ii southern-summer school on mathematical biology · references j.d.murray: mathematical biology i...
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II Southern-Summer School on MathematicalBiology
Roberto André Kraenkel, IFT
http://www.ift.unesp.br/users/kraenkel
Lecture III
São Paulo, January 2013
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Outline
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Competition
Consider competition betwenn two species.
We say that two species compete if the presence of one of them isdetrimental for the other, and vice versa.
The underlying biological mechanisms can be of two kinds;exploitative competition: both species compete for a limited resource.
Its strength depends also on the resource .Interference competition: one of the species actively interferes in theacess to resources of the sother .Both types of competition may coexist.
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Models for species in competition
We are speaking of inter-specific competitionIntra-specific competition gives rise to the models like thelogistic that we studied in the first lecture.In a broad sense we can distinguish two kinds of models forcompetition:
implicit: that do not take into account the dynamics of theresources.explicit where this dynamics is included.Here is a pictorial view of the possible cases:
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Competition
Figura : A single species. Only intra-specific competition indicated by theblue arrow
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Competition
Figura : Two species. Besides intra-specific competition, both speciescompete. This is an implicit model as we do not even mention the resources.No distinction is made between exploitative or interference competition
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Competition
Figura : Two species (A and B) that feed on C. Intra-specific competition hasbeen omitted, but may exist. Here we have an explicit model for exploitativecompetition. A interaction of A and C and between B and C is usually of theantagonistic kind.
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Competition
Figura : Two species (A and B) that feed on C but also interfere. Intra-specificcompetition has again been omitted, but may exist. We have an explicit model withboth exploitative and interference competition.
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Competition
Figura : A model where two species, A and B, compete for resources,(AND) they have also exclusive resources (A ↔ C) e (B ↔ D). Andinterference competition is also indicated.
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Mathematical Model
Let us begin with the simplest case:Two species,Implicit competition,intra-specific competition taken into account.
We proceed using the same rationale that was used for thepredator-prey system.
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Lotka-Volterra model for competition
Let N1 and N2 be the two species in question.
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Lotka-Volterra model for competition
Each of them increases logistically in the absence of the other:
dN1
dt= r1N1
[1−
N1
K1
]
dN2
dt= r2N2
[1−
N2
K2
]
where r1 and r2 are the intrinsic growth rates and K1 and K2 arethe carrying capacities of both species in the absence of the other..
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Lotka-Volterra model for competition
We introduce the mutual detrimental influence of one species onthe other:
dN1
dt= r1N1
[1−
N1
K1− aN2
]
dN2
dt= r2N2
[1−
N2
K2− bN1
]
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Lotka-Volterra model for competition
Or, in the more usual way :
dN1
dt= r1N1
[1−
N1
K1− b12
N2
K1
]
dN2
dt= r2N2
[1−
N2
K2− b21
N1
K2
]
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Lotka-Volterra model for competition
Or, in the more usual way:
dN1
dt= r1N1
1− N1
K1−
↓︷︸︸︷b12
N2
K1
dN2
dt= r2N2
1− N2
K2−
↓︷︸︸︷b21
N1
K2
where b12 and b21 are the coefficients that measure the strength
of the competition between the populations.
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Lotka-Volterra model for competition
This is a Lotka-Volterra type model for competing species. Payattention to the fact that both interaction terms come in withnegative signs. All the constants r1, r2,K1,K2, b12and b21 are
positive.
dN1
dt= r1N1
[1−
N1
K1− b12
N2
K1
]
dN2
dt= r2N2
[1−
N2
K2− b21
N1
K2
]
Let’s now try to analyze this system of two differential equations .
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Analyzing the model I
dN1
dt= r1N1
[1 −
N1
K1− b12
N2
K1
]
dN2
dt= r2N2
[1 −
N2
K2− b21
N1
K2
]
We will first make a change of variables,by simple re-scalings.
Define:
u1 =N1
K1, u2 =
N2
K2, τ = r1t
In other words,we are measuring popula-tions in units of their carrying capacitiesand the time in units of 1/r1.
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Analyzing the model II
du1
dt= u1
[1− u1 − b12
K2
K1u2
]
du2
dt=
r2
r1u2
[1− u2 − b21
K1
K2u1
]
The equations in
the new varia-
bles.
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Analyzing the model III
du1
dt= u1 [1− u1 − a12u2]
du2
dt= ρu2 [1− u2 − a21u1]
Defining:
a12 = b12K2
K1,
a21 = b21K1
K2
ρ =r2r1
we get these equations.It’s a system of nonlinear ordi-nary differential equations.
We need to study the behavior of their solutions
.
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Analyzing the model IV
du1
dt= u1 [1− u1 − a12u2]
du2
dt= ρu2 [1− u2 − a21u1]
No explicit solutions!.
We will develop a qualitative analysis of these equations.
Begin by finding the points in the (u1 × u2) plane such that:
du1
dt=
du2
dt= 0,
the fixed points.
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Analyzing the model V
du1
dt= 0⇒ u1 [1− u1 − a12u2] = 0
du2
dt= 0⇒ u2 [1− u2 − a21u1] = 0
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Analyzing the model V
u1 [1− u1 − a12u2] = 0
u2 [1− u2 − a21u1] = 0
These are two algebraic equations for ( u1 e u2).We FOUR solutions. Four fixed points.
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Fixed points
u∗1 = 0
u∗2 = 0
u∗1 = 1
u∗2 = 0
u∗1 = 0
u∗2 = 1 u∗
1 =1− a12
1− a12a21
u∗2 =
1− a211− a12a21
The relevance of those fixed points depends on their stability. Which, in turn, dependon the values of the parameters a12 e a21. We have to proceed by a phase-spaceanalysis, calculating community matrixes and finding eigenvalues......take a look atJ.D. Murray ( Mathematical Biology).
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Stability
Se a12 < 1 e a21 < 1
u∗1 =
1− a121− a12a21
u∗2 =
1− a211− a12a21
is stable.
Se a12 > 1 e a21 > 1
u∗1 = 1 e u∗
2 = 0
u∗1 = 0 e u∗
2 = 1
are both stable.
Se a12 < 1 e a21 > 1
u∗1 = 1 e u∗
2 = 0
is stable.
Se a12 > 1 e a21 < 1
u∗1 = 0 e u∗
2 = 1
is stable.
The stability of the fixed points depends on the values of a12 and a21.
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Phase space
To have a more intuitive understanding of the dynamics it isuseful to consider the trajectories in the phase spaceFor every particular combination of a12 and a21 – but actuallydepending if they are smaller or greater than 1 – ,we will havea qualitatively different phase portrait.
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Phase Space II
Figura : The four cases. The four different possibilities for the phaseportraits.
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Coexistence
Figura : a12 < 1 and a21 < 1. The fixed point u∗1 and u∗
2 is stable andrepresents the coexistence of both species. It is a global attractor.
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Exclusion
Figura : a12 > 1 and a21 > 1. The fixed point u∗1 and u∗2 is unstable. The points (1.0) and (0, 1)are stable but have finite basins of attraction, separated by a separatrix. The stable fixed pointsrepresent exclusionof one of the species.
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Exclusion
Figura : a12 < 1 and a21 > 1. The only stable fixed is (u1 = 1, u2 = 0).A global attractor. Species(2) is excluded.
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Exclusion
Figura : This case is symmetric to the previous. a12 > 1 and a21 < 1. Theonly stable fixed point is (u1 = 1, u2 = 0). A global attractor. Species (1) isexcluded
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Interpretation of the results
What is the meaning of these results?Let us recall the meaning of a12 and a21:
du1
dt= u1 [1− u1 − a12u2]
du2
dt= ρu2 [1− u2 − a21u1]
a12 is a measure of the influence of species 2 on species 1. Howdetrimental 2 is to 1.a21 measures the influence of species 1on species 2. How detrimental 1 isto 2.
So, we may translate the results as:
a12 > 1⇒ 2 competes strongly with 1 for resources.a21 > 1⇒ 1 competes strongly with 2 for resources.
This leads us to the following rephrasing of the results :
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If a12 < 1 and a21 < 1The competition is weak and both can coexist.
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If a12 > 1 and a21 > 1The competition is mutually strong . One species always excludes
the other. Which one "wins"depends on initial conditions.
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If a12 < 1 e a21 > 1Species 1 is not strongly affected by species 2. But species 2 is
affected strongly be species 1. Species 2 is eliminated, and species1 attains it carrying capacity.
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Se a12 > 1 e a21 < 1This is symmetric to the previous case. Species 1 is eliminated
and Species 2 attains its carrying capacity
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Competitive exclusion
In summary: the mathematical model predicts patterns ofexclusion. Strong competition always leads to the exclusion ofa speciesCoexistence is only possible with weak competition.The fact the a stronger competitor eliminates the weaker oneis known as the competitive exclusion principle.
Georgiy F. Gause (1910-1986), Russian bi-
ologist, was the first to state the principle
of competitive exclusion (1932).
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Paramecium
The experiences of G.F. Gause where performed with a protozoagroup called Paramecia.
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Paramecium
The experiences of G.F. Gause where performed with a protozoagroup called Paramecia .Gause considered two of them: Paramecium aurelia e ParameciumCaudatum.
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Paramecium
The experiences of G.F. Gause where performed with a protozoagroup called Paramecia .Gause considered two of them: Paramecium aurelia e Parameciumcaudatum. They where allowed to grow initially separated, with alogistic like growth .
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Paramecium
The experiences of G.F. Gause where performed with a protozoagroup called Paramecia .Gause considered two of them: Paramecium aurelia e ParameciumCaudatum. They where allowed to grow initially separated, with alogistic like growth .When they grow in the same culture, P. aurelia survives and P.caudatum is eliminated.
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Paramecium
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Paramecium
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Paramecium
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Ants
Figura : The Argentinean ant (Linepithema humile) and the Californianone( Pogonomyrmex californicus)
The introduction of the Argentinean ant in California had theeffect to exclude Pogonomyrmex californicus.Here is a plot with data....
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Ants II
Figura : The introduction of the Argentinean ant in California had theeffect of excluding Pogonomyrmex californicus
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Plankton
In view of the principle of competitive exclusion, consider the situation ofphytoplankton.
Phytoplankton are organisms that live in seasand lakes, in the region where there is light.
You won’t see a phytoplankton with nakedeye..
You can see only the visual effect of a largenumber of them.
It needs light + inorganic molecules.
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The Plankton Paradox
The plankton paradox consists of the following:There are many species of phytoplankton. It used a verylimited number of different resources. Why is there nocompetitive exclusion?
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One paradox, many possible solutions
Competitive exclusion is aproperty of the fixed points.But if the environmentchanges, the equilibria mightnot be attained. We arealways in transient dynamics.
We have considered no spatialstructure. Different regionscould be associated withdifferent limiting factors, andthus could promote diversity.
Effects of trophic webs.
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References
J.D. Murray: Mathematical Biology I (Springer, 2002)F. Brauer e C. Castillo-Chavez: Mathematical Models inPopulation Biology and Epidemiology (Springer, 2001).N.F. Britton: Essential Mathematical Biology ( Springer,2003).R. May e A. McLean: Theoretical Ecology, (Oxford, 2007).N.J. Gotelli: A Primer of Ecology ( Sinauer, 2001).G.E. Hutchinson: An Introduction to Population Ecology (Yale, 1978).