Premium
EFFECTIVE POPULATION SIZE IN A CONTINUOUSLY DISTRIBUTED POPULATION
Author(s) -
Kawata Masakado
Publication year - 1995
Publication title -
evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.84
H-Index - 199
eISSN - 1558-5646
pISSN - 0014-3820
DOI - 10.1111/j.1558-5646.1995.tb04432.x
Subject(s) - biology , population size , population , effective population size , evolutionary biology , demography , genetics , genetic variation , sociology , gene
An individual‐based simulation model was created to examine genetic variability, time until fixation and spatial genetic structure in a continuously distributed population. Previous mathematical models for continuously distributed populations have the difficulty that the assumption of independent reproduction and independent dispersal of offspring cause clumped spatial distribution and thus violate an assumption of random spatial distribution. In this study, this problem is avoided by considering the dispersal behavior of offspring. The simulation results showed that the inbreeding effective population size estimated by the rate of decrease of heterozygosity during the first 15 generations corresponds to the neighborhood size calculated by the standard deviation of the dispersal distance (σ T ). This inbreeding effective population size does not greatly change with the area of simulation when the densities and σ T are the same. However, the inbreeding effective population size estimated by heterozygosity using the first 500 generations is larger than the neighborhood size calculated by the dispersal distance and increases with the area of simulation with the same densities. The variance effective population size, estimated by time until fixation of alleles, increases with dispersal distance (σ T ) and with the area of simulation given the same densities. The inbreeding effective population size and variance effective population size were smaller than the actual population size unless σ T is sufficiently large (2 σ T > approximate L /2, where L is a side of the simulation square).