BIRDSEY PHD THESIS-done-NO FIELDCODES

PhD Thesis
Animal associations and the impact of introduced
species in marine ecosystems
Emma M. Birdsey
B.Sc. Hons (University of Plymouth), M.Sc. (University of Exeter)
Evolution and Ecology Research Centre
School of Biological, Earth and Environmental Sciences
University of New South Wales
Sydney, NSW 2052
AUSTRALIA
Thesis submitted in fulfilment of the requirements for the degree of Doctor of
Philosophy at the University of New South Wales. August 2010.

ORIGI ALITY STATEME T
‘I hereby declare that this submission is my own work and to the best of my
knowledge it contains no materials previously published or written by another
person, or substantial proportions of material which have been accepted for the
award of any other degree or diploma at UNSW or any other educational
institution, except where due acknowledgement is made in the thesis. Any
contribution made to the research by others, with whom I have worked at
UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare
that the intellectual content of this thesis is the product of my own work,
except to the extent that assistance from others in the project's design and
conception or in style, presentation and linguistic expression is
acknowledged.’
Signed ……………………………………………..............
Date ……31/08/2010……………………………………..

Acknowledgements
First of all, I would like to thank my supervisors, Drs Alistair Poore and Emma Johnston, for all their
advice, guidance and enthusiasm during meetings and designing experiments; also, for their comments
and patience when reading drafts of my work. Thank you for having me in your lab.
Next, I am grateful to everyone in the Subtidal Ecology and Ecotoxicology Group for fieldwork
assistance. Your professionalism has made fieldwork a pleasure. Thanks especially to Damon Bolton,
Graeme Clark, Derrick Cruz, Katherine Dafforn, Luke Hedge, Chris Hellyer, Martin Hing, Louise
McKenzie, and David Roberts - for help with fieldwork. Also, Shin Ushiama for being such an
excellent summer scholarship student to me and sifting through those many samples of skeleton
shrimp.
Thanks, also, to:
Assoc. Prof. Ross Coleman for comments on my conference presentations and posters.
Dr. Jim Lowry of the Australian Museum for taxonomy expertise
Denise Bunting comments on Chapter 4.
Andrew McIntyre, CEO of Sydney Royal Yacht Squadron, for a secure field site away from the
meddling public.
Prof. Bob Clarke and Assoc. Prof. Marti Anderson for statistical discussion.
Last, though by no means least, my thanks to Matthew Pinter for being my most active fieldwork
helper. Thanks for the use of the ‘re-breather’, being my chauffer in hellish Sydney traffic, reading
drafts of my PhD thesis and making hugely helpful comments, and finally, for knowing to turn on
country music to cheer me up.

i
Table of contents
Appendices..................................................................................................................viii
List of Tables ................................................................................................................ix
List of Figures and Boxes ...........................................................................................xiv
Abstract..........................................................................................................................1
Chapter 1. General Introduction .....................................................................4
Biological diversity and ecosystem functioning................................................4
Surrogate measures of biodiversity....................................................................5
Species identity in ecosystem functioning..........................................................6
Species associations between habitat-forming species and epifauna ................7
Introduced species..............................................................................................8
Impacts to biodiversity.......................................................................................9
Associations between mobile animals and their sessile hosts in marine
ecosystems .........................................................................................................9
Impact of introduced habitat-forming marine species ....................................11
Thesis outline...................................................................................................12
Chapter 2. Species diversity, cover, and functional identity of sessile
animal assemblages do not predict associated mobile fauna ...............15
SUMMARY.....................................................................................................15
INTRODUCTION ...........................................................................................17
METHODS ......................................................................................................21
Experimental survey .......................................................................................21
Relatedness between sessile and mobile assemblages ....................................23

ii
Predictors of the diversity, abundance, and composition mobile species........24
Statistical analyses ...........................................................................................26
RESULTS ........................................................................................................28
Sessile and mobile assemblage relatedness .....................................................28
Predictors of the diversity, abundance, and composition mobile species........29
DISCUSSION..................................................................................................47
Sessile species diversity and associated fauna ................................................47
Habitat cover and functional type fails to predict mobile composition...........50
Non-indigenous habitat forming species ........................................................53
Conclusions..........................................................................................................................55
Chapter 3. The effect of varying the numbers and identities of
functional types of hosts on associated biodiversity ................................57
SUMMARY.....................................................................................................57
INTRODUCTION ...........................................................................................60
The functional role of diversity........................................................................60
Complentarity and sampling effects.................................................................60
Redundancy......................................................................................................61
Identity versus richness of species ...................................................................62
Habitat-forming organisms..............................................................................63
Marine ecosystems...........................................................................................64
Aims.................................................................................................................67
METHODS ......................................................................................................69
Study site and organisms .................................................................................69
Variation among mobile assemblages with host functional types...................69

iii
Variation in mobile assemblages with the number and composition of host
functional types................................................................................................72
Variation in mobile assemblages with the composition of functional types of
neighbouring hosts...........................................................................................73
Relatedness between specific compositions of sessile species and assemblages
of mobile fauna ................................................................................................74
Statistical analyses ...........................................................................................74
RESULTS .......................................................................................................76
Variation among mobile assemblages with host functional types...................76
Variation in mobile assemblages with the number and composition of host
functional types................................................................................................77
Variation in mobile assemblages with the composition of neighbouring
functional types of hosts .................................................................................78
Relatedness between assemblages of sessile species and mobile fauna..........79
DISCUSSION..................................................................................................91
Effectiveness of functional characteristics as predictors of epifauna .............93
Other explanatory factors driving associated fauna.........................................94
Species-specific associations ..........................................................................94
Structural complexity of hosts..........................................................................95
Species richness of host assemblages ..............................................................96
Other biotic and abiotic factors.......................................................................97
Conclusions .....................................................................................................98
Chapter 4. Estimating the biodiversity associated with individual
species of sessile animals ..................................................................................100

iv
SUMMARY...................................................................................................100
INTRODUCTION .........................................................................................102
Associations between sessile and mobile animals in marine systems...........104
METHODS ....................................................................................................107
Study systems and sites..................................................................................107
Predicting biodiversity...................................................................................109
Statistical analyses ........................................................................................110
RESULTS ......................................................................................................112
Host species and associated mobile species...................................................112
Estimating biodiversity..................................................................................113
DISCUSSION................................................................................................121
Species specific associations..........................................................................121
Host origin ....................................................................................................124
Estimating biodiversity..................................................................................127
Conclusions....................................................................................................129
Chapter 5. Associations between sessile and mobile marine animals
on a global scale: A review and meta-analysis .........................................130
SUMMARY...................................................................................................130
INTRODUCTION .........................................................................................132
Marine ecosystems.........................................................................................135
Aims...............................................................................................................136
METHODS ....................................................................................................137
Taxonomy......................................................................................................138
Hypotheses.....................................................................................................139

v
Variation among sessile host phyla ...............................................................139
Variation between orders of the Mollusca ....................................................140
Structural complexity of hosts........................................................................140
Global region and environment of host .........................................................141
Statistical analyses ........................................................................................142
RESULTS ......................................................................................................144
Variation among sessile host phyla................................................................144
Variation between orders of the Mollusca ....................................................145
Role of host structural complexity.................................................................145
Variation of mobile fauna with host geographic region and
environment. .................................................................................................146
DISCUSSION................................................................................................156
Variation among host identities ....................................................................156
Structural host complexity and associated fauna...........................................159
Species diversity from different global regions and environments ..............162
Future work – other potential predictors of associated fauna........................164
Conclusions....................................................................................................164
Chapter 6. Mobile epifauna associated with similar native and
invasive species of bryozoa and the effect of avicularia........................166
SUMMARY...................................................................................................166
INTRODUCTION .........................................................................................168
METHODS ....................................................................................................172
Study system ..................................................................................................172
Fauna associated with invasive versus native Bugula spp ............................172

vi
Manipulating Bugula dentata avicularia........................................................173
Statistical analyses .......................................................................................174
RESULTS ......................................................................................................176
Fauna associated with invasive versus native Bugula spp. ...........................176
DISCUSSION................................................................................................183
Invasive vs. native Bugula and associated diversity......................................183
Explanations for finding no difference in epifauna between a native and
invasive host species......................................................................................184
Conclusions....................................................................................................187
Chapter 7. First record of invasive skeleton shrimp Caprella
hirayamai in Australian waters......................................................................189
SUMMARY...................................................................................................189
INTRODUCTION .........................................................................................190
METHODS ....................................................................................................192
RESULTS ......................................................................................................193
Identifying Caprella hirayamai ....................................................................193
Distribution of C. hirayamai .........................................................................194
DISCUSSION................................................................................................200
Chapter 8. General discussion ......................................................................206
Species associations and predictors of diversity............................................207

vii
Assemblage composition................................................................................208
Species diversity.............................................................................................208
Functional types.............................................................................................209
Habitat diversity.............................................................................................209
Introduced hosts.............................................................................................210
Explanations for results..................................................................................211
Future research...............................................................................................212
Final remarks ................................................................................................214
References..............................................................................................................216

viii
Appendices
Supplementary Appendix 5.1. Studies and data compiled from the literature search..244

ix
List of Tables
Table 2.1. Sessile species accounting for most of the variation in associated
assemblages of mobile species analysed (BVSTEP) using the entire dataset. Origin
indicates whether they are native, cryptogenic, non-indigenous, or unknown............32
Table 2.2. BVSTEP results for the best selection model and the correlation of the
composition of sessile species to associated assemblages of mobile species. The best
selection model column shows the number of habitat-forming species in the subset of
habitat-forming species that best correlated with mobile assemblage composition
(from a total listed in parentheses)...............................................................................33
Table 2.3. Analyses of covariance contrasting the (A) diversity and (B) abundance,
(C) and multivariate analysis of covariance contrasting the composition of mobile
species with the diversity (H’) of sessile species collected from two sites and from six
sampling months. The % cover of sessile species on each plate was used as a
covariate. When interactions between the covariate and the categorical variables were
non-significant these terms were removed from the model. P values were calculated
by 9999 permutations of the raw data. ........................................................................34
species with ordinal diversity (H’) of sessile species among plates collected from two
sites and from six sampling months. The ordinal diversity of sessile hosts was
included as a covariate. When interactions between the covariate and the categorical
variables were non-significant these terms were removed from the model. P values
were calculated by 9999 permutations of the raw data. ..............................................36

x
species with the cover (%) of sessile species on plates collected from two sites and
from six sampling months. The cover was included as a covariate. When interactions
between the covariate and the categorical variables were non-significant these terms
were removed from the model. P values were calculated by 9999 permutations of the
raw data. ......................................................................................................................38
species with the functional diversity (H’) of sessile species on plates collected from
two sites and from six sampling months and including the diversity of sessile
functional types as a covariate. When interactions between the covariate and the
categorical variables were non-significant these terms were removed from the model.
P values were calculated by 9999 permutations of the raw data. ...............................40
species with the proportion of sessile non-indigenous species (NIS) on plates
collected from two sites and from six sampling months and including the proportion
of sessile NIS as a covariate. When interactions between the covariate and the
categorical variables were non-significant these terms were removed from the model.
P values were calculated by 9999 permutations of the raw data .................................42
Table 3.1. Results of the multivariate ANOVAs testing whether the composition,
abundance, and richness of mobile groups significantly differed with the identity of
functional types of host assemblages (monocultures of barnacles, bryozoans,
serpulids). . .................................................................................................................80

xi
Table 3.2. ANOVA results testing that the abundance of individual mobile groups
differs with the identity of functional types of host assemblages hosts (monocultures
of barnacles, bryozoans, serpulids). ............................................................................80
Table 3.3. Results from analysing whether the composition, abundance, and richness
of mobile groups differs with the number of functional types and composition of
functional types in assemblages of hosts. ...................................................................81
Table 3.4. Results from testing whether the abundance of individual taxonomic
groups of mobile fauna differ with the number of functional types and composition of
functional types in assemblages of hosts. ...................................................................81
Table 3.5. Permutational MANOVA results detailing whether the mobile
composition differs with the functional richness and functional composition of hosts
neighbouring barnacle, serpulid, and bryozoan assemblages. ....................................82
Table 4.1. Permutational analyses of variance contrasting the (A) raw data and (B)
weighted (proportioned to weight in g of sessile species sample) data of the
abundance of mobile species among different sessile species collected using cores
from two sites (Kirribilli and Chowder Bay). Sessile species were nested in site and
origin (introduced, native, or cryptogenic). .............................................................115
Table 4.2. Permutational analyses of variance contrasting the richness of mobile
species among different sessile species collected using cores from two sites (Kirribilli
and Chowder Bay). The richness of sessile species was nested in (A) site and (B)
origin (native, cryptogenic, and non-indigenous). ...................................................115
Table 4.3. Permutational multivariate analyses of variance contrasting the (A) raw
data and (B) weighted (standardised by weight of g of sessile sample) data of the
composition of mobile species among different sessile species collected using cores

xii
from Kirribilli and Chowder Bay. Sessile species were nested in site and origin
(native, cryptogenic, and non-indigenous). ...............................................................116
Table 5.1. Table 5.1. Permutational multivariate ANOVA contrasting the
composition of mobile phyla from all data and the balanced data (A-B), classes (C)
and orders (D) and richness of mobile species (E) among host phyla from the
balanced data..............................................................................................................147
Table 5.2. The mobile orders and classes which contribute most to the similarities in
composition of mobile fauna within each host phylum. The percentage contributions
to within-phylum similarities were obtained from the SIMPER routine (similarities
listed for orders > 10%, classes > 15%). ..................................................................147
Table 5.3. Permutational multivariate ANOVA contrasting the composition of
mobile orders and richness of mobile species associated with host orders within
Mollusca from all data and the balanced dataset .......................................................148
mobile orders and richness of mobile species associated with hosts, in the Annelida
and Porifera, of differing levels of structural complexity. ........................................148
mobile orders and richness of mobile species associated with hosts in the Mollusca
from different geographic regions and between intertidal and subtidal environments.
149
Table 5.6. Distribution of secondary metabolites in sessile animals (McClintock and
Baker 2001). ..............................................................................................................149
Table 6.1. Results of PERMANOVA contrasting the composition of mobile species
associated with Bugula dentata and B. neritina (A) has the analysis of all data and (B)
is the analysis with treatments balanced to equal sample sizes per g........................178

xiii
Table 6.2. Results of PERMANOVA contrasting the composition of mobile species
associated with different manipulative treatments to avicularia on colonies of Bugula
dentata........................................................................................................................178
Table 7.1. Caprellid species present in the 50 samples collected from Port Jackson,
their base substrata (a) = animal, (p) = plant and the sessile species of host.............198
Table 7.2. List of Caprella spp. known to have extended beyond their native range.
Including their countries of origin, invasive distribution, any association they have
with an animal or plant species, and the speculated or recorded route of transfer from
their native range. ......................................................................................................199

xiv
List of Figures and Boxes
Figure 2.1. MDS ordination contrasting the composition of mobile species collected
from the experimental plates in different months. The assemblage data were 4th
-root
transformed, and the Bray-Curtis similarity index was used as the measure of
similarity among samples. ..........................................................................................44
Figure 2.2. Mean diversity of mobile species (Shannon-Weiner diversity index H’)
collected throughout the study period, from Chowder Bay and Watsons Bay, from
eight and 16 week old assemblages. Error bars are ± standard error. ........................45
Figure 2.3. Correlations between the diversity of mobile species (H’) and (A) the
diversity of sessile species, (B) % cover of habitat-forming species, (C) functional
type diversity, and (D) the proportion of sessile NIS. Eight week old assemblages are
circles ○, 16 weeks are multiplication signs ×. ...........................................................46
Box 3.1. Compositions of functional types present in assemblages of sessile species. 75
Figure 3.1. Example of experimental assemblage arrangement of mono cultures, bi-
cultures, tri-cultures, and controls (1: random species removal and 2: emergence
disturbance) of functional types of sessile animal groups. See Methods and Box 3.1
for further details and number of replicates per combinations of different cultures.
Control 1: disturbance from gardening and control 2: disturbance from assemblage
emergence. ..................................................................................................................83
Figure 3.2. Mean (A) abundance (black bars) and (B) richness (white bars) of mobile
fauna contrasted across monocultures of different functional types (bryozoans,
serpulids and barnacles). Mobile fauna did not differ with host identity. + SEM is the
standard error of the mean. ........................................................................................84
Figure 3.3. CAP ordinations of assemblages of mobile composition associated with
(A) different functional monocultures of sessile species and (B) sessile assemblages

xv
composed of one, two, or three different functional types and the control
assemblages..................................................................................................................85
Figure 3.4. The abundance of individual mobile groups associated with assemblages
of host monocultures differing in identity of functional type. Only copepods differed
with host functional type. The Y axes, showing abundance, varies with mobile group.
+ SEM is the standard error of the mean. ..................................................................86
Figure 3.5. Mean (A) richness and (B) abundance of mobile fauna associated with
different levels of richness and composition of functional types of host assemblages.
Only mobile abundance significantly differed with hosts varying in the richness of
functional types. Asterisks represent significant differences between host
compositions. + SEM is the standard error of the mean. ...........................................87
Figure 3.6. Influence of sessile assemblages differing in the richness and
composition of functional types, to the abundance of associated mobile groups
(Amphipoda, Annelida, Copepoda, and Nematoda). Only Amphipod abundance
differed with the richness of functional types (i.e., mono, bi and tri-cultures, and
controls) per host assemblage. Letters ‘A’ and ‘B’ on the Amphipod graph represent
significant differences between host compositions. + SEM is the standard error of the
mean. ...........................................................................................................................88
Figure 3.7. CAP ordinations (for the significant results) and an MDS ordination (for
the non significant result) of assemblages of mobile composition associated with (A)
barnacles, (B) serpulids, and (C) bryozoans with neighbouring hosts differing in
functional richness and functional composition (0: similar neighbour, 1: neighbour
differing in one functional type; 2: neighbour differing in two functional types). .....90
Figure 4.1. Mean (A) abundance and (B) richness of mobile species collected from
cores of sessile species, from sites Chowder Bay and Kirribilli. Bars sharing a letter

xvi
do not differ significantly in pair-wise comparisons. The origin of sessile species is
show in brackets (Na. – native; NIS – non-indigenous species; and Cryp. –
cryptogenic). Error bars are + the standard error of the mean. ................................117
Figure 4.2. CAP ordinations of the composition of mobile species (standardised by
weight of sessile species core) which significantly differed with different species of
sessile host. ...............................................................................................................118
Figure 4.3. Sample - based rarefaction curves. Species richness of mobile fauna as a
function of the number of core samples of individual sessile species. .....................119
Figure 4.4. Species - based rarefaction curves. Species richness of mobile fauna as a
function of the number of core samples taken of sessile species pooled to assemblages
of varying species richness, (i.e., 1 sessile species × 1 - 8 samples, to 7 sessile species
× 7 - 56 samples). Assemblages of sessile species were created by random selection
of species combination data from sites Kirribilli and Chowder Bay.........................120
Box 5.1. Search terms and criteria. ..........................................................................138
Figure 5.1. Locations where the sessile species were sampled in the studies compiled
for this paper. ............................................................................................................150
Figure 5.2. The frequency of studies used in the quantitative review of literature
investigating the mobile species associated with sessile animals in marine ecosystems.
Studies are classified by (A) hosts of sessile phyla, (B) sessile orders, and (C) the
latitudinal zone, geographic region and the type of environment..............................151
Figure 5.3. MDS ordination of the composition of mobile phyla associated with
different host phyla (balanced results shown, n = 9). ...............................................152
Figure 5.4. Ordinations derived from canonical analysis of principal (CAP)
coordinates, where axes best distinguish the groups. Ordinations contrast the

xvii
composition of (A) orders and (B) classes of mobile fauna across host phyla, The
results shown are those with balanced numbers of studies per host phyla (n = 9). ..153
Figure 5.5. Mean richness of mobile species associated with (A) host phyla
(balanced data shown n = 9), (B) orders of host within Mollusca, (C and D) the
structural complexity of hosts (complexity increasing with number) [host phyla (C)
Annelida and (D) Porifera], (E) the geographic region, and (F) the type of
environment samples were from (intertidal or subtidal). +SEM is the standard error
of the mean.................................................................................................................154
Figure 5.6. CAP ordinations contrasting the composition of orders of mobile fauna
associated with Mollusca hosts across different (A) geographic regions and (B)
marine environments. ................................................................................................155
Figure 6.1. Figure 6.1. Branches of invasive (a) Bugula neritina and (b) native B.
dentata........................................................................................................................179
Figure 6.2. Mean (A) richness and (B) abundance of mobile species associated with
B. dentata and B. neritina hosts, contrasting the non-significant difference between
mobile fauna and Bugula species. Non-significant results were maintained when data
was balanced. ± SEM is the standard error of the mean...........................................180
Figure 6.3. The mean (A) abundance and (B) richness of mobile species found on
colonies of Bugula neritina with no avicularia, avicularia and the procedural control
‘disturbed avicularia’ treatment. ± SEM is the standard error of the mean..............181
Figure 6.4. Ordination derived from the canonical analysis of principal (CAP)
coordinates where axes best distinguish the groups. The ordination contrasts the
composition of mobile species on B. dentata that had avicularia removed, to
procedural controls and unmanipulated controls.......................................................182
Map 7.1. Locations of Caprella hirayami sampling sites in Sydney Harbour. ........195

xviii
Figure 1. Male Caprella hirayamai. (A) lateral view, (B) gnathopod 2. Scale bar 1
mm. Drawing by EM Birdsey...................................................................................196
Figure 7.2. Photograph of male Caprella hirayamai. (A) Lateral view, scale bar 5
mm; (B) gnathopod 2, scale 2 mm. Specimen from Parsley Bay shark net. Photos
taken on a Leica microscope and camera. Photos were edited (i.e., contrast and
brightness adjusted) with Adobe Photoshop Lightroom 2.5. Photograph by EM
Birdsey.......................................................................................................................197

Abstract
1
ABSTRACT
It has long been postulated that biological diversity is important for ecosystem
functioning. Marine ecosystems support a significant proportion of the
Earth’s biodiversity and yet we have a limited understanding of the functional
importance of marine biodiversity. Knowledge of the specificity of species
associations is fundamental for predicting how changes to assemblages of
species may affect local biodiversity and subsequently ecosystem functioning.
Such research is fundamental in light of increasing anthropogenic activities
threatening coastal systems. Shallow marine environments are greatly
disturbed and heavily invaded by exotic species, posing a considerable threat
to the diversity of species that rely on native animals for habitat. In this thesis,
I explored species associations between sessile and mobile animals and
attempted to identify some of the key factors influencing the diversity of
mobile species. I also considered the effects of host assemblages harbouring
introduced sessile species on associated mobile diversity.
Using a combination of field surveys, manipulative experiments, studies on
single focal species of habitat forming animals, and a global scale meta-
analysis, I found that the diversity and composition of mobile species
associated with hosts of sessile animals are difficult to predict from traits of
their host assemblages. While the specific composition of sessile assemblages
and certain individual sessile species clearly influenced the composition and
diversity of associated mobile species, other factors which I expected to be
good predictors of associated diversity were unreliable. For instance, the
extent of habitat provided by the host, species and ordinal diversity, and the

Abstract
2
identity and composition of functional types of host assemblages all failed to
reliably predict diversity of associated species. These results suggest that, in
contrast to terrestrial systems, mobile species are more likely to be habitat
generalists in that they are not influenced by many differing features of their
host assemblage. Additionally, factors which I did not measure (e.g., biofilms,
host microbial make-up and chemical properties) may be more important than
the identity of sessile macrofaunal hosts in determining the distribution of
associated mobile species. My results suggest that many introduced sessile
species, in this system, are functionally redundant.
Surprisingly, host assemblages with non-indigenous host species present, and
individual species of non-indigenous hosts, did not correlate with differences
in the diversity of associated species. A study with two focal species
demonstrated that very similar species of arborescent bryozoa, a native and an
invasive, hosted a similar diversity and composition of epifauna.
Additionally, I described a non-indigenous species of amphipod, Caprella
hirayamai, previously unrecorded in Australia, and using Chapman and
Carlton’s (1991) criteria to recognise it as introduced.
In summary this research investigated the functional importance of sessile
marine animals as hosts to mobile animals. Through examining the diversity
of species associated with animal assemblages, I tested the effect of changes to
different components of host assemblages on the diversity and composition of
epifauna. This research is also one of the few to document a predominantly

Abstract
3
inconsequential effect of introduced animals, at least in this environment, on
associated biodiversity.

1. Introduction
4
Chapter 1
GE ERAL I TRODUCTIO
Biological diversity and ecosystem functioning
Noticeable changes to the earth’s biodiversity has generated a recent surge of
ecological research focussing on the link between biodiversity and ecosystem
functioning (Bruno et al. 2005, Hooper et al. 2005). Put simply, without
species diversity there is no ecosystem processing (i.e., nutrient cycles and
energy pathways) (Loreau et al. 2009). Therefore, knowledge of the influence
of diversity on ecosystem functioning is fundamental (Bruno et al. 2005).
Further, understanding the key mechanisms which drive changes in
biodiversity (e.g., species loss or species introductions) may let us make
generalisations of the consequences to ecosystem functioning of biodiversity
change (Giller et al. 2004). Such research is important considering the
increasing levels of anthropogenic disturbance resulting in changes to
biodiversity, in a wide range of ecosystems.
Anthropogenic activity has the potential to affect ecosystem functioning by
changing species diversity in a variety of ways, often with detrimental results.
From changes to diversity come impacts to ecosystem services followed by an
effect on economically important goods, products and other services bestowed
by the ecosystem (Hooper et al. 2005). These impacts may be especially
strong when disturbances affect assemblages of primary trophic species, which
then could influence the composition of dependent species via cascading
effects (Hooper et al. 2005).

1. Introduction
5
Surrogate measures of biodiversity
In studies of ecosystem functioning, the range of attributes associated with
biological diversity that may affect functioning should be considered (Giller et
al. 2004). The relative merits of species richness and functional type measures
of diversity have been subject to debate (Hooper and Vitousek 1997). Changes
to species richness often correlate with effects to biodiversity (Gaston and
Spicer 2003). However, species richness may have little explanatory power in
experimental studies for predicting diversity and ecosystem functioning
(Loreau et al. 2009). For example, habitat degradation, overexploitation, and
species invasions may reduce species richness on large scales (Rosenzweig
2001). However, in other ecosystems, species richness may increase as a
result of these disturbances (Rosenzweig 2001, Sax and Gaines 2003, Bruno et
al. 2005). This indicates that species richness alone is not a reliable predictor
of the importance of diversity within an ecosystem, the effects of disturbance
on ecosystem functioning has much to do with the specific functions of
species. Measures of functional types are being increasingly recognised as
important considerations in whole-community investigations of biodiversity,
as they are closely in-line with the process of ecosystem functioning (Loreau
et al. 2009). Grouping species by their functional characteristics is a
constructive method for elucidating the level of species redundancy (i.e., when
species have the same function) in ecosystem processing (Giller et al. 2004).
In addition to species richness and functional types, the composition of
assemblages may be as important as changes in diversity (Hooper and

1. Introduction
6
Vitousek 1997) and thus an important predictor of associated biodiversity.
Previous experiments have demonstrated that ecosystem processes are
dependent on the composition of species assemblages and associations, e.g.
the likelihood of invasion (Stachowicz et al. 1999); primary productivity
(Bruno et al. 2005); and associated species diversity (Parker et al. 2001).
Consequently, ecologists have urged that the composition of assemblages be
considered empirically (Warwick and Clarke 2001, Giller et al. 2004). Habitat
diversity has also been recognised as a vital component of diversity.
Experimentally testing differences in the complexity of habitats may allow us
to measure the potential ecosystem-level effects of diversity.
Species identity in ecosystem functioning
How well an ecosystem functions depends often on the type of species present
(Hooper and Vitousek 1997, Giller et al. 2004), with some having a much
larger or smaller influence on ecosystem functions than other species. For
example, energy pathways can be strongly influenced by relatively rare
species (Hooper et al. 2005), i.e. ‘keystone species’ (Paine 1966, 1969). Other
species can be described as ‘ecosystem engineers’ which have the ability to
create, modify, or destroy habitats (Jones et al. 1997); and foundation species
have the ability to drastically change biological communities through altering
species assemblages or community dynamics (Dayton 1972). Other species
may be facilitators in that they often have a positive association with other
species through the provision of refuges (Bruno and Bertness 2001). Many
facilitators are sessile organisms which support a vast diversity of
invertebrates, fish, birds and mammals. On the other hand, changes to host

1. Introduction
7
diversity (via loss or addition of invasive species) may not alter ecosystem
functioning if a number of species have little influence or function similarly
these species are functionally redundant (Hooper et al. 2005). For example,
habitats created by two sessile species which are similar in structural
complexity and food resources may host similar compositions of fauna,
making one of the sessile species functionally redundant in the ecosystem.
Recognising which species have important roles will be essential to predicting
how changes to individual species may affect associated biodiversity and
subsequently overall ecosystem structure, when subject to anthropogenic
disturbances.
Species associations between habitat-forming species and epifauna
An important ecosystem functioning is facilitation of mobile species provided
by close associations with host species (Stachowicz et al. 2007). Knowledge
of the specificity of these species associations is essential for predicting how
changes to the number and composition of host species may affect the
diversity of associated species and subsequently overall ecosystem structure.
Knowledge of species associations is particularly relevant for studies of
biodiversity when investigating sessile organisms and epifauna.
We know that sessile species are important for creating habitat refuges for a
vast array of mobile species, and this relationship has been observed in many
systems, e.g., insects on plants (Bernays and Graham 1988); protozoans,
invertebrates, and fish on sea grasses, barnacles, and coral (Harley 2006). The
mobile organisms may utilise sessile hosts for shelter and/or food resources.

1. Introduction
8
However, we have a limited understanding of the mechanisms driving the
relationships between the diversity of sessile species and the diversity of their
mobile associates. Whilst previous research has typically explored
associations at the scale of a single host species, it is important to consider
several different factors that may alter species associations. For example, is
the diversity of associated species driven by the identity of the host organisms,
or components of host assemblages such as the richness of species or
functional types, their composition, or by the diversity of habitat conferred by
associated species?
Introduced species
An important change to the composition of host species in many environments
is the addition of invasive species. It is universally accepted that invasive
species can cause severe problems for the environment and economy. They
have become one of the most profound elements of global biodiversity change,
impacting natural ecosystem functioning and costing hundreds of billions of
dollars (USD) to manage (Pimentel et al. 2000). Additionally, the detrimental
impact to diversity from some invasive species rivals that of habitat
degradation and pollution (Pimentel et al. 2000). Consequently, invasive
species have recently generated a surge of studies in ecology and evolution in
many ecosystems. Species invasions are ubiquitous in regions where
anthropogenic activities prevail, and can cause major effects in ecosystems
(Stachowicz and Byrnes 2006). Although, only 10% of the introduced species
worldwide become established (Williamson 1996) and even fewer become
invasive species, these few species may result in considerable change to the

1. Introduction
9
structure and function of associated assemblages (Elton 1958, Mack et al.
2000, Mitchell and Power 2003).
Impacts to biodiversity
Invasive species may influence diversity at many different levels. Frequently
the direct and indirect effects of invasive species lead to native species loss
and reduced abundances or evenness resulting in a more homogenous
community (Ruiz et al. 1997). Impacts from invaders may be predicted from
the similarity between their function in an ecosystem and that of co-occurring
native species. The largest impacts may occur if an invading species is not
functionally equivalent to any native species, thereby directly modifying the
ecosystem and creating a cascading effect for remaining species (Crooks
2002). It is therefore important to define the functional roles of invasive
species in the ecosystem, with the likely consequences of invasion being
dependent on this functional identity (Crooks 2002).
Associations between mobile animals and their sessile hosts in marine
ecosystems
Currently, the majority of biodiversity and ecosystem functioning research has
focused on terrestrial and model freshwater microbial systems. The functional
importance of marine diversity has been rarely addressed, indicating that
research exploring changes to the composition and diversity of species is
needed (Hooper et al. 2005). Disturbances to marine life are ubiquitous
around coastal cities and areas of development. Changes to the diversity of
marine assemblages can strongly affect the provision of habitat by sessile

1. Introduction
10
organisms. This can lead to changes in the diversity of associated species and
subsequently alter ecosystem function. Therefore, attempts to elucidate the
specificity of species associations and quantify the extent to which functional
redundancy occurs in the facilitation of species, is driving much ecological
and evolutionary research (Bruno and Bertness 2001).
Marine systems are unique in that many animal species provide habitat for
smaller, mobile animals. Such associations are unusual in terrestrial systems
(exceptions are parasitic species), where plants are the common host
organisms. In marine ecosystems, host animals are often sessile fouling
species, which resemble plants and algae in structure. Such sessile animals
facilitate a wide diversity of associated species, and thus, the effect of changes
to assemblages of sessile species may not only directly influence associated
species, but also whole communities in different ways including alleviating
stresses through the provision of refuges (Stachowicz and Hay 1999). For
example, when comparing the habitat provided by sessile organisms with
neighbouring bare substrata, the composition of associated fauna will likely
differ greatly between the two types of habitat.
The groups of species primarily associated with sessile animal hosts (e.g.,
small mobile invertebrates) are trophically important to a wide range of
species, such as larger predators including commercially important species
(such as fish) and socio-economically important species (such as whales,
birds, and turtles).

1. Introduction
11
Impact of introduced habitat-forming marine species
In marine ecosystems, increasing anthropogenic disturbances are resulting in
greater numbers of exotic species introductions, especially in coastal cities or
industrially busy bays, where ship ballast waters and hulls act as effective
vectors for traversing species. Near shore marine communities are particularly
vulnerable to increased propagule pressure from invasive species, potentially
changing local biodiversity and ecosystem structure (Vitousek et al. 1996,
Mack et al. 2000, Ruiz et al. 2000). Sessile species are especially at risk from
anthropogenic stresses as they are permanently fixed to substrata and unable to
evade contact with threats from expanding coastal developments (Johnston
and Keough 2003). However, compared with terrestrial systems, introduced
species have received little attention in marine systems. Our current
knowledge of invasive species and associated species diversity comes mainly
from terrestrial and freshwater microbial model systems (e.g., Ruiz et al.
1997). The majority of existing marine studies have concerned invasive algae
(see Williams and Smith 2007). Although, see Sellheim et al. (2010) for an
exceptions.
In marine systems, invasive species can influence the species diversity of
sessile host species at many different levels. Frequently the direct and indirect
effects lead to species loss and reduced abundances or evenness resulting in a
more homogenous community (Ruiz et al. 1997). Therefore, invasive host
species may not only impact mobile fauna but also local native hosts and
epifauna. On the other hand, some invasive hosts may be extremely similar to
native hosts (in terms of morphology, phylogeny, and functional role) and

1. Introduction
12
result in no change to the diversity of associated mobile species. Research
needs to focus on assemblages of introduced species, with different functional
roles, to test whether they modify the habitats created by native hosts for
associated biodiversity.
Thesis outline
Understanding the mechanisms driving diversity in marine systems is essential
for economical and social management practices to diminish the rate of
species loss. The main objective of my research was to understand species
associations between sessile organisms and mobile fauna in order to predict
how changes to sessile species may affect the diversity of entire assemblages.
Additionally, due to the disturbed nature of shallow marine environments, I
tested whether species of sessile hosts which are non-indigenous to the region
have a different diversity and composition of associated species. I used a
mixture of manipulative experiments, surveys, and meta-analyses in a
tractable subtidal study system to achieve my aims. Analyses used were
univariate and multivariate routines to test components of assemblage
composition. These methods allowed for powerful quantitative tests which
conformed to previous findings in biodiversity research that replicated
richness and composition effects should be tested (Schmid et al. 2002, Giller
et al. 2004).
First, I used experimental surveys to investigate temporally broad associations
between assemblages of sessile species and mobile species (Chapter 2). Here,
I wanted to know whether there was a particular factor of sessile the

1. Introduction
13
assemblages that best predicted the diversity of assemblages of associated
mobile species (i.e., percent cover, species and ordinal diversity, diversity
functional type, and specific composition of sessile species). Additionally, I
tested whether the diversity of mobile species changed with differing
proportions of non-indigenous hosts naturally occurring in assemblages of
sessile species. In Chapter 3, I investigated whether the sessile species
grouped by their functional type could predict the richness, abundance and
composition of associated mobile fauna. I manipulated assemblages of sessile
species to create different host cultures of functional types, separating identity
from richness and composition effects. In Chapter 4, I focused on the
diversity and composition of mobile species associated with individual species
of sessile hosts. Here, I used rarefaction curves to estimate mobile species
diversity from different combinations of sessile species. Chapter 5 is a global
review and meta-analysis of the composition of mobile fauna known to inhabit
a range of sessile hosts. In addition to the type of sessile species, I accounted
for geographic region, habitat and structural complexity when attempting to
find an effective predictor of mobile diversity and composition. In Chapter 6,
I contrasted the fauna associated with an invasive species of bryozoan to a
congeneric native which was similar in terms of morphology, phylogeny and
potentially function. With these species, I tested whether the presence of
avicularia (small, non-feeing zooids, with a jaw-like hinged mandible) in the
native bryozoan affected the abundance and richness of fouling mobile
species. Lastly (Chapter 7), I described the existence of a non-indigenous
mobile invertebrate, previously unrecorded in Australian waters, the skeleton

1. Introduction
14
shrimp Caprella hirayamai. I used Carlton and Chapman’s (1991) ten criteria
to establish whether C. hirayamai was an introduced species.
Chapters 2-7 of this thesis have been prepared in the style of stand-alone
manuscripts for peer-review journal publication. As such there is some
repetition between chapters. Additionally, the tense may vary among chapters
as they are tailored to specific journal styles.

2. Associations between sessile and mobile marine animals
15
Chapter 2
Species diversity, cover, and functional identity of sessile animal
assemblages do not predict associated mobile fauna
SUMMARY
1. Habitat-forming organisms often determine the structural properties and
food resources available to a wide diversity of associated mobile species. If
co-occurring habitat-forming species play different ecological roles, then
changes to ecosystem processes may be predicted by changes to the
composition of habitat-forming assemblages. Understanding the specificity of
associations is thus essential for predicting how changes to the diversity and
composition of host organisms may affect local diversity.
2. I test whether the composition of a marine assemblages of sessile animals
predict the diversity and composition of associated mobile fauna. I tested
whether the species diversity, ordinal diversity, cover, or functional diversity
of sessile assemblages can predict associated mobile species. I also tested
whether the proportion of sessile species which are non-indigenous is
associated with changes to the diversity of mobile assemblages.
3. I recorded 53 sessile invertebrate species on experimental substrata, with
14 of these being non-indigenous species (NIS). The sessile fauna was
dominated by ascidians, bryozoans and serpulid worms. A total of 141 mobile
species were associated with these sessile assemblages, dominated numerically
by species of amphipods.

16
4. Similar assemblages of sessile species supported similar compositions of
mobile species. However, no single variable of the sessile assemblages
effectively predicted the diversity or composition of associated mobile species.
The percent cover, species and ordinal diversity, or the functional diversity of
the sessile assemblages was not able to predict variation in associated mobile
assemblages. Furthermore, the proportion of sessile animals that were NIS did
not correlate with the diversity of mobile fauna.
5. My study suggests that it is difficult to predict the species diversity of
mobile invertebrate assemblages based on surrogate measures for biodiversity,
such as habitat structure, diversity, or functional types of hosts. Rather,
knowledge of assemblage composition will be required. My results differ
from those in other systems, suggesting that results from one ecosystem
cannot be reliably applied to another, and indicating that there are still
considerable challenges in explaining patterns of diversity.

17
I TRODUCTIO
Understanding patterns of biological diversity is a major aim of community
ecology and in recent years much research has focussed on the relationships
between diversity and ecosystem function (see Loreau et al. 2009). Changes
to diversity and community composition often have strong consequences for
ecosystem processes. Yet these effects can vary among ecosystem types and
processes measured (Hooper et al. 2005), and they are strongly dependent on
the specificity of associations among co-occurring species in the community.
If co-occurring species play the same ecological role (i.e., are functionally
redundant), changes to ecosystem processes may not be simply predicted from
changes in species richness (Micheli and Halpern 2005).
An important functional role for many species is the provision of habitat.
Most species of eukaryotes live in close association with other organisms, and
habitat-forming organisms often determine the structural properties and the
food resources available to a diverse assemblage of associated species (Bruno
and Bertness 2001). More diverse assemblages of habitat-forming organisms
may support a greater diversity of associated species due to increasing
physical complexity of the habitat (Lawton 1983), through the inclusion of
species with host-specific associates, or the inclusion of functional groups of
species with ecological roles that are not represented in species poor
communities (Petchey and Gaston 2002). Understanding the nature of these
associations is essential for predicting how changes to the diversity and
composition of host organisms may affect local diversity. If interactions are
mostly generalised, individual species of hosts will be redundant in their role

18
of habitat provision. Alternatively, the loss of hosts involved in greatly
specialised interactions will strongly affect organisms associated with those
hosts.
Understanding the specificity of associations among organisms and their hosts
is also essential for predicting diversity in assemblages for which data on the
relative richness of species are rarely available. With limited resources for
species surveys, conservation efforts have frequently used surrogates for
diversity measures that aim to predict diversity but are obtained at lower cost
(Caro and O'Doherty 1999). Such measures have included the diversity and
composition of habitat-forming species, in particular the use of plant diversity
as a predictor of more diverse animal assemblages (Siemann et al. 1998,
Haddad et al. 2001, Schaffers et al. 2008,). An understanding of host
specificity is needed to predict change at local scales and has been central to
estimates of biodiversity at global scales, largely dependent on the host
specificity of insect herbivores (Novotny et al. 2002). Additionally,
knowledge of functional roles of sessile species may predict how assemblages
with respond to change. Such research may allow conservation managers to
predict community composition with limited resources.
Marine environments are unique in having extensive habitat provided by
sessile, filter-feeding animals, which support very diverse assemblages of
smaller organisms. Some examples of sessile animals are corals (Jones et al.
2004), sponges (Poore et al. 2000); ascidians (Castilla et al. 2004); bryozoans
(Conradi et al. 2000); cnidarians (Bradshaw et al. 2003); and bivalves (Crooks

19
2002). The positive effect of these sessile species on the diversity of
associated assemblages is well established – mostly by a simple comparison
with bare rock or sedimentary environments (Chapman et al. 2005). Poorly
understood, however, is the degree to which individual sessile species may be
redundant in their facilitation of other species (Bruno and Bertness 2001).
There is some evidence that individual sessile species will support distinct
assemblages of invertebrates, e.g., amphipods on sponges (Poore et al. 2000);
opisthobranchs on corals (Ritson-Williams et al. 2003). If such patterns are
typical of invertebrate assemblages, more diverse assemblages should support
more associated animals, and changes to the sessile hosts due to natural
(predation, wave action) or human-induced (addition of invasive species,
pollution, habitat loss) disturbances should result in large changes in the
composition of the associated fauna. Despite the likelihood of such effects,
few studies have considered how changes to the species or functional diversity
of sessile animals affect the diversity and composition of associated animals
(but see Sellheim et al. 2010).
In this study, I quantify the relationships between the diversity, cover, and
functional diversity of sessile animals in a hard-substrate assemblage with the
diversity and composition of associated mobile invertebrates. If mobile fauna
are strongly host specific, I predict that the diversity and composition of
mobile species will be strongly dependent on the diversity of sessile hosts.
The host organisms are fouling invertebrates (sponges, bryozoans, ascidians,
polychaetes and barnacles) commonly associated with subtidal hard substrates

20
on natural rocky reefs and artificial substrata (i.e., wooden, pylons and
pontoons) in an urbanised estuary, Sydney Harbour (Australia). Changes to
the diversity and composition of sessile invertebrate assemblages occur due to
predation, physical disturbances from storms, and commercial and recreational
boat traffic pollution from heavy metals and the frequent addition of
introduced species (Glasby and Creese 2007, Dafforn et al. 2009b). Sessile
invertebrate assemblages support a diverse and abundant assemblage of
mobile invertebrates, mostly crustaceans, polychaetes, gastropods and
nematodes (Perrett et al. 2006). Knowledge of the relationships between
mobile and sessile animals is needed to understand effects of frequent shifts in
habitat structure on the wider ecosystem.
I asked the following specific questions: (1) Do habitats with a similar
composition of sessile invertebrates support a similar composition of mobile
invertebrates? (2) Is the diversity and composition of mobile species predicted
by: a) the species or ordinal diversity of the sessile assemblage; b) the amount
of available habitat (% cover of sessile invertebrates); c) the functional
diversity of the sessile assemblage, and d) the prevalence of non-indigenous
species (NIS) of sessile invertebrate?

21
METHODS
Experimental survey
Assemblages of sessile invertebrates readily colonize plastic settlement plates
allowing for replicated assemblages of known age to be sampled at multiple
sites and times. Experimental substrata were deployed at two sheltered sites
within Port Jackson: Watsons Bay (33° 50’42”S, 151° 16’ 50” E) and
Chowder Bay (33° 50’ 23” S, 151° 15’ 10” E). Both sites are in close
proximity (< 1 km) to sandstone rocky reefs and a range of artificial structures
that support fouling assemblages. Substrata were deployed for multiple
periods of eight or 16 weeks over the course of 13 months from October 2007
to November 2008.
Black perspex settlement plates (110 × 110 mm) were hung vertically at a
depth of one metre below the low water mark at each of the sites. Prior to
submersion, settlement plates were lightly sanded and fixed to backing panels
(60 × 60 cm) (n = 16 each panel) via a cable-tie through two small holes (6
mm diameter) in the centre of each plate and fastened tight, flush against the
panel, through corresponding holes on the backing panel. This method of
plate attachment allowed for fast removal underwater with minimum
disturbance to the mobile species in-situ. From each site, after eight or 16
weeks of submersion, eight plates were randomly chosen and removed in-situ
by swiftly cutting the cable-tie holding the plate in place and immediately
enclosing the plate in a plastic bag underwater. Each plate was then
transferred to a secure 2l plastic tub. Collections were done whilst holding
breath on snorkel, rather than SCUBA, to avoid air bubble vibrations

22
disturbing any highly mobile invertebrates present on the plates (Schmidt and
Gassner 2006). After each collection, eight new plates were re-attached to the
backing panels. This was done underwater to not disturb the remaining plates
via emergence.
In the laboratory, plates were placed in formalin (5% formaldehyde) for at
least 24 h and then thoroughly rinsed in freshwater, with the rinse water being
passed through a 300 µm mesh sieve to collect the mobile animals. All plates
and associated mobile fauna were then separately preserved in ethanol (70%).
Using a dissecting light microscope, mobile species were sorted to morpho-
species, identified to species level where possible, and counted. The percent
cover of sessile animals on each plate was quantified for each species using a
quadrat measuring 110 × 110 mm divided into 10 × 10 sections. The quadrat
was placed over the top of a plate, recording the presence and identity of the
sessile species at every cross-hair of the quadrat. A general scan of the plate
was then done for other species which did not fall under a cross-hair, with
these noted as present and having a nominal cover of ‘1%’.
At each site, eight weeks after the first collection, eight plates which were
eight weeks old were again collected from each site, along with eight 16 week
old plates, which had been submerged since the start of the experiment. Plate
collection continued like this for 12 months, with settlement plates being
collected and replaced with blanks every eight and 16 weeks. Thus, at the end
of the survey, I had collected six sets of eight week old plates (eight collected
from two sites in February, April, June, August, September, and November, N

23
= 96 plates), and three sets of 16 week old plates (eight from collected from
two sites in April, August, and November, N = 48 plates).
Relatedness between sessile and mobile assemblages
If the composition of mobile invertebrates was strongly influenced by the
composition of sessile invertebrates, I would expect settlement plates with
similar sessile assemblages to be associated with similar assemblages of
mobile invertebrates. I used the RELATE procedure (Somerfield and Clarke
1995) in the software package PRIMER (v6.0) (Clarke and Warwick 2001) to
test this hypothesis. The abundance data for mobile species were fourth-root
transformed and the % cover data for sessile species data were loge (χ+1)
transformed. Dissimilarity matrices among samples were calculated
separately for each data set using the Bray-Curtis similarity index and
including a dummy variable of 1. RELATE uses a Spearman’s rank
correlation coefficient to establish the correlation between indices in the two
dissimilarity matrices and tests the significance of this relationship by a
randomisation test (n = 9999 permutations).
The procedure BVSTEP in PRIMER (Clarke and Warwick 2001) was used to
identify sub-sets of sessile species which are mostly strongly correlated with
the variation (rho (ρ) = 0.95) in mobile species composition (see Hirst 2006
for a similar example with algae and epifauna). BVSTEP uses a forward
selection / backward elimination stepwise algorithm to compare dissimilarity
matrices generated for combinations of explanatory variables (here, the
composition of sessile species) with the dissimilarity matrix generated for

24
mobile species data. Each comparison uses a Spearman’s correlation
coefficient (ρ) to quantify the correlation between the two dissimilarity
matrices (akin to RELATE above). During the stepwise selection,
combinations of variables were added only to the model if they increased the
correlation coefficient by > 0.001. In the model, mobile species data were the
fixed similarity matrix and the random selection method was used, here the
routine was re-started 10 times and a maximum of 6 ‘trial variables’ was used
[see Clarke and Warwick (1998) for a full explanation of the assumptions
made by these processes]. BVSTEP was also used to identify important
explanatory variables when the dataset on sessile species was converted to
orders and functional types (see below). The results of the BVSTEP ‘best
model’ with the entire dataset contained a wide range of ‘key species’,
therefore datasets were separated by month of collection and age of
assemblage to help further understand which key species were driving the
variation.
Predictors of the diversity, abundance, and composition mobile species
I treated the data of the sessile assemblages in four ways to identify which
assemblages attributes best correlated with the abundance, diversity and
composition of mobile species: (1) the diversity (at species and ordinal levels),
(2) the % cover of sessile species, (3) the diversity of functional types, and (4)
the prevalence of non-indigenous species (NIS).
First, the diversity (H’) of the sessile assemblages, a proxy for habitat
heterogeneity (Tews et al. 2004), was used to test the hypotheses that an

25
increase in diversity of sessile animals will positively correlate with the
richness and abundance, and alter the composition, of the mobile assemblage.
Separate analyses were run for diversity of sessile assemblage at the species
and ordinal levels. The mobile species data (the dependent variables) were
represented in three ways: species diversity (measured as the Shannon-Weiner
index, H’), total abundance (pooling species), and the composition data set
composed of the abundance and richness of all mobile species recorded. The
univariate variables (abundance and diversity) were analysed using
permutational ANCOVAs, with site and month of collection as random factors
and the diversity of sessile species / orders as the covariate. Mobile
composition was analysed with permutational MANCOVA with the same
independent variables. Eight and 16 week old assemblages were analysed
separately to ensure a balanced dataset and to avoid the problem of different
sampling start times (Underwood and Chapman 2005). Throughout the
analyses, interactions between the covariate and random factors (site and
month) were included. When these terms were non-significant, they were
removed from the model and the analyses re-run.
Second, the percent cover of sessile animals was used as an independent
variable to test the hypothesis that the mobile fauna was simply dependent on
the amount of available habitat. The analyses were as described above,
replacing % cover with diversity of sessile species as the covariate.
Third, sessile species were grouped into functional types, on the basis of
feeding mechanisms and morphology, to test the hypothesis that the mobile

26
assemblage was dependent on functional diversity, rather than species
diversity (Petchey and Gaston 2002). Functional groups were arborescent
filter-feeders (e.g., arborescent bryozoa), encrusting filter-feeders (calcified)
(e.g., encrusting bryozoa), encrusting filter-feeders (gelatinous) (e.g., colonial
ascidia), stemmed filter-feeders (e.g., solitary ascidians), algae, and other
(Bremner et al. 2003). The analyses were as described above, using diversity
of functional groups as the covariate.
Fourth, the proportion of NIS in the sessile assemblage was analysed to test
the hypothesis that these NIS more strongly relate to community traits than
other species in the system. The fouling assemblages in Sydney Harbour are
heavily invaded (Glasby et al. 2007) and I considered those sessile
invertebrates assigned as NIS as reported in Dafforn et al. (2009a). Sessile
NIS present were arborescent bryozoans Bowerbankia gracilis, Bugula
flabellata, and B. neritina; encrusting bryozoans Schizoporella errata,
Watersipora subtorquata, Conopeum seurati and Microporella umbracula;
barnacles Amphibalanus amphitrite and Megabalanus coccopoma; serpulid
polychate, Hydroides elegans; and ascidians Botrylloides leachi, Botryllus
schlosseri, Diplosoma listerianum and Styela plicata. The analyses were as
described above with the proportion of sessile NIS as the covariate.
Statistical analyses
Analyses of covariance and permutational MANCOVA were run in the
PERMANOVA routine in PRIMER (Anderson 2001). During ANCOVA, the
sum of squares was taken as Type 1 (sequential), and the probabilities

27
calculated using 9999 permutation of residuals under a reduced model. Prior
to analyses, the species data of sessile animals was loge χ+1transformed.
Multivariate analyses of the mobile species assemblage used a dissimilarity
matrix created using the modified Gower (loge χ+1) index after a fourth root
transformation, and including a dummy variable of 1 to avoid problems with
double zeros in the dataset and reduce the influence of outliers during
resemblance measures. For the analyses with single independent variables
(e.g., proportion of NIS) the Euclidean distance routine was used to create a
dissimilarity matrix. The significance level was α < 0.05.

28
RESULTS
Sessile and mobile assemblage relatedness
In total, 53 sessile species were recorded, with an average of 7.7 species
covering 55% of the experimental substrata in the eight week old assemblages
and 10.5 species covering 75% in the more mature 16 week old assemblages.
This difference in sessile richness significantly varied between age of
assemblage (age nested in site, F = 12.44, P = 0.0001). The sessile fauna was
dominated by ascidians, bryozoans and serpulid polychaetes. Diverse and
abundant assemblages of mobile species were recorded from these
assemblages of sessile species, with a total of 141 species recorded, and
averages of 117 and 170 individuals collected from each of the eight and 16
week old assemblages. The mobile fauna was dominated numerically by
species of amphipods.
Plates that had more similar assemblages of sessile species supported more
similar assemblages of mobile species, although the rank correlation between
the pair of dissimilarity coefficients was relatively weak (RELATE, ρ = 0.244,
P < 0.001, pooling sampling dates and sites). A stronger correlation was
found from the BVSTEP algorithm (ρ = 0.95) using a sub-set of 15 sessile
species. These species comprised members from every major taxonomic
group sampled (Table 2.1). Further BVSTEP analyses from each sampling
date (run with the aim of identifying a smaller subset of influential sessile
species) failed to isolate common sessile species that most strongly correlated
with mobile assemblages. The best selection models ranged from one to 13

29
sessile species and rank correlations were below that recorded for the full data
set (Table 2.2, Fig. 2.1). Only in the analysis of the eight week old
assemblages, collected in February, did the BVSTEP analysis indentify a
single sessile species, the encrusting bryozoan Schizoporella errata, as the
best selection model (with a correlation, ρ = 0.59) (Table 2.2, Fig. 2.1).
Pooling the sessile species into orders and functional groups also failed to
identify a small number of groups that most strongly correlated with mobile
assemblages. When pooled into orders, the best selection model included five
of the possible 17 orders and had a low rank correlation (ρ = 0.36), When
pooled into functional groups, the best selection model included four of the
seven groups and low rank correlation (ρ = 0.28) (Table 2.2).
Predictors of the diversity, abundance, and composition mobile species
The diversity of sessile species did not predict the diversity of mobile species
in eight or 16 week old assemblages. For eight week old assemblages, the
diversity of mobile species did not vary with sessile diversity or among sites
and months of sampling (Table 2.3A; Figs, 2.2 and 2.3A). For the 16 week
old assemblages, the diversity of mobile species varied among sampling
months (Fig. 2.2), and there was a significant interaction between site and
sessile diversity (Table 2.3A). The abundance of mobile fauna (pooling all
species) was not predicted by the diversity of sessile species, and did not vary
between sites and sampling month (Table 2.3B). In the multivariate analyses,
the composition of mobile species was not affected by the species diversity,
but did vary between sites and sampling months, with these two factors also

30
displaying a significant interaction (Table 2.3C). When sessile species were
pooled into orders for a measure of habitat-heterogeneity at a courser grain,
diversity of habitat again failed to strongly predict the diversity, abundance
and composition of mobile species (Table 2.4).
The total percent cover of sessile animals was not an effective predictor of
abundance, diversity or composition of associated mobile species. There was
no significant effect of % cover, or site on the diversity of mobile species in
eight or 16 week old assemblages (Table 2.5A; Figs 2.2 and 2.3B). The
diversity of mobile species varied among sampling months for the 16 week
old, but not eight week old assemblages (Fig. 2.2). The total abundance of
mobile species was unaffected by the % cover of sessile species, with the only
significant effect being a site by month interaction for the eight week old
assemblages (Table 2.5B). Similarly, the composition of mobile fauna was
not predicted by the cover of sessile species in both the eight and 16 week old
assemblages, but varied between sites and sampling months, with these two
factors interacting (Table 2.5C).
When sessile species were pooled into their functional groups, mobile
diversity was not predicted by the functional diversity of sessile assemblages.
For 8 week old assemblages, interacting terms of month × functional type, and
site × month were predictors of mobile diversity (Table 2.6A). In 16 week old
assemblages, the diversity of mobile species varied among the month samples
were collected (Fig. 2.3C, Table 2.6A). Functional diversity did not predict
the abundance of mobile species, with abundance only varying among

31
sampling month for the 16 week old assemblages (Table 2.6B). The
composition of mobile fauna was unaffected by the diversity of functional
groups, varying only between sites and sampling month in the 16 week old
assemblages; in the eight week old assemblages, there was also an interaction
between functional diversity and sampling month indicating composition is
affected by functional diversity in some months (Table 2.6C).
A total of 14 non-indigenous species (NIS) of sessile invertebrates were
present in assemblages. The diversity of mobile species for all assemblages
was not correlated with the proportion of NIS in those assemblages, only
varying among sampling months in that analysis (Fig. 2.3D; Table 2.7A). The
total abundance (Table 2.7B) and composition of mobile species was not
affected by the proportion of NIS in the eight or 16 week old assemblages
(Table 2.7C).

32
Table 2.1. Sessile species accounting for most of the variation in associated
assemblages of mobile species analysed (BVSTEP) using the entire dataset.
Origin indicates whether they are native, cryptogenic, non-indigenous, or
unknown.
Sessile species Origin
Conopeum seurati NIS
Membranipora membranacea Cryptogenic
Spirobid sp. Cryptogenic
Amphibalanus variegatus Native
Celleporaria nodulosa Native
Didemnid (ascidian) Cryptogenic
Leathesia diffornis (algae) unknown
Brown algae sp. 2 unknown
Fenestrulina mutabilis Native
Beania magelanica Native
Syconoid sponge sp. 1 unknown
Hydroides elegans NIS
Watersipora subtorquata NIS
Bowerbankii sp. NIS
Diplosoma listerianum NIS

33
Table 2.2. BVSTEP results for the best selection model and the correlation of
the composition of sessile species to associated assemblages of mobile
species. The best selection model column shows the number of habitat-
forming species in the subset of habitat-forming species that best correlated
with mobile assemblage composition (from a total listed in parentheses).
Plate samples (month and # weeks
old) (n = 16)
Best selection model
(# sessile species)
Correlation (ρ)
All (n = 144) 15 (57) 0.95
All-major taxonomic groups 5 (17) 0.36
All-functional types 4 (7) 0.28
Feb. (8 w) 1 (17) 0.59
Apr. (8 w) 8 (27) 0.59
Apr. (16 w) 9 (27) 0.65
Jun. (8 w) 3 (26) 0.39
Aug. (8 w) 8 (31) 0.56
Aug. (16 w) 13 (37) 0.68
Sept. (8 w) 5 (20) 0.38
Nov. (8 w) 9 (34) 0.57
Nov. (16 w) 7 (32) 0.68

34
Table 2.3. Analyses of covariance contrasting the (A) diversity and (B) abundance, (C) and multivariate analysis of covariance contrasting the
composition of mobile species with the diversity (H’) of sessile species collected from two sites and from six sampling months. The % cover of
sessile species on each plate was used as a covariate. When interactions between the covariate and the categorical variables were non-significant
these terms were removed from the model. P values were calculated by 9999 permutations of the raw data.
2.3A. Diversity of mobile species
Eight week old assemblages 16 week old assemblages
Source df MS F P df MS F P
Diversity H’ 1 0.000 0.152 0.705 1 0.000 0.01 0.782
Site 1 0.000 0.01 0.790 1 0.000 7.702 0.120
Month 5 0.001 0.481 0.858 2 0.001 44.257 0.024
Habitat diversity× site - - - - 1 0.000 6.400 0.036
Habitat diversity × month - - - - 2 0.000 1.04 0.357
Site × month 5 0.003 1.218 0.278 2 0.000 0.469 0.631
Habitat diversity × site × month - - - - 2 0.000 0.004 0.960
Res 83 0.003 36
Total 95 47
2.3B. Abundance of mobile species
df MS F P df MS F PSource
Diversity H’ 1 0.000 0.002 0.90 1 0.0004 0.358 0.556
Site 1 0.001 0.797 0.510 1 0.006 8.251 0.109
Month 5 0.007 2.589 0.07 2 0.002 2.798 0.236
Site × month 5 0.003 0.971 0.478 2 0.0006 0.944 0.390
Res 83 0.003 41 0.0007
Total 95 47

35
2.3C. Composition of mobile species
df MS Pseudo-F P(perm) df MS Pseudo-F P(perm)Source
Diversity H’ 1 0.983 0.841 0.752 1 0.617 0.842 0.754
Site 1 1.10 1.953 0.001 1 1.055 1.967 0.008
Month 5 1.635 3.104 0.0001 2 1.508 3.062 0.0001
Site × month 5 0.540 1.932 0.0001 2 0.510 1.892 0.0001
Res 83 0.280 41 0.270
Total 95 47
Notes: ‘ - ’ indicates’ terms covariate × site, covariate × month, and covariate × month × site were excluded from analysis as P(perm) = NS in full model.

36
composition of mobile species with ordinal diversity (H’) of sessile species among plates collected from two sites and from six sampling
months. The ordinal diversity of sessile hosts was included as a covariate. When interactions between the covariate and the categorical
variables were non-significant these terms were removed from the model. P values were calculated by 9999 permutations of the raw data.
Ordinal diversity (H’) 1 0.002 1.057 0.313 1 0.000 0.244 0.622
Site 1 0.000 0.003 0.902 1 0.000 10.031 0.079
Month 5 0.002 0.500 0.845 2 0.001 147.67 0.007
Group × site - - - - 1 0.0001 3.027 0.101
Group × month - - - - 2 0.0001 2.082 0.143
Site × month 5 0.003 1.420 0.164 2 0.000 0.171 0.843
Group × site × month - - - - 2 0.0002 3.948 0.030
Res 83 0.002 36 0.000
Total 95 47
Site 1 0.000 0.0001 0.983 1 0.001 0.798 0.478
Month 5 0.104 2.503 0.085 2 0.007 4.799 0.171
Site × month 5 0.004 1.549 0.129 2 0.001 3.369 0.041
Res 83 0.003 41 0.0004
Total 95 47

37
Site 1 1.113 2.052 0.001 1 1.029 2.012 0.006
Month 5 1.711 3.272 0.0001 2 1.491 3.190 0.0001
Site × month 5 0.527 1.885 0.0001 2 0.480 1.774 0.0001
Res 83 0.280 41 0.270
Total 95 47

38
composition of mobile species with the cover (%) of sessile species on plates collected from two sites and from six sampling months. The cover
was included as a covariate. When interactions between the covariate and the categorical variables were non-significant these terms were
removed from the model. P values were calculated by 9999 permutations of the raw data.
Cover 1 0.001 0.350 0.546 1 0.0001 0.528 0.471
Site 1 0.000 0.001 0.915 1 0.0004 6.173 0.092
Month 5 0.001 0.459 0.895 2 0.002 19.996 0.025
Site × month 5 0.003 1.229 0.270 2 0.0001 1.205 0.307
Res 83 0.003 41 0.0001
Total 95 47
Cover 1 1.5456 3.306 0.0692 1 0.003 3.086 0.10
Site 1 0.001 0.002 0.988 1 0.000 0.226 0.860
Month 5 1.0127 2.2188 0.1848 2 0.006 22.826 0.05
Cover x site - - - - 1 0.001 4.265 0.05
Cover x month - - - - 2 0.001 3.704 0.05
Site × month 5 0.4522 2.853 0.0117 2 0.0002 0.512 0.604
Cover x site x month - - - - 2 0.001 2.839 0.07
Res 83 0.1585 36 0.0004
Total 95 47

39
Cover 1 0.781 1.205 0.174 1 0.485 1.012 0.442
Site 1 1.204 2.193 0.0004 1 1.163 2.280 0.001
Month 5 1.679 3.069 0.0001 2 1.594 3.105 0.0001
Site × month 5 0.533 1.915 0.0001 2 0.521 1.962 0.0001
Res 83 0.278 41 0.266
Total 95 47

40
composition of mobile species with the functional diversity (H’) of sessile species on plates collected from two sites and from six sampling
months and including the diversity of sessile functional types as a covariate. When interactions between the covariate and the categorical
df MS F P df MS F P
Source
Functional diversity 1 0.1 2.504 0.135 1 0.001 2.913 0.102
Site 1 0.005 0.171 0.953 1 0.001 1.714 0.307
Month 5 0.05 0.854 0.614 2 0.0001 14.925 0.014
Functional × site 1 0.032 0.841 0.394 - - - -
Functional × month 5 0.065 2.981 0.028 - - - -
Site × month 5 0.05 2.763 0.037 2 0.0001 1.314 0.284
Functional × site × month 5 0.042 2.510 0.08 - - - -
Res 72 0.017 41 0.0001
Total 95 47
Site 1 0.0004 0.129 0.872 1 0.0008 0.572 0.561
Month 5 0.008 2.374 0.100 2 0.006 4.957 0.079
Site × month 5 0.004 1.315 0.230 2 0.001 3.387 0.042
Res 83 0.003 41 0.0004

41
Total 95 47
Site 1 1.113 2.052 0.0005 1 1.029 2.012 0.006
Month 5 1.711 3.272 0.0001 2 1.491 3.190 0.0001
Functional type × site 1 0.785 2.066 0.001 1 0.310 0.913 0.605
Functional type × month 5 0.373 1.249 0.013 2 0.260 0.913 0.605
Site × month 5 0.527 1.885 0.0001 2 0.480 1.774 0.0001
Functional type × site x month 5 0.298 1.097 0.178 2 0.291 1.087 0.296
Res 83 0.280 41 0.270
Total 95 47

42
composition of mobile species with the proportion of sessile non-indigenous species (NIS) on plates collected from two sites and from six
sampling months and including the proportion of sessile NIS as a covariate. When interactions between the covariate and the categorical
NIS richness 1 <0.0001 0.328 0.600 1 0.303 0.151 0.714
Site 1 <0.0001 0.249 0.952 1 1.775 2.124 0.297
Month 5 <0.0001 0.498 0.859 2 2.721 8.045 0.111
NIS × site 1 <0.0001 2.189 0.234 1 0.047 0.288 0.769
NIS × month 5 <0.0001 0.501 0.712 2 0.336 2.004 0.225
Site × month 5 <0.0001 2.043 0.132 2 0.196 1.734 0.194
NIS × site × month 5 <0.0001 1.007 0.356 2 0.123 1.089 0.348
Res 72 <0.0001 36 0.113
Total 95 47
2.7B. Abundance mobile of species
NIS richness 1 0.247 0.075 0.795 1 7963 0.876 0.598
Site 1 2.155 0.559 0.735 1 6589.3 1.617 0.059
Month 5 6.004 1.512 0.364 2 13091 6.253 0.0001
NIS × site 1 0.571 0.254 0.773 1 3611.2 2.582 0.008
NIS × month 5 1.687 0.987 0.529 2 1944.8 1.537 0.068
Site × month 5 2.18 4.800 0.004 2 1263.3 0.996 0.476

43
NIS × site × month 5 0.654 1.440 0.223 2 1648.6 1.299 0.129
Res 72 0.454 36 1268.7
Total 95 47
NIS richness 1 6583.6 0.71296 0.785 1 7963 0.876 0.598
Site 1 8035.2 2.7796 0.0002 1 6589.3 1.617 0.056
Month 5 14989 3.5561 0.0001 2 13091 6.253 0.0001
NIS × site 1 1516.8 0.74242 0.7483 1 3611.2 2.582 0.011
NIS × month 5 3161.1 1.5438 0.0224 2 1944.8 1.537 0.076
Site × month 5 2131.4 1.5205 0.0053 2 1263.3 0.996 0.474
NIS × site × month 5 1692.1 1.2071 0.1204 2 1648.6 1.299 0.130
Res 72 1401.8 36 1268.7
Total 95 47

44
Figure 2.1. MDS ordination contrasting the composition of mobile species
collected from the experimental plates in different months. The assemblage
data were 4th
-root transformed, and the Bray-Curtis similarity index was used
as the measure of similarity among samples.
2D Stress: 0.24
MONTH
Feb
Apr
Jun
Aug
Sept
Nov
Mobile species assemblages
2D Stress: 0.24
MONTH
Feb
Apr
Jun
Aug
Sept
Nov
2D Stress: 0.24
MONTH
Feb
Apr
Jun
Aug
Sept
Nov
2D Stress: 0.24
MONTH
Feb
Apr
Jun
Aug
Sept
Nov
2D Stress: 0.24
MONTH
Feb
Apr
Jun
Aug
Sept
Nov
2D Stress: 0.24
MONTH
Feb
Apr
Jun
Aug
Sept
Nov
2D Stress: 0.24
MONTH
Feb
Apr
Jun
Aug
Sept
Nov
2D Stress: 0.24
MONTH
Feb
Apr
Jun
Aug
Sept
Nov
2D Stress: 0.24
MONTH
Feb
Apr
Jun
Aug
Sept
Nov
2D Stress: 0.24
MONTH
Feb
Apr
Jun
Aug
Sept
Nov
2D Stress: 0.24
MONTH
Feb
Apr
Jun
Aug
Sept
Nov
2D Stress: 0.24
MONTH
Feb
Apr
Jun
Aug
Sept
Nov

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