Rodríguez, A. et al. Future directions in conservation research on petrels and shearwaters. Front. Mar. Sci. 6, 94 (2019).
Google Scholar
Thomson, S. A. et al. Taxonomy based on science is necessary for global conservation. PLoS Biol. 16, e2005075 (2018).
Google Scholar
Friesen, V. L., Burg, T. M. & McCoy, K. D. Mechanisms of population differentiation in seabirds. Mol. Ecol. 16(9), 1765–1785 (2007).
Google Scholar
Lombal, A. J., O’Dwyer, J. E., Friesen, V. L., Woehler, E. J. & Burridge, C. P. Identifying mechanisms of genetic differentiation among population in vagile species: historical factors dominate genetic differentiation in seabirds. Biol. Rev. 95(3), 625–651 (2020).
Google Scholar
Friesen, V. L. Speciation in seabirds: why are there so many species…and why aren’t there more?. J. Ornithol. 156, 27–39 (2015).
Google Scholar
Morris-Pocock, J. A., Steeves, T. E., Estela, F. A., Anderson, D. J. & Friesen, V. L. Comparative phylogeography of brown (Sula leucogaster) and red-footed boobies (Sula sula): the influence of physical barriers and habitat preference on gene flow in pelagic seabirds. Mol. Phylogenet. Evol. 54(3), 883–896 (2010).
Google Scholar
Morris-Pocock, J. A., Anderson, D. J. & Friesen, V. L. Biological barriers to dispersal and rare gene flow shape population genetic structure in red-footed boobies (Sula sula). J. Biogeogr. 43(11), 2125–2135 (2016).
Google Scholar
Nuss, A., Carlos, C. J., Moreno, I. B. & Fagundes, N. J. R. Population genetic structure of the Magnificent Frigatebird Fregata magnificens (Aces, Suliformes) breeding colonies in the western Atlantic Ocean. PLoS ONE 11, e0149834 (2016).
Google Scholar
Friesen, V. L., González, J. A. & Cruz-Delgado, F. Population genetic structure and conservation of the Galápagos Petrel (Pterodroma phaeopygia). Conserv. Genet. 7, 105–115 (2006).
Google Scholar
Frugone, M. J. et al. Contrasting phylogeographic patterns among Eudyptes penguins around the Southern Ocean. Sci. Rep. 8, 17481 (2018).
Google Scholar
Croxall, J. P. et al. Seabird conservation status, threats and priority actions: a global assessment. Bird Conserv. Int. 22(1), 1–34 (2012).
Google Scholar
Montevecchi, W. A. Interactions between fisheries and seabirds. In The Biology of Marine Birds (eds Schrieber, E. A. & Burger, J.) 527–557 (CRC Press, 2002).
Hamer, K. C. Breeding biology, life histories and life history-environment interaction in seabirds. In The Biology of Marine Birds (eds Schrieber, E. A. & Burger, J.) 217–261 (CRC Press, 2002).
Frankham, R., Briscoe, D. A. & Ballou, J. D. Introduction to Conservation Genetics (Cambridge University Press, 2002).
Wan, Q. H., Wu, H., Fujihara, T. & Fang, S. G. Which genetic marker for which conservation geneitic issue?. Electrophoresis 25, 2165–2176 (2004).
Google Scholar
Paleczny, M., Hammill, E., Karpouzi, V. & Pauly, D. Population trend of the world’s monitored seabirds, 1950–2010. PLoS ONE 10(6), e0129342 (2015).
Google Scholar
Pinet, P. et al. Barau’s Petrel Pterodroma baraui: history, biology and conservation of an endangered endemic petrel. Mar. Ornithol. 37, 107–113 (2009).
Lougnon, A. Sous le Signe de la Tortue. Voyage Anciens a I’Ile Bourbon, (1611–1725). Saint-Denis, La Reunion, France. (Editions Orphie, 2006).
Milot, E., Weimerskirch, H. & Bernatchez, L. The seabird paradox: dispersal, genetic structure and population dynamics in a highly mobile, but philopatric albatross species. Mol. Ecol. 17(7), 1658–1673 (2008).
Google Scholar
Antaky, C. C., Coklin, E. E., Toonen, R. J., Knapp, I. S. S. & Price, M. R. Unexpectedly high genetic diversity in a rare and endangered seabird in the Hawaiian Archipelago. PeerK 8, e8463 (2020).
Google Scholar
Smith, A. L. & Friesen, V. L. Differentiation of sympatric populations of the Band-rumped Storm Petrel in the Galapagos Islands: an examination of genetics, morphology, and vocalizations. Mol. Ecol. 16(8), 1593–1603 (2007).
Google Scholar
Wiley, A. E. et al. Foraging segregation and genetic divergence between geographically proximate colonies of a highly mobile seabird. Oecologia 168, 119–130 (2011).
Google Scholar
Hardy, O. J., Charbonnel, N., Fréville, H. & Heuertz, M. Microsatellite allele sizes: a simple test to assess their significance of genetic differentiation. Genetics 163(4), 1467–1482 (2003).
Google Scholar
Faubet, P., Waples, R. S. & Gaggiotti, O. E. Evaluating the performance of a multilocus Bayesian method for the estimation of migration rates. Mol. Ecol. 16(6), 1149–1166 (2008).
Google Scholar
Brooke, M. Albatrosses and Petrels Across the World (Oxford University, 2004).
Pinet, P., Jaquemet, S., Phillips, R. A. & Le Corre, M. Sex-specific foraging strategies throughout the breeding season in a tropical sexually monomorphic small petrel. Anim. Behav. 83(4), 979–989 (2012).
Google Scholar
Friesen, V. L. et al. Sympatric speciation by allochrony in a seabird. PNAS 104(47), 18589–18594 (2007).
Google Scholar
Gay, L. et al. Speciation with gene flow in the large white-headed gulls: Does selection counterbalance introgression?. Heredity 102, 122–146 (2009).
Google Scholar
Zidat, T. et al. Reproductive isolation maintains distinct genotypes, phenotypes and chemical signatures in mixed colonies of the two European Calonectris shearwaters (Procellariiformes: Procellariidae). Zool. J. Linnean. Soc. 181(3), 711–726 (2017).
Google Scholar
Abbott, C. L. & Double, M. C. Genetic structure, conservation genetics and evidence of speciation by range expansion in shy and white-capped albatrosses. Mol. Evol. 12, 2953–2962 (2003).
Welch, A. J. et al. Population divergence and gene flow in an endangered and highly mobile seabird. Heredity 109, 19–28 (2012).
Google Scholar
Pinet, P. Biologie, écologie & conservation d’un oiseau marin endemique de La Réunion: Le Petrel de Barau (Pterodroma baraui), Thèse de Doctorat de l’Université de La Réunion (2012).
Coulson, J. C. A review of philopatry in seabirds and comparisons with other waterbird species. Waterbirds 39, 229–326 (2016).
Google Scholar
Cristofari, R. et al. Unexpected population fragmentation in an endangered seabird: the case of the Peruvian diving-petrel. Sci. Rep. 9, 2021 (2019).
Google Scholar
Pinet, P. et al. Migration, wintering distribution and habitat use of an endangered tropical seabird, Barau’s Petrel Pterodroma baraui. Mar. Ecol. Prog. Ser. 423, 291–302 (2011).
Google Scholar
Danckwerts, D. K., Corré, S., Pinet, P. L., Corre, M. & Humeau, L. Isolation and characterization of 15 polymorphic microsatellite loci for the Barau’s Petrel (Pterodroma baraui), an endangered endemic of Réunion Island (Indian Ocean). Waterbirds 39, 413–416 (2016).
Google Scholar
Schuelke, M. An economic method for the fluorescent labelling of PCR fragments. Nat. Biotechnol. 18(2), 233–234 (2000).
Google Scholar
van Oosterhout, C., Hutchinson, W. F., Wills, D. P. M. & Shipley, P. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Resour. 43(3), 535–538 (2004).
Google Scholar
Rousset, F. Genepop’007: a complete re-implementation of the GENEPOP software for windows and linus. Mol. Ecol. Resour. 8, 103–106 (2008).
Google Scholar
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57(1), 289–300 (1995).
Google Scholar
Agapow, P. M. & Burt, A. Indices of multi-locus linkage disequilibrium. Mol. Ecol. Resour. 1, 101–102 (2001).
Google Scholar
Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. Peer J 4, 2281 (2014).
Nei, M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89, 583–590 (1978).
Google Scholar
Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 28, 2537–2539 (2012).
Google Scholar
Hammer, Ø., Harper, D. A. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4(1), 1–9 (2001).
El Mousadik, A. & Petit, R. J. High level of genetic differentiation for allelic richness among population of the argan tree [Argania spinose (L.) Skeels] endemic to Morocco. Theor. Appl. Genet. 92, 832–839 (1996).
Google Scholar
Goudet, J. FSTAT (version 1.2): a computer program to calculate F-statistics. J. Hered. 86, 485–486 (1995).
Google Scholar
Paradis, E. Pegas: an R package for population genetics with an integrated-modular approach. Bioinformatics 26, 419–420 (2010).
Google Scholar
Wright, S. Coefficients of inbreeding and relationship. Am. Nat. 56, 330–338 (1922).
Google Scholar
Wright, S. The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution 19, 395–420 (1965).
Google Scholar
Weir, B. S. & Cockerham, C. C. Estimating F-statistics of population structure. Evolution 38(6), 1358–1370 (1984).
Google Scholar
Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24(11), 1403–1405 (2008).
Google Scholar
Hardy, O. J. & Vekemans, X. SPAGEDI: a versatile computer program to analyse spatial genetic structure at the individual and population levels. Mol. Ecol. Resour. 2, 618–620 (2002).
Google Scholar
Meirmans, P. G. GENODIVE version 3.0: easy-to-use software for the analysis of genetic data of diploids and polyploids. Mol. Ecol. Resour. 20(4), 1126–1131 (2020).
Google Scholar
Slatkin, M. A measure of population subdivision based on microsatellite allele frequencies. Genetics 139, 457–462 (1995).
Google Scholar
Pons, O. & Petit, R. J. Measuring and testing genetic differentiation with ordered vs. unordered alleles. Genetics 144, 1237–1245 (1996).
Google Scholar
Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 155(2), 945–959 (2000).
Porras-Hurtado, L. et al. An overview of STRUCUTE: applications, parameter settings, and supporting software. Front. Genet. 4, 98 (2013).
Google Scholar
Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164(4), 1567–1587 (2003).
Google Scholar
Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferrign weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332 (2009).
Google Scholar
Pritchard, J., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
Google Scholar
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).
Google Scholar
Earl, D. A. & von Holdt, B. M. Structure harvester: a website and program for visualizing structure output and implementing the Evanno method. Cons Genet Res 4, 359–361 (2012).
Google Scholar
Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. CLUMPAK: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15(5), 1179–1191 (2015).
Google Scholar
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).
Google Scholar
Carlen, E. & Munshi-South, J. Widespread genetic connectivity of feral pigeons across the Northeastern megacity. Evol. Appl. 14, 1–13 (2020).
Nomura, T. Estimation of effective number of breeders from molecular coancestry of single cohort sample. Evol. Appl. 1(3), 462–474 (2008).
Google Scholar
Do, C. et al. NeEstimator V2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14(1), 209–214 (2014).
Google Scholar
Piry, S., Luikart, G. & Cornuet, J. M. BOTTLENECK: a computer program for detecting recent reduction in effective population size using allele frequency data. J. Hered. 90, 502–503 (1999).
Google Scholar
Peery, M. Z. et al. Reliability of genetic bottleneck tests for detecting recent population declines. Mol. Ecol. 21(14), 3403–3418 (2012).
Google Scholar
Luikart, G., Cornuet, J. M. & Allendorf, F. W. Temporal changes in allele frequencies provide estimates of population bottleneck size. Cons. Biol. 13(3), 523–530 (1999).
Google Scholar
Archer, F. I., Adams, P. E. & Schneiders, B. B. stratag: an r package for manipulating, summarizing and analyzing population genetic data. Mol. Ecol. Resour. 17(1), 5–11 (2017).
Google Scholar
Nikolic, N. & Chevalet, C. Detecting past changes of effective population size. Evol. Appl. 7(6), 663–681 (2014).
Google Scholar
Garza, J. C. & Williamson, E. G. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol. 10(3), 305–318 (2001).
Google Scholar
Wickman, H. François, R. Henry, L. & Müller, K. dplyr: A grammar of data manipulation. R package version 1.0.2. https://CRAN.R-project.org/package=dplyr (2020).
Humeau, L. et al. Genetic structuring among colonies of a pantropical seabird: Implication for subspecies validation and conservation. Ecol. Evol. (in press).
Nunes, G. T. & Bugoni, L. Local adaptation drives population isolation in a tropical seabird. J Biogeogr. 45(2), 332–341 (2018).
Google Scholar
Barlow, E. J. et al. Weak large-scale population genetic structure in a philopatric seabird, the European Shag Phalacrocorax aristotelis. Ibis 153(4), 768–778 (2011).
Google Scholar
QGIS.org. QGIS Geographical Information System version 3.10. QGIS Association. http://www.qgis.org (2020).
Source: Ecology - nature.com
