English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The introduction of the spatial Lambda-Fleming-Viot model (LV) in populationgenetics was mainly driven by the pioneering work of Alison Etheridge, incollaboration with Nick Barton and Amandine V\'eber about ten years ago (1,2).The LV model provides a sound mathematical framework for describing theevolution of a population of related individuals along a spatial continuum. Italleviates the "pain in the torus" issue with Wright and Mal\'ecot's isolationby distance model and is sampling consistent, making it a tool of choice forstatistical inference. Yet, little is known about the potential connectionsbetween the LV and other stochastic processes generating trees and the spatialcoordinates along the corresponding lineages. This work focuses on a version ofthe LV whereby lineages move infinitely rapidly over infinitely smalldistances. Using simulations, we show that the induced LV tree-generatingprocess is well approximated by a birth-death model. Our results also indicatethat Brownian motions modelling the movements of lineages along birth-deathtrees do not generally provide a good approximation of the LV due to habitatboundaries effects that play an increasingly important role in the long run.Finally, we describe efficient algorithms for fast simulation of the backwardand forward in time versions of the LV model.

On the connections between the spatial Lambda-Fleming-Viot model and other processes for analysing geo-referenced genetic data