The framework provided by the Dynamic Energy Budget (DEB) theory allows the quantification of metabolic processes and the associated biological rates that are of interest for aquaculture, such as growth and feeding. The DEB parameters were estimated for farmed European sea bass (Dicentrarchus labrax), a species of major importance for the Mediterranean aquaculture, using zero-and uni-variate literature data and achieving an overall good fit. The obtained parameter set was used to validate the model on sites representatively covering the geographic distribution of the aquaculture activity in Greece via comparison of model predictions to observations. Inter-individual variability of farmed fish was introduced through: 1) an individual initial weight and 2) a factor that acts as an individual-specific multiplier for some of the model parameters and produces scatter in maximum size, and age and size at puberty. Growth of E. sea bass was adequately predicted by the model while feeding tended to be underestimated, particularly during the period following the summer months when warmer temperatures promote high growth rates. The results suggest robustness of the model since it is able to simulate growth and food intake in several independent aquaculture production units, using a common parameter set. The accuracy of growth predictions supports the applicability of the model in variable environmental conditions in the context of climate change. Reconstruction of the feeding history from growth data revealed variations in the scaled functional response ( f ), i.e., the feeding rate as fraction of maximum possible one of an individual of a given size, throughout the production cycle. However, model simulations with constant f result in reasonably good predictions for growth and feeding in variable environmental conditions. Tendency of the model to underestimate the feeding process revealed both model weaknesses associated with higher temperatures as well as irregularities in the feeding protocols applied at the farm level.Our work demonstrates the capacity and potential of DEB theory for further development of tools that contribute to the assessment and improvement of feeding practices in aquaculture.
Climate change is expected to have a drastic effect on aquaculture worldwide. As we move forward with the agenda to increase and diversify aquaculture production, rising temperatures will have a progressively relevant impact on fish farming, linked to a multitude of issues associated with fish welfare. Temperature affects the physiology of both fish and pathogens, and has the potential to lead to significant increases in disease outbreaks within aquaculture systems, resulting in severe financial impacts. Significant shifts in future temperature regimes are projected for the Mediterranean Sea. We therefore aim to review and discuss the existing knowledge relating to disease outbreaks in the context of climate change in Mediterranean finfish aquaculture. The objective is to describe the effects of temperature on the physiology of both fish and pathogens, and moreover to list and discuss the principal diseases of the three main fish species farmed in the Mediterranean, namely gilthead seabream (Sparus aurata), European seabass (Dicentrarchus labrax), and meagre (Argyrosomus regius). We will attempt to link the pathology of each disease to a specific temperature range, while discussing potential future disease threats associated with the available climate change trends for the Mediterranean Sea.
Finfish aquaculture in the Mediterranean Sea faces increasing challenges due to climate change, while potential adaptation requires a robust assessment of the arising threats and opportunities. This paper presents an approach developed to investigate effects of climate drivers on Greek aquaculture, a representative Mediterranean country with a leading role in the sector. Using a farm level approach, dynamic energy budget models for European seabass and meagre were developed, and environmental forcing was used to simulate changes in production and farm profitability under IPCC scenarios RCP45 and RCP85. The effects of temperature and extreme weather events at the individual and farm levels were considered along with that of husbandry parameters such as stocking timing, market size, and farm location (inshore, offshore) for nine regions. The simulations suggest that at the individual level, fish may benefit from warmer temperatures in the future in terms of growth, thus reaching commercial sizes faster, while the husbandry parameters may have as large an effect on growth as the projected shifts in climatic cues. However, this benefit will be largely offset by the adverse effects of extreme weather events at the population level. Such events will be more frequent in the future and, depending on the intensity one assigns to them, they could cause losses in biomass and farm profits that range from mild to detrimental for the industry. Overall, these results provide quantification of some of the potential threats for an important aquaculture sector while suggesting possibilities to benefit from emerging opportunities. Therefore, they could contribute to improving the sector’s readiness for tackling important challenges in the future.
Monitoring and understanding fish behavior is crucial for achieving precision in everyday husbandry practices (i.e. for optimizing farm performance), and for improving fish welfare in aquaculture. Various intelligent monitoring and control methods, using mathematical models, acoustic methods and computer vision, have been recently developed for this reason. Here, a tracking algorithm based on computer vision that extracts short trajectories of individual European seabass in both recirculating aquaculture systems and sea cages was developed using videos from network cameras. Using this methodology, parameters such as instantaneous normalized speed, travel direction and preference for the tank surface by European seabass could be quantified. When testing the sensitivity of this algorithm for detecting fish swimming variations under different husbandry scenarios, we found that the algorithm could detect variations in all of the abovementioned parameters and could potentially be a useful tool for monitoring the behavioral state of European seabass.
Marine aquaculture has been expanding rapidly in recent years, driven by the growing demand for marine products. However, this expansion has led to increased competition for space and resources with other coastal zone activities, which has resulted in the need for larger facilities and the relocation of operations to offshore areas. Moreover, the complex environment and exposure to environmental conditions and external threats further complicate the sustainable development of the sector. To address these challenges, new and innovative technologies are needed, such as the incorporation of remote sensing and in-situ data for comprehensive and continuous monitoring of aquaculture facilities. This study aims to create an integrated monitoring and decision support system utilizing both satellite and in-situ data to monitor aquaculture facilities on various scales, providing information on water quality, fish growth, and warning signs to alert managers and producers of potential hazards. This study focuses on identifying and estimating parameters that affect aquaculture processes, establishing indicators that can act as warning signs, and evaluating the system’s performance in real-life scenarios. The resulting monitoring tool, called “Aquasafe”, was evaluated for its effectiveness and performance by test users through real-life scenarios. The results of the implemented models showed high accuracy, with an R2 value of 0.67. Additionally, users were generally satisfied with the usefulness of the tool, suggesting that it holds promise for efficient management and decision making in marine aquaculture.
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