Canoco 5.1职能 - 环境分析和微生物群系数据分析的新步骤。 现有的Canoco 5客户能够使用Canoco 5中的通例更新流程更新到Canoco 5.1。

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设计用于微生物组分析的新步骤是加权对数比率PCA（Greenacre和Lewi，2009），并且RDA。 新版本中了Canoco用于微生物组分析的工作事俘。

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职能 - 环境分析用于通过社区加权均匀值（CWM）有关性或第四角有关性进行。 这些步骤将单个性状与单个环境变量有关联。 这些分析此刻扩大到多属性多环境变量案例。 新步骤允许您检测哪些属性与环境的有关性，反之，哪些环境变量显示与属性的有关性。 该步骤成立了回归模型，允许您量化属性，环境及其组合诠释的变动量。

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新的特质 - 环境步骤的根基道理已在一份汇报和两篇已颁发的论文中进行了总结。文章链接了第四个基于GLM的回归，并给出了多属性多环境变量案例的扩大，这是的双约束对应分析（dc-CA）。 篇文章给出了dc-CA和Canoco使用的算法的描述。

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Cormont等人（2011）的线性属性环境模型已经对双约束主成分分析（dc-PCA）进行了扩大。

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新版本随新附带了沉新编写的手册。更新Canoco5 / pdf文件夹中的pdf，其中Canoco 5.1中的重要更改.

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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.

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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.

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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.

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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.

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

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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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