Climate change is already known to cause irreversible impacts on ecosystems that are difficult to accurately predict due to the multiple scales at which it will interact. Predictions at the community level are mainly focused on the future distribution of marine species biomass using ecological niche modelling, which requires extensive efforts concerning the effects that trophic interactions could have on the realized species dynamics. In this study, a set of species distribution models predictions were used to force the spatially‐explicit trophic model Ecospace in order to evaluate the potentials impacts that two 2,100 climate scenarios, RCP2.6 and RCP8.5, could have on a highly exploited ecosystem, the Bay of Seine (France). Simulations demonstrated that both scenarios would influence the community of the Bay of Seine ecosystem: as expected, more intense changes were predicted with the extreme scenario RCP8.5 than with the RCP2.6 scenario. Under both scenarios, a majority of species underwent a decrease of biomass, although some increased. However, in both cases the stability of the majority of species dynamics was lowered, the sustainability of the fishery. Differences between niche modelling predictions and those obtained through the forcing in Ecospace highlighted the paramount importance of considering trophic interactions in climate change simulations. These results illustrate the requirement of multiplying novel approaches for efficiently forecasting potential impacts of climate change.

Sharks are vulnerable to exploitation as a result of their biological characteristics. Markrecapture models were applied to conventional tag recapture data and acoustic telemetry data to estimate abundance, apparent survival, recapture probability and temporary emigration for the pyjama shark, Poroderma africanum in Mossel Bay, South Africa over a five-year period. This study applied Pollock's robust design (with the conventional tag data) and Cormack-Jolly-Seber (CJS) models (with the acoustic tag data) to analyze the mark-recapture data. In addition, a von Bertalanffy model was fit to the data to estimate individual growth. The best-fit robust design model showed the population as having no temporary emigration, survival probability that is dependent on the length at first capture, and time-constant capture probabilities. The best-fit CJS model showed the population also having time-constant survival, but sex dependent capture probabilities. Robust design abundance estimates (with 95% C.I.) in Mossel Bay varied from 279 (102-787) sharks to 733 (320-1777) sharks, although confidence intervals were quite large. CJS apparent annual survival (95% C.I.; CJS) was estimated to be 0.254 year-1 (0.04 to 0.56) and annual recapture probability (95% C.I.) was estimated to be 0.008 year-1 (0.003-0.20), indicating that survival and recaptures for this endemic species are relatively low. Annual somatic growth rate (k) was estimated to be 0.213 year-1 , indicating that this population is slow growing, a characteristic common in most shark species. Overall, the results in this study provide baseline knowledge on this population in Mossel Bay and can be used to implement proper management techniques. This knowledge can be further expanded upon to give a more in-depth understanding of all size and age classes in the population and the role that the environment and anthropogenic activities play in the population structure.

The P/B and P/Q values for meso-and macrozooplankton were based on Hutchings et al. (1991), and the P/B value for microzooplankton from Hutchings et al. (1995). The Q/B ratio of microzooplankton and the diet of the micro-, meso-and macrozooplankton were derived from the Southern Benguela model (Shannon et al. 2020). For gelatinous zooplankton, the biomass, P/B and Q/B values from the Southern Benguela model (Shannon et al. 2020) were used. Benthos (8, 9)Meiobenthos and macrobenthos biomass was estimated by EwE using the P/B, Q/B and EE (0.95) values for the southern Benguela model (Shannon et al. 2020). Diet information was also derived from this model. Chokka squid (10) and other cephalopods (11)The biomass of chokka squid was derived from the demersal trawl surveys for the south coast of South Africa using information from years 2010, 2011 and 2014 for the depth stratum 0-100 m. The abundance estimate (in tons) was divided by the stratum area (nm 2 ), and then converted from t nm 2 to t km 2 . The average over the 2010-2014 period was input in the model. The P/B and P/Q values from the southern Benguela model were used. Diet information was obtained from Sauer and Lipiński (1991).In terms of other cephalopods, Augustyn et al. (1995) reported that the most common species of Sepioidea in the