Editing Phenetics (section)

==Current usage==
Traditionally there was much debate between pheneticists and cladists, as both methods were proposed initially to resolve evolutionary relationships. One of the most noteworthy applications of phenetics were the [[DNA–DNA hybridization]] studies by [[Charles G. Sibley]], [[Jon E. Ahlquist]] and [[Burt L. Monroe Jr.]], from which resulted the 1990 [[Sibley-Ahlquist taxonomy]] for [[bird]]s. Controversial at its time, some of its findings (e.g. the [[Galloanserae]]) have been vindicated, while others (e.g. the all-inclusive "[[Ciconiiformes]]" or the "[[Corvida]]") have been rejected. However, with computers growing increasingly powerful and widespread, more refined cladistic [[algorithm]]s became available which could test the suggestions of [[Willi Hennig]]. The results of cladistic analyses were proven superior to those of phenetic methods, at least for resolving phylogenies.

Many systematists continue to use phenetic methods, particularly to address species-level questions. While a major goal of taxonomy remains describing the 'tree of life' – the evolutionary relationships of all species – for [[fieldwork]] one needs to be able to separate one [[taxon]] from another. Classifying diverse groups of closely related organisms that differ very subtly is difficult using a cladistic method. Phenetics provides numerical methods for examining patterns of variation, allowing researchers to identify discrete groups that can be classified as species.

Modern applications of phenetics are common for [[botany]], and some examples can be found in most issues of the journal ''[[Systematic Botany]]''. Indeed, due to the effects of [[horizontal gene transfer]], [[polyploid complex]]es and other peculiarities of plant [[genomics]], phenetic techniques of botany – though less informative altogether – may, for these special cases, be less prone to errors compared with cladistic analysis of [[DNA sequence]]s.

In addition, many of the techniques developed by phenetic taxonomists have been adopted and extended by [[community ecology|community ecologists]], due to a similar need to deal with large amounts of data.<ref>Legendre, Pierre & Louis Legendre. 1998. Numerical ecology. 2nd English edition. Elsevier Science BV, Amsterdam. xv + 853 pages.</ref>