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

hist

ory

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

istor

y

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 }∗𝐷𝐴

growth

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 }∗𝐷𝐴

growth

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 ∗𝑉 ω 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