Éditeur(s) : HAL CCSD Inter-Research
Résumé : Precise knowledge of lifetime migrations is vital in exploited fish species, since all essential habitats must be protected to maintain sustainable stock levels. The present study used multi-element otolith fingerprints of the gilthead sea bream Sparus aurata (L.) to discriminate its main juvenile and adult habitats in the Languedoc-Roussillon region (Gulf of Lions, northwest Mediterranean) and infer the lifetime migrations of 12 individuals from the area (11 from the present day and 1 from the Roman era). This allowed for the first time the identification of key habitats for the successful completion of the species' life cycle in the Gulf of Lions, and the connectivity between them. Our results revealed that lagoon use by S. aurata is probably ancient (>2500 yr) and confirmed its current commonness. Yet, although most observed migration patterns were in accordance with the migratory behavior previously described for the species, strong inter-individual variations and new patterns in habitat use were detected. At the juvenile stage, a preference for shallow lagoons with low salinities was evidenced. Nevertheless, the first year of life can also be successfully completed in marine conditions. At the adult stage, lagoon use was shown to occur until at least age 4 yr, with periods of lagoon residency of up to 11 mo in a year, often including winter months. Because overwintering in the lagoons was previously thought to be impossible for S. aurata due to low temperatures, this finding has important implications for future stock management, especially since the species breeds in winter.
Marine Ecology Progress Series

hal-00682003
https://hal.archives-ouvertes.fr/hal-00682003
DOI : 10.3354/meps09444

Éditeur(s) : HAL CCSD Ecological Society of America
Résumé : Reliable assessment of fish origin is of critical importance for exploited species, since nursery areas must be identified and protected to maintain recruitment to the adult stock. During the last two decades, otolith chemical signatures (or "fingerprints") have been increasingly used as tools to discriminate between coastal habitats. However, correct assessment of fish origin from otolith fingerprints depends on various environmental and methodological parameters, including the choice of the statistical method used to assign fish to unknown origin. Among the available methods of classification, Linear Discriminant Analysis (LDA) is the most frequently used, although it assumes data are multivariate normal with homogeneous within-group dispersions, conditions that are not always met by otolith chemical data, even after transformation. Other less constrained classification methods are available, but there is a current lack of comparative analysis in applications to otolith microchemistry. Here, we assessed stock identification accuracy for four classification methods (LDA, Quadratic Discriminant Analysis [QDA], Random Forests [RF], and Artificial Neural Networks [ANN]), through the use of three distinct data sets. In each case, all possible combinations of chemical elements were examined to identify the elements to be used for optimal accuracy in fish assignment to their actual origin. Our study shows that accuracy differs according to the model and the number of elements considered. Best combinations did not include all the elements measured, and it was not possible to define an ad hoc multielement combination for accurate site discrimination. Among all the models tested, RF and ANN performed best, especially for complex data sets (e. g., with numerous fish species and/or chemical elements involved). However, for these data, RF was less time-consuming and more interpretable than ANN, and far more efficient and less demanding in terms of assumptions than LDA or QDA. Therefore, when LDA and QDA assumptions cannot be reached, the use of machine learning methods, such as RF, should be preferred for stock assessment and nursery identification based on otolith microchemistry, especially when data set include multispecific otolith signatures and/or many chemical elements.
ISSN: 1051-0761

hal-00644549
https://hal.archives-ouvertes.fr/hal-00644549
DOI : 10.1890/09-1887.1

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : The analysis of fine-scale spatial genetic structure (FSGS) within populations can provide insights into eco-evolutionary processes. Restricted dispersal and locally occurring genetic drift are the primary causes for FSGS at equilibrium, as described in the isolation by distance (IBD) model. Beyond IBD expectations, spatial, environmental or historical factors can affect FSGS. We examined FSGS in seven African and Neotropical populations of the late-successional rain forest tree Symphonia globulifera L. f. (Clusiaceae) to discriminate the influence of drift-dispersal vs. landscape/ecological features and historical processes on FSGS. We used spatial principal component analysis and Bayesian clustering to assess spatial genetic heterogeneity at SSRs and examined its association with plastid DNA and habitat features. African populations (from Cameroon and São Tomé) displayed a stronger FSGS than Neotropical populations at both marker types (mean Sp = 0.025 vs. Sp = 0.008 at SSRs) and had a stronger spatial genetic heterogeneity. All three African populations occurred in pronounced altitudinal gradients, possibly restricting animal-mediated seed dispersal. Cyto-nuclear disequilibria in Cameroonian populations also suggested a legacy of biogeographic history to explain these genetic patterns. Conversely, Neotropical populations exhibited a weaker FSGS, which may reflect more efficient wide-ranging seed dispersal by Neotropical bats and other dispersers. The population from French Guiana displayed an association of plastid haplotypes with two morphotypes characterized by differential habitat preferences. Our results highlight the importance of the microenvironment for eco-evolutionary processes within persistent tropical tree populations.
ISSN: 1932-6203

hal-01608324
https://hal.archives-ouvertes.fr/hal-01608324
DOI : 10.1371/journal.pone.0182515
PRODINRA : 403154
PUBMED : 28771629