Ningaloo Reef as a Plankton Filter: Changes in the Size

Ningaloo Reef as a Plankton Filter: Changes in the

Size Spectrum and Community Structure of Zooplankton

across a Fringing Reef

Honours Dissertation

Kate Philp

10209171

School of Environmental Systems Engineering

30 October 2007

ii

Cover photo: Coral reef community off Sandy

Bay, Ningaloo Reef, with reef shark, fish and

pelagic plankton community. Photo courtesy of

Kim Brooks.

iii

Acknowledgments

The assistance and support that I received during this study was crucial to its successful

completion and I would like to thank the following people: firstly, my supervisor Associate

Professor Anya Waite for providing me with the opportunity to undertake this project and for

her advice and suggestions during the data analysis phase and on the writing of this paper;

Nick Mortimer and Joanna Strzelecki for their time and patience in assisting me with the

image analysis at CSIRO and for providing me with the opportunity to use the PlanktonJ

software; Dianne Krikke and Kim Brooks for their extensive help and advice during the

fieldwork at Ningaloo and for the loan of their beautiful photos of the trip; Danielle Kapeli for

being my Ningaloo buddy and for all the crazy times we had; Harriet Patterson for her

assistance in identifying some of the zooplankton; Iain Suthers for his advice regarding the

zooplankton size spectra; Stuart Humphries for assisting me with the statistics; Alex Wyatt

for advice regarding the subject literature and Patricia Kershaw for proof reading this

document.

I would also like to thank several people for their support throughout the year: my friends and

family; the crew at SESE for the fun times; my team at URS for their understanding and

flexibility; Jayne Richards for being a fantastic friend over our 11 years of education together

and for always being there for me this year and Simon Davis, our housemate, for putting up

with us. Finally, I would like to thank Michael Gillen for his amazing love and support this

year and for his extensive help during the final stages of the project, I could not have

successfully completed this project or this year without him.

iv

Abstract

Benthic-pelagic coupling of coral reefs and the associated role of oceanographic processes has

so far received little attention. This study addresses a component of this deficit and will

increase the understanding of pelagic food sources within coral reef food webs. The study site

consists of a transect across a coral reef section at Sandy Bay, Ningaloo Marine Park, Western

Australia. This study is part of the Ningaloo Biological Oceanography Project being

conducted by Associate Professor Anya Waite. Through a detailed investigation of changes in

the daytime pelagic zooplankton community structure across Ningaloo Reef and in

conjunction with other studies being conducted as part of the Ningaloo Biological

Oceanography Project, it is intended that this study will contribute to the understanding of the

influence of oceanic inputs and benthic-pelagic coupling on coral reefs.

Zooplankton samples were collected using horizontal plankton net tows from a small boat at

six sampling sites across the reef section and were analysed using image analysis. Images of

the samples were taken using a scanner and a microscope attached to a digital camera and

these images were analysed using the image analysis programs ImageJ and PlanktonJ. The

image analysis technique produced comprehensive datasets describing the taxonomic

structure and size structure of the zooplankton communities, as well as their total biomass and

abundance. Water samples were also taken from the sampling sites and these were analysed to

produce chlorophyll a and phaeophyton a data.

The results of this study have produced clear trends, both in terms of temporal variation in the

source waters outside the reef and spatial variation across the reef. The temporal variation was

highlighted by the chlorophyll data and by the occurrence of a salp bloom in the second set of

tow samples at the outside reef stations. The spatial variation was highlighted by several

results, specifically: a decrease in total biomass across the reef; the change in taxa structure

across the reef and; a significant decrease in total chlorophyll across the reef. As well as the

trends observed, there are specific sites where it would seem there are complex processes

occurring; in particular, a ‘hot-spot’ on top of the reef and a conundrum of high picoplankton

and high zooplankton biomass just outside of the reef. While this study is not able to produce

conclusive results as to the mechanisms that are occurring on the reef, it is likely that there is

a combination of biological and physical mechanisms occurring and it is clear that there is

some interaction between the reef and the surrounding waters.

v

Glossary

Benthic community Those organisms living on the reef bottom

Pelagic community Those organisms living in the water column

Benthic-pelagic coupling The interactions between the sea or reef bottom and the water

column

Biomass The total carbon content of an organism

Biovolume The total volume of an organism

Trophic level Describing the position of an organism in the food web

Fringing reef A reef which fringes the edge of a continent or island, with a

lagoon contained between the reef and the land.

Heterotrophic Organisms that require an input of organic food

Autotrophic Organisms capable of sustaining themselves through production

of their own food

Chlorophyll a Pigment used for photosynthesis and found in phytoplankton

Phaeophyton a Pigment produced when zooplankton digest chlorophyll a

ESD The estimated spherical diameter of an organism, based on its

biovolume

Normalised biomass-size

(NB-S) spectrum

The plot of logarithmically increasing size intervals against the

total biomass for each interval, normalised by the width of the

interval – the slope indicates the efficiency of biomass transfer to

larger organisms

ImageJ General image analysis program

PlanktonJ Zooplankton image analysis program currently being developed

vi

Table of Contents

ACKNOWLEDGMENTS .................................................................................................... III

ABSTRACT ............................................................................................................................ IV

GLOSSARY ............................................................................................................................. V

TABLE OF CONTENTS ....................................................................................................... VI

LIST OF FIGURES ............................................................................................................ VIII

LIST OF TABLES ............................................................................................................... XII

1 INTRODUCTION ............................................................................................................ 1

2 LITERATURE REVIEW ................................................................................................ 3

2.1 CORAL REEFS AS TROPHIC SYSTEMS ........................................................................... 3

2.1.1 Benthic-pelagic coupling ...................................................................................... 4

2.2 ZOOPLANKTON DYNAMICS ............................................................................................. 5

2.2.1 Scale of studies ..................................................................................................... 5

2.3 ZOOPLANKTON AS PART OF THE TROPHIC STRUCTURE OF CORAL REEFS ....... 6

2.4 MOTIVATION FOR STUDY ................................................................................................ 8

2.5 NINGALOO REEF ................................................................................................................. 8

2.5.1 Physical oceanography ........................................................................................ 9

2.5.2 Biological oceanography ................................................................................... 11

2.6 IMAGE ANALYSIS ............................................................................................................. 11

2.6.1 Automated and semi-automated techniques ....................................................... 12

2.6.2 Using image analysis to estimate zooplankton biomass .................................... 14

2.7 SIZE STRUCTURE ANALYSIS ......................................................................................... 16

3 METHODOLOGY ......................................................................................................... 19

3.1 STUDY SITE ........................................................................................................................ 19

3.2 FIELD WORK ...................................................................................................................... 21

3.2.1 Water column data collection ............................................................................ 22

3.2.2 Water sampling .................................................................................................. 23

3.2.3 Zooplankton tows ............................................................................................... 23

3.3 PRELIMINARY LABORATORY WORK .......................................................................... 25

3.3.1 Zooplankton samples .......................................................................................... 25

3.3.2 Phytoplankton ..................................................................................................... 25

vii

3.3.3 Chlorophyll ......................................................................................................... 26

3.4 IMAGE ANALYSIS ............................................................................................................. 26

3.4.1 Sample preparation ............................................................................................ 27

3.4.2 Microscope ......................................................................................................... 28

3.4.3 Scanner ............................................................................................................... 30

3.5 DATA ANALYSIS ............................................................................................................... 35

3.5.1 Biovolume and biomass ...................................................................................... 36

3.5.2 Normalized biomass-size spectra ....................................................................... 36

3.5.3 Taxonomy ........................................................................................................... 38

4 RESULTS ........................................................................................................................ 41

4.1 CHLOROPHYLL DATA ...................................................................................................... 41

4.2 ZOOPLANKTON ABUNDANCE AND BIOMASS ........................................................... 43

4.2.1 Biomass .............................................................................................................. 43

4.2.2 Abundance .......................................................................................................... 45

4.2.3 Mean zooplankton size ....................................................................................... 46

4.3 COMMUNITY STRUCTURE .............................................................................................. 47

4.3.1 Biomass-size spectra .......................................................................................... 47

4.3.2 Taxonomy ........................................................................................................... 51

5 DISCUSSION ................................................................................................................. 55

5.1 SOURCE WATERS .............................................................................................................. 56

5.2 TRENDS ACROSS THE REEF ........................................................................................... 58

5.2.1 Chlorophyll and phaeophyton ............................................................................ 58

5.2.2 Biomass and abundance ..................................................................................... 59

5.2.3 Community structure .......................................................................................... 59

5.3 VALIDITY OF THE IMAGE ANALYSIS TECHNIQUE .................................................. 63

5.4 CONCLUDING REMARKS ................................................................................................ 65

6 CONCLUSIONS............................................................................................................. 67

7 RECOMMENDATIONS ............................................................................................... 69

REFERENCES ....................................................................................................................... 70

APPENDICES ........................................................................................................................ 74

APPENDIX A: SAMPLING AND IMAGE ANALYSIS DETAILS FOR EACH SAMPLE ......... 75

APPENDIX B: PROTOCOL FOR PREPARATION OF IMAGE ANALYSIS SAMPLES ........... 77

APPENDIX C: NB-S SPECTRA MATLAB CODE ........................................................................ 78

viii

List of Figures

Figure 2.1: Map of Ningaloo Marine Park (Department of the Environment and Water

Resources 2006). ........................................................................................................................ 9

Figure 2.2: Simplified version of the Leeuwin Current down the Western Australian coast

(Caputi et al. 1996). .................................................................................................................. 10

Figure 2.3: Generalized pattern of currents around Ningaloo Reef (Woo et al. 2006) ........... 11

Figure 2.4: Stylised representation of a normalised biomass-size spectrum. ........................... 17

Figure 2.5: Log normalised relationship between the zooplankton particle concentration and

particle size class in the disturbed and undisturbed regions. Note that e.s.d. is equivalent

spherical diameter (modified from Rissik et al. [1997]) .......................................................... 18

Figure 3.1: North West Cape - Sandy Bay around 45km south-west of Exmouth. (Adapted

from Waite et al. (2007)) .......................................................................................................... 19

Figure 3.2: Northern and southern reef sections, on the right and left of the photo respectively

Sandy Bay (D. Krikke) ............................................................................................................. 20

Figure 3.3: Sampling sites at Sandy Bay, with stylised version of water flow. Note the

inclusion of site R02, which was originally considered, but was not included in the final set

decided on for sampling (Adapted from Waite et al. (2007)). ................................................ 21

Figure 3.4: a) Deploying the LI-COR, b) Hydrolab taking measurements. ............................. 23

Figure 3.5: Simple schematic diagram of the plankton net used, with the beaker attached to

the end. The dimensions of the net were 105µm. .................................................................... 24

Figure 3.6: (a) Filter stack (b) 2mL, 5mL and 10mL sub-samplers. ........................................ 27

Figure 3.7: (a) Bogorov tray (b) Microscope ........................................................................... 29

Figure 3.8: Image taken using a digital camera attached to a microscope. The lines on the

copepod represent those which were drawn in ImageJ to measure the major and minor axes of

the copepod approximated as an ellipse. .................................................................................. 30

Figure 3.9: Scanner with sample trays. .................................................................................... 31

ix

Figure 3.10: Vignettes produced by PlanktonJ (clockwise from top left): copepod; crab

larvae; euphausid; decapod. ..................................................................................................... 33

Figure 3.11: Main taxa groups found in samples. Note that the pictures are not to scale. ....... 39

Figure 4.1: Mean of the chlorophyll a measurements (mgL-1

) at each station moving from the

outside or fore-reef (R07, R06) across the reef (R05) into the lagoon (R04, R03) and the

channel (C01). The error bars represent one standard error above and below the mean. ........ 41

Figure 4.2: Mean of the >5µm chlorophyll a measurements (mgL-1

) at each station moving

across the reef as in Figure 4.1. The error bars represent one standard error above and below

the mean. .................................................................................................................................. 41

Figure 4.3: Mean of the phaeophyton a measurements (mgL-1

) at each station moving from

the outside or fore-reef (R07, R06) across the reef (R05) into the lagoon (R04, R03) and the


Figure 4.4: Mean of the chlorophyll a to phaeophyton a ratio at each station moving from the

outside or fore-reef (R07, R06) across the reef (R05) into the lagoon (R04, R03) and the


Figure 4.5: Biomass (mg C m-3

) across the reef for the smallest size fraction (filter dimension

of 105µm), from the fore-reef (R07, R06) to the reef break (R05) and into the lagoon and the

channel (R04, R03, C01). Tow E was conducted between the 2nd

and 4th

May and Tow L

between the 21st and 23

rd May. Note the negative trend from the fore-reef to the lagoon and

the spike in biomass at the reef station R05. ............................................................................ 43

Figure 4.6: Mean of the normalized biomass of the smallest size fraction (105µm) at each

station for the two tows. The biomass is normalized to be a fraction of the highest biomass in

each of the tows. The error bars indicate the standard error for the calculated mean at each

point. ......................................................................................................................................... 44

Figure 4.7: Total biomass across the reef (mg C m-3

) from the fore-reef (R07, R06) to the reef

break (R05) into the lagoon and channel (R04, R03, C01). Tow E was conducted between the

2nd

and 4th

May and Tow L between the 21st and 23

rd May. Note the negative trend from the

fore-reef to the lagoon and the large spike in biomass at the reef station R06. ....................... 44

x

Figure 4.8: Mean of the normalized total biomass at each station for the two tows. The

biomass is normalized to be a fraction of the highest biomass in each of the tows. The error

bars indicate the standard error for the calculated mean at each point. ................................... 45

Figure 4.9: Abundance in individuals per m3 at each station across the reef for tow E and tow

L. See section 4.2.1 for further tow and station details. ........................................................... 45

Figure 4.10: Mean of the normalized abundance for tow E and tow L at each station. The

abundance is normalized to be a fraction of the highest abundance for each tow. The error

bars represent the standard error of each calculated mean. ...................................................... 46

Figure 4.11: Mean of the values for mean zooplankton ESD (mm) for the two tows at each

station across the reef. The error bars represent the standard error of each calculated mean.

See section 4.2.1 for more details on the tows and stations. .................................................... 46

Figure 4.12: Raw biomass-size spectrum displaying total biovolume (mm3m

-3) distributed

over even size classes of ESD (mm) for tow E of station R07. ............................................... 47


-3) distributed

over even size classes of ESD (mm) for tow L of R07. ........................................................... 47


-3) distributed

over even size classes of ESD (mm) for tow E of R06. Note the differing scale of the vertical

axis compared to Figure 4.12 and Figure 4.13 due to the much larger biovolume of station

R06. .......................................................................................................................................... 48

Figure 4.15: NB-S spectrum for tow E at station R07. Normalized biovolume (total

biovolume for each size class, divided by the mean of that size class) plotted against the size

classes (mm3 per individual). ................................................................................................... 48

Figure 4.16: NB-S spectrum for tow L at station R07. Normalized biovolume (total





classes (mm3 per individual) .................................................................................................... 49

xi

Figure 4.18: NB-S spectrum for tow L at station R05. Normalized biovolume (total






Figure 4.20: Copepod abundance, as a percentage of total abundance, across the reef, from the

outside stations (R07, R06) to the reef station (R05) to the lagoon and the channel (R04, R03,

C01). ......................................................................................................................................... 52

Figure 4.21: Copepod biovolume, as a percentage of total biovolume, across the reef, from the

outside stations (R07, R06) to the reef station (R05) to the lagoon and the channel (R04, R03,

C01). ......................................................................................................................................... 52

Figure 4.22: General taxa abundance, number of individuals m-3

, for tow E of R06 .............. 53


, for tow L of R06. ............. 53


, for tow E of R07 .............. 53


, for tow L of R07. ............. 54


, for tow E of lagoon station

R04. .......................................................................................................................................... 54

Figure 5.1: Changes in chlorophyll (mgL-1

) for outside reef stations R06 and R07 over the

month of sampling. ................................................................................................................... 56

Figure 5.2: Total and copepod abundance m-3

for tow E and tow L of R06 (a) and R07 (b). . 57

Figure 5.3: Change of the chlorophyll a to phaeophyton ratio over the month of sampling at

outside reef station R06. ........................................................................................................... 61

Figure 5.4: Concentration (mgL-1

) of chlorophyll a and phaeophyton a over the month of

sampling at reef station R05. Note the consistency of the data when compared to Figure 5.1.

.................................................................................................................................................. 63

xii

List of Tables

Table 3.1: Record of complete datasets achieved for each station. .......................................... 35

Table 3.2: Size intervals for the NB-S spectra describing the upper and lower limits in terms

of ESD, the geometric mean ESD of each interval and the equivalent biovolume. ................. 37

Table 4.1: List of the slopes and correlation coefficients for the NB-S spectra, demonstrating

that each graph has a p-value of less then 0.001. ..................................................................... 51

Table 4.2: Abundance of net tows for each station. Abundance of taxa groups is rounded to

the nearest percent and only those over 1% are shown. ........................................................... 51

Chapter 1 Introduction

1

1 Introduction

Plankton form the base of the marine food web, supporting the vast majority of marine

production (Tang et al. 1998, Barnes and Hughes 1999). Coral reefs have traditionally been

viewed as systems closed to inputs from the oceanic pelagic community, islands of high

biodiversity and biomass in a relatively sparse ocean (Odum and Odum 1955); however, reef

systems are now considered to be more affected by external forcing and trans-boundary fluxes

then earlier models suggested. This idea of a more open reef system, stripping plankton and

nutrients from the ocean water as it flows over the reef, puts a new emphasis on the

importance of the pelagic marine environment in the trophic structure of coral reefs (Hatcher

1997, Bozec et al. 2004).

The objective of this study was to undertake a detailed investigation of changes in the daytime

pelagic zooplankton community structure across a reef, with a view to contributing to the

understanding of the role of the pelagic marine environment in a coral reef system. The site

selected for this study is a reef section of Ningaloo Reef, offshore from Sandy Bay in the

Cape Range National Park, Western Australia. Ningaloo Reef is the only extensive fringing

reef in the world found on a west coast (Taylor and Pearce 1999) and is considered to be of

great ecological significance due to the diverse biota and iconic megafauna found in the area

(Commonwealth of Australia 2002). The combination of a growing international reputation

for nature-based tourism at Ningaloo and an expanding industrial presence on the North West

Shelf increases the need for mechanistic understanding of the influence of oceanic inputs on

the reef, and development of sustainable management strategies in the context of continuing

global change.

The sampling regime of the study involved taking water samples and conducting zooplankton

tows at six stations across a transect of the reef section and in the channel between the reef

sections. The water samples were analysed to produce chlorophyll a data and the tow samples

were preserved for image analysis. Image analysis of the tow samples was used to provide

data on the changes in zooplankton community structure and total zooplankton biomass

across the reef. The image analysis program PlanktonJ, which is currently being developed at

CSIRO, was used to analyse the samples and feedback on the program was provided to

CSIRO, allowing further development.

Chapter 1 Introduction

2

This study is part of the Ningaloo Biological Oceanography Project being run by Associate

Professor Anya Waite. Through a detailed investigation of changes in the daytime pelagic

zooplankton community structure across Ningaloo Reef and in conjunction with a benthic-

pelagic coupling study being conducted by Alex Wyatt and a phytoplankton study being

conducted by Danielle Kapeli, both at the same study site, it is intended that this study will

contribute to the understanding of the influence of oceanic inputs and benthic pelagic

coupling on coral reefs.

This dissertation is divided into chapters as follows: Chapter 2 presents a critical discussion of

literature in the areas of coral reefs as trophic systems, benthic-pelagic coupling, zooplankton

dynamics and zooplankton as a part of the trophic system of coral reefs. The motivation for

this study is then presented, followed by a brief description of Ningaloo Reef and a critical

discussion of the literature regarding the methods of image analysis and size structure

analysis; Chapter 3 presents the methodology used to undertake the study, including field

methodology, lab methodology and data analysis; Chapter 4 presents the results obtained

from the methodology described in Chapter 3; Chapter 5 presents a discussion of the results

obtained and Chapter 6 and Chapter 7 present the conclusions that can be drawn from the

study and recommendations for future work, respectively.

Chapter 2 Literature Review

3

2 Literature Review

This chapter commences with a review of the current literature regarding: the trophic structure

of coral reefs and the benthic-pelagic coupling which occurs; the dynamics of zooplankton

communities with a focus on the wide range of scales at which plankton studies can be

conducted; and zooplankton as a part of the trophic structure of coral reefs and the

interactions which occur between the pelagic zooplankton communities and the benthic reef

communities. The motivation for this study is then presented, followed by a brief description

of Ningaloo Reef and a discussion of the literature regarding the methods of image analysis

and size structure analysis.

2.1 Coral reefs as trophic systems

An important consideration when observing community structure is the trophic interactions of

the community; trophic structure can be considered a characteristic feature of all ecosystems

(Nybakken 2001). Odum and Odum (1955) postulated that there was much that could be

learnt from the trophic structure of a reef community, specifically relating to the relationships

between productivity, efficiency and the standing crop structure of the community. The

trophic spectrum has also been shown to be useful as an ecosystem indicator to compare

systems in time and space; with regard to coral reefs, it has been used to display the impacts

of fishing and change of habitat on the trophic structure of coral reef fish communities and the

associated affects on the reefs (Gascuel et al. 2005, Mumby et al. 2007).

Citing a lack of studies regarding the flux of energy and matter in reef ecosystems, Arias-

Gonzalez et al. (1997) modelled the trophic structure of two reef habitats, a fringing and a

barrier reef in the same area, in order to determine the primary energy pathways in the two

reef types. It was established that the trophic structures of both reef habitats incorporated two

forms of internal recycling to efficiently conserve energy within the ecosystems: one

involving detritus and a microbial food web and a second involving predation. Furthermore,

the results from the models suggested a ‘globally efficient and rapid use of energy within reef

ecosystems’ (Arias-Gonzalez et al. 1997 pg. 244).

Coral reefs have traditionally been viewed as closed systems, islands of biodiversity and high

biomass in a relatively sparse ocean, within the boundaries of which accurate budgets of

biomass and energy transfer may be derived (eg. Odum and Odum 1955). Later models have

shifted to the idea that reef systems may be more affected by external forcing and trans-


4

boundary fluxes then the earlier models suggested. This view of a more open reef system,

which strips plankton and nutrients from the ocean water as it flows over the reef, puts a new

emphasis on the importance of the pelagic marine environment in the trophic structure of

coral reefs and therefore on benthic-pelagic coupling (Hatcher 1997, Bozec et al. 2004).

2.1.1 Benthic-pelagic coupling

While there have been several comprehensive studies on the trophic interactions between reef

species, there has been minimal intensive study on the planktonic food web of coral reefs and

comprehensive studies investigating the benthic-pelagic coupling in reef systems are scarce

(Bozec et al. 2004). In fact, it would seem that despite the importance of pelagic marine

organisms as a potential reef food source and their global influence on biomass production

and atmospheric composition, their diversity and trophic importance in reef systems has not

been well studied (Barnes and Hughes 1999, Duffy and Stachowicz 2006).

Investigations and models of the benthic-pelagic coupling of the ocean floor (sediment) and

the water column are available in the literature, although there has traditionally been a focus

on the deposition of detritus, or non-living organic matter, to the seabed (Raffaelli et al. 2003,

Porter et al. 2004). Studies accounting for and observing benthic-pelagic coupling on coral

reefs are less common (eg. Linley and Koop 1986, Bozec et al. 2004) and there is a need for

further investigations into this area.

Bozec et al. (2004) accounted for both the pelagic and benthic communities in order to model

the complete trophic structure of a shallow lagoon. It was established that the benthic

community required an input of food (specifically zooplankton) from the pelagic community

in order to sustain the biomass of predatory fishes. While predation was established as a major

structuring force in the trophic food web, it was also noted that water circulation within the

lagoon influenced the amount of primary resources (such as plankton and detritus). This

included the possibility of water flow passing over the lagoon and exporting phytoplankton

and zooplankton biomass into the open ocean (Bozec et al. 2004). This emphasises the

importance of oceanic inputs, through the pelagic community, in the trophic structure of coral

reefs.


5

2.2 Zooplankton dynamics

Plankton are a fundamental part of marine ecosystems, forming the base for a vast majority of

marine food resources, and an understanding of the ecological and physical processes

controlling the population dynamics of plankton communities, over a wide range of scales, is

essential for understanding and predicting the impacts of climate change and human activities

on the marine environment (Tang et al. 1998). Zooplankton form the larger part of the size

spectrum of plankton communities; they are between 100µm and 5000µm in size and feed on

other organisms, defining them as heterotrophic (Waite and Suthers 2007). These meso-

planktonic organisms have the important function of channelling matter and energy towards

higher trophic levels and constitute the main food source for many important fish stocks

(Nogueira et al. 2004).

Often the trophic status of plankton can be difficult to determine; many marine invertebrates

are defined as omnivorous, meaning that they cannot be assigned to a particular trophic level.

Zooplankton size can often be used to determine production (ie. secondary and tertiary

producers), and relationships between size and trophic level can be used to describe

ecosystem structure (Waite and Suthers 2007).

2.2.1 Scale of studies

It is important to understand the processes controlling plankton dynamics over a wide range

of time and space scales (Daly and Smith 1993, Tang et al. 1998). Investigations into

zooplankton dynamics have considered scales ranging from the Atlantic Ocean (San-Martin et

al. 2006) to a continental shelf (Williams et al. 1988, Nogueira et al. 2004) and down to a

coastal reef lagoon (Gilabert 2001). Alcaraz et al. (2007) considered small, meso- and large

scales in their investigation into the physical controls of zooplankton communities in the

Catalan Sea and established that ‘the spatial distribution of zooplankton biomass and

metabolic rates appeared to be closely related to the physical characteristics of the different

hydrographic features’ (Alcaraz et al. 2007 pg. 294).

The study conducted by San Martin et al. (2006) is a good example of an investigation into

plankton dynamics on a large scale. Citing a lack of data on which open ocean plankton

biomass-size spectra can be constructed, San Martin et al. (2006) calculated the normalized

biomass-size spectra of plankton communities, as well as the mean zooplankton size, over a

latitudinal distribution in the Atlantic Ocean. It was discovered that higher latitude areas and


6

areas around the equator contained the highest zooplankton biomass and the highest mean

zooplankton size, with a dip in biomass and size found in the oligotrophic gyres in the middle

latitudes. Essentially, zooplankton size followed the same pattern as production; the more

productive areas contained larger zooplankton (San-Martin et al. 2006).

On a smaller scale, Strzelecki et al. (in press) compared the structure and biomass of the

zooplankton communities in a pair of eddies formed by the Leeuwin Current, and compared

these to the parent water masses from which the eddies were formed. Nogueira et al. (2004)

observed the distribution of mesoplankton biomass and size structure along the Northwest and

North Iberian Shelf, and the relationship with the hydrographic context, in order to justify the

use of the in situ optical plankton counter. Williams et al. (1988) used a transect across the

outer continental shelf at the Great Barrier Reef to observe the distribution of copepods across

the shelf and found that the biomass of surface copepods decreased with distance from the

shore; however, there was no gradient for the bottom samples.

On a smaller scale again, Gilabert (2001) observed the seasonal changes in the planktonic size

structure in a Mediterranean coastal lagoon; however, the whole planktonic spectrum from

pico- to mesoplankton was considered and there was no specific focus on zooplankton. The

present study has adopted a similar scale to that used by Gilabert (2001), with the focus on

zooplankton communities across a reef.

2.3 Zooplankton as part of the trophic structure of coral reefs

The spatial distribution and abundance of zooplankton has been shown to be closely linked

with the feeding of benthic communities, specifically suspension feeders (Gili and Coma

1998, Yahel et al. 2005). The densities of oceanic zooplankton entering and crossing a reef

have been found to decrease across the reef and it has been presumed that this is due to

predation by planktivorous fish (Williams et al. 1988, Roman et al. 1990). As well as

plankton being washed in from oceanic surface waters, upwelling can cause near-bottom

populations of zooplankton to become available for predation by reef fish (Williams et al.

1988).

While it has often been assumed that zooplankton found around coral reefs are brought in

from surrounding oceanic waters, increases in zooplankton biomass around reefs at night time

suggests that there is also an important community of resident demersal reef zooplankton

(Roman et al. 1990). Sale et al. (1976) demonstrated that the abundance of zooplankton on the


7

substratum of a reef was higher than that at the surface, suggesting that certain organisms

were perhaps epibenthic rather than strictly planktonic as they were able to maintain position

with respect to the reef.

Corals are able to behave as autotrophs, with the symbiotic zooxanthellae carrying out

photosynthesis, or as heterotrophs feeding on bacteria and zooplankton found in the water

column (Muscatine and Porter 1977). Coral reef zooplankton can be seen as an important

trophic link between primary producers and higher trophic levels on coral reefs (Heidelberg et

al. 2004); however, the level to which they are essential for coral growth is a topic that has

been extensively debated (Johannes et al. 1970, Muscatine and Porter 1977). Despite corals

having developed mechanisms to catch prey, such as sieving particles out of the water column

(Sebens et al. 1996), rarely is zooplanktonic food available in concentrations great enough to

satisfy the daily energy requirements of coral (Muscatine and Porter 1977). Due to this, it has

been postulated that zooplankton capture is likely to be important to corals as a source of

nutrients which are unable to be obtained through photosynthesis (Johannes et al. 1970,

Sebens et al. 1996).

The predation of corals on zooplankton can also be seen in the near-bottom depletion of

zooplankton over coral reefs (Yahel et al. 2005). Depletion has been found to be greatest at

night, due to the fact that coral reef planktivores capture much of their prey at dusk, dawn and

during the night when the zooplankton are migrating up and down through the tentacles of the

coral and habitats of the reef fish (Sebens et al. 1996). While near-bottom depletion of

zooplankton is partly due to predation, it is also due to avoidance mechanisms used by the

zooplankton and was found to be greatest for taxa groups deemed to have strong swimming

ability (copepods and polychaetes) (Holzman et al. 2005). Sebens et al. (1996) found that

while small size classes of zooplankton were not detected efficiently by the coral and large

size classes had very effective escape behaviours, intermediate size classes were most

effective in avoidance of coral predation.

The extensive interaction of zooplankton populations with the benthic community of coral

reefs highlights the need for a more detailed understanding of zooplankton dynamics in coral

reef systems.


8

2.4 Motivation for study

There is currently a lack of studies on the benthic-pelagic interactions occurring on coral

reefs, specifically with regard to the role of zooplankton. Historically, investigations into the

plankton communities of coral reefs have focused on atolls and barrier reefs (Sale et al. 1976,

Williams et al. 1988, Roman et al. 1990), although there have also been studies into the

plankton dynamics of coastal lagoons (eg. Gilabert 2001).

It is intended that this study will contribute to the understanding of benthic-pelagic

interactions and the influence of oceanic inputs on coral reefs through a detailed investigation

of changes in the pelagic zooplankton community structure across the reef. The location of

this study at Ningaloo Reef, off the north-west coast of Australia, will also ensure that a

contribution is made to the currently limited literature regarding zooplankton communities

across fringing reefs.

2.5 Ningaloo Reef

Ningaloo Reef stretches approximately 260km down the coast of Western Australia from

North West Cape to Coral Bay, and is adjacent to the Cape Range National Park (Figure 2.1).

The continental shelf in the northern area of Ningaloo is the narrowest in Australia with the

shelf break at a depth of approximately 100m occurring 6 to 10km offshore (Taylor and

Pearce 1999). Ningaloo is the only extensive fringing reef found on a west coast in the world,

partially enclosing a coastal lagoon and ranging from 200m to 7km offshore (Taylor and

Pearce 1999). The ecological significance of Ningaloo Reef is defined by the diverse biota

found on the reef, including over 500 species of fish, 200 species of coral and 600 species of

molluscs (Commonwealth of Australia 2002). The reef also attracts iconic megafauna such as

whale sharks and manta rays. The combination of the unique wildlife and the accessibility of

the reef from the shore has Ningaloo rapidly gaining an international reputation for nature-

based tourism (Commonwealth of Australia 2002).


9

Figure 2.1: Map of Ningaloo Marine Park (Department of the Environment and Water Resources 2006).

In order to effectively preserve Ningaloo Reef, there must be an understanding of the

interaction between the biophysical situation of the ocean and the biophysical situation of the

reef. Without this understanding, it is impossible to predict the impact on the reef of

anthropogenic and climatically induced changes in the surrounding ocean (Wyatt 2007).

2.5.1 Physical oceanography

The dominant current system found off the coast of Western Australia (WA) is the

oligotrophic (nutrient sparse) Leeuwin Current (Figure 2.2). The Leeuwin Current is defined

as an eastern boundary current, but is unlike other eastern boundary currents in the Southern

Hemisphere as it flows poleward and is downwelling (Caputi et al. 1996). The warm, low

nutrient waters of the Leeuwin Current are a major influence on the abundance of marine

species off the WA coast; an example of this is the relatively high occurrence of sea-floor

invertebrate species in Western Australian waters compared with finfish (Caputi et al. 1996).


10

Figure 2.2: Simplified version of the Leeuwin Current down the

Western Australian coast (Caputi et al. 1996).

Other currents in the Ningaloo Reef area are the Ningaloo Current and the Capes Current

(Figure 2.3). These currents are wind generated and flow towards the equator as

countercurrents to the Leeuwin Current, generating localized upwelling (Hanson et al. 2005).

The counter-currents are dominant over the summer, when the Leeuwin Current is weakest,

with the Leeuwin Current being dominant over autumn and winter (Smith et al. 1991, Gianotti

2003).


11

Figure 2.3: Generalized pattern of currents around

Ningaloo Reef (Woo et al. 2006)

2.5.2 Biological oceanography

Physical oceanography is strongly linked with biological oceanography and this can be seen

in the Leeuwin Current (specifically within the eddies that are formed) (Waite et al. 2006), as

well as in the interaction between the Leeuwin Current and the Ningaloo Current. For

example, the upwelling caused by the Ningaloo Current brings nutrients to the surface and is

likely to be responsible for the active food chain around Ningaloo reef over the summer and

the presence of whale sharks just after this time (Taylor and Pearce 1999, Hanson et al. 2005).

McKinnon and Duggan (2003) found that copepod abundance, biomass and production off

the North West Cape decreased dramatically between the summer of 1997/1998 and

1998/1999, corresponding with the increase in strength of the Leeuwin Current due to the

change from El Nino to El Nina.

2.6 Image analysis

Image analysis is a useful tool for minimizing tedious microscope work and provides a non-

destructive method for determining biovolume (Alcaraz et al. 2003). In general, it involves


12

the capture of images using a scanner or a digital camera attached to a microscope and the

analysis of these images using image analysis software.

While there have been many updates to the technology, automated and semi-automated image

analysis has been used for plankton biomass calculations since the 1980s (Jeffries et al. 1984,

Bjornsen 1986). It was established that image analysis could be used to efficiently process

large numbers of samples, measuring numbers of organisms or cells, their dimensions and

biovolumes (Krambeck et al. 1981, Bjornsen 1986, Estep et al. 1986, Legendre and Yentsch

1989). Previously, the biovolume technique for establishing the biomass of a plankton

population had been rarely used as it was extremely tedious and time consuming (Krambeck

et al. 1981). Another benefit of the image analysis technique is that it provides information on

the population structure, rather than just an overall biomass number (Krambeck et al. 1981).

2.6.1 Automated and semi-automated techniques

Both automated and semi-automated techniques of image analysis have been used to analyse

plankton communities and there are benefits and limitations to both. As a general rule,

automated image analysis has been used to analyse smaller matter; for example, to discover

the biomass of the smaller types of plankton, such as bacterioplankton, phytoplankton and

nano- and microzooplankton, as well as to discover quantities of suspended particulate matter

(Bjornsen 1986, Verity et al. 1996, Lam and Bishop 2007). For analysis of the larger

mesozooplankton, semi-automated techniques are frequently used to allow for manual

distinction of different species and particulate matter, as well as more accurate measurements

given the often complex shapes of the animals (Billones et al. 1999, Alcaraz et al. 2003).

There is significant variation in the techniques used and both techniques are used for all sizes

of plankton. There has been software developed for the automated image analysis of

mesozooplankton and recently there have been significant advances in the development of

automatic classification of zooplankton (Jeffries et al. 1984, Tang et al. 1998, Culverhouse et

al. 2006).

An example of automated image analysis is the application of image analysis to

epifluorescence microscopy by Bjornsen (1986), in order to determine bacterioplankton

biomass. The plankton samples were stained with acridine orange, filtered onto pre-stained

black filters and placed under a microscope. The contrast of the plankton to the background

allowed a computer system to analyse the images taken by a camera attached to the

microscope, producing values for the area and perimeter of each particle. Using these values,


13

the biovolume was calculated and then converted to biomass (Bjornsen 1986). Verity et al.

(1996) used this same method for bacterioplankton and used a very similar method for

phytoplankton and nano- and microzooplankton, staining them with proflavine and

diamindinophenylindole rather then acridine orange.

Lam and Bishop (2007) used a similar method of automated image analysis to those described

above to determine particle loadings at various depths in the Southern Ocean. Rather then

epifluoresence, this method used more advanced camera technology to compare the image

obtained from a blank filter to that of a sample filter. This allowed the development of a

relationship between the optical density of the images and the amount of material on the filter,

providing an estimate of the relative particle loadings at each depth (Lam and Bishop 2007).

Semi-automated image analysis still involves a significant amount of automated computer

work; however, to achieve the final result, there is an element of human input or influence in

the procedure. This is sometimes necessary to achieve the desired results. Krambeck et al.

(1981) tested automatic systems for pattern recognition of bacteria and established that only

half of the bacteria were detected. It was decided that this was due to some of the properties of

the images obtained from the scanning electron microscope (such as shadows), so the system

was designed to be semi-automated, leaving the complex pattern recognition to the human

brain (Krambeck et al. 1981).

Automated image analysis systems often have difficulty recognising and accurately

measuring individual zooplankton, so it may be necessary to manually choose and measure

them. Billones et al. (1999) used the Magiscan Imaging System to manually measure the

length and width of each zooplankton body, using these measurements to calculate the

biovolume, and hence biomass, of the zooplankton. The biovolume was calculated by

approximating the zooplankton body as an ellipsoid. A similar method was employed by

Alcaraz et al. (2003); however, the operator only had to decide which particles in the image

were zooplankton and which were detritus, as the measurements of the chosen zooplankton

were automatic. This approach allowed the operator discretion and control over the particles

that were chosen to be zooplankton and also allowed accurate classification of the organisms

if required (Alcaraz et al. 2003).

While some authors have chosen to use a level of manual input to ensure accurate

measurement and identification, others have developed pattern recognition systems in an

attempt to automatically identify and measure zooplankton in a sample using image analysis


14

(Jeffries et al. 1984, Tang et al. 1998). Jeffries et al. (1984) created a database of manually

measured morphological features of eight taxonomic groups of zooplankton commonly

occurring off the coast of New England and used it to automatically measure and classify the

zooplankton using an image analysis system. They were able to achieve automatic

classification of the eight major taxonomic groups; however, genus or species identification

was not possible. Issues related to the orientation of the plankton and the optical contrast

produced were encountered (Jeffries et al. 1984). It should also be mentioned that the system

was tested on contrived samples containing only selected and well placed zooplankton with

no detritus, or clusters of plankton, such as there would be in a field sample.

Tang et al. (1998) developed a pattern recognition system which was able to accurately

classify six plankton taxa using classification algorithms, which could potentially be attached

to the Video Plankton Recorder (VPR) system to allow automatic classification of plankton in

real time at sea. This technique would allow more accurate mapping of plankton abundance in

space and time, than is currently possible with conventional techniques; however, it will be

necessary to develop further algorithms to classify larger numbers of plankton taxa and this

system must still be integrated into the VPR system (Tang et al. 1998). Culverhouse et al.

(2006) established that for accurate large scale automatic classification of species to occur,

there must be very large databases of 3D rotatable images of plankton species available.

Whereas automatic classification of zooplankton is complex and problematic, automatic

measurement without classification is slightly more straightforward. Where Alcaraz et al.

(2003) used manual interaction to separate the zooplankton from the detritus for automatic

measurement, San Martin et al. (2006) stained the zooplankton with eosin allowing them to be

recognized by the image analysis software, Plankton Visual Analyser. The software then

measured the equivalent spherical diameter (ESD) of each plankton (>200µm); the ESD was

used to calculate the volume and the biomass was calculated following the methods of

Alcaraz et al. (2003). These methods are described in more detail in Section 2.6.2.

2.6.2 Using image analysis to estimate zooplankton biomass

As described above, an important step when using image analysis to analyse a zooplankton

sample is to decide on the level of manual interaction in the process. This is determined by the

level of automation in the image analysis software, the level of complexity within the sample

and the data set required from analysis of the sample. Following image analysis, a set of


15

measurements of the zooplankton will be produced, which can be manipulated to produce the

biovolume of the sample and subsequently the biomass.

Billones et al. (1999) captured images of estuarine copepods using a camera attached to a

microscope, and manually measured their body length and width using the Magiscan Imaging

System. The copepods were assumed to be of an ellipsoid shape and the length and width

measurements were used to produce a body volume according to Equation 1:

𝑉𝐵 = 4

3 𝜋

𝐿

2

𝑊

2

2

(1)

Where L = body length; W = body width; VB = volume of body.

The body volume was then converted into carbon weight, or biomass, according to the

following conversion factors:

106 µm

3 body volume = 1 mg wet weight;

dry weight = 20% wet weight;

carbon weight = 45% dry weight.

Comparing the biomass values produced using the above method with literature data

concerning the same copepod species, Billones et al. (1999) determined that biomass

measured by image analysis was comparable to that measured using other methods.

As noted in Section 2.6.1 a similar, although more automated, method to that of Billones et al.

(1999) was employed by Alcaraz et al. (2003). Rather than relying on conversion factors to

convert biovolume to biomass, Alcaraz et al. (2003) analysed the zooplankton samples with a

Carlo-Erba C-H-N Analyser to obtain carbon (C) and nitrogen (N) contents of the samples.

Aliquots containing 150-350 individuals were taken from the zooplankton samples and

images were captured of all individuals present using an EDI CCD video camera. These

images were analysed using the software NIH-Image 1.62. The software automatically fitted

an ellipse to the area of each organism, providing the major and minor axes of each ellipse;

these were then used to calculate the approximate volume of the organism, as it was assumed

to be equivalent to the ellipsoid volume (see Equation 1 above). Using these methods, Alcaraz

et al. (2003) were able to calculate the relationship between the zooplankton biovolume and

biomass.


16

Alcaraz et al. (2003) also compared the C and N measurements for the formalin preserved

zooplankton samples with those of fresh samples. The percentage loss of organic C and N was

found to be comparable to the percentage reduction observed in the dry weight of copepods

under similar conditions. There are also aspects to be considered when using formalin

preserved samples for image analysis. Omori (1978) noted a significant decrease in the weight

of zooplankton following preservation by formaldehyde and Alcaraz et al. (1998) found that

salps are very easily affected by the preservation process and their gelatinous bodies are often

shrunken and deformed; the only structure within the salp not affected by preservation is the

nucleus. Alcaraz et al. (2003) made use of this fact by measuring only the nucleus of the salp

using image analysis and then establishing the relationship of the nucleus of the salp with the

C and N contents of the whole volume.

The method of calculating zooplankton biomass described by Alcaraz et al. (2003) has been

used in several other studies (eg. San-Martin et al. 2006, Alcaraz et al. 2007, Strzelecki et al.

in press). Strzelecki et al. (in press) altered the method slightly by assuming the shape of

crustaceans to be an ellipsoid, but the shape of siphonophores to be a cube and the shape of

chaetognaths and appendicularians to be a cylinder.

2.7 Size structure analysis

The discovery of regular patterns in the size distribution of particles in the ocean by Sheldon

et al. (1972), and the extension of this idea by Platt and Denman (1978) to create a normalized

biomass-size (NB-S) spectrum of the pelagic zone, has led to the possibility of using the size

structure of pelagic communities to compare ecosystems with different species composition

(San-Martin et al. 2006). It should also be mentioned that that there is a large body of

published work which discusses the dependence of organism size on physiological processes,

enhancing the reliability of using NB-S distributions (Platt and Denman 1978). A NB-S

spectrum is created by dividing biomass into size classes and plotting the total normalised

biomass for each size class against the size classes on a log-log plot (Figure 2.4); the spectrum

should produce a linear trend with the slope indicating the efficiency of biomass transfer to

larger organisms (Gaedke 1993).


17

Figure 2.4: Stylised representation of a normalised biomass-size spectrum.

Gaedke (1993) observed the NB-S distribution of plankton in a large lake, using seasonal

changes in the NB-S distribution to estimate seasonal fluctuations in the biomass transfer

efficiency and metabolic activity of the community. The observations were compared to

predictions made by NB-S spectra models such as the continuum model of Platt and Denman

(1978) and it was found that no significant differences occurred between the observed and

predicted slopes. The study also investigated which seasonal changes in the pelagic ecosystem

were reflected in the NB-S spectra. It was determined that the analysis of NB-S spectra

presents a promising tool for investigating fluxes in complex pelagic food webs (Gaedke

1993).

Rissik et al. (1997) used zooplankton NB-S spectra to observe the effect of flow disturbance

on the distribution and abundance of zooplankton around an isolated reef. Log normalized

plots of particle abundance against size classes were produced and it was found that the plot

for the flow disturbed region was steeper than that of the undisturbed region (Figure 2.5). This

steeper plot suggests a higher concentration of the smaller particles indicating greater nutrient

concentrations, with smaller size classes responding quickly to increased nutrients (Rissik et

al. 1997).


18

Figure 2.5: Log normalised relationship between the zooplankton particle concentration and

particle size class in the disturbed and undisturbed regions. Note that e.s.d. is

equivalent spherical diameter (modified from Rissik et al. [1997])

Citing that the NB-S spectra approach had not been used for coastal lagoons, Gilabert (2001)

used this approach to observe the seasonal variability in the planktonic size structure of a

Mediterranean coastal lagoon. It was shown that the spectra followed a seasonal trend, with

more negative (steeper) values in late summer and less negative values in winter. This implies

that in late summer there is a higher proportion of smaller organisms and in winter there is a

higher proportion of larger organisms (Gaedke 1993). These results suggested that the system

had two different trophic configurations; however, it was noted that due to the shallowness of

the lagoon and interaction with the benthic community, it was difficult to trace a clear

separation between the two configurations (Gilabert 2001). It was also noted that the inclusion

of the smallest (pico) size range affected the slope and the seasonal trend, suggesting that the

microbial loop has an important role in the lagoon (Gilabert 2001).

As discussed in Section 2.2.1, San Martin et al. (2006) considered the NB-S spectra of

plankton communities on a much larger scale, observing the changes in NB-S spectra with

changing latitude in the Atlantic Ocean. It was established that the slopes of the NB-S spectra

were shallowest at the equatorial upwelling regions and became steeper moving from the

equator to higher latitudes, suggesting that the efficiency of biomass transfer decreases

moving from the equator to the higher latitudes (San-Martin et al. 2006).

Chapter 3 Methodology

19

3 Methodology

3.1 Study site

The study site chosen for this project is located on Ningaloo Reef, just offshore from Sandy

Bay in the Cape Range National Park, on the western side of the North West Cape, 45 km

south-west of Exmouth (Figure 3.1). Sampling was conducted around the southern end of the

northern reef section in this area. The northern and southern reef sections can be seen in the

right and left of Figure 3.2, respectively.

Figure 3.1: North West Cape - Sandy Bay around 45km south-west

of Exmouth. (Adapted from Waite et al. (2007))


20

Figure 3.2: Northern and southern reef sections, on the right and

left of the photo respectively Sandy Bay (D. Krikke)

It was originally proposed to sample from transects across two consecutive reef sections, as

well as from the channel between the reef sections; however, due to the data received from

deployed drifters, it was decided to focus around the northern reef section and the channel.

When the drifters were deployed, it was expected that at a certain point on each reef section,

the flow coming in over the reef would then flow out through the channel. It was found that

the flow across the northern reef section behaved in this way; however, the flow across the

southern section did not clearly flow north and out of the channel, with a significant amount

of the flow heading southwards and creating a ‘messy’ flow pattern.

With the focus on the Northern reef section, it was decided to maintain the general idea of a

transect across the reef and sample from two sites just outside the reef break (R07 and R06)

one site on the reef (R05) and three sites in the lagoon (R04, R03 and R02) and one site in the

channel between the reef sections (C01). Initial measurements indicated sharp changes in

chlorophyll a concentration across the reef, with smaller changes in the lagoon; due to this, it

was decided to disregard the lagoon site R02. This produced a total of six sampling sites

around the reef section. The original seven sites are demonstrated in Figure 3.3.


21

Figure 3.3: Sampling sites at Sandy Bay, with stylised version of water flow. Note the inclusion of site R02,

which was originally considered, but was not included in the final set decided on for sampling

(Adapted from Waite et al. (2007)).

3.2 Field work

Field work was conducted between the 2nd

May and the 23rd

May as part of the Ningaloo

Biological Oceanography expedition which was led by Associate Professor Anya Waite. The

field work involved conducting water column data collection, water sampling and

zooplankton tows from a small boat. Two to three stations were sampled each sampling day

and zooplankton tows were taken at each station two to three times over this period. The

stations were sampled at roughly the same time each day – between 6am and 9am.


22

3.2.1 Water column data collection

At each station the LI-COR was used to give light data for the water column and the Hydrolab

was used to provide salinity, dissolved oxygen (DO), pH, chlorophyll, turbidity and

conductivity measurements.

LI-COR

The LI-COR probe was used to obtain light measurements from the water column (Figure

3.4a). Measurements were taken just above the surface and then every half metre to either the

seabed or eight metres water depth. Readings are produced on a hand held digital display and

are calculated as an average of all of the light levels detected by the sensor over 10 seconds.

The device cannot store values and each measurement was recorded by hand. Some issues

were encountered regarding the unclear depth markings on the rope used to lower the sensor,

as well as the difficulty with holding the LI-COR still in turbulent water. At the stations on

top of the reef and in the lagoon, where the water was shallow, it was difficult to get near-

bottom readings due to the importance of avoiding any damage to the coral.

Hydrolab

The Hydrolab probe (Figure 3.4b) takes measurements of salinity, dissolved oxygen, pH,

chlorophyll, turbidity and conductivity. The probe is attached to a hand held computer which

stores the data and this data can then be transferred to a computer. Measurements were taken

every half metre starting with a surface measurement. As with the LI-COR, there were

difficulties measuring around the coral and in turbulent water; however, the Hydrolab can

monitor the depth, so there was not the same error with regards to depth measurement as there

was with the LI-COR probe. The Hydrolab could also be attached to a deep sea cable,

allowing measurements to be taken to the bottom for the outer stations’ measurements.


23

(a) (b)

Figure 3.4: a) Deploying the LI-COR, b) Hydrolab taking measurements.

3.2.2 Water sampling

At each site, a 20L carboy was filled with water from just below the surface, beside the boat.

The carboys were completely filled to ensure that there would be enough water for the various

samples to be taken and analysed.

It was originally intended to use Niskin bottles to obtain samples from a greater depth to

complement the surface samples; however, these could not be obtained in time for the

expedition. There was also the option of using a pump and hose system to obtain bottom

water samples but it was decided that this was not compatible with the small boat that was

being used. It is expected that this will not have a major impact on the results as the water is

considered to be well mixed (pers comm. R. Lowe, May 2007).

Error associated may have been introduced to the water sampling due to the disturbance of the

natural system by the boat, but it is beyond the scope of this project to quantify that error.

3.2.3 Zooplankton tows

Zooplankton samples were collected using a 105µm net with an opening of diameter 0.3m

and a beaker attached at the opposite end (Figure 3.5). The net was towed for around 10min at

a boat speed of between 1.2kn and 1.8kn. The speed was not recorded for some of the tows,

so for the purposes of this study it has been assumed that all tows were taken at 1.5kn. Only

horizontal surface tows were performed as the water column was considered to be well mixed

(pers comm. R. Lowe, May 2007). Once the tow was completed, the contents of the beaker


24

were emptied into a labelled placo jar. It was intended to do two 10 minute zooplankton tows

at each station to collect zooplankton samples for both image analysis and isotope analysis;

however, due to weather conditions, it was sometimes necessary to reduce the duration or

number of tows.

Figure 3.5: Simple schematic diagram of the plankton net used, with the beaker attached to the end.

The dimensions of the net were 105µm.

Using the known values of the speed of the boat, time of the tow and diameter of the net

opening, a rough value of the volume of water filtered by the net can be calculated according

to the following Equation (2):

𝑉𝑜𝑙𝑢𝑚𝑒 = (𝜋 × 𝐷𝑛𝑒𝑡

2

2

) × 𝑣𝑏𝑜𝑎𝑡 × 𝑡𝑡𝑜𝑤 × 𝑓𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝑠𝑢𝑏𝑚𝑒𝑟𝑔𝑒𝑛𝑐𝑒 (2)

There was initially some difficulty in keeping the opening of the net fully submersed below

the water; hence the fraction of submergence incorporated into the above equation. Sampling

data for the zooplankton tows can be found in Appendix A.

It was important to check that the plankton net which was used for the zooplankton sampling

was working correctly. This is done by calculating the Open Area Ratio (OAR) of the net

using the following equation (3):

𝑂𝐴𝑅 = 𝑡𝑜𝑡𝑎𝑙 𝑓𝑖𝑙𝑡𝑒𝑟𝑖𝑛𝑔 𝑎𝑟𝑒𝑎 𝑜𝑓 𝑛𝑒𝑡 × 𝑝𝑜𝑟𝑜𝑠𝑖𝑡𝑦

𝑎𝑟𝑒𝑎 𝑜𝑓 𝑜𝑝𝑒𝑛𝑖𝑛𝑔 𝑜𝑓 𝑛𝑒𝑡 (3)

If the calculated value of the OAR is greater than 6, then the net is working correctly;

otherwise a correction must be made to account for the error in the net. The plankton net

which was used gave an OAR of greater than 6.


25

3.3 Preliminary laboratory work

Laboratory work was conducted in the field and on return to Perth. In the field, a temporary

laboratory was set up at the ranger station near the study site to complete the initial laboratory

work required to prepare the samples to be stored and transported back to Perth. This section

will discuss the preparation of the zooplankton samples, as well as the laboratory work

undertaken for generation of the chlorophyll data and will also briefly summarise the

laboratory work undertaken to prepare the phytoplankton samples for analysis. While the

phytoplankton are not the focus of this study, it is intended that following this study and the

corresponding phytoplankton study by D. Kapeli, the data from both the phytoplankton and

the zooplankton analyses will be brought together to form an extensive dataset on the

plankton life existing in the study area.

3.3.1 Zooplankton samples

Ideally, there were two zooplankton tows completed for each station sampled, providing two

zooplankton sample jars for each station. The first zooplankton sample was size fractionated

and prepared for isotope analysis, while the second was preserved for image analysis.

Originally it was intended to focus this study on isotope analysis, but it was decided that that

comprehensive dataset on the zooplankton community structure produced through image

analysis would be more useful. The isotope samples will be useful for further work in this

area.

The complete set of samples for image analysis included one set of samples from the stations

taken in early May (2nd

-4th

) and one set taken in late May (21st-23

rd), termed ‘tow E’ and ‘tow

L’ respectively. The samples were kept in the 1000mL placo jars in which they were

collected. In order to preserve them for transport to Perth and storage until they were able to

be processed for image analysis, 50mL of formaldehyde was added to each one, producing a

final concentration of 5% formaldehyde or formalin.

3.3.2 Phytoplankton

Sample water from the 20L carboys filled at each sampling station was used in an incubation

experiment to determine phytoplankton uptake rates of carbon and nitrogen. One black

incubation bottle and two clear incubation bottles were filled for each station and a known

amount of 13

C and 15

N was injected into each bottle. These bottles were then securely

attached to a mooring about 20m offshore and left to incubate for around six hours. Following


26

incubation, the water from the bottles was filtered onto GFF filters and these were frozen for

storage and transport back to Perth for analysis.

Some of the sample water was also preserved for phytoplankton and picoplankton observation

and taxonomy; 5mL of Lugols solution was added to 500mL of sample water for the

phytoplankton and 0.2mL of gluteraldehyde was added to 1.8mL of sample water for the

picoplankton.

3.3.3 Chlorophyll

Total chlorophyll a and >5µm chlorophyll a were measured by fluorometry for each station

using water from the carboy samples. For the total chlorophyll a, one litre of sample water

was filtered onto GFF filters; the >5µm involved two litres of sample water filtered onto

>5µm Nitex filters. The filter papers were placed in glass scint vials with 8mL of acetone and

left in the freezer for 5 to 24 hours. After this time the vials were removed from the freezer

and the liquid in each poured into a glass tube. The exterior of the tube was cleaned and

placed in a calibrated fluorometer, which produced a ‘before acid’ reading. Two millilitres of

hydrochloric acid was added and the fluorescence again measured, producing the ‘after acid’

reading. The difference between these readings, multiplied by a constant, constituted the

chlorophyll a concentration. The ‘after acid’ reading, multiplied by a constant, constituted the

phaeophyton a concentration.

3.4 Image analysis

Image analysis was chosen as the technique for analysis of the zooplankton samples,

following the methods of Alcaraz et al. (2003) and Strzelecki et al. (in press). The technique

produced total biomass values for the zooplankton at the different sites, as well as the size

structure and taxa structure of the zooplankton communities. While there are other options for

calculating the biomass of a sample, such as dry weight or chemical analysis, image analysis

provides a non-destructive technique which is effective in sorting detritus particulate matter

from zooplankton (Alcaraz et al. 2003). It should also be noted that a filter stack was used in

this method to size fractionate the sample, ensuring a more accurate distribution of particle

sizes when sub-samples are taken; furthermore, some of the inherent inaccuracies associated

with filter fractionation are negated by the individual measurement of particle sizes during

image analysis.


27

A total of 11 tow samples were analysed using image analysis and the details of each of these

samples can be found in Appendix A. The process of image analysis involved preparing each

sample, the capture of images and their processing and analysis. The image analysis software

used was a combination of Image J (NIH-Image for the PC, National Institute of Health,

Bethesda, Md., USA) and PlanktonJ (Mortimer and Koslow 2007). The preparation of the

samples, capture of images and some of the image processing was conducted at the CSIRO

Marine and Atmospheric Research Laboratories in Floreat, Western Australia. A copy of the

protocol for the preparation of the image analysis samples can be found in Appendix B.

3.4.1 Sample preparation

Tow samples had to be properly prepared prior to capturing the images. This involved size

fractionation of the tow sample followed by sub-sampling of each size fraction. Due to the

formaldehyde contained in the preserved samples, initial sample preparation was conducted

under the fumehood, using gloves and safety glasses.

The tow sample was first poured into a measuring cylinder and the total volume recorded; the

sample jar and the measuring cylinder were then washed into a filter stack, using de-ionised

water. The filter stack consisted of filter screens with dimensions 105µm, 300µm, 500µm,

1000µm, 2000µm and 4000µm, stacked in order with the smallest size fraction at the base as

demonstrated in Figure 3.6a.

(a) (b)

Figure 3.6: (a) Filter stack (b) 2mL, 5mL and 10mL sub-samplers.


28

Once the cylinder and jar appeared to be empty of zooplankton, they were each washed out

into the filter stack three times. Following this, a shower was put through the top of the filter

stack for one minute to wash the zooplankton through to the appropriate filters. The filters

were then separated; the bottom of each filter was washed into the filters below and the edges

rinsed again to ensure the smaller zooplankton had been washed through the mesh. The

contents of each filter were then washed into an ice cream container, with tweezers used to

remove zooplankton which remained stuck in the mesh. The contents of the ice cream

container were washed into a jar, labelled with the appropriate filter size and stored in the

fridge until sub-sampling.

Sub-samples were obtained using a 2mL, 5mL or 10mL aliquot sampler (Figure 3.6b). The

size fractionated tow sample jars were shaken upside down ten times in a random motion to

ensure complete mixing, the aliquot was then taken from roughly the centre of the sample jar.

Sub-samples were imaged using either a microscope attached to a digital camera (Section

3.4.2) or a scanner (Section 3.4.3) depending on the size of the organisms in the sub-sample.

3.4.2 Microscope

The smallest size fraction (105µm) was deemed too small for the scanner and subsequently

PlanktonJ, so the images were captured using a Q imaging digital camera attached to a

microscope and a computer. In some cases, when the next larger size fraction seemed to

consist mainly of smaller organisms, the microscope was also used to analyse that sample. A

Bogorov tray (Figure 3.7a) was used under the microscope, which allowed the user to keep

track of where in the sub-sample they were up to, ensuring accurate counting and

measurement of all of the zooplankton in the sub-sample. Initially, a sub-sample of 5mL was

used; however, for the more productive outer stations, the analysis involved the time-

consuming counting and measurement of many hundreds of organisms. It was decided to start

with a 2mL sub-sample to minimise counting time and to follow this with a further 2mL sub-

sample if required. The sub-samples were always transferred directly from the sub-sampler to

the Bogorov tray. Originally, it was intended to measure between 100 and 200 organisms for

each sample but this figure was revised to between 40 and 200 following an initial

observation of low numbers of organisms in some inner reef stations.


29

(a) (b)

Figure 3.7: (a) Bogorov tray (b) Microscope

The Bogorov tray with the sub-sample in it was observed from end to end under the

microscope (Figure 3.7b), with images captured of any zooplankton found in the tray. The

camera program on the computer was opened directly through ImageJ, so that any images

captured were immediately present in the ImageJ program. Once the first image was captured,

it was necessary to set the scale within the ImageJ program; the images were taken at a

magnification of 12 times, producing a scale of 1mm to 270 pixels.

Once the scale had been set, the length and width of all zooplankton in the image were

measured using the line function in ImageJ (Figure 3.8). There were often several

zooplankton in each image and several images would be measured consecutively, producing a

list of alternating length and width measurements. These were transferred into Microsoft

Excel (Excel) and each pair of measurements was transposed, producing a column of length

measurements and a column of width measurements; in other words, two columns

representing the major and minor axes of ellipses. A list was compiled in Excel of the width

and length measurements of each zooplankton, as well as their general taxonomic group. The

taxa were composed mostly of copepods, nauplii and gastropods, with some polychaetes.


30

Figure 3.8: Image taken using a digital camera attached to a microscope. The lines on the copepod

represent those which were drawn in ImageJ to measure the major and minor axes of the copepod

approximated as an ellipse.

There were several issues encountered using this method of image analysis. The most

significant was the time required for a thorough application of the method. The time required

for identification of the zooplankton, capture of the images, manual measure of the

zooplankton and transfer of results to Excel for up to 200 zooplankton, and in one case 500,

was a severe constraint. Another issue encountered was that copepods were often found

clumped in groups with detritus and sand; this made it difficult to discern individual

zooplankton and in some cases estimates had to be made as to the exact shape and size of an

animal. There was also some difficulty encountered in separating copepods from copepod

shells and it is possible that this distinction was not always made correctly.

3.4.3 Scanner

The scanner was used to capture images of the five larger size fractions (300µm, 500µm,

1000µm, 2000µm and 4000µm) of each tow sample, where the resolution would provide

appropriate detail for processing and analysis with the PlanktonJ program.

Production of images

The scans were conducted in a conventional flatbed scanner with the sub-samples contained

in glass-bottomed trays. The scanner could fit in three trays for each scan (see Figure 3.9) and

in order to minimise scanning and processing time it was decided that the five larger size

fractions would have to be combined to produce three new size fractions for each tow sample.


31

The size fractions were combined, based on a brief visual analysis of the amount of matter in

each size fraction; for example, two consecutive size fractions which contained less matter

then the other size fractions around them would be combined. As a general rule, the larger

size fractions would contain less matter, so it was common that 4000µm and 2000µm would

be combined as would 1000µm and 500µm, leaving 300µm as the final size fraction. The

exact combinations chosen for each tow sample are shown in Appendix A.

Figure 3.9: Scanner with sample trays.

Once the final size fractions had been determined, they needed to be sub-sampled to provide a

sub-sample of appropriate size and mass to fit in the scanner trays. Due to all the washing

required to transfer zooplankton from the filter stack to the jars, each original size fraction

contained between 250mL and 500mL of water. Depending on the amount of matter within

the size fractions, this could be too much water to provide a sensible sub-sample, so whilst

combining the size fractions some of the water was filtered out. This involved pouring the

size fraction sample though a small filter of dimensions much smaller than that of the size

fraction, providing for easier washing and therefore a decreased volume of water. Once the

size fractions had been combined they were sub-sampled as described in Section 3.4.1. It was

important to achieve a representative sample without over-filling the tray with either water or

zooplankton mass; this resulted in sub-samples ranging between 10mL and 40mL, depending

on the mass distribution within the samples.


32

Once the trays had been transferred to the scanner, it was necessary to ensure that the

individual zooplankton were as separated and as close to the bottom of the tray as possible. A

plastic pen lid was used, to avoid damage to the glass bottom, to attempt to separate the

zooplankton in the trays as much as possible to assist the program in distinguishing between

each. It was also necessary to move any zooplankton away from the edges of the tray to avoid

a similar problem. The earlier scans were taken immediately after the zooplankton had been

separated to avoid them floating back together; however, it was realised that this caused a

different problem of the zooplankton not being appropriately settled when the scans were

taken, producing blurry images. For later scans the zooplankton were left to settle for a few

minutes before the scans were taken and this appeared to produce images of better quality.

The trays were scanned in an Epson 4990 conventional flatbed office scanner; the scanner

was set to transparency mode, so rather than light reflecting off the objects to be scanned, it

shines through them. The resolution was set to 2400 dpi, which is roughly 10µm a pixel,

allowing adequate detail for the smaller size fractions which were scanned, and producing

high quality images 140MB in size. The images were captured as 16 bit greyscale images.

Processing and analysis of images

Once the images of the sub-samples were obtained, the next step was the processing of these

images using the zooplankton image analysis software PlanktonJ. PlanktonJ functions as a

plugin to ImageJ – it is opened through ImageJ allowing ImageJ utilities to be used within

PlanktonJ. PlanktonJ is currently being developed by Nick Mortimer at the CSIRO Marine

and Atmospheric Research Laboratories and it is in the final stages of development. An

ancillary function of this study was to provide feedback on the use of the software to analyse

the zooplankton images, supporting improvement of the program for zooplankton sample

analysis.

Once the tow and size fraction details of the sub-sample were entered into PlanktonJ, the

image processing commenced. It was important to enter the size fractions as this would allow

the program to filter out any material, such as small items of detritus, which should not be

considered in that particular image. For the initial stages of processing, PlanktonJ uses the

sample treatment process which is written into another zooplankton image analysis program

Zooimage - also a plugin to ImageJ. Initial stages of processing involve the following steps:

1. The image is processed to an 8 bit gif image, which is corrected for the optical density.

2. The image is run through a particle analysis process:


33

a. Thresholds the image at 230 (white is 255 and black is 0)

b. Objects in the picture are found according to the size specifications

c. Objects are measured

3. Images are divided into a series of vignettes displaying the images (Figure 3.10).

Figure 3.10: Vignettes produced by PlanktonJ (clockwise from top left):

copepod; crab larvae; euphausid; decapod.

PlanktonJ then proceeds by running the images through auto-thresholding. This runs an

adaptive threshold on the vignettes in order to segment the appendages from the bodies of the

zooplankton and fit an ellipse to the new body outline. It then completes the set of

measurements produced in the initial stages by adding the major and minor axes and area of

the ellipses. These measurements are written to a measurement file (zim file) and a file is also

created of the outlines and ellipses of the zooplankton.

Once the image has been processed, the zim file can be opened in PlanktonJ and all the

vignettes as well as their associated measurements, outlines and ellipses can be viewed.

Observing the vignettes, it was necessary to separate the plankton from the detritus and class

the plankton into their general taxa groups. Due to the limited classification experience of the

user, it was not possible to classify all of the taxa, so a small proportion were classed into a

group titled ‘Other’. For certain animals, such as fully grown copepods, the ellipses produced

by PlanktonJ were quite accurate in fitting to the outline of the zooplankton; however, many

of the measurements had to be re-calculated as the ellipses contained more area then the

organism did. In some cases, the organism was not adequately defined for the auto-threshold

to fit an ellipse to it and in some cases the organism was curled around so the ellipse which

PlanktonJ fitted was not accurate. Where required, manual measurement was possible using


34

the line tool in ImageJ (as for the microscope method); these measurements could then be

stored in PlanktonJ. For animals which were curled, it was still possible to measure the major

and minor axes using the segmented line option in ImageJ.

Following sorting and measurement the data was transferred to Excel and, where appropriate,

the automatic measurements were replaced with the manual measurements. As with the

microscope samples, the final Excel sheet contained a list of all of the organisms classed by

taxa and the major and minor axis of each.

Development of PlanktonJ

There were several issues encountered with PlanktonJ. These issues were reported to the

developer and this feedback was used to improve the functionality of the software and rectify

the issues.

Once the images had been processed, PlanktonJ produced a folder for each image which

contained between 400 and 2000 vignettes. When considering the numbers of plankton

expected in these samples, the numbers of vignettes produced by PlanktonJ would seem quite

high. This was due in part to the detritus in the samples, as well as to PlanktonJ picking up

smudges and scratches on the glass as particles in the sample. It was also discovered that the

software was not filtering out particles based on the provided size fractions; the software was

processing everything at a predetermined size threshold not equal to the provided size

fraction. In addition, the auto-threshold was breaking the larger animals up into many smaller

vignettes. Due to the high resolution of the images, the production of so many large images

was overloading the software and the computer, causing them to crash. The smallest size

fraction scans were able to be processed, because the organisms in these images were small

enough for the problems mentioned above not to be prohibitive. The other size fractions

would not load in PlanktonJ due to insufficient processing capability, so in order to obtain a

complete dataset the issues had to be addressed.

These problems were addressed by the developer in several ways. Firstly, the software was

corrected so that it did use the size fractions that were provided by the user. In order to ensure

that all of the appropriate organisms were included, the lower size fraction of the sub-sample

was halved and this was considered the lower limit that PlanktonJ should observe. There was

no upper limit set as some thin organisms such as appendicularians were observed to slip

through the larger filter screens, so would be considerably larger, in terms of estimated

spherical diameter (ESD), than the upper limit of the size fraction. It was also decided that for


35

the largest size fraction, the auto-threshold function would be turned off to avoid the large

images being divided up into many smaller ones. For the smaller size fractions, the auto-

threshold function was limited to dividing one picture into four smaller ones; this avoided one

picture being separated into many pictures and draining the available memory of the

computer.

Following the implementation of these solutions, PlanktonJ could load the larger size

fractions; however, it was still slow and had trouble loading all of the vignettes without

overloading for some scans. This was improved by altering the software slightly. Previously,

it was trying to load two sets of all of the vignettes at the same time. The software was

changed so it would only load the vignettes when they were required; in other words, when

the they were actually being viewed on the screen. It was now possible to load most of the

size fractions in PlanktonJ; however, it was still a little slow due to the high resolution of

images and in the future this could be improved by creating the vignettes with a smaller

border around the organisms, thereby reducing the size and memory required for the

vignettes.

3.5 Data analysis

Following analysis of the vignettes in PlanktonJ, complete datasets were produced for both

tows at all stations, with the exception of stations C01 and R03, as described in Table 3.1.

Table 3.1: Record of complete datasets achieved for each station.

Stations Tow E Tow L

C01

R03

R04

R05

R06

R07

A zooplankton tow sample for image analysis was not taken for station C01 during the early

May tows (tow E), so it was not possible to produce a working dataset. With regards to the

missing full dataset of tow L for station R03, there was a sample taken and analysed, but

PlanktonJ did not correctly process one of the size fractions and a full dataset was not able to

be produced. In the results (Section 4.2.1), data for the smallest size fraction of tow L at R03

is presented; however, for the rest of the results where a full dataset was required, tow L is

missing for R03.


36

3.5.1 Biovolume and biomass

The microscope and scanner image analysis produced sets of data containing the general taxa

and the major and minor axis measurements of each animal found. The measurements were

used to calculate the biovolume of each animal, either approximating them as ellipsoids or, in

the case of chaetognaths and appendicularians, as cylinders, according to Equation 4 and

Equation 5 respectively:

𝑉𝑂𝐿𝑒𝑙𝑙𝑖𝑠𝑜𝑖𝑑 = 4

3 𝜋

𝑀𝑎𝑗𝑜𝑟

2

𝑀𝑖𝑛𝑜𝑟

2

2

(4)

𝑉𝑂𝐿𝑐𝑦𝑙𝑖𝑛𝑑𝑒𝑟 = 𝜋 𝑀𝑖𝑛𝑜𝑟

2

2

× 𝑀𝑎𝑗𝑜𝑟 (5)

Due to the effects of preservation on the body shape of salps, the nucleus of each salp was

measured, rather than the body, and the biovolume of this nucleus was converted to biomass

according to the equation from Alcaraz et al. (2003): C=0.0515×VOLnucleus. For the other

zooplankton, the conversion to biomass was also taken from Alcaraz et al. (2003) and was

used by Strzelecki et al. (in press): C=0.0699×VOL. The estimated spherical diameter (ESD),

based on the biovolume, was also calculated for each animal.

Mesozooplankton can be considered to be between 100µm and 5000µm in size (Waite and

Suthers 2007) and it is common for a smaller upper limit, often 2000µm ESD, to be imposed

when considering the zooplankton spectrum in the field due to typically low particle

concentrations in the larger sizes of zooplankton (Rissik et al. 1997, Gilabert 2001). As a filter

stack has been used to size fractionate the samples in this study, it is considered that the larger

size fractions are better represented and it has been decided to impose an upper limit of

5000µm ESD when considering total biomass. Biomass totals were calculated for the smallest

size fraction at each station and also for the complete mesozooplankton size spectrum (105µm

to 5000µm ESD) at each station.

3.5.2 Normalized biomass-size spectra

Normalized biomass-size (NB-S) spectra were also created for each station. As discussed in

Section 2.7, NB-S spectra can be used to compare ecosystems regardless of species

composition and the slope of the spectrum provides information regarding the efficiency of

biomass transfer to larger organisms within the system. A pelagic community which is close

to steady state should have a slope of -1 or -1.22 depending on whether biomass is expressed


37

in terms of volume or carbon, respectively (San-Martin et al. 2006). A steeper slope indicates

a higher proportion of smaller organisms and a shallower slope indicates a higher proportion

of larger organisms.

The NB-S spectra were calculated for each tow sample from the spectrum of biovolume

concentrations (mm3m

-3) over logarithmic size intervals of ESD. The intervals used can be

found in Table 3.2 and ranged from 115 µm to 4230 µm ESD. The biovolume was normalized

by dividing the total biovolume in each interval by the average individual biovolume which

corresponds to the geometric mean ESD of that interval. This normalized biovolume was then

plotted against the average individual biovolume for each size class on a log-log plot using

Excel. A MATLAB program was written to calculate the total biovolume in each interval for

a given sample; this was done by importing data from and exporting data to Excel; the code is

attached as Appendix C. It should be noted that due to sub-sampling, the biovolume of each

individual had to be adjusted to the equivalent biovolume for the entire sample before the

biovolume totals could be calculated. These biovolumes were also adjusted to be equivalent to

the biovolume concentration by dividing them by the estimated volume of water processed by

the plankton net for each sample.

Table 3.2: Size intervals for the NB-S spectra describing the upper and lower limits in terms of ESD, the

geometric mean ESD of each interval and the equivalent biovolume.

Lower Limit ESD (mm) Upper Limit ESD (mm) Geometric Mean ESD (mm)

Equivalent Biovolume (mm3)

0.115 0.155 0.133510299 0.001246071

0.155 0.2 0.176068169 0.002857861

0.2 0.25 0.223606798 0.005854012

0.25 0.305 0.276134025 0.011024487

0.305 0.365 0.333654012 0.01944857

0.365 0.43 0.396169156 0.032556717

0.43 0.5 0.463680925 0.052198294

0.5 0.575 0.536190265 0.080715213

0.575 0.655 0.613697808 0.121021446

0.655 0.74 0.696203993 0.176688446

0.74 0.83 0.78370913 0.252036442

0.83 0.925 0.876213444 0.352231636

0.925 1.025 0.973717105 0.483389285

1.025 1.13 1.076220238 0.652682676

1.13 1.235 1.181333992 0.863210519

1.235 1.345 1.288826986 1.120940305

1.345 1.46 1.401320806 1.440825322

1.46 1.58 1.518815328 1.834482345

1.58 1.705 1.641310452 2.315105719


38

Lower Limit ESD (mm) Upper Limit ESD (mm) Geometric Mean ESD (mm)

Equivalent Biovolume (mm3)

1.705 2.835 2.19856203 5.564354518

2.835 2.97 2.90171501 12.7927199

2.97 3.11 3.039193972 14.69852921

3.11 3.255 3.181674088 16.8641981

3.255 3.405 3.329155298 19.31971814

3.405 3.56 3.481637546 22.09781298

3.56 3.72 3.639120773 25.23411216

3.72 3.885 3.801604924 28.76733076

3.885 4.055 3.969089946 32.73945494

4.055 4.23 4.141575787 37.19593338

A trendline was added to each NB-S dataset and a p-value was produced for each regression

line. ANOVA and Tukey’s test were used to compare and contrast the slopes of each dataset.

While the slopes of the NB-S spectra are useful, it was decided to also plot the changes in

biovolume, without normalisation, over constant sizes. This allows simple visual observation

of the changes in biovolume across the size spectrum. A new set of intervals was established

with constant widths and the biovolume contained in each interval was again calculated for

each station using the MATLAB program and plotted using Excel.

3.5.3 Taxonomy

When each organism was measured, it was also classified into a general taxa group; any

further classification was out of the scope and timeline of the project as it would have

required further external input. The main groups are shown in Figure 3.11, with a typical

picture of each. It should also be noted that there was no distinction between copepods,

copepodites and copepod nauplii - these were all classed into a general ‘copepod’ group. In

order to calculate the percentage abundance of the various groups in each tow sample, the

abundance in each sub-sample needed to be standardized against the total volume of the tow

sample. This was achieved by dividing the sub-sample abundance by the sub-sample to tow

sample volume ratio and assuming this as the total abundance of each group in the tow

sample. Using these adjusted numbers, the total number of individuals in the sample, the total

number of individuals for each general taxa group and their relative percentage abundances

could be calculated. The numbers were also divided by the volume of water filtered through

the net to achieve abundances per metre cubed, allowing for comparison between the samples.

Abundances based on biovolume were also calculated using the same method.


39

Copepod

Chaetognath

Crab Larvae

Euphausid

Decapod

Doliolid

Salp

Gastropod

Polychaete

Figure 3.11: Main taxa groups found in samples. Note that the pictures are not to scale.


40

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Chapter 4 Results

41

4 Results

4.1 Chlorophyll data

Chlorophyll data was taken over a month, from the 27th

April to the 26th

May 2007, with each

sampling station being sampled a number of times during that month. As can be seen in

Figure 4.1, there is a distinct negative gradient of total chlorophyll a moving from the fore-

reef to the lagoon.

Figure 4.1: Mean of the chlorophyll a measurements (mgL-1

) at each station moving from the outside or

fore-reef (R07, R06) across the reef (R05) into the lagoon (R04, R03) and the channel (C01). The error

bars represent one standard error above and below the mean.

There were also measurements conducted of the >5µm chlorophyll a. These measurements

differed to the total as there was a spike over the reef station R05 and no discernible gradient

across the reef (Figure 4.2).

Figure 4.2: Mean of the >5µm chlorophyll a measurements (mgL-1

) at each station moving across the reef

as in Figure 4.1. The error bars represent one standard error above and below the mean.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

R07 R06 R05 R04 R03 C01

Ch

loro

ph

yll a

(m

gL-1

)

Station number

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

R07 R06 R05 R04 R03 C01

Ch

loro

ph

yll a

(m

gL-1

)

Station number

Chapter 4 Results

42

Phaeophyton a is the pigment produced when zooplankton feed on phytoplankton, digesting

the chlorophyll a. Figure 4.3 shows a positive gradient in phaeophyton a moving from the

fore-reef to the lagoon, although the gradient is not as strong as the negative gradient shown

by chlorophyll a.

Figure 4.3: Mean of the phaeophyton a measurements (mgL-1

) at each station moving from the outside or



As might be expected from the graphs above, the chlorophyll a to phaeophyton ratio

decreases dramatically across the reef (Figure 4.4).

Figure 4.4: Mean of the chlorophyll a to phaeophyton a ratio at each station moving from the outside or



0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

R07 R06 R05 R04 R03 C01

Ph

aeo

ph

yto

n a

(m

gL-1

)

Station number

0.00

10.00

20.00

30.00

40.00

50.00

R07 R06 R05 R04 R03 C01

Rat

io

Station number

Chapter 4 Results

43

4.2 Zooplankton abundance and biomass

4.2.1 Biomass

Figure 4.5 depicts the change in biomass across the reef for the smallest size fraction of

zooplankton (105µm). It can be noted that there is a distinct negative trend between the fore-

reef (R07, R06) and the lagoon (R04, R03); however, there is a spike in biomass at reef

station R05. Tow E was taken between the 2nd

May and the 4th

May 2007 and did not include

a channel station (C01) sample, while Tow L was taken between the 21st May and the 23

rd

May 2007.

The variability between the two tows can be attributed to both the temporal variation between

the two tows and some error associated with the methodology. A paired t-test on the two sets

of data (tow E and tow L) gave a p-value of 0.043, a mean difference of 0.12 and a standard

error of 0.04. This demonstrates that the tows are significantly different, suggesting that

temporal variation is important. Figure 4.6 represents the mean of the normalized biomass for

the smallest size fraction; the biomass has been normalized by representing it as the fraction

of the highest biomass in each tow, thereby allowing an observation of the data without the

influence of temporal variation.

Figure 4.5: Biomass (mg C m-3

) across the reef for the smallest size fraction (filter dimension of 105µm),

from the fore-reef (R07, R06) to the reef break (R05) and into the lagoon and the channel (R04, R03, C01).

Tow E was conducted between the 2nd

and 4th

May and Tow L between the 21st and 23

rd May. Note the

negative trend from the fore-reef to the lagoon and the spike in biomass at the reef station R05.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

R07 R06 R05 R04 R03 C01

Bio

mas

s (m

g C

m-3

)

Station number

Tow E

Tow L

Chapter 4 Results

44

Figure 4.6: Mean of the normalized biomass of the smallest size fraction (105µm) at each station for the

two tows. The biomass is normalized to be a fraction of the highest biomass in each of the tows. The error

bars indicate the standard error for the calculated mean at each point.

The total biomass exhibits a slightly different trend to that of the smallest size fraction. There

is still a general decreasing trend in biomass between the outside and the inside of the reef;

however, the spike now sits above R06 (Figure 4.7). Figure 4.8 represents the mean of the

normalized total biomass for each tow; the biomass at each station has been normalized by

representing it as a fraction of the highest biomass for that tow. By comparing Figure 4.6 and

Figure 4.8, it can quite clearly be noted that the biomass at R06 is derived from larger

organisms while the biomass at R05 is derived from smaller organisms.

Figure 4.7: Total biomass across the reef (mg C m-3

) from the fore-reef (R07, R06) to the reef break (R05)

into the lagoon and channel (R04, R03, C01). Tow E was conducted between the 2nd

and 4th

May and Tow

L between the 21st and 23

rd May. Note the negative trend from the fore-reef to the lagoon and the large

spike in biomass at the reef station R06.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

R07 R06 R05 R04 R03 C01

Frac

tio

n o

f h

igh

est

bio

mas

s

Station number

0

5

10

15

20

25

30

R07 R06 R05 R04 R03 C01

Bio

mas

s (m

g C

m-3

)

Station number

Tow E

Tow L

Chapter 4 Results

45

Figure 4.8: Mean of the normalized total biomass at each station for the two tows. The biomass is

normalized to be a fraction of the highest biomass in each of the tows. The error bars indicate the

standard error for the calculated mean at each point.

4.2.2 Abundance

The abundance, in terms of the number of individuals per m3, was calculated for each sample

by adjusting the number of individuals found in each size fraction sub-sample to the

equivalent number that would be found in the whole tow sample and totalling the number for

all of the size fractions. The raw abundance data for tow E and tow L has been plotted in

Figure 4.9. The normalized mean of the two tows has been plotted in Figure 4.10 and shows a

similar trend across the reef to the smallest size fraction biomass (Figure 4.6). This would be

expected as the smaller size fractions contribute the highest number of organisms.

Figure 4.9: Abundance in individuals per m3 at each station across the reef for tow E and tow L. See

section 4.2.1 for further tow and station details.

0

0.2

0.4

0.6

0.8

1

R07 R06 R05 R04 R03 C01

Frac

tio

n o

f h

igh

est

bio

mas

s

Station number

0

500

1000

1500

2000

2500

3000

3500

C01R03R04R05R06R07

Ab

un

dan

ce (

ind

m-3

)

Station number

Tow E

Tow L

Chapter 4 Results

46

Figure 4.10: Mean of the normalized abundance for tow E and tow L at each station. The abundance is

normalized to be a fraction of the highest abundance for each tow. The error bars represent the standard

error of each calculated mean.

4.2.3 Mean zooplankton size

Mean zooplankton size was calculated in terms of mean zooplankton ESD for each sample.

As with the abundance, this was done by adjusting the average mean of each size fraction so

that it was weighted based on the size of the sub-sample and therefore the equivalent

contribution of that size fraction to the whole sample. The mean individual ESD of the whole

sample was then calculated. These mean values were plotted (Figure 4.11) and the trend is

similar to that of the total biomass (Figure 4.8), with larger zooplankton found outside the

reef.

Figure 4.11: Mean of the values for mean zooplankton ESD (mm) for the two tows at each station across

the reef. The error bars represent the standard error of each calculated mean. See section 4.2.1 for more

details on the tows and stations.

0

0.2

0.4

0.6

0.8

1

C01R03R04R05R06R07

Frac

tio

n o

f h

igh

est

ab

un

dan

ce

Station number

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

R07 R06 R05 R04 R03 C01

Me

an in

div

idu

al E

SD (

mm

)

Station number

Chapter 4 Results

47

4.3 Community structure

4.3.1 Biomass-size spectra

Biomass-size spectra were required to observe the distribution of biovolume over the size

spectrum of meso-zooplankton for each station. Raw biomass-size spectra were created and

examples of these can be seen in Figure 4.12, Figure 4.13 and Figure 4.14, the spectra for tow

E and tow L of R07 and tow E of R06 respectively.


-3) distributed over even size

classes of ESD (mm) for tow E of station R07.



classes of ESD (mm) for tow L of R07.

0

5

10

15

20

25

0 1 2 3 4

Tota

l bio

volu

me

(m

m3

m-3

)

Size classes of ESD (mm)

0

5

10

15

20

25

0 1 2 3 4

Tota

l bio

volu

me

(m

m3

m-3

)


Chapter 4 Results

48



classes of ESD (mm) for tow E of R06. Note the differing scale of the vertical axis compared to Figure 4.12

and Figure 4.13 due to the much larger biovolume of station R06.

While the raw biomass-size spectra allow for some interesting general visual observations of

the patterns in biovolume distribution at the different stations, normalized biomass-size (NB-

S) spectra allow for a more quantitative representation of the community size structure. As

they are normalized, NB-S spectra also eliminate the difficulties with scale when presenting

communities with significantly different biovolumes. The following figures (Figure 4.15,

Figure 4.16 and Figure 4.17) represent the equivalent NB-S spectra for tow E and tow L of

R07 and tow E of R06. The slopes of the two R07 spectra are variable, although both are

closer to -1 than the slope of the R06 spectrum. This shallower slope at station R06 indicates

higher biovolumes of larger organisms, which can be observed visually in Figure 4.14.

Figure 4.15: NB-S spectrum for tow E at station R07. Normalized biovolume (total biovolume for each size

class, divided by the mean of that size class) plotted against the size classes (mm3 per individual).

0

10

20

30

40

50

60

70

80

90

0 1 2 3 4

Tota

l bio

volu

me

(m

m3

m--3

)


y = -0.981x - 0.499R² = 0.899

-6

-4

-2

0

2

4

6

8

10

-12 -10 -8 -6 -4 -2 0 2 4 6

Log 2

[no

rmal

ize

d b

iovo

lum

e]

( m

m3

m-3

)/si

ze c

lass

( m

m3

)

Log2 [size class ( mm3 )]

Chapter 4 Results

49

Figure 4.16: NB-S spectrum for tow L at station R07. Normalized biovolume (total biovolume for each size



class, divided by the mean of that size class) plotted against the size classes (mm3 per individual)

The reef station R05 demonstrated a slope of just above -1 suggesting a larger population of

smaller organisms when compared with the outside stations (Figure 4.18). The slopes of the

spectra inside the reef (stations C01, R03 and R04) tended to be around or just under -1 and

this is demonstrated in Figure 4.19, the NB-S spectrum of tow E at R04.

y = -0.762x + 2.330R² = 0.832

-6

-4

-2

0

2

4

6

8

10

-12 -10 -8 -6 -4 -2 0 2 4 6

Log 2

[no

rmal

ize

d b

iovo

lum

e]

( m

m3

m-3

)/si

ze c

lass

( m

m3

)

Log2 [size class ( mm3 )]

y = -0.666x + 3.045R² = 0.915

-6

-4

-2

0

2

4

6

8

10

-12 -10 -8 -6 -4 -2 0 2 4 6 8

Log 2

[no

rmal

ize

d b

iovo

lum

e]

(mm

3m

-3)/

size

cla

ss (

mm

3 )

Log2 [size class (mm3)]

Chapter 4 Results

50

Figure 4.18: NB-S spectrum for tow L at station R05. Normalized biovolume (total biovolume for each size




The NB-S spectra were all well correlated, with all of the graphs having p-values of less than

0.001. The slopes of the NB-S spectra of each sample and the related statistics can be seen in

Table 4.1. ANOVA and Tukey’s test were run for the station slopes to determine whether

they were different across the reef. It was determined that there was difference within the

slopes; however, a significant difference was only noted between R06 and R05.

y = -1.057x - 0.541R² = 0.920

-6

-4

-2

0

2

4

6

8

10

-12 -10 -8 -6 -4 -2 0 2 4 6

Log 2

[no

rmal

ize

d b

iovo

lum

e]

( m

m3

m-3

)/si

ze c

lass

( m

m3 )

Log2 [size class ( mm3)]

y = -0.955x - 1.703R² = 0.909

-6

-4

-2

0

2

4

6

8

10

-12 -10 -8 -6 -4 -2 0 2 4 6

Log 2

[no

rmal

ize

d b

iovo

lum

e]

( m

m3

m-3

)/si

ze c

lass

( m

m3)

Log2 [size class ( mm3)]

Chapter 4 Results

51

Table 4.1: List of the slopes and correlation coefficients for the NB-S spectra, demonstrating that each

graph has a p-value of less then 0.001.

Station Tow Slope R2 n p-value <

C01 L -1.01 0.936 18 0.001

R03 E -0.90 0.935 9 0.001

R04 E -0.96 0.909 21 0.001

L -0.74 0.772 16 0.001

R05 E -1.04 0.929 16 0.001

L -1.06 0.920 22 0.001

R06 E -0.67 0.915 28 0.001

L -0.68 0.935 21 0.001

R07 E -0.98 0.899 22 0.001

L -0.76 0.832 19 0.001

4.3.2 Taxonomy

The zooplankton were classed into general taxa groups and percentage composition was

calculated based on biovolume and abundance, once again taking into account the adjustment

for sub-sample volumes. Based on abundance, copepods dominated at every station; the

remaining composition was similar for the inside reef and reef break samples and varied for

the samples outside the reef (Table 4.2).

Table 4.2: Abundance of net tows for each station. Abundance of taxa groups is rounded to the nearest

percent and only those over 1% are shown.

Station Tow Individuals m-3

% Copepods Composition of remainder (%)

C01 L 1400 71 Gastropods (24), polychaetes (3)

R03 E 830 62 Gastropods (30), polychaetes (7)


L 450 86 Gastropods (12)

R05 E 3060 90 Gastropods (8), crab larvae (1)

L 2240 82 Gastropods (16)

R06 E 2100 85 Euphausids (3), polychaetes (3),

chaetognaths (2), gastropods (2)

L 1550 82 Salps (7), gastropods (6),

polychaetes (1)


L 2080 75 Salps (11), gastropods (7),

polychaetes (2), siphonophore (1)

The change in copepod dominance across the reef is demonstrated in Figure 4.20. It can be

noted that while it appears constant outside the reef, across the reef break and into the lagoon

closest to the reef break, the channel/lagoon area of R03 and C01 appears to have slightly

decreased copepod abundance. Based on biovolume, the trend is significantly different with

Chapter 4 Results

52

little difference between the outside and inside of the reef, but a significant spike at the reef

station, R05 (Figure 4.21).

Figure 4.20: Copepod abundance, as a percentage of total abundance, across the reef, from the outside

stations (R07, R06) to the reef station (R05) to the lagoon and the channel (R04, R03, C01).

Figure 4.21: Copepod biovolume, as a percentage of total biovolume, across the reef, from the outside

stations (R07, R06) to the reef station (R05) to the lagoon and the channel (R04, R03, C01).

Based on biovolume, copepods remained dominant for the reef break samples and for tow E

of R07; however, chaetognaths dominated tow E of R06 and salps/doliolids dominated tow L

of R06 and R07. It is interesting to observe the effect of the salp bloom which occurred in tow

L of R06 and R07 by comparing the abundances (ignoring copepod abundance) of tow E and

tow L for both R06 (Figure 4.22 and Figure 4.23) and R07 (Figure 4.24 and Figure 4.25). The

taxa structures have neglected copepods due to the difficulty of representing the highly

abundant copepods on the same scale as the other taxa.

0

10

20

30

40

50

60

70

80

90

100

R07 R06 R05 R04 R03 C01

Co

pe

po

d a

bu

nd

ance

(%

)

Station number

0

10

20

30

40

50

60

70

80

90

100

R07 R06 R05 R04 R03 C01

Co

pe

po

d b

iovo

lum

e (

%)

Station number

Chapter 4 Results

53


, for tow E of R06


, for tow L of R06.


, for tow E of R07

0

50

100

150

200

250

Ind

ivid

ual

s m

-3

General taxa groups

0

50

100

150

200

250

Ind

ivid

ual

s m

-3

General taxa groups

0

50

100

150

200

250

Ind

ivid

ual

s m

-3

General taxa groups

Chapter 4 Results

54


, for tow L of R07.

Compared with the diversity and change which can be seen in the general taxa at R07 and

R06, the inside and reef break stations are consistent, containing mainly gastropods with a

small amount of polychaetes and assorted individuals from other groups. An example of this

can be seen in Figure 4.26, the taxa abundance (neglecting copepods) for tow E of station

R04.


, for tow E of lagoon station R04.

It should also be noted that, while it hasn’t been represented in the previous figures due to the

general classification of copepods representing all copepodites, copepod nauplii and adult

copepods, it was noticed that in comparison to the inside and reef-top stations, there were

very few copepod nauplii found at the outside stations.

0

50

100

150

200

250

Ind

ivid

ual

s m

-3

General taxa groups

0

50

100

150

200

250

Ind

ivid

ual

s m

-3

General taxa groups

Chapter 5 Discussion

55

5 Discussion

The results of this study have produced clear trends. High temporal variation in the source

waters and spatial variation across the reef are apparent throughout the results, forming clear

trends.

Temporal variation can be noticed in the high variability of the chlorophyll a results of the

outside stations (R06 and R07), as well as in the significant differences in total biomass

between tow E and tow L for the two stations. Distinct changes in the community structure

due to a salp bloom in tow L are also apparent for both outside stations. A general spatial

trend across the reef is a decrease in total biomass between the outside and inside of the reef,

with a spike over station R06. This trend is also apparent for the mean zooplankton size. The

biomass of the smallest size fraction of zooplankton also exhibited a general decreasing trend

from the outside to the inside; however, in this case there is a spike over the reef station

(R05). Zooplankton abundance shows a similar trend to that of the small size fraction

biomass, as do the >5µm chlorophyll a measurements. There is a clear difference in the

general taxa distributions between the outside and inside of the reef, with the outside having a

more diverse and changing distribution and the inside having a consistent and less diverse

distribution.

Due to field constraints, specifically where some areas of the reef became non-navigable by

small boats at certain times of the day, all of the sampling was conducted in the morning

between 6am and 9am. This limited sampling time has meant that diel migration, or diurnal

changes in the zooplankton communities, has been neglected from this study. Roman et al.

(1990) found that water column zooplankton biomass on top of Davies Reef, Great Barrier

Reef, increased by a factor of 2 to 3 at night time due to the diel migration of dermersal reef

zooplankton. For these reasons, it is expected that the values of zooplankton biomass and

abundance presented in this study would be underestimates of the overall biomass and

abundance around the reef. Diel migration also affects different taxa groups of zooplankton

differently; Roman et al. (1990) found the biomass contribution of mysids, prawns and crab

larvae to be significantly greater at night and also found that other groups such as ostracods

and amphipods were only present in the water column at night. Therefore, it must be stated

that the taxa distributions presented in this study can be considered relevant for the daytime

water column zooplankton community, but not for the reef zooplankton community as a

whole.


56

5.1 Source waters

Hydro-dynamically, the reef front is the source for pelagic inputs into the reef from the ocean.

In order to observe the impacts of oceanic inputs on the reef, it is important to characterise

these source waters and determine the variation which occurs within them. As would be

expected with waters exposed to the dynamic system of the ocean, the source waters on the

outside of the reef exhibit large temporal variation.

The temporal variation is demonstrated in the chlorophyll a data for the stations in the source

water area (R06, R07). The data varies considerably over the month of sampling with data

from station R06 having the largest variation of around 1.6mgL-1

over the month; both

stations exhibit the same patterns over time, with slight differences in the quantities of

chlorophyll a (Figure 5.1).

Figure 5.1: Changes in chlorophyll (mgL-1

) for outside reef stations R06 and R07

over the month of sampling.

The total mesozooplankton biomass at the outside stations also exhibits some temporal

variation over the month when comparing tow E to tow L, although in this case the patterns of

the two stations are quite different (Figure 4.7). The station furthest out from the reef (R07)

saw an increase of biomass at the end of the month when compared to the beginning, while

station R06 underwent a decrease. In order to ascertain the reasons for this difference, it is

necessary to examine the changes in community structure over this period.

When observing the taxa structure (without copepods) of R06 and R07 during tow L (Figure

4.23 and Figure 4.25 respectively) it can be noticed that a salp bloom occurred at the outside

stations during this time. For further indication as to why a salp bloom would affect the

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Ch

loro

ph

yll a

(m

g L-1

)

Date

R07

R06


57

biomass of the two stations differently, the taxa structure of tow L must be compared to that

of the tow at the beginning of the month, tow E, for stations R06 and R07 (Figure 4.22 and

Figure 4.24 respectively). From observation of these two figures depicting the taxa structure

for tow E, it can be noticed that compared to R06, R07 has low numbers of all taxa groups

except gastropods. For this reason, a salp bloom occurring at R07 actually increased the

biomass at the station through the large biomass of the salps, despite the fact that salps tend to

feed on everything (Newell and Newell 1977), producing a smaller biomass and decreased

diversity than before the bloom, as can be seen occurring at R06.

Observing the copepod abundance (as a percentage of the total abundance) of tow E and tow

L at stations R06 and R07 (Table 4.2), it is noted to drop by 3% between tow E and tow L at

R06 and by 14% at R07. This significant decrease at R07 may suggest that the salps are

feeding primarily on copepods due to the lack of other taxa groups; however, remembering

that the salp bloom caused an increase in total biomass at R07, simply observing the

percentage abundance of copepods is not adequate to draw a conclusion. Figure 5.2 compares

the change in total abundance and the change in copepod abundance between tow E and tow

L for R06 and R07. In this figure, note the increase in the abundance of copepods at R07

between tow E and tow L despite the decrease in percentage abundance due to the high

numbers of salps.

(a) (b)

Figure 5.2: Total and copepod abundance m-3

for tow E and tow L of R06 (a) and R07 (b).

This would be an appropriate point in the discussion to mention that although it is interesting

to compare the image analysis data from the beginning of the month (2nd

to 4th

May, tow E)

and the end of the month (21st to 23

rd May, tow L), the lack of more constant data over the

month makes it difficult to draw conclusions simply by comparing the two tows. While it is

clear the salp bloom had some impact on the taxa structure, particularly at station R06, it is

difficult to draw specific conclusions with regard to relative abundance of taxa groups.

0

500

1000

1500

2000

2500

tow E tow L

Ind

ivid

ual

s m

-3

Total Copepods

0

500

1000

1500

2000

2500

tow E tow L

Ind

ivid

ual

s m

-3

Total Copepods


58

Indeed, it is possible that there were changes between the beginning and the end of the month

that were not related to the salp bloom. The same argument can be applied to R07. The most

definitive conclusion that can be drawn is that there is significant temporal variation in the

zooplankton community structures outside the reef.

Finally, it is interesting to note where the temporal variation in the outside stations actually

becomes apparent. It is clear that there is a difference in the taxonomic structure and

abundance of the communities of R06 and R07 between tow E and tow L; however, the size

structure of the communities does not present such a clear difference. Observing Table 4.1 it

can be noted that the slopes of the NB-S spectra for tow E and tow L at R06 are very similar,

suggesting that although the salp bloom does change the taxa structure of the community, the

size distribution of biovolume in the community remains unchanged. At station R07, the slope

changes considerably with the salp bloom, presenting a shallower slope for tow L, which

indicates a higher proportion of larger organisms. The shallow slope may be related to the

change in percentage abundance of copepods, as a reduced proportion of copepods would

mean a reduced proportion of smaller organisms.

5.2 Trends across the reef

As mentioned above, this study has shown some significant trends in the zooplankton

communities across the reef, specifically: a decreasing total biomass and mean zooplankton

size moving from the outside of the reef to the inside, with a spike over outside station R06;

an overall decrease in abundance moving from the outside to the inside of the reef, with a

spike over the reef station R05; and significant differences in the taxonomy of the inside of

the reef compared to the outside. There are also significant trends found in the chlorophyll a

and phaeophyton a data.

5.2.1 Chlorophyll and phaeophyton

The decreasing trend of chlorophyll a from the outside to inside of the reef, indicates a

decrease in the production rate of phytoplankton across the reef (Figure 4.1). Observing the

>5µm chlorophyll a measurements across the reef (Figure 4.2) it can be noted that the high

total chlorophyll a measurements found outside the reef in Figure 4.1 are due to the small

picoplankton, not the large phytoplankton; instead, the large phytoplankton have a spike in

production over the reef station R05.

The phaeophyton a measurements increase moving from the outside to the inside of the reef,

with a peak at lagoon station R03 (Figure 4.3). This implies that the rate of zooplankton


59

feeding on phytoplankton is increasing in relation to phytoplankton production with reduced

distance from shore. The ratio of chlorophyll a to phaeophyton a decreases dramatically

across the reef, as might be expected from Figure 4.1 and Figure 4.3. There is a spike at

station R06, just outside the reef, implying this is the point of greatest phytoplankton

production when compared to zooplankton feeding.

5.2.2 Biomass and abundance

Biomass totals, for both the smallest size fraction and the whole size spectrum, have produced

a general decreasing trend between the outside reef stations and the lagoon stations. For the

smallest size fraction there was a peak in biomass over the reef station R05 (Figure 4.6);

however, for the total biomass there was a peak over R06 (Figure 4.8) suggesting that the

smaller organisms are the main contributors to biomass at R05 and the larger ones are the

main contributors to biomass at R06. This is reinforced by the abundance and the mean

zooplankton size across the reef (Figure 4.10 and Figure 4.11 respectively); the abundance

shows a spike over reef station R05, while the mean zooplankton size shows a spike over the

outside reef station R06.

Some of the patterns and trends which are observed in the biomass totals are reflected in the

chlorophyll and phaeophyton measurements. The spike over reef station R05 which is seen in

the small biomass is reflected in the large phytoplankton >5µm chlorophyll a measurements

(Figure 4.2). In other words, the station with the largest community of small meso-

zooplankton also has the largest community of large phytoplankton; this station, R05, can be

seen as a ‘hot-spot’ across the reef. The trend of the total biomass matches the trend of the

chlorophyll to phaeophyton ratio, with a spike over station R06 and significantly lower values

on the inside of the reef compared to the outside. These results appear to conflict as they

suggest that the rate of zooplankton feeding on phytoplankton compared to phytoplankton

growth is at its lowest at the point where there is the greatest zooplankton biomass. It is

necessary to further examine the structure of the zooplankton communities in order to gain

more information on what is occurring at the various stations.

5.2.3 Community structure

Based on the above discussion regarding biomass changes across the reef, the slope of the

NB-S spectrum for each station turned was as expected. The shallowest slopes were

documented at station R06, just outside the reef; the steepest were found at the reef station

R05 (Table 4.1). The two slopes indicate a dominance of larger organisms and a dominance


60

of smaller organisms respectively. The results from Tukey’s test suggest that stations R05 and

R06 were the only two stations, out of the seven that were sampled, to exhibit significantly

different slopes, conversely implying consistency in the slopes for the remainder of the

stations.

The most interesting trend across the reef, as far as taxonomic community structure is

concerned, is the change from a diverse and varied range of taxa groups outside the reef to a

consistent and uniform range of taxa groups inside the reef. The reader should again be

reminded that only general taxa groups were considered and there was no enumeration to

more complex levels; therefore, when discussing diversity, this is with regard to the general

taxa groups and not species diversity. The most diverse sample tow E of station R06,

demonstrated in Figure 4.22, and this can be contrasted with tow E of the lagoon station R04

which is indicative of the taxa structure found at the lagoon stations and the reef station R05

(Figure 4.26).

Another significant difference between the outside stations (R06 and R07) and the remainder

was the lack of any euphausids or chaetognaths at R05 or any of the inside stations – they

were only found outside the reef. Based on biovolume, chaetognaths were in fact the

dominant taxa group for tow E of R06 and this may present some reasoning as to why R06

displayed not only the highest zooplankton biomass, but also the highest chlorophyll to

phaeophyton ratio. Chaetognaths are carnivores and thus would not be consuming

phytoplankton and producing the phaeophyton pigment, meaning that they would be

contributing to the biomass at station R06, but not the phaeophyton levels; however, this

presents the issue that the chaetognaths would most likely be eating copepods, which in turn

would need to be eating the large phytoplankton. As demonstrated in Figure 4.2, the large

phytoplankton were not found to be abundant at station R06, so we are presented with the

conundrum of high picoplankton levels and high zooplankton levels.

In order investigate this issue further it is useful to observe the temporal variation in the

chlorophyll a to phaeophyton a ratio at station R06 (Figure 5.3). Tow E was conducted

towards the beginning of the month and tow L towards the end and it can be noted that both

of these exhibit lower ratios, below 10 for tow E and below 20 for tow L. Observing the low

ratio for tow E, it could be suggested that the conundrum produced when examining the mean

ratio at station R06 in Figure 4.4 was a little exaggerated. For the higher ratio observed during

tow L, a possible explanation could be related to the salp bloom present during this tow with

the salps acting as a conduit between the picoplankton and the zooplankton.


61

Figure 5.3: Change of the chlorophyll a to phaeophyton ratio

over the month of sampling at outside reef station R06.

This reiterates the issue of temporal variation at the outside stations as discussed in Section

5.1 – it is difficult to draw conclusions from the image analysis data when there is only data

from the beginning and end of the month. It would be interesting to observe the community

structure of station R06 at a time when there was a very high chlorophyll to phaeophyton ratio

and compare that with the structures observed for the lower ratios at the beginning and end of

the month.

Returning to the significantly different taxa groups found outside and inside the reef, there are

several theories which can be postulated as to why this is the case. Firstly, it could be

suggested that the coral is selectively feeding on certain taxa groups, such as the euphausids

and the chaetognaths, as they are washed across the reef. Corals are not thought to be

selective with their prey, but zooplankton behaviour is considered to be the determining factor

for coral predation (Sebens et al. 1996) and it is considered likely that relatively large

individuals, such as euphausids and chaetognaths, will be more easily captured by coral.

However, it is also believed that coral mainly feed on zooplankton during dawn, dusk and

night time during the migration of the demersal zooplankton (Sebens et al. 1996) and it would

seem unlikely that the considerable biomass of chaetognaths found during tow E of station

R06 could have been consumed by the coral prior to reaching the inside stations.

The second possibility is that the taxa groups found outside the reef but not inside could be

avoiding the reef break - swimming down or away from the reef. Zooplankton avoidance

around coral reefs is a common occurrence, with certain species exhibiting better avoidance

than others due to size and swimming ability (Sebens et al. 1996, Holzman et al. 2005, Yahel

et al. 2005). While it was difficult to source information on the swimming speed of specific

0.00

10.00

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30.00

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50.00

60.00

70.00

80.00

90.00

100.00

30-Apr-07 05-May-07 10-May-07 15-May-07 20-May-07

Rat

io (

Ch

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62

taxonomic groups, an in-situ study of three-dimensional swimming behaviour of zooplankton

found that the average individual swimming speed was 11cms-1

(McGehee and Jaffe 1996).

Comparing this to the speed of the source water flow towards the reef at R06, an estimated 1

to 2cms-1

(pers comm. R Lowe, October 2007), it can be seen that there is likely to be an

order of magnitude difference between the water flow and the zooplankton swimming speed

at station R06. This suggests that zooplankton avoidance of the reef break is a possible reason

for the different taxa found across the reef.

Given the limited scope of this study, there is no single definitive conclusion or explanation to

be drawn from the taxa results and there are several other factors to consider. The main factor,

which has only been considered briefly in this study as it was not within the scope, is the

hydrodynamic conditions around the reef. For the purposes of the study, it has been assumed

that water is flowing from the outside stations, across the reef, through the lagoon and then

out through the channel. This general flow pattern was confirmed by gliders; however, it is

possible that the hydrodynamic conditions, especially just outside the reef break, are more

complex. The very high zooplankton biomass at R06 and the difference in taxa structure

between R06 and R05 suggest that the hydrodynamic conditions are playing an important role

in the reef break area; however, it is difficult at this stage to speculate on the nature and extent

of that role. It is interesting to note that Strzelecki et al. (in press) also found chaetognaths to

be the dominant taxa by biovolume when observing the zooplankton communities in a pair of

warm-core and cold-core eddies. While there is perhaps some hint of the importance of the

hydrodynamic conditions around the reef in producing such a high biomass of chaetognaths at

station R06, it is not possible to draw a conclusion.

Another factor to consider is the possible impact on the results due to the sampling regime. As

has already been mentioned, there are issues to contend with regarding the high temporal

variation found outside the reef (Section 5.1) and the time lapse between the two sets of

image analysis data. This temporal variation causes another issue with regard to each set of

stations being sampled over a period three days; specifically, the outside stations were always

sampled on a different day from the inside stations. Given the high temporal variability of the

outside stations, it is entirely possible that the actual source waters (ie. the source waters

crossing the reef on the same morning as sampling) for the sampled inside stations are quite

different to those source waters that were sampled one to two days before or after. This

presents the issue of whether or not the structures of the source water, reef break and inside

reef communities are valid for an across reef comparison. While the source waters have been

found to be highly variable over time, the reef break and inside reef stations have been found


63

to be relatively consistent and this can be demonstrated by comparing Figure 5.1 with Figure

5.4. The comparison of these figures demonstrates the consistency of the chlorophyll a and

phaeophyton a measurements at R05 over the month when compared with the chlorophyll a

measurements of R06 and R07 over the month.

Figure 5.4: Concentration (mgL-1

) of chlorophyll a and phaeophyton a over the month of sampling at reef

station R05. Note the consistency of the data over the month when compared to Figure 5.1.

Due to the observable consistency in the chlorophyll a measurements of the reef stations

(R05) and inside reef stations coupled with the consistency in the zooplankton community

structure between tow E and tow L for these stations, it has been assumed that the

zooplankton community structure at the inside stations remained consistent throughout the

month of sampling, allowing the comparison of the outside reef stations with inside reef

stations. While general comparisons are possible, it is still not possible to demonstrate the

structure of a zooplankton community on the outside of the reef and then demonstrate the

structure of the same community once it has been washed across the reef. Given the

uncertainty of zooplankton sampling in a dynamic marine environment and the technical

difficulties of navigating around a reef break, it is unlikely that this level of comparison could

be achieved for a study of this nature.

5.3 Validity of the image analysis technique

The image analysis technique, using the software PlanktonJ, was found to produce

comprehensive datasets describing the samples analysed; however, the technique was also

very time-consuming and this detracted from the study in a number of ways. The use of

PlanktonJ in this study has assisted in developing the software. Feedback from this study and

corresponding improvements to the software are discussed in Section 3.4.3.

00.20.40.60.8

11.21.41.61.8

2

Co

nce

ntr

atio

n m

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Date

chl

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64

The most important benefit of the image analysis technique was the extensive dataset

produced, describing not only the total biomass for each station, but also the community size

and taxa structure. From this dataset, it was also possible to calculate the mean zooplankton

size at each station and the abundance in terms of number of individuals and biovolume. The

restriction of classification to general taxa groups and the time constraints of the study have

meant that the results of the image analysis have not been utilised to their full potential. The

resolution of the images presents an opportunity for a comprehensive analysis of the

taxonomy of the samples and of changes in species abundance across the reef, and this has not

been taken advantage of as it was outside the scope of the study.

As would be expected in the production of such an extensive dataset, the image analysis

technique was found to be very time-consuming. This was due in part to the software still

being in a development phase, but also to the samples containing plankton of varied size and

shapes, and significant amounts of detritus and the time required for preparation of the

samples for image analysis. Unfortunately, due to the image analysis technique used in this

study, only a small number of samples were able to be analysed, producing an incomplete

temporal view.

An additional contributing factor to the time required for the image analysis was the need to

manually remeasure many of the plankton after the image had been processed by PlanktonJ.

This was due in part to the incomplete program, but also to the presence of plankton which

did not fit the ellipsoid shape. Plankton such as copepods and doliolids which are ellipsoid in

shape were generally measured correctly by PlanktonJ; however, plankton of different shapes,

such as chaetognaths and euphausids, had to be remeasured. There were also problems with

the automatic measurement of plankton without defined outlines, such as polychaete larvae

and copepod nauplii.

There are a number ways in which the time taken by the image analysis technique could have

been reduced. Firstly, it must be noted that analysis of further samples would have been less

time-consuming due to the continual improvement of the PlanktonJ software and the

increased experience of the author. One option for reducing the time taken to prepare the

samples for image analysis would be to avoid size fractionating the samples prior to image

analysis. The samples were size fractionated to allow a more accurate representation of the

size range being considered; however, by skipping this step any inaccuracies associated with

the filter stack technique would be avoided and the preparation time would be greatly

reduced. There are two main issues associated with not size fractionating the sample: firstly,

the large organisms would no longer be accurately represented in the scanned sub-samples, so


65

a smaller upper limit would need to be imposed to avoid skewing the figures; secondly, as the

smallest organisms are not separated from the rest of the sample they cannot be imaged using

the microscope and since they are too small for the scanner, a larger lower limit would have to

be imposed on the plankton size range being considered. Given these issues, the use of size

fractionation prior to image analysis would seem to be justified.

Overall, while time consuming, image analysis did provide an extensive dataset which

allowed a significantly more in-depth analysis of the changes in the zooplankton communities

across the reef then a simple biomass measurement and taxa observation would have

presented. The development of PlanktonJ through the feedback provided by this study was an

added benefit of using the image analysis technique.

5.4 Concluding remarks

From the discussion above it can be seen that while this study has produced some interesting

results with strong trends, it is not yet possible to reach definitive conclusions regarding the

mechanisms that are occurring. It is clear that around the reef break (stations R05 and R06)

there are certain processes that are producing a ‘hot-spot’ at R05 and the conundrum of high

picoplankton and high zooplankton at R06; however, the exact nature of these processes

remains unknown.

It is interesting to note that other studies have defined coastal lagoons as having a non-steady

state character due to their shallowness, causing a tight physical-biological coupling (Silvert

1983 in Gilabert 2001); however, the result of this study would suggest that this is not the

case, at least not as far as taxa distribution is concerned. Upon closer examination of the size

structure results, it can be seen that the only lagoon station for which there were two full

datasets (R04) has a large variance in the slopes of its size spectra (Table 4.1), perhaps

representing the non-steady state character of the lagoon. This is, however, purely speculation

and there would need to be further study to confirm.

While this study is not conclusive as to exactly which mechanisms are occurring around the

reef, it is clear that the reef is interacting with the water column to some extent and it would

seem likely that there is a combination of physical and biological mechanisms producing the

results which have been observed. This study is part of the preliminary stages of the Ningaloo

Biological Oceanography Project and thus has been focused on formulating methodology and

on data collection. It is anticipated that further conclusions will be produced through future

study on both the biological and physical aspects of the reef.


66


Chapter 6 Conclusions

67

6 Conclusions

The results of this study have produced clear trends, both in terms of temporal variation

outside the reef in the source waters and spatial variation across the reef. The temporal

variation in the source waters was highlighted by the chlorophyll data and by the occurrence

of a salp bloom during the second tow at the outside reef stations, R06 and R07. Compared to

the first tow, it was clear that the salp bloom significantly changed the zooplankton

community structure at both stations. The spatial variation across the reef was highlighted by

several results, specifically: the significant decrease in total biomass across the reef; the

difference in taxa structure of the outside reef stations compared with the rest of the stations;

the significant decrease in total chlorophyll a across the reef and the spike in the biomass of

the small mesozooplankton and large phytoplankton at the reef station R05.

As well as the spatial and temporal trends in the results, there are specific sites where it would

seem there are complex processes occurring; in particular, the ‘hot-spot’ at reef station R05

and the conundrum of high picoplankton and high zooplankton biomass at outside station

R06. While the study is not able to produce definitive conclusions as to the mechanisms that

are occurring at these sites, it is likely that there is a combination of biological and physical

mechanisms.

This study has successfully completed the objective of characterising the daytime pelagic

zooplankton community across a section of Ningaloo Reef and while it was not possible to

produce conclusive results as to the mechanisms that are occurring on the reef, it is clear that

there is some interaction between the reef and surrounding waters. This study is part of the

initial stages of the Ningaloo Biological Oceanography Project and has focused on baseline

data collection and analysis. When these results are combined with the results from the Wyatt

benthic-pelagic coupling study, the Kapeli phytoplankton study and other future work, it will

be possible to produce definitive conclusions as to the processes which are occurring on the

reef and the interactions between the reef and surrounding oceanic waters.

Chapter 6 Conclusions

68


Chapter 7 Recommendations

69

7 Recommendations

There are several recommendations which can be made following the conclusions of this

study. Firstly, in order to understand the relationship between the zooplankton community of

the reef and the benthic community of the reef, it is necessary to produce more detail

regarding the whole zooplankton community. This would include:

More detailed taxa classification down to species level, using the images which have

been produced. This would allow observations of the changes in species diversity

across the reef rather than simply general taxa diversity and would perhaps allow

improved comparisons to other studies, as far as taxonomy is concerned;

Isotope analysis of the tow samples which were collected for that purpose. Isotopes

can be used to determine the position of the zooplankton in the food web occurring at

the reef, in relation to the benthic fauna and phytoplankton. This will produce a much

clearer picture of the changes in the reef community structure across the reef;

Diurnal investigations to gain an idea of the demersal communities of zooplankton and

also to take into account the fact that the majority of coral feeding on zooplankton

occurs at night. The importance of the demersal reef communities in terms of the

whole zooplankton community of the reef should not be understated; however, the

practical difficulty of sampling around a reef break at night remains a barrier in the

characterisation of the whole reef community of zooplankton.

Secondly, it is recommended that a sampling regime be undertaken which takes into account

the temporal variation which has been found outside the reef. In other words, zooplankton

sampling needs to be conducted more frequently to allow a more direct comparison of the

sample sets. Given the time which would be required to analyse a larger number of samples

using image analysis, it is suggested that only a small number of the sample sets be analysed

by image analysis. The complete set of samples could be analysed using a simpler method for

total biomass measurement and some general taxa observation; given the non-destructive

nature of the image analysis technique, this could include the samples analysed using image

analysis.

Finally, it is necessary to conduct an in-depth investigation into the physical mechanisms

which potentially could be occurring around the reef break and producing the interesting

situations observed at reef station R05 and outside station R06.

Chapter 8 References

70

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Appendices

74

Appendices

Appendix A: Sampling and image analysis details for each sample

Tow Station Date Tow time (min)

Net Submergence (%)

Volume of water

sampled (m^3)

Sample Volume (mL)

Size Fraction (µm)

Volume (mL)

Sub-sample (mL)

Fraction of total sample

E R05 2-May 6 90 17.613288 840 4000 + 2000 1 1 1

1000 + 500 360 20 0.055555556

300 415 10 0.024096386

105 680 2 0.002941176

E R04 2-May 12 90 35.226576 780 4000 + 2000 1 1 1

1000 + 500 410 35 0.085365854

300 400 20 0.05

105 855 2 0.002339181

E R03 2-May 10 100 32.6172 850 4000 Nothing in it

2000 + 1000 1 1 1

500 + 300 640 4 0.00625

105 860 2 0.002325581

E R06 4-May 10 100 32.6172 925 4000 + 2000 790 30 0.037974684

1000 860 20 0.023255814

500 600 10 0.016666667

300 730 4 0.005479452

105 520 2 0.003846154

E

R07 4-May 10 100 32.6172 805 4000 + 2000 1 1 1

1000 1 1 1

500 + 300 520 10 0.019230769

105 530 2 0.003773585

Tow Station Date Tow time (min)

Net Submergence (%)

Volume of water

sampled (m^3)

Sample Volume (mL)

Size Fraction (µm)

Volume (mL)

Sub-sample (mL)

Fraction of total sample

L R05 21-May 10 100 32.6172 695 4000 + 2000 1 1 whole sample

1000 + 500 390 30 0.076923077

300 635 20 0.031496063

105 520 5 0.009615385

L C01 21-May 10 100 32.6172 430 4000 + 2000 1 1 1

1000 + 500 340 40 0.117647059

300 460 20 0.043478261

105 580 2 0.003448276

L R06 22-May 10 100 32.6172 1030 4000 650 20 0.030769231

2000 + 1000 500 40 0.08

500 + 300 960 15 0.015625

103 400 5 0.0125

L R07 22-May 10 100 32.6172 690 4000 810 10 0.012345679

2000 + 1000 440 20 0.045454545

500 + 300 650 5 0.007692308

105 460 2 0.004347826

L R04 23-May 10 100 32.6172 1000 4000 Nothing in it

2000 + 1000 1 1 1

500 + 300 450 40 0.088888889

105 720 4 0.005555556

L R03 23-May 10 100 32.6172 730 4000 1 1 1

2000 + 1000 380 40 0.105263158

500 + 300 375 20 0.053333333

105 600 2 0.003333333

Appendix B: Protocol for preparation of image analysis samples

Separate into Size Fractions

1. Pour original sample from jar into measuring cylinder through a funnel and record volume.

2. Wash funnel into original jar and pour jar through filter stack, rinse jar until there is nothing

visible on it, then rinse three times.

3. Pour cylinder into filter stack and rinse until you can’t see anything, then rinse three times.

4. Put shower on filter stack for one minute.

5. Wash each filter into a dish or icecream container, until there is nothing visible on it and also

rinse the bottom of the filter into the rest of the container stack.

6. Pour from dish into individual jars for each size fraction. Wash dish out until there is nothing

visible on it, then rinse three times.

7. Label each jar with the relevant size fraction.

Microscope for smallest size fraction (105µm)

1. Pour jar into cylinder through funnel and record volume. Return to jar, taking note of any

extra volume used to wash out the cylinder.

2. Invert jar 10 times to mix.

3. Take sub-sample of 2mL.

4. Wash off side of sampler and put sub-sample into Bogorov tray and wash sampler out three

times.

5. Open ImageJ on computer and switch microscope to camera mode. Go to Plugins and Aquire

QCam.

6. Start at one end of the tray and work along taking photos of any zooplankton.

7. Within the image, measure the major and minor axis of any zooplankton and transfer the

results to excel.

8. Repeat for another 2mL if necessary.

Scanner for larger size fractions (300µm, 500µm, 1000µm, 2000µm, 4000µm)

1. Decide on which size fractions to combine, based on amount found in each. Need to have

three samples as opposed to five. Combine chosen samples in one jar each.

2. Pour jar into cylinder through funnel and record volume. Return to jar, taking note of any

extra volume used to wash out the cylinder.

3. Invert jar 10 times to mix.

4. Take a sub-sample. Start with 5mL or 10mL depending on how much is in the jar and add

more subsamples accordingly. Wash sampler if there are any zooplankton seen on it. May

need to strain some of the water out to get a decent number of zooplankton within 60mL of

sub-samples. Tray will hold a maximum of 60mL.

5. Attach waterproof paper to sides of scanner.

6. Place tray on scanner.

7. Repeat for other combined size fractions.

8. Once all three trays are in place, turn on scanner and open ZooImage, click on camera icon

and open scanning program. Do a preview scan to check that the trays are correctly placed.

9. Using a pen lid, ensure that all the zooplankton are separate from each other and away from

the sides of the tray (as much as possible).

10. Take a final scan and check image. Ensure that image has been saved to the C drive.

Appendix C: NB-S spectra MATLAB code

%This code imports two columns of data (adjusted biovolume %and ESD for each zooplankton ordered from smallest to largest ESD) from an %excel file, divides the data up into the provided ESD intervals, then %calculates the total biovolume for each interval. The solution matrix is %then exported to a new excel file.

%Import excel file containing the zooplankton data, define the cells in %excel to be used and define the size of the imported dataset excelfile1 = ddeinit('excel','R05a.xls'); dataset = ddereq(excelfile1,'r2c1:r1500c2'); size_dataset=size(dataset);

%Import excel file containing the interval data, with the lower limit and %upper limit of each interval in the 1st and 2nd columns respectively %define the cells to be used and the size of the dataset. excelfile2 = ddeinit('excel','intervals.xls'); interval = ddereq(excelfile2,'r1c1:r29c2'); size_interval=size(interval);

%Define the size of the solution array. solution = size(29,1);

%Loop through each size interval using a for loop for i = 1:size_interval(1) counter=1; while dataset(counter,2)<interval(i,1) && counter<size_dataset(1) counter = counter+1; %Run while loop until the ESD is end %equal to the lower interval %limit, increasing the counter. sum=0;

while dataset(counter,2)<interval(i,2) && counter<size_dataset(1) sum = sum + dataset(counter,1); %Run while loop until the ESD is counter=counter+1; %equal to the upper limit. end

solution(i,1)=sum; %Fill solution matrix.

end %Export solution matrix to a new excel file xlswrite('R05a_soln.xls', solution, 'Biovolume', 'A1');

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Ningaloo Reef as a Plankton Filter: Changes in the Size