Update notes: Canoco, version 3.10

TL;DR: In this article, a method for detecting and quantifying spatial patterns over a wide range of scales is proposed by eigenvalue decomposition of a truncated matrix of geographic distances among the sampling sites.

TL;DR: After pointing out the key assumptions underlying CCA, the paper focuses on the interpretation of CCA ordination diagrams and some advanced uses, such as ranking environmental variables in importance and the statistical testing of effects are illustrated on a typical macroinvertebrate data-set.

TL;DR: Weighted averaging regression and calibration form a simple, yet powerful method for reconstructing environmental variables from species assemblages as discussed by the authors, which performs well with noisy, species-rich data that cover a long ecological gradient (>3 SD units).

TL;DR: In this article, the advantages and disadvantages of the preferred technique (weighted averaging partial least squares) are reviewed and the problems in model selection are discussed and the need for evaluation and validation of reconstructions is emphasised.