The Italian Museo Nazionale dell’Antartide

These abstract proceedings were produced based on the program for the POLAR2018 SCAR/IASC Open Science Conference, updated until 25 May 2018

Cross-Disciplinarity in the Advance of Antarctic Ecosystem Research

The biodiversity, ecosystem services and climate variability of the Antarctic continent, and the Southern Ocean are major components of the whole Earth system. Antarctic ecosystems are driven more strongly by the physical environment than many other marine and terrestrial ecosystems. As a consequence, to understand ecological functioning, cross-disciplinary studies are especially important in Antarctic research. The conceptual study presented here is based on a workshop initiated by the Research Programme Antarctic Thresholds - Ecosystem Resilience and Adaption of the Scientific Committee on Antarctic Research, which focused on challenges in identifying and applying cross-disciplinary approaches in the Antarctic. Novel ideas, and first steps in their implementation, were clustered into eight themes, ranging from scale problems, risk maps, organism and ecosystem responses to multiple environmental changes, to evolutionary processes. Scaling models and data across different spatial and temporal scales were identified as an overarching challenge. Approaches to bridge gaps in the research programmes included multi-disciplinary monitoring, linking biomolecular findings and simulated physical environments, as well as integrative ecological modelling. New strategies in academic education are proposed. The results of advanced cross-disciplinary approaches can contribute significantly to our knowledge of ecosystem functioning, the consequences of climate change, and to global assessments that ultimately benefit humankind

A Bayesian semiparametric GLMM for historical and newly collected presence-only data: An application to species richness of Ross Sea Mollusca

Historical data sets from vast and relatively inaccessible areas are sources of potentially unique information still valuable for biodiversity studies today. In many research fields, ranging from climate change to projection of species loss, great efforts have been made to integrate historical data sets with recent data to create databases that are as complete as possible. Unlocking the information contained in presence-only data, largely prevalent in such databases, presents a challenge for statistical modeling because of insidious observational errors due to the opportunistic nature of the data-gathering process. In this article, we propose an appropriate statistical method for the joint analysis of historical and newly collected presence-only data, that is, a Bayesian semiparametric generalized linear mixed model with Dirichlet process random effects. The potential of the method is illustrated by considering the Ross Sea section of the SOMBASE, an international compilation of Southern OceanMollusc distributional records, from 1899 to 2004 and beyond. Despite the presence of sampling bias and non detection errors, the proposedmodel draws latent information from the data, such that the resulting estimates of the parameters of interest not only are coherent with those obtained in indirectly related studies based on well-structured data but also suggest interesting ideas for further research