Modelling Oceanic Mid-trophic Levels - Bridging the gap

PAGE 1

© 2008 Connaître aujourd’hui, mieux vivre demain

Patrick Lehodey, Inna Senina, Julien Jouanno, Beatriz Calmettes

CLS, MEMMS (Marine Ecosystems Modelling and Monitoring by Satellites), Satellite Oceanography Division, 8-10 rue Hermes, 31520 Ramonville, France

PFRP Principal Investigators Workshop, November 18 - 19, 2008

Modelling Oceanic Mid-trophic Levels -Bridging the gap from ocean models to population dynamics of large marine

predators

PAGE 2


Mid-trophic levels

The OBJECTIVE is to model abundance and spatial dynamics of micronekton. This group is composed by a myriad of species, mostly crustaceans, fish, and cephalopods with size roughly from 2 to 20 cm, which constitute the bulk of the food for ocean top predators, such as tunas, billfish, sharks, marine mammals, seabirds or some turtles species.

day night

acoustic backscatter transect kindly from R. Kloser, CSIRO

PAGE 3


day

nightsunset, sunrise

Epipelagic layerT, U, V

surface1 2 3 4 5 6

Mesopelagic layerT, U, V

BathypelagicLayerT, U, V

Day length= f(Lat, date)PP

E

En ’

The MODEL: 6 functional groups in 3 vertical layers. Several components exhibit diel vertical migrations, transferring energy from surface to deep layers. The biomass of each component is computed with an energy transfer coefficient directly from the observed or modelled vertically integrated primary production

Mar-ECO station North Atlantic, kindly from Nils Olav Handegard, IMR, Bergen Norway

Mid-trophic conceptual model in SEAPODYM

PAGE 4


Functional group = multi-species population

tr +1/λ

PP

Eco

logi

cal t

rans

fer

5%

t0

F

( )teF’Fλ

λ−−= 1

tmax =-1/λ Ln(0.05) + trλ1tr

“mean age”

“lifespan”

t

λF’

teF’ λ−

F’

E

a window in the biomass size (weight) spectrum defined by:

E: energy transfer from PP to the functional micronekton group (trophic level ~2.5)

λ: mortality coefficient

tr : time of development for reaching the minimum size (weight)

Lehodey (2001)

PAGE 5


Parameterizing E

Jennings (2005):

PTL = PP x TETL-1

(TE trophic transfer efficiency; TL trophic level)

Similar slopes suggest invariant processes leading to constant energy transfer through size spectrum

Boudreau and Dickie 1992,

in Jennings, 2005

≠P

Lindeman (1942), Schaeffer (1965), Ryther (1969), and Iverson (1990):

F’yr = Pyr ’ · E n · c (with n the trophic level)

(n = 2.5 for mid-trophic)

Iverson (1990)

micronekton size window

PAGE 6


Can we link λ to meaningful biological parameters that are used to characterize the turnover of a population, i.e., generation time (~age at maturity tm or lifespan tmax )?

Parameterizing λ

Froese and Bihnolan (2000)

log tmax =0.5496+0.957*log(tm ) (n=432, r2=0.77)

* tmax = age at L∞

*0·95 (Taylor, 1958)

*rm txt ⋅+

⋅−= 5496.05496.0

957.0

101

10)ln(λ

substituting tmax by previous definition of lifespan (i.e., -1/λ Ln(0.05) + tr ), we obtain:

that, given the range of standard error of the original regression, can be simplified as:

tm = 1/λ + 1/3 tr

PAGE 7


Age at maturity tm

Gillooly et al. (2002) propose a model explaining relation between temperature and development time of post-embryonic (hatching to adult) zooplankton species (rotifers, copepods and cladocerans) incubated at different constant temperatures ranging from 5 to 30°C

y = -0.1252x + 7.6541R2 = 0.8834

0123456789

0 4 8 12 16 20 24 28Tc / (1+ (Tc/273))

Ln(t m

)

CephalopodCrustaceanFish

we obtain similar result using age at maturity and ambient temperature of micronekton species

metabolism of ectotherm animals is linked to ambient temperature

PAGE 8


0

300

600

900

1200

1500

1800

2100

2400

0 4 8 12 16 20 24 28 32T°

td (d

ay)

tr

based on a (very) few obs., we fixed tr to the development time needed to reach a weight of 1g, which is also linked to temperature (with same slope) and lead to tr ~ ¼ tm

Parameterizing tr

0

300

600

900

1200

1500

1800

2100

2400

0 4 8 12 16 20 24 28 32T°

td (d

ay)

0

300

600

900

1200

1500

1800

2100

2400

0 4 8 12 16 20 24 28 32T°

td (d

ay)

tr

tm = 1/λ + 1/3 tr

1/λ

PAGE 9


Problem: Parameterizing E’n

Matrix of Energy transfer coefficients used for the 3-layer 6-components mid-trophic levels model, according to the depth and the number of corresponding layers

Mid-trophic functional groups

Nb of Layers

epi meso m- meso

bathy m- bathy

hm-bathy

0 0 0 0 0 0 0

1 1 0 0 0 0 0

2 0.34 0.27 0.39 0 0 0

3 0.17 0.10 0.22 0.18 0.13 0.20

Currently, coefficients are tuned to approach existing observations (1st order)

next step: optimization using large data sets of acoustic data?

(See presentations by Julien and by Nils Olav for CLIOTOP-MAAS project )

PAGE 10


Spatial dynamics

⎣ ⎦ijrmn

mn

rmn

mn

mn

mn

mn

tmSS

tmnSvy

Suxy

S

x

SD

t

S

max

for ,

... 6,... ),ˆ()ˆ(

≤≤=

==∂∂

−∂∂

−⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

=∂∂

− 1

11

1

2

2

2

2

Mid-trophic Production

')ˆ()ˆ(2

2

2

2

nnnnnnn FFFv

yFu

xyF

xF

Dt

F+−

∂∂

−∂∂

−⎟⎟⎠

⎞⎜⎜⎝

⎛∂∂

+∂∂

=∂∂

λ

Mid-trophic biomass

Initial condition:

Neumann boundary conditions (impermeability):

ijnnij PcES =0

( ) , ,ˆˆ Ω∂∈∀=∂

∂=

∂

∂== ji

y

S

x

Svu ijij

ijij 0

PAGE 11


Applications: Foraging habitat of predators

Feeding habitat of Atlantic leatherback turtles

(Master T. Bastian; data kindly from J-Y Georges)

PAGE 12


Atlantic bluefin tuna

temperature x prey

Predicted habitat and obs. individual tracks

= habitat

Application: Foraging habitat of predators

Problem: high resolution/mesoscale requires highly realistic oceanic forcing (i,e., models using data assimilation)

UNH LPRC project: ABFT habitat (Tagging data kindly from M. Lutcavage)

PAGE 13


Applications: Spawning habitats of pelagic sp.

larvae survival leading to recruits in SEAPODYM

The number of larvae recruited in each cell of the grid at each time step is the product of a Beverton-Holt relationship coefficient linking the number of larvae to the density of mature fish and the spawning index IsIs : combines the effect of temperature and a measure of the trade-off ratio between food (~PP) and predators (micronekton) of larvae

Mid-trophic level species are predators of eggs and larvae of all pelagic species

skj betbft

PAGE 14


Spatial population dynamics of predators

SPC - SCIFISH

WCPO adult bigeye

0.20

0.25

0.30

0.35

0.40

0.45

1965 1970 1975 1980 1985 1990 1995 20000.15

0.20

0.25

0.30

0.35

0.40

0.45

Bio

mas

s (1

06 m

t) optimization hindcast

WCPO EPO

predicted adult biomass of bigeye tuna and observed longline catch (circles)

Parameter optimization with

Pacific fishing data

Test the validation in other Oceans

Distribution of skj larvaeGlobal simulation (several environmental forcing data sets)

SEAPODYM model: parameter optimization based on catch data in the Pacific Ocean

(Lehodey et al., 2008; Senina et al., 2008)

ICCAT envelop of prediction for main tuna species

PFRP - Tuna and climate

15

2008 results: yellowfin (still preliminary)

16

2008: South Pacific albacore (preliminary without size frequency data)

PAGE 17



PFRP - Tuna and climate CLIOTOP WG5 (modelling and synthesis)

comparison between two modelling approaches:

SEAPODYM (functional groups)

and

APECOSM (O. Maury) (size spectrum)

PAGE 18



2000

Big

eye 1950

2000

1950

Skip

jack

Bet SkjE Pac.

108- 142

282- 439

W Pac.

117- 134

1136- 1370

Ind. 115- 135

422- 489

Atl. 76- 103

115- 160

W Pac

E Pac

W Pac

E Pac

Range of annual catch

(‘000 t) by ocean 2000-04

(FAO,2007)

2050

2099

2050

2099

Clim

ate

chan

ge s

cena

rios

Global picture: why Atl. O. less “tuna productive” ?

PAGE 19


Conclusion

• Most pending questions on habitats and dynamics of large predators are linked to the lack of knowledge (i.e. observation and modeling) of Mid Trophic Level species!

• we need data (assimilation)...

20

Thank you

Questions?

Documents

Modelling Oceanic Mid-trophic Levels - Bridging the gap