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Coupling OSMOSE and ROMS‐NPZD models:towards end‐to‐end modelling of the Benguela
upwelling ecosystem
Yunne SHINMorgane TRAVERS
PICES XVI th annual meeting, October 26 - November 5, 2007, Victoria BC, Canada
DEFINITION (Travers, Shin, Jennings, Cury 2007. Progress in Oceanography)
• aims to represent the entire food web and the associated abiotic environment, • requires the integration of physical and biological processes at different scales, • implements two-way interaction between ecosystem components• accounts for the dynamic forcing effect of climate and human impacts at multiple trophic levels.
End‐to‐end modelling (E2E) ResultsInput/OutputE2E Description
Food web for the Benguela ecosystem (Field et al. 1991)
Primary production
Recruitment success
Spatial distributionMatch-mismatch
Physiological rates
E2E models for EBFM
WHAT FOR ?
- What are the combined effects of F & C on target and non-target species?
ResultsInput/OutputE2E Description
- How fishing and climate (F & C) effects propagate down and up marine foodwebs?
HOW ?
- Vertical integrationCoupling of LTL and HTL models
- needs integration of multi-disciplinary knowledge- relies on pre-existing models- ensures that processes addressed at appropriate scales at each TL
- Horizontal integrationBiodiversity integration / simplification► How can we select the key components to be explicitly modelled?
Structure of E2E models ResultsInput/OutputE2E Description
(Shin et al. MS)
Stibor et al. 2004
Van der Lingen et al. 2006
Parsons and Lalli 2002The Effect of Fishing Downthe Food Chains of the Sea
Physicaland
ChemicalForcing
Diatoms Macrozooplankton Fish
Fishing
SmallerFish
Flagellates Mesozooplankton Jellyfish
Alternative foodchains ResultsInput/OutputE2E Description
PhytoplanktonZooplankton
FishFish
FishFish
Fish Fish Fish Fish
Pathways‐oriented E2E models ResultsInput/OutputE2E Description
Rhomboïd E2E approach (De Young et al. 2004)
HTL model
LTL model
HORIZONTAL / BIODIVERSITY integration
VE
RTI
CA
L / T
RO
PH
IC in
tegr
atio
n ►Fish-centered models
Fish
Zooplankton ZooplanktonZooplankton
PhytoplanktonPhytoplankton Detritus
HTL model
LTL model
HORIZONTAL / BIODIVERSITY integration
VE
RTI
CA
L / T
RO
PH
IC in
tegr
atio
n
Pathways‐oriented E2E models ResultsInput/OutputE2E Description
Rhomboïd E2E approach (De Young et al. 2004)
►Plankton-centered models
Zooplankton ZooplanktonZooplankton
PhytoplanktonPhytoplankton Detritus
HTL model
LTL model
HORIZONTAL / BIODIVERSITY integration
VE
RTI
CA
L / T
RO
PH
IC in
tegr
atio
n
FishFish
FishFish
Fish Fish Fish Fish
Pathways‐oriented E2E models ResultsInput/OutputE2E Description
Pathways-oriented E2E approach (Shin et al. MS)
Lowenergy FC
Highenergy FC
Fast TORFC
Slow TORFC
Top predators
Phytoplankton
Environment
Nutrients
Forage species
Detritus
Zooplankton
« NPZD»
OSMOSE
Larva
l IBM
Souris
seau
NEMURO.FishSEAPODYM
APECOSMEwE
ERSEMIG
BEM / BM2
Size sp
ectra
E2E models: review ResultsInput/OutputE2E Description
COMPONENTS
MODELS
Spatial model
Non spatial model
2-ways trophic coupling
Functional groups
Multi-species
1 or 2 species
Adapted from Travers, Shin et al. 2007
Representation of fishing (Osmose) and climate (Roms-NPZD) drivers
Vertical integration
Predation as the coupling process: propagation of C & F effects via trophic interactions
OSMOSE
Horizontal integration
Multi-species and multi-compartment coupled model: possible alternative foodchains
ResultsInput/OutputE2E Description
Top predators
Phytoplankton
Environment
Nutrients
Forage species
Detritus
Zooplankton
ROMS-NPZD
Osmose‐Npzd‐Roms
OSMOSEObject‐oriented Simulator of Marine ecOSystems Exploitation
Variability in time and space of fish diets
Cannibalism
Omnivory
Patterns in fish diets
ResultsInput/OutputDescriptionE2E
Shin & Cury 2001, 2004
Spe
cies
Size
PREDATION MORTALITY
Size and marine foodwebs ResultsInput/OutputDescriptionE2E
log Size
log abd
1 µm 1 mm 1 m log Size
log abd
1 µm 1 mm 1 m
Size‐based predation
Ratio max
Ratio min
pred size
Prey size
1- Thresholds for predator/prey size ratio
Modelled food webs are variable in structure
Opportunistic predation: dampening role on the foodweb
#
#
#
#
#
#
Gansbay
Lamberts Bay
Saldanha Bay
Port ElizabethHout Bay
St Helena Bay
200 m
500 mLamberts Bay
16 18 20 22 24 26 28
-36 -36
-34 -34
-32 -32
0 200 400 Km
#
#
#
#
#
#
Gansbay
Lamberts Bay
Saldanha Bay
Port ElizabethHout Bay
St Helena Bay
200 m
500 mLamberts Bay
16 18 20 22 24 26 28
-36 -36
-34 -34
-32 -32
0 200 400 Km
2- Spatio-temporal co-occurrence
ResultsInput/OutputDescriptionE2E
OSMOSE structure
- Size-based predation
►Size-structured and spatial model
- Fisheries management and conservation issues
►Species-based model
Model dimensions: Abundance and biomass by species, age, size, space and time
ResultsInput/OutputDescriptionE2E
Class COHORT
- species- abund., biomass
- n schools Class SCHOOL
- species, age- abundance, biomass- spatial coordinates- length, weight- predation efficiency
Class SYSTEM
- abundance, biomass- species richness S- carrying capacity- size spectrum
- S species
Class SPECIES
- abundance, biomass- growth parameters- reproduction parameters- distribution area/age- fishing mortality/age
- (longevity+1) cohorts
OSMOSE structure ResultsInput/OutputDescriptionE2E
Fish life cycle
Eggs and larvae surplus mortality (export, sinking, non-fecundation, starvation, critical stage..)
Mortality due to other predators (marine mammals, birds etc)
► Estimation by calibration (genetic algorithm,Versmisse et al. MS)
Spatial distribution
Natural mortality
anchovy
• By age/size class
• Implicit migrationpatterns
ResultsInput/OutputDescriptionE2E
Fish life cycle
Spatial distribution
Natural mortality
Predation
Forage
Piscivores
3 constraints:
► predator/prey size ratio
► spatio-temporal co-occurrence
► maximum ingestion rate
Predation efficiency ξ
ResultsInput/OutputDescriptionE2E
Fish life cycle
Spatial distribution
Natural mortality
Forage
Predation
Piscivores
Starvationξ
N N eMM
ξξ= − −( )1
MM
Mcrit
ξξ
ξξ=− +max
maxξ
10
Mξ max
ξcritξ
Μξ
ration of maintenance
maximal rationξcrit =
ResultsInput/OutputDescriptionE2E
Fish life cycle
Spatial distribution
Natural mortality
Forage
Predation
Starvation
Piscivores
ξ
Growth
ξ
ΔL L e es aK K a t
ss s s,
( )( )= −∞− − −1 0Von Bertalanffy
model:
Δ
ΔΔ
L
LL
s a i
s a is a
critcrit
, ,
, ,, ( )
= <
=−
− ≥
⎧⎨⎪
⎩⎪
021
si
si
ξ
ξξ ξξi
Δ
ΔΔ
L
LL
s a i
s a is a
critcrit
, ,
, ,, ( )
= <
=−
− ≥
⎧⎨⎪
⎩⎪
021
si
si
ξ
ξξ ξξi
if ξi < ξcrit
if ξi ≥ ξcrit
ResultsInput/OutputDescriptionE2E
Fish life cycle
Spatial distribution
Natural mortality
Forage
Predation
Starvation
Piscivores
ξ
Growth
Fishing mortality
ξ
Fishing periods
Fishing spatial distribution - MPAs(Yemane, Shin, Field MS)
ResultsInput/OutputDescriptionE2E
Fish life cycle
Spatial distribution
Natural mortality
Forage
Predation
Starvation
Piscivores
ξ
Growth
Fishing mortality
Reproduction
ξ N0 = φ . SSB . SR
ResultsInput/OutputDescriptionE2E
Spatial distribution
Natural mortality
Forage
Predation
Starvation
Piscivores
ξ
Growth
Fishing mortality
Reproduction
ξ
Carrying capacity
Non piscivores
maximal biomass of non-piscivorousfish which implicitly feed on plankton
Fish life cycle ResultsInput/OutputDescriptionE2E
Forcing/coupling: E2E approach
Spatial distribution
Natural mortality
Forage
Predation
Starvation
Growth
Fishing mortality
Reproduction
Food availability
Bt,x,y,i
Predation mortality
Mt,x,y,i
(Travers and Shin, MS)
ROMS-N2P2Z2D2(Penven, Machu, Koné)
Flagellates Diatoms
Copepods
Nitrates
Large detritus
Ciliates
Small detritus
Ammonium
20 µm
200 µm20 µm20 µm2 µm
2 mm200 µm200 µm
PROCESSES: Grazing, growth, excretion, egestion, mortality, sinking, photosynthesis, respiration, nitrification, remineralization
ResultsInput/OutputDescriptionE2E
12 fish species modelled: 76% total fish biomass, 94% total catch
Lanternfish Lightfish
Anchovy
Sardine
Redeye
Chub mackerel
Horse mackerel
Shallow water hakeDeep water hake
Snoek
Silver kob
Kingklip
Application southern Benguela ResultsInput/OutputDescriptionE2E
SNOEK
SILVER KOB
SARDINE
REDEYE
LIGHTFISH
LANTERNFISH
KINGKLIP
HORSE MACKEREL
HAKE deep water
HAKE shallow water
CHUB MACKEREL
ANCHOVY
SNOEK
SILVER KOB
SARDINE
REDEYE
LIGHTFISH
LANTERNFISH
KINGKLIP
HORSE MACKEREL
HAKE deep water
HAKE shallow water
CHUB MACKEREL
ANCHOVY
0.018-0.10.294115.3
0.007-1.470.12116
0.009-0.170.9521.4
0.0090.280.7130.1
0.0080.061.156
0.0080.061.667
0.0010.050.142132.6
0.009-0.650.18354.5
0.005-0.820.046230.3
0.005-0.820.046230.3
0.005-0.980.20768
0.007-0.031.3714.8
ct0KLinf
GROWTH
0.018-0.10.294115.3
0.007-1.470.12116
0.009-0.170.9521.4
0.0090.280.7130.1
0.0080.061.156
0.0080.061.667
0.0010.050.142132.6
0.009-0.650.18354.5
0.005-0.820.046230.3
0.005-0.820.046230.3
0.005-0.980.20768
0.007-0.031.3714.8
ct0KLinf
GROWTH
3130
2150
2400
1750
0.5334
0.5646
5500
3250
4500
4500
3300
18000
amatφREPRODUCTION
3130
2150
2400
1750
0.5334
0.5646
5500
3250
4500
4500
3300
18000
amatφREPRODUCTION
20.210.19
30.180.23
10.310.5
10.030.18
10.000.27
10.000.27
30.110.25
20.090.36
30.390.27
30.280.31
20.110.24
10.300.41
arecFMSURVIVAL
20.210.19
30.180.23
10.310.5
10.030.18
10.000.27
10.000.27
30.110.25
20.090.36
30.390.27
30.280.31
20.110.24
10.300.41
arecFMSURVIVAL
Biological parameters
Ex: Southern Benguela (Shin, Shannon, Cury 2004; Travers et al. 2006)
ResultsInput/OutputDescriptionE2E
Ratio max
Ratio min
pred size
Prey size
No a priori functional response and pre-determined diets, but predation constraints:
1- Maximum ingestion rate
2- Predator/prey size ratios
Predation constraints ResultsInput/OutputDescriptionE2E
#
#
#
#
#
#
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
#
#
#
#
#
#
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
Age 0
Ages 1-2 Ages 3-4-5 Ages 6+
REFERENCES• Maps- Badenhorst and Smale 1991- Payne, 1989- Punt 1994- Punt et al. 1992
Shallow water hake
Southern Benguela
Spatial distributions ResultsInput/OutputDescriptionE2E
► Size-based indicators
► Species-based indicatorsLmean, L95%, L at ageSize spectrum
Shannon indexW-statistic
0
20
40
60
80
100
120
0 5 10
Age
Siz
e
Size
Ln(a
bd)
OSMOSE Outputs
Abundance
Biomass
0%
20%
40%
60%
80%
100%
1 10 100
W
ResultsInput/OutputDescriptionE2E
TL distribution
Mean TL, TL-at-age
Diet matrix/species/size
► Trophodynamic indicators
2
2.5
3
3.5
4
4.5
5
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Shallow water hake
TL
age
OSMOSE Outputs
Community mean length
► Spatial indicators
ResultsInput/OutputDescriptionE2E
Trophic structure of the Southern Benguela using Osmose-LTL
► Zoo-plankton spatio-temporal mortality
► Fish trophic levels and life-history
Preliminary results ResultsDescriptionE2E Input/Output
FEEDBACK FEEDBACK fromfrom OSMOSE toOSMOSE to NPZD NPZD modelmodel
Applying fish-induced mortality rates on plankton
m=0.05 day-1 in the biogeochemical modelfish-induced mortality: 0.012 day-1 in average ~ ¼ of the mortality
due to modelled fish
For copepods:
(Travers and Shin, MS)
Copepods mortality – spatial patterns ResultsDescriptionE2E Input/Output
32°
30°
28°
S
16° 18° 24°22°20° E
34°
36°
St Helena Bay
Agulhas Bank
0
0.1
0.4
0.3
0.2
0.5
0.6
Fish-induced mortality on copepods (month-1)
Upwelling
Mor
talit
yra
te (m
onth
-1) B
iom
ass
J F M A M JJ A S O N D0.2
0.5
0.4
0.3
0
2
-2
Fish-induced mortality on copepods (month-1)
Upwelling
Copepods/Fish – seasonal patterns ResultsDescriptionE2E Input/Output
(Travers and Shin, MS)
Mean TL per species
Trophic structure – mean TL ResultsDescriptionE2E Input/Output
(Travers, Shin, Jennings)
0 1 2 3 4 5
anchovy
euphausiids
shall. w. hake
shall. w. hake 2+
deepw. hake
deepw. Hake 2+
horse mack.
horse mack. 2+
kingklip
lanternfish
lightfish
redeye
sardine
silver kob
snoek
EcopathModelECOPATH
OSMOSE
Comparison OSMOSE-NPZD-ROMS with ECOPATH
Species TL distributions ResultsDescriptionE2E Input/Output
TL
Den
sity
TL OSMOSE TL ECOPATH
anchovy
round herring
euphausiids
horse mackerel sardine
lantern fish
► Small forage species not always specialists► Sardine more generalists than anchovy – Van der Lingen et al. 2006
TL
Den
sity
ResultsDescriptionE2E Input/OutputSpecies TL distributionssilver kob kingklip
shallow-water hake deep-water hake snoek
► Large fish species are life-history omnivores (TL species may vary F, csq on trophic spectrum)
► In the 1990s, low predation of eggs and larvae of demersal fish by small pelagics (cultivation hypothesis)
Conclusion
► What’s next: Defining and simulating « What if scenarios » for quantifying propagation of climate and fishing changes
► Strategy 1: appropriate vertical and horizontal integration of E2E models for being able to anticipate ecological « surprises »
► Strategy 2: Comparative approach across models for strengthening simulation results
► Strategy 3: Validation using Pattern-Oriented Modelling approach (Grimm & Railsback 2005)
Multiple patterns validation at different hierarchical levels (Cury, Shin, Travers et al. 2007)
INDIVIDUAL PATTERNS
Diets
POPULATION PATTERNS
Speciescomposition
Predator prey size ratio
Communityindicators
Population indicators
Spatial patterns
Predator size
Pre
y si
ze
Evolution of size frequency
COMMUNITY PATTERNS
Meansize
Trophic level
Chladistribution
TL hake = 4.2TL redeye = 3.6
ABC curves
Size spectrum
PelagicDemersal
MANY THANKS FOR YOUR ATTENTION !!!