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Selection by differential mortality rates

M. S. Cecconello
M. S. Cecconello
, 
R. A. de Assis
R. A. de Assis
, 
L. M. E. de Assis
L. M. E. de Assis
 und 
E. Venturino
E. Venturino
   | 26. Mai 2021
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Online veröffentlicht: 26. Mai 2021
Seitenbereich: 2049 - 2062
Eingereicht: 24. Juni 2020
Akzeptiert: 26. Feb. 2021
DOI: https://doi.org/10.2478/amns.2021.2.00018
Schlüsselwörter
© 2021 M. S. Cecconello et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Schematic representation of a mortality function for directional selection.
Schematic representation of a mortality function for directional selection.

Fig. 2

Non-zero equilibrium solutions of Eq. (2) with Neumann (left) and Dirichlet (right) boundary conditions, respectively. For this simulation we consider μ(x) = 0.15(5 − x). These simulations show the typical behaviour of evolution by directional selection.
Non-zero equilibrium solutions of Eq. (2) with Neumann (left) and Dirichlet (right) boundary conditions, respectively. For this simulation we consider μ(x) = 0.15(5 − x). These simulations show the typical behaviour of evolution by directional selection.

Fig. 3

Schematic representation of a mortality function for disruptive selection.
Schematic representation of a mortality function for disruptive selection.

Fig. 4

Numerical simulations of nonzero equilibrium solutions for Eq. (2) with Neumann (left) and Dirichlet (right) boundary conditions, respectively. Here we consider μ(x) = max{0,0.192(6.25 − x2)}. These simulations show the typical behaviour of evolution by disruptive selection.
Numerical simulations of nonzero equilibrium solutions for Eq. (2) with Neumann (left) and Dirichlet (right) boundary conditions, respectively. Here we consider μ(x) = max{0,0.192(6.25 − x2)}. These simulations show the typical behaviour of evolution by disruptive selection.

Fig. 5

Schematic representation of a mortality function for stabilising selection.
Schematic representation of a mortality function for stabilising selection.

Fig. 6

Numerical simulations of nonzero equilibrium solutions for Eq. (2) with Neumann (left) and Dirichlet (right) boundary conditions, respectively. For these simulations we consider μ(x) = min{0.184x2,1.15}. These simulations show the typical evolution behaviour by stabilising selection.
Numerical simulations of nonzero equilibrium solutions for Eq. (2) with Neumann (left) and Dirichlet (right) boundary conditions, respectively. For these simulations we consider μ(x) = min{0.184x2,1.15}. These simulations show the typical evolution behaviour by stabilising selection.

Fig. 7

Numerical simulation showing the transition from the fittest to the flattest as the mutation rate increases. Left: the function μ(x); right: the soultion for small V. Here, the horizontal line is y = 1.
Numerical simulation showing the transition from the fittest to the flattest as the mutation rate increases. Left: the function μ(x); right: the soultion for small V. Here, the horizontal line is y = 1.

Fig. 8

Numerical simulation showing the transition from the fittest to the flattest as mutation rate increases. Left V = 0.4; right V = 0.52.
Numerical simulation showing the transition from the fittest to the flattest as mutation rate increases. Left V = 0.4; right V = 0.52.

Fig. 9

Numerical simulation showing the transition from the fittest to the flattest as the mutation rate increases. Left V = 0.54; right V = 0.7.
Numerical simulation showing the transition from the fittest to the flattest as the mutation rate increases. Left V = 0.54; right V = 0.7.
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