Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
A mathematical framework forevolutionary ecology
Yosef Cohen
University of Minnesota St. Paul, Minnesota
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Outline
Key references
Games vs ED
Formal definition
ApplicationsSingle-trait competitionTwo-traits competitionPredator prey
Point processED
Host pathogenPoint processED
Conclusions
Extensions
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Key references
Cohen, Y. 2003. Distributed evolutionary games.Evolutionary Ecology Research 5:1-14.
Cohen, Y. 2003. Distributed predator prey coevolution.Evolutionary Ecology Research 5: 819-834.
Cohen Y. 2005 Evolutionary distributions in adaptivespace. Journal of Applied Mathematics 2005:403–424.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Key references
Cohen, Y. 2003. Distributed evolutionary games.Evolutionary Ecology Research 5:1-14.
Cohen, Y. 2003. Distributed predator prey coevolution.Evolutionary Ecology Research 5: 819-834.
Cohen Y. 2005 Evolutionary distributions in adaptivespace. Journal of Applied Mathematics 2005:403–424.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Key references
Cohen, Y. 2003. Distributed evolutionary games.Evolutionary Ecology Research 5:1-14.
Cohen, Y. 2003. Distributed predator prey coevolution.Evolutionary Ecology Research 5: 819-834.
Cohen Y. 2005 Evolutionary distributions in adaptivespace. Journal of Applied Mathematics 2005:403–424.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Key references
Cohen, Y. 2003. Distributed evolutionary games.Evolutionary Ecology Research 5:1-14.
Cohen, Y. 2003. Distributed predator prey coevolution.Evolutionary Ecology Research 5: 819-834.
Cohen Y. 2005 Evolutionary distributions in adaptivespace. Journal of Applied Mathematics 2005:403–424.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Outline
Key references
Games vs ED
Formal definition
ApplicationsSingle-trait competitionTwo-traits competitionPredator prey
Point processED
Host pathogenPoint processED
Conclusions
Extensions
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
From evolutionary games to evolutionarydistributions
I We start with the case of a single population density,z and a single adaptive trait x.
I Thenz′ = f (z, x, t) .
I Next, we derive the strategy dynamics in some way
x′ = g (z, x, t)
I and solve for x (and sometimes for z also) to obtainstability or dynamics in a game theoretic context.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
From evolutionary games to evolutionarydistributions
I We start with the case of a single population density,z and a single adaptive trait x.
I Thenz′ = f (z, x, t) .
I Next, we derive the strategy dynamics in some way
x′ = g (z, x, t)
I and solve for x (and sometimes for z also) to obtainstability or dynamics in a game theoretic context.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
From evolutionary games to evolutionarydistributions
I We start with the case of a single population density,z and a single adaptive trait x.
I Then
z′ = f (z, x, t) .
I Next, we derive the strategy dynamics in some way
x′ = g (z, x, t)
I and solve for x (and sometimes for z also) to obtainstability or dynamics in a game theoretic context.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
From evolutionary games to evolutionarydistributions
I We start with the case of a single population density,z and a single adaptive trait x.
I Thenz′ = f (z, x, t) .
I Next, we derive the strategy dynamics in some way
x′ = g (z, x, t)
I and solve for x (and sometimes for z also) to obtainstability or dynamics in a game theoretic context.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
From evolutionary games to evolutionarydistributions
I We start with the case of a single population density,z and a single adaptive trait x.
I Thenz′ = f (z, x, t) .
I Next, we derive the strategy dynamics in some way
x′ = g (z, x, t)
I and solve for x (and sometimes for z also) to obtainstability or dynamics in a game theoretic context.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
From evolutionary games to evolutionarydistributions
I We start with the case of a single population density,z and a single adaptive trait x.
I Thenz′ = f (z, x, t) .
I Next, we derive the strategy dynamics in some way
x′ = g (z, x, t)
I and solve for x (and sometimes for z also) to obtainstability or dynamics in a game theoretic context.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
From evolutionary games to evolutionarydistributions
I We start with the case of a single population density,z and a single adaptive trait x.
I Thenz′ = f (z, x, t) .
I Next, we derive the strategy dynamics in some way
x′ = g (z, x, t)
I and solve for x (and sometimes for z also) to obtainstability or dynamics in a game theoretic context.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Evolutionary Distributions (ED)
I Decompose f to components that reflect growth anddecline:
f (z, x, t) = β̃ (z, x, t)− µ (z, x, t) .
I There are good reasons to assume that β̃ is linear. Sowe write
β̃ (z, x, t) = βz(x, t).
I Assume random mutations on progeny with fractionη.
So ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Evolutionary Distributions (ED)
I Decompose f to components that reflect growth anddecline:
f (z, x, t) = β̃ (z, x, t)− µ (z, x, t) .
I There are good reasons to assume that β̃ is linear. Sowe write
β̃ (z, x, t) = βz(x, t).
I Assume random mutations on progeny with fractionη.
So ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Evolutionary Distributions (ED)
I Decompose f to components that reflect growth anddecline:
f (z, x, t) = β̃ (z, x, t)− µ (z, x, t) .
I There are good reasons to assume that β̃ is linear. Sowe write
β̃ (z, x, t) = βz(x, t).
I Assume random mutations on progeny with fractionη.
So ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Evolutionary Distributions (ED)
I Decompose f to components that reflect growth anddecline:
f (z, x, t) = β̃ (z, x, t)− µ (z, x, t) .
I There are good reasons to assume that β̃ is linear. Sowe write
β̃ (z, x, t) = βz(x, t).
I Assume random mutations on progeny with fractionη.
So ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Evolutionary Distributions (ED)
I Decompose f to components that reflect growth anddecline:
f (z, x, t) = β̃ (z, x, t)− µ (z, x, t) .
I There are good reasons to assume that β̃ is linear. Sowe write
β̃ (z, x, t) = βz(x, t).
I Assume random mutations on progeny with fractionη.
So ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Evolutionary Distributions (ED)
I Decompose f to components that reflect growth anddecline:
f (z, x, t) = β̃ (z, x, t)− µ (z, x, t) .
I There are good reasons to assume that β̃ is linear. Sowe write
β̃ (z, x, t) = βz(x, t).
I Assume random mutations on progeny with fractionη.
So ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Evolutionary Distributions (ED)
∂tz (x, t) = (1− η)βz (x, t) +12βη [z (x + ∆) + z (x−∆)]− µ (z, x, t) .
With Taylor series expansion of z around x, we obtainapproximately
∂tz = z +12∆2βη∂xxz − µ (z, x, t) .
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Evolutionary Distributions (ED)
∂tz (x, t) = (1− η) βz (x, t) +12βη [z (x + ∆) + z (x−∆)]− µ (z, x, t) .
With Taylor series expansion of z around x, we obtainapproximately
∂tz = z +12∆2βη∂xxz − µ (z, x, t) .
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Evolutionary Distributions (ED)
∂tz (x, t) = (1− η) βz (x, t) +12βη [z (x + ∆) + z (x−∆)]− µ (z, x, t) .
With Taylor series expansion of z around x, we obtainapproximately
∂tz = z +12∆2βη∂xxz − µ (z, x, t) .
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Evolutionary Distributions (ED)
∂tz (x, t) = (1− η) βz (x, t) +12βη [z (x + ∆) + z (x−∆)]− µ (z, x, t) .
With Taylor series expansion of z around x, we obtainapproximately
∂tz = z +12∆2βη∂xxz − µ (z, x, t) .
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
ED (continued)
For a single ED with m orthogonal adaptive traits, wehave
∂tz = z +12∆2β
m∑i=1
ηi∂xixiz − µ (z,x, t) .
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
ED (continued)
For a single ED with m orthogonal adaptive traits, wehave
∂tz = z +12∆2β
m∑i=1
ηi∂xixiz − µ (z,x, t) .
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Outline
Key references
Games vs ED
Formal definition
ApplicationsSingle-trait competitionTwo-traits competitionPredator prey
Point processED
Host pathogenPoint processED
Conclusions
Extensions
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
A formal definition of ED
Define the mth order mutation operator
mA := 1 + km∑
i=1
ηi∂xixi
where k := ∆2β/2.
zi ∈ R0+, i = 1, . . ., n is the distribution of the density oftypes with mi adaptive traits xi.
x = [x1, . . . ,xn].
Define the bounded open set X ⊂ RM (where M =∑ni=1 mi) with boundary ∂X . Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
A formal definition of ED
Define the mth order mutation operator
mA := 1 + km∑
i=1
ηi∂xixi
where k := ∆2β/2.
zi ∈ R0+, i = 1, . . ., n is the distribution of the density oftypes with mi adaptive traits xi.
x = [x1, . . . ,xn].
Define the bounded open set X ⊂ RM (where M =∑ni=1 mi) with boundary ∂X . Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
A formal definition of ED
Define the mth order mutation operator
mA := 1 + k
m∑i=1
ηi∂xixi
where k := ∆2β/2.
zi ∈ R0+, i = 1, . . ., n is the distribution of the density oftypes with mi adaptive traits xi.
x = [x1, . . . ,xn].
Define the bounded open set X ⊂ RM (where M =∑ni=1 mi) with boundary ∂X . Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
A formal definition of ED
Define the mth order mutation operator
mA := 1 + k
m∑i=1
ηi∂xixi
where k := ∆2β/2.
zi ∈ R0+, i = 1, . . ., n is the distribution of the density oftypes with mi adaptive traits xi.
x = [x1, . . . ,xn].
Define the bounded open set X ⊂ RM (where M =∑ni=1 mi) with boundary ∂X . Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
A formal definition of ED
Define the mth order mutation operator
mA := 1 + k
m∑i=1
ηi∂xixi
where k := ∆2β/2.
zi ∈ R0+, i = 1, . . ., n is the distribution of the density oftypes with mi adaptive traits xi.
x = [x1, . . . ,xn].
Define the bounded open set X ⊂ RM (where M =∑ni=1 mi) with boundary ∂X . Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
A formal definition of ED
Define the mth order mutation operator
mA := 1 + k
m∑i=1
ηi∂xixi
where k := ∆2β/2.
zi ∈ R0+, i = 1, . . ., n is the distribution of the density oftypes with mi adaptive traits xi.
x = [x1, . . . ,xn].
Define the bounded open set X ⊂ RM (where M =∑ni=1 mi) with boundary ∂X . Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
A formal definition of ED
Define the mth order mutation operator
mA := 1 + k
m∑i=1
ηi∂xixi
where k := ∆2β/2.
zi ∈ R0+, i = 1, . . ., n is the distribution of the density oftypes with mi adaptive traits xi.
x = [x1, . . . ,xn].
Define the bounded open set X ⊂ RM (where M =∑ni=1 mi) with boundary ∂X . Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Definition An ED, zi (x, t), is the solution of the system
∂tzi (x, t) = βi miAzi (xi, t)− µi (z,x, t) ,
with the data
zi (x, 0) = z0 (x)
and
∂xizi (x, t)|x=∂X = 0, i = 1, . . . , n.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Definition An ED, zi (x, t), is the solution of the system
∂tzi (x, t) = βi miAzi (xi, t)− µi (z,x, t) ,
with the data
zi (x, 0) = z0 (x)
and
∂xizi (x, t)|x=∂X = 0, i = 1, . . . , n.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Definition An ED, zi (x, t), is the solution of the system
∂tzi (x, t) = βi miAzi (xi, t)− µi (z,x, t) ,
with the data
zi (x, 0) = z0 (x)
and
∂xizi (x, t)|x=∂X = 0, i = 1, . . . , n.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Definition An ED, zi (x, t), is the solution of the system
∂tzi (x, t) = βi miAzi (xi, t)− µi (z,x, t) ,
with the data
zi (x, 0) = z0 (x)
and
∂xizi (x, t)|x=∂X = 0, i = 1, . . . , n.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Definition An ED, zi (x, t), is the solution of the system
∂tzi (x, t) = βi miAzi (xi, t)− µi (z,x, t) ,
with the data
zi (x, 0) = z0 (x)
and
∂xizi (x, t)|x=∂X = 0, i = 1, . . . , n.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Outline
Key references
Games vs ED
Formal definition
ApplicationsSingle-trait competitionTwo-traits competitionPredator prey
Point processED
Host pathogenPoint processED
Conclusions
Extensions
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Applications
I With this framework, we can now port all pointprocess population ecology models.
I Here are some applications ....
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Applications
I With this framework, we can now port all pointprocess population ecology models.
I Here are some applications ....
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Applications
I With this framework, we can now port all pointprocess population ecology models.
I Here are some applications ....
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait without selection
The point process is
z′ = rz − r
kz2.
The ED is
∂tz = rAz − r
kz2,
with data
z (x, 0) = 20 + sin (x) ,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0.
We obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait without selection
The point process is
z′ = rz − r
kz2.
The ED is
∂tz = rAz − r
kz2,
with data
z (x, 0) = 20 + sin (x) ,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0.
We obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait without selection
The point process is
z′ = rz − r
kz2.
The ED is
∂tz = rAz − r
kz2,
with data
z (x, 0) = 20 + sin (x) ,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0.
We obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait without selection
The point process is
z′ = rz − r
kz2.
The ED is
∂tz = rAz − r
kz2,
with data
z (x, 0) = 20 + sin (x) ,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0.
We obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait without selection
The point process is
z′ = rz − r
kz2.
The ED is
∂tz = rAz − r
kz2,
with data
z (x, 0) = 20 + sin (x) ,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0.
We obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait without selection
The point process is
z′ = rz − r
kz2.
The ED is
∂tz = rAz − r
kz2,
with data
z (x, 0) = 20 + sin (x) ,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0.
We obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait without selection
The point process is
z′ = rz − r
kz2.
The ED is
∂tz = rAz − r
kz2,
with data
z (x, 0) = 20 + sin (x) ,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0.
We obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait without selection
The point process is
z′ = rz − r
kz2.
The ED is
∂tz = rAz − r
kz2,
with data
z (x, 0) = 20 + sin (x) ,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0.
We obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
No selection
x
t
z
x
t
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
No selection
x
t
z
x
t
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait with selection
Assume single trait adaptation to competition and bestadaptation to some value of carrying capacity. Then ...
α (x, ξ) = kα(1 + k exp
[−1
2
(x− ξ
σα
)2])
and
k (x) = km(1 + exp
[−1
2
(x− 5π/2
σk
)2])
and the ED is now ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait with selection
Assume single trait adaptation to competition and bestadaptation to some value of carrying capacity. Then ...
α (x, ξ) = kα(1 + k exp
[−1
2
(x− ξ
σα
)2])
and
k (x) = km(1 + exp
[−1
2
(x− 5π/2
σk
)2])
and the ED is now ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait with selection
Assume single trait adaptation to competition and bestadaptation to some value of carrying capacity. Then ...
α (x, ξ) = kα(1 + k exp
[−1
2
(x− ξ
σα
)2])
and
k (x) = km(1 + exp
[−1
2
(x− 5π/2
σk
)2])
and the ED is now ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait with selection
Assume single trait adaptation to competition and bestadaptation to some value of carrying capacity. Then ...
α (x, ξ) = kα(1 + k exp
[−1
2
(x− ξ
σα
)2])
and
k (x) = km(1 + exp
[−1
2
(x− 5π/2
σk
)2])
and the ED is now ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait with selection
Assume single trait adaptation to competition and bestadaptation to some value of carrying capacity. Then ...
α (x, ξ) = kα(1 + k exp
[−1
2
(x− ξ
σα
)2])
and
k (x) = km(1 + exp
[−1
2
(x− 5π/2
σk
)2])
and the ED is now ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait with selection
Assume single trait adaptation to competition and bestadaptation to some value of carrying capacity. Then ...
α (x, ξ) = kα(1 + k exp
[−1
2
(x− ξ
σα
)2])
and
k (x) = km(1 + exp
[−1
2
(x− 5π/2
σk
)2])
and the ED is now ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait with selection(continued)
∂tz = rAz − r
kmz (x, t)
∫ 9π/2
π/2α (x, ξ) z (ξ, t) dξ,
and data
z (x, 0) = 0.005,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0
and we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait with selection(continued)
∂tz = rAz − r
kmz (x, t)
∫ 9π/2
π/2α (x, ξ) z (ξ, t) dξ,
and data
z (x, 0) = 0.005,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0
and we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait with selection(continued)
∂tz = rAz − r
kmz (x, t)
∫ 9π/2
π/2α (x, ξ) z (ξ, t) dξ,
and data
z (x, 0) = 0.005,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0
and we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait with selection(continued)
∂tz = rAz − r
kmz (x, t)
∫ 9π/2
π/2α (x, ξ) z (ξ, t) dξ,
and data
z (x, 0) = 0.005,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0
and we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Competition - single trait with selection(continued)
∂tz = rAz − r
kmz (x, t)
∫ 9π/2
π/2α (x, ξ) z (ξ, t) dξ,
and data
z (x, 0) = 0.005,
∂xz (π/2, t) = ∂xz (9π/2, t) = 0
and we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Single trait selection for α and k
x
t
z
x
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Single trait selection for α and k
x
t
z
x
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two-traits competition
I x1 selected for carrying capacityI x2 selected for competitive abilityI The traits are orthogonal
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two-traits competition
I x1 selected for carrying capacity
I x2 selected for competitive abilityI The traits are orthogonal
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two-traits competition
I x1 selected for carrying capacityI x2 selected for competitive ability
I The traits are orthogonalThen ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two-traits competition
I x1 selected for carrying capacityI x2 selected for competitive abilityI The traits are orthogonal
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two-traits competition
I x1 selected for carrying capacityI x2 selected for competitive abilityI The traits are orthogonal
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two-traits single ED
∂tz = r 2Az − r
k (x1)z
∫ 9π/2
π/2α (x2, ξ) z (x1, ξ, t) dξ,
and data
z (x, 0) = 20∂x1z (π/2, x2, t) = ∂x1z (9π/2, x2, t) = 0,
∂x2z (x1, π/2, t) = ∂x2z (x1, 9π/2, t) = 0,
we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two-traits single ED
∂tz = r 2Az − r
k (x1)z
∫ 9π/2
π/2α (x2, ξ) z (x1, ξ, t) dξ,
and data
z (x, 0) = 20∂x1z (π/2, x2, t) = ∂x1z (9π/2, x2, t) = 0,
∂x2z (x1, π/2, t) = ∂x2z (x1, 9π/2, t) = 0,
we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two-traits single ED
∂tz = r 2Az − r
k (x1)z
∫ 9π/2
π/2α (x2, ξ) z (x1, ξ, t) dξ,
and data
z (x, 0) = 20∂x1z (π/2, x2, t) = ∂x1z (9π/2, x2, t) = 0,
∂x2z (x1, π/2, t) = ∂x2z (x1, 9π/2, t) = 0,
we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two-traits single ED
∂tz = r 2Az − r
k (x1)z
∫ 9π/2
π/2α (x2, ξ) z (x1, ξ, t) dξ,
and data
z (x, 0) = 20∂x1z (π/2, x2, t) = ∂x1z (9π/2, x2, t) = 0,
∂x2z (x1, π/2, t) = ∂x2z (x1, 9π/2, t) = 0,
we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two-traits single ED
∂tz = r 2Az − r
k (x1)z
∫ 9π/2
π/2α (x2, ξ) z (x1, ξ, t) dξ,
and data
z (x, 0) = 20∂x1z (π/2, x2, t) = ∂x1z (9π/2, x2, t) = 0,
∂x2z (x1, π/2, t) = ∂x2z (x1, 9π/2, t) = 0,
we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two-traits single ED
x1
x2
z
x1
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two-traits single ED
x1
x2
z
x1
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey
Next, an application with regard to predator prey.
We start with the point process and then move on to ED...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey
Next, an application with regard to predator prey.
We start with the point process and then move on to ED...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey - point process
Let
z1 preyz2 predator
z′1 = rz1 −r
kz21 −
az1
b + cz1z2,
z′2 = daz1
b + cz1z2 − µz2
2 .
With certain parameter values we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey - point process
Let
z1 preyz2 predator
z′1 = rz1 −r
kz21 −
az1
b + cz1z2,
z′2 = daz1
b + cz1z2 − µz2
2 .
With certain parameter values we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey - point process
Let
z1 prey
z2 predator
z′1 = rz1 −r
kz21 −
az1
b + cz1z2,
z′2 = daz1
b + cz1z2 − µz2
2 .
With certain parameter values we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey - point process
Let
z1 preyz2 predator
z′1 = rz1 −r
kz21 −
az1
b + cz1z2,
z′2 = daz1
b + cz1z2 − µz2
2 .
With certain parameter values we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey - point process
Let
z1 preyz2 predator
z′1 = rz1 −r
kz21 −
az1
b + cz1z2,
z′2 = daz1
b + cz1z2 − µz2
2 .
With certain parameter values we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey - point process
Let
z1 preyz2 predator
z′1 = rz1 −r
kz21 −
az1
b + cz1z2,
z′2 = daz1
b + cz1z2 − µz2
2 .
With certain parameter values we obtain ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Limit cycle
0 200 400 600 8001000t
010203040506070
z
prey�thin,predator�thick
0 10 20 30 40 50 60 70z1�t�
2345678
z 2�t�
limit cycle
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Limit cycle
0 200 400 600 8001000t
010203040506070
zprey�thin,predator�thick
0 10 20 30 40 50 60 70z1�t�
2345678
z 2�t�
limit cycle
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey ED
I z1 evolves on x1
I z2 evolves on x2
I Predation is at its maximum when x1 = x2 withsome phenotypic plasticity σ
I Then ...
α (x1, x2) = exp
[−1
2
(x1 − x2
σ
)2]
.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey ED
I z1 evolves on x1
I z2 evolves on x2
I Predation is at its maximum when x1 = x2 withsome phenotypic plasticity σ
I Then ...
α (x1, x2) = exp
[−1
2
(x1 − x2
σ
)2]
.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey ED
I z1 evolves on x1
I z2 evolves on x2
I Predation is at its maximum when x1 = x2 withsome phenotypic plasticity σ
I Then ...
α (x1, x2) = exp
[−1
2
(x1 − x2
σ
)2]
.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey ED
I z1 evolves on x1
I z2 evolves on x2
I Predation is at its maximum when x1 = x2 withsome phenotypic plasticity σ
I Then ...
α (x1, x2) = exp
[−1
2
(x1 − x2
σ
)2]
.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey ED
I z1 evolves on x1
I z2 evolves on x2
I Predation is at its maximum when x1 = x2 withsome phenotypic plasticity σ
I Then ...
α (x1, x2) = exp
[−1
2
(x1 − x2
σ
)2]
.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Predator prey ED
I z1 evolves on x1
I z2 evolves on x2
I Predation is at its maximum when x1 = x2 withsome phenotypic plasticity σ
I Then ...
α (x1, x2) = exp
[−1
2
(x1 − x2
σ
)2]
.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The mutation operators
Let
zi ≡ zi (x1, x2, t) ,
Az1 := z1 +12∆2η1∂x1x1z1
and
Az2 := z2 +12∆2η2∂x2x2z1.
Then the point process becomes ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The mutation operators
Let
zi ≡ zi (x1, x2, t) ,
Az1 := z1 +12∆2η1∂x1x1z1
and
Az2 := z2 +12∆2η2∂x2x2z1.
Then the point process becomes ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The mutation operators
Let
zi ≡ zi (x1, x2, t) ,
Az1 := z1 +12∆2η1∂x1x1z1
and
Az2 := z2 +12∆2η2∂x2x2z1.
Then the point process becomes ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The mutation operators
Let
zi ≡ zi (x1, x2, t) ,
Az1 := z1 +12∆2η1∂x1x1z1
and
Az2 := z2 +12∆2η2∂x2x2z1.
Then the point process becomes ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The mutation operators
Let
zi ≡ zi (x1, x2, t) ,
Az1 := z1 +12∆2η1∂x1x1z1
and
Az2 := z2 +12∆2η2∂x2x2z1.
Then the point process becomes ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The mutation operators
Let
zi ≡ zi (x1, x2, t) ,
Az1 := z1 +12∆2η1∂x1x1z1
and
Az2 := z2 +12∆2η2∂x2x2z1.
Then the point process becomes ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The mutation operators
Let
zi ≡ zi (x1, x2, t) ,
Az1 := z1 +12∆2η1∂x1x1z1
and
Az2 := z2 +12∆2η2∂x2x2z1.
Then the point process becomes ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two ED two traits
∂tz1 = rAz1 −r
kz21 − α (x)
az1
b + cz1z2,
∂tz2 = dα (x)az1
b + cz1Az2 − µz2
2 ,
with initial conditions
z1 (x, 0) = 10,
z2 (x, 0) = 1
and boundary conditions
∂x1z1 (π/2, x2, t) = ∂x1z1 (9π/2, x2, t) = 0,
∂x2z2 (x1, π/2, t) = ∂x2z2 (x1, 9π/2, t) = 0.
Now ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two ED two traits
∂tz1 = rAz1 −r
kz21 − α (x)
az1
b + cz1z2,
∂tz2 = dα (x)az1
b + cz1Az2 − µz2
2 ,
with initial conditions
z1 (x, 0) = 10,
z2 (x, 0) = 1
and boundary conditions
∂x1z1 (π/2, x2, t) = ∂x1z1 (9π/2, x2, t) = 0,
∂x2z2 (x1, π/2, t) = ∂x2z2 (x1, 9π/2, t) = 0.
Now ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two ED two traits
∂tz1 = rAz1 −r
kz21 − α (x)
az1
b + cz1z2,
∂tz2 = dα (x)az1
b + cz1Az2 − µz2
2 ,
with initial conditions
z1 (x, 0) = 10,
z2 (x, 0) = 1
and boundary conditions
∂x1z1 (π/2, x2, t) = ∂x1z1 (9π/2, x2, t) = 0,
∂x2z2 (x1, π/2, t) = ∂x2z2 (x1, 9π/2, t) = 0.
Now ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two ED two traits
∂tz1 = rAz1 −r
kz21 − α (x)
az1
b + cz1z2,
∂tz2 = dα (x)az1
b + cz1Az2 − µz2
2 ,
with initial conditions
z1 (x, 0) = 10,
z2 (x, 0) = 1
and boundary conditions
∂x1z1 (π/2, x2, t) = ∂x1z1 (9π/2, x2, t) = 0,
∂x2z2 (x1, π/2, t) = ∂x2z2 (x1, 9π/2, t) = 0.
Now ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two ED two traits
∂tz1 = rAz1 −r
kz21 − α (x)
az1
b + cz1z2,
∂tz2 = dα (x)az1
b + cz1Az2 − µz2
2 ,
with initial conditions
z1 (x, 0) = 10,
z2 (x, 0) = 1
and boundary conditions
∂x1z1 (π/2, x2, t) = ∂x1z1 (9π/2, x2, t) = 0,
∂x2z2 (x1, π/2, t) = ∂x2z2 (x1, 9π/2, t) = 0.
Now ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Two ED two traits
∂tz1 = rAz1 −r
kz21 − α (x)
az1
b + cz1z2,
∂tz2 = dα (x)az1
b + cz1Az2 − µz2
2 ,
with initial conditions
z1 (x, 0) = 10,
z2 (x, 0) = 1
and boundary conditions
∂x1z1 (π/2, x2, t) = ∂x1z1 (9π/2, x2, t) = 0,
∂x2z2 (x1, π/2, t) = ∂x2z2 (x1, 9π/2, t) = 0.
Now ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Phenotypic plasticity σ = π/3
Prey
x1
x2
z1
x1
Predator
x1
x2
z2
x1
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Phenotypic plasticity σ = π/3
Prey
x1
x2
z1
x1
Predator
x1
x2
z2
x1
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Phenotypic plasticity σ = π
Prey
x1
x2
z1
x1
Predator
x1
x2
z2
x1
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Phenotypic plasticity σ = π
Prey
x1
x2
z1
x1
Predator
x1
x2
z2
x1
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen
The model is from ...
Anderson and May (1980, equations 3,5 and 6; 1981).
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen
The model is from ...
Anderson and May (1980, equations 3,5 and 6; 1981).
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen
The model is from ...
Anderson and May (1980, equations 3,5 and 6; 1981).
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The point process
I z1 - density of hostI z2 - density of infected hostI z3 - density of pathogens
We have ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The point process
I z1 - density of host
I z2 - density of infected hostI z3 - density of pathogens
We have ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The point process
I z1 - density of hostI z2 - density of infected host
I z3 - density of pathogens
We have ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The point process
I z1 - density of hostI z2 - density of infected hostI z3 - density of pathogens
We have ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The point process
I z1 - density of hostI z2 - density of infected hostI z3 - density of pathogens
We have ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The point process (continued)
z′1 = (a− b) z1 − α̃z2,
z′2 = νz3 (z1 − z2)− (α̃ + b + γ) z2,
z′3 = λz2 − (µ̃ + νz1) z3.
The relevant parameters are
α̃ additional death rate due to infectionµ̃ death rate of infective stages
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The point process (continued)
z′1 = (a− b) z1 − α̃z2,
z′2 = νz3 (z1 − z2)− (α̃ + b + γ) z2,
z′3 = λz2 − (µ̃ + νz1) z3.
The relevant parameters are
α̃ additional death rate due to infectionµ̃ death rate of infective stages
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The point process (continued)
z′1 = (a− b) z1 − α̃z2,
z′2 = νz3 (z1 − z2)− (α̃ + b + γ) z2,
z′3 = λz2 − (µ̃ + νz1) z3.
The relevant parameters are
α̃ additional death rate due to infectionµ̃ death rate of infective stages
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The point process (continued)
z′1 = (a− b) z1 − α̃z2,
z′2 = νz3 (z1 − z2)− (α̃ + b + γ) z2,
z′3 = λz2 − (µ̃ + νz1) z3.
The relevant parameters are
α̃ additional death rate due to infectionµ̃ death rate of infective stages
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The point process (continued)
z′1 = (a− b) z1 − α̃z2,
z′2 = νz3 (z1 − z2)− (α̃ + b + γ) z2,
z′3 = λz2 − (µ̃ + νz1) z3.
The relevant parameters are
α̃ additional death rate due to infectionµ̃ death rate of infective stages
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The point process (continued)
z′1 = (a− b) z1 − α̃z2,
z′2 = νz3 (z1 − z2)− (α̃ + b + γ) z2,
z′3 = λz2 − (µ̃ + νz1) z3.
The relevant parameters are
α̃ additional death rate due to infection
µ̃ death rate of infective stages
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
The point process (continued)
z′1 = (a− b) z1 − α̃z2,
z′2 = νz3 (z1 − z2)− (α̃ + b + γ) z2,
z′3 = λz2 − (µ̃ + νz1) z3.
The relevant parameters are
α̃ additional death rate due to infectionµ̃ death rate of infective stages
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen ED
I x1 - adaptive trait that affects death rate of hostsdue to infection (α)
I x2 - adaptive trait that affects pathogen death rate ofinfective stages (µ)
I At some value of x1 the value of α is at its minimumI At some value of x2 the value of µ is at its minimum
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen ED
I x1 - adaptive trait that affects death rate of hostsdue to infection (α)
I x2 - adaptive trait that affects pathogen death rate ofinfective stages (µ)
I At some value of x1 the value of α is at its minimumI At some value of x2 the value of µ is at its minimum
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen ED
I x1 - adaptive trait that affects death rate of hostsdue to infection (α)
I x2 - adaptive trait that affects pathogen death rate ofinfective stages (µ)
I At some value of x1 the value of α is at its minimumI At some value of x2 the value of µ is at its minimum
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen ED
I x1 - adaptive trait that affects death rate of hostsdue to infection (α)
I x2 - adaptive trait that affects pathogen death rate ofinfective stages (µ)
I At some value of x1 the value of α is at its minimum
I At some value of x2 the value of µ is at its minimum
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen ED
I x1 - adaptive trait that affects death rate of hostsdue to infection (α)
I x2 - adaptive trait that affects pathogen death rate ofinfective stages (µ)
I At some value of x1 the value of α is at its minimumI At some value of x2 the value of µ is at its minimum
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen ED
I x1 - adaptive trait that affects death rate of hostsdue to infection (α)
I x2 - adaptive trait that affects pathogen death rate ofinfective stages (µ)
I At some value of x1 the value of α is at its minimumI At some value of x2 the value of µ is at its minimum
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Coevolution
α (x1) = α̃
(1− 0.1 exp
[−1
2
(x1 − 5π/2
σα
)2])
,
µ (x2) = µ̃
(1 + 0.1 exp
[−1
2
(x2 − 5π/2
σµ
)2])
.
Let
Az1 = z1 +12∆2η1∂x1x1 ,
Az3 = z3 +12∆2η2∂x2x2 .
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Coevolution
α (x1) = α̃
(1− 0.1 exp
[−1
2
(x1 − 5π/2
σα
)2])
,
µ (x2) = µ̃
(1 + 0.1 exp
[−1
2
(x2 − 5π/2
σµ
)2])
.
Let
Az1 = z1 +12∆2η1∂x1x1 ,
Az3 = z3 +12∆2η2∂x2x2 .
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Coevolution
α (x1) = α̃
(1− 0.1 exp
[−1
2
(x1 − 5π/2
σα
)2])
,
µ (x2) = µ̃
(1 + 0.1 exp
[−1
2
(x2 − 5π/2
σµ
)2])
.
Let
Az1 = z1 +12∆2η1∂x1x1 ,
Az3 = z3 +12∆2η2∂x2x2 .
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Coevolution
α (x1) = α̃
(1− 0.1 exp
[−1
2
(x1 − 5π/2
σα
)2])
,
µ (x2) = µ̃
(1 + 0.1 exp
[−1
2
(x2 − 5π/2
σµ
)2])
.
Let
Az1 = z1 +12∆2η1∂x1x1 ,
Az3 = z3 +12∆2η2∂x2x2 .
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Coevolution
α (x1) = α̃
(1− 0.1 exp
[−1
2
(x1 − 5π/2
σα
)2])
,
µ (x2) = µ̃
(1 + 0.1 exp
[−1
2
(x2 − 5π/2
σµ
)2])
.
Let
Az1 = z1 +12∆2η1∂x1x1 ,
Az3 = z3 +12∆2η2∂x2x2 .
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Coevolution
α (x1) = α̃
(1− 0.1 exp
[−1
2
(x1 − 5π/2
σα
)2])
,
µ (x2) = µ̃
(1 + 0.1 exp
[−1
2
(x2 − 5π/2
σµ
)2])
.
Let
Az1 = z1 +12∆2η1∂x1x1 ,
Az3 = z3 +12∆2η2∂x2x2 .
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Coevolution
α (x1) = α̃
(1− 0.1 exp
[−1
2
(x1 − 5π/2
σα
)2])
,
µ (x2) = µ̃
(1 + 0.1 exp
[−1
2
(x2 − 5π/2
σµ
)2])
.
Let
Az1 = z1 +12∆2η1∂x1x1 ,
Az3 = z3 +12∆2η2∂x2x2 .
Then ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen ED
∂tz1 = aAz1 − bz1 − α (x1) z2,
∂tz2 = νAz3 (z1 − z2)− (α (x1) + b + γ) z2,
∂tz3 = λz2 − (µ (x2) + νz1) z3,
with data
zi (x, 0) = 1000zi (π/2, x2, t) = zi (9π/2, x2, t)zi (x1, π/2, t) = zi (x2, 9π/2, t)
i = 1, 2, 3.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen ED
∂tz1 = aAz1 − bz1 − α (x1) z2,
∂tz2 = νAz3 (z1 − z2)− (α (x1) + b + γ) z2,
∂tz3 = λz2 − (µ (x2) + νz1) z3,
with data
zi (x, 0) = 1000zi (π/2, x2, t) = zi (9π/2, x2, t)zi (x1, π/2, t) = zi (x2, 9π/2, t)
i = 1, 2, 3.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen ED
∂tz1 = aAz1 − bz1 − α (x1) z2,
∂tz2 = νAz3 (z1 − z2)− (α (x1) + b + γ) z2,
∂tz3 = λz2 − (µ (x2) + νz1) z3,
with data
zi (x, 0) = 1000zi (π/2, x2, t) = zi (9π/2, x2, t)zi (x1, π/2, t) = zi (x2, 9π/2, t)
i = 1, 2, 3.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen ED
∂tz1 = aAz1 − bz1 − α (x1) z2,
∂tz2 = νAz3 (z1 − z2)− (α (x1) + b + γ) z2,
∂tz3 = λz2 − (µ (x2) + νz1) z3,
with data
zi (x, 0) = 1000zi (π/2, x2, t) = zi (9π/2, x2, t)zi (x1, π/2, t) = zi (x2, 9π/2, t)
i = 1, 2, 3.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Host pathogen ED
∂tz1 = aAz1 − bz1 − α (x1) z2,
∂tz2 = νAz3 (z1 − z2)− (α (x1) + b + γ) z2,
∂tz3 = λz2 − (µ (x2) + νz1) z3,
with data
zi (x, 0) = 1000zi (π/2, x2, t) = zi (9π/2, x2, t)zi (x1, π/2, t) = zi (x2, 9π/2, t)
i = 1, 2, 3.
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Anticipated effect of α and µ
Host
x1
x2
�Α
x2Pahogen
x1
x2
�Μ
x2
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Anticipated effect of α and µ
Host
x1
x2
�Α
x2Pahogen
x1
x2
�Μ
x2
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Stable surfaces of ED
Host
x1
x2
z1
x2Pathogen
x1
x2
z3
x2
The rise and fall ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Stable surfaces of ED
Host
x1
x2
z1
x2Pathogen
x1
x2
z3
x2
The rise and fall ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Stable surfaces of ED
Host
x1
x2
z1
x2Pathogen
x1
x2
z3
x2
The rise and fall ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Outline
Key references
Games vs ED
Formal definition
ApplicationsSingle-trait competitionTwo-traits competitionPredator prey
Point processED
Host pathogenPoint processED
Conclusions
Extensions
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Conclusions
I With ED, it is clear how one can obtain ESS atminimum fitness
I For organisms with small number of genes, we canhope to map the power set of genes to phenotypictraits
I Then population genetics problems become algebraicproblems
I For smooth games (not matrix games) ED bypassesgames
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Conclusions
I With ED, it is clear how one can obtain ESS atminimum fitness
I For organisms with small number of genes, we canhope to map the power set of genes to phenotypictraits
I Then population genetics problems become algebraicproblems
I For smooth games (not matrix games) ED bypassesgames
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Conclusions
I With ED, it is clear how one can obtain ESS atminimum fitness
I For organisms with small number of genes, we canhope to map the power set of genes to phenotypictraits
I Then population genetics problems become algebraicproblems
I For smooth games (not matrix games) ED bypassesgames
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Conclusions
I With ED, it is clear how one can obtain ESS atminimum fitness
I For organisms with small number of genes, we canhope to map the power set of genes to phenotypictraits
I Then population genetics problems become algebraicproblems
I For smooth games (not matrix games) ED bypassesgames
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Conclusions
I With ED, it is clear how one can obtain ESS atminimum fitness
I For organisms with small number of genes, we canhope to map the power set of genes to phenotypictraits
I Then population genetics problems become algebraicproblems
I For smooth games (not matrix games) ED bypassesgames
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Conclusions (continued)
I A stable ED surface (homogeneous or not) is an ESSin the context of point processes
I Because of stability of non-homogeneous surfaces,fitness of phenotypes can have any value
I ED are functions in Sobolev space; they need not besmooth; they even need not be continuous
Example ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Conclusions (continued)
I A stable ED surface (homogeneous or not) is an ESSin the context of point processes
I Because of stability of non-homogeneous surfaces,fitness of phenotypes can have any value
I ED are functions in Sobolev space; they need not besmooth; they even need not be continuous
Example ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Conclusions (continued)
I A stable ED surface (homogeneous or not) is an ESSin the context of point processes
I Because of stability of non-homogeneous surfaces,fitness of phenotypes can have any value
I ED are functions in Sobolev space; they need not besmooth; they even need not be continuous
Example ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Conclusions (continued)
I A stable ED surface (homogeneous or not) is an ESSin the context of point processes
I Because of stability of non-homogeneous surfaces,fitness of phenotypes can have any value
I ED are functions in Sobolev space; they need not besmooth; they even need not be continuous
Example ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Conclusions (continued)
I A stable ED surface (homogeneous or not) is an ESSin the context of point processes
I Because of stability of non-homogeneous surfaces,fitness of phenotypes can have any value
I ED are functions in Sobolev space; they need not besmooth; they even need not be continuous
Example ...
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Mutual parasitism
05
10x
010
2030
40t
0
10
20
30
40
z1
05
10x
010
2030t
05
10x
010
2030
40t
0
20
40
z2
05
10x
010
2030t
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Mutual parasitism
05
10x
010
2030
40t
0
10
20
30
40
z1
05
10x
010
2030t
05
10x
010
2030
40t
0
20
40
z2
05
10x
010
2030t
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Outline
Key references
Games vs ED
Formal definition
ApplicationsSingle-trait competitionTwo-traits competitionPredator prey
Point processED
Host pathogenPoint processED
Conclusions
Extensions
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Extensions
I ED and learningI Mating systemsI Sexual reproductionI Thanks for you attention
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Extensions
I ED and learning
I Mating systemsI Sexual reproductionI Thanks for you attention
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Extensions
I ED and learningI Mating systems
I Sexual reproductionI Thanks for you attention
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Extensions
I ED and learningI Mating systemsI Sexual reproduction
I Thanks for you attention
Amathematicalframework forevolutionary
ecology
Yosef Cohen
Key references
Games vs ED
Formaldefinition
Applications
Single-traitcompetitionTwo-traitscompetitionPredator preyPoint processEDHost pathogenPoint processED
Conclusions
Extensions
Extensions
I ED and learningI Mating systemsI Sexual reproductionI Thanks for you attention