Jump-growth model for predator-prey dynamics
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Transcript of Jump-growth model for predator-prey dynamics
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Jump-growth model forpredator-prey dynamics
Gustav W. Delius
Department of MathematicsUniversity of York
I will present a simple stochastic model using the techniques presented in thisworkshop but for modelling not spatial structure but size structure.
work with Samik Datta, Mike Planck, Richard Lawarxiv:0812.4968
ICMS Edinburgh, 15 - 20 June 2009
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Approaches to Ecosystem ModellingIndividual Based ModelPopulation level model
Approaches to ecosystem modelling: food webs
Traditionally, interactionsbetween species in anecosystem are described with afood web, encoding who eatswho.
Food Web
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Approaches to Ecosystem ModellingIndividual Based ModelPopulation level model
Marine ecosystems are special
Fish grow over several orders of magnitude during their lifetime.
Example: an adult female cod of 10kg spawns 5million eggs every year, each hatching to a larvaweighing around 0.5mg.”
All species are prey at some stage. Wrong picture:
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Approaches to Ecosystem ModellingIndividual Based ModelPopulation level model
Approaches to ecosystem modelling: size spectrum
Ignore species altogether anduse size as the sole indicatorfor feeding preference.
Large fish eats small fish
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Approaches to Ecosystem ModellingIndividual Based ModelPopulation level model
Individual based model
We can model predation as a Markov process on configurationspace. A configuration γ = w1,w2, . . . is the set of theweights of all individuals in the system.
The primary stochastic event comprises a predator of weightwa consuming a prey of weight wb and, as a result, increasingto become weight wc = wa + Kwb.
The Markov generator L is given heuristically as
(LF )(γ) =∑
wa,wb∈γk(wa,wb) (F (γ\wa,wb ∪ wc)− F (γ)) .
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Approaches to Ecosystem ModellingIndividual Based ModelPopulation level model
Population level model
We introduce weights wi with 0 = w0 < w1 < w2 < · · · andweight brackets [wi ,wi+1), i = 0,1, . . . .
Let n = [n0,n1,n2, . . . ], where ni is the number of organisms ina large volume Ω with weights in [wi ,wi+1].
Now the Markov generator is
(LF )(n) =∑i,j
k(wi ,wj)((ni + 1)(nj + 1)F (n − ν ij)− ninjF (n)
),
where n − ν ij = (n0,n1, . . . ,nj + 1, . . . ,ni + 1, . . . ,nl − 1, . . . )and l is such that wl ≤ wi + Kwj < wl+1.
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Approaches to Ecosystem ModellingIndividual Based ModelPopulation level model
Master equation
The time evolution of the probability P(n, t) that the system is inthe state n at time t is then given by the master equation
∂P(n, t)∂t
=∑i,j
kij
Ω
[(ni + 1)(nj + 1)P(n − ν ij , t)− ninjP(n, t)
],
(1)This is conveniently written using the step-operator notation:
∂P(n, t)∂t
=∑i,j
kij
Ω
(EiEjE−1
l − I) (
ninjP(n, t)). (2)
A step operator Ei acts on any function f (n) asEi f ([n0, . . . ,ni , . . . ]) = f ([n0, . . . ,ni + 1, . . . ]).
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Approaches to Ecosystem ModellingIndividual Based ModelPopulation level model
van Kampen expansion
Following the method used by van Kampen, we separate eachrandom variable ni into a deterministic component φi(t) and arandom fluctuation component ξi(t) as
ni = Ωφi(t) + Ω12 ξi(t),
where the deterministic component satisfies
ddtφi =
∑j
(−kijφiφj − kjiφjφi + kmjφmφj
),
Substituting this back into the Master equation gives a linearFokker-Planck equation for the fluctuations ξi(t) plus terms ofhigher-order in Ω.
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Approaches to Ecosystem ModellingIndividual Based ModelPopulation level model
Linear Fokker-Planck equation
The linear Fokker-Planck equation for the probabilitydistribution Π(ξ) of the fluctuations is
∂Π
∂t= −
∑ij
Aij∂
∂ξi
(ξjΠ)
+12
∑ij
Bij∂2
∂ξi∂ξjΠ,
where the coefficients Aij and Bij are independent of thefluctuations ξ.
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Approaches to Ecosystem ModellingIndividual Based ModelPopulation level model
Fokker-Planck equation
If we introduce the objects kijl and fijk by
kijl =
kij if wl ≤ wi + Kwj < wl+10 otherwise
,
fijl =12(kijl + kjil
)then we can give the succinct expressions
Aii =∑
jl
fijlφj , Aij =∑
l
(fijlφi − fljiφl
),
Bii =∑
jl
fjliφjφl , Bij =∑
l
(fijlφiφj − filjφiφl − fljiφlφj
).
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
SymmetriesSteady StateTravelling Waves
Continuum limit
When we take the limit of vanishing width of weight brackets thedeterministic equation becomes
∂φ(w)
∂t=
∫(− k(w ,w ′)φ(w)φ(w ′)
− k(w ′,w)φ(w ′)φ(w)
+ k(w − Kw ′,w ′)φ(w − Kw ′)φ(w ′))dw ′. (3)
The function φ(w) describes the density per unit mass per unitvolume as a function of mass w at time t .
We will now assume that the feeding rate takes the form
k(w ,w ′) = Awαs(w/w ′
). (4)
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
SymmetriesSteady StateTravelling Waves
Symmetries
The jump-growth equation is invariant under the followingtransformations
weight scale transformation
φ(w , t) 7→ να+1φ(νw , t),
where ν is the parameter for the scale transformation.time scale transformation
φ(w , t) 7→ µφ(w , µt).
time translation
φ(w , t) 7→ φ(w , t + a).
There is no translation invariance in weight space.Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
SymmetriesSteady StateTravelling Waves
Restoring translation invariance
If we introduce the log weight x so that
w = w0ex
and the rescaled density
v(x) = w1+αφ(w)
then the deterministic jump growth equation reads
∂v(x)
∂t= −A
∫s(ez) (eαzv(x)v(x − z) + v(x)v(x + z)
−eα(z+ε)v(x − ε)v(x − z − ε))
dz,
where ε = ln(1 + Ke−z). This is manifestly invariant undertranslation in x .
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
SymmetriesSteady StateTravelling Waves
Power law steady-state
Substituting an Ansatz φ(w) = w−γ into the deterministicjump-growth equation gives
0 = f (γ) =
∫s(r)
(−rγ−2−rα−γ+rα−γ(r+K )−α+2γ−2
)dr . (5)
If we assume that predators are bigger than their prey, then forγ < 1 + α/2, f (γ) is less than zero. Also, f (γ) increasesmonotonically for γ > 1 + α/2, and is positive for large positiveγ. Therefore there will always be one γ for which f (γ) is zero.
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
SymmetriesSteady StateTravelling Waves
The size spectrum slope
When s(r) = δ(r − B) we can find an approximate analyticexpression for γ
γ ≈ 12
(2 + α +
W(B
K log B)
log B
). (6)
For reasonable values for the parameters this gives γ ≈ 2. Forexample with K = 0.1, B = 100, α = 1 we get γ = 2.21.
This is consistent with observation.
Note that in steady state v(x) = e(1+α−γ)x , i.e., steady state isnot homogeneous.
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
SymmetriesSteady StateTravelling Waves
Travelling waves
The power-law steady state becomes unstable for narrowfeeding preferences. The system undergoes a supercriticalHopf bifurcation.
The new attractor is a stable limit cycle and describes atravelling wave.
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
SymmetriesSteady StateTravelling Waves
Comparison of stochastic and deterministic equations
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Reformulation as 3-d local lattice model
We usually think of the jump-growth model as a model on thereal line with some long-range interactions. In the case ofdelta-function feeding preference:
But we can alternatively arrange the points on a square lattice:
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Between x and x + ε there are other points x + δ, x + 2δ, . . .. Itis most natural to put these points in additional layers, stackedin the third dimension.
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
In the case of fixed predator-prey weight ratio there will be nocoupling between the individual layers. If we allow a predator ofweight x to eat prey of weight x − z or of weight x − z − δ, thenwe introduce inter-layer couplings.
Gustav W. Delius Jump-growth model
The Stochastic Jump-Growth ModelSolutions of the Deterministic Jump-Growth Equation
Reformulation as 3-d local lattice model
Summary
Simple stochastic process of large fish eating small fishcan explain observed size spectrum.Described by a configuration space model with athree-point non-local interaction.Instead of moment closure we use van Kampen expansion.Translation-invariant model has nonhomogeneous steadystate.
OutlookGet more analytical results (in progress).Treat rigorously directly in the continuum.Match with data.Model reproduction and coexistent species.
Gustav W. Delius Jump-growth model