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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 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) Constraints define what traits are possible Trait constraints + environmental conditions = Emergent community structure light, nutrients, temperature, grazers functional groups tradeoffs allometric scaling 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 growth = mmax S K +S µmax 1.0 Growth rate 0.8 0.6 0.4 K 0.2 0.0 0 1 2 3 Nutrient concentration 4 5 Monod model Rivkin & Swift 1985, Marine Biology Monod model growth = 1.0 mmax S mmax +S a Affinity = α = µmax/K Growth rate 0.8 0.6 0.4 0.2 0.0 0 1 2 3 Nutrient concentration 4 5 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 æ Qmin ö growth = m¥ ç1÷ Q è ø µ∞ 1.0 Growth rate 0.8 0.6 0.4 0.2 Qmin 0.0 0.0 0.2 0.4 0.6 0.8 Internal nutrient concentration (per cell or per C) 1.0 Quota model • Growth should depend more directly on limiting nutrient in the cell æ Qmin ö growth = m¥ ç1÷ Q è ø 1.0 Growth rate 0.8 µmax 0.6 0.4 0.2 Qmin 0.0 0.0 Qmax 0.2 0.4 0.6 0.8 Internal nutrient concentration (per cell or per C) 1.0 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 1.0 Uptake rate 0.8 0.6 0.4 Kuptake 0.2 0.0 0 1 2 3 Nutrient concentration 4 5 Quota model • Uptake affinity Uptake affinity = Vmax/Kuptake 1.0 Uptake rate 0.8 0.6 0.4 0.2 0.0 0 1 2 3 Nutrient concentration 4 5 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 Quota model • What are the key traits? Vmax 1 K uptake Qmin = 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) 1.0 Growth rate 0.8 0.6 0.4 mortality rate 0.2 0.0 0 1 2 3 Nutrient concentration 4 5 How traits determine community structure – R* theory 1.0 Growth rate 0.8 0.6 0.4 0.2 Initial nutrient concentration 0.0 0 1 2 3 Nutrient concentration 4 5 How traits determine community structure – R* theory 1.0 Growth rate 0.8 0.6 0.4 0.2 Population increase draws down nutrient 0.0 0 1 2 3 Nutrient concentration 4 5 How traits determine community structure – R* theory 1.0 When growth = mortality, steady-state Growth rate 0.8 0.6 0.4 0.2 0.0 0 1 2 3 Nutrient concentration 4 5 How traits determine community structure – R* theory Nutrient concentration Population Size R* Time How traits determine community structure – R* theory 1.0 Steady-state nutrients = R* Growth rate 0.8 0.6 0.4 0.2 0.0 0 1 2 3 Nutrient concentration 4 5 How traits determine community structure – R* theory 1.0 R1* R2* Growth rate 0.8 0.6 0.4 0.2 0.0 0 1 2 3 Nutrient concentration 4 5 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* ~ 1 K = Qmin specific uptake affinity Vmax 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 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 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 Cellular allocation • How does ecological context select for cellular stoichiometry? Distribution of N:P across species Klausmeier et al. 2004, Nature 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 Diatoms Coscinodiscus wailesii Ditylum brightwellii Eucampia zodiacus Nitzschia closterium Psuedo-nitzschia pungens Skeletonema costatum Asterionellopsis glacialis Cocco Emiliania huxleyi Dinos Gleaners and opportunists: L4 English Channel time series Prorocentrum micans Alexandrium tamarense Gymnodinium catenatum Gleaners and opportunists: L4 English Channel time series • • • - nitrate - mean PAR in the mixed layer - algal biovolume 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. storage capacity rapid growth vs. storage capacity Residual P affinity Residual P affinity R* vs. rapid growth 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 Nutrient (R*) Not enough nutrient for bigger to persist After Armstrong 1994, L&O Predators eat Nutrient Nutrient After Armstrong 1994, L&O Top-down control of the phytoplankton (R* for the grazer) Nutrient After Armstrong 1994, L&O Now a larger species can persist Which is too big for to eat Nutrient 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?