CONSULTANCY AND RESEARCH IN AQUACULTURE AND THE AQUATIC ENVIRONMENT A Company in the NIVA-group Environmental impacts of aquaculture

Factors affecting impact  Analysis of monitoring results from 168 environmental surveys on 80 Salmon farm sites in Norway (Carroll, 2003) has shown that management practices as well as environmental factors play a strong role on the impact of sediments below the cages.  For salmon production in cold waters, management practices such as years in operation (without fallowing) and feeding strategy were found to have greater influence on impact than environmental factors such as current speed and water depth.

Use of models to test mitigation scenarios  The MERAMOD model is designed to predict the solids deposition from seabass and seabream mariculture operations in the Mediterranean.  The model uses site information on bathymetry, cage layouts, current speed and direction.  For each cage at the site, feed input (FI) (i.e. ration) and species is specified. Using information on feed digestibility, water content and wastage (uneaten), the rates of discharge of faecal material and uneaten feed can be calculated.

Scenario 1 – shallow site versus deep site The majority of fish farm sites in the Mediterranean are located inshore in relatively shallow and protected areas. However, in Cyprus and Malta, farms are located at relatively exposed sites in deeper water. For continued growth of the industry, it will be necessary to develop new sites offshore in deeper areas. Scenario 1 tests the effect between cages sited in a shallow site (15 m) and a deep site (30 m) and compares the waste solids deposition.

Scenario 1 – shallow site versus deep site Model predictions of flux (g m-2 yr-1) showing the larger footprint area around the cages (centres shown as □) at the deeper site. The deeper site also has lower flux (impact) below the cages as there are no dark areas shown

Scenario 2 – spacing between cages  The development of aquaculture in the Mediterranean has progressed from the use of small square wooden cages used in the 80’s to large round plastic cages or large square metal cages in the 90’s. The mooring system for the large round cages is based on a fixed mooring grid to which individual round cages are attached which provides spacing between cages. This compares with the large square metal cages that are connected to each other by hinges and forms a relatively tight cluster of cages. Scenario 2 tests the effect of round cages spaced out by 6 m against square tightly clustered cages on waste solids deposition. A depth of 30 m was used

Scenario 2 – spacing between cages  Model predictions of flux (g m-2 yr-1) showing the difference in deposition footprint shape when tightly clustered square cages are replaced by circular cages spaced by 6 m.

Scenario 3 – large spacing between cages  This scenario is similar to Scenario 2 but a larger spacing of 30 metres was used between the circular cages. This is to test the effect of round, largely spaced out cages against square tightly clustered cages. A depth of 30 m was used.

Scenario 3 – large spacing between cages  Model predictions of flux (g m-2 yr-1) showing the significant difference in deposition footprint severity and extent when tightly clustered square cages are replaced by circular cages spaced by 30 m. For the spaced out cages, areas of lower flux are shown in between lines of cages which will tend to assist sediment processes.

Scenario 4 – effect of different species (feed input slightly higher for bream due to SFR in tables)  Some farm sites could be more suitable for seabass than for seabream and visa versa due to seabass having a faster faecal settling velocity than seabream. This scenario tests the difference in impact on sediments depending on whether seabass or seabream are stocked in the cages. A shallow site (15 m) was used in this test, using faster settling velocities for bass.

Scenario 4 – effect of different species  Model predictions of flux (g m-2 yr-1) showing the significant difference in deposition footprint shape between bass and bream cages.  Higher flux (impact) is predicted below bass cages and wastes from bream are dispersed more widely.  This indicates that bass should be placed in more dispersive, deeper areas of the site.

Scenario 5 – effect of locating seabass in deeper and more dispersive sites  As the findings in Scenario 4 indicate that it may be better to place seabass in more dispersive sites, Scenario 5 tests the effect of locating bass in deeper more dispersive areas to take account of the higher faecal settling rates. In scenario 5a, a depth of 30 m was used to test the effect of bass in deeper sites. In scenario 5b, a depth of 30 m was also used but the current was increased by 50 % to represent a more dispersive site

Scenario 5 – effect of locating seabass in deeper and more dispersive sites  Model predictions of flux (g m-2 yr-1) showing the difference in the deposition footprint when bass are moved to deeper and more dispersive sites. The effect of depth is seen by comparing Figure 4a and Figure 5a; the effect of higher current is seen by comparing Figure 5a and 5b. This indicates bass should be located in deeper and/or more dispersive areas

Scenario 6 – test efficient FCR and less efficient FCR  Food conversion rate in seabass and seabream farms varies between 1.4:1 and 2.2:1 depending on the feeding strategy and close feed management. This overfeeding leads to feed wastage and potential higher environmental impact. Scenario 6 tests the effect between cages with a FCR of 1.6:1 (FI = kg cage-1 d-1) and 2.0:1 (FI = kg cage-1 d-1). A depth of 15 m was used.

Scenario 6 – test efficient FCR and less efficient FCR  Model predictions of flux (g m-2 yr-1) showing the difference in deposition for different values of FCR. The darkest area representing high flux (impact), covers more area underneath the cages with the less efficient FCR scenario

Scenario 7 – feeding method  The majority of farms in the Mediterranean still use hand feeding of fish rather than automatic feeding. This results in less frequent feeding of larger portions. Scenario 7 tests the effect between undertaking hand feeding twice a day and automatic feeding. Hand feeding was undertaken twice per day (am and pm) with 70% of ration (and defecation) in the morning feed. Automatic feeding was constant feeding and defecation between 09:00 and 16:00 local time. These can be compared with scenario 1b. A depth of 30 m was used.

Scenario 7 – feeding method  Model predictions of flux (g m-2 yr-1) showing little difference in deposition for the different feeding methods. If the model was used to examine the effect of feeding method over a shorter period and at a site where a strong diurnal pattern of wind occurs, a difference might be more obvious.

Scenario 8 – low and high stocking density  Scenario 1b uses cages with a stocking density of 12 kg m-3. To test the effect of stocking density, stocking density in scenario 8a was reduced to 6 kg m-3 and increased to 20 kg m-3 in scenario 8b and the predictions compared. A depth of 30 m was used.

Scenario 8 – low and high stocking density  Model predictions of flux (g m-2 yr- 1) showing a significant difference between deposition footprints for low and high stocking density. The high stocking density will cause a high level of impact underneath the cages.

Conclusions  Greater dispersion of wastes resulting in low severity and high extent of the deposition footprint occurs where sites are deeper (scenario 1), cages are highly spaced (scenario 2, 3) and bream is farmed (scenario 4) (Table 1). In particular, where 30 m spacing of cages is used, the severity of impact underneath the cage groups is reduced by four times. Scenarios with a high FCR (scenario 6) and stocking density (scenario 8) resulted in higher severity and extent of footprint, an undesirable situation. Scenario 5 showed bass are more suitable for deep dispersive sites as this results in a low severity­– high extent situation. No real difference was detected for feeding method (scenario 7), particularly for the extent of the deposition footprint.

Conclusions  These scenarios show deeper, dispersive sites result in less severe impact over a larger area. In addition, spacing out of cages reduces predicted deposition markedly especially where a large spacing is used. The modelling also suggests bass potentially have more impact than bream as a result of faster faecal settling velocities, despite the slightly lower feed input used for bass. Therefore, bass should be sited in deeper more dispersive sites or, where farmed at the same site as bream, bass should be placed in the outer (deeper) areas of the farm. Consideration should be given to modifying management practices to reflect this.

Conclusions  The effect of inefficient feeding and high stocking density is clear. A more severe impact over a larger area will result, with a higher probability of problems with sediment and fish health.  Little difference was found between the scenarios where feeding method was tested. However, where a strong diurnal pattern of wind and circulation exists at a site, the effect of feeding larger portions by hand in two feeding events may result in periods of higher deposition. This would mainly be a result of a larger feeding event in the morning coinciding with lighter winds and therefore less potential for dispersion.