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生物多样性的动态和机制的研究，尤其是物种多样性和生态系统多样性的研究的核心问题之一就是物种和系统空间分布定量规律及其动态的研究，群落数量分析则是完成上述研究的主要手段。过去20 年里植物群落学的数量分析方怯得到了极大的发展，其重要原因之一是由于计算机技术的高速发展和在生态学领域里的日益广泛的应用梯度分析方法，作为群落生态学定量研究的重要方法也经厉了从简到繁从直接梯度分析到间接梯度分析又回到直接梯度分析（可称组合直接梯度分析）的发展过程。梯度分析所隐含的基本假设是研究对象范围内的植物群落中的各植物种及其所处的环境中的各种生态因子在空间作连续或近似连续分布同其他数量分析方法一样，梯度分析的根本目的是为了揭示植物群落的结构的空间变化与环境因子空间的关系(Digby 和Kempton, 1987 Gauch, 1982 随着分析理论和方法的不断改进和完善各种通用性计算机软件程序也不断出现目前国际上比较成熟的和应用较为广泛的计算软件CANOCO(Ter Braak,1988)。
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Canoco for Windows 4.5介绍

Canoco for Windows is the next generation of CANOCO software, the most popular tool for constrained and unconstrained ordination in ecological applications. Canoco for Windows integrates ordination with regression and permutation methodology, so as to allow sound statistical modelling of ecological data. Canoco for Windows contains both linear and unimodal methods. Ordination with Canoco for Windows can provide insight into:

1. the structure of biological communities,
2. the relations between plant and animal communities and their environment,
3. the effects of a putative impact on the environment and/or its biological communities, and
4. the effects of treatments of complex ecological and ecotoxicological experiments on biological communities.

Ordination diagrams can be displayed on screen immediately after an ordination has been calculated. Canoco is unique in its capability to account for background variation specified by covariables and in its extensive facilities for permutation tests, including tests of interaction effects. These unique features make Canoco for Windows particularly effective in solving applied research problems.

Canoco has been designed for ecologists, but Canoco has also been used in toxicology, soil science, geology, public health research and market research, to name a few.

Canoco 软件使用平台： WIN98 WIN2000 WINXP。

Canoco for Windows 4.5 多维统计分析方法:

A. Unconstrained ordination methods -
methods to describe the structure in a single data set:

• principal components analysis (PCA), with various combinations of data standardization by rows and/or by columns (so supporting, among others, PCA on a covariance matrix and PCA on a correlation matrix). A special case is Aitchison's log-ratio PCA for compositional data
• correspondence analysis (CA), also known as reciprocal averaging
• detrended correspondence analysis (DCA), often incorrectly named as DECORANA, after the original program implementing DCA
• principal coordinates analysis (PCO), a classical method of the metric multidimensional scaling

B. Canonical ordination methods -
methods to explain one data set by another data set (ordinations constrained by explanatory variables):

• redundancy analysis (RDA), also called reduced-rank regression, the canonical form of PCA. Special cases are simple and multiple regression, analysis of variance and the log-ratio form of reduced-rank regression
• canonical correspondence analysis (CCA), the canonical form of CA
• detrended canonical correspondence analysis (DCCA), the canonical form of CCA
• canonical variate analysis (CVA), better known as Fisher linear discriminant analysis
• distance-based redundancy analysis (db-RDA), a constrained form of principal coordinates analysis (PCO)

C. Partial ordination methods -
methods to describe the structure in a data set after accounting for variation explained by a second data set (covariable data):

• partial PCA
• partial CA
• partial DCA

D. Partial canonical ordination methods -
methods to explain one data set by another data set after accounting for variation by a third data set (covariable data):

• partial RDA
• partial CCA
• partial DCCA
• partial CVA

For all the listed multivariate methods, you can have a supplementary data set with explanatory variables, that are projected a posteriori into the ordination space to facilitate the interpretation of results.

The statistical significance of the explanatory variables in (partial) canonical methods can be determined by Monte Carlo permutation tests. Explanatory variables can be tested jointly (overall test) or separately after adjusting for other explanatory variables (partial tests). The problem of (auto-)correlation between samples can be overcome by using special permutation schemes. Canoco for Windows has built-in schemes for:

• data from one or more equi-spaced time series, line transects, or rectangular grids of samples
• data originating from repeated measurement designs, Before-After-Control-Impact (BACI design) and
• data from nested and crossed designs with fixed and random factors

Other useful features include forward selection of explanatory variables, and ordination diagnostics on outliers and influential data points.

In addition, the plotting program CanoDraw for Windows contains both elementary and advanced methods for interpreting ordination diagrams. Elementary methods include:

• plotting values of species or explanatory variables in the ordination diagram
• plotting diversity values in the ordination diagram
• plotting samples or species by group symbols

More advanced methods include:

• fitting and plotting species response curves along ordination axes and
• contouring species or explanatory variables in the ordination diagram by:
  • generalized linear modelling (e.g. Gaussian response curves/surfaces)
  • loess smoothing
  • generalized additive modelling (GAM)

Canoco for Windows 软件包组成 

Canoco for Windows: to specify and calculate ordination analyses, and to view and plot the results. The Canoco for Windows module is truly interactive and utilizes the rich Microsoft Windows® user interface, with context-sensitive online help at each step. 
WCanoImp: to import spreadsheet data. 
CanoDraw 4.0 for Windows: to graph all basic types of ordination diagrams and to fully explore ordination results. CanoDraw is launched directly from the Canoco for Windows module. 
CANOCO 4.5 Console Version: to run Canoco in batch for simulation studies and tailor-made applications. Also for users who want to stay with the CANOCO 3.1 textual user interface. 
CanoMerge: to merge column-wise two or more data tables, to export data in TAB-separated format, or to remove rare species from data tables. 
PrCoord: to compute metric multidimensional scaling (= principal coordinates analysis, PCO) for specified dataset and to support calculation of distance-based RDA.
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Canoco 试用版本其相关资料
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