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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, Belgium2Department of Earth Sciences, University of Oxford, UK
45th International Liege Colloquium13th – 17th May 2013
Liege, Belgium
MIRO (Lancelot et al., 2005)
• MIRO: a Plankton Functional Type (PFT) model
PFT models: aggregation of many species into one single group (e.g. diatoms)
“average behaviour” prediction ability with scenarios?
Data 1989-1999: diatoms counts + spp identification
Trait-based approach Trait-based module Results ConclusionsThe MIRO model
Represent diatom diversity in MIRO(based on size)
Relative presence of size classes in the community & Mean Cell Vol
Diatom diversity ↑
Phytoplankton functional traits*
Reproduction Resource acquisition Predator avoidance
Trai
t typ
ePh
ysio
logi
cal
Mor
phol
ogic
alBe
havi
oral
Life
his
tory
Ecological function
Litchman and Klausmeier 2008
• How to characterize diversity among phytoplankton?
*Trait: a well-defined, measurable property of organisms, usually measured at the individual level and used comparatively across species (McGill et al., 2006)
Trait values ecological functions
Trade-offs (cannot maximize all trait values)
Fitness is environment-dependent
Principle Many spp in competition, selection of the fittest
Size Many key traits co-vary with size
Trait-based module Results ConclusionsThe MIRO model Trait-based approach
The trait-based approach
Diatoms diversity is represented, based on size Size is related to ecological functions
Trait-based module Results ConclusionsThe MIRO model Trait-based approach
Trait values ecological functions
Trade-offs (cannot maximize all trait values)
Fitness is environment-dependent
Principle Many spp in competition, selection of the fittest
Size Many key traits co-vary with size
• How to characterize diversity among phytoplankton?
Phytoplankton functional traits
The trait-based approach
Susceptibility to grazing
Photosynthesis
Nutrient uptakeBiomass synthesis
Cell size
Reproduction Resource acquisition Predator avoidance
Trai
t typ
e
Ecological function
Phys
iolo
gica
lM
orph
olog
ical
Beha
vior
alLi
fe h
isto
ry
Diatom
Cell volume (VDA)Nutrients(N, P, Si)
growth grazing Copepodsµ𝑚𝑎𝑥 affinity
• Trait-based diatom module in MIRO
Biomass (DA)
sed lysis
Results ConclusionsThe MIRO model Trait-based approach Trait-based module
00𝑓 𝑙𝑖𝑚𝑃𝐴𝑅
𝑓 𝑙𝑖𝑚𝑁𝑈𝑇
Diatom dynamics:
𝑑𝐷𝐴𝑑𝑡
= {µ𝑚𝑎𝑥 (𝑽 𝑫𝑨 )∗ 𝑓 𝑙𝑖𝑚𝑃𝐴𝑅 (𝑽 𝑫𝑨 )∗ 𝑓 𝑙𝑖𝑚𝑁𝑈𝑇 (𝑽 𝑫𝑨 ) −𝑔𝑟𝑎𝑧𝑖𝑛𝑔(𝑽 𝑫𝑨)− 𝑙𝑦𝑠𝑖𝑠−𝑠𝑒𝑑𝑖𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛 }∗𝐷𝐴growth
Diatom
Cell volume (VDA)Nutrients(N, P, Si)
growth grazing Copepodsµ𝑚𝑎𝑥 affinity
Biomass (DA)
sed lysis
Diatom dynamics:
Mean cell volume dynamics:𝑑𝑉 𝐷𝐴
𝑑𝑡=variance∗( 𝜕𝑔𝐷𝐴
𝜕𝑉 𝐷𝐴)
𝑑𝐷𝐴𝑑𝑡
= {µ𝑚𝑎𝑥 (𝑽 𝑫𝑨 )∗ 𝑓 𝑙𝑖𝑚𝑃𝐴𝑅 (𝑽 𝑫𝑨 )∗ 𝑓 𝑙𝑖𝑚𝑁𝑈𝑇 (𝑽 𝑫𝑨 ) −𝑔𝑟𝑎𝑧𝑖𝑛𝑔(𝑽 𝑫𝑨)− 𝑙𝑦𝑠𝑖𝑠−𝑠𝑒𝑑𝑖𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛 }∗𝐷𝐴growth
𝑔𝐷𝐴
the mean cell volume depends on environmental conditions (nutrients, light, zooplankton)
• Trait-based diatom module in MIRO
Results ConclusionsThe MIRO model Trait-based approach Trait-based module
The diatom community is approximated in terms of total biomass and mean Cell volume
00𝑓 𝑙𝑖𝑚𝑃𝐴𝑅
𝑓 𝑙𝑖𝑚𝑁𝑈𝑇
(Wirtz and Eckhardt, 1996; Norberg et al., 2001; Merico et al., 2009)
• Variability in diatom parametersMany diatom traits co-vary with their cell volume
𝑡𝑟𝑎𝑖𝑡=𝑡𝑟𝑎𝑖𝑡𝑟𝑒𝑓 ∗𝑉ω allometric relationships : (linear on log-log scale)
slope and scaling factor : optimized
max growth rate
Sarthou et al., 2005 (JSR)
half-saturation constant
Litchman et al., 2007 (Ecol. Lett.)
photosynthetic efficiency
Geider et al., 1986 (MEPS)
Parameter Fittest diatoms
maximum growth rate Small
Small
photosynthetic efficiency Small
susceptibility to grazing Large
trade-offSmall vs Large diatoms
Gismervik et al., 1996 (Mar Pollut Bull)
susceptibility to grazing
BCZ range
Results ConclusionsThe MIRO model Trait-based approach Trait-based module
• Results: seasonal cycle (climatology 1989-1999)
ConclusionsThe MIRO model Trait-based approach Trait-based module Results
Diatom biomass (optimized)2 blooms
• Results: seasonal cycle (climatology 1989-1999)
ConclusionsThe MIRO model Trait-based approach Trait-based module Results
Diatom biomass (optimized)2 blooms
Mean cell volume (validation) information on the community structure
• Results: seasonal cycle (climatology 1989-1999)
ConclusionsThe MIRO model Trait-based approach Trait-based module Results
summer bloom: larger diatoms (103-106 µm3)
spring bloom: smaller diatoms (102-104 µm3)
Diatom biomass (optimized)2 blooms
Mean cell volume (validation) information on the community structure
Chaetoceros spp
Thalassiosira spp
Rhizosolenia spp
Guinardia spp
• Results: seasonal cycle (climatology 1989-1999)
Diatom biomass (optimized)2 blooms
ConclusionsThe MIRO model Trait-based approach Trait-based module Results
top-down pressure
bottom-up pressure
Sink and source terms of the mean cell volume 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
Mean cell volume (validation) information on the community structure
top-down pressure “pushes” towards larger size• copepods: build on 1st bloom present for the 2d bloom
• 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)
The MIRO model Trait-based approach Trait-based module Results Conclusions
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
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