In addition to answering Hutchinson's question ''Why are there so many species?'', we need to understand why certain species are found only under certain environmental conditions and not others. Trait-based approaches are being increasingly used in ecology to do just that: explain and predict species distributions along environmental gradients. These approaches can be successful in understanding the diversity and community structure of phytoplankton. Among major traits shaping phytoplankton distributions are resource utilization traits, morphological traits (with size being probably the most influential), grazer resistance traits, and temperature responses. We review these trait-based approaches and give examples of how trait data can explain species distributions in both freshwater and marine systems. We also outline new directions in trait-based approaches applied to phytoplankton such as looking simultaneously at trait and phylogenetic structure of phytoplankton communities and using adaptive dynamics models to predict trait evolution.

We compiled light utilization traits for 56 species of freshwater phytoplankton to analyze group differences, trait trade‐offs, and allometric scaling relationships. We also used these traits to explain differences in major group distributions along the light availability gradient in 527 lakes in the continental United States. Major taxonomic groups differed significantly in their light utilization traits. Cyanobacteria had the highest initial slope of the growth‐irradiance curve (ɑ) and low irradiance at the onset of photoinhibition, indicating adaptation to low light environments. Green algae had the highest maximal growth rates and low ɑ, indicating adaptation to higher light environments. Groups capable of mixotrophy had traits indicative of poor light competitive abilities and high light requirements. Key light utilization traits scaled allometrically with cell size and exhibited trade‐offs leading to contrasting ecological strategies; ɑ and cell size were conserved at the highest taxonomic level (domain), indicating a fundamental trait divergence between prokaryotic and eukaryotic phytoplankton. In line with these trait differences, major groups showed different responses to light availability in natural conditions. The relative abundances of low light–adapted groups declined with increasing light availability and vice versa. The genera mean values of the initial slopes of the growth‐irradiance curves were significantly negatively correlated with the slopes of the relationships between the genus's relative abundance and light availability characterized by Secchi depth in 527 lakes. This indicates that light utilization traits can be used to explain phytoplankton distributions in nature.

1. Phytoplankton are key players in the global carbon cycle, contributing about half of global primary productivity. Within the phytoplankton, functional groups (characterized by distinct traits) have impacts on other major biogeochemical cycles, such as nitrogen, phosphorus and silica. Changes in phytoplankton community structure, resulting from the unique environmental sensitivities of these groups, may significantly alter elemental cycling from local to global scales. 2. We review key traits that distinguish major phytoplankton functional groups, how they affect biogeochemistry and how the links between community structure and biogeochemical cycles are modelled. 3. Finally, we explore how global environmental change will affect phytoplankton communities, from the traits of individual species to the relative abundance of functional groups, and how that, in turn, may alter biogeochemical cycles. 4. Synthesis. We can increase our mechanistic understanding of the links between the community structure of primary producers and biogeochemistry by focusing on traits determining functional group responses to the environment (response traits) and their biogeochemical functions (effect traits). Identifying trade-offs including allometric and phylogenetic constraints among traits will help parameterize predictive biogeochemical models, enhancing our ability to anticipate the consequences of global change.