Final Report The People s Trust for Endangered Species Project: Conservation genetics and migratory patterns of sea turtles in Southern Brazil

Final Report The People s Trust for Endangered Species Project: Conservation genetics and migratory patterns of sea turtles in Southern Brazil Project Team M.Sc. Maíra Carneiro Proietti M.Sc. Júlia Wiener Reisser Ph.D. Eduardo Resende Secchi Ph.D. Luis Fernando Marins M.Sc. Rodrigo Kerr November 2009

INDEX INTRODUCTION... 3 Life cycle... 4 Genetic markers applied to sea turtle research... 5 Bayesian Mixed Stock Analysis... 5 Ocean currents and sea turtle dispersal... 7 Implications for conservation... 8 Objectives... 8 METHODS... 9 RESULTS... 12 FINAL CONSIDERATIONS... 15 CITED LITERATURE... 16 PROJECT DETAILS... 20 Summary... 20 Unforeseen difficulties... 20 Budget and expenditure... 21 Next steps... 21 Sharing results... 22 2

INTRODUCTION The green and hawksbill turtles The green (Chelonia mydas) and hawksbill (Eretmochelys imbricata) sea turtles (Figure 1) belong to order Testudinata, suborder Cryptodira, superfamily Chelonioidea, family Cheloniidae. This family comprises four other sea turtle species (Caretta caretta, Lepidochelys olivacea, Lepidochelys kempii, Figure 1. Chelonia mydas (left) and Eretmochelys imbricata (right). Natator depressus), and only the leatherback turtle, Dermochelys coriacea, belongs to a different family (Dermocheliidae). Cheloniids possess bony carapaces, with green turtles presenting fused, predominantly brown and green with radial and streaked scutes, and hawksbills presenting imbricate brown and yellow scutes and a hawk-like beak. Green turtles are larger than hawksbills, reaching up to 140 cm of curved carapace length (CCC) and 300 kg, but usually do not surpass 120 cm and 240 kg (Pritcher & Mortimer 1995). Hawksbill sea turtles may reach 100 cm CCC and 90 kg. Green sea turtles present an omnivorous diet with a tendency for herbivory, and hawksbills are omnivorous with a preference for sponges (Bjorndal 1997). They are present in all of the world s oceans, at tropical and subtropical latitudes, with hawksbill turtles more confined to lower latitudes with decrease in occurrence as latitudes increase. The life cycles of these animals are extremely complex. They occupy various ecological niches and distinct habitats throughout their lives, and may perform large-scale migrations between feeding and nesting areas upon reaching sexual maturity (Bolten 2003, Godley et al. 2003). In Brazil, these species occur along the entire coast for feeding and development, with lower occurrence of hawksbills at higher latitudes. Reproduction, however, is restricted to oceanic islands for green turtles (Trindade Island, Rocas Atoll and Fernando de Noronha) and beaches located at the states of Bahia, Sergipe and Rio Grande do Norte for hawksbills. All sea turtle species are currently threatened, with green turtles classified as endangered and hawksbills critically endangered by the World Conservation Union (IUCN 2009). Factors such as overexploitation, disorderly occupation of the coast, habitat destruction, marine pollution and fisheries bycatch have led to such status and continue to threaten these animals today. 3

Life cycle Green and hawksbill turtles reach sexual maturity with between 20 and 50 years, and present a phylopatric reproductive behavior, that is, they return to the same region to reproduce in subsequent reproductive cycles (Miller 1997, Avise 2007). Courtship and mating usually occur in waters close to the nesting beach, and courtship of one female by more than one male is common (Miller 1997). Females nest more than once at each nesting season, and 50-60 days after nest deposition, eggs hatch and Figure 2. General life cycle of sea turtles. hatchlings run to sea and swim against the waves to the open ocean, where predation is supposedly lower (Musick & Limpus 1997). Little is known on the post-hatchling phase of these animals, which remain in the pelagic environment being theoretically transported by ocean currents for an unknown period, believed to be between one and three years ( the lost years Carr 1967, Musick & Limpus 1997). After reaching an adequate size (usually between 20 and 35 cm CCC, varying according to population; Bjorndal 1997, Musick & Limpus 1997, Bolten 2003), juveniles recruit to coastal feeding and development areas, where individuals from various reproductive areas mix, forming mixed stocks (Bass et al. 2006). Sea turtles may present fidelity to feeding areas, but may also perform movements between different areas (Godley et al. 2003, Reisser et al. 2008). When approaching sexual maturity, they may move to adult feeding areas (Godley et al. 2003), possibly due to larger proximity to nesting areas. Upon sexual maturity, they migrate to reproductive areas, reproduce, and afterwards return to feeding areas where they remain and stock energy until the next reproductive cycle. Figure 2 presents the most accepted general model of the life cycle of these animals. 4

Genetic markers applied to sea turtle research The complexity of the life cycles of these animals, and the large spatial and temporal scales involved, make the direct study of these animals difficult, and indirect approaches through molecular analyses can elucidate many aspects of their biology and behavior (Bowen & Karl 1997, Bowen & Karl 2007, Avise 2007). Molecular markers have been utilized for the investigation of mating systems and paternity, population structure and gene flow, philogeography, systematics, taxonomy, natal homing hypothesis, and natal origins of animals at non-reproductive phases of their life cycles (Avise 2007, Bowen & Karl 2007). The natal homing hypothesis was first proposed by Carr (1967), based on the observation that female sea turtles present elevated degree of philopatry, many times quite precise (Miller 1997, Musick & Limpus 1997, Formia et al. 2007, Avise 2007). Two hypotheses were postulated for explaining this site fidelity: natal homing, in which females return to the area where they were born in order to reproduce, and social facilitation, in which inexperienced females follow experienced ones to a nesting area and would thereon utilize this area for reproduction (Bowen & Karl 2007). These hypotheses are difficult to test directly, considering the difficulty in recovering decades later a tag applied to a 5 cm hatchling, in an adult measuring over one meter. These hypotheses however generate genetically-testable prediction, in terms of matriarchal population structure: if a female returns faithfully to its nesting area, each area would present a genetic signature in terms of maternally-inherited mtdna; on the other hand, if nesting areas are chosen by social facilitation, there would be high gene flow between nesting populations that overlap in feeding areas (Bowen & Karl 2007). Green turtle tagging studies demonstrated that feeding areas in northeastern Brazil are shared by animals from the Surinam and Ascension Island nesting areas. Despite this overlap in feeding grounds, genetic studies demonstrated high genetic differentiation between samples from Ascension and Surinam, revealing that female dispersion between these areas is low and supporting the natal homing hypothesis (Bowen et al. 1992, Allard et al. 2004, Bowen & Karl 2007). Bayesian Mixed Stock Analysis Based on the premise that structuring exists among rookeries, a Bayesian approach to Mixed Stock Analysis (MSA) has been increasingly applied for the 5

determination of rookery (stock) contributions to sea turtle foraging aggregations (mixed stocks). This analysis, initially developed for the evaluation of fishing stocks, utilizes differences in the frequency of genetic characters (especially mtdna) for determining contributions of each stock to a mixed stock (Pella & Masuda 2001). This analysis frequently presents high standard deviations and is based on the assumption that all stocks are adequately sampled, which is not the case, with many areas presenting little or no genetic characterization (Avise 2007). Despite these potential sources of error, MSA is extremely useful for quantitative estimates and qualitative descriptions of origins of sea turtles in feeding habitats (Bowen & Karl 2007). These estimates should be considered with caution, and when possible, compared and associated with other data and hypotheses. The Bayesian approach to MSA permits the incorporation of informative priors for improving estimates, and ecological data such as rookery population size and distance from stock to mixed stock are frequently employed, assuming that these factors influence the mixed stock composition. Oceanographic data, such as surface drifter trajectories, can be good indicators of hatchling dispersal, considering that most researchers accept that hatchlings are pelagic and transported by ocean currents (Bolten 2003). Such data therefore possess a high potential for incorporation in Bayesian MSA. Some examples of natal origin estimates of green turtles in the Atlantic and Pacific can be seen in Bass et al. (2006), Bolker et al. (2007), Bjorndal & Bolten (2008), and Dutton et al. (2008). In the southwestern and south-central Atlantic there are four green turtle nesting areas, listed as follows in decreasing population size: Ascension Island (United Kingdom), Trindade Island (ES, Brazil), Rocas Atoll (RN, Brazil), and Fernando de Noronha Archipelago (PE, Brazil). Origins of juvenile green turtles in Brazil have been described for Rocas Atoll and Fernando de Noronha Archipelago (grouped into one area in Bjorndal et al. 2006) Ubatuba (SP) and Almofala (CE) (Naro- Maciel et al. 2007). For all these areas, high contributions from Ascension Island were consistently observed, followed by lower yet important contributions from Trindade Island and almost null contributions from Rocas Atoll and Fernando de Noronha. These authors attribute such contributions to the pattern of ocean currents that flow near these rookeries. 6

Ocean currents and sea turtle dispersal The role of ocean currents in sea turtle dispersal and migration has been thoroughly discussed (see Luschi et al. 2003a). As cited previously, hatchlings are thought to rely on oceanic currents for dispersal until recruiting to their coastal foraging zone, and data obtained through satellite telemetry indicate that sea turtle movements and migrations are frequently shaped by ocean currents (Luschi et al. 2003a, 2003b; Craig et al. 2004). Parallels between MSA and ocean currents have been made for North Atlantic mixed stocks by Luke et al. (2004) and Bass et al. (2006), in which it is assumed that the compositions of these foraging aggregations depend on local major and minor current systems. For Brazil, it has been suggested that Ascension Island hatchlings drift with major Equatorial currents towards South America, while hatchlings from other rookeries may drift away with prevailing currents (Naro-Maciel et al. 2007). The large-scale upper-layer (< 100 m) general circulation pattern which could influence sea turtle dispersal in the South Atlantic, affecting the composition of Brazilian foraging areas, is characterized by a dominating anticyclonic subtropical gyre. The westerlybound Southern Equatorial Current bifurcates at the South American continental shelf at approximately 10 S originating the northernbound North Brazil Current and the southern-bound Brazil Current (BC). The BC travels southward alongside the coast until reaching the Subtropical Convergence Zone Figure 3. Upper-layer currents system for the southern Atlantic Ocean, from Stramma & England 1999. (approximately 33-38º S), where it encounters the Falkland Current and separates from the coast forming the eastern-bound South Atlantic Current (SAC). When it approaches the African continent, part of the SAC flows to the Indian Ocean and part forms the northern-bound Benguela Current, which in turn will form the CSE and complete the (Stramma & England 1999 Figure 3). 7

Implications for conservation The relevance of identifying natal origins of mixed stocks for conservation lies in the fact that rookeries, despite being generally independent reproductively, are linked at the non-nesting phases of the female green turtle life cycle (Avise 2007). Therefore, impacts at foraging grounds and migratory routes may affect many breeding stocks at different levels. The determination of migratory routes and identification of which breeding areas will be impacted by mortality at non-reproductive areas, may help in the practical management of populations, through the elaboration of management and conservation plans (Avise 2007, Bowen & Karl 2007). Nevertheless, caution is needed when using MSA estimates to understand stock contribution to feeding grounds, and when possible, compared and associated with other data. The Bayesian approach to MSA allows the incorporation of informative priors for improving estimates, and ecological data such as rookery population size and distance from source to mixture are commonly employed based on the assumption that foraging ground composition may be related to these factors. Hatchlings are considered by most authors as pelagic, dispersing almost passively with ocean currents until reaching a certain size (Bolten 2003). Therefore, oceanographic data such as surface drifter trajectories can be viewed as an indicative of early life stage dispersal routes, and have potential to be incorporated as ecologically-informative priors in MSA. Objectives Considering that genetic studies potentially elucidate many aspects of sea turtle biology and ecology, including foraging ground composition, hatchling dispersal and migrations, this study aimed at: a) determining genetic differences amongst southern Brazil foraging areas of Arvoredo Island (AI) and Cassino Beach (CB) and other mixed aggregations in the Atlantic; b) estimating contributions of different rookeries to the AI and CB mixed stocks; c) developing novel informative priors for Bayesian Mixed Stock Analysis; d) assessing the effect of incorporating different ecological priors in Bayesian Mixed Stock Analysis; and e) determining possible dispersal patterns from rookeries to the studied foraging areas. We highlight that although the analysis of Eretmochelys imbricata samples was foreseen in the submitted proposal, we were able to obtain only a very small number of samples of this species (n = 5 for Arvoredo Island and n = 2 for 8

Cassino Beach). These samples have not yet been analysed; however, analyses will be performed after further sampling for a more robust sample size. METHODS Samples were collected at Arvoredo Island (27º51 S 48º26 W), in Santa Catarina state (n = 115), and Cassino Beach (from 31 21 S 51 02 W to 33 44 S 53 22 W), Rio Grande do Sul state (n = 101) (Figure 4). Arvoredo Island lies within the Figure 4. Location of study areas. Arvoredo Marine Biological Reserve and presents rocky shores with a high diversity of benthic organisms and frequent occurrences of green turtles, and less frequent reports of hawksbills. Reisser et al. (2008) reported recaptures of individuals at the island over three years after initial captures, usually within 200 m of initial location, indicating that some of these animals present fidelity to this foraging ground and to specific sites within the area. Cassino Beach is an extensive and continuous sandy beach composed of predominantly unconsolidated substrate and few substantial hard substrates. Green turtles are frequently observed stranded at this beach (Bugoni et al. 2001), but its exact role in the life cycles of these animals is unknown. At Arvoredo Island, skin samples were collected using 5 mm disposable biopsy punches from the flippers of live individuals hand-captured through free and SCUBA dives in expeditions carried out from July 2005 to April 2008. Animals were also measured (curved and straight carapace length and width CCL/CCW and SCL/SCW), weighed, and photographed for photo-id. At Cassino Beach, samples were collected using disposable scalpels from 9

stranded live animals or carcasses found washed ashore during beach surveys conducted from January 2005 to May 2007 by NEMA (Núcleo de Educação e Monitoramento Ambiental), an NGO acting towards environment education, monitoring and conservation at the southernmost coast of Brazil. These animals were only measured (CCL/CCW). All samples were conserved in absolute ethanol and maintained at -20 ºC until DNA extraction. Sea turtle sizes ranged from 33.5-83 cm (mean 49.2 cm) and 29-71.5 cm (mean 40.1 cm) curved carapace length (CCC), respectively for Arvoredo Island and Cassino Beach. Tissue samples were macerated in a Tris-HCl lysis buffer containing Proteinase K and submitted to digestion in an oven at 37 C until complete digestion (from five to 24 hours). DNA was extracted through DNAExtraction Kits (Tissue Bioamerica Inc.) or standard phenol:chlorophorm method with precipitation in absolute ethanol (adapted from Hillis et al. 1996). Approximately 500 bp-fragments of the mitochondrial DNA control region were amplified via polymerase chain reaction (PCR), using primers LTCM1 and HDCM1 (Allard et al. 1994) or LTCM2 and HDCM2 (longer versions of the prior primers, designed by Lahanas et al. 1994). Illustra GFX purification kits (GE Healthcare, U.S.A.) were employed for purification, and samples were sequenced in both directions using DYEnamic ET dye terminator kit in a MegaBACE 500 DNA sequencer (GE Healthcare, U.S.A.). AI C Figure 5. Foraging (squares) and nesting (dots) areas in the Atlantic used for comparison with study areas (triangles). Letters stand for: Arvoredo (AI), Cassino (CB), Ubatuba (UB), Trindade (TI), Rocas/Noronha (R/N), Ascension (AS), Almofala (AF), Surinam (SU), BA (Barbados), Aves (AI), Costa Rica (CR), Nicaragua (NI), Mexic (MX), Bahamas (BH), Florida (FL), North Carolina (NC), Guinea Bissau (GB), Bioko and São Tomé (BI and ST, referred to in text as Gulf of Sequences were aligned and haplotypes (491 bp, according to previouslydescribed haplotypes for Chelonia mydas) classified according to the Archie Carr Center for Sea Turtle Research online genetic bank (Florida University). Relationships among haplotypes were demonstrated through a statistical parsimony network. Genetic differentiation tests were conducted in order to verify differences between the study 10

areas and other previously-described Atlantic foraging grounds. 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) (Bjorndal et al. 2006). The last two were grouped into one unit for all analyses due to geographic proximity (c.a. 150 km) and small sample sizes and will hereafter be referred to as Rocas/Noronha. Nicaragua (Bass et al. 1998), Barbados (Luke et al. 2004), Bahamas (Lahanas et al. 1998), Florida (Bass and Witzell 2000) and North Carolina (Bass et al. 2006), in the Caribbean and North Atlantic, were also considered for comparison (Figure 5). In order to compare Mixed Stock Analysis results with surface current data and develop two novel informative priors for MSA analysis, surface drifter data available for the Atlantic and Mediterranean (5842 drifters, from February 1979 to January 2009), was downloaded from NOAA s Global Drifter Program (http://www.aoml.noaa.gov/envids/gld). We then evaluated the number of drifters that passed through the nesting areas considered in the natal origin analyses and reached a target area consisting in the eastern Brazilian coast, from the southernmost limit to the northeastern corner. The northern portion of the country was not included due to evidence of genetic structuring between this area and the East Brazil coast. Based on these data, the probability of a drifter which reached the target area being from a determined rookery was calculated in a Bayesian framework. Such data were taken into consideration assuming that hatchling sea turtles are dispersed mainly by surface currents. The nesting areas considered as possible sources correspond to all rookeries in the Atlantic and Mediterranean with mtdna description, as reported by Encalada et al. (1996), Kaska (2000), Bjorndal et al. (2005, 2006), Formia et al. (2006, 2007): (1) Trindade Island, (2) Rocas/Noronha (Brazil), (3) Ascension Island (United Kingdom), (4) Poilão (Guiné Bissau), (5) Bioko Island (Equatorial Guinea), São Tomé and Príncipe (Democratic Republic of São Tomé and Príncipe), (6) Aves Island (Venezuela), (7) Matapica (Surinam), (8) Quintana Roo (Mexico), (9) Tortuguero (Costa Rica), (10) Florida (United States) and (11) Lara Bay (Cyprus) (Figure 4). Bioko, São Tomé and Principe were grouped into one area due to proximity and lack of genetic differentiation, and hereafter will be referred to as Gulf of Guinea. 4 x4 (latitude and longitude) areas were delineated around all these considered rookeries, and in the case of non-insular 11

rookeries, the area was designed in order to incorporate the largest possible oceanic area. Probable natal origins were determined employing mtdna data from the study areas and all rookeries with mtdna description, through Bayesian Mixed Stock Analysis (MSA) implemented. Arvoredo Island and Cassino Beach were grouped into one area due to geographic proximity and genetic similarity, and four MSAs were performed considering uninformative priors (MSA 1 ) and priors weighed according to: number of females/year of each source (MSA 2 ); probabilities calculated from surface drifter data (MSA 3 ); and a combination of the two previous informative priors (MSA 4 ). For a detailed description of priors, refer to Annex 1. Source populations considered as possible contributors to the study areas correspond to the same area used in surface drifter analysis. RESULTS Each study area presented 12 previously-described haplotypes, of which ten were shared (CM-A5, CM-A6, CM-A8, CM-A9, CM-A10, CM-A23, CM-A24, CM- A32, CM-A42 and CM-A45) and four were not shared (CM-A3 and CM-A39, present only at AI, and CM-A25 and CM-A36, present only at CB). Both areas were characterized by a high predominance of haplotypes CM-A8 (61% for both areas) and CM-A5 (22% and 20% for AI and CB, respectively). All remaining haplotypes were present in frequencies lower than 5%. Figure 6. Network of haplotypes encountered at Arvoredo Island (left) and Cassino Beach (right). Rare haplotypes were observed, such as CM-A10, CM-A23 and CM-A24, encountered only at Ascension and Trindade islands; CM-A25 and CM-A32 only at Rocas Atoll and Ascension Island; CM-A39 and CM-A45 at Ascension Island; and CM-A42 in only two individuals at the Almofala foraging ground in northeast Brazil, with no observations in 12

rookeries. The number of polymorphic sites defining these haplotypes was 19 for AI and CB, with a maximum of 12 variations distinguishing them (Figure 6). Foraging ground h π Arvoredo Island 0.5831 ± 0.0451 0.0025 ± 0.0018 Cassino Beach 0.5857 ± 0.0501 0.0025 ± 0.0018 Ubatuba 0.4460 ± 0.0556 0.0020 ± 0.0015 Rocas/Noronha 0.5887 ± 0.0911 0.0019 ± 0.0015 Almofala a 0.7168 ± 0.0306 0.0067 ± 0.0039 Barbados 0.7734 ± 0.0276 0.0105 ± 0.0057 Bahamas 0.3703 ± 0.0650 0.0066 ± 0.0038 Nicaragua 0.1831 ± 0.0621 0.0039 ± 0.0025 Florida 0.4855 ± 0.0668 0.0032 ± 0.0021 North Carolina 0.6778 ± 0.0310 0.0052 ± 0.0031 Average 0.5410 0.0045 Table 1. Haplotype (h) and nucleotide (π) diversity estimates for all compared foraging aggregations. Haplotype (h) and nucleotide (π) diversities of AI (h = 0.5831 ± 0.0451; π = 0.00246 ± 0.00176) and CB (h = 0.5857 ± 0.0501 and π = 0.00251 ± 0.00178), and the averaged diversities of all compared foraging aggregations (h = 0.5410; π = 0.0045), were similar, as shown in Table 1. Differentiation tests revealed an overall structuring among foraging areas (p < 0.001 for both analyses); however, genetic structuring was non-significant between AI and CB, with a slightly negative Φ ST value (Φ ST = -0.0066, p > 0.05). Both analyses revealed that AI and CB are genetically different from most areas (Almofala, Nicaragua, Barbados, North Carolina, Florida and Bahamas, p < 0.05), but showed no difference in relation to Ubatuba and Rocas/Noronha (p > 0.05), both located in the Southwestern Atlantic. Drifters coming from Ascension and Trindade Islands were dominant at the target area, as clearly shown in Figure 7, which illustrates the trajectories of all surface drifters which passed :through the eleven 4 x4 areas (all rookeries considered in the MSAs). In this figure is a table listing the total number of drifters which passed through each area (N), the number of these that reached the target area (Y), and the probability that drifters at the Brazilian coast are from each rookery (P). This table shows that only Ascension, Trindade, Rocas/Noronha and Gulf of Guinea supplied drifters to the target area. The first two areas presented posterior probabilities of around 40% of reaching the target, while Rocas/Noronha and Gulf of Guinea presented near 2% and slightly over 5%, respectively. Although Costa Rica and Guinea Bissau exhibited posterior probabilities of over 2%, they are not considered relevant due to the fact that this estimate is simply a result of the small number of drifters passing through the areas. The remaining rookeries presented probabilities lower than 1%. 13

Figure 7. Surface drifter trajectories in the Atlantic Ocean, with study areas (black circles), rookeries (4 x4 squares), and target area. Letters stand for Rocas/Noronha (R/N), Ascension Island (AS), Trindade Island (TR), Guinea Bissau (GB), Gulf of Guinea (GG), Aves Island (AV), Mexico (MX), Costa Rica (CR), Suriname (SU), Florida (FL), Cyprus (CY). Natal origin analyses indicated that the main contributors to the southern Brazil foraging areas were Ascension, Aves and Trindade Islands (Figure 8). Ascension Island consistently presented the largest contributions, ranging from 53.3 to 66.5% in the four performed MSAs, while Aves and Trindade Islands exhibited contributions that ranged from 21.6 to 22% and 7.6 to 17.7%, respectively. Remaining stocks presented low contributions in all MSAs (less than 1% in a general manner), with the exception of the Gulf of Guinea, with estimated contributions from 2.1 to 7.3%. MSA 3 (which used surface drifter Figure 8. Mixed Stock Analyses estimates for the southern Brazil foraging aggregations. MSA 1 uninformative prior; MSA 2 prior reflecting surface drifter data; MSA 3 prior reflecting number of females nesting per year at each rookery; MSA 4 prior constructed to weigh both previous priors. Prior weights are represented in black, and MSA estimates in gray. 14

data as ecological information) slightly increased Ascension Island contribution estimates when compared to the uninformative MSA 1, while MSA 2 (prior weighing rookery population size) and MSA 4 (combination of both ecological priors) increased estimates in slightly over 12% for the former and 6% for the latter analysis. Contributions from Trindade Island increased to 17.7% in MSA 3, while MSA 2 decreased this contribution to slightly less than 8%. Gulf of Guinea s contributions in MSA 1 was relatively high (around 7%), but decreased to 5.2% in MSA 3, and when inserting the ecological variable rookery size (MSA 2 ) and the combination rookery size/surface drifters (MSA 4 ), contribution from this stock dropped to 2.1%. Of the largest contributors, Aves Island was the least variable throughout MSAs, varying less than 1%. FINAL CONSIDERATIONS Arvoredo Island and Cassino Beach presented extremely similar diversities and haplotype frequencies, and were not genetically distinct. The study areas were also genetically similar other relatively close feeding areas in the southwestern Atlantic (Ubatuba SP and Rocas/Noronha PE). However, they were genetically different from Almofala (CE), a slightly more distant feeding area in Brazil, as well as from feeding areas in the Caribbean and North America. Surface drifter tracks revealed that Ascension and Trindade Islands, and at a smaller proportion Gulf of Guinea and Rocas/Noronha, present favorable current conditions for leading drifters to the Brazilian coast. Natal origin analyses showed that the main stock contributing to the study areas was Ascension Island, followed by Aves and Trindade Islands, and Gulf of Guinea. We developed novel priors for MSA by combining different ecological data, but their insertion in the analyses did not greatly alter estimates. However, we believe that the combination of ecological data is the ideal scenario for obtaining more realistic contribution estimates. Despite being genetically indistinct units, we suggest that different management strategies be adopted at the areas due to some differences in terms of green turtle occurrence and habitat use. Mean curved carapace size of sampled animals revealed that green turtles at Arvoredo and Cassino are at different stages of their life cycles, with the latter hosting smaller animals than the former. These different stages present different vulnerabilities and their survival influences population growth rates in different manners. Also, these habitats may represent different roles in the life 15

cycles of juvenile green turtles. While at least short-term fidelity has been observed at Arvoredo Island, the role of Cassino Beach is still unclear due to lack of mark-recapture or telemetry studies and it is possibly more important as a passing-through area when compared to Arvoredo Island, perhaps due to factors such as lower preferred food availability and temperatures. Marine turtle bycatch in fisheries is today one of the major obstacles for the recovery of populations reduced by overexploitation and habitat degradation, and in southern Brazil, it has been evidenced that sea turtle mortality due to fishery interaction, as well as ingestion of human debris, is an issue of concern. Such impacts on the developmental stages of green turtles prevent the fulfillment of their ecological role of reaching maturity and reproducing, and the conservation of juveniles along the coast leads to the protection of rookeries which are frequently thousands of kilometers away. The highly migratory behavior of green turtles, which may occupy the waters of many countries as demonstrated by demographic and genetic studies, makes international cooperation essential for the conservation of these animals. The identification of stock contributions to mixed aggregations has important conservation implications, and if is to be seriously considered as a tool for the adequate elaboration of conservation and management plans, it is necessary that nesting populations be adequately described in terms of mtdna, in order to provide complete and accurate baseline genetic data for estimates of natal origins. The use of other genetic markers is also advisable for better description of populations and possible inclusion in MSA. CITED LITERATURE Allard MW, Miyamoto MM, Bjorndal KA, Bolten AB, Bowen BW (1994). Support for natal homing in green turtles from mitochondrial DNA sequences. Copeia, 1994(1), 34-41. Avise JC (2007). Conservation genetics of marine turtles ten years later. In: Frontiers in Wildlife Science: Linking Ecological Theory and Management Applications (eds. Hewitt D, Fulbright T), pp. 295-314. CRC Press, Boca Raton, Florida. Bass AL, Lagueux CJ, Bowen BW (1998). Origin of green turtles, Chelonia mydas, at Sleeping Rocks off the northeast coast of Nicaragua. Copeia, 1998(4), 1064-1069. 16

Bass AL, Witzell WN (2000). Demographic composition of immature green turtles (Chelonia mydas) from the east central Florida coast: evidence from mtdna markers. Herpetologica, 56, 357-367. Bass AL, Epperly SP, Braun-Mcneill J (2006). Green Turtle (Chelonia mydas) foraging and nesting aggregations in the Caribbean and Atlantic: impact of currents and behavior on dispersal. Journal of Heredity, 97(4), 346-354. Bjorndal KA (1997). Foraging ecology and nutrition of sea turtles. In: The Biology of Sea Turtles (eds. Lutz PL, Musick JA), pp. 199-231. CRC Press, Boca Raton, Florida. Bjorndal KA, Bolten AB, Troeng S (2005). Population structure and genetic diversity in green turtles nesting at Tortuguero, Costa Rica, based on mitochondrial DNA control region sequences. Marine Biology, 147, 1449-1457. Bjorndal KA, Bolten AB, Moreira L, Bellini C, Marcovaldi MA (2006). Population structure and diversity of Brazilian green turtle rookeries based on mitochondrial DNA sequences. Chelonian Conservation and Biology, 5, 262-268. Bjorndal KA & Bolten AB (2008). Annual variation in source contributions to a mixed stock: implications for quantifying connectivity. Molecular Ecology, 17, 2185-2193. Bolker BM, Okuyama T, Bjorndal KA, Bolten AB (2007). Incorporating multiple mixed stocks in mixed stock analysis: many-to-many analyses. Molecular Ecology, 16, 685-695. Bolten A (2003). Variation in sea turtle life history patterns: neritic vs. oceanic developmental stages. In: The Biology of Sea Turtles Volume II (eds. Lutz PL, Musick JA, Wyneken J), pp. 243-257. CRC Press, Boca Raton, Florida. Bowen BW, Meylan AB, Ross JP, Limpus CJ, Balazs GH, Avise JC (1992). Global population structure and natural history of the green turtle (Chelonia mydas) in terms of matriarchal phylogeny. Evolution, 46(4), 865-881. Bowen BW, Karl SA (1997). Population genetics, phylogeography, and molecular evolution. In: The Biology of Sea Turtles (eds. Lutz PL, Musick JA), pp. 29-50. CRC Press, Boca Raton, Florida. Bowen BW, Karl SA (2007). Population genetics and phylogeography of sea turtles. Molecular Ecology, 16, 4886-4907. 17

Bugoni L, Krause L, Petry MV (2001). Marine debris and human impacts on sea turtles in Southern Brazil. Marine Pollution Bulletin, 42(12), 1330-1334. Carr AF (1967). So excellent a Fishe: a Natural History of Sea Turtles. Scribner, New York. Craig P, Parker D, Brainard R, Rice M, Balazs G. (2004). Migrations of green turtles in the central South Pacific. Biological Conservation, 116, 433-438. Dutton PH, Balazs GH, LeRoux RA, Murakawa SKK, Zarate P, Martínez LA (2008). Composition of Hawaiian green turtle foraging aggregations: mtdna evidence for a distinct regional population. Endangered Species Research, 5, 37-44. Encalada SE, Lahanas PN, Bjorndal KA, Bolten AB, Miyamoto MM, Bowen BW (1996). Phylogeography and population structure of the Atlantic and Mediterranean green turtle Chelonia mydas: a mitochondrial DNA control region sequence assessment. Molecular Ecology, 5, 473-483. Formia A, Godley BJ, Dontaine J-F, Bruford MW (2006). Mitochondrial DNA diversity and phylogeography of endangered green turtle (Chelonia mydas) populations in Africa. Conservation Genetics, 7, 353 369. Formia A, Broderick AC, Glen F, Godley BJ, Hays GC, Bruford MW (2007). Genetic composition of the Ascension Island green turtle rookery based on mitochondrial DNA: implications for sampling and diversity. Endangered Species Research, 3, 145-158. Godley BJ, Lima EHSM, Åkesson S et al. (2003). Movement patterns of green turtles in Brazilian coastal waters described by satellite tracking and flipper tracking. Marine Ecology Progress Series, 253, 271-288. Hillis DM, Mable BK, Larson A, Davis SK, Zimmer EA (1996). Nucleic acids IV: sequencing and cloning. In: Molecular Systematics, 2 nd edition (eds. Hillis DM, Moritz C, Mable BK), pp. 321-381. Sunderland, MA: Sinauer Associates. IUCN (2009). The World Conservation Union/International Union for Conservation of Nature and Natural Resources Red List. http://www.redlist.org, acessado 18 de junho de 2009. Kaska Y (2000). Genetic structure of Mediterranean sea turtle populations. Turk Journal of Zoology, 24, 191-197. 18

Lahanas PN, Miyamoto MM, Bjorndal KA, Bolten AB (1994). Molecular evolution and population genetics of Greater Caribbean green turtles (Chelonia mydas) as inferred from mitochondrial DNA control region sequences. Genetica, 94, 57-67. Lahanas PN, Bjorndal KA, Bolten AB et al. (1998). Genetic composition of a green turtle (Chelonia mydas) feeding ground population: evidence for multiple origins. Marine Biology, 130, 345-352. Luke K, Horrocks JA, LeRoux RA, Dutton PH (2004). Origins of green turtle (Chelonia mydas) feeding aggregations around Barbados, West Indies. Marine Biology, 144, 799-805. Luschi P, Hays GC, Papi F (2003a). A review of long-distance movements by marine turtles, and the possible role of ocean currents. Oikos, 103, 293-302. Luschi P, Sale A, Mencacci R, Hughes GR, Lutjeharms JRE, Papi F (2003b). Current transport of leatherback sea turtles (Dermochelys coriacea) in the ocean. Proceedings of the Royal Society of London B, 270, S129-S132. Miller JD (1997). Reproduction in sea turtles. In: The Biology of Sea Turtles (eds. Lutz PL, Musick JA), pp. 51-81. CRC Press, Boca Raton, Florida. Musick JA & Limpus CJ (1997). Habitat utilization and migration in juvenile sea turtles. In: The Biology of Sea Turtles (eds. Lutz PL, Musick JA), CRC Press, Boca Raton, Florida, 137-163. Naro-Maciel E, Becker JH, Lima EHSM, Marcovaldi MA, Desalle R (2007). Testing dispersal hypotheses in foraging green sea turtles (Chelonia mydas) of Brazil. Journal of Heredity, 98, 29-39. Pella J, Masuda M (2001). Bayesian methods for analysis of stock mixtures from genetic characters. Fishery Bulletin, 9, 151-167. Pritchard PCH & Mortimer JA (1995). Taxonomía, morforlogía externa e identificación de las especies. In: Estratégia Mundial para la Conservación de las Tortugas Marinas (eds. Eckert KL, Bjorndal KA, Abreu Grobois FA, Donnelly M), pp 23-41. Publicación nº 4 de lo Grupo Especialista em Tortugas Marinas, UICN/CSE. Reisser JR, Proietti MC, Kinas PG, Sazima I (2008). Photographic identification of sea turtles: method description and validation, with an estimation of tag loss. Endangered Species Research, 5, 73-82. Stramma L, England M (1999). On the water masses and mean circulation of the South Atlantic Ocean. Journal of Geophysical Research, 104(C9), 20,863-20,883. 19

PROJECT DETAILS Summary This project aimed at understanding the genetic structure and natal origins of green and hawksbill sea turtles in southern Brazil foraging areas, in terms of mitochondrial DNA. We were able to describe mtdna of green turtles at Arvoredo Island and Cassino Beach, which lacked genetic studies. This was a major achievement of the project. Another important outcome was the observation that study areas are genetically indistinct in terms of mtdna, composing one genetic unit, but which should not be treated as one management unit. Finally, the determination of the main stocks that contribute to the Arvoredo and Cassino foraging areas was a very important result. Genetically, it was determined that Ascension, Aves and Trindade Islands are the main contributors to the study areas, and some animals arrive from the African continent. Through the analysis of surface drifter data, it was clear that surface currents favor arrival of drifters passing by Ascension and Trindade Islands. We developed a novel prior for Bayesian Mixed Stock Analysis based on such surface drifter data, which we consider to be more ecologically realistic. Unforeseen difficulties Some difficulties arose during the project. The predicted field expedition for sampling was successfully performed, and we were able to conduct an additional expedition for the collection of more samples due to additional financial aid received from the Rufford Small Grants Foundation. Therefore, sample size was larger than anticipated, resulting in a total of 216 green turtle samples. This increase in sample number was very welcome but contributed to the delay in project completion. As cited beforehand, hawksbill turtles were scarce and only seven tissue samples were collected. These samples are currently stored and will be analyzed when a more robust sample size is obtained. A major setback involved laboratorial problems, and many PCRs had to be redone for sequencing. I had to travel to a different University in order to sequence DNA samples, which also caused delay. Sequencing is a very sensitive procedure and some samples were not clear enough for analysis, and since it was impossible to travel once more for re-sequencing, samples had to be sent to a private laboratory a few times in order to sequence all samples. This also proved to be a setback in project completion. 20

However, with hard work from the project team, it was possible to successfully conclude the project in terms of green turtles. Budget and expenditure Item Amount requested Amount spent Comments Supplies for expeditions 384.00 Disposable skin biopsy punches 274.00 mtdna control primers 179.40 Taq polymerase 332.60 Purification kit 337.23 746.88 (R$2517.00) 284.87 (R$960.00) 179.40 (R$605.00) 660.26 (R$2225.08) 773.29 (R$2606.00) Amount was higher than budgeted due to the additional expedition Amount was slightly higher due to shipping. Punches were sterilized and reused, therefore we did not need to buy more units than foreseen Primers were sufficient for all samples Four units were necessary due to additional samples Two units were necessary due to additional samples Sequencing kit 1121.37 Separate sequencing reactions 0 Extraction kits 0 1287.00 (R$4340.00) 884.21 (R$2845.00) 566.00 (R$1907.42) Total 2628.63 5381.91 Amount was higher due to adjustment of kit value Separate sequencing reactions were necessary for obtaining clear sequences for all samples Since funding was provided also by the RSGF, we were able to acquire DNA extraction kits for simplifying DNA extraction Additional funding was provided by the RSGF Next steps The first next step is to publish the full results in a scientific journal. The article on green turtle mtdna in southern Brazil is almost finished and will be submitted to Molecular Ecology by the end of the year. Obtainment of more hawksbill turtle samples for analysis is also an important step for concluding this aspect of the project. Other important steps are to continue genetic description of sea turtles in Brazilian waters for identification of genetically-distinct units and natal origins of these endangered animals. Also, the improvement of natal origin analysis through the elaboration of more informative priors is important for a better definition of mixed stock origins. Finally, continuing information exchange with other researchers and conservation projects is essential for taking effective measures for the conservation of these endangered animals. I am currently enrolled in the Biological Oceanography Doctorate program, and intend to continue research on sea turtle genetics. My thesis project involves green and hawksbill turtles, continuing studies in southern Brazil 21

(especially in terms of hawksbills), but also expanding the study areas to include northeast Brazil and the analysis of different molecular markers. I also plan on continuing the development of more informative priors for natal origins analysis. Sharing results I have published one short communication in the journal Genetics and Molecular Biology - GMB, and presented this work in three conferences. I am currently finishing another scientific paper for submission to journal Molecular Ecology (see attachment for the published paper and posters). Information on the project and link to the GMB paper is at the Pata da Cobra Diving website, www.patadacobra.com.br/biologia-marinha/projetos/proietti2009_gmb.pdf. The PTES was acknowledged in all materials produced during the project (three posters and two scientific papers see attachments), and the logo was printed on all posters. Although direct involvement of local communities was not an objective of the project, some local people were indirectly involved. At Arvoredo Island, we were in direct contact with the Navy officials responsible for operating the lighthouse, and they accompanied the work and were able to participate in some activities, learning more about sea turtles and the marine environment, and why conservation is so important. Also, activities were developed on board the boat of the diving school (Pata da Cobra Diving) that provided logistic support at the island, and dive staff and divers also accompanied the work and learned more about sea turtles and conservation. 22