Modeling Antarctic Marine Ecosystems PDF Download

Among tools that are used to fill knowledge gaps on natural systems, ecological modelling has been widely applied during the last two decades. Ecological models are simple representations of a complex reality. They allow to highlight environmental drivers of species ecological niche and better understand species responses to environmental changes. However, applying models to Southern Ocean benthic organisms raises several methodological challenges. Species presence datasets are often aggregated in time and space nearby research stations or along main sailing routes. Data are often limited in number to correctly describe species occupied space and physiology. Finally, environmental datasets are not precise enough to accurately represent the complexity of marine habitats. Can we thus generate performant and accurate models at the scale of the Southern Ocean ? What are the limits of such approaches ? How could we improve methods to build more relevant models ? In this PhD thesis, three different model categories have been studied and their performance evaluated. (1) Mechanistic physiological models (Dynamic Energy Budget models, DEB) simulate how the abiotic environment influences individual metabolism and represent the species fundamental niche. (2) Species distribution models (SDMs) predict species distribution probability by studying the relationship between species presences and the environment. They represent the species realised niche. (3) Dispersal lagrangian models predict the drift of propagules in water masses. Results show that physiological models can be developed for marine Southern Ocean species to simulate the metabolic variations in link with the environment and predict population dynamics. However, more data are necessary to highlight detailed physiological contrasts between populations and to accurately evaluate models. Results obtained for SDMs suggest that models generated at the scale of the Southern Ocean and future simulations are not relevant, given the lack of data available to characterise species occupied space, the lack of precision and accuracy of future climate scenarios and the impossibility to evaluate models. Moreover, model extrapolate on a large proportion of the projected area. Adding information on species physiological limits (observations, results from experiments, physiological model outputs) was shown to reduce extrapolation and to improve the capacity of models to estimate the species realised niche. Spatial aggregation of occurrence data, which influenced model predictions and evaluation was also succefully corrected. Finally, dispersal models showed an interesting potential to highlight the role of geographic barriers or conversely of spatial connectivity and also the link between species distribution, physiology and phylogeny history. This PhD thesis provides methodological advices, annoted codes and tutorials to help implement future modelling works applied to Southern Ocean marine species.