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Time-dynamic modeling, Ecosim
Biogeochemical processes and fish dynamics in food web models
International Centre for Theoretical Physics
Trieste, Nov 26-27, 2007
Villy Christensen & Carl Walters
UBC Fisheries Centre
Toward ecosystem management
• Consider feeding interactions: fish eat fish
• But living organisms are smart
– Behavior impacts interactions
– Human behavior impacts fishing
2
Main elements of Ecosim
• Includes biomass and size-structure dynamics
– mixed differential and difference equations
• Mass-balance (Ecopath) for initial state
• Variable speed splitting
– dynamics of both ‘fast’ (phytoplankton) and ‘slow’ groups (whales)
• Effects of micro-scale behaviors on macro-scale rates
Size-structured dynamics
• Multi-stanza size/age structure by monthly cohorts, density- and risk-dependent growth;
• Adult numbers, biomass, mean size accounting via delay-difference equations;
• Recruitment relationship as ‘emergent’ property of competition/predation interactions of juveniles.
Biomass dynamics in Ecosim
• Gross food conversion Efficiency, GE = Production / Consumption
• dB/dt = GE · Consumption - Predation - Fishery + Immigration - Emigration - Other Mort.
• Consumption = Σ micro-scale rates
• Predation = Σ micro-scale rates
6
Unavailable prey B-V
Available prey, V
Predator, P
Foraging arena
v = behavioral exchange rate (‘vulnerability’)
aVP
Walters, Christensen & Pauly 1997
7
Top-down vs. bottom-up control
Carrying capacity?
8
Predation mortality: effect of vulnerability
Predator abundance
Predicted predation mortality
Bottom-upTop-Down
High v Low v
Carrying capacity
0 Ecopath baseline
V = = 2
Foraging arena theory
• argues that the same fine-scale variation that drives us crazy when we try to survey abundances in the field is also critical to long-term, large-scale dynamics and stability
• Foraging arenas can now be shared between predators, (e.g., by different year-classes)
• EwE now allows bout feeding
Fine-scale arena dynamics: food concentration seen by predators should be highly sensitive to
predator abundance
‘Invulnerable’prey (N-V)
‘Vulnerable’prey (V)
Predationrate:
aVP
(mass actionencounters,within arena)
This structure implies ‘ratio-dependent’ predation rates:
V=v1N/(v1+v2+aP)
(rate per predator decreases with increasing predator abundance P)
v1
v2
Food concentration in arenas should be highly sensitive to density of animals
foraging there
dV/dt = (mixing in)-(mixing out)-(consumption)= vN - v’V - aVP
Fast equilibration of concentration implies
V = vN / ( v’ + aP )
Effect of local competition on food density
• Fast equilibration of concentration implies: V = vN / ( v’ + aP )
0
0.2
0.4
0.6
0.8
1
1.2
0 5 10 15
Are
na
foo
d d
ensi
ty (V
)
Competitor density (P)
Predicts strong effects at low densities
0
100
200
300
400
500
600
0 500 1000 1500 2000 2500 3000
Fin
al b
od
y w
eig
ht (g
)
Yearling density (fish/ha)
Ungrazed, Lo Fry
Ungrazed, Hi Fry
Grazed, Lo Fry
Grazed, Hi Fry
Power (Series5)
Trout in BC lakes
Moving predictions to larger scales
Hour
Season/
Year
Day
Decade
Meter Patch Reach Landscape
Arena
Dynamics
Local
Recruitment
Population
Dynamics
Beverton-Holt
equation
Ideal Free Distn.,
simulations
Behavior implies Beverton-Holt recruitment model(1) Foraging arena effect of density on food available:
Food
density
Juvenile fish density(2) implies linear effect on required activity and predation risk:
(3) which in turn implies the Beverton-Holt form:
Net recruits
surviving
Initial juvenile fish density
Activity,
mortality
Juvenile fish density
Strong empirical
support
Emerging empirical
support (Werner)
Massive empirical
support
Beverton-Holt shape and recruitment “limits” far below trophic potential (over
600+ examples now):
Predicting consumption
Qij
=a
ij • v
ij • B
i• P
j• T
i• T
j• S
ij• M
ij/ D
j
vij
+ vij
• Ti
• Mij
+ aij
• Mij
• Pj • Sij
• Tj/ D
j
Q = consumptiona = effective search ratev = vulnerabilityB = biomass; P = predator biomass or number
Qij
=a
ij• v
ij• B
i• P
j
vij
+ vij
+ aij
• Pj
Basic consumption equation:
Adding additional realism to the consumption equation:
S = seasonality or long-term forcingM = mediationT = search timeD = f(handling time)
Mediation
• Non-trophic mediation of interaction between a consumer and a prey group
– Tuna – small pelagics – albatrosses
– Piscivores – macroalgae – juvenile fish
– Trawling – resuspension – primary production
– Primary production – shading – macroalgae
– …
18
“D = f(handling time)”Functional response?
• Serves to limit consumption rates when moving away from initial condition
• Foraging arena provides a flexible representation of functional response
• Foraging arena: main difference from Holling’s functional response formulation– Incorporates prey behavior
19
Ecosim seeks to predict changes in mortality rates, Z
• Zi = Fi + sum of Mij
(predation components of M)
where Mij is Qij/Bi
(instantaneous risk of being eaten)
– Mij varies with
– Changes in abundance of type j predators
– Changes in relative feeding time by prey i and predator j
Time predictions from an ecosystem model of the Georgia Strait, 1950-2000
With mass-action (Lotka-Volterra) interactions only:
With foraging arena interactions:
How can we ‘test’ complex ecosystem models?
• No model fully represents natural dynamics, and hence
every model will fail if we ask the right questions;
• A ‘good’ model is one that correctly orders a set of
policy choices, i.e. makes correct predictions about the
relative values of variables that matter to policy choice;
• No model can predict the response of every variable to
every possible policy choice, unless that model is the
system being managed (experimental management
approach).
So how can we decide if a given model is likely to correctly order a set of
specific policy choices?
• Can it reproduce the way the system has responded to similar choices/changes in the past (temporal challenges)?
• Can it reproduce spatial patterns over locations where there have been differences similar to those that policies will cause (spatial challenges)?
• Does it make credible extrapolations to entirely novel circumstances, (e.g., cultivation/depensation effects)?
26
Fitting to time series: learning from ecosystem history
• Numerous ecosystem (EwE) models have in recent years produced credible fit to historical data, and made plausible policy predictions
–have clearly shown that this requires inclusion of environmental as well as fisheries impact
Formal estimation
Ecosystem model (predation,
competition, mediation,
age structured)
Climate Nutrientloading
Fishing
Predicted C, B, Z, W, diets
ObservedC,B,Z,W, diets
Log Likelihood
( BCC/B0)
(Diet0)
(Z0)
Habitat area
Errorpattern
recognition
Choice of parametersto include in final
estimation (e.g., climate anomalies)
Judgmental evaluation
Search for
minimi-zation
Christensen & Walters 2005
Modeling process: fitting & drivers
Confounding of fishery, environment, and trophic effects: monk seals in NWHI
• Initial Ecosim runs: fishing & trophic interactions together could not explain monk seal decline. Predicted lobster recovery
Low Chl
Fishing effort:
1970 2000
• Satellite chlorophyll data indicated persistent ~40% decline in primary production around 1990. ‘Explains’ both continued monk seal decline and persistent low lobster abundance
Are seals causing fish declines in the Georgia Strait?
Is it fishing?
Is it environ-mental change?
Or, is it all three?
1950 19502000 2000‘50 ‘50 ‘90‘90
PP anomaly and climate indicators
• Strait of Georgia– Race Rocks Salinity, Feb-May
0.7
0.9
1.1
1.3
1950 1960 1970 1980 1990 2000
pro
duct
ion a
nom
aly
-20
-10
0
10
upw
ellin
g (m
3/1
00m
/s)
PPA 10
MJJ 10
0.7
0.9
1.1
1.3
1950 1960 1970 1980 1990 2000
pro
duct
ion a
nom
aly
-1.3
-0.9
-0.5
-0.1
0.3
0.7
1.1
PD
O in
dex
PPA 30
AMJJ 30
• BC shelf– Upwelling, May-July, 54°N
• NE Pacific– PDO, April-July
Preikshot (UBC FCRR) 200730
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1950 1960 1970 1980 1990 2000
Nu
trie
nt
loa
din
g (
rela
tiv
e)
Environmental forcing • Force nutrient supply
to environment
• Force primary production – or secondary, or any other
• Force temperature, salinity, or any other environmental parameter through mediation
• Force (residual) currents
• Long time series should be used for contrast – Often, detailed hydrographic models only cover
short time period; not desirable31
Nutrient loading, Chesapeake Bay
Year