Patrick Lehodey

Marine Ecosystems Modeling and Monitoring by Satellites

CLS, Space Oceanography Division

8-10 rue Hermes, 31520 Ramonville, France

plehodey@cls.fr

One more step towards

operational management of the

world largest tuna fishery

mailto:plehodey@cls.fr

SEAPODYM

Ocean biogeoch.

3-D Models

Satellite data

Primary Prod.

From physics to fish (er)

Ocean Physics

3-D models

Satellite data

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Total prises 90-97 8000YFTSKJ

BFT

ALB

BET

Blue = skipjack; yellow = yellowfin; Red = bigeye

FISHERIES

(kindly from A. Fonteneau)

Fish population

dynamic models

predator

Ocean Mid-

Trophic Levels

prey

?

3

stomach content

of a yellowfin tuna

Credit: Frédéric MENARD, IRD, France

First step: modeling MTL

Time of development in days (Log scale)

of mid-trophic organisms in relation to

their ambiant habitat temperature Tc

Lehodey P., Murtugudde R., Senina I. (2010). Bridging

the gap from ocean models to population dynamics of

large marine predators: a model of mid-trophic

functional groups. Progress in Oceanography, 84: 69–84

day

night

sunset, sunrise

Epipelagic layer

T, U, V

surface 1 2 3 4 5 6

Mesopelagic layer

T, U, V

Bathypelagic

Layer

T, U, V

Day length

= f(Lat, date) PP

E

En’

Mar-ECO station North Atlantic, (IMR, Bergen Norway) showing acoustic detection of micronekton

Micronekton conceptual Model

0 m

500 m

A model of micronekton

(small prey organisms)

The MODEL: 6 functional groups in 3 vertical

layers. Three components exhibit diel vertical

migrations, transferring energy from surface to

deep layers.

The source of energy is the primary production

PP.

Epipelagic (daytime)

micronekton (2005)

Production (g m-2 d-1)

Biomass (g m-2)

Results: P/B epipelagic micronekton

¼ deg x 6 day

Physical fields from

MERCATOR

(http://www.mercator-ocean.fr/)

Satellite derived Primary

production

http://www.mercator-ocean.fr/http://www.mercator-ocean.fr/http://www.mercator-ocean.fr/

day

night

sunset, sunrise

Epipelagic layer

surface1 2 3 4 5 6

Mesopelagic layer

Bathypelagic layer

day

night

sunset, sunrise

Epipelagic layer

surface1 2 3 4 5 6

Mesopelagic layer

Bathypelagic layer

References:

Lehodey et al. (1998). Fish. Oceanog.

Lehodey, (2001). Prog. Oceanog.

Lehodey et al. (2010 ) Prog. Oceanog.

Results: day/night epipelagic biomass

1/12th deg x 6 day

Physical fields from MERCATOR

(http://www.mercator-ocean.fr/)

Satellite derived Primary

production

http://www.mercator-ocean.fr/http://www.mercator-ocean.fr/http://www.mercator-ocean.fr/

Assimilation of acoustic data in the MTL model:

The ratios Predicted biomass / Observed signal (NASC) between layers and between day and

night are used to optimize the coefficient matrix of Energy transfer between functional groups.

Data assimilation

Page 8

Optimization: Hawaiian transect

Page 9

Validation: Tasmanian transect

Data provided by R. Kloser (CSIRO)

Age-structured

Population Growth

mortality

by

cohort

Feeding Habitat = Food (MTL) abundance

x accessibility (T,DO)

Spawning Habitat = Food & T for larvae

Absence of larvae

predators (MTL)

IF MATURE

Seasonal

switch

Movement toward

feeding grounds

Movement toward

spawning grounds

Mortality

Spawning success

Recruitment

(larval drift)

Page

10

Fisheries

Predicted

catch

Observed

effort/catch

Calibration

The prediction of MTL is the key to understand and model The population dynamics of their predators, e.g., tunas

Application to exploited species

Parameter estimation approach

K

a rji

jiajifaf

rji

jiajifaf

predraft

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a

jiaaafjiftfpred

jift

yxNE

yxNEs

Q

yxNwsEqC

1 ,

,,,,,

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,,,,,

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2sin1

2

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ln

2

,,,,,,

,,,2

,,,

,

kkkkk

prraft

obsraft

raft f

LF

t f t f

obsft

predft

obsft

t f

predft

obs

QQL

CCC

CCL θ

Model predictions

Likelihoods, parametric space

The world’s largest tuna fishery catch ~2 Million t/yr of skipjack

in the western central Pacific Ocean. This is the main

economical resource of Pacific Island Countries, that want to

manage it in a sustainable way and to obtain a MSC label.

Background:

Application to the largest tuna fishery

To conserve this label, PICs needs to demonstrate they have appropriate (spatial)

tools to manage the stock, accounting for the fishing effort outside of their EEZs.

Total predicted skipjack biomass (2009)

And observed catch rates (circles)

-> Detailed spatial population dynamics (larvae to oldest adults) and catch

0.25°

SODA, February 2002 , 1° GLORYS, February 2002 , 0.25° NCEP, February 2002, 2°

OPA-NCEP (http://www.nemo-ocean.eu/; http://www.cgd.ucar.edu/cas/guide/Data/ncep-ncar_reanalysis.html)

SODA (http://www.atmos.umd.edu/~ocean/)

GLORYS (http://www.mercator-ocean.fr/)

Interest for operational approach

Before the real time monitoring, there is a strong interest for

improving the stock estimates and their fluctuations under

both Climate (natural and anthropic) and Fishing impacts

A realistic mesoscale provides a better signal for parameter estimation

Spatial and temporal fit is improved

http://www.nemo-ocean.eu/http://www.nemo-ocean.eu/http://www.nemo-ocean.eu/http://www.cgd.ucar.edu/cas/guide/Data/ncep-ncar_reanalysis.htmlhttp://www.cgd.ucar.edu/cas/guide/Data/ncep-ncar_reanalysis.htmlhttp://www.cgd.ucar.edu/cas/guide/Data/ncep-ncar_reanalysis.htmlhttp://www.atmos.umd.edu/~ocean/http://www.mercator-ocean.fr/http://www.mercator-ocean.fr/http://www.mercator-ocean.fr/

ESSIC ORCA2-PISCES SODA-PPsat

GLORYS-PPsat

30d x 2

30d x 1

7d x 0.25

Note: Parameterization obtained with SODA is simply scaled for GLORYS

Stock estimation is

improved (less diffusion)

ORCA2-PISCES

Interest for operational approach

Goodness-of-fit for catch and CPUE (1975-2003)

Validation (Skipjack)

Indian Ocean experiment

1997, II quarter

1997, IV quarter

Predicted vs. observed catch & LF

Sub-tropical pole-and-line (solid lines – predicted catch)

Tropical purse-seine (free schools)

7th regular session of

the Scientific Committee

Pohnpei, Micronesia,

8-17 August 2011

Observed catch rates (black dots) and monthly

predictions of skipjack total biomass (2009)

EEZ of Papua New Guinea

(weekly)

(weekly)

Towards real time monitoring?

Page 17

By providing realistic reanalyses of ocean physics, OO allows a better

parameter estimation of biological models (by increasing spatio-temporal fit

between data and prediction).

OM applications are being developed. The coming next step is the

validation and use of real time applications for the monitoring of fisheries.

Operational biogeochemical models are complementary to PPsat with

several advantages:

- Fully coherent with physics (resolution, no gaps, meso and submeso…)

- Include other key variables (DO, pH?, pollutants…)

- Provide hindcasts that are absolutely critical for estimating and

validating fish stock evolution over historical periods (50 yrs typically),

and limiting the impact of initial conditions in the optimization approach.

- Provide forecasts (week, season, internannual, decadal, IPCC)

- Allow testing scenarios for impact studies, e.g., tracers Cs, Hg…

- PPsat is also an (empirical) model with many sources of uncertainties.

Conclusions