Functional traits, convergent evolution, and periodic tables of niches. (21st June 2015)

• Record Type: Journal Article
• Authors: Winemiller, Kirk O.; Fitzgerald, Daniel B.; Bower, Luke M.; Pianka, Eric R.
• Editors: Arita, Hector
• Is Part Of: Ecology letters. Volume 18:Number 8(2015:Aug.)
• Page Start: 737
• Page End: 751
• Publication Date: 2015-06-21
• Subjects: Adaptive peak -- bioassessment -- ecological classification -- ecological restoration -- life history strategy -- niche dimension -- niche scheme -- species ordination; Ecology -- Periodicals; 577
• DOI: 10.1111/ele.12462
• Languages: English
• ISSNs: 1461-023X

Abstract

Abstract: Ecology is often said to lack general theories sufficiently predictive for applications. Here, we examine the concept of a periodic table of niches and feasibility of niche classification schemes from functional trait and performance data. Niche differences and their influence on ecological patterns and processes could be revealed effectively by first performing data reduction/ordination analyses separately on matrices of trait and performance data compiled according to logical associations with five basic niche 'dimensions', or aspects: habitat, life history, trophic, defence and metabolic. Resultant patterns then are integrated to produce interpretable niche gradients, ordinations and classifications. Degree of scheme periodicity would depend on degrees of niche conservatism and convergence causing species clustering across multiple niche dimensions. We analysed a sample data set containing trait and performance data to contrast two approaches for producing niche schemes: species ordination within niche gradient space, and niche categorisation according to trait‐value thresholds. Creation of niche schemes useful for advancing ecological knowledge and its applications will depend on research that produces functional trait and performance datasets directly related to niche dimensions along with criteria for data standardisation and quality. As larger databases are compiled, opportunities will emerge to explore new methods for data reduction, ordination and classification.