Currently, in the salmon farming industry, the fattening of salmon takes place in commercial sea cages. These circular cages have a circumference ranging from 60 to 240 meters, and each cage can accommodate up to 200,000 individuals, making it challenging for fish farmers to estimate fish growth based on population samples.
In the salmon farming industry, accurate estimation of biomass and fish size plays a crucial role in ensuring optimal management practices. Traditionally, these estimates have been based on models and small sample sizes, leaving room for improvements in representativity.
The advent of machine learning has driven rapid advances in automated size estimation, particularly through the use of stereovision cameras.
Existing methods, often based on limited individual samples, may not fully capture the true diversity and distribution of fish within sea cages.
To address this, scientists from the Norwegian University of Science and Technology designed a case study using stereovision cameras to monitor the vertical and horizontal distributions of salmon in a commercial sea cage.
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Stereovision systems have been used as a non-invasive technique to measure fish length and biomass in aquaculture facilities. This methodology employs at least two synchronized cameras to cover the field of view from different positions.
Currently, fully automated fish biomass estimation cameras based on stereovision measurements are available in the market, but at higher costs and with limited accessible information on measurement accuracy.
Revealed Vertical Stratification
The use of stereovision cameras allowed researchers to explore the vertical stratification of salmon size within the sea cage. Four simultaneous recordings were made four times throughout the day at 16 positions, covering four horizontal positions and four depths.
The study findings revealed a heterogeneous vertical distribution, with larger individuals predominantly located in the deeper zones (9m and 14m) of the sea cage, exhibiting higher abundance and slower swimming speeds. In contrast, smaller individuals, including growth-delayed fish, were found closer to the surface (1m) with lower abundance and faster swimming speeds.
“The use of stereovision to measure salmon in our study reduced possible bias in the results, as fish were not afraid of the stereo equipment and were randomly selected for measurement. Fish could display their usual swimming behavior in sea cages without any restraint, such as swimming through a frame and getting accustomed to it,” reported the study authors.
Homogeneity in Horizontal Distribution
While vertical distribution posed challenges, the study data supported the assumption of a homogeneous horizontal distribution. Fish within the sea cage exhibited a circular swimming pattern, similar to observations in smaller sea cages and under slow current conditions.
This consistency in horizontal behavior adds a layer of predictability to the dynamics within commercial sea cages.
Implications for Industry Practices
The study contributes valuable data to the limited knowledge set on the behavior and distribution of fish on a commercial scale. It highlights the potential of stereovision cameras for biomass and size estimation, emphasizing the need for nuanced approaches that consider both vertical and horizontal dimensions.
The findings suggest that previous models derived from smaller sea cages may not directly translate into larger industrial conditions.
“Our case study suggests a heterogeneous vertical distribution and a homogeneous horizontal distribution of salmon in a commercial sea cage. Fish abundance differed between depths, as did the average size of fish, with larger individuals being more densely located in the depths of the sea cage,” conclude the scientists.
In the pursuit of optimal management in Norwegian Atlantic salmon production in cages, the study underscores the transformative potential of stereovision cameras to refine biomass and size estimates.
The researchers indicate that stereovision is a suitable tool for random sampling of salmon for size measurements and, if used, should alternate at least between two different depths: near the surface and where the majority of the fish population is located.
By unraveling the complexities of vertical and horizontal distributions, researchers pave the way for more accurate and representative models, setting a new standard for sustainable and efficient management of Atlantic salmon populations in sea cages.
The study has been funded by the Department of Biological Sciences Ålesund at the Norwegian University of Science and Technology (NTNU).
Norwegian University of Science and Technology
Larsgårdsvegen 2, 6009 Ålesund, Norway
Reference (open access)
Clara Sauphar, Christian Stolz, Stig Atle Tuene, Lars Christian Gansel, Grete Hansen Aas. 2023. Atlantic salmon (Salmo salar) distribution and vertical size-stratification in a commercial sea cage: A case study, Aquaculture, 2023, 740356, ISSN 0044-8486, https://doi.org/10.1016/j.aquaculture.2023.740356.