Lisa Lumley

Dr. Lisa Lumley

Alberta Biodiversity Monitoring Institute, University of Alberta, Edmonton, Alberta, Canada

Research Focus & Metrics

Academic Career Length

18Years (2009-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 July 2019 in Ecological Indicators
Verified
Journal Article
Closed access
MMMatthew L. Meehan
Matthew L. Meehan
ZSZhuoyan Song
Zhuoyan Song
LLLisa Lumley
Lisa Lumley
TCTyler P. Cobb
Tyler P. Cobb
HPHeather Proctor
Heather Proctor

1 June 2017 in Molecular Phylogenetics and Evolution
Verified
Journal Article
Closed access
JDJ.R. Dupuis
J.R. Dupuis
BBBryan Brunet
Bryan Brunet
HBH.M. Bird
H.M. Bird
LLLisa Lumley
Lisa Lumley
GFG. Fagua
G. Fagua
FSF.A.H. Sperling
F.A.H. Sperling

2 August 2019 in Evolutionary Applications
Verified
Journal Article
Open access
PDF file
JLJeremy Larroque
Jeremy Larroque
SLSimon Legault
Simon Legault
RJRob Johns
Rob Johns
LLLisa Lumley
Lisa Lumley
Michel CussonMichel Cusson
Michel Cusson
PJPatrick M. A. James
Patrick M. A. James
Spatial synchrony is a common characteristic of spatio‐temporal population dynamics across many taxa. While it is known that both dispersal and spatially autocorrelated environmental variation (i.e., the Moran effect) can synchronize populations, the relative contributions of each, and how they interact, are generally unknown. Distinguishing these mechanisms and their effects on synchrony can help us to better understand spatial population dynamics, design conservation and management strategies, and predict climate change impacts. Population genetic data can be used to tease apart these two processes as the spatio‐temporal genetic patterns they create are expected to be different. A challenge, however, is that genetic data are often collected at a single point in time, which may introduce context‐specific bias. Spatio‐temporal sampling strategies can be used to reduce bias and to improve our characterization of the drivers of spatial synchrony. Using spatio‐temporal analyses of genotypic data, our objective was to identify the relative support for these two mechanisms to the spatial synchrony in population dynamics of the irruptive forest insect pest, the spruce budworm (Choristoneura fumiferana), in Quebec (Canada). AMOVA, cluster analysis, isolation by distance, and sPCA were used to characterize spatio‐temporal genomic variation using 1,370 SBW larvae sampled over four years (2012–2015) and genotyped at 3,562 SNP loci. We found evidence of overall weak spatial genetic structure that decreased from 2012 to 2015 and a genetic diversity homogenization among the sites. We also found genetic evidence of a long‐distance dispersal event over >140 km. These results indicate that dispersal is the key mechanism involved in driving population synchrony of the outbreak. Early intervention management strategies that aim to control source populations have the potential to be effective through limiting dispersal. However, the timing of such interventions relative to outbreak progression is likely to influence their probability of success.

9 January 2015 in Molecular Ecology
Verified
Journal Article
Closed access
PDF file
PJPatrick M. A. James
Patrick M. A. James
BCBarry Cooke
Barry Cooke
BBBryan Brunet
Bryan Brunet
LLLisa Lumley
Lisa Lumley
FSFelix A. H. Sperling
Felix A. H. Sperling
BSBrian R. Sturtevant
Brian R. Sturtevant