Testing Dispersal Hypotheses in Foraging Green Sea Turtles (Chelonia mydas) of Brazil

Abstract

Testing theories of dispersal is challenging in highly migratory species. In sea turtles, population size, geographic distance, natal homing, and ocean currents are hypothesized to affect dispersal. Little is known, however, about these mechanisms in sea turtles foraging along the South American coast. Green sea turtles feeding at Ubatuba (UB, n = 114) and Almofala (AF, n = 117), Brazil, were sequenced at the mitochondrial DNA (mtDNA) control region (486 bp) and genotyped at 7 microsatellite loci to test dispersal hypotheses. Fifteen mtDNA haplotypes were revealed, including a previously undescribed sequence, and the average observed heterozygosity (H(o)) was 76.4%. Overall short-term temporal differences were not detected, and differentiation was less pronounced in microsatellite than in mtDNA analyses. Mitochondrial results reveal significant differentiation between the Brazilian feeding grounds and most other Atlantic groups, whereas microsatellites uncover similarities to some of the geographically closest populations. Ubatuba and Almofala are mixed stocks, drawn primarily from Ascension, with lesser contributions from Surinam/Aves and Trindade. Costa Rica is also a significant source of individuals feeding at AF. The results are consistent with a model of juvenile natal homing impacted by other factors. Effective protection of turtles foraging along the extensive Brazilian coast may enhance breeding populations thousands of kilometers away.

Finding the ‘lost years’ in green turtles: insights from ocean circulation models and genetic analysis

“…Dispersal patterns (figures 1, 2 and 3) were consistent with previous genetic and oceanographic studies that suggest considerable connectivity is possible among green turtle populations during the 'lost years' [12,15,[25][26][27]. Dispersal from rookeries east of 148W (including Ascension Island) account for approximately 70-90% of the particles reaching South American FGs, and approximately 5-65% of particles reaching Caribbean and North American FGs (see the electronic supplementary material, tables S3 and S4).…”

“…Genetic surveys of southwestern Indian Ocean rookeries revealed patterns consistent with limited migration between the Atlantic and Indian basins [25,26], lending support to our findings. Mixing of diverse genetic stocks has also been documented in FGs where older turtles occur [10,13,27]. Thus, evidence increasingly suggests that the management of many turtle populations will require a broad approach across life-stages and regions [6,13].…”

Green turtle Chelonia mydas mixed stocks in the western South Atlantic, as revealed by mtDNA haplotypes and drifter trajectories

“…Haplotype frequencies of AI and CB were similar to other Atlantic rookeries and foraging aggregations: high CM-A8 frequency, consistent with the suggestion that this is the closest relative to an ancestral haplotype in the Atlantic basin, followed by a high occurrence of CM-A5, a common haplotype in Caribbean rookeries (Bjorndal et al 2005, Formia et al 2006, 2007, Naro-Maciel et al 2007, and rare haplotypes. Increasing sample size did not change the proportion of CM-A8 and CM-A5 or the diversity indexes found by Proietti et al (2009) for green turtles around AI; however, the detection of rarer haplotypes increased.…”

“…This software was also used to calculate pairwise Φ-statistics for an analysis of molecular variance (AMOVA) with 10 000 permutations and using the Kimura-2P model of nucleotide substitution as determined by jModelTest (Posada 2008). The Brazilian foraging grounds included in these analyses for comparative purposes were Ubatuba (SP), Almofala (CE) (Naro-Maciel et al 2007), Rocas Atoll (RN) and Fernando de Noronha (PE) ). The last 2 were clumped for all analyses due to geographic proximity (~150 km), small sample sizes and genetic similarity, following Bjorndal et al (2006), and will hereafter be referred to as Rocas/ Noronha.…”

“…(e.g. Okuyama & Bolker 2005, Bass et al 2006, Naro-Maciel et al 2007, Bolker et al 2007, Monzón-Argüello et al 2010. Nonetheless, the influence of priors on posterior inferences depend on the quality of analyzed data; when data is strong and large sample sizes are available, prior distributions will have minor effects on posterior estimates (Gelman et al 2004).…”