Using correlative and mechanistic niche models to assess the sensitivity of the Antarctic echinoid Sterechinus neumayeri to climate change

Abstract

The Southern Ocean is undergoing rapid environmental changes that are likely to have a profound impact on marine life, as organisms are adapted to sub-zero temperatures and display specific adaptations to polar conditions. However, species ecological and physiological responses to environmental changes remain poorly understood at large spatial scale owing to sparse observation data. In this context, correlative ecological niche modeling (ENMc) can prove useful. This approach is based on the correlation between species occurrences and environmental parameters to predict the potential species occupied space. However, this approach suffers from a series of limitations amongst which extrapolation and poor transferability performances in space and time. Mechanistic ecological niche modeling (ENMm) is a process-based approach that describes species functional traits in a dynamic environmental context and can therefore represent a complementary tool to understand processes that shape species distribution in a changing environment. In this study, we used both ENMc and ENMm projections to model the distribution of the Antarctic echinoid Sterechinus neumayeri. Both models were projected according to present (2005–2012) and future IPCC scenarios RCP 4.5 and 8.5 for (2050–2099). ENMc and ENMm projections are congruent and predict suitable current conditions for the species on the Antarctic shelf, in the Ross Sea and Prydz Bay areas. Unsuitable conditions are predicted in the northern Kerguelen Plateau and South Campbell Plateau due to observed lower food availability and higher sea water temperatures compared to other areas. In contrast, the two models diverge under future RCP 4.5 and 8.5 scenarios. According to ENMm projections, the species would not be able to grow nor reach sexual maturity over the entire ocean, whereas the Antarctic shelf is still projected as suitable by the ENMc. This study highlights the complementarity and relevance of EMN approaches to model large scale distribution patterns and assess species sensitivity and potential response to future environmental conditions.