Selecting statistical models and variable combinations for optimal classification using otolith microchemistry.

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.

Ancient and recent evolutionary history of the bruchid beetle, Acanthoscelides obtectus Say, a cosmopolitan pest of beans.

Acanthoscelides obtectus Say is a bruchid species of Neotropical origin, and is specialized on beans of the Phaseolus vulgaris L. group. Since the domestication and diffusion of beans, A. obtectus has become cosmopolitan through human-mediated migrations and is now a major pest in bean granaries. Using phylogeographic methods applied to mitochondrial DNA (mtDNA) and nuclear microsatellite molecular markers, we show that the origin of this species is probably further south than Mesoamerica, as commonly thought. Our results also indicate that A. obtectus and its Mesoamerican sister species Acanthoscelides obvelatus, two morphologically close species differing principally in voltinism, speciated in allopatry: A. obtectus (multivoltine) arising in Andean America and A. obvelatus (univoltine) in Mesoamerica. In contrast to Mesoamerica where beans fruit once yearly, wild beans in Andean America fruit year-round, especially in regions showing little or no seasonality. In such habitats where resources are continuously present, multivoltinism is adaptive. According to existing hypotheses, multivoltinism in A. obtectus is a new adaptation that evolved after bean domestication. Our data suggest the alternative hypothesis that multivoltinism is an older trait, adapted to exploit the year-round fruiting of wild beans in relatively aseasonal habitats, and allowed A. obtectus to become a pest in bean granaries. This trait also permitted this species to disperse through human-mediated migrations associated with diffusion of domesticated beans. We also show that diversity of Old World A. obtectus populations can be quite well explained by a single colonization event about 500 bp. Human-mediated migrations appear not to be rare, as our results indicate a second more recent migration event from Andean America to Mexico.