Habitat Preferences and Fitness Consequences for Fauna Associated with Novel Marine Environments

Home , Ecological trap, Little weed whiting

Habitat preferences and fitness consequences for fauna associated with novel marine environments

Luke T Barrett

orcid.org/0000-0002-2820-0421

Submitted in total fulfilment of the requirements of the degree of Doctor of Philosophy

September 2018

School of BioSciences

University of Melbourne

ABSTRACT

The rapidly expanding reach of anthropogenic environmental change means that animals must now navigate landscapes comprised largely of modified and degraded habitats. Individuals that correctly perceive habitat quality will be best placed to survive and reproduce in novel environments, but where environmental change outpaces the evolution of behavioural responses, mismatches can arise between cues and the underlying value of habitats. These mismatches can lead individuals to select habitats that offer relatively poor fitness outcomes, creating ecological traps. In environments where ecological traps are likely to occur, data on habitat preferences and fitness consequences can fundamentally change predictions of metapopulation models and increase our understanding of the role that novel habitats play in population persistence, but such data are rarely collected. In this thesis, I first conduct a global meta-analysis to assess the state of knowledge on habitat preference and fitness metrics in animal populations, using wildlife populations associated with aquaculture as a case study. My findings reveal that responses to aquaculture vary widely across taxa and farming systems, ranging from large increases in abundance to near complete displacement. However, the influence of aquaculture on wildlife populations remains poorly understood, as researchers rarely obtain appropriate measures of habitat preference, survival or reproductive success. Accordingly, in subsequent chapters I apply the ecological trap framework to assess marine habitats modified by aquaculture or invasive species. In the first application, I collect wild Atlantic cod (a species known to be attracted to salmon farms) from areas of high and low salmon farming intensity, and compare reproductive fitness via a captive spawning trial with hatchery-rearing of offspring. I found limited negative effects of high farming intensity on quality of offspring. In the second application, I show that the threat of predation by a native keystone predator may limit the ability of an invasive seastar to exploit a food-rich habitat at shellfish farms. In the third application, I show that an invasive canopy-forming marine macroalga provides viable habitat for native fishes and may help to maintain fish biodiversity in areas where urban impacts have driven a decline in native macroalgal canopy cover. Together, this thesis demonstrates the utility of individual-level data on habitat preference and fitness outcomes— via the application of the ecological trap conceptual framework—in assessing the impacts of novel habitats on animals, and recommends greater use of this approach in future investigations into the impacts of human-induced rapid environmental change in coastal marine ecosystems.

DECLARATION

This is to certify that:

The thesis comprises only my original work towards the PhD except where indicated in the Preface.

Due acknowledgement has been made in the text to all other material used.

The thesis is fewer than 100 000 words in length, exclusive of tables, maps, bibliographies and appendices.

Luke Barrett

September 2018

Cover image: Mesocosm reef stocked with invasive wakame kelp (Undaria pinnatifida)

PREFACE

I am the primary author and principle contributor on all chapters presented in this thesis. My supervisors, Stephen E Swearer and Tim Dempster, are co-authors on all chapters.

Article publication status and author contributions

Chapter Two: Published by Reviews in Aquaculture on 14 Aug 2018. Co-authored by Tim Dempster and Stephen E Swearer. LTB, TD and SES conceived and designed the experiment; LTB conducted the experiment and collected data with assistance from technical staff and volunteers; LTB analysed the data and wrote the manuscript; TD and SES provided editorial comments.

Contributions: LTB 80 %, TD 10 %, SES 10 %

Chapter Three: Published by Aquaculture Environment Interactions on 16 Aug 2018. Co- authored by Tim Dempster, Stephen E Swearer, Ørjan Karlsen, Torstein Harboe and Sonnich Meier. LTB, TD, SES, ØK and TH conceived and designed the experiment; LTB conducted the experiment and collected data with assistance from ØK, TH and SM, as well as technical staff at the Norwegian Institute of Marine Research; LTB analysed the data and wrote the manuscript; TD, SES and SM provided editorial comments.

Contributions: LTB 75 %, TD 5 %, SES 5 %, ØK 5 %, TH 5 %, SM 5 %

Chapter Four: Unpublished material not submitted for publication. Co-authored by Tim Dempster and Stephen E Swearer. LTB, TD and SES conceived and designed the experiment; LTB conducted the experiment and collected data with assistance from technical staff and volunteers; LTB analysed the data and wrote the manuscript; TD and SES provided editorial comments.

Contributions: LTB 80 %, TD 10 %, SES 10 %

Chapter Five: Unpublished material not submitted for publication. Co-authored by Stephen E Swearer, Tim Dempster. LTB, SES and TD conceived and designed the experiment; LTB conducted the experiment and collected data with assistance from technical staff and volunteers; LTB analysed the data and wrote the manuscript; SES and TD provided editorial comments.

Contributions: LTB 80 %, SES 10 %, TD 10 %

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This research was funded by grants from the Holsworth Wildlife Research Endowment (Chapters Four and Five), the PADI Foundation (Chapter Five), the Victorian Environmental Assessment Council (Chapter Five), the Sustainable Aquaculture Lab – Temperate and Tropical (all chapters), the Research on the Ecology and Evolution (REEF) Lab (all chapters), and the Norwegian Seafood Research Fund (Chapter Three). All animal research was conducted in accordance with the animal ethics requirements of the University of Melbourne (Chapter Five: approval numbers 1413133 and 1413193) and Norwegian legislation on animal experimentation (Chapter Three: approval number 8264). Permits were obtained from the Victorian state government for collection and translocation of marine animals and algae for (Chapters Four and Five: RP919, RP1185, NP280, NP282).

ACKNOWLEDGMENTS

I would firstly like to thank my supervisors, Steve Swearer and Tim Dempster, for their unwavering support over the duration of my PhD. They have been everything I could have hoped for in a pair of supervisors.

My friends and colleagues in the REEF and SALTT labs, past and present, provided helpful discussions and comments on my thesis chapters, and were universally great company to have a coffee or beer with, as were all my officemates in 131 and others around BioSciences 4. Special mentions go to Simon, Emily, Ben, Fran, Qike, Valeriya, Tyler, Matt, James, Jack, Ollie and Fletch for making me feel welcome in my first couple of years in Melbourne.

Many people combined forces with me to get fieldwork done on the cold and murky waters of Port Phillip Bay. In alphabetical order: Dean Chamberlain, Seann Chia, Ben Cleveland, Emily Fobert, Molly Fredle, Akiva Gebler, Kevin Jensen, Valeriya Komyakova, Nina Kriegisch, Kevin Menzies, Rebecca Morris, Jack O’Connor, Simon Reeves, Juan Manuel Valero Rodriguez, Kyler Tan, Chris Taylor, João Teixiera, Oliver Thomas and Rod Watson (Victorian Marine Science Consortium). Apologies if I forgot anyone! Lance Wiffen provided access to his aquaculture leases at Clifton Springs and Grassy Point. John Ahern and Tania Long averted a couple of aquarium-related catastrophes in my absence, thanks and sorry!

Thanks to my Norwegian collaborators and surrogate supervisors during my time there: Torstein Harboe, Ørjan Karlsen and Sonnich Meier. The work was made possible by numerous technical staff, researchers, expert cod fishermen and all-round nice people. I’d especially like to thank Margareth Møgster, Stig Ove Utskot, Theresa Aase, Michal Rejmer, Inger Semb Johansen, Nele Gunkel-Sauer, Kristine Hovland Holm, Yvonne Rong, Terje van der Meeren, Tord Skår, Velimir Nola and Glenn Sandtorv. I’d also like to thank the staff and students at Austevoll High School for taking us to Brandasund and back with a boatload of live cod.

My family and friends back home in WA tolerated my long absence and my incommunicativeness during the busy times, and largely stayed away from the question “when will you be finished?”. Well played!

Finally, but most importantly, I’d like to thank my partner Marina, who provided constant love and support, and made quite a few sacrifices to ensure that I had a clear run at this thesis. I hope it’s been worth it!

CONTENTS

List of Tables ...... ix

List of Figures ...... x

Chapter One | General introduction

General introduction ...... 1

References ...... 9

Chapter Two | Impacts of marine and freshwater aquaculture on wildlife: a global meta- analysis

Abstract ...... 14

Introduction ...... 14

Methods ...... 17

Results ...... 21

Discussion...... 28

References ...... 36

Chapter Three | Limited evidence for differential reproductive fitness of wild Atlantic cod in areas of high and low salmon farming density

Abstract ...... 50

Introduction ...... 50

Methods ...... 53

Results ...... 62

Discussion...... 66

References ...... 71

Chapter Four | Native predator prevents an invader from exploiting food-rich habitat

Abstract ...... 79

Introduction ...... 79

Methods ...... 82

Results ...... 86

Discussion...... 89

References ...... 97

Chapter Five | An invasive habitat-former mitigates impacts of native habitat loss for endemic reef fishes

Abstract ...... 102

Introduction ...... 103

Methods ...... 105

Results ...... 113

Discussion...... 119

References ...... 123

Chapter Six | General discussion and conclusions

General discussion and conclusions ...... 128

References ...... 137

Appendices

Appendix 2.1 ...... 142

Appendix 2.2 ...... 152

Appendix 2.3 ...... 153

Appendix 2.4 ...... 154

Appendix 2.5 ...... 155

vii

Appendix 3.1 ...... 156

Appendix 3.2 ...... 157

Appendix 3.3 ...... 158

Appendix 4.1 ...... 161

Appendix 4.2 ...... 164

Appendix 5.1 ...... 165

Appendix 5.2 ...... 166

Appendix 5.3 ...... 167

Appendix 5.4 ...... 168

Appendix 5.5 ...... 170

Appendix 5.6 ...... 171

Appendix 5.7 ...... 172

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LIST OF TABLES

Table 2.1. Mean effects of aquaculture sites on wildlife populations ...... 33

Table 3.1. Body size and condition metrics for low and high farm density groups ...... 62

Table 3.2. Egg and larval quality metrics for cod from low and high farm density areas ...... 67

Table 5.1. Summary of fish recruitment to artificial reefs stocked with Undaria pinnatifida, Ecklonia radiata, or left barren...... 114

Table 5.2. Comparison of reef fish relative abundance estimated by diver catch per unit effort in Undaria and Ecklonia habitats...... 117

Table 5.3. Comparison of body condition and reproductive condition metrics in reef fishes collected from Undaria and Ecklonia habitats...... 118

LIST OF FIGURES

Figure 1.1. Conceptual representation of habitat selection responses to habitats of varying quality ...... 2

Figure 2.1. Distribution of research effort for studies that met the criteria for inclusion ...... 20

Figure 2.2. Distribution of research effort on interactions between aquaculture sites and wild fauna among countries and territories ...... 22

Figure 2.3. Summary statistics for log response ratios for each variable in our meta-analysis. 25

Figure 3.1. Map of collection sites relative to active salmon farms in south-western Norway . 54

Figure 3.2. Weight-at-length relationship for female and male cod ...... 61

Figure 3.3. Multidimensional scaling (MDS) plot showing dissimilarly (Euclidean distance) of multivariate fatty acid profiles in Atlantic cod ovaries according to salmon farm density ...... 63

Figure 3.4. Daily egg production per tank during the captive spawning period ...... 64

Figure 4.1. Population density of Asterias amurensis and Coscinasterias muricata inside and outside the Clifton Springs and Grassy Point Fisheries Aquaculture Reserves ...... 87

Figure 4.2. Armspan and gutted weight of Asterias amurensis inside and outside the Clifton Springs Fisheries Aquaculture Reserve ...... 88

Figure 4.3. Condition metrics for Asterias amurensis inside and outside the Clifton Springs Fisheries Aquaculture Reserve ...... 90

Figure 4.4. Density plot of Asterias amurensis size distribution inside and outside the farm boundary at Clifton Springs Aquaculture Fisheries Reserve ...... 91

Figure 4.5. Effect of prey, conspecifics and predators on habitat selection decisions by the invasive seastar Asterias amurensis in laboratory trials ...... 92

Figure 5.1. Archetypal examples of three rocky reef habitats in northern Port Phillip Bay ..... 103

Figure 5.2. Map of study locations in Port Phillip Bay, Australia ...... 106

Figure 5.3. Weight-at-length relationships for Heteroclinus perspicillatus, H. heptaeolus and Neoodax balteatus ...... 113

Figure 5.4. Habitat choice trial results for Heteroclinus perspicillatus and Neoodax balteatus ...... 114

Figure 5.5. Canonical analysis of principle coordinates (CAP) showing variation in fish communities across underwater visual census (UVC) plots with and without Undaria pinnatifida canopy ...... 120

Figure 5.6. Fish community metrics from underwater visual census plots ...... 122

Figure 6.1. Conceptual diagram showing speculative positioning of responses of focal species to novel habitats ...... 130

Figure. 6.2. Conceptual representation of the ecological trap framework applied to an assessment of the role of artificial reefs for fish production ...... 132

Figure. 6.3. Current distribution of Undaria pinnatifida within Port Phillip Bay in 2017 ...... 133

CHAPTER ONE: GENERAL INTRODUCTION

Anthropogenic impacts on the natural environment are occurring at an unprecedented rate and spatial scale, with nearly every part of the globe affected by one or more human impacts (Vitousek et al. 1997, Sanderson et al. 2002, Halpern et al. 2008, Vörösmarty et al. 2010). The effects of these drivers are referred to collectively as human-induced rapid environmental change (HIREC), and include those arising from habitat loss or habitat change, pollution, species introductions, human harvesting, and climate change (Sih et al. 2011). As a result, animals must now navigate a landscape comprised of modified or degraded environments that have altered the ecological playing field from that of their evolutionary past (often to the advantage of non-native over native species: Byers 2002, Crooks et al. 2011).

Impacts of HIREC on animal populations will depend on how individuals respond to novel cues and environmental conditions. Animals will be best placed to survive and reproduce in modified or degraded habitats if they exhibit adaptive behaviours, such as selecting the best available habitats, choosing suitable food items and recognising and evading novel predators (Sih et al. 2011, Sih 2013, Wong & Candolin 2014). However, HIREC frequently outpaces the evolution of the indirect decision-making cues used by animals to assess current and future conditions, which can result in individuals incorrectly evaluating risks and resources (Schlaepfer et al. 2002).

Novel habitats, ecological traps and metapopulation ecology

These scenarios, in which ecological novelty leads individuals to choose behaviours or habitats that lead to poor fitness outcomes, are termed evolutionary traps (Schlaepfer et al. 2002, Robertson et al. 2013). Incorrect evaluations may arise through one of two sensory mechanisms: a reliance on ‘outdated’ cues that no longer reliably predict fitness outcomes because conditions have changed, or the introduction of novel cues that mimic or overpower pre-existing cues but lead to poor fitness outcomes (Schlaepfer et al. 2002, Robertson et al. 2013, Wong & Candolin 2014).

A specific case of the evolutionary trap, the ecological trap, occurs when individuals either prefer or fail to avoid low quality habitats when higher quality alternatives are available (Robertson & Hutto 2006, Hale, Treml, et al. 2015). In doing so, they also fall into the corollary,

1 a perceptual trap, whereby individuals avoid or fail to prefer a relatively high quality habitat (Fig. 1.1, Kokko and Sutherland 2001; Gilroy and Sutherland 2007; Patten and Kelly 2010).

Although the ecological trap concept concerns individual-level phenomena, traps directly drive population-level processes, and can exacerbate population-level effects of environmental change in impacted landscapes. Attractive population sinks caused by ecological traps can impact metapopulation persistence disproportionately by drawing in animals that would otherwise settle in source habitats (Battin 2004, Hale, Treml, et al. 2015). Similarly, perceptual traps cause potential source habitats to be underutilised (Fig. 1.1), increasing effective habitat loss in fragmented landscapes and potentially driving Allee effects for any individuals that do choose to reside in the perceptual trap habitat (Kokko & Sutherland 2001, Gilroy & Sutherland 2007, Patten & Kelly 2010).

Habitat quality

High Low (potential source) (potential sink)

Habitat selection Preferred Correct decision Ecological trap response Avoided Perceptual trap Correct decision

Figure 1.1. Conceptual representation of the ecological trap framework in terms of possible habitat selection responses to habitats of varying quality, sensu Patten and Kelly (2010).

In early source-sink metapopulation models, population growth was considered to be approximately proportional to the spatial extent of source and sink habitats, with populations in sink habitats maintained by passive dispersal from source habitats (Holt 1985). Later, researchers began to incorporate active dispersal, allowing individuals to preferentially settle in the best available habitats (population sources), and then spill over into more marginal habitats in a density-dependent fashion (Pulliam 1988). Such models have significantly better predictive ability than their predecessors and drove an important shift in thinking about metapopulation ecology (Dias 1996). However, by assuming that individuals will tend to make adaptive habitat selections, these models are vulnerable to making overly optimistic predictions about population persistence whenever this assumption is violated, with potentially dire consequences for the management of threatened animal populations (Battin 2004). The ecological trap concept provides a framework for assessing habitats through the lens of individual habitat preferences and habitat quality (Fig. 1.1). This framework can, by

2 linking habitat-specific indices of habitat preference and fitness, distinguish between attractive population sources (‘traditional’ population sources), attractive population sinks (ecological traps), unattractive potential population sources (perceptual traps), and unattractive population sinks (‘traditional’ population sinks) (Fig. 1.1, Patten and Kelly 2010; Hale et al. 2015; Hale and Swearer 2016). Incorporating such an assessment can improve the predictive ability of source-sink models, and in some cases may fundamentally alter expectations of population persistence in degraded or modified environments (Kokko & Sutherland 2001, Battin 2004, Hale, Treml, et al. 2015). Despite this, uptake of the ecological trap assessment framework has been relatively slow (Hale & Swearer 2016).

Current evidence for ecological traps

In their influential review, Robertson and Hutto (2006) proposed a set of criteria for demonstrating an ecological trap: (1) there must be a suitable measure of habitat preference indicating preference for, or non-avoidance of, the putative trap habitat relative to alternative habitats, and (2) there must be a reasonable surrogate measure of fitness that is significantly lower in the putative trap habitat relative to alternative habitats. Put simply, the preferred habitat should not confer the best fitness outcomes. Habitat preference can be particularly difficult to assess in the field, especially in systems where observed population densities are determined by a combination of passive recruit supply, active habitat preference and early stage survival (Underwood & Fairweather 1989, Hixon & Beets 1993, Shima 2001, Stevens 2003, Railsback et al. 2003). Robertson and Hutto (2006) proposed that estimates of habitat preference should be based on one or more of the following lines of evidence: settlement patterns, distribution of dominant individuals, temporal variance in population size, and controlled choice experiments. Robertson and Hutto (2006) considered that only 5/45 putative demonstrations at that time had provided sufficient evidence of habitat preference.

Terrestrial systems

While ecological traps may arise naturally (where a broadly adaptive preference is maladaptive in certain situations), most proposed and demonstrated cases have been driven by HIREC (Hale & Swearer 2016). The bulk of evidence for ecological traps comes from terrestrial systems,

3 with birds and ovipositing insects providing especially convenient model systems as fitness can be easily compared between habitats by tracking clutch sizes and survivorship of adults or offspring.

Habitat changes associated with human land use are perhaps the most well-documented drivers of ecological traps. The creation of artificial materials such as plastic, asphalt, oils, glass, and paint can provide hyperattractive cues that lead insects to choose unsuitable ovipositing sites (e.g. Horváth et al. 1998; Kriska et al. 1998), while built structures may mimic natural/ nesting sites for birds, but offer poor fitness outcomes (Sumasgutner et al. 2014).

Similarly, human disturbance of native vegetation may mimic natural grasslands or disturbed forest, but present novel risks for animals that choose to reside in these areas. Balme et al. (2010) found that the efficacy of a wildlife reserve was reduced by a failure of leopards to avoid adjacent areas of farmland, where they come into conflict with humans. Negative edge effects were also observed in birds nesting in vegetation patches fragmented by human land use: indigo buntings preferentially nested in patches with a high edge length to area ratio—an adaptation that allows them to exploit natural disturbance—but experienced high chick mortality in anthropogenic patches (Weldon & Haddad 2005).

Habitat structure is also influenced by exotic or invasive habitat-forming plants, and such species can drive the creation of ecological traps by altering the quality of the habitat without affecting associated attractive cues. Lloyd and Martin (2005) found that the presence of an exotic grass did not alter the attractiveness of a native prairie habitat but was associated with lower nest survival for longspurs, while Rodewald et al. (2011) reported lower reproductive success in cardinals that selected exotic honeysuckle as nesting substrate in urban areas. Some traps may arise due to phenology of exotic habitat-formers: returning migrant blackcaps preferentially settled in plantations of an exotic tree, perhaps because the associated shrubs develop spring foliage earlier, but experienced reduced nesting success (Remeš 2003).

Aquatic systems

Marine and freshwater systems have not escaped the reach of HIREC (Halpern et al. 2008, Vörösmarty et al. 2010), yet there has been comparatively little research effort on behavioural responses of aquatic fauna to habitat changes (Hale, Coleman, et al. 2015, Hale & Swearer

2016). This lack of research reflects difficulties in obtaining habitat preference and fitness metrics in many aquatic animals, especially broadcast spawning fishes, but it is nonetheless important to broaden the research effort beyond North American and European birds, as terrestrial and aquatic fauna are likely to differ in their vulnerability to the various causes of ecological traps (Hale & Swearer 2016).

Aquatic fauna are likely to be particularly vulnerable to ecological traps caused by pollution, given the tendency for aquatic environments to transport and accumulate pollutants from surrounding watersheds, intensifying exposure to toxins (Hale, Coleman, et al. 2015). Perhaps as a result, pollution-driven ecological traps were among the earliest documented in the aquatic environment. Juvenile flatfish detect and avoid heavily-oiled sediments, but not lightly- oiled sediments (Moles et al. 1994). Likewise, juvenile crabs select preferred sediment grain size regardless of the presence of oil, despite avoiding the oil when it was present on less preferred sediments (Moles & Stone 2002). Negative fitness effects of oil contamination mean that affected sediments are likely to function as ecological traps (Moles & Norcross 1998, Khan 2003). Other forms of pollution, such as noise or seismic pollution tend to be avoided, although exposure prior to avoidance, or sub-avoidance levels of exposure, may have deleterious effects that could lead to ecological trap formation to the extent that they co- occur with particular habitats or locations (Codarin et al. 2009, Miller et al. 2014).

Much of the subsequent work on marine ecological traps has assessed habitat selection in relation to fishing pressure. Natural floating debris signals oceanic convergence zones and high food availability for pelagic predators, but there is now broad-scale use of artificial fish aggregation devices (FADs) deployed by fishing fleets in non-convergence zones. These FADs are associated with lower food availability and high fishing mortality for fish that are attracted to them (Marsac et al. 2000, Hallier & Gaertner 2008). Similarly, casitas—concrete shelters used to attract lobsters for harvesting—act as ecological traps for lobsters that find the casitas more attractive than natural reefs (Gutzler et al. 2015). Fishing pressure may drive ecological traps even without the need for attractive structures: Abrams et al. (2012) predicted that some animals may find harvested areas to be more attractive than adjacent marine protected areas because: (a) potential predators and competitors are rare due to harvesting pressure, (b) prey species are abundant due to the rarity of predators, and (c) the risk of harvesting mortality is undetectable. Indirect fishing-driven traps also arise for predators that compete with fisheries for food. Verhulst et al. (2004) found that oystercatcher populations did not redistribute following the establishment of a shellfish reserve, despite individuals outside reserves having fewer shellfish in the diet, poorer condition indices and higher mortality rates.

Similarly, African penguins rely on sea surface temperature and primary productivity cues to find optimum feeding areas, but a combination of fishing pressure and climate change has driven large scale changes in the size and distribution of forage fish populations such that these cues no longer reliably predict prey abundance (Sherley et al. 2017).

High densities of wildlife at some marine and freshwater aquaculture systems have prompted research into the effects of association with farms on wildlife populations. Šigutová et al. (2015) found that pond management regimes caused high mortality rates for endangered dragonfly larvae, and recommended measures to make the ponds less attractive to ovipositing dragonflies. Stocking of fish ponds or sea cages for aquaculture can also alter the availability of food for piscivorous birds, leading to ecological traps where food availability is lower than expected (Kloskowski 2012), where high densities of breeding waterbirds attracted to fish ponds in turn attract nest predators (Broyer et al. 2017), or where culling is carried out (Quick et al. 2004, Callier et al. 2017). Wild fish attracted to sea cage fish farms had higher condition indices and a lower incidence of internal parasites, but also higher incidences of external parasites (Dempster et al. 2011), while captive feeding trials indicate that consuming farm waste may have deleterious effects on reproduction (Lavens et al. 1999, Mazorra et al. 2003, Salze et al. 2005, Bogevik et al. 2012). Other studies indicate that marine infrastructure (whether for aquaculture or other purposes) is probably both attractive and productive for many fish and benthic invertebrate populations (Reubens et al. 2013, Borgert 2015).

Exotic or invasive species can have disastrous effects on native animal populations (Clavero & García-Berthou 2005), and are drivers of several demonstrated terrestrial ecological traps (Robertson & Hutto 2006, Hale & Swearer 2016). Despite this, there have been few assessments of potential ecological traps driven by non-native aquatic species (candidates may include novel predators, prey, competitors or habitats). One such case concerns the invasive macroalga Caulerpa taxifolia, a habitat-forming species that functions as an ecosystem engineer in soft sediment habitats. In Australia, native bivalves do not avoid C. taxifolia as a substrate, and recruit in large numbers (Gribben & Wright 2006, Gribben et al. 2009). Recruits fare well, but there is evidence that mortality risk over an individual’s lifetime is higher in the invasive habitat (Gribben & Wright 2006, Wright & Gribben 2008, Byers et al. 2010).

Thesis aims and structure

Habitat association studies are a cornerstone of the animal ecology field, yet only a fraction provide sufficient evidence to place habitats within a source–sink metapopulation framework, let alone determine whether individuals utilising or avoiding habitats are making adaptive habitat selection decisions (Hale & Swearer 2016). This is especially true for marine systems, where most investigations provide only basic population- or community-level data. The examples cited in the previous section constitute nearly all published investigations of potential ecological traps in the marine environment. This is despite the ecological trap concept being first articulated more than four decades ago (Dwernychuk & Boag 1972, Gates & Gysel 1978), and refined over subsequent decades (Battin 2004, Robertson & Hutto 2006, Gilroy & Sutherland 2007, Patten & Kelly 2010, Hale & Swearer 2016). Related calls have been made regarding the analogous attraction–production controversy around fish populations on artificial reefs (Bohnsack 1989, Osenberg et al. 2002, Brickhill et al. 2005), but studies that separate these key population processes remain rare.

Accordingly, this thesis examines habitat selection and fitness consequences for animals in several human-impacted coastal marine environments using the ecological trap assessment framework, and aims to link these individual-level traits to the potential for population persistence in degraded environments. The aim was not necessarily to demonstrate the existence of ecological traps in these environments, but rather to demonstrate the application of the ecological trap assessment framework to questions around the value of novel habitats for fauna. I worked across three study systems modified by aquaculture and invasive species to demonstrate the broad applicability of this individual-level approach.

Chapters 2-5 were written as standalone manuscripts, with minor changes to fit the thesis format. As a result, some repetition of key concepts—particularly HIREC and ecological trap theory—has been unavoidable. I have minimised unnecessary repetition by confining descriptions of my study systems to the relevant chapters.

In Chapter Two, I conduct a global systematic review and meta-analysis of research on interactions between all forms of aquaculture and vertebrate wildlife, with an emphasis on the impacts of aquaculture on the distribution, fitness and population persistence of wildlife. I demonstrate that there are considerable knowledge gaps around very basic questions, including individual responses to aquaculture (i.e. the degree of attraction or repulsion relative

7 to natural habitats), and whether proximity to aquaculture is likely to have positive or negative effects on fitness and population persistence of vertebrate wildlife.

Chapter Three concerns one of the key knowledge gaps highlighted by Chapter 2: the effect of proximity to aquaculture on the fitness of wild fish. We have multiple lines of evidence that wild fish are attracted to aquaculture (see Chapter 2), where they experience high food availability (Dempster et al. 2011), but the effects of proximity to aquaculture on reproductive fitness are poorly known. In this chapter, I collect adult Atlantic cod (Gadus morhua) in spawning condition from sites within two areas of high and low salmon farming density, and conduct a hatchery spawning experiment to compare egg production, egg quality, and larval fitness metrics.

In Chapter Four, I study a complex interaction between shellfish aquaculture, a native keystone predator (the eleven-arm seastar Coscinasterias muricata) and an invasive keystone predator (the northern Pacific seastar Asterias amurensis). Shellfish aquaculture provides a large trophic subsidy for benthic fauna, and has the potential to act as an important population source for both species. However, as the native seastar has been reported to prey upon the invasive seastar, there may be some predation pressure occurring that reduces the value of the shellfish farm habitat for the invader, potentially leading to an ecological trap. Accordingly, I investigate the behavioural responses of the invasive seastar to the shellfish farm habitat and the native predator, and assess fitness metrics for seastars inside and outside the farms.

In Chapter Five, I consider the attractiveness and fitness value of habitat created by an invasive ecosystem engineer (the wakame kelp Undaria pinnatifida) for native fish on urban- impacted temperate reefs. This invasive habitat-forming kelp provides a seasonal canopy on reefs where urchin-grazing and urban impacts have driven the decline of native kelp cover. I combine a laboratory habitat choice experiment, recruitment data on mesocosm reefs, fish community surveys on natural reefs, and fitness metrics to provide a holistic assessment of the relative value of this novel habitat.

Finally, Chapter Six provides a general discussion of this body of work, and reflects on the utility of the ecological trap framework in assessing the role of novel habitats in population persistence of marine fauna.

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CHAPTER TWO: IMPACTS OF MARINE AND FRESHWATER AQUACULTURE ON WILDLIFE: A GLOBAL META-ANALYSIS

ABSTRACT

The global expansion of aquaculture has raised concerns about its environmental impacts, including effects on wildlife. Aquaculture farms are thought to repel some species and function as either attractive population sinks (‘ecological traps’) or population sources for others. We conducted a systematic review and meta-analysis of empirical studies documenting interactions between aquaculture operations and vertebrate wildlife. Farms were associated with elevated local abundance and diversity of wildlife, although this overall effect was strongly driven by aggregations of wild fish at sea cages and shellfish farms (abundance: 72x; species richness: 2.0x). Birds were also more diverse at farms (1.1x), but other taxa showed variable and comparatively small effects. Larger effects were reported when researchers selected featureless or unstructured habitats as reference sites. Evidence for aggregation ‘hotspots’ is clear in some systems, but we cannot determine if farms act as ecological traps for most taxa, as few studies assess either habitat preference or fitness in wildlife. Fish collected near farms were larger and heavier with no change in body condition, but also faced higher risk of disease and parasitism. Birds and mammals were frequently reported preying on stock, but little data exists on the outcomes of such interactions for birds and mammals – farms are likely to function as ecological traps for many species. We recommend researchers measure survival and reproduction in farm-associated wildlife to make direct, causal links between aquaculture and its effects on wildlife populations.

INTRODUCTION

Aquaculture infrastructure (farms hereafter) presents a novel environment for wild animal populations. High stocking densities within farms aggregate biomass far beyond natural levels (commonly 5-45 kg m-3 final biomass: FAO Fisheries and Aquaculture 2018), and in open systems, provide considerable trophic subsidies for animals that take advantage of the opportunity, potentially benefitting some wildlife. However, there are also deleterious effects associated with proximity to farms, and the net impact of aquaculture on productivity and

14 persistence of wildlife populations will depend both on behavioural responses to farms and the fitness consequences of those responses. Where individuals are attracted to a habitat that confers poorer fitness outcomes than other available habitats, they have fallen into an ‘ecological trap’ (Robertson & Hutto 2006; Hale & Swearer 2016). While the concept is defined at the individual level, trap habitats have population-level consequences by drawing individuals from surrounding habitats into attractive population sinks (Hale et al. 2015). Even in the absence of an ecological trap, changes in the abundance and spatial distribution of influential species may indirectly affect other species and drive large-scale shifts in biodiversity and ecosystem function (Gamfeldt et al. 2015).

A range of attractive and repulsive mechanisms for wildlife can occur simultaneously at farms (Callier et al. 2017). The primary attractive mechanism in most systems is probably food availability, either in the form of direct predation on stock, or an indirect trophic subsidy in the form of farm waste (spilled feed, faeces and dead stock). Birds, pinnipeds and otters are well- documented predators of stock at sea cage or pond fish farming systems (Carss 1993; Pitt & Conover 1996; Adámek et al. 2003; Güçlüsoy & Savas 2003; Quick et al. 2004; Freitas et al. 2007; Dorr et al. 2012; Sepúlveda et al. 2015), while farm waste from sea cages also attracts significant aggregations of opportunistic wild fish (Dempster et al. 2002, 2009; Tuya et al. 2006; Sanchez-Jerez et al. 2011). A high local abundance of fish is likely to lead to secondary attraction of large predators, such as dolphins (Diaz López 2006; Piroddi et al. 2011). Shellfish and algae farming do not require inputs of feed, but high densities of filter feeding shellfish in farms do accumulate biomass, attracting wild fish and invertebrate species (Dealteris et al. 2004; Powers et al. 2007; McKindsey et al. 2011; Segvic-Bubic et al. 2011), while algae farming attracts wild herbivores (Hehre & Meeuwig 2016). Farm structures themselves may also be attractive, functioning in a similar manner to fish aggregation devices or artificial reefs (Tallman & Forrester 2007; Sanchez-Jerez et al. 2011). Farm structures provide three- dimensional habitat complexity, and associated light, noise and novel biofouling communities may all be attractive to a range of wild taxa (Dumont et al. 2011; Callier et al. 2017). Paradoxically, many of these environmental changes associated with farms, such as light, noise, eutrophication and high densities of predators, may have repulsive effects on wary or functionally specialised taxa (Markowitz et al. 2004; Becker et al. 2011).

Attraction to farms may increase or decrease the fitness of wildlife. One expectation is that increased food availability will lead to faster growth, higher body condition and increased reproductive output. Accordingly, there is some evidence that farm-associated wild fish have higher body condition and reproductive investment indices than fish from reference sites

(Dempster et al. 2011), but little is known about potential benefits for other taxa. In broadcast spawning taxa, high local population densities at farms are likely to confer greater mating efficiency (Inglis & Gust 2003). Such benefits for farm-associated wildlife are likely to be at least partially counteracted by potential deleterious fitness effects related to dietary shifts, contamination, disease, parasitism, and elevated mortality rates. For example, a shift from fish oils to terrestrially-derived ingredients in aquaculture feed may result in deficiencies of long- chain polyunsaturated fatty acids in animals that feed regularly at farms (eg. Salze et al. 2005; Fernandez-Jover et al. 2007a; Gonzalez-Silvera et al. 2017). Additionally, farm waste can create an anoxic environment with significant effects on benthic and estuarine communities (Wu 1995; Yucel-Gier et al. 2007; Herbeck et al. 2013; Valdemarsen et al. 2015), while in some areas, wildlife may also accumulate elevated tissue loadings of contaminants such as antibiotics, pyrethroid parasiticides, metals and organohalogens (Samuelsen et al. 1992; Boyd & Massaut 1999; Burridge et al. 2010; Bustnes et al. 2010) with potentially nontrivial effects (e.g. Crump & Trudeau 2009; Berg et al. 2016). For fish, the primary concern may be the effect of proximity to farms on disease and parasitism rates: high population densities within farms create favourable conditions for outbreaks of diseases and parasites such as sea lice (Krkosek et al. 2005, 2006; Costello 2009; Lafferty et al. 2015; Krkošek 2017). Wild fish populations may also act as reservoirs for parasites and diseases, and as they move between cages to take advantage of feeding opportunities, they act as potential transmission vectors that may increase reinfection rates for farms, driving positive feedbacks (Uglem et al. 2009; Hayward et al. 2011).

Despite this suite of environmental concerns, the aquaculture industry is the world’s fastest- growing food production sector (FAO Fisheries and Aquaculture 2015). For this growth to be sustainable in terms of environmental impacts and ‘social license’ to operate, the industry must grapple with issues arising from interactions between aquaculture activities and the natural environment and develop solutions to minimise negative effects on wildlife (and vice versa). The first step should be to assess the state of knowledge on these issues and identify the most severe effects. Recent reviews have outlined the range of interactions that occur between aquaculture activities and wild fauna (e.g. Uglem et al. 2014; Taranger et al. 2015; Glover et al. 2017; Callier et al. 2017), but there has been not yet been a quantitative global synthesis of the impacts of aquaculture on wildlife. Here, we conduct a systematic review and meta-analysis of studies documenting interactions between aquaculture activities and wildlife, primarily to quantify the effects of these interactions on abundance, diversity, and fitness of farm-associated wildlife, and secondarily to highlight potential drivers of conflict between

16 wildlife and aquaculture. Thereafter, we recommend directions for future research to address key knowledge gaps in this area.

MATERIALS AND METHODS

Literature search and systematic review

Primary publications up to November 2017 were discovered by searching for the following terms using the ISI Web of Science: (aquaculture OR mariculture OR "fish farm*" OR "shellfish farm*" OR "mussel farm*" OR "oyster farm*" OR "sea cage*" OR "net pen*" OR "fish pond*" OR "seaweed farm*" OR "macroalgal farm*" OR "algal farm*") AND (attract* OR avoid* OR wild OR aggreg* OR impact* OR depredat* OR predat* OR disease) AND (wildlife OR animal* OR fauna* OR fish* OR shark* OR mammal* OR dolphin* OR cetacean* OR otter* OR seal* OR sea lion* OR bird* OR avian OR reptile* OR snake* OR amphibian* OR frog*). >9000 results were manually screened on an individual basis, by title and abstract alone where the topic was clearly irrelevant, or else after accessing the full text. Additional articles missed by our initial search were discovered using informal exploratory searches using Google Scholar, and by reading the reference lists of all relevant articles returned by our initial search. Our search focused on interactions with vertebrate wildlife (defined here as fish, birds, mammals and reptiles), as these animals are typically highly mobile and are therefore more able to make decisions about whether to reside at and interact with farms.

For inclusion, publications were required to have provided empirical field data on at least one of the following: (1) distribution, behaviour, condition, disease or mortality of wildlife in the vicinity of aquaculture sites, or (2) direct interactions between wildlife and stock at aquaculture sites (e.g. predation of stock). To minimise potential duplication of data, we only included peer-reviewed English-language journal articles.

To document the distribution of research effort in the field, we recorded the year, country, region, environment, culture system, culture taxa and the wild taxa for each study.

Meta-analysis

Studies were included in the subsequent meta-analysis if they provided quantitative data sufficient to calculate effect sizes for variables at aquaculture sites relative to ‘natural’ or ‘reference’ sites. We extracted a range of quantitative variables that were representative of the dominant types of interactions between aquaculture operations and wild vertebrates, relating to spatial distribution (Abundance, Species Richness), size structure (Length, Weight), food availability (Body Condition, Stomach Fullness), disease and parasite infection levels – either infection loads on individuals or prevalence of infected individuals in the population (Infection Level), as well as direct measures of Mortality and Fertility. Reproductive condition metrics (e.g. relative gonad size) were considered a component of Body Condition.

Natural log response ratios were calculated for each variable: RR = ln(F/R), where F is the trait mean at farm sites and R is the trait mean at reference sites. Taking the natural log of the response ratio normalises the error distribution by reducing the influence of positive responses (Hedges et al. 1999). Studies employed a variety of sampling designs, including random or matched farm and reference sites, and stocked or unstocked farms. All were treated as random for the purposes of this meta-analysis, with RR calculated from the mean trait values across all farm and reference sites regardless of how sites were selected by the authors. Where multiple complementary measures were available for a response variable, we took the mean of those measures (for example, Fulton’s K, hepatosomatic index and gonadosomatic index all contribute to the Body Condition variable). Where a study provided data on a response variable from multiple species or sites, we combined data to provide a single replicate, except where data spanned multiple culture systems (e.g. cages and ponds), taxonomic classes (e.g. birds and mammals), environments (e.g. marine and freshwater), or countries. No article contributed more than two studies to our database. This was done to prevent studies that provided data on numerous species from having a disproportionate influence on our findings, and to ensure spatial independence between replicates given the high mobility of most species studied. Where data was provided for farms with and without exclusion measures (e.g. fenced and unfenced sites), we used data from sites without exclusion measures.

Some variables were not easily quantified for statistical analysis but were nonetheless important in understanding interactions between farms and wild fauna. These included changes in tissue fatty acid profiles, trace elements and stable isotopes, contamination from

18 antibiotics, heavy metals and other substances, and behavioural data such as residence time or visitation rates. For these variables, we recorded the response ratio if possible, otherwise we noted the direction or nature of the effect.

Statistical analyses

To test for a significant effect of farm-association on response variables, we checked normality before conducting one sample t-tests on RR data (mean RR under null hypothesis of no farm effect = 0) using R software (R Core Team 2017).

Exploratory model selection was used to determine which of the following factors best predicted effects of farms on wildlife (abundance and species richness responses only, as remaining responses had insufficient sample sizes for exploratory analysis): Year, Country, Continent, Environment (Marine, Freshwater), Culture System (Cage, Pond, Longline, Rack, Bed), Cultured Taxa (Fish, Shellfish, Crustacean, Alga), Wild Taxa (Fish, Bird, Mammal, Reptile, Amphibian), and Reference Habitat (Structured, Unstructured). We fitted a global general linear model using R, and employed the dredge() function in the MuMIn package (Barton 2016) to compare the second-order Akaike’s Information Criterion (AICC) score of every possible subset of the global model. AICC includes a correction for finite sample sizes and yields more conservative models than AIC (Burnham and Anderson 2002). We selected the model with the lowest AICC score, and then used the likelihood ratio to test whether the selected model offered a significantly better fit than the null (intercept only) model, tested the significance of model terms, and then conducted post-hoc tests with a Tukey correction to test pairwise effects within significant model terms.

There was orders-of-magnitude variation in RRs for abundance and species richness among studies and systems, and accordingly, the overall trends that we report may be strongly influenced by a small number of studies with unusually large RRs. To test this possibility, we conducted a sensitivity analysis by ranking studies (replicates) according to the absolute value of the RR, removing the studies with the largest RR in a stepwise fashion, and rerunning the model between each removal (Bancroft et al. 2007; Kroeker et al. 2010). We then report the number of studies than can be removed from the dataset without altering the statistical significance of the farm effect.

To test whether the geographic distribution of research effort on this topic corresponds to the distribution of aquaculture production, we fitted a zero-inflated Poisson model (using the pscl package for R: Zeileis et al. 2008) to compare the number of studies contributed by each country with the reported aquaculture production (t) by that country (FAO Fisheries and Aquaculture 2017). To account for the large disparity in peer-reviewed English language research output between developed and developing nations, we also included the United Nations Human Development Index as a model term (United Nations Development Programme 2017).

Figure 2.1. Distribution of research effort for studies that met the criteria for inclusion in our database, according to (A) Country, (B) Culture system, (C) Region, (D) Culture taxa, (E) Environment, and (F) Wild taxa.

RESULTS

Our searches discovered 204 relevant studies across 191 articles published between 1978 and 2017 (Appendix 2.1). 91 studies provided comparative data on wildlife populations at farms and reference sites suitable for inclusion in the meta-analysis of log response ratios (RR).

Distribution of research effort

There was a clear geographical bias in research effort within our database, with 114 peer- reviewed English language studies conducted in Europe and 46 in North America (Fig. 2.1). Among nations, Norway, the United States and Spain accounted for the most research (Fig. 2.1). Research effort across nations was significantly predicted by an interaction between the size of the nation’s aquaculture industry and the developmental index of the nation (p = 0.03, Appendix 2.2), wherein highly developed nations (especially those in Europe and North America: Fig. 2.2) with large production contributed more studies than those with low production (p <0.0001, Appendix 2.2). Several major aquaculture-producing nations were either poorly represented or entirely absent from our database: most notably, mainland China is by an order of magnitude the largest aquaculture producer in the world (FAO Fisheries and Aquaculture 2017), yet was entirely absent from our database. Other leading producers, namely Indonesia, India, Vietnam, Philippines and Bangladesh, were also either absent or represented by only a single study.

Most studies in our database assessed interactions with wildlife in marine or estuarine environments (Fig. 2.1), despite global animal aquaculture production being considerably higher in freshwater environments (47 cf. 27 million t in 2014) (FAO Fisheries and Aquaculture 2015). 105/144 studies in the marine environment took place at sea cage farms, while 49/60 freshwater systems were pond-based (Fig. 2.1). Fish were the most common cultured taxa studied (163 studies) – primarily salmonids (69 studies) in western Europe and the Americas, and sea bream (Sparus aurata) and sea bass (Dicentrarchus labrax) in southern Europe (43 studies). The research effort on environmental effects of salmon farming is in line with the predominance of salmonids in the marine fish farming sector, although freshwater cyprinid culture is the most productive pisciculture sector overall (FAO Fisheries and Aquaculture 2015). Sea bream, sea bass and marine shellfish systems are overrepresented in our dataset

21 relative to the size of these sectors, perhaps due to their importance for nations with high marine research activity (particularly Spain). Algal and crustacean culture (5 and 3 studies, respectively) were dramatically underrepresented here relative to the size of the sectors (FAO Fisheries and Aquaculture 2015).

Most studies reported interactions with wild fish (108 studies), followed by birds (53 studies), mammals (38 studies), reptiles (3 studies) and amphibians (2 studies) (Fig. 2.1).

Figure 2.2. Distribution of research effort on interactions between aquaculture sites and wild fauna among countries and territories. Production data taken from the Fishstatj database (FAO Fisheries and Aquaculture Department 2017).

Effects on wildlife

Abundance

We discovered 65 studies that quantified the abundance of wildlife at aquaculture farming sites compared to reference sites, using various forms of Control-Impact (CI), Before-After (BA) and Control-Impact-Before-After (BACI) designs. These studies used a variety of sampling methods, including visual census, catch-per-unit-effort and tagging/tracking. 17 studies reported a lower abundance near farms, two no difference, and 46 a higher abundance. The

22 mean effect was a 49x increase in abundance near farms (RR = 1.05, t64 = 4.3, p <0.0001), but this value was strongly influenced by a few outlier studies reporting very large aggregations of wild fish around sea cages (for example, a mean 1327x increase over three sampling dates at one Australian offshore farm compared to featureless mid-water reference sites: Dempster et al., 2004). Fish demonstrated the largest abundance changes, while changes in bird and mammal abundance were highly variable in both effect size and direction and not significantly different to zero (Fig. 2.3, Table 1). We were not able to calculate RR for an additional six studies reporting differential abundance at farms (fish: 2/2 higher; mammals: 1/2 higher; birds: 2/2 higher).

A sensitivity analysis revealed that it was possible to conduct stepwise removal of 25/65 replicates with the largest effect sizes without losing statistical significance, indicating that the overall trend was robust. However, when studies that assessed changes in wild fish abundance at sea cage systems were omitted from the analysis, the remaining studies did not provide support for an overall effect of aquaculture on wildlife abundance (t38 = 0.81, p = 0.42), indicating that wild fish aggregations around sea cages were largely responsible for this overall effect.

Model selection indicated that differential abundance was best predicted by a model containing Environment, Cultured Taxa and Reference Habitat (R2 = 0.33, F = 7.4, p <0.0001; Appendix 2.3). The Cultured Taxa term was significant (p = 0.0001), as was Reference Habitat (p = 0.003), while Environment was not (p = 0.12). Post-hoc testing revealed that increases in abundance of wild fauna tended to be higher at fish farms than at shellfish farms (p = 0.001) and that studies comparing abundance at farm sites to unstructured or featureless reference sites (e,g, sandy seabed or open ocean) generally found a larger response than those that chose natural reef, unstocked farms or other structured habitats as reference sites (p = 0.006; Appendix 2.4).

Species richness

Most studies only assessed a limited number of target species, but 37 studies provided useful data on species richness at farms and reference sites. Of these, all but six reported higher species richness at farm sites, with a mean 1.7x increase (RR = 0.30, t36 = 3.1, p = 0.004). This effect was strongest in fish (RR = 0.43) but was also significant in birds (RR = 0.13) (Table 1).

Only one study in our database quantified differential species richness in mammals, and one in amphibians (Roycroft et al. 2004; Kloskowski 2010, Table 1).

There was large variation in effect size and direction across studies, but a sensitivity analysis found the overall trend to be remarkably robust (25/37 studies removed without losing statistical significance). As with abundance, the effect was not significant when sea cage systems were omitted from the analysis (t21 = 1.6, p = 0.11).

Species richness effects were best predicted by a model containing Reference Habitat and Wild Taxa (R2 = 0.27, F = 4.3, p = 0.007; Appendix 2.3). Post-hoc testing revealed that the only significant pairwise effect was between fish and amphibians, with fish species richness positively affected and amphibian species richness negatively affected by the presence of aquaculture sites in their respective environments (p = 0.03; Appendix 2.5).

Size structure, body condition and stomach fullness

Wild fish collected near aquaculture sites were on average 1.2x larger and 1.7x heavier than their counterparts from reference sites (Table 1), but no size comparisons were available for non-fish taxa.

Most studies (11) reported trends toward higher condition metrics in farm-associated wildlife, while two found no difference and two lower condition metrics at farms, although there was no significant effect overall (Table 1). Similarly, 8/11 studies found higher rates of stomach fullness in farm-associated wildlife, but these effects tended to be small and were not significant overall (Table 1). All but two comparisons of body condition or stomach fullness data concerned wild marine fish, while Gregory and Nelson (1991) estimated a 1.9x higher rate of stomach fullness in snakes at fish hatcheries, and Kloskowski et al. (2017) reported higher physiological stress indicators (=lower condition for our purposes) in grebes nesting on fish ponds.

Physiological changes

All 16/17 studies that reported looking for physiological or dietary changes in farm-associated wild fish relative to those from reference sites found evidence of dietary shifts, while the

24 remainder found only minor differences in stable isotopes (Johnston et al. 2010). Evidence for dietary shift included farm feed pellets in the stomachs of farm-associated wild fish (Skog et al. 2003; Fernandez-Jover et al. 2011; Arechavala-Lopez et al. 2011), taxonomic changes in stomach contents (Demétrio et al. 2012; Fernandez-Jover & Sanchez-Jerez 2015), higher tissue fat content and altered tissue fatty acid profiles that reflected the terrestrial-origin of lipids in farm feed (Skog et al. 2003b, Fernandez-Jover et al. 2007, 2011, Arechavala-Lopez et al. 2011, 2015, Abaad et al. 2016). Arechavala-Lopez et al. (2015) also reported differing trace element profiles in saithe near and far from salmon farms, while two studies reported altered taste and other metrics of quality (Skog et al. 2003; Bogdanović et al. 2012).

Figure 2.3. Summary statistics for log response ratios (RR) for each variable in our meta- analysis. All taxa are included. Boxes denote median, lower (25 %) and upper (75 %) quartiles, whiskers denote 1.5x interquartile range. Data points are ‘jittered’ for clarity. Asterisk indicates variables for which higher RR corresponds to poorer outcomes.

Contamination

Comparisons of contaminant levels in the tissues of farm-associated and non-associated fish revealed mixed results. All three studies that tested for antimicrobial contamination in farm- associated wild fish at farms where antimicrobials were in use found evidence of antimicrobial

25 residue in the majority of fish sampled, including oxytetracycline (0.2-1.3 µg g-1 muscle tissue: Björklund et al. 1990), oxolinic acid (0.4-4.4 µg g-1 muscle tissue at two farms: Samuelsen et al. 1992) and flumequine (1.0-4.9 µg g-1 muscle tissue: Ervik et al. 1994). In each case, mean concentrations for positive samples exceed the current European Union limits for these substances in skin and muscle of finfish for human consumption: oxytetracycline: 0.1 µg g-1; oxolinic acid: 0.1 µg g-1; flumequine: 0.6 µg g-1 (European Union 2010). It should be noted that the development of new vaccines has allowed fish farmers in some areas (e.g. salmonid farms in Norway and Scotland) to largely cease antimicrobial use despite rapid expansion of the industry, but use remains high in other regions (Watts et al. 2017). It remains unclear whether antimicrobial residue impacts fitness in farm-associated wild fish, whether through toxicity, loss of gut microbiota or antimicrobial resistance in pathogens.

There have also been assessments of organohalogens and metals in the tissues of farm- associated wild fish. One study reported significantly higher levels of organochlorines and polybrominated diphenyl ethers in farm-associated fish relative to those from reference sites (1.5x higher in cod, 1.2x higher in saithe: Bustnes et al. 2010). Another reported higher levels (2.1x) of mercury in tissues of farm-associated rockfish (Sebastes spp.), potentially related to an increase in trophic level near farms (DeBruyn et al. 2006). In the most comprehensive study to date, Bustnes et al. (2011) measured concentrations of 30 elements in cod and saithe livers from three regions in Norway. In saithe, Hg (2.0x), U (1.4x), Cr (1.9x) and Mn (1.6x) concentrations were significantly higher in farm-associated fish, while Se, Zn, Cd, Cs and As were higher at reference sites. In cod, U (1.4x), Al (1.5x) and Ba (1.9x) were higher in farm- associated fish, while Se, Zn, Cd, Cs and As were higher at reference sites. While there is evidence that some metals accumulate in sediments under fish farms, there is little evidence so far that farm-associated wild fish are accumulating high concentrations in their tissues.

Infection rates

We discovered 22 studies that empirically investigated viral, bacterial or parasite transmission between farmed and wild populations. In all cases, the authors concluded that the risk of infection was either unchanged or elevated by interactions between farms and wild fish populations. Of the 11 studies that quantified changes in infection levels with the presence of active fish farms, all found higher levels of infection in farm-associated wild fish, with a mean

16x increase overall (RR = 2.1, t10 = 5.5, p = 0.0003). This large effect was primarily driven by

26 eight studies of sea louse infection loads on wild salmonids near salmon farms (3.5-73x increase, RR = 2.5). One study reported higher infection densities of external parasites but lower densities of internal parasites in farm-associated gadids, probably as a result of consuming fewer infected wild fish and invertebrates in favour of commercial feed (Dempster et al. 2011). Three studies provided molecular evidence for likely viral or bacterial transmission between cultured and wild fish in the Mediterranean Sea (Zlotkin et al. 1998; Diamant et al. 2000; Colorni et al. 2002), and a molecular analysis of stomach contents revealed that wild cod consumed escaped salmon stock infected with piscine reovirus (Glover et al. 2013). However, molecular evidence did not always support the transmission hypothesis: Mladineo et al. (2009) reported that monogenean and isopod parasites were not transmitted between wild and farmed fish at one Mediterranean Sea farm.

Survivorship and fertility rates

Only two studies in our database estimated differential mortality rates in farm-associated fauna. Kilambi et al. (1978) used age structure to infer a 21 % increase in survivorship of largemouth bass following the establishment of cage culture in a freshwater lake, while in contrast, Broyer et al. (2017) recorded 39 % higher mortality of ducklings at fish ponds. In sea cage systems, elevated external parasitism rates (especially sea louse infections on salmonids) may increase mortality in farm-associated fish, but to our knowledge, differential mortality between farm and reference sites has not yet been empirically demonstrated. A further six studies quantified culling of numerous birds at farms but did not compare mortality rates at farms to those at reference sites. Two others reported dolphins being accidentally drowned in anti-predator nets (Kemper & Gibbs 2001; Diaz López & Bernal-Shirai 2007), but again, did not benchmark these against natural mortality rates. Several studies noted higher fishing effort adjacent to sea cages, although we are only aware of two studies that quantified fishing effort and catch rates (Akyol & Ertosluk 2010; Bacher & Gordoa 2016), and none assessed fishing mortality rates among farm-associated fish.

Estimates of fertility (i.e. reproductive success) for wildlife at farms are similarly rare, but two recent examples were returned by our search, both documenting probable ecological traps: Kloskowski (2012) reported that fledging rates of grebes nesting on fish ponds stocked with +1 carp were only 37 % of those nesting on unstocked ponds, while Broyer et al. (2017) found

27 that high food availability was outweighed by high predation rates for ducks nesting on stocked ponds (Table 1).

Conflict with aquaculture operations

Birds were usually predators of stock. 45/53 studies that documented interactions with birds considered predation on stock to be the major habitat use, whether in cages, ponds, shellfish beds or longlines. The most common avian predators were cormorants and herons. 24/38 studies of interactions with wild mammals considered predation to be the major habitat use, in most cases by otters in ponds or sea cages (12 studies) or pinnipeds in sea cages (10 studies). Five studies reported herbivorous fishes inhabiting algae farms, but only one presented clear evidence of fish consuming algal crops (Anyango et al. 2017). One study reported predation of farmed mussels by wild fish (Segvic-Bubic et al. 2011), while three reported snakes taking stock from hatchery ponds (Plummer & Goy 1984; Gregory & Nelson 1991; Nelson & Gregory 2000).

Of the 77 studies that reported predation on stock or damage to infrastructure, only 11 quantified stock losses as a proportion of potential production, with a mean loss of 15 % (range 0-50 %). The lower end of that range was due to mammals taking only dead or moribund fish from hatcheries (Pitt & Conover 1996), while the upper was due to predation by cormorants in fish ponds (Barlow & Bock 1984). Other studies quantified consumption of stock by individual predators without placing it in the context of potential production (e.g. Glahn et al. 1999). In addition to predating stock, pinnipeds were reported to damage nets and cause fish escapes (e.g. Güçlüsoy & Savas 2003; Sepúlveda & Oliva 2005).

DISCUSSION

Responses to aquaculture by wildlife vary greatly across taxonomic groups and culturing systems, but our systematic review and meta-analysis reveals several key and well-supported trends within taxonomic groups and culturing systems and identifies clear knowledge gaps to inform future research.

Are wildlife attracted to aquaculture?

Fish

Multiple lines of evidence suggest that many fish species prefer aquaculture sites over natural habitats, and on average, farms are associated with a much higher density and diversity of wild fish. The few available tracking studies indicate that farm-associated wild fish tend to be either residents or regular visitors (Otterå & Skilbrei 2014; Arechavala-Lopez et al. 2015a; Loiseau et al. 2016; Tsuyuki & Umino 2017), with spilled feed and waste likely to be the major attractive cues driving wild fish aggregations (Tuya et al. 2006; Ballester-Moltó et al. 2015; Bacher et al. 2015). Effects on fish abundance and diversity are also likely to depend on the functional group being assessed, with most surveys of fish populations at farms and reference sites targeting mobile generalist carnivores (either by design or through choice of sampling method).

Birds

Studies of bird abundance revealed highly variable responses to farms, but our meta-analysis indicates that aquaculture sites are associated with higher bird species richness overall. Numerous studies documented large bird populations at farms without comparing them to natural waterways, making it difficult to draw conclusions about the influence of farms on the spatial distribution of wildlife. Furthermore, little work has been done to assess responses at the individual level (i.e. migration or site fidelity) that can assist in inferring habitat preferences (Robertson & Hutto 2006), but it is likely that many bird species (especially herons, cormorants and gulls) find the availability of prey at fish and crustacean farms highly attractive (Barlow & Bock 1984; Stickley et al. 1992, 1995; Carss 1993; Glahn et al. 1999; Harrison 2009). Shellfish farms also increase local abundance of generalist or molluscivorous bird functional groups (Roycroft et al. 2004; Kirk et al. 2007), but others, such as invertivorous wading birds, may be displaced by shellfish farm infrastructure or associated ecological changes (Kelly et al. 1996; Godet et al. 2009; Broyer & Calenge 2010).

Mammals

Marine mammals (pinnipeds and dolphins) also showed highly variable responses to the presence of aquaculture, ranging from resident nuisance animals (Pemberton & Shaughnessy 1993; Hume et al. 2002; Güçlüsoy & Savas 2003; Sepúlveda and Oliva 2005), to periodic visitors (Diaz López 2012, 2017; Díaz López and Methion 2017), to active avoidance of farms (Markowitz et al. 2004; Watson-Capps & Mann 2005; Pearson 2009; Becker et al. 2011). Otters were common at freshwater fish ponds (Kloskowski 2005; Kortan et al. 2007) and estuarine sea cages in Europe (Freitas et al. 2007; Sales-Luis et al. 2009), but our search did not reveal any data on abundance or attraction to farms relative to natural waterways.

How does aquaculture affect fitness of wildlife?

Fish

Our meta-analysis indicated that farm-associated fish tend to be larger and heavier, a finding that is consistent with either aggregation of adult fish or higher growth rates due to a trophic subsidy. This larger average size, together with greater abundance overall, results in a very high local biomass of farm-associated wild fish. Despite this, farm-associated fish had similar or higher body condition metrics and rates of stomach fullness than fish from reference sites (Fernandez-Jover et al. 2007a; Dempster et al. 2011), indicating that farm environments may have a higher carrying capacity for wild fish than reference sites. However, any potential positive effects—such as higher reproductive potential—may be opposed by orders of magnitude higher infection loads near farms (especially sea lice on salmonids: Krkošek 2017) and possible impacts of a dietary shift from marine-derived to terrestrially-derived fatty acids in commercial aquaculture feed (Lavens et al. 1999; Mazorra et al. 2003; Salze et al. 2005; Bogevik et al. 2012; Arechavala-Lopez et al. 2015b). Little is known about how the plurality of environmental changes at farms combine to influence survival and reproduction in wild fish. Mortality rates are difficult to measure directly, but Kilambi et al. (1978) compared age structure and recapture rates in a lake before and after the commencement of cage farming and inferred that survivorship had increased with farming.

In this study, we only assessed direct interactions between aquaculture activities and wildlife, but indirect interactions also occur, and are likely to have a considerable bearing on outcomes for fish populations in farming areas. Dietary shifts may occur indirectly via benthic nutrient loading and subsequent ecological changes across multiple trophic levels (Brown et al. 1987; Wu 1995; Yucel-Gier et al. 2007; White et al. 2017), and potential deleterious effects of direct or indirect dietary shifts or other changes may be most apparent in eggs or offspring of farm- associated fish (Salze et al. 2005; Barrett et al. 2018). Aggregations of large predators around sea cages may also reduce survivorship of fishes that inhabit the same area (Güçlüsoy & Savas 2003). Fish that escape from farms can reduce fitness in native populations through disease transmission (Arechavala-López et al. 2013; Glover et al. 2013), genetic mixing (Glover et al. 2017), and interference with spawning or competition with offspring and adults (Jensen et al. 2010; Sundt-Hansen et al. 2015).

Birds

In birds, the effects of farm proximity on fitness are even less clear; only in a few cases were we able to extract usable data on direct or indirect fitness metrics. Numerous studies reported birds taking stock from ponds and cages, but none in our database compared feeding rates to those on natural waterways. Nonetheless, we expect food availability to be high provided that birds are able to access suitable food items (e.g. feed, stock or wild prey co-occurring at farms). However, predatory birds also experience high mortality from culling and anti-predator net entanglements where such methods are employed, potentially causing fish farms to act as ecological traps for birds if mortality rates outweigh any benefits of higher food availability (Carss 1994; Belant et al. 2000; Blackwell et al. 2000; Bechard & Márquez-Reyes 2003; Quick et al. 2004). Negative effects will be exacerbated if food availability is lower than advertised, for example if piscivores are attracted to fish ponds but cannot access fish due to anti-predator nets, or if stocking regimes lead to cohorts of fish that are too large to be consumed. This latter scenario was observed by Kloskowski (2012), who reported that European carp farms were acting as ecological traps for red-necked grebes, as farmed fish were too large for fledglings to consume leading to starvation. Predation risk for clutches may also be elevated at farms: Broyer et al. (2017) observed high densities of breeding pairs and high food availability, but also high offspring mortality – a probable ecological trap. Conversely, tuna ranching in Australia was associated with a population boom for silver gulls and appears to be a clear case

31 of fish farms functioning as a strong population source for wildlife – reproductive success for the gulls was dramatically increased by the trophic subsidy obtained by exploiting farm feed (Harrison 2009). Similarly, long term trends in wading bird populations closely tracked the scale of crayfish aquaculture in the southern USA (Fleury & Sherry 1995).

Mammals

Effects of aquaculture on mortality and reproduction of aquatic mammals are little known, but as with piscivorous birds, are probably determined by the trade-off between high food availability and high risk from culling and entanglements. Cetaceans may benefit from easy food when they visit farms (Diaz López 2017) and culling and entanglements are relatively rare (Diaz López & Bernal-Shirai 2007; Callier et al. 2017). As a result, attraction to farms may be an adaptive trait that results in increased fitness on balance, although we lack direct evidence for this. In contrast to cetaceans, pinnipeds experience heavy mortality from culling (Güçlüsoy & Savas 2003; Quick et al. 2004; Callier et al. 2017) and are more vulnerable to accidental entanglement (Callier et al. 2017). High mortality rates are likely to outweigh any increase in food availability for a long-lived, slow breeding animal, such that seals that are attracted to farms may be vulnerable to ecological traps driven by culling at farms.

Conflict and potential mutualism between aquaculture and wildlife

Our meta-analysis revealed that the nature of interactions between wild fauna and aquaculture were highly dependent on the taxon. Wild fish generally do not interact directly with stock unless small enough to enter sea cages through the mesh (although in rare cases wild fish may damage nets: Moe et al. 2007, Sanchez-Jerez et al. 2008). Of more concern is the role that wild populations play as reservoirs for pathogens and parasites, facilitating reinfection of farms (Uglem et al. 2014b). This is an inevitable risk of farming in open systems, but research is underway to lower infection rates by minimising spatiotemporal overlap between stock and zones of high infection risk (Samsing et al. 2016; Wright et al. 2017). Together with post-infection treatments, such measures also minimise the role that farms play as amplifiers of pathogen and parasite populations.

Table 2.1. Mean effects of aquaculture sites on wildlife populations. F:W = mean at farms / mean at reference sites. RR = ln(F:W). Positive RR indicates metric is higher at aquaculture sites. t-stat and p refer to one sample t-test comparing RR data to null expectation of RR = 0. Taxa are omitted where no comparative data are available. Asterisk indicates variables for which higher RR corresponds to poorer outcomes.

N F:W RR ± SE t-stat p Abundance Fish 44 72 1.65 ± 0.29 5.7 <0.0001 Birds 13 1.8 0.13 ± 0.31 0.40 0.70 Mammals 7 1.1 -0.68 ± 0.67 -1.0 0.35 Amphibians 1 0.31 -1.17 – – All taxa 65 49 1.05 ± 0.24 4.3 <0.0001

Species richness Fish 28 2.0 0.43 ± 0.11 3.9 0.0005 Birds 7 1.1 0.13 ± 0.04 3.3 0.02 Mammals 1 0.50 -0.69 – – Amphibians 1 0.32 -1.15 – – All taxa 37 1.7 0.30 ± 0.10 3.1 0.004

Size (length) Fish 18 1.2 0.15 ± 0.03 4.6 0.0002

Size (weight) Fish 12 1.7 0.40 ± 0.13 3.0 0.01

Condition metrics Fish 14 1.3 0.17 ± 0.09 1.9 0.08 Birds 1 0.7 -0.31 – – All taxa 15 1.2 0.14 ± 0.09 1.5 0.15

Stomach fullness Fish 10 1.4 0.04 ± 0.30 0.13 0.90 Reptiles 1 1.9 0.66 – – All taxa 11 1.5 0.10 ± 0.28 0.35 0.73

Infection level+ Fish 11 16 2.09 ± 0.38 5.5 0.0003

Fertility Birds 2 0.60 -0.60 ± 0.40 -1.5 0.37

Mortality+ Fish 1 0.82 -0.20 – – Bird 1 1.4 0.33 – –

Most studies returned by our search concluded that predation or damage by birds and mammals is an ongoing problem for managers, but stock losses were rarely quantified (but see some recent examples: Sun et al. 2004; Sepúlveda & Oliva 2005; Morrison & Vogel 2009; Dorr et al. 2012) Where suitably habituated, pinnipeds have a propensity to become ‘nuisance animals’, damaging nets (leading to fish escapes) and consuming or stressing stock (Kemper et al. 2003; Quick et al. 2004; Sepúlveda & Oliva 2005). Such problems are difficult to solve. Culling is undesirable as it increases environmental impacts and negatively affects public perceptions of aquaculture. Relocation is expensive and often ineffective (Hume et al. 2002) and scaring devices have a limited effective lifespan before animals are desensitised. Exclusion using steel mesh appears to be the only viable option in some cases (Pemberton & Shaughnessy 1993).

While there tends to be a focus on negative interactions between farms and wild fauna, wild fish can provide ecosystem services to aquaculture operations by increasing animal welfare and reducing local environmental impacts of farming. Invertivorous fish that are small enough to gain access to sea cages (such as wrasse and lumpfish in Norwegian salmon farms) can act as cleaner fish and significantly reduce parasite loads on stock. Cleaner fish are now being deployed in large numbers for this purpose (Imsland et al. 2014; Skiftesvik et al. 2014). Wild fish and invertebrates ameliorate and disperse benthic nutrient loads by consuming spilled feed, faeces and dead stock (Vita et al. 2004; Felsing et al. 2005; Fernandez-Jover et al. 2007b). However, resident populations of large predators at fish farms may impede this waste amelioration service by scaring or consuming wild fish (Diaz López 2006), resulting in more severe benthic impacts, but such predators also prey on escaped fish, potentially reducing the risk of genetic introgression from farmed to wild fish populations (Glover et al. 2017).

For fish farming to continue to grow, farmers need to demonstrate environmental sustainability and good animal welfare standards. Protecting wild fish aggregations to take advantage of the ecosystem services they provide may be an important part of achieving these goals. Continued development of non-lethal bird and pinniped exclusion methods will be a necessary step.

Recommendations for future research on impacts of aquaculture on wildlife

Simply documenting behaviour of wildlife at farms or changes in wildlife abundance provides little information on the effects of aquaculture on persistence of wildlife populations.

Aquaculture can have qualitatively distinct effects on wildlife that are superficially indistinguishable in the absence of data on habitat selection decisions, movement or fitness. For example, an elevated density of wildlife at a farm relative to a reference site may support various contradictory hypotheses, including but not limited to: (a) high survivorship or fertility causing the farm to function as a productive population source, with or without strong attraction, and typically with density-dependent spillover to surrounding areas (Pulliam 1988), or (b) strong attraction to the farm habitat but high mortality rates or low reproductive success for residents. The latter scenario describes an attractive population sink (ecological trap) that draws animals in from surrounding areas and causes deleterious population effects disproportionate to its area (Hale et al. 2015). Our meta-analysis reveals that in most cases we do not have sufficient data on fitness outcomes, either direct or indirect, and as a result cannot distinguish between attractive or productive population effects, or their resultant positive or negative effects on wild populations.

Conceptual frameworks have been developed to distinguish between these two (non- mutually-exclusive) processes on artificial reefs and fish aggregation devices (Osenberg et al. 2002; Brickhill et al. 2005; Reubens et al. 2013), and may be applied to aquaculture sites. Evidence for attraction without significant production of wild fauna at aquaculture sites may include: (i) rarity of younger cohorts relative to older cohorts, (ii) population declines at adjacent reference sites corresponding to increases at farms, (iii) high mortality or reproductive failure rates at farms, or (iv) tracking, microchemistry, tissue fatty acid or stable isotope analysis indicating recent immigration to farms. Conversely, evidence for high individual fitness leading to productive wild populations at farms may include, depending on the taxa: (i) successful breeding pairs residing at farms, (ii) high densities of larvae or juveniles, (iii) increases in abundance at farms followed by increases at adjacent reference sites consistent with density-dependent spillover, (iv) tracking, microchemistry, tissue fatty acid and stable isotope analysis indicating that most individuals are not recent immigrants.

Importantly, the above criteria for separating attraction and production are most relevant when compared to reference habitats (i.e. is residing at a farm a good decision for an individual, or likely to lead to an ecological trap?). Only 91/204 studies included in our database allowed us to infer changes in at least one variable in farm-associated wildlife by making comparison to reference sites or timepoints. In many cases, changes in distribution or health of wildlife were not central to the study, but in other cases, there was a lost opportunity to understand more about these interactions. Where relevant, we recommend that studies of abundance or fitness of wild fauna at farms should benchmark their findings against reference

35 sites or timepoints (Underwood 1994; Osenberg et al. 2002; Brickhill et al. 2005). Reference sites should be appropriate for the hypotheses being tested. For example, our meta-analysis revealed that inferred increases in population densities at sea cage fish farms vary by orders of magnitude depending on whether the reference habitat is a nearby natural reef or featureless open water. Accordingly, researchers should be clear in their reasons for selecting a given reference habitat.

Most importantly, we have highlighted the paucity of data on mortality rates and reproductive success in farm-associated fauna. Such data are central to our understanding of the environmental impacts of aquaculture but can be difficult to obtain. Population-level metrics can be effective in closed or semi-closed systems (Kilambi et al. 1978), and researchers have long been capable of tracking mortality and breeding success in birds, including at aquaculture sites (Kloskowski 2012; Broyer et al. 2017). Open systems with highly mobile taxa (such as wild fish associated with sea cage aquaculture) present a greater challenge, but in coastal environments, acoustic tags in conjunction with external tags can provide excellent data on spatiotemporal movement and mortality rates in areas with differing levels of farm activity (Olsen & Moland 2011; Olsen et al. 2012; Fernández-Chacón et al. 2015).

It is now well established that wild fish are typically more abundant at sea cage fish farms than reference sites, and that such fish are likely to consume farm waste, experience nutritional shifts and depending on the system, be exposed to elevated parasite loads. The challenge now is to develop an equivalent state of knowledge for other wild taxa and aquaculture systems, and to obtain more direct measures of the effects of farm-association on wildlife populations.

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CHAPTER THREE: LIMITED EVIDENCE FOR DIFFERENTIAL REPRODUCTIVE FITNESS OF WILD ATLANTIC COD IN AREAS OF HIGH AND LOW SALMON FARMING DENSITY

ABSTRACT

Sea cage fish aquaculture attracts large aggregations of wild fish that feed on farm waste. Fish that associate closely with farms undergo physiological changes, and captive feeding trials indicate possible negative effects on reproductive fitness. However, little is known about the significance of this phenomenon for reproduction in wild fish over larger spatial scales. To assess if coastal areas with intensive aquaculture impact the fitness of wild fish, we collected Atlantic cod (Gadus morhua) from two areas of high and low salmon farming density (HFD and LFD respectively) in south-western Norway, a region that hosts the world’s largest coastal fish aquaculture industry. We conducted a captive spawning trial and compared a range of reproductive fitness metrics. Two fatty acids that occur at high levels in commercial feed, oleic and lineoleic acid, were strongly correlated in the ovaries of HFD fish, but a comparison of lipid profiles between HFD and LFD fish showed no differences in total lipids or essential fatty acids. Although HFD fish were slightly larger than LFD fish and had similar body condition, the volume of eggs produced did not differ, indicating relatively lower reproductive investment by HFD fish. HFD eggs were 5 % smaller than LFD eggs, which did not lead to differential hatching success but may have contributed to HFD larvae being 8 % smaller than their LFD counterparts at 40 days post spawning. The potential for cumulative effects of smaller eggs and larvae on fitness at later life stages warrants further investigation.

INTRODUCTION

Intensive culture of fish within sea cages leads to considerable benthic nutrient loads via spilled feed and waste, providing a trophic subsidy that attracts large and persistent aggregations of ‘farm-associated’ wild fish (Dempster et al. 2002, 2009, 2010). Individuals may reside in the vicinity of farms for months to years (Uglem et al. 2009, Otterå & Skilbrei 2014), and during this time are exposed to a variety of environmental changes including elevated infection risk from parasites and other pathogens (Dempster et al. 2011, Johansen et al. 2011, Arechavala-López et al. 2013, Glover et al. 2013), artificial lighting regimes that may delay

50 maturation and alter behaviour (Porter et al. 1999, Hansen et al. 2001, McConnell et al. 2010, Otterå and Skilbrei 2014, Skilbrei and Otterå 2016, reviewed in Trippel 2010), contamination from chemicals and metals used in aquaculture (Burridge et al. 2010), elevated predation risk due to the abundance of large predatory fish, and where permitted, fishing mortality (Akyol & Ertosluk 2010, Bagdonas et al. 2012). However, perhaps the most obvious change is the high availability of waste feed that typically results in higher somatic and gonadal condition indices for farm-associated wild fish (e.g. Dempster et al. 2011). 67-90% of fish sampled near Spanish sea cages had consumed pellets (Fernandez-Jover et al. 2008), while pellets made up 71 and 25%, respectively, of the diet for farm-associated saithe and cod in Norway (Dempster et al. 2011).

Superficially, this trophic subsidy appears to benefit wild fish, but it also results in a dietary shift from marine-derived highly-unsaturated omega-3 fatty acids to terrestrially-derived omega-6 fatty acids. This in turn translates to a compositional shift in tissues (Fernandez-Jover et al. 2007, Fernandez-Jover et al. 2011, Olsen et al. 2014, Arechavala-López et al. 2015), and given that dietary lipids are reflected in the egg stores (e.g. Lavens et al. 1999, Czesny et al. 2000, Salze et al. 2005), may result in deficiencies in several of the fatty acids required for reproduction and development. For example, captive feeding trials have found that essential fatty acids 20:5 n-3 (eicosapentaenoic acid, EPA) and 22:6 n-3 (docosahexaenoic acid, DHA), as well as 20:4 n-6 (arachidonic acid, AA), are important for fecundity, egg and sperm quality, hatching success and larval development in fish (Reitan et al. 1994, Rainuzzo et al. 1997, Sargent et al. 1999, Sargent et al. 1999, Rahman et al. 2014). Dietary deficiencies in these reproductive nutrients contribute to low fertilisation and hatching rates in Atlantic cod (Gadus morhua) broodstock relative to their wild counterparts (Salze et al. 2005) and have been linked to changes in egg quality metrics in turbot (Scophthalmus maximus) (Lavens et al. 1999), Atlantic halibut (Hippoglossus hippoglossus) (Mazorra et al. 2003) and cod (Bogevik et al. 2012). Furthermore, while data are lacking for fish, evidence from other taxa indicate that additional effects may become apparent only after fertilisation; sea urchins (Echinus acutus) reared on commercial salmon feed had higher gonad indices but lower fertilization and larval survival rates, leading to an overall reduction in reproductive fitness (White et al. 2016, White et al. 2017a).

The net effect of farm proximity on fitness, whether positive or negative, has a considerable bearing on wild fish populations in farming areas. If trophic subsidies and associated conditions provide a net fitness benefit, farm-associated fish populations will experience higher production than those in neighbouring areas, and farms will act as a population source.

However, because farms are highly attractive to many fish species, any decline in fitness in farm-associated wild fish may cause farms to function as ‘ecological traps’ (Robertson & Hutto 2006, Hale & Swearer 2016). Ecological traps are attractive but low quality habitats that can have significant metapopulation-level impacts by drawing in individuals from higher quality adjacent habitats, thus acting as attractive population sinks (Hale et al. 2015, Hale & Swearer 2016). To demonstrate the existence of an ecological trap, we must show that (1) individuals prefer or show equal preference for the putative trap habitat relative to other available habitats, and (2) that fitness outcomes in the putative trap habitat are lower than they would have been in the other available habitats (Robertson & Hutto 2006, Patten & Kelly 2010). We have strong empirical evidence that ecological traps affect birds and mammals in modified terrestrial and aquatic environments (e.g. Remeš 2003, Weldon & Haddad 2005, Balme et al. 2010, Kloskowski 2012), but to date there have been very few tests of the theory in the marine environment (Hallier and Gaertner 2008, Dempster et al. 2011, Sherley et al. 2017), and none that have directly assessed fitness.

Norway operates the largest coastal fish aquaculture industry in the world, with Atlantic salmon production reaching ~1.4 million tons in 2016 (Norwegian Directorate of Fisheries 2016). Fjord cod stocks, which are distinct from the more mobile offshore cod stocks (Robichaud & Rose 2004), coexist with salmon farming in southern Norway and are at historically low levels, with recent recruitment rates also low (Knutsen et al. 2016). Given these concerns, the population can ill afford a potential ecological trap scenario. The >300 active salmon farms in south-western Norway have widespread effects on fish movement and population distribution and represent hundreds of potential ecological traps for fjord cod. Gadid populations are strongly attracted to salmon grow-out cages (Dempster et al. 2011) and move from farm to farm (Uglem et al. 2009, Otterå & Skilbrei 2014). This satisfies the first component of an ecological trap assessment by showing preference for the putative trap habitat (Robertson & Hutto 2006). However, we have no data on direct fitness measures in any farm-associated fish, including gadids.

Farms may impact fitness in wild fish by altering either the survival or reproductive success of individuals. Both pathways are difficult to measure directly in marine fishes, particularly highly mobile broadcast spawning species, and as a result most previous work has relied on proxies such as body condition (Fernandez-Jover et al. 2007, Dempster et al. 2011). Here we employ a more direct approach by conducting a captive spawning experiment with wild-caught Atlantic cod, to investigate whether reproductive fitness differs between areas of high and low salmon farming intensity in south-western Norway. We assess potential reproductive effects spanning

52 initial adult body condition, ovarian fatty acid profiles and volumetric egg production, through to egg quality metrics, hatching rates, and larval quality metrics including growth rates, deformity rates and behaviour.

MATERIALS AND METHODS

Experimental design and fish husbandry

The density of salmon farms throughout Norway means that there are no longer any true reference sites for the impacts of salmon farming on highly mobile wild fish in most parts of the country. Instead, we make a comparison between two areas with differential farm density, reflecting the typical spectrum of farm exposure for wild fish populations. We collected two groups of mature live wild Atlantic cod (Gadus morhua) from the outer fjords of Hordaland county in south-western Norway during February 2016. The first group (High Farm Density; HFD) was collected by commercial fishers and technical staff from six locations along an 8 km stretch of coastline in the Austevoll archipelago and 4 km away at Reksteren (Fig. 3.1). HFD collection locations were all in relatively close proximity (300–1200 m) to six active salmon farms, in an area of generally dense farming activity. The collections took place over ten days between 1-15 February. The second group (Low Farm Density; LFD) was collected by commercial fishers from two locations within the Bømlo archipelago, a neighbouring area with relatively little fish farming activity (Fig. 3.1), between 5–15 February. HFD fish were collected from sites exposed to a mean of 4.0 ha (range 1.5–5.2 ha) of sea cage surface area within 4 km of their collection location, while LFD collection sites were exposed to mean 0.8 ha (range 0– 3.2 ha) within 4 km. These estimates include all sea cages and holding pens, including enclosures that were unstocked at times during 2015 (for locations of active salmon grow-out cages relative to collection sites, see Fig. 3.1). 75 % of LFD fish were collected ~9 km from the nearest salmon cages, although it should be noted that the remaining 25 % of LFD fish were collected within 2 km of a small salmon farm (2340 t capacity). As all fish were pooled within groups, this is likely to reduce overall effect sizes observed in this study. We judged the farm density within 4 km of the collection site to be a suitable metric of farm exposure based on telemetry data describing the movements of tagged wild coastal cod in Norway and Shetland (Neat et al. 2006, Svåsand et al. 2008, Uglem et al. 2008), but even if some proportion of fish move larger distances, a considerable difference in the average level of farm exposure

53 between HFD and LFD groups will be maintained (Fig. 3.1). These two areas are otherwise comparable environments in terms of gross hydrology, geology and ecology. Several logistical factors prevented us from including additional sampling areas. Specifically, our need to collect both HFD and LFD fish from shallow water to minimise barotrauma reduced the number of suitable locations, as farms are typically placed in much deeper water. We were also restricted to areas with similar latitude and water temperature to minimise temporal effects on spawning (Kjesbu et al. 1994). In consideration of this geographic restriction, we consider our results to be representative of outer Hardangerfjord but with relevance for similar systems worldwide.

Figure 3.1. Map of collection sites relative to active salmon farms in south-western Norway. Active outgrowing locations are taken from Norwegian Directorate of Fisheries aquaculture biomass geodata.

Fish were captured using gill nets and fyke nets over reef or mixed sand-reef substrates at 5 - 30 m depth, and held in marine net pens, unfed, until the commencement of the experiment. The experiment was conducted at the Austevoll Research Station, Norwegian Institute of Marine Research (IMR). On 24 February 2016, we sedated all fish using 20 g L-1 tricaine methanesulfonate (MS-222: Finquel). Fish with unhealed injuries or other welfare concerns

54 were killed with a blow to the head while sedated. We recorded length, wet weight and sex of the remaining fish, inserted a Passive Integrated Transponder (PIT) tag into the peritoneal cavity of every fish (allowing us to track individual weight loss between the start and finish of spawning), and took ovarian biopsies from all females for storage at -80 °C. Within LFD and HFD groups, 54 females and 24 males were allocated randomly among six cylindrical 7 m3 tanks per group (nine females and four males per tank, total 12 tanks).

Tanks were supplied with 6-8 °C sand-filtered and aerated seawater from 168 m depth and exposed to a natural photoperiod through light-reducing shades. Hatchery facilities at IMR Austevoll are described in detail by Karlsen et al. (2015). Winter spawning typically occurs in the dark, during the early hours of the morning every 2-3 days from February to April. Each spawning tank was appended with a cylindrical 100 L egg collection tank that filtered the full volume of the spawning tank outflow via outlets at the top and bottom of the water column. A constant circular flow was maintained within the egg collector to prevent eggs from being pressed against the filter and damaged. The egg collectors were emptied every morning for the duration of spawning and the volume of floating and sinking eggs was recorded.

Fish in this experiment were not fed for the duration of captivity to better preserve any effects of diet prior to capture and to prevent clogging of egg collectors with waste matter. Reduced feeding and significant weight loss is typical for both wild and captive cod during spawning (Lambert & Dutil 2000), but to improve animal welfare, we removed fish from the experiment early if their body condition dropped below acceptable levels (indicated by loss of muscle mass, cessation of normal swimming behaviour, or unhealed wounds). Eggs were present after the first night of egg collection, indicating that spawning had already begun. Accordingly, some caution must be applied to interpretations of egg production and quality. However, the two groups were collected at similar latitudes and housed in almost identical temperatures (the main determinant of spawning commencement time: Kjesbu 1994), while temporal trends in egg production do not suggest differential start or end times between groups (Fig. 3.4). Egg production declined gradually toward the end of the spawning season, with the season considered complete when all tanks had failed to produce viable eggs for four consecutive days. All fish were humanely killed with a blow to the head while sedated with 20 g L-1 MS-222 before recording final length and weight.

On two occasions during the spawning season (3-4 March and 24 March), we took eggs for incubation and hatching. Up to 350 mL eggs per tank were disinfected in 400 mg L−1

55 glutaraldehyde for 8 min to limit harmful bacterial growth (Harboe et al. 1994), and transferred to 70 L black polyethylene conical incubators (one per spawning tank) with 0.5 L min-1 seawater flow at 6 °C. Each morning during incubation, dead eggs were drained from the bottom of the incubator and measured volumetrically. On 22 March, when the majority of eggs from the first collection had hatched, we took approximately 6000 larvae from each incubator and divided them across duplicate 50 L larval feeding tanks (24 tanks in total) at 8 °C. Larvae remaining in the incubators were killed with a lethal dose of MS-222. Larvae were fed size-fractionated copepod nauplii collected from the IMR sea-pond facility at Svartatjern. Techniques for harvesting and preparation of copepods for rearing of cod larvae are described in detail by van der Meeren et al. (2014) and Karlsen et al. (2015). Twice daily, larval rearing tanks received 150000 nauplii, with 1.5 mL of algal paste per tank added to improve feeding performance (Naas et al. 1992). The experiment was concluded on 13 April, with early season larvae at day 42 and late season larvae at day 21 post fertilisation.

Ovarian fatty acid composition

We randomly selected ten ovarian samples per group (LFD and HFD) from the biopsies stored at the beginning of the experiment. The samples weighed 60-100 mg each. All samples were methylated and the respective fatty acid methyl esters were analysed on a HP-7890A gas chromatograph (Agilent, USA) with a flame ionization detector (GC-FID), according to the method described in Meier et al. (2006). The fatty acid 19:0 was added as an internal standard and 2.5 M dry HCl in methanol was used as a methylation reagent. The methyl esters were extracted using 22 mL of hexane, and the solution diluted or concentrated to obtain a suitable chromatographic response. 1 μL was injected splitless (the split was opened after 2 min) with the injection temperature set to 270 °C. The column was a 25 m × 0.25 mm fused silica capillary, coated with polyethylene-glycol of 0.25 μm film thickness, CP-Wax 52 CB (Varian-Chrompack, Middelburg, The Netherlands). Helium (99.9999 %) was used as mobile phase at 1 ml/min for 45 min, then 3 ml/min for 25 min. The temperature of the flame ionization detector was set at 300 °C. The oven temperature was programmed to hold at 90 °C for 2 min, then heated to 150 °C at 30 °C/min and then to 240 °C at 2.5 °C/min and held steady for 30 min. Total analysis time was 70 min. Seventy well-defined peaks in the chromatogram were selected, and identified by comparing retention times with a fatty acid methyl ester standard (GLC-463 from Nu-Chek Prep. Elysian, MN, USA) and retention index maps and mass spectral libraries (GC-MS) (http://www.chrombox.org/index.html) performed under the

56 same chromatographic conditions as the GC-FID (Wasta & Mjøs 2013). Chromatographic peak areas were corrected by empirical response factors calculated from the areas of the GLC-463 mixture. The chromatograms were integrated using the EZChrom Elite software (Agilent Technologies). Only the 39 fatty acids that contributed more than 0.1 % of the total fatty acid amount were included in the calculation. The total amount of fatty acids and cholesterol was calculated using the internal standard 19:0.

Reproductive fitness traits

Body condition and weight loss

The amount and quality of egg production is likely to depend on fish condition. To allow quantification of any such relationship, we compared body condition between LFD and HFD groups (within sexes) using the relative condition index Krel = 100*(W/ExpW), where W is the measured wet weight of the individual and ExpW is the expected weight (LeCren 1951). The expected weight was calculated using a power function of the form ExpW = aLb fitted to the full dataset including both LFD and HFD fish (Fig. 3.2). In this case, a = 0.0272 and b = 2.76 (R2 = 0.89). We also calculated proportional weight loss between the start and end of the spawning season. This provides a general index of relative reproductive investment that is typically closely correlated with egg production (e.g. Kjesbu et al. 1996).

Egg production

We quantified volumetric egg production in terms of daily egg production per tank, both with and without a correction for the size of the females within a given tank, as well as any loss of females during the season. Corrected egg production (relative daily egg production; RDEP) was calculated as follows: RDEP = eggV / CFL, where eggV is the volume (mL) of eggs collected from the tank, and CFL is the combined length (cm) of all females in the tank at the time of egg collection. Four females (all HFD) experienced ≤10% weight loss, indicating little or no egg release.

Egg quality

Viability, fertilization and early development

Egg viability rates were estimated from the proportion of eggs that were floating when the egg collectors were emptied each morning. Fertilisation rates were assessed in subsamples of 100 eggs per tank on four occasions (three occasions for one HFD tank that finished spawning early) during the spawning season (early season: 3-4 and 8-9 March, late season: 17 and 24-25 March), as egg quality typically declines during the spawning season (e.g. Kjesbu et al. 1996). Where egg production was low on a given occasion, we combined fertilisation estimates from two successive days. Eggs were scored as fertilised and normal (symmetrical cell divisions), fertilised and abnormal (asymmetry or other abnormalities), or unfertilised.

Egg size and variability

Mean egg size and variability was measured on two occasions (3-4 March and 11 April) during the spawning season. Eggs were taken from the egg collectors, stored in 6 °C seawater for 2-3 hours, and placed on a petri dish and photographed using a digital camera mounted on a light microscope. The images had a 19 mm field of view at a resolution of 1024x768 pixels. We measured the diameter of up to 50 eggs per tank (range 18-50) using the image analysis software package ImageJ (Schneider et al. 2012), calibrated against a micrometer slide.

Hatching success

To estimate the proportion of viable eggs that successfully hatch, duplicate egg subsamples (>100 eggs) were taken from incubators on two occasions, early and late in the season (early season: 10 March, Day 8 of incubation; late season: 1 April, Day 9 of incubation) for a hatching trial. Eggs were rinsed with filtered seawater, placed in covered containers filled with 200 mL of filtered seawater, and maintained at 6-7 °C until hatching was complete. Dead eggs and live and dead hatchlings were scored and removed daily until no viable embryos remained. As there is large variation in growth rates between individual larvae, cannibalism makes it

58 impossible to reliably estimate impacts of farm density on larval survival in this system beyond first feeding.

Larval quality

Larval development and growth rates

Subsamples of at least 30 larvae per tank were collected 40 days after spawning (23-28 days post hatching), killed by a lethal dose of MS-222 and stored at 6 °C until required for photography (<4 hrs). Larvae were transferred to a petri dish with a thin layer of seawater and photographed laterally under dark field illumination on a light microscope with mounted digital camera. The images provided a 12 mm field of view at a resolution of 1024x768 pixels. Larvae were measured for length using the ImageJ measuring tool calibrated against a micrometer slide. The measurement was taken along a polyline running from the tip of the snout to the cranial vertebra and along the spine to the end of the caudal peduncle.

Phototaxis

Development of visual and cognitive systems in larvae (at day 42, ca. 23 days since first feeding) was compared by means of a phototaxis trial in which we tested the proportion of larvae exhibiting behavioural responses to a light gradient. Phototactic responses in this context correlate with larval fitness (e.g. Karlsen and Mangor-Jensen 2001, Forsgren et al. 2013). Approximately 70 larvae (± 16.9 SD) were placed in a 60 x 10 x 5 cm tank and allowed to disperse. We then completely covered the tank with black plastic to block any light. After 8 mins, we removed the covering on a 10 x 10 x 5 cm end section of the tank (thus exposing it to ambient light from a fluorescent bulb) and scored the number of larvae already present in the end section. This was treated as the control score: the number of larvae present in the end section due to random dispersal without stimulus. The tank was then left for a further 5 mins, and the number of larvae in the end section scored again. We then removed the rest of the covering and counted the larvae that had not showed preference for light. The trial was conducted for each of the 12 spawning tanks. The change in spatial distribution (the difference

59 in the proportion of larvae in the end section before and after the light treatment) provided a measure of responsiveness to this environmental stimulus.

Statistical analysis

We compared initial fish size, weight-at-length and condition (Krel) metrics across LFD and HFD groups (Group), using linear analysis of variance models constructed using the lm function in R (R Core Team 2016). Data were log transformed as necessary to improve normality (and to linearise weight-at-length curves). Proportional weight loss was compared between LFD and HFD groups using a beta regression generalised linear model fitted using the betareg package for R (Cribari-Neto & Zeileis 2010).

We conducted several ovarian fatty acid analyses: initally, we made univariate comparisons of total lipids, cholesterol, and aquafeed markers oleic and linoleic acid across LFD and HFD groups using linear analysis of variance models. We then compared the entire suite of fatty acids across groups using a multivariate permutational analysis of variance (Permanova) fitted to a Euclidean dissimilarity matrix using Primer 6 software (Anderson et al. 2008, White et al. 2017c).

Egg production metrics were compared between treatments and over time using negative binomial generalised linear mixed models fitted using the glmmTMB package (Brooks et al. 2017). Group (farm density group) and Day (sampling day) were fitted as fixed effects. As tanks were sampled repeatedly, nonindependence between samples was addressed by including a random intercept term with TankID (tank identity) nested within Group. We report significance of individual model terms within glmmTMB models by comparing the fit of the full model and a null model with the term removed (X2 test on 1 df).

Effects of farm density on egg quality metrics were tested by fitting linear mixed models, also using the glmmTMB package. Egg size data were best fitted with a gaussian model family, while proportion data were fitted using a beta regression family. We included three fixed terms: Group, Time (early or late season collections) and MeanFL (mean female length at the tank level, to account for possible effects of female age on egg quality). As with egg production models, we included the nested random intercept term Group/TankID. Proportion or rate responses were analysed at the tank level. Egg size data were analysed at the egg level, with Group, Time and MeanFL as fixed terms and Group/TankID as a random intercept term.

Following the full analysis described above, egg quality datasets were split into early and late season collections and re-analysed using beta regression models fitted using the betareg package. These results are reported alongside those from the full analyses (Table 3.2), using a z-test of significance for the Group term.

Data on deformity rates, maximum sizes and phototaxis were analysed at the tank level using beta regression models fitted with Group and MeanFL as fixed terms (betareg package). The Time and TankID terms were not necessary as all larvae were reared from eggs collected over a two day period early in the season. Larval size data were analysed at the level of individual larvae using a linear mixed model fitted by glmmTMB, with Group and MeanFL as fixed terms and Group/TankID as a random intercept term.

As tank-level analyses come with a cost to statistical power, we calculated Cohen’s d effect sizes (calculated using individuals as replicates for fish size and condition, and tanks as replicates for all egg and larval quality data) to provide an estimate of effects independent of sample size and statistical significance (Cohen 1988).

Figure 3.2. Weight-at-length relationship for female (left panel) and male (right panel) cod used in this study. The best fitting power function (y = axb) is shown for each combination of sex and group (HFD: red triangles, solid red fitted line; LFD: blue circles, dashed blue fitted line).

RESULTS

Body condition and weight loss

Female Atlantic cod collected from the high farm density environment (HFD) were significantly longer (7%) and heavier (16%) than fish from the area of low farm density (LFD), with no difference in body condition between females from HFD and LFD sites (Table 3.1). HFD males were not significantly different in length or weight from LFD males, but did have a higher body condition than LFD males (Table 3.1). The log-transformed weight-at-length slope was significantly higher for HFD females than LFD females (although the effect was small: partial R2 = 0.06), while weight-at-length did not differ for HFD and LFD males (Appendix 3.2, Fig. 3.2). During the captive spawning season (mean 37 days LFD, 39 days HFD), females lost 29 ± 9% body weight, while males lost 18 ± 6% (mean ± SD). LFD females lost more weight than HFD females (mean ± SE: 30 ± 1.1% and 27 ± 1.2% respectively, z1,9 = 2.4, p = 0.02), consistent with greater reproductive effort by LFD females.

Table 3.1. Body size and condition metrics for low (LFD) and high (HFD) farm density groups at the commencement of the experiment (group means ± standard error). Significant p values are highlighted in bold. Positive direction indicates that metric was higher in HFD fish.

LFD HFD Statdf p Cohen’s d Females n = 54 n = 54 Length (cm) 60.1 ± 1.1 64.6 ± 1.2 F1,106 = 7.2 0.008 +0.49 Weight (g) 2442 ± 145 2839 ± 164 F1,106 = 4.2 0.043 +0.35 Condition (Krel) 1.03 ± 0.03 0.98 ± 0.01 F1,106 = 3.1 0.08 -0.34

Males n = 24 n = 24 Length (cm) 60.3 ± 1.2 62.8 ± 2.0 F1,46 = 0.9 0.34 +0.29 Weight (g) 2204 ± 148 2624 ± 243 F1,46 = 1.7 0.20 +0.37 Condition (Krel) 0.98 ± 0.02 1.04 ± 0.01 F1,46 = 4.7 0.04 +0.63

Ovarian fatty acid composition

We found no evidence that ovarian lipid modification reflected the density of salmon farms, with no significant difference in total fatty acid content or cholesterol content, nor in total saturated, monounsaturated or polyunsaturated fatty acids (Appendix 3.1). Likewise, multivariate fatty acid composition was not significantly different between LFD and HFD

62 groups (pseudo-F18 = 0.58, p = 0.71; Fig. 3.3). Multidimensional scaling (MDS) revealed that three individuals—one LFD and two HFD—were separated from the main cluster. These three fish were not remarkable in size or body condition (57-72 cm, 2022-3620 g, Krel 0.99-1.11), but both HFD fish were high in oleic acid 18:1 (n-9) (15.2 and 17.5%) and one was also high in linoleic acid 18:2 (n-6) (3.7%). The outlying LFD fish was also high in linoleic acid (2.5%). Both oleic and linoleic acid can indicate consumption of commercial feed (White et al. 2017c), and were marginally higher in the HFD group on average (6% and 18% increases, respectively), but this effect was not significant for either oleic acid (F1,18 = 1.9, p = 0.2) or linoleic acid (F1,18 = 0.4, p = 0.6). Levels of these fatty acids were strongly positively correlated with each other in the

HFD group (Pearson’s r = 0.90, t8 = 6.0, p <0.001), while this effect was nonsignificant in the

LFD group (r = 0.44, t8 = 1.4, p = 0.2). The highest levels of both oleic and linoleic acid (17.5% and 3.7% respectively) occurred in one individual from the HFD group.

Figure 3.3. Multidimensional scaling (MDS) plot showing dissimilarly (Euclidean distance) of multivariate fatty acid profiles in Atlantic cod ovaries according to salmon farm density. Individuals are grouped by high (HFD; red triangles) and low (LFD; blue circles) salmon farm density. Model stress is 0.08.

Egg production

Twelve spawning tanks produced a total of 137 L of eggs over the spawning season, with no difference in the total egg volume from LFD and HFD tanks (11.3 ± 0.9 L and 11.3 ± 0.7 L,

63 respectively; Fig. 3.4). A model comparing raw daily egg production between HFD and LFD tanks over time found no effect of the farm density factor (p = 0.08; Table S3). There was a significant temporal decline in daily egg production (p <0.0001, Fig. 3.4), and a positive effect of total female length in the tank on daily egg production (p <0.0001).

The relative daily egg production metric (daily egg production corrected for total female length) also did not significantly differ across groups (LFD: 64.6 ± 6.2 cf. HFD: 57.2 ± 3.4 mL female m-1 day-1 tank-1; p = 0.52), but again, there was a significant decline over time (p <0.0001, Fig. 3.4).

Initial female body condition (tank mean values) did not significantly predict either raw daily egg production or relative daily egg production metrics overall (p >0.10 in both cases), or within farm density groups (p >0.05 in each case). However, mean female weight loss during the season was strongly correlated with both egg production metrics (p <0.0004 in each case).

Figure 3.4. Daily egg production per tank during the captive spawning period (25 Feb 2016 – 11 April 2016). Tanks are grouped by high (HFD; red triangles, solid red fitted line) and low (LFD; blue circles, dashed blue fitted line) farm density. Production is quantified by (left panel) raw daily egg volume per tank and (right panel) with a correction for the total length of females in the tank. The temporal trend within groups is fitted by third order polynomial functions.

Egg quality and survivorship

The proportion of viable (floating) eggs was almost identical across farm density groups (Table 3.2; Table S3), with the viable proportion significantly declining over the season (p <0.0001).

Neither mean female length (p = 0.18) nor body condition (p = 0.60) were correlated with egg viability rates at the tank level, regardless of farm density group.

The LFD group produced larger eggs over the duration of the season (Table 3.2; Appendix 3.3). LFD eggs were 4.8 % larger by diameter at the time of the early season collections, with no significant difference later in the season (Table 3.2). We did not test for a temporal decline in egg size, as late but not early season samples were fixed in formalin. Again, neither mean female length (p = 0.065) nor body condition (p = 0.32) were significantly correlated with egg size, regardless of farm density group.

Rates of asymmetrical cell division in pre-blastula eggs did not differ between LFD and HFD groups in either early or late season collections (Table 3.2; Appendix 3.3). Neither mean female size nor body condition were correlated with egg symmetry (p >0.06 in each case). Mean asymmetry rates did not exceed 10% in either group on any collection date, although samples ranged from 0-53% asymmetry.

Early season eggs hatched 12-17 days after collection, while late season eggs hatched 14-18 days after collection. Egg survival during incubation prior to the hatching trial did not differ significantly between groups (Table 3.2; Appendix 3.3). Declines in survivorship between early and late season collections did not differ (p >0.10), nor did the effect of mean female length or body condition (p >0.10 in each case).

Hatching success rates were similar between groups (Table 3.2; Appendix 3.3), with no decline between early and late season collections. One HFD tank ceased spawning before the late season egg sampling, so late season HFD hatching data comes from only five tanks.

Larval quality

Larvae from LFD tanks were 8 % larger than those from HFD tanks (Table 3.2; Appendix 3.3). Neither mean female size (p = 0.97) nor body condition (p = 0.34) were correlated with larval size either overall or within farm density groups. The size of the largest larvae from each tank did not differ across farm density groups, nor did the rate of deformities (Table 3.2; Appendix 3.3). Neither were significantly correlated with mean female length or body condition (p >0.10 in each case). Overall rates of larval deformity were 48% at day 40 from both LFD and HFD groups (Table 3.2). Most deformities were of the spine (lordosis, kyphosis or vertebral misalignment) (152/173 deformities), followed by deformities of the jaw (15/173) and skull

(6/173). Larvae from both groups exhibited phototaxis in response to a horizontal light gradient (proportion in end section before exposure: 0.25 ± 0.03 cf. after exposure: 0.69 ±

0.04; t-test: t22 = -9.5, p <0.0001), but we found no evidence that the farm density groups differed in the extent of the phototactic response (Table 3.2; Appendix 3.3), nor any strong evidence for an effect of mean female length or body condition (p >0.13 in each case).

DISCUSSION

This study presents limited evidence for negative impacts of high salmon farm density on reproductive fitness in the studied Atlantic cod (Gadus morhua) population. Female cod collected from locations with a high density of salmon farms (HFD) were larger and heavier than fish from the low farm density location (LFD), but with no consistent changes in female body condition. Effects on ovarian lipid composition were also small and largely limited to two HFD individuals. There was no significant difference in egg production, viability, fertilisation rates, symmetry or hatching success, but eggs from HFD tanks were 5 % smaller and this likely contributed to the observed -8 % disparity in mean larval length at 40 days post spawning for the HFD group relative to the LFD group.

Among the viable proportion, fertilisation rates were similar for LFD and HFD tanks respectively (Table 3.2; Appendix 3.3), but declined during the season (p = 0.009). Neither mean female length nor body condition significantly predicted fertilisation rates overall (p >0.06 in each case), although the effect of female length was significant within the LFD group (p = 0.04). Given the very small difference in egg viability and fertilisation rates between groups, and nonsignificant effects of male length and body condition on fertilisation rates either overall or within groups (p >0.13 in each case), we did not test for differences in sperm quality.

Gadid fishes accumulate significant energy reserves for reproduction, with lipids stored primarily in the liver and proteins in muscle tissue (Kjesbu et al. 1991, Lambert & Dutil 1997). Accordingly, body condition indices during vitellogenesis are typically good predictors of fecundity in coastal cod from this region (Skjæraasen et al. 2006). In our case, mean female size and percentage weight loss during the season positively tracked egg production at the tank level; both were better predictors of egg production than initial female body condition. Table 3.2. Egg and larval quality metrics for cod from low (LFD) and high (HFD) farm density areas (group mean ± standard error). Positive effect sizes indicate that quality was higher in

66 the HFD group. Significance has not been corrected for false discovery rate of multiple comparisons.

LFD HFD N (HFD, LFD) Stat p Cohen’s d Viability rate Early season 0.96 ± 0.01 0.94 ± 0.03 102, 102 z = 0.21 0.83 -0.25 Late season 0.78 ± 0.15 0.76 ± 0.11 70, 74 z = 0.56 0.56 -0.19 Overall 0.87 ± 0.04 0.85 ± 0.04 172, 176 X2 = 0.23 0.61 -0.14

Egg diameter (μm) (of viable eggs) Early season 1183 ± 16 1129 ± 16 257, 289 X2 = 5.8 0.016 -1.36 Late season* 1261 ± 30 1210 ± 13 221, 271 X2 = 5.3 0.07 -0.92 Overall 1222 ± 20 1166 ± 16 478, 560 X2 = 6.5 0.011 -0.90

Fertilisation rate (of viable eggs) Early season 0.75 ± 0.04 0.77 ± 0.03 19, 20 z = 0.68 0.49 +0.27 Late season 0.59 ± 0.09 0.68 ± 0.06 15, 18 z = 0.47 0.63 +0.46 Overall 0.67 ± 0.08 0.73 ± 0.04 34, 38 X2 = 1.0 0.60 +0.36

Egg symmetry rate (of fertilised viable eggs) Early season 0.92 ± 0.01 0.90 ± 0.02 19, 20 z = 0.69 0.49 -0.29 Late season 0.88 ± 0.18 0.88 ± 0.08 15, 18 z = 0.48 0.63 -0.10 Overall 0.90 ± 0.03 0.89 ± 0.04 34, 38 X2 = 0.35 0.84 -0.08

Egg survival rate during incubation Early season 0.85 ± 0.03 0.83 ± 0.05 6, 6 z = 0.66 0.49 -0.23 Late season 0.56 ± 0.11 0.68 ± 0.09 5, 6 z = 0.48 0.63 +0.41 Overall 0.70 ± 0.08 0.76 ± 0.06 11, 12 X2 = 0.01 0.95 +0.23

Hatching success rate Early season 0.90 ± 0.03 0.85 ± 0.03 12, 12 z = 1.56 0.12 -0.64 Late season 0.92 ± 0.03 0.88 ± 0.08 11, 12 z = 0.15 0.88 -0.07 Overall 0.91 ± 0.02 0.86 ± 0.04 23, 24 X2 = 0.18 0.67 -0.48

Larval length (mm) Early season 6.46 ± 0.05 5.98 ± 0.18 180, 176 X2 = 3.9 0.048 -1.46

Max larval length (mm)

Early season 8.05 ± 0.17 7.68 ± 0.21 6, 6 t1, 10 = 1.32 0.21 -0.77

Larval deformity rate

Early season 0.48 ± 0.06 0.48 ± 0.07 6, 6 z1,9 = 0.01 0.99 0

Larval phototaxis rate

Early season 0.71 ± 0.07 0.67 ± 0.04 6, 6 z1,9 = 0.79 0.43 -0.28 *Late season eggs were fixed in formalin prior to examination, which may have affected egg diameter.

However, the direction of the body condition trend was positive, and together, our findings are consistent with greater reproductive investment relative to body length by LFD females.

Egg quality metrics revealed similar quality overall in LFD and HFD groups, but with significantly smaller eggs from HFD fish. This was somewhat unexpected, as HFD females were slightly larger; female size and age in cod is usually positively correlated with egg size (e.g. Marteinsdottir & Steinarsson 1998, Vallin & Nissling 2000), although body condition can be equally important (Chambers & Waiwood 1996, Marteinsdottir & Steinarsson 1998). The egg size effect that we observed did not correspond to any significant decrease in other metrics of egg quality, and the cumulative effect of the more direct metrics (viability, fertilisation, developmental symmetry, survival during incubation and hatching success) was such that eggs collected from HFD and LFD tanks did not differ substantially in their likelihood of successfully hatching (HFD: 40 % cf. LFD: 37 %). However, previous studies have indicated that egg size can predict larval quality, with larger size at hatching, faster growth rates and successful development of the swim bladder leading to a survival advantage for larvae from larger eggs (Knutsen & Tilseth 1985, Marteinsdottir & Steinarsson 1998). This prediction was consistent with our larval size data; HFD larvae were 8 % smaller on average than their LFD counterparts 40 days after spawning.

Larger larvae often exhibit differing responses to stimuli such as light (e.g. Colton & Hurst 2010). In some fish, phototaxis along a horizontal light gradient correlates positively with other metrics of development (e.g. first feeding: Karlsen & Mangor-Jensen 2001) and is affected by the environment (Forsgren et al. 2013). In our case, LFD and HFD fish did not differ in their phototactic response to a horizontal light gradient, although the effect was in the direction of the greater phototaxis by LFD larvae (Cohen’s d = 0.28). Regardless, even small differences in hatching size and larval growth rates can affect survival during the planktonic stage and even influence post settlement fitness (e.g. Sclafani et al. 1993, Shima & Swearer 2010), so it is likely that the HFD larvae in this study would have experienced nontrivial negative fitness effects later in development.

Condition indices for fish in the present study were similar to those from wild caught fish in the same region prior to the expansion of salmon farming (Botros 1962, cited in Kjesbu 1989). Egg sizes in our study (mean 1.2 mm) were at the lower end of those reported by previous captive spawning studies with Norwegian coastal cod (1.2-1.4 mm: Kjesbu et al. 1996, Otterå et al. 2006, van der Meeren & Ivannikov 2006, Bogevik et al. 2012, Karlsen et al. 2015), but this may be related to the lack of feed during spawning rather than condition at the time of collection (Kjesbu et al. 1990). Egg fertilisation, symmetry and hatching rates were all within the range of

68 those reported by previous studies (Morgan et al. 1999, van der Meeren & Ivannikov 2006, Bogevik et al. 2012, Karlsen et al. 2015), while larval growth rates (6.2 mm cf. 7.4 mm at 25 days post hatching) were slightly below those reported by Karlsen et al. (2015).

While previous studies have found significant shifts in ovarian fatty acid composition in captive-fed sea bream (Cejas et al. 2003) and farm-associated bogue (L. Martinez-Rubio unpubl. data, cited in Fernandez-Jover et al. 2011b), the absence of such clear effects in this study should not be taken as strong evidence that these fish have not fed at salmon farms. Lipid profiles of the gonads are less affected by diet than those of the liver or muscle tissue, as the gonads are composed almost entirely of phospholipids rather than dietary fatty acids. Accordingly, our analysis of ovarian lipid composition may only have detected effects of a large dietary shift, while the presence of a single active salmon farm near one LFD location (affecting 25 % of LFD fish) may have reduced overall differences in fatty acid profiles between HFD and LFD groups. Indirect intake of farm waste via predation of farm-associated invertebrates and fish, combined with dietary sparing and biosynthesis, may also weaken or mask fatty acid signals in fish that are not strongly farm-associated (White et al. 2017c). The strong correlation between levels of oleic and linoleic acids in the HFD group (less so in the LFD group) is consistent with (but not necessarily strong evidence for) a spectrum of farm association, with the most strongly farm-associated individuals in the HFD group having higher levels of oleic and linoleic acids than their counterparts in the LFD group. Both oleic and linoleic acid are present in the natural diet but are especially abundant in commercial salmon feed, and captive feeding trials revealed a strong correlation between these two fatty acids in muscle, liver and gonad tissue of saithe fed on salmon feed (Karlsen et al., unpublished data).

Our collection sites were selected to represent levels of farm exposure experienced by cod populations in the south-western fjords (Fig. 3.1). Importantly, cod do not associate with farms as closely or persistently as other species (e.g. saithe), and likely do not feed exclusively at farms for extended periods (Dempster et al. 2011), so our experiment was designed to test for effects of farm density on cod whose home ranges at the time of collection overlap with areas of farming influence. Salmon farms have relatively localised nutrient footprints, with acute deposition within 50-250 m and very diffuse deposition beyond 500 m (Kutti et al. 2007, Bannister et al. 2016, White et al. 2017b). Accordingly, our collection sites are likely to reflect the two dominant types of farm exposure for wild Norwegian fjord cod: temporally dynamic or partial association versus little or no association. Tissue fatty acid profiles in cod are altered within 3 weeks of a dietary shift (Kirsch et al. 1998), with vitellogenesis commencing 3-4 months prior to spawning (Skjæraasen et al. 2006). Individuals that reside within farm

69 footprints throughout vitellogenesis are likely to show the largest shifts in ovarian fatty acid profiles and concomitant effects on reproductive physiology and output. We do not know how long HFD and LFD cod have resided at their capture locations, but available telemetry and mark-recapture data indicate that wild coastal cod have relatively restricted home ranges over a scale of weeks and months. 87 % of tag returns for wild cod released in Heimarkspollen, a 2.9 km2 semi-enclosed fjord in the Austevoll archipelago, were recaptured within the fjord (Svåsand 1990), while in Balsfjord in northern Norway, the majority of wild cod tagged and released at a farm were still present at the same farm 9-12 weeks later (Uglem et al. 2008). In the Shetland Isles, 133 wild cod were tagged and released, with 37/39 recaptures over a two year period occurring within 15 km of the release site (Neat et al. 2006). While some individuals move larger distances and may have spent time in areas that differ from their capture location in terms of farm density, our study only assumes that fish collected from LFD locations will, on average, be less affected by farms than fish from HFD locations.

Previous captive feeding experiments have shown that gadids and other fishes fed commercial diets experience changes in reproductive fitness (e.g. Salze et al. 2005, Bogevik et al. 2012), but very little work has been done to assess potential impacts in a real world ecological context. Taken together, our findings indicate that salmon farming in this region has some negative effects on the reproductive physiology of Atlantic cod on a fjord-wide scale, with potential cumulative effects of egg and larval size on later developmental stages. More work is needed to track fitness effects later in development, including potential effects of decreased egg and larval size on later life stages. In addition, as our study was restricted in its geographic extent and spatial replication, we are cautious of generalising these findings beyond our study environment. We encourage others to replicate and extend this important line of research. Future work may also consider the other potential pathway for fitness impacts: individual mortality. Mortality may decline due to the provision of a trophic subsidy (Kilambi et al. 1978), or increase due to elevated levels of contamination, infection, or predation at farms. Wild fish aggregations are also an easy target for fishers, and fishing mortality will take on greater importance if the current 100 m fishing restriction around Norwegian farms is lifted (Bagdonas et al. 2012). Previous studies have employed acoustic tracking to good effect in comparing spatiotemporal movement and mortality rates of Atlantic cod individuals across habitats (Olsen et al. 2011, Olsen et al. 2012, Fernández-Chacón et al. 2015), and it would be entirely feasible to apply the same approach to quantify differential mortality rates in wild fish across multiple farm-affected and non-affected areas.

ACKNOWLEDGMENTS

We thank the staff at IMR Austevoll for their expert advice and assistance in the collection and maintenance of the spawning fish, egg incubation and feeding of larvae: Margareth Møgster, Stig Ove Utskot, Michal Rejmer, Inger Semb Johansen, Nele Gunkel-Sauer, Kristine Hovland Holm, Yvonne Rong, Terje van der Meeren, Tord Skår, Velimir Nola and Glenn Sandtorv. We also thank the staff and students at Austevoll High School for their assistance in transporting live fish to the research station. Theresa Aase prepared the fatty acid analysis. Camille White provided useful comments on lipid data. Francisca Samsing assisted with the creation of figures. The manuscript was improved by comments from three anonymous reviewers. All procedures were conducted in accordance with Norwegian animal welfare regulations by experienced personnel.

This project was funded by the Sustainable Aquaculture Laboratory, University of Melbourne, and the Norwegian Seafood Research Fund (Fiskeri og Havbruksnæringens Forskningsfond).

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CHAPTER FOUR: NATIVE PREDATOR PREVENTS AN INVADER FROM EXPLOITING FOOD-RICH HABITAT

ABSTRACT

Biodiverse ecosystems may be inherently resistant to invasion, but environmental change can facilitate invasion by disturbing natural communities and providing resources that are underutilised by native species. In such cases, abundant native predators may be crucial in limiting invasive population growth. We studied invasive seastars feeding at shellfish farms together with a larger native predatory seastar, to examine whether farms are reproductive hotspots for the invader or whether the native predator causes farms to function as ecological traps. Invaders were not more abundant at farms, despite individuals residing at farms having higher body condition and reproductive investment than those on reference habitats. The native predator was 25x more abundant at farms than surrounding habitats and was negatively correlated with invader abundance. We also observed several native-on-invasive predation events and a conspicuous absence of small invaders at farms despite high larval recruitment to farms, consistent with biotic control by the native predator. In a choice experiment, invaders were strongly attracted to mussels, but the presence of a single native predator nullified the attractive effect, indicating that the invader recognises and avoids the native predator. Attractive habitats add spatial structure and may leave invasive populations more susceptible to top-down control. We provide evidence that the presence of a native predator prevents an invader from exploiting an attractive rich food resource.

INTRODUCTION

Invasive species are economically costly and an important contributor to biodiversity loss worldwide (Clavero and García-Berthou, 2005; Pimentel et al., 2001). Species introductions are increasing in frequency as a result of human transport, with the likelihood of a newly introduced species establishing and becoming invasive depending on multiple interacting factors including environmental conditions, ecosystem characteristics and species traits (Carlton, 1996; Keane and Crawley, 2002; Kennedy et al., 2002; Kimbro et al., 2013; Papacostas et al., 2017).

Intact, biodiverse ecosystems sometimes have inherent biotic invasion resistance (Elton, 1958), conferred by low niche vacancy rates and strong competition for resources (Kennedy et al., 2002), as well as a higher likelihood of the new arrival encountering predators or other enemy species (Keane and Crawley, 2002; Kimbro et al., 2013). However, ecosystem-level invasion resistance is being eroded by large scale habitat disturbance and loss of biodiversity associated with human-induced rapid environmental change (HIREC: Vitousek et al. 1997; Sanderson et al. 2002; Halpern et al. 2008; Vörösmarty et al. 2010; Sih et al. 2011). Non-native species can benefit from the impacts of HIREC by taking advantage of man-made or degraded habitats that are low in biodiversity and have resources that are underutilised by native competitors or predators (Byers, 2002; Didham et al., 2005; MacDougall and Turkington, 2005; Mack et al., 2000). In the early stages of invasion, or at range limits, these refuge habitats can become invasion hubs: satellite populations that facilitate persistence and expansion (Carlton, 1996; Letnic et al., 2014; Russell et al., 2011; Suarez et al., 2001; With, 2002). Later, they may function as reproductive hotspots for both native and invasive species, for example by permitting high local population densities that overcome Allee effects (Inglis & Gust 2003; Ling et al. 2012).

A single abundant predator species can exert considerable top-down control and play an important role in limiting the eventual population size and distribution of an invader, essentially providing invasion resilience when invasion resistance has failed (DeRivera et al., 2005; Kimbro et al., 2013; Papacostas et al., 2017). Predator-mediated biotic control may be particularly effective in cases where the invader does not recognise or respond appropriately to the native predator; a scenario that is most likely to occur when the invader has no evolutionary history with the native predator or its relatives (Cox and Lima, 2006; Pintor and Byers, 2015; Sih et al., 2010). Such conditions favour the formation of an evolutionary trap: where an individual chooses a habitat (ecological trap: Robertson & Hutto 2006; Hale & Swearer 2016) or behaviour that leads to poor fitness outcomes (Robertson et al., 2013). Traps usually arise as a result of environmental change outpacing the evolution of behaviour, and account for some of the disastrous effects of invasive predators on naïve native prey (e.g. juvenile fish avoid reefs with a resident native predator but not those with a resident invasive predator: Benkwitt 2017). Most work has focused on mitigating traps that affect native species, but evolutionary traps also have potential as tools in the management of non-native species (Letnic et al., 2015; Robertson et al., 2017). One potential approach is to increase mortality rates at attractive sites to create attractive sinks – Letnic et al. (2015) recently demonstrated that excluding invasive toads from water sources converts invasion hubs into

80 ecological traps. Native predators may also contribute to the formation of ecological traps for non-native species, but such traps have not yet been empirically demonstrated.

In this study, we employ the ecological trap framework to study interactions between native and invasive seastars at shellfish farms in southern Australia. To demonstrate an ecological trap, it is necessary that individuals either prefer or fail to avoid the putative trap habitat when better options are available, and that fitness is reduced in the putative trap habitat (Robertson and Hutto, 2006). Shellfish farms are an excellent model for a range of anthropogenic marine habitats worldwide that are simultaneously highly disturbed and rich in food for benthic predators (D’Amours et al., 2008; Inglis and Gust, 2003; McKindsey et al., 2011). This combination of traits may cause such habitats to be highly vulnerable to exploitation by invasive species (e.g. Ling et al. 2012). The invasive seastar Asterias amurensis (Asterias hereafter) was introduced to Tasmania during the 1980s (Grannum et al., 1996), and mainland Australia in 1995 (Parry and Cohen, 2001), with the mainland population reaching 75 million by 2000 (Parry and Cohen, 2001). The invader has decimated naïve bivalve populations (Hutson et al., 2005; Ross et al., 2003), and has few predators in its new environment, but there have been reports of predation by a native predator, the eleven-arm seastar Coscinasterias muricata (Coscinasterias hereafter) (Byrne et al., 2013; Parry, 2017). The trophic subsidy provided by shellfish farms attracts aggregations of both species, but the ability of the invader to benefit from the high food availability will depend on competition and predation risk from the native predator (Appendix 4.1). If the native predator does not impede the ability of the invader to access the food resource, then such habitats are likely to act as population sources (Letnic et al., 2014; Ling et al., 2012). Alternatively, if the invader is attracted to these sites but then suffers high predation rates or competitive exclusion, there is potential for an ecological trap to arise (Hale and Swearer, 2016). We combine data on population distribution, individual body condition, reproductive investment and habitat preference to assess the likely role of two coastal mussel farms for populations of the native and invasive seastars. In doing so, we provide evidence indicating that an abundant native predatory seastar (a) prevents an established invader from accessing a disturbed but food-rich habitat.

METHODS

Study sites and focal species

Port Phillip Bay is a 1930 km2 semi-enclosed marine water body in south-eastern Australia subject to multiple impacts from invasive species and urban inputs (Hewitt et al., 2004; Sampson et al., 2014). We studied native (Coscinasterias) and invasive (Asterias) seastar populations at two shellfish aquaculture reserves 12 km apart in the south-western part of the bay: Clifton Springs and Grassy Point. These leases primarily produce blue mussels (Mytilus galloprovincialis) grown on suspended longlines. Clifton Springs and Grassy Point contain approximately 81 and 42 ha of active bivalve aquaculture areas, respectively. Active areas consist of parallel longlines approximately 20 m apart, which are stocked year-round with short turnaround times between harvesting and restocking. Live mussels (typically 40-60 mm shell length) were abundant on the seabed throughout the study (pers. obs.), with the natural substrate entirely replaced by mussel shell debris within active areas. Both reserves are located on soft sediment between 8-14 m depth, with constant mixing and exchange of water by tidal cycling and wind-generated currents. Clifton Springs is the more sheltered of the two locations and is dominated by silty substrate, while Grassy Point is characterised by sandy substrate and lower turbidity. The natural benthos within farms and surrounding areas at both locations is dominated by Halophila australis seagrass beds, with paper mussels (Electroma georgiana) attached to available seagrass or macroalgae. Caulerpa spp. beds are common at Grassy Point. Seastars are the predominant benthic predators within and around both reserves.

Coscinasterias muricata (Asteroidea: Asteriidae) are a large (armspan commonly 30-45 cm) subtidal reef dwelling invertivore. Bivalves are preferentially consumed, especially the mussels that are abundant on reefs and marine infrastructure in southern Australia, but will opportunistically consume a variety of invertebrate prey (Day et al., 1995; Parry, 2017). Asterias amurensis (Asteroidea: Asteriidae) are smaller (armspan commonly 20-30 cm) but highly mobile invertivores, and are typically more abundant on soft sediment habitats where they consume primarily bivalve prey (e.g. clams, scallops and mussels) (Ross et al., 2003; Ross and Johnson, 2002).

Experiment 1: Effect of shellfish farming on population density of native and invasive seastars

We surveyed Asterias and Coscinasterias population densities along diver transects inside and outside the two aquaculture reserves: Clifton Springs on six occasions between July 2014 and June 2016, and Grassy Point on seven occasions between July 2014 and June 2015. On each sampling occasion, we surveyed three replicate 2 x 25 m transects (extended to 2 x 50 where <10 seastars were recorded) inside and outside the reserve boundaries (a total of six transects per reserve per sampling date). ‘Inside’ transects were haphazardly placed within active mussel longline areas where live mussels were present on the seabed. ‘Outside’ transects were haphazardly placed on natural soft sediment habitat within a ~6 km2 area 1-2 km outside the reserve boundary. Inside and Outside habitats were comparable in terms of depth (10-13 m), water flow and sediment characteristics. Each transect consisted of the diver laying out a transect centreline on a predetermined random heading and counting all seastars with their central point within 1 m of the transect centreline. On rare occasions where a transect strayed beyond the area of fallen mussel debris (typically >20 m from the outermost longline) we altered the heading of the remainder of transect. We placed transects at least 50 m apart to ensure that no seastars were sampled twice.

To test for effects of shellfish farms on seastar population density, we constructed generalised linear mixed models (GLMMs) for each species using the glmmTMB package for R (Brooks et al., 2017; R Core Team, 2017). A negative binomial model family was specified as there was a higher frequency of low or zero counts than would be contained within a Poisson distribution. We included Habitat (Inside or Outside) and Location (Clifton Springs or Grassy Point) as fixed effects and Date as a random intercept term to account for temporal variation. As there was a very strong effect of Location (largely driven by the rarity of Asterias at Grassy Point), we then split the dataset by Location and re-ran the models without the Location term, reporting both sets of results. In each case, we tested the significance of the Habitat effect by comparing the fit of models with and without the Habitat term using a likelihood ratio test. To test for a potential effect of Coscinasterias abundance on Asterias abundance, we fitted a negative binomial GLMM in which Asterias density is regressed against Coscinasterias density, with Habitat and Location also as fixed effects and Date as a random intercept term.

Experiment 2: Effect of shellfish farming on size and condition of native and invasive seastars

To track any differences in size between habitats, we measured the arm span of all seastars encountered on each transect (or the first 30 of each species) and took the mean arm span for each species on each transect.

We compared condition metrics between habitats by collecting up to 20 individuals per species per farm per sampling occasion (collected on transects and supplemented with off- transect collections when necessary). When fewer than 20 individuals were available in one habitat on a given day, we limited our sample size in the other habitat to avoid temporal bias caused by seasonal dynamics in condition metrics. Drained and gutted carcass weight provided a measure of size that was largely independent of body condition, as seastars store nutrients primarily in the coelomic fluid and digestive organs. The relative size of the pyloric caeca (digestive organs that increase in size with feeding activity) provided a measure of body condition and an indirect measure of food availability inside and outside the aquaculture reserves (Oudejans et al., 1979; Xu and Barker, 1990). We calculated the pyloric caeca index relative to somatic weight: PCI = PCW/CW, where PC = pyloric caeca wet weight and CW = gutted carcass weight drained of coelomic fluid. We considered reproductive condition in terms of two correlated metrics: relative reproductive investment (gonadosomatic index: GSI = GW/CW, where GW = gonad weight) and potential fecundity (gonad size). A pilot study demonstrated that PCI and GSI can be accurately estimated from a single limb (whole-body vs. limb-only linear R2 with n = 20 for Asterias PCI = 0.79, GSI = 0.90; with n = 23 for Coscinasterias PCI = 0.88, GSI = 0.78). This permitted the release of the native Coscinasterias after the removal of a single limb. As Asterias is a declared noxious aquatic species, entire Asterias individuals were taken, but only a single limb dissected. Asterias are sufficiently abundant and mobile that the removal of up to 20 individuals per farm per sampling occasion likely had a negligible effect on numbers in later surveys (Andrews et al., 1996). Limbs were cut open longitudinally, drained of coelomic fluid and weighed. The pyloric caeca and gonads were then removed and weighed separately. Individuals were sexed based on the colour of the gonads, a method that was validated by microscopy during a pilot study (n = 20 per species, 100 % accuracy).

To test for effects of shellfish farms on size and condition, we fitted linear mixed models with Habitat as a fixed term and Date and Sex as a random intercept term. We tested the Habitat

84 term as per Experiment 1. Responses were transformed as necessary to improve linearity and

th normality: log10 for Asterias armspan and 4 root for PCI and GSI.

Experiment 3: Interactions between native and invasive seastars

Any fitness benefits of high food availability may be offset if there is an elevated predation risk. We recorded evidence of predation on transects as for population surveys in Experiment 1, including predation events in progress and sublethal damage that is unlikely to be explained by asexual reproduction (e.g. damage to arms consistent with crab or fish predation: Ling & Johnson 2013). Predation may influence the size distribution of a prey species whenever predation events and/or success rates depend on the relative size of the predator and prey. Coscinasterias predation on Asterias is strongly size dependent, with a higher likelihood of prey escaping when relative sizes are close (Parry 2017). Cannibalism of small Asterias by larger conspecifics also occurs (pers. obs.). In light of this, we compared the size distributions of Asterias inside and outside farms for evidence of size-dependent predation within and/or avoidance of the farm environment.

Given evidence that the native seastar does prey on the invasive seastar, we devised a laboratory-based choice experiment to test the strength of the invader’s attraction to fallen mussels, and whether this attraction is moderated by the presence of either conspecifics or native predators. We conducted the experiment in a 72 L flume tank (120 x 30 x 20 cm), housed within a recirculating seawater facility containing ca. 12000 L of continuously filtered and UV-treated seawater. A flow rate of 4 L min-1 was divided between the two chambers at the head of the tank, with both chambers opening into an undivided downstream portion of the tank containing the Asterias subject. We compared rates of attraction to four treatments: a seawater control with no predatory nor prey cues (‘SW’), five thawed mussels (40-60 mm shell length) placed in a perforated compartment at the upstream end of the chamber (‘M’), five thawed mussels with three conspecifics in the chamber with the mussels (‘AM’), and five thawed mussels with a large Coscinasterias individual also present in the chamber (‘CM’). All seastars placed in the treatment chamber for AM and CM treatments remained by the end of the trial. We applied treatments singly, with the cue randomly assigned to one of the two chambers (all treatments were tested relative to a seawater control). We left the subjects overnight (16 hrs), and considered them to be attracted to the habitat cue if they were present inside the corresponding chamber at the completion of the trial, providing a binary outcome.

The flume tank was cleaned and flushed between trials. Subjects were randomly selected from a large captive population (>200) collected from the wild within 2 months of the trial, fasted for 2-4 days prior to the trial, and randomly assigned to a treatment. The size range of subjects was 4-17 cm in every treatment group (mean 11 ± 0.2 cm).

We tested for differential attraction to treatments by fitting a binomial generalised linear model using the glm function in R, before conducting post-hoc pair-wise X2 tests of proportions using the prop.test function.

RESULTS

Experiment 1: Effect of shellfish farming on population density of native and invasive seastars

The invasive seastar Asterias was common on silty substrate and mussel shell debris both inside and outside the farm boundary at Clifton Springs, with very high abundance inside throughout winter 2014 (Fig. 4.1). However, Asterias population density was highly variable, with no significant effect of the farm habitat over the full study duration (2014-2016) (Table 4.1). Asterias was nearly absent from Grassy Point, being recorded on a single transect only inside the Grassy Point boundary in winter 2014. (Fig. 4.1).

The native seastar Coscinasterias showed contrasting habitat preference (Fig. 4.1). At Grassy Point, Coscinasterias was common inside and outside the Grassy Point farm boundary, with a 14x higher population density inside (Table 4.1). They were also common inside the boundary at Clifton Springs, but absent on our transects outside the Clifton Springs boundary (Fig. 4.1). Across the two locations, Coscinasterias occurred at a higher density (mean 25x) inside farms on every sampling occasion between 2014-2016.

If both species were distributed according to availability of a common food source, we would expect a positive correlation between the abundance of the two species. Instead, our population density data reveal that Asterias density is negatively dependent on Coscinasterias density (negative binomial mixed effects model with Coscinasterias density, Habitat and Location as fixed effects and Date as random effect: X2 = 4.1, p = 0.04).

Experiment 2: Effect of shellfish farming on size and condition of native and invasive seastars

Both seastar species were larger on average inside farm boundaries. Due to the rarity of Asterias outside Grassy Point and the rarity of Coscinasterias outside Clifton Springs, we compared the size distribution of both species inside and outside a single farm (Asterias: Clifton Springs, Coscinasterias: Grassy Point).

Figure 4.1. Population density of Asterias amurensis (plots A and B) and Coscinasterias muricata (plots C and D) inside and outside the Clifton Springs and Grassy Point Fisheries Aquaculture Reserves. Boxes denote median, lower (25 %) and upper (75 %) quartiles, whiskers denote 1.5x interquartile range.

Asterias inside the farm had a 25 % wider arm span than their counterparts outside, corresponding to a 92 % increase in gutted weight (Table 4.1). This effect was consistent across

87 all but one sampling date (Fig. 4.2). The difference in mean arm span of Asterias was driven by a contraction at the lower end of the size distribution inside the Clifton Springs farm, rather than the presence of larger individuals (Fig. 4.4). All body and reproductive condition metrics that we assessed in Asterias were higher inside the farm than outside: drained weight was 77 % higher, gonad weight 57 % higher, PCI 26 % higher and GSI 57 % higher (Table 4.1). The PCI effect was highly consistent over the study duration, while differences in GSI varied over time (Fig. 4.3).

Figure 4.2. Armspan (A) and gutted weight (B) of Asterias amurensis inside and outside the Clifton Springs Fisheries Aquaculture Reserve.

Likewise, Coscinasterias inside the farm had a 7 % wider arm span and 42 % heavier gutted weight than those outside (Table 4.1). Individuals inside the farm also had higher body condition (11 %) but there was no difference in either gonad size or GSI (Table 4.1).

Experiment 3: Interactions between native and invasive seastars

We observed three native-on-invasive seastar predation events on our transects inside the Clifton Springs farm (total 3325 m2 surveyed, 160 Asterias scored), with none observed on the same number of transects outside (3900 m2 surveyed, 126 Asterias scored). We also noted a further ten predation events off-transect, as well as five skeletons of recently-consumed

Asterias, all inside the farm boundary. We observed one cannibalism event (a large Asterias consuming a smaller conspecific), also on a transect within the Clifton Springs farm.

We found strong effects of prey and predator cues on habitat selection decisions by the invasive Asterias in captive behavioural trials (n = 40 per treatment, p <0.0001) (Fig. 4.5). The mussel cue (M) attracted Asterias subjects at a significantly higher rate than the seawater control (SW) (93 vs. 28 % respectively, X2 = 32, p <0.0001). Mussels remained more attractive than seawater when three Asterias conspecifics were present feeding on the mussels (AM) (88 %, X2 = 27, p <0.0001), and there was no difference between rates of attraction to M or AM cues (93 % vs. 88 % respectively, X2 = 0.1, p = 0.7). In contrast, the presence of a single Coscinasterias individual feeding on the mussels (CM) resulted in a dramatic decline in attraction rates relative to both the M (28 vs. 93 % respectively, X2 = 32, p <0.0001) and AM cues (28 vs. 88 % respectively, X2 = 27, p <0.0001). 2/11 individuals that selected the chamber with the CM cue were predated by the Coscinasterias (one fully, one partially consumed overnight). Asterias individuals that avoided the chamber with the CM cue exhibited a variety of behaviours, such as moving immediately to the cue-free (clean seawater) chamber and remaining there for the duration of the study, circling the downstream portion of the tank, or attempting to reach the mussels before evading a predation attempt by Coscinasterias. We found no evidence that the size of the subject influenced their response to the presence of either conspecifics (z38 = 1.1, p = 0.3) or Coscinasterias (z38 = 1.0, p = 0.3). The size differential between the subject and the three conspecifics in the AM treatment (natural log of subject arm span / mean conspecific arm span) also did not influence the likelihood of attraction to the mussels (z38 = 0.6, p = 0.5).

DISCUSSION

In disturbed ecosystems, abundant native predators may be an important line of defence against invaders, limiting the probability of establishment and subsequent population growth. In Experiment 1 we showed that a native predator occurs at 25x higher abundance inside two shellfish farms relative to reference habitats, with the shellfish farm accounting for almost the entire population in an area with non-preferred silty substrate. In contrast, the invasive species was not significantly more abundant inside the farms and was negatively associated with the native predator. Experiment 2 revealed that invasive seastars residing inside one farm possessed considerably higher body condition and reproductive condition metrics than

89 conspecifics at reference sites, supporting the hypothesis that this shellfish farm may be a reproductive hotspot for the invader. When contrasted with the lack of a clear effect on abundance in Experiment 1, this suggests a failure or inability to take advantage of a high quality food resource. Accordingly, in Experiment 3 we provided observational field evidence of elevated predation risk for the invasive species inside shellfish farms, and found that in captivity, the invader recognises and avoids the native predator even if it means forgoing a favoured prey item. Taken together, these findings raise the possibility that the presence of the native predator is preventing the invader from accessing a highly disturbed food-rich habitat.

Figure 4.3. Condition metrics for Asterias amurensis inside and outside the Clifton Springs Fisheries Aquaculture Reserve: drained carcass weight (A), gonad weight (B), gonadosomatic index (C), and pyloric caeca index (D).

In the absence of abundant native predators, man-made habitats such as shellfish farms, fish farms and mussel-fouled infrastructure can function as reproductive hotspots for invasive

Asterias amurensis populations, simultaneously supporting orders-of-magnitude higher population density and elevated individual reproductive output (Ling et al. 2012). Within their native range, Asterias species also have highly elevated population densities at shellfish farms, perhaps indicating release from native predators such as crabs and sunstars (e.g. Olaso Toca 1979, 1982; Saranchova & Kulakovskii 1982, cited in McKindsey et al. 2011; D’Amours et al. 2008). The present study indicates that top-down control may alter the role played by these food-rich habitats.

Figure 4.4. Density plot of Asterias amurensis size distribution inside and outside the farm boundary at Clifton Springs Aquaculture Fisheries Reserve. N = 165 (inside) + 107 (outside).

Effective top-down control of non-native animals by native predators has been reported in numerous marine, freshwater and terrestrial systems (e.g. DeRivera et al. 2005; Cheng & Hovel 2010; Tetzlaff et al. 2011; Freed & Leisnham 2014), but to our knowledge, this is the first clear evidence of a native predator preventing invaders from accessing a high value food resource. Asterias is thought to be primarily food limited in its new range: growth rates declined during the first three years following its introduction to mainland Australia, coinciding with increasing population densities and large declines in prey abundance (Parry and Cohen, 2001). Our body condition and reproductive investment data show elevated condition inside farms, indicating that food limitation still exists outside farm habitats. This means that regardless of the frequency of predation events, if Coscinasterias discourage Asterias from accessing farms, the

91 native predator may exert indirect biotic control on the invasive population by maintaining food limitation and thus a lower equilibrium population size.

Figure 4.5. Effect of prey, conspecifics and predators on habitat selection decisions by the invasive seastar Asterias amurensis in laboratory trials. Treatments: SW = seawater control, M = mussels, AM = mussels + Asterias conspecifics, CM = mussels + Coscinasterias. Bars show the proportion of subjects that are attracted to the cue chamber (‘Y’) or not (‘N’) according to the treatment. Individuals that were not attracted to the cue chamber either remained in the downstream portion or moved into the control chamber. N = 160 (40 per treatment). Matching letters indicate proportions that do not differ statistically.

Our data also raise the possibility that juveniles of the non-native species may be falling into a predation-mediated ecological trap at shellfish farms. Asterias has a long pelagic larval duration (79-112 days: Bruce et al., 1995), and in Australia, planktonic Asterias recruit in large numbers directly to mussel lines and other suspended structures (e.g. Dommisse & Hough 2004). This creates the conditions for an ecological trap affecting juvenile Asterias, as juveniles living on mussel lines are (a) unlikely to reach sexual maturity before the mussel lines are harvested, and (b) likely to experience high predation risk if they fall to a benthic environment populated by >2000 Coscinasterias ha-1 (this study). The truncated size distribution for Asterias on the seabed under mussel farms is consistent the hypothesis that juvenile Asterias falling to the seabed from mussel lines either leave the area or are consumed, although we cannot rule out individuals leaving or avoiding the farm environment due to innate preferences (such as

92 for soft sediment rather than biofouled mussel shell substrate). The risk of predation and potential for an ecological trap may be increased if juveniles do not have sufficiently strong predator avoidance behaviour (Cox and Lima, 2006; Sih et al., 2010); in our habitat choice experiment, small individuals were no more likely to avoid the native predator than large individuals, despite being at higher risk of predation than larger conspecifics (size-dependent predation success: Parry 2017).

As adults, the potential for an ecological trap at shellfish farms depends on whether individuals are attracted to the farm (i.e. the strength of their preference for bivalve prey versus their avoidance response to the native predator) and the net fitness effects of any preference for the farm (i.e. high food availability versus high predation risk). Our choice experiment found that Asterias were strongly attracted to bivalves, but only in the absence of the native predators, and while actual predation rates are almost certainly higher inside the farms (this study; Parry 2017), so is reproductive output (this study; Ling et al. 2012). Further work is needed to calculate the net effect of these opposing factors, and to test whether Asterias individuals are able to make informed habitat selection decisions at ecologically-relevant distances. The high densities of mussels and predators at farms provide a relatively strong scent trail, and all else being equal, even random movement of seastars will tend to result in higher densities in preferred habitats if individuals move less after arrival (Benhamou 2011; see also elevated density of Coscinasterias inside farms, this study).

Invading Asterias benefitted from the naivety of Australian bivalves (Hutson et al., 2005; Ross et al., 2003), but to date there has been little evidence that Asterias responds naïvely to novel predators, despite theoretical expectations arising from a lack of shared evolutionary history (Cox and Lima, 2006; Sih et al., 2010). Behavioural data on predator-prey interactions in the early days of invasion are not available, but the establishment and expansion of Asterias in Australia may have been assisted by pre-existing predator cues evolved in response to functionally similar predators in its native range (Sih et al., 2010). In particular, the sun star Solaster paxillatus and king crab Paralithodes camtschaticus of the northern Pacific region are notably similar in form and functional role to the major invertebrate predators in its new range: the seastar Coscinasterias and the spider crab Leptomithrax gaimardii (Byrne et al., 2013; Ling and Johnson, 2013), and may emit similar sensory cues.

Little is known about how the Asterias invasion affected native predators. A meta-analysis by Pintor and Byers (2015) found that simultaneous access to native and non-native prey was associated with increased native predator abundance, but exceptions are common where native and non-native species rely on a shared food resource or where abundant non-native

93 species drive severe ecological change (e.g. Pothoven et al. 2001; Suarez & Case 2002). Here, the invader competes with the native species for mussel prey, but localised high densities of mussels, such as those on rocky reefs, shellfish farms and marine infrastructure may suit the native species by improving their ability to exclude Asterias from the food resource, as well as supplement the mussel diet with opportunistic intraguild predation of the invader.

Eradication of this invader is not feasible, but any top-down pressure may have ecological benefits by partially releasing vulnerable prey populations from predation and reducing the likelihood of new populations establishing (DeRivera et al., 2005; Letnic et al., 2009; Mack et al., 2000). Conversely however, in cases where a non-native prey species provides a large trophic subsidy for native predators, native predators can become overabundant and severely impact their native prey species (Noonburg and Byers, 2005). It is unclear whether the presence of Asterias at farms is sufficient to maintain locally elevated Coscinasterias populations, but such predation events could conceivably be important during rare periods when preferred prey (mussels) are scarce.

A range of harvesting methods have been employed to control seastars globally, but none are cost-effective (Barkhouse et al., 2007). In particular, a lack of spatial population structure on homogeneous landscapes limits the efficiency of control methods, but attractive habitats such as shellfish farms and other marine infrastructure add spatial structure and may leave invasive populations more vulnerable if mortality can be induced at such sites (Bascompte et al., 2002; Letnic et al., 2015; Russell et al., 2011). In our study, biotic control by the native predator apparently limits the ability of the invader to exploit a food-rich resource, and we provide evidence that these habitats may function as ecological traps for the invader by combining attractive cues from bivalve prey with a high predation risk from the native predator. In such a case, the native predator provides a potentially valuable ecosystem service by suppressing invasive populations. It may be feasible to improve the impact of this service by protecting and augmenting predator populations in key areas such as localised sites of new introductions (Parry, 2017); an approach has been used with some success in other invaded marine environments (Atalah et al., 2016, 2013).

ACKNOWLEDGMENTS

This work was funded by a Holsworth Wildlife Research Endowment grant awarded to LB. Lance Wiffen provided access to aquaculture leases, while Chris Taylor, Simon Reeves, Emily

Fobert, Dean Chamberlain, Jack O'Connor, Ben Cleveland, Kevin Jensen, Kevin Menzies, Oliver Thomas and Rod Watson (Victorian Marine Science Consortium) assisted with fieldwork.

Table 4.1. Population metrics for native (Coscinasterias muricata) and invasive (Asterias amurensis) seastars inside and outside farms at Grassy Point and Clifton Springs Aquaculture Fisheries Reserves. The Habitat (Inside vs. Outside) effect is tested by comparison of linear mixed effects model fit with and without the Habitat term. Data are missing for Asterias inside and outside Grassy Point and Coscinasterias outside Clifton Springs because too few individuals were present in these habitats. Sample sizes are reported as follows: n = Inside n, Outside n.

Clifton Springs Grassy Point Inside Outside % n X2 p Inside Outside % n X2 p Asterias amurensis Population density (ha- 1300 ± 311 755 ± 92 72 20, 20 1.4 0.23 22 ± 22 0 ± 0 ∞ 18, 18 1.4 0.23 Armspan1)a (cm) 19.5 ± 0.6 15.6 ± 0.8 25 20, 20 21 <0.0001 12.6 ± 12.6 – – 1, 0 – – Gutted wt (g) 9.0 ± 0.5 4.7 ± 0.3 92 96, 96 71 <0.0001 – – – 0, 0 – – Drained wt (g) 15.2 ± 0.7 8.6 ± 0.6 77 96, 96 59 <0.0001 – – – 0, 0 – – PCI 0.24 ± 0.01 0.19 ± 0.01 26 96, 96 16 <0.0001 – – – 0, 0 – – Gonad wt (g) 3.6 ± 0.5 2.3 ± 0.5 57 96, 96 12 0.0005 – – – 0, 0 – – GSI 0.22 ± 0.02 0.14 ± 0.03 57 96, 96 22 <0.0001 – – – 0, 0 – – Coscinasterias Populationmuricata density (ha- 2240 ± 460 0 ± 0 ∞ 20, 20 72 <0.0001 3194 ± 829 233 ± 55 14 18, 18 21 <0.0001 Armspan1)b (cm) 30.1 ± 0.7 – – 20, 0 – – 30.7 ± 0.4 28.6 ± 0.7 7 18, 18 8.8 0.003 Gutted wt (g) 16.5 ± 0.4 – – 0, 0 – – 16.5 ± 0.3 15.5 ± 0.3 6 79, 79 13 0.0003 Drained wt (g) 23.5 ± 0.7 – – 104, 0 – – 21.8 ± 0.6 19.1 ± 0.8 14 79, 79 12 0.0005 PCI 0.21 ± 0.01 – – 104, 0 – – 20 ± 0.01 0.18 ± 0.01 11 79, 79 6.5 0.01 Gonad wt (g) 3.5 ± 0.3 – – 104, 0 – – 1.9 ± 0.2 1.9 ± 0.2 0 79, 79 0.0 1.0 GSI 0.20 ± 0.02 – – 104, 0 – – 0.11 ± 0.01 0.12 ± 0.01 -9 79, 79 0.1 0.8 aFull dataset (Clifton Springs + Grassy Point) with location as fixed term: n = 38, 38, X2 = 1.8, p = 0.18 bFull dataset (Clifton Springs + Grassy Point) with location as fixed term: n = 38, 38, X2 = 43, p <0.0001

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Russell, B.G., Letnic, M., Fleming, P.J.S., 2011. Managing feral goat impacts by manipulating their access to water in the rangelands. Rangel. J. 33, 143–152 Sampson, J., Easton, A., Singh, M., 2014. Port Phillip Bay, in: Wolanski, E. (Ed.), Estuaries of Australia in 2050 and Beyond. Springer, pp. 49–68 Sanderson, E.W., Jaiteh, M., Levy, M.A., Redford, K.H., Wannebo, A. V, Woolmer, G., 2002. The human footprint and the last of the wild. Bioscience 52, 891–904 Saranchova, O.L., Kulakovskii, É.E., 1982. Effect of environmental salinity on different developmental stages of sea stars Asterias rubens and mussels Mytilus edulis. Sov. J. Mar. Biol. 8, 32–36 Sih, A., Bolnick, D.I., Luttbeg, B., Orrock, J.L., Peacor, S.D., Pintor, L.M., Preisser, E., Rehage, J.S., Vonesh, J.R., 2010. Predator-prey naïveté, antipredator behavior, and the ecology of predator invasions. Oikos 119, 610–621 Sih, A., Ferrari, M.C.O., Harris, D.J., 2011. Evolution and behavioural responses to human- induced rapid environmental change. Evol. Appl. 4, 367–387 Suarez, A. V, Case, T.J., 2002. Bottom-up effects on persistence of a specialist predator: ant invasions and horned lizards. Ecol. Appl. 12, 291–298 Suarez, A. V, Holway, D.A., Case, T.J., 2001. Patterns of spread in biological invasions dominated by long-distance jump dispersal: insights from Argentine ants. Proc. Natl. Acad. Sci. USA 98, 1095–1100 Tetzlaff, J.C., Roth, B.M., Weidel, B.C., Kitchell, J.F., 2011. Predation by native sunfishes (Centrarchidae) on the invasive crayfish Orconectes rusticus in four northern Wisconsin lakes. Ecol. Freshw. Fish 20, 133–143 Vitousek, P.M., Mooney, H.A., Lubchenco, J., Melillo, J.M., 1997. Human domination of Earth’s ecosystems. Science 277, 494–499 Vörösmarty, C.J., McIntyre, P.B., Gessner, M.O., Dudgeon, D., Prusevich, A., Green, P., Glidden, S., Bunn, S.E., Sullivan, C.A., Liermann, C.R., others, 2010. Global threats to human water security and river biodiversity. Nature 467, 555–561 With, K.A., 2002. The landscape ecology of invasive spread. Conserv. Biol. 16, 1192–1203 Xu, R.A., Barker, M.F., 1990. Laboratory experiments on the effects of diet on the gonad and pyloric caeca indices and biochemical composition of tissues of the New Zealand starfish Sclerasterias mollis (Hutton) (Echinodermata: Asteroidea). J. Exp. Mar. Biol. Ecol. 136, 23– 45

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CHAPTER FIVE: AN INVASIVE HABITAT-FORMER MITIGATES IMPACTS OF NATIVE HABITAT LOSS FOR ENDEMIC REEF FISHES

ABSTRACT

Impacts of environmental change on animal populations depend on how individuals respond to novel or degraded habitats. Animals that make adaptive habitat selection choices can maximise their fitness in altered landscapes, while maladaptive selection of low quality habitats can lead to the formation of ‘ecological traps’ with increased mortality or lowered reproductive success. Habitat-forming invasive species provide evolutionarily unfamiliar habitat with the potential to create ecological traps. In southern Australia, urchin grazing has driven a decline in native kelps enabling an invasive kelp (Undaria pinnatifida) to fill the vacant niche and provide replacement canopy with unknown effects. We assessed the value of this novel habitat for macroalgae-associated fishes by combining laboratory habitat choice experiments, an artificial reef experiment, before-after-control-impact field surveys and estimates of fitness. In captive choice experiments, macroalgae-associated fishes preferred rock with macroalgal cover to rock without, but did not distinguish between invasive and native macroalgae. Cryptobenthic reef fishes occurred in higher abundance and diversity on urchin-grazed reefs with invasive kelp than those without, while the invader did not affect communities of large or highly mobile fishes. More fish recruited to artificial reefs that were stocked with kelp compared to barren reefs, with no preference between native and invasive kelp canopy. Fitness metrics, including stomach contents, body condition, liver and gonad indices and fecundity in fish collected from invasive kelp habitats were similar to those in fish from adjacent native kelp patches, indicating that this invader can function as a partial replacement for lost native habitat-forming species. These findings illustrate how invasive habitat-formers can play a role in maintaining biodiversity in heavily impacted ecosystems, and reinforces the need for managers to consider effects of habitat-forming invasive species within the broader context of environmental change.

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INTRODUCTION

Ecosystems will continue to undergo dramatic anthropogenic shifts in the coming century, with impacts on fauna likely to depend on how individuals respond to modified or degraded habitats. Animals that make adaptive decisions by responding appropriately to novel risks and resources and that prefer habitats conferring the best fitness outcomes will be most able to persist in impacted landscapes (Sih et al. 2011, Wong & Candolin 2014). However, a lack of evolutionary history in altered environments can create a mismatch between cues and outcomes, causing individuals to make maladaptive decisions (evolutionary trap: Robertson, Rehage & Sih 2013). In the case of habitat selection decisions, this may result in individuals avoiding high quality but unattractive habitats (perceptual traps) or selecting low quality but attractive habitats (ecological traps) (Robertson & Hutto 2006; Patten & Kelly 2010; Hale & Swearer 2016). In impacted landscapes, such individual-level responses to altered habitats could exacerbate population-level effects of environmental change, as animals are either drawn into attractive population sinks from surrounding higher quality habitats, or fail to take advantage of remaining viable habitats in fragmented landscapes because habitat cues have changed (Hale et al. 2015).

Traditional density-based habitat association studies may come to erroneous conclusions about the ecological value of a habitat if individual-level processes are not considered (van Horne 1983). Density-dependent source-sink population models account for differential fitness between habitats (Pulliam 1988), but such models typically assume (often incorrectly) that individuals will choose the best available habitat, and thus ignore the potential for attractive sinks or underutilised sources (Kokko & Sutherland 2001, Battin 2004). By linking individual habitat selection and fitness outcomes, the ecological trap framework provides a more informative approach to assessing the role of habitats in population persistence (Hale et al. 2015, Hale & Swearer 2016).

Invasive habitat-forming ecosystem engineers, such as plants, algae and sessile marine fauna, can alter ecosystems by competing for space with native habitat-formers and by changing the availability of food and shelter for animals (Crooks 2002, MacDougall & Turkington 2005, Gribben & Wright 2006, Pyšek et al. 2012). It is often assumed that invasive species reduce biodiversity in invaded areas, but the body of empirical evidence is equivocal, particularly for animals (Gribben & Wright 2006, Pyšek et al. 2012, Dijkstra et al. 2017). Effects on biodiversity will depend on a range of conditions, including the state of the ecosystem prior to invasion,

103 the quality of the novel habitat for native fauna, and on behavioural responses by native fauna to the novel habitat.

Figure 5.1. Archetypal examples of three rocky reef habitats in northern Port Phillip Bay, Australia: (A) dense canopy of the native kelp Ecklonia radiata, (B) urchin-grazed barren reef, and (C) spring growth of the invasive kelp Undaria pinnatifida on urchin-grazed reef.

Where anthropogenic stressors lead to declines in native habitat-forming species, invasive habitat-formers may take advantage of vacant niches (MacDougall & Turkington 2005). A marine macroalga, the Japanese kelp (Undaria pinnatifida), is one such invader. Native to the north-west Pacific Ocean, and has now established in Europe, the Americas, New Zealand and Australia. However, despite its high profile as a serial invader and a considerable body of literature on its physiology and reproductive ecology (Schaffelke & Hewitt 2007, Davidson et al. 2015), very little is known about its impacts on animal populations in invaded ecosystems (Raffo et al. 2009, 2014, Thomsen et al. 2009, Irigoyen et al. 2011, Howland 2012). In southeastern Australia, Undaria fills a macroalgal niche on artificial substrates and degraded reefs where poor water quality and urchin grazing have driven the decline of native kelps such as Ecklonia radiata (Fig. 5.1). Undaria is a weak competitor in undisturbed macroalgal communities, especially those with laminarian kelps (Valentine & Johnson 2003, Edgar et al. 2004, Farrell & Fletcher 2006, de Leij et al. 2017), but quickly takes advantage of bare substrate and appears better able to persist on degraded reefs (Campbell & Burridge 1998, Valentine & Johnson 2003, Edgar et al. 2004, South & Thomsen 2016).

Seasonal Undaria growth may mitigate loss of fish biodiversity on urchin-grazed rocky reefs if native fish utilize the potential refugia provided by the invasive habitat, and the invasive habitat is of higher quality than the urchin barren habitats that it replaces. Potential benefits

104 will be maximised if the invasive habitat does not act as an ecological trap by attracting fish that might otherwise settle in higher quality native habitats (Robertson & Hutto 2006, Patten & Kelly 2010, Hale & Swearer 2016). In this study, we employ the ecological trap framework to test three predictions about the effects of the invasive habitat-former on native fauna, by surveying fish populations on comparable natural rocky reefs and artificial boulder reefs with differing canopy cover. The directions of our predictions are based on a common expectation that invaded habitats will be both less attractive and of poorer quality than uninvaded habitats, but that the invasive habitat-former may offer better habitat than urchin barrens: native fish will prefer native over invasive kelps over urchin barrens (Prediction 1); native fish communities will differ and be more abundant and diverse in invasive kelp than urchin barrens (Prediction 2); and measures of fitness will be higher for fish in native than invasive kelp habitat (Prediction 3). In testing these predictions, we inform management responses to this established invader, and demonstrate how the ecological trap framework can be employed in future assessments of the effects of invasive habitat-formers on native fauna.

METHODS

Study system

We conducted this study at five locations in Port Phillip Bay (Fig. 5.2; see Appendix 5.1 for location descriptions), a 1930 km2 semi-enclosed marine embayment in south-eastern Australia. The bay is affected by numerous anthropogenic impacts arising from population centres and industrial inputs (Sampson et al. 2014). Soft sediment habitats are predominant, with scattered rocky shorelines and reef patches in 0-8 m depth. Rocky reef habitats in the northern half of the bay have been degraded by a combination of abiotic conditions and urchin grazing, leading to large areas of urchin barrens and epilithic sediment matrix with turfing algae (Ling et al. 2010, Filbee-Dexter & Wernberg 2018). Canopy-forming macroalgae such as the native common kelp (Ecklonia radiata) have been in decline for decades (Jung et al. 2011). Concurrently, the invasive kelp Undaria pinnatifida has been spreading since its introduction in the late 1980s, and is now widespread on rocky reefs and marine infrastructure throughout the bay, with high densities in the northern half (pers. obs.). Throughout its native range and most of its introduced range (including Australia), Undaria is a winter annual, forming dense stands during winter-spring and senescing in summer.

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Figure 5.2. Map of study locations in Port Phillip Bay, Australia. Round markers denote locations with established populations of Undaria pinnatifida. Governor Reef (square marker) is a comparatively unimpacted reef with dense Ecklonia radiata canopy.

Prediction 1: Native fish will prefer native over invasive kelps and urchin barrens habitat

The first criterion for demonstrating an ecological trap is to show that animals either prefer or do not avoid the novel habitat (Robertson & Hutto 2006).

Habitat choice experiment

To investigate whether native reef fish prefer native over invasive kelp, we collected common weedfish (Clinidae: Heteroclinus perspicillatus) and little weed whiting (Odacidae: Neoodax balteatus) individuals from rocky reef habitats and subjected them to a habitat choice trial. These species are among the most common macroalgae-associated fishes in the Bay and are easily housed in aquaria for the duration of the experiment. Both species use macroalgae and seaweed structure as shelter. Clinids are predators of small fish and motile invertebrates and use available cover to stalk and ambush prey. N. balteatus are more active in browsing macroalgal surfaces for epifaunal invertebrate prey such as molluscs and crustaceans.

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We placed individuals in the centre of a four-chambered crucifix-shaped tank (McDermott and Shima 2006; Appendix 5.2) and offered them a simultaneous choice between one reference (bare rock, simulating an urchin barren) and three macroalgal habitat cues (similarly-sized rocks with a Undaria, Ecklonia or Sargassum linearifolium thallus attached via a black cable tie on the holdfast). We provided equal volumes of each species of macroalgae (measured by water displacement) and randomised their positions within the tank for each replicate fish. Undaria and Ecklonia are very similar in height and gross morphology at a given volume, with large fronds making up the bulk of the volume and spreading to cover a large area of substrate. Sargassum tends to be more upright, with gas-filled bladders holding foliage above the substrate. At least 10 thalli of each macroalgal species were used throughout the experiment; for each replicate trial, a thallus was randomly selected and attached to a randomly-selected rock. We held fish in a transparent plastic cylinder in the centre of the tank for 5 min to visually survey the habitat options prior to the commencement of the trial. The cylinder was then raised by a string-and-pulley system with the researcher out of sight (but able to view the fish through a small hole in a screen), and the fish allowed to swim freely around the choice tank. As measures of preference, we recorded both the initial choice of the fish, which was likely to be a primarily vision-based decision (first chamber entered after release from the cylinder) and the location of the fish after 20 mins, which is expected to be a decision based on multiple senses. No fish were recorded in the centre of the tank at the 20 min mark.

Recruitment to artificial reefs

To complement the laboratory habitat choice experiment, we conducted an artificial reef experiment at Half Moon Bay to test the effect of native and invasive kelp canopies on fish recruitment under controlled field conditions. Half Moon Bay was selected as it offered a large expanse of sandy substrate at a suitable depth and in an area of the Bay where Undaria has been established since at least 2009 (Primo et al. 2010). During September 2014, we constructed 20 replicate boulder reefs (1 m2) on sandy substrate, arrayed 20 m apart in a 4 x 5 grid pattern, and randomly assigned each reef to one of three treatments (n = 7 with Undaria canopy, n = 7 with E. radiata canopy and n = 6 as unstocked controls) using kelp thalli collected from an adjacent subtidal reef and attached to the artificial reef using rope and cable ties. We stocked kelp treatments in late July and early August 2015 and recorded natural recruitment of

107 reef fishes on three occasions throughout spring (16 September, 26 October and 31 November 2015). Kelp canopies were replenished as necessary to maintain similar percent coverage (Undaria treatments required regular replenishment as thalli regressed during October and November). As artificial reefs were initially devoid of fish, relative recruitment was quantified in terms of the abundance and diversity of fish present on the reef at each survey date.

Prediction 2: Native fish will be more abundant and diverse in invasive kelp than urchin barrens

To test whether habitat preferences in experimental conditions correspond to patterns of fish abundance in the field, we compared fish communities on heavily urchin-grazed rocky reefs with and without seasonal Undaria canopy using the underwater visual census (UVC) survey method for benthic fish species and baited remote underwater video (BRUV) for larger, more mobile fish species. Survey locations were selected based on the presence of Undaria patches on urchin-grazed subtidal reefs. We primarily focused on a comparison of fish populations in invasive Undaria habitat relative to urchin barrens—rather than native Ecklonia habitat— because (a) the small size and rarity of remaining Ecklonia habitats in northern Port Phillip Bay limited the potential for spatial replication of comparable sites, with Williamstown currently the only known location where urchin barrens, Undaria patches and remnant Ecklonia patches co-occur on rocky reef, and (b) this invader fills vacant habitat on degraded reefs or artificial substrates rather than directly outcompeting native kelps, making the comparison to barren reefs more ecologically-relevant. Nonetheless, we did compare fish populations in Undaria and Ecklonia beds using a diver catch-per-unit-effort metric at one location (Williamstown), as well as BRUV deployments at two locations (Williamstown and St Leonards) (Fig. 5.2). All surveys and collections were conducted by the same researcher (LB) under calm weather conditions (<15 knots) between 1000-1600 hours. Survey and collection efforts alternated randomly between the two habitat types to avoid temporal bias.

Underwater visual census survey

The UVC was conducted on paired circular plots of 12.6 m2 (2 m radius) with comparable rugosity and depth but differing in their coverage of Undaria. The paired design only extended

108 to placement of plots to maximise comparability of plots with and without Undaria, rather than a paired statistical analysis. We employed a before-after-control-impact (BACI) design by surveying plots with and without seasonal Undaria canopy, during and after the Undaria growing season (when the canopy was at its most dense and after it had completely regressed). In this environment, adult Undaria undergo a complete die-off over summer, with no holdfasts remaining by the time of the “after” surveys. We haphazardly placed paired plots 10-15 m apart, and >30 m from adjacent pairs at that location. Populations of small benthic and cryptic fishes are strongly influenced by habitat characteristics at this scale (Willis & Anderson 2003). We dropped a weight to mark the middle of the plot and surveyed larger benthic and benthopelagic fish initially by swimming a circular path outside the plot. Small or cryptic species were detected by a diver moving in concentric circular paths from the outer edge to the centre of the plot, searching within the reef structure and among algal cover. If fish of the same species were observed multiple times within the plot, they were counted as a single fish unless they were clearly distinct individuals (size, sex or markings). Several members of the Heteroclinus genus (H. perspicillatus, H. heptaeolus, H. adelaidae, H. wilsoni, H. eckloniae, H. macrophthalmus) are difficult to reliably distinguish in situ, so were treated as a single species. Accordingly, the species richness metric is likely underestimating the true diversity of the Heteroclinus genus. Upon completing the fish census, we counted the number of urchins present within the plot and estimated percent coverage of kelp, all macroalgae, and algal turf.

Baited video survey

The BRUV system consisted of a GoPro Hero3+ camera mounted on a weighted milk crate with a 1 m long bait arm made from 20mm PVC conduit. Two pilchards (Sardinops sagax) were cut in half and placed in a 20 x 20 cm plastic mesh bag attached to the end of the bait arm. Larger baits are typically used in BRUV systems, but we wished to minimise the spread of the bait plume as elevated rocky reefs in the bay are often very small (10-100 m). BRUV deployments were made at least 50 m apart on comparable reef substrate at depths of 2-4.5 m. Fish observations were made for 20 min from the time when the BRUV unit settled on the sea floor. To assess relative abundance of each species at each site while preventing potential double counting of individual fish, we used a conservative metric termed maxN, where maxN is the maximum number of individuals of a given species occurring simultaneously in the video

109 field of view (Willis & Babcock 2000). In the case of sexually dimorphic species or juvenile fishes, we summed the maxN counts for males, females and juveniles of that species. Where an individual could not be identified to species level, we identified it to the lowest taxonomic rank possible (usually Family or Genus). We included the southern calamari squid (Sepioteuthis australis) in the analysis as it is similar in habit to many inshore fishes.

Catch-per-unit-effort

Collection efforts for N. balteatus, H. perspicillatus and H. heptaeolus in Ecklonia and Undaria habitat patches at Williamstown were timed to allow quantification of catch per unit effort (CPUE). The Williamstown location contained a mosaic of seasonal Undaria growing on urchin- grazed reefs between well-defined remnant patches of Ecklonia and Sargassum. Collections were made by hand net on SCUBA. We calculated CPUE on a per species basis, with each collection dive treated as a single statistical replicate where CPUE = N fish collected / duration of dive. Collection dives alternated between habitats over the course of nine days between 24 October to 28 November 2016.

Prediction 3: Measures of fitness will be higher for fish in native than invasive kelp habitat

The second criterion for demonstrating an ecological trap is to show that a suitable measure of fitness is lower in the putative trap habitat than other habitat options (Robertson & Hutto 2006). We expect that the native kelp habitat will be of higher quality and may lead to higher fitness metrics, although individual fitness (Prediction 2) may not be independent of population density (Prediction 1); population density tends to be higher in high quality habitats, leading to equalisation of individual fitness between habitats of differing quality (ideal free distribution: Fretwell & Lucas 1969).

Fish collected from natural reefs

We compared body condition metrics in N. balteatus, H. perspicillatus and Ogilby's weedfish (Heteroclinus heptaeolus) collected at Williamstown from Undaria and Ecklonia habitats

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(collection efforts described above). The habitat occupied by each individual was defined by the dominant kelp species within 2 m of the collection site, with percentage coverage of macroalgal species estimated visually. Fish were collected using handnets and killed using clove oil, and placed in sealed plastic bags in an ice slurry. Within 24 hours of collection, specimens were weighed (wet weight), measured (total length) and dissected to determine sex and weight of the stomach contents, liver and gonads. Overall body condition was quantified using the relative condition metric recommended by (Le Cren 1951): Krel = W/Wexp, where W is the measured gutted weight and Wexp is the gutted weight predicted by the weight-at-length power curve fitted to all available samples (Fig. 5.3). Individuals with Krel values >1 are heavier than the population mean for their length. One particularly large H. perspicillatus individual from Undaria habitat was removed from the Krel analysis (145 mm, 4.7 standard deviations above the mean). Liver and gonad condition was quantified using hepatosomatic and gonadosomatic indices respectively (HSI or GSI = OW/GW, where OW = wet organ weight and GW = gutted weight). For gravid females, we also photographed and counted subsamples of ova (N. balteatus) or embryos (Heteroclinus spp.) to assess fecundity (Neoodax are broadcast spawners, while Heteroclinus are livebearers).

Fish collected from artificial reefs

We collected H. perspicillatus individuals from artificial reefs (see Prediction 1) from Jan-Nov 2015 and compared body condition and reproductive investment metrics in fish from reefs stocked with Undaria or Ecklonia. Collection methods were identical to those on natural reefs, with collection efforts alternating between Undaria and Ecklonia stocking treatments. There were not enough H. perspicillatus recruits on control (unstocked) reefs for meaningful comparisons of condition on reefs with and without kelp.

Statistical analysis

Choice data (Prediction 1) were tested for habitat preference using a X2 test of proportions implemented in R (R Core Team 2017), with expected (null) proportions equally distributed across the four habitat cues. Artificial reef recruit abundance and species richness data (Prediction 1) were compared across kelp treatments using Poisson generalised linear mixed

111 effects models implemented in the lme4 package for R (Bates et al. 2015). We included Treatment (stocked kelp species: Undaria, Ecklonia or barren) as a fixed term, KelpCover (percentage coverage of canopy) as a covariate, and as reefs were surveyed repeatedly, a reef identity random intercept term (ReefID) nested within Treatment. There was some data overdispersion, so we included an observation-level random term to account for excess variation and avoid overestimating the predictive ability of model terms (Harrison 2014). We tested for a significant Treatment effect by comparing the fit of models with and without the Treatment term, while the lsmeans package for R provided Tukey’s pairwise post-hoc comparisons of the three treatments (Lenth 2016).

Fish community data from UVC and BRUV surveys (Prediction 2) were fitted to a permutational multivariate ANOVA model implemented in PRIMER 6 with the PERMANOVA+ add-on (Anderson et al. 2008). Data were log(x+1) transformed to reduce the influence of a few highly abundant species and fitted to a Bray-Curtis similarity resemblance matrix with a dummy variable of 1. The models contained two fixed factors, Habitat and Location, as well as an interaction term (Habitat×Location). The Location factor contained two levels (North and West), reflecting environmental differences between sites in the north (Point Cook, Altona Bay, Williamstown, Half Moon Bay) and west (Kirk Point) of the Bay. The models were fitted using Type III sums of squares, with unrestricted permutation of raw data and 9999 permutations. We also extracted diversity metrics using the Diversity function and identified species that were associated most strongly with the observed differences between habitats and locations using the similarity percentage (SIMPER) function. Finally, data were visualised using multidimensional scaling (MDS) and canonical analysis of principal coordinates (CAP) plots.

Catch per unit effort data were log(x+1) transformed to improve normality, and compared across Ecklonia and Undaria habitats using a linear analysis of covariance (lm function in R) with Habitat as a fixed factor and sampling Date as a temporal covariate.

We compared fitness metrics (Prediction 3) in native and invasive kelp habitats using a series of univariate linear models implemented in R. Response variables were checked for normality and equality of variance and transformed as necessary. We analysed both weedfish species (H. perspicillatus and H. heptaeolus) with a single model and included terms for kelp habitat type (‘Habitat’), species (‘Species’), day of the season (‘Day’), and sex (‘Sex’). Models for the sequentially hermaphroditic N. balteatus included only Habitat and Day terms. Fitness metrics for H. perspicillatus recruits collected from artificial reefs (Prediction 3) were compared across kelp treatments using univariate linear mixed models implemented in lme4, and included

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Treatment, KelpCover and Sex as fixed terms and Treatment/ReefID as a random intercept term. In all cases, we tested the significance of the effect of interest by comparing the fit of models with and without the relevant term.

Plots were produced using the ggplot2 package for R (Wickham 2009).

Figure 5.3. Weight-at-length relationships for (A) Heteroclinus perspicillatus and H. heptaeolus and (B) Neoodax balteatus individuals collected from Undaria pinnatifida and Ecklonia radiata habitats in Port Phillip Bay.

RESULTS

Prediction 1: Native fish will prefer native over invasive kelps and urchin barrens habitat

Habitat choice experiment

Common weedfish (Heteroclinus perspicillatus) were more likely to select macroalgal cover than barren rock, both initially (1.25x more than expected; p <0.0001) and after 20 mins (1.22x more than expected; p <0.0001) (Fig. 5.4A; Appendix 5.3). Of those that chose macroalgae during their initial decision, Undaria was preferable to Ecklonia or Sargassum (2.0x and 2.7x more likely, respectively; p = 0.015), but after 20 mins, individuals were evenly distributed across the three macroalgal options (p = 0.9) (Fig. 5.4A; Appendix 5.3).

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Figure 5.4. Habitat choice trial results for (A) common weedfish (Heteroclinus perspicillatus): N = 48; (B) little weed whiting (Neoodax balteatus): N = 23.

Little weed whiting (N. balteatus) also preferred macroalgae to bare rock, both initially (1.16x more than expected; p = 0.0004) and after 20 mins (1.22x more than expected; p <0.0001) (Fig. 5.4B; Appendix 5.3). However, we observed no clear preference among macroalgal options, either initially (p = 0.12) or after 20 mins (p = 0.4) (Fig. 5.4B; Appendix 5.3).

Table 5.1. Summary of fish recruitment to artificial reefs stocked with Undaria pinnatifida, Ecklonia radiata, or left barren.

Treatment Kelp canopy cover (%) Abundance reef-1 Species richness reef-1 N surveys Undaria 42 ± 3 0.9 ± 0.1 0.8 ± 0.1 89 Ecklonia 73 ± 2 1.1 ± 0.1 1.1 ± 0.1 89 Barren 5 ± 5 0.4 ± 0.2 0.4 ± 0.2 18

Pairwise U-E z = 1.7, p = 0.21 z = 1.6, p = 0.24 U-B z = 2.3, p = 0.05 z = 2.8, p = 0.02 E-B z = 3.0, p = 0.008 z = 3.3, p = 0.003

Recruitment to artificial reefs

A 2.5x higher abundance (p = 0.01) and 2.4x higher species richness (p = 0.007) of reef fish recruited to artificial reefs stocked with kelp relative to those with turfing algae only (Table

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5.1). There was no evidence for differential recruitment between reefs stocked with either kelp (p = 0.21), despite the Undaria treatment having less canopy cover (42 vs. 73 %) (Table 5.1). The canopy cover covariate positively predicted recruit abundance (p = 0.05), indicating that the amount of cover may be more important than the macrophyte species providing the cover. Canopy cover did not affect species richness (p = 0.36).

Prediction 2: Native fish will be more abundant and diverse in invasive kelp than urchin barrens

Visual census survey

We recorded 19 fish species across 25 Undaria and 26 barren plots during in-season surveys (September-October 2016) (Appendix 5.4) and found a significant effect of Habitat (Undaria or Barren), Location (west or north Port Phillip Bay), and Habitat×Location on dissimilarity between plots (PERMANOVA: Appendix 5.5). Canonical analysis of principle coordinates (CAP) plots revealed visual separation of sites with and without Undaria, driven primarily by a greater abundance of Heteroclinus spp. and Diodon nicthemerus in Undaria plots (Fig. 5.5). SIMPER analysis determined that weedfish (Heteroclinus spp.: 28 %), Clarke’s threefin (Trinorfolkia clarkei: 23 %), Tasmanian blenny (Parablennius tasmanianus: 10 %) and globefish (D. nichthemerus: 7 %) contributed most to the observed dissimilarity between habitats (weedfish and globefish in Undaria plots, threefins and blennies in Barren plots). Gross community metrics also differed; species richness and total abundance were both higher on Undaria plots (2.0x and 1.9x higher, respectively: Fig. 5.6A, Appendix 5.6). Barren and Undaria plots were placed at comparable depths (mean ± SD: 2.6 ± 0.5 m cf. 2.7 ± 0.4 m, respectively). Barren plots contained 6 ± 2 % macroalgal cover, mostly sea lettuce (Ulva spp). Undaria plots contained 72 ± 3 % macroalgal cover, dominated by Undaria (58 %) with some secondary cover from other ephemeral macroalgae, including Ulva spp. and Gracilaria spp. Undaria plots did not contain any other brown algal species such as Ecklonia radiata or Sargassum spp. Where Ulva or Gracilaria were present on Undaria plots, clinids (Heteroclinus spp. and Cristiceps australis) were generally found in Undaria microhabitat (12/16 fish).

We resurveyed 18 plots (9 Undaria, 9 Barren) after the Undaria had completely regressed (April-May 2017). Sparse early stage Undaria recruits (<10 cm) were present in May, but

115 provided a negligible amount of macroalgal cover. The effect of the Habitat factor on overall fish community structure was significant (PERMANOVA: Appendix 5.5), but neither species richness nor abundance significantly differed (Appendix 5.6).

Analysing in-season and off-season data together revealed significant overall effects of Habitat, Season and Location on fish community structure, as well as a Habitat×Location interaction (PERMANOVA: Appendix 5.5). Off-season Undaria and Barren plots both contained abundant non-canopy-forming ephemeral macroalgae, dominated by Ulva spp., Gracilaria spp. and Caulerpa spp., with greater mean coverage (51 ± 12 %) on Undaria plots relative to Barren plots (32 ± 12 %).

Overall, macroalgal cover negatively correlated with urchin density (r = -0.42, t67 = -3.7, p = 0.0004). We found no evidence that off-season macroalgal cover on resurveyed plots was predicted by in-season cover of either Undaria (F16 = 0.5, p = 0.5) or all macroalgae (F16 = 1.5, p = 0.24).

Baited video survey

We recorded 28 fish species across 14 Undaria and 15 barren deployments (Appendix 5.4). Fish communities did not differ between habitats or locations (PERMANOVA: Appendix 5.6), and nor did species richness or combined MaxN (Appendix 5.6). BRUV data included some non- reef-associated species, but restricting the analysis to reef-associated species did not alter our interpretation.

Diver catch per unit effort

Collection efforts provided 45 Neoodax balteatus, 24 Heteroclinus perspicillatus and 18 Heteroclinus heptaeolus from 12 dives (total 465 min) in Undaria and 13 dives in Ecklonia (total 690 min) habitats over 9 days between 24 October and 28 November 2016. Catch per unit effort (CPUE) of N. balteatus was 2.6x higher in Ecklonia than Undaria habitat (p = 0.04), but CPUE of Heteroclinus spp. did not differ (Table 5.2). CPUE of N. balteatus also increased throughout spring, coinciding with warmer water and greater fish activity (Date covariate: R2 = 0.26, p = 0.007; Table 5.2). There was no evidence that CPUE of Heteroclinus spp. increased over time (p = 0.6; Table 5.2).

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Table 5.2. Comparison of reef fish relative abundance, estimated by diver catch per unit effort (CPUE), in Undaria and Ecklonia habitats, and over time, at Williamstown. Number of collection efforts (N dives) are given as ‘Undaria, Ecklonia’. N dives differ between species as not all dives targeted all species. Terms are tested by maximum likelihood ratio comparison of fitted and null models (X2). Positive Cohen’s d effect sizes indicate metrics were higher in Undaria habitat.

Habitat effect Undaria Ecklonia N dives X2 p Cohen’s d Heteroclinus perspicillatus 0.9 ± 0.3 2.0 ± 0.6 13, 10 1.4 0.12 -0.74

Heteroclinus heptaeolus 0.7 ± 0.2 1.0 ± 0.4 13, 12 0.04 0.91 -0.30 Heteroclinus spp. 1.5 ± 0.4 2.7 ± 0.8 13, 12 0.32 0.73 -0.48 Neoodax balteatus 1.6 ± 0.4 4.1 ± 1.0 9, 10 2.1 0.04 -1.08

Temporal effect R2 N dives X2 p Heteroclinus perspicillatus -0.02 13, 10 0.74 0.33 Heteroclinus heptaeolus 0.10 13, 12 0.77 0.19 Heteroclinus spp. 0.03 13, 12 0.14 0.87 Neoodax balteatus 0.26 9, 10 3.3 0.007

CPUE was not formally assessed in barren habitats, as both Heteroclinus spp. and N. balteatus are rare in areas without macroalgae. In the course of surveying 26 barren UVC plots during the Undaria growing season in the northern part of the Bay, we only observed one clinid—a H. perspicillatus individual inhabiting a macroalgal microhabitat on an otherwise barren plot— and no N balteatus (Appendix 5.4). Accordingly, CPUE in the barrens would likely be close to zero.

Prediction 3: Measures of fitness will be higher for fish in native than invasive kelp habitat

Fish collected from natural reefs

N. balteatus individuals collected from Ecklonia habitat were 1.3x larger (p = 0.007) and 2.4x heavier (p = 0.01) than those in seasonal Undaria habitat (Table 5.3), and a higher proportion were male (Ecklonia: 7/23; Undaria: 1/22). We found no evidence that body condition or reproductive fitness metrics differed between Ecklonia and Undaria habitats, although the direction of effect for reproductive metrics was generally positive in Undaria habitats (Table 5.3).

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We also found little evidence that H. perspicillatus or H. heptaeolus collected from Ecklonia or Undaria habitats experience differential habitat quality, with no difference in size, body condition or reproductive investment between habitat types (Table 5.3). However, the proportion of fertilised eggs was 1.4x higher in weedfish living in Ecklonia habitats (p = 0.003; Table 5.3).

Table 5.3. Comparison of body condition and reproductive condition metrics in reef fishes collected from Undaria and Ecklonia habitats at Williamstown (natural reefs) and Half Moon Bay (artificial reefs). N = ‘Undaria, Ecklonia’. Habitat effect is tested by comparing fit of models with and without Habitat term. Positive Cohen’s d effect sizes indicate metrics were higher in Undaria habitat.

Undaria Ecklonia N Stat p d Neoodax balteatus Natural reefs F Length (mm) 73 ± 4 96 ± 6 22, 23 8.2 0.007 -0.97 Gutted weight (g) 3.9 ± 0.9 9.5 ± 1.7 22, 23 7.2 0.01 -0.87 Wrm 101 ± 1 99 ± 1 22, 23 4.0 0.05 +0.50 Hepatosomatic index (*1000) 30 ± 2 28 ± 2 22, 23 2.1 0.15 +0.20 Stomach index (*1000) 25 ± 2 27 ± 2 22, 23 1.0 0.32 -0.19 Gonadosomatic index (*1000) 51 ± 8 48 ± 10 22, 23 0.2 0.67 +0.07 Mature eggs 789 ± 245 516 ± 213 17, 10 0.5 0.49 +0.30 Eggs 3162 ± 936 2789 ± 850 17, 10 0.5 0.49 +0.11 Mature eggs gutted weight-1 198 ± 39 116 ± 29 17, 10 0.6 0.63 +0.59 Eggs gutted weight-1 742 ± 128 568 ± 176 17, 10 0.3 0.34 +0.32 Egg maturity (%) 26 ± 4 24 ± 8 17, 10 <0.1 0.93 +0.06

Heteroclinus spp. Natural reefs F Length (mm) 74 ± 5 65 ± 2 20, 22 1.3 0.26 +0.52 Gutted weight (g) 4.1 ± 0.9 2.6 ± 0.3 20, 22 1.6 0.21 +0.50 Wrm 102 ± 3 100 ± 3 20, 22 0.2 0.63 +0.12 Hepatosomatic index (*1000) 19 ± 1 21 ± 3 20, 22 <0.1 0.98 -0.09 Stomach index (*1000) 24 ± 3 42 ± 17 20, 22 2.0 0.16 -0.31 Gonadosomatic index (*100) 12 ± 2 11 ± 3 20, 22 0.6 0.45 +0.10 Embryos 256 ± 35 198 ± 33 13, 12 0.8 0.39 +0.48 Eggs 516 ± 102 274 ± 66 13, 12 2.9 0.10 +0.81 Embryos gutted weight-1 68 ± 9 92 ± 16 13, 12 2.4 0.14 -0.52 Eggs gutted weight-1 124 ± 21 126 ± 28 13, 12 0.3 0.58 -0.02 Egg fertilisation (%) 60 ± 6 81 ± 7 13, 12 11 0.003 -0.90 Artificial reefs: X2 Length (mm) 51 ± 4 62 ± 4 11, 20 0.6 0.45 -0.71 Gutted weight (g) 1.3 ± 0.3 2.3 ± 0.4 11, 20 0.9 0.35 -0.70 Wrm 1.3 ± 0.1 1.3 ± 0.1 11, 20 0.3 0.58 +0.01 Hepatosomatic index (*1000) 43 ± 17 21 ± 3 11, 20 5.9 0.01 +0.53 Stomach index (*1000) 16 ± 4 15 ± 3 11, 20 <0.1 0.99 +0.08 Gonadosomatic index (*1000) 12 ± 5 55 ± 19 11, 20 0.8 0.37 -2.39

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Dietary assessment revealed that almost all clinids had consumed mysid shrimp, both in Undaria and Ecklonia habitats. Some individuals had also consumed amphipods (Undaria and Ecklonia), isopods (Undaria only) or decapods (Ecklonia only). Amphipods were the most frequently identifiable prey items for N. balteatus (Ecklonia only). Animal prey items from N. balteatus in Undaria habitats were not identifiable, although there was no significant difference in the weight of stomach contents (Table 5.3).

Fish collected from artificial reefs

Hepatosomatic index was 2.0x higher in H. perspicillatus individuals collected from reefs stocked with Undaria canopy versus Ecklonia canopy (p = 0.01), while other fitness metrics were unaffected by kelp canopy treatment (Table 5.3).

DISCUSSION

Some results indicate that the presence of the invasive habitat-forming ecosystem engineer does not create an ecological trap for native fish. Contrary to Prediction 1, that native fish will prefer native to invasive kelp, the laboratory habitat choice experiment revealed that two macroalgal-associated reef fishes were equally willing to utilise shelter provided by native and invasive kelp. Similarly, fish recruits did not distinguish between artificial reefs stocked with invasive or native kelp, but preferred both to barren reefs. Our population survey data broadly supported Prediction 2, that fish communities will be more abundant and diverse in native kelp than invasive kelp than urchin barrens, with some evidence for higher population densities in native kelp beds, but invasive kelp patches greatly improved fish abundance and diversity on heavily urchin-grazed reefs where native canopy-forming macroalgae are absent. Body condition and reproductive investment metrics indicate that fish inhabiting these invasive kelp patches have similar or better body condition to those in adjacent native kelp beds, contradicting Prediction 3 (that fish in native kelp habitats will have higher fitness metrics than those in invasive kelp habitats), although we did not make any direct comparisons of mortality rates between habitats. Overall, this invasive habitat-forming kelp appears to provide valuable habitat for native fishes on urchin-grazed urban-impacted rocky reefs.

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Invasive habitat-forming species can deleteriously affect faunal populations in invaded landscapes whether the invasive habitat is avoided, leading to habitat loss (Trammell & Butler 1995, Valentine et al. 2007), or occupied, leading to poor fitness outcomes (Remeš 2003, Lloyd & Martin 2005, Rodewald et al. 2010). However, where the invader adds physical structure or a novel food source, there can be beneficial effects for some native taxa and increases in local biodiversity (Baldwin & Lovvorn 1994, Crooks 2002, Castilla et al. 2004, Byers et al. 2012, Wright et al. 2014). Our study highlights the potential benefits of a weakly-competitive invasive habitat-forming species in mitigating the impacts of habitat degradation on native biodiversity.

Figure 5.5. Canonical analysis of principle coordinates (CAP) showing variation in fish communities across underwater visual census (UVC) plots with and without Undaria pinnatifida canopy (Undaria: grey; Barren: black), during and after the Undaria growing season. Radiating lines indicate direction and strength of influence of fish species on the observed variation between UVC plots. Only species with Pearson’s correlation >0.40 are shown. Full species names: Brachaluteres jacksonianus, Diodon nicthemerus, Foetorepus calauropomus, Heteroclinus perspicillatus, Ophiclinus ningulus, Parablennius tasmanianus, Trinorfolkia clarkei, Trachinops caudimaculatus.

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In this study system, the invasive habitat-former is highly seasonal, and may influence the reproductive success of local reef fishes to the extent that it coincides with reproductive provisioning and larval settlement phases (conceptual model: Appendix 5.7). Common macroalgae-associated reef fishes such as Heteroclinus spp. and N. balteatus inhabit Undaria patches throughout vitellogenesis and into the spawning period that occurs from late winter to late spring (Gunn & Thresher 1991; Neira & Sporcic 2002; this study). Our data on stomach contents, body condition and reproductive investment indicate that adults residing in Undaria habitat experience comparable food availability to those in native Ecklonia habitats – perhaps as a result of lower population density and therefore less competition in Undaria patches. This would be consistent with the ideal free distribution theory (Fretwell & Lucas 1969). Regardless, it is unlikely that individuals that choose to reside in Undaria habitats during vitellogenesis are falling into a condition-driven ecological trap. We did find evidence that fertilisation rates were lower in the ovaries of H. perspicillatus and H. heptaeolus collected from Undaria patches. This may reflect mate-finding difficulty in small habitat patches that contain few if any conspecifics and are separated from adjacent patches by urchin barrens. Undaria patches on these degraded reefs are increasing in size with successive seasons (pers. obs.), and we expect that this possible Allee effect will ameliorate over time.

We focus on juvenile and adult fish, but larval settlement is also important in assessing the role of the invasive habitat-former. At present, no literature exists on the settlement preferences of larval Heteroclinus spp. or Neoodax balteatus. Clinid larvae are at peak densities in the water column in spring, with settlement occurring during spring-summer (Gunn & Thresher 1991, Neira & Sporcic 2002). This coincides with peak densities of Undaria canopy, and it is likely that if larvae have similar habitat preferences to adults, large numbers may recruit to these habitats shortly before the summer die-off of Undaria canopy (Appendix 5.7). Such a loss of cover on otherwise barren reefs warrants investigation as a potential temporal ecological trap for clinid recruits. Such a trap may occur if the benefits of inhabiting the invasive kelp are outweighed by the eventual loss of cover. However, the rapid appearance of late stage juvenile and adult fish on our plots and artificial reefs in summer—together with anecdotal reports of clinids ‘rafting’ in unattached macroalgae—suggests that migration is common and perhaps does not greatly increase mortality rates. As quantifying movement and mortality of cryptobenthic fishes settling in invaded and non-invaded habitats remains a considerable challenge, future studies would benefit from developing novel approaches to discriminate between these two key demographic processes. N. balteatus settlement peaks later in summer (Neira & Sporcic 2002), by which time Undaria thalli have regressed to sporophyll and

121 holdfasts and may no longer offer attractive habitat for recruits. Such species likely disperse into the invasive habitat as adults, perhaps through spillover from areas of high population density in remnant native kelp habitats.

Figure 5.6. Fish community metrics from underwater visual census plots on urchin-grazed reefs (A) during and (B) after the Undaria pinnatifida growing season.

Implications for management

In this study we demonstrated the application of the ecological trap theory in assessing the impacts of novel habitats on native fauna. In this case, rather than revealing the existence of an ecological trap, our findings provide evidence that an invasive habitat-forming species can have value for faunal communities when it fills a niche left vacant by native habitat-formers. Previous work has shown that in contrast to many invasive ecosystem engineers, this invader appears to be a passenger rather than a driver of ecological change (Valentine & Johnson 2003, Edgar et al. 2004, South & Thomsen 2016), and that it supports diverse invertebrate communities (Howland 2012). Together with our findings of benign effects on at least some key native fish taxa and its functional replacement of native macroalgae in large areas, this leads us to recommend that removal of Undaria should not be a high management priority within the Bay. Eradication is certainly impossible here, and where an invasive species has functionally replaced a native species (as is the case with Undaria on urchin-grazed reefs), removal may not be sufficient to restore the natural ecosystem (Reid et al. 2009), and furthermore, is likely to drive additional biodiversity loss where native fauna depend on the invasive habitat for food or shelter (Zavaleta et al. 2001). Instead, we recommend that managers (a) focus on measures to restore the native kelp canopy, and (b) consider the

122 potential for beneficial effects of introduced habitat-formers, particularly in degraded environments, and when possible, focus control or eradication efforts on invaders that are highly competitive in the invaded environment and/or drive declines in biodiversity of native species.

ACKNOWLEDGEMENTS

We wish to thank Dean Chamberlain, Seann Chia, Ben Cleveland, Emily Fobert, Molly Fredle, Akiva Gebler, Kevin Jensen, Valeriya Komyakova, Nina Kriegisch, Kevin Menzies, Jack O’Connor, Simon Reeves, Juan Manuel Valero Rodriguez, Kyler Tan, Chris Taylor and João Teixiera for their assistance with fieldwork. Members of the SALTT and REEF labs provided useful comments on the manuscript. All work was conducted in accordance with permits from the Victorian Fisheries Authority (RP919) and the University of Melbourne’s Faculty of Science Animal Ethics Committee (1413193 and 1413133). This research was funded by grants to LB from the Holsworth Wildlife Research Endowment, the PADI Foundation, and the Victorian Environmental Assessment Council. The authors have no competing interests to declare.

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CHAPTER SIX: GENERAL DISCUSSION

Incorporating data on individual-level responses to environmental change can fundamentally improve our understanding of associations between novel habitats and wild animal populations, and especially the role of novel habitats for population persistence in a source– sink metapopulation framework. The ecological trap literature highlights the importance of combining individual- and population-level data to distinguish between attractive population sources, unattractive population sources, attractive population sinks and unattractive population sinks.

HOW PREVALENT ARE ECOLOGICAL TRAPS IN THE MARINE ENVIRONMENT?

Ecological traps are probably more common than the current evidence indicates. Clear demonstrations of traps are rare, but it would be surprising if this reflects consistently adaptive habitat selection by animals in novel and degraded environments (Sih et al. 2011, Robertson et al. 2013, Sih 2013). More likely it reflects a lack of research effort on the topic, with the vast majority of habitat association studies focusing on community-level survey data. As a result, most researchers are not obtaining suitable estimates of habitat preference, nor studying the fitness consequences of those habitat selection decisions. Furthermore, Battin (2004) noted that researchers in terrestrial systems frequently presented findings that were consistent with the existence of an ecological trap but did not interpret the findings within the ecological trap framework. Similar cases exist within the marine literature: several of the examples I cite in this thesis as evidence for marine ecological traps were not interpreted as such by the authors, including flatfish and crabs selecting oil-contaminated sediment (Moles et al. 1994, Moles & Norcross 1998, Moles & Stone 2002), bivalves selecting sediment modified by Caulerpa taxifolia (Wright & Gribben 2008, Gribben et al. 2009a, 2009b, Byers et al. 2010), and fish recruits avoiding reefs with resident native predators but not those with invasive lionfish (Benkwitt 2017). In some cases, authors referred to probable ecological trap habitats as ‘population sinks’ (e.g. Gribben et al. 2009b), consistent with greater awareness of the source-sink framework, or otherwise noted likely deleterious population effects. However, as ecological trap models demonstrate, an attractive population sink (ecological trap) has more severe implications for population persistence than a ‘traditional’ non-preferred population

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sink as it actively draws in individuals from surrounding higher quality areas (Battin 2004, Abrams et al. 2012, Hale et al. 2015). In other words, non-preferred population sinks are likely to conform to the ideal free distribution (with each individual optimising its fitness within available habitats: Fretwell & Lucas 1969), while ecological traps do not, causing fauna populations to underperform relative to expectations.

Additional evidence for the likely prevalence of ecological traps comes from the rate at which researchers looking for traps find them. While it may be impossible to account for bias in research effort (i.e. researchers selecting systems that are likely to be vulnerable to ecological traps, or setting aside the ecological trap hypothesis when no evidence is found), there are notably few cases of researchers looking for traps and not finding them (but see Dempster et al. 2011; Reubens et al. 2013). In Chapters 3-5, I investigated three novel marine systems that I considered to be vulnerable to the formation of ecological traps. In Chapter 3 I found some limited evidence that offspring of Atlantic cod from an area of high salmon farming density produced smaller eggs and larvae, potentially leading to reduced fitness. In Chapter 4, an absence of small Asterias seastars within mussel farms was consistent with either high mortality or emigration in this habitat, while adult Asterias may be avoiding a habitat rich in food but also potential predators. In Chapter 5, I concluded that juvenile and adult weedfish and weed whiting are probably making broadly adaptive habitat-selection decisions in Undaria habitats (consistent with ideal free distribution), but that there are possible Allee effects in small patches and that loss of canopy during summer senescence might be costly for weedfish larvae that recruit to the Undaria canopy in spring if the value of the Undaria canopy is outweighed by its eventual disappearance.

Intriguingly, in all cases, my findings indicated that fauna associated with novel marine habitats were most likely either making adaptive habitat selection decisions or falling into ecological traps. I found no evidence that any of these novel habitats were likely to function as perceptual traps (Fig. 6.1). This is consistent with the current body of literature, in which novel habitats are more commonly found to be ecological traps than perceptual traps (but see Patten and Kelly 2010). I see three possible reasons for this trend: (1) novel habitats may, on average, offer poorer fitness outcomes than natural habitats for native species due to a lack of evolutionary adaptation to the novel habitat and likely disadvantages relative to non-native competitors or predators (Byers 2002, Crooks et al. 2011); (2) if a novel habitat appears similar to an evolutionarily-familiar habitat, individuals will likely attempt to utilise it; and (3) an abundance of a native species in a novel habitat is more likely to arouse the interest of

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ecologists than an absence, as we are accustomed to native taxa avoiding novel habitats and implicitly assume this to be a broadly adaptive response.

Figure 6.1. Conceptual diagram showing speculative positioning of responses of focal species to novel habitats, indicating potential differences in responses and fitness outcomes across life stages (Asterias and Coscinasterias at shellfish farms; Heteroclinus and Neoodax in Undaria canopy; Atlantic cod at salmon farms) placed within the ecological trap framework. Species placed within the green area exhibit adaptive habitat selection responses. Juvenile Coscinasterias were not observed inside the shellfish farm, and it is unclear whether this reflects habitat preferences or high mortality.

HOW DOES THE ECOLOGICAL TRAP CONCEPT RELATE TO THE ATTRACTION–PRODUCTION DEBATE?

Ultimately, the ecological trap and attraction–production concepts are directed toward the same objective: understanding the role of a given habitat for population growth, with a focus on ecologically-novel habitats. The ecological trap concept pre-dates both source–sink models and the attraction–production debate (Dwernychuk & Boag 1972, Gates & Gysel 1978), but the source–sink model experienced more rapid uptake among ecologists, while the attraction– production debate arose in response to questions about the role of artificial reefs for production of targeted fish species during the 1980s (Lindberg 1997). 130

Uptake of the ecological trap concept has been slow in the fisheries management literature, but in my view, it offers something that the attraction–production distinction does not. The typical application of the attraction–production models looks at two questions: are fish attracted to the given habitat, and does the existence of this habitat lead to more fish being produced? Under the attraction hypothesis, fish move from surrounding areas to the new habitat, with no net increase in production. Under the production hypothesis, the new habitat increases the carrying capacity of a habitat-limited environment and leads to increased fish production. These two hypothesis are not mutually exclusive and likely co-occur in many cases, but the limitation of the attraction–production framework is that researchers are liable to miss a third possibility – that poor individual fitness outcomes in an attractive novel habitat will create an ecological trap that leads to serious negative effects on fish production, such that it would be better for the habitat not to exist at all (Reubens et al. 2014). The ecological trap assessment framework (see Fig. 1.1) accounts for this possibility, and its corollary, an unattractive but high quality habitat that is underutilised (termed a perceptual trap by Patten and Kelly 2010). Gutzler et al. (2015) considered both frameworks in their assessment of the role of casitas for targeted lobsters as individuals and as a population, while Reubens et al. (2014) proposed a hybrid ‘attraction–ecological trap–production’ framework for assessing the role of artificial reefs for fish populations, within which researchers ask the following questions:

(1) Does attraction towards the artificial reef occur? (2) If there is attraction, is it age-group specific? (3) Which mechanisms or processes influence production in the ecosystem investigated, and are these mechanisms/processes affected by the artificial reef? (4) What is the species-specific behavioural ecology in this ecosystem? (5) If there is production, is it sufficient to offset associated fishing mortality?

This integration of two assessment frameworks, with two largely independent bodies of research focusing on terrestrial birds (ecological traps) and artificial reefs (attraction– production), can also be expressed as a conceptual diagram (Fig. 6.2). The four scenarios outlined in the diagram are extreme outcomes, and intermediate outcomes are possible. For example, newly-created, unattractive, low quality habitats may have a small positive effect on production in habitat-limited systems if they offer a sufficient fitness value for individuals spilling over from densely-populated preferred habitats (adaptively following the ideal free distribution).

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Relative fitness outcomes for individuals

Good Poor

Relative habitat Attractive Source ↑ Ecological trap ↓ attractiveness Unattractive Perceptual trap — Sink —

Figure 6.2. Conceptual representation of the ecological trap framework applied to an assessment of the role of artificial reefs for fish production. “↑” indicates an increase in fish production (e.g. fish utilise a viable new habitat in a habitat-limited system), “↓” indicates a decrease in fish production (e.g. fish are attracted to a habitat that offers low availability of food or mates, or confers high mortality), and “—" indicates a neutral effect on fish production (as the new habitat is not utilised).

BETTER LEFT ALONE? LESSONS FOR THE MANAGEMENT OF HABITAT-FORMING INVASIVE SPECIES

Most human-mediated species introductions do not establish, but of those that do, a few will become invasive (Richardson & Pyšek 2012). Such invasions may be an important factor in native species extinctions worldwide (Clavero & García-Berthou 2005), and some particularly influential invasive ecosystem engineers have driven phase shifts in natural ecosystems (Crooks 2002). Management of non-native arrivals often begins with immediate eradication attempts, but once the window for effective eradication has passed, management strategies must be reassessed. In cases where the invader is an opportunistic passenger rather than a driver of ecological change, control measures may be neither effective nor desirable (Zavaleta et al. 2001, Didham et al. 2005, MacDougall & Turkington 2005). This may be especially true where invasive habitat-forming species replace native habitat-formers and native animals come to rely on the invasive habitat (Zavaleta et al. 2001). However, a major limitation in our understanding of the impacts of invasive habitat-formers is a scarcity of data on habitat preferences and fitness outcomes for native fauna inhabiting invasive habitats.

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Figure 6.3. Current distribution of Undaria pinnatifida within Port Phillip Bay in 2017. Red markers denote documented established populations on rocky reefs or marine infrastructure; blue markers denote recent infestations. A satellite population has also established at Apollo Bay (97 km SW) and another was discovered and eradicated at Flinders (45 km SE). Both expansions were likely the result of boat transport.

The invasive kelp Undaria pinnatifida, a habitat-forming ecosystem engineer, provides an illustrative case study. Despite being one of only two macroalgal taxa included on the IUCN’s list of 100 of the world’s worst invasive alien species (Lowe et al. 2000), the subsequent literature on invasive Undaria populations in Australia, New Zealand and Europe indicates that this species is a passenger of general ecological degradation, taking advantage of rocky reef substrate left vacant by declining native kelp canopy but only persisting at low population densities in intact native macroalgal habitats (Valentine & Johnson 2003, Edgar et al. 2004, South & Thomsen 2016, Kriegisch et al. 2016, de Leij et al. 2017, South et al. 2017). Effects of the invasive canopy on native faunal biodiversity are less well known, but in South America, invasive Undaria cover was associated with elevated macroinvertebrate species richness, diversity and abundance compared to plots where early stage Undaria recruits were removed to prevent canopy formation (Irigoyen et al. 2011). Similarly, work in Australia found that invasive Undaria and native Ecklonia radiata holdfasts contain comparable abundance and species richness of epibenthic invertebrates (Howland 2012), indicating that Undaria is likely to have neutral or positive effects on biodiversity in degraded areas. In Chapter Five, I

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demonstrated that the presence of Undaria on degraded reefs is associated with elevated fish biodiversity and provides viable habitat for a variety of endemic fish species in the months leading up to spring spawning. This work is a rare assessment of outcomes for native species utilising invasive habitats.

Despite this growing body of evidence that Undaria canopy is utilised by native fish and invertebrates, coupled with broadly benign effects on native macroalgal communities, authorities are undertaking ongoing eradication efforts at local sites. Such efforts are unlikely to be effective in the long term (e.g. Hewitt et al. 2005), and as they necessarily occur during the Undaria growing season in spring and summer, are likely to displace resident macroalgae- associated fish in spawning condition. Accordingly, I recommend that eradication efforts cease in locations where Undaria is established (see Fig. 6.3 for distribution in Port Phillip Bay), and instead focus on eradicating satellite populations using approaches modelled on successful removals of localised infestations at Flinders (Parry & Cohen 2001) and the Chatham Islands (Wotton et al. 2004). Where the invader is established, management efforts may be better directed toward promoting the recovery of Ecklonia and other native macrophytes that compete with Undaria (Ling et al. 2009, Carnell & Keough 2016, Kriegisch et al. 2016), for example by investigating methods to reduce sea urchin grazing pressure (e.g. Pert et al. 2018) and other stressors.

Farming of Undaria may also be considered in areas where the invader is already abundant and where farming is unlikely to increase the severity of any ecological impacts. Accordingly, the New Zealand government allowed farming of Undaria in several heavily infested areas in 2012, with interest along similar lines in Europe (Peteiro et al. 2016). Farming would greatly increase propagule pressure locally if the reproductive plants reach maturity before harvesting, so the potential for increased local impacts as well as the risk of accelerated range expansion should be considered carefully. Undaria spores do not naturally disperse large distances (Forrest et al. 2000), but caution is required given the likelihood of detached mature plants drifting large distances (Russell et al. 2008), and a history of long distance dispersal mediated by maritime transport.

Undaria may not be alone in being more benign than initially feared. A recent global review of non-native seaweeds found that introduced seaweeds generally had negative effects on macroalgal diversity, but neutral or positive effects on faunal communities (Thomsen et al. 2016). In many cases, there is evidence that the spread of non-native taxa is facilitated by prior declines in native cover (Ceccherelli et al. 2014, Gennaro & Piazzi 2014, Luigi & Giulia 2017).

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More research is needed to understand the value of non-native habitats for native fauna, and to avoid conflating effects of non-native habitat-formers with negative effects of other forms of HIREC.

WHERE TO NEXT FOR THIS WORK?

The meta-analysis in Chapter Two revealed substantial gaps in our understanding of the impacts of marine and freshwater aquaculture on wildlife. In particular, studies that rigorously demonstrate attractive or repulsive effects of farms are rare, as are studies quantifying direct or indirect fitness effects on wild taxa. Chapter Three addressed one of these key knowledge gaps—effects of farm-association on reproductive fitness of wild fish—and revealed some evidence for negative effects, but the study was nonetheless limited in spatial replication, experimental power and duration of larval rearing. Ideally, the geographical extent of this work should be expanded, with additional resources to support larger sample sizes, and/or raise larvae to juvenile or adult stages to test for delayed effects of farm-association on offspring. Adult mortality is also important in assessing the impacts of aquaculture on wild fish populations, especially where farms bring elevated infection, predation and fishing mortality rates. Long term acoustic tracking of adult fish within farm-affected or non-affected areas, replicated across multiple locations, may be the most viable approach for quantifying mortality and emigration in semi-enclosed systems such as fjords (Olsen & Moland 2011, Olsen et al. 2012, Fernández-Chacón et al. 2015).

In Chapter Four, I showed that despite an underlying attraction to shellfish prey and benefits of high food availability for individuals residing inside shellfish farms, the invasive seastar Asterias amurensis was not more abundant inside farms. Predator avoidance experiments together with a truncated size distribution indicated that the threat of predation by the native seastar Coscinasterias muricata may be preventing the invader from exploiting the food-rich resource. However, I was unable to clearly partition the effects of behavioural predator avoidance and mortality. A follow-up study quantifying relative predation risk for tethered seastars, as well as mass mark-recapture of seastars at locations inside and outside farms (Loosanoff 1937, Lamare et al. 2009, Barahona & Navarrete 2010) may shed light on the relative importance of these processes.

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In Chapter Five, I provided estimates of habitat preference (via a controlled habitat choice experiment and recruitment patterns to mesocosm reefs) and relative fitness (via condition and fecundity metrics) for native reef fish inhabiting native and invasive kelp canopy, but further work is required to quantify patterns of movement, site fidelity and mortality, especially during the summer senescence of the Undaria canopy. Acoustic tags can be appropriate for tracking movement of large-bodied taxa (Parsons et al. 2003), while site fidelity of smaller site-attached taxa may be estimated by a mark-recapture study using visible implant elastomer tags (Willis et al. 2001). However, a pilot mark-recapture study of the common weedfish Heteroclinus perspicillatus translocated to mesocosm reefs in Chapter Five indicated low retention rates, with simultaneous high immigration of new juveniles and adults indicating that this species is highly transient, consistent with previous work tracking temporal trends in fish communities on artificial seaweed beds (Jenkins & Sutherland 1997). Novel techniques may be required to track movement and mortality of small cryptobenthic fishes during and after the Undaria growing season. The little weed whiting Neoodax balteatus may be a better focal species for a study of survivorship rates in native and invasive kelp habitats, as dominant terminal phase males maintain harems and appear to be more strongly site- attached (pers. obs.), such that it may be reasonable to infer mortality rates from the loss of dominant individuals, although a full assessment of the ecological role of Undaria patches should consider a wider suite of associated vertebrate and invertebrate fauna.

CONCLUSION

A reliance on population- and community-level metrics can result in a failure to detect important individual-level processes that influence population growth and persistence, such as habitat selection, survivorship and reproductive output. Wherever possible, researchers assessing the role of novel or degraded habitats as population sources or sinks should complement population- and community-level metrics with direct or indirect measures of habitat preference and individual fitness. Applying the ecological trap assessment framework can also improve our understanding of source–sink dynamics by accounting for maladaptive habitat selection decisions that can lead to attractive population sinks or underutilised population sources. Correctly characterising the value of specific habitats for fauna will assist in targeting of management measures, for example by: (1) protecting attractive, highly productive habitats, (2) disarming ecological traps by removing attractive cues or drivers of 136

poor fitness outcomes, (3) investigating ecological engineering approaches to increase utilisation of perceptual traps by native fauna, or (4) leveraging ecological traps to assist in the control of non-native fauna.

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APPENDICES

Appendix 2.1. List of 191 articles included in systematic review and meta-analysis.

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Appendix 2.2. Summary of results for zero-inflated Poisson model comparing research effort on interactions between wildlife (number of articles included in our systematic review) and aquaculture between nations according to both domestic aquaculture production (t) in 2015 and Human Development Index (HDI) in 2015. N = 188 nations.

Model term Estimate SE z p HDI 9.4 2.3 4.1 <0.0001 Production -0.01 0.006 -2.2 0.02 HDI × Production 0.02 0.01 2.2 0.03

HDI > 0.8, N = 55 Production 0.008 0.002 5.6 <0.0001

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Appendix 2.3. List of the six most parsimonious linear models predicting log response ratios for wildlife abundance (a) and species richness (b) at aquaculture sites relative to reference sites. In each case, the full model was specified using the following terms: Response ~ Year + Country + Region + Environment + Culture System + Culture Organism + Wild Taxa + Reference Habitat. A model selection process using the MuMIn package for R (Barton 2016) fitted every possible

combination of these terms using maximum likelihood estimation and ranked them by AICC score. ‘+’ indicates the corresponding term was included in the given model.

Model Cultured Wild Env Ref Hab Year logLik AICC ΔAICC Wt ID Taxa Taxa 27 + + + -115.6 247.2 0 0.33 91 + + + + -111.7 247.6 0.3 0.28 155 + + + + -115.3 249.2 2.0 0.12 75 + + + -113.9 249.2 2.0 0.12 83 + + + -114.3 250.0 2.8 0.08 219 + + + + + -111.6 250.3 3.1 0.07

Model Cultured Wild Env Ref Hab Year logLik AICC ΔAICC Wt ID Taxa Taxa 81 + + -24.8 64.4 0 0.34 65 + -26.5 65.0 0.6 0.26 209 + + + -24.1 66.0 1.7 0.15 89 + + + -24.4 66.7 2.4 0.11 73 + + -26.2 67.2 2.9 0.08 193 + + -26.5 67.8 3.4 0.06

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Appendix 2.4. ANOVA table and Tukey’s post-hoc test results (multiple comparisons of means with 95% family-wise confidence level) for best fitting linear model for factors predicting log response ratios (RR) for wildlife abundance at aquaculture sites. The best fitting model was selected from a full model containing the following factors: Year, Country, Continent, Environment (Marine, Freshwater), Culture System (Cage, Pond, Longline, Rack, Bed), Cultured Taxa (Fish, Shellfish, Crustacean, Alga), Wild Taxa (Fish, Bird, Mammal, Reptile, Amphibian), and Reference Habitat (Structured, Unstructured). Post-hoc testing compared all pairwise combinations of levels of significant model terms (Cultured Taxa and Reference Habitat). Positive values for ‘Difference’ indicate that the former level has a higher response to farms than the latter level in the pairwise comparison.

ANOVA Model term df SS MS F p Environment 1 6.87 6.87 2.7 0.11 Cultured Taxa 3 62.0 20.7 8.1 0.0001 Reference Habitat 1 24.9 24.9 9.8 0.003 Residuals 57 144 2.54

POST HOC Significant model term Difference Lower Upper Adjusted p Cultured Taxa Crustacean-Alga -2.33 -6.95 2.29 0.54 Fish-Alga 1.77 -0.24 3.78 0.10 Shellfish-Alga 0.09 -2.01 2.19 0.99 Fish-Crustacean 4.11 -0.17 8.38 0.06 Shellfish-Crustacean 2.42 -1.89 6.74 0.45 Shellfish-Fish -1.68 -2.84 -0.52 0.002 Reference Habitat Unstructured-Structured 1.17 0.37 1.97 0.005

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Appendix 2.5. ANOVA table and Tukey’s post-hoc test results for best fitting linear model for factors predicting log response ratios (RR) for wildlife species richness at aquaculture sites. The best fitting model was selected from a full model containing the following factors: Year, Country, Continent, Environment (Marine, Freshwater), Culture System (Cage, Pond, Longline, Rack, Bed), Cultured Taxa (Fish, Shellfish, Crustacean, Alga), Wild Taxa (Fish, Bird, Mammal, Reptile, Amphibian), and Reference Habitat (Structured, Unstructured). Post-hoc testing compared all pairwise combinations of wild taxonomic groups represented in the dataset. Positive values for ‘Difference’ indicate that the former level has a higher response to farms than the latter level in the pairwise comparison.

ANOVA Model term df SS MS F p Wild Taxa 3 3.63 1.21 4.4 0.01 Reference Habitat 1 0.81 0.81 3.1 0.09 Residuals 32 8.27 0.26

POST HOC Significant model term Difference Lower Upper Adjusted p Wild Taxa Bird-Amphibian 1.22 -0.25 2.70 0.13 Fish-Amphibian 1.53 0.13 2.93 0.03 Mammal-Amphibian 0.41 -1.54 2.35 0.94 Fish-Bird 0.30 -0.28 0.89 0.50 Mammal-Bird -0.82 -2.29 0.65 0.44 Mammal-Fish -1.12 -2.53 0.28 0.15

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Appendix 3.1. Ovarian fatty acid profiles in cod (Gadus morhua) collected from areas of high and low salmon farming density. Fatty acids that represent less than 0.1 % of the total fatty acids are omitted. Data are presented as percentage of total fatty acids (mean ± SD, n = 10 per group). Statistical comparisons are from univariate linear analyses of variance with 1 on 18 df.

Fatty acids Low farm density High farm density F p 14:0 1.63 ± 0.36 1.53 ± 0.39 Iso 15:0 0.25 ± 0.12 0.20 ± 0.04 Iso 16:0 0.17 ± 0.06 0.15 ± 0.04 Iso 17:0 0.46 ± 0.09 0.46 ± 0.07 Antiso 17:0 0.23 ± 0.09 0.21 ± 0.05 17:0 0.37 ± 0.06 0.41 ± 0.08 iso 18:0 0.18 ± 0.05 0.19 ± 0.04 18:0 2.44 ± 0.40 2.55 ± 0.54 ∑SFA 23.87 ± 0.71 24.00 ± 1.05 0.10 0.76 16:1 (n-11) 0.14 ± 0.04 0.13 ± 0.05 16:1 (n-9) 1.27 ± 0.13 1.30 ± 0.28 18:1 (n-9) 12.77 ± 0.71 13.57 ± 1.67 1.9 0.18 18:1 (n-7) 4.25 ± 0.89 3.98 ± 0.47 18:1 (n-5) 0.28 ± 0.04 0.31 ± 0.07 20:1 (n-11) 0.33 ± 0.23 0.20 ± 0.08 20:1 (n-9) 1.02 ± 0.56 0.61 ± 0.23 22:1 (n-11) 0.38 ± 0.32 0.21 ± 0.10 22:1 (n-9) 0.08 ± 0.04 0.07 ± 0.01 24:1 (n-9) 0.94 ± 0.20 1.02 ± 0.34 24:1 (n-7) 0.24 ± 0.05 0.32 ± 0.12 ∑MUFA 26.16 ± 3.10 26.12 ± 1.51 0.00 0.97 18:2 (n-6) 1.06 ± 0.53 1.26 ± 0.89 0.34 0.57 18:3 (n-6) 0.18 ± 0.05 0.17 ± 0.04 20:2 (n-6) 0.32 ± 0.15 0.25 ± 0.07 20:3 (n-6) 0.10 ± 0.01 0.09 ± 0.03 20:4 (n-6) 3.90 ± 0.69 4.11 ± 1.72 22:4 (n-6) 0.68 ± 0.20 0.50 ± 0.20 22:5 (n-6) 0.36 ± 0.04 0.40 ± 0.05 18:3 (n-3) 0.39 ± 0.07 0.52 ± 0.25 18:4 (n-3) 0.44 ± 0.13 0.68 ± 0.59 20:3 (n-3) 0.14 ± 0.02 0.14 ± 0.03 20:4 (n-3) 0.45 ± 0.11 0.50 ± 0.19 20:5 (n-3) 11.14 ± 1.00 10.34 ± 0.94 21:5 (n-3) 0.19 ± 0.04 0.17 ± 0.03 22:5 (n-3) 1.54 ± 0.47 1.35 ± 0.45 22:6 (n-3) 26.64 ± 3.43 26.87 ± 2.08 ∑PUFA 47.53 ± 2.78 47.33 ± 1.03 0.04 0.84 ∑PUFA (n-3) 40.92 ± 3.26 40.57 ± 2.34 0.08 0.78 ∑PUFA (n-6) 6.61 ± 0.84 6.77 ± 1.78 0.07 0.80 (n-3)/(n-6) 6.32 ± 1.20 6.47 ± 2.12 0.03 0.86 Fatty acids relative to 2.12 ± 0.47 2.27 ± 0.73 0.32 0.58 sample wet weight (%) Cholesterol relative to 0.21 ± 0.06 0.20 ± 0.03 0.17 0.68 sample wet weight (%)

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Appendix 3.2. Model summaries for linear regression of log-transformed weight-at-length relationships in female and male Atlantic cod from areas of high and low salmon farming density.

FEMALES Model term Estimate SE t p Intercept 1.63 0.10 17 <0.0001 log(W) 0.32 0.01 26 <0.0001 Treatment(LFD) -0.02 0.01 -2.5 0.014

R2 = 0.88 Residual df = 105

MALES Model term Estimate SE t p Intercept 1.56 0.96 19 <0.0001 log(W) 0.33 0.00 31 <0.0001 Treatment(LFD) 0.02 0.00 2.0 0.06

R2 = 0.95 Residual df = 45

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Appendix 3.3. Model summaries for effects of farm density on egg production, egg quality and larval quality of wild Atlantic cod. Model terms are Farm density group (LFD and HFD), Day, Time (early or late season sampling), total female length (TotalFL) and mean female length (MeanFL). Model summaries for X2 likelihood ratio tests of model terms are omitted, as are model summaries for analyses within farm density groups.

Daily egg production (DEP) Model specification: DEP ~ Group + Day + TotalFL + (1|Group/Tank) Model term Estimate SE z p Intercept 1.55 0.48 3.2 0.001 Group (LFD) 0.53 0.28 1.9 0.056 Day -0.017 0.008 -2.0 0.042 TotalFL 0.008 0.006 14 <0.0001

Observations 420 Residual df 413

Relative daily egg production (RDEP) Model specification: RDEP ~ Group + Day + (1|Group/Tank) Model term Estimate SE z p Intercept 5.32 0.19 27 <0.0001 Group (LFD) 0.12 0.18 0.7 0.52 Day -0.07 0.007 -10 <0.0001

Observations 420 Residual df 414

Egg viability rate (ViablePr) Model specification: ViablePr ~ Group + Time + meanFL + (1|Group/Tank) Model term Estimate SE z p Intercept 3.7 1.48 2.5 0.012 Group (LFD) -0.09 0.18 -0.5 0.61 Time (late season) -0.81 0.03 -6.3 <0.0001 MeanFL -0.03 0.02 -1.4 0.18

Observations 348 Residual df 341

Egg diameter (EggSize) Model specification: EggSize ~ Group + Time + meanFL + (1|Group/Tank) Model term Estimate SE z p Intercept 0.82 0.16 5.6 <0.0001 Group (LFD) 0.07 0.02 3.9 0.0001 Time (late season) 0.08 0.004 20 <0.0001 MeanFL 0.005 0.002 2.1 0.045

Observations 1038 Residual df 1031

Egg fertilisation rate (FertPr) Model specification*: FertPr ~ Group + Time + (1|Group/Tank) *MeanFL omitted due to poor model fit Model term Estimate SE z p

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Intercept 0.96 0.22 4.4 <0.0001 Group (LFD) -0.16 0.25 -0.6 0.52 Time (late season) -0.64 0.25 -2.6 0.009

Observations 72 Residual df 66

Egg symmetry rate (SymPr) Model specification: SymPr ~ Group + Time + (1|Group/Tank) Model term Estimate SE z p Intercept -2.0 2.7 -0.7 0.50 Group (LFD) 0.15 0.35 0.4 0.67 Time (late season) 0.36 0.26 1.4 0.18 MeanFL 0.05 0.04 1.3 0.20

Observations 72 Residual df 65

Egg survival rate during incubation (SurvPr) Model specification: SurvPr ~ Group + Time + MeanFL + (1|Group/Tank) Model term Estimate SE z p Intercept -2.2 3.6 -0.6 0.54 Group (LFD) 0.03 0.41 0.07 0.95 Time (late season) -1.0 0.39 -2.7 0.007 MeanFL 0.06 0.06 1.0 0.31

Observations 23 Residual df 16

Egg hatching rate (HatchPr) Model specification: HatchPr ~ Group + Time + MeanFL + (1|Group/Tank) Model term Estimate SE z p Intercept 3.1 2.6 1.2 0.24 Group (LFD) 0.13 0.30 0.43 0.67 Time (late season) 0.57 0.26 2.2 0.030 MeanFL -0.02 0.04 -0.54 0.59

Observations 45 Residual df 38

Larval length (LarvL) Model specification: LarvL ~ Group + MeanFL + (1|Group/Tank) Model term Estimate SE z p Intercept 6.4 1.7 3.6 0.0002 Group 0.46 0.21 2.2 0.029 MeanFL -0.006 0.03 -0.2 0.83

Observations 356 Residual df 350

Maximum larval length (MaxLarvL) Model specification: MaxLarvL ~ Group + MeanFL Model term df SS F p Group 1 0.39 1.6 0.24 MeanFL 1 0.01 0.04 0.84 Residuals 9 2.2

159

Observations 12

Larval deformity rate (DeformPr) Model specification: DeformPr ~ Group + MeanFL Model term Estimate SE z p Intercept 0.88 3.1 0.28 0.77 Group -0.07 0.38 -0.18 0.85 MeanFL -0.02 0.05 -0.31 0.75

Observations 12 Residual df 8

Larval phototaxis rate (PhotoPr) Model specification: PhotoPr ~ Group + MeanFL Model term Estimate SE z p Intercept 0.07 3.3 0.02 0.98 Group 0.30 0.41 0.74 0.46 MeanFL 0.01 0.05 0.19 0.85

Observations 12 Residual df 8

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Appendix 4.1. (Left) Large Asterias amurensis individuals (>20 cm) feeding on fallen mussel clumps with Coscinasterias muricata. (Right) Attempted predation of Asterias amurensis by Coscinasterias muricata. Image credit: Emily Fobert.

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Appendix 4.2. Model summaries for effects of shellfish farms on seastar population metrics. Model summaries for X2 likelihood ratio tests of model terms are omitted, as are model summaries for analyses within locations.

Asterias population density (Density) Model specification: Density ~ Habitat + Location + (1|Date) Model term Estimate SE z p Intercept 7.6 0.96 8.1 <0.0001 Habitat (Out) -0.81 0.60 -1.4 0.18 Location (GP) -10.5 1.5 -6.8 <0.0001

Observations 76 Residual df 71

Asterias mean transect arm span at Clifton Springs (Size) Model specification: Size ~ Habitat + (1|Date) Model term Estimate SE z p Intercept 3.0 0.05 55 <0.0001 Habitat (Out) -0.2 0.04 -5.5 <0.0001

Observations 38 Residual df 34

Asterias gutted weight at Clifton Springs (GuttedW) Model specification: GuttedW ~ Habitat + (1|Date) + (1|Sex) Model term Estimate SE z p Intercept 9.2 1.1 8.4 <0.0001 Habitat (Out) -4.3 0.46 -9.4 <0.0001