Trait-based representation of diatom diversity in a Plankton Functional Type model N. T ERSELEER 1, J. B RUGGEMAN 2, C. L ANCELOT 1 AND N. G YPENS 1 1 Écologie des Systèmes Aquatiques, Université Libre de Bruxelles, Belgium 2 Department of Earth Sciences, University of Oxford, UK 45 th International Liege Colloquium 13 th – 17 th 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 : diatoms counts + spp identification Trait-based approachTrait-based moduleResultsConclusionsThe 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* ReproductionResource acquisitionPredator avoidance Trait type Physiological Morphological Behavioral Life history 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 moduleResultsConclusionsThe MIRO modelTrait-based approach  The trait-based approach

 Diatoms diversity is represented, based on size  Size is related to ecological functions Trait-based moduleResultsConclusionsThe MIRO modelTrait-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 ReproductionResource acquisitionPredator avoidance Trait type Ecological function Physiological Morphological Behavioral Life history

Diatom Cell volume (V DA ) Nutrients (N, P, Si) growthgrazing Copepods affinity Trait-based diatom module in MIRO Biomass (DA) sedlysis ResultsConclusionsThe MIRO modelTrait-based approachTrait-based module 00 Diatom dynamics: growth

Diatom Cell volume (V DA ) Nutrients (N, P, Si) growthgrazing Copepods affinity Biomass (DA) sedlysis Diatom dynamics: Mean cell volume dynamics: growth  the mean cell volume depends on environmental conditions (nutrients, light, zooplankton) Trait-based diatom module in MIRO ResultsConclusionsThe MIRO modelTrait-based approachTrait-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)

Variability in diatom parameters Many 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) ParameterFittest diatoms 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 ResultsConclusionsThe MIRO modelTrait-based approachTrait-based module

Results: seasonal cycle (climatology ) ConclusionsThe MIRO modelTrait-based approachTrait-based moduleResults Diatom biomass (optimized) 2 blooms

Results: seasonal cycle (climatology ) ConclusionsThe MIRO modelTrait-based approachTrait-based moduleResults Diatom biomass (optimized) 2 blooms Mean cell volume (validation)  information on the community structure

Results: seasonal cycle (climatology ) ConclusionsThe MIRO modelTrait-based approachTrait-based moduleResults  summer bloom: larger diatoms ( µm 3 )  spring bloom: smaller diatoms ( µm 3 ) 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 ) Diatom biomass (optimized) 2 blooms ConclusionsThe MIRO modelTrait-based approachTrait-based moduleResults 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 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 modelTrait-based approachTrait-based moduleResultsConclusions 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)

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