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生态排序分析软件Canoco 5.1 已正式发布

Canoco 5.1实现了特征 - 环境分析和微生物群系数据分析的新方法。 现有的Canoco 5客户可以使用Canoco 5中的常规更新流程更新到Canoco 5.1。


设计用于微生物组分析的新方法是加权对数比率PCA(Greenacre和Lewi,2009),并且类似RDA。 新版本中包含了Canoco用于微生物组分析的完整工作实例。


特征 - 环境分析用于通过社区加权平均值(CWM)相关性或第四角相关性进行。 这些方法将单个性状与单个环境变量相关联。 这些分析现在完全扩展到多特征多环境变量案例。 新方法允许您检测哪些特征与环境的相关性,反之,哪些环境变量显示与特征的相关性。 该方法建立了一个回归模型,允许您量化特征,环境及其组合解释的变化量。


新的特质 - 环境方法的基本原理已在一份报告和两篇已发表的论文中进行了总结。文章链接了第四个基于GLM的回归,并给出了多特征多环境变量案例的扩展,这是简单的双约束对应分析(dc-CA)。 第二篇文章给出了dc-CA和Canoco使用的算法的完整描述。


Cormont等人(2011)的线性特征环境模型已经对双约束主成分分析(dc-PCA)进行了扩展。


新版本随每个新许可证附带了重新编写的手册。更新包含Canoco5 / pdf文件夹中的pdf,其中包含Canoco 5.1中的主要更改.


Canoco 5.1 implements new methods for trait-environment analysis and for micro-biome data analysis. Existing Canoco 5 customers can update to Canoco 5.1 with the usual update process from within Canoco 5.


The new method designed for micro-biome analysis is weighted log-ratio PCA (Greenacre and Lewi, 2009), and in similar vein, RDA. A fully worked example of the possibilities of Canoco for microbiome analysis is included in the new release.


Trait-environment analysis used to proceed via community weighted means (CWM) correlation or the fourth-corner correlation. These methods correlated a single trait to a single environmental variable. These analysis are now fully extended to the multi-trait multi-environmental variable case. The new method allows you to detect which traits show the highest correlation with the environment and, reversely, which environmental variables show the highest correlation with the traits. The method builds a regression model that allows you to quantify how much variation is explained by traits, by environment and by their combination.


The rationale for the new trait-environment methods has been summarized in a presentation and two published papers. The first paper links the fourth-corner GLM-based regression and gives the extension to the multi-trait multi-environmental variable case, which is simple double constrained correspondence analysis (dc-CA). The second paper gives a full description of dc-CA and the algorithm used by Canoco.


The linear-trait environment model of Cormont et al. (2011) has been extended similarly to double constrained principal component analysis (dc-PCA).


The new release has a reworked manual that comes with each new license. The free update comes with pdfs in the Canoco5/pdf folder containing the major changes in Canoco 5.1 (see details). 



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