Among- and within-patch components of genetic diversity respond at different rates to habitat fragmentation: an empirical demonstration

Habitat fragmentation is a ubiquitous by-product of human activities that can alter the genetic structure of natural populations, with potentially deleterious effects on population persistence and evolutionary potential. When habitat fragmentation results in the subdivision of a population, random genetic drift then leads to the erosion of genetic diversity from within the resulting subpopulations and greater genetic divergence among them. Theoretical and simulation analyses predict that these two main genetic effects of fragmentation, greater differentiation among resulting subpopulations and reduced genetic diversity within them, will proceed at very different rates. Despite important implications for the interpretation of genetic data from fragmented populations, empirical evidence for this phenomenon has been lacking. In this analysis, we carry out an empirical study in populations of an alpine meadow-dwelling butterfly, which have become fragmented by increasing forest cover over five decades. We show that genetic differentiation among subpopulations (G(ST)) is most highly correlated with contemporary forest cover, while genetic diversity within subpopulations (expected heterozygosity) is better correlated with the spatial pattern of forest cover 40 years in the past. Thus, where habitat fragmentation has occurred in recent decades, genetic differentiation among subpopulations can be near equilibrium while contemporary measures of within subpopulation diversity may substantially overestimate the equilibrium values that will eventually be attained

A multiethnic genetic approach for the minimum conflict weighted spanning tree problem

This paper addresses a variant of the minimum spanning tree problem in which, given a list of conflicting edges, the primary goal is to find a spanning tree with the minimum number of conflicting edge pairs and the secondary goal is to minimize the weight of spanning trees without conflicts. The problem is NP-hard and it finds applications in the design of offshore wind farm networks. We propose a multiethnic genetic algorithm for the problem in which the fitness function is designed to simultaneously manage the two goals of the problem. Moreover, we introduce three local search procedures to improve the solutions inside the population during the computation. Computational results performed on benchmark instances reveal that our algorithm outperforms the other heuristic approach, proposed in the literature, for this problem