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A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator prey Point process ED Host pathogen Point process ED Conclusions Extensions A mathematical framework for evolutionary ecology Yosef Cohen University of Minnesota St. Paul, Minnesota
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Page 1:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 2:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 3:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 4:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 5:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 6:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 7:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 8:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 9:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 10:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 11:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 12:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 13:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 14:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 15:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 16:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 17:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 18:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 19:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 20:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 21:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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) .

Page 22:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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) .

Page 23:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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) .

Page 24:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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) .

Page 25:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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) .

Page 26:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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) .

Page 27:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 28:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 29:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 30:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 31:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 32:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 33:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 34:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 35:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 36:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 37:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 38:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 39:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 40:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 41:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ....

Page 42:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ....

Page 43:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ....

Page 44:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 45:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 46:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 47:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 48:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 49:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 50:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 51:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 52:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 53:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 54:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 55:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 56:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 57:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 58:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 59:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 60:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 61:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 62:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 63:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 64:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 65:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 66:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 67:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 68:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 69:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 70:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 71:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 72:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 73:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 74:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 75:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 76:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 77:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 78:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 79:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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...

Page 80:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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...

Page 81:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 82:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 83:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 84:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 85:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 86:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 87:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 88:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 89:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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]

.

Page 90:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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]

.

Page 91:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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]

.

Page 92:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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]

.

Page 93:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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]

.

Page 94:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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]

.

Page 95:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 96:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 97:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 98:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 99:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 100:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 101:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 102:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 103:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 104:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 105:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 106:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 107:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 108:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 109:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 110:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 111:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 112:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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).

Page 113:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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).

Page 114:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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).

Page 115:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 116:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 117:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 118:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 119:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 120:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 121:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 122:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 123:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 124:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 125:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 126:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 127:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 128:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 129:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 130:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 131:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 132:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 133:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 134:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 135:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 136:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 137:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 138:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 139:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 140:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 141:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 142:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 143:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 144:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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.

Page 145:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 146:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 147:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 148:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 149:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 150:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 151:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 152:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 153:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 154:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 155:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 156:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 157:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 158:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 159:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 160:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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 ...

Page 161:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 162:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 163:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 164:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 165:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 166:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 167:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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

Page 168:  · A mathematical framework for evolutionary ecology Yosef Cohen Key references Games vs ED Formal definition Applications Single-trait competition Two-traits competition Predator

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


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