Contrasting biogeographical patterns in Margarella (Gastropoda: Calliostomatidae: Margarellinae) across the Antarctic Polar Front

Members of the trochoidean genus Margarella (Calliostomatidae) are broadly distributed across Antarctic and sub-Antarctic ecosystems. Here we used novel mitochondrial and nuclear gene sequences to clarify species boundaries and phylogenetic relationships among seven nominal species distributed on either side of the Antarctic Polar Front (APF). Molecular reconstructions and species-delimitation analyses recognized only four species: M. antarctica (the Antarctic Peninsula), M. achilles (endemic to South Georgia), M. steineni (South Georgia and Crozet Island) and the morphologically variable M. violacea (=M. expansa, M. porcellana and M. pruinosa), with populations in southern South America, the Falkland/Malvinas, Crozet and Kerguelen Islands. Margarella violacea and M. achilles are sister species, closely related to M. steineni, with M. antarctica sister to all these. This taxonomy reflects contrasting biogeographic patterns on either side of the APF in the Southern Ocean. Populations of Margarella north of the APF (M. violacea) showed significant genetic variation but with many shared haplotypes between geographically distant populations. By contrast, populations south of the APF (M. antarctica, M. steineni and M. achilles) exhibited fewer haplotypes and comprised three distinct species, each occurring across a separate geographical range. We hypothesize that the biogeographical differences may be the consequence of the presence north of the APF of buoyant kelps – potential long-distance dispersal vectors for these vetigastropods with benthic-protected development – and their near-absence to the south. Finally, we suggest that the low levels of genetic diversity within higher-latitude Margarella reflect the impact of Quaternary glacial cycles that exterminated local populations during their maxima

Interpretable and Reliable Rule Classification Based on Conformal Prediction

This paper deals with the challenging problem of simultaneously integrating interpretablility and reliability into prediction models in machine learning. It proposes to combine the interpretable models of decision rules with the reliable models based on conformal prediction. The result is a new technique of conformal decision rules. Given a test instance, the technique is capable of providing a point prediction, an explanation, and a confidence value for that prediction plus a prediction set. The experiments show when and how conformal decision rules can be used for interpretable and reliable machine learning