Community trait structure in phytoplankton: seasonal dynamics from a method for sparse trait data. Issue 12 (2nd December 2016)

• Record Type: Journal Article
• Authors: Edwards, Kyle F.
• Is Part Of: Ecology. Volume 97:Issue 12(2016)
• Page Start: 3441
• Page End: 3451
• Publication Date: 2016-12-02
• Subjects: diversity -- English Channel -- fourth‐corner problem -- functional biogeography -- ocean -- species distribution model -- trait‐based model -- uptake kinetics; Ecology -- Periodicals; Ecology -- Periodicals; Écologie -- Périodiques; Ecologie; Écologie; Écologie animale; Écologie végétale; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecy.1581
• Languages: English
• ISSNs: 0012-9658

Abstract

Abstract: The distribution of functional traits in communities, and how trait distributions shift over time and space, is critical information for understanding community structure, the maintenance of diversity, and community effects on ecosystem function. It is often the case that traits tightly linked to ecological performance, such as physiological capacities, are laborious to measure and largely unknown for speciose communities; however, these traits are particularly important for unraveling the mechanistic basis of community structure. Here I develop a method combining sparse trait data with a statistical niche model to infer trait distributions for phytoplankton communities and how they vary over 10 yr in the western English Channel. I find that community‐average nitrate affinity, light‐limited growth rate, and maximum growth rate all show major seasonal patterns, reflecting alternate limitation by light vs. nitrogen. Trait diversity exhibits a variety of patterns distinct from community trait means, which suggests complex regulation of functional diversity. Patterns such as these are important for predicting how ocean ecosystems will respond to global change, and for developing trait‐based models of emergent community structure. The statistical approach used here could be applied to any kind of organism, if it exhibits strong relationships between traits and statistical niche estimates.