A spatial approach to jointly estimate Wright's neighborhood size and long-term effective population size.
Spatially continuous patterns of genetic differentiation, which are common in nature, are often poorly described by existing population genetic theory or methods that assume either panmixia or discrete, clearly definable populations. There is therefore a need for statistical approaches in population...
| Published in: | Genetics Vol. 227; no. 4; pp. 1 - 15 |
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| Main Authors: | , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Oxford University Press / USA
Aug2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=178974548&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178974548 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00166731 GNT jtl: Genetics issn: 00166731 maglogo: N pubinfo: dt: Aug2024 vid: 227 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 178974548 178974548 178974548 10.1093/genetics/iyae094 178974548 ppf: 1 ppct: 14 formats: tig: atl: A spatial approach to jointly estimate Wright's neighborhood size and long-term effective population size. aug: au: Hancock, Zachary B Toczydlowski, Rachel H Bradburd, Gideon S affil: Department of Ecology and Evolutionary Biology, University of Michigan , Ann Arbor, MI 481103 , USA sug: subj: Population Density Evaluation Genetics Neighborhood Characteristics Evaluation Geographic Factors Funding Source Animal Studies Models, Statistical Research Personnel Social Isolation Spatial Perception Computer Simulation ab: Spatially continuous patterns of genetic differentiation, which are common in nature, are often poorly described by existing population genetic theory or methods that assume either panmixia or discrete, clearly definable populations. There is therefore a need for statistical approaches in population genetics that can accommodate continuous geographic structure, and that ideally use georeferenced individuals as the unit of analysis, rather than populations or subpopulations. In addition, researchers are often interested in describing the diversity of a population distributed continuously in space; this diversity is intimately linked to both the dispersal potential and the population density of the organism. A statistical model that leverages information from patterns of isolation by distance to jointly infer parameters that control local demography (such as Wright's neighborhood size), and the long-term effective size (Ne) of a population would be useful. Here, we introduce such a model that uses individual-level pairwise genetic and geographic distances to infer Wright's neighborhood size and long-term Ne. We demonstrate the utility of our model by applying it to complex, forward-time demographic simulations as well as an empirical dataset of the two-form bumblebee (Bombus bifarius). The model performed well on simulated data relative to alternative approaches and produced reasonable empirical results given the natural history of bumblebees. The resulting inferences provide important insights into the population genetic dynamics of spatially structured populations. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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