In order to achieve sustainable and ethical aquaculture practices, it is imperative to gain a comprehensive understanding of the interactions between fish species and their environment. The aquaculture industry is in need of instruments capable of objectively and accurately monitoring fish health and welfare in real time, without disturbing the fish or interfering with standard management practices. Precision livestock farming is a subject that has gathered increased attention given its potential to enhance animal welfare, whilst concomitantly increase production and environmental sustainability (Føre et al. 2018; Brijs et al. 2021). The utilisation of biological sensors, capable of measuring diverse parameters (e.g. depth, acceleration, muscle activity, heart rate), has emerged as a pertinent tool to assess fish health and welfare in response to aquaculture environments and practices (e.g. Carbonara et al. 2019, 2021; Morgenroth et al. 2024; Toomey et al. 2025) . The present study evaluated the effectiveness of wireless environmental and accelerometer/pressure sensors in monitoring fish welfare and in gathering insights into relationships between fish acceleration/depth and environmental/farm operational factors.
An experimental trial was conducted within a sea cage of industrial scale at the Rehomare S.r.l. farm in Torre Suda, Italy. In this trial, a variety of environmental sensors were deployed in the water to monitor environmental parameters, namely temperature, oxygen saturation, salinity, tilt (i.e. used as a proxy for water current), and turbidity. Furthermore, we implanted accelerometer or accelerometer/pressure sensors, with a tailbeat mode algorithm, in gilthead sea breams (Sparus aurata), thus enabling the measurement of fish acceleration (m.s-2 ), which serves as a proxy for energy expenditure (Alfonso et al. 2021), and depth (m). The experiment was conducted over a period of 305 days, commencing in September 2023 and concluding in September 2024, the experiment being subjected to intermittent interruptions due to technical issues. A total of 40 fish were tagged at different periods during the experiment. A ll sensors provided real-time data visibility to the farmers, and a total of 1,855,363 detections (acceleration + environmental variables) were obtained after the data was cleaned (i.e. false detections were excluded). A comprehensive data set was compiled, encompassing the quantity of feed administered daily, in addition to any occurrence of disruptions (e.g., feeding, sensor maintenance, or the introduction of tagged fish into the sea cage). Linear mixed models were employed to assess the variation of acceleration according to the period (night/day), the different environmental variables (i.e. temperature, salinity, oxygen, sensor tilt, turbidity), as well as the amount of feed provided and the occurrence of disturbances. The effect of these factors on the switch between aerobic and anaerobic metabolism was also investigated using a binomial generalised linear model.
The utilisation of wireless technology allowed for the acquisition of precise temporal data on various pivotal environmental parameters to explore relationships with fish behaviour/physiology. A range of significant variations were observed across time for the environmental data. For instance, the mean temperature was found to be 27.7 ± 5.7°C, with a range between 13.6 and 29.7°C. Similarly, the levels of dissolved oxygen varied from 48.3 to 126.8%. Overall, fish exhibited clear differences in day/night patterns both in terms of acceleration and depth, with fish being in shallower waters and displaying lower acceleration values during the night . Furthermore, the observed pattern of acceleration across hours indicates a possible anticipatory behaviour in relation to feeding. The study also identified the influence of environmental parameters on fish acceleration. In addition to photoperiod, a significant positive correlation was observed between acceleration and temperature, tilt, turbidity, and dissolved oxygen. Conversely, an inverse correlation was observed between acceleration and salinity or feed supply. In the switch between aerobic and anaerobic metabolism, temperature and disturbances appeared to be particularly important factors. In terms of depth, a positive correlation was observed with turbidity and salinity, and fish were in deeper waters in the absence of disturbance . Conversely, a negative relationship was evident with temperature and oxygen. T he results of this study are overall in agreement with those previously obtained for Gilthead seabream in other contexts (e.g. Alfonso et al., 2024; Muñoz et al., 2020; Toomey et al., 2025) . However, we provide here various new insights on relationships with new environmental and farm operational parameters and over a longer time period. A more in-depth discussion of these findings is required, with particular emphasis on the season and the inherent variability among individual fish.
This study provided preliminary insights into the complex interplay between environmental factors and Gilthead seabream acceleration and depth, which are useful information for future welfare prediction indicators. The next steps are the integration of growth data to facilitate the creation of predictive models. The integration of acoustic telemetry technology with environmental sensors provides fish farmers with real-time data, which could function as an alert system for adverse events , enabling timely adjustments to be made to management practices, thus enhancing productivity and fish welfare.
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