Introduction
Nutrition is of prime interest in finfish aquaculture and plays a critical role not only because it influences production costs but also because it affects fish growth and waste production. Protein is the forefront of finfish nutritional research as fish have high dietary protein requirements. In addition, nitrogen derived from protein catabolism is one of the main water pollutants in recirculating aquaculture. The quantitative study of the energy and nutrients entering the organism via food and its partitioning into various metabolic processes, is one of the main goals of many experiments with fish. Such experiments usually involve extensive use of laboratory facilities for long periods, which make, from both ethical (3Rs) and cost perspectives, mathematical models important tools for designing and planning of scientific experiments.
The nutritional bioenergetics model we propose is based on an ensemble of rules that describe the processes of digestion, absorption and the allocation of energy derived from food to metabolic processes of growth, maintenance, maturation, and reproduction according to a set of priority rules. The model is based on the Dynamic Energy Budget (DEB) theory, a qualitative and quantitative framework to study individual metabolism throughout the entire life cycle of an organism making explicit use of energy and mass balances (Kooijman, 2010).
Material and Methods
The model is an extension of the standard DEB model (Sousa et al., 2008; Kooijman, 2010) and assumes three life stages (larvae, juvenile and adult) as well as metabolic accelerated development for early stages which is an established practice for studying fish species in the DEB context (Kooijman, 2014; Lika et al., 2014, Stavrakidis-Zachou, 2019). Additionally, the model incorporates a digestion-assimilation module to simulate the food dynamics in the gut and the process of assimilation of the macronutrients from the gut wall. The model allows to track the fate of nitrogenous waste. The conceptual organization of metabolism is presented in Figure 1.
The model was parametrized for three fish species (Sparus aurata, Salmo salar, and Oncorhynchus mykiss) using commonly available data for fish growth, feeding, reproduction and gastric evacuation time. The model was validated using published data on weight, oxygen consumption, carbon dioxide production and total ammonia nitrogen (TAN) excretion for the three species reared in a range of temperatures and under different food compositions.
Results
Overall, the model performed well and was able to capture the diverse nature of the inputs of the validation datasets. On average the model predicted better the body weight than gaseous exchange and nitrogenous waste. Results of this study show that growth is strongly linked with the amount of feed consumed where a higher ration results in higher weight gain. Moreover, diets rich in protein translate in high production of Total Ammonia Nitrogen while an increase in feeding frequency can result in lower daily fluctuations of gas exchanges.
An emerging property of the model is that it captures the effects of food composition on assimilation, which in essence translates to the effects of protein-energy (PE) ratio in the diet. Food either low or high in protein, results in low assimilation rate. It follows, that for a given ratio of fats and carbohydrates, there exists a specific protein fraction where assimilation is maximized. The protein fraction that maximizes assimilation as well as the maximum value depends on the fat to carbohydrate ratio and, thus, on energy content.
Conclusions
We have developed a mechanistic model that focus on both energy and nutrient fluxes. The approach followed allowed to model the bioenergetics of fish throughout their life cycle as a function of temperature, food quantity and quality, and feeding frequency. The model allows simulations of growth, feeding, oxygen consumption, carbohydrate production, Total Ammonia Nitrogen, and solids, with hourly resolution. The model is one of the main components in the AQUAEXCEL2020 virtual laboratory (https://ae2020virtuallab.sintef.no/), which has been developed to enable virtual experiments in aquaculture research facilities.
Acknowledgements
The study was financially supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No. 652831 (AQUAEXCEL2020).
References
Kooijman, S.A.L.M. (2010) Dynamic Energy Budget theory for metabolic organisation. Cambridge University Press.
Kooijman, S.A.L.M. (2014). Metabolic acceleration in animal ontogeny: an evolutionary perspective. Journal of Sea Research, 94, 19–28.
Lika, K., Kooijman, S.A.L.M., Papandroulakis, N. (2014) Metabolic acceleration in Mediterranean Perciformes. Journal of Sea Research, 94, 37-46.
Sousa, T., Domingos, T. Kooijman, S.A.L.M. (2008) From empirical patterns to theory: a formal metabolic theory of life. Phil. Trans. R. Soc. B 363, 2453–2464
Stavrakidis-Zachou, O., Papandroulakis, N., Lika, K., (2019) A DEB model for European sea bass (Dicentrarchus labrax): parameterisation and application in aquaculture. Journal of Sea Research. 143, 262-271.