Farming macroalgae to mitigate coastal nutrification from finfish aquaculture : a modelling study

Aquaculture is an increasingly expanding industry driven both by economic opportunity and necessity, as countries seek contemporary solutions to food security. However there are risks with unchecked or poorly managed expansion, including the potential to harm natural ecosystems in the vicinity of aquaculture farms. It is unknown exactly how sensitive estuarine biogeochemistry is to a major input of dissolved inorganic nitrogen (DIN) from finfish aquaculture. DIN is the limiting nutrient in autotrophic growth in temperate Australian estuaries, and increase in DIN loading has the potential to greatly increase primary phytoplankton production with possible consequences ranging from general decrease in water quality to harmful algal blooms and a trophic shift to eutrophication. The present work investigates the impact of salmon farming on the marine environment and key ecological processes in the D'Entrecasteaux Channel and Huon Estuary in Tasmania. This study uses purpose built ecosystem models to assess the increase of nutrients in the region due to the nutrient input from fish farms. The results indicate a need to develop strategies to deal with nutrient loading from salmon aquaculture, particularly if the industry were to increase production beyond current levels. One method gaining increasing interest worldwide is Integrated Multitrophic Aquaculture (IMTA) in which species that utilize the waste products from the primary species are farmed alongside the focus species. Here I construct an IMTA process model to identify the most suitable macroalgae, from a set of potential species for this region, and present a thorough uncertainty analysis of the model. The model is then used within larger estuary models to quantify the potential benefits of IMTA at the system level. The thesis comprises separate chapters for the General Introduction, General Conclusion and four standalone chapters that focus on quantifying the potential of using macroalgae as an agent for IMTA in conjunction with finfish aquaculture in southeast Tasmania. In Chapter 2 the aim was to identify a suitable species of macroalgae for IMTA in the D'Entrecasteaux Channel and Huon Estuary. To achieve this aim, a macroalgae growth model was developed and then applied in a simulation of IMTA in a near field experiment, whereby macroalgae are grown close to a point source of nutrients. The model was used to compare the capacity of three species of macroalgae (Macrocystis pyrifera, Ulva lactuca and Porphyra umbilicalis) to remove 'waste' DIN under a range of scenarios. The species were selected based on certain assumptions about their intrinsic worth; M. pyrifera is a species that has largely disappeared from the region and so has environmental and conservation value, P. umbilicalis has high economic value in the seafood industry, while U. lactuca has the highest absolute growth rate of these three rapidly-growing algae and so is an obvious candidate as a potential 'nutrient pump'. The model distinguishes between the species based on parameters representing sub-processes that control growth. An allometric growth term was developed to allow M. pyrifera to vary its height and thus exploit its ability to occupy the water column. The results show that M. pyrifera vastly out-performs the other two species in terms of its ability to remove the DIN output from the finfish cages in the near field case, largely as a result of its size advantage over the other species. Quantifying the potential optimization of IMTA considering cultivation depth, site selection and harvesting, suggests that varying cultivation depth up to a maximum of 5m impacts M. pyrifera production but has no effect on the other two species; DIN removal varied with flow rate (for all 3 species) and the appropriate harvesting scheme can improve bioremediation by a factor of 15 compared to non-harvested crops. In Chapter 3 a thorough uncertainty analysis of the model was conducted and a method to incorporate empirical data to improve model performance was also developed. A Bayesian inference method was used to quantify uncertainty in the IMTA model with M. pyrifera used as the extractive species. The deterministic model was reformulated into a stochastic form through the representation of sub-processes (e.g. mortality and maximal growth rate) as time varying, using first order auto-regressive processes. Parameter uncertainty was accounted for using prior distributions. We used data from three empirical growth experiments to test the effect of seeding density on ropes supporting M. pyrifera grown around salmon pens. The data were assimilated into the model using a Sequential Monte Carlo method. Through conditioning the state variables on the parameter priors alone we obtained a comprehensive uncertainty analysis of the model, and were able to constrain the model output to observed values. The results showed learning in a subset of model parameters, and overall the data assimilation method resulted in a 90% reduction in model uncertainty in both the state and parameters. These results will assist in future applications of the model by providing a more realistic parameter set. We were also able to show that low to medium density as an initial seeding of M. pyrifera resulted in best uptake of DIN. This approach offers a method by which empirical data from IMTA experiments can be used to improve IMTA process models. The next two chapters incorporate the model into a three-dimensional coupled hydrodynamic, sediment and biogeochemical model. The 3D model, which was used in the original study that prompted this work, has been developed through numerous case studies to offer a realistic simulation of estuarine dynamics. In chapter 4 an idealized 'test' estuary was created as the setting of finfish aquaculture. Firstly, through incremental increases in DIN output from finfish aquaculture a relationship between nutrient loading rates and water quality as determined by chlorophyll concentration, was obtained. Through the simulation of IMTA farms adjacent to the finfish sites, the capacity of M. pyrifera to remediate the estuary was then established. The results showed that M. pyrifera could effectively bioremediate the output from the finfish aquaculture as loading increased. This ensured a classification of 'good' water quality based on chlorophyll concentrations, was retained within the estuary. The hydrodynamic conditions was determined to be the primary driver of both the distribution of chlorophyll and successful IMTA. Farms in the southern section of the estuary achieved the highest biomass of macroalgae, but had little impact on the reduction of primary production due to this area being well flushed from strong river flow; which limited phytoplankton growth in this area. A region of freshwater influence was responsible for the high productivity observed in the northern region of the estuary, and IMTA in this section was solely responsible for reducing chlorophyll concentration. The last phase of the study (Chapter 5) focused on a more realistic simulation of macroalgae-based IMTA by incorporating the stylized model into a model of the D'Entrecasteaux Channel and Huon Estuary - a region of intensive salmon aquaculture in southeast Tasmania. In this chapter the aim was to estimate phytoplankton production in this region stimulated from 'waste' DIN from finfish aquaculture, and investigate the effectiveness of IMTA in remediating any increase in production. We identify the spatial pattern and magnitude of phytoplankton production in the region under a range of outputs from finfish aquaculture. Scenario analysis showed that the most productive area to grow M. pyrifera is in the immediate vicinity of the salmon farms, and that growing giant kelp in this way can mitigate undesirable effects on chlorophyll concentration of DIN loading from the farms if activity expands beyond current levels. However, mitigation using IMTA is non-linear and there are limits to the magnitude of salmon aquaculture activity beyond which significant declines in water quality seem inevitable, even if M. pyrifera is grown extensively around farms. This study provides a thorough investigation of the potential of macroalgae-based IMTA to prevent the potentially damaging waste DIN output from finfish aquaculture from adversely affecting water quality (as assessed by chlorophyll concentration). It provides important baseline information that will help both management and future studies into IMTA by: (i) identifying vertical distribution as the most important feature for potential macroalgae culture species in near field IMTA; (ii) showing the impact of farm arrangement on DIN uptake by macroalgae for a range of potential IMTA species; (iii) describing a method for data assimilation to improve the validity of model results and reduce uncertainty, and (iv) providing a general guide to considerations for the successful implementation of macroalgae-based IMTA to mitigate reduction in water quality resulting from the addition of anthropogenic nutrification in estuaries.