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      Intraspecific evolutionary relationships among peregrine falcons in western North American high latitudes

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          Abstract

          Subspecies relationships within the peregrine falcon ( Falco peregrinus) have been long debated because of the polytypic nature of melanin-based plumage characteristics used in subspecies designations and potential differentiation of local subpopulations due to philopatry. In North America, understanding the evolutionary relationships among subspecies may have been further complicated by the introduction of captive bred peregrines originating from non-native stock, as part of recovery efforts associated with mid 20 th century population declines resulting from organochloride pollution. Alaska hosts all three nominal subspecies of North American peregrine falcons– F. p. tundrius, anatum, and pealei–for which distributions in Alaska are broadly associated with nesting locales within Arctic, boreal, and south coastal maritime habitats, respectively. Unlike elsewhere, populations of peregrine falcon in Alaska were not augmented by captive-bred birds during the late 20 th century recovery efforts. Population genetic differentiation analyses of peregrine populations in Alaska, based on sequence data from the mitochondrial DNA control region and fragment data from microsatellite loci, failed to uncover genetic distinction between populations of peregrines occupying Arctic and boreal Alaskan locales. However, the maritime subspecies, pealei, was genetically differentiated from Arctic and boreal populations, and substructured into eastern and western populations. Levels of interpopulational gene flow between anatum and tundrius were generally higher than between pealei and either anatum or tundrius. Estimates based on both marker types revealed gene flow between augmented Canadian populations and unaugmented Alaskan populations. While we make no attempt at formal taxonomic revision, our data suggest that peregrine falcons occupying habitats in Alaska and the North Pacific coast of North America belong to two distinct regional groupings–a coastal grouping ( pealei) and a boreal/Arctic grouping (currently anatum and tundrius)–each comprised of discrete populations that are variously intra-regionally connected.

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          Maximum likelihood estimation of a migration matrix and effective population sizes in n subpopulations by using a coalescent approach.

          A maximum likelihood estimator based on the coalescent for unequal migration rates and different subpopulation sizes is developed. The method uses a Markov chain Monte Carlo approach to investigate possible genealogies with branch lengths and with migration events. Properties of the new method are shown by using simulated data from a four-population n-island model and a source-sink population model. Our estimation method as coded in migrate is tested against genetree; both programs deliver a very similar likelihood surface. The algorithm converges to the estimates fairly quickly, even when the Markov chain is started from unfavorable parameters. The method was used to estimate gene flow in the Nile valley by using mtDNA data from three human populations.
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            Detection of reduction in population size using data from microsatellite loci.

            We demonstrate that the mean ratio of the number of alleles to the range in allele size, which we term M, calculated from a population sample of microsatellite loci, can be used to detect reductions in population size. Using simulations, we show that, for a general class of mutation models, the value of M decreases when a population is reduced in size. The magnitude of the decrease is positively correlated with the severity and duration of the reduction in size. We also find that the rate of recovery of M following a reduction in size is positively correlated with post-reduction population size, but that recovery occurs in both small and large populations. This indicates that M can distinguish between populations that have been recently reduced in size and those which have been small for a long time. We employ M to develop a statistical test for recent reductions in population size that can detect such changes for more than 100 generations with the post-reduction demographic scenarios we examine. We also compute M for a variety of populations and species using microsatellite data collected from the literature. We find that the value of M consistently predicts the reported demographic history for these populations. This method, and others like it, promises to be an important tool for the conservation and management of populations that are in need of intervention or recovery.
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              Do island populations have less genetic variation than mainland populations?

              R Frankham (1997)
              Island populations are much more prone to extinction than mainland populations. The reasons for this remain controversial. If inbreeding and loss of genetic variation are involved, then genetic variation must be lower on average in island than mainland populations. Published data on levels of genetic variation for allozymes, nuclear DNA markers, mitochondrial DNA, inversions and quantitative characters in island and mainland populations were analysed. A large and highly significant majority of island populations have less allozyme genetic variation than their mainland counterparts (165 of 202 comparisons), the average reduction being 29 per cent. The magnitude of differences was related to dispersal ability. There were related differences for all the other measures. Island endemic species showed lower genetic variation than related mainland species in 34 of 38 cases. The proportionate reduction in genetic variation was significantly greater in island endemic than in nonendemic island populations in mammals and birds, but not in insects. Genetic factors cannot be discounted as a cause of higher extinction rates of island than mainland populations.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Funding acquisitionRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: InvestigationRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: ResourcesRole: Writing – review & editing
                Role: Funding acquisitionRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                17 November 2017
                2017
                : 12
                : 11
                : e0188185
                Affiliations
                [1 ] U.S. Geological Survey Alaska Science Center, Anchorage, Alaska, United States of America
                [2 ] Fairbanks Fish and Wildlife Field Office, U. S. Fish and Wildlife Service, Fairbanks, Alaska, United States of America
                [3 ] Alaska Maritime National Wildlife Refuge, U. S. Fish and Wildlife Service, Homer, Alaska, United States of America
                [4 ] Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America
                [5 ] Yukon-Charley River National Preserve and Gates of the Arctic National Park and Preserve, National Park Service, Fairbanks, Alaska, United States of America
                [6 ] Migratory Bird Management, U. S. Fish and Wildlife Service, Juneau, Alaska, United States of America
                [7 ] Alaska Regional Office, National Park Service, Anchorage, Alaska, United States of America
                [8 ] Falcon Research Group, Bow, Washington, United States of America
                [9 ] Department of Plant and Wildlife Sciences and Monte L. Bean Life Science Museum, Brigham Young University, Provo, Utah, United States of America
                University of Arkansas Fayetteville, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-3312-7214
                Article
                PONE-D-16-25138
                10.1371/journal.pone.0188185
                5693296
                29149202
                5b00def2-2de4-4b49-ad15-d045fcd8b8a4

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 22 June 2016
                : 2 November 2017
                Page count
                Figures: 3, Tables: 4, Pages: 25
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000203, U.S. Geological Survey;
                Funded by: funder-id http://dx.doi.org/10.13039/100000202, U.S. Fish and Wildlife Service;
                Funded by: funder-id http://dx.doi.org/10.13039/100006756, Brigham Young University;
                Funded by: National Park Service (US)
                Funded by: Los Alamos National Laboratory (US)
                Funded by: Falcon Research Group
                Funded by: U. S. Bureau of Land Management
                Funding for this research was provided by the Alaska Science Center, U. S. Geological Survey (SLT), U. S. Fish and Wildlife Service (JCW, TS, SA, SBL), Brigham Young University (CMW), National Park Service (MJF, LP), Falcon Research Group (CA), Los Alamos National Laboratory (JLL), and U. S. Bureau of Land Management (TS). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                People and places
                Geographical locations
                North America
                United States
                Alaska
                Biology and Life Sciences
                Genetics
                Heredity
                Gene Flow
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Gene Flow
                Biology and Life Sciences
                Genetics
                Population Genetics
                Gene Flow
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Gene Flow
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Biology and Life Sciences
                Genetics
                Population Genetics
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Earth Sciences
                Geomorphology
                Topography
                Landforms
                Islands
                Biology and life sciences
                Genetics
                DNA
                Forms of DNA
                Mitochondrial DNA
                Biology and life sciences
                Biochemistry
                Nucleic acids
                DNA
                Forms of DNA
                Mitochondrial DNA
                People and places
                Geographical locations
                North America
                Canada
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Mapping
                Haplotypes
                People and places
                Geographical locations
                North America
                Custom metadata
                Microsatellite genotype data are accessioned at the USGS-ASC data repository ( http://dx.doi.org/10.5066/F7F18WV0). All sequence data are archived in GenBank and GenBank accession numbers are included in sample data accessioned at the USGS-ASC data repository ( http://dx.doi.org/10.5066/F7F18WV0).

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