Trait-based representation of diatom diversity in a
Plankton Functional Type model
N. TERSELEER1, J. BRUGGEMAN2, C. LANCELOT1 AND N. GYPENS1
1Écologie
des Systèmes Aquatiques, Université Libre de Bruxelles, Belgium
of Earth Sciences, University of Oxford, UK
2Department
45th International Liege Colloquium
13th – 17th May 2013
Liege, Belgium
The MIRO model
Trait-based approach
Trait-based module
Results
Conclusions
• MIRO: a Plankton Functional Type (PFT) model
Data 1989-1999: diatoms counts + spp identification
Diatom diversity ↑
Relative presence of size classes in the community & Mean Cell Vol
MIRO (Lancelot et al., 2005)
PFT models: aggregation of many species
into one single group (e.g. diatoms)
 “average behaviour”
 prediction ability with scenarios?
Represent diatom diversity in MIRO
(based on size)
The MIRO model
Trait-based approach
Trait-based module
Results
Conclusions
• How to characterize diversity among phytoplankton?
 The trait-based approach
Ecological function
Resource acquisition
Predator avoidance
Trait values  ecological functions
Physiological
Trade-offs (cannot maximize all trait values)
Fitness is environment-dependent
Principle
Many spp in competition, selection of the fittest
Behavioral
Life history
Trait type
Morphological
Reproduction
Size
Many key traits co-vary with size
Litchman and Klausmeier 2008
Phytoplankton functional traits*
*Trait: a well-defined, measurable property of organisms, usually measured
at the individual level and used comparatively across species (McGill et al., 2006)
The MIRO model
Trait-based approach
Trait-based module
Results
Conclusions
• How to characterize diversity among phytoplankton?
 The trait-based approach
Ecological function
Predator avoidance
Cell size
Susceptibility to
grazing
Photosynthesis
Trait values  ecological functions
Trade-offs (cannot maximize all trait values)
Fitness is environment-dependent
Biomass synthesis
Nutrient uptake
Principle
Many spp in competition, selection of the fittest
Behavioral
Physiological
Resource acquisition
Size
Many key traits co-vary with size
Life history
Trait type
Morphological
Reproduction
Phytoplankton functional traits
 Diatoms diversity is represented, based on size
 Size is related to ecological functions
The MIRO model
Trait-based approach
Trait-based module
Results
Conclusions
• Trait-based diatom module in MIRO
Diatom
𝑃𝐴𝑅
𝑓𝑙𝑖𝑚
00
µ𝑚𝑎𝑥
growth
𝑁𝑈𝑇
𝑓𝑙𝑖𝑚
sed
Nutrients
(N, P, Si)
Diatom dynamics:
Biomass (DA)
grazing
affinity
Copepods
lysis
Cell volume (VDA)
growth
𝑑𝐷𝐴
𝑃𝐴𝑅
𝑁𝑈𝑇
= µ𝑚𝑎𝑥 𝑽𝑫𝑨 ∗ 𝑓𝑙𝑖𝑚
𝑽𝑫𝑨 ∗ 𝑓𝑙𝑖𝑚
𝑽𝑫𝑨 − 𝑔𝑟𝑎𝑧𝑖𝑛𝑔(𝑽𝑫𝑨 ) − 𝑙𝑦𝑠𝑖𝑠 − 𝑠𝑒𝑑𝑖𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛 ∗ 𝐷𝐴
𝑑𝑡
The MIRO model
Trait-based approach
Trait-based module
Results
Conclusions
• Trait-based diatom module in MIRO
Diatom
𝑃𝐴𝑅
𝑓𝑙𝑖𝑚
00
µ𝑚𝑎𝑥
growth
𝑁𝑈𝑇
𝑓𝑙𝑖𝑚
sed
Nutrients
(N, P, Si)
Diatom dynamics:
Biomass (DA)
grazing
affinity
Copepods
lysis
Cell volume (VDA)
growth
𝑑𝐷𝐴
𝑃𝐴𝑅
𝑁𝑈𝑇
= µ𝑚𝑎𝑥 𝑽𝑫𝑨 ∗ 𝑓𝑙𝑖𝑚
𝑽𝑫𝑨 ∗ 𝑓𝑙𝑖𝑚
𝑽𝑫𝑨 − 𝑔𝑟𝑎𝑧𝑖𝑛𝑔(𝑽𝑫𝑨 ) − 𝑙𝑦𝑠𝑖𝑠 − 𝑠𝑒𝑑𝑖𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛 ∗ 𝐷𝐴
𝑑𝑡
𝑔𝐷𝐴
The diatom community is approximated in terms of total biomass and mean Cell volume
Mean cell volume dynamics:
𝑑𝑉𝐷𝐴
𝜕𝑔𝐷𝐴
= variance ∗
𝑑𝑡
𝜕𝑉𝐷𝐴
(Wirtz and Eckhardt, 1996; Norberg et al., 2001; Merico et al., 2009)
 the mean cell volume depends on environmental conditions
(nutrients, light, zooplankton)
The MIRO model
Trait-based approach
Trait-based module
Results
Conclusions
• Variability in diatom parameters
Many diatom traits co-vary with their cell volume
 allometric relationships : 𝑡𝑟𝑎𝑖𝑡 = 𝑡𝑟𝑎𝑖𝑡𝑟𝑒𝑓 ∗ 𝑉 ω (linear on log-log scale)
slope and scaling factor : optimized
max growth rate
half-saturation constant
photosynthetic efficiency
susceptibility to grazing
BCZ range
Sarthou et al., 2005 (JSR)
Litchman et al., 2007 (Ecol. Lett.)
Geider et al., 1986 (MEPS)
Parameter
Fittest diatoms
maximum growth rate µ𝑚𝑎𝑥
Small
half−saturation constant 𝐾𝑛𝑢𝑡
Small
photosynthetic efficiency
Small
susceptibility to grazing
Large
Gismervik et al., 1996
(Mar Pollut Bull)
trade-off
Small vs Large diatoms
The MIRO model
Trait-based approach
Trait-based module
Results
• Results: seasonal cycle (climatology 1989-1999)
Diatom biomass (optimized)
2 blooms
Conclusions
The MIRO model
Trait-based approach
Trait-based module
Results
• Results: seasonal cycle (climatology 1989-1999)
Diatom biomass (optimized)
2 blooms
Mean cell volume (validation)
 information on the community structure
Conclusions
The MIRO model
Trait-based approach
Trait-based module
Results
Conclusions
• Results: seasonal cycle (climatology 1989-1999)
Diatom biomass (optimized)
2 blooms
Mean cell volume (validation)
 information on the community structure
 spring bloom: smaller diatoms (102-104 µm3)
Chaetoceros spp
Thalassiosira spp
 summer bloom: larger diatoms (103-106 µm3)
Rhizosolenia spp
Guinardia spp
The MIRO model
Trait-based approach
Trait-based module
Results
Conclusions
• Results: seasonal cycle (climatology 1989-1999)
Diatom biomass (optimized)
2 blooms
Mean cell volume (validation)
 information on the community structure
Sink and source terms of the mean cell volume
top-down pressure
 Evolving environmental constrains
bottom-up pressure “pushes” towards smaller size
• light: more limiting in winter
• nutrients: abundant in winter, progressively depleted…
import from adjacent waters
bottom-up pressure
top-down pressure “pushes” towards larger size
•copepods: build on 1st bloom  present for the 2d bloom
The MIRO model
Trait-based approach
Trait-based module
Results
Conclusions
• Conclusions/perspectives
Trait-based approach
- attractive way to add details without increasing uncertainty (allometric relationships)
- enables the use of additional data set (+ requires quantitative knowledge about trade-offs)
Application to the Belgian Coastal Zone (MIRO)
- good representation of the mean cell volume
- understanding of the drivers of changes in community structure
Perspectives
- added benefit under different scenarios
- model portability in space (variation across regions) and time (interannual runs)
THANK YOU