Summer distribution of marine mammals encountered along transects between South Africa and Antarctica during 2007-2012 in relation to oceanographic features

The at-sea summertime distribution of marine mammals between South Africa and Antarctica was determined along eight transects surveyed between December 2007 and January 2012. During 1930 30-minute transect counts, 1390 marine mammal individuals were attributed to 19 species: eight toothed whales (Odontoceti), six pinnipeds, and five baleen whales (Mysticeti). An additional two toothed-whale species were encountered ‘out of effort’. The four most numerous species accounted for 85% of the total number of individuals encountered: crabeater seal (Lobodon carcinophagus), humpback whale (Megaptera novaeangliae), Antarctic Minke whale (Balaenoptera bonaerensis) and fin whale (B. physalus). The distribution of these species was related to oceanographic features, such as water masses and fronts, pack ice and ice edge: These differences were statistically highly significant. Biodiversity was compared with other polar marine ecosystems

Predicting the future is hard and other lessons from a population time series data science competition

Population forecasting, in which past dynamics are used to make predictions of future state, has many real-world applications. While time series of animal abundance are often modeled in ways that aim to capture the underlying biological processes involved, doing so is neither necessary nor sufficient for making good predictions. Here we report on a data science competition focused on modelling time series of Antarctic penguin abundance. We describe the best performing submitted models and compare them to a Bayesian model previously developed by domain experts and build an ensemble model that outperforms the individual component models in prediction accuracy. The top performing models varied tremendously in model complexity, ranging from very simple forward extrapolations of average growth rate to ensembles of models integrating recently developed machine learning techniques. Despite the short time frame for the competition, four of the submitted models outperformed the model previously created by the team of domain experts. We discuss the structure of the best performing models and components therein that might be useful for other ecological applications, the benefit of creating ensembles of models for ecological prediction, and the costs and benefits of including detailed domain expertise in ecological modelling. Additionally, we discuss the benefits of data science competitions, among which are increased visibility for challenging science questions, the generation of new techniques not yet adopted within the ecological community, and the ability to generate ensemble model forecasts that directly address model uncertainty