Abstract
Trait-based approaches are increasingly used to help understanding community structure and ecosystem functioning. A large proportion of trait-based studies define a species by its mean trait values and assume intraspecific trait variability to be negligible compared to interspecific differences. However, this assumption is rarely tested. Phenotypic cell size plasticity can be particularly important in phytoplankton species, which are known for their rapid changes in cell size in response to variations in environmental conditions. While phytoplankton traits show clear systematic trends with mean cell size across species, how size-related plasticity influences the dynamics of a species remains unknown. In this study, we evaluate the effects of cell size plasticity on the nitrogen (N) utilization traits of the green alga Desmodesmus armatus (Chlorophyta), reared in different inorganic nitrogen sources (nitrate, ammonium or both) and nutrient histories (N-replete and N-deplete). Results show that traits for per-cell ammonium uptake, maximum cell growth rate and minimum N-quota change substantially within the study species, depending on mean cell size and nutrient history. In contrast, per-cell nitrate uptake was independent of cell size. These results indicate that representing phytoplankton species only by their mean trait values could underestimate the physiological performance of a species by as much as one order of magnitude. This study highlights the extent to which explicit incorporation of within-species trait variability can enhance our understanding of how species performance changes along environmental gradients. A lay summary is available for this article.
Original language | English |
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Pages (from-to) | 1745-1755 |
Number of pages | 11 |
Journal | Functional Ecology |
Volume | 30 |
Issue number | 11 |
DOIs | |
Publication status | Published - 1 Nov 2016 |
Keywords
- allometric scaling
- environmental gradients
- green alga
- intraspecific trait variability
- kinetic parameters
- nitrogen assimilation
- observation error
- process noise
- process-based models
- time series