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Published in: Dynamic Games and Applications 3/2020

28-08-2019

Evolutionary Substitution and Replacement in N-Species Lotka–Volterra Systems

Authors: Ross Cressman, Miklós Koller, M. Barnabás Garay, József Garay

Published in: Dynamic Games and Applications | Issue 3/2020

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Abstract

The successful invasion of a multi-species resident system by mutants has received a great deal of attention in theoretical ecology but less is known about what happens after the successful invasion. Here, in the framework of Lotka–Volterra (LV) systems, we consider the general question where there is one resident phenotype in each species and the evolutionary outcome after invasion remains one phenotype in each species, but these include all the mutant phenotypes. In the first case, called evolutionary substitution, a mutant appears in only one species, the resident phenotype in this species dies out, and the mutant coexists with the original phenotypes of the other species. In the second case, called evolutionary replacement, a mutant appears in each species, all resident phenotypes die out, and the evolutionary outcome is coexistence among all the mutant phenotypes. For general LV systems, we show that dominance of the resident phenotype by the mutant (i.e. the mutant is always more fit) in each species where the mutant appears leads to evolutionary substitution/replacement. However, it is shown by example that, when dominance is weakened to only assuming the average fitness of the mutants is greater than the average for the resident phenotype, the residents may not die out. We also show evolutionary substitution occurs in two-species competitive LV systems when the initial invasion of the resident system (respectively, of the new coexistence system) is successful (respectively, unsuccessful). Moreover, if sequential evolutionary substitution occurs for either order that the two mutant phenotypes appear (called historically independent replacement), then it is shown evolutionary replacement occurs using a generalization of the dominance argument.

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Appendix
Available only for authorised users
Footnotes
1
Here the resident and mutant phenotypes are fixed. Another approach to phenotype evolution (that is not pursued in this article) is based on adaptive dynamics [11] with continuous phenotype space whereby the resident phenotypes change continuously in the direction of nearby mutant phenotypes that can successful invade. Adaptive dynamics also questions whether invasion leads to substitution/replacement [22].
 
2
The notation used here is consistent with the more complicated LV systems in the remainder of the paper (e.g. (3)) . It can be considerably simplified in this special case. For instance, with \(r_{1}=r>0\) and \( m_{11}^{RR}=m<0\), (1) is the logistic equation (i.e. positive intrinsic growth and negative density dependence) in more standard notation.
 
3
That is, the intrinsic growth rate is independent of species phenotype. The discussion here does not rely on this assumption. In fact, for the N-species systems of Sects. 3 and 4, Theorems 1 to 4 do not depend on our assumption that, for each species k, the resident and mutant phenotypes have the same intrinsic growth rate \(r_{k}\). Moreover, the proofs of these results do not rely on this assumption either.
 
4
In evolutionary game theory, it is usually assumed that each individual has one random pairwise interaction per unit time. Here, the number of such interactions for an individual is proportional to the density in each species.
 
5
The Jacobian matrix at the resident equilibrium \(\rho ^{*}\) is \(\left[ \begin{array}{ccc} \rho _{1}^{*} &{} 0 &{} 0 \\ 0 &{} \ddots &{} 0 \\ 0 &{} &{} \rho _{N}^{*} \end{array} \right] M^{RR}\).
 
6
In the threshold case when the resident phenotype is initially selectively neutral (i.e. \(\lambda _{1}^{R}=0\)), the analogue of \(M^{II}-M^{IR}\left( M^{RR}\right) ^{-1}M^{RI}\) must now be negative (see the B-matrix method of Cressman and Garay [6]).
 
7
Under these assumptions, we say that the invasion conditions are satisfied.
 
8
That is, every neigborhood of an equilibrium with all \(N+1\) components positive contains another equilibrium.
 
9
The first and fourth authors refer to this general result as the Calgary lemma, the location where they obtained its proof that has as yet been unpublished as far as they are aware.
 
10
Since \(0<\rho _{2}<\frac{1}{2}\) implies \({{\dot{\rho }}}_{2}<0\) and thus \({{\dot{\rho }}}_{2}<-\frac{1}{2}\rho _{2}<0\) as well as \({{\dot{\mu }}}_{1}<-\frac{1}{2} \mu _{1}<0\) , the origin attracts all points of the open, unbounded 3D rectangle \(0<\rho _{1}<\infty \), \(0<\rho _{2}<\frac{1}{2}\), \(0<\mu _{1}<\infty \). The region of attraction of the origin is separated from the rest of the phase portrait by (the nonnegative part of) the two-dimensional, unbounded stable manifold of the saddle point S.
 
11
Here, and everywhere else in the present paper, for both LV and replicator systems, global asymptotic stability (gas) of an equilibrium means that it is locally asymptotically stable (las) and attracts all interior trajectories. Similarly, “gas restricted to a face” means “las restricted to this face and attracts all interior trajectories on this face”.
 
12
That is, each trajectory is defined for all \(t\ge 0\) and all its components are less that D for some \(D>0\) that may depend on the trajectory.
 
13
Specifically, by the dominance assumption, \(\rho _{i}/\mu _{i}\) for \( i=1,2,\ldots ,N\) are all strictly decreasing when \(\rho _{i}\) and \(\mu _{i}\) are initially positive. Since all forward trajectories are bounded, the proof of Theorem 3 shows that \(\lim _{t\rightarrow \infty }\rho _{i}(t)=0\) for all i (i.e. all resident phenotypes go extinct). Moreover, since no interior trajectories converge to the boundary of the N-dimensional mutant system, each such trajectory has a limit point in the interior of the N-dimensional mutant face. Global asymptotic stability of \((\mu _{1}^{*},\mu _{2}^{*},\ldots ,\mu _{N}^{*})\) on this face combined with its local asymptotic stability in the full resident-mutant system guarantees that it is the only limit point of each interior trajectory.
 
14
To see this, consider \(\left( \prod \limits _{i=1}^{N}\rho _{i}^{w_{i}}\right) /\left( \prod \limits _{i=1}^{N}\mu _{i}^{v_{i}}\right) \) where \(w_{i}>0\) are the weights for the residents and \(v_{i}>0\) are the weights for the mutants. These weights satisfy \(\sum w_{i}=1=\sum v_{i}\). In view of (12), this expression is strictly decreasing at all interior points and so there is no interior equilibrium.
 
15
Note that a purely algebraic proof of inequality \(y_{14}=\frac{b_{34}- {\mathbf {b}}_{24}}{b_{14}-{\mathbf {b}}_{24}}> \frac{b_{23}}{b_{23}-{\mathbf {b}}_{13} }=y_{13}\) is considerably harder. Elementary examples show that \(y_{14}\ge y_{13}\) does not follow from \(x_{04}<x_{03}<1\) and \(0<x_{14}<x_{13}\). Thus, the equivalence between \(y_{14}\le 1\) and \(x_{14}\ge 1\) (due to the fact that the slope of \(L_{4}\) is positive) leads to a crucial improvement of (25).
 
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Metadata
Title
Evolutionary Substitution and Replacement in N-Species Lotka–Volterra Systems
Authors
Ross Cressman
Miklós Koller
M. Barnabás Garay
József Garay
Publication date
28-08-2019
Publisher
Springer US
Published in
Dynamic Games and Applications / Issue 3/2020
Print ISSN: 2153-0785
Electronic ISSN: 2153-0793
DOI
https://doi.org/10.1007/s13235-019-00324-0

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