model N Terseleer 1 J Bruggeman 2 C Lancelot 1 and N Gypens 1 1 Écologie des Systèmes Aquatiques Université Libre de Bruxelles Belgium 2 Department of Earth Sciences ID: 264401
Download Presentation The PPT/PDF document "Trait-based representation of diatom div..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.
Slide1
Trait-based representation of diatom diversity in a Plankton Functional Type modelN. Terseleer1, J. Bruggeman2, C. Lancelot1 and N. Gypens11Écologie des Systèmes Aquatiques, Université Libre de Bruxelles, Belgium2Department of Earth Sciences, University of Oxford, UK
45
th
International
Liege
Colloquium
13
th
– 17
th
May 2013
Liege
, Belgium Slide2
MIRO (Lancelot et al., 2005)MIRO: a Plankton Functional Type (PFT) modelPFT 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
Conclusions
The MIRO model
Represent diatom diversity in MIRO
(based on size)
Relative presence of size classes in the community & Mean Cell
Vol
Diatom diversity ↑Slide3
Phytoplankton functional traits*ReproductionResource acquisitionPredator avoidanceTrait typePhysiologicalMorphologicalBehavioralLife historyEcological 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
Conclusions
The MIRO model
Trait-based approach
The
trait-based approachSlide4
Diatoms diversity is represented, based on sizeSize is related to ecological functionsTrait-based moduleResultsConclusionsThe 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 uptake
Biomass synthesis
Cell size
Reproduction
Resource acquisition
Predator avoidance
Trait type
Ecological function
Physiological
Morphological
Behavioral
Life historySlide5
DiatomCell volume (VDA)Nutrients(N, P, Si)growth
grazing
Copepods
affinity
T
rait-based diatom module in MIRO
Biomass
(DA)
sed
lysis
Results
Conclusions
The MIRO model
Trait-based approach
Trait-based module
00
Diatom dynamics:
growthSlide6
DiatomCell volume (VDA)Nutrients(N, P, Si)growthgrazingCopepods
affinity
Biomass
(DA)
sed
lysis
Diatom dynamics:
Mean cell volume dynamics:
growth
the m
ean cell volume depends on environmental conditions
(nutrients, light, zooplankton)
T
rait-based diatom module in MIRO
Results
Conclusions
The MIRO model
Trait-based approach
Trait-based module
The diatom community is approximated in terms of total biomass and mean Cell volume
00
(
Wirtz
and
Eckhardt
, 1996;
Norberg
et al., 2001;
Merico
et al., 2009)Slide7
Variability in diatom parametersMany diatom traits co-vary with their cell volume
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
Parameter
Fittest diatoms
Small
Small
photosynthetic efficiency
Small
susceptibility to grazing
Large
trade-off
Small
vs
Large diatoms
Gismervik
et al.,
1996
(Mar
Pollut
Bull)
susceptibility to grazing
BCZ range
Results
Conclusions
The MIRO model
Trait-based approach
Trait-based moduleSlide8
Results: seasonal cycle (climatology 1989-1999)ConclusionsThe MIRO modelTrait-based approach
Trait-based moduleResults
Diatom biomass
(
optimized
)
2 bloomsSlide9
Results: seasonal cycle (climatology 1989-1999)ConclusionsThe MIRO model
Trait-based approachTrait-based module
Results
Diatom biomass
(
optimized
)
2 blooms
Mean cell volume
(
validation
)
information on the community structureSlide10
Results: seasonal cycle (climatology 1989-1999)Conclusions
The MIRO modelTrait-based approach
Trait-based module
Results
summer bloom: larger
diatoms (10
3
-10
6
µm
3
)
spring bloom: smaller
diatoms (10
2
-10
4 µm3)
Diatom biomass
(
optimized
)
2 blooms
Mean cell volume
(
validation
)
information on the community structure
Chaetoceros
spp
Thalassiosira
spp
Rhizosolenia
spp
Guinardia
sppSlide11
Results: seasonal cycle (climatology 1989-1999)
Diatom biomass
(
optimized
)
2 blooms
Conclusions
The 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 1
st
bloom
present for the 2
d
bloomSlide12
Conclusions/perspectivesTrait-based approachattractive 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)Slide13
THANK YOU