Ecology and bioenergetics of the gudgeon (Hypseleotris spp.) in Maroon Dam: a zooplanktivorous fish in a whole-lake

biomanipulation

Shaun Meredith B.Sc. (Hons). M.Sc

School of Natural Resource Sciences Queensland University of Technology

Submitted in fulfillment of the requirements for the degree of Doctor of Philosophy

August 2005

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“…some to the yard-arm, shiver my timbers, and some by the board, and all to feed the fishes”

Long John Silver (17__), as cited in Stevenson (1882)

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Abstract: Gudgeon (Hypseleotris spp.) are the most widespread and abundant native

Australian freshwater fish and the dominant zooplanktivore in Maroon Dam, the site of

Australia’s first whole-lake biomanipulation experiment. The spatial (littoral and pelagic) and

temporal (diurnal and seasonal) distribution and diet of Hypseleotris was examined following

the addition of 100,000 piscivorous Australian Bass (Macquaria novemaculeata) to Maroon

Dam in the summer of 1998/99. A strong spatial and temporal ontogeny was observed, with

smaller (<16 mm SL) Hypseleotris dominating the pelagic, an intermediate (12-20 mm SL)

size class diurnally migrating between littoral and pelagic, and larger fish (>20 mm SL)

remaining in the littoral throughout the day and night. Spatial ontogeny affected diet also,

with fish consuming a decreasing proportion of zooplankton and an increasing proportion of

macro-invertebrates as fish length increased and habitat use changed. A bioenergetics model

was constructed to examine these distribution and diet patterns. Laboratory derived

consumption and respiration parameters were combined with caloric densities and commonly

accepted excretion and activity scalars to produce modeled growth estimates that were

validated against Hypseleotris age-at-growth data collected from a diversity of habitats.

Using this model, it was concluded that the spatial and temporal ontogeny and diet of

Hypseleotris in Maroon Dam described the most energetically advantageous life history.

Unlike many zooplanktivores in northern hemisphere lakes, Hypseleotris did not appear to

engage in migratory predator avoidance behaviour. This is discussed in a context of

Australia’s paucity of pelagic piscivores. It is concluded that top-down biomanipulation by

stocking of native piscivores has only a limited application in Australia, and that other

biomanipulation techniques may prove more successful.

Key words: Hypseleotris; ontogeny; bioenergetics; biomanipulation

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List of Manuscripts

Manuscript 1: Meredith, S.N., Matveev, V.F. and Mayes, P. (2003). Spatial and temporal variability in

the distribution and diet of the gudgeon (Eleotridae : Hypseleotris spp.) in a sub-tropical Australian Reservoir. Marine and Freshwater Research. 54, 1009-1017.

Manuscript 2: Meredith, S.N., Johnson, T.B., Sharpe, C. and Matveev, V.F. (in prep). A bioenergetic

model for gudgeon (Eleotridae : Hypseleotris spp.). (submitted to Transactions of the American Fisheries Society).

Manuscript 3: Meredith, S.N., Johnson, T.B., Matveev, V.F. and Jones, G.J. (in prep). Spatial and

temporal ontogeny of gudgeon (Eleotridae : Hypseleotris spp.) in a sub-tropical lake: a bioenergetic analysis

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STATEMENT OF ORIGINAL AUTHORSHIP ....................................................................................... 7 ACKNOWLEDGEMENTS ......................................................................................................................... 8 INTRODUCTION ........................................................................................................................................ 9 LITERATURE REVIEW ...........................................................................................................................12

THE MANAGEMENT OF NUISANCE ALGAE.................................................................................................12 BIOMANIPULATION IN AUSTRALIA ............................................................................................................16 THE GUDGEONS (HYPSELEOTRIS SPP.)........................................................................................................18 ZOOPLANKTIVORY BY FISH .......................................................................................................................19 PREDATION BY ZOOPLANKTIVOROUS FISH................................................................................................20

Feeding motivation ..............................................................................................................................21 Prey location........................................................................................................................................22 Pursuit..................................................................................................................................................24 Capture ................................................................................................................................................27 Retention ..............................................................................................................................................28 Digestion..............................................................................................................................................29

MEASURING CONSUMPTION BY FISH.........................................................................................................29 Models that use stomach content .........................................................................................................30 Popular Gut Content Based Consumption Models ..............................................................................30

The Bajkov (1935) model ............................................................................................................................... 31 The Elliot and Persson (1978) Models ............................................................................................................ 32 The Eggers (1979) Model ............................................................................................................................... 33

Using the models..................................................................................................................................35 Model Selection....................................................................................................................................36

BIOENERGETICS MODELS ..........................................................................................................................37 Limitations ...........................................................................................................................................40

REFERENCES..............................................................................................................................................44 MANUSCRIPT 1: SPATIAL AND TEMPORAL VARIABILITY IN THE DISTRIBUTION AND DIET OF THE GUDGEON (ELEOTRIDAE : HYPSELEOTRIS SPP.) IN A SUB-TROPICAL AUSTRALIAN RESERVOIR. ...................................................................................................................65

ABSTRACT.................................................................................................................................................67 INTRODUCTION..........................................................................................................................................68 MATERIALS AND METHODS.......................................................................................................................70

Study Site..............................................................................................................................................70 Sampling Regime .................................................................................................................................71 Hypseleotris Taxonomy........................................................................................................................73 Dietary Analysis...................................................................................................................................73 Statistical Analysis ...............................................................................................................................74

RESULTS....................................................................................................................................................75 Spatial distribution...............................................................................................................................75

Near-shore littoral vs Pelagic .......................................................................................................................... 75 Hypseleotris diet ............................................................................................................................................. 75

Temporal Distribution .........................................................................................................................77 Diurnal – Near-shore littoral zone................................................................................................................... 77 Diurnal – Pelagic zone .................................................................................................................................... 77 Seasonal– Near-shore littoral zone catch rate.................................................................................................. 78 Seasonal – Pelagic zone catch rate ................................................................................................................. 78

DISCUSSION...............................................................................................................................................78 Spatial and Temporal Distribution ......................................................................................................78 Pelagic .................................................................................................................................................79 Near-shore littoral ...............................................................................................................................82 Diet ......................................................................................................................................................83 Conclusion ...........................................................................................................................................85

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ACKNOWLEDGEMENTS ..............................................................................................................................86 REFERENCES: ............................................................................................................................................87 LIST OF TABLES.........................................................................................................................................92 LIST OF FIGURES........................................................................................................................................94

MANUSCRIPT 2: A BIOENERGETIC MODEL FOR GUDGEON (ELEOTRIDAE : HYPSELEOTRIS SPP.). ............................................................................................................................102

ABSTRACT...............................................................................................................................................104 INTRODUCTION........................................................................................................................................105 METHODS ................................................................................................................................................106

Taxonomy...........................................................................................................................................106 Respiration.........................................................................................................................................107 Consumption ......................................................................................................................................108 Energy Density...................................................................................................................................110 Other Parameters (SDA, Egestion, Excretion) ..................................................................................111 Model Validation ...............................................................................................................................112

RESULTS..................................................................................................................................................113 Respiration.........................................................................................................................................113 Consumption ......................................................................................................................................114 Energy Density...................................................................................................................................114 Model Validation ...............................................................................................................................114 Sensitivity Analysis.............................................................................................................................115

DISCUSSION.............................................................................................................................................115 ACKNOWLEDGEMENTS ............................................................................................................................118 REFERENCES............................................................................................................................................119 LIST OF TABLES.......................................................................................................................................123 LIST OF FIGURES......................................................................................................................................127

MANUSCRIPT 3: SPATIAL AND TEMPORAL ONTOGENY OF GUDGEON (ELEOTRIDAE: HYPSELEOTRIS SPP.) IN A SUB-TROPICAL LAKE: A BIOENERGETIC ANALYSIS ..............132

ABSTRACT...............................................................................................................................................134 INTRODUCTION........................................................................................................................................135 METHODS ................................................................................................................................................137

Study Site............................................................................................................................................137 Bioenergetic Modelling......................................................................................................................137

RESULTS..................................................................................................................................................139 DISCUSSION.............................................................................................................................................140 ACKNOWLEDGEMENTS ............................................................................................................................144 REFERENCES............................................................................................................................................146 LIST OF TABLES.......................................................................................................................................149 LIST OF FIGURES......................................................................................................................................151

GENERAL DISCUSSION........................................................................................................................158 REFERENCES............................................................................................................................................166

APPENDIX 1 .............................................................................................................................................169

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Statement of Original Authorship

This work contains no material which has been accepted for the award of any other degree or diploma in any university or other tertiary institution and, to the best of my knowledge and belief, contains no material previously published or written by another

person, except where due reference has been made in the text.

Signed: Date:

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Acknowledgements Many thanks to my helpful and encouraging CSIRO supervisor, Vlad Matveev for his trust,

comments and prodding. Great thanks to my other supervisor, Ian Williamson, for his timely

support and help which were important and much appreciated. I also owe much to my

unofficial supervisors – Tim Johnson (whose generosity of time and advice made the

modelling component possible), and Gary Jones (who encouraged me through the dark

times). And thanks, as always, to my lovely parents, who have always let me follow my own

path – pitfalls and all.

To the abundance of field workers, and in particular Andrew Palmer, Juanita Renwick, Paul

Mayes, David Elmouttie, Geoff DeZylva and Grant Hamilton, for their time and effort on

cold and wet nights, I thank you very much. For sometimes important and sometimes not so

important conversations over a quiet beverage, I thank you all again. Especially to Grant,

who excelled in both conversation and beverage.

To Lilian Matveeva and Cheryl Orr, thankyou for help in the laboratory. To Clayton Sharpe

for his efforts in and around Mildura, and to Oliver Scholz, Bernard McCarthy and Paul

Humphries who have provided valuable comment on the papers and thesis, thankyou. To Ben

Gawne and Gary Jones, who suggested a trip to Canada to finish off the modelling and have

provided support in the final writing up of this work, a very big thankyou to you both. To

Brendan Ebner for being the single most important source of enthusiasm and knowledge

during my foray into the field of fish ecology, particularly in the early years - thanks mate,

you are the source of my interest in this stuff. To Clare Mason for helping with this last nasty

bit – thanks for the love, support and dumplings. And finally to my beautiful puppy Kahlua,

who didn’t live to see the end of this - I miss you girl. xx

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Introduction

Since the introduction of the biomanipulation concept by Shapiro et al. (1975) and the

replicated system-scale trophic cascade work by Carpenter et al. (1987), whole-lake

biomanipulation has become an accepted method for the biological control of nuisance

algae in the Northern Hemisphere. Early attempts at biomanipulation focused on top-

down piscivore stocking, and these met with variable success (Drenner and Hambright

1999). The expansion of the concept to include the removal of zooplanktivorous and

benthivorous fish (e.g. Van Berkum et al. 1995; Meijer et al. 1990, 1994, 1999) and the

harvesting of aquatic macrophytes (e.g. Galanti et al. 1990; Giussani et al. 1990),

combined with a shift in focus to define the trophic conditions under which

biomanipulation is likely to succeed (Carpenter et al. 2001), has significantly improved

the success of biomanipulation in freshwater systems (Drenner and Hambright 1999).

As a result of this success, the first Australian biomanipulation was undertaken in the

summer of 1998/1999 in Maroon Dam, an irrigation supply and recreation facility in

south-east Queensland. Approximately 100,000 Australian Bass (Macquaria

novemaculeata) fingerlings were stocked into Maroon Dam in an attempt to reduce the

abundance of Hypseleotris, the dominant zooplanktivorous fish. Through a simple trophic

cascade, it was proposed that the subsequent release of predation pressure on zooplankton

would lead to increased algal consumption by zooplankton, and therefore a reduction in

the algal biomass.

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This thesis represents one of four independent studies undertaken to examine and assess

the response of different trophic levels to the Maroon Dam whole-lake biomanipulation

experiment. Here the focus is on the role of the dominant zooplanktivore (Hypseleotris

spp.) within the biomanipulated food web. The primary objectives of this work were to

examine the ecology of Hypseleotris in Maroon Dam, and to model spatial and temporal

ontogeny and diet such that it can be discussed in a context relevant to the

biomanipulation experiment. Within this, five specific aims were developed:

1. To define the spatial and temporal distribution of Hypseleotris in Maroon Dam

over an annual cycle

2. To examine the diets of different life stages of Hypseleotris and relate this to

spatial and temporal distribution

3. To construct a broadly applicable bioenergetic model for Hypseleotris

4. To use this bioenergetic model to energetically interpret the observed spatial and

temporal distribution and diet of Hypseleotris in Maroon Dam

5. To discuss, using the Maroon Dam biomanipulation as an example, the potential

for nuisance algal control using top-down biomanipulation in Australian lakes and

dams.

This thesis incorporates three manuscripts and a general discussion, the combination of

which addresses each of these aims to provide an examination of the role of the

zooplanktivorous Hypseleotris in the biomanipulated Maroon Dam, and to discuss this in

a broader Australian context.

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The literature review describes the evolution of biomanipulation from a broader

background of nuisance algal control techniques, and explains in detail the experimental

biomanipulation of Maroon Dam. Significant knowledge gaps in our understanding of the

ecology and systematics of Hypseleotris are then described. Zooplanktivory by fish and

the behavioural components of predation are outlined and methods for measuring

consumption by fish (including bioenergetic models) are discussed.

Manuscript 1 addresses the first and second aims of this thesis, examining the ontogenetic

and diurnal movement and feeding ecology of Hypseleotris in Maroon Dam using field

data collected over a 13 month period. The third aim of the thesis is addressed in

manuscript 2, where laboratory experiments and field data are combined with literature

defined parameters to construct and validate a bioenergetic model for Hypseleotris, the

first for an Australian freshwater fish. To achieve the fourth aim, manuscript 3 applies the

model constructed in Manuscript 2 to describe the bioenergetic implications of the

distribution and feeding ecology observed in Manuscript 1. In this final manuscript, the

interaction of fish behaviour, food consumption, lake morphology and water temperature

is incorporated into a discussion of Hypseleotris energetic advantage and predator

avoidance. The fifth aim of this thesis is addressed in the general discussion. Here, results

from the biomanipulation of Maroon Dam are presented and comparisons between

Australia’s relatively depauperate freshwater fish fauna and that of the Northern

Hemisphere, particularly in relation to the presence of pelagic piscivores, are discussed

with reference to the potential for future success of biomanipulation in Australia.

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Literature Review

The Management of Nuisance Algae

In Australia, as overseas, early management practices for controlling nuisance algae and

cyanobacterial blooms focussed on nutrient limitation. At the catchment scale, the

reduction of topsoil run-off (Boulton and Ward 1993; Murray et al. 1993), point source

nutrients from industry and sewerage plants (Cullen and Forsberg 1988; Kairesalo et al.

1999), and the use of high nutrient products such as fertilisers (Wendt and Corey 1980;

Behrendt and Boekhold 1993; Cullen 1995) and laundry detergents (Booman and Sedlak

1986; Cullen et al. 1995) have all been targeted. Strategies implemented to achieve these

objectives include the improvement of nutrient removal techniques at water treatment

plants (Carrondo et al. 1980, 1981), the development of non-phosphate based alternatives

in commercial laundry detergents (Coffey and Gudowicz 1990), legislation banning

phosphate based detergents (Maki et al. 1984; Lee and Jones-Lee 1986, 1995), the use of

wetlands as nutrient sinks (Howard-Williams 1985; Greenway and Simpson 1996;

Cerezo et al. 2001), and large-scale public education programs (Albury City Council

1995).

In addition to external factors, internal nutrient loading has also been broadly recognised

as pivotal to nutrient reduction strategies. The flux of nutrients between the sediment and

the water column has received particular attention (Bostrom et al. 1982; Cooke and

White 1987; Triska et al. 1989; Faafeng and Roseth 1993; Lijklema 1993; Moss et al.

1996), as has fish excretion (Brabrand et al. 1990; Schaus et al. 1997), bioturbation

(Meier et al. 1990; Meredith 1996; Tatrai et al. 1997; Lougheed et al. 1998), and the role

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of macrophytes (Dieter 1990; Galanti et al. 1990; Stansfield et al. 1997; Perrow et al.

1999).

The success of nutrient limitation in controlling nuisance algae has, however, been

equivocal. In some cases, nutrient levels were significantly reduced but algal abundance

remained high due to ecosystem resilience (a function of the turnover rate of the limiting

nutrient and high food web complexity), failure to sufficiently control internal nutrient

loading (e.g. transport of nutrients from the littoral to the pelagic via fish excretion) and

insensitivity of algal standing crop to phosphorus fluctuations (Carpenter et al. 1987,

1992; DeMelo et al. 1992). In many more cases, nutrient concentrations could not be

reduced to levels required to limit algal growth (Marsden 1989; Meijer et al. 1989; Van

Donk et al. 1989; Perrow et al. 1999).

Either separate or in conjunction with nutrient limitation practices, biomanipulation

offers a different approach to the management of nuisance algae. Shapiro et al. (1975)

coined the term ‘biomanipulation’ to describe the alteration of any biological component

of a lake to achieve a more desirable water quality. In its practical application, however,

biomanipulation has come to define nuisance algal management through manipulation of

the fish stock (Drenner and Hambright 1999). This commonly means increasing the ratio

of piscivorous to zooplanktivorous fish such that predation pressure on large zooplankters

(in particular Daphnia sp.) is reduced, and, through an increase in zooplankton biomass,

the consumption of algae and cyanobacteria increased (Fig. 1).

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Figure 1: Simple top-down food web that forms the basis for the biomanipulation of

Maroon Dam (from Matveev and Matveeva, unpubl.)

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The manipulation of fish stocks is achieved in a variety of ways. In the simplest cases,

piscivorous fish are added to the lake in large numbers (e.g. Baca and Drenner 1995;

Sondergaard et al. 1997; Dorner et al. 1999) or benthivorous and zooplanktivorous fish

are removed (e.g. Galanti et al.1990; Moss et al. 1996). More complex biomanipulations

incorporate both strategies (e.g. Meijer et al. 1994, 1999). In some cases, the total fish

stock is simply eliminated such that zooplankton will be released from predation and

exert a greater grazing pressure on nuisance algae (e.g. Stenson et al. 1978). Fish

elimination is also often accompanied by a restocking of a more desirable fish fauna (e.g.

Van Donk et al. 1989; Reinertsen et al. 1990; Goldyn et al. 1997).

Each of these biomanipulation techniques has, as with nutrient management strategies,

met with variable success. In a recent review of 41 whole lake biomanipulations, Drenner

and Hambright (1999) defined a successful study as one which lowered phytoplankton

biomass and/or increased water transparency several years after biomanipulation. They

found that the removal of benthivorous or zooplanktivorous fish alone to be the most

successful biomanipulation technique (9 out of 10 studies successful), followed by fish

elimination and restocking (6 out of 9 studies successful), piscivore stocking and removal

of benthivorous and zooplanktivorous fish (6 out of 10 studies successful), elimination of

fish alone (2 out of 5 studies successful), and piscivore stocking alone (2 out of 7 studies

successful). A diversity of lake-specific management practices that accompanied

biomanipulation experiments (e.g. macrophyte harvesting, sediment removal) could not

be suitably generalised for the purposes of comparison due to lack of replication. A

conclusion regarding an optimum biomanipulation technique, if indeed such a thing

16

exists, remains elusive. Despite this, it is broadly accepted that the concurrent application

of fish stock manipulation, nutrient management strategies (both internal and external

loading) and the stabilisation of the more desirable food webs through the establishment

of littoral macrophytes represents the approach most likely to achieve long-term

reductions in nuisance algae and increases in water clarity (Kairesalo et al. 1999;

Peltonen et al. 1999; Perrow et al. 1999; Zalewski 1999; Mehner et al. 2001).

Biomanipulation in Australia

Despite successful international case studies, the potential for biomanipulation control of

cyanobacteria in Australia has been contested. Although several authors suggest that

biomanipulation offers significant scope for the reduction of nuisance algal blooms in

Australian water storages (e.g. Matveev et al. 1994; Matveev and Matveeva 1997;

Matveev 1998; Jones and Poplawski 1998; Kobayashi and Church 2003), Boon et al.

(1994) disagree, suggesting that Australian zooplankton lack the physical and

behavioural requirements necessary to exert a predatory effect on phytoplankton

concentration.

A basic tenet of top-down food web biomanipulation requires that an increase in

zooplankton abundance will lead to an increase in the consumption (and therefore a

decrease in the biomass) of nuisance algae (Carpenter et al. 1987). Variation in

phytoplankton consumption by zooplankton has been explained by variability in

zooplankton species composition, body size and biomass (Dawidowicz 1990; Kobayashi

and Church, 2003; Hunt and Matveev 2005), cyanobacterial palatability (Chow-Fraser

17

and Sprules 1986) and the effect of toxins on cladoceran survivorship (Gilbert 1990;

Matveev et al. 1994). Using both experimental and observational analysis of these

factors, Boon et al. (1994) concluded that Australian zooplankton were unlikely to exert

sufficient consumptive pressure on cyanobacteria to facilitate top-down biomanipulation.

This was later refuted by Matveev and Matveeva (1997) and Matveev (1998), who found

negative correlations between zooplankton body size and phytoplankton biovolume in a

diversity of Australian reservoirs, and then used these results to justify the establishment

of Australia’s first top-down whole lake biomanipulation experiment.

This project examines the role of a zooplanktivorous fish (Hypseleotris spp.) in this

whole-lake biomanipulation. This is a co-operative project between Queensland

University of Technology, CSIRO Land and Water, and the Queensland Department of

Natural Resources. The work presented in this thesis complements that of Dr Paul Mayes

(Queensland University of Technology) who examined the piscivorous Australian Bass

(Macquaria novemaculeata), Richard Hunt (CSIRO Land and Water), a PhD student who

examined the interactive effects of fish, zooplankton and nutrients on algal growth, and

Dr Vladimir Matveev, Andrew Palmer and Lilian Matveeva (CSIRO Land and Water)

who performed all of the whole-lake routine sampling, analysis and interpretation.

Piscivore stocking, the simplest biomanipulation technique, was chosen for this first

attempt at biomanipulation in Australia. Two dams approximately 100km south-east of

Brisbane, Queensland, were selected because of their similar water quality, bathymetry

and biota. Moogerah Dam was chosen as the ‘reference’ lake, while approximately

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100,000 Australian Bass fingerlings were added to Maroon Dam in the summer of

1998/99 in an attempt to reduce the abundance of the gudgeons (Hypseleotris spp.), the

dominant zooplanktivores in the lake. Routine fortnightly water quality monitoring has

been undertaken since 1996, with weekly samples taken during the warmer months. The

effects of this biomanipulation on phytoplankton concentrations are presented in the

discussion.

The Gudgeons (Hypseleotris spp.)

The gudgeons (Eleotridae: Hypseleotris spp.) are the most widespread genus within

Australia (Hoese and Allen 1983; Herbert and Peeters 1995; Larson and Hoese 1996;

Unmack 1997, 2001; Allen et al. 2002). Despite this, little is known of their biology with

several species remaining undescribed (Allen et al. 2002; Unmack 2000). Within

southeastern Australia, Hypseleotris often dominate fish communities in terms of

abundance (e.g., Gehrke et al. 1995) with between two and four species commonly

occurring sympatrically (Unmack 2000). Considerable taxonomic uncertainty has

surrounded the group due to a lack of easy morphological characters for discriminating

each species (Larson and Hoese 1996), which is further complicated by the recently

discovered common occurrence of hybrids (Bertozzi et al. 2000).

Despite their abundance and likely importance in aquatic ecosystems, the ecology and

behaviour of Hypseleotris have received only limited attention. Many multi-species

studies provide generic catch and distribution data (e.g. Lloyd and Walker 1986; Gehrke

et al. 1995; Harris 1995; Harris and Silveira 1999; Humphries et al. 1999, 2002; Lieschke

19

and Closs 1999; Norris and Thoms 1999; Syarifuddin 2001), however few provide more

detailed analysis. Recently, prey selectivity (Hall 1998) and predation effects on macro-

invertebrate (Nielsen et al. 1999), and micro-invertebrate (Nielsen et al. 2000a,b)

populations have shown Hypseleotris to be a facultative zooplanktivore. Habitat use by

Hypseleotris has been examined on only three occasions and in all cases the focus has

been on fish in or adjacent to the littoral zone (Stoffels 1998; Balcombe and Closs 2000;

Stoffels and Humphries 2003). There are no studies examining Hypseleotris ecology in

the pelagic zone.

Zooplanktivory by Fish

The zooplankton provide an easily digestible prey of an appropriately small size,

abundance and geographic distribution for small fish. As such, almost all fishes rely on

zooplankton as their primary food source during some stage of their life cycle (Lazarro

1987; Bone et al. 1995). Although many fish species switch to more energetically

advantageous prey such as macroinvertebrates and other fish, zooplankton provide such

an expedient food source that many fish remain zooplanktivorous throughout their life

(Werner and Hall 1988; Bone et al. 1995).

Planktivorous fishes are broadly classified as obligate or facultative according to their

diet. As the name implies, obligate planktivores feed exclusively on phytoplankton and

zooplankton whereas facultative planktivores utilise other dietary items such as insects,

plants and epiphytic algae (Lazarro 1987; Bone et al. 1995). Both feeding modes are

further categorised according to their mode of foraging. Particulate feeders (often called

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visual planktivores) rely on visual stimuli to identify and attack suitable planktonic food

items. In contrast, filter feeders do not target individual prey, instead they ingest a

volume of water and retain suitable prey using gill rakers or branchial teeth (Vinyard

1980; Lazzaro 1987). Filter feeders can further be divided into pump feeders, which

expand and contract the muscles around the mouth to ‘pump’ water into their buccal

cavity, and tow-net feeders, which force water into their buccal cavity by swimming with

an open mouth (Lazzaro 1987).

These classifications describe well most freshwater fish species, yet they are neither

conclusive nor exclusive. Alewife (Alosa pseudoharengus) and cisco (Coregonus

artedii) have size selective ‘gulping’ feeding behaviours (Janssen 1978) which classifies

them as visually oriented pump feeders (Lazzaro 1987), while gizzard shad (Dorosoma

cepedianum) combine pump and tow-net feeding behaviours to catch their prey (Drenner

1977, as cited in Lazzaro 1987). Further, many fish (in particular, the clupeids) switch

between feeding behaviours depending on age, light conditions and prey composition,

size and density (Bone et al. 1995).

Predation by Zooplanktivorous Fish

In his examination of the European sawfly, Holling (1959) developed a model describing

predation as a series of discrete behavioural components. This model provides the basis

for the majority of zooplanktivorous fish predation models (Hart 1993). Although many

variants of Holling’s (1959) basic model exist, a general model applicable to a wide

range of fish species is presented in Figure 2. Components of this model are independent,

21

and can thus be modelled separately and recombined to describe predation rates (Wright

and O’Brien 1984; Lazzaro 1987; Hart 1993).

Motivation

Prey Location

Pursuit

Capture

Retention

Digestion

Figure 2: A generalised model of zooplanktivory by fish (based on Holling 1959)

Feeding motivation

The motivation to feed in fish is driven by two physiological components – a gastric

factor which is based on gut fullness, and a systemic factor which reflects metabolic

balance (Colgan 1993). Some authors use gut fullness alone to model feeding motivation

(e.g. Hart and Gill 1992) due to its relative ease of estimation, and (perhaps) an

anthropomorphic understanding of ‘hunger’. Despite its simplicity, this method does have

22

merit as relationships between gut fullness and both the quantity of food consumed

(Colgan 1993) and prey size selection (Colgan 1993; Wanzenbock 1995) have been

demonstrated. However, the importance of a systemic factor to feeding motivation has

also been demonstrated, with water temperature, and hence metabolism, affecting both

the total food intake and the individual meal size taken by fish (Cuenco et al. 1985;

Koskela et al. 1997).

Prey location

The probability of a zooplanktivorous fish locating a prey item is a function of the

physical and behavioural characteristics of the predator (visual acuity, searching

behaviour), prey (size, pigmentation, predator avoidance behaviour), and the

environmental conditions (structural complexity, stratification). In quantitative models,

prey location is often approximated by the ‘detection rate’, and is mathematically

represented as a function of the concentration of the prey item, and the volume of water

searched by a predator per unit time (e.g. Wright and O’Brien 1984). The volume of

water searched is defined by a predator’s reactive distance (the maximum distance from a

prey item the fish will attempt to strike - a function of vision, behavioural and physical

characteristics) and the area searched by fish for prey (Bone et al. 1995). The limiting

factor in such ‘detection rate’ models is most often related to vision.

Terrestrial vertebrates use the refractive power of the cornea to gather the light required

to project an image onto the retina. Due to the higher refractive index of water, the cornea

of a fish eye is less effective, thus fish have evolved a large spherical lens to capture as

23

much light as possible (Wootton 1998; Wolken 1995). This, coupled with a unique

increasing refractive gradient within the lens (cf. the photographic ‘fish-eye lens’)

provides some fish with enough light to discern objects up to 40m away (Wootton 1998;

Wolken 1995).

Facultative zooplanktivores rely solely on vision to locate prey. The components of

vision most important are the contrast perception threshold, spectral sensitivities (for

colour discrimination) and visual acuity (Wootton 1998). Both the acuity and the stimuli

used to identify prey vary ontogenetically (Wu and Culver 1992) and between species.

Some fish (e.g. Rainbow trout, Salmo gairdneri – Ware 1973 as cited in Lazzaro 1987)

have specialised motion detecting retinal cells, and thus feed only on moving prey.

Motion detecting cells in the goldfish (Carassius auratus) are particularly well

developed, enabling goldfish to identify different prey species based on their movement

patterns and adjust attack strategy accordingly (Ingle 1968, as cited in Lazzaro 1987).

Alternately, many species (e.g. bluegill, Lepomis macrochirus) rely more heavily on the

use of contrast receptors to discern prey items from the background (Janssen 1982).

Lazzaro (1987) suggests that regardless of the use of motion detecting cells, contrast

receptors are the most important determinants of prey location as they limit the maximum

visibility range of a predator. As such, zooplanktivorous fish diet is usually modelled

with consideration given only to the contrast of zooplankton prey against the background

(e.g. O’Brien et al. 1976; Vinyard 1980). The probability of locating a small,

morphologically cryptic zooplankter (e.g. Bosmina sp) is therefore lower than that of a

large, pigmented prey (e.g. Daphnia carinata).

24

Outside of the physiological capabilities of the fish, the light intensity and turbidity of the

water in which zooplanktivorous fish forage also have great bearing on the visibility of

prey (Diehl 1988; Amundsen et al. 1999; Vogel and Beauchamp 1999). Further, the

ability to locate prey is also affected by the ability of zooplankton to seek refuge. In

shallow systems, refugia are commonly provided by structurally complex macrophytes

(Tonn and Paszkowski 1987; Copp and Jurajda 1993; Pierce and Hinrichs 1997;

Stansfield et al. 1997; Weaver et al. 1997). In deeper lakes, the reduced light penetration

and greater tolerance of zooplankton to reduced oxygen concentrations often excludes

fish from the deep pelagic (Shapiro 1990; Dini and Carpenter 1991; Pijanowska et al.

1993).

Such a predation refuge is, however, often not an ideal feeding environment for

zooplankton, thus many species undertake diurnal migrations to balance the need to feed

with predator avoidance (Dini and Carpenter 1991; Pijanowska et al. 1993). Diurnal

migrations often only occur in the presence of piscivorous fish and have been linked to

chemical stimuli (kairomones) exuded by piscivores (Petranka et al. 1987; Dodson and

Wagner 1996; Jacobsen et al. 1997; Pijanowska and Kowalczewski 1997).

Pursuit

The ‘pursuit’ component of the foraging model requires that, following ‘location’, a

decision is made by the fish as to whether or not to pursue a potential prey item. This

25

decision is affected by the physical characteristics of the predator and prey, and the

availability of prey in the environment.

As zooplanktivorous fish consume their prey whole, the size of a potential prey item in

relation to the size of the predator’s mouth is the primary consideration in whether or not

to pursue. The ontogenetic nature of this relationship means that (generally) the older and

bigger the predator, the larger prey it consumes (Boisclair and Leggett 1985; Hart and

Hamrin 1988; Hambright et al. 1991; Hislop et al. 1991; Wu and Culver 1992). This is

not only due to the physical constraints of fitting prey into the mouth, but also because a

greater buccal volume produces greater suction, decreasing the chance of prey escape

(Schmidt and O’Brien 1982).

Once it has been determined that the located prey item can be ingested, the decision as to

whether or not to attack depends primarily on the predators perceived availability of prey.

When overall prey availability is low, zooplanktivorous fish have been shown to feed in a

non-selective manner, attacking edible prey as they are encountered, whilst at higher prey

abundances, feeding becomes more selective (Ivlev 1961, as cited in Lazzaro 1987;

Gliwicz 1994).

The major determinant of such selectivity is prey size. Brooks and Dodson (1965) found,

during a survey of ten lakes in New England, USA, that lakes containing

zooplanktivorous fish had smaller sized zooplankters (on average) than those without.

They argued that since larger prey generally represent greater individual packages of

26

nutrient, fish preferentially selected large zooplankters, eventually causing a change in

zooplankton community size structure. This idea is now commonly referred to as the

‘size selective hypothesis’, and is supported by many authors (e.g. Post and McQueen

1987; Vanni and Findlay 1990; Pont et al. 1991; Ramcharan et al. 1995; Mookerji et al.

1998) yet refuted by others (e.g. Vinyard 1980; Pastorok 1981; Elhigzi et al. 1995;

Wanzenbock 1995; Link 1996; Mayer and Wahl 1997; Garcia-Berthou 1999).

Much of this debate is based around the notion of ‘apparent size’ of zooplankton, and the

trade off between energy gain and expenditure for different zooplankton species.

Apparent size is defined as the maximum visual angle subtended by prey (O’Brien et al.

1976). While the actual size of a zooplankter is an important determinant (Brooks and

Dodson 1965; Pastorok 1981), pigmentation greatly affects the apparent size of

zooplankton to predatory fish (Confer and Blades 1975; Schmidt and O’Brien 1982), and

hence the likelihood that zooplankton will be pursued. Pigmentation of eyes (Zaret and

Kerfoot 1975), mandibles (Stenson 1980), and even ingested algae (Vinyard and O’Brien

1975) have all been shown to affect the apparent size of zooplankton to fish.

Apparent size offers only a partial rebuttal to Brooks and Dodson’s (1965) size selection

hypothesis. There are many adaptations of both predator and prey which affect prey

profitability, and hence the decision of the predator to pursue. These often relate to the

capture, retention and digestion components of the generalist predation model. As a

zooplanktivorous fish learns the advantages of attacking some species relative to others,

this knowledge will feed back into the ‘pursuit’ component. For example, Vinyard (1980)

27

explains the learned preference of bluegill (Lepomis macrochirus) for smaller Daphnia

pulex over larger copepoda (Diaptomus pallidus) due to the greater swimming strength,

and hence ability to escape, of the copepod. Thus, escape ability of the prey, which is a

factor in the ‘capture’ component of the generalist predation model (see below), will

affect the ‘pursuit’ decision after sufficient experience has been gained by the predator.

Capture

The capture of prey by zooplanktivorous fish is a function of the efficiency of the

predator and the escape ability of the prey. Predator efficiency is dependent on the size

and function of the mouth (as above) and learned behavioural adaptations. In

experimental tanks, facultative zooplanktivores have been shown to increase their capture

success rate as experience of the prey type increases (Confer and Blades 1975; Doi et al.

1997). Similarly, when multiple prey species are available, zooplanktivorous fish able to

recognise prey according to their motion behaviour can significantly improve their

capture success (Beukema 1968).

The escape ability of zooplankton reflects the morphological adaptations and behavioural

techniques employed to avoid capture (Menge and Sutherland 1987), often at

considerable energetic cost (Schwartz 1991; Gliwicz 1994). At the most basic level, the

swimming speed and reaction time of zooplankton is used to quantify escape ability

(Viitasalo et al. 1998). This has been used to explain the preference of many species of

fish for the slow moving Daphnia sp. over more agile copepoda in many fresh water

systems (Drenner and McComas 1980; Vinyard 1980; Arcifa et al. 1986; Carrillo et al.

28

1990; Arumugam and Geddes 1996). More specialised morphological adaptations usually

involve an enlargement or change in shape of the prey organism beyond the gape size of

potential predators (e.g. Benzie 1991; Schwartz 1991; Kolar and Wahl 1998). Specialist

behavioural adaptations take many varied forms, from the individual ‘swarming and

somersaulting behaviour’ of Daphnia sp. (Pijanowska and Kowalczewski 1997) to the

complex co-operative behaviours of some rotifera which form gelatinous matrices to take

them out of the edible size range of larval fish (Felix et al. 1995).

Retention

Despite the early popularity of the theory that a relationship exists between minimum size

of prey retained and the distance between the gill rakers, this has been shown to be

incorrect for many species (Galbraith 1967; Langeland and Nost 1995). Some authors,

however, maintain that the relationship between gill raker spacing and minimum prey

size breaks down only as fish size increases (Schmidt and O’Brien 1982).

For particulate feeders, this debate is purely academic. They target suitable individuals,

thus once these prey are located, pursued and captured, they are almost always retained

and digested. Physical defences of some zooplankton species that affect their palatability,

such as warts, extendable bristles and retractable feet, may cause them to be ejected from

the predator’s mouth (Williamson 1987; Felix et al. 1995). However, fish do learn to

avoid such prey, thus excluding them via a feedback loop at the ‘pursuit’ level of the

general model.

29

Digestion

Some zooplankters survive the predation process, passing through the digestive system of

zooplanktivorous fish. Survival is dependent on the composition of fish digestive

enzymes, the composition of the zooplankters cuticle, and the transit time of prey in the

gut (Lazzaro 1987). Vinyard (1979) found 26% of ostracods ingested by juvenile bluegill

sunfish (Cypriodopsis vidua) appeared undamaged and fully active after passage through

the gut. This high rate of survival was thought due to the ability of ostracods to close their

shells and thus exclude digestive enzymes from soft tissues, and because the large

number of ostracods ingested reduced the time the zooplankton spent in the gastro-

intestinal tract of the fish.

Zooplankton able to survive the digestion phase have a dual advantage over more

digestible species. In addition to a reduced post-ingestion mortality, Vinyard (1979)

found evidence for learning by predators who rejected ostracods in favour of more

digestible prey at the ‘pursuit’ level of the general foraging model.

Measuring Consumption by Fish

In most cases it is not possible to observe fish feeding in their natural environment and it

is because of this that indirect measures of consumption must be employed. Although a

wide variety of novel measures have been tried (e.g. Kohlemainen 1974; Karjalainen and

30

Viljanen 1992), by far the most commonly used determinants of consumption are

stomach content and fish growth.

Models that use stomach content

A distinction must be made here between studies that simply quantify the occurrence of

food items in the gastro-intestinal tract by either weight, number, volume or a more

subjective point-score system (see Hynes 1950 and Hyslop 1980 for reviews), and those

that attempt to relate stomach content data to appropriate time-scales with a view to

calculating consumption rates. The former are most often used to determine selectivity of

food items (e.g. Price 1975; Chesson 1978, 1983; Cortes 1997) or to determine diel

feeding periodicity (e.g. Staples 1975), and will not be discussed in detail here as this

section is concerned only with the calculation of food consumption rates.

Popular Gut Content Based Consumption Models

By applying a simple mass balance assumption, gastric evacuation rate and fish gut

content can be combined to produce estimates of consumption. Many different

consumption models have been produced (see Bromley 1994), however only three such

models are still widely in use today – the Bajkov (1935), Elliot and Persson (1978) and

Eggers (1979) models.

31

The Bajkov (1935) model

Bajkov (1935) was the first to construct a model quantifying consumption by fish using

stomach weight as the dependent variable. He calculated daily consumption according to

the equation:

D = A(24/n) (1)

where: D = daily food consumption, A = average amount of food in the gut at the time of

sampling (which can be estimated more efficiently using a linear regression method

devised by Hayward 1991 – see also Madon 1998), and n = the number of hours

necessary for complete evacuation determined by periodic dissection of fish collected

from the field. This model assumes a linear gastric evacuation (ie. a constant volume of

food is evacuated per unit time) and thus can only be applied to species for which this is

the case.

Estimates of consumption produced by Bajkov’s (1935) model rely heavily on the time at

which initial sampling occurred (ie. to determine ‘A’), and as such it is most applicable to

species who feed at a constant rate throughout the day. Further, the model is highly prey

specific because of variable retention times of different organisms in the gut of fish

predators (Elliot 1972; Kionka and Windell 1972; Andersen 1999). As such, assumptions

regarding the uniformity of diet must be made, and extrapolation of consumption

estimates to novel environments limited (Elliot and Persson 1978).

32

Another more didactic limitation of Bajkov’s (1935) model is proposed by Swenson and

Smith (1973). They addressed a ‘grey area’ surrounding the definition of an empty

stomach required for calculation of ‘n’ in the Bajkov model. In many fishes, the easily

digestible portion of the food is evacuated rapidly, however more rigid structures such as

sclerotin or bone remain much longer, confounding determination of ‘complete

digestion’. Bromley (1994) suggests a common sense solution to this problem, where

stomachs containing only skeletal remains are classified as empty.

Many of the limitations of Bajkov’s (1935) model have been addressed in subsequent

more complex ‘improvements’ on the original model (e.g. Darnell and Meierotto 1962;

Swenson and Smith 1973; Thorpe 1977). However, in many studies, the original model is

considered adequate, and is often used for fish shown to exhibit linear gastric evacuation

due to its simplicity (e.g. Jobling 1986).

The Elliot and Persson (1978) Models

Elliot and Persson (1978) describe two consumption models, both of which assume an

exponential rate of gastric evacuation. The first model describes consumption by fish

with constant feeding rates between sampling times:

Ct = (St – S0e-Rt) Rt(1-e-Rt)-1 (2)

where Ct = the amount of food consumed in t hours,

33

St = weight of food present in the stomach after time t, S0 = weight of food present in the

stomach at time zero, and Rt = the exponential rate of gastric evacuation. The major

advantage this model has over the Bajkov (1935) model is that it produces more accurate

daily consumption estimates for strongly diurnal feeders because it calculates

consumption, rather than just gut fullness, over short (maximum 3 hour) periods (Elliot

and Persson 1978; Olsen and Mullen 1986). Equation 2 does, however, require that

feeding is not affected by stomach fullness. Correction for this gives us the second of

Elliot and Persson’s (1978) consumption models:

Ct = Cmax – ae-bt (3)

where a = Cmax – S0, and b is a constant. This model

assumes food consumption by fish decreases with time as stomach fullness approaches an

experimentally determined maximum level (Cmax). Application of this model does,

however, require considerable extra laboratory work to determine Cmax and the

introduction of a new set of assumptions associated with this calculation (ie.

independence of Cmax from temperature and ontogenetic variability). As a result, Elliot

and Persson’s first model is much more widely used (e.g. Boisclair and Leggett 1985;

Jobling 1986; Hayward 1991; Amundsen et al. 1999).

The Eggers (1979) Model

Eggers (1979) argued that the Bajkov (1935) model was theoretically sound, with the

exception of the assumption of linear gastric evacuation. In agreement with Elliot and

34

Persson (1978), Eggers believed that gastric evacuation by fish was best described

exponentially. This resulted in the following model:

C24 = 24R . S (4)

Where C24 = daily food consumption, S = mean stomach weight during a 24 hour period

(which can be estimated more efficiently using a linear regression method devised by

Hayward 1991 – see also Madon 1998), and R = the exponential rate of gastric

evacuation.

Due to its similarity to Bajkov’s model, the Eggers model is subject to similar limitations

of predator and prey specificity. Hayward (1991) suggests a further limitation of the

Eggers (1979) model involves the steady state assumption that stomach weights at the

beginning and end of the 24 hour period are equal. This assumption is rarely met,

resulting in an error Hayward (1992) termed ‘lack of closure bias’. Eggers (1979) offers a

solution to this problem, suggesting the difference between the initial and final stomach

weights is simply subtracted from the final estimate of consumption. The theoretical basis

for this correction is not clear, however its application has been shown to produce

consumption estimates more similar to those produced by the Elliot and Persson (1978)

model (Hayward 1991; Boisclair and Marchand 1993). This increase in precision of the

two models is considered an advantage for both.

35

Using the models

Procedural and analytical variations on the methods outlined by Bajkov (1935), Elliott

and Persson (1978) and Eggers (1979) have been suggested by several authors. Boisclair

and Marchand (1993) show that the use of the complete gastro-intestinal tract, and not

just the stomach, significantly reduces variance around daily ration estimates. While this

practice has been widely adopted throughout the literature (e.g. Boisclair and Leggett

1988, 1989a; Shepherd and Mills 1996), others still use only the stomach (e.g. Hislop et

al. 1991; Mookerji et al. 1998).

Fish consumption estimates are also affected by stress induced regurgitation (Bowman

1986; Hislop et al. 1991). This problem is confounded because although the presence of

digested food in the mouth and the thickness of the stomach lining are good indicators, it

is often not possible to determine if regurgitation has occurred (Bowman 1986). A variety

of post-capture procedures have been employed to decrease the likelihood of

regurgitation. These range from killing fish “immediately by a blow on the head”

(Amundsen and Klemetsen 1986), on-site dissection of the stomach (e.g. Chapman and

Fernando 1994), placing specimens directly onto ice (e.g. Grant and Kott 1999), or

anaesthetisation of specimens (e.g. Swenson and Smith 1973; Andersen 1999), however

none of these methods appear failsafe. The exclusion of fish showing any sign of

regurgitation is the only possible recourse, however this can significantly reduce sample

size and increase sampling effort. For example, Hislop et al. (1991) rejected data from

9200 of the 19000 fish they examined because of regurgitation.

36

Fish consumption estimates using gut content are further affected by statistical

limitations. Stomach and/or whole gastro-intestinal tract data from fish are rarely

normally distributed, with the high proportion of empty guts often either skewing or

polarising raw data (Amundsen and Klemetsen 1986). Comparison of data using standard

parametric statistical techniques is therefore not possible without prior application of a

transformation. This is undisputed, however there is some argument over the most

appropriate transformation. Amundsen and Klemetsen (1986) suggest a log transformed

geometric mean, whereas Grant and Kott (1999) argue for an arcsine square root

transform. In contrast, Hayward et al. (1991) avoid statistical transformations and use the

median datum for comparison.

Model Selection

The Bajkov model is used only for fish with a linear gastric evacuation rate, and is

therefore excluded from comparison with the other models here. Both the Elliot and

Persson (1978) and the Eggers (1979) models assume an exponential evacuation rate, and

can therefore be used interchangeably. As a result, the relative merits and limitations of

these two models have received considerable attention in the literature from authors

required to choose between them.

Because in most cases it is simply not possible to assess the accuracy of these models in a

natural situation, selection criteria include model precision, ease of application,

theoretical soundness and versatility. Model precision is of little use in separating the two

models. Indeed authors testing their own novel models use both Elliot and Persson (1978)

37

and the Eggers (1979) model outputs as a yardstick (Nakashima and Leggett 1978; Allen

and Wootton 1984; Hayward et al. 1991). Authors concerned mainly with ease of

application favour the Eggers model, as this is the most often cited reason for its use (e.g.

Amundsen and Klemetsen 1986; Boisclair and Leggett 1988; Boisclair and Marchand

1993; Grant and Kott 1999). The complexity of the Elliot and Persson models does,

however, greatly increase their versatility. The calculation of sub-daily consumption

rates, and the inclusion of fish with either constant or stomach-fullness limited

consumption rates allow the application of the Elliot and Persson model to a wider range

of fish species. This is often described as a greater ‘theoretical soundness’ by many

authors who suggest that the fewer assumptions required by the Elliot and Persson model

make it more robust than the Eggers model (Cochran 1979; Elliot 1979; Boisclair and

Leggett 1985; Jobling 1986; Hayward 1991).

Both the Elliot and Persson and the Eggers models have been used by many authors to

estimate daily food consumption of fish. Neither is universally preferred and model

selection is in many cases a purely subjective decision. Both Allen and Wootton (1984)

and Amundsen et al. (1999) circumvent the selection process by reporting their results

using both models.

Bioenergetics Models

In contrast to the stomach weight based methods, bioenergetics models most commonly

derive consumption estimates from the growth rate of fish. In its most basic form, the

relationship between consumption and the fate of the ingested food energy is:

38

C = M + W + Gr (5)

where C = the energy consumed by a fish, M = energy used in metabolism, W = energy

lost to waste, and Gr = energy used for growth of the fish.

Metabolic energy expenditure (M) can be further divided into respiration (R – the amount

of energy required for routine metabolism), active metabolism (A - the amount of energy

required during active periods such as searching for prey), and specific dynamic action (S

– the energy required for assimilation/digestion of food). Waste loss (W) is further

divided into egestion (F - faecal loss), and excretion, or nitrogenous loss (U - urine).

Growth (Gr) is divided into two components, somatic (B – body growth), and

reproductive (G – gonadal growth) (Jobling 1993; Hanson et al. 1997). The above

equation can thus be expanded to:

C = (R + A + S) + (F + U) + (B + G) (6)

This basic equation is the starting point of all bioenergetic estimates of consumption by

fish. Most commonly, parameters describing the R, A, S, F, U and G components are

calculated experimentally in a laboratory, and for many species (particularly those

important commercially) the complete parameter suite is available in the literature (see

Hanson et al. 1997).

39

Once fully parameterised, models use field based measures of the abundance, growth,

diet and mortality rates of fish, and data on water temperature and energy density of both

predator and prey to balance the bioenergetics equation. The most common use of fish

bioenergetics models is to determine the amount of food consumed by fish (e.g. Rudstam

1989; Cianelli et al 1998; Worischka and Mehner 1998; Tolonen 1999; Essington et al.

2000) or, particularly in aquaculture, to determine the growth rate of fish under different

feeding and temperature regimes (e.g. Johnston 1999; Railsback and Rose 1999;

Bjornsson et al. 2001). The mass balance nature of the model does, however, allow for

each of its components to be derived given estimates of the others (e.g. Activity –

Boisclair and Leggett 1989b; Cooke et al. 2001; and Excretion – Mehner et al. 1998;

Schindler et al. 2001). This diversity of modelling possibilities has resulted in an eclectic

application of fish bioenergetics models. Models have been used to describe the diurnal

and ontogenetic migrations of fish (Bevelheimer and Adams 1993; Hartman and Brandt

1995a; Madon et al. 2001), the productivity of aquatic ecosystems (Hartman and Brandt

1995a; Johnson et al. 1998), the size structure of fish prey items (Blumenshine et al.

2000), habitat quality for fishes (Nislow et al. 2000; Tyler and Brandt 2001) and nutrient

cycling in aquatic systems (Mehner et al. 1998; Schindler et al. 2001).

This study uses the Fish Bioenergetics v3.0 model developed at the Wisconsin Sea Grant

Institute. Although other bioenergetics models have been suggested (e.g. Winberg 1956;

Karas and Thoresson 1992; Ney 1993), the ‘Wisconsin model’ was chosen because of its

widespread use in the literature and because of the scrutiny it has undergone due to its

popularity.

40

Limitations

Debate on the limitations of the Wisconsin model has focussed on the accuracy and

complexity of the input variables. Ney (1990, 1993) argues that the Wisconsin model is

excessively ‘data hungry’, with 12-30+ physiological parameters and field measured

inputs required. Each of these inputs is associated with some degree of error, and Ney

(1993) argues that errors are, by the nature of the model, additive and multiplicative. He

provides three much simpler (3 and 4 parameter) production based models, suggesting

that these models facilitate easier calculations, reduce the possibility for data entry error

and “focus concern on the accurate determination of only a small number of energetics

parameters”.

Hansen et al. (1993), citing the work of Bartell et al. (1986), refute the arguments of Ney

(1990, 1993), arguing that the mass balance requirement of all bioenergetics models force

them to be balanced and that this limits error propagation. Further, Hansen et al. (1993)

suggest that the complexity of the Wisconsin model is necessary to produce the fine scale

resolution outputs required by many applications of the model. It follows that both the

complex Wisconsin bioenergetics model and the much simpler models of fish

consumption proposed by Ney (1993) are both well suited to different tasks.

In addition to the mathematical complexity, Ney (1993) lists four major areas of

deficiency in the use of the Wisconsin model, these are discussed and rebutted below;

41

1. Unknown activity costs. One of the most difficult to measure parameters in the

Wisconsin model is the energy consumed as a result of the swimming activity of fish.

Three methods are used. The Winberg multiplier is a constant value, usually between

1 and 4.4 (Hanson et al. 1997), which is multiplied by the basal respiration

component of the model to approximate energy consumption due to fish activity. The

selection of a value for the Winberg multiplier is based on a perception of the fishes

lifestyle, and despite being too simplistic (Ney 1993), it is still widely in use (e.g.

Johnston 1999; Railsback and Rose 1999; Schaeffer et al.1999). A second method

describes activity as a fixed proportion of consumption (Kerr 1982). While this works

well for fish whose feeding success is determined primarily by the area searched, and

does have some support in the literature (Boisclair and Leggett 1989b), it does not

hold well for more sedentary species (Ney 1993), nor does it account for temporal

variability due to temperature or fish ontogeny (Lucas et al. 1993; Madon and Culver

1993). Temperature and ontogeny are, however, incorporated into a third approach

developed by Stewart et al. (1983). They express the mass and temperature

dependence of the swimming speed of a fish as a function of basal metabolism based

on telemetry (Stewart and Ibarra 1991; Lucas et al. 1993; Rand et al 1993) or

observations in the laboratory (Lantry and Stewart 1993) or field (Boisclair and Sirois

1993; Cooke et al 2001). However, food availability, prey type and predator

avoidance all affect fish activity on a variety of time scales (Bevelheimer and Adams

1993; Bone et al. 1995), and these have not been included in any bioenergetics

model. Thus, despite its recognised importance as a source of significant error in

bioenergetics models (Boisclair and Leggett 1989b; Hansen et al. 1993; Lucas et al.

42

1993; Madon and Culver 1993; Worischka and Mehner 1998), no suitable alternative

method for modelling the energetic requirement of fish activity exists.

2. Extrapolation of allometric functions. For many larger fish species, determination

of allometric functions for consumption and respiration is not practicable because of

the equipment and time requirements. This has resulted in the extrapolation of

laboratory data to fish outside the size range examined (Ney 1993). Post (1990)

showed this to produce erroneous estimates of consumption for yellow perch (Perca

flavescens). To account for this, most recent bioenergetics models target a particular

age class of fish (e.g. Hartman and Brandt 1993; Madon and Culver 1993; Cianelli et

al 1998; Huuskonen et al. 1998).

3. Unjustified species borrowing. As with the extrapolation of allometric functions,

the exclusive equipment and time requirements of model parameterisation often leads

to the borrowing of parameter values from similar fish species (e.g Rudstam 1989;

Madon and Culver 1993; Qin et al. 1997). This is particularly the case for parameters

such as excretion and egestion, and Ney (1993) argues that such borrowing is

unjustified. Sensitivity analyses, however, show without exception that the most

commonly borrowed parameters (egestion and excretion) are insensitive to model

outputs, and many authors argue this as a justification for borrowing (see Bartell et al.

1986; Hanson et al. 1997).

4. Inadequate measurement of field based variables. Ney (1993) highlights the need

for rigorous field sampling on appropriately small time-scales when gathering

information on fish abundance, growth, diet and mortality rates, water temperature

and energy density of both predator and prey required for bioenergetics simulations.

43

Energy densities of predator and prey vary according to season, size and sex (Adams

et al. 1982; Luecke and Brandt 1993), and while the option to incorporate this into the

Wisconsin model is available, most simply enter energy density as a constant value

(Hanson et al. 1997). Further, despite being disproved (Spigarelli 1982), the

assumption that fish reside in waters nearest their optimum temperature is often made

when water temperature data file for use in the Wisconsin model (Ney 1993). The

issue of inadequate field sampling, most commonly due to time and resource

constraints, is endemic and applicable to all field based estimates of consumption,

thus no solution to this limitation is offered.

Despite these ongoing limitations, bioenergetic modelling has a growing history of

predictive management success (Hansen et al. 1993; Rand et al 1993; Cianelli et al.

1998; Worischka and Mehner 1998). This, combined with its inherent flexibility,

widespread use, commercial availability, and the continual improvements being

suggested in the literature, will ensure the continued use of the Wisconsin fish

bioenergetics model to understand fish population dynamics. For these reasons, it was

decided that the Wisconsin fish bioenergetics model would be used in preference to the

other models of consumption described above to examine the roles that diet and

consumption play in determining the spatial and temporal distribution of Hypseleotris in

Maroon Dam.

44

References

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Allen, G.R., Midgely, S.H. and Allen, M. (2002). Field Guide to the Freshwater Fishes of Australia. Western Australian Museum, Perth. 394pp.

Allen, J. R. M, and R. J. Wootton, (1984). Temporal Patterns in Diet and Rate of Food Consumption of the Three Spined Stickleback (Gasterosteus aculeatus L.) in Llyn Frongoch, an Upland Welsh Lake. Freshwater Biology, 14: 335-46.

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Manuscript 1: Spatial and temporal variability in the distribution and diet of the gudgeon (Eleotridae : Hypseleotris spp.) in a sub-tropical Australian Reservoir. Authors Contributions Shaun Meredith:

• Wrote the manuscript • Designed the field sampling program and helped construct the equipment required

to implement it • Undertook all fieldwork, statistical analysis and interpretation

Vlad Matveev

• Conceived the overall project • Provided sampling design, statistical and interpretational advice throughout the

project • Provided significant comment on draft manuscripts

Paul Mayes

• Undertook all field work and helped construct the equipment required to implement it.

• Provided intellectual input into sampling design and interpretation of results • Provided significant comment on draft manuscripts

Note: This paper has been accepted for publication. However, for consistency of format within the thesis, the final manuscript prior to publication is presented here. A re-print of the journal article is presented in Appendix 1.

66

Spatial and temporal variability in the distribution and

diet of the gudgeon (Eleotridae : Hypseleotris spp.) in a

sub-tropical Australian Reservoir

Shaun N. Meredith1,2,3,4, Vladimir F. Matveev2, Paul Mayes1 1Queensland University of Technology, School of Natural Resource Sciences,

PO Box 2434, Brisbane QLD 4001, Australia 2CSIRO Land and Water, 120 Meiers Rd, Indooroopilly, QLD, 4068

3Present address: Cooperative Research Centre for Freshwater Ecology, Lower Basin

Laboratory, PO Box 3428, Mildura, Victoria 3502, Australia.

e-mail: Shaun.Meredith@csiro.au 4Author for correspondence

Abbreviated Title: Distribution and Diet of Gudgeon in Maroon Dam

Key words: Hypseleotris, spatial, temporal, diet

67

Abstract

The diurnal distribution and diet of Hypseleotris spp. was examined over a 14-month

period in Maroon Dam, a productive sub-tropical reservoir in south-east Queensland,

Australia. Three distinct size classes of Hypseleotris were observed. The smallest (<16

mm SL) fish undertake a vertical diurnal migration in the pelagic throughout the year,

spending daylight hours near the surface and night hours in deeper waters. The diet of

this size class consists almost exclusively of zooplankton (98.6% total prey volume), with

cladocera and copepoda dominating the identifiable prey items. A larger (12-20 mm SL)

size class group of sub-adult/adult fish occupy the near-shore littoral throughout the

daylight hours, but move out of the sampled area during the night. The diet of this size

class is more diverse (zooplankton 58.5%, macro-invertebrate 25.0%, other 16.5%). The

largest size class (>20 mm SL) of adult Hypseleotris remain in the near-shore littoral

throughout seasonal and diurnal cycles and have a more eclectic diet than the other two

size classes (zooplankton 28.8%, macro-invertebrate 28.9%, other 42.3%). Spatial and

temporal differences in the distribution and diet of these three size class groups are

discussed with reference to the abundance and availability of suitable prey, intra-specific

competitive exclusion, predator avoidance, and water quality.

101

102

Manuscript 2: A bioenergetic model for gudgeon (Eleotridae : Hypseleotris spp.). Authors Contributions Shaun Meredith:

• Wrote the manuscript • Co-designed the laboratory experiments in consultation with Tim Johnson. • Undertook all experimental work, statistical analysis and model validation.

Tim Johnson:

• Co-designed the laboratory experiments • Provided significant intellectual input and modelling expertise into model

construction and validation • Provided significant intellectual input into the manuscript and provided

significant comments on draft manuscripts. Clayton Sharpe:

• Provided significant intellectual and laboratory input into the examination of Hypseleotris otoliths

• Provided significant field help in Lindsay River system Vlad Matveev:

• Conceived the overall project • Provided sampling design, statistical and interpretational advice throughout the

project • Provided significant comment on draft manuscripts

103

A bioenergetic model for gudgeon (Eleotridae : Hypseleotris spp.)

Shaun N. Meredith1,2,3,5, Timothy B. Johnson4, Clayton Sharpe3 and Vladimir F.

Matveev2 1Queensland University of Technology, School of Natural Resource Sciences,

PO Box 2434, Brisbane QLD 4001, Australia 2CSIRO Land and Water, 120 Meiers Rd, Indooroopilly, QLD, 4068

3Present address: Cooperative Research Centre for Freshwater Ecology, Lower Basin

Laboratory, PO Box 3428, Mildura, Victoria 3502, Australia.

e-mail: Shaun.Meredith@csiro.au 4Aquatic Research and Development Section, Ontario Ministry of Natural Resources.

Lake Erie Fisheries Station, Wheatley, Ontario, Canada N0P2P0 5Author for correspondence

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Abstract

The gudgeon (Hypseleotris spp.) is the most widely distributed native fish in Australia.

Facultative zooplanktivores that display strong ontogeny in diet and distribution,

gudgeons often dominate fish communities in terms of abundance and therefore have the

potential to structure food webs both as a predator, and as the principal prey for

piscivorous fish. We constructed a bioenergetic model for gudgeon by combining

laboratory derived measurements of allometric and temperature dependent coefficients

for respiration and consumption with published estimates of other key physiological

processes. The resulting model was validated by fitting model outputs to weight-at-age

data collected from the field and a sensitivity analysis was undertaken. Experimentally

derived respiration and consumption allometric slope coefficients (λ = -0.3909, β = -0.53,

respectively) and intercepts (φ = 0.001, α = 0.122, respectively) were generally lower

than for previous bioenergetic models, producing uniquely steep respiration and

consumption functions. The nature of these functions is discussed with reference to the

small size, behavioural ontogeny and habitat of Hypseleotris and to gross conversion

efficiency (GCE). This bioenergetic model, the first published for an Australian native

freshwater fish, has applications in the fields of biomanipulation and food web research.

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132

Manuscript 3: Spatial and temporal ontogeny of gudgeon (Eleotridae: Hypseleotris spp.) in a sub-tropical lake: a bioenergetic analysis Authors Contributions Shaun Meredith:

• Wrote the manuscript • Undertook all bioenergetic analyses of fish behaviour in consultation with Tim

Johnson. • Provided all interpretation and analysis of results

Tim Johnson:

• Provided significant intellectual input and modelling expertise into bioenergetic analyses of fish behaviour

• Provided significant intellectual input into the manuscript and provided significant comments on draft manuscripts.

Vlad Matveev:

• Conceived the overall project • Provided sampling design, statistical and interpretational advice throughout the

project • Provided significant comment on draft manuscripts

Gary Jones

• In consultation with Vlad Matveev, conceived and funded the overall project • Provided sampling design, statistical and interpretational advice throughout the

project

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Spatial and temporal ontogeny of gudgeon (Eleotridae : Hypseleotris spp.) in a sub-tropical lake: a bioenergetic

analysis

Shaun N. Meredith1,2,3,6, Timothy B. Johnson4, Vladimir F. Matveev2 and Gary J. Jones5 1Queensland University of Technology, School of Natural Resource Sciences,

PO Box 2434, Brisbane QLD 4001, Australia 2CSIRO Land and Water, 120 Meiers Rd, Indooroopilly, QLD, 4068

3Present address: Cooperative Research Centre for Freshwater Ecology, Lower Basin

Laboratory, PO Box 3428, Mildura, Victoria 3502, Australia.

e-mail: Shaun.Meredith@csiro.au 4Aquatic Research and Development Section, Ontario Ministry of Natural Resources.

Lake Erie Fisheries Station, Wheatley, Ontario, Canada N0P2P0 5Cooperative Research Centre for Freshwater Ecology, Building 15, University of

Canberra, ACT 2601, Australia 6Author for correspondence

134

Abstract

In Maroon Dam, zooplanktivorous gudgeons (Hypseleotris spp.) display a strong spatial

and temporal ontogeny. The smallest functional size class (<16 mm SL) reside in the

pelagic, undergoing diurnal vertical migration. An intermediate size class (12-20 mm SL)

diurnally migrate horizontally between the littoral and the pelagic, while the largest 5

Hypseleotris (>20 mm SL) remain in the littoral throughout the diurnal cycle. The

choices made by Hypseleotris in producing this spatial and temporal ontogeny were

examined using a bioenergetic model previously described for this species. Water column

temperature profiles and size-class specific dietary composition data were input,

revealing that the minimum time to sexual maturity (268 d) corresponded with the 10

distributions of Hypseleotris observed in the field. It was concluded that considering only

dietary and thermoregulatory determinants in Maroon Dam, Hypseleotris are occupying

their energetically optimum niche at all size classes. Further, because the energetically

optimum diurnal migrations of the smaller (<20 mm SL) Hypseleotris are likely to

increase predation mortality, we concluded that predator avoidance was not a strong 15

driver of juvenile Hypseleotris behaviour in Maroon Dam.

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General Discussion In Maroon Dam, the spatial ontogeny of Hypseleotris is pronounced over seasonal and

diurnal timescales. Hypseleotris hatch in the littoral then undertake a horizontal migration

into the pelagic where they diurnally migrate vertically, spending daylight hours in the

surface waters and moving to deeper waters at night. Upon attaining a size of 12 mm SL,

Hypseleotris begin to move into the near-shore littoral during daylight hours, and migrate

offshore at night. Once they have attained a size of 20 mm SL, Hypseleotris remain in the

near-shore littoral throughout seasonal and diurnal cycles. Elsewhere, preliminary

research suggests that similar size class distributions observed in Maroon Dam are also

found for Hypseleotris in Australian lowland river systems, and particularly so in Murray

River weir pools (S. Meredith unpubl. data).

This spatial and temporal ontogeny is manifest in the diet of Hypseleotris in Maroon

Dam. The diet of the smallest size class in the pelagic (<16 mm SL) consists almost

entirely of zooplankton (98.6% total prey volume). As Hypseleotris size and time spent in

the near-shore littoral increases, the proportion of zooplankton in the diet decreases while

that of macroinvertebrates increases (total prey volumes: 12-20 mm SL: 58.5%

zooplankton, macroinvertebrates 25.0%, other 16.5%; >20 mm SL: 28.8% zooplankton,

28.8% macroinvertebrate, 42.3% other). Similar ontogenetic shifts from obligate to

facultative zooplanktivory and changes in dietary proportion have been shown for other

species to influence trophic structure (e.g. McDermot and Rose 2000) and this has strong

implications for the role of Hypseleotris in the Maroon Dam biomanipulation experiment.

159

To further examine this, a bioenergetic model based on Hewett and Johnson (1992) was

constructed for Hypseleotris. Laboratory experiments measuring allometric and

temperature dependent respiration and consumption rates and the energy density of

Hypseleotris were combined with broadly accepted scalars of excretion and activity to

parameterize the model. Predicted growth rates generated by the model were validated

against two sets of age-at-growth data from the Lindsay Island Hypseleotris population.

The tightness of fit of the model derived from fish in Maroon Dam to field-observed

growth at Lindsay Island reflects the robustness of the model across geographic and

species complex (Bertozzi et al. 2000) boundaries.

The fully parameterized and validated bioenergetics model was then used to examine the

observed spatial and temporal ontogeny of Hypseleotris in Maroon Dam. In contrast to

many other zooplanktivorous fish species for which predator avoidance and interspecific

competition appear to be important determinants of distribution and diet (e.g. Bluegill,

Lepomis macrochirus – Werner and Hall 1988; Pike, Esox lucius - Eklov 1997), it was

demonstrated that Hypseleotris simply follow the spatial and temporal ontogeny that

facilitates fastest growth to sexual maturity. In a context of Australian whole lake

biomanipulation experiments, the failure of Hypseleotris to alter behaviour in the face of

increasing piscivorous fish density represents a potentially significant limitation to the

potential for algal control via top-down biomanipulation.

The addition of piscivorous predators to an aquatic system can have a dual effect on both

the abundance and behaviour of their prey. At its simplest, an increase in predation

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mortality results in a decrease in zooplanktivorous fish abundance. This was the original

pathway by which Shapiro et al. (1975) suggested top-down control – a reduced

zooplanktivorous fish abundance releases predation pressure on zooplankton, and in turn,

increases the consumption of nuisance algae by zooplankton. Zooplanktivorous fish have

also demonstrated strong behavioural responses following the addition of piscivores. In

many cases (e.g. Dini and Carpenter 1991; Werner and Hall 1988; Jacobsen et al. 1997;

Holker et al. 2002), the addition of a pelagic piscivore results in the migration of

zooplanktivorous fish from the open water of the pelagic (where foraging by both

piscivorous and zooplanktivorous fish is unobstructed by structurally complex

macrophytes), to the relatively shallow littoral where zooplanktivory is less profitable but

piscivorous predation pressure is lower. From a biomanipulation perspective, piscivore

additions that result in both increased zooplanktivore mortality and the relative

confinement of zooplanktivorous fish to the vegetated (i.e. structurally complex) littoral

is more likely have greatest affect on nuisance algal abundance. To achieve this,

however, stocking with a pelagic piscivore is required.

Australian Bass are, however, strongly associated with the littoral (Harris and Rowland

1996; Allen et al. 2002) and diet data suggest that Bass target the late juvenile and adult

growth stages of Hypseleotris in Maroon Dam (Paul Mayes, unpubl. data). The early

juvenile obligate zooplanktivorous Hypseleotris in the pelagic thus appear to be under

little, if any, predation pressure from piscivorous fish. It is therefore unsurprising that, as

indicated in the bioenergetic modelling, the behaviour of both the pelagic (<16 mm SL)

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and the diurnally horizontally migrating (12 – 20 mm SL) life stages of Hypseleotris

reflects a life history strategy aimed at growth maximisation, and not predator avoidance.

Stocking with a littoral piscivore can still, however, produce a successful

biomanipulation through a simple predation mortality pathway if the dominant

zooplanktivore in the system utilizes the littoral zone at some stage during its life cycle.

Increased predation mortality of the littoral dwelling adult and sub-adult Hypseleotris can

reduce juvenile pelagic Hypseleotris abundance through a reduced recruitment resulting

from a similarly reduced brood stock abundance. Such a pathway is, however, subject to

complex and often non-linear relationships between spawning stock biomass and

population reproductive potential, such as density dependent recruitment (Ricker 1954),

egg size, quality and hatching success, and timing and duration of spawning (Trippel et

al. 1997) making it likely less effective than a biomanipulation where the predation of

pelagic zooplanktivores is targeted directly.

Comparison of results from stocking of a littoral piscivore in Maroon Dam with pelagic

piscivore stocking in the northern hemisphere reflect this. In Maroon Dam, algal

abundance following piscivore stocking was reported to have shown a small downward

trend in comparison with the control – Lake Moogerah (V. Matveev and L. Matveeva,

unpubl. data; Fig. 1). By the standards of Drenner and Hambright (1999), however, this

data is inconclusive. In contrast, biomanipulation experiments in northern hemisphere

lakes where pelagic piscivores are stocked often produce marked declines in algal

abundance (e.g. Jayaweera and Asaeda 1995; Fig. 2).

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Figure 1: Small downward trend in phytoplankton biovolume following the addition of piscivorous fish to Maroon Dam (V. Matveev and L. Matveeva, unpublished data)

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Figure 2: Comparison of simulated and measured chlorophyll-a concentration in eutrophic and biomanipulated parts of Lake Bleiswijske Zoom in 1987 (From Jayaweera and Asaeda 1995)

The future for pelagic piscivore stocking in Australia is limited, however, because there

exists no native Australian freshwater pelagic piscivore. Evolutionarily, this may be

because until the recent construction of dams and weirs, there has been no permanent

freshwater pelagic zone on the Australian continent since the desiccation of Lake

Bungunnia approximately 500,000 years ago (Evans et al. 1990).

The rivers of the Murray Darling Basin are characterised by higher hydrological

variability than many other lowland systems worldwide (Puckridge et al. 1998).

Typically large episodic flood events create a highly productive but ephemeral ‘pelagic’

which provides a key high prey and low predator density niche (cf. Junk et al. 1989)

optimal for foraging by zooplanktivorous fish (Harris and Gehrke 1994). The movement

164

of zooplanktivorous fish into the ephemeral pelagic therefore likely has an historical basis

over evolutionary timescales. The use of a similar, but more permanent, pelagic present

in impoundments today thus does not represent an evolutionary change in

zooplanktivorous fish behaviour, possibly explaining the broadly observed abundance

and diversity of zooplanktivorous fishes currently occupying these zones (e.g. Flyspecked

hardyheads, Craterocephalus stercusmuscarum fulvus in Moogerah Dam (Qld) – S.

Meredith, unpubl. data; Australian Smelt Retropinna semoni and Galaxias brevipinnis in

Hume Dam (NSW) – Matveev et al. 2002; Australian Smelt in Lake Benanee (Vic) –

B.Ebner unpubl. data; Flathead gudgeon (Phylipnodon grandiceps) in Lake Dartmouth

(Vic) – V.Matveev unpubl. data; and Hypseleotris in Maroon Dam (current study)).

For piscivores, flood-created ephemeral pelagic zones more likely represent sub-optimal

foraging conditions. In contrast to zooplankton, the fish prey of piscivores do not respond

to increases in productivity associated with over-bank flooding on timescales relevant to

the creation of ephemeral pelagic zones due to the general lack of a flood related

spawning cue (Humphries et al. 1999) and slow (months to years) development from

larval to juvenile and adult phases. During flooding, appropriate prey of piscivorous fish

are therefore likely to be distributed throughout a much greater volume of water,

decreasing the probability of prey location (Wright and O’Brien 1984). Thus although

piscivore foraging in the more recently created permanent pelagic zones of dams and

storages within the Murray-Darling Basin may be sustainable, the lack of a truly pelagic

piscivore in Australia may be a reflection of the historical unsuitability of the pelagic.

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This is not the case in the northern hemisphere, where large lakes with permanent pelagic

zones have existed over evolutionary timescales. Here the fish fauna comprises an

abundance of pelagic piscivores (e.g. European perch Perca fluviatilis; Walleye Sander

vitreus; Rainbow trout Oncorhyncus mykiss; Chinook salmon O. gorbuscha; Lake trout

O. namaycush; Largemouth bass Micropterus salmoides).

In Australia, where political and climatic constraints limit the stocking of exotic

piscivores into large storages, the pelagic zone of large deep lakes will therefore likely

remain an area where zooplanktivorous fishes can forage unobstructed by structurally

complex macrophytes and without the need to undertake predator avoidance behaviour.

In a biomanipulation context, and highlighted in the current study examining

Hypseleotris and Australian Bass interactions in Maroon Dam, it therefore appears that

stocking Australian native piscivorous fish to achieve top-down biomanipulation control

over nuisance algae in large dams is likely to be less successful than other

biomanipulation strategies. Direct planktivorous fish removal (e.g. Jeppesen et al.

1990a,b; Sanni and Waervagen 1990), control of benthivorous fish (e.g. Tatrai et al.

1997), manipulation of water levels to reduce zooplanktivore and benthivore abundance

(e.g. Zalewski et al. 1990), bottom-up control by nutrient limitation (e.g. Moss et al.

1996; Mehner et al. 2001), manipulation of littoral macrophytes (e.g. Galanti et al. 1990;

Kornijow et al. 1990), or a combination of techniques (see review by Drenner and

Hambright 1999) warrant further and higher priority consideration.

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Appendix 1 Reprint of : Meredith, S.N., Matveev, V.F. and Mayes, P. (2003). Spatial and temporal variability in

the distribution and diet of the gudgeon (Eleotridae : Hypseleotris spp.) in a sub-tropical Australian Reservoir. Marine and Freshwater Research. 54, 1009-1017.