Introduction
Sustainable aquaculture requires tools to address the key environmental issues associated with animal husbandry. Because water is the medium, the fate of waste substances is more difficult to determine than in agriculture, and engineering solutions for mitigating the impact of waste are more challenging.
In open water aquaculture, organic particle emissions are tightly coupled with the bottom, although water movement in a 3D environment results in deposition plumes that transport and deform the farm footprint due to horizontal transport and dispersion during the sinking process.
Regulations are normally set on the basis of (i ) loading rates to the sediment (1 gC m-2 d-1 is often used); and (ii) thresholds of selected indicators such as sulphide, redox potential, and particulate organic carbon (POC) in the sediment below the farm. While the sediment indicators are measurable quantities, loading rates are measurable only by means of sediment traps, which often give ambiguous results in relatively shallow water (tens of metres).
In order to quantify the relationship between particulate emissions from a farm (waste feed for fed aquaculture, faeces, pseudofaeces for bivalves) and sediment quality, it is necessary to measure and model emissions, model the trajectory to the sea (or lake/reservoir) bottom, and use the calculated loads to feed diagenetic models that can predict sediment indicators—these can then be compared with measured values, closing the loop between emission and impact.
Far-field effects of aquaculture are often neglected; these include eutrophication due to the emission of dissolved nutrients (typically up to 5X more nitrogen than in particulate waste), and potential pathogen contamination, which may occur following a disease outbreak on a farm and will impact neighbouring farms depending on connectivity and other risk factors.
The Farming In Natural Systems (FINS) framework was developed to address the various issues described above, including the production and ecological pillars (e.g. Inglis et al, 2000;McKindsey et al, 2006) of carrying capacity, with the far-field ecological component including both eutrophication and disease.
Models that quantify near- and far-field impacts of aquaculture are available, but typically their use is beyond the scope of farmers and managers, which limits their application in any screening process. This is particularly the case in data-poor areas, which is often where expansion pressure is highest in the aquaculture sector.
FINS was designed to integrate complex models into a simple platform, allowing practitioners and policy makers to review potential siting and stocking, evaluate risk, and analyse development options in a multi-stakeholder environment.
In that context, this work aims to:
Approach
The standard approach used in FINS for both near-field and far-field simulations was to (i) use inputs from more detailed models such as FVCOM (Chen et al, 2002), FARM (e.g. Cubillo et al, 2016; Cubillo et al, 2021), and ABC (Ferreira et al, 2021); (ii) simplify these inputs in terms of temporal and spatial resolution while maintaining acceptable accuracy; and (iii) develop a map-based platform (
) to allow managers and industry to interact with the models, optimising runtimes and providing a rich user experience.
At present, FINS is coupled offline with the ORGANIX model (Cubillo et al, 2016) which simulates emission and dispersion of particulates for near-field effects, and ammonia, oxygen demand, chlorophyll uptake (bivalves), and pathogen dispersal.
The FINS framework has been applied to a number of ecosystems i n the Canadian province of Nova Scotia , including Liverpool Bay, Whitehead Bay, Lobster Bay, and Port Mouton. Hydrodynamic inputs were generated through the application of the FVCOM model and optimised for offline coupling, since one of the priorities for FINS is a very fast execution.
The addition of the disease component to the model required the development of an AI algorithm to identify individual farms (such as the three shown in Fig. 1), implementation of pathogen decay (through turbulence and natural mortality), and automated farm-by-farm modelling of pathogen emission and dispersal to calculate a connectivity matrix for a bay, estuary, or lake.
The calculations required to plot the FINS output surfaces on a map are computationally intensive, but a significant optimisation can be obtained if they are performed in parallel. Consequently, the computations are executed by the GPU, resulting in a considerable increase in calculation speed allowing for real-time display and quasi-instant updates when maps are panned or zoomed.
Results and Discussion
The results presented in this paper focus on the pathogen component of FINS and on the link between particulate emissions from cages and diagenetic processes.
Fig. 2 shows conceptual pathogen emissions from each of three farms in Liverpool Bay, Nova Scotia, over a fourteen-day cycle. Because pathogen outbreaks are notifiable for any major disease, the maximum period for a pathogen simulation in FINS is 30 days. The results show that the farms have different connectivity with each other, not only in terms of trajectory but also in terms of magnitude.
For the hydrodynamic conditions considered, farm 3 has the most significant effect on the others—this is expressed in Table 1 as a percentage, based on the highest overall value at a target farm using a normalised pathogen emission.
The table, which shows relative risk, shows that the maximum risk from farm 1 is on farm 3, but only about a quarter of the risk of farm 3 on farm 2. The effect of farm 1 on farm 2 is about 10% of the maximum risk, and farm 2 has a very low risk on the other farms (less than 1%) under these water circulation conditions. The data can be interpreted as follows: siting of farm 2 (Brooklyn) at the proposed location poses little hazard in terms of disease to other farms considered, but a disease outbreak in farm 3 (Coffin Island) would have potentially serious consequences on the new farm.
Although not shown due to space constraints, we also present results of the cage-to-sediment chain with respect to loading of organic particles and its effects on sediment key performance indicators. The representation of these results is shown in the web version of the FINS application.
Acknowledgements
The authors wish to acknowledg e funding from the Atlantic Fisheries Fund, Canada, and the Horizon Europe NovaFoodies project.
References
Chen, C. H. Liu, R. C. Beardsley, 2002. An unstructured, finite-volume, three-dimensional, primitive equation ocean model: application to coastal ocean and estuaries. Journal of Atmospheric and Oceanic Technology,20, 159-186.
Cubillo, A.M., J.G. Ferreira, S.M.C. Robinson, C.M. Pearce, R.A. Corner, J. Johansen, 2016. Role of deposit feeders in integrated multi-trophic aquaculture - a model analysis. Aquaculture, 453, 54-66. ntegrated multi-trophic aquaculture - a
Cubillo, A., Ferreira, J.G., Lencart -Silva, J., Taylor, N.G.H., Kennerley, A., Guilder, J., Kay, S., Kamermans, P., 2021. Direct effects of climate change on productivity of European aquaculture. Aquaculture International 29(1–2) DOI:10.1007/s10499-021-00694-6
Ferreira, J.G., Taylor, N.G.H., Cubillo, A., Lencart -Silva, J., Pastres, R., Bergh, Ø., Guilder, J., 2021. An integrated model for aquaculture production, pathogen interaction, and environmental effects. Aquaculture 536, 1-16, doi.org/10.1016/j.aquaculture.2021.736438
Inglis, G.J., Hayden, B.J., Ross, A.H., 2000. An overview of factors affecting the carrying capacity of coastal embayments for mussel culture. NIWA Client Report CHC00/69, Christchurch, New Zealand.
McKindsey , C. W., Thetmeyer, H., Landry, T., Silvert, W., 2006. Review of recent carrying capacity models for bivalve culture and recommendations for research and management. Aquaculture, 261(2):451-462.