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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 2. P 4. P 1. P 3. I. N. Many Species. Z. P 1. P 1. - PowerPoint PPT Presentation
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Functional traits, trade-offs and community structure in phytoplankton and other microbes
Elena Litchman, Christopher Klausmeier and Kyle Edwards
Michigan State University
Z
NI
P
NPZ
P2
P1
Z
NI
P3
P4
Plankton Functional Groups
P1 P1 P1 P1P1 P1 P1 P1P1 P1 P1 P1P3
Z
P1 P2 P4
P1P1P1P5
P1P1P1P6
P28
P21
P14
P7
Many Species
NI
Z
Continuum of Strategies
NI
Continuum of Strategies
lightcompetitive
nutrientcompetitive
grazing
resistant
com-
petitive
Trait-Based Approaches
• Traits • Environmental gradients• Species interactions• Performance currencies (fitness measures)
McGill et al. 2006 TREE
Trait-Based Approach
1. Ecologically relevant traits2. Trade-offs between these traits3. Mechanistic models of population
interactions4. Fitness5. Source of novel phenotypes
Questions1. 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 2008Annual Rev. Ecol. Evol. Syst.
Example: Nutrient Utilization TraitsBasic model (modified Droop)
nutrientuptake
growth
Traits: µ∞, growth rate at infinite quotaQmin , minimum internal nutrient contentQmax , maximum internal nutrient contentVmax, maximum uptake rate of nutrientK, half-saturation constant for nutrient uptake
Qmin
Vmax
V
K R QQmaxQmin
Q
Trait relationships
Linking traits and community structure:Resource competition
R* decreases (competitive ability increases) when
•∞ (growth at max Q)•Vmax (max uptake rate)
•K (half-saturation constant)•Qmin (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. 2007 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 distributionsEnglish Channel phytoplankton time series
Using traits to explain species distributionsEnglish 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. 2009 PNAS
Diatom Size Evolution
BQR
Litchman et al. 2009 PNAS
Qmin
Q
Vmax
K R
QQmaxQmin
×
Allometries (power relationships)Freshwater Marine
R2=0.49
R2=0.73
R2=0.76
R2=0.61
Litchman et al. 2009 PNAS
ESS (N limitation) at different fluctuation periods, mixed layer depth and sinking
Evolution Experiments3. Assess trait distribution (mean and variance)
before and after experiment under identical conditions
Selection pressure
Mean change Variance changeor 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