Trait-based approaches are increasingly used in ecology. Phytoplankton communities, with a rich history as model systems in community ecology, are ideally suited for applying and further developing these concepts. Here we summarize the essential components of trait-based approaches and review their historical and potential application to illuminating phytoplankton community ecology. Major ecological axes relevant to phytoplankton include light and nutrient acquisition and use, natural enemy interactions, morphological variation, temperature sensitivity, and modes of reproduction. Trade-offs between these traits play key roles in determining community structure. Freshwater and marine environments may select for a different suite of traits owing to their different physical and chemical properties. We describe mathematical techniques for integrating traits into measures of growth and fitness and predicting how community structure varies along environmental gradients. Finally, we outline challenges and future directions for the application of trait-based approaches to phytoplankton ecology.

Local Optima for Plankton: Recent ocean warming has changed the seasonality and composition of the marine phytoplankton.Thomaset al.(p.1085, published online 25 October) investigated the direct effect of temperature on phytoplankton. By fitting published data to reaction temperature norms for 194 phytoplankton strains isolated from a wide latitudinal range, the resultant temperature-related traits (maximum growth rate, temperature optimum, and thermal niche width) reveal latitudinal trends in temperature optima and diversity. Biogeographical differences indicate an increased susceptibility of tropical strains to further warming, and modeling predicts poleward shifts of tropical strains and a loss of phytoplankton diversity in the tropics within the next hundred years.

Zooplankton are major primary consumers and predators in most aquatic ecosystems. They exhibit tremendous diversity of traits, ecological strategies and, consequently, impacts on other trophic levels and the cycling of materials and energy. An adequate representation of this diversity in community and ecosystem models is necessary to generate realistic predictions on the functioning of aquatic ecosystems but remains extremely challenging. We propose that the use of trait-based approaches is a promising way to reduce complexity while retaining realism in developing novel descriptions of zooplankton in ecosystem models. Characterizing zooplankton traits and trade-offs will also be helpful in understanding the selection pressures and diversity patterns that emerge in different ecosystems along major environmental gradients. Zooplankton traits can be characterized according to their function and type. Some traits, such as body size and motility, transcend several functions and are major determinants of zooplankton ecological strategies. Future developments of trait-based approaches to zooplankton should assemble a comprehensive matrix of key traits for diverse groups and explore it for general patterns; develop novel predictive models that explicitly incorporate traits and associated trade-offs; and utilize these traits to explain and predict zooplankton community structure and dynamics under different environmental conditions, including global change scenarios.

Nutrient utilization traits can be used to link the ecophysiology of phytoplankton to population dynamic models and the structure of communities across environmental gradients. Here we analyze a comprehensive literature compilation of four traits: maximum nutrient uptake rate; the half‐saturation constant for nutrient uptake; the minimum subsistence quota, measured for nitrate and phosphate; and maximum growth rate. We also use these traits to analyze two composite traits, uptake affinity and scaled uptake affinity. All traits tend to increase with cell volume, except for scaled uptake affinity and maximum growth rate, which tend to decline with cell volume. Most scaling relationships are the same for freshwater and marine species, although important differences exist. Most traits differ on average between major taxa, but between‐taxon variation is nearly always due to between‐taxon variation in volume. There is some evidence for between‐trait correlations that could constrain trait evolution, but these correlations are difficult to disentangle from correlation driven by cell volume. These results should enhance the parameterization of models that use size or taxonomic group to structure physiological variation in phytoplankton communities.