modelling oceanic mid-trophic levels - bridging the gap
TRANSCRIPT
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
© 2008 Connaître aujourd’hui, mieux vivre demain
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
© 2008 Connaître aujourd’hui, mieux vivre demain
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
© 2008 Connaître aujourd’hui, mieux vivre demain
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
© 2008 Connaître aujourd’hui, mieux vivre demain
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
© 2008 Connaître aujourd’hui, mieux vivre demain
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
© 2008 Connaître aujourd’hui, mieux vivre demain
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
© 2008 Connaître aujourd’hui, mieux vivre demain
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
© 2008 Connaître aujourd’hui, mieux vivre demain
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
© 2008 Connaître aujourd’hui, mieux vivre demain
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
© 2008 Connaître aujourd’hui, mieux vivre demain
Applications: Foraging habitat of predators
Feeding habitat of Atlantic leatherback turtles
(Master T. Bastian; data kindly from J-Y Georges)
PAGE 12
© 2008 Connaître aujourd’hui, mieux vivre demain
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
© 2008 Connaître aujourd’hui, mieux vivre demain
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
© 2008 Connaître aujourd’hui, mieux vivre demain
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
© 2008 Connaître aujourd’hui, mieux vivre demain
Spatial population dynamics of predators
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
© 2008 Connaître aujourd’hui, mieux vivre demain
Spatial population dynamics of predators
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
© 2008 Connaître aujourd’hui, mieux vivre demain
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?