Individual origin assignment Sample Clauses

Individual origin assignment. Introduction During Phase 6, individual classification techniques were applied to δ13C and δ18O values to predict the origin of bluefin tuna at individual scale. Samples analyzed in Task 1 were also assigned to origin individually, with associated levels of probability. The identification of individual origin is needed for at least two main reasons: the construction of stock-age-length-keys, and the comparison/improvement of individual assignments based on different types of markers (i.e. genetic, otolith shape and stable isotopes). Moreover, it allows to table the results according to any stratification that might be used during the stock assessment or MSE process.
Sample 1
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Individual origin assignment. Introduction During GBYP Phase 2 to Phase 4 otoliths from different regions of the Atlantic Ocean (Central North Atlantic, Bay of Biscay, Strait of Gibraltar and northwestern African coast) and Mediterranean Sea (Levantine Sea, Ionian Sea, Adriatic Sea, Tyrrhenian Sea, Ligurian Sea and Balearic Sea) have been analyzed for stable carbon and oxygen (δ13C and δ18O) composition. The results from these analyses have been used to estimate eastern and western population mixing proportions in each of the areas. However, this does not allow knowing the origin of individual fish, and this information is needed for at least two main reasons: the construction of stock-age-length-keys, and the comparison/improvement of individual assignments based on different types of markers (i.e. genetic, otolith shape and stable isotopes). Moreover, it allows to table the results according to any stratification that might be used during the stock assessment or MSE process. During Phase 5, individual classification techniques were applied to δ13C and δ18O values to predict the origin of bluefin tuna at individual scale. Material and Methods In total, 1371 individual bluefin were assigned to their natal origin (Gulf of Mexico or Mediterranean Sea), including 1084 from Phase 2 to Phase 4 and 287 presented in Task- 1 of the current project. Detailed information on sample source is presented in Table 4.2. δ13C and δ18O values of bluefin tuna otoliths were statistically analyzed and individuals were assigned to source populations with associated levels of probability. Classification performance of four classification methods was compared including classical standard procedures like Quadratic Discriminant Analysis (QDFA), a Bayesian estimator such as Naive Bayes (NB), and two different machine learning algorithms: Random Forest (RF) and Support Vector Machine (SVM). Each of the classification method was previously tested with the baseline dataset and attained similar classification accuracies. RF achieved the best performance, with an overall classification accuracy of 82%, followed by NB estimator (81% of accuracy) and QDFA and RF, both with 80% of correct classification. In order to increase robustness of our results, we only considered individuals with a probability >70% or <30% for the subsequent data analysis.
Sample 1

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