ALBERT

Description: The hypothesis of this work was that exposure to diverse abiotic factors in two sites with different sediment and iron input (Peñón de Pesca: low impact; Island D: high impact, both areas in Potter Cove, King George Island, Antarctica) affects the physiological and oxidative profile of Gigartina skottsbergii and Himantothallus grandifolius. Daily metabolic carbon balance was significantly lower in both macroalgae from Island D compared to Peñón de Pesca. Lipid radical (LRradical dot) content was significantly higher in G. skottsbergii collected from Island D compared to Peñón de Pesca. In contrast, H. grandifolius showed significantly lower values in Island D compared to Peñón de Pesca. The β-carotene (β-C) content was significantly lower in G. skottsbergii from Island D compared to Peñón de Pesca, and the ratio LRradical dot/β-C showed a 6-fold increase in Island D samples compared to Peñón de Pesca. On the other hand, β-C content in H. grandifolius showed no significant differences between both areas. The LRradical dot/β-C content ratio in this alga was significantly lower (26%) in Island D as compared to Peñón de Pesca. Total iron content was significantly higher in both macroalgae from Island D compared to samples from Peñón de Pesca. Results with G. skottsbergii suggested changes in the oxidative cellular balance, probably related to the higher environmental iron in Island D as compared to Peñón de Pesca. The species H. grandifolius seems to be better adapted to the environmental conditions especially through a higher antioxidant capacity to cope with oxidative stress.

Description: Ensemble habitat modeling is a tool in the multivariate analysis of arbitrary species or community distribution which combines models of best fit to an optimized model (ensemble model, EM). To simulate spatial variation of communities and predict the impact of climate change, it is essential to identify the distribution-controlling factors. Macroalgae biomass production in polar regions is determined by environmental factors such as irradiance, which are modified under climate change impact. In coastal fjords of King George Island/Isla 25 de Mayo, Antarctica, suspended particulate matter (SPM) from glacial melting causes shading of algal communities during summer. Ten different species distribution models (SDMs) were applied to predict macroalgae distribution based on their statistical relationships with environmental variables. The suitability of the SDMs was assessed by two different evaluation methods. Those SDMs based on a multitude of decision trees such as Random Forest and Classification Tree Analysis reached the highest predictive ability followed by generalized boosted models and maximum-entropy approaches. We achieved excellent results for the current status EM (true scale statistics 0.833 and relative operating characteristics 0.975). The environmental variables hard substrate and SPM were identified as the best predictors explaining more than 60 % of the modelled distribution. Additional variables distance to glacier, total organic carbon, bathymetry and slope increased the explanatory power proved by cross-validation. Presumably, the SPM load of the meltwater streams on the Potter Peninsula will continue to increase at least linearly. We therefore coupled the EM with changing SPM conditions representing enhanced or reduced melt water input. Increasing SPM by 25% decreased predicted macroalgal coverage by approximately 38%. The ensemble species distribution modelling helps to identify the important factors controlling spatial distribution and can be used to link causes to effects in (Antarctic) coastal community change.