Sabrina Speich S. Russo, O. Aumont, E. Machu, C. Messager

Institut Universitaire Européen de la Mer & LMI ICEMASAV. Garçon, B. Le Vu (LEGOS, Toulouse)Y. Shin (UMR EME & LMI ICEMASA)

L. Shannon, C. Molooney (UCT, Afrique du Sud)

MEECE is a FP7 Integrated Project which aims to push forward the state-of-the-art of our understanding of impacts of global climate change and direct

anthropogenic drivers on marine ecosystems end to end

The specific goals of MEECE are:

To improve the knowledge base on marine ecosystems and their response to climate and anthropogenic driving forces

To develop innovative predictive management tools and strategies to resolve the dynamic interactions of the global change driver, changes in ocean circulation, climate, ocean acidification, pollution, over fishing and alien invasive species on the structure and functioning of marine ecosystems

Coordinator: Icarus Allen Plymouth Marine Laboratory (PML), UK meece@pml.ac.uk | www.meece.eu

MEECE integrated ecosystem changes

approach

meece@pml.ac.uk www.meece.eu

Climate Global Models:underestimation of climate subsystem processes

Climate Global Models:underestimation of climate subsystem processes

Global Climate Models not yet adequate to reproduce the whole spectra of atmospheric, oceanic and air-sea exchanges processes

HadCM3 SST error (model-simulated)

Emission scenarios

Modeling approaches to ‘downscaling’ from global to

regional scale

Modeling approaches to ‘downscaling’ from global to

regional scale1. using a regional climate model (RCM) – often referred to as

‘dynamical downscaling’. Note that this involves a two-step process, driving RCM at its boundaries by results from a GCM.

2. making use of empirical relationships between large and smaller scales based on historical observations – referred to as ‘statistical downscaling’. Note that this requires long-term and high-quality observations at the location/region in question.

3. using a ‘stretched grid’ global model, with high resolution over the domain of interest and lower resolution elsewhere. Note that this poses challenges for physical parameterizations, flow distortion, etc., but avoids problems at boundaries.

4. use global climate model to produce ‘high resolution time slices’. Note that this avoids boundary problems, but there may be issues with initial conditions, parameterizations, ocean boundary conditions, etc.

1. using a regional climate model (RCM) – often referred to as ‘dynamical downscaling’. Note that this involves a two-step process, driving RCM at its boundaries by results from a GCM.

2. making use of empirical relationships between large and smaller scales based on historical observations – referred to as ‘statistical downscaling’. Note that this requires long-term and high-quality observations at the location/region in question.

3. using a ‘stretched grid’ global model, with high resolution over the domain of interest and lower resolution elsewhere. Note that this poses challenges for physical parameterizations, flow distortion, etc., but avoids problems at boundaries.

4. use global climate model to produce ‘high resolution time slices’. Note that this avoids boundary problems, but there may be issues with initial conditions, parameterizations, ocean boundary conditions, etc.

but climate predictions & projections must be done at global scale, because the system’s response is fundamentally global

First step: A dynamical downscaling of the ocean using the

Regional Ocean Model System (ROMS)

Climate Scenario DownscalingClimate Scenario Downscaling

Dynamical downscaling runs regional (climate) models in reduced (regional) domain

with boundary conditions given by the (AR4) GCMs

Russo & Speich in prep.

Climate Scenario DownscalingClimate Scenario DownscalingSecond step:

A statistical calibration of the climate (IPSL A1B) scenario

COADS

Hyp.: Find an empirical function T that downscales (or corrects the model outputs) cumulative distribution function (CDF) of a climate variable from large- (the predictor) to local-scale (the predictand) by

applying an equivalent of proportionality transformation1

Russo & Speich in prep. 1Michelangeli et al. 2009

SOUTHERN SOUTHERN AFRICAAFRICA

50°S

30°S

25°S

40°S

45°S

10°W 0 10°E 20°W 30°W

Climate Scenario DownscalingClimate Scenario Downscaling

Future steps: 1.Improving the physical downscaling by using a coupled atmosphere-ocean regional system forced at boundaries by the statistically corrected AR4 (AR5) GCMs;

2.Adding the biogeochemistry components to the regional coupled system (NPZD, ecosystems, end-to-end models)

3.Implementing a full coupled regional system (including land biosphere, hydrology, atmosphere chemistry, etc.) ?

WRF forcedWRF forcedby OSTIA SSTby OSTIA SST

Latent Heat FluxLatent Heat Flux

MEECE integrated ecosystem changes

approach

Application to South Benguela for 1990-

1997

11 explicit species ¾ fish biomass >90% of captures

SOUTH AFRICA

0.15° x 0.15°

OSMOSE Model (high trophical levels) in the BenguelaModel dimensions:

Abundance and Biomass by:

•Species•Age•Size•Space unit•Time unit

Model dimensions:

Abundance and Biomass by:

•Species•Age•Size•Space unit•Time unit

log Size

log abd

1 µm 1 mm 1 m

log Size

log abd

1 µm 1 mm 1 m

Ratio max

Ratio min

Predator size

Prey size

1. Min-Max limits for the size pred/prey ratio2. Spatio-temporal co-occurrence

Variable structure of the trophical network

Opportunist predation: buffer role

#

#

#

#

#

#

Gansbay

Lamberts Bay

Saldanha Bay

Port ElizabethHout Bay

St Helena Bay

200 m

500 m

South Africa

Namibia

Lesotho

Lamberts Bay

Orange river

16

16

18

18

20

20

22

22

24

24

26

26

28

28

-36 -36

-34 -34

-32 -32

-30 -30

-28 -28

-26 -26

S

N

EW

0 200 400 Km

#

#

#

#

#

#

Gansbay

Lamberts Bay

Saldanha Bay

Port ElizabethHout Bay

St Helena Bay

200 m

500 m

South Africa

Namibia

Lesotho

Lamberts Bay

Orange river

16

16

18

18

20

20

22

22

24

24

26

26

28

28

-36 -36

-34 -34

-32 -32

-30 -30

-28 -28

-26 -26

S

N

EW

0 200 400 Km

Spatial distribution

1

2

3

4

5

6

1

(x,y)

1

1

(x,y+1) (x+1,y+1)

(x+1,y-1)(x,y-1)(x-1,y-1)

(x-1,y)

Processes

Natural mortality2

Explicit predation3

Growth or

Mortality by starving4

Mortality by fishing5

Reproduction6

Age 01st semester

Age 02nd semester

Ages 1-2 Ages 3+Age 01st semester

Age 02nd semester

Ages 1-2 Ages 3+Ex : harengAge 0 – sem 1

Ex : harengAge 3+

OSMOSE: Modelling the life cycle

Forcing & Coupling: ROMS-NPZD-OSMOSETravers et al. 2009. Ecol. Model.Travers et al. 2009. Ecol. Model.

Natural mortality

Predation

Growth

Fishing mortality

Reproduction

ξ

Small Detritus

Copepods

Flagellates

Nitrates

Large Detritus

Ciliates

Ammonium

Diatoms

Food availabilit

y(x,y,t,size)

1

1One-way coupling = Forcing

Starvation mortality

2Predation mortality

2Two-way coupling (feedback) = Coupling

OSMOSEOSMOSE ROMS-NPZDROMS-NPZD

- Parametrization

Shin et al. 2004. S. Afr. J. Mar. Sci.Travers et al. 2006. Can. J. Fish. Aquat. Sci.

- Sensibility analyses

Ferrer 2008, Msc thesis

- Calibration by genethic algorithm

Versmisse 2008, PhD thesisDuboz et al. 2010. Ecol. Model.

- Parametrization

Shin et al. 2004. S. Afr. J. Mar. Sci.Travers et al. 2006. Can. J. Fish. Aquat. Sci.

- Sensibility analyses

Ferrer 2008, Msc thesis

- Calibration by genethic algorithm

Versmisse 2008, PhD thesisDuboz et al. 2010. Ecol. Model.

-Validation – POM approach

Travers 2010, PhD thesis

- Crossed validation with Ecopath-Ecosim

Shin et al. 2004. S. Afr. J. Mar. Sci.Travers et al., 2010. J. Mar. Sys.

ROMS-NPZD coupling

Travers et al., 2009. Ecol. ModellingTravers et Shin, 2010. Progress Oceanogr

Scenarios with fishing and climate variability Y. Shin (UMR EME), L. Shannon (UCT)

Three questions will be addressed in the Benguela, using Roms-Npzd-Osmose and EwE:

1.How would climate change affects fishing reference levels?

2.Would climate change and fishing scenarios modify the trophic structure of the ecosystem?

3.To what extent are ecological indicators of fishing effects sensitive and exclusive to fishing pressure (vs sensitive to climate forcing)?

MEECE integrated approach on the Benguela ecosystem

Climate variability and impact S. Speich, S. Russo, E. Machu, O. Aumont, C. Messager (LPO IUEM), V. Garçon, B. Le Vu (LEGOS), Y. Shin (UMR EME), C. Mooloney (UCT)

Two questions adressed:

1.How climate change impacts the regional climate system ?

2.How this affects the local ecosystems (adresses via different coupled systems: ROMS-NPZD-OSMOSE et ROMS-PISCES-APECOSM)

Would climate change and fishing scenarios modify the trophic structure of the ecosystem?

Shift between different alternative trophic pathways?

Comparing ecological indicators across world’s marine ecosystemsthe IndiSeas Working Group

OBJECTIVESThe IndiSeas WG was established in 2005 under the auspices of EUROCEANS to:

•Develop a set of synthetic ecological indicators;

•Build a generic dashboard using a common set of interpretation and visualisation methods;

•Evaluate the exploitation status of marine ecosystems in a comparative framework

A suite of papers published in ICES Journal of Marine Science (2010) presents initial results of comparative analyses of the 19 fished marine ecosystems (Shin and Shannon 2010; Shin et al. 2010a).

In blue, the first 19 ecosystems considered in the IndiSeas WG. In yellow, the participating countries

The IndiSeas WG relies strongly on a multi-institutional collaboration for assembling a common dataset, and for allowing the global comparative approach to keep a good track of the data which underlie the indicators, and to account for the local scientific knowledge in the final diagnosis. The first phase of the WG (2005-2009) assembled the expertise of 31 scientific experts around the world, from 21 research institut

Indicators Headline label

Mean length Fish size

Trophic level of landings Trophic level

Proportion of under to moderately exploited species

% Healthy stocks

Proportion of predatory fish % Predators

Mean life span Life span

1/CV of total biomass Biomass stability

Total biomass of surveyed species

Biomass

Biomass:Landings Inverse fishing pressure

www.indiseas.org

Yunne-Jai SHINIRD, UMR EME 212shin@ird.fr

Lynne SHANNONUCT, Zoology DptLynne.Shannon@uct.ac.za

Comparing ecological indicators across world’s marine ecosystems

Next stepsBuilding bridges with other scientific fieldsTo strengthen the ecosystem diagnosis, additional indicators from other scientific fields need to be considered, allowing to:• Quantify the joint effects of climate and fishing changes• Integrate conservation and biodiversity issues• Integrate socio-economic issues

Testing the performance of ecosystem indicators in fisheries managementPerformance testing will allow to assess whether an indicator and accompanying decision rules actually guide decision-makers to make the “right” decision, in hindsight. The suite of indicators collected by the Indiseas WG provides a unique opportunity to test their performance across a range of ecosystems.

Developing reference levels for indicatorsEstablishing reference levels for ecosystem indicators has proven to be a major challenge to implementing EAF, due to the complexity of ecosystems and their response to fishing in a changing environment. Ecosystem models (EwE, Osmose, Atlantis) will be used for identifying baseline unexploited reference levels and limit reference levels.

For each ecosystem, a synthetic overview is displayed with state and trends indicators. A summary diagnosis is provided by each

ecosystem expert. Viewing options include time series for each indicator, descriptions of ecosystem and key species.

The IndiSeas websiteThe website www.indiseas.org has been developed as a platform to disseminate the results of the analyses beyond the scientific audience. It is intended to inform scientists, managers, policy makers and the public at large of the state of the world’s marine ecosystems as a result of fisheries exploitation.

Yunne-Jai SHINIRD, UMR EME 212shin@ird.fr

Lynne SHANNONUCT, Zoology DptLynne.Shannon@uct.ac.za

Spatio-temporal variation of fish-induced mortality on plankton

2.4E-03

2.5E-03

2.6E-03

2.7E-03

2.8E-03

2.9E-03

J F M A M J J A S O N D

2.4 10-3

2.6 10-3

2.8 10-3

Predation mortality rate on copepods (d-1)

Forçage/couplage ROMS-NPZD et OSMOSE

Travers et Shin 2010 - Progr.Ocean.

1 10-3

2 10-3

3 10-3

4 10-3

5 10-3

6 10-3

Predation mortality rate on copepods

(day-1)

Travers et al. 2009 - Ecol. Model.Travers et al. 2009 - Ecol. Model.

Forçage Couplage

1.104

2.104

3.104

4.104

Bio

masse

(t)

Diatomées Diatomées

Couplage = moins de plancton dans la zone de nourricerie

Quel est l’effet de la rétroaction?

- Des scénarios d’Aires Marines Protégées (ANR AMPED, coord. D. Kaplan)

Avec Y. Shin (UMR EME), D. Yemane (MCM), C. van Der Lingen (MCM), N. Bez (IRD)

Deux effets à tester avec ROMS-NPZD-OSMOSE:

1- Variabilité spatiale des réseaux trophiques

2- Changements d’habitats des espèces exploitées (scénarios IPCC)

- Des scénarios d’Aires Marines Protégées (ANR AMPED, coord. D. Kaplan)

Avec Y. Shin (UMR EME), D. Yemane (MCM), C. van Der Lingen (MCM), N. Bez (IRD)

Deux effets à tester avec ROMS-NPZD-OSMOSE:

1- Variabilité spatiale des réseaux trophiques

2- Changements d’habitats des espèces exploitées (scénarios IPCC)

Vers des scénarios prospectifs dans le Benguela sud

Hutchings et al. 2002

Life-history migration

Weeks et al. 2006Agulhas current

Agulhas current

Benguela current

Benguela current

The same species occur in the South

and West coasts so

many interactions

between the 2 zones

The same species occur in the South

and West coasts so

many interactions

between the 2 zones

1) How would climate change affects fishing reference levels?

- Simulate FMSY present conditions (already done in MSC LTLWG – T. Smith), and compare with simulations under IPCC scenarios (at least A1B, time slice 2080-2100)

- For a set of key target species (monospecies approach). In the Benguela: anchovy, sardine, redeye, horse mackerel, shallow water hake, deep water hake

2) Would climate change and fishing scenarios modify the trophic structure of the ecosystem?

Shift between different alternative trophic pathways?

Combined fishing and IPCC scenarios. 4 fishing scenarios:

- F status quo- Increase in F(global), F(small pelagics), F(large demersals)

2) Sensitivity and responsiveness of ecological indicators to fishing vs climate forcing

indicator

Fishing mortality F

? Linear decrease ?

? Environmental noise ?

Theoretical climate and fishing forcing:

- Implement present climate conditions, increase in wind stress (trend), interannual variability

- multiplier of F(global)- F(small pelagics): 0 to Fdepletion- F(demersal fish): 0 to Fdepletion

Set of indicators to be tested:

Mean size of fish, proportion of predatory fish, mean lifespan, 1/CV tot biomass, tot B, TL landings