Feeding behavior can be described through a variety of factors, involving anticipation, stress, health, and activity. Measuring each one can be hard, because they are interdependent or underly circadian clocks.
Accurately describing the behavior preceding feeding is difficult but can lead to improvements on feeding schedules to optimize nutrition usage. We therefore investigate the nature of Food Anticipatory Activity (FAA) displayed in sea caged European seabass, especially with respect to diving behavior and acceleration activity. Our work expands the aquatic knowledge in FAA and is a step towards precision aquaculture with accurate feeding schedules independent from internal rhythm patterns.
Materials and methods
Around 10 thousand individuals of European seabass (Dicentrarchus labrax) with an estimated weight of 450 grams each were held in a sea cage of 40m diameter and 9m cage depth at an aquaculture facility off the island Crete, Greece. The feeding schedule consisted of once a day manually feeding between 8am and 10am local time. 24 fish were prepped with a tag with two transmitters, which sent depth and GPS position every 5 minutes, and alternately temperature and activity using a 3-axis accelerometer. Three receivers positioned around the cage collected positional, temperature and activity data from the transmitters between 28.05.2021-06.06.2021 to analyze positional and behavioral changes in the feeding process. We split the entire water column of the sea cage into three segments: The upper water column ranged from 0-3m depth, the middle water column ranged from 3-6m depth, and the lower water column ranged from 6-9m depth.
Figure 1 shows an almost identical relative distribution of recorded fish positions between the three water columns. The day-night distribution was around 75% to 25% across the water columns, if the different twilights (official, nautical astronomical) are counted in as day. The difference in time spent in each water column during and before the feeding window (Fig. 2, red and green chunk in the first vertical bar) hinted at: 50% of the time where the seabasses were in the upper water column (0-3m) was before and during feeding, whereas for the other two water columns, less time was spent there before and during feeding. When residing in the middle water column, around half of the fish position pings were recorded after 2pm. Figure 3 illustrates 24-hour periods where activity was higher during day and fell lower during night with the twilights as transitional periods. On a weekly scale, we noted that activity followed the temperature curve, and slightly increased towards the end of the experiment (see Fig. 3).
Discussion and conclusion
The day-night distribution of 75%-25% described in Figure 1 was not surprising, since the tags transmitted in an even interval combined with long days in Crete. For example, the night stretching from the astronomical sunset on the 26.05.21 to the astronomical sunrise on the 27.05.21 is 6 hours 11 minutes long, which is approximately 25% of a 24-hour day, verifying our data quality. We were not able to detect a significant positional change throughout the day in Fig. 1, neither water column-wise nor timewise, suggesting that the fish were using the entire water column.
Due to the discrepancy between the different water columns just around and before the feeding time, we argue that the E. seabass was anticipating food and swimming up to the upper water column to prepare for food intake, which falls under FAA. We conclude that the cyclic regular feeding method created a habit in the fish to await food and therefore to swim up and be “prepared”. It appears therefore as the FAA was also at least correlated with the time of day, since we could not observe any FAA during nighttime.
Higher activity throughout the first week of June was expected, since we know that temperature correlates with the activity of fish, as Figure 3 illustrates. It is remarkable that individual fish tend to stay in their hierarchy when sorted after “most active” to “least active”, which we argue is due to the different physiological traits the individuals have. The spike in the beginning of the experiment with the high complex interweaving of the lines may be due to the initial habituation period where the fish had not established activity profiles yet. It remains to be discussed more thoroughly though.
This work was funded by the iFishIENCi project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 818036 and the Norwegian Research Council, Project number 32330.