Allocating C-S-R Plant Functional Types: A Soft Approach to a Hard Problem

TLDR

The C‑S‑R classification of plant functional types, developed from extensive long‑term research, is broadly applicable to vegetation and offers substantial potential for interpreting and predicting ecosystem properties worldwide. The study aims to develop and test a rapid method for attributing C‑S‑R types to species lacking prior ecological data, evaluating its accuracy against a more laborious classification in Britain. Based on a long‑term northern‑England research programme, the authors present a rapid C‑S‑R attribution method that assigns functional types to unknown herbaceous species using a few simple predictor variables, implemented via downloadable spreadsheets available from the UCPE website. The study confirms that patterns of covariation among the traits used in the C‑S‑R classification have been validated in this journal.

Abstract

A long-term research programme, conducted mainly in northern England, has involved field surveys (1965-77), laboratory screening (1974-96), monitoring of permanent plots (1958 to date) and manipulative experiments (1987 to date). The so-called C-S-R classification of plant functional types developed from all this activity. Patterns of covariation among the traits used in the classification have recently been validated in this journal. The C-S-R classification appears to be applicable to vegetation in general. It thus has considerable potential for interpreting and predicting vegetation and ecosystem properties on a world-wide scale. However, to realize this potential we need to develop simplified procedures to extrapolate the C-S-R system to the many species which have not been the subject of previous ecological investigation. Here we describe a rapid method for attribution of C-S-R type and we test its accuracy in Britain by comparing it with an independent classification based upon more laborious procedures. The new method allocates a functional type to an unknown herbaceous subject using few, simple predictor variables. We have developed spreadsheets to perform all of the necessary calculations. These may be downloaded from the UCPE website at http://www.shef.ac.uk/uni/academic/N-Q/ nuocpe, or obtained by direct application to the E-mail address [e-mail].