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International audience; Otolith microchemistry can provide crucial information to address gaps of knowledge in spatio-temporal ecology of fish species. However, understanding the seasonal variability of water chemistry and its effect on otolith signatures is needed to interpret fish movements. Otolith multi-elemental signatures were used to examine the diadromous migration and small-scale movements of a tropical goby (Sicyopterus lagocephalus) within a watershed in La Réunion Island, Indian Ocean. The elemental ratios Sr:Ca, Ba:Ca and Sr:Ba recorded monthly in the water of nine sampling sites varied between localities and were correlated with otolith signatures, whereas the Mg:Ca signature was not. The incorporation of some trace elements in otoliths was influenced by the size of fish and site where they were caught, reflecting an effect of endogenous and exogenous factors. Despite these sources of variation, the elemental signatures did not overlap for three river sections distributed along the 26-km-long watershed. The diadromous migration and fish lifetime movements between these river sections were reconstructed using an original process based on random forest analysis. This approach revealed distinct migratory behaviours after fish entered in the river, with some individuals progressing slowly in the watershed whereas other rapidly reached the upper localities. As adults, S. lagocephalus showed a restricted exploratory behaviour and a strong site fidelity to a particular part of the watershed. The detection of adult movement was typically only between adjacent locations and is possibly explained by shifts in local intraspecific hierarchies and/or displacement during extreme discharge caused by cyclones.

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.