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Mixed stock analysis in R: getting started with the mixstock package
... A mixed stock analysis was conducted to estimate the geographic origins of the specimens, using haplotype frequencies from Atlantic and Mediterranean rookeries as baseline data [49,86], following the approach applied by Tolve et al. [38] and Loisier et al. [23]. This analysis was performed with the MIXSTOCK package v0.9.9 [87] in RStudio v2024.04.2. ...
Background: The conservation of loggerhead sea turtles (Caretta caretta) in the central Mediterranean benefits from an in-depth understanding of its population genetic structure and diversity. Methods: This study, therefore, investigates C. caretta in Maltese waters by genetically analysing 63 specimens collected through strandings and in-water sampling, using mitochondrial DNA control region and microsatellites. Additionally, the two nests detected in Malta in 2023 were analysed for the same markers. Results: Mitochondrial data identified 10 haplotypes, with mixed stock analyses tracing 87.5% of the specimens to Mediterranean origins, primarily from Libyan rookeries, with contributions from Lebanon, Israel and Turkey. Three Atlantic haplotypes were identified in six specimens, with CC-A17.1 linking central Mediterranean foraging individuals to rookeries in Cape Verde. Five of these six Atlantic haplotype records were from recently sampled individuals (2022-2023), possibly indicating a recent eastward expansion of Atlantic haplotypes into the Mediterranean. Bayesian clustering (K = 2) of microsatellite data using haplotypes as priori revealed similar proportions for clusters across most specimens, except for three specimens with Atlantic haplotypes CC-A1.1 and CC-A1.3, which exhibited distinct patterns. The two nests examined here displayed Mediterranean haplotypes, with nuclear DNA matching the predominant Mediterranean profiles found in foraging individuals, suggesting that local clutches originated from Mediterranean parents. Conclusions: Increasing nesting activity on Maltese beaches and this archipelago's geographical position highlight the need for ongoing genetic monitoring to track changes in genetic diversity and develop conservation strategies that support the effective protection of this species and its habitats.
https://doi.org/10.3390/genes15121565
... Eight of the haplotypes from Clarke et al. (2015) were shared between the Indo-Pacific clade, the Atlantic clade, and the market-derived samples (Figure 4(a)). Therefore, we used the haplotype frequencies from Clarke et al. (2015) and the frequencies of those haplotypes in the shark fin markets (Figure 4(a)) to conduct a mixed-stock analysis (MSA) with the R-package mixstock (Bolker, 2012). Mixstock was used to estimate the contribution of each source population (i.e., Indo-Pacific and Atlantic) to both shark fin markets using a Markov Chain Monte Carlo (MCMC) estimation with 100,000 iterations following a burn-in of 50,000. ...
The silky shark is the second most common shark in Southeast Asia’s dried fin markets and is managed in the Atlantic Ocean by the International Commission for the Conservation of Atlantic Tuna (ICCAT) and by three Indo-Pacific regional fisheries management organizations (RMFOs). ICCAT reports ~ 7% of global silky landings but there is a moratorium on the export of their fins. During a four-year period (2014- 2017) we used genetic differentiation observed between Atlantic and Indo-Pacific silky sharks to assess the contribution of Atlantic individuals to fins randomly obtained in the two largest shark fin markets in the world, Hong Kong and Guangzhou, China (N=604). We did not detect any Atlantic fins in either market despite robust sampling effort with an estimated Indo-Pacific contribution of 99.8% to these markets. These findings indicate that supply chains for silky shark fins in Hong Kong and Guangzhou primarily originate in the Indo-Pacific and are mainly under the purview of three RFMOs. Our results are consistent with the possibility that ICCAT parties have achieved high compliance with the ban on silky sharks. We suggest research and monitoring improvements that could enhance our understanding of the global trade of silky sharks and enable better fisheries management.
... An MSA was performed using the Bayesian approach implemented in the MIXSTOCK package in R (Bolker and Okuyama 2014). The package compares mtDNA sequences from one or more mixed populations with baseline haplotypes from source populations (rookeries). ...
Migration is one of several marine vertebrate traits increasingly affected by human encroachment. The Adriatic Sea is an important foraging and wintering site for loggerhead turtles (Caretta caretta), and one of the Mediterranean regions where they are most heavily impacted, particularly by fisheries bycatch. Conservation measures concern foraging and wintering areas and nesting grounds, and must, therefore, be informed by the natal origin of individuals in these mixed aggregates. Genetic diversity was investigated among 488 loggerheads found stranded or incidentally captured in fishing gear across the Adriatic and the origin of individuals was assessed through mixed-stock analysis based on comparison of 755 bp of mitochondrial DNA control region sequence. In addition, we highlight the importance of the Gulf of Manfredonia (Apulia, Italy), and of the northeastern Adriatic—a previously genetically undescribed foraging ground. No significant genetic divergence was recorded among sampling areas, between turtles sampled in cold and warm months or between adults and juveniles. The distribution of turtles across the Adriatic Sea appeared not to depend on individual origin. Rookeries in western Greece and Crete provided the most important contributions to the Adriatic mixed stock. In particular, the Greek populations were the most abundant locally in the Gulf of Manfredonia, so they are likely to experience an even greater impact than previously thought because of the severe fishery bycatch levels in this area. This study also provides an example of how substantial increases in sample sizes permit a relatively comprehensive testing of genetic structure across groupings in foraging aggregations.
... Mixed stock analysis (MSA) has become a key tool for marine turtle research aiming to connect turtles sampled away from nesting beaches (e.g., foraging areas, migratory corridors, fisheries bycatch, or strandings) to their natal rookeries. Software packages like BAYES (Pella and Masuda, 2001) and the " mixed stock " R package (Bolker et al., 2007;Bolker, 2008) uses the frequencies of genetic markers for estimating the most likely proportions of " source populations " (rookeries) sampled in a " mixed population. " Robust MSA depends on three key factors: (1) a comprehensive sampling of potential source populations (rookeries), (2) suitable sample size of the mixed population and (3) strong genetic structure to differentiate between source populations. ...
Marine turtles migrate across long distances, exhibit complex life histories, and occupy habitats that are difficult to observe. These factors present substantial challenges to understanding fundamental aspects of their biology or assessing human impacts, many of which are important for the effective conservation of these threatened and endangered species. The early development and application of genetic tools made important contributions to understanding marine turtle population and evolutionary biology, such as providing evidence of regional natal homing by breeding adults, establishing connectivity between rookeries and foraging habitats, and determining phylogeography and broad scale stock structure for most marine turtle species. Recent innovations in molecular technologies, statistical methods, and creative application of genetic tools have significantly built upon this knowledge to address key questions in marine turtle biology and conservation management. Here, we evaluate the latest major advances and potential of marine turtle genetic applications, including improved resolution and large-scale syntheses of population structure, connectivity and phylogeography, estimation of key demographic rates such as age to maturity and operational or breeding sex ratios, insight into reproductive strategies and behavior, and assessment of differential human impacts among populations. We then discuss remaining challenges and emerging capabilities, such as rapid, multiplexed genotyping, and investigation of the genomic underpinnings of adaptive variation afforded by high-throughput sequencing technologies.
... Mixed-stock analysis was carried out with the use of the Markov-chain Monte Carlo (MCMC) sampling method implemented in the program MIXSTOCK (Bolker, 2009). MIXSTOCK implements the Bayesian method of Pella and Masuda (2001) but accounts for sampling error of rare genotypes (Bolker et al., 2003). ...
Chelonia mydas(Green Turtle) foraging areas, where juveniles, subadults, and adults of diverse natal origins coalesce and spend a large portion of their lives, can be located thousands of kilometers from nesting beaches. Unfortunately, the natal origin of turtles in many foraging areas remains unknown. Resolution of this issue was recently listed among the conservation priorities for the species by a global panel of Green Turtle researchers. We examined the genetic diversity and natal origins of Green Turtles from a well-known foraging area in the western Gulf of Mexico. Bayesian mixed-stock analysis of mitochondrial DNA haplotypes was used to demonstrate that an overwhelming percentage (~95%) of individuals in the western Gulf of Mexico foraging group likely originate in other Gulf of Mexico and northern Caribbean rookeries, with smaller contributions from the western and southern Caribbean, and potentially the Mediterranean Sea. Management of Green Turtles in the western Gulf of Mexico will be improved by linking conservation efforts aimed at this foraging group to turtle aggregates occurring in other critical habitats within the recently defined northwest Atlantic Green Turtle regional management unit.
Gorgona Island represents a characteristic foraging ground for the green and black turtles (Chelonia mydas/agassizii) that connects rookeries in the Eastern Tropical Pacific Ocean (ETPO). We used mitochondrial DNA Control Region sequences of 176 individuals from a seven years sampling (2005 - 2011) at Gorgona foraging ground (FG). Control Region sequences (940bp) revealed the presence of twenty-three haplotypes, with high haplotype (h) and low nucleotide (π) diversities of h = 0.7869 ± 0.0185 and π = 0.006576 ± 0.003472 respectively. At spatial level, we identified two Stock Management Units (SMU) in the Eastern Pacific. We determined that orphan Gorgona FG haplotypes came from three different phylogeographic clades. At temporal level, we found substantial variations in genetic diversity. Changes in the haplotypes frequency and unique annual and, seasonal haplotypes. There are temporal variations in the contribution by differential recruitment from different pacific rookeries, where Gorgona FG comprised mostly of animals from the Galapagos rookery (59-93%). Seasonal variations of connectivity in the suitable habitat were found for the species. The suitable habitat models allowed us to define that Sea Surface Temperature (SST), nitrates, dissolved oxygen and salinity have a crucial role in the Pacific distribution of the green and black turtles. We highlighted Gorgona FG is part of the Eastern Pacific SMU with connectivity based on genetic and suitable habitat analyses. Temporal genetic variations could be useful for the conservation and management strategies of the green and black turtles recruited from distant rookeries. It is necessary to implement mainly more sampling efforts at spatial and temporal levels, satellite telemetric analyses, nuclear genetic markers as complement to this study to reinforce trans-national conservation programs and to protect the ETPO corridor in international waters throughout the eastern Pacific managed by non-governmental organizations.
The worldwide population of the leatherback turtle ( Dermochelys coriacea ) encompasses seven subpopulations among the Pacific, Atlantic and Indian Oceans. It has experienced declines across parts of its distribution, with the subpopulation of the South-west Atlantic listed as critically endangered by the IUCN Red List. The main threats to this subpopulation include its interaction with fisheries, coastal development, pollution and climate change. In this study, we sequenced mitochondrial DNA from 52 leatherback turtles in Brazil and combined these with published data from other Atlantic Ocean rookeries. The haplotype diversities of the Atlantic population rookeries ranged from 0.112 to 0.533 and are not directly proportional to current rookery sizes. The Brazilian rookery, despite recording low nest numbers per year, had the second-highest haplotype diversity among all Atlantic rookeries ( h = 0.532). A mixed-stock analysis revealed that the South American pelagic aggregate is primarily composed of individuals from West Africa (84%), with contributions from the North Atlantic rookeries (14%). Leatherback turtles appear to have a complex phylogeographic pattern, showing evidence of multiple colonization events and a lack of isolation by distance. Our novel dataset, based on DNA sequences of 695 base pairs, will provide baseline data needed to understand population dynamics in the region, building comprehensive population assessments to support and develop management strategies. Having both the only known regular rookery in the South-west Atlantic, and a mixed-origin foraging area for the species along its coast, Brazil has a key role in the conservation of the leatherback turtle.
Molecular markers based on mitochondrial (mt) DNA control region sequences were used to test the hypothesis that juvenile loggerhead sea turtles (Caretta caretta) in pelagic habitats of the eastern Atlantic are derived from nesting populations in the western Atlantic. We compared mtDNA haplotypes from 131 pelagic juvenile turtles (79 from the Azores and 52 from Madeira) to mtDNA haplotypes observed in major nesting colonies of the Atlantic Ocean and Mediterranean Sea. A subset of 121 pelagic samples (92%) contained haplotypes that match mtDNA sequences observed in nesting colonies. Maximum likelihood analyses (UCON, SHADRACQ) estimate that 100% of these pelagic juveniles are from the nesting populations in the southeastern United States and adjacent Yucatan Peninsula, Mexico. Estimated contributions from nesting populations in south Florida (0.71, 0.72), northern Florida to North Carolina (0.19, 0.17), and Quintana Roo, Mexico (0.11, 0.10) are consistent with the relative size of these nesting aggregates. No contribution was detected from nesting colonies in the Mediterranean (Greece) or South Atlantic (Brazil), although samples sizes are insufficient to exclude these locations with finality. The link between west Atlantic nesting colonies and east Atlantic feeding grounds provides a more complete scientific basis for assessing the impact of subadult mortality in oceanic fisheries. Demographic models for loggerhead turtles in the western Atlantic can now be improved by incorporating growth and mortality data from juvenile turtles in pelagic habitats. These data demonstrate that the appropriate scale for loggerhead turtle conservation efforts is vastly larger than the current scale of management plans based on political boundaries.
The contributions of different sea turtle rookeries to mixed-stock populations on foraging grounds can only be estimated by indirect methods such as analysis of mito-chondrial DNA samples from the mixed stocks and rookery populations. We explain and evaluate methods for genetic stock estimation using simulations and data from previous studies. We focus on Markov Chain Monte Carlo (MCMC) estimation, a relatively new method. MCMC differs from older combinations of maximum likelihood (ML) with non-parametric bootstrapping in (1) using a Bayesian prior to quantify previous knowledge; (2) taking account of multiple modes in the probability distribution of contributions; and (3) incorporating sampling error more flexibly, allowing for the possibility that rare haplotypes actually present in a particular rookery were not detected in a small sample. In the context of sea turtle stock analysis, the differences in point estimates between ML and MCMC methods are relatively small, but MCMC gives wider and more accurate confidence limits than ML with bootstrapping, which tends to underestimate small contributions as zero.