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Application of network methods for understanding evolutionary dynamics in discrete habitats.

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TLDR
The goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats.
Abstract
In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene-flow patterns with other evolutionary forces may be exceedingly complex. Several models describing gene flow between discrete habitat patches have been presented in the population-genetics literature; however, these models have usually addressed relatively simple settings of habitable patches and have stopped short of providing general methodologies for addressing nontrivial gene-flow patterns. In the last decades, network theory - a branch of discrete mathematics concerned with complex interactions between discrete elements - has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. We review the main branches of network theory that have been, or that we believe potentially could be, applied to population genetics and molecular ecology research. We address applications to theoretical modelling and to empirical population-genetic studies, and we highlight future directions for extending the integration of network science with molecular ecology.

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Citations
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Models and methods in social network analysis

TL;DR: Models and Methods in Social Network Analysis presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s.
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Network science of biological systems at different scales: A review

TL;DR: This work presents research highlights ranging from determination of the molecular interaction network within a cell to studies of architectural and functional properties of brain networks and biological transportation networks, and focuses on synergies between network science and data analysis, which enable us to determine functional connectivity patterns in multicellular systems.
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Seascape genetics and biophysical connectivity modelling support conservation of the seagrass Zostera marina in the Skagerrak–Kattegat region of the eastern North Sea

TL;DR: This work estimates connectivity of Z. marina in the Skagerrak–Kattegat region of the North Sea based on genetic and biophysical modelling and finds clusters, barriers and networks of connectivity to be very similar based on either genetic or oceanographic analyses.
Posted Content

Unwinding the "hairball" graph: a pruning algorithm for weighted complex networks.

TL;DR: In this article, a simple and intuitive null model based on the configuration model of network generation is introduced, and a significance filter from it is derived from it and applied to the network of air traffic volume between US airports and recover a geographically faithful representation of the graph.
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graph4lg: A package for constructing and analysing graphs for landscape genetics in R

TL;DR: Graph4lg as mentioned in this paper is a software package for comparing landscape and genetic graphs, which includes functions for converting and importing genetic data and for genetic distance computing, as well as time-efficient geodesic and cost-distance calculations from spatial data.
References
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Estimating F-statistics for the analysis of population structure.

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TL;DR: This paper presents mathematical representation of social networks in the social and behavioral sciences through the lens of Dyadic and Triadic Interaction Models, which describes the relationships between actor and group measures and the structure of networks.
Journal ArticleDOI

Community structure in social and biological networks

TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Journal ArticleDOI

Finding and evaluating community structure in networks.

TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
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