Trait-based models of phytoplankton Kyle Edwards 2015 CMORE Microbial Oceanography Course

Modeling phytoplankton: why? Central players in ocean ecosystem + biogeochemical processes We need models to test whether we can explain the present, and to predict the future

Cell size: export, microbial loop vs. higher trophic levels Modeling phytoplankton communities: why? Community structure matters for function Cell size: export, microbial loop vs. higher trophic levels Variable cellular stoichiometry Some cyanobacteria fix N

Modeling phytoplankton communities: why? Aggregate responses are different from single-species responses How does community diversity scale up to aggregate ecosystem processes? Scaling of bulk phytoplankton growth Niches of individual species

How to make complexity tractable? 1000s of species Genetic diversity 1000s of genes

How to make complexity tractable? Key traits (parameters) light, nutrients, temperature, grazers functional groups tradeoffs allometric scaling Constraints define what traits are possible Trait constraints + environmental conditions = Emergent community structure Optimal strategies Global community patterns

How to make complexity tractable? Define the upper envelope of temperature responses Let the environment select for the optimal strategy (or coexisting strategies) We don’t need to measure the temperature response of every phytoplankter on the planet

Outline Case study: Models and traits for nutrient-limited growth Ecological theory for traits  community structure Trait diversity and potential constraints Emergent community structure at the global scale: Mick

Monod model How is population growth/dynamics affected by nutrient limitation? Growth rate depends on the external concentration of the limiting substrate µmax K

Monod model Rivkin & Swift 1985, Marine Biology

Monod model Affinity = α = µmax/K

Monod model Very simple model, very phenomenological Two very important traits: 1) Growth under chronic nutrient limitation (affinity) stratified, well-lit waters 2) Growth under (transiently) high nutrients (µmax) upwelling, large mixing events

Limitations of the Monod model Measured & works best under relatively steady nutrients (or slow change) Uptake rate = (Growth rate)*(Nutrient per cell) Assumes constant stoichiometry No luxury uptake of transiently elevated nutrients Can be difficult to estimate K

Quota model (Droop model) Growth should depend more directly on limiting nutrient in the cell µ∞ Qmin (per cell or per C)

Quota model Growth should depend more directly on limiting nutrient in the cell µmax Qmin Qmax (per cell or per C)

Quota model Caperon and Meyer 1972, Deep Sea Research

Quota model Timmermans et al. 2005, Journal of Sea Research

Quota model Can model flexible stoichiometry Can decouple uptake from growth: Michaelis-Menten uptake Vmax Kuptake

Quota model Uptake affinity Uptake affinity = Vmax/Kuptake

Can model growth under variable nutrient concentration, with luxury uptake Chlorella sp. Cells mL-1 P per cell (10-15 mol) Time (d) Grover 1991, J. Phycol

Equivalent to affinity in the Monod model Quota model What are the key traits? = specific uptake affinity Uptake rate, under limitation, relative to demand Equivalent to affinity in the Monod model

Quota model What are the key traits? µmax high nutrients for many generations Vmax and Qmax high nutrients for <1 to several generations

How traits determine community structure – R* theory How does nutrient limitation determine community structure? Start simple: a steady-state system (e.g., permanently stratified systems) mortality rate

How traits determine community structure – R* theory Initial nutrient concentration

How traits determine community structure – R* theory Population increase draws down nutrient

How traits determine community structure – R* theory When growth = mortality, steady-state

How traits determine community structure – R* theory Nutrient concentration Population Size R* Time

How traits determine community structure – R* theory Steady-state nutrients = R*

How traits determine community structure – R* theory

Under steady-state nutrient supply the species with the lowest R* competitively excludes all others because it draws down nutrients below what other species need to persist Tilman 1982

Tilman 1982

Which phytoplankton are the best competitors? 1000s of species Ideally, we won’t have to measure R* for every species + nutrient What constrains R*?

Which phytoplankton are the best competitors? Insights from the Quota model For low mortality: R* ~ Specific uptake affinity ~ competitive ability, under chronic nutrient limitation

Which phytoplankton are the best competitors? To be a better competitor Increase the ratio of uptake affinity : nutrient content

Cell size Finkel et al. 2010, JPR

specific affinity ~ 1/radius2 Cell size Specific nitrate affinity (L µmol N-1 d-1) Cell volume (µm3) specific affinity ~ 1/radius2 competitive ability for nitrate varies over 4 orders of magnitude! Edwards et al. 2012, L&O

specific affinity ~ 1/radius2 Cell size Specific nitrate affinity (L µmol N-1 d-1) Cell volume (µm3) specific affinity ~ 1/radius2 competitive ability for nitrate varies over 4 orders of magnitude! scaling relationships greatly simplify model complexity

Cellular composition – ways to reduce Qmin Reduce iron demand by reducing iron-rich photosynthetic machinery (Strzepek and Harrison 2004) Reduce phosphorus demand by using non-phosphorus membrane lipids (Van Mooy et al. 2009) What are the costs / tradeoffs? Can we quantify the impact of these decisions on growth, competition, etc?

Cellular allocation Major physiological components: chloroplasts, ribosomes, nutrient acquisition Clark et al. 2013, L&O

Cellular allocation How does allocation to rapid growth (ribosomes) relate to ecological outcomes? Optimize rapid growth N:P = 8 Optimize R* for N N:P = 37 Allocation to biosynthesis Klausmeier et al. 2004, Nature

How does ecological context select for cellular stoichiometry? Cellular allocation How does ecological context select for cellular stoichiometry? Distribution of N:P across species Klausmeier et al. 2004, Nature

How does ecological context select for cellular stoichiometry? Cellular allocation How does ecological context select for cellular stoichiometry? Distribution of N:P across species R* Redfield µmax Klausmeier et al. 2004, Nature

Theory for variable nutrient supply Seasonal stratification, shorter-term events, etc. Simplest version: tradeoff between R* and µmax ‘Opportunist’ ‘Gleaner’ Kremer and Klausmeier 2013, JTB

Theory for variable nutrient supply Seasonal stratification, shorter-term events, etc. Simplest version: tradeoff between R* and µmax ‘Opportunist’ ‘Gleaner’ Kremer and Klausmeier 2013, JTB

Theory for variable nutrient supply In general, greater resource fluctuation favors rapid growth strategy (live fast die young) Coexistence of strategies: Large resource pulses + periods of scarcity

Gleaners and opportunists: L4 English Channel time series Coscinodiscus wailesii Ditylum brightwellii Eucampia zodiacus Nitzschia closterium Psuedo-nitzschia pungens Skeletonema costatum Asterionellopsis glacialis Emiliania huxleyi Prorocentrum micans Alexandrium tamarense Gymnodinium catenatum Diatoms Cocco Dinos

Gleaners and opportunists: L4 English Channel time series - nitrate - mean PAR in the mixed layer - algal biovolume

Species vary in their response to nitrate Gleaners and opportunists: L4 English Channel time series Species vary in their response to nitrate Edwards et al. 2013, Ecology Letters

Gleaners and opportunists: L4 English Channel time series Specific nitrate affinity Species with higher affinity increase in relative abundance as nitrate decreases Edwards et al. 2013, Ecology Letters

Gleaners and opportunists: L4 English Channel time series µmax Species with higher µmax increase in relative abundance when both irradiance and nitrate are high Edwards et al. 2013, Ecology Letters

Gleaners and opportunists: L4 English Channel time series Seasonal succession of opportunists vs. gleaners Also good light competitors during winter Edwards et al. 2013, Ecology Letters

Quota model: more complex Storage capacity (Qmax) and/or rapid luxury uptake (Vmax) Favored by regular-ish events on the scale of day-weeks Meso/sub-meso Shorter-term ‘opportunists’

Pulsing begins (7 µmol L-1, twice daily) Cermeño et al. 2011, MEPS

Cermeño et al. 2011, MEPS

Sommer 1984, L&O

Are there constraints to simplify this? R* vs. rapid growth R* vs. storage capacity rapid growth vs. storage capacity Residual P affinity Residual P affinity Excelling at any one function diminishes the others (multidimensional tradeoff) Empirical tradeoffs can explain the coexistence of strategies Mechanistic basis?

Predation is difficult Many kinds of grazers for many kinds of phytoplankton Trophic interactions less well developed than nutrients, light, temperature Size provides some important constraints

Theory for size-structured predation Specific nitrate affinity (L µmol N-1 d-1) Cell volume (µm3) Why do large phytoplankton exist? Bad at (nearly) everything.

Theory for size-structured predation

Theory for size-structured predation Fuchs and Franks 2010

Growth Nutrient Nutrient (R*) After Armstrong 1994, L&O

Not enough nutrient for bigger to persist After Armstrong 1994, L&O

Predators eat Nutrient Nutrient After Armstrong 1994, L&O

Nutrient Top-down control of the phytoplankton (R* for the grazer) After Armstrong 1994, L&O

Nutrient Now a larger species can persist Which is too big for to eat After Armstrong 1994, L&O

Nutrient After Armstrong 1994, L&O

Nutrient After Armstrong 1994, L&O

A size-structured food web Nutrient After Armstrong 1994, L&O

A size-structured food web Nutrient After Armstrong 1994, L&O

A size-structured food web Important features: Size classes are added as nutrient input increases Individual populations experience strong grazing But the total phytoplankton biomass is controlled by nutrient input Ecosystem co-limitation by grazing and nutrient supply Explains the coexistence of large phytoplankton After Armstrong 1994, L&O

A size-structured food web Important features: Both phytoplankton and zooplankton traits can be constrained by size After Armstrong 1994, L&O

A size-structured food web Fuchs and Franks 2010 JPR

Summary The parameters of phytoplankton growth models are key ecological traits Tradeoffs and other constraints are essential for parsing community complexity These constraints determine how community structure and diversity emerges under different environmental conditions

Future directions What are the constraints? Physiological / genetic basis for trait variation and tradeoffs Models of allocation Synthesize omics approaches with continuous ecological traits Better validate trait-based community models Lab/mesocosm experiments, distributional data Interactions: how temperature modulates resource competition How do aggregate patterns emerge from community complexity?