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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


VE

RTI

CA

L / T

RO

PH

IC in

tegr

atio

n


Rhomboïd E2E approach (De Young et al. 2004)

►Plankton-centered models

Zooplankton ZooplanktonZooplankton

PhytoplanktonPhytoplankton Detritus

HTL model

LTL model


VE

RTI

CA

L / T

RO

PH

IC in

tegr

atio

n

FishFish

FishFish

Fish Fish Fish Fish


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


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


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


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


Fish life cycle


Natural mortality

Predation

Forage

Piscivores

3 constraints:

► predator/prey size ratio

► spatio-temporal co-occurrence

► maximum ingestion rate

Predation efficiency ξ


Fish life cycle


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 =


Fish life cycle


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


Fish life cycle


Natural mortality

Forage

Predation

Starvation

Piscivores

ξ

Growth

Fishing mortality

ξ

Fishing periods

Fishing spatial distribution - MPAs(Yemane, Shin, Field MS)


Fish life cycle


Natural mortality

Forage

Predation

Starvation

Piscivores

ξ

Growth

Fishing mortality

Reproduction

ξ N0 = φ . SSB . SR



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


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


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)


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


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


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


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


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


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


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:


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


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 !!!

yunne shin morgane travers - home - pices · morgane travers pices xvi th annual meeting, october...

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