ECOSYSTÈMES SUD AFRICAINS AU
CARREFOUR D’INTERFACES ET
INTERACTION D’ÉCHELLES
Sabrina Speich
S. Russo, O. Aumont, E. Machu, C. Messager
Institut Universitaire Européen de la Mer & LMI ICEMASA
V. Garçon, B. Le Vu (LEGOS, Toulouse)
Y. Shin (UMR EME & LMI ICEMASA)
L. Shannon, C. Molooney (UCT, Afrique du Sud)
Coordinator: Icarus Allen
Plymouth Marine Laboratory (PML), UK
meece@pml.ac.uk | www.meece.eu
MEECE is a FP7 Integrated Project which aims to push forward the state-ofthe-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
MEECE integrated
ecosystem changes
approach
meece@pml.ac.uk
www.meece.eu
Climate Global Models:
underestimation of climate subsystem processes
Emission scenarios
Global Climate Models not yet
adequate to reproduce the whole
spectra of atmospheric, oceanic
and air-sea exchanges processes
HadCM3 SST error
(model-simulated)
Modeling approaches to
‘downscaling’ from global to
regional scale
1. using a regional climate model (RCM) – often referred to as
‘dynamical downscaling’.
downscaling’. Note
Note that
that this
this involves
involves aa two-step
two-step process,
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’.
downscaling’. Note
Note that
that this
this requires
requires long-term
long-term and
and highhighquality 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.
but climate predictions & projections must be done at global
4. use global response
climate model tois
produce
‘high resolution time
scale, because the system’s
fundamentally
global
slices’. Note that this avoids boundary problems, but there may be issues
with initial conditions, parameterizations, ocean boundary conditions, etc.
Climate Scenario Downscaling
First step:
A dynamical downscaling of the ocean using the
Regional Ocean Model System (ROMS)
Dynamical
downscaling runs
regional (climate)
models in reduced
(regional) domain
with boundary
conditions given by
the (AR4) GCMs
Russo & Speich in prep.
Climate Scenario Downscaling
Second step:
A statistical calibration of the climate (IPSL A1B) scenario
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
COADS
Russo & Speich in prep.
1Michelangeli
et al. 2009
Climate Scenario Downscaling
SOUTHERN
AFRICA
25°
S
30°
S
40°
S
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, endto-end models)
3.Implementing a full coupled regional system
(including land biosphere, hydrology, atmosphere
chemistry, etc.) ?
45°
S
50°
S
Latent Heat Flux
10°W
0 WRF forced
10°
E SST
by OSTIA
Coupled
simulation
WRF-ROMS
20°W
30°W
MEECE integrated
ecosystem changes
approach
OSMOSE Model (high trophical levels) in the Benguela
SOUTH AFRICA
Application to South
Benguela for 19901997
Model dimensions:
0.15° x 0.15°
Abundance and Biomass by:
 11 explicit species
•Species
•Age
•Size
•Space unit
•Time unit
 ¾ fish biomass
 >90% of captures
16
18
20
22
24
26
28
-26
-26
N
W
E
Namibia
16
18
20
-28
24
22
26
Lesotho
-30
-26
N
-30
Min-Max limits for the size pred/prey ratio
Spatio-temporal co-occurrence
-28
28
Or ange r ive r
-26
1.
2.
S
South Africa
W
E
Namibia
-28
-28
S
20 0 m
#
-32
-32
La m b erts B ay
5 00 m
Or ange r ive r
#
St H ele n a B ay
#
-30
Lesotho
Sald a nh a B
ay
-30
South Africa
-34
#
Po rt E liz ab eth
H o ut B ay
-34
#
Ga
ns b ay
#
200 m
#
-32
-32
La m b erts B ay
5 00 m
Ratio
max
Ratio min
Prey size
#
-36
-36
St H ele n a B ay
#
Sald a nh a B ay
-34
#
0
Po rt E liz ab eth
H o ut B ay
200
400
Km
-34
#
Ga ns b ay
#
16
18
20
22
24
26
28
-36
-36
0
16
18
20
22
200
24
400
26
Km
28
Predator size
log
abd
Variable structure of the
trophical network
Opportunist predation:
buffer role
1 µm
1 mm
1 m
log Size
OSMOSE: Modelling the life cycle
Processes
(x-1,y-1)
(x,y-1)
(x-1,y)
(x,y)
(x+1,y-1)

4
3
2
1
Spatial distribution
2
Natural mortality
3
Explicit predation
4
Growth or
Mortality by starving
5
Mortality by fishing
6
Reproduction
5
6
1
1
Age 0
1st semester
(x,y+1)
1
Age 0
nd semester
2
(x+1,y+1)
Ex : Ages
hareng
Age
1-20
1st0semester
Age
– sem 1
Ex : Ages
hareng
Age3+0
2ndAge
semester
3+
Forcing & Coupling: ROMS-NPZD-OSMOSE
Travers et al. 2009. Ecol. Model.
Food
availability
OSMOSE
ROMS-NPZD
(x,y,t,size)
Natural
mortality
Copepods
Ciliates
Flagellates
Predation
Reproduction
1
ξ
Fishing
mortality
Growth
Starvation
mortality
Diatoms
Nitrates
Ammonium
2
Predation
mortality
Small Detritus
Large Detritus
1 One-way coupling = Forcing
2 Two-way coupling (feedback) = Coupling
- Parametrization
Shin et al. 2004. S. Afr. J. Mar. Sci.
Travers et al. 2006. Can. J. Fish. Aquat. Sci.
- Sensibility analyses
-Validation – POM approach
Ferrer 2008, Msc thesis
Travers 2010, PhD thesis
- Calibration by genethic algorithm
- Crossed validation with Ecopath-Ecosim
ROMS-NPZD coupling
Versmisse 2008, PhD thesis
Duboz et al. 2010. Ecol. Model.
Shin et al. 2004. S. Afr. J. Mar. Sci.
Travers et al., 2010. J. Mar. Sys.
Travers et al., 2009. Ecol. Modelling
Travers et Shin, 2010. Progress Oceanogr
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)
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)?
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 ecosystems
the IndiSeas Working Group
OBJECTIVES
www.indiseas.org
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).
The 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
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
Yunne-Jai SHIN
IRD, UMR EME 212
shin@ird.fr
Lynne SHANNON
UCT, Zoology Dpt
Lynne.Shannon@uct.ac.za
Indicators
Headline label
Mean length
Fish size
Trophic level of landings
Trophic level
Proportion of under to
% Healthy stocks
moderately exploited species
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
Comparing ecological indicators across world’s
marine ecosystems
Yunne-Jai SHIN
IRD, UMR EME 212
shin@ird.fr
Lynne SHANNON
UCT, Zoology Dpt
Lynne.Shannon@uct.ac.za
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 website
The 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.
Next steps
Building bridges with other scientific fields
To 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 management
Performance 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 indicators
Establishing 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.
Forçage/couplage ROMS-NPZD et OSMOSE
Spatio-temporal variation of fishinduced mortality on plankton
Quel est l’effet de la rétroaction?
Predation mortality rate on copepods (d-1)
2.9E-03
Forçage
2.82.8E-03
10-3
Diatomées
2.7E-03
Couplage
Diatomées
2.62.6E-03
10-3
4.104
2.42.4E-03
10-3
J
F
M
A
M
J
J
A
S
O
N
D
2.104
6 10-3
Predation mortality rate
on copepods (day-1)
1.104
5 10-3
4 10-3
3 10-3
2 10-3
1 10-3
Travers et Shin 2010 - Progr.Ocean.
Couplage = moins de plancton
dans la zone de nourricerie
Travers et al. 2009 - Ecol. Model.
Biomasse (t)
3.104
2.5E-03
Vers des scénarios prospectifs dans le Benguela sud
- 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
Life-history
IPCC)
The same
migration
species occur
in the South
and West
coasts so
many
interactions
between the 2
zones
Weeks et al. 2006
Hutchings et al. 2002
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
? Linear decrease ?
? Environmental noise ?
Set of indicators to be tested:
Fishing mortality F
Theoretical climate and fishing forcing:
Mean size of fish, proportion of
predatory fish, mean lifespan,
1/CV tot biomass, tot B, TL
landings
- 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