Modelling of Atlantic salmon (Salmo salar L.) behaviour in sea-cages: Using artificial light to control swimming depth
Highlights
► Mathematical model of salmon behaviour in response to submerged artificial lights. ► Verification of model against observation data. ► Estimates of efficiency in using lights to control farmed salmon swimming depths. ► Potential tool for intelligent placement of artificial lights in salmon cages.
Introduction
Modern salmon aquaculture has advanced from small, shallow cages containing thousands of fish placed at sheltered inshore locations to large, deep cages (157 m circumference, 50 m depth) holding 100,000–250,000 individual salmon in more exposed locations (Fredheim and Langan, 2009). Although larger production units offer considerable economies of scale, they introduce distinct challenges for production management that differ from smaller sea-cages, including feeding control (Talbot et al., 1999), photoperiod control (Hansen et al., 1992), sea-lice management (Costello, 2009) and maintaining optimal oxygen levels (Johansson et al., 2007). To counter such production challenges, cage management strategies customised to large-scale production conditions are required. A key element in developing such strategies is knowledge on how salmon behave in response to the culture conditions.
Within the confines of sea-cages, the behaviours of farmed Atlantic salmon are determined by a complex environment containing spatially and temporally varying factors, such as temperature, light, dissolved oxygen (DO) and salinity. Whereas the full effects of DO and salinity on adult salmon behaviour are still not known (Oppedal et al., 2011a), responses to temperature and light levels drive the vertical distribution of salmon within sea-cages outside feeding periods (Fernö et al., 1995, Johansson et al., 2006). Salmon have distinct ranges of preference for water temperature and light intensity, and regulate these factors behaviourally when they are outside their preference ranges (Dempster et al., 2008, Fernö et al., 1995, Johansson et al., 2007). As both temperature and light vary less horizontally than with depth in the water column, this often results in vertical movement. When the most preferable temperature and light levels occur at different depths, salmon may swim at depths that are a trade-off between these factors (Oppedal et al., 2011a). The distributions resulting from these trade-offs tend to be much denser than the initial stocking density of the cage (Oppedal et al., 2011a, Oppedal et al., 2011b).
The use of artificial light sources is a management strategy originally developed to inhibit fish maturation in cages (Hansen et al., 1992, Oppedal et al., 1997, Porter et al., 1999), but has also been shown to have positive effects on fish growth (Nordgarden et al., 2003, Oppedal et al., 1997, Oppedal et al., 1999, Oppedal et al., 2003). Submerged artificial lights also affect fish behaviour (Juell and Fosseidengen, 2004, Juell et al., 2003, Korsøen et al., 2012, Oppedal et al., 2001). Placing light sources at specific depths could be a strategy to shift fish vertically within the cage to distribute fish more evenly within the cage or to steer fish away from areas in the cage where conditions are sub-optimal for growth and welfare (Oppedal et al., 2007).
Individual-based (or Lagrangian) modelling (IBM) is a technique in which each individual animal in a population is modelled to produce a group-level outcome. This technique has been applied with great success to other livestock industries (e.g. Pomar and Pomar, 2005, Tedeschi et al., 2004) to improve production efficiency through precision livestock farming (Wathes et al., 2008). An IBM of salmon behaviour in sea-cages has been developed and verified against detailed observational data on salmon distributions (Føre et al., 2009). The model accurately simulates the behavioural trade-offs made by salmon between temperature and natural light levels when positioning themselves vertically in a sea-cage. However, the model does not currently include behavioural responses to artificial light sources which are in widespread use by the industry.
We aimed to model the behavioural effects of artificial lights in salmon aquaculture and verify the model using observational data on the swimming depths of salmon in full-scale sea-cages subject to artificial light sources. We expanded the model of Føre et al. (2009) by adding a new model module describing the responses of salmon to underwater artificial light sources, and verified model outputs against observational data provided by Oppedal et al. (2007). Through a series of simulation experiments using the model, we predicted the effects of artificial light placement depth, season, and water column thermal profiles (stratified versus well mixed), to determine which combinations were most likely to yield growth advantages for commercial production.
Section snippets
Materials and methods
The model described below is based on an individual-based model built and verified by Føre et al. (2009) which simulates the behaviours and swimming depths of Atlantic salmon in sea-cages in response to a range of environmental variables. Here, we only briefly explain the main features of the model and detail modifications to adapt the model to account for behavioural responses of salmon toward artificial light sources. The model explanation partly follows the ODD (Overview, Design concepts,
Winter period
The model results matched the results from Oppedal et al. (2007) well in that the predicted vertical distributions of fish were similar to those observed for all three winter scenarios (Fig. 1). In W1, temperatures varied from about 1.5 °C near the surface to approximately 7 °C at the cage bottom, with a sharp thermocline at about 2 m depth, while the maximal surface light levels exceeded 600 μEm− 2s− 1. At night-time (00:00–05:00 and 17:00–24:00), the majority of the fish were observed to reside at
Model verification: Oppedal et al. (2007)
The experiments performed by Oppedal et al. (2007) were conducted under a wide variety of environmental conditions. Temperature profiles ranged from having the lowest values near the surface (winter scenarios), through being almost invariant with depth (spring scenarios), to featuring clear maximums in the upper layers and distinct thermoclines (summer scenarios). Together with the randomised placement depths of the artificial lights, these variations produced nine different scenarios, all with
Acknowledgements
Funding for this work was provided by SINTEF Fisheries and Aquaculture through the Centre for Research-based Innovation in Aquaculture Technology (CREATE).
References (36)
- et al.
Growth rate estimates for cultured Atlantic salmon and rainbow trout
Aquaculture
(1987) - et al.
Behaviour and growth of Atlantic salmon (Salmo salar L.) subjected to short-term submergence in commercial scale sea-cages
Aquaculture
(2008) - et al.
Vertical distribution of Atlantic salmon (Salmo salar L.) in net pens: trade-off between surface light avoidance and food attraction
Aquaculture
(1995) - et al.
Modelling of Atlantic salmon (Salmo salar L.) behaviour in sea-cages: a Lagrangian approach
Aquaculture
(2009) - et al.
New Technologies in Aquaculture: Improving Production Efficiency, Quality and Environmental Management
- et al.
A standard protocol for describing individual-based and agent-based models
Ecological Modelling
(2006) - et al.
The effect of temperature and fish size on growth, feed intake, food conversion efficiency and stomach evacuation rate of Atlantic salmon post-smolts
Aquaculture
(2008) - et al.
The effect of artificial light treatment and depth on the infestation of the sea louse Lepeophtheirus salmonis on Atlantic salmon (Salmo salar l.) culture
Aquaculture
(2003) - et al.
The influence of the pycnocline and cage resistance on current flow, oxygen flux and swimming behaviour of Atlantic salmon (Salmo salar L.) in production cages
Aquaculture
(2007) - et al.
Swimming depth and thermal history of individual Atlantic salon (Salmo salar L.) in production cages under different ambient temperature conditions
Aquaculture
(2009)
Effect of environmental factors on swimming depth preferences of Atlantic salmon (Salmo salar L.) and temporal and spatial variations in oxygen levels in sea cages at a fjord site
Aquaculture
Use of artificial light to control swimming depth and fish density of Atlantic salmon (Salmo salar) in production cages
Aquaculture
Individual variation in swimming depth and growth in Atlantic salmon (Salmo salar L.) subjected to submergence in sea-cages
Aquaculture
Environmental drivers of Atlantic salmon behaviour in sea-cages: a review
Aquaculture
Thermo- and photoregulatory swimming behaviour of caged Atlantic salmon: implications for photoperiod management and fish welfare
Aquaculture
Growth performance and sexual maturation in diploid and triploid Atlantic salmon (Salmo salar l.) in seawater tanks exposed to continuous light or simulated natural photoperiod
Aquaculture
Fluctuating sea-cage environments modify the effects of stocking densities on production and welfare parameters of Atlantic salmon (Salmo salar L.)
Aquaculture
A knowledge-based decision support system to improve sow farm productivity
Expert Systems with Applications
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