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A growing population on a planet with limited resources demands finding new sources of protein. Hence, fisheries are turning their perspectives towards mesopelagic fish, which have, so far, remained relatively unexploited and poorly studied. Large uncertainties are associated with regards to their biomass, turn-over rates, susceptibility to environmental forcing and ecological and biogeochemical role. Models are useful to disentangle sources of uncertainties and to understand the impact of different processes on the biomass. In this study, we employed two food-web models – OSMOSE and the model by Anderson et al. (2019, or A2019) – coupled to a regional physical–biogeochemical model to simulate mesopelagic fish in the Eastern Tropical South Pacific ocean. The model by A2019 produced the largest biomass estimate, 26 to 130% higher than OSMOSE depending on the mortality parameters used. However, OSMOSE was calibrated to match observations in the coastal region off Peru and its temporal variability is affected by an explicit life cycle and food web. In contrast, the model by A2019 is more convenient to perform uncertainty analysis and it can be easily coupled to a biogeochemical model to estimate mesopelagic fish biomass. However, it is based on a flow analysis that had been previously applied to estimate global biomass of mesopelagic fish but has never been calibrated for the Eastern Tropical South Pacific. Furthermore, it assumes a steady-state in the energy transfer between primary production and mesopelagic fish, which may be an oversimplification for this highly dynamic system. OSMOSE is convenient to understand the interactions of the ecosystem and how including different life stages affects the model response. The combined strengths of both models allow us to study mesopelagic fish from a holistic perspective, taking into account energy fluxes and biomass uncertainties based on primary production, as well as complex ecological interactions.

Marine ecosystems are subjected to increasing top–down and bottom–up anthropogenic pressures. Top–down pressures affect higher trophic levels (HTL) such as fish, for instance, by harvesting them. Bottom–up pressures affect the ecosystem by changing the environment, which in turn affects lower trophic levels (LTL), such as plankton. The northern Humboldt Current System (NHCS) is the most productive eastern boundary upwelling system in terms of fish catches. In this project, I used physical-biogeochemical and HTL models to understand how the NHCS is affected by top–down and bottom–up drivers. Over four studies, I explored two key questions: How does variability in the biogeochemistry affect fish? And how does fishing pressure affect fish and how does variability in fish affect the biogeochemistry? The first study looks at the impact that fish variability may have on the plankton community. Zooplankton mortality in the physical–biogeochemical model CROCO-BioEBUS was modified to implicitly simulate the change in the biomass of fish which would prey on zooplankton. Large zooplankton was the main driver of the community response. For the second study, the impacts of LTL variability on the HTL of the system were studied by coupling CROCO-BioEBUS with the multispecies HTL model OSMOSE. Interannual variability had an impact on fish biomasses but this was small compared to the high variability seen in observations. Then, I explored the top–down impact of fishing on the Peruvian anchovy and Peruvian hake. I observed a higher resilience of anchovy to increased fishing pressure than hake. Finally, mesopelagic fish in OSMOSE were compared with another model. It was concluded that the life-cycle and trophic interactions in OSMOSE affect the response of simulated mesopelagic fish to changes in LTL. The thesis concludes with a reflection on possible next steps for improving the representation of the NHCS ecosystem.

Small pelagic fish off the coast of Peru in the Eastern Tropical South Pacific (ETSP) support around 10 % of the global fish catches. Their stocks fluctuate interannually due to environmental variability which can be exacerbated by fishing pressure. Because these fish are planktivorous, any change in fish abundance may directly affect the plankton and the biogeochemical system. To investigate the potential effects of variability in small pelagic fish populations on lower trophic levels, we used a coupled physical-biogeochemical model to build scenarios for the ETSP and compare these against an already published reference simulation. The scenarios mimic changes in fish predation by either increasing or decreasing mortality of the model's large and small zooplankton compartments. The results revealed that large zooplankton was the main driver of the response of the community. Its concentration increased under low mortality conditions and its prey, small zooplankton and large phytoplankton, decreased. The response was opposite, but weaker, in the high mortality scenarios. This asymmetric behaviour can be explained by the different ecological roles of large, omnivorous zooplankton, and small zooplankton, which in the model is strictly herbivorous. The response of small zooplankton depended on the antagonistic effects of mortality changes as well as the grazing pressure by large zooplankton. The results of this study provide a first insight on how the plankton ecosystem might respond if variations in fish populations were modelled explicitly.