Farming macroalgae to mitigate coastal nutrification from finfish aquaculture

186 Pages·2015·7.51 MB·English

by  Scott Hadley

Farming macroalgae to mitigate coastal nutrification

from finfish aquaculture: a modelling study

By

Scott Alistair Hadley

BSc. (Hons) Grad Dip. App. Comp. MSc.

Quantitative Marine Sciences,
Institute of Marine and Antarctic Sciences (IMAS)

Submitted in fulfillment of the requirements for the degree of
Doctor of Philosophy in Biogeochemical Modelling,
University of Tasmania April, 2015.

STATEMENTS AND DECLARATIONS
Declaration of Originality
This thesis contains no material which has been accepted for a degree or
diploma by the University or any other institution, except by way of
background information and duly acknowledged in the thesis, and to the best
of my knowledge and belief no material previously published or written by
another person except where due acknowledgement is made in the text of
the thesis, nor does the thesis contain any material that infringes copyright.
Authority of Access
This thesis may be made available for loan and limited copying and
communication in accordance with the Copyright Act 1968.
The publisher of the papers in chapters 2 and 4 holds the copyright for that
content, and access to the material should be sought from the journal.
Chapters 3 and 5 have been submitted for publication and upon acceptance
access to the material contained within similarly should be obtained from the
respective journals. The remaining non-published content of the thesis may
be made available for loan and limited copying and communication in
accordance with the Copyright Act 1968.
Statement of Ethical Conduct (where applicable)
The research associated with this thesis abides by the international and
Australian codes on human and animal experimentation, the guidelines by
the Australian Government's Office of the Gene Technology Regulator and
the rulings of the Safety, Ethics and Institutional Biosafety Committees of the
University.
Signed Date 29th October 2015
Scott Hadley
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Statement of Co-authorship
The following people and institutions contributed to the publication of work
undertaken as part of this thesis:
Scott Hadley IMAS QMS = Candidate
Craig Johnson, IMAS, University of Tasmania = Author 1
Catriona Macleod, IMAS, University of Tasmania = Author 2
Karen Wild-Allen, CSIRO = Author 3
Emlyn Jones, CSIRO = Author 4
Author details and their roles:
Paper 1 Modelling macroalgae growth and nutrient dynamics for Integrated
Multitrophic Aquaculture:
Located in chapter 2
Candidate was the primary author and largely contributed to the idea, its
formalisation and development. Author 1, author 2 and author 3 contributed
to the idea, its formalisation and development and in an editorial capacity.
Paper 2 A Bayesian approach to data assimilation in a macroalgae base
Integrated Multitrophic Aquaculture:
Located in chapter 3
Candidate was the primary author and largely contributed to the idea, its
formalisation and development. Author 4 was responsible for expert advice
and guidance on applying the model with the LibBI framework. Author 1,
author 2, author 3 and author 4 contributed to the idea, its formalisation and
development and in an editorial capacity.
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Paper 3 Quantification of the impacts of finfish aquaculture and
bioremediation capacity of Integrated Multitrophic Aquaculture using a 3D
estuary model:
Located in chapter 4
Candidate was the primary author and largely contributed to the idea, its
formalisation and development. Author 1, author 2 and author 3 contributed
to the idea, its formalisation and development and in an editorial capacity
Paper 4 Investigation of broad scale implementation of IMTA in a
temperate estuary using a 3D model
Located in chapter 5
Candidate was the primary author and largely contributed to the idea, its
formalisation and development. Author 1, author 2 and author 3 contributed
to the idea, its formalisation and development and in an editorial capacity.
We the undersigned agree with the above stated for each of the above
published (or submitted) peer-reviewed manuscripts contributing to this
thesis:
Signed: __________________ ______________________
Craig Johnson Nathan Bindoff
Supervisor Head, Oceans & Cryosphere Centre
Head, Ecology & Biodiversity Centre IMAS
IMAS University of Tasmania
University of Tasmania
Date: 29th October 2015
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ACKNOWLEDGEMENTS
I would like to thank the Fisheries Research and Development Council (FRDC)
whose funding made this project possible. I would also like to thank Catriona
Macleod in particular for securing this funding. The research was also made possible
through the support of the Institute of Marine and Antarctic Studies (IMAS),
University of Tasmania (Quantitative Marine Sciences Scholarship) and the CSIRO. I
would also like to acknowledge the support given by TASSAL in allowing me conduct
research at one of their farms.
I would like to thank the Coastal Environmental Modelling Team at CSIRO
whose generous support enabled me to become a biogeochemical modeller. In
particular I would like to acknowledge Mark Baird for his insights into the modelling
process. I would also like to thank Emlyn Jones for his assistance with the Bayesian
statistical analysis performed in chapter 3. Without LibBI chapter 3 would not have
been possible.
I am extremely grateful to my supervisors for helping me through this
process. I would like to say thank you to Karen Wild-Allen for her generous support
and for sharing her knowledge of modelling with me, Craig Johnson for his generous
support and insights into the problem at hand, and Catriona Macleod for her
generous support and her focus on the important details.
Finally I would like to thank my family. To my wife Kristin for her love and
support, my mother Maureen who made it possible, and my son Fergus for showing
me the myriad of solutions possible if you remove logic from the equation.
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GENERAL ABSTRACT
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
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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
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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.
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