Description: Ports are associated with negative impacts to the environment such as habitat loss, collisions with whales, noise pollution and chemical and physical pollution. Structures in ports such as jetties are known to attract a high diversity of fish. However, a large amount of recreational fishing pressure is also associated with these structures. The Port of Albany in Western Australia is situated in Princess Royal Harbour. A complete fishing restriction within the port’s boundaries was implemented in 2013. We aimed to assess the effect of the fishing restriction by comparing assemblages inside the port to outside. Some of the targeted species in the port are open water species as well. We also looked whether or not distance from the mouth of the harbour affected the abundance of open water species in the assemblage. Baited Remote Underwater Video Systems were used to estimate the relative abundance and richness of fish along two distance gradients: one in the port and the other one in a control site outside the port. Higher total abundance, targeted species and open water species abundance, and species richness were found in the port than in the open area. Open water species presented an abundance gradient in the port, with the highest abundance closer to the mouth of the harbour. Few species, including at least one targeted species, preferred the area outside the port. Habitat inconsistence between the port and the control site limited the interpretation of the results. Despite this problem, the results of this study suggest that the port is likely to have a positive effect on the abundance of fish in the harbour. Distance from the mouth of the harbour also had an effect on the assemblage, which is a advised to be considered in future studies carried out in harbours.

Description: 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.

Description: 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.

Description: 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.

Description: Highlights: • Modelled fish biomass was affected by interannual variability in the plankton food. • The effects were small compared with the high variability in observations. • Fish were highly affected by changes in the larval mortality of anchovy. Abstract: The Northern Humboldt Current System is the most productive eastern boundary upwelling system, generating about 10 % of the global fish production, mainly coming from small pelagic fish. It is bottom-up and top-down affected by environmental and anthropogenic variability, such as El-Niño Southern Oscillation and fishing pressure, respectively. The high variability of small pelagic fish in this system, as well as their economic importance, call for a careful management aided by the use of end-to-end models. This type of models represent the ecosystem as a whole, from the physics, through plankton up to fish dynamics. In this study, we utilised an end-to-end model consisting of a physical–biogeochemical model (CROCO-BioEBUS) coupled one-way with an individual-based fish model (OSMOSE). We investigated how time-variability in plankton food production affects fish populations in OSMOSE and contrasted it against the sensitivity of the model to two parameters with high uncertainty: the plankton accessibility to fish and fish larval mortality. Relative interannual variability in the modelled fish is similar to plankton variability. It is, however, small compared with the high variability seen in fish observations in this productive ecosystem. In contrast, changes in larval mortality have a strong effect on anchovies. In OSMOSE, it is a common practice to scale plankton food for fish, accounting for processes that may make part of the total plankton in the water column unavailable. We suggest that this scaling should be done constant across all plankton groups when previous knowledge on the different availabilities is lacking. In addition, end-to-end modelling systems should consider environmental impacts on other biological processes such as larval mortality in order to better capture the interactions between environmental processes, plankton and fish.