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

2. 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

3. 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

4. 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

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

6. 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

7. 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

8. 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

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

10. Monod model
Rivkin & Swift 1985,
Marine Biology

11. Monod model
Affinity = α = µmax/K

12. 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

13. 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

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

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

16. Quota model
Caperon and Meyer 1972,
Deep Sea Research

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

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

19. Quota model
Uptake affinity
Uptake affinity = Vmax/Kuptake

20. 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

21. 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

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

23. 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

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

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

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

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

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

29. How traits determine community structure – R* theory

30. 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

31. Tilman 1982

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

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

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

36. Cell size
Finkel et al. 2010, JPR

37. 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

38. 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

39. 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?

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

41. 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

42. 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

43. 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

44. 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

45. 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

46. 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

47. 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

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

49. 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

50. 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

51. 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

52. 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

53. 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’

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

55. Cermeño et al. 2011, MEPS

56. Sommer 1984, L&O

57. 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?

58. 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

59. 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.

60. Theory for size-structured predation

61. Theory for size-structured predation
Fuchs and Franks 2010

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

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

64. Predators eat
Nutrient
Nutrient
After Armstrong 1994, L&O

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

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

67. Nutrient
After Armstrong 1994, L&O

68. Nutrient
After Armstrong 1994, L&O

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

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

71. 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

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

73. A size-structured food web
Fuchs and Franks 2010 JPR

74. 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

75. 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?