efficiently fit species response data. They propose a sound theoretical background for ecological interpretation. Extended HOF models as presented here are judged as an effective tool for univariate species response modelling.