title: Microsatellite genetic distances between oceanic populations of the humpback whale (Megaptera novaeangliae).
abstract: Mitochondrial DNA haplotypes of humpback whales show strong segregation between oceanic populations and between feeding grounds within oceans, but this highly structured pattern does not exclude the possibility of extensive nuclear gene flow. Here we present allele frequency data for four microsatellite loci typed across samples from four major oceanic regions: the North Atlantic (two mitochondrially distinct populations), the North Pacific, and two widely separated Antarctic regions, East Australia and the Antarctic Peninsula. Allelic diversity is a little greater in the two Antarctic samples, probably indicating historically greater population sizes. Population subdivision was examined using a wide range of measures, including Fst, various alternative forms of Slatkin's Rst, Goldstein and colleagues' delta mu, and a Monte Carlo approximation to Fisher's exact test. The exact test revealed significant heterogeneity in all but one of the pairwise comparisons between geographically adjacent populations, including the comparison between the two North Atlantic populations, suggesting that gene flow between oceans is minimal and that dispersal patterns may sometimes be restricted even in the absence of obvious barriers, such as land masses, warm water belts, and antitropical migration behavior. The only comparison where heterogeneity was not detected was the one between the two Antarctic population samples. It is unclear whether failure to find a difference here reflects gene flow between the regions or merely lack of statistical power arising from the small size of the Antarctic Peninsula sample. Our comparison between measures of population subdivision revealed major discrepancies between methods, with little agreement about which populations were most and least separated. We suggest that unbiased Rst (URst, see Goodman 1995) is currently the most reliable statistic, probably because, unlike the other methods, it allows for unequal sample sizes. However, in view of the fact that these alternative measures often contradict one another, we urge caution in the use of microsatellite data to quantify genetic distance.
authors.affiliation: Department of Zoology, Cambridge University, UK.
authors.forename: E
authors.initials: E
authors.lastname: Valsecchi
authors.forename: P
authors.initials: P
authors.lastname: Palsbøll
authors.forename: P
authors.initials: P
authors.lastname: Hale
authors.forename: D
authors.initials: D
authors.lastname: Glockner-Ferrari
authors.forename: M
authors.initials: M
authors.lastname: Ferrari
authors.forename: P
authors.initials: P
authors.lastname: Clapham
authors.forename: F
authors.initials: F
authors.lastname: Larsen
authors.forename: D
authors.initials: D
authors.lastname: Mattila
authors.forename: R
authors.initials: R
authors.lastname: Sears
authors.forename: J
authors.initials: J
authors.lastname: Sigurjonsson
authors.forename: M
authors.initials: M
authors.lastname: Brown
authors.forename: P
authors.initials: P
authors.lastname: Corkeron
authors.forename: B
authors.initials: B
authors.lastname: Amos
mesh_terms: D000483:Alleles; D000818:Animals; D004276:DNA, Satellite / Q000235:genetics*; D014644:Genetic Variation; D011110:Polymorphism, Genetic; D014907:Whales / Q000235:genetics*
chemical_list: D004276:DNA, Satellite