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DESCRIPTION:Spatial pattern detection in genetic and ecological data\n\nPed
 ro Peres-Neto\, Concordia University\n	Tuesday December 7\, 12-1pm\n	Zoom Li
 nk: https://mcgill.zoom.us/j/85428056343\n\nAbstract: Landscape genetics s
 tudies using neutral markers have focused on the relationship between gene
  flow and landscape features. In the same way that landscape ecologists ar
 e interested in modelling the relationships between ecological data and la
 ndscape features. Spatial patterns in the genetic distances among individu
 als may reflect spatially uneven patterns of gene flow caused by landscape
  features that influence movement and dispersal. We present a method for i
 dentifying spatial patterns in genetic data that adopts a regression frame
 work where the predictors are generated using Moran’s eigenvectors maps (M
 EM)\,a multivariate technique developed by us for spatial ecological analy
 ses and applicable to landscape genetics (MEMGENE). Using simulated and re
 al genetic data\, we show that our MEMGENE method can recover patterns ref
 lecting the landscape features that influenced gene flow. We developed the
  MEMGENE package for R to detect and visualize relatively weak or cryptic 
 spatial genetic patterns and aid researchers in generating hypotheses abou
 t the ecological processes that may underlie these patterns. MEMGENE provi
 des a flexible set of R functions that can be used to different types of a
 nalyses.\n
DTSTART:20211207T170000Z
DTEND:20211207T180000Z
SUMMARY:QLS Seminar Series - Pedro Peres-Neto
URL:/qls/channels/event/qls-seminar-series-pedro-peres
 -neto-335013
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