Description: Euphausiids constitute major biomass component in shelf ecosystems and play a fundamental role in the rapid vertical transport of carbon from the ocean surface to the deeper layers during their daily vertical migration (DVM). DVM depth and migration patterns depend on oceanographic conditions with respect to temperature, light and oxygen availability at depth, factors that are highly dependent on season in most marine regions. Changes in the abiotic conditions also shape Euphausiid metabolism including aerobic and anaerobic energy production. Here we introduce a global krill respiration model which includes the effect of latitude (LAT), the day of the year of interest (DoY), and the number of daylight hours on the day of interest (DLh), in addition to the basal variables that determine ectothermal oxygen consumption (temperature, body mass and depth) in the ANN model (Artificial Neural Networks). The newly implemented parameters link space and time in terms of season and photoperiod to krill respiration. The ANN model showed a better fit (r**2=0.780) when DLh and LAT were included, indicating a decrease in respiration with increasing LAT and decreasing DLh. We therefore propose DLh as a potential variable to consider when building physiological models for both hemispheres. We also tested for seasonality the standard respiration rate of the most common species that were investigated until now in a large range of DLh and DoY with Multiple Linear Regression (MLR) or General Additive model (GAM). GAM successfully integrated DLh (r**2= 0.563) and DoY (r**2= 0.572) effects on respiration rates of the Antarctic krill, Euphausia superba, yielding the minimum metabolic activity in mid-June and the maximum at the end of December. Neither the MLR nor the GAM approach worked for the North Pacific krill Euphausia pacifica, and MLR for the North Atlantic krill Meganyctiphanes norvegica remained inconclusive because of insufficient seasonal data coverage. We strongly encourage comparative respiration measurements of worldwide Euphausiid key species at different seasons to improve accuracy in ecosystem modelling.

Description: Human-induced habitat destruction, overexploitation, introduction of alien species and climate change are causing species to go extinct at unprecedented rates, from local to global scales. There are growing concerns that these kinds of disturbances alter important functions of ecosystems. Our current understanding is that key parameters of a community (e.g. its functional diversity, species composition, and presence/absence of vulnerable species) reflect an ecological network's ability to resist or rebound from change in response to pressures and disturbances, such as species loss. If the food web structure is relatively simple, we can analyse the roles of different species interactions in determining how environmental impacts translate into species loss. However, when ecosystems harbour species-rich communities, as is the case in most natural systems, then the complex network of ecological interactions makes it a far more challenging task to perceive how species' functional roles influence the consequences of species loss. One approach to deal with such complexity is to focus on the functional traits of species in order to identify their respective roles: for instance, large species seem to be more susceptible to extinction than smaller species. Here, we introduce and analyse the marine food web from the high Antarctic Weddell Sea Shelf to illustrate the role of species traits in relation to network robustness of this complex food web. Our approach was threefold: firstly, we applied a new classification system to all species, grouping them by traits other than body size; secondly, we tested the relationship between body size and food web parameters within and across these groups and finally, we calculated food web robustness. We addressed questions regarding (i) patterns of species functional/trophic roles, (ii) relationships between species functional roles and body size and (iii) the role of species body size in terms of network robustness. Our results show that when analyzing relationships between trophic structure, body size and network structure, the diversity of predatory species types needs to be considered in future studies.