%AMilligan, Brook [Department of Biology New Mexico State University Las Cruces NM USA]%AMilligan, Brook [Department of Biology; New Mexico State University; Las Cruces NM USA]%AArcher, Frederick [NOAA Fisheries Southwest Fisheries Science Center La Jolla CA USA]%AArcher, Frederick [NOAA Fisheries; Southwest Fisheries Science Center; La Jolla CA USA]%AFerchaud, Anne‐Laure [Institut de Biologie Intégrative et des Systèmes (IBIS) Université Laval Québec QC Canada]%AFerchaud, Anne-Laure [Institut de Biologie Intégrative et des Systèmes (IBIS); Université Laval; Québec QC Canada]%AHand, Brian [Flathead Lake Biological Station University of Montana Polson MT USA]%AHand, Brian [Flathead Lake Biological Station; University of Montana; Polson MT USA]%AKierepka, Elizabeth [Biology Department Trent University Peterborough ON USA]%AKierepka, Elizabeth [Biology Department; Trent University; Peterborough ON USA]%AWaples, Robin [NOAA Fisheries Northwest Fisheries Science Center Seattle WA USA]%AWaples, Robin [NOAA Fisheries; Northwest Fisheries Science Center; Seattle WA USA]%BJournal Name: Evolutionary Applications; Journal Volume: 11; Journal Issue: 7; Related Information: CHORUS Timestamp: 2023-08-17 10:25:39 %D2018%IWiley-Blackwell %JJournal Name: Evolutionary Applications; Journal Volume: 11; Journal Issue: 7; Related Information: CHORUS Timestamp: 2023-08-17 10:25:39 %K %MOSTI ID: 10055156 %PMedium: X %TDisentangling genetic structure for genetic monitoring of complex populations %XAbstract

Genetic monitoring estimates temporal changes in population parameters from molecular marker information. Most populations are complex in structure and change through time by expanding or contracting their geographic range, becoming fragmented or coalescing, or increasing or decreasing density. Traditional approaches to genetic monitoring rely on quantifying temporal shifts of specific population metrics—heterozygosity, numbers of alleles, effective population size—or measures of geographic differentiation such asFST. However, the accuracy and precision of the results can be heavily influenced by the type of genetic marker used and how closely they adhere to analytical assumptions. Care must be taken to ensure that inferences reflect actual population processes rather than changing molecular techniques or incorrect assumptions of an underlying model of population structure. In many species of conservation concern, true population structure is unknown, or structure might shift over time. In these cases, metrics based on inappropriate assumptions of population structure may not provide quality information regarding the monitored population. Thus, we need an inference model that decouples the complex elements that define population structure from estimation of population parameters of interest and reveals, rather than assumes, fine details of population structure. Encompassing a broad range of possible population structures would enable comparable inferences across biological systems, even in the face of range expansion or contraction, fragmentation, or changes in density. Currently, the best candidate is the spatial Λ‐Fleming‐Viot (SLFV) model, a spatially explicit individually based coalescent model that allows independent inference of two of the most important elements of population structure: local population density and local dispersal. We support increased use of theSLFVmodel for genetic monitoring by highlighting its benefits over traditional approaches. We also discuss necessary future directions for model development to support large genomic datasets informing real‐world management and conservation issues.

%0Journal Article