Functional traits, trade-offs and
community structure in
phytoplankton and other microbes
Elena Litchman, Christopher
Klausmeier and Kyle Edwards
Michigan State University
NPZ
Z
P
I
N
Plankton Functional Groups
Z
P
P
P
2
1
P
I
3
N
4
Many Species
Z
P1 P1 P1 P1 P1 P1 P
P1 P1 P1 P1 P1 P1 P
P 1 P1 P1 P1 P1 P1 P
P P P P P P P
21
14
1
2
3
4
5
I
6
N
7
28
Continuum of Strategies
Z
I
N
Continuum of Strategies
light
competitive
nutrient
competitive
Trait-Based Approaches
•
•
•
•
Traits
Environmental gradients
Species interactions
Performance currencies (fitness measures)
McGill et al. 2006 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)
growth

æ Qmin ö
m = m ¥ç1÷
Q ø
è

Qmin
nutrient
uptake V = Vmax
R
Qmax - Q
K + R Qmax - Qmin
V
Q
Vmax
K
Traits:
µ∞, growth rate at infinite quota
Qmin , minimum internal nutrient content
Qmax , maximum internal nutrient content
Vmax, maximum uptake rate of nutrient
K, half-saturation constant for nutrient uptake
R
Qmin Qmax Q
Trait relationships
Linking traits and community structure:
Resource competition
Species with the lowest minimum nutrient requirements
to sustain growth, R* (Tilman 1982)
m
Q
K
m
=
R*
V (m - m) - m Q m
¥
max
¥
min
¥
min
R* decreases (competitive ability increases) when
•∞ (growth at max Q)
•Vmax (max uptake rate)
•K (half-saturation constant)
•Qmin (min quota)
•m (mortality)
What are the trade-offs between
traits?
Functional Group Distribution along a
Trade-off Curve
Niche differentiation?
dinoflagellates
diatoms
coccolith
greens
Litchman et al. 2007 Ecol. Lett.
Other measures of nutrient competitive
ability
m- > 0
K
R* ®
Qmin m
Vmax
Vmax
C»
KQmin
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
3.5
2.5
0.5
1.5
Log10 C P
2.5
1.5
0.5
Log10 C P
3.5
Three-way trade-off
-1
0
1
Log10 C N
2
-1
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
CV = 10.1
mean = 4.3
CV = 8.9
4
2
4
400
600
0
0.0
200
0
200
400
600
0
2
1.5
2
1
0
0
200
400
600
600
0
200
400
600
Leptocylindrus danicus
Nitzschia closterium
Pseudo-nitzschia pungens
Rhizosolenia robusta
CV = 5.3
mean = 1.6
0.0
0
0.0
2
1.5
1.5
CV = 2.6
200
400
600
0
200
400
600
0
200
400
600
mean = 0.4
CV = 7
mean = 0
CV = 5.1
0.3
mean = 12.1
0.0
CV = 4.6
0.6
Eucampia zodiacus
0.0 1.0 2.0 3.0
Sample
3.0
Sample
6
Sample
0
200
400
600
0
200
400
600
Sample
Sample
Skeletonema costatum
Thalassiosira rotula
Alexandrium tamarense
Prorocentrum micans
Prorocentrum minimum
400
600
0
200
400
600
0
200
400
600
mean = 27.1
CV = 9.2
8
CV = 4.6
4
0
0
200
400
600
0
200
400
600
Sample
Emiliania huxleyi
Phaeocystis pouchetii
Asterionellopsis glacialis
Gymnodinium cf. catenatum
Thalassiosira cf gravida
400
Sample
600
0
200
400
Sample
600
mean = 0
CV = 10.6
0
200
400
Sample
600
mean = 0.2
CV = 11.1
1.5
CV = 18.1
0.0
4
0
200
mean = 0
0.3
CV = 5.7
0.0
mean = 97
8
CV = 3.7
3.0
Sample
0.6
Sample
0.0 0.5 1.0 1.5
Sample
12
Sample
mean = 53.3
0
mean = 0.4
CV = 9.8
0.0
0.0
2
0
200
mean = 0
0.4
1.5
CV = 7.9
2.0
mean = 0.4
1.0
CV = 9.4
0.0
mean = 6.2
0.8
Sample
3.0
Sample
4
6
400
Sample
0
0 2 4 6 8
200
CV = 3.7
Sample
mean = 0.7
0
0
mean = 0
Sample
4
3.0
0
Ditylum brightwellii
0.0 0.5 1.0 1.5
mean = 6.6
CV = 16.8
Chaetoceros simplex
6
mean = 0.1
Chaetoceros debilis
6
CV = 6.7
Chaetoceros curvisetus
3.0
mean = 0.8
3
4
Chaetoceros affinis
0
200
400
Sample
600
0
200
400
Sample
600
Using traits to explain species distributions
English Channel phytoplankton time series
abundance vs scaled N affinity
2
0
-2
-6
-4
log mean abundance
6
4
-8
2
log mean biovolume
8
4
biovolume vs scaled N affinity
-1
0
1
2
3
4
-1
log Scaled N affinity
0
1
2
3
log Scaled N affinity
When N is low
4
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
Possible responses to changing
environmental conditions
•
•
•
•
Phenotypic plasticity
Species/group replacements
Trait evolution, niche shifts
Combinations of the above
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
10
8
marine vs. freshwater p<0.0001
AB
B
B
C
6
CD
D
D
4
Log
10
3
cell volume (mm )
A
2
0
Pacific
Mediter
Barents
Baltic Finnish lakes Biwa
Crater L. Madison
Litchman et al. 2009 PNAS
Diatom Size Evolution
R
Q
B
Vmax
K
×

R
Qmin
Q
Qmin Qmax Q
Litchman et al. 2009 PNAS
Allometries (power relationships)
Freshwater
-6
10
-7
10
10
-6
10
-7
-5
10
-6
10
1
10
2
10
3
10
4
10
5
-7
10
10
2
10
10
3
10
4
10
5
6
10
R2=0.61
1
N
k (mM)
R2=0.73
-8
Q
min
P
10
1
1
-1
(mmol cell )
10
10
max
-8
Marine
-4
R2=0.76
V
V
max
P
N
10
-1
R2=0.49
-1
10
-1
-5
(mM cell day )
-1
(mM cell day )
10
10
-9
10
1
2
3
4
10
10
10
3
cell volume (mm )
10
5
0.1
1
10
10
2
3
10
10
4
10
3
cell volume (mm )
5
10
6
ESS (N limitation) at different fluctuation
periods, mixed layer depth and sinking
Litchman et al. 2009 PNAS
Evolution Experiments
3. Assess trait distribution (mean and variance)
before and after experiment under identical
conditions
Selection pressure
Variance change
Mean change
or both!
Single strain (mutation)
Multiple strains (mutation or clonal selection)
Evolution Experiments
A. Single species experiments (single or multiple
strains)
B. Species in a community
– Limits on trait evolution
– Species replacement instead?
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