A stochastic Model for the Size Spectrum in a Marine Ecosystem
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Transcript of A stochastic Model for the Size Spectrum in a Marine Ecosystem
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
A stochastic Model for the Size Spectrum in aMarine Ecosystem
Samik Datta, Gustav W. Delius, Richard Law
Department of Mathematics/BiologyUniversity of York
Stochastics and Real World Models 2009
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Nature of this talk
The GoodA very simple stochastic modelReal-world application (Fish Abundances)Analytic result (Power-law size spectrum)
The BadCompletely non-rigorous (Challenge for the audience)Hand-waving approximations to derive stochastic DEConcentrating on the deterministic macroscopic equations
The Ugly
Travelling-wave solutions only found numerically
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Outline
1 The Stochastic Jump-Growth Model
2 Derivation of the Jump-Growth SDE
3 Solutions of the Deterministic Jump-Growth Equation
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Observed Phenomenon: Size SpectrumApproaches to Ecosystem ModellingIndividual Based ModelPopulation level model
Observed phenomenon: Power law size spectrum
Let φ(w) be the abundance of marine organisms of weight wso that
∫ w2
w1φ(w)dw is the number of organisms per unit volume
with weight between w1 and w2.
Observed power law:
φ(w) ∝ w−γ
with γ ≈ 2.
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Observed Phenomenon: Size SpectrumApproaches 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
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Observed Phenomenon: Size SpectrumApproaches to Ecosystem ModellingIndividual Based ModelPopulation level model
Size is more important than species
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:
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Observed Phenomenon: Size SpectrumApproaches 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
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Observed Phenomenon: Size SpectrumApproaches to Ecosystem ModellingIndividual Based ModelPopulation level model
Individual based model
We can model predation as a Markov process on configurationspace (Kondratiev). A configuration γ = w1,w2, . . . is the setof the weights of all organisms in the system. The primarystochastic event comprises a predator of weight wa consuminga prey of weight wb and, as a result, increasing to becomeweight wc = wa + Kwb (K < 1).
The Markov generator L is given heuristically as
(LF )(γ) =∑
wa,wb∈γk(wa,wb) (F (γ\wa,wb ∪ wc)− F (γ)) .
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Observed Phenomenon: Size SpectrumApproaches 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− νij)− ninjF (n)
),
where n− ν ij = (n0,n1, . . . ,nj + 1, . . . ,ni + 1, . . . ,nl − 1, . . . )and l is such that wl ≤ wi + Kwj < wl+1.
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Evolution Equation for Stochastic ProcessApproximationsThe stochastic differential equation
Evolution Equation for Stochastic Process
The random proces n(t) describing the population numberssatisfies
n(t + τ) = n(t) +∑i,j
Rij(n(t), τ)νij ,
where the Rij(n(t), τ) are random variables giving the numberof predation events taking place in the time interval [t , t + τ ] thatinvolve a predator from weight bracket i and a prey from weightbracket j .
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Evolution Equation for Stochastic ProcessApproximationsThe stochastic differential equation
Approximation 1: events approximately independent
The propensity of each individual predation event aij dependson the numbers of individuals
aij(n) = k(wi ,wj)ninj .
This introduces a dependence between predation events. If wechoose τ small we can approximate
aij(n(t ′)) ≈ aij(n(t)) ∀t ′ ∈ [t , t + τ ].
Then predation events are independent and Rij(n, τ) is Poissondistributed, Rij(n, τ) ∼ Pois(τaij(n)).
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Evolution Equation for Stochastic ProcessApproximationsThe stochastic differential equation
Approximation 2: large number of events
Next we assume that τaij(n(t)) is either zero or large enoughso that Pois(τaij(n)) ≈ N(τaij(n), τaij(n)). Then
Rij(N(t), τ) = aij(N(t))τ +√
aij(n(t))τ rij
where the rij are N(0,1). This gives the approximate evolutionequation
n(t + τ)− n(t) =∑
ij
aij(n(t))νijτ +∑
ij
√aij(n(t))νijτ
1/2rij .
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Evolution Equation for Stochastic ProcessApproximationsThe stochastic differential equation
Approximation 3: continuous time limit
We now approximate th equation
n(t + τ)− n(t) =∑
ij
aij(n(t))νijτ +∑
ij
√aij(n(t))νijτ
1/2rij ,
which is valid for small but finite τ , by the stochastic differentialequation obtained by taking the limit τ → 0,
dN(t) =∑
ij
aij(n(t))νijdt +∑
ij
√aij(n(t))νijdWij(t),
where Wij are independent Wiener processes.
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Evolution Equation for Stochastic ProcessApproximationsThe stochastic differential equation
The Jump-Growth SDE
More explicitly
dNi(t) =∑
j
(−kijNi(t)Nj(t)− kjiNj(t)Ni(t) + kmjNm(t)Nj(t)
)dt
+∑
j
(−√
kijNi(t)Nj(t)dWij(t)−√
kjiNj(t)Ni(t)dWji
+√
kmjNm(t)Nj(t)dWmj
),
where m is such that wm ≤ wi − Kwj < wm+1.
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Evolution Equation for Stochastic ProcessApproximationsThe stochastic differential equation
Rescaling
When we write the equation in terms of the population densitiesΦi = Ω−1Ni we see that the fluctuation terms are supressed bya factor of Ω−1/2.
dΦi(t) =∑
j
(−kijΦi(t)Φj(t)− kjiΦj(t)Φi(t) + kmjΦm(t)Φj(t)
)dt
+ Ω−1/2∑
j
(−√
kijΦi(t)Φj(t)dWij −√
kjiΦj(t)Φi(t)dWji
+√
kmjΦm(t)Φj(t)dWmj
).
From now on we will drop the stochastic terms.
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Steady 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 ′. (1)
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 ′
). (2)
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Steady StateTravelling Waves
Power law solution
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 . (3)
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.
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Steady 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
). (4)
For reasonable values for the parameters this gives γ ≈ 2. Forexample with K = 0.1, B = 100, α = 1 we get γ = 2.21.
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Steady StateTravelling Waves
Travelling waves
The power-law steady state becomes unstable for narrowfeeding preferences.
The new attractor is a travelling wave.
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Steady StateTravelling Waves
Comparison of stochastic and deterministic equations
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish
The Stochastic Jump-Growth ModelDerivation of the Jump-Growth SDE
Solutions of the Deterministic Jump-Growth Equation
Steady StateTravelling Waves
Summary
Simple stochastic process of large fish eating small fishcan explain observed size spectrum.arXiv:0812.4968Samik Datta, Gustav W. Delius, Richard Law: Ajump-growth model for predator-prey dynamics: derivationand application to marine ecosystems
OutlookTreat configuration space model rigorously.Understand travelling waves analytically.Model coexistent species.
Samik Datta, Gustav W. Delius, Richard Law The Size of Fish