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The significance of macroalgae to the
diets of juvenile fish and ecosystem
function in a tropical coral reef lagoon
Submitted by
Cameron Jose Desfosses
B(Sc) Marine Science
B(Sc) Biological Science
This thesis is presented for the degree of Honours in Marine Science
(H1257)
2016
Murdoch University
School of Veterinary and Life Sciences
I declare this thesis is my own account of my research and contains as its
main content work which has not been previously submitted for a degree at
any tertiary education institution.
Cameron Jose Desfosses
iii
Contents
ACKNOWLEDGEMENTS ........................................................................ v
ABSTRACT ................................................................................................ vi
1. General Introduction .............................................................................. 9
1.1 Macroalgae in Tropical Systems ...................................................... 9
1.2 Modelling Ecosystem Dynamics ..................................................... 10
1.3 The Ningaloo Reef Ecosystem ......................................................... 11
2. Literature Review.................................................................................. 14
2.1 General Introduction ....................................................................... 14
2.2 Tropical Lagoon Habitats ............................................................... 16
2.2.1 Role of Lagoon Habitats ................................................................................. 16
2.2.2 Macroalgae ..................................................................................................... 23
2.2.3 Ontogenetic Shifts .......................................................................................... 27
2.3 The Ningaloo Reef Marine System ................................................. 28
2.3.1 Prevalence of Macroalgae in the Reef Ecosystem .......................................... 29
2.3.2 Fish Present in the Ningaloo Reef Lagoon ..................................................... 31
2.4 Ecosystem Models ............................................................................ 34
2.4.1 Use of Ecosystem Models in Ecosystem/Natural Resource Management ..... 34
2.4.2 Ecopath with Ecosim ...................................................................................... 35
2.4.3 Ecosystem Model Requirements .................................................................... 37
2.4.4 Potential Effects of Disturbances on Macroalgae at Ningaloo Reef .............. 39
2.4.5 Implications for Management ......................................................................... 40
2.4.6 Decisions Regarding Marine Sanctuaries ....................................................... 41
2.5 Summary ........................................................................................... 43
3. The Importance of Macroalgae to the Diets of Juvenile Fish in the
Ningaloo Reef Lagoon ............................................................................... 44
3.1 Introduction ...................................................................................... 44
3.2 Materials and Methods .................................................................... 45
3.2.1 Study Area ...................................................................................................... 45
3.2.2 Sampling Fish in Macroalgal Beds ................................................................. 48
3.2.3 Fish Processing ............................................................................................... 50
3.2.4 Gut Contents Analyses ................................................................................... 50
iv
3.2.5 Importance of Dietary Items ........................................................................... 52
3.2.6 Statistical Analysis .......................................................................................... 54
3.3 Results ............................................................................................... 55
3.3.1 Annual Dietary Composition .......................................................................... 55
3.3.2 Seasonal Dietary Composition ....................................................................... 59
3.3.3 Analysis of Prey Specific Abundance and Frequency of Occurrence ............ 64
3.4 Discussion .......................................................................................... 68
3.4.1 Contribution of Macroalgae to Diets of Fish Species in the NMP Lagoon .... 68
3.4.2 Enhancing Knowledge of Food Webs in the NMP ........................................ 73
3.4.3 Future Research .............................................................................................. 74
3.5 Conclusion ........................................................................................ 75
4. Modelling the Trophic Flows to Fish in the Macroalgal Beds of the
Ningaloo Reef Lagoon in North-Western Australia .............................. 76
4.1 Introduction ...................................................................................... 76
4.2 Methods ............................................................................................. 77
4.2.1 Ecopath Model ................................................................................................ 77
4.2.2 Model Structure, Parameterisation and Balancing ......................................... 81
4.2.3 Evaluating the Ecopath Model ........................................................................ 88
4.2.4 Ecosim ............................................................................................................ 94
4.3 Results ............................................................................................... 99
4.3.1 Energy and Mass Flows .................................................................................. 99
4.3.2 Mixed trophic impacts (MTI) ....................................................................... 104
4.3.3 Network Analysis and Ecosystem Properties ............................................... 104
4.3.4 Ecosim Scenarios .......................................................................................... 108
4.4 Discussion ........................................................................................ 116
4.4.1 Characteristics of the Macroalgal System .................................................... 117
4.4.2 Ecosim simulations ....................................................................................... 118
4.4.3 Evaluation of Model Performance ................................................................ 124
4.4.4 Utility for ecosystem managers .................................................................... 127
4.5 Conclusions ..................................................................................... 128
REFERENCES ........................................................................................ 130
APPENDIX .............................................................................................. 149
v
ACKNOWLEDGEMENTS
I cannot give enough acknowledgement to the people who have been integral in getting
this thesis over the line. My supervisors Neil, Shaun, and Hector who, if patience is a
virtue, must be saints. Glenn Moore, Corey Wakefield and Joe DiBattista, for their
assistance in identifying the cursed juvenile Lethrinid. Dan Yeoh, Tom Holmes, Chris
Fulton, Martial Depczynski and Josh van Leer, for their assistance chasing fish around
Ningaloo. Pete Coulson and James Tweedley for their assistance in identifying contents
from the world’s smallest fish guts and making sense of multivariate analyses, Halina
Kobryn for her help with the GIS and habitat data, and Jacqueline Dyer for her
understanding of the foibles of Word formatting.
Last, but certainly not least, I’d like to thank my gorgeous wife and son for putting up
with me studying for another two years. With any luck there’ll be a summer off.
vi
ABSTRACT
Little information is available on the contribution of macroalgae to the food web of the
Ningaloo lagoon and its importance in the diets of fish associated with it. This information
is important for understanding potential trophic flows from macroalgae to juvenile fish
and provides the fundamental data for constructing ecosystem models.
In my Honours research, I have examined: the significance of macroalgae, its associated
epibionts and infauna in the diets of juvenile and subadult fish in the Ningaloo lagoon,
focussing on:
1. the significance of macroalgae in the diets of juvenile fish and how this varies between
summer (February) and winter (July) (Chapter 3); and
2. the development and use of an Ecopath with Ecosim ecosystem model to assess trophic
flows of macroalgae to functional fish groups (Chapter 4).
Fish were sampled in macroalgal beds by a variety of techniques (herding into fence nets
by SCUBA, hand spear, and rod and line fishing) in February and July, 2015. A total of
181 fish were caught representing 11 species, with six species caught in both months.
Stomach contents were identified to the lowest taxonomic resolution possible and the
percent volume of items recorded. Multivariate analyses were used to identify guilds
(species with similar diets), and to assess differences in diets between February and July.
The results showed that: fleshy macroalgae (e.g. Sargassum spp.) were not as important
as filamentous algae to the diets of the juveniles of two nominally herbivorous fish species
in February, but became more important in July. Macroalgae were not an important
component in the diets of juvenile Lethrinidae, Lutjanidae, or Mullidae, though the
infauna associated with the macroalgal beds was important in the diets. Analysis of
feeding specialisation found that the smaller size classes of fish in February had a
narrower trophic width and a more specialised feeding strategy than larger bodied fish of
vii
the same species in July. At this time, fish tended to show a generalist feeding strategy
and broad niche width, possibly associated with increasing gape size of the larger fish
sampled at this time. These studies provided the basis for defining three distinct functional
feeding groups for the Ecopath model: herbivores, zoobenthivores, and carnivores
(Chapter 4).
The Coral Bay Ecopath model was constructed by modifying an Ecopath model for the
Ningaloo system and applying it to an area of the macroalgal beds to examine trophic
flows from macroalgae to higher trophic levels. The Coral Bay model had 29 functional
groups based on the functional fish feeding groups (adults and juveniles of herbivores,
zoobenthivores, carnivore, Lethrinus species and Lethrinus nebulosus - 10 groups) and
broad dietary categories (13 groups) identified in Chapter 3, a competitor for algal
resources (e.g. turtles - 1 group), predators to the fish groups (e.g. reef sharks and dolphins
- 2 groups) and extra groups that were included due to having different functional roles
(e.g. phytoplankton, squid and octopus - 3 groups). The model was balanced by adjusting
biological parameters, with an emphasis on changing those with the fewest data from the
region the most. Macroalgae were the dominant primary producers in the system, and
comprised more than 70% of the total consumption of trophic level I groups.
Ecopath with Ecosim was used to evaluate the effects of three categories of disturbance,
(17 scenarios), affecting primary production, fishing effort and simultaneous changes in
both primary production and fishing effort. The results from these predicted that changes
in the rate of primary production had a much larger effect on the biomass of functional
groups within the Ningaloo lagoon than changes to fishing effort. Since this model was
developed with a focus on trophic understanding and not fishing, and it was not possible
to tune the model with data from recreational fisheries, therefore, the predictions from the
scenarios involving fishing effort should be treated with caution
viii
The results from this study show that the macroalgal beds are important in the diets of
some fish species and contribute to trophic flows in the Ningaloo lagoon. This adds to the
understanding of their function as habitat for different species and highlights the value of
including them in conservation planning for the Ningaloo Marine Park.
9
1. General Introduction
Coral reef ecosystems comprise a small fraction of oceanic habitat in global tropical and
subtropical regions, however, they are among the world’s most biodiverse productive
ecosystems (Moberg and Folke 1999; Pandolfi et al. 2011). Interconnected, shallow-
water habitats separate the reef from the coastline and include lagoons, patch reefs,
estuaries, tidal creeks, and subtidal sand and mud flats (Dahlgren and Marr 2004; Adams
et al. 2006; Sheaves et al. 2015).
Macrophytes, including mangroves, seagrass, and macroalgae can make up a large
component of tropical shallow-water habitats (Kobryn et al. 2013), have high rates of
productivity (Beck et al. 2001), and support diverse fish and benthic communities
(Nagelkerken et al. 2000; Dahlgren and Marr 2004). They are essential providers of
ecological services, including foraging habitat for a range of organisms, and settlement
and refuge habitat for cryptic species and juveniles of organisms that inhabit coral reefs
(Dahlgren and Marr 2004; Dorenbosch 2006).
1.1 Macroalgae in Tropical Systems
While the ecology of macroalgae has been well studied in temperate systems (Edgar and
Shepherd 2013), it has often been overlooked as shallow-water habitat in tropical systems,
and has been considered symptomatic of coral reef decline globally due to the effects of
overfishing, warming sea temperatures, and excessive nutrient flows (Diaz-Pulido and
McCook 2003; Hughes 2003; Bellwood et al. 2004; Hughes et al. 2007; Norström et al.
2009). However, recent research has recognised macroalgae as an important component
of coral reef systems (Vroom et al. 2006; Wismer et al. 2009; Johansson et al. 2010;
Vroom and Braun 2010; Bruno et al. 2014), providing nursery habitat for juvenile fish
(Wilson et al. 2010; Evans et al. 2014; Wilson et al. 2014), a food source for herbivorous
fish (Randall 1967; Pillans et al. 2004; Tolentino-Pablico et al. 2008; Green and Bellwood
10
2009; Bessey and Heithaus 2015), and habitat for invertebrate infauna (Downie et al.
2013; Wernberg et al. 2013).
Since coral reefs are generally oligotrophic systems, herbivory is an important component
for transferring primary production and nutrients to higher trophic levels within the
system (McCook 1999; Bellwood et al. 2004; Fulton et al. 2014). Therefore,
understanding which macroalgae are consumed by fish, their rates of consumption and
assimilation, and the transfers to secondary consumers, is essential in understanding
resource partitioning in juvenile and adult habitats, and the trophodynamics of the
ecosystem (Choat et al. 2002; Horinouchi et al. 2012; Bessey and Heithaus 2015). It also
provides fundamental information to guide the development of the structure and trophic
flows in ecosystem models (Christensen and Pauly 1992; Christensen 2009).
1.2 Modelling Ecosystem Dynamics
Ecosystem models are designed to understand ecosystems and their trophic flows,
illustrate issues with fisheries management, describe theoretical ecology, investigate
policy questions, and address uncertainty around the effectiveness of protected areas
(Christensen 2009). They simulate processes that influence species in an ecosystem, and
are vital for providing managers, researchers, and stakeholders with detailed scientific
information about the structure and functioning of the systems (Food and Agriculture
Organisation 2008). Ecosystem models can also be used in planning processes to manage
and protect marine species (Evans et al. 2014).
Ecopath with Ecosim (EwE) has become one of the most widely used tools for modelling
ecosystem dynamics, and due to its ease of use, ability to be adapted to the system being
analysed, and ready availability (Christensen 2009; Coll et al. 2015; Gartner et al. 2015),
more than 400 models have been published worldwide (Colléter et al. 2013). Ecopath is
a static, mass-balanced model (Pauly et al. 2000; Plagányi 2007) used primarily to
11
parameterise a system for dynamic simulations in Ecosim (Christensen 2009). Ideally,
data should be collected from the region being modelled rather than importing parameters
from other systems (Heymans et al. 2016), though this is often necessary given the lack
of detailed biological data for many systems (Dambacher et al. 2009; Metcalf et al. 2009).
Though output from models may not have high degrees of certainty (Food and Agriculture
Organisation 2008; Christensen 2009) due to the lack of detailed information for
biological parameters in poorly studied systems (Dambacher et al. 2009) and problems
with quality control and validation of data being used in the model (Coll et al. 2015),
EwE models can play an important role in determining habitats that require protection to
enhance conservation of important fish species (Beck et al. 2001; Dahlgren et al. 2006;
Vergés et al. 2011; Coll et al. 2015). These models may be used to inform management
and decision making by improving understanding of the ecosystem and defining
knowledge gaps and scenarios for future research (Fulton et al. 2011).
1.3 The Ningaloo Reef Ecosystem
The Ningaloo Reef is a relatively pristine fringing coral reef in the Ningaloo Marine Park
(NMP), Western Australia (Department of Conservation and Land Management and
Marine Parks and Reserves Authority 2005), where low rainfall and terrestrial runoff
result in low nutrient levels relative to other fringing reefs worldwide (Johansson et al.
2010; Kobryn et al. 2013). Recreational fishing is a key activity within the NMP, with
recreational catches of species such as Lethrinus nebulosus exceeding commercial
catches for the region (Fletcher and Santoro 2015).
Macroalgae comprises a large proportion of the benthic habitat in lagoon areas (Cassata
and Collins 2008; Kobryn et al. 2013; Fulton et al. 2014), thought to be due to the lack
of reef structure or rugosity (Johansson et al. 2010; Vergés et al. 2011). Sargassum
species are the dominant component of macroalgal beds within the Ningaloo lagoon
12
(Johansson et al. 2010), and they undergo seasonal fluctuations in biomass, peaking in
the austral summer (December to February) and senescing in winter (June to August;
Fulton et al. 2014).
While juveniles of fish species that have ecological significance and are important in
commercial and recreational fisheries (Department of Conservation and Land
Management and Marine Parks and Reserves Authority 2005) are known to settle onto
macroalgal habitat within the Ningaloo lagoon (Wilson et al. 2010; Evans et al. 2014;
Wilson et al. 2014), it is not known whether the macroalgae contributes to juvenile diets
(Lugendo et al. 2006), and it is difficult to discern the dietary importance of macroalgae
compared to the protection afforded by the macroalgae as nursery habitat, or other food
sources that are available (Zieman et al. 1984; Choat et al. 2002; Fulton et al. 2016).
Since Ningaloo macroalgal communities are not influenced by high nutrient levels, and
herbivorous fish are not targeted by local fisheries, the macroalgal beds should be close
to their naturally occurring state (Department of Conservation and Land Management and
Marine Parks and Reserves Authority 2005; Johansson 2012; Fulton et al. 2014). They
thus provide a unique tropical ecosystem for researching the trophic role of macroalgae
for juvenile fish.
The primary objective of this Honours project is to determine the importance of
macroalgae to the diets of juvenile fish in the Ningaloo Reef lagoon. This has been
achieved by investigating the following:
1. Review the literature on the contribution of macroalgae to the diets of juvenile
fish in tropical ecosystems, and assess where there are gaps in the published
literature (Chapter 2);
13
2. Determine the diets of juvenile fish that associate with macroalgal beds in the
Ningaloo lagoon to assess the importance of broad dietary categories and how the
diets vary among species and seasons (Chapter 3);
3. Develop an Ecopath with Ecosim (EwE) model for the macroalgal beds of the
lagoon and use the model to evaluate potential changes the productivity of the
system, and changes in fishing effort (Chapter 4).
14
2. Literature Review
2.1 General Introduction
Tropical marine ecosystems contain an array of interconnected habitats and communities
that extend from the shoreline to the open ocean (Dahlgren and Marr 2004; Unsworth et
al. 2014). Shallow-water habitats separate the reef from the coastline, and make up some
of the most ecologically significant and productive ecosystems on Earth (Adams et al.
2006). They include coastal fringe, lagoon and patch reef habitats, estuaries, tidal creeks,
saltmarshes, and intertidal and subtidal sand and mud flats (Dahlgren and Marr 2004;
Adams et al. 2006; Sheaves et al. 2015). Macrophytic shallow-water habitats include
macroalgae, mangroves and seagrass, and are integral components of coastal ecosystems
(Loneragan et al. 2013). They provide essential ecosystem services (Costanza et al.
1997), and provide fauna with a source of primary production, nursery grounds, habitat,
food sources, and protection from predation (Dahlgren and Marr 2004).
A variety of abundant and diverse animal species utilise shallow-water habitats for at least
part of their life cycle, especially during juvenile stages (Nagelkerken et al. 2000;
Dorenbosch 2006; Wilson et al. 2010; Wilson et al. 2014). These ecosystems are often
referred to as nurseries due to the variety of resources provided to juveniles, and flow-on
effects for adult productivity (Beck et al. 2001). Juvenile fish use these habitats to
maximise the growth rate to predation ratio until they are less susceptible to predation
(Sogard 1997; Dahlgren and Eggleston 2000; Adams et al. 2004; Grol et al. 2008;
Nakamura et al. 2012). The benefits from these habitats to fish production and diversity
extend to fisheries (Dahlgren and Marr 2004), with the diversity and extent of
macrophytes being positively correlated with key metrics, including total catch, catch per
unit effort, maximum sustainable yield (Loneragan et al. 2013).
15
While there has been abundant research into the roles of seagrass and mangrove habitats
in tropical ecosystems (Heck Jr. and Weinstein 1989; Loneragan et al. 1997; Loneragan
et al. 1998; Nagelkerken et al. 2000; Cocheret de la Morinière et al. 2003; Heck Jr. et al.
2003; Nakamura et al. 2003; Adams et al. 2004; Dahlgren and Marr 2004; Manson et al.
2005; Adams et al. 2006; Dahlgren et al. 2006; Dorenbosch 2006; Lugendo et al. 2006;
Grol et al. 2008; Mumby and Hastings 2008; Horinouchi et al. 2012; Nakamura et al.
2012; Loneragan et al. 2013; Sheaves et al. 2015), macroalgae has historically been
ignored (Manson et al. 2005; Nagelkerken et al. 2015) or considered to be symptomatic
of degraded coral reef ecosystems (Chong-Seng et al. 2012). Recent research in the north-
west of Western Australia has shown that macroalgal beds act as nursery habitat,
analogous to seagrass and mangrove habitats elsewhere (Wilson et al. 2010; Wilson et al.
2014; Evans et al. 2014; Fulton et al. 2014); however, little is known about the role of
macroalgae for the dietary requirements of juvenile fish. Increasing our knowledge of the
ecological importance of macroalgal shallow-water habitats is essential in determining
the appropriate management and conservation measures (Adams et al. 2006) required to
sustain diversity and productivity of a suite of ecologically and commercially important
fish species at Ningaloo Reef (Wilson et al. 2010).
Dietary information provides an understanding of trophic flows and helps to identify the
major functional groups in ecosystems (Green and Bellwood 2009), fundamental
information for the structure of ecosystem models (Christensen and Pauly 1992;
Christensen 2009). Ecosystem models vary in complexity and purpose, simulate
processes that influence species in an ecosystem, and are vital for providing managers,
researchers, and stakeholders with detailed scientific information (Food and Agriculture
Organisation 2008) and identifying knowledge gaps in the structure and function of the
systems (Food and Agriculture Organisation 2008). Ecopath with Ecosim (EwE) has
become one of the most widely used tools for modelling ecosystem dynamics due to its
16
ease of use, ability to be adapted to the system being analysed and free availability
(www.ecopath.org) (Christensen 2009; Gartner et al. 2015). Though output from models
may not have high degrees of certainty (Food and Agriculture Organisation 2008;
Christensen 2009), they can play an important role in determining habitats that require
protection to enhance conservation of important fish species (Beck et al. 2001; Dahlgren
et al. 2006; Vergés et al. 2011; Coll et al. 2015).
This review will provide the background to the role of macrophytes, particularly
macroalgae, in the natural history of ecologically and socially important fish, with a focus
on their importance to tropical ecosystems. It also summarises the knowledge of
macroalgae and its contribution to trophic flows in tropical marine ecosystems. It will
illustrate the importance of ecosystem models in providing information based on
simulations derived from local data and how they can be used to improve management of
shallow-water macroalgal habitats, which can have positive flow-on effects for fish
communities.
2.2 Tropical Lagoon Habitats
2.2.1 Role of Lagoon Habitats
Ecosystem Services
Shallow-water lagoon ecosystems serve many important functions through the provision
of ecosystem services (Costanza et al. 1997) and are a key component in the lives of
millions of people worldwide (Moberg and Folke 1999; McManus et al. 2000). These
include protecting shorelines against erosion, removing suspended solids from the water
column, oxygenating waters, carbon sequestration, recycling nutrients, providing habitat,
and supporting fisheries production (Costanza et al. 1997; Dahlgren and Marr 2004;
Manson et al. 2005; Unsworth et al. 2014), with an estimated average global value of
US$375 billion per year (Costanza et al. 1997).
17
Macrophytic shallow-water habitats such as mangroves, seagrass beds, and macroalgal
patches have high rates of primary productivity, and support biologically diverse fish and
benthic communities (Beck et al. 2001; Dahlgren and Marr 2004). They provide
important ecological functions such as nursery, refuge and foraging habitats for juveniles
of species that are common on coral reefs (Dahlgren and Marr 2004; Dorenbosch 2006)
and are crucial in maintaining reef diversity and stability (Lee and Lin 2015). However,
separating their role as a dietary source from that of habitat is difficult (Zieman et al.
1984; Fulton et al. 2016)
Habitat
Fish community composition varies across shallow-water macrophyte habitats
(Laegdsgaard and Johnson 2001; Boyer et al. 2004; Fulton et al. 2016) and structural
complexity of marine habitats is a major factor in determining localised abundance and
diversity of coral reef fish (Adams et al. 2004; Almany 2004). Habitat complexity impacts
on competition, predation rates and foraging ability, ultimately having an impact on fish
abundance, biomass, and fitness (Botsford et al. 1997; Dahlgren and Eggleston 2000;
Almany 2004; Hoey and Bellwood 2011; Fulton et al. 2016). Habitat selection aims to
maximise energy intake while minimising the risk of predation (Adams et al. 2004; Grol
et al. 2008), and understanding the links of fish to habitats is important for understanding
the vulnerability and resilience of marine ecosystems, as well as for future fisheries
management (Manson et al. 2005; Munday et al. 2008; Loneragan et al. 2013).
Until recently, most research has focused on mangrove and seagrass habitats since they
are the most abundant nearshore habitat adjacent to tropical coral reef ecosystems (Heck
Jr. and Weinstein 1989; Loneragan et al. 1997; Loneragan et al. 1998; Nagelkerken et al.
2000; Cocheret de la Morinière et al. 2003; Heck Jr. et al. 2003; Nakamura et al. 2003;
Adams et al. 2004; Dahlgren and Marr 2004; Manson et al. 2005; Dorenbosch 2006;
18
Adams et al. 2006; Dahlgren et al. 2006; Lugendo et al. 2006; Grol et al. 2008; Mumby
and Hastings 2008; Horinouchi et al. 2012; Nakamura et al. 2012; Blaber 2013;
Loneragan et al. 2013; Sheaves et al. 2015), and many fish species depend on them for
all or part of their lifecycle (Manson et al. 2005). Recently, research has focused on the
importance of macroalgae as habitat in tropical ecosystems with mixed results (Wilson et
al. 2010; Wilson et al. 2012; Wilson et al. 2014; Evans et al. 2014).
In addition to providing habitat for small invertebrates (Wernberg et al. 2013), studies at
Ningaloo Reef, Western Australia (Figure 2.1) suggest that macroalgal beds provide
habitat for juveniles of a number of fishes with ecological and fisheries significance
(Wilson et al. 2010). The significance of macroalgal cover to fish production and diversity
however, varies among studies; several have found a positive correlation with fish
biomass (Wismer et al. 2009; Wilson et al. 2012; Evans et al. 2014), while others report
declines in foraging, species richness, diversity and the number of functional groups as
Figure 2.1 Ningaloo Reef, Western Australia
19
homogenous macroalgae dominates coral reef habitats (Hoey and Bellwood 2011;
Chong-Seng et al. 2012; Rasher et al. 2013). This illustrates the importance of habitat
complexity to fish rather than the type of habitat, since homogenous macroalgae appears
to have negative effects on fish assemblages (Nagelkerken et al. 2000; Heck Jr. et al.
2003; Adams et al. 2004; Wilson et al. 2012).
Food Webs
Tropical ecosystems typically have high rates of herbivory (Hay 1991), with herbivorous
fish being more abundant and diverse than in temperate ecosystems (Ogden and Lobel
1978; Tolentino-Pablico et al. 2008; Green and Bellwood 2009). Herbivory plays a range
of ecological roles in coral reef functioning that maintain spatial heterogeneity and
convert primary production into forms available to higher trophic levels (Choat 1991).
Herbivorous fish have varied feeding modes and consume a range of plant matter (Choat
1991; Green and Bellwood 2009; Horinouchi et al. 2012; Blaber 2013). Species that feed
on macroalgae are a key functional group since they help to prevent or reverse phase
shifts from coral dominated systems to those dominated by macroalgae when reefs have
been impacted by external disturbances such as severe storms or coral bleaching events
(McCook 1999; Bellwood et al. 2004; Hughes et al. 2007; Green and Bellwood 2009;
Rasher et al. 2013; Welsh and Bellwood 2014). Healthy reefs are characterised by high
feeding complementarity or functional redundancy (Bellwood et al. 2004; Hoey and
Bellwood 2009; Green and Bellwood 2009; Pratchett et al. 2011; Rasher et al. 2013).
Feeding complementarity occurs when taxa consume different taxa or perform different
roles in an ecosystem that have a complementary or additive effect (Burkepile and Hay
2011; Hoey et al. 2013; Rasher et al. 2013), while functional redundancy occurs when
sympatric species perform the same ecological function (niche overlap) in an ecosystem
(Pratchett et al. 2011; Burkepile and Hay 2011; Welsh and Bellwood 2014).
20
Herbivorous fish feed in a variety of modes and their presence in macroalgal beds should
not imply that they are feeding on it (Fox and Bellwood 2008). In shoreward zones of the
Great Barrier Reef, the abundance of herbivorous fish was not related to their rate of
removing or reducing macroalgal biomass; only juveniles fed on macroalgae, while
mature individuals and other species were hypothesised to be feeding on epiphytes (Fox
and Bellwood 2008). Furthermore, some species belonging to important functional
groups may not be conspicuous in underwater visual censuses (Fox and Bellwood 2008).
The diet of larval or post-larval stages of herbivorous fish species may also differ from
that of older life history stages, with some studies finding that they are planktivorous,
then switching to omnivory and herbivory with ontogeny (Choat 1991; Benavides et al.
1994a; Choat et al. 2002).
Not all algae eaten by fish may have been consumed intentionally (Ogden and Lobel
1978) and less than half of reportedly herbivorous species had stomach contents
dominated by living algae (Choat et al. 2002). Macrophytes are also used as habitat,
protection, and a food source by small, mobile invertebrates (Wernberg et al. 2013;
Downie et al. 2013), which may be targeted as prey. In southern Japan, 44% of fish in a
coral reef system had ingested seagrass, though it may have been ingested incidentally
with epifaunal prey, since harpacticoid copepods were the most important food item in
gut contents analyses (Nakamura et al. 2003) and invertivorous fish are highly abundant
in censuses of macroalgal associated fish assemblages (Yamada et al. 2012; Evans et al.
2014; Wilson et al. 2014).
Nursery Function
Shallow-water ecosystems provide many functions that influence and support abundance
and diversity of juvenile fish, and are often referred to as nurseries (Moberg and Folke
1999; Laegdsgaard and Johnson 2001; Beck et al. 2001; Heck Jr. et al. 2003; Dahlgren
21
and Marr 2004; Manson et al. 2005; Adams et al. 2006; Dahlgren et al. 2006; Dorenbosch
2006; Wilson et al. 2010; Evans et al. 2014; Sheaves et al. 2015; Nagelkerken et al. 2015).
The nearly complete absence of juveniles in deeper habitats of a coral reef illustrates the
importance of shallow-water habitats for nurseries (Nagelkerken et al. 2000). The nursery
hypothesis stipulates that species that associate with reef habitat as adults, principally
inhabit shallow-water habitats at a distance from the reef after settling out of their pelagic
larval phase (Beck et al. 2001; Dorenbosch 2006). This is followed by a directional
migration from juvenile to adult habitat (Heck Jr. and Weinstein 1989; Cocheret de la
Morinière et al. 2003).
In comparison to adult habitats, nursery habitats are beneficial to juveniles due to the high
availability of food, increased living space, high structural complexity, and reduced
visibility, which combined, result in high growth rates and reduced levels of predation
(Nagelkerken et al. 2000; Dahlgren and Eggleston 2000; Adams et al. 2004; Manson et
al. 2005; Dahlgren et al. 2006; Dorenbosch 2006; Lee and Lin 2015). In the last decade,
extensive research has investigated the dependence of tropical fish on tropical shallow-
water habitats as estuaries due to their role in supporting marine species of economic and
functional value (Manson et al. 2005; Blaber 2013).
The value of nurseries varies with geography, and is influenced by biotic, abiotic, and
landscape factors (Nagelkerken et al. 2000; Beck et al. 2001; Dorenbosch et al. 2005).
Four components are considered essential in determining the value of nursery habitat:
density of juvenile fish, growth rates, survival rates, and connectivity with adult habitats
(Sogard 1997; Beck et al. 2001; Grol et al. 2008; Sheaves et al. 2015), though a high
value for one factor does not necessarily increase recruitment to adult populations (Beck
et al. 2001; Dorenbosch et al. 2005; Manson et al. 2005). In fact, juvenile survival is not
always significantly higher on macrophyte habitat compared to coral reef habitat
22
(Dorenbosch 2006; Nakamura et al. 2012; Lee and Lin 2015); the juveniles choose the
macrophytes as a trade-off to maximise the ratio between mortality risk and rate of growth
so that the fish can grow quickly to a size that is less susceptible to predation (Sogard
1997; Adams et al. 2004; Grol et al. 2008; Nakamura et al. 2012).
The role of mangroves and seagrass as nursery habitats has been well documented,
particularly in the Caribbean (Dahlgren and Eggleston 2000; Nagelkerken et al. 2000;
Dorenbosch et al. 2004; Manson et al. 2005; Adams et al. 2006; Dorenbosch et al. 2006;
Nakamura et al. 2012). Historically, macroalgae has been overlooked as a potential
nursery, or as a bridging habitat for ontogenetic migrations (Manson et al. 2005;
Nagelkerken et al. 2015; see also Nagelkerken et al. 2000; Beck et al. 2001; Dorenbosch
et al. 2005; Dorenbosch et al. 2006). Recently, shallow-water macroalgal beds have been
identified as important nurseries in the Indo-Pacific, particularly for large-bodied species
(Wilson et al. 2010; Evans et al. 2014).
Our understanding of the importance of shallow-water nursery habitats to maintaining
adult coral reef fish and invertebrate communities is limited, because the assumption that
there are large contributions of individuals to adult populations is relatively untested
(Beck et al. 2001; Manson et al. 2005; Adams et al. 2006; Loneragan et al. 2013; Sheaves
et al. 2015). Knowledge of the life history of individual species is essential, and data on
the functional roles of nursery habitats, factors that create site-specific variability, and
drivers of ontogenetic habitat shifts is required to understand population dynamics
(Dahlgren and Eggleston 2000; Beck et al. 2001; Adams et al. 2006). This will help to
conserve and manage both nursery habitat quality, and the fish communities that benefit
from the juveniles that migrate from them (Beck et al. 2001).
23
2.2.2 Macroalgae
Macroalgal beds are a prominent component of tropical and temperate shallow-water
ecosystems (Carr 1989; Anderson 1994; Steneck and Erlandson 2002; Cassata and
Collins 2008; Wilson et al. 2010; Wilson et al. 2012; Evans et al. 2014; Fulton et al.
2014). They play an important role in primary production, stabilising sediments,
competing with benthic organisms, and providing a source of food and complex habitat
(Thomsen et al. 2010; Wilson et al. 2010); however their importance to coral reef
community dynamics is poorly understood (Evans et al. 2014; Wilson et al. 2014) and
there is conflicting information on their role in influencing the diversity and abundance
of fish assemblages. For example, where Hoey et al. (2011) observed herbivorous fish
avoided high density macroalgae, Wilson et al. (2014) found that heterogeneity in
structural complexity of macroalgal habitat influenced fish distribution, and high holdfast
density was associated with greater density, biomass and species richness of functionally
important fish.
The majority of studies on macroalgae focus either on temperate regions (Carr 1989;
Anderson 1994; Steneck and Erlandson 2002), or the competitive effects of algae on coral
reefs (McCook 1999; McCook et al. 2001; Hughes et al. 2007; Webster et al. 2015); yet
approximately 90% of herbivorous marine fish species live in coral reef ecosystems
(Tolentino-Pablico et al. 2008). In temperate rocky reefs, the abundance of macroalgae
explains local variation in the species composition of fish recruitment (Carr 1989;
Anderson 1994), while there is little published research for patterns of recruitment onto
macroalgae in tropical regions outside of Western Australia (Wilson et al. 2010; Yamada
et al. 2012; Wilson et al. 2014; Evans et al. 2014), and their role as nurseries is poorly
understood compared to temperate systems (Adams et al. 2006). Important fishery
species recruit to macroalgal beds during the peak macroalgal biomass in summer
(Wilson et al. 2010; Terazono et al. 2012; Loneragan et al. 2013), and reductions in
24
macroalgal biomass during winter may affect their contributions to fisheries (Evans et al.
2014). This makes macroalgal beds worthy of increased attention to try to understand
their role in contributing to adult populations.
Presence in Tropical Ecosystems
Macroalgae, such as Sargassum species, grow in oligotrophic areas adjacent to coral reefs
(Littler and Littler 2007; Fulton et al. 2014), though there is considerable spatial variation
in abundance and composition (Vergés et al. 2011). Frondose algae can be fast growing
with high turnover rates, therefore, they increase their abundance when settlement space
is made available through reef disturbances (Moberg and Folke 1999; Diaz-Pulido and
McCook 2003; Hughes et al. 2007; Littler and Littler 2007). Macroalgal abundance is
important as a bio-indicator of reef degradation (Littler and Littler 2007; Bruno et al.
2014), and overabundance may be due to several factors, such as high nutrient levels,
terrestrial runoff, overfishing leading to trophic cascades, and reduced herbivory
(McCook et al. 2001; Littler and Littler 2007; Hughes et al. 2007; Johansson et al. 2010;
Fulton et al. 2014).
Many studies show that there is a negative correlation between macroalgal abundance
and coral recruits (McCook et al. 2001; Webster et al. 2015), and phase shifts to
macroalgal dominance over corals can result in reduced fecundity, recruitment, and
survival of coral (Hughes et al. 2007; Bruno et al. 2014); this has implications for a
system’s resilience, ecological communities and economic potential (Hughes et al. 2007;
Chong-Seng et al. 2012). Phase shifts can be initiated by: storms, climate change, reduced
light levels and settlement habitat for coral larvae, the effects of microbes and
allelochemicals, reduced oxygen exchange, uptake of nutrients and food particles by
corals and abrasion by macroalgae (McCook 1999; McCook et al. 2001; Biber et al. 2004;
Fulton et al. 2014; Webster et al. 2015).
25
Alternatively, the absence of macroalgae in tropical ecosystems may be as detrimental as
an overabundance, since it may be critical for nursery habitat and food webs (Johansson
et al. 2010; Bruno et al. 2014). Though high levels of macroalgae are symptomatic of
degraded coral reef habitat, it may be a natural component of many tropical ecosystems
(Vroom et al. 2006; Tolentino-Pablico et al. 2008; Wismer et al. 2009; Wilson et al. 2010;
Vergés et al. 2012; Hoey et al. 2013; Wernberg et al. 2013; Bruno et al. 2014; Evans et
al. 2014; Wilson et al. 2014). Research shows that macroalgae may also have positive
effects on corals, and extended periods of overgrowth have not had significant effects on
coral populations (McCook et al. 2001). Therefore, there are questions regarding how
much macroalgae is considered “normal” in tropical ecosystems, and whether it can be
considered responsible for coral reef degradation (Vroom et al. 2006; Hoey and Bellwood
2011; Bruno et al. 2014). It has been argued that tropical ecosystems can have abundant
assemblages of macroalgae, yet remain healthy and functionally intact (Vroom et al.
2006; Wismer et al. 2009). There are distinct reef systems operating at the Great Barrier
Reef with high abundances of macroalgae in inshore zones (Wismer et al. 2009), which
is also evident at Ningaloo Reef (Cassata and Collins 2008; Johansson et al. 2010; Kobryn
et al. 2013).
Herbivory and Trophic Flows
Macroalgae are a key component of food webs in tropical ecosystems (Bruno et al. 2014),
whether as a direct food source for herbivorous fish (Randall 1967; Tolentino-Pablico et
al. 2008), or as habitat supporting invertebrates that are prey to fish from higher trophic
levels (Wernberg et al. 2013). Since tropical ecosystems are generally oligotrophic
systems, transfers of nutrients and primary productivity to higher trophic levels are an
essential component of ecosystem function (McCook 1999) that is reliant on herbivorous
functional groups (Bellwood et al. 2004). Senescent macroalgae that detaches and drifts
to other areas may be important for transferring primary productivity (Fulton et al. 2014),
26
as are fish that undergo ontogenetic migrations, though there are differing opinions about
the functional role of adult fish (Vermeij et al. 2013; Welsh and Bellwood 2014).
Understanding the dietary targets of herbivorous fish and the rates of consumption,
assimilation and transfer to secondary consumers is essential to understanding the
trophodynamics of the system (Choat et al. 2002; Bessey and Heithaus 2015).
In tropical ecosystems, herbivorous fish are the dominant consumer balancing
macroalgae production (Bellwood et al. 2004; Hughes et al. 2007; Tolentino-Pablico et
al. 2008), a role predominantly filled by invertebrates in temperate systems (Tolentino-
Pablico et al. 2008). There is strong evidence that herbivores provide a top-down role in
moderating competition between macroalgae and coral reefs to prevent phase shifts to
algal dominated systems (Boyer et al. 2004; Hughes et al. 2007; Hoey and Bellwood
2011; Rasher et al. 2013), which marks them as the most important fish functional group
on coral reefs (Bellwood et al. 2004; Hughes et al. 2007; Pratchett et al. 2011). In the
absence of this herbivorous control, macroalgae may dominate tropical ecosystems,
having a neutral or negative effect on herbivore activity and creating ecological feedbacks
from which corals may not recover (McCook et al. 2001; Hoey and Bellwood 2009; Hoey
and Bellwood 2011; Webster et al. 2015).
The epilithial algal matrix (EAM) consists of filamentous and turf algae among other
microbenthos and phytoplankton components, which is an important component of
herbivorous fish diets, and is often selectively consumed (Bellwood et al. 2004;
Tolentino-Pablico et al. 2008); however there is limited data on the role of algae in the
diets of juvenile coral reef fish (Lugendo et al. 2006), especially from macroalgal beds
that the fish recruit to. Sargassum species are actively eaten by herbivorous species
(Vergés et al. 2011) and may be an important source of primary production in nutrient
27
poor tropical systems (Fulton et al. 2014) that has not been investigated for juvenile fish
species.
2.2.3 Ontogenetic Shifts
Many coral reef fish undertake ontogenetic habitat shifts, and the movement of fish from
nursery grounds to adult coral reef habitats is well documented (Dahlgren and Eggleston
2000; Nagelkerken et al. 2000; Cocheret de la Morinière et al. 2003; Gillanders et al.
2003; Manson et al. 2005; Dorenbosch et al. 2005; Adams et al. 2006; Wilson et al. 2010;
Lee and Lin 2015); however the full ecological significance of ontogenetic dispersal is
poorly understood (Gillanders et al. 2003; Mumby and Hastings 2008). Coral reef fish
can be divided into three categories based on the benthic phase of their life history strategy
(Adams et al. 2006). Two categories, habitat specialists and habitat generalists, have
minor or poorly defined patterns of ontogenetic habitat shift, while the other category,
ontogenetic shifters, undergo complex habitat, dietary, and behavioural shifts from
settlement to adult life stages (Adams et al. 2006; Mumby and Hastings 2008). Those
species that undergo ontogenetic habitat shifts are thought to maintain functional
connectivity between habitats, and are considered key components of coral reef
ecosystem resilience (Vermeij et al. 2013; Welsh and Bellwood 2014).
The availability of food resources and avoidance of predators are the main drivers of
population dynamics and distribution patterns in tropical shallow-water systems (Werner
et al. 1983; Werner and Hall 1988; Dahlgren and Eggleston 2000; Grol et al. 2008;
Kimirei et al. 2013; Lee and Lin 2015). Large bodied fish species inhabit particular
habitat niches and have changing dietary requirements as they mature (Cocheret de la
Morinière et al. 2003; Horinouchi et al. 2012; Nakamura et al. 2012). As fish grow, it is
also thought that they become too large to receive optimal benefits provided by nursery
habitats, forcing them to migrate to deeper habitats to maximise the ratio of risk of
28
mortality to rate of growth (Werner et al. 1983; Werner and Hall 1988; Dahlgren and
Eggleston 2000; Nagelkerken et al. 2000; Laegdsgaard and Johnson 2001; Adams et al.
2006; Dorenbosch 2006; Kimirei et al. 2013; Lee and Lin 2015). Over large spatial and
temporal scales, dispersal may also occur to seek resources, breed, and avoid
unfavourable environmental conditions (Sugden and Pennisi 2006).
Research in the Caribbean has shown that the extent and type of seagrass and mangrove
habitats may strongly influence the structure of adult fish and invertebrate populations on
nearby reefs (Dorenbosch et al. 2004; Mumby 2004; Dorenbosch et al. 2005), which may
also impact fisheries (Dahlgren and Marr 2004; Adams et al. 2006). The effect may not
be as strong in the Indo-West Pacific, and macroalgae may be just as significant for
populations of coral reef fish (Manson et al. 2005; Blaber 2013). In north-western
Australia, juveniles were observed exclusively on macroalgal beds, while adults were
frequently seen on coral reefs, implying some ontogenetic habitat shift analogous to that
observed from seagrass and mangrove habitats (Wilson et al. 2010; Fulton et al. 2014;
Evans et al. 2014). Understanding large-scale ontogenetic habitat use for ecologically and
functionally important species is critical for ecosystem, fisheries, and species
conservation and management (Manson et al. 2005; Adams et al. 2006; Welsh and
Bellwood 2014).
2.3 The Ningaloo Reef Marine System
The Ningaloo Reef, located in the north-west of Western Australia, is Australia’s largest
fringing coral reef and is located in the Ningaloo Marine Park (state waters) within the
central western IMCRA transition provincial bioregion (Commonwealth of Australia
2006; Commonwealth of Australia 2015c). It is the only large fringing coral reef on the
western margin of a continent, which is due to the southerly flow of the tropical Leeuwin
current (Taylor and Pearce 1999). The average annual rainfall is less than 270 mm
29
(Commonwealth of Australia 2015a) and the average solar exposure ranges from 4.0 to
8.1 kWh.m-2 (Commonwealth of Australia 2015b). Due to the low rainfall and runoff,
land-based sources of nutrients are low relative to other fringing coral reefs worldwide
(Johansson et al. 2010; Kobryn et al. 2013), though there are pulses of freshwater runoff
associated with cyclone events that change surface salinities and provide a physical
disturbance to the system (Biber et al. 2004; Loneragan et al. 2013; Feng et al. 2015).
Commercial fishing has been limited in the marine park and herbivorous fish are seldom
targeted by recreational fishers, leaving populations relatively untouched (Webster et al.
2015). These factors, as well as the remoteness of the reef and the undeveloped coastline,
combine to maintain the reef in a relatively pristine state (Johansson et al. 2010; Webster
et al. 2015).
Ningaloo Reef contains a shallow lagoon (0-4 m depth) that ranges from 1 to 5 km wide
(Cassata and Collins 2008) and is characterised by rocky pavement and unconsolidated
sand substrates vegetated by macroalgal patches and sparse corals (Cassata and Collins
2008; Kobryn et al. 2013; Fulton et al. 2014). Since the Leeuwin Current depresses
coastal upwelling that is common on western continental coasts and allochthonous
sources of nutrients are low (Hanson et al. 2005), Ningaloo macroalgal communities are
not influenced by elevated nutrient levels. As a consequence, they should be relatively
close to their naturally-occurring state (Department of Conservation and Land
Management and Marine Parks and Reserves Authority 2005; Johansson et al. 2010;
Fulton et al. 2014), providing a unique tropical ecosystem for research into the role of
macroalgae for juvenile fish.
2.3.1 Prevalence of Macroalgae in the Reef Ecosystem
Although the extent and biomass of macroalgal beds at Ningaloo vary spatially (Cassata
and Collins 2008; Johansson et al. 2010; Kobryn et al. 2013; Fulton et al. 2014; Webster
30
et al. 2015), it has been estimated that these habitats cover approximately 2,200 ha of the
lagoon area in the Ningaloo Marine Park (Department of Conservation and Land
Management and Marine Parks and Reserves Authority 2005), representing
approximately 51% of the benthic habitats shallower than the 20 m depth contour (Kobryn
et al. 2013). One genus, Sargassum, comprises more than 40% of the macroalgal cover
(Johansson et al. 2010) with reports of up to 88% (Doropoulos et al. 2013). Since there
are low levels of terrestrial nutrient input, the lack of both reef structure and rugosity are
thought to be responsible for high macroalgal abundance due to decreased protective
habitats for herbivore populations (Johansson et al. 2010; Vergés et al. 2011).
Macroalgal habitats at Ningaloo have pulses in structure and biomass (Wilson et al. 2014)
that are influenced by seasonal factors such as photosynthetically active radiation (PAR),
surface wind speed, and sea temperature (Fulton et al. 2014). Sargassum biomass differed
up to 18-fold in a 26 month period (Fulton et al. 2014), and holdfast density was greater
in summer than winter, though the differences were not uniform between locations
(Wilson et al. 2014). Nutrients, light, wave-driven water flow, and herbivory were not
found to be significant drivers of algal biomass at Ningaloo (Fulton et al. 2014). Since
Ningaloo Reef is a relatively pristine habitat, and there are no consistent sources of
nutrients to support increased macroalgal biomass, the high areas of macroalgal cover
probably represent the natural state and do not indicate a degraded tropical ecosystem
(Johansson et al. 2010).
Stable habitats are essential for fish life cycles and those that maintain structure during
biomass fluctuations are likely to be essential in supporting fish communities across
seasons (Wilson et al. 2014). Juvenile fish species often recruit to macroalgal beds at
Ningaloo (Wilson et al. 2010) coinciding with the summer peak in algal biomass (Wilson
et al. 2010; Terazono et al. 2012) from November to December (McIlwain 2003).
31
Macroalgal beds provide ecological functions similar to that afforded by seagrass or
mangrove habitats in other systems (Thomsen et al. 2010; Wilson et al. 2010), therefore,
fauna residing in unstable algal habitats must migrate or adapt in order to survive (Wilson
et al. 2014).
2.3.2 Fish Present in the Ningaloo Reef Lagoon
Surveys have recorded over 500 species of finfish from 234 genera and 86 families within
the Ningaloo Reef, some of which are important to commercial and recreational fisheries
(Department of Conservation and Land Management and Marine Parks and Reserves
Authority 2005). Many species are essential for ecosystem function, fulfilling key
ecological roles in maintaining ecosystem health and resilience (Bellwood et al. 2004;
Green and Bellwood 2009). Hoey et al. (2013) defined ecosystem functions as “the
movement of energy through trophic and/or bioconstructional pathways” and Green &
Bellwood (2009) define functional groups as “a collection of species that perform a
similar function, irrespective of their taxonomic affinities”. Understanding the trophic
guild structure of fish that use the algal beds is important for identifying resource
partitioning in both juvenile habitats and adult habitats (Horinouchi et al. 2012). A review
of estuarine functional groups categorises feeding mode into seven functional groups
(Elliott et al. 2007) which are applicable to tropical as well as temperate habitats (Blaber
2013).
Functional Groups
A key process occurring on coral reefs is herbivory, which incorporates several groups
that mediate competition between algae and coral (Bellwood et al. 2004; Vergés et al.
2012). At Ningaloo, herbivorous fish contain the most abundant functional groups in
back-reef (69% of individuals) and lagoon (99% of individuals) habitats, and the
distribution of those groups impacts benthic community structure (Johansson et al. 2010).
32
These groups are categorised according to diet and mode of feeding, into browsers, and
grazers (which includes scrapers and bioeroders) (Bellwood et al. 2004; Green and
Bellwood 2009; Hoey and Bellwood 2009; Hoey and Bellwood 2011), all of which play
specific, though complementary, roles in preventing macroalgal dominance of coral reefs
(Bellwood et al. 2004; Green and Bellwood 2009; Rasher et al. 2013).
Piscivores are another functional group that has the potential to influence fish populations
and macroalgal abundance (Wilson et al. 2012). Overfishing has the potential to locally
reduce the abundance of predatory species (Roberts 1995), which has been shown in
differences between sanctuary and recreational fishing zones along the Ningaloo Reef
(Babcock et al. 2008). Overfishing can lead to a decline in piscivorous species causing
fishers to target more abundant species from lower trophic levels (McManus et al. 2000),
a trend that has been occurring worldwide (Pauly et al. 1998). As herbivore abundance
decreases, top-down controls of macroalgae decline, and coral reefs may be more
susceptible to phase shifts to a macroalgal dominated state after a disturbance event
(McManus et al. 2000). This scenario may be prevented by encouraging resilient reefs
and maintaining healthy predator/herbivore populations (Munday et al. 2008).
Other functional groups of interest that associate with macroalgal beds at Ningaloo Reef
include omnivores, and zoobenthivores. Representative species found in macroalgal beds
of Ningaloo include members of the families: Acanthuridae, Lethrinidae, Lutjanidae,
Mullidae, and Siganidae (Table 2.1).
Role of Macroalgal Patches for Ningaloo Fish Species
The composition of larval and adult fish communities at Ningaloo Reef varies both
between and within years (McIlwain 2003; Wilson et al. 2014), with habitats being
selectively used at different life stages (Wilson et al. 2010; Wilson et al. 2014). Juvenile
fish density and species richness are significantly higher in summer months than winter
33
(Wilson et al. 2014), and there is evidence that shallow-water macroalgal habitats play a
role as nursery habitat for juvenile fish in the north-west of Western Australia (Wilson et
al. 2010; Evans et al. 2014; Wilson et al. 2014). Macroalgal beds are also a source of
primary productivity for adult herbivorous fish, and provide prey resources for predatory
fish that feed on juvenile fish and invertebrates associated with the algal habitat
(Wernberg et al. 2013; Wilson et al. 2014).
Table 2.1 Summary of some representative families found in macroalgal habitats of the Ningaloo
Reef lagoon and their functional groups, as defined by Elliott et al.(2007), for adults and
juveniles. Classification of adult functional groups based on information in FishBase (Froese
and Pauly 2015). Classification of juveniles based on mixed age group studies (Randall 1967;
Choat et al. 2002; Cocheret de la Morinière et al. 2003; Nakamura et al. 2003; Pillans et al.
2004; Tolentino-Pablico et al. 2008; Farmer and Wilson 2011; Horinouchi et al. 2012; Hoey et
al. 2013) due to lack of data on post-recruitment juveniles.
Fish Family
Representative
species at
Ningaloo
Functional
group as an
adult
Functional
group as a
juvenile
Importance at
Ningaloo Reef
Acanthuridae
(surgeonfish) Naso fageni
herbivore
detritivore
zooplanktivore
detritivore
zooplanktivore grazer
Lethrinidae
(emperors)
Lethrinus
atkinsoni
L. nebulosus
piscivore
zoobenthivore
opportunist
zoobenthivore
targets of
recreational
fishery
Lutjanidae
(snappers)
Lutjanus kasmira
L. fulviflamma
piscivore
zoobenthivore
zooplanktivore
piscivore
zoobenthivore
zooplanktivore
targets of
recreational
fishery
Mullidae
(goatfish)
Parupeneus
spilurus
P. barberinoides
zoobenthivore
piscivore
zoobenthivore
zooplanktivore
bioturbation
by-catch of
recreational
fishery
Siganidae
(rabbitfish)
Siganus
fuscescens herbivore
detritivore
herbivore
zooplanktivore
grazer
browser
While the diets of many species that associate with macroalgal beds as adults have been
relatively well studied (Randall 1967; Pillans et al. 2004; Elliott et al. 2007; Tolentino-
Pablico et al. 2008; Green and Bellwood 2009; Wismer et al. 2009; Farmer and Wilson
2011; Vergés et al. 2012; Rasher et al. 2013; Wernberg et al. 2013; Downie et al. 2013;
34
Welsh and Bellwood 2014), little is known of the diets of post-settlement juveniles and
sub-adult fish in these systems (Table 2.1). It is not known whether juveniles of
herbivorous species use macroalgae or their epiphytes as a primary source of nutrition, or
whether they undergo an ontogenetic shift in diet (Choat 1991; Benavides et al. 1994a;
Choat et al. 2002), since it can be difficult to determine the target of fish feeding on
macroalgae, particularly when there is a mosaic of algae, epiphytes, meiofauna and
detritus available (Choat et al. 2002).
2.4 Ecosystem Models
Ecosystem models simulate processes that influence species in an ecosystem, and are vital
for providing managers, researchers, and stakeholders with detailed scientific information
about the structure and functioning of the systems (Food and Agriculture Organisation
2008). They have been built to understand ecosystems and their trophic flows, illustrate
issues with fisheries management, describe theoretical ecology, investigate policy
questions, and address uncertainty around the effectiveness of protected areas
(Christensen 2009). They offer a complementary technique to empirical studies (Gartner
et al. 2015) and are an important component of management strategy evaluation (MSE),
which attempts to identify and model uncertainty, determine the effects of management
actions, balance ecosystem dynamics, and identify trade-offs between management
strategies (Food and Agriculture Organisation 2008; Thébaud et al. 2014).
2.4.1 Use of Ecosystem Models in Ecosystem/Natural Resource Management
The output derived from ecosystem models can be used in a variety of ways, though it
needs a level of insight to be of any value (Food and Agriculture Organisation 2008).
Problems with acceptance of model output are driven by various factors (Jorgensen and
Chon 2009; Fulton et al. 2011), however, decisions must be made, and the models are
invaluable for reproducing events that may not be ethically replicated in real systems or
35
on limited time-frames (Food and Agriculture Organisation 2008; Fulton et al. 2011).
Other benefits derived from ecosystem models include permitting less biased
interpretation of information, facilitating the learning of skills and attitudes required to
deal with complex problems, and providing a conduit for developing partnerships and
communication (Fulton et al. 2011). In addition, they summarise the knowledge available
for a system and identify significant gaps in data and understanding of system functioning
(Food and Agriculture Organisation 2008).
Successful management of predatory and sport fish is dependent on the management of
prey species (Chipps and Garvey 2007), and these interactions are frequently explored by
using ecosystem models (Pauly et al. 2000). The management and protection of marine
resources utilises many methods which can be incorporated into ecosystem models to
facilitate knowledge-based decisions in planning processes (Evans et al. 2014). An
improved understanding of the role macroalgal habitats play in influencing the
survivorship of juvenile coral reef fish, and the ensuing contributions to adult
communities, is fundamental in improving the success of conservation and management
of the Ningaloo marine resources (Evans et al. 2014).
2.4.2 Ecopath with Ecosim
Ecosystem models vary in their complexity and purpose (Fulton et al. 2011), ranging
from minimum realistic models that focus on a subset of an ecosystem, to fully
quantitatively-detailed and statistically-based models that attempt to describe an entire
system (Plagányi 2007; Food and Agriculture Organisation 2008) (Table 2.2). With ease
of parameterisation, models capable of varying in complexity by orders of magnitude to
suit the system being analysed, and being freely available (www.ecopath.org), Ecopath
with Ecosim (EwE) has become one of the most common tools for modelling ecosystem-
36
based management of aquatic systems (Christensen 2009; Coll et al. 2015); it has 3 main
components, Ecopath, Ecosim, and Ecospace (Pauly et al. 2000).
Table 2.2 Summary of the types of models used to build ecosystem understanding,
their main purposes and examples of their application (Fulton et al. 2011)
Model
classification Purpose Example
Conceptual model 2, 3, 7 - one of the first steps in
determining the main drivers of a
system
Toy model 2, 3, 5, 6, 7 - stocks and flows model
- feedback loop model
Single-system
model
1, 3, 4, 5 - ELFSim
- tourist destination model
Shuttle model 2, 3, 4, 5, 6, 7 - ScenarioLab
- Ecopath with Ecosim
Full-system model 1, 2, 3, 4 - InVitro
1 predict/hindcast
2 understand system functioning
3 understand causal relationships
4 explore system behaviour
5 build tools for others to use
6 train specific skills and develop useful learning attitudes
7 foster communication and collaboration
Ecopath provides a standardised, static, mass-balanced view of the system of interest
(Pauly et al. 2000; Plagányi 2007), and is primarily used to parameterise a system for
dynamic simulations (Christensen 2009). The central tenet behind the simulations is the
conservation of energy in trophic transfers between functional groups (Pauly et al. 2000;
Christensen 2009; Gartner et al. 2015). Ecosim provides predictive, temporally-dynamic
simulation models to examine system processes and management policies (Christensen
2009; Gartner et al. 2015). This permits stakeholders to investigate trade-offs between
social, economic, and ecological strategies (Pauly et al. 2000; Christensen 2009).
Ecospace does not assume homogeneous spatial behaviour (Pauly et al. 2000), and
provides spatially- and temporally-dynamic models that examine how the placement of
protected areas impacts populations, including dispersal and advection effects
37
(Christensen 2009; Fulton et al. 2011). It is useful because it allows simultaneous
consideration of population dynamics of functional groups and their food webs (Gartner
et al. 2015).
Even though EwE modelling is widely applied, there are challenges associated with it,
including communication of results, improving output validation, performing quality
control over the model and keeping the model up-to-date (Coll et al. 2015). The Ningaloo
EwE model was originally developed to analyse the impacts of anthropogenic fisheries
on various ecological groups (Fulton et al. 2011), but has not been updated with the most
recent research from the region, including the role of macroalgae to important fishery
species (Lozano-Montes, CSIRO, pers. comm.). It has the capacity to influence regional
management and decision making by: improving how the Ningaloo Reef ecosystem is
described, providing a framework for the various interactions between stakeholders and
their motives and goals, having models available for future use, and defining scenarios
for future research (Fulton et al. 2011).
2.4.3 Ecosystem Model Requirements
Ecosystem models can be complex and have requirements that are too extensive to
include in this review. The collection of data and verification of the model are essential
components that will be addressed here. Other components, with best practices, have been
summarised by the Food and Agriculture Organisation (2008) and Heymans et al. (2016).
Data
The data requirements for ecosystem models depend on the research questions and
hypotheses, the resolution of the data, the type of model required to analyse the
hypotheses, and how the data are collected (Food and Agriculture Organisation 2008);
and reliable predictions depend on the level of knowledge in the system being examined
(Fulton et al. 2011). For less complex models, a small amount of data may be sufficient
38
to help understand critical gaps in data and ecosystem interactions (Food and Agriculture
Organisation 2008), though time-dynamic models (Ecosim) have more intense data
requirements, making the certainty of predictions derived from them less reliable
(Christensen 2009).
Types of data that need to be considered for ecosystem models are removals of biomass
(dominated by anthropogenic interactions), indices of abundance, and vital rates (Food
and Agriculture Organisation 2008). The main input parameters for each functional group
in EwE models are: biomass, the ratios of production over biomass (P/B) and
consumption over biomass (Q/B), catches, dietary composition, and ecotrophic efficiency
(EE) (Christensen and Pauly 1992; Christensen 2009). Dietary and species interaction
data are crucial for modelling trophic flows (Farmer and Wilson 2011) and are critical
additions to models that allow them to develop, from stock assessments, to looking at
ecosystem interactions (Food and Agriculture Organisation 2008). However, these are
often limited for species or regions, which impacts on the credibility of ecosystem models
if data needs to be imported (Farmer and Wilson 2011). Ecotrophic efficiency is a
measure of the proportion of the production that is used in the system, and it includes all
production terms apart from ‘other mortality’ (Christensen et al. 2005). It is often fitted
by the model because it is difficult to quantify and is usually the most uncertain input
parameter (Christensen et al. 2005).
Data should be collected from the region or species of interest, before importing data from
other ecosystems (Food and Agriculture Organisation 2008). Documentation of the
sources and quality of the data is termed “pedigree”, and refers to the data
representativeness, specificity, and bias due to limited coverage (Food and Agriculture
Organisation 2008). Input from local data, or from expert opinion is scored higher than
input from model generated data or “guesstimates”(Ainsworth and Pitcher 2005).
39
Model Verification - Calibration and Validation
The complexity of interactions, between organisms and their environment may result in
unexpected changes to population dynamics (Chong-Seng et al. 2012), which may not be
reproduced by the modelled predictions when forcing factors such as increased sea
temperature or a decrease in water pH are applied (Biber et al. 2004). Therefore, testing
of the model helps to determine its suitability, and how confident end users can be in the
inferences and predictions produced by the models (Rykiel 1996). Model calibration and
validation are two components of the basic steps for best practice in model development
(Food and Agriculture Organisation 2008).
Model calibration involves ensuring the modelled output is as consistent as it can be, with
the available data (Rykiel 1996) by ensuring that parameters and constants are correctly
programmed (Food and Agriculture Organisation 2008). Sensitivity analysis is used to
determine the input, parameters, structures, and conditions that have the greatest effect
on predictions, allowing programmers to ensure that they have been estimated with the
greatest level of certainty possible (Biber et al. 2004).
Model validation occurs when the model is ratified as being useful in addressing the
research question and producing satisfactorily accurate simulations (Rykiel 1996), which
is conducted against independent field observations (Biber et al. 2004) to build credibility
for the model (Rykiel 1996). It is considered best practice to use statistical methods as a
structured approach to compare modelled output and data from the system (Biber et al.
2004; Food and Agriculture Organisation 2008).
2.4.4 Potential Effects of Disturbances on Macroalgae at Ningaloo Reef
There is considerable naturally occurring, spatial and temporal variation in the biomass
and diversity of many tropical communities (Munday et al. 2008; Hoey et al. 2013)
(macrophytes, corals, invertebrates, fish), driven by large scale episodic disturbances and
40
complex interactions between physical processes, predation, and food and habitat
resources (Munday et al. 2008; Green and Bellwood 2009). Tropical ecosystems are
generally resilient enough to recover from natural disturbances such as cyclones in the
absence of anthropogenic stressors (e.g. destructive fishing practices or terrestrial runoff
affecting nutrients or turbidity) (Green and Bellwood 2009); however few reefs
worldwide are unaffected by human-induced disturbances (Hodgson 1999).
While there are fundamental gaps in our knowledge of the impact climate change will
have on tropical habitats, it is expected to dramatically impact ecological systems, be the
dominant disturbance affecting them in the future (Munday et al. 2008; Graham et al.
2015), and is expected to increase in frequency and severity, based on climate change
scenarios (Freeman et al. 2013; Webster et al. 2015). Effects are expected to include loss
of diversity, altered recruitment and distribution dynamics, changes in seasonal behaviour
and trophic flows, and increased incidences of diseases and invasive species due to a
combination of increased sea level and ocean temperatures, decreased ocean pH levels,
altered ocean currents, and more frequent extreme weather events (Hughes 2003; Munday
et al. 2008; Green and Bellwood 2009; Harley et al. 2012; Koch et al. 2013).
While many studies focus on the effects of climate change to a particular functional group,
ecosystem models allow us to examine the positive and negative effects of interactions
between many functional groups (Food and Agriculture Organisation 2008; Christensen
2013; Gartner et al. 2015). This will allow managers to predict how changes in macroalgal
habitats due to external pressures, will impact food resources for juvenile coral reef fish,
and examine the flow-on effects for adult populations.
2.4.5 Implications for Management
The effective management of marine systems is more difficult than terrestrial systems
and requires a thorough understanding of the dynamics of the associated communities
41
and environmental systems (Botsford et al. 1997). Ecosystem modelling can be used to
examine interactions between fish species and their habitat, and analyse the implications
of spatial and temporal disturbances, and various management strategies (Lozano-Montes
et al. 2011; Lozano-Montes et al. 2012; Christensen 2013; Lozano-Montes et al. 2013).
EwE has become a fundamental tool in developing ecosystem models (Coll et al. 2015),
even though ecosystem models have not been refined enough that a “management” model
could be developed for reliable tactical recommendations (Food and Agriculture
Organisation 2008). The output from models helps us to improve our understanding of
the importance of linkages between coral reef habitats and macroalgal nursery systems,
the natural or anthropogenic processes that maintain or alter the structure of those
linkages, and facilitate the development and use of ecosystem-based management
approaches (Adams et al. 2006; Littler and Littler 2007; Coll et al. 2015). Improved
understanding of how ecosystems may change will inform decisions regarding habitat
conservation, population dynamics, ecosystem-based management, sustainable fisheries,
and adaptive strategies (Adams et al. 2006; Coll et al. 2015).
The adoption of ecosystem modelling for fisheries management has been slow (Coll et
al. 2015), and a lack of confidence in the predictions of models may impact how decisions
are accepted (Christensen 2009), particularly when using data from outside the local area
(Farmer and Wilson 2011). Reliable data are essential for prioritising management plans
(Littler and Littler 2007) and improved confidence in decisions will result from
independent convergence of models when analysing the same questions, even though
certainty may be lower than would be ideal (Food and Agriculture Organisation 2008).
2.4.6 Decisions Regarding Marine Sanctuaries
There is a need to identify the most important habitats for both juvenile and adult fish,
and the spatial patterns of ecological processes between them, so that adult populations
42
remain sustainable (Beck et al. 2001; Dahlgren et al. 2006; Vergés et al. 2011; Lozano-
Montes et al. 2011; Lozano-Montes et al. 2012; Lozano-Montes et al. 2013). While
simulations using EwE can be used to inform managers about the implementation of
marine protected areas (MPAs) (Lozano-Montes et al. 2011; Lozano-Montes et al. 2012;
Lozano-Montes et al. 2013; Coll et al. 2015), it is often not used due to uncertainty and a
lack of confidence in the predictions (Christensen 2009; Heymans et al. 2016).
It is important to manage and protect habitats that contribute fish to adult populations,
since macroalgal habitats that maintain a stock of desirable fish can replenish distant
seascapes with new recruits (Dorenbosch et al. 2006). Coral reef resilience is strongly
dependent on the matrix of habitats surrounding them (Vergés et al. 2011), and protecting
algal habitats may improve reef resilience and the long-term sustainability of fisheries
resources by protecting ecologically important fish species that have the potential to
prevent phase shifts after disturbance events (Hughes et al. 2007; Wilson et al. 2010;
Loneragan et al. 2013; Rasher et al. 2013).
Understanding the value of macroalgal habitat for the contribution of recruits to adult
populations is essential for improving conservation outcomes for sustainable populations
of ecologically and recreationally important fish species; however ignoring other sources
of data may reduce the efficacy of decisions (Sheaves et al. 2015). Knowledge of
herbivorous fish and the species that comprise their diets are important for the design and
management of MPAs (Tolentino-Pablico et al. 2008) because herbivory rates are thought
to be indicators of resilience in tropical ecosystems (Littler and Littler 2007; Green and
Bellwood 2009). The management of functional groups represents a paradigm shift from
standard management practices, which focus on individual taxa or habitats, by supporting
the taxa that sustain dynamic ecological processes rather than trying to maintain the status
quo (Hughes 2003; Bellwood et al. 2004).
43
2.5 Summary
This literature review has delved into the current knowledge of macrophyte habitat in
tropical shallow-water habitats, revealing the historic bias against macroalgal habitat in
coral reef systems, and how perceptions are changing. Macroalgae is now becoming
considered a natural component in lagoon areas of pristine coral reef systems in Australia
and Hawaii, is recognised as important settlement habitat for juvenile fishes and a food
source for the many herbivorous fish species that inhabit coral reefs. However, there are
still knowledge gaps regarding its dietary importance for juvenile fish, and its role in
trophic flows in coral reef systems via the juvenile fishes.
Ecosystem models are designed to incorporate multiple functional groups into a model to
understand systems and their trophic flows, illustrate issues with fisheries management,
describe theoretical ecology, assess the impacts of hypotheses that can’t be ethically
tested in real ecosystems, and address uncertainty around the effectiveness of protected
areas. Although there are issues with data availability in complex ecosystems, Ecopath
with Ecosim models have become one of the most widely used tools for modelling
ecosystem dynamics due to its ease of use, adaptability, a free availability.
Based on the gaps in published knowledge, realised through this literature review, this
study aims to increase the understanding of macroalgal trophic ecology within lagoon
areas of the Ningaloo Marine Park by: determining the diets of juvenile fish that associate
with the macroalgal beds in the Ningaloo lagoon; assessing the importance of macroalgae
and other dietary categories to the juvenile fish; and using the data gleaned from the
dietary study to construct an Ecopath with Ecosim model, that can then be used to
simulate changes in physical and biological drivers of the system to examine the dynamics
of the lagoon.
44
3. The Importance of Macroalgae to the Diets of Juvenile Fish in the
Ningaloo Reef Lagoon
3.1 Introduction
Coral reef ecosystems make up a small component of oceanic habitat in tropical and
subtropical regions globally, however, they are among the world’s most biodiverse
systems (Moberg and Folke 1999; Pandolfi et al. 2011). Habitats between coral reefs and
the shoreline, such as mangroves, seagrasses and macroalgae, provide important
ecosystem services to a range of organisms including humans (Costanza et al. 1997;
Moberg and Folke 1999; McManus et al. 2000). These include oxygenating the water,
protecting shorelines against erosion, recycling nutrients, and providing habitat, nursery
grounds and food to fish and benthic communities (Costanza et al. 1997; Dahlgren and
Marr 2004; Manson et al. 2005).
Macroalgae have often been considered a symptom of unhealthy reefs in the Caribbean,
eastern Africa and the eastern Pacific, as coral habitat becomes dominated by macroalgae
(Diaz-Pulido and McCook 2003; Hughes 2003; Hughes et al. 2007; Norström et al. 2009).
However, recent research has recognised that macroalgae is a natural component of coral
reef systems (Vroom et al. 2006; Wismer et al. 2009; Johansson et al. 2010; Vroom and
Braun 2010; Bruno et al. 2014), providing nursery habitat for juvenile fish (Wilson et al.
2010; Evans et al. 2014; Wilson et al. 2014), a food source for herbivorous fish (Pillans
et al. 2004; Green and Bellwood 2009; Bessey and Heithaus 2015), and habitat for
invertebrate infauna (Martin-Smith 1993; Peart 2004).
Ningaloo reef is a relatively pristine, fringing coral reef system in the north-west of
Western Australia (Chin et al. 2008). In places, Sargassum dominated macroalgal beds
make up more than 40% of the benthic habitat within the Ningaloo lagoon (Cassata and
Collins 2008; Kobryn et al. 2013; Fulton et al. 2014), and have been shown to be
important habitat for juvenile fish species (Wilson et al. 2010; Evans et al. 2014; Wilson
45
et al. 2014) that play important ecological roles (Michael et al. 2013) or are prized target
species for recreational fishers in Western Australia (Smallwood and Beckley 2012; Ryan
et al. 2015). Macroalgae in the genus Sargassum are cosmopolitan species that undergo
seasonal pulses in biomass and productivity (Vuki and Price 1994; Ateweberhan et al.
2006; Fulton et al. 2014); however, little is known about the trophic role of Sargassum or
other macroalgae in the Ningaloo lagoon for these juvenile fish populations. The
importance of Sargassum and other macroalgae to the diets of these juvenile fishes is not
known, nor whether fish diets change seasonally in response to the seasonal dynamics of
Sargassum.
The overall aim of this chapter is to investigate the diets of juvenile fish that associate
with macroalgal beds in the Ningaloo Reef lagoon, and to evaluate the contribution of
macroalgae to the diets of these fish. The specific aims of the Chapter are to:
1. Determine the diets of juvenile fish that associate with macroalgal beds in the
Ningaloo lagoon to assess the importance of broad dietary categories and how
the diets vary among species and seasons;
2. Assess the diets to determine the feeding strategy and trophic width of the
juveniles and determine whether they differ between seasons;
3. Use the information from the dietary analyses to determine the species
composition of functional feeding groups (i.e. fish with similar diets) for an
Ecopath with Ecosim model developed in Chapter 4.
3.2 Materials and Methods
3.2.1 Study Area
Coral Bay (23° 08.683’ S, 113° 46.583’ E) is a small settlement, approximately 1,200 km
north-northwest of Perth, Western Australia (Figure 3.1). Mean annual temperatures
range from 17.7° C to 31.9° C, with an average annual rainfall of 260.7 mm falling
46
predominantly between January and July (Commonwealth of Australia 2016b). The
average summer sea temperature is approximately 26° C with an average winter sea
temperature of approximately 23° C (Commonwealth of Australia 2016a). The settlement
is situated towards the southern end of the Ningaloo Reef, the largest fringing coral reef
in Australia.
The Ningaloo Reef is encompassed by the Ningaloo Marine Park (NMP) and currently
covers approximately 263,400 hectares (Department of Conservation and Land
Management and Marine Parks and Reserves Authority 2005). The region has diverse
marine floral and faunal communities due to the convergence of temperate and tropical
currents, making it a high conservation priority (Department of Conservation and Land
Management and Marine Parks and Reserves Authority 2005). The area also has
important cultural significance for local Aboriginal people (Commonwealth of Australia
2016c), a thriving commercial tourism sector that operates out of Coral Bay and Exmouth
(Department of Conservation and Land Management and Marine Parks and Reserves
Authority 2005), and is a target destination for recreational fishers, with an annual average
of 206,000 survey respondents nominating fishing as one of their top leisure activities in
the region (Tourism Western Australia 2015).
The catch by recreational fishers in the Ningaloo Marine Park matches or exceeds the
commercial catch in the Gascoyne region, for example, the 2014 recreational catch of
Lethrinus nebulosus was 17 t compared to 4 t for charter fishing and 2 t for commercial
fishing (Fletcher and Santoro 2015). Lethrinus nebulosus is the most targeted species by
recreational fishers in the NMP, followed by squid (Order Teuthoidea), snappers
(Lutjanus sebae, Pristipomoides multidens), and emperors (Lethrinus laticaudis,
Lethrinus miniatus) (Sumner et al. 2002; Ryan et al. 2015).
47
Figure 3.1 The lagoon site that fish were sampled from and which was used as the area for the Ecopath
with Ecosim model (Chapter 4). The site is bounded by the reef to the west (blue), the coastline to the
east (white), and the Maud and Pelican Sanctuary zones to the north and south (red cross-hatch),
respectively. The figure is derived from a benthic hyperspectral study (Kobryn et al. 2013). Habitat
codes are explained in Appendix 1.
Habitat Legend
48
Ningaloo Marine Park is considered to be a near-pristine ecosystem due to the low
population density, small amount of terrestrial freshwater runoff, and limited industrial
activity in the area (Chin et al. 2008); however, lagoon areas of the reef are characterised
by large areas of macroalgae (Cassata and Collins 2008; Kobryn et al. 2013) which are
considered an inherent part of the system (Cassata and Collins 2008; Kobryn et al. 2013).
Recent research has highlighted the importance of these macroalgal beds as habitat for
post-larval juvenile fish, with many of the species targeted by recreational fishers found
in macroalgal habitats over several years (Wilson et al. 2010; Evans et al. 2014; Wilson
et al. 2014), though little is known about the contribution of macroalgae to the food webs
of these fish and its significance to their diets.
3.2.2 Sampling Fish in Macroalgal Beds
Juvenile and subadult fish were targeted for dietary studies, from lagoon areas of the
Ningaloo Marine Park (NMP), over one week periods in summer (February) and winter
(July), 2015. Fish species were selected based on their importance to ecological processes
within the lagoon (Naso fageni, Siganus fuscescens), their importance to the recreational
fishery (Choerodon rubescens, Lethrinus nebulosus, Lutjanus spp.) and their abundance
at the time of sampling (Lethrinus atkinsoni, Parupeneus barberinoides, Parupeneus
spilurus). All fieldwork was undertaken south of Coral Bay, Western Australia
(Figure 3.1), under Murdoch University ethics permit (RW2748/15), Western Australian
Department of Fisheries permit (Exemption No. 2548), and Department of Parks and
Wildlife (DPaW) permits (SF010284, CE004784, SW016903).
The macroalgal beds were dominated by Sargassum spp. in February, but had more of an
even mix of Lobophora spp., Dictyota spp., and Dictyopteris spp. in July (Fulton et al.
2014). Sample sites were located within the lagoon, at a depth of 1-5 m, and were
protected from oceanic swells. They were selected based on previous knowledge of
49
abundant fish biomass from DPaW researchers or from surveys undertaken with a manta-
board towed at low speed (<4 km.h-1) behind a small boat.
Immature fish were collected from well-defined macroalgal patches, where possible, and
sampling took place between 10:00 and 16:00, the period when grazing activity is at its
peak (Michael et al. 2013). Fish were caught using monofilament nylon fence nets (7 m
long, 1.5 m high, 6 mm stretched mesh size) deployed by SCUBA divers, who then herded
the targeted fish into the fence net and caught them with a hand-held scoop net. In the
July sampling period, fish larger than approximately 150 mm were caught using either a
small spear gun, taking care not to damage gut contents, or by line fishing using scented
plastic lures on monofilament trace, which could not confound gut contents analysis if
the fish swallowed the lure. In July, some carnivorous fish species (Lutjanus fulviflamma,
Lutjanus quinquelineatus) were caught from isolated coral outcrops (bombies) within
macroalgal beds. Lethrinus nebulosus were difficult to find on macroalgal beds while
diving in July, so rod and line fishing with plastic lures while drifting over macroalgal
habitat was used to collect this species. After capture, fish were placed in an ice slurry
and euthanased to prevent decomposition of the fish and their gut contents. All samples
were frozen at -20°C on returning to shore, and kept frozen until fish were processed and
guts were removed.
Fish were identified in the field, where possible (Allen and Swainston 1988; Allen 2009),
before colours faded after death. Otherwise fish were identified in the laboratory when
they were processed using either: identification sources relevant to species found in the
lagoon (Sainsbury et al. 1984; Russ and Williams 1994; Wilson 1998), resources at the
Western Australian Museum (Food and Agriculture Organisation 2001a; Food and
Agriculture Organisation 2001b; Moore, WAM, pers. comm.), or with genetic barcoding
for the most cryptic individuals (Wakefield, DoF, pers. comm.).
50
3.2.3 Fish Processing
Total length (LT) and fork length (LF) were measured for each fish to the nearest mm.
Wet weight and carcass weight were recorded to the nearest 10 mg. Carcass weight was
recorded after all organs posterior of the gills were removed for gut contents analysis.
Care was taken to process the fish quickly so that the guts could be returned to the freezer
quickly to minimise decomposition of their contents. All measurement and dissection
tools were sterilised in an ethanol solution (70%) and rinsed with distilled water between
individual fish to prevent contamination of subsequent samples. For fish that required
genetic barcoding, a section of flesh anterior to the caudal fin was taken using tools
sterilised in bleach then an ethanol solution between each individual fish. Fish were
classified according to length at maturity (LM) for the target species if the information
was available (Ebisawa 1999; Grandcourt et al. 2007; Marriott et al. 2010; Froese and
Pauly 2015), otherwise data were used for fish from the same genus or Family that have
a similar total length. Fish that with a LT smaller than 50% of LM were classified as
juveniles, while fish with a LT larger than 50% of LM but smaller than LM were classified
as subadults.
3.2.4 Gut Contents Analyses
The stomach, or foregut where the pyloric sphincter was indistinguishable (Rust 2002;
Barton 2007; Bone and Moore 2008), was separated from the visceral organs and assessed
for its fullness using a scale of 0 (empty) to 10 (full). Gut contents were spread evenly
over a petri dish that was scored into a 4 mm x 4 mm grid. Photographs were taken of all
guts before dissection for later cross-reference for gut fullness. Gut contents were also
photographed for later verification if necessary.
Gut contents were classified into broad dietary categories: algae, invertebrate, vertebrate,
sediment, detritus and unidentified (Cocheret de la Morinière et al. 2003), at 10- to 40-X
51
magnification. Algae were classified according to phyla (Huisman 2000) and macroalgal
growth form (filamentous, encrusting, foliose, thallate, Sargassum; Table 3.1) according
to descriptions published in Steneck and Dethier (1994) and Choat et al. (2002).
Invertebrates were identified through morphometric features to Order where possible
(Barnes et al. 1993; Jones and Morgan 1994; Ruppert et al. 2004), though unidentified
crustaceans were classified as Malacostraca. Teleosts were identified from persistent
body parts (scales, fin rays). Digested matter and unidentifiable organisms were classified
as detritus and small coral fragments were considered to be part of the sediment.
Table 3.1 Broad dietary categories and their classification for statistical analyses and functional groups for an
ecosystem model, for fish sampled south of Coral Bay, 2015.
Gut Contents Categories Description EwE Functional
Groups (Ch. 4)
Filamentous algae Small fibrous algae (see Fig. 1 in Steneck and
Dethier 1994; Choat et al. 2002)
Non-fleshy
macroalgae
Encrusting algae See Fig. 1 in Steneck & Dethier (1994)
Foliose algae
Small, less structurally complex species (often
sheet-like) (see Fig. 1 in Steneck and Dethier
1994; Choat et al. 2002)
Thallate macroalgae Large, structurally complex species (Choat et al
2002) (see Fig. 1 Steneck & Dethier 1994) Fleshy macroalgae
Sargassum Sargassum spp. Sargassum
Porifera Sponge fragments Sponge/Bryozoan
Bryozoa Colonial organisms
Zooplankton Eggs, diatoms Zooplankton
Nematoda/Nematomorpha Gut parasites Annelid/Nematode
Annelida Polychaeta, clitellata
Mollusca Gastropods, bivalva, polyplacophora Mollusc (exc
cephalopods)*
Mollusca Cephalopods Squid*
Octopus*
Echinodermata Ophiuroids and other sea stars Echinoderms
Crustacea
Copepod, cirripedia, ostracod, unidentified
malacostraca, anaspidaceae, stomatopod,
isopod, and amphipod groups (<1 mm)
Micro-crustaceans
Crustacea Decapods (1 - 4 mm) Meso-crustaceans
Teleostei Undigested fish parts (scales, rays) Teleosts
Detritus Unidentified digested organic matter Detritus
Unidentified Unidentified organisms
Cnidaria Small coral fragments Sediment
Sediment Sand, sediment
* Mollusc groups were retained as a single group for statistical analyses in this study and were only
separated for the ecosystem model (Ch.4)
The gut contents were spread to a uniform depth and relative abundance was quantified
by percent volume (% Vol) of gut (Hyslop 1980) and averaged for the species to
determine its feeding strategy for use in an ecosystem model (Ch. 4). The volumetric data
52
were used to calculate prey specific abundance (Amundsen et al. 1996) and frequency of
occurrence (Hyslop 1980) for each species of fish to help determine niche width, feeding
strategy and prey importance for the species. Prey specific abundance (Pi) is the
proportion of the prey item only from guts containing the item (Eq. 3.1):
𝑃𝑆𝐴𝑖 = (∑ 𝑆𝑖
∑ 𝑆𝑡𝑖) ×100 (3.1)
where Pi is the prey-specific abundance of food item i, Si is the relative abundance of the
food item i, and Sti is the total stomach content only from consumers with the food item i
in their stomach (Amundsen et al. 1996). The frequency of occurrence (%FOi) is the
proportion of guts with the food item i (Eq. 3.2)
%𝐹𝑂𝑖 = (∑ 𝑁𝑖
∑ 𝑁) ×100 (3.2)
where Ni is the number of consumers with the food item i in their gut and N is the total
number of consumers with non-empty guts(Amundsen et al. 1996).
3.2.5 Importance of Dietary Items
Niche theory and niche width have long been used to describe the ecological position of
an organism in its ecosystem (Van Valen 1965; Pielou 1972; Vandermeer 1972). A
graphical analysis adapted from the Costello method (Figure 3.2; Costello 1990;
Amundsen et al. 1996) was used to determine niche width contribution (broad niche
width = high within phenotype contribution; narrow niche width = high between
phenotype contribution), feeding strategy (specialist vs. generalist) and prey importance
(dominant vs. rare in the diet) (Amundsen et al. 1996) for broad dietary categories and to
distinguish for these between individual fish and the population for each species.
The importance of prey items along the diagonal between the lower left (white shading)
and top right (darker blue shading) quadrants is represented by a non-linear function
53
(emphasised by the dashed contours along that axis) of prey specific abundance (PSA)
and frequency of occurrence (FO) (Amundsen et al. 1996) (Figure 3.2). The feeding
strategy of the predator is indicated by the distribution of points along the y-axis, with
high values of prey specific abundance (PSA) indicating specialisation, and low values
of PSA representing generalisation. The individual versus population feeding strategy is
indicated by data-points in the upper left quadrant (high PSA, low FO) indicating
specialisation by the individual predator for the prey item, while data-points in the lower
right quadrant indicating generalisation by the population for the prey item (Amundsen
et al. 1996). Therefore, if an item is located in the upper left or lower right of the plot, it
can indicate the same contribution to a species’ diet (e.g. 5%), but represent very different
feeding strategies between an individual and the population (Amundsen et al. 1996).
Points in the upper right quadrant (high PSA and high FO above the diagonal) indicate
Figure 3.2 Interpretation of the graphical representation of prey specific
abundance (PSA) plotted against the frequency of occurrence (FO) for dietary
items. The distribution of dietary items within the plot helps to determine niche
width, feeding strategy, and prey importance. Adapted from the Costello
method (Costello 1990; Amundsen et al. 1996).
Prey
Impo
rtance co
nto
urs (%
)
75
50
25
5
54
specialisation by the population and should be limited to few points (Amundsen et al.
1996), representing narrow niche width. All points in the lower left quadrant (low PSA
and low FO below the diagonal) represent broad niche for the population (Amundsen et
al. 1996).
3.2.6 Statistical Analysis
The similarity in gut contents among species and months were analysed using Primer
(v. 6; Clarke and Warwick 2001). Sediment categories, unidentified organisms, digested
organic matter and all samples with zero gut contents (n = 23) were removed from the
dataset for analyses. Dietary items (variables) were grouped together according to broad
dietary categories (Table 3.1) used in an Ecopath with Ecosim (EwE) model (Ch. 4),
except for cephalopod groups (squid and octopus) that were retained in the mollusc
category. Data were standardised to calculate the relative abundance (the proportion in
the gut) for each dietary category rather than an absolute abundance (volumetric
measure), then square root transformed. These data pre-treatments reduce the influence
of abundant or consistently occurring species on the results (Clarke 1993).
Bray-Curtis similarity (Clarke 1993) was used to construct resemblance matrices for: all
species (as a group), the species that were present in both seasons (as a group), and each
individual species (as individual species). Non-metric multi-dimensional scaling plots
(nMDS), dendrograms (CLUSTER), analysis of similarity (ANOSIM) tests, and
similarity percentages (SIMPER) analyses (Clarke 1993) were used to assess the data for
similarities, differences and patterns within and between seasons and species. Bray-Curtis
similarity was used for all similarity analyses. Two-way ANOSIM analyses (crossed with
replicates) were used to determine whether there were differences in diets between
species and seasons. When significant differences were found, one-way ANOSIMs were
used to help interpret the differences. Where data were averaged within species, the
55
arithmetic average was used rather than distance among centroids (Lek et al. 2011).
Primer (v. 7; (Clarke, Gorley, et al. 2014) was used to construct a shade plot (Clarke,
Tweedley, et al. 2014) for broad dietary groups in the species that were present in both
seasons.
3.3 Results
3.3.1 Annual Dietary Composition
A total of 181 fish were collected during this study, 85 in February, 2015 and 96 in July,
2015. Ten species were collected in February: three Lethrinidae, three Mullidae and one
in the Acanthuridae, Labridae, Lutjanidae and Siganidae (Table 3.2). The total lengths
(LT) for all fish caught in February ranged from 22-93 mm with an average LT (±1 S.E.)
of 47.6 ± 1.33 mm and had an average wet weight of 2.2 ± 0.23 g (Table 3.2). Seven
species were collected in July: two Lethrinidae, two Mullidae, and one in the
Acanthuridae, Lutjanidae and Siganidae (Table 3.2). The mean lengths of all fish caught
in July (LT range = 22-93 mm, mean = 47.6 ± 1.33 mm) were 3-times longer than the
mean in February (LT range = 53-364 mm, mean = 168.8 ± 7.77 mm). The average wet
weight of all fish caught in July (114.2 ± 13.54 g) was much heavier than in February
(Table 3.2). Note that sample sizes for some individual species were small and not all
species were caught in both seasons (Table 3.2).
All fish caught in February were juveniles, compared with July, where 35.4% were
juveniles and 47.9% were subadults; the remainder (16.7%) were adults. A one-way
ANOSIM test showed that, for each species, the gut composition did not differ
significantly (Global R = 0.054, p = 0.204) between fish larger than the LM and the
juvenile/subadult fish in July. Gut contents from all fish were therefore included in the
analyses.
56
Table 3.2 The numbers, average weight, average lengths and length ranges of fish species collected south of Coral Bay
in February and July, 2015, that were used for gut contents analysis. S.E. = 1 standard error.
Of the 181 fish caught, 175 were analysed for gut contents, with 164 (93.7% of guts
analysed) having contents (Table 3.3). Four S. fuscescens from February were not used
as their gut contents were used in developing the methodology, and two P. spilurus from
July were not analysed as they were damaged during collection.
The gut contents of all Lethrinus species were dominated by annelids and digested organic
matter with teleosts, crustaceans and molluscs making up other principal groups (Table
3.3). The annelid component was dominated by polychaete worms for all Lethrinus
species; however, the mollusc and crustacean categories differed between species.
Cephalopods were the dominant item for molluscs in L. atkinsoni (LeA, n = 27) guts,
while gastropods dominated the mollusc component in L. nebulosus (LeN, n = 25) guts:
L. genivittatus (LeG, n = 2) guts contained no mollusc component. Decapods dominated
Species
Total
Caught
Average Wet
Weight (g)
Average Total
Length (mm)
Length Range
(mm)
Feb Jul Feb
(S.E.)
Jul
(S.E.)
Feb
(S.E.)
Jul
(S.E.) Feb Jul
Choerodon
rubescens 1 -
9.75
(-) -
73
(-) - 73 -
Lethrinus
atkinsoni 6 22
1.34
(0.36)
31.59
(11.14)
38.17
(4.75)
108.73
(7.77) 22-56 54-251
Lethrinus
genivittatus 2 -
1.15
(0.51) -
40.5
(6.5) - 34-37 -
Lethrinus
nebulosus 18 12
2.06
(0.18)
324.80
(49.08)
47.89
(1.89)
278.83
(17.82) 23-57 161-364
Lutjanus
fulviflamma - 8 -
186.93
(16.69) -
218.50
(7.01) - 194-249
Lutjanus
quinquelineatus 6 -
2.46
(0.58) -
48.83
(4.42) - 31-61 -
Naso fageni 10 4 2.24
(0.22)
31.86
(2.29)
43.60
(1.40)
123.00
(3.51) 38-53 116-130
Parupeneus
barberinoides 8 16
1.42
(0.44)
7.59
(1.39)
45.25
(3.26)
78.88
(4.49) 39-67 53-116
Parupeneus
spilurus 8 27
3.89
(1.73)
141.76
(21.48)
56.25
(8.08)
205.56
(8.06) 38-93 154-330
Siganus
fuscescens 15 7
1.63
(0.11)
114.69
(7.33)
46.55
(1.04)
202.43
(5.46) 41-53 183-219
Upeneus sp. 11 - 2.09
(0.70) -
50.73
(4.57) - 38-84 -
Grand Total 85 96 2.19
(0.23)
114.24
(13.54)
47.60
(1.33)
168.82
(7.77) 22-93 53-364
57
the guts of both LeA and LeN, while amphipods dominated the guts of LeG. The guts of
LeA and LeN had small amounts of algal material (<1%) that were not present in LeG
guts (Table 3.3).
The gut contents of Lutjanus fulviflamma (LuF, n = 7) were characterised by crustaceans
(42.0%) and teleosts (36.7%), while L. quinquelineatus (LuQ, n = 4) had mainly
unidentifiable digested organic matter (45.3%), annelids (27.1%) and crustaceans
(18.8%) (Table 3.3). Decapods were the sole component of crustaceans in LuF guts, while
stomatopods were the sole component of LuQ guts. Clitellata items were the only annelid
component in the guts of LuQ, which were the only species analysed that had a larger
proportion of clitellata than polychaetes. The dominant item in the C. rubescens (CR, n =
1) gut was molluscs (74.1%), made up primarily of bivalves, with the remainder
consisting of unidentifiable digested organic matter (25.9%) (Table 3.3).
Crustaceans were the dominant items for the goat fishes P. barberinoides (PB, n = 24),
P. spilurus (PS, n = 33) and Upeneus species (Usp, n = 10), though the composition of
crustaceans in guts varied among the three fish species (Table 3.3). The various
microscopic crustacean groups (<1 mm; hereafter referred to as micro-crustaceans) made
up 94.8% and 65.2% of the crustacean total in Usp. and PB, respectively, with amphipods
dominating Usp guts and the combined amphipod and unidentified malacostracan volume
making up 51.8% of the crustacean total in PB. Small decapods (1 - 4 mm; hereafter
referred to as meso-crustaceans) made up 55.5% of the crustacean total in PS and were
the largest individual group in PB guts (Table 3.3).
Naso fageni (NF, n = 13) guts contained mainly filamentous (45.0%), thallate (32.2%)
and foliose (7.8%) algae, though there were also small amounts of fish scales (4.7%),
crustaceans (0.04%) and annelids (0.04%) present (Table 3.3). Rhodophytes and phaeo-
58
Table 3.3 Average volumetric diet contents (with standard error of the mean) for 11 species of fish caught in February and July (2015) from macroalgal beds in the Ningaloo Reef lagoon, south of Coral Bay, Western Australia. Bold type is the sum of small font groups beneath it (if applicable). Standard error of the mean (SEM) was only calculated for individual dietary groups where n >1, not summed groups. Percentage of guts analysed that had
contents ranged from 66.7% (LuQ) to 100% (CR, LeG, PB, PS, SF). Fish codes are Choerodon rubescens = CR; Lethrinus atkinsoni = LeA; Lethrinus genivittatus = LeG; Lethrinus nebulosus = LeN; Lutjanus fulviflamma =
LuF; Lutjanus quinquelineatus = LuQ; Naso fageni = NF; Parupeneus barberinoides = PB; Parupeneus spilurus = PS; Siganus fuscescens = SF; Upeneus species = Usp.
Fish Species CR LeA LeG LeN LuF LuQ NF PB PS SF Usp
# Analysed (% non-empty) 1 (100) 28 (96.4) 2 (100) 30 (83.3) 8 (87.5) 6 (66.7) 14 (92.9) 24 (100) 33 (100) 18 (100) 11 (90.9) Avg Avg SEM Avg SEM Avg SEM Avg SEM Avg SEM Avg SEM Avg SEM Avg SEM Avg SEM Avg SEM
Filamentous algae 0 0.05 0.05 0 0 0 0 1.43 1.43 0 0 44.98 5.09 0 0 0 0 29.49 4.60 0 0
Encrusting algae 0 0 0 0 0 0 0 0 0 0.52 0 Phaeophyta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.07 0.07 0 0
Chlorophyta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.45 0.27 0 0
Rhodophyta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Foliose algae 0 0.03 0 0 0 0 7.84 0 0 3.67 0 Phaeophyta 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.25 0.14 0 0
Chlorophyta 0 0.03 0.02 0 0 0 0 0 0 0 0 7.47 3.68 0 0 0 0 1.59 1.28 0 0
Rhodophyta 0 0 0 0 0 0 0 0 0 0 0 0.37 0.31 0 0 0 0 1.83 0.79 0 0
Thallate macroalgae 0 0.42 0 0.04 0.82 0 32.21 0 0.06 29.16 0 Phaeophyta 0 0.37 0.36 0 0 0.04 0.04 0 0 0 0 13.04 3.97 0 0 0.05 0.05 22.16 5.81 0 0
Chlorophyta 0 0.05 0.05 0 0 0 0 0 0 0 0 0.48 0.25 0 0 0.01 0.01 3.01 0.92 0 0
Rhodophyta 0 0 0 0 0 0 0 0.82 0.82 0 0 18.69 4.94 0 0 0 0 4.00 1.12 0 0
Sargassum 0 0 0 0 0 0.02 0.02 0 0 0 0 3.06 1.78 0 0 0 0 20.56 5.56 0 0
Porifera 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.02 0.02 0 0
Bryozoa 0 0 0 0 0 0.01 0.01 0 0 0 0 2.01 1.17 0 0 0 0 2.21 1.08 0 0
Zooplankton 0 0 0 0 0 0.00 0.00 0 0 0 0 0 0 0 0 0.19 0.13 0 0 6.00 6.00
Nematoda 0 0.34 0.34 0 0 2.04 2.08 0 0 0 0 0 0 0.09 0.04 0.02 0.02 0.25 0.16 0.08 0.08
Annelida 0 23.32 54.92 30.75 0 27.08 0.04 1.50 6.76 0 0 Polychaeta 0 22.40 6.65 54.92 37.68 26.83 7.56 0 0 0 0 0.04 0.04 1.50 1.50 6.65 3.02 0 0 0 0
Clitellata 0 0.93 0.93 0 0 3.92 3.25 0 0 27.08 24.38 0 0 0 0 0.12 0.08 0 0 0 0
Mollusca 74.14 2.72 0 15.43 0 0 0 0.18 0.72 0.11 0 Polyplacophora 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.17 0.17 0 0 0 0
Gastropoda 5.17 0 0 0 0 10.01 5.77 0 0 0 0 0 0 0 0 0.32 0.13 0.06 0.03 0 0
Bivalva 68.97 0.40 0.40 0 0 4.92 2.48 0 0 0 0 0 0 0.18 0.18 0.23 0.15 0.03 0.03 0 0
Cephalopoda 0 2.32 2.22 0 0 0.50 0.46 0 0 0 0 0 0 0 0 0.01 0.00 0.02 0.01 0 0
Echinodermata 0 1.21 0 0 0 0 0 0.08 4.11 0 0 Ophiuroidea 0 1.21 1.21 0 0 0 0 0 0 0 0 0 0 0 0 4.10 1.61 0 0 0 0
spp. 0 0 0 0 0 0 0 0 0 0 0 0 0 0.08 0.05 0.01 0.01 0 0 0 0
Crustacea 0 12.04 8.75 8.43 42.00 18.75 0.04 87.73 66.10 0.22 76.45 Copepoda 0 0 0 0 0 0.04 0.04 0 0 0 0 0.02 0.02 3.06 1.06 1.05 0.95 0 0 0.72 0.48
Cirripedia 0 0 0 0 0 0.21 0.21 0 0 0 0 0 0 0.90 0.56 0.08 0.06 0 0 0.14 0.14
Ostracoda 0 0 0 0 0 0 0 0 0 0 0 0.02 0.02 0.35 0.14 0.06 0.03 0 0 0 0
Malacostraca 0 12.04 8.75 8.18 42.00 18.75 0.01 83.42 64.92 0.22 75.59
Malacostraca (unidentified) 0 2.00 1.85 0 0 0 0 0 0 0 0 0 0 25.40 3.99 14.17 3.94 0 0 12.07 7.26
Anaspidacea 0 0 0 0 0 0 0 0 0 0 0 0 0 0.50 0.50 0 0 0 0 0 0
Stomatopoda 0 0 0 0 0 0 0 0 0 18.75 18.75 0 0 4.13 2.15 0.56 0.47 0 0 2.98 2.04
Isopoda 0 0 0 0 0 0 0 0 0 0 0 0 0 2.00 1.15 0.38 0.37 0 0 0 0
Amphipoda 0 0.74 0.74 8.75 5.04 1.07 1.03 0 0 0 0 0.01 0.01 20.85 5.09 13.12 4.57 0.16 0.12 56.54 11.81
Decapoda 0 9.30 3.78 0 0 7.11 4.34 42.00 16.96 0 0 0 0 30.54 6.38 36.68 5.00 0.05 0.04 4.00 4.00
Teleost-scales/rays 0 15.75 4.82 0 0 0.15 0.15 36.70 13.96 3.29 3.29 4.69 3.30 1.56 1.56 1.54 0.90 0 0 0 0
Digested organic matter 25.86 35.71 7.34 34.48 34.48 26.87 7.43 4.76 4.76 45.33 21.67 4.30 2.18 2.70 1.88 2.91 1.10 11.52 3.37 4.90 3.33
Sediment 0 0.80 1.85 6.97 0 0 0.71 5.27 17.15 2.10 12.57 Sediment 0 0.79 0.41 1.85 1.85 6.97 3.31 0 0 0 0 0.71 0.25 5.26 1.47 17.01 3.12 2.10 0.72 12.57 5.34
Cnidaria 0 0.00 0.00 0 0 0 0 0 0 0 0 0 0 0.01 0.01 0.14 0.06 0.01 0.01 0 0
Unidentified 0 7.60 4.09 0 0 9.27 5.53 14.29 14.29 5.55 3.46 0.13 0.10 0.89 0.59 0.43 0.27 0.15 0.15 0 0
59
phytes dominated the thallate algae component of NF guts, and chlorophytes made up the
majority of the foliose component. Algal groups (filamentous = 29.5%, thallate = 29.2%,
Sargassum = 20.6%) were also the principal items in S. fuscescens (SF, n = 18) guts, with
phaeophytes dominating thallate component (Table 3.3).
3.3.2 Seasonal Dietary Composition
Temporal Differences
The gut composition differed significantly between species across seasons (Global R = 0.61, p
≤ 0.001; Table 3.4), and between each pair of species, except for between the two mullids,
P. barberinoides and P. spilurus (R = 0.010, p ≤ 0.360). The largest difference (greatest R
value) was between N. fageni and P. barberinoides, while the smallest significant difference
was the two herbivorous species, N. fageni and S. fuscescens.
Table 3.4 R-statistics and p-values (in parentheses) derived from two-way crossed species x month ANOSIM
analysis testing for differences between 6 fish species from macroalgal fields south of Coral Bay, sampled in both
February and July, 2015. Bold values indicate no significant difference.
Species: Global R = 0.608, p ≤ 0.001
Month: Global R = 0.352, p ≤ 0.001
S. fuscescens P. spilurus P. barberinoides N. fageni L. nebulosus
L. atkinsoni 0.633 (0.001) 0.508 (0.001) 0.524 (0.001) 0.457 (0.003) 0.257 (0.001)
L. nebulosus 0.930 (0.001) 0.770 (0.001) 0.908 (0.001) 0.948 (0.001)
N. fageni 0.130 (0.038) 0.981 (0.001) 1.000 (0.001)
P. barberinoides 0.994 (0.001) 0.010 (0.360)
P. spilurus 0.974 (0.001)
Interspecific Differences
There were significant differences in dietary compositions between February and July for 5 of
the 6 species, except for L. atkinsoni, which had a similar diet in both seasons (R = 0.031, p ≤
0.395; central diagonal Table 3.5). The diets differed significantly among species in February
(Global R = 0.777, p ≤ 0.001; lower left section Table 3.5) and between all except two pairwise
comparisons: L. atkinsoni and L. nebulosus (R = -0.230, p ≤ 0.927), and N. fageni and
S. fuscescens (R = 0.001, p ≤ 0.405). In July, the composition of gut contents differed
60
significantly among species (Global R = 0.551, p ≤ 0.001; upper right section Table 3.5), and
for all pairwise comparisons except P. spilurus and P. barberinoides (R = -0.010, p ≤ 0.497).
Table 3.5 R-statistics and p-values (in parentheses) derived from one-way ANOSIM analyses testing for differences
between 6 fish species sampled from macroalgal fields south of Coral Bay within February (lower left) and July (upper
right), 2015. Values in the dark grey diagonal show the Global R statistics and p- values (in parentheses) derived from
one-way ANOSIM analyses testing for differences between February and July, 2015 for each species. Bold values
indicate no significant difference.
February: Global R = 0.777, p ≤ 0.001
July: Global R = 0.551, p ≤ 0.001
L. atkinsoni L. nebulosus N. fageni P. barberinoides P. spilurus S. fuscescens
L. atkinsoni 0.031
(0.395)
0.431
(0.003)
0.345
(0.021)
0.469
(0.001)
0.498
(0.001)
0.529
(0.001)
L. nebulosus -0.230
(0.917)
0.633
(0.002)
0.746
(0.003)
0.871
(0.001)
0.791
(0.001)
0.743
(0.001)
N. fageni 1.000
(0.001)
1.000
(0.001)
0.571
(0.003)
1.000
(0.002)
0.978
(0.001)
0.574
(0.009)
P. barberinoides 1.000
(0.002)
0.970
(0.001)
1.000
(0.001)
0.643
(0.001) -0.010
(0.497)
1.000
(0.001)
P. spilurus 0.654
(0.002)
0.683
(0.001)
1.000
(0.001)
0.175
(0.015)
0.273
(0.003)
0.973
(0.001)
S. fuscescens 0.984
(0.001)
0.991
(0.001) 0.001
(0.405)
0.983
(0.001)
0.980
(0.001)
0.585
(0.001)
In February, L. atkinsoni guts had only 3 dietary groups compared to 11 in July. After
removing digested organic matter and sediment from the data, the composition changed from
one dominated by annelid (95.4%) groups in February to a relatively even mix of teleosts
(34.9%) and annelids (28.2%) in July (Figure 3.3). However, these differences were not
significant (R = 0.031; p ≤ 0.39). The L. nebulosus diet changed significantly between seasons
(R = 0.633; p ≤ 0.005). Annelid composition declined from 43.7% in February to 13.4% in
July, with larger contributions from molluscs (29.8%), meso-crustaceans (10.3%), fleshy
macroalgae (6.7%) and sediment (17.4%) groups in July (Figure 3.3).
The dietary composition of P. barberinoides differed significantly between February and July
(R = 0.643; p ≤ 0.001), changing from one dominated by micro-crustaceans in February (70%
by volume), to roughly equal contributions from meso- (45.8%) and micro-crustaceans (47.3%)
in July (Figure 3.3). The diet of P. spilurus also changed significantly between months (R =
0.273; p ≤ 0.005). The combined proportion of micro- and meso-crustaceans was similar
between February (60.1%) and July (66.9%) samples; however, the relative contribution
61
changed from being micro-crustacean dominated (78.1% of crustaceans) in the February cohort
to meso-crustacean dominated (65.2% of crustaceans) in July (Figure 3.3).
Primary producers dominated the gut contents of N. fageni in both February (92.0%) and July
(74.0%); however, a large proportion of teleost scales (15.9%) was also found in the July guts
(Figure 3.3). The magnitude of difference between months was relatively large (R = 0.571; p
≤ 0.005). Although the proportion of primary producer material in the guts of S. fuscescens was
very similar in February (86.3%) and July (85.4%), the percent contribution of individual
groups changed markedly (Figure 3.3). The volume of fleshy macroalgae decreased from
41.5% to 7.4%, while that of Sargassum increased from 4.4% to 47.4% (Figure 3.3), resulting
in a small, significant difference between seasons (R = 0.273; p ≤ 0.005).
Figure 3.3 Seasonal variation in volumetric gut contents of broad dietary categories for
juvenile and subadult fishes from the Ningaloo Reef lagoon, south of Coral Bay, 2015. The
x-axis is categorised by number of guts that had contents (n) for each month of sampling for each species. Species code is the same as Table 3.1.
CLUSTER analyses applied to a nMDS plot were used to organise the six species that were
sampled in both February and July into guilds based on an ordination of their gut contents
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 0 6 21 2 0 16 9 4 7 9 4 8 16 8 25 11 7 10 0
Feb Jul Feb Jul Feb Jul Feb Jul Feb Jul Feb Jul Feb Jul Feb Jul Feb Jul Feb Jul
CR LeA LeG LeN LuQ LuF NF PB PS SF Usp
Gu
t co
nte
nt
rela
tive
ab
un
dan
ce (
%V
ol)
Number of guts analysed per sample month per species
Non-fleshy algae
Fleshy macroalgae
Sargassum
Sponge/Bryozoan
Zooplankton
Annelid/Nematode
Echinoderm
Mollusc
Cephalopod
Micro-crustacean
Meso-crustacean
Teleost
Sediment
Digested OMn
Month
Species
62
(Figure 3.4). The analysis, based on the composition of individual guts, clearly shows the
separation between the herbivorous species (NF and SF) and the other two groups, which were
also very distinct, with relatively few overlapping samples (Figure 3.4): this is emphasised by
the ordination of averaged data for each species in both months showing three major groups
(inset of Figure 3.4). The mullidae species (PB, PS) were grouped as a zoobenthivorous guild
in the lower left, the Lethrinus species (LeA, LeN) were grouped together as a carnivorous
guild (top left) distinct to the mullids, and N. fageni (NF) and S. fuscescens (SF) were grouped
into a herbivorous guild (lower right; Figure 3.4). The Lutjanid species that were only sampled
in one season were excluded from this analysis, but were grouped together as a carnivorous
guild based on the gut contents composition (Figure 3.3).
Figure 3.5 illustrates the similarity in relative dietary composition among the three guilds
identified in Figure 3.4. The three pairs of species that had no significant difference in their
diets (N. fageni/S. fuscescens and L. atkinsoni/L. nebulosus in February, and P.
Figure 3.4 nMDS ordination plot of the individual standardised and transformed (main figure) and arithmetic
average (inset) for volumetric gut contents of six species of fish that were sampled in both February and July,
2015. February samples of L. atkinsoni (LeA) are positioned behind July samples, and so are hidden. Averaged
data is grouped according to CLUSTER analysis (based on Bray-Curtis similarity) at 30% similarity.
63
barberinoides/P. spilurus in July) had very similar relative gut contents, as shown by the depth
of shading, especially for the dominant dietary categories. The similarity is reflected in the
branching of the dendrogram along the top axis (Figure 3.5).
Fleshy macroalgae and non-fleshy algae were common in the guts of three of the herbivorous
groups (N. fageni from both months, and S. fuscescens from February) with the relative volume
of Sargassum being the main difference for S. fuscescens in July (Figure 3.5). Annelids were
abundant in the gut composition of Lethrinus species in February, while in July, teleosts were
abundant in L. atkinsoni and molluscs in L. nebulosus. The two crustacean groups were most
abundant in three of the Parupeneus groups (P. barberinoides in July and P. spilurus from both
months), while micro-crustaceans were the only important group for P. barberinoides in
February (Figure 3.5).
Figure 3.5 Shade plot showing, by depth of shading, relative abundance of volumetric gut composition for six
fish species sampled in both February (hollow symbols and red cross) and July (solid symbols and red
asterisk), 2015. The dendrograms used CLUSTER analysis based on Bray-Curtis similarity. The species:
L. atkinsoni (LeA), L. nebulosus (LeN), N. fageni (NF), P. barberinoides (PB), P. spilurus (PS), and
S. fuscescens (SF), have been reordered from the original seriated Primer output to position the same species
over different seasons next to each other.
64
3.3.3 Analysis of Prey Specific Abundance and Frequency of Occurrence
Lethrinid species
Analysis of the Lethrinid species plots (Figure 3.6a-b) shows that annelids were an important
(50 - 75% on the nonlinear scale; Figure 3.2), specialised prey item in February (hollow
symbols), making up about 80% of the relative abundance in 66% of the L. atkinsoni guts
(Figure 3.6a) and more than 50% of the relative abundance of 75% of the L. nebulosus guts
(Figure 3.6b). The importance of annelids in Lethrinid diets decreased in July (10 - 25%, filled
symbols), and more dietary items, with smaller relative abundance, were present in fewer fish,
indicating a more generalist feeding strategy and broader niche width.
Meso-crustaceans (1-4 mm decapods) had a low importance (≈ 10%) to individuals of both
L. nebulosus and fish L. atkinsoni sampled in February (Figure 3.6a-b). Zooplankton, molluscs,
micro-crustaceans, teleosts and sediment made up a very low abundance of a small proportion
of L. nebulosus individuals indicating a low importance (< 5%) for the species in February
(Figure 3.6b). Algal groups were present in the guts of a small proportion of individuals
sampled in July, but were not present in any guts from February and were not important to the
diet (< 5%) of either species (Figure 3.6a-b).
In July, both species had a broad niche width, with all organic categories (exc. digested organic
matter and sediment) falling on or below the diagonal between the upper left and lower right
quadrants (Figure 3.6a-b). In L. nebulosus diets, meso-crustaceans were a highly specialised
prey item (high PSA, low FO) for individuals sampled in July (in contrast to L. atkinsoni),
indicating high between phenotype contribution, while the low PSA and high FO for molluscs
in the July cohort indicates high within phenotype contribution.
65
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Figure 3.6 Abundance of 13 dietary categories for 8 fish species, grouped by broad feeding groups used in the Ecopath
with Ecosim model (Chapter 4). Fish species include: Lethrinid species: a) Lethrinus atkinsoni, (nFeb = 6; nJul = 21),
b) Lethrinus nebulosus (nFeb = 16; nJul = 9); carnivorous species: c) Lutjanus quinquelineatus (nFeb = 4), d) Lutjanus
fulviflamma (nJul = 7); zoobenthivorous species: e) Parupeneus barberinoides (nFeb = 8; nJul = 16), f) Parupeneus
spilurus (nFeb = 8; nJul = 25); herbivorous species: g) Naso fageni (nFeb = 9; nJul = 4), h) Siganus fuscescens (nFeb = 11;
nJul = 7). Prey specific abundance is the proportion of the dietary category, only from guts that contained the dietary
category. Frequency of occurrence is the proportion of the dietary item in guts that were not empty (adapted from Amundsen et al. (1996)). Hollow and filled symbols indicate February and July data respectively.
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66
Carnivorous species
Temporal comparison of the diet between carnivorous fish species (Figure 3.6c-d) is not
possible because both species were not caught in each month. L. quinquelineatus,
sampled in February, had a broad niche width (low PSA, low FO), with all values on the
lower left of the niche width contribution diagonal (Figure 3.6c). Individuals tended to be
generalist feeders of annelids and teleosts, though some individuals specialised on
consuming micro-crustaceans. The L. fulviflamma gut contents, sampled in July, showed
a narrow niche width for teleosts and meso-crustaceans with values for these groups in
the upper right quadrant (Figure 3.6d), indicating some specialisation in consuming these
groups. Algal groups were a rare item in the guts of some individuals and were not an
important component of the diet.
Zoobenthivorous species
Both zoobenthivorous species, P. barberinoides and P. spilurus, showed a generalist
feeding strategy over both the February and July samples (Figure 3.6e-f), with almost all
dietary categories falling in the two lower quadrants. Meso-crustaceans were the
dominant organic dietary category for both species; however, where its importance was
relatively consistent between months for P. barberinoides (≈ 50%; Figure 3.6e), it
increased in importance between February (≈ 25%) and July (≈ 50%) samples for P.
spilurus (Figure 3.6f).
In February, mollusc, annelid and teleost groups had similar importance (< 10%) to the
two species, though molluscs occurred in a larger proportion of guts in P. spilurus
juveniles (Figure 3.6f). The importance of annelids in P. barberinoides (< 10%; Figure
3.6e), and zooplankton, molluscs and teleosts in P. spilurus (< 5%; Figure 3.6f) didn’t
change between February and July, though there was an increase in the importance of
annelids for P. spilurus.
67
Micro-crustaceans were not present in February gut samples, but were present in low PSA
(≤20%) with medium to high FO (0.65-1.0) for both species (Figure 3.6e-f). This showed
high within phenotype contribution for micro-crustaceans in July for both species. Other
dietary groups that were only present in the July samples were present in low PSA (<40%)
and low FO (<0.4), and were not important (< 5%) prey items. Algal groups did not
contribute to the diet of either species.
Herbivorous species
Both herbivorous species showed, largely, a generalist feeding strategy, with algal groups
being the most important dietary categories for both species (20 - 50%; Figure 3.6g-h).
The only exception to the generalist strategy was the importance of non-fleshy algae to
N. fageni juveniles (≈ 70%), with the group making up more than 65% of relative
abundance in 100% of the guts analysed (Figure 3.6g). The importance of non-fleshy
algae decreased between February and July for N. fageni; however, it was still the most
important dietary category in both months (Figure 3.6g).
The importance of fleshy macroalgae and Sargassum spp. to N. fageni remained constant
between months (< 10%), even though the frequency of occurrence of Sargassum spp.
increased from 10% of guts in February to more than 75% of guts in July (Figure 3.6g).
For S. fuscescens, the PSA and FO of Sargassum spp. in the diet increased from February
to July, increasing in importance from approximately 10% to over 40% (Figure 3.6h).
The FO of the other two algal groups also increased, however, the PSA decreased only
slightly, decreasing their importance (Figure 3.6h).
Apart from teleost fragments, which occurred in 75% of N. fageni in July (Figure 3.6g),
faunal groups were not an important component (<10%) to the diets of either species
(Figure 3.6g-h). However, there were mollusc and bryozoan/sponge groups present in low
PSA in 100% of the guts analysed for S. fuscescens in July (Figure 3.6h).
68
3.4 Discussion
This study analysed the diets of eleven species of juvenile and subadult fish, collected
from macroalgal beds within the southern Ningaloo Marine Park (NMP) lagoon in
February and July, 2015 (Table 3.3). Gut contents analyses revealed that only two species
had largely herbivorous diets, while the other nine species displayed varying degrees of
carnivory on micro-crustaceans (<1 mm), meso-crustaceans (1-4 mm), annelids,
molluscs, or teleosts (Table 3.3).
Six species were caught in both sampling periods and multi-variate analyses revealed
three distinct species groupings based on their dietary composition (Figure 3.4). The
groups were: herbivorous fish (Naso fageni, Siganus fuscescens), characterised by fleshy
and non-fleshy algae in their guts; zoobenthivorous fish (Parupeneus barberinoides,
Parupeneus spilurus), characterised by micro- and meso-crustaceans in their guts; and
carnivorous fish (Lethrinus atkinsoni, Lethrinus nebulosus), characterised by annelids,
molluscs and teleosts in their guts (Figure 3.5).
Although sample sizes were not uniform among species, and were sometimes small, the
results have provided a basis for examining the degree of specialisation and generalisation
in the diets of these juvenile fishes, the importance of broad dietary categories to each
species of fish, and provided information to update the Ningaloo Ecopath with Ecosim
model.
3.4.1 Contribution of Macroalgae to Diets of Fish Species in the NMP Lagoon
Macroalgae are an important settlement habitat for post-larval fish in the Ningaloo Marine
Park lagoon (Wilson et al. 2010; Evans et al. 2014; Wilson et al. 2014); however, this
study infers that macroalgal species are an important component to the diets of nominally
herbivorous fish species, and are not a directly important component to the diets of the
Lethrinidae, Lutjanidae and Mullidae species that were analysed, some of which are
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targeted by recreational fishers in the NMP. Of the eleven species that were collected
from macroalgal beds south of Coral Bay in February and July, 2015, only two species
(the Acanthurid, Naso fageni, and the Siganid, Siganus fuscescens) had large volumes of
algal or macroalgal material in their guts (Table 3.3). While herbivorous species have
ecological roles within the NMP ecosystem (Vergés et al. 2011; Michael et al. 2013) and
are targeted as an important fishery in other regions of the world, such as the Caribbean
and throughout the Indo-Pacific (Lam 1974; Food and Agriculture Organisation 2001b),
they are not targeted by recreational fishers in the NMP (Fletcher and Santoro 2015).
Herbivorous species
Macroalgae, especially Sargassum spp., are an important component (≈50% of diet
composition;Table 3.3) to the diet of S. fuscescens in the NMP lagoon, south of Coral
Bay. Evidence from this study shows that there is an increase in the importance of
Sargassum species in the winter as S. fuscescens mature. This is the time when the
biomass of Sargassum in the lagoon declines compared with summer (Fulton et al. 2014).
The apparent increase in importance of Sargassum may be explained by changes in its
properties that make it more palatable as a food source to S. fuscescens in winter (e.g.
chemical (Boyer et al. 2004), physical, or epifaunal composition (Pennings et al. 2000)
of Sargassum spp., may change) because algal traits have been shown to explain more
variation in the impacts of herbivory than the identity of the consumer (Poore et al. 2012).
Another explanation could be that some fish increase herbivory with ontogeny, as the
length of their alimentary canal increases (Benavides et al. 1994a; Kramer and Bryant
1995). A third explanation for the apparent shift to Sargassum may be that it is not
represented in February gut samples because the algal fragments were too small to be
correctly identified, due to the small gape or bite size of the juvenile fish.
70
It is likely that the difference between juvenile and adult diets was due to ontogenetic
changes, as a similar ontogenetic shift was evident for S. fuscescens in Green Island,
within the Great Barrier Reef (Pitt 1997). In this study, S. fuscescens gut contents from
February showed a high PSA and high FO for non-fleshy algae, indicating specialisation
at the population level (Figure 3.6h), analogous to Green Island, where the dominant
items in juvenile guts were easily digestible rhodophytes (Pitt 1997). The shift to
Sargassum in S. fuscescens diets in July is similar to the shift in diets from rhodophytes
to seagrass for the Green Island study. The trend to change from a specialised diet as
juveniles to a broader diet at larger body sizes was also evident in the Lethrinus species
(see below).
Although the rabbitfishes in the Siganidae are well known herbivores (Pitt 1997;
Grandcourt et al. 2007; Yamaguchi et al. 2010), the importance of algae in the diets of
S. fuscescens and Siganus canaliculatus, a synonymous/hybrid species (Kuriiwa et al.
2007; Hsu et al. 2011) appears to vary. Thus, in the current study, algal material
dominated the gut composition of S. fuscescens, while other studies have found that
S. canaliculatus diets consist of less than 5% macroalgal content (Wu 1984) and that it
may eat whatever is available to it (reviewed in Lam 1974; Wu 1984). Though several
individual S. fuscescens contained a quantity of nematode-like organisms in their guts in
July, this was assumed to be a parasitic organism and not a targeted prey item.
Studies based on gut contents analysis provide a snapshot of the diet at a particular time
and do not necessarily represent the longer-term dietary composition or indicate what
items are being assimilated (Chipps and Garvey 2007; Ashworth et al. 2014). Stable
isotope analysis, which measures the ratios of isotopes (e.g. 𝐶13 and 𝑁15 ) in tissues,
provides a means of assessing the sources of nutrition derived over longer time periods
(Vander Zanden and Rasmussen 2001; Cocheret de la Morinière et al. 2003). Used in
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conjunction with the gut contents analysis, it may help confirm whether the change in diet
between winter and summer was real, or a function of a snapshot of the day and small
sample sizes (Cocheret de la Morinière et al. 2003; Lugendo et al. 2006; Ashworth et al.
2014).
Little is known of the diet of the surgeon fish, Naso fageni, since it is a relatively minor
component of the herbivorous fish assemblage in coral reef ecosystems (Moore, Western
Australian Museum, pers. comm.). This study clearly shows that alga (non-fleshy algae,
fleshy macroalgae and Sargassum spp.) make an important contribution to the diet of
immature N. fageni, in both summer and winter, in lagoon areas of the NMP, south of
Coral Bay.
The small (<1%) amounts of crustaceans and annelids in the guts of N. fageni were likely
consumed incidentally with the algae that were eaten. Some faunal material was expected
in the guts of February samples of nominally herbivorous fish because it has been inferred
that juvenile fish target sources of protein to meet their high nitrogen requirements (White
2011; Day et al. 2011) or because the alimentary canal is not capable of digesting fleshy
macroalgae (Benavides et al. 1994b; Kramer and Bryant 1995). However, the presence
of fish scales in the guts of N. fageni from the July samples was unexpected. Naso fageni
have distinctively shaped scales (Moore, Western Australian Museum, pers. comm.),
different to the scales that were found in the guts of these fish. This indicates that the
presence of these scales was not the result of intra-species fighting, and may have been
due to consumption of other fish, or more probably, inter-species fighting over food
resources or protecting a home patch, similar to the territorial behaviour of many
Pomacentrid species (Clarke 1970; Low 1971).
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Carnivorous species
Annelids were a specialised prey item for both Lethrinus species in February, though their
importance decreased in July as more items were present in smaller relative abundance
(Figure 3.6a-b). This mirrored the trend for S. fuscescens juveniles to specialise on a
dietary item before broadening their diet as they grew and became capable of consuming
a larger variety of items (Figure 3.6g-h). This is described as “ontogenetic niche shift”,
where patterns in an organism’s resource use change as it grows (Werner and Gilliam
1984). The evidence of ontogenetic niche shift by L. nebulosus highlights the importance
of the macroalgal habitats for the juvenile stages (Kimirei et al. 2013).
Several species (L. atkinsoni, L. nebulosus, L. fulviflamma, P. spilurus) had small
amounts (< 2.5%;Table 3.3) of macroalgae in their guts, but it only occurred in winter
samples (Figure 3.6). Macroalgal beds provide structure for habitat to many cryptic
vertebrate and invertebrate organisms (Martin-Smith 1993; Peart 2004); including
molluscs, annelids, echinoderms and cryptic fish species (Everett 1994), many of which
are an important component of the diets of the species in this study (Figure 3.6). Many
predatory fish ingest their prey through suction or “gulping” (e.g. Lethrinidae,
Lutjanidae), and the macroalgae present in the guts of the species above were likely
consumed as an incidental component of their target diet (Ogden and Lobel 1978;
Nakamura et al. 2003). However, this still illustrates the importance of macroalgae to the
diets of these species, even if it wasn’t a direct food source, since it provides habitat for
cryptic species such as polychaete worms, ophiuroid brittle stars, molluscs and fish
(Everett 1994) that are an important component to the fishes’ diets. As mentioned
previously, it would be valuable to complement the results of the current study with stable
isotope analyses (Cocheret de la Morinière et al. 2003; Lugendo et al. 2006) to determine
whether the presence of algae was an incidental component of the diet or if it was more
important.
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3.4.2 Enhancing Knowledge of Food Webs in the NMP
The diets and biological characteristics of many fish species found within the lagoon areas
of the NMP are relatively unknown. Species such as L. quinquelineatus, N. fageni, P.
barberinoides, S. fuscescens and Upeneus spp. are neither iconic species, nor the target
of recreational or commercial fisheries in the NMP. They have not been the subject of
detailed research regarding diet, size at maturity, age and growth in this region. There is
also a dearth of information for juveniles of these species, though dietary studies have
been published on S. fuscescens in the Great Barrier Reef (Pitt 1997) and parts of Asia
(reviewed in Lam 1974), and L. atkinsoni in Japan (Nakamura et al. 2003).
While this study has helped increase the knowledge of juvenile diets of fish associated
with macroalgal beds within the NMP, it was only conducted over two, 1-week periods
in the summer and winter of 2015. The effect of seasonal or inter-annual changes in
variables such as sea temperature, algal growth, fish larvae recruitment, and consumption
by the fishes can not be inferred from this study. Several species that were anticipated to
be included in the study (e.g. Choerodon rubescens, Lethrinus miniatus, Lethrinus
variegatus) were not seen during the sampling periods, but are often target species for
recreational fishers (Ryan et al. 2015; Fletcher and Santoro 2015), and have been seen
during surveys of macroalgal beds (Wilson et al. 2010; Evans et al. 2014; Wilson et al.
2014). Collection of samples over different seasons in several years would have helped
overcome this, but was not possible with the temporal and budgetary constraints posed
by an Honours project.
The regurgitation of gut contents when fish are threatened can affect the results of dietary
studies (Bowman 1986). Regurgitation was not observed during this study and more than
90% of the individuals had stomachs with food items present for 8 of the 11 species in
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the current study (Table 3.3). This suggests that regurgitation was unlikely to influence
the results of this study.
3.4.3 Future Research
While this study has helped to improve the knowledge for juvenile life-stages of several
fish species within lagoon areas in the south of the NMP, several interesting questions
have come from its findings.
There are many more juveniles of species that are important for ecological processes or
fisheries in the region that weren’t present when sampling took place during this study.
Determining their trophic niche in the Ningaloo lagoon is the next step for increasing our
knowledge of the ecology of the NMP.
Macroalgae were an important component to the diets of the two juvenile herbivorous
fish species in July and herbivorous fish play a significant role in regulating the amount
of macroalgal biomass present in areas of the NMP (Johansson et al. 2010; Vergés et al.
2011; Michael et al. 2013). One species, S. fuscescens, is increasing its range as temperate
waters become warmer in Western Australia (Vergés et al. 2014; Wernberg et al. 2016),
the question arises whether they will have an impact on temperate macroalgal habitat in
those waters? The second species, N. fageni, contained a relatively large proportion of
fish scales in their guts in July. This raises the question of whether they consume fish as
part of their diet, or were these scales the result of patch protection behaviour similar to
the behaviour of many species of Pomacentrids?
Lastly, gut contents analysis shows only a snapshot of dietary composition over a short
time scale (Ashworth et al. 2014). Stable isotope analyses of juvenile and subadult life-
stages for these species will help to develop the findings of this research, and determine
the primary source of nutrition over seasons within the lagoon.
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3.5 Conclusion
This study has provided information to assess the contribution of macroalgae to the diets
of juvenile and subadult fish, of both iconic and cryptic species in the macroalgal beds of
the Ningaloo Reef lagoon. It has identified clear differences in feeding guilds found in
the macroalgal beds and separated Lethrinus species from zoobenthivorous and
carnivorous species to define guilds that can be used as functional groups for the
ecosystem model developed in Chapter 4. It has also identified that diet varies between
summer and winter when the macroalgal community composition changes, and fish attain
a size where they can broaden their trophic niche. Further work to increase the number of
fish sampled, complemented with stable isotope studies would enhance our understanding
of the significance of macroalgae to the food web and trophic flows in the Ningaloo Reef
lagoon.
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4. Modelling the Trophic Flows to Fish in the Macroalgal Beds of the
Ningaloo Reef Lagoon in North-Western Australia
4.1 Introduction
Ecosystem models are used for a variety of reasons, including providing managers and
researchers with information about the structure and functioning of a system, and
simulating dynamic processes that influence multiple functional groups in a system (Food
and Agriculture Organisation 2008). They are designed to help understand trophic flows,
assess uncertainty around policy decisions, to identify trade-offs between potential
management strategies, and can be used in the planning process to manage and protect
species (Food and Agriculture Organisation 2008; Evans et al. 2014; Thébaud et al.
2014).
Ecopath with Ecosim (EwE) has become one of the most widely used tools for modelling
ecosystem dynamics and fisheries (Coll et al. 2015). Its relative simplicity for novice
users, adaptability to various systems, and free access have resulted in more than 400
models being published worldwide (Christensen 2009; Colléter et al. 2013; Gartner et al.
2015). Ecopath is a static, mass-balanced model that parameterises the system for
dynamic, temporal simulations in Ecosim (Christensen 2009). Combined, EwE provides
the user with an understanding of the dynamics of the system and allows predictions to
be made about the impact of potential disturbances through simulation of different
scenarios.
Though EwE is widely used to understand ecosystem dynamics, a number of issues
should be considered when developing the model and interpreting the results from
simulating different scenarios. These include estimating values for the large number of
parameters in the model, parameterising the model in data-poor systems, verifying the
relevance of the model to the system, performing quality control over the data, keeping
the data up-to-date and inherent uncertainty in the predictions (Coll et al. 2015). Even so,
77
the models can play an important role in understanding system function, interactions
between different components of the system, and can inform management decisions by
identifying knowledge gaps within a system (Beck et al. 2001; Dahlgren et al. 2006;
Loneragan et al. 2010; Fulton et al. 2011; Vergés et al. 2011; Lozano-Montes et al. 2012).
The primary aims of this chapter are to:
1. Revise the Ningaloo Ecopath model with data derived from a dietary study
that looked at the importance of macroalgae to the diets of juvenile fishes from
the Ningaloo lagoon, and other data from the Ningaloo Marine Park since the
original model was developed;
2. Use the revised model to examine the trophic flows of macroalgae through the
system, and determine its importance to the ecosystem; and
3. Develop an Ecosim model to evaluate the consequences of various changes to
physical and biological drivers to the system. These drivers include an
increase in temperature and macroalgal productivity, and an increase in
fishing effort.
Based on these objectives, the hypotheses to be tested are:
H0: Macroalgae are a significant component of primary production in the system
and will have a positive influence on juvenile fish biomass.
H0: Extreme increases in fishing effort will have an impact on adult L. nebulosus
biomass
4.2 Methods
4.2.1 Ecopath Model
An Ecopath model (Coral Bay model) was constructed to describe the trophic flows from
macroalgal patches in the Ningaloo lagoon south of Coral Bay to fish species that are
78
important to recreational fishers in the NMP. Ecosim was used to simulate several
scenarios where algal production increased or decreased according to expected outcomes
from external drivers (see section 4.2.3). The Coral Bay model is based on the CSIRO
developed, Ningaloo EwE ecosystem model (Fulton et al. 2011; Jones et al. 2011;
Lozano-Montes, CSIRO, pers.comm.), which was developed as part of the Ningaloo
Collaboration Cluster (Fulton et al. 2011). It covers 10,400 km2, extending from the east
coast of the Exmouth Gulf (21° 41.460’ S) in the north to between Warroora and Gnaraloo
stations (23° 33.420’ S) in the south, with the modelled area split 75:25 between marine
and terrestrial systems.
The geographic bounds of the model developed during this research cover 19.42 km2 of
the lagoon, bounded by the Maud Sanctuary Zone in the north, the Pelican Sanctuary
Zone in the south, the backreef to the west and the shoreline to the east (Figure 4.1). The
Coral Bay model was limited to the backreef and lagoon compared to the Ningaloo model
which includes the reef slope, backreef, lagoon and shoreline. These boundaries were
chosen to maintain relative homogeneity of the area regarding habitat, management
practices and fishery access. The sanctuary zones to the north and south are closed to
fishing, however the modelled area is fully open to recreational fishing and accessible by
boat or four-wheel drive vehicle from either Coral Bay or 14-Mile Beach campsite (Figure
4.1). Hyperspectral imaging of the modelled area in 2006 found that the benthic habitat
consisted of sand (≈38.5%), limestone pavement (≈36.7%), macroalgae (≈18.0%), hard
coral (≈3.3%), rubble (≈1.1%), turf algae (≈0.7%), intact dead coral (≈0.5%) and soft
coral (≈0.1%) (Kobryn et al. 2013) (Figure 4.1).
The Ningaloo model contains 53 functional groups including both terrestrial and marine
groups (Fulton et al. 2011). The model developed in this study focussed on the
macrophyte beds in the lagoon and was reduced to 29 marine functional groups. Fish
groups were reclassified as carnivorous (Lutjanidae), zoobenthivorous (Mullidae), herbi-
79
Figure 4.1 The area covered by the Coral Bay Ecopath with
Ecosim model, bounded by the reef to the west (blue), the
coastline to the east (white), and the Maud and Pelican
Sanctuary Zones to the north and south (red cross-hatch),
respectively. The modelled area is derived from a benthic
habitat hyperspectral survey (Kobryn et al. 2013). Habitat
codes are explained in Appendix 1.
0 40 km
Habitat Legend
80
vorous (Siganidae and Acanthuridae) or other (Table 4.1) based on the importance of
macroalgae as habitat, prey habitat or dietary item to each group. Lethrinus nebulosus and
Lethrinidae were retained as distinct groups due to their importance to recreational
fishers. Macrophytes were reclassified into Sargassum spp., fleshy macroalgae and non-
fleshy algae to better analyse the varying influence of each group. This model also limited
human influences only to fisheries, while the Ningaloo model included broad tourism,
camping and fisheries groups.
Ecopath with Ecosim (EwE) is a mass-balanced technique based on two master equations,
one for the production term (Eq. 4.1) and one for the energy balance (Eq. 4.2)
(Christensen and Walters 2004). The total production rate of a group (i) is defined by:
𝐵𝑖 ∙ (𝑃 𝐵⁄ )𝑖 ∙ 𝐸𝐸𝑖 − ∑ 𝐵𝑗 ∙ (𝑄 𝐵⁄ )𝑗 ∙ 𝐷𝐶𝑗𝑖 − 𝑌𝑖 − 𝐸𝑖 − 𝐵𝐴𝑖𝑛𝑗=1 = 0 (4.1)
where: Bi is the biomass of group i (Bj is biomass of a predator group j)
P/Bi is the production/biomass ratio of group i
EE is the proportion of production of group i utilised within the system
Q/Bi is the consumption/biomass ratio of group i
DCji is the fraction of prey (i) in the average diet of predator (j)
Yi is the total fishery catch rate of group i
Ei is the net migration (emigration - immigration) of group i
BAi is the biomass accumulation rate of group i (Christensen and Walters 2004).
Few data were available for the modelled area for many of these parameters. Ecopath is
able to solve for one unknown parameter out of B, P/B, Q/B or EE (Christensen et al.
2008), and in the Coral Bay model, the model was generally used to estimate the
81
ecotrophic efficiency (EE), a measure of the proportion of a group’s production that is
used within the system (Christensen et al. 2008) (Table 4.1).
Once any missing parameters have been estimated, the flow of energy needs to be
balanced to achieve mass-balance of the model. This is defined through the equation:
𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 = 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 + 𝑟𝑒𝑠𝑝𝑖𝑟𝑎𝑡𝑖𝑜𝑛 + 𝑢𝑛𝑎𝑠𝑠𝑖𝑚𝑖𝑙𝑎𝑡𝑒𝑑 𝑓𝑜𝑜𝑑 (4.2)
so that a group’s consumption accounts for respiration losses and changes in biomass,
and its biomass production accounts for losses through fishery landings and consumption
by its predators (Christensen et al. 2008).
4.2.2 Model Structure, Parameterisation and Balancing
The model was constructed with 29 functional groups (Table 4.1), determined by
recreationally targeted fish species that are significant to users of the NMP (Lethrinus
nebulosus, other Lethrinus and Lutjanus species), fish groups sampled for dietary studies
(see Ch. 3, Table 3.2), their dominant dietary categories (see Ch. 3, Table 3.3), predators
of the modelled fish groups (e.g. reef sharks, dolphins; Table 4.2), competitors for
macroalgal resources (e.g. turtles; Table 4.2), and inorganic/detrital groups (e.g.
sediment).
Fish functional groups were categorised based on the results of the dietary study and
placed in multi-stanza categories including both juvenile and adult life stages for selected
species. Lethrinus nebulosus and Lethrinus spp. represent opportunist/omnivorous
species while other multi-stanza fish groups were categorised into carnivorous (e.g.
Lutjanus fulviflamma, Lutjanus quinquelineatus), zoobenthivorous (e.g. Parupeneus
barberinoides, Parupeneus spilurus), and herbivorous (e.g. Naso fageni, Siganus
fuscescens) fish species. Age at maturity, growth, mortality and consumption parameters
were taken from local data wherever possible (Marriott et al. 2010; Marriott et al. 2011).
Otherwise literature that focussed on the species (or genera) studied in the dietary analysis
82
Table 4.1 Summary of the parameters for each functional group in the Ecopath mass-balanced Coral Bay model. Bold
values were estimated by the Ecopath model.
Functional Group
Biomass in
habitat area
(t.km-2.yr-1)
Biomass
(t.km-2.yr-1)
P/B
(yr-1)
Q/B
(yr-1)
Ecotrophic
Efficiency
Catch
(t.km-2.yr-1)
Trophic
Level
Reef Sharksa,e 0.077 0.051 0.33 3.80 0.000 - 3.55
Dolphinsa,e 0.031 0.031 0.11 4.50 0.000 - 3.50
Lethrinus
nebulosus (ad)a,e 1.680 4.200 0.45f 4.06 0.464 0.0906 3.31
Lethrinus species
(ad)a,e 1.152 10.800 0.35f 3.50 0.061 0.0086 3.24
Octopusa,d 0.293 0.147 3.72 15.10 0.887 - 3.11
Zoobenthivorous
fish (ad)a,d 3.440 4.300 0.45f 3.51 0.515 0.0018 3.07
Carnivorous fish
(ad)a,e 0.532 2.662 0.35f 3.86 0.300 0.0259 2.99
Squida,e 2.333 2.333 4.55 18.39 0.965 0.0100 2.98
Lethrinus species
(juv)a,d 0.201 1.117 0.72f 7.91 0.937 - 2.92
Zoobenthivorous
fish (juv)a,d 0.642 0.802 0.80f 7.62 0.540 - 2.92
Carnivorous fish
(juv)a,d 0.043 0.215 0.69f 8.56 0.973 - 2.71
Lethrinus
nebulosus (juv)a,d 0.112 1.250 0.67f 8.60 0.954 - 2.55
Fish species
(other)a,d,e 8.591 8.591 0.81 3.47 0.953 - 2.54
Crustacean
(meso)a,d 9.663 5.798 5.66 19.48 0.981 - 2.46
Turtlesa,e 1.487 1.487 0.33 17.08 0.100 - 2.18
Crustacean
(micro)a,d 0.551 0.551 44.00 135.00 0.983 - 2.16
Annelid/
Nematodea,d 2.533 2.533 6.57 23.28 0.828 - 2.11
Herbivorous fish
(ad)a,e 2.758 6.896 0.34f 9.70 0.426 - 2.07
Mollusc (exc.
cephalopod)a,d 6.821 5.456 2.85 13.31 0.992 - 2.06
Herbivorous fish
(juv)a,d 0.725 1.813 0.50f 14.09 0.884 - 2.04
Echinoderma,d 1.169 0.994 11.98 39.70 0.878 - 2.02
Zooplanktona,e 8.800 8.800 67.31 226.38 0.130 - 2.01
Sponge/
Bryozoana,d 34.000 3.628 2.74 24.47 0.610 - 2.00
Sargassumb,d 360.000 64.800 4.00 - 0.201 - 1.00
Fleshy
macroalgaeb,d 175.100 31.518 13.25 - 0.121 - 1.00
Non-fleshy
algaeb,d 53.510 26.755 17.00 - 0.137 - 1.00
Phytoplanktonb,e 25.510 25.510 70.00 - 0.912 - 1.00
Sedimentc 60.000 60.000 - - 0.905 - 1.00
Detritusc 70.000 70.000 - - 0.284 - 1.00 a Consumer group b Producer group c Detritus group d Benthic group e Pelagic group
f Total mortality (Z) used as a proxy for P/B values for multi-stanza groups(Christensen et al. 2008)
83
(Lam 1974; Russ and Williams 1994; Ebisawa 1999; Grandcourt et al. 2007; Currey et
al. 2013), Fishbase (Froese and Pauly 2015), and the Ningaloo model (Jones et al. 2011)
were used. Where recent data were unavailable for the Coral Bay modelled groups, data
from relevant functional groups in the Ningaloo model were compiled. The total mortality
rate (Z) was used as a proxy for the P/B ratio based on assumptions in the Ecopath master
equation (Christensen et al. 2008). Since the Ningaloo model had 25% of the habitat area
dedicated to terrestrial sources, where habitat area fraction was calculated from the
Ningaloo model (Eq. 4.3), it was adjusted to account for the modelled area being 100%
marine according to the equation:
𝛼𝐶𝐵 =4
3𝛼𝑁 (4.3)
where 𝛼𝐶𝐵 is the habitat area for the Coral Bay model, and 𝛼𝑁 is the habitat area for the
Ningaloo model.
Lethrinus nebulosus was retained as a distinct functional group (Table 4.1) because of its
importance to recreational fishers. Species of the macroalga genus Sargassum were also
retained as a functional group, as they were the dominant species of macroalgae within
the modelled area and this allowed the productivity to be analysed and adjusted separately
from other algal groups.
Initial Model Parameterisation
Data on the biomasses of fish functional groups were obtained from a monitoring program
carried out by the Department of Parks and Wildlife (Wilson et al., DPaW, unpublished
data). Biomass, P/B and Q/B data for other groups were obtained from recent research at
Ningaloo (Marriott et al. 2010; Marriott et al. 2011; Herwig et al. 2012; Doropoulos et
al. 2013; Kobryn et al. 2013; Fulton et al. 2014) where possible, then from research
conducted in the Gascoyne/Pilbara/Kimberley regions of Western Australia (Fry et al.
2008; Keesing et al. 2011; Ryan et al. 2015; Fletcher and Santoro 2015). Where recent,
84
Table 4.2 Functional groups and representative species within each group for the Coral Bay
Ecopath model of the Ningaloo lagoon south of Coral Bay, north-western Australia.
Functional groups with more than one life stage were input as multi-stanza groups. The
geographic extent of the modelled area was 19.42 km2, covering the backreef and lagoon.
Functional group Representative organisms
Reef sharks
Carcharhinus melanopterus
Negaprion acutidiens
Triaenodon obesus
Dolphins
Delphinus delphis
Sousa chinensis
Tursiops truncatus
Turtles Caretta caretta
Chelonia mydas
Lethrinus nebulosus (adult)
Lethrinus nebulosus (juvenile) Lethrinus nebulosus
Lethrinus species (adult)
Lethrinus species (juvenile)
Lethrinus atkinsoni
Lethrinus genivittatus
Carnivorous fish (adult)
Carnivorous fish (juvenile)
Choerodon rubescens
Lutjanus fulviflamma
Lutjanus quinquelineatus
Zoobenthivorous fish (adult)
Zoobenthivorous fish (juvenile)
Parupeneus barberinoides
Parupeneus spilurus
Upeneus spp.
Herbivorous fish (adult)
Herbivorous fish (juvenile)
Naso fageni
Siganus fuscescens
Other fish species
Gobiidae spp.
Labridae spp.
Pomacentridae spp.
Terapontidae spp.
Squid Cephalopodidae spp.
Octopus Octopus cyanea
Mollusc
Bivalvia spp.
Bursa spp.
Cerithium spp.
Conus spp.
Echinoderm Holothuria spp.
Ophiuroidea spp.
Annelid/Nematode Polychaeta spp.
Sipuncula spp.
Crustacean (meso) Panulirus spp.
Penaeus spp.
Crustacean (micro)
Amphipoda spp.
Copepoda spp.
Isopoda spp.
Sponge/Bryozoan
Cheilostomata spp.
Palythoa spp.
Pocillopora spp.
Zooplankton
Sargassum spp. Sargassum myriocystum
Fleshy macroalgae
Cystoceira spp.
Dictyoptera spp.
Dictyota spp.
Lobophora spp.
Non-fleshy algae
Ceramium spp.
Hincksia spp.
Ulva spp.
Phytoplankton
Sediment Fine gravel, sand, silt, shell and coral fragments
Detritus Non-living organic matter
85
local data were not available, data from the Ningaloo EwE model (Jones et al. 2011), then
from models in tropic/subtropic regions of Australia, the Pacific and Atlantic Oceans
were used (Aliño et al. 1993; Arias-Gonzalez et al. 1997; Okey and Mahmoudi 2002;
Bulman 2006; Freire et al. 2008). The remaining data were estimated by formulae within
the model according to the Ecopath thermodynamic equation (Christensen et al. 2008).
Diet Composition
Dietary data for juvenile fish groups were taken from gut contents analysis of fishes
sampled from macroalgal beds in the Ningaloo lagoon (Ch. 3). Data for adult fish groups
came from gut contents of fish that were larger than the length at maturity (n = 6 for
zoobenthivorous adults, n = 4 for herbivorous fishes), relevant literature for the sampled
species (Choat et al. 2002; Nakamura et al. 2003; Debenay et al. 2011; Farmer and
Wilson 2011), and Fishbase (Froese and Pauly 2015). Data for other functional groups
were collated from the Ningaloo model (Jones et al. 2011) or ecosystem models from
subtropical/tropical regions where the functional groups were analogous to those in the
Coral Bay model (Arreguín-Sánchez et al. 1993; Opitz 1996; Zetina-Rejón et al. 2003;
Bulman 2006; Tsehaye and Nagelkerke 2008). A summary of the predator/prey dietary
matrix (Figure 4.2) shows the dominant dietary categories for each functional group in
the model.
The fate of all detritus from the non-detrital groups was contained within the system. The
majority of biomass from all groups flowed to detritus, with a nominal amount flowing
to sediment if the organism contained persistent body parts (e.g. coralline algae, shell,
exoskeleton).
Fishery
Fishery parameters were defined for the recreational fishery that operates from Coral Bay
in the southern NMP. Recreational fishing is an important activity in Western Australia,
86
Co
nsu
mer
gro
up
Ree
f sh
ark
Do
lphin
Tu
rtle
L.
neb
ulo
sus
(ad
)
L.
neb
ulo
sus
(juv
)
Let
hri
nu
s sp
p. (a
d)
Let
hri
nu
s sp
p. (j
uv
)
Car
niv
oro
us
fish
(ad
)
Car
niv
oro
us
fish
(juv
)
Zoo
ben
thiv
oro
us
fish
(ad
)
Zoo
ben
thiv
oro
us
fish
(ju
v)
Her
biv
oro
us
fish
(ad
)
Her
biv
oro
us
fish
(juv
)
Fis
h s
pp
.
Sq
uid
Oct
opu
s
Moll
usc
Ech
inod
erm
An
nel
id/N
emat
ode
Mes
o-c
rust
acea
n
Mic
ro-c
rust
acea
n
Sp
ong
e/B
ryozo
an
Zoo
pla
nkto
n
Dietary Item (x)
Reef shark
Dolphin
Turtle
Lethrinus nebulosus (ad)
Lethrinus nebulosus (juv)
Lethrinus spp. (ad)
Lethrinus spp. (juv)
Carnivorous fish (ad)
Carnivorous fish (juv)
Zoobenthivorous fish (ad)
Zoobenthivorous fish (juv)
Herbivorous fish (ad)
Herbivorous fish (juv)
Fish spp.
Squid
Octopus
Mollusc
Echinoderm
Annelid/Nematode
Meso-crustacean
Micro-crustacean
Sponge/Bryozoan
Zooplankton
Sargassum
Fleshy macroalgae
Non-fleshy algae
Phytoplankton
Sediment
Detritus
Import
Figure 4.2 Dietary matrix showing relative dietary composition by volume for each functional group in the Coral Bay Ecopath model.
87
with an estimated 29.6% of the population aged older than 15 years being active
participants (Ryan et al. 2015; Department of Fisheries 2015). The recreational fishing
effort in the NMP is highly seasonal, with peak effort between April and October, and in
areas with infrastructure providing access to the coast (Sumner et al. 2002; Smallwood et
al. 2011; Smallwood and Beckley 2012). Boat-based fishing activity is greater than shore-
based activity (Sumner et al. 2002) with the most intensive effort occurring inside the
lagoon (Smallwood et al. 2011; Ryan et al. 2015). In a 2007 aerial survey of the NMP
coastline there was generally a high level of compliance with legal catch sizes and bag
limits (Sumner et al. 2002; Smallwood and Beckley 2012), although the Maud and
Pelican sanctuary zones had the highest levels of noncompliance (Smallwood and
Beckley 2012).
Both boat- and shore-based catches are dominated by Lethrinus nebulosus (Spangled
emperor) (Smallwood and Beckley 2012; Ryan et al. 2015), with Order Teuthoidea (squid
species) and other species (Epinephelus rivulatus (Chinaman cod), Lethrinus miniatus
(Redthroat emperor), Lutjanus sebae (Red emperor), Lethrinus laticaudis (Grass
emperor)) that associate with macroalgal habitat (Wilson et al. 2012; Wilson et al. 2014)
also well represented in boat-based catches (Sumner et al. 2002; Ryan et al. 2015).
While there are no large-scale commercial fishing activities within the NMP, recreational
catch for certain species in the Ningaloo Marine Park matches or exceeds the commercial
catch for those same species in the Gascoyne region (e.g. 2014 L. nebulosus catch;
recreational = 17 t, charter = 4 t, commercial = 2 t) (Fletcher and Santoro 2015) and
management practices were altered in 2013/14 after localised depletion of L. nebulosus
in 2007/08 (Fletcher and Santoro 2015).
Catch data were estimated for adults of L. nebulosus, Lethrinus spp., carnivorous fish,
zoobenthivorous fish, fish spp., and squid. These landings were designed to be
88
proportionally higher by area, than those described in the literature for the entire NMP
(Sumner et al. 2002; Ryan et al. 2015; Fletcher and Santoro 2015): because even though
there is lower fishing intensity south of Coral Bay than in the northern NMP (Smallwood
et al. 2011; Smallwood and Beckley 2012), most fishing occurs within the lagoon areas
and the presence of the Coral Bay boat ramp and launching facilities at 14-Mile Beach
increase the density of boats in the modelled area compared to areas outside the lagoon
(see Figures 4 and 5 in Smallwood and Beckley 2012). Landings differ by an order of
magnitude between L. nebulosus and Lethrinus spp. based on length to weight
calculations and the assumption that the ratio of L. nebulosus: Lethrinus spp. caught is
5:1. The landings ranged from approximately 0.002 t.km-2.yr-1 for zoobenthivorous fish
to 0.09 t.km-2.yr-1 for L. nebulosus (Table 4.1).
4.2.3 Evaluating the Ecopath Model
Several metrics and procedures were used to evaluate sources of uncertainty and the
model’s predictions, including pedigree (uncertainty in data), mixed trophic impacts
(sensitivity of model), network analysis (system maturity), balance and calibration of the
model (Heymans et al. 2016). These are summarised briefly below.
Model Pedigree
The Pedigree of the model was calculated in Ecopath to provide a measure of the
reliability and uncertainty of data sources in the model (Christensen and Walters 2004;
Morissette 2007). The model provides an index and confidence interval for the data
source with higher pedigree values indicating better quality data (Table 4.3). The pedigree
index assumes that parameters originating from local data are of higher quality than those
originating from other systems. The equation (Eq. 4.4) used to calculate the pedigree
index (τ) is:
𝜏 = ∑𝜏𝑖,𝑝
𝑛𝑛𝑖=1 (4.4)
89
where τi,p is the pedigree index value for group i and input parameter p for each of the n
living groups in the ecosystem and p represents any of the parameters in Eq. 4.1 (B, P/B,
Q/B, Y, DC) (Christensen and Walters 2004). Data sources for this model were entered
using pre-determined Ecopath categories and confidence intervals (Table 4.3).
The measure of fit (t*) uses the pedigree index to describe how well the model is based
on local data rather than data from other models (Christensen and Walters 2004), with
higher values indicating the model is better entrenched in local data. It is defined based
on the calculation for the t-value for a regression (Eq. 4.5):
𝑡∗ = 𝜏×√𝑛−2
√1−𝜏2 (4.5)
where τ is the pedigree index, and n is the number of living groups in the system
(Christensen and Walters 2004).
Sensitivity
Mixed trophic impacts (MTI) analysis describes how a small increase in a group’s
biomass directly and indirectly impacts all other groups in the model (Christensen et al.
2008). The MTI values for an impacting group (i) in relation to an impacted group (j) are
defined by Eq. 4.6:
𝑀𝑇𝐼𝑖,𝑗 = 𝐷𝐶𝑖,𝑗 − 𝐹𝐶𝑗,𝑖 (4.6)
where DCi,j is the diet composition term expressing how much j contributes to the diet of
i, and FCj,i is a host composition term describing the proportion of predation on j that is
due to i as a predator: fishing fleets (i.e. recreational fishers) are treated as a predator,
with catches treated as a dietary item (Christensen et al. 2008). The MTI analyses for the
Coral Bay model were used to evaluate the sensitivity of the model to an incremental
increase in recreational fisher, adult Lethrinus nebulosus, and Sargassum spp. biomass.
90
Table 4.3 Pedigree indices with confidence intervals (±CI%) in parentheses for each functional
group in the Coral Bay EwE model. Indices are shaded based on the size of the confidence
interval ranging from light grey (0-25%) to black (75-100%).
Functional group Biomass in
habitat area P/B Q/B Diet Catch
Legend
Reef Sharks 2 (±80) 3 (±60) 3 (±60) 2 (±80) 0< CI ≤25
Dolphins 2 (±80) 3 (±60) 3 (±60) 2 (±80) 25< CI ≤50
Turtles 2 (±80) 1 (±80) 3 (±60) 2 (±80) 50< CI ≤75
Lethrinus nebulosus (ad) 6 (±10) 3 (±60) 3 (±60) 5 (±30) 5 (±30) 75< CI ≤100
Lethrinus nebulosus (juv) 6 (±10) 3 (±60) 1 (±80) 6 (±10)
Lethrinus species (ad) 6 (±10) 3 (±60) 3 (±60) 5 (±30) 5 (±30)
Lethrinus species (juv) 6 (±10) 3 (±60) 1 (±80) 6 (±10)
Carnivorous fish (ad) 6 (±10) 3 (±60) 5 (±40) 6 (±10) 5 (±30)
Carnivorous fish (juv) 6 (±10) 3 (±60) 1 (±80) 6 (±10)
Zoobenthivorous fish (ad) 6 (±10) 3 (±60) 5 (±40) 5 (±30) 1 (±70)
Zoobenthivorous fish (juv) 6 (±10) 3 (±60) 1 (±80) 6 (±10)
Herbivorous fish (ad) 6 (±10) 3 (±60) 5 (±40) 5 (±30)
Herbivorous fish (juv) 6 (±10) 3 (±60) 1 (±80) 6 (±10)
Fish spp. 6 (±10) 3 (±60) 3 (±60) 2 (±80)
Squid 2 (±80) 3 (±60) 3 (±60) 2 (±80) 5 (±30)
Octopus 6 (±10) 3 (±60) 3 (±60) 2 (±80)
Mollusc 2 (±80) 3 (±60) 3 (±60) 2 (±80)
Echinoderm 4 (±50) 3 (±60) 3 (±60) 1 (±80)
Annelid/Nematode 4 (±50) 3 (±60) 3 (±60) 2 (±80)
Crustacean-meso 2 (±80) 3 (±60) 3 (±60) 2 (±80)
Crustacean-micro 5 (±30) 3 (±60) 3 (±60) 2 (±80)
Sponge/Bryozoan 4 (±50) 3 (±60) 3 (±60) 1 (±80)
Zooplankton 2 (±80) 3 (±60) 3 (±60) 2 (±80)
Sargassum 6 (±10) 7 (±20)
Fleshy macroalgae 6 (±10) 3 (±60)
Non-fleshy algae 3 (±80) 3 (±60)
Phytoplankton 2 (±80) 3 (±60)
Sediment 3 (±80)
Detritus 2 (±80)
Network Analysis
Ecopath produces network analyses with a range of summary statistics to compare models
and describe the maturity of the system, often used as a proxy for ecosystem health
(Christensen and Walters 2004). Total system throughput (TST; the sum of all flows
through the system (Heymans et al. 2014)), total biomass (excluding detritus) and the
total biomass:total throughput ratio were used as an overall measure of the system. Sum
of production, net system production, calculated total net primary production, the total
primary production:total respiration ratio, and the total primary production:total biomass
ratio were used to describe the role of primary production in the system. Total catch, mean
trophic level of the catch, and gross efficiency (the fraction of primary production that is
used within the system (Christensen et al. 2008)) were used to compare fishing
91
parameters between models. The connectance index (the ratio of the number of actual
links to the number of possible links (Christensen and Walters 2004)), system omnivory
index (a measure of variance in prey trophic levels (Heymans et al. 2014)), Ecopath
pedigree index (τ), measure of fit (t*), and Shannon diversity index (a modification of the
Shannon-Wiener index and used to represent evenness, or distribution of biomass, across
functional groups (Ainsworth et al. 2011)) were used to define trophic flows in the
ecosystem and compare them to five published models from the Indo-Pacific region, the
Red Sea and the Caribbean.
The biomasses for functional groups in the Coral Bay model and the comparison models
were grouped according to macrofauna (excluding teleosts), teleosts, invertebrates,
primary producers and detritus groups. If terrestrial groups or non-coastal birds were part
of the comparison model (e.g. foxes in the Ningaloo model), they were not included.
These grouped biomasses were used to calculate invertebrate:teleost, primary
producer:teleost, primary producer:invertebrate, and primary producer:detritus ratios for
comparison of ecosystem structure.
Mass-Balancing the Model
Ecopath’s pre-balance tool was used to test underlying assumptions regarding key
diagnostics of the model’s performance (biomass, vital rates, production; Figure 4.3) for
each functional group, when ranked by trophic level, before balancing the model
(Heymans et al. 2016). This indicates how well the collated values matched the “real-
world”, and the scale of adjustment for parameters in each trophic level to get a closer fit
to the recommended guidelines during mass-balance. These guidelines include: biomass
should span 5-7 orders of magnitude, the biomass slope should decline by 5-10% on a log
scale, and the predator to prey vital rate ratios should be less than 1 (Link 2010). The
initial model did not fit these guidelines, however, after the mass-balancing process the
model fitted well.
92
Ecopath models must be balanced according to several ecological and thermodynamic
logical constraints (Christensen et al. 2008; Heymans et al. 2016). These include:
ecotrophic efficiency (EE) must be less than 1.0, since it is not possible for more than
100% of the biomass produced to be passed on to the next trophic level; gross food
conversion efficiency (GE), the ratio between production and consumption (P/Q), should
generally be between 0.1 and 0.3, except for small, fast growing organisms (Christensen
et al. 2008); net efficiency, the production divided by the assimilated portion of food
(Christensen et al. 2008), can not be lower than GE (Christensen et al. 2008; Heymans et
al. 2016) and is generally less than 1.0 (Christensen et al. 2008); the proportion of an
organism’s biomass lost through respiration can not exceed the biomass of food
assimilated ([RA/AS]<1.0) (Heymans et al. 2016); and the production/respiration ratio,
which represents the fate of assimilated food, should generally not exceed 1.0 either
(Heymans et al. 2016).
As part of the mass-balancing process, the EE for each group was estimated by the model
according to the EwE thermodynamic equation (Christensen and Walters 2004). Where
the estimated EE exceeded 1.0, parameters were adjusted to reduce it for the functional
group. This was achieved by adjusting those parameters with the weakest pedigree and
greatest uncertainty (Table 4.3), adjusting dietary information (i.e. changing values for
consumers of the pressured group), followed by the consumption ratio, production ratio,
habitat fraction and biomass in habitat area of the pressured group (Lozano-Montes,
CSIRO, pers. comm.). Parameters were changed by 5-20% at a time and each change was
recorded. Initially, sediment was included as a dietary category, but was changed to a
detritus group due to the instability it was creating in the model. Phytoplankton was
originally not included in the initial model, but was added as a functional group during
mass-balance to better represent its importance to trophic flows in the system.
93
Figure 4.3 Changes in the key parameters for each functional group between the original data (hatched fill) and the mass-balanced Ecopath model (solid fill) for a) trophic level, b) biomass, c)
production/biomass (P/B) ratio, and d) consumption/biomass (Q/B) ratio in the Coral Bay EwE model.
0 1 2 3 4 5 6 7
Reef SharksDolphins
Lethrinus nebulosus (ad)Lethrinus species (ad)
OctopusZoobenthivorous fish (ad)
Carnivorous fish (ad)Squid
Lethrinus species (juv)Zoobenthivorous fish (juv)
Carnivorous fish (juv)Lethrinus nebulosus (juv)
Fish speciesCrustacean (meso)
TurtlesCrustacean (micro)Annelid/Nematode
Herbivorous fish (ad)Mollusc (exc cephalopod)
Herbivorous fish (juv)Echinoderm
ZooplanktonSponge/Bryozoan
SargassumFleshy macroalgae
Nonfleshy macroalgaePhytoplankton*
Sediment*Detritus
Trophic levela)0.0001 0.001 0.01 0.1 1 10 100
Reef SharksDolphins
Lethrinus nebulosus (ad)Lethrinus species (ad)
OctopusZoobenthivorous fish (ad)
Carnivorous fish (ad)Squid
Lethrinus species (juv)Zoobenthivorous fish (juv)
Carnivorous fish (juv)Lethrinus nebulosus (juv)
Fish speciesCrustacean (meso)
TurtlesCrustacean (micro)Annelid/Nematode
Herbivorous fish (ad)Mollusc (exc cephalopod)
Herbivorous fish (juv)Echinoderm
ZooplanktonSponge/Bryozoan
SargassumFleshy macroalgae
Nonfleshy macroalgaePhytoplankton*
Sediment*Detritus
Biomass (t.km-2)b)
0.01 0.1 1 10 100 1000
Reef SharksDolphins
Lethrinus nebulosus (ad)Lethrinus species (ad)
OctopusZoobenthivorous fish (ad)
Carnivorous fish (ad)Squid
Lethrinus species (juv)Zoobenthivorous fish (juv)
Carnivorous fish (juv)Lethrinus nebulosus (juv)
Fish speciesCrustacean (meso)
TurtlesCrustacean (micro)Annelid/Nematode
Herbivorous fish (ad)Mollusc (exc cephalopod)
Herbivorous fish (juv)Echinoderm
ZooplanktonSponge/Bryozoan
SargassumFleshy macroalgae
Nonfleshy macroalgaePhytoplankton*
Sediment*Detritus
P/B ratio (yr-1)c) 0.01 0.1 1 10 100 1000
Reef SharksDolphins
Lethrinus nebulosus (ad)Lethrinus species (ad)
OctopusZoobenthivorous fish (ad)
Carnivorous fish (ad)Squid
Lethrinus species (juv)Zoobenthivorous fish (juv)
Carnivorous fish (juv)Lethrinus nebulosus (juv)
Fish speciesCrustacean (meso)
TurtlesCrustacean (micro)Annelid/Nematode
Herbivorous fish (ad)Mollusc (exc cephalopod)
Herbivorous fish (juv)Echinoderm
ZooplanktonSponge/Bryozoan
SargassumFleshy macroalgae
Nonfleshy macroalgaePhytoplankton*
Sediment*Detritus
Q/B ratio (yr-1)d)
94
Apart from these two functional groups that were changed during the mass-balance
process, the largest differential in parameters between the initial model and the mass-
balanced model were in: the trophic levels of zooplankton (-68%), echinoderms (-67%),
molluscs (-57%), other fish species (-51%) and squid (-51%); the biomass of octopus
(4.3 x 104 %), micro-crustaceans (2.6 x 103 %), echinoderms (688%), annelids (554%),
and squid (125%); the production ratio (P/B) of dolphins (175%), micro-crustaceans
(125%), annelids (95%) and echinoderms (93%); and the consumption ratio (Q/B) of
micro-crustaceans (166%), echinoderms (61%) and other fish spp. (-69%) (Figure 4.3).
4.2.4 Ecosim
Scenario Parameterisation
The Ecosim component allows dynamic simulations to be performed over the modelled
ecosystem, based on parameters input into the Ecopath model (Christensen et al. 2008).
Seventeen scenarios were modelled, based on the extremity and duration of the forcing
function that was applied (Table 4.4). All scenarios tested for this model were run from
2015 for 35 years, with forcing functions applied for 5 or 20 years, and then allowed to
stabilise to observe how the system recovered.
Since both fishing pressure and climatic drivers can act independently or in combination
on ecosystems (Alexander et al. 2015), forcing functions were individually applied to the
production rate of primary producer groups (Sargassum, fleshy macroalgae, non-fleshy
algae, phytoplankton) and the consumption (i.e. effort) of an apex consumer (recreational
fishers), then combined to determine their importance in driving biomass of groups in the
lagoon ecosystem south of Coral Bay. Scenarios were graded as low, intermediate and
extreme, for changes to either primary producer or fisher parameters, or a combination of
effects, and selected to examine: the impact of changing primary producer productivity
on recreational fish species (seven scenarios), the effect of changing recreational fishing
pressure on recreational fish species and any subsequent effects on Sargassum spp. (six
95
scenarios), and a combination of changing both fishing pressure and primary producer
production to simulate a “worst-case” scenario for recreational fish species (four
scenarios; Table 4.4).
Functional Group Information
All EwE settings were kept at the default values except for maximum relative feeding
time, feeding time adjustment rate, predator effect on feeding time, and density-dependent
catchability for certain groups, as outlined below.
The maximum relative feeding time describes how much a predator’s feeding time may
increase if prey becomes scarce (Christensen et al. 2008). In Ecosim, a value of 2.0
represents a potential doubling in feeding time, while a value of 1.0 represents no change.
This parameter was adjusted for juvenile life stages of multi-stanza fish groups, based on
field observations. Juvenile carnivorous fish were assigned a lower value (1.25) than
adults due to their site-specific fidelity (closely associated with isolated massive corals
[bombies]), Lethrinus spp. and L. nebulosus juveniles were assigned an intermediate
value (1.5) because they had a wider habitat area (macroalgal beds), and juvenile
zoobenthivorous and herbivorous fish were assigned a higher value (1.75) due to their
wide-ranging foraging method. All other functional groups were assigned the Ecosim
default value (2.0).
Feeding time adjustment rate represents the speed that an organism can adjust feeding
time to stabilise Q/B, and can be assigned a value between 0.0 and 1.0. Lower values
cause feeding time (and time exposed to predation) to be constant: changes in Q/B lead
to changes in growth rate. Higher values lead to fast response times, and vulnerability to
predation changes instead of growth rates. Input values were assigned to each functional
group following the recommendations in the EwE user manual (Christensen et al. 2008),
where dolphins were assigned a value of 0.5, juvenile multi-stanza groups were assigned
96
values between 0.5 (L. nebulosus, L. species, carnivorous fish) and 0.75 (zoobenthivorous
fish, herbivorous fish) and all other groups were kept at zero. This results in compensatory
recruitment and compensatory natural mortality when the stock size decreases, a potential
weakness in the model (Plagányi and Butterworth 2004).
Table 4.4 Summary of the scenarios evaluated, the forcing effects applied and the potential source of
the forcing effect applied to the Coral Bay Ecopath with Ecosim lagoon model.
Groups affected Scenario Forcing effect
Primary producer
groups
- Sargassum
- Fleshy macroalgae
- Non-fleshy algae
- Phytoplankton
1 Linear 20% increase in primary production rate
2 Linear 50% increase in primary production rate
3 Linear 20% decrease in primary production rate
4 Linear 50% decrease in primary production rate
5a Rapid 50% decrease in primary production rate
6 Rapid 50% decrease in primary production rate
7 Rapid 90% decrease in primary production rate
Recreational fishers
8 Linear 20% increase in fishing effort
9 Linear 50% increase in fishing effort
10 Linear 200% increase in fishing effort
11 Linear 20% decrease in fishing effort
12 Linear 50% decrease in fishing effort
13b Sudden cessation (0%) of fishing effort
Combination
- Recreational fishers
- Primary producer
groups
14 Scenario 2
Scenario 9
15 Scenario 4
Scenario 9
16 Scenario 2
Scenario 12
17 Scenario 4
Scenario 12
Predator effect on feeding time can be set between 0.0 and 1.0, and indicates changes in
feeding time and consumption rate relative to changes in predator abundance. Higher
values assume that consumption rates are discretionary and they will be reduced up to
that fraction if predator abundance increases, leading to a reduction in time exposed to
predation (Christensen et al. 2008). This was adjusted for juvenile stages of multi-stanza
fish groups based on the assumption that the density-dependent juvenile mortality was
associated with changes in feeding time and predation risk (non-zero feeding time
adjustment rate) (Christensen et al. 2008), and the juveniles would alter feeding habits in
the presence of predators. Values were inversely related to maximum relative feeding
time values, with carnivorous fish most impacted (0.9), followed by L. nebulosus and L.
spp. (0.75), then zoobenthivorous and herbivorous fish (0.5).
97
Density-dependent catchability was increased for all groups subjected to recreational
fishery pressure. All fish groups were increased to 5, while the squid group was increased
to 10. This was increased because recreational fishers often do not fill their bag limit
(Smallwood and Beckley 2012) and to allow increased landings of these groups if
biomass increased in scenarios where fishing pressure was increased.
Vulnerabilities
The vulnerability parameter estimates the degree that an increase in predator biomass
causes predation mortality for that prey (Christensen et al. 2008). Values were set
between 1.001 and 8.0 with lower values describing bottom-up (prey-controlled)
interactions and higher values describing top-down (predator-controlled) interactions
(Christensen et al. 2008). Vulnerabilities were not increased above 10.0 due to decreasing
effects on the relevant prey group (Lozano-Montes, CSIRO, pers. comm.). Although the
recommendation is to have a consistent vulnerability for each consumer group
(Christensen et al. 2008), individual values were assigned based on broad predator/prey
relationships since the modelled area was relatively small and the detailed dietary data
available for fish groups provided information to discern this. In general, all groups were
defined as bottom-up interactions (v = 1-3) except for the consumption of algal groups
by herbivorous fishes which were intermediate (v = 5 for herbivorous juveniles) or top-
down (v = 8 for herbivorous adults) (see Vergés et al. 2011; Downie et al. 2013; Michael
et al. 2013 for herbivory in NMP).
Forcing Functions
Two sinusoidal forcing functions were designed to simulate an annual summer and winter
peak, with each having a peak of 1.5 (lull of 0.5) from the base value (1). The summer
peak (January) was applied to Sargassum spp. and the winter peak (July) was applied to
phytoplankton to simulate seasonal changes in biomass (Rousseaux et al. 2012; Fulton et
al. 2014). The winter peak was also applied to fishing effort to better match seasonal
98
Figure 4.4 The stable Ecosim model for the Coral Bay region of the Ningaloo Marine Park showing the
seasonal fluctuations in biomass due to forcing functions applied to Sargassum spp., phytoplankton and
fishing effort over a) 50 years, b) 2.5 years, and c) 2.5 years with selected groups of interest.
a)
c)
b)
99
changes in fishing pressure that parallel the seasonal dynamics of tourism in the region
(Smallwood et al. 2011; Smallwood and Beckley 2012). This produced seasonal peaks
and troughs in the biomass of most groups in the model (Figure 4.4). These seasonal
factors were applied to the forcing functions of these two functional groups and the
fishing pressure for all scenarios that were run (Table 4.4).
Simulations
Seventeen simulations were run in Ecosim, based on the scenarios summarised in Table
4.4. Seven simulations were run for primary producer groups, covering both linear
increases/decreases in production and a sudden decrease in production over varying
timeframes. Six simulations were run for fishing effort that incorporated linear changes
in fishing effort (20%, 50%, 200%) over 20 years, as well as a sudden cessation in all
fishing effort. Four simulations were run for a combination of changes to primary
production and fishing effort (Table 4.4). Combined scenarios both increased and
decreased primary production based on the assumption that production will increase to a
point with warming sea temperatures (Brown et al. 2010) and then decrease after
temperatures exceed the thermal tolerance of the algal group (Eppley 1972; Harley et al.
2006; Ateweberhan et al. 2006; Pandolfi et al. 2011; Harley et al. 2012; Koch et al. 2013;
Poore et al. 2016; Wernberg et al. 2016). As noted earlier, the parameterisation may have
had a flaw built into the feeding time adjustment rate. Scenarios involving changes to
fishing effort should be interpreted with caution, since an in-depth evaluation and change
in model setup is beyond the time constraints of this Honours project.
4.3 Results
4.3.1 Energy and Mass Flows
Total biomass was dominated by trophic level (TL) I (78.9%) with 53.3% of that made
up by living matter (algal/phytoplankton producers) and 46.7% made up of detritus (Table
100
4.1). Trophic level I groups were dominated by Sargassum spp. which made up 18.4% of
the total biomass in the system followed by fleshy macroalgae (8.9%), non-fleshy algae
(7.6%), and phytoplankton (7.2%). The biomass for organisms between TL II and III
comprised 14.84% of the total biomass with the remainder (6.24%) made up of fauna
higher than TL III. Invertebrates, comprising sponge/bryozoan, zooplankton,
echinoderm, annelid/nematode, mollusc (including octopus and squid), and crustacean
(micro- and meso-) groups, made up 8.6% of the biomass of the modelled area. Teleost
groups made up 12.1% of the biomass, with the remainder (0.4%) made up by macrofauna
(reef sharks, dolphins, turtles). Of the non-detrital groups, benthic organisms (including
half of other fish spp.) comprised 69.9% of the biomass compared with 30.1% for pelagic
organisms (Table 4.1).
Trophic levels for the modelled system ranged from 1.0 for detritus and primary producer
groups to 3.55 for reef sharks (Table 4.1) with a mean TL (± 1 SD) of 2.29 ± 0.82 for the
system. The mean TL for all fish groups was 2.76 ± 0.43, with juvenile herbivorous fishes
having the lowest TL (2.04) and L. nebulosus adults having the highest TL (3.31).
Invertebrate groups had a mean TL of 2.32 ± 0.43, almost half a trophic level lower than
the fish groups, with sponge/bryozoans having the lowest TL (2.0) and squid having the
highest TL (2.98).
Primary producers were an important component to the diets of 17 of the 23 consumer
groups in the model (Figure 4.5). The total primary production required to sustain
consumption of all groups in the models was 2,740.1 t.km-2.yr-1, while primary production
required to sustain the harvest of fished groups was 28.3 t.km-2.yr-1, 53.3% and 0.55% of
the total primary production, respectively. Although Sargassum spp. dominated the
biomass of primary producers, they were not as important as other primary producer
101
Figure 4.5 Direct consumption of a) Sargassum spp., b) fleshy macroalgae, c) non-fleshy algae, and d) phytoplankton
primary producer groups (trophic level I) based on functional groups in the Coral Bay Ecopath model. The width
of lines indicates the relative biomass consumed.
a)
b)
c)
d)
102
Figure 4.6 Direct predation of, and consumption by some recreationally important fish species: a) adult Lethrinus
nebulosus, b) juvenile Lethrinus nebulosus, c) juvenile Lethrinus spp., and d) juvenile carnivorous fish, based on
functional groups in the Coral Bay Ecopath model. Green lines show consumption by the fish group and pink lines
show predation on the fish group. The width of lines indicates the relative biomass consumed.
a)
b)
c)
d)
103
groups to the diets of L. nebulosus adults (Figure 4.6a) or juveniles of recreationally
important fish groups (Figure 4.6b-d).
The mean trophic transfer efficiency from all TL I groups was 9.0% (primary
producers = 8.7%, detritus = 9.9%). The total system throughput (TST) for the Coral Bay
model was 7,938.8 t.km-2.yr-1, with 5,145.5 t.km-2.yr-1 made up by TL I groups (Figure
4.7a. Almost half of the TST (48.3%) originating from TL I groups was consumed by
predators, of which primary producers comprised 72.2% (1,793.3 t.km-2.yr-1) and detritus
comprised 27.8% (691.6 t.km-2.yr-1).
Figure 4.7 Proportion of flows between trophic levels for the Coral Bay EwE model for a) all groups combined,
and b) primary producers as a proportion of total system throuput (TST).
Consumption of TL I functional groups increased to 61.5% of the TST when detritus
groups were excluded (Figure 4.7b), indicating the importance of primary producers at
the base of the food web. The proportion of TST consumed by predators for TL II to IV
was much less, ranging from 5.8% to 17.4% (Figure 4.7a). This shows the strength of the
interactions originating from TL I compared to that originating from TL II or higher.
Respiration increased at each successive group from TL II to IV comprising between
53.0% and 66.5% of the TST while flow to detritus comprised 21.8% to 41.2% of the
TST (Figure 4.7a).
0%
20%
40%
60%
80%
100%
IVIIIIII
Pro
po
rtio
n o
f TS
T
Trophic levelb)
Respiration
Flow todetritusExport
Consumptionby predatorsImport
0%
20%
40%
60%
80%
100%
IVIIIIII
Pro
po
rtio
n o
f TS
T
Trophic levela)
Respiration
Flow todetritusExport
Consumptionby predatorsImport
104
4.3.2 Mixed trophic impacts (MTI)
When looking at interactions driven by primary producers, the MTI analysis of the Coral
Bay model shows that a small increase in Sargassum biomass has a negative impact on
the biomass of itself (-18.7%), followed by sponge/bryozoans (-15.4%) and non-fleshy
algae (-9.7%), and a positive impact on the biomass of adult herbivorous fish (30.4%).
Unexpectedly, the biomass of juvenile L. nebulosus and adult Lethrinus spp. declined
with the increase in Sargassum biomass, possibly due to an increase in the biomass of
their predators (Figure 4.8a).
A small increase in adult L. nebulosus biomass negatively impacted benthic invertebrate
biomass the most (octopus = -37.9%; mollusc = -21.4%), along with adult Lethrinus spp.
(-22.0%) biomass. The largest positive impact was experienced by recreational fisher
biomass (68.2%), followed by carnivorous fish (adult = 12.1%; juvenile = 9.7%) and
herbivorous fish biomass (juvenile = 10.9%; adult = 9.2%) (Figure 4.8b).
For MTI analysis, fishers are included as a predator group to assess how an increase in
their biomass (equivalent to an increase in fishing effort) will affect functional groups
(Christensen et al. 2008). A small increase in recreational fishing biomass had a large
negative impact on the biomass of adult Lethrinus spp. (-24.9%). All other negatively
affected groups were impacted by less than 2.5% (Figure 4.8c). However, it had a positive
impact on squid (14.3%), and adult carnivorous fish (12.6%), functional groups that are
targeted by recreational fishers. All juvenile fish groups also showed an increase in
biomass (0.8% to 8.8%) (Figure 4.8c).
4.3.3 Network Analysis and Ecosystem Properties
The total system throughput (TST) describes the ecological size of the system in terms of
flow (Finn 1976). The TST for the Coral Bay was 7,938.8 t.km-2.yr-1, 15.1% higher than
the Ningaloo Marine Park model though only 33.6% of the TST for the Australia
105
-30 -20 -10 0 10 20 30 40
SargassumSponge/Bryozoan
Nonfleshy algaeFleshy macroalgae
TurtlesLethrinus spp. (ad)
Annelid/NematodeEchinoderm
ZooplanktonL. nebulosus (juv)
Carnivorous fish (juv)Zoobenthivorous fish (ad)
Lethrinus spp. (juv)Octopus
PhytoplanktonSquid
DetritusRecreational fishers
Zoobenthivorous fish (juv)Mollusc
L. nebulosus (ad)Crustacean (meso)
SedimentCarnivorous fish (ad)
Reef SharksDolphins
Herbivorous fish (juv)Crustacean (micro)
Fish spp.Herbivorous fish (ad)
% changea)-60 -40 -20 0 20 40 60 80
OctopusLethrinus spp. (ad)
MolluscFish spp.
Sponge/BryozoanCrustacean (meso)
EchinodermSargassum
Annelid/NematodeFleshy macroalgae
PhytoplanktonSquid
Lethrinus spp. (juv)Detritus
ZooplanktonL. nebulosus (ad)L. nebulosus (juv)
Nonfleshy algaeZoobenthivorous fish (ad)
Crustacean (micro)Turtles
SedimentReef Sharks
DolphinsZoobenthivorous fish (juv)
Herbivorous fish (ad)Carnivorous fish (juv)Herbivorous fish (juv)Carnivorous fish (ad)Recreational fishers
% changeb)-30 -20 -10 0 10 20
Lethrinus spp. (ad)Mollusc
ZooplanktonFish spp.
Zoobenthivorous fish (ad)Sargassum
Nonfleshy algaeTurtles
Fleshy macroalgaeSponge/Bryozoan
DolphinsCrustacean (micro)
Reef SharksAnnelid/Nematode
EchinodermCrustacean (meso)Lethrinus spp. (juv)
SedimentL. nebulosus (juv)
OctopusDetritus
PhytoplanktonHerbivorous fish (ad)Carnivorous fish (juv)
Recreational fishersL. nebulosus (ad)
Herbivorous fish (juv)Zoobenthivorous fish (juv)
Carnivorous fish (ad)Squid
% changec)
Figure 4.8 Mixed trophic impacts analyses of the Coral Bay EwE model showing the percent change on functional groups due to a small increase in the biomass of a) Sargassum spp., b) adult
Lethrinus nebulosus, and c) recreational fishers, who are included as a predator group for this analysis.
106
northwest shelf (NWS) model (Table 4.5). The TST is markedly lower than coral reef
models from Eritrea and the British Virgin Islands (BVI) and a coastal lagoon model from
the Philippines (Bolinao), though higher than a coastal lagoon model from Mexico
(Huizache-Caimanero: HC) and an open ocean Indo-Pacific regional model (Western
Tropical Pacific Ocean: WTPO) (Table 4.5). The total biomass/total throughput ratio
(0.028) is similar to the Eritrea model (0.022), higher than the two Australian models and
the WTPO model, and lower than the remaining overseas models (Table 4.5).
Primary production (PP) parameters also varied widely between models. The biomass of
primary producers (148.58 t.km-2) was 50.1% higher than the Australian NWS model
(99 t.km-2), 5-times higher than the Ningaloo model (27.91 t.km-2) and an order of
magnitude (1,120%) higher than the WTPO model (13.26 t.km-2). However, it was much
lower (9% to 36%) than the primary producer biomass of the other overseas models. The
calculated total net PP for Coral Bay (2,917.35 t.km-2.yr-1) fell between the two Western
Australian models (Ningaloo = 2,036.25 t.km-2.yr-1; NWS = 9,776.00 t.km-2.yr-1);
however, it is an order of magnitude less than three of the five overseas models (Table
4.5) due to the large difference in primary producer biomass. The total PP/total biomass
ratio (13.1) is comparable to the Eritrea model (12.0), is lower than the two Australian
models and the WTPO model, and higher than the other overseas models (Table 4.5).
This indicates that the primary production in this model is closer to the Eritrean coral reef
model, characterised by high biomass and low production, than the other Australian
models which are characterised by a higher proportion of phytoplankton to benthic algal
biomass.
The total estimated recreational catch for Coral Bay (0.137 t.km-2.yr-1) is similar in
magnitude to the Western Tropical Pacific Ocean (WTPO; 0.148 t.km-2.yr-1), North-West
Shelf (NWS; 0.084 t.km-2.yr-1) and Eritrea (0.190 t.km-2.yr-1), but lower than that
estimated for Ningaloo and markedly lower than those for Bolinao and HC (Table 4.5).
107
Table 4.5 Network analyses and model indices for several Australian (light grey columns) and overseas (white columns) Ecopath with Ecosim models.
Statistics and Flows Units Coral Bay Ningaloo Northwest
Shelf Western Tropical
Pacific Ocean Bolinao
Huizache-Caimanero
Eritrea Virgin Islands
Author/s Desfosses &
Lozano-Montes
Fulton (Jones et al.
2011)
Bulman (Bulman 2006)
Godinot (Godinot and Allain
2003)
Aliño (Aliño et al.
1993)
Zetina-Rejón
(Zetina-Rejón et al. 2003)
Tsehaye (Tsehaye and Nagelkerke
2008)
Opitz (Opitz 1996)
Country/region Western Australia
Western Australia
Western Australia
Indo-Pacific Philippines Mexico Eritrea British Virgin
Islands
Type of model coastal lagoon
coral reef continental
shelf open ocean
coastal lagoon
coastal lagoon
coral reef coral reef
Modelled period yr 2015 2007 1986-91 1990-2000 1980-1981 1984-1986 1998 1960-1999
Latitude 23.173° S - 23.294° S
21.690° S - 23.560° S
18.000° S - 21.000° S
15.000° N - 15.000° S
16.162° N - 16.250° N
22.850° N - 23.200° N
12.689° N - 18.326° N
17.373° N - 19.373° N
Area km2 30 10400 70000 2.55 x 107 240 175 6000 - Number of groups 29 53 37 20 26 26 19 21
Total system throughput t.km-2.yr-1 7938.31 6894.75 23635.22 3343.98 39613.08 6668.56 70184.97 80464.85 Total biomass (excluding detritus) t.km-2 223.04 106.25 366.11 24.76 1895.95 486.33 1521.16 3902.49
Total biomass/total throughput yr-1 0.028 0.015 0.015 0.007 0.048 0.073 0.022 0.048
Sum of all production t.km-2.yr-1 3653.59 2401.55 11799.50 1651.71 19519.69 4319.74 25927.75 30392.28 Net system production t.km-2.yr-1 1461.48 140.31 7462.60 813.71 17711.25 2678.27 3972.96 157.23 Calculated total net PPa t.km-2.yr-1 2917.35 2036.25 9776.00 1332.76 18977.87 3816.43 18179.00 19968.75
Total PP/total respiration 2.004 1.074 4.226 2.568 14.983 3.353 1.280 1.008 Total PP/total biomass 13.080 19.164 26.702 53.831 10.010 7.847 11.951 5.117
Total catch t.km-2.yr-1 0.137 0.503 0.084 0.148 13.079 7.400 0.190 - Mean trophic level of the catch 3.22 2.80 3.50 3.90 2.20 2.52 3.85 - Gross efficiency (catch/net PP) 4.695 x 10-5 2.470 x 10-4 8.540 x 10-6 1.113 x 10-4 6.892 x 10-4 0.002 1.045 x 10-5 -
Connectance Index 0.328 0.230 0.258 0.364 0.173 0.302 0.463 0.352 System Omnivory Index 0.241 0.227 0.184 0.265 0.182 0.254 0.206 0.227 Ecopath pedigree index 0.305 - - - - - - -
Measure of fitb t* 1.600 - - - - - - - Shannon diversity index 2.402 2.427 2.009 1.762 1.299 0.978 1.475 1.783
Invertebrate:Teleost Biomass Ratio 0.709 1.940 2.022 1.639 38.825 5.797 18.705 8.871 Prim. Prod.:Teleost Biomass Ratio 3.484 2.645 2.978 3.053 238.129 40.365 19.971 5.525 Prim. Prod.:Invert. Biomass Ratio 4.914 1.363 1.473 1.863 6.133 6.963 1.068 0.623
Prim. Prod:Detritus Ratio 1.143 0.310 0.990 0.102 1.624 0.109 0.951 0.700 a primary production (PP) b unit defined by Ecopath (not tonnes)
108
The mean trophic level of the catch (3.23) was comparable to both Australian models
(Ningaloo = 2.80; NWS = 3.50), lower than the WTPO and Eritrea models, and higher
than the Bolinao and HC models (Table 4.5). The mean TL of the system was 2.29
(± 0.82), which shows that the level of recreational fishing in the system has not been
high enough for lower trophic levels to be targeted due to over-exploitation of higher-
level predators. The estimated gross catch efficiency represents the yield relative to
primary production and was relatively low (4.7 x 10-5), higher only than those estimated
for NWS (8.5 x 10-6) and Eritrea (1.0 x 10-5) (Table 4.5).
The connectance index (CI = 0.33) and the omnivory index (OI = 0.24) closely match
those of the HC model (CI = 0.30; OI = 0.25; Table 4.). These are moderately high and
show that the system has a broad level of interactions between trophic levels, indicating
that the system is relatively mature (Odum 1971).
The pedigree index and the measure of fit were 0.304 and 1.600, respectively, which are
moderately low values showing that much of the data in the model are not from local
sources. Comparative data were not available for any of the selected models, though a
2007 study of 50 EwE models found that 40% of the models had pedigrees ranging
between 0.20 and 0.40 (Morissette 2007).
4.3.4 Ecosim Scenarios
Scenarios Acting on Primary Productivity
Of the seventeen Ecosim scenarios modelled, the simulations where forcing functions
were applied to primary production rate (PPr) in the four algal groups (Sargassum spp.,
fleshy macroalgae, non-fleshy algae, phytoplankton) showed the greatest changes in the
system (Figure 4.9).
Slow, linear increases or decreases in PPr predicted reciprocal increases or decreases in
biomass for all functional groups over the 35-year simulation. The linear change
109
consistently predicted the largest effect on herbivorous fish (both juveniles and adults),
dolphins and reef sharks, while squid, phytoplankton, meso-crustacean, echinoderm and
annelid/nematode groups were predicted to be the least affected (Figure 4.9a-d). Lesser
Figure 4.9 Ecosim scenarios for the Coral Bay model where linear forcing functions were applied to primary
production rate (PPr) over 20 years and allowed to run for a further 15 years before predicted values were
assessed against a base model with no forcing function. Forcing functions applied were: a) 20% linear
increase in PPr, b) 50% linear increase in PPr, c) 20% linear decrease in PPr, d) 50% linear decrease in PPr.
The figures correspond to scenarios 1-4 in Table 4.4.
changes in primary production (Scenarios 1 and 3) predicted squid biomass to increase
by 9.9% in Scenario 1 (Figure 4.9a) and decrease by 7.8% in Scenario 3 (Figure 4.9c).
Predicted juvenile herbivorous fish biomass almost doubled for Scenario 1 (+92%) and
more than halved in Scenario 3 (-62%). Intermediate scenarios (Scenarios 2 and 4)
resulted in predicted squid biomass increasing by 26% (Scenario 2; Figure 4.9b) and
0 20 40 60 80 100
SquidPhytoplankton
EchinodermCrustacean (meso)Annelid/Nematode
MolluscSargassum
Fish spp.Detritus
SedimentCrustacean (micro)Lethrinus spp. (juv)
TurtlesNonfleshy algae
OctopusL. nebulosus (juv)
Lethrinus spp. (ad)Carnivorous fish (juv)
ZooplanktonFleshy macroalgaeSponge/BryozoanL. nebulosus (ad)
Zoobenthivorous fish (ad)Zoobenthivorous fish (juv)
Carnivorous fish (ad)Reef Sharks
DolphinsHerbivorous fish (ad)Herbivorous fish (juv)
% changea)0 100 200 300
SquidPhytoplankton
EchinodermCrustacean (meso)Annelid/Nematode
MolluscSargassum
Fish spp.Detritus
SedimentCrustacean (micro)
Nonfleshy algaeTurtles
Lethrinus spp. (juv)Octopus
L. nebulosus (juv)Carnivorous fish (juv)
Lethrinus spp. (ad)Fleshy macroalgae
ZooplanktonSponge/BryozoanL. nebulosus (ad)
Zoobenthivorous fish (ad)Zoobenthivorous fish…Carnivorous fish (ad)
Reef SharksDolphins
Herbivorous fish (juv)Herbivorous fish (ad)
% changeb)
-100 -50 0
Herbivorous fish (juv)Herbivorous fish (ad)
DolphinsReef Sharks
Zoobenthivorous fish (ad)Zoobenthivorous fish (juv)
Fleshy macroalgaeSponge/Bryozoan
Carnivorous fish (ad)L. nebulosus (ad)Nonfleshy algae
Carnivorous fish (juv)Turtles
OctopusL. nebulosus (juv)
Crustacean (micro)Lethrinus spp. (ad)
ZooplanktonSediment
Lethrinus spp. (juv)Sargassum
DetritusFish spp.Mollusc
Annelid/NematodeEchinoderm
Crustacean (meso)Phytoplankton
Squid
% changec)-100 -50 0
Herbivorous fish (juv)Herbivorous fish (ad)
Fleshy macroalgaeNonfleshy algae
DolphinsZoobenthivorous fish (ad)Zoobenthivorous fish (juv)
Sponge/BryozoanL. nebulosus (ad)
TurtlesCarnivorous fish (ad)
Reef SharksSargassum
Crustacean (micro)Carnivorous fish (juv)
L. nebulosus (juv)Sediment
OctopusLethrinus spp. (juv)Lethrinus spp. (ad)
DetritusFish spp.Mollusc
ZooplanktonEchinoderm
Annelid/NematodeCrustacean (meso)
PhytoplanktonSquid
% changed)
110
decreasing by 15% (Scenario 4; Figure 4.9d), while predictions had adult herbivorous fish
biomass increasing by 254% (Scenario 2) and juvenile herbivorous fish biomass
decreasing by 97% (Scenario 4).
All the major recreational fish groups were predicted to undergo moderate increases or
decreases in biomass with the respective linear increase or decrease in PPr. Adult
L. nebulosus biomass was predicted to increase by 33% in Scenario 1 (Figure 4.9a) and
85% in Scenario 2 (Figure 4.9b) while decreasing by the 33% and 85% for Scenarios 3
(Figure 4.9c) and 4 (Figure 4.9d), respectively. Similar patterns emerged for juvenile L.
nebulosus and both stanzas of Lethrinus spp.
Scenarios involving a collapse in productivity showed a different pattern to those for
linear changes (Figure 4.10a-c). After the 35-year simulation, the magnitude of the impact
was markedly less than the linear scenarios, with top predators (dolphins, reef sharks) and
both herbivorous fish stanzas the most severely impacted in all three grades of sudden
change. The biomass of the dolphin group was the most affected in all three scenarios,
with decreases of 2.3% in the lowest increase in PPr scenario (Scenario 5; Figure 4.10a),
21% in the intermediate scenario (Scenario 6; Figure 4.10b), and 45% in the extreme
scenario (Scenario 7; Figure 4.10c). All fish groups targeted by recreational fishers
showed small, positive biomass gains up to 2050, however this is not representative of
the true dynamics of the simulation (see below).
Scenarios Acting on Fishing Effort
Forcing functions were applied to targeted fish species by modifying fishing effort. Low
(±20%), intermediate (±50%) and extreme (> ±100%) scenarios were applied to assess
the impact of recreational fishers on the biomass of fish species. The magnitude of effects
on fishing effort were much smaller than those predicted by altering primary production
rates. The pattern of change in biomass of the functional groups was similar across all
111
-25 -20 -15 -10 -5 0 5
DolphinsReef Sharks
Herbivorous fish (ad)Herbivorous fish (juv)
SquidPhytoplankton
Crustacean (meso)Zooplankton
Annelid/NematodeLethrinus spp. (juv)Lethrinus spp. (ad)
OctopusEchinoderm
DetritusCarnivorous fish (juv)
Fleshy macroalgaeMollusc
Crustacean (micro)Carnivorous fish (ad)
L. nebulosus (juv)Sponge/Bryozoan
Fish spp.Sediment
TurtlesZoobenthivorous fish (juv)
Nonfleshy algaeL. nebulosus (ad)
Zoobenthivorous fish (ad)Sargassum
% changeb)-3 -2 -1 0 1
DolphinsReef Sharks
Herbivorous fish (ad)Herbivorous fish (juv)
SquidCrustacean (meso)
PhytoplanktonZooplankton
Annelid/NematodeOctopus
Lethrinus spp. (juv)Lethrinus spp. (ad)
Carnivorous fish (juv)Echinoderm
MolluscDetritus
Fleshy macroalgaeCrustacean (micro)
Fish spp.Sponge/BryozoanL. nebulosus (juv)
SedimentNonfleshy algae
Zoobenthivorous fish (juv)Carnivorous fish (ad)
TurtlesZoobenthivorous fish (ad)
SargassumL. nebulosus (ad)
% changea)-50 -40 -30 -20 -10 0 10
DolphinsHerbivorous fish (ad)
Reef SharksHerbivorous fish (juv)
SquidPhytoplankton
ZooplanktonCrustacean (meso)Annelid/NematodeLethrinus spp. (juv)
Carnivorous fish (ad)Detritus
EchinodermCarnivorous fish (juv)
TurtlesFleshy macroalgaeCrustacean (micro)Lethrinus spp. (ad)
L. nebulosus (juv)Mollusc
Sponge/BryozoanOctopus
Zoobenthivorous fish (juv)Fish spp.
SedimentL. nebulosus (ad)Nonfleshy algae
Zoobenthivorous fish (ad)Sargassum
% changec)
Figure 4.10 Ecosim scenarios for the Coral Bay model, where forcing functions were applied to primary production rate (PPr) over 5 or 20 years and allowed to run for a total of 35
years before predicted values were assessd against a base model with no forcing function. Forcing functions applied were: a) 50% sudden decrease in PPr with a recovery after 5 years,
b) 50% sudden decrease in PPr with recovery after 20 years, c) 90% sudden decrease in PPr with recovery after 20 years. The figures correspond to scenarios 5-7 in Table 4.4.
112
scales of change, with only the magnitude and the ranking of groups changing (Figure
4.11a-f).
All changes to fishing effort predicted the largest effect on adult L. nebulosus biomass
with an inverse effect on octopus biomass (Figure 4.11a-f). A small (±20%) increase
(Scenario 8; Figure 4.11a) or decrease (Scenario 11; Figure 4.11b) in fishing effort was
predicted to have a negligible (< 1%) impact on the biomass of all groups, while an
intermediate (±50%) increase (Scenario 9; Figure 4.11c) or decrease (Scenario 12; Figure
4.11d) in fishing effort was predicted to change the biomass of all groups by less than
3%. In the most extreme scenario, where the fishing effort was increased by 200%
(Scenario 10; Figure 4.11e), the model predicted decreases of 10% in adult L. nebulosus
biomass and 4.5% in adult carnivorous fish biomass, with a predicted 8.2% increase in
octopus biomass. All other groups were predicted to change by less than 5%. Simulating
the introduction of a sanctuary zone through complete cessation of fishing effort
(Scenario 13; Figure 4.11f) predicted an increase in the biomasses of adult L. nebulosus
(4.9%) and adult carnivorous fish species (2.3%) with a predicted decrease of 3.6% in
octopus biomass. All other functional groups were predicted to change by less than ±2%.
Combined Scenarios
A combination of forcing functions from both the primary producer and recreational
fisher scenarios were applied to the EwE model to assess if a mixture of factors would
have a compounding effect on functional groups. Intermediate forcing functions where
both fishing effort and rate of primary production were changed by ±50% were evaluated.
When the Ecosim output was compared to the stable model (i.e. the base model with no
forcing functions applied; Figure 4.4) the predicted change in biomass closely resembled
scenarios 2 and 4, where only primary production was affected (Scenarios 2 & 4; Figure
4.9b,d). Therefore, due to the strong influence of primary producers, the summary
113
Figure 4.11 Ecosim scenarios for the Coral Bay model where linear forcing functions were applied to recreational
fishing effort (RFE) over 20 years and allowed to run for a further 15 years before predicted values were assessed
against a base model with no forcing function. Forcing functions applied were: a) 20% linear increase in RFE, b)
20% linear decrease in RFE, c) 50% linear increase in RFE, d) 50% linear decrease in RFE, e) 200% increase in
RFE f) cessation of RFE (sanctuary zone). The figures correspond to scenarios 8-13 in Table 4.4.
-1 -0.5 0 0.5 1
L. nebulosus (ad)Carnivorous fish (ad)
Crustacean (micro)Dolphins
L. nebulosus (juv)Reef Sharks
Carnivorous fish (juv)Nonfleshy algae
TurtlesSediment
SargassumSponge/Bryozoan
DetritusFleshy macroalgae
ZooplanktonPhytoplankton
Herbivorous fish (ad)Echinoderm
Herbivorous fish (juv)Zoobenthivorous fish (ad)
Lethrinus spp. (ad)Zoobenthivorous fish (juv)
Annelid/NematodeCrustacean (meso)
SquidLethrinus spp. (juv)
Fish spp.MolluscOctopus
% changea)
-1 -0.5 0 0.5 1
OctopusMollusc
Fish spp.Lethrinus spp. (juv)
SquidCrustacean (meso)Annelid/Nematode
Zoobenthivorous fish (juv)Lethrinus spp. (ad)
Zoobenthivorous fish (ad)Herbivorous fish (juv)
EchinodermHerbivorous fish (ad)
PhytoplanktonZooplankton
Fleshy macroalgaeDetritus
Sponge/BryozoanSargassum
SedimentTurtles
Nonfleshy algaeCarnivorous fish (juv)
Reef SharksL. nebulosus (juv)
DolphinsCrustacean (micro)
Carnivorous fish (ad)L. nebulosus (ad)
% changeb)
-3 -2 -1 0 1 2
L. nebulosus (ad)Carnivorous fish (ad)
Crustacean (micro)Dolphins
L. nebulosus (juv)Reef Sharks
Carnivorous fish (juv)Nonfleshy algae
TurtlesSediment
SargassumSponge/Bryozoan
DetritusFleshy macroalgae
ZooplanktonPhytoplankton
Herbivorous fish (ad)Echinoderm
Herbivorous fish (juv)Zoobenthivorous fish (ad)
Lethrinus spp. (ad)Zoobenthivorous fish (juv)
Annelid/NematodeCrustacean (meso)
SquidLethrinus spp. (juv)
Fish spp.MolluscOctopus
% changec)-2 -1 0 1 2 3
OctopusMollusc
Fish spp.Lethrinus spp. (juv)
SquidCrustacean (meso)Annelid/Nematode
Zoobenthivorous fish (juv)Lethrinus spp. (ad)
Zoobenthivorous fish (ad)Herbivorous fish (juv)
EchinodermHerbivorous fish (ad)
PhytoplanktonZooplankton
Fleshy macroalgaeDetritus
Sponge/BryozoanSargassum
SedimentTurtles
Nonfleshy algaeCarnivorous fish (juv)
Reef SharksL. nebulosus (juv)
DolphinsCrustacean (micro)
Carnivorous fish (ad)L. nebulosus (ad)
% changed)
-10 -5 0 5 10
L. nebulosus (ad)Carnivorous fish (ad)
Crustacean (micro)L. nebulosus (juv)
DolphinsReef Sharks
Carnivorous fish (juv)Nonfleshy algae
TurtlesSediment
SargassumSponge/Bryozoan
DetritusFleshy macroalgae
ZooplanktonPhytoplankton
Herbivorous fish (ad)Echinoderm
Herbivorous fish (juv)Zoobenthivorous fish (ad)
Lethrinus spp. (ad)Zoobenthivorous fish (juv)
Annelid/NematodeCrustacean (meso)
SquidLethrinus spp. (juv)
Fish spp.MolluscOctopus
% changee)-4 -2 0 2 4 6
OctopusMollusc
Fish spp.Lethrinus spp. (juv)
SquidCrustacean (meso)Annelid/Nematode
Zoobenthivorous fish (juv)Lethrinus spp. (ad)
Zoobenthivorous fish (ad)Herbivorous fish (juv)
EchinodermHerbivorous fish (ad)
PhytoplanktonZooplankton
Fleshy macroalgaeDetritus
Sponge/BryozoanSargassum
SedimentTurtles
Nonfleshy algaeCarnivorous fish (juv)
Reef SharksL. nebulosus (juv)
DolphinsCrustacean (micro)
Carnivorous fish (ad)L. nebulosus (ad)
% changef)
114
Figure 4.12 Ecosim scenarios for the Coral Bay model where linear forcing functions were applied to both primary
production rate (PPr) and recreational fishing effort (RFE) over 20 years and allowed to run for a further 15 years
before predicted values were assessed against the relevant PPr scenario. Forcing functions applied were: a) 50%
linear increase in RFE with a 50% linear increase in PPr, b) 50% linear increase in RFE with a 50% linear decrease
in PPr, c) 50% linear decrease in RFE with a 50% linear increase in PPr, d) 50% linear decrease in RFE with a
50% linear decrease in PPr. The figures correspond to scenarios 14-17 in Table 4.4.
figures for the combined scenarios (Figure 4.12a-d) are shown as the percent change from
the primary producer scenario that was used, rather than from the base model where no
forcing functions were applied. This gave a better indication of the impact of fishing in
the simulation.
When both fishing effort and PPr had a 50% linear increase applied (Scenario 14) the
overall biomass of adult L. nebulosus was predicted to increase by 83% from the base
model; however, when compared to Scenario 2 (Figure 4.9b), there was a predicted
decrease in adult L. nebulosus biomass of 1.3% (Figure 4.12a), showing that the change
-1.5 -1 -0.5 0 0.5 1
L. nebulosus (ad)Carnivorous fish (ad)
DolphinsReef Sharks
Crustacean (micro)L. nebulosus (juv)
Nonfleshy algaeTurtles
SedimentSargassum
Sponge/BryozoanDetritus
Herbivorous fish (ad)Fleshy macroalgae
ZooplanktonPhytoplankton
EchinodermCarnivorous fish (juv)Herbivorous fish (juv)
Zoobenthivorous fish (ad)Lethrinus spp. (ad)
Zoobenthivorous fish (juv)Annelid/NematodeLethrinus spp. (juv)Crustacean (meso)
SquidFish spp.MolluscOctopus
% changea)
-15 -10 -5 0 5
L. nebulosus (ad)Carnivorous fish (ad)
L. nebulosus (juv)Crustacean (micro)
Carnivorous fish (juv)Nonfleshy algae
TurtlesDolphins
SedimentSponge/Bryozoan
Reef SharksSargassum
DetritusPhytoplankton
ZooplanktonHerbivorous fish (ad)
SquidFleshy macroalgae
Herbivorous fish (juv)Echinoderm
Zoobenthivorous fish (juv)Zoobenthivorous fish (ad)
Fish spp.Annelid/NematodeLethrinus spp. (ad)Crustacean (meso)Lethrinus spp. (juv)
MolluscOctopus
% changeb)
-1 -0.5 0 0.5 1 1.5
OctopusMollusc
Fish spp.Squid
Crustacean (meso)Lethrinus spp. (juv)Annelid/Nematode
Zoobenthivorous fish (juv)Lethrinus spp. (ad)
Zoobenthivorous fish (ad)Herbivorous fish (juv)
EchinodermPhytoplankton
Carnivorous fish (juv)Zooplankton
Fleshy macroalgaeHerbivorous fish (ad)
DetritusSponge/Bryozoan
SargassumSediment
TurtlesNonfleshy algae
L. nebulosus (juv)Crustacean (micro)
Reef SharksDolphins
Carnivorous fish (ad)L. nebulosus (ad)
% changec) -10 -5 0 5 10 15
OctopusMollusc
Lethrinus spp. (juv)Lethrinus spp. (ad)Crustacean (meso)
Fish spp.Annelid/Nematode
Zoobenthivorous fish (ad)Zoobenthivorous fish (juv)
EchinodermSquid
Herbivorous fish (juv)Fleshy macroalgae
Herbivorous fish (ad)Zooplankton
PhytoplanktonDetritus
SargassumReef Sharks
Sponge/BryozoanSedimentDolphins
TurtlesNonfleshy algae
Carnivorous fish (juv)L. nebulosus (juv)
Crustacean (micro)Carnivorous fish (ad)
L. nebulosus (ad)
% changed)
115
in PPr accounted for approximately 87% of the increase. The predicted change from the
base model for all other groups ranged from a 14% decline for squid biomass to a 97%
decline for juvenile herbivorous fish biomass; though when compared to Scenario 3, the
predicted change in biomass was < 1% for these groups (Figure 4.12a).
Scenario 15 combined a 50% linear increase in fishing effort (Scenario 9) with a 50%
linear decrease in primary production (Scenario 4). When compared to the base model,
adult L. nebulosus biomass was predicted to decrease by 87%, and by 13% when
compared to Scenario 4 (Figure 4.12b). Other groups that were predicted to be impacted
included adult carnivorous fish (-6.3%), juvenile L. nebulosus (-5.2%), micro-crustaceans
(-4.6%), molluscs (2.3%) and octopus (6.7%). All other groups were predicted to change
by < 2% (Figure 4.12b).
Scenario 16 had a combination of a 50% linear decline in fishing effort (Scenario 12) with
a 50% linear increase in PPr (Scenario 2). Though the difference in predicted biomass
from the base model ranged from increases of 26% (squid) to 253% (adult herbivorous
fish), when compared to Scenario 2 (Figure 4.9c), the predicted change due to decreased
fishing effort was < 2% for all groups ranging from a decrease of 0.7% in the octopus
group to an increase of 1.2% in the adult L. nebulosus group (Figure 4.12c).
Scenario 17 combined a 50% linear decrease in both fishing effort (Scenario 12) and
primary production (Scenario 4). When compared to the base model, the greatest
predicted change in biomass was for juvenile herbivorous fish (97%) with the smallest
decrease in biomass occurring in squid (15%). When compared to the output from
scenario 4 to isolate the impact of reduced fishing effort, the biomass changed very little
(<2%) and the greatest changes in predicted biomasses were all less than 10%, except for
a predicted 14% increase in adult L. nebulosus (Figure 4.12d).
116
4.4 Discussion
An Ecopath model was constructed for a small lagoon region, south of Coral Bay, that
was representative of lagoon habitats in the southern Ningaloo Marine Park (NMP) in
north-western Australia. The primary focus for the model was to examine the trophic role
of macroalgal beds within the system, since little is known about their significance as a
food source to fish species that play an important role in structuring the ecosystem and as
a target of the recreational fishing sector in the region. Primary producers dominate the
biomass in the system (Table 4.1) and comprised a large proportion (72.2%) of the
consumption of trophic level I (TL I) organisms, though they were not an important
dietary component for multi-stanza fish species (Figure 4.2). Detritus made up the largest
gross primary producer biomass (70 t.km-2.yr-1) though Sargassum comprised the largest
living primary producer biomass, both per area of habitat (360 t.km-2.yr-1) and the overall
extent of the modelled area (64.8 t.km-2.yr-1; Table 4.1).
The results of the Coral Bay Ecopath model were compared to other models in the region
(Ningaloo (N), North-West Shelf (NWS), and Western Tropical Pacific Ocean (WTPO)
Ecopath models) and overseas (Eritrea (E), British Virgin Islands (BVI), Huizache-
Caimanero (HC), and Bolinao (B) Ecopath models). While ratios that incorporated
invertebrate data indicated that their biomass was relatively low in the Coral Bay model,
other indices were comparable to models for similar systems (Table 4.5). The pedigree
index was moderate to low, even though the dietary data for 10 of the 29 groups was
excellent, highlighting the lack of biological data for many organisms in the Ningaloo
lagoon ecosystem, particularly cryptic groups.
An Ecosim model was constructed to simulate changes to both low and high trophic levels
to assess how changes in primary production rate and fishing effort impacted the overall
fish biomass (Figures 4.9, 4.11). The results of these simulations indicated that changes
in macroalgal production rate contributed more to predicted effects on fish biomass than
117
changes in fishing effort (Figure 4.12). This shows that, at current levels, the fishing
pressure seems to be sustainable and moderate increases or decreases in effort would have
no long-term impact on adult fish biomass. However, it should be noted that this model
was designed to examine trophic dynamics more than the influence of fishing, and as a
consequence, the predictions on changes in fishing effort require further examination.
4.4.1 Characteristics of the Macroalgal System
The system was characterised by high levels of benthic primary producer biomass
(123.1 t.km-2) compared to other Western Australian coastal models (Ningaloo:
8.8 t.km-2; NWS: 64.0 t.km-2), and benthic producers which comprised 66.6% of the total
biomass in the modelled system (excluding detritus). While this value is high when
compared to coral reef systems worldwide (Steneck and Dethier 1994; Bruno et al. 2009),
it is the paradigm for lagoon areas of the NMP (Cassata and Collins 2008; Doropoulos et
al. 2013; Kobryn et al. 2013; Fulton et al. 2014), and is much lower than the overseas
comparison models (Aliño et al. 1993; Opitz 1996; Zetina-Rejón et al. 2003; Tsehaye and
Nagelkerke 2008). The value is much higher than for the Ningaloo EwE model, because
the Coral Bay model is confined to a nearshore region in the south of the NMP where the
biomass of Sargassum spp. has been shown to be up to 3-times higher than the biomass
in lagoon areas of the northern NMP (Fulton et al. 2014). The modelled area is also
contained within the backreef and lagoon of the Ningaloo Reef system and does not
incorporate reef flat or reef slope areas, where herbivorous species are more abundant and
benthic algae biomass is lower (Cassata and Collins 2008; Downie et al. 2013;
Doropoulos et al. 2013).
The estimated consumption of primary producers by higher trophic levels comprised
72.2% of the total consumption of TL I groups, showing primary producers make up a
large segment at the base of the food web in this system. The transfer efficiency from
primary producer groups (8.7%) is relatively low when compared to the open ocean
118
system (WTPO = 18.7%), though comparable to other coral reef systems (B = 9.5%, BVI
= 9.3%, HC = 7.0%); and, when looking at global trends in EwE models it is comparable
to the median value for other Indian Ocean models, which are typically shallow-water
systems, characterised by lower mean transfer efficiency (Heymans et al. 2014). This may
be explained by higher flow to detritus and more benthic interactions at low efficiency in
lagoon, estuary and bay systems (Heymans et al. 2014), a trend that is evident in the Coral
Bay system with a greater proportion of TL II flow going to detritus than being consumed
by predators (Figure 4.7).
It should be noted though, that care must be taken with comparing models, since there
may be problems with comparisons of different numbers of functional groups and
different methods of parameterisation (Baird et al. 1991; Freire et al. 2008; Loneragan et
al. 2010). Six of the seven comparison models have similar numbers of functional groups
to the Coral Bay model, though the Ningaloo model has many more.
4.4.2 Ecosim Simulations
When forcing functions were combined in simulating a scenario, changes to macroalgal
production accounted for more of the change in biomass of the functional group than
changes to fishing effort (Figure 4.12). This is evidence of the importance of primary
producers in driving the interactions in this ecosystem, though this differs from the
findings of several studies where fish biomass was responsible for controlling algal
biomass over small scales (Vergés et al. 2011; Vergés et al. 2012; Johansson 2012;
Michael et al. 2013).
Scenarios Acting on Primary Productivity
A slow, linear increase or decrease in primary production over 20 years predicted that the
biomass of herbivorous fish, both juveniles and adults, would be most affected, with flow-
on effects to higher level predators (reef sharks, dolphins) (Figure 4.9). In the medium-
119
term (until year 2050), the slower, linear changes in primary productivity predicted a
larger, more sustained impact on the biomass of functional groups than a sudden, large
(up to 90%) change in productivity that recovered over 5 to 20 years (Figure 4.10). The
relationship between medium-term changes in macroalgal productivity and the biomass
of all functional groups indicates the importance of macroalgae at the base of the food
web in the Coral bay lagoon system. The lack of medium-term effects on the biomass of
functional groups when macroalgal productivity recovers illustrates the stability of the
lagoon ecosystem in the southern NMP, where macroalgae is an essential component and
a natural part of the ecosystem (Vroom et al. 2006; Tolentino-Pablico et al. 2008; Bruno
et al. 2009; Wismer et al. 2009; Wilson et al. 2010; Johansson et al. 2010; Vergés et al.
2012; Hoey et al. 2013; Wernberg et al. 2013; Bruno et al. 2014; Evans et al. 2014;
Wilson et al. 2014). This has implications for temperate systems where tropical algal
species are dominating temperate species (Wernberg et al. 2016), and indicates that the
tropical species will be difficult to shift once they become stabilised.
The ability for the macroalgal biomass to return to high levels after a large decline aligns
well with research predicting difficulty in reverting to a coral-dominated system from a
macroalgal-dominated one due to a phase shift (Mumby et al. 2007; Nyström et al. 2008;
Norström et al. 2009). There are predictions that warming sea temperatures will
contribute to phase shifts from coral- to algal-dominated ecosystems (Hughes 2003;
Pandolfi et al. 2011), as well as a rise of sea temperatures above the thermal tolerance of
algal species resulting in a decline in biomass (Ateweberhan et al. 2006; Wernberg et al.
2012; Harley et al. 2012; Wernberg et al. 2016; Poore et al. 2016). Based on the limited
system modelled here, an increase in sea temperatures, within the thermal tolerance range
of algal groups in the lagoon, has the potential to increase biomass of recreationally
important fish species as algal biomass increases (Brown et al. 2010) (Figure 4.9a-b).
However, if sea temperatures rise above the thermal tolerance of the algae and primary
120
production rates decline by 50% from the levels modelled here, then 27 of the 29
functional groups in the system are predicted to have massive (>50%) declines in biomass
(Figure 4.9d).
The predictions from the rapid loss of PPr scenarios (scenarios 5-7; Figure 4.10) revealed
that there were relatively small changes in the predicted biomass of most functional
groups after the 35-year simulation. However, this was due to the rapid stabilisation of
functional groups after primary productivity rates returned to their base levels after 5 or
20 years (Figure 4.13a-c). Figure 4.13a shows how L. nebulosus stanzas had a predicted
decline of 40-60%, but had recovered to normal levels after approximately 10 years, 5
years after primary production rates returned to normal levels. Herbivorous fishes were
predicted to be slower to recover (Figure 4.13b) and there was a distinct lag between
Sargassum spp. recovery and the recovery of herbivorous fish biomass which had still
not recovered in 2050, when the output data was taken. Though it is not shown in these
figures, due to the difficulty in distinguishing groups when too many are plotted, top
predators such as reef shark and dolphin groups were even slower in their recovery
explaining why their biomass was the most affected in these scenarios. In reality, the
decline in these groups would likely be due to them emigrating from the model
boundaries, rather than a large-scale decline in biomass (Wilson, DPaW, pers. comm.).
When the biomass of a group falls below a threshold, a phase shift can occur and the
group will not recover to levels observed before the disturbance leading to an alternative
stable state (Knowlton 1992; Scheffer et al. 2001; Norström et al. 2009). This did not
happen in these scenarios, even when the rate of primary production dropped by 90% for
a sustained period, and algal biomass was reduced by 100% for more than 15 years
(Figure 4.13c). This either indicates that the system is both resistant to disturbances and
resilient in recovering from them, or that there is a deficiency in the model and it is not
accurately reflecting “real world” situations. Sargassum biomass is known to have
121
seasonal peaks and troughs in biomass, decreasing by up to 90% under cooler sea
temperatures at Ningaloo Reef (Fulton et al. 2014) and the Great Barrier Reef (Vuki and
Price 1994), and warmer temperatures in the Red Sea (Ateweberhan et al. 2006);
however, it recovers the following season, indicating that the simulations predicted under
this model are relevant even for extreme declines in productivity.
Figure 4.13 Selected functional group dynamics in three scenarios where primary production rates
collapsed by a) 50% (5-year recovery), b) 50% (20-year recovery), and c) 90% (20-year recovery)
for the Coral Bay model.
122
Scenarios Acting on Fishing Effort
Changes to fishing effort had the largest impacts on adult L. nebulosus, with flow-on
effects to their main prey groups, including molluscs, fish spp., octopus, squid, annelid,
and meso-crustacean groups, which were consistently impacted inversely to the change
in L. nebulosus biomass (Figure 4.11). These items are the main components in the diets
of adult L. nebulosus (Figure 4.2) and are responding to the increase or decrease in
predator biomass. Under the most extreme scenarios the magnitude of predicted change
in adult L. nebulosus biomass underwent relatively low fluctuations, ranging from a
decline of 10% under a 200% increase in fishing effort, to an increase of 4.9% under a
complete cessation of fishing effort (Figure 4.11e-f). This indicates either that the fishing
pressure is sustainable and not having a long-term impact on fish biomass, even when
effort changes dramatically, the seasonal nature of the fishing effort is akin to temporal
closures of the fishery (Department of Fisheries 2010; Fouzai et al. 2012) allowing
targeted species to maintain the biomass over large changes in fishing effort, or the fished
stocks are so low that there is not enough biomass for biomass to recover in the absence
of fishing effort (Alexander et al. 2015). The first option is unlikely, since the Gascoyne
demersal scalefish resources have been classified as moderate to high risk from the fishery
(Department of Fisheries 2015). Surprisingly, while a small increase in fishing effort was
predicted to have adverse effects on L. nebulosus carnivorous fish stanzas, the predicted
impacts on other target species (Lethrinus spp., squid) were positive (Figure 4.11).
Though these predictions may be explained in several ways (e.g. the fishery was
underexploited, base levels of exploitation for the groups in question were set too low in
the model, a reduction in predator (L. nebulosus) biomass resulted in lower natural
mortality (Hinke et al. 2004; Mohammed et al. 2008), or a reduction in the biomass of
competitors for food resources permitted the group to take advantage of the extra
resources (Mohammed et al. 2008)). However, the parameterisation of the model may
123
also have contributed to this low sensitivity of the simulations to changes in fishing effort.
Analysis of the Ecosim input revealed that these results were probably due to having a
combination of non-zero feeding time adjustment for juvenile multi-stanza groups, a fixed
period in the juvenile stanza, and high EE that is sensitive to changes in predator feeding
time (Christensen and Walters 2004). This combination creates compensatory recruitment
and compensatory natural mortality, mechanisms that sustain fisheries yields when
fishing reduces the stock size (Christensen and Walters 2004), a recognised weakness in
the model structure (Plagányi and Butterworth 2004). This model was set up to examine
trophic linkages and interactions within the lagoon ecosystem: the recreational fishery
component was included as a contrast to the primary production scenarios, and did not
incorporate time series data from single species stock assessments, that would have been
used to tune the model (e.g. rock lobster data in Lozano-Montes et al. 2013) and enhance
predictions (Christensen et al. 2008). Therefore, the predictions produced by this model,
especially in relation to scenarios involving changes to fishing effort, should be assessed
with caution in terms of utility for management decisions. The settings of the model and
their influence on the simulations of different scenarios should also be reviewed in future
model developments.
Combined Scenarios
All combined scenarios were compared to the relevant increasing or decreasing primary
productivity forcing scenario because most of the changes in biomass were driven by the
change in primary productivity, as shown below. Due to the issues in model
parameterisation, and the emphasis on understanding trophic dynamics, the predictions
on the significance of fishing pressure should be interpreted with caution. Under the
combined scenarios, where the primary productivity increased, it slightly moderated the
impact of the change in fishing pressure. For example, instead of L. nebulosus biomass
decreasing by 2.5% under a 50% increase in fishing pressure (Scenario 9), it decreased
124
by about 1.3% (Figures 4.11-4.12). In the combined scenario where primary productivity
decreased and fishing pressure increased, there was a larger decline in L. nebulosus
biomass (13%) than in the model where only fishing pressure changed (2.5%).
This indicates that the algal biomass, including non-fleshy algae, fleshy macroalgae and
Sargassum, determine the biomass of fish and other organisms in this system. Intuitively,
one would assume that this would result in a higher abundance of herbivorous fish on the
macroalgal beds within the lagoon than is currently seen. However, the macroalgal habitat
does not provide enough protection for the larger-bodied fish, and most of the herbivorous
adult fish are located closer to reef habitat for more complex structure (Vergés et al.
2011).
4.4.3 Evaluation of Model Performance
Parameterisation
The composition of predation and consumption in several of the functional groups is not
always intuitive (e.g. low algal component in echinoderm group, high detritus component
in carnivorous fish stanzas; Figures 4.1, 4.5-4.6). The echinoderm functional group in
the model was established based on diet composition, with Ophiuroids (brittle stars) as
the dominant group (Ch. 3). The high detritus component in the carnivorous fish was a
result of the dietary analyses conducted as part of this study in Chapter 3. Rather than
being detritus per se, it was probably digested animal material that could not be identified,
since the species analysed would not be expected to consume detritus (Wilson, DPaW,
pers. comm.). For future updates of this model the detritus component could be removed
from those juvenile fish groups.
The ratio of invertebrate to teleost biomass was very low when compared to other models
(Table 4.5), indicating that the invertebrate biomass may be underestimated or the teleost
biomass may be overestimated. The ratio of primary producers to invertebrates was also
125
high when compared to most other selected models. When viewed together, this indicates
that the invertebrate biomass used for the model was too low for the system. This may be
due to several of the biomass values for the invertebrate groups being sourced from
Gourdon Bay in the Kimberley, in the north of Western Australia (Keesing et al. 2011).
This is an area with a high diversity of macroalgae, though lower biomass than the NMP
(Keesing et al. 2011; Fulton et al. 2014). Since many of the micro-crustaceans may be
associated with macroalgal habitat (Martin-Smith 1993; Peart 2004), this may have
resulted in underestimates of biomass. The sediment structure may also differ between
Gourdon Bay and the NMP lagoon, resulting in differing biomasses for infaunal
invertebrates (annelids, micro-crustaceans, echinoderms) (Przeslawski et al. 2013).
Therefore, improved data for invertebrates from the macroalgal habitat and sediment core
samples in the NMP lagoon would improve this parameter for future modelling.
Pedigree of the Data
The use of detailed, dietary data from juvenile fish that associate with macroalgal beds
was a novel component of this model, and potentially improved the pedigree and quality
of local data from the Ningaloo Ecopath model. The pedigree index for the Coral Bay
model (0.31) was intermediate to low, though comparable to 40% of a global sample of
EwE models (Morissette 2007) (Figure 4.14). The quality of the input data for the Coral
Bay model was excellent for multi-stanza dietary data, habitat area biomass of octopus
and macroalgal groups, and the production/biomass ratio of Sargassum (Figure 4.3). It is
difficult to quantify whether the Coral Bay Ecopath model is an improvement on the
Ningaloo Ecopath model, since the Ningaloo model did not have a pedigree index, and
the literature associated with the model does not provide sources for the data (Jones et al.
2011; Fulton et al. 2011).
126
The use of local dietary data will have enhanced dietary parameters; however, data were
limited for the diets of non-fish groups, the consumption/biomass ratios of juvenile fish
groups, and the habitat area biomass of macrofauna (sharks, dolphins, turtles), squid,
mollusc, meso-crustacean, zooplankton, non-fleshy algae, phytoplankton, sediment and
detritus groups (Figure 4.3), mainly non-charismatic species. Even for models of very
high pedigree (e.g. 0.72 in Jurien Bay; Loneragan et al. 2010), the authors recommend
that parameters need to be reviewed and included as new data becomes available
(Loneragan et al. 2010). It is essential to have local data in order to analyse local issues
(Metcalf et al. 2009), but highly complex ecosystems make it difficult to quantify data
for species that are cryptic, or are not valued by recreational or commercial industries
(Ainsworth and Pitcher 2005; Menon et al. 2005). This development of the Coral Bay
Ecopath model has reinforced the lack of local knowledge for key biological parameters
in the Ningaloo ecosystem. More information is needed for groups that are not part of the
charismatic megafauna (whale sharks, whales) or commercially and recreationally
important fish species (L. nebulosus) to enhance the detailed dietary data of this model.
0
0.1
0.2
0.3
0.4
0.5
0.6
0 - 0.199 0.2 - 0.399 0.4 - 0.599 0.6 - 1.0
Pro
po
rtio
n o
f sa
mp
le
Pedigree index
Figure 4.14 Histogram of the proportion for a range
of pedigree indices from a sample of 50 Ecopath
models globally (Morissette 2007), with the
position of the Coral Bay Ecopath model
represented by the orange point and vertical
dashed line.
127
4.4.4 Utility for Ecosystem Managers
Ecosystem managers in the Ningaloo Marine Park need to understand the dynamics of
the associated ecosystem and consider the impacts of increasing infrastructure and
development, fishing, changes in habitat health and biomass, and the variable recruitment
of species within the ecosystem (Botsford et al. 1997; Department of Conservation and
Land Management and Marine Parks and Reserves Authority 2005). The use of
ecosystem models allows managers to understand the importance of linkages in an
ecosystem, analyse the effects of processes that impact on those linkages, and facilitate
management decisions for complex ecosystems (Adams et al. 2006; Littler and Littler
2007; Coll et al. 2015).
The Coral Bay Ecopath with Ecosim model has excellent juvenile dietary data, sourced
from macroalgal beds in the Ningaloo reef lagoon. Juvenile data is often a weak link in
ecosystem models (Lozano-Montes, CSIRO, pers. comm.), and this is the first detailed
juvenile fish data available for an EwE model in Western Australia. It will help managers
to understand the linkages between important macroalgal habitat for the juveniles (Wilson
et al. 2010; Evans et al. 2014; Wilson et al. 2014) and the wider ecosystem and illustrates
the importance of benthic production in the Ningaloo lagoon. This provides valuable
information for the conservation of macroalgal habitat in both protected areas and general
use areas.
The model also identifies knowledge gaps for the region, particularly in relation to
biological data for cryptic and non-charismatic species. Improved data for these
functional groups can only increase the precision of predictions, and therefore, the utility
of this model. Predictions could also be enhanced by incorporating an Ecospace
component based on habitat structure and the movement patterns of functional groups.
This could also incorporate the movement of fish with ontogeny to more rugose coral
habitats (Cocheret de la Morinière et al. 2003; Vergés et al. 2011), and the effect of higher
128
algal biomass on coral biomass (Webster et al. 2015), with the subsequent effects on fish
community composition (Wismer et al. 2009; Wilson et al. 2010; Hoey and Bellwood
2011; Chong-Seng et al. 2012; Rasher et al. 2013; Evans et al. 2014), both of which
would add an extra level of detail to the model.
While this EwE model contains scenarios involving an increase in fishing effort, it was
primarily constructed to examine trophic interactions, and the recreational fishery data
was not verified against time series data for the region. There are several reasons for this,
including the lack of detailed data for the region, particularly for the recreational fishery
(Fletcher and Santoro 2015) and the temporal and budgetary constraints associated with
an Honours project. Therefore, caution should be applied when interpreting the fishing
scenarios produced by this model (Pauly et al. 2000; Plagányi and Butterworth 2004),
and the use of more detailed fisheries data would be better for management decisions
regarding fishing catch and effort.
4.5 Conclusions
Ecopath with Ecosim (EwE) is an extremely useful tool for synthesising information on
a system and the functional groups within it, exploring scenarios of change to it, and
identifying knowledge gaps for future research. The simulations from the extreme EwE
scenarios illustrate the utility of the model for managers, where devastating scenarios can
be run over short timeframes, without damaging the system.
This model has built on the Ningaloo model, and incorporated detailed dietary data for
juvenile fish species that associate with macroalgal beds within the Ningaloo lagoon. By
updating the Ningaloo model with recent, local data along with data from similar regions,
the Coral Bay model has highlighted the importance of benthic primary producers to the
Ningaloo lagoon ecosystem and their role in trophic linkages to recreationally important
fish species.
129
Though there are still knowledge gaps for many functional groups within the region, the
data for these can be incorporated into the model as they are researched, improving its
pedigree and therefore the precision of its predictions.
130
REFERENCES
Adams, A. J., Dahlgren, C. P., Kellison, G. T., Kendall, M. S., Layman, C. A., Ley, J. A.,
Nagelkerken, I., and Serafy, J. E. (2006). Nursery function of tropical back-reef
systems. Marine Ecology Progress Series 318, 287–301. doi:10.3354/meps318287
Adams, A. J., Locascio, J. V., and Robbins, B. D. (2004). Microhabitat use by a post-
settlement stage estuarine fish: evidence from relative abundance and predation among
habitats. Journal of Experimental Marine Biology and Ecology 299, 17–33.
doi:10.1016/j.jembe.2003.08.013
Ainsworth, C. H., and Pitcher, T. J. (2005). Using local ecological knowledge in
ecosystem models. In ‘Fisheries Assessment and Management in Data-Limited
Situations’. (Eds G. H. Kruse, V. F. Gallucci, D. E. Hay, R. I. Perry, R. M. Peterman,
T. C. Shirley, P. D. Spencer, B. Wilson, and D. Woodby.) pp. 289–304. (Alaska Sea
Grant College Program, University of Alaska: Fairbanks, U.S.A.)
doi:10.4027/famdis.2005.17
Ainsworth, C. H., Samhouri, J. F., Busch, D. S., Cheung, W. W. L., Dunne, J., and Okey,
T. A. (2011). Potential impacts of climate change on northeast Pacific marine
foodwebs and fisheries. ICES Journal of Marine Science 68, 1217–1229.
doi:10.1093/icesjms/fsr043
Alexander, K. A., Heymans, J. J., Magill, S., Tomczak, M. T., Holmes, S. J., and Wilding,
T. A. (2015). Investigating the recent decline in gadoid stocks in the west of Scotland
shelf ecosystem using a foodweb model. ICES Journal of Marine Science 72, 436–
449.
Aliño, P. M., McManus, L. T., McManus, J. W., Nafiola, C., Fortes, M. D., Trono G. C.,
and Jacinto, G. S. (1993). Initial parameter estimations of a coral reef flat ecosystem
in Bolinao, Pangasinan, northwestern Philippines. In ‘Trophic Models of Aquatic
Ecosystems’. (Eds V. Christensen and D. Pauly.) pp. 252–267. (ICLARM Conference
Procedings.)
Allen, G. R. (2009). ‘Field Guide to Marine Fishes of Tropical Australia and South-East
Asia’ 4th ed. (Western Australian Museum: Perth, Western Australia.)
Allen, G. R., and Swainston, R. (1988). ‘The Marine Fishes of North-Western Australia:
A field guide for anglers and divers’ 3rd ed. (Western Australian Museum: Perth,
Western Australia.)
Almany, G. R. (2004). Differential effects of habitat complexity, predators and
competitors on abundance of juvenile and adult coral reef fishes. Oecologia 141, 105–
13. doi:10.1007/s00442-004-1617-0
Amundsen, P. A., Gabler, H. M., and Staldvik, F. J. (1996). A new approach to graphical
analysis of feeding strategy from stomach contents data: modification of the Costello
(1990) method. Journal of Fish Biology 48, 607–614. doi:10.1006/jfbi.1996.0060
Anderson, T. W. (1994). Role of macroalgal structure in the distribution and abundance
of a temperate reef fish. Marine Ecology Progress Series 113, 279–290.
doi:10.3354/meps113279
Arias-Gonzalez, J. E., Delesalle, B., Salvat, B., and Galzin, R. (1997). Trophic
functioning of the Tiahura reef sector, Moorea Island, French Polynesia. Coral Reefs
16, 231–246. doi:10.1007/s003380050079
131
Arreguín-Sánchez, F., Valero-Pacheco, E., and Chavez, E. A. (1993). A trophic box
model of the coastal fish communities of the southwestern Gulf of Mexico. In ‘Trophic
Models of Aquatic Ecosystems’. (Eds V. Christensen and D. Pauly.) pp. 197–205.
(ICLARM Conference Procedings.)
Ashworth, E. C., Depczynski, M., Holmes, T. H., and Wilson, S. K. (2014). Quantitative
diet analysis of four mesopredators from a coral reef. Journal of Fish Biology 84,
1031–1045. doi:10.1111/jfb.12343
Ateweberhan, M., Bruggemann, J. H., and Breeman, A. M. (2006). Effects of extreme
seasonality on community structure and functional group dynamics of coral reef algae
in the southern Red Sea (Eritrea). Coral Reefs 25, 391–406. doi:10.1007/s00338-006-
0109-6
Babcock, R. C., Haywood, M., Vanderklift, M. A., Clapin, G., Dennis, D., Skewes, T.,
Milton, D., Murphy, N., Pillans, R. D., and Limbourn, A. (2008). Ecosystem impacts
of human usage and the effectiveness of zoning for biodiversity conservation: broad-
scale fish census. Final analysis and recommendations 2007. CSIRO, Cleveland, Qld.
Baird, D., McGlade, J. M., and Ulanowicz, R. E. (1991). The comparative ecology of six
marine ecosystems. Philosophical Transactions of the Royal Society of London B:
Biological Sciences 333, 15–29. doi:10.1098/rstb.1991.0058
Barnes, R. S. K., Calow, P., and Olive, P. J. W. (1993). ‘The Invertebrates: A new
synthesis’ 2nd ed. (Blackwell Scientific Publishing: Oxford.)
Barton, M. (2007). ‘Bond’s Biology of Fishes’ 3rd ed. (Thomson Brooks/Cole:
California.)
Beck, M. W., Heck Jr., K. L., Able, K. W., Childers, D. L., Eggleston, D. B., Gillanders,
B. M., Halpern, B. S., Hays, C. G., Hoshino, K., Minello, T. J., Orth, R. J., Sheridan,
P. F., and Weinstein, M. P. (2001). The identification, conservation, and management
of estuarine and marine nurseries for fish and invertebrates. BioScience 51, 633.
doi:10.1641/0006-3568(2001)051[0633:TICAMO]2.0.CO;2
Bellwood, D. R., Hughes, T. P., Folke, C., and Nyström, M. (2004). Confronting the coral
reef crisis. Nature 429, 827–833. doi:10.1038/nature02691
Benavides, A. G., Cancino, J. M., and Ojeda, F. P. (1994a). Ontogenetic change in the
diet of Aplodactylus punctatus (Pisces, Aplodactylidae) - an ecophysiological
explanation. Marine Biology 118, 1–6. doi:10.1007/BF00699213
Benavides, A. G., Cancino, J. M., and Ojeda, F. P. (1994b). Ontogenetic changes in gut
dimensions and macroalgal digestibility in the marine herbivorous fish, Aplodactylus
punctatus. Functional Ecology 8, 46–51. doi:10.2307/2390110
Bessey, C., and Heithaus, M. R. (2015). Ecological niche of an abundant teleost Pelates
octolineatus in a subtropical seagrass ecosystem. Marine Ecology Progress Series 541,
195–204. doi:10.3354/meps11542
Biber, P. D., Harwell, M. A., and Cropper, W. P. (2004). Modeling the dynamics of three
functional groups of macroalgae in tropical seagrass habitats. Ecological Modelling
175, 25–54. doi:10.1016/j.ecolmodel.2003.10.003
Blaber, S. J. M. (2013). Fishes and fisheries in tropical estuaries: the last 10 years.
Estuarine, Coastal and Shelf Science 135, 57–65. doi:10.1016/j.ecss.2012.11.002
Bone, Q., and Moore, R. H. (2008). ‘Biology of Fishes’ 3rd ed. (Taylor and Francis
132
Group: New York.)
Botsford, L. W., Castilla, J. C., and Peterson, C. H. (1997). The management of fisheries
and marine ecosystems. Science 277, 509–515.
Bowman, R. E. (1986). Effect of regurgitation on stomach content data of marine fishes.
In ‘Contemporary studies on fish feeding: the proceedings of GUTSHOP ’84’. (Eds C.
A. Simenstad and G. M. Cailliet.) pp. 1771–182. (Springer Netherlands.)
Boyer, K. E., Fong, P., Armitage, A. R., and Cohen, R. A. (2004). Elevated nutrient
content of tropical macroalgae increases rates of herbivory in coral, seagrass, and
mangrove habitats. Coral Reefs, 530–538. doi:10.1007/s00338-004-0421-y
Brown, C. J., Fulton, E. A., Hobday, A. J., Matear, R. J., Possingham, H. P., Bulman, C.,
Christensen, V., Forrest, R. E., Gehrke, P. C., Gribble, N. A., Griffiths, S. P., Lozano-
Montes, H., Martin, J. M., Metcalf, S., Okey, T. A., Watson, R., and Richardson, A. J.
(2010). Effects of climate-driven primary production change on marine food webs:
implications for fisheries and conservation. Global Change Biology 16, 1194–1212.
doi:10.1111/j.1365-2486.2009.02046.x
Bruno, J. F., Precht, W. F., Vroom, P. S., and Aronson, R. B. (2014). Coral reef baselines:
how much macroalgae is natural? Marine Pollution Bulletin 80, 24–29.
doi:10.1016/j.marpolbul.2014.01.010
Bruno, J. F., Sweatman, H., Precht, W. F., Selig, E. R., and Schutte, V. G. W. (2009).
Assessing evidence of phase shifts from coral to macroalgal dominance on coral reefs.
Ecology 90, 1478–1484. doi:10.1890/08-1781.1
Bulman, C. (2006). Trophic webs and modelling of Australia’s North West Shelf:
Technical report No. 9. , 85. Available at: file://c/dev/app/pdf-msp\MARINE_478.pdf
Burkepile, D. E., and Hay, M. E. (2011). Feeding complementarity versus redundancy
among herbivorous fishes on a Caribbean reef. Coral Reefs 30, 351–362.
doi:10.1007/s00338-011-0726-6
Carr, M. H. (1989). Effects of macroalgal assemblages on the recruitment of temperate
zone reef fishes. Journal of Experimental Marine Biology and Ecology 126, 59–76.
doi:10.1016/0022-0981(89)90124-X
Cassata, L., and Collins, L. B. (2008). Coral reef communities, habitats, and substrates in
and near sanctuary zones of Ningaloo Marine Park. Journal of Coastal Research 241,
139–151. doi:10.2112/05-0623.1
Chin, A., Sweatman, H., Forbes, S., Perks, H., Walker, R., Jones, G., Williamson, D.,
Evans, R., Hartley, F., Armstrong, S., Malcolm, H., and Edgar, G. (2008). Status of
the Coral Reefs in Australia and Papua New Guinnea. In ‘Status of Coral Reefs of the
World’. (Ed C. Wilkinson.) pp. 159–176. (Global Coral Reef Monitoring Network:
Townsville, Australia.)
Chipps, S. R., and Garvey, J. E. (2007). Assessment of diets and feeding patterns. In
‘Analysis and Interpretation of Freshwater Fisheries Data’. (Eds C. S. Guy and M.
Brown.) pp. 473–514. (American Fisheries Society: Bethesda, Maryland.)
Choat, J. H. (1991). The biology of herbivorous fishes on coral reefs. In ‘The Ecology of
Fishes on Coral Reefs’. (Ed P. F. Sale.) pp. 120–155. (Academic Press, Inc.: San
Diego, California.)
Choat, J. H., Clements, K. D., and Robbins, W. D. (2002). The trophic status of
133
herbivorous fishes on coral reefs. Marine Biology 140, 613–623. doi:10.1007/s00227-
001-0715-3
Chong-Seng, K. M., Mannering, T. D., Pratchett, M. S., Bellwood, D. R., and Graham,
N. A. J. (2012). The influence of coral reef benthic condition on associated fish
assemblages. PLoS ONE 7, e42167. doi:10.1371/journal.pone.0042167
Christensen, V. (2013). Ecological networks in fisheries: predicting the future? Fisheries
38, 76–81. doi:10.1080/03632415.2013.757987
Christensen, V. (2009). Ecopath with Ecosim: linking fisheries and ecology. In
‘Handbook of Ecological Modelling and Informatics’. (Eds S. E. Jorgensen, T.-S.
Chon, and F. Recknagel.) pp. 55–70. (WIT Press: Boston, U.S.A.)
Christensen, V., and Pauly, D. (1992). ECOPATH II: a software for balancing steady-
state ecosystem models and calculating network characteristics. Science 61, 169–185.
Christensen, V., and Walters, C. J. (2004). Ecopath with Ecosim: methods, capabilities
and limitations. Ecological Modelling 172, 109–139.
doi:10.1016/j.ecolmodel.2003.09.003
Christensen, V., Walters, C. J., and Pauly, D. (2005). Ecopath with Ecosim: a user’s
guide. Vancouver, Canada. doi:10.1016/0304-3800(92)90016-8
Christensen, V., Walters, C. J., Pauly, D., and Forrest, R. (2008). Ecopath with Ecosim
version 6 User Guide. Vancouver, Canada. Available at:
http://sources.ecopath.org/trac/Ecopath/wiki/UsersGuide
Clarke, K. R. (1993). Non-parametric multivariate analyses of changes in community
structure. Australian Journal of Ecology 18, 117–143.
Clarke, K. R., Gorley, R. N., Somerfield, P. J., and Warwick, R. M. (2014). ‘Change in
marine communities: an approach to statistical analysis and interpretation’ 3rd ed.
(PRIMER-E: Plymouth, U.K.)
Clarke, K. R., Tweedley, J. R., and Valesini, F. J. (2014). Simple shade plots aid better
long-term choices of data pre-treatment in multivariate assemblage studies. Journal of
the Marine Biological Association of the United Kingdom 94, 1–16.
doi:10.1017/S0025315413001227
Clarke, K. R., and Warwick, R. M. (2001). ‘Change in marine communities: an approach
to statistical analysis and interpretation’ 2nd ed. (PRIMER-E: Plymouth, U.K.)
Clarke, T. A. (1970). Territorial behavior and population dynamics of a Pomacentrid fish,
the Garibaldi, Hypsypops rubicunda. Ecological Monographs 40, 189–212.
Cocheret de la Morinière, E., Nagelkerken, I., van der Velde, G., Pollux, B. J. A.,
Hemminga, M. A., and Huiskes, A. H. L. (2003). Ontogenetic dietary changes of coral
reef fishes in the mangrove-seagrass-reef continuum: stable isotopes and gut-content
analysis. Marine Ecology Progress Series 246, 279–289. doi:10.3354/meps246279
Coll, M., Akoglu, E., Arreguín-Sánchez, F., Fulton, E. A., Gascuel, D., Heymans, J. J.,
Libralato, S., Mackinson, S., Palomera, I., Piroddi, C., Shannon, L. J., Steenbeek, J.,
Villasante, S., and Christensen, V. (2015). Modelling dynamic ecosystems: venturing
beyond boundaries with the Ecopath approach. Reviews in Fish Biology and Fisheries
25, 413–424. doi:10.1007/s11160-015-9386-x
Colléter, M., Valls, A., Guitton, J., Morissette, L., Arreguin-Sánchez, F., Christensen, V.,
134
Gascuel, D., and Pauly, D. (2013). EcoBase: A repository solution to gather and
communicate information from EwE models. Canada.
Commonwealth of Australia (2006). A guide to The Integrated Marine and Coastal
Regionalisation of Australia: Version 4.0 - June 2006. Canberra, Australia.
Commonwealth of Australia (2016a). Australian climate variability and change: average
maps. Available at: http://www.bom.gov.au/cgi-
bin/climate/change/averagemaps.cgi?map=sst&season=0608 [Verified 15 September
2016]
Commonwealth of Australia (2015a). Climate statistics for Australian locations:
Learmonth Airport. Available at:
http://www.bom.gov.au/climate/averages/tables/cw_005007.shtml [Verified 16
November 2015]
Commonwealth of Australia (2015b). Daily global solar exposure: Learmonth Airport.
Available at:
http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?p_nccObsCode=193&p_displa
y_type=dailyDataFile&p_stn_num=005007&p_startYear= [Verified 16 November
2015]
Commonwealth of Australia (2016b). Monthly climate statistics. Climate statistics for
Australian locations. Available at:
http://www.bom.gov.au/climate/averages/tables/cw_005007.shtml [Verified 15
September 2016]
Commonwealth of Australia (2015c). Ningaloo Commonwealth Marine Reserve.
Available at: https://www.environment.gov.au/topics/marine/marine-reserves/north-
west/ningaloo [Verified 16 November 2015]
Commonwealth of Australia (2016c). Place details: the Ningaloo coast. Australian
Heritage Database. Available at: http://www.environment.gov.au/cgi-
bin/ahdb/search.pl?mode=place_detail;place_id=105881 [Verified 15 September
2016]
Costanza, R., D’Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K.,
Naeem, S., O’Neill, R. V., Paruelo, J., Raskin, R. G., Sutton, P., and van den Belt, M.
(1997). The value of the world’s ecosystem services and natural capital. Nature 387,
253–260. doi:10.1038/387253a0
Costello, M. J. (1990). Predator feeding strategy and prey importance: a new graphical
analysis. Journal of Fish Biology 36, 261–263. doi:10.1111/j.1095-
8649.1990.tb05601.x
Currey, L. M., Williams, A. J., Mapstone, B. D., Davies, C. R., Carlos, G., Welch, D. J.,
Simpfendorfer, C. A., Ballagh, A. C., Penny, A. L., Grandcourt, E. M., Mapleston, A.,
Wiebkin, A. S., and Bean, K. (2013). Comparative biology of tropical Lethrinus
species (Lethrinidae): Challenges for multi-species management. Journal of Fish
Biology 82, 764–788.
Dahlgren, C. P., and Eggleston, D. B. (2000). Ecological processes underlying
ontogenetic habitat shifts in a coral reef fish. Ecology 81, 2227–2240.
doi:10.1890/0012-9658(2000)081[2227:EPUOHS]2.0.CO;2
Dahlgren, C. P., Kellison, G. T., Adams, A. J., Gillanders, B. M., Kendall, M. S., Layman,
C. A., Ley, J. A., Nagelkerken, I., and Serafy, J. E. (2006). Marine nurseries and
135
effective juvenile habitats: concepts and applications. Marine Ecology Progress Series
312, 291–295. doi:10.3354/meps312291
Dahlgren, C. P., and Marr, J. (2004). Back reef systems: important but overlooked
components of tropical marine ecosystems. Bulletin of Marine Science 75, 145–152.
Dambacher, J. M., Gaughan, D. J., Rochet, M. J., Rossignol, P. A., and Trenkel, V. M.
(2009). Qualitative modelling and indicators of exploited ecosystems. Fish and
Fisheries 10, 305–322. doi:10.1111/j.1467-2979.2008.00323.x
Day, R. D., German, D. P., and Tibbetts, I. R. (2011). Why can’t young fish eat plants?
neither digestive enzymes nor gut development preclude herbivory in the young of a
stomachless marine herbivorous fish. Comparative Biochemistry and Physiology 158,
23–29. doi:10.1016/j.cbpb.2010.09.010
Debenay, J.-P., Sigura, A., and Justine, J.-L. (2011). Foraminifera in the diet of coral reef
fish from the lagoon of New Caledonia: predation, digestion, dispersion. Revue de
Micropaleontologie 54, 87–103. doi:10.1016/j.revmic.2010.04.001
Department of Conservation and Land Management, W. A. G., and Marine Parks and
Reserves Authority, W. A. G. (2005). Management Plan for the Ningaloo Marine Park
and Muiron Islands Marine Management Area 2005-2015. Fremantle, W.A.
Department of Fisheries, W. A. (2010). West Coast Demersal Scalefish Resource,
Fisheries Management Paper No. 247. Perth, W.A.
Department of Fisheries, W. A. G. (2015). Research, monitoring, assessment and
development plan. Fisheries Occasional Publication. Department of Fisheries, Perth,
Western Australia.
Diaz-Pulido, G., and McCook, L. J. (2003). Relative roles of herbivory and nutrients in
the recruitment of coral-reef seaweeds. Ecology 84, 2026–2033.
Dorenbosch, M. (2006). Connectivity between fish assemblages of seagrass beds,
mangroves and coral reefs. Evidence from the Caribbean and the western Indian
Ocean. Radboud University Nijmegen, The Netherlands. Available at:
http://repository.ubn.kun.nl/bitstream/2066/57039/1/24______.PDF
Dorenbosch, M., Grol, M. G. G., Christianen, M. J. A., Nagelkerken, I., and van der
Velde, G. (2005). Indo-Pacific seagrass beds and mangroves contribute to fish density
and diversity on adjacent coral reefs. Marine Ecology Progress Series 302, 63–76.
Dorenbosch, M., Grol, M. G. G., Nagelkerken, I., and van der Velde, G. (2006). Seagrass
beds and mangroves as nurseries for the threatened Indo-Pacific Humphead wrasse,
Cheilinus undulatus and Caribbean Rainbow parrotfish, Scarus guacamaia. Biological
Conservation 129, 277–282.
Dorenbosch, M., van Riel, M. C., Nagelkerken, I., and van der Velde, G. (2004). The
relationship of reef fish densities to the proximity of mangrove and seagrass nurseries.
Estuarine, Coastal and Shelf Science 60, 37–48.
Doropoulos, C., Hyndes, G. A., Abecasis, D., and Vergés, A. (2013). Herbivores strongly
influence algal recruitment in both coral- and algal-dominated coral reef habitats.
Marine Ecology Progress Series 486, 153–164. doi:10.3354/meps10325
Downie, R. A., Babcock, R. C., Thomson, D. P., and Vanderklift, M. A. (2013). Density
of herbivorous fish and intensity of herbivory are influenced by proximity to coral
reefs. Marine Ecology Progress Series 482, 217–225. doi:10.3354/meps10250
136
Ebisawa, A. (1999). Reproductive and sexual characteristics in the Pacific yellowtail
emperor, Lethrinus atkinsoni, in waters off the Ryukyu Islands. Ichthyological
Research 46, 341–358. doi:10.1007/BF02673977
Edgar, G., and Shepherd, S. (2013). ‘Ecology of Australian Temperate Reefs: The unique
south’. (CSIRO Publishing.)
Elliott, M., Whitfield, A. K., Potter, I. C., Blaber, S. J. M., Cyrus, D. P., Nordlie, F. G.,
and Harrison, T. D. (2007). The guild approach to categorizing estuarine fish
assemblages: a global review. Fish and Fisheries 8, 241–268. doi:10.1111/j.1467-
2679.2007.00253.x
Eppley, R. W. (1972). Temperature and phytoplankton growth in the sea. Fishery Bulletin
70, 1063–85.
Evans, R. D., Wilson, S. K., Field, S. N., and Moore, J. A. Y. (2014). Importance of
macroalgal fields as coral reef fish nursery habitat in north-west Australia. Marine
Biology 161, 599–607. doi:10.1007/s00227-013-2362-x
Everett, R. A. (1994). Macroalgae in marine soft-sediment communities: effects on
benthic faunal assemblages. Journal of Experimental Marine Biology and Ecology
175, 253–274. doi:10.1016/0022-0981(94)90030-2
Farmer, B. M., and Wilson, S. K. (2011). Diet of finfish targeted by fishers in north west
Australia and the implications for trophic cascades. Environmental Biology of Fishes
91, 71–85. doi:10.1007/s10641-010-9761-3
Feng, M., Benthuysen, J., Zhang, N., and Slawinski, D. (2015). Freshening anomalies in
the Indonesian throughflow and impacts on the Leeuwin Current during 2010–2011.
Geophysical Research Letters 42, 1–8. doi:10.1002/2015GL065848
Finn, J. T. (1976). Measures of ecosystem structure and function derived from analysis
of flows. Journal of Theoretical Biology 56, 363–380. doi:10.1016/S0022-
5193(76)80080-X
Fletcher, W. J., and Santoro, K. (2015). Status reports of the fisheries and aquatic
resources of Western Australia 2014/15: state of the fisheries. Perth.
Food and Agriculture Organisation (2001a). Bony Fishes: Part 3 (Menidae to
Pomacentridae). In ‘FAO Species Identification Guide for Fishery Purposes. The
Living Marine Resources of the Western Central Pacific’. (Eds K. E. Carpenter and V.
H. Niem.) pp. 2791–3380. (Food and Agriculture Organisation of the United Nations:
Rome.)
Food and Agriculture Organisation (2001b). Bony Fishes: Part 4 (Labridae to
Latimeriidae), estuarine crocodiles, sea turtles, sea snakes and marine mammals. In
‘FAO Species Identification Guide for Fishery Purposes. The living marine resources
of the Western Central Pacific’. (Eds K. E. Carpenter and V. H. Niem.) pp. 3381–4218.
(Food and Agriculture Organisation of the United Nations: Rome.)
Food and Agriculture Organisation (2008). Fisheries Management. Supp. 2 Add. 1 No. 4.
Food and Agriculture Organisation of the United Nations, Rome.
Fouzai, N., Coll, M., Palomera, I., Santojanni, A., Arneri, E., and Christensen, V. (2012).
Fishing management scenarios to rebuild exploited resources and ecosystems of the
Northern-Central Adriatic (Mediterranean Sea). Journal of Marine Systems 102–104,
39–51. doi:10.1016/j.jmarsys.2012.05.003
137
Fox, R. J., and Bellwood, D. R. (2008). Remote video bioassays reveal the potential
feeding impact of the rabbitfish Siganus canaliculatus (f: Siganidae) on an inner-shelf
reef of the Great Barrier Reef. Coral Reefs 27, 605–615. doi:10.1007/s00338-008-
0359-6
Freeman, L. A., Kleypas, J. A., and Miller, A. J. (2013). Coral reef habitat response to
climate change scenarios. PloS one 8, e82404. doi:10.1371/journal.pone.0082404
Freire, K. M. F., Christensen, V., and Pauly, D. (2008). Description of the East Brazil
Large Marine Ecosystem using a trophic model. Scientia Marina 72, 477–491.
doi:10.3989/scimar.2008.72n3477
Froese, R., and Pauly, D. (2015). FishBase. World wide web electronic publication (v.
10/2015). Available at: www.fishbase.org [Verified 1 November 2015]
Fry, G., Heyward, A., Wassenberg, T. J., Ellis, N., Taranto, T., Keesing, J. K., Irvine, T.
R., Steiglitz, T., and Colquhoun, J. (2008). Benthic habitat surveys of potential LNG
hub locations in the Kimberley region. Available at: file://c/dev/app/pdf-
msp\BI_HM_275.pdf
Fulton, C. J., Depczynski, M., Holmes, T. H., Noble, M. M., Radford, B., Wernberg, T.,
and Wilson, S. K. (2014). Sea temperature shapes seasonal fluctuations in seaweed
biomass within the Ningaloo coral reef ecosystem. Limnology and Oceanography 59,
156–166. doi:10.4319/lo.2014.59.01.0156
Fulton, C. J., Noble, M. N., Radford, B., Gallen, C., and Harasti, D. (2016). Microhabitat
selectivity underpins regional indicators of fish abundance and replenishment.
Ecological Indicators 70, 222–231. doi:10.1016/j.ecolind.2016.06.032
Fulton, E. A., Gray, R., Sporcic, M., Scott, R., Little, R., Hepburn, M., Gorton, B.,
Hatfield, B., Fuller, M., Jones, T., De la Mare, W., Boschetti, F., Chapman, K., Dzidic,
P., Syme, G., Dambacher, J., and McDonald, D. (2011). Ningaloo collaboration
cluster: adaptive futures for Ningaloo.
Gartner, A., Lavery, P. S., and Lonzano-Montes, H. (2015). Trophic implications and
faunal resilience following one-off and successive disturbances to an Amphibolis
griffithii seagrass system. Marine Pollution Bulletin 94, 131–143.
doi:10.1016/j.marpolbul.2015.03.001
Gillanders, B. M., Able, K. W., Brown, J. A., Eggleston, D. B., and Sheridan, P. F. (2003).
Evidence of connectivity between juvenile and adult habitats for mobile marine fauna:
an important component of nurseries. Marine Ecology Progress Series 247, 281–295.
doi:10.3354/meps247281
Godinot, O., and Allain, V. (2003). A preliminary Ecopath model of the warm pool
pelagic ecosystem. doi:10.1002/jor.1100150514
Graham, N. A. J., Jennings, S., MacNeil, M. A., Mouillot, D., and Wilson, S. K. (2015).
Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature
518, 1–17. doi:10.1038/nature14140
Grandcourt, E., Al Abdessalaam, T., Francis, F., and Al Shamsi, A. (2007). Population
biology and assessment of the white-spotted spinefoot, Siganus canaliculatus (Park,
1797), in the southern Arabian Gulf. Journal of Applied Ichthyology 23, 53–59.
doi:10.1111/j.1439-0426.2006.00796.x
Green, A. L., and Bellwood, D. R. (2009). Monitoring functional groups of herbivorous
138
reef fishes as indicators of coral reef resilience: a practical guide for coral reef
managers in the Asia Pacific region. , 70. Available at:
http://cmsdata.iucn.org/downloads/resilience_herbivorous_monitoring.pdf
Grol, M. G. G., Dorenbosch, M., Kokkelmans, E. M. G., and Nagelkerken, I. (2008).
Mangroves and seagrass beds do not enhance growth of early juveniles of a coral reef
fish. Marine Ecology Progress Series 366, 137–146. doi:10.3354/meps07509
Hanson, C. E., Pattiaratchi, C. B., and Waite, A. M. (2005). Sporadic upwelling on a
downwelling coast: phytoplankton responses to spatially variable nutrient dynamics
off the Gascoyne region of Western Australia. Continental Shelf Research 25, 1561–
1582. doi:10.1016/j.csr.2005.04.003
Harley, C. D. G., Anderson, K. M., Demes, K. W., Jorve, J. P., Kordas, R. L., Coyle, T.
A., and Graham, M. H. (2012). Effects of climate change on global seaweed
communities. Journal of Phycology 48, 1064–1078. doi:10.1111/j.1529-
8817.2012.01224.x
Harley, C. D. G., Randall Hughes, A., Hultgren, K. M., Miner, B. G., Sorte, C. J. B.,
Thornber, C. S., Rodriguez, L. F., Tomanek, L., and Williams, S. L. (2006). The
impacts of climate change in coastal marine systems. Ecology Letters 9, 228–241.
doi:10.1111/j.1461-0248.2005.00871.x
Hay, M. E. (1991). Fish-seaweed interactions on coral reefs: effects of herbivorous fishes
and adaptations of their prey. In ‘The Ecology of Fishes on Coral Reefs’. (Ed P. F.
Sale.) pp. 96–119. (Academic Press, Inc.: San Diego, California.)
Heck Jr., K. L., Hays, G., and Orth, R. J. (2003). Critical evaluation of the nursery role
hypothesis for seagrass meadows. Marine Ecology-Progress Series 253, 123–136.
doi:10.3354/meps253123
Heck Jr., K. L., and Weinstein, M. P. (1989). Feeding habits of juvenile reef fishes
associated with Panamanian seagrass meadows. Bulletin of Marine Science 45, 629–
636.
Herwig, J. N., Depczynski, M., Roberts, J. D., Semmens, J. M., Gagliano, M., and
Heyward, A. J. (2012). Using age-based life history data to investigate the life cycle
and vulnerability of Octopus cyanea. PLoS ONE 7. doi:10.1371/journal.pone.0043679
Heymans, J. J., Coll, M., Libralato, S., Morissette, L., and Christensen, V. (2014). Global
patterns in ecological indicators of marine food webs: a modelling approach. PLoS
ONE 9, e95845. doi:10.1371/journal.pone.0095845
Heymans, J. J., Coll, M., Link, J. S., Mackinson, S., Steenbeek, J., Walters, C., and
Christensen, V. (2016). Best practice in Ecopath with Ecosim food-web models for
ecosystem-based management. Ecological Modelling 331, 173–184.
doi:10.1016/j.ecolmodel.2015.12.007
Hinke, J. T., Kaplan, I. C., Aydin, K., Watters, G. M., Olson, R. J., and Kitchell, J. F.
(2004). Visualizing the food-web effects of fishing for tunas in the Pacific Ocean.
Ecology and Society 9, 24.
Hodgson, G. (1999). A global assessment of human effects on coral reefs. Marine
Pollution Bulletin 38, 345–355.
Hoey, A. S., and Bellwood, D. R. (2009). Limited functional redundancy in a high
functional diversity system: single species dominates key ecological process on coral
139
reefs. Ecosystems 12, 1316–1328. doi:10.1007/sl0021-009-9291-z
Hoey, A. S., and Bellwood, D. R. (2011). Suppression of herbivory by macroalgal
density: a critical feedback on coral reefs? Ecology Letters 14, 267–273.
doi:10.1111/j.1461-0248.2010.01581.x
Hoey, A. S., Brandl, S. J., and Bellwood, D. R. (2013). Diet and cross-shelf distribution
of rabbitfishes (f. Siganidae) on the northern Great Barrier Reef: implications for
ecosystem function. Coral Reefs 32, 973–984. doi:10.1007/s00338-013-1043-z
Horinouchi, M., Tongnunui, P., Furumitsu, K., Nakamura, Y., Kanou, K., Yamaguchi,
A., Okamoto, K., and Sano, M. (2012). Food habits of small fishes in seagrass habitats
in Trang, southern Thailand. Fisheries Science 78, 577–587. doi:10.1007/s12562-012-
0485-5
Hsu, T. H., Adiputra, Y. T., Burridge, C. P., and Gwo, J.-C. (2011). Two spinefoot colour
morphs: mottled spinefoot Siganus fuscescens and white-spotted spinefoot Siganus
canaliculatus are synonyms. Journal of Fish Biology 79, 1350–1355.
doi:10.1111/j.1095-8649.2011.03104.x
Hughes, T. P. (2003). Climate change, human impacts, and the resilience of coral reefs.
Science 301, 929–933. doi:10.1126/science.1085046
Hughes, T. P., Rodrigues, M. J., Bellwood, D. R., Ceccarelli, D., Hoegh-Guldberg, O.,
McCook, L. J., Moltschaniwskyj, N., Pratchett, M. S., Steneck, R. S., and Willis, B.
L. (2007). Phase shifts, herbivory, and the resilience of coral reefs to climate change.
Current Biology 17, 360–365. doi:10.1016/j.cub.2006.12.049
Huisman, J. M. (2000). ‘Marine Plants of Australia’. (University of Western Australia
Press: Perth, W.A.)
Hyslop, E. J. (1980). Stomach contents analysis: a review of methods and their
application. Journal of Fish Biology 17, 411–429.
Johansson, C. L. (2012). A functional analysis of herbivory on Ningaloo Reef, Australia.
James Cook University.
Johansson, C. L., Bellwood, D. R., and Depczynski, M. (2010). Sea urchins, macroalgae
and coral reef decline: a functional evaluation of an intact reef system, Ningaloo,
Western Australia. Marine Ecology Progress Series 414, 65–74.
doi:10.3354/meps08730
Jones, D. S., and Morgan, G. J. (1994). ‘A Field Guide to Crustaceans of Australian
Waters’. (Western Australian Museum: Perth, W.A.)
Jones, T. A., Fulton, E. A., and Wood, D. (2011). Challenging tourism theory through
integrated models: how multiple model projects strengthen outcomes through a case
study of tourism development on the Ningaloo Coast of Western Australia. In
‘MODSIM 2011 - 19th International Congress on Modelling and Simulation’. (Eds F.
Chan, D. Marinova, and R. S. Anderssen.) pp. 3112–3120. (Modelling and Simulation
Society of Australia and New Zealand: Perth, W.A.) Available at:
http://www.scopus.com/inward/record.url?eid=2-s2.0-
84858822190&partnerID=40&md5=496c073ae7b02cbede9180bcb61f95f4
Jorgensen, S. E., and Chon, T.-S. (2009). Overview of the model types available for
ecological modelling. In ‘Handbook of Ecological Modelling and Informatics’. (Eds
S. E. Jorgensen, T.-S. Chon, and F. Recknagel.) pp. 9–47. (WIT Press: Boston, U.S.A.)
140
Keesing, J. K., Irvine, T. R., Alderslade, P., Clapin, G., Fromont, J., Hosie, A. M.,
Huisman, J. M., Phillips, J. C., Naughton, K. M., Marsh, L. M., Slack-Smith, S. M.,
Thomson, D. P., and Watson, J. E. (2011). Marine benthic flora and fauna of Gourdon
Bay and the Dampier Peninsula in the Kimberley region of North-Western Australia.
Journal of the Royal Society of Western Australia 94, 285–301.
Kimirei, I. A., Nagelkerken, I., Trommelen, M., Blankers, P., van Hoytema, N.,
Hoejimakers, D., Huijbers, C. M., Mgaya, Y. D., and Rypel, A. L. (2013). What drives
ontogenetic niche shifts of fishes in coral reef ecosystems? Ecosystems 16, 783–796.
doi:10.1007/s10021-013-9645-4
Knowlton, N. (1992). Thresholds and multiple stable states in coral reef community
dynamics. American Zoologist 32, 674–682. doi:10.1093/icb/32.6.674
Kobryn, H. T., Wouters, K., Beckley, L. E., and Heege, T. (2013). Ningaloo Reef: shallow
marine habitats mapped using a hyperspectral sensor. PLoS ONE 8, e70105.
doi:10.1371/journal.pone.0070105
Koch, M., Bowes, G., Ross, C., and Zhang, X.-H. (2013). Climate change and ocean
acidification effects on seagrasses and marine macroalgae. Global Change Biology 19,
103–132. doi:10.1111/j.1365-2486.2012.02791.x
Kramer, D. L., and Bryant, M. J. (1995). Intestine length in the fishes of a tropical stream:
2. Relationships to diet - the long and short of a convoluted issue. Environmental
Biology of Fishes 42, 129–141.
Kuriiwa, K., Hanzawa, N., Yoshino, T., Kimura, S., and Nishida, M. (2007). Phylogenetic
relationships and natural hybridization in rabbitfishes (Teleostei: Siganidae) inferred
from mitochondrial and nuclear DNA analyses. Molecular Phylogenetics and
Evolution 45, 69–80. doi:10.1016/j.ympev.2007.04.018
Laegdsgaard, P., and Johnson, C. (2001). Why do juvenile fish utilise mangrove habitats?
Journal of Experimental Marine Biology and Ecology 257, 229–253.
Lam, T. J. (1974). Siganids: their biology and mariculture potential. Aquaculture 3, 325–
354.
Lee, C.-L., and Lin, H.-J. (2015). Ontogenetic habitat utilization patterns of juvenile reef
fish in low-predation habitats. Marine Biology 162, 1799–1811. doi:10.1007/s00227-
015-2712-y
Lek, E., Fairclough, D. V., Platell, M. E., Clarke, K. R., Tweedley, J. R., and Potter, I. C.
(2011). To what extent are the dietary compositions of three abundant, co-occurring
labrid species different and related to latitude, habitat, body size and season? Journal
of Fish Biology 78, 1913–1943. doi:10.1111/j.1095-8649.2011.02961.x
Link, J. S. (2010). Adding rigor to ecological network models by evaluating a set of pre-
balance diagnostics: a plea for PREBAL. Ecological Modelling 221, 1580–1591.
doi:10.1016/j.ecolmodel.2010.03.012
Littler, M. M., and Littler, D. S. (2007). Assessment of coral reefs using
herbivory/nutrient assays and indicator groups of benthic primary producers: a critical
synthesis, proposed protocols, and critique of management strategies. Aquatic
Conservation: Marine and Freshwater Ecosystems 17, 195–215. doi:10.1002/aqc.790
Loneragan, N. R., Babcock, R. C., Lozano-Montes, H., and Dambacher, J. M. (2010).
Evaluating how food webs and the fisheries they support are affected by fishing
141
closures in Jurien Bay, temperate Western Australia. Available at: file://c/dev/app/pdf-
bdp\00197.pdf
Loneragan, N. R., Bunn, S. E., and Kellaway, D. M. (1997). Are mangroves and
seagrasses sources of organic carbon for penaeid prawns in a tropical Australian
estuary? A multiple stable-isotope study. Marine Biology 130, 289–300.
doi:10.1007/s002270050248
Loneragan, N. R., Kangas, M., Haywood, M. D. E., Kenyon, R. A., Caputi, N., and
Sporer, E. (2013). Impact of cyclones and aquatic macrophytes on recruitment and
landings of tiger prawns Penaeus esculentus in Exmouth Gulf, Western Australia.
Estuarine, Coastal and Shelf Science 127, 46–58.
Loneragan, N. R., Kenyon, R. A., Staples, D. J., Poiner, I. R., and Conacher, C. A. (1998).
The influence of seagrass type on the distribution and abundance of postlarval and
juvenile tiger prawns (Penaeus esculentus and P. semisulcatus) in the western Gulf of
Carpentaria, Australia. Journal of Experimental Marine Biology and Ecology 228,
175–195. doi:10.1016/S0022-0981(98)00029-X
Low, R. M. (1971). Interspecific territoriality in a Pomacentrid reef fish, Pomacentrus
flavicauda Whitley. Ecology 52, 648–654.
Lozano-Montes, H. M., Babcock, R. C., and Loneragan, N. R. (2012). Exploring the
effects of spatial closures in a temperate marine ecosystem in Western Australia: a
case study of the western rock lobster (Panulirus cygnus) fishery. Ecological
Modelling 245, 31–40. doi:10.1016/j.ecolmodel.2012.05.015
Lozano-Montes, H. M., Loneragan, N. R., Babcock, R. C., and Caputi, N. (2013).
Evaluating the ecosystem effects of variation in recruitment and fishing effort in the
western rock lobster fishery. Fisheries Research 145, 128–135.
doi:10.1016/j.fishres.2013.01.009
Lozano-Montes, H. M., Loneragan, N. R., Babcock, R. C., and Jackson, K. (2011). Using
trophic flows and ecosystem structure to model the effects of fishing in the Jurien Bay
Marine Park, temperate Western Australia. Marine and Freshwater Research 62, 421–
431. doi:10.1071/MF09154
Lugendo, B. R., Nagelkerken, I., van der Velde, G., and Mgaya, Y. D. (2006). The
importance of mangroves, mud and sand flats, and seagrass beds as feeding areas for
juvenile fishes in Chwaka Bay, Zanzibar: gut content and stable isotope analyses.
Journal of Fish Biology 69, 1639–1661. doi:10.1111/j.1095-8649.2006.01231.x
Manson, F. J., Loneragan, N. R., Skilleter, G. A., and Phinn, S. R. (2005). An evaluation
of the evidence for linkages between mangroves and fisheries: a synthesis of the
literature and identification of research directions. In ‘Oceanography and Marine
Biology: An Annual Review’. (Eds R. N. Gibson, R. J. A. Atkinson, and J. D. M.
Gordon.) pp. 483–513. (CRC Press-Taylor & Francis Group: Boca Raton.)
Marriott, R. J., Adams, D. J., Jarvis, N. D. C., Moran, M. J., Newman, S. J., and Craine,
M. (2011). Age-based demographic assessment of fished stocks of Lethrinus
nebulosus in the Gascoyne Bioregion of Western Australia. Fisheries Management
and Ecology 18, 89–103.
Marriott, R. J., Jarvis, N. D. C., Adams, D. J., Gallash, A. E., Norriss, J., and Newman,
S. J. (2010). Maturation and sexual ontogeny in the spangled emperor Lethrinus
nebulosus. Journal of Fish Biology 76, 1396–1414.
142
Martin-Smith, K. M. (1993). Abundance of mobile epifauna: the role of habitat
complexity and predation by fishes. Journal of Experimental Marine Biology and
Ecology 174, 243–260. doi:10.1016/0022-0981(93)90020-O
McCook, L. J. (1999). Macroalgae, nutrients and phase shifts on coral reefs: scientific
issues and management consequences for the Great Barrier Reef. Coral Reefs 18, 357–
367. doi:10.1007/s003380050213
McCook, L. J., Jompa, J., and Diaz-Pulido, G. (2001). Competition between corals and
algae on coral reefs: a review of evidence and mechanisms. Coral Reefs 19, 400–417.
doi:10.1007/s003380000129
McIlwain, J. L. (2003). Fine-scale temporal and spatial patterns of larval supply to a
fringing reef in Western Australia. Marine Ecology Progress Series 252, 207–222.
doi:10.3354/meps252207
McManus, J. W., Meñez, L. A. B., Kesner-Reyes, K. N., Vergara, S. G., and Ablan, M.
C. (2000). Coral reef fishing and coral-algal phase shifts: implications for global reef
status. ICES Journal of Marine Science 57, 572–578. doi:10.1006/jmsc.2000.0720
Menon, M. M., Gallucci, V. F., and Conquest, L. L. (2005). Sampling designs for the
estimation of longline bycatch. In ‘Fisheries Assessment and Management in Data-
Limited Situations’. (Eds G. H. Kruse, V. F. Gallucci, D. E. Hay, R. I. Perry, R. M.
Peterman, T. C. Shirley, P. D. Spencer, B. Wilson, and D. Woodby.) pp. 851–870.
(Alaska Sea Grant College Program, University of Alaska: Fairbanks, U.S.A.)
Metcalf, S. J., Gaughan, D. J., and Shaw, J. (2009). Conceptual models for ecosystem
based fisheries management (EBFM) in Western Australia. , 42.
Michael, P. J., Hyndes, G. A., Vanderklift, M. A., and Vergés, A. (2013). Identity and
behaviour of herbivorous fish influence large-scale spatial patterns of macroalgal
herbivory in a coral reef. Marine Ecology Progress Series 482, 227–240.
doi:10.3354/meps10262
Moberg, F., and Folke, C. (1999). Ecological goods and services of coral reef ecosystems.
Ecological Economics 29, 215–233. doi:10.1016/S0921-8009(99)00009-9
Mohammed, E., Vasconcellos, M., Mackinson, S., Fanning, P., and Carocci, F. (2008).
Scientific basis for ecosystem-based management in the Lesser Antilles including
interactions with marine mammals and other top predators. Food and Agriculture
Organisation of the United Nations, Barbados.
Morissette, L. (2007). Complexity, cost and quality of ecosystem models and their impact
on resilience: a comparative analysis, with emphasis on marine mammals and the Gulf
of St. Lawrence. University of British Columbia.
Mumby, P. J. (2004). Mangroves enhance the biomass of coral reef fish communities in
the Caribbean. Nature 427, 533–536. doi:10.1029/2001JB001194
Mumby, P. J., and Hastings, A. (2008). The impact of ecosystem connectivity on coral
reef resilience. Journal of Applied Ecology 45, 854–862. doi:10.1111/j.1365-
2664.2008.01459.x
Mumby, P. J., Hastings, A., and Edwards, H. J. (2007). Thresholds and the resilience of
Caribbean coral reefs. Nature 450, 98–101.
Munday, P. L., Jones, G. P., Pratchett, M. S., and Williams, A. J. (2008). Climate change
and the future for coral reef fishes. Fish & Fisheries 9, 261–285. doi:10.1111/j.1467-
143
2979.2008.00281.x
Nagelkerken, I., Sheaves, M., Baker, R., and Connolly, R. M. (2015). The seascape
nursery: a novel spatial approach to identify and manage nurseries for coastal marine
fauna. Fish and Fisheries 16, 362–371. doi:10.1111/faf.12057
Nagelkerken, I., van der Velde, G., Gorissen, M. W., Meijer, G. J., van’t Hof, T., and den
Hartog, C. (2000). Importance of mangroves, seagrass beds and the shallow coral reef
as a nursery for important coral reef fishes, using a visual census technique. Estuarine,
Coastal and Shelf Science 51, 31–44. doi:10.1006/ecss.2000.0617
Nakamura, Y., Hirota, K., Shibuno, T., and Watanabe, Y. (2012). Variability in nursery
function of tropical seagrass beds during fish ontogeny: timing of ontogenetic habitat
shift. Marine Biology 159, 1305–1315. doi:10.1007/s00227-012-1911-z
Nakamura, Y., Horinouchi, M., Nakai, T., and Sano, M. (2003). Food habits of fishes in
a seagrass bed on a fringing coral reef at Iriomote Island, southern Japan.
Ichthyological Research 50, 15–22. doi:10.1007/s102280300002
Norström, A. V., Nyström, M., Lokrantz, J., and Folke, C. (2009). Alternative states on
coral reefs: beyond coral-macroalgal phase shifts. Marine Ecology Progress Series
376, 293–306. doi:10.3354/meps07815
Nyström, M., Graham, N. A. J., Lokrantz, J., and Norström, A. V. (2008). Capturing the
cornerstones of coral reef resilience: linking theory to practice. Coral Reefs 27, 795–
809. doi:10.1007/s00338-008-0426-z
Odum, E. P. (1971). ‘Fundamentals of Ecology’ 3rd ed. (Saunders: Philadelphia.)
Ogden, J. C., and Lobel, P. S. (1978). The role of herbivorous fishes and urchins in coral
reef communities. Environmental Biology of Fishes 3, 49–63.
doi:10.1007/BF00006308
Okey, T. A., and Mahmoudi, B. (2002). An Ecosystem model of the West Florida Shelf
for use in fisheries management and ecological research: Volume II. Model
construction. St. Petersburg, Florida.
Opitz, S. (1996). Trophic interactions in Caribbean coral reefs.
Pandolfi, J. M., Connolly, S. R., Marshall, D. J., and Cohen, A. L. (2011). Projecting coral
reef futures under global warming and ocean acidification. Science 333, 418–422.
doi:10.1126/science.1204794
Pauly, D., Christensen, V., Dalsgaard, J., Froese, R., and Torres Jr, F. (1998). Fishing
down marine food webs. Science 279, 860–863. doi:10.1126/science.279.5352.860
Pauly, D., Christensen, V., and Walters, C. (2000). Ecopath, Ecosim, and Ecospace as
tools for evaluating ecosystem impact of fisheries. ICES Journal of Marine Science
57, 697–706. doi:10.1006/jmsc.2000.0726
Peart, R. A. (2004). Amphipoda (Crustacea) collected from the Dampier Archipelago,
Western Australia. Western Australian Museum, Perth, W.A.
Pennings, S. C., Carefoot, T. H., Zimmer, M., Danko, J. P., and Ziegler, A. (2000).
Feeding preferences of supralittoral isopods and amphipods. Canadian Journal of
Zoology 78, 1918–1929. doi:DOI 10.1139/cjz-78-11-1918
Pielou, E. C. (1972). Niche width and niche overlap: a method for measuring them.
Ecology 53, 687–692.
144
Pillans, R. D., Franklin, C. E., and Tibbetts, I. R. (2004). Food choice in Siganus
fuscescens: influence of macrophyte nutrient content and availability. Journal of Fish
Biology 64, 297–309. doi:10.1111/j.0022-1112.2004.00261.x
Pitt, J. M. (1997). The feeding ecology of rabbitfish (Siganidae) at Green Island Reef:
ontogenetic and interspecific differences in diet, morphology and habitat utilisation.
James Cook Universdity.
Plagányi, E. E. (2007). Models for an ecosystem approach to fisheries. FAO Fisheries
Technical Paper No. 477. FAO, Rome. doi:9789251057346
Plagányi, É. E., and Butterworth, D. S. (2004). A critical look at the potential of Ecopath
with ecosim to assist in practical fisheries management. African Journal of Marine
Science 26, 261–287. doi:10.2989/18142320409504061
Poore, A. G. B., Campbell, A. H., Coleman, R. A., Edgar, G. J., Jormalainen, V.,
Reynolds, P. L., Sotka, E. E., Stachowicz, J. J., Taylor, R. B., Vanderklift, M. A., and
Emmett Duffy, J. (2012). Global patterns in the impact of marine herbivores on benthic
primary producers. Ecology Letters 15, 912–922. doi:10.1111/j.1461-
0248.2012.01804.x
Poore, A. G. B., Graham, S. E., Byrne, M., and Dworjanyn, S. A. (2016). Effects of ocean
warming and lowered pH on algal growth and palatability to a grazing gastropod.
Marine Biology 163, 1–11. doi:10.1007/s00227-016-2878-y
Pratchett, M. S., Hoey, A. S., Wilson, S. K., Messmer, V., and Graham, N. A. J. (2011).
Changes in biodiversity and functioning of reef fish assemblages following coral
bleaching and coral loss. Diversity 3, 424–452. doi:10.3390/d3030424
Przeslawski, R., McArthur, M. A., and Anderson, T. J. (2013). Infaunal biodiversity
patterns from Carnarvon Shelf (Ningaloo Reef), Western Australia. Marine and
Freshwater Research 64, 573–583. doi:10.1071/MF12240
Randall, J. E. (1967). ‘Food habits of reef fishes of the West Indies’. (Institute of Marine
Sciences: University of Miami.)
Rasher, D. B., Hoey, A. S., and Hay, M. E. (2013). Consumer diversity interacts with
prey defenses to drive ecosystem function. Ecology 94, 1347–1358. doi:10.1890/12-
0389.1
Roberts, C. R. (1995). Effects of fishing on the ecosystem structure of coral reefs.
Conservation Biology 9, 988–995. doi:10.1002/jcc.540110605
Rousseaux, C. S. G., Lowe, R., Feng, M., Waite, A. M., and Thompson, P. A. (2012).
The role of the Leeuwin Current and mixed layer depth on the autumn phytoplankton
bloom off Ningaloo Reef, Western Australia. Continental Shelf Research 32, 22–35.
doi:10.1016/j.csr.2011.10.010
Ruppert, E. E., Fox, R. S., and Barnes, R. D. (2004). ‘Invertebrate Zoology: A functional
evolutionary approach’ 7th ed. (Thomson Brooks/Cole: California.)
Russ, G. R., and Williams, D. M. (1994). Review of data on fishes of commercial and
recreational fishing interest in the Great Barrier Reef. Report. Great Barrier Reef
Marine Park Authority. Available at: http://hdl.handle.net/11017/235
Rust, M. B. (2002). Nutritional Physiology. In ‘Fish Nutrition’. (Eds J. E. Halver and R.
W. Hardy.) pp. 367–452. (Elsevier Science: U.S.A.)
145
Ryan, K. L., Hall, N. G., Lai, E. K., Smallwood, C. B., Taylor, S. M., and Wise, B. S.
(2015). State-wide survey of boat-based recreational fishing in Western Australia
2013/14. Perth.
Rykiel, E. J. (1996). Testing ecological models: the meaning of validation. Ecological
Modelling 90, 229–244.
Sainsbury, K. J., Kailola, P. J., and Leyland, G. G. (1984). ‘Continental Shelf Fishes of
Northern Australia and North-Western Australia: An illustrated guide’. (Clouston &
Hall: Canberra, Australia.)
Scheffer, M., Carpenter, S., Foley, J. A., Folke, C., and Walker, B. (2001). Catastrophic
shifts in ecosystems. Nature 413, 591–596. doi:10.1038/35098000
Sheaves, M., Baker, R., Nagelkerken, I., and Connolly, R. M. (2015). True value of
estuarine and coastal nurseries for fish: incorporating complexity and dynamics.
Estuaries and Coasts 38, 401–414. doi:10.1007/s12237-014-9846-x
Smallwood, C. B., and Beckley, L. E. (2012). Spatial distribution and zoning compliance
of recreational fishing in Ningaloo Marine Park, north-western Australia. Fisheries
Research 125–126, 40–50. doi:10.1016/j.fishres.2012.01.019
Smallwood, C. B., Beckley, L. E., Moore, S. A., and Kobryn, H. T. (2011). Assessing
patterns of recreational use in large marine parks: a case study from Ningaloo Marine
Park, Australia. Ocean and Coastal Management 54, 330–340.
doi:10.1016/j.ocecoaman.2010.11.007
Sogard, S. M. (1997). Size-selective mortality in the juvenile stage of teleost fishes: a
review. Bulletin of Marine Science 60, 1129–1157. Available at:
http://www.ingentaconnect.com/content/umrsmas/bullmar/1997/00000060/00000003
/art00029
Steneck, R. S., and Dethier, M. N. (1994). A functional group approach to the structure
of algal-dominated communities. Oikos 69, 476–498. doi:10.2307/3545860
Steneck, R. S., and Erlandson, J. M. (2002). Kelp forest ecosystems: biodiversity,
stability, resilience and future. Environmental Conservation 29, 436–459.
Sugden, A., and Pennisi, E. (2006). When to go, where to stop. Science 313, 775.
doi:10.1126/science.313.5788.775
Sumner, N. R., Williamson, P. C., and Malseed, B. E. (2002). A 12-month survey of
recreational fishing in the Gascoyne bioregion of Western Australia during 1998-99.
Perth, Western Australia.
Taylor, J. G., and Pearce, A. F. (1999). Ningaloo Reef currents: implications for coral
spawn dispersal, zooplankton and whale shark abundance. Journal of the Royal Society
of Western Australia 82, 57–65.
Terazono, Y., Nakamura, Y., Imoto, Z., and Hiraoka, M. (2012). Fish response to
expanding tropical Sargassum beds on the temperate coasts of Japan. Marine Ecology
Progress Series 464, 209–220. doi:10.3354/meps09873
Thébaud, O., Little, L. R., and Fulton, E. A. (2014). Evaluation of management strategies
in Ningaloo Marine Park, Western Australia. International Journal of Sustainable
Society 6, 102. doi:10.1504/IJSSOC.2014.057892
Thomsen, M. S., Wernberg, T., Altieri, A., Tuya, F., Gulbransen, D., McGlathery, K. J.,
146
Holmer, M., and Silliman, B. R. (2010). Habitat cascades: the conceptual context and
global relevance of facilitation cascades via habitat formation and modification.
Integrative and Comparative Biology 50, 158–175. doi:10.1093/icb/icq042
Tolentino-Pablico, G., Bailly, N., Froese, R., and Elloran, C. (2008). Seaweeds preferred
by herbivorous fishes. Journal of Applied Phycology 20, 933–938.
doi:10.1007/s10811-007-9290-4
Tourism Western Australia, W. A. G. (2015). Australia’s Coral Coast: overnight visitor
fact sheet. , 9.
Tsehaye, I., and Nagelkerke, L. A. J. (2008). Exploring optimal fishing scenarios for the
multispecies artisanal fisheries of Eritrea using a trophic model. Ecological Modelling
212, 319–333. doi:10.1016/j.ecolmodel.2007.10.044
Unsworth, R. K. F., Hinder, S. L., Bodger, O. G., and Cullen-Unsworth, L. C. (2014).
Food supply depends on seagrass meadows in the coral triangle. Environmental
Research Letters 9, 94005. doi:10.1088/1748-9326/9/9/094005
Van Valen, L. (1965). Morphological variation and width of ecological niche. The
Amercian Naturalist 99, 377–390.
Vandermeer, J. H. (1972). Niche theory. Annual Review of Ecology and Systematics 3,
107–132.
Vergés, A., Bennett, S., and Bellwood, D. R. (2012). Diversity among macroalgae-
consuming fishes on coral reefs: a transcontinental comparison. PloS one 7, e45543.
doi:10.1371/journal.pone.0045543
Vergés, A., Steinberg, P. D., Hay, M. E., Poore, A. G. B., Campbell, A. H., Ballesteros,
E., Heck, K. L., Booth, D. J., Coleman, M. A., Feary, D. A., Figueira, W., Langlois,
T., Marzinelli, E. M., Mizerek, T., Mumby, P. J., Nakamura, Y., Roughan, M., van
Sebille, E., Gupta, A. Sen, Smale, D. A., Tomas, F., Wernberg, T., and Wilson, S. K.
(2014). The tropicalization of temperate marine ecosystems: climate-mediated
changes in herbivory and community phase shifts. Proceedings of The Royal Society
B 281, 1–10. doi:10.1098/rspb.2014.0846
Vergés, A., Vanderklift, M. A., Doropoulos, C., and Hyndes, G. A. (2011). Spatial
patterns in herbivory on a coral reef are influenced by structural complexity but not by
algal traits. PLoS ONE 6, e17115. doi:10.1371/journal.pone.0017115
Vermeij, M. J. A., van der Heijden, R. A., Olthuis, J. G., Marhaver, K. L., Smith, J. E.,
and Visser, P. M. (2013). Survival and dispersal of turf algae and macroalgae
consumed by herbivorous coral reef fishes. Oecologia 171, 417–425.
doi:10.1007/s00442-012-2436-3
Vroom, P. S., and Braun, C. L. (2010). Benthic composition of a healthy subtropical reef:
baseline species-level cover, with an emphasis on algae, in the Northwestern Hawaiian
Islands. PLoS ONE 5, 1–9. doi:10.1371/journal.pone.0009733
Vroom, P. S., Page, K. N., Kenyon, J. C., and Brainard, R. E. (2006). Algae-dominated
reefs. American Scientist 94, 430–437. Available at: http://0-
search.proquest.com.prospero.murdoch.edu.au/docview/215260496/fulltext?accounti
d=12629 [Verified 16 November 2015]
Vuki, V. C., and Price, I. R. (1994). Seasonal changes in the Sargassum populations on a
fringing coral reef, Magnetic Island, Great Barrier Reef region, Australia. Aquatic
147
Botany 48, 153–166. doi:10.1016/0304-3770(94)90082-5
Webster, F. J., Babcock, R. C., Van Keulen, M., and Loneragan, N. R. (2015). Macroalgae
inhibits larval settlement and increases recruit mortality at Ningaloo Reef, Western
Australia. Plos One 10, e0124162. doi:10.1371/journal.pone.0124162
Welsh, J. Q., and Bellwood, D. R. (2014). Herbivorous fishes, ecosystem function and
mobile links on coral reefs. Coral Reefs 33, 303–311. doi:10.1007/s00338-014-1124-
7
Wernberg, T., Bennett, S., Babcock, R. C., de Bettignies, T., Cure, K., Depczynski, M.,
Dufois, F., Fromont, J., Fulton, C. J., Hovey, R. K., Harvey, E. S., Holmes, T. H.,
Kendrick, G. A., Radford, B., Santana-Garcon, J., Saunders, B. J., Smale, D. A.,
Thomsen, M. S., Tuckett, C. A., Tuya, F., Vanderklift, M. A., and Wilson, S. (2016).
Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172.
doi:10.1126/science.aad8745
Wernberg, T., Smale, D. a., Tuya, F., Thomsen, M. S., Langlois, T. J., de Bettignies, T.,
Bennett, S., and Rousseaux, C. S. (2012). An extreme climatic event alters marine
ecosystem structure in a global biodiversity hotspot. Nature Climate Change 3, 78–82.
doi:10.1038/nclimate1627
Wernberg, T., Thomsen, M. S., and Kotta, J. (2013). Complex plant–herbivore–predator
interactions in a brackish water seaweed habitat. Journal of Experimental Marine
Biology and Ecology 449, 51–56. doi:10.1016/j.jembe.2013.08.014
Werner, E. E., and Gilliam, J. F. (1984). The ontogenetic niche and species interactions
in size-structured populations. Ecology 15, 393–425.
Werner, E. E., and Hall, D. J. (1988). Ontogenetic habitat shifts in Bluegill: the foraging
rate-predation risk trade-off. Ecology 69, 1352–1366. doi:10.2307/1941633
Werner, E. E., Mittelbach, G. G., Hall, D. J., and Gilliam, J. F. (1983). Experimental tests
of optimal habitat use in fish: the role of relative habitat profitability. Ecology 64,
1525–1539. doi:10.2307/1937507
White, T. C. R. (2011). When is a herbivore not a herbivore? Oecologia 67, 596–597.
Wilson, G. G. (1998). A description of the early juvenile colour patterns of eleven
Lethrinus species (Pisces: Lethrinidae) from the Great Barrier Reef, Australia. Records
of the Australian Museum 50, 55–83. doi:10.3853/j.0067-1975.50.1998.1274
Wilson, S. K., Babcock, R. C., Fisher, R., Holmes, T. H., Moore, J. A. Y., and Thomson,
D. P. (2012). Relative and combined effects of habitat and fishing on reef fish
communities across a limited fishing gradient at Ningaloo. Marine Environmental
Research 81, 1–11. doi:10.1016/j.marenvres.2012.08.002
Wilson, S. K., Depczynski, M., Fisher, R., Holmes, T. H., O’Leary, R. A., and Tinkler,
P. (2010). Habitat associations of juvenile fish at Ningaloo Reef, Western Australia:
the importance of coral and algae. PLoS ONE 5, e15185.
doi:10.1371/journal.pone.0015185
Wilson, S. K., Fulton, C. J., Depczynski, M., Holmes, T. H., Noble, M. M., Radford, B.,
and Tinkler, P. (2014). Seasonal changes in habitat structure underpin shifts in
macroalgae-associated tropical fish communities. Marine Biology 161, 2597–2607.
doi:10.1007/s00227-014-2531-6
Wismer, S., Hoey, A. S., and Bellwood, D. R. (2009). Cross-shelf benthic community
148
structure on the Great Barrier Reef: relationships between macroalgal cover and
herbivore biomass. Marine Ecology Progress Series 376, 45–54.
doi:10.3354/meps07790
Wu, R. S. S. (1984). The feeding habits of seven demersal fish species in a subtropical
estuary. Asian Marine Biology 1, 17–26.
Yamada, H., Nanami, A., Ohta, I., Fukuoka, K., Sato, T., Kobayashi, M., Hirai, N.,
Chimura, M., Akita, Y., and Kawabata, Y. (2012). Occurrence and distribution during
the post-settlement stage of two Choerodon species in shallow waters around Ishigaki
Island, southern Japan. Fisheries Science 78, 809–818. doi:10.1007/s12562-012-0506-
4
Yamaguchi, A., Furumitsu, K., Yagishita, N., and Kume, G. (2010). Biology of
herbivorous fish in the coastal areas of Western Japan. In ‘Coastal Environmental and
Ecosystem Issues of the East China Sea’. (Eds A. Ishimatsu and H.-J. Lie.) pp. 181–
190. (TERRAPUB and Nagasaki University.) Available at: http://naosite.lb.nagasaki-
u.ac.jp/dspace/handle/10069/23510
Vander Zanden, M. J., and Rasmussen, J. B. (2001). Variation in d 15 N and d 13 C
trophic fractionation: Implications for aquatic food web studies. Limnology and
Oceanography 46, 2061–2066.
Zetina-Rejón, M. J., Arreguín-Sánchez, F., and Chávez, E. A. (2003). Trophic structure
and flows of energy in the Huizache-Caimanero lagoon complex on the Pacific coast
of Mexico. Estuarine, Coastal and Shelf Science 57, 803–815. doi:10.1016/S0272-
7714(02)00410-9
Zieman, J. C., Macko, S. A., and Mills, A. L. (1984). Role of seagrass and mangroves in
estuarine food webs: temporal and spatial changes in stable isotope composition and
amino acid content during decomposition. Bulletin of Marine Science 35, 380–392.
149
APPENDIX
Appendix 1 Biotic and abiotic benthic habitat types and their codes, derived from a hyperspectral
benthic survey of the Ningaloo Marine Park in 2006 (Kobryn et al. 2013), as used in Figure 4..
Benthic habitat Code
Hard coral HC
Soft coral (e.g. Sinularia spp.) SC
Branching coral CB
“Blue tip” branching coral (Acropora cervicornis) CBT
Digitate coral CD
Encrusting coral CE
Submassive coral CS
Tabulate coral CT
Massive coral CM
Foliaceous coral CF
Macroalgae (consisting largely of Sargassum myriocystium) MA
Turfing algae TA
Turfing algae or macroalgae-covered intact dead coral or
rubble
TA- or MA-covered IDC or R
Limestone pavement LP
Sand S
Rubble R