Functional traits, trade-offs and community structure in phytoplankton and other microbes Elena Litchman, Christopher Klausmeier and Kyle Edwards Michigan State University

Z N I P NPZ

P2P2 P1P1 Z N I P3P3 P4P4 Plankton Functional Groups

P1P1 P1P1 P1P1 P1P1 P1P1 P1P1 P1P1 P1P1 P1P1 P1P1 P1P1 P1P1 P3P3 Z P1P1 P2P2 P4P4 P1P1 P1P1 P1P1 P5P5 P1P1 P1P1 P1P1 P6P6 P 28 P 21 P 14 P7P7 Many Species N I

Z Continuum of Strategies N I

light competitive nutrient competitive grazing resistant com- petitive

Trait-Based Approaches Traits Environmental gradients Species interactions Performance currencies (fitness measures) McGill et al TREE

Trait-Based Approach 1.Ecologically relevant traits 2.Trade-offs between these traits 3.Mechanistic models of population interactions 4.Fitness 5.Source of novel phenotypes

Questions 1.What are key traits of (phyto)plankton? 2.What are the constraints on and trade-offs between traits? Can they be predicted from first principles? (How) can they be broken? 3.How are traits distributed along environmental gradients? Can traits explain species distributions? 4.How to link traits below (genomes, gene regulation, physiology) and above (community assembly evolutionary dynamics, phylogeny)?

Ecologically relevant traits (phytoplankton) Litchman and Klausmeier 2008 Annual Rev. Ecol. Evol. Syst.

Example: Nutrient Utilization Traits Basic model (modified Droop) nutrient uptake growth Traits: µ ∞, growth rate at infinite quota Q min, minimum internal nutrient content Q max, maximum internal nutrient content V max, maximum uptake rate of nutrient K, half-saturation constant for nutrient uptake Q min V max   V KRQQ max Q min Q

Trait relationships

Linking traits and community structure: Resource competition R* decreases (competitive ability increases) when  ∞ (growth at max Q) V max (max uptake rate) K (half-saturation constant) Q min (min quota) m (mortality) Species with the lowest minimum nutrient requirements to sustain growth, R* (Tilman 1982)

What are the trade-offs between traits?

Functional Group Distribution along a Trade-off Curve Niche differentiation? diatoms coccolith dinoflagellates greens Litchman et al Ecol. Lett.

Other measures of nutrient competitive ability Nutrient affinity

Three-way trade-offs  Assembled trait information for all species we could find the data for  Considerable number of missing traits  Used statistical imputation techniques to infer missing trait values  Examined relationships between traits and competitive abilities for N and P

Three-way trade-off Edwards et al. in press

Three-way trade-off Edwards et al. in press

Three-way trade-off Edwards et al. in press

Light utilization traits vs group distribution in nature (US lakes)

Using traits to explain species distributions English Channel phytoplankton time series

Using traits to explain species distributions English Channel phytoplankton time series When N is low

Traits in a Food Web Perspective Litchman et al. 2010

Traits in a Food Web Perspective Need to find ways to reduce dimensionality of traits that describe interactions between trophic levels Use scaling relationships and stoichiometry to define traits

Phenotypic plasticity Species/group replacements Trait evolution, niche shifts Combinations of the above Possible responses to changing environmental conditions

Adaptive Dynamics Approach (a trait-based approach to evolutionary ecology) Eco-physiological traits & allometric relationships Abiotic factors Growth rate of invader vs resident (competition) ESS or other long-term evolutionary outcome (size)

Marine vs Freshwater Diatom Cell Sizes Litchman et al PNAS

Diatom Size Evolution B QR Litchman et al PNAS Q min  Q V max KR QQ max Q min ×

Allometries (power relationships) FreshwaterMarine R 2 =0.49 R 2 =0.73 R 2 =0.76 R 2 =0.61

Litchman et al PNAS ESS (N limitation) at different fluctuation periods, mixed layer depth and sinking

Evolution Experiments 3. Assess trait distribution (mean and variance) before and after experiment under identical conditions Selection pressure Mean change Variance change or both! Single strain (mutation) Multiple strains (mutation or clonal selection)

A.Single species experiments (single or multiple strains) B.Species in a community – Limits on trait evolution – Species replacement instead? Evolution Experiments

Challenges and future directions Still very few species with known traits Significant gaps in trait coverage With sparse trait data it is difficult to infer trade-offs, especially their shape Need to characterize intraspecific variation and compare with interspecific differences— important for potential evolutionary changes