Embed Size (px)
Citation preview
A dynamic stochastic food web model
for the Barents Sea
Benjamin Planque and Ulf Lindstrøm
A Dynamic Stochastic Food Web model for the Barents Sea
A Dynamic Stochastic Food Web model for the Barents Sea
• Food Web
• Stochastic
A dynamic stochastic food web model
? ?
?
?
?
?
?
?
?
A Dynamic Stochastic Food Web model for the Barents Sea
Stochasticity in prey-predator functional relationships?
Pre
y co
nsum
ptio
n (g
/day
)
Prey biomass (g/nm2)
Edda Johannesen. Pers. com.
A Dynamic Stochastic Food Web model for the Barents Sea
• Food Web
• Stochastic
• Constrained– Mass Balanced– Satiety and inertia
A dynamic stochastic food web model
? ?
?
?
?
?
?
?
?
Mullon et al. 2009. A minimal model of the variability of marine ecosystems. Fish and Fisheries, 10: 115-131.
A Dynamic Stochastic Food Web model for the Barents Sea
Model Principles: Mass-balance
Sp 1 Sp 2Import Trophic flow Export
Metabolic losses
‘Other’ losses
A Dynamic Stochastic Food Web model for the Barents Sea
Model Principles 2. Mass-balance
Sp 1 Sp 2Import Trophic flow Export
Metabolic losses
‘Other’ losses
I1 F1,2E2
B1 B2
(1-γ1) (1-γ2)
(1-EE1) (1-EE2)
F2,2
A Dynamic Stochastic Food Web model for the Barents Sea
• Satiety
• Inertia
Model Principles: Additional Constraints
time
abun
danc
e Too high
Too low
A Dynamic Stochastic Food Web model for the Barents Sea
6 trophospecies12 fluxes1 import
Initial biomasse4 coeficientsFor each species
The minimal Barents Sea model
Cop.Euph.
Phytopl.
Cod
Minkewhales
Capelin
A Dynamic Stochastic Food Web model for the Barents Sea
Results 1. Diet fractions0 5 10 150
1
2
3
4
5
6
Copepod biomass
Fee
din
g r
ate
of
cope
pod
s
0 50 100 1500
2
4
6
8
10
Euphausiid biomass
Fee
din
g r
ate
of
eu
pha
usiid
s
0 5 10 150
1
2
3
4
5
6
Capelin biomass
Fee
din
g r
ate
of
cape
lin
Whale Cod Cap. Euph.0
0.2
0.4
0.6
0.8
1D
iet
fra
ctio
n
1 2 3 40
0.2
0.4
0.6
0.8
1
Phytopl.Cop.Euph.Cap.Cod
A Dynamic Stochastic Food Web model for the Barents Sea
Results 2 trophic functional relationships
A Dynamic Stochastic Food Web model for the Barents Sea
Results 3. Biomass time series
• Key graphs for the results (3 slides)
A Dynamic Stochastic Food Web model for the Barents Sea
Johannesen et al. In prep.
A Dynamic Stochastic Food Web model for the Barents Sea
Results 4. bottom up & top –down controls ?
A Dynamic Stochastic Food Web model for the Barents Sea
decadal fluctuations in top-down/bottom-up control
Bottom-up
Top-down
Johannesen et al.
A Dynamic Stochastic Food Web model for the Barents Sea
decadal fluctuations in top-down/bottom-up control
Bottom-up
Top-down
A Dynamic Stochastic Food Web model for the Barents Sea
Conclusions
• Stochastic model with a few constraints…• Mass-balance, satiation, inertia
• …and few parameters• EE, Metabolic efficiency, Lifespan, Satiation, import,
Export
• Simple, Fast and Transparent
• Simulates realistic ecosystem features
• Set a reference for expected ecosystem properties under a minimal set of assumptions
A Dynamic Stochastic Food Web model for the Barents Sea
On going work
• In-depth testing
• Spatial compartments
• Age-structured populations
• Known functional relationships
A Dynamic Stochastic Food Web model for the Barents Sea
Challenges and future work
• Linear programing & random solutions
• Model complexity and lack of solutions
• Model evaluation using summary statistics
• Model optimisation using summary statistics
• Data assimilation
A Dynamic Stochastic Food Web model for the Barents Sea
A Dynamic Stochastic Food Web model for the Barents Sea
The Master equation
Ecotrophicefficiency
Transition Matrix
Import Metabolicefficiency
Biomass at t-1 Influx of preys
Export
Outflux to predators
A Dynamic Stochastic Food Web model for the Barents Sea
Barents Sea model coefficients
Species Phytoplankton Zooplankton Euphausiiids Capelin Cod Whales
Metabolic Efficiency 1.00 0.36 0.40 0.21 0.35 0.002
Ecotrophic Efficiency 0.55 0.50 0.90 0.75 0.99 0.97
Life-span 0.02 0.65 1.00 3.00 15.00 40.00
Satiation 54.00 50.00 40.00 20.00 7.00 25.60
