DOCTOR of PHILOSOPHY Trophic Relevance of Gelatinous

Home , Blue jellyfish, Ecopath, Psenes maculatus

DOCTOR OF PHILOSOPHY

Trophic relevance of gelatinous zooplankton for commercial fisheries in the Irish Sea

Griffin, Donal

Award date: 2020

Awarding institution: Queen's University Belfast

Link to publication

Terms of use All those accessing thesis content in Queen’s University Belfast Research Portal are subject to the following terms and conditions of use

• Copyright is subject to the Copyright, Designs and Patent Act 1988, or as modified by any successor legislation • Copyright and moral rights for thesis content are retained by the author and/or other copyright owners • A copy of a thesis may be downloaded for personal non-commercial research/study without the need for permission or charge • Distribution or reproduction of thesis content in any format is not permitted without the permission of the copyright holder • When citing this work, full bibliographic details should be supplied, including the author, title, awarding institution and date of thesis

Take down policy A thesis can be removed from the Research Portal if there has been a breach of copyright, or a similarly robust reason. If you believe this document breaches copyright, or there is sufficient cause to take down, please contact us, citing details. Email: [email protected]

Supplementary materials Where possible, we endeavour to provide supplementary materials to theses. This may include video, audio and other types of files. We endeavour to capture all content and upload as part of the Pure record for each thesis. Note, it may not be possible in all instances to convert analogue formats to usable digital formats for some supplementary materials. We exercise best efforts on our behalf and, in such instances, encourage the individual to consult the physical thesis for further information.

Download date: 26. Sep. 2021 Declaration

I declare that:

(i) The thesis is not one for which a degree has been or will be conferred by

any other university or institution;

(ii) The thesis is not one for which a degree has already been conferred by this

university;

(iii) The work for the thesis is my own work and that, where materials are

submitted by me for another degree or work undertaken by me as part of a

research group has been incorporated into the thesis, the extent of the

work thus incorporated has been clearly indicated;

(iv) The composition of the thesis is my own work

The format of this thesis is in accordance with the guidelines published by Queen’s

University of Belfast.

Donal Christopher Griffin

June 2019

Trophic relevance of gelatinous zooplankton for

commercial fisheries in the Irish Sea

Acknowledgements

I would like to thank everyone who has helped me complete this PhD; the list is long. Firstly, thank you to my supervisors. Dr Jonathan Houghton and Dr Chris Harrod who I have known since my undergraduate degree. Your expertise and advice have helped me become not only a better scientist but a better person. I appreciate everything you have done for me, not least your patience in guiding me to the completion of this thesis. For that I will always be grateful. To my third supervisor Dr Steven Beggs, the time I spent on the AFBI research ship are some of my happiest memories of the PhD. Without your effort and help in the field this PhD would not have been possible, thank you. A huge thank you to AFBI and all the crew and scientific staff on the RV Corystes who let me ‘piggy-back’ on their surveys and collect the data contributing to this thesis. Thank you to the funders and match funders of this PhD including DAERA NI, AFBI NI, NERC, QML who made this PhD project possible. Thank you to all my collaborative partners who have helped me carry out the various aspects of this multidisciplinary thesis including; Victoria Dominguez Almela and Dr Christophe Eizaguirre from Queen Mary London University, NERC Life Sciences Mass Spectrometry Facility at Bristol University, University of Hull. Special thanks to Dr Isabella Capellini, Dr Jason Newton and Alison Kuhl who introduced me to whole other worlds of scientific techniques and methods, and for your never-ending patience in helping me to use those to answer my jelly related questions. I would also like to thank Gillian Riddell for her field work know how and help throughout my time at QUB, for being a good friend during the darkest times of my PhD and for organising various field courses I attended at undergraduate level. Those courses are the reason I chose to become a marine biologist, and for that I will always be grateful. I would like to thank my friends. I would not be here without you all. This PhD has produced some of the best and worst times of my life and whether you realise or not, you all have been the constant I needed to keep going. A special thanks to those who read over drafts of my thesis, I owe you a large bottle of gin. Finally, I want to thank my family. Mum, Patrick, Kym and Lockie. I love you all and appreciate everything you have ever done for me. I am a better person because of your kindness, patience and generosity, thank you!

Abstract

Jellyfish can compete with fish for food resources, or feed on fish eggs and larvae. They also provide habitat and space for developing larval and juvenile fish which use their hosts as means of protection from predators and feeding opportunities. However, the broader ecological relevance of jellyfish is often neglected beyond their role as stressors to the marine environment, including fish communities. However, this is a gross over-simplification of the true role of jellyfish in the marine environment, yet the link between field biologists and modellers remains a limiting factor in their inclusion in ecosystem and fisheries models. Using a multidisciplinary approach, this thesis aimed to provide a balanced understanding of the net impact of jellyfish on fish communities in the Irish Sea, and to provide a means for researchers to do the same in marine systems around the world.

Chapter one of this thesis is a broad synthesis of the traditional, as well as the more recent and nuanced view of the role of jellyfish in the marine environment. Chapter two employs comparative phylogenetic techniques and indicates that jellyfish association is a probable adaptive anti-predator strategy for juvenile fish, more likely to evolve in benthic (fish living on the sea floor), benthopelagic (fish living just above the bottom of the seafloor) and reef-associating species than those adapted to other marine habitats.

Latter experimental chapters predominantly employ trophic investigation and analysis to assess fish-jellyfish predator-prey interactions in the Irish Sea. Chapter three investigates jellyfish inter-tissue isotopic variation and discusses whether tissue and/or size-based isotopic grouping are suitable parameters for the inclusion of jellyfish in ecosystem, fisheries and food web models in the future. Evidence for inter-tissue δ15N variation within and across scyphozoan jellyfish species is provided. Furthermore, findings indicate how δ15N may be influenced by jellyfish size, which in certain instances may be a useful tool in accounting for the nuances of jellyfish trophic ecology. Chapter four employs the latest Bulk Stable Isotope Analysis (BSIA) and Compound Specific Isotope Analysis (CSIA) techniques to quantify robust jellyfish Trophic Discrimination Factors (TDFs) to aid the estimation of jellyfish Trophic Position (TP). Findings suggest that estimates based on a bulk TDF of 2.9‰ and an amino-acid TDF of 7.6‰ overlapped

4 for all comparisons indicating that these TDF values may be appropriately representative of jellyfish: allowing for their inclusion in isotopic food web studies in the future. In the last experimental chapter, we used jellyfish appropriate TDFs to resolve jellyfish trophic positions and explore the role of jellyfish in an Irish Sea food- web context. More specifically we explored whether trophic fish-jellyfish interactions are a two-way street i.e. do fish eat jellyfish and do jellyfish eat fish. Finally, in chapter six we bring together all the strands of the thesis and discuss the broader relevance of the findings. As pressure on fin-fish stocks increase year on year due to overfishing, habitat disturbance and climate change, acknowledging and accounting for additional but lesser known factors such as fish-jellyfish interactions such as two-way predation is important to inform and safeguard sustainable fisheries and fishing in the future. More specifically, to ignore fish-jellyfish interactions and their trophic importance in marine systems and food webs, runs the risk of misrepresenting the ecological role of a broad array of species, not least commercial fish upon which jellyfish can impact both negatively and positively.

Contents

Declaration ...... 1

Trophic relevance of gelatinous zooplankton for commercial fisheries in the Irish Sea ...... 2

Acknowledgements ...... 3

Abstract ...... 4

Contents ...... 6

List of appendices ...... 10

Chapter 1 - General introduction: the good, the bad and the jellyfish ...... 12

1.1 Overarching rationale ...... 13 1.2 Negative perception of jellyfish ...... 13 1.2.1 Impacts on tourism, power and aquaculture industries ...... 13 1.2.2 Jellyfish as predators and competitors of fish ...... 16 1.3 Changing attitudes to the role of jellyfish in marine systems ...... 19 1.3.1 Ecosystem services provided by jellyfish ...... 20 1.3.2 Regulating services ...... 21 1.3.4 Cultural services ...... 24 1.3.5 Supporting services ...... 26 1.4 Fish-jellyfish interactions ...... 28 1.4.1 Jellyfish as prey ...... 28 1.4.2 Fish-jellyfish associations ...... 31 1.4.3 The knock-on effect on humans and global food security ...... 34 1.5 Knowledge gaps and research objectives ...... 34 1.5.1 Addressing positive impacts of fish-jellyfish interactions ...... 35 1.5.3 Producing reliable trophic position estimates for jellyfish ...... 36 1.5.4 Trophic fish-jellyfish interactions in the Irish Sea ...... 38 1.6 References ...... 39 Chapter 2 - Unravelling the macro-evolutionary ecology of fish-jellyfish associations: life in the ‘gingerbread house’ ...... 53

2.1 Abstract ...... 54 2.2 Introduction ...... 55 2.3 Methods ...... 59 2.3.1 Data collection ...... 59 2.3.2 Evolutionary history of fish-jellyfish association ...... 61 6

2.3.3 Independent and dependent models of evolution ...... 61 2.4 Results ...... 63 2.4.1 Fish-jellyfish associations in the literature ...... 63 2.4.2 Evolutionary history of fish-jellyfish association and lifestyle ...... 65 2.4.3 Correlated evolution between fish-jellyfish association and lifestyle ...... 65 2.5 Discussion ...... 69 2.6 References ...... 74 Chapter 3 - Inter-tissue isotopic variation in three scyphozoan jellyfish ...... 79

3.1 Abstract ...... 80 3.2 Introduction ...... 81 3.3 Methods ...... 84 3.3.1 Sampling and SIA preparation ...... 84 3.3.2 Statistical analyses ...... 87 3.4 Results ...... 88 3.5 Discussion ...... 92 3.5.1 Conclusions ...... 95 3.6 References ...... 96 Chapter 4 - Regional and individual variation in jellyfish trophic position shown using bulk and amino acid δ15N ...... 100

4.1 Abstract ...... 101 4.2 Introduction ...... 102 4.3 Methods ...... 105 4.3.1 Sample collection and processing for SIA ...... 106 4.3.2 Bulk Stable Isotope Analysis (BSIA) ...... 107 4.3.3 Compound Specific Isotope Analysis (CSIA) ...... 108 4.3.4 Liberation of free amino acids by acid hydrolysis ...... 108 4.3.5 Purification of amino acids by cation exchange column chromatography ...... 109 4.3.6 Preparation of N-acetyl, O-isopropyl derivatives ...... 109 4.3.7 Gas chromatography with Flame ionisation Detection (GC-FID) analyses ...... 110 4.3.8 Gas chromatography-combustion-isotope ratio mass spectrometry ...... 110 4.3.9 Estimation of trophic position using BSIA and CSIA ...... 111 4.4 Results ...... 113 4.4.1 Bulk Trophic Position Estimation ...... 113 4.4.2 Amino Acid Trophic Position Estimation ...... 114 4.4.3 Bulk vs amino acid trophic position comparisons ...... 116 4.5 Discussion ...... 121

4.5.1 Conclusions ...... 124 4.6 References ...... 125 Chapter 5 - The jellyfish paradox: are trophic interactions with fishes a two-way street? 133

5.1 Abstract ...... 134 5.2 Introduction ...... 135 5.3 Methods ...... 138 5.3.1 Study site ...... 138 5.3.2 Sample collection ...... 138 5.3.3 Sample processing for SIA ...... 139 5.3.4 Bulk stable isotope analysis ...... 139 5.3.5 Statistical analysis ...... 140 5.4 Results ...... 141 5.3.1 δ15N variation between fish and jellyfish species ...... 141 5.4.2 Irish Sea fish and jellyfish trophic position estimates ...... 144 5.4.3 Jellyfish contributions to fish diet ...... 149 5.4.4 Fish contribution to jellyfish diet ...... 152 5.5 Discussion ...... 155 5.5.1 δ15N variation between fish and jellyfish species ...... 155 5.5.2 Irish Sea fish and jellyfish trophic position estimates ...... 156 5.5.3 Are fish eating jellyfish? ...... 156 5.5.4 Are jellyfish eating fish? ...... 157 5.5.5 Conclusions ...... 158 5.6 References ...... 160 Chapter 6 - General discussion ...... 167

6.1 Synthesis of main findings ...... 168 6.1.1 Evolution of jellyfish-fish associations ...... 168 6.1.2 Majority of fish-jellyfish associations involve commercial fish species ...... 169 6.1.3 Inter-tissue and inter-specific δ15N variation among jellyfish could mask selective feeding by predators ...... 171 6.1.4 Size-scaled δ15N in jellyfish ...... 173 6.1.5 Jellyfish specific TDF estimates open the door for their effective inclusion in ecosystem and fisheries-based models in the future ...... 174 6.1.6 Two-way trophic interactions with fishes ...... 175 6.2 Concluding comment on the jellyfish paradox ...... 175 6.3 References ...... 178 Appendix I ...... 183

Appendix II ...... 189

Appendix III ...... 193

Appendix IV ...... 197

Appendix V ...... 199

Appendix VI ...... 200

Appendix VII ...... 202

Appendix VIII ...... 203

List of appendices

I - 183

List of papers documenting jellyfish-fish associations in the literature used to create phylogenetic comparative analysis dataset in chapter 2: Unravelling the macro- evolutionary ecology of fish-jellyfish associations: life in the ‘gingerbread house’…

II - 189

Phylogenetic comparative dataset showing whether each species displays jellyfish association behaviour, and whether each fish is a demersal type (benthic, benthopelagic or reef-associated) or fully pelagic living. This data set was used to carry out the ancestral state reconstruction and the correlated evolution analyses in chapter

2: Unravelling the macro-evolutionary ecology of fish-jellyfish associations: life in the

‘gingerbread house’

III - 193

Bell diameter, wet mass, and δ15N of different body parts of jellyfish sampled from

Strangford Lough, Northern Ireland. Data used in chapter 3: Inter-tissue isotopic variation in three scyphozoan jellyfish

IV - 197

Glutamine acid (Glu) and phenylalanine (Phe) amino acid and bulk δ15N data from five jellyfish species collected from the North East Atlantic, Mediterranean and South East

Pacific from chapter 4: Regional and individual variation in jellyfish trophic position shown using bulk and amino acid δ15N.

V - 199

Amino acid mean and ± SD δ15N for four scypyhozoan and one hydrozoan jellyfish sampled from the Irish Sea, Mediterranean and S.E. Pacific. Chapter 4: Regional and individual variation in jellyfish trophic position shown using bulk and amino acid δ15N.

VI - 200

Individual plots of raw and mean δ15N and δ13C for jellyfish and fish sampled from the

Irish Sea and isotopic phytoplankton baselines. zooplankton in regards to δ15N but have a lower mean δ13C. Chapter 5: Fish-jellyfish trophic ecology in the Irish Sea

VII - 202

As a part of the field work and data collection carried out for this thesis, we contributed to a large collaborative project which investigated the role of ocean currents in the secondary spread of a marine invasive comb jelly across western

Eurasia. Our findings were published: Jaspers, Cornelia, et al. "Ocean current connectivity propelling the secondary spread of a marine invasive comb jelly across western Eurasia." Global ecology and biogeography 27.7 (2018): 814-827.

VIII - 203

List of publications and conferences presented in arising from the work detailed in this thesis.

Chapter 1 - General introduction: the good, the bad and the

jellyfish

1.1 Overarching rationale

This thesis is presented as three experimental chapters that come together under the general theme of gelatinous zooplankton ecology. The project arose from an ongoing collaboration between Queen’s University Belfast and the Agri-Food and Biosciences

Institute (AFBI) in Northern Ireland. PhD Funding was awarded from the Department of Agriculture, Environment and Rural Affairs (DAERA) in direct response to their stated environmental objectives. Specifically, this project was aligned directly with objective

‘Productivity and sustainability of local sea fisheries and aquaculture’ in 2014. In this opening chapter I provide an up to date overview of gelatinous zooplankton ecology as a comprehensive backdrop for this thesis before outlaying the synthesis and interconnectivity of the experimental chapters.

1.2 Negative perception of jellyfish

1.2.1 Impacts on tourism, power and aquaculture industries

Over the years jellyfish have acquired a “pest” status in relation to their negative impact on human endeavour in the marine environment (Baird & Ulanowicz, 1989;

Richardson et al., 2009; Boreo, 2013). There are many areas in which they cause considerable disruption and financial loss, making them a prime target for such labelling (Purcell, 2012). However, among the general public it is primarily their ability to sting bathers and force beach closures that has secured their negative characterisation. Yet, because jellyfish are so widely distributed and often form blooms in coastal areas, it is inevitable during summer months that they will come into contact with swimmers (Mariottini & Pane, 2010; Boreo, 2013). However, the sting from many jellyfish species causes little to mild discomfort, while a small minority of cnidarian

13 venoms are known to produce toxic effects in humans (Mariottini & Pane, 2010;

Cegolon et al., 2013).

Disruption to the power production and desalination industry is another reason jellyfish have become known as “nuisance” species (Purcell et al., 2007; Purcell, 2012).

There have been many reports of large blooms of scyphozoan jellyfish clogging seawater intake systems, causing frequent production reductions or shutdowns most notably in Japan but also in parts of Europe, Middle East, India and North America

(Purcell et al., 2007). Graham et al. (2014) pointed out that 44% of the world’s human population live within 100km of the coast and that energy demands are largely met by power plants equipped with seawater-cooled condensers. Adding to this is the fact that many of the world’s energy production sites, including nuclear power, overlap in space with recurring blooms of jellyfish, it is clear these types of disruptions present an ongoing threat for power production generation (Graham et al. 2014).

The aquaculture industry has expanded exponentially over the past two decades wherein it now plays an ever increasing role in global food security, providing roughly half of the fish consumed worldwide (Troell et al., 2014). However, the industry has sustained huge financial losses during this same period owing to jellyfish blooms

(Baxter et al., 2011). Small jellyfish and the tentacles of larger jellyfish enter fish pens, where nematocyst cells irritate fish gills resulting in haemorrhage and subsequent suffocation (Johnston et al., 2005). In one particularly severe incident in 2007, a bloom of Pelagia noctiluca (Fig. 1.1) killed 250,000 harvest ready Atlantic salmon (Salmo salmar) in Northern Ireland, incurring losses of over £1 million (Doyle et al., 2008; Hay

& Murray, 2008). Other notable fish kill events due to jellyfish have been recorded in

France, Scotland, India, Norway and the US (Rajagopal et al., 1989; Merceron et al.,

1995; Purcell et al., 1999; Johnson, 2002; Heckmann, 2004). Such incidents have been a major driver for targeted research into the impact of jellyfish on aquaculture farmed fish (Baxter et al., 2011).

To mitigate against negative impacts of jellyfish blooms, researchers have often concentrated on forecasting jellyfish spatial and temporal distribution (Houghton et al.,

2007; Bastian et al., 2011; Brodeur et al., 2016). By collecting data on abundance, biomass and location in combination with other environmental factors, scientists have sought to generate spatial predictive models in an attempt to lessen the impact of jellyfish blooms in the future (Houghton et al., 2006; Nickell et al., 2010; Fleming et al.,

2013).

Figure. 1.1. The mauve stinger (Pelagia noctiluca) (Cnidaria: Scyphozoa) is a small pelagic jellyfish generally pink-mauve or light-brown coloured, with a phosphorescent bell. Pelagia noctiluca has direct development, so its reproductive cycle does not comprise the benthic scyphistoma stage. ©Hans Hillewaert

1.2.2 Jellyfish as predators and competitors of fish

Large aggregations of jellyfish can impact negatively on the fishing industry through the loss and degradation of catch (Acha et al., 2014). However, ecological interactions such as jellyfish preying on pelagic fish eggs and larvae, or jellyfish competing for food resources with adult fish, may be having a significant but less obvious impact on fisheries. For example, Graham et al. (2003) demonstrated extraordinarily high consumption rates of fish eggs by Phylorrhiza punctata jellyfish in the Gulf of Mexico while Lynam et al. (2004) found Aurelia aurita had a negative impact on herring (Clupea harengus) survival and recruitment in the North Sea. While the environmental context of how, why and to what extent jellyfish can impact negatively on fish communities varies, in some circumstances the impacts are detectable over considerable temporal and spatial scales. The invasive ctenophore Mnemiopsis leidyi contributed to the collapse of the pelagic fish population in the Black Sea by feeding on the food supply of anchovy (Engraulis encrasicolus) as well as their eggs and larvae (Kideys, 2002). After the Mnemiopsis leidyi invasion the Black Sea was characterised as an ecosystem with low biodiversity dominated by ‘dead end’ gelatinous food web (Vinogradov et al. 1992) from which it is still recovering (Tsikliras et al., 2015).

The apparent phenomenon whereby marine systems shift from fish to jellyfish dominated, aided by human induced stresses, has been labelled the ‘jellyfish joyride’ by Richardson et al. (2009) (Fig. 1.2). Research showing a profound ecosystem change including an increase in jellyfish biomass in northern Benguela (Lynam et al., 2006) was purported to demonstrate how the ‘jellyfish joyride’ had already begun (Richardson et al., 2009). Furthermore, some researchers commented such shifts towards a more gelatinous state may be irreversible as jellyfish impede fish stock recovery by feeding

16 on fish eggs and larvae and competing for fish food with lasting ecological, economic and social consequences (Lynam and Brierley, 2006; Richardson et al., 2009 Sommer et al., 2002). However, while it is beyond doubt jellyfish can impact on coastal industries and ecosystems in which they occur in large numbers, the concept of the ‘jellyfish joyride’ prompted a debate where some argued there was a risk of focussing too narrowly on the negative. Subsequently, a more nuanced view of the ecological role of jellyfish emerged.

Figure 1.2. Probable mechanisms promoting jellyfish outbreaks. (a) Summary of the impacts of habitat modification (Lo et al., 2008), translocations (Graham & Bayha, 2008) and overfishing (Bakun & Weeks, 2006) on jellyfish outbreaks; (b) Summary of the impacts of eutrophication and climate change (Purcell et al., 2007) on jellyfish outbreaks. Jellyfish symbols represent jellyfish blooms. Scientific illustration and caption taken from Richardson et al. (2009).

1.3 Changing attitudes to the role of jellyfish in marine systems

Jellyfish research has traditionally been prompted by a need to foresee and overcome the negative impacts of jellyfish on marine environments and industry (Lynam et al.,

2005; Doyle et al., 2008; Tomlinson et al., 2015). Recently however, there has been a notable shift in how jellyfish are viewed and defined in marine ecosystems. After

Richardson et al. (2009) coined the term ‘jellyfish joyride’, it became synonymous with not only the adverse effects jellyfish have directly on marine ecosystems and human endeavour but also with the proposed contributing factors which were supposed to be integral to their ability to ‘take over’ marine systems. These factors included overfishing, eutrophication, climate change, translocation and habitat modification. However, the consensus that global jellyfish abundance is increasing was called into question when

Condon et al. (2013) showed how jellyfish abundances undergo large worldwide oscillations operating over decadal scales. The broader inference is that these cyclical trends are likely the causes of perceived jellyfish abundance increases rather than a definite continual rise in jellyfish numbers globally. Rather, the emphasis of jellyfish research has shifted from the rise and fall of jellyfish abundances to nuanced investigation of their ecological role in marine systems (Condon et al., 2012, 2013;

Gibbons & Richardson, 2013). In the context of this changing attitude among scientists,

Doyle et al. (2014) reviewed the evidence that jellyfish provided beneficial ecosystem services in their own right suggesting that the story is not entirely to the detriment of other marine organisms, as was suggested by Richardson et al. (2009). On the back of this sentiment, ‘The secret lives of jellyfish’ was explored by Hamilton (2016) in a feature article in Nature. The author documented the evolution in our understanding of how jellyfish function within marine systems and began the article with a fitting

19 recognition of how jellyfish were long regarded as minor players in ocean ecology but are actually an important part of marine food webs. Finally disposing of the trope that jellyfish are trophic dead ends which are ignored by most predators because of their low energy content, Hays et al. (2018) went further, asserting that there has been a paradigm shift in the trophic importance of jellyfish. Pointing to new research driven by cutting edge technologies such as stable isotope analysis, animal borne cameras and

DNA metabarcoding, Hays et al. (2018) showcased overwhelming evidence that jellyfish are integral components of marine food webs and warrant attention for reasons far exceeding their negative impacts on human enterprise.

1.3.1 Ecosystem services provided by jellyfish

Ecosystem services are the benefits that people obtain from ecosystems, including food, natural fibres, a steady supply of clean water, regulation of pests and diseases to name a few (Anonymous, 2005). Jellyfish contribute to the four categories of ecosystem services defined by the Millennium Ecosystem Assessment including regulating, provisioning, cultural and supporting services. While studies showing the

‘positive’ impacts of jellyfish were not lacking, they were often overshadowed by the well documented ‘negative’ aspects of jellyfish blooms. However, by gathering all the evidence for jellyfish as ‘positive contributors’ under the headings of each ecosystem service category, Doyle et al. (2014) highlighted the importance of balance when considering the net impact of jellyfish in marine systems. Furthermore, the ecosystem services jellyfish provide may have far-reaching implications for society in relation to the fate of our oceans, tackling climate change as well as human health and endeavour.

1.3.2 Regulating services

Regulating ecosystem services are defined as the benefits obtained from the regulation of ecosystem processes such as climate regulation, natural hazard regulation, water purification, waste management or pollination (Anonymous, 2005). However, carbon sequestration and its role in climate regulation is an important ecosystem services in which jellyfish are involved (Doyle et al., 2014). For example, the global average atmospheric carbon dioxide (CO2) concentration has risen to levels higher than in the previous 80,000 years (Lüthi et al., 2008). While much of the efforts to combat the rise in atmospheric CO2 and subsequent global warming has concentrated on the reduction of CO2 emissions, another approach has been to combine this strategy with the conservation of natural ecosystems with high carbon sequestration rates and capacity

(Canadell & Raupach, 2008). Rapid sinking of organic particles to the deep sea is a key process of the biological pump in sequestering carbon from the atmosphere (Graham et al., 2014) and it is in this respect that jellyfish and jellyfish falls (i.e. when gelatinous organisms die and sink from the water column to the sea floor) can aid in this process

(Lebrato et al., 2012). Mass deposition events of dead jellyfish to the sea floor have been recorded worldwide (Lebrato et al., 2012) and the amount of carbon deposited from a single jellyfish-fall event may be approximately four times the annual carbon input to the seabed from other means (Lebrato & Jones, 2009). Thus jellyfish-falls are considered one of the most important vertical carbon transport processes in the sea

(Madin et al., 2006; Lebrato et al., 2012; Henschke et al., 2013).

The global significance of jellyfish as carbon vectors to the seafloor is amplified

considering that the continued increase in atmospheric concentration of CO2 due to anthropogenic emissions is predicted to lead to an expected warmer ocean which is

21 more stratified, acidic, oxygen-poor and characterised by reduced upwelling (Cox et al.,

2000; Gregg et al., 2003). Due to these potential changes, the amount of carbon exported to depth through traditional mechanisms is expected to be reduced as phytoplankton communities shift from large diatom-based assemblages to picoplanktonic with lower export efficiency (Buesseler et al., 2007; Smith et al., 2008).

Therefore, if classic carbon transport (phyto-detritus) processes become less important in future oceans, an increased amount of Jellyfish Particulate Organic Matter (J-POM) sinking to the seabed could mitigate some of the losses of carbon from phytoplanktonic sources (Lebrato et al., 2012).

Jellyfish-falls are just one mechanism by which jellyfish can facilitate the transport of carbon to the sea floor. Due to their widespread occurrence and tendency to form large blooms, salps (pelagic tunicates belonging to the Order Salpida) which feed on small phytoplankton and produce organic-rich faecal pellets, have rapid defecation and faecal pellet sinking rates. These faecal sinking rates are much faster when compared to phytoplankton-derived particles that sink passively. Conversely, a recent study has shown how microplastic ingestion by jellyfish may lower the efficiency of salp faecal sinking rates under high ambient levels off microplastics in the future (Wieczorek et al.,

2019). However, at current levels of ocean microplastics, rapid conveyance of small particulate matter from surface to deep water makes salps a noteworthy transport mechanism in this regard, contributing disproportionately to the vertical flux to the benthos (Madin, 1982; Turner, 2002).

1.3.3 Provisioning services

Provisioning services are the products obtained from ecosystems such as food, fresh water, fuel or biochemicals (Anonymous, 2005) and while it may be little-known in 22

Western countries, jellyfish have been a source of food for humans for more than 1,700 years (Omori & Nakano, 2001; Li & Hsieh, 2004). Jellyfish fisheries are based predominantly in Asian countries such as China but also Japan, Malaysia and Korea for example (Brotz, 2016). More recently however, smaller jellyfish fisheries have opened elsewhere including Australia and the U.S.A, driven by market demand in Asia as well as worldwide collapses of more traditional local finfish and shellfish stocks (Brotz,

2016). A global catch reconstruction based on jellyfish landing data from 1950 to 2013 revealed the number of fishing countries, the number of targeted species and the magnitudes of catches have all been largely underestimated until recently (Brotz, 2016).

Contemporary global landings of jellyfish were estimated to be at least 750,000 tonnes per-annum, double previous estimates and exceeding the catch of many traditional fisheries (e.g. lobster) (Graham et al., 2014; Brotz & Pauly, 2016). The importance of jellyfish fisheries to China economically and in terms of food security is highlighted by

You et al. (2007) who stated that jellyfish-related businesses generate millions of dollars each year in Asia. Furthermore, because jellyfish are considered a delicacy in

China and attributed special medicinal values by consumers, the Asian markets are often undersupplied (You et al., 2007). Research into breeding young medusae in culture ponds as a means for expanding the production of the jellyfish Rhopilema esculentum was initiated in the 1980s, primarily to meet the demand for human consumption and medicinal products (You et al., 2007). This practice became so successful that the jellyfish aquaculture industry replaced the more traditional shrimp and fish aquaculture industry in many areas (Guan et al., 2004).

In terms of the health benefits of jellyfish, it is believed the consumption of jellyfish can effectively cure arthritis, hypertension, back pain, ulcers, tracheitis, asthma, burns, and

23 other illnesses as well as relieve fatigue, though You et al., (2007) warned that more scientific experiments are needed to verify many of these claims. However, jellyfish tissue is rich in collagen and research into the potential for jellyfish collagen to be used in biomedical applications found it had a similar biological effect on human cells to that of mammalian sourced collagen (Addad et al., 2011). Thus, the authors suggested jellyfish collagen makes a good replacement candidate for use in various biomedical applications, including rebuilding muscle, cartilage, and bone. Furthermore, studies have shown the anticancer and antioxidant potential of Chrysaora quinquecirrha (sea nettle) nematocyst venom, are comparable to that of animals treated with current standard treatment drugs (Balamurugan et al., 2010).

Arguably one of the greatest benefits jellyfish have had on medical science has been the discovery and subsequent development of Green Fluorescent Protein (GFP) (Ohba et al., 2013). GFP is a fluorescent protein that was originally isolated from the luminous organ of the jellyfish Aequorea victoria by Dr. Osamu Shimomura (Morise et al., 1974).

Following its initial discovery, it provided the molecular biology field with a non- invasive luminescent marker that revolutionised studies of cell biology and physiology

(Graham et al., 2014). It became commonplace in studies involving protein dynamics, fluorescence microscopy, reporting genes, transgenic plants, fungal biology, bacterial protein localisation and real time molecular and cellular analysis often with a range of medical applications including cellular research and diagnosis (Zimmer, 2002).

1.3.4 Cultural services

Cultural services are non-physical benefits that humans obtain from ecosystems through spiritual enrichment, cognitive development, reflection, recreation or aesthetic experiences (Anonymous, 2005). These can include cultural diversity,

24 spiritual and religious values, knowledge systems, social relations, cultural heritage and tourism. Surprisingly, even in countries that do not have a tradition of fishing for and eating jellyfish, highly aestheticised representations of jellyfish in video and still photography proliferate in pop culture (Alaimo, 2013). For example, ‘Jellies: living art exhibition’ at the Monterey Bay Aquarium (MBA) California, combines live jellyfish displays with a collection of artworks including works on paper, paintings and sculpture.

To date, this has been the most popular display in the aquarium history attracting over

10 million visitors (Doyle et al., 2014) (Fig. 1.3). Alaimo (2013) stated that while there are undoubtedly many reasons to be interested in jellyfish strictly from a scientific perspective, the scientific representation of these creatures is also extraordinarily aesthetic.

Figure 1.3. Moon jellyfish on display at Aquarium Berlin ©Samantha Ward, 2019.

Whilst jellyfish can deter people from beaches, they are also a driver of tourism in certain areas around the world. According to Dawson et al. (2001) one of the most remarkable sights in the Western Pacific is a perennial swarm of 1.5 million golden

25 jellyfish (Mastigias papua) crowded into a land locked marine lake in Palau, Micronesia.

Since the marine lakes were brought to the attention of the general public, many nature and diving magazines, radio and television shows have explored their inhabitants (Dawson et al., 2001). This publicity established jellyfish lake in Palau as one of the most popular snorkelling areas in the Tropical Pacific where, on average,

30,000 tourists visit jellyfish lake each year, providing a valuable source of revenue for the country.

1.3.5 Supporting services

Supporting services are those which are necessary for the production of all other ecosystem services (Anonymous, 2005). They differ from other services as their impacts on humans are indirect or occur over a long-time period such as production of atmospheric O2 (through photosynthesis), soil formation, nutrient cycling and provisioning of habitat, for example (de Groot et al., 2002). Appendicularians are small, gelatinous, free swimming tunicates which are common in most oceans and contribute significantly to nutrient recycling (Deibel, 1998; Vargas et al., 2002). A unique characteristic of appendicularians is their external filtering apparatus which sieves and concentrates a wider range of particle sizes from 0.2 – 3.0 µm, thus capturing organisms from bacteria to microplankton (Gorsky, 1998). It is this ability to obtain energy from the microbial loop, rather than the classic ‘diatom-copepod-fish’ food chain (Gorsky, 1998), which aids their nutrient recycling role in marine systems by providing an alternative energy pathway (Doyle et al., 2014). More generally, due to their boom and bust population dynamics, the potential for jellyfish to contribute significantly to nutrient recycling is large and proportionate to the size of their populations (Pitt et al., 2005). By determining the contribution to inorganic nutrient

26 recycling of co-occurring jellyfishes, Pitt et al. (2005) found nitrogen excretion can account for approximately 8% of the inorganic nitrogen requirements of phytoplankton consumed in intermittently open coastal lagoon in New South Wales, Australia.

Furthermore, a manipulative mesocosm experiment in a comparable saline coastal lake in Australia found that increased primary production was due largely to a 10-fold increase in the diatom Chaetoceros sp. (West et al., 2009). This exponential diatom growth was predominantly driven by phosphate excreted by jellyfish (Catostylus mosaicus) which was the limiting nutrient in the lake. West et al. (2009) suggested that, while blooms of both zooxanthellate and non-zooxanthellate jellyfish deplete mesoplankton and alter the composition of microzooplankton via top down processes, excretion of nutrients by blooms of non- zooxanthellate jellyfish can also greatly increase phytoplankton production.

Another way in which jellyfish can facilitate production is by contributing to ocean mixing. More broadly, ocean mixing is one of the major determinants of ocean circulation and its climatological influences (Bryden & Imawaki, 2001; Wunsch, 2017).

Recent studies have evoked debate about whether biologically generated ocean mixing

(or biogenic) affects overall mixing processes in a substantial way (Katija, 2012).

However, estimates of jellyfish biogenic inputs have shown that their contribution to ocean mixing is of the same order as winds and tides (Katija, 2012). Based on their vertical migration behaviour and variables such as morphology, swimming mode and body orientation, it is likely some species have the potential to be strong sources of biogenic mixing (Katija, 2012). As jellyfish swim between different layers in the water column they facilitate the transport of nutrients and other dissolved water across physiochemical boundaries stimulating production in these areas. Again, considering

27 the boom and bust nature of jellyfish populations and the high abundances they can attain, such contributions to ocean mixing may be significant, having an impact on ecosystem functioning, for example, via the resupply of nutrients to depleted surface waters which may enhance surface primary production (Doyle et al., 2014).

1.4 Fish-jellyfish interactions

Jellyfish are involved in a range of ecological fish-jellyfish interactions, also falling under the category of ‘supporting’ ecosystem services. Of these, competition and predator interactions have already been mentioned (see 1.2.2), which ultimately may have a significant negative impact on fish stocks and annual recruitment. Conversely however, jellyfish as prey and jellyfish as hosts for juvenile and larval fish can serve to positively influence fish stocks and annual recruitment (e.g. Cardona et al., 2012; Lynam and

Brierley, 2006).

1.4.1 Jellyfish as prey

The reconsideration of how jellyfish factor into marine food webs is the most significant shift in attitude by the jellyfish research community in recent years (Doyle et al., 2014;

Hays et al., 2018). Traditionally viewed as trophic dead ends (i.e. nutrients directed towards jellyfish are lost to taxa higher up the food chain; Hansson and Norrman, 1995;

Lynam et al., 2006), this idea has been overturned thanks to numerous studies demonstrating how jellyfish are key prey for a range of different species (Arai 2005;

Purcell and Arai, 2001). Other than the leatherback sea turtle (Dermochelys coriacea) which is widely known to be an obligate jellyfish feeder, Arai (2005) drew attention to numerous fish, mollusc, arthropod, reptile and bird species which utilise jellyfish as prey. Many jellyfish predator observations involve the pelagic or medusa stage of the 28 jellyfish. However, predation on the sessile benthic life stage of jellyfish is also a viable food source for benthic fauna which can potentially impact on subsequent abundances of pelagic medusae. Hernroth and Grondahl (1985) showed how the marine gastropod nudibranch (Coryphella verrucose) ingested benthic Aurelia sp. polyps on settling plates at a rate up to 200 polyps per day which is believed to be responsible for a drastic decline in polyp abundance in Gullmar Fjord, Sweden. Furthermore, Hoover et al.

(2012) showed how four different species of nudibranch fed on Aurelia labiata polyp, again emphasising their potential role as a source of top-down control on benthic polyp populations. Indeed, instances of predation and scavenging of jellyfish (pelagic scyphozoa, hydrozoan and ctenophore) by benthic invertebrates were reviewed by

Ates (2017), revealing a long list of species (n>55) dominated by sea anemones and decapod crabs as being directly involved in such interactions.

Despite studies to the contrary, jellyfish continued to be depicted as dead ends in marine food chains or as a rare source of food for predators up until relatively recently

(e.g. Lynam et al., 2006; Ruzicka, 2007; Condon et al., 2011). Two independent studies in the late 1980s showed over 50 species of fish were identified as consuming jellyfish

(Arai, 1988; Ates, 1988). Although representing a small proportion of fish species worldwide, the number of fish identified as feeding on jellyfish slowly began to rise as scientists became increasingly aware of the inherent difficulties involved when assessing jellyfish contributions to diet when using gut content analysis (Hays et al.,

2018) (Fig. 1.5). Due to extremely high clearance rates of soft bodied taxa such as jellyfish, their significance is often underestimated in studies using gut content analysis to attribute proportionate energy flow within food webs (Symondson, 2002). For example, spiny dogfish (Squalus acanthias) were considered to rarely prey on jellyfish

29 until gut content analysis was carried out on freshly caught samples, rather than preserved, as was standard practice at the time. As a result, 30-40% of stomach content volume was found to be jellyfish (Bowman et al., 1984; McFarlane, 1984). More recently, a large-scale stomach content analysis showed 39 of the 107 fish species sampled consumed jellyfish, 23 of which had not been documented as doing so previously (Brodeur et al., 2019). Again, gut content analysis in this study was carried out directly after the fish were caught, a method which is becoming more common in fisheries and ecology research, specifically to mitigate problems with soft bodied taxa and preservation (Brodeur et al., 2019).

In recent years, studies have used a variety of techniques such as stable isotope analysis of predator tissues, animal-borne cameras, remotely operated vehicles and molecular analysis of stomach contents and fecal samples and discovered a broad range of marine predators frequently feed on jellyfish (e.g. flying sea birds: McInnes et al., 2017; penguins: Jarman et al., 2013; Baptiste et al., 2016; Mcinnes et al., 2016;

Thiebot et al., 2017; fish including fish larvae: Cardona et al., 2012; Arkhipkin &

Laptikhovsky, 2013; D’Ambra et al., 2014; Milisenda et al., 2014; Lamb et al., 2017;

Ayala et al., 2018; turtles: Fossette et al., 2012; Heaslip et al., 2012; González Carman et al., 2014; crab: Sweetman et al., 2014; Dunlop et al., 2017; rock lobster larvae: Rorke et al., 2012). These approaches have also increased our knowledge of predators previously well known to feed on jellyfish such as leatherback turtles and ocean sunfish.

Single observations of fish feeding events on jellyfish have also greatly helped our understanding of these interactions. For example, Dias and Almeida (2015) documented the first record of predation on the jellyfish Chrysaora lactea by an aggregation of French Angelfishes (Pomacanthus paru) on Brazilian rocky reefs,

30 suggesting temporarily available food resources such as jellyfish benefit species with a more generalist diet. Cardona et al. (2012) used stable isotopes of carbon and nitrogen to discover massive consumption of jellyfish by apex predators in the Mediterranean including bluefin tuna (Thunnus thynnus), little tunny (Euthynnus alletteratus), spearfish (Tetrapturus belone), swordfish (Xiphias gladius), loggerhead sea turtles

(Caretta caretta) and ocean sunfish (Mola mola). Similarly, novel use of mitochondrial

DNA assays showed that predation on jellyfish by commercially important species such as herring (Clupea harengus) and whiting (Merlangius merlangus) was prevalent in the

Irish Sea (Lamb et al., 2017, 2019). Taken together, the multi-disciplinary approach to investigate jellyfish predator-prey interactions involving stable isotope analysis (SIA),

DNA metabarcoding and more traditional gut content analysis has been key in revaluating the ecological role of jellyfish in marine systems (Hays et al., 2018).

1.4.2 Fish-jellyfish associations

Associative behaviour is defined as the spatial relationship between an animal (or a group) of a given species and an animal of another species or object, based on a decision by at least one of the two individuals to maintain contact or close proximity

(Castro et al., 2001). Fish-jellyfish associations are reported as early as the 19th century beginning with Beneden (1876). Later, Step (1924) and Dean et al. (1923) listed some of the earliest known fish-jellyfish symbionts. Meanwhile, the number of fish species recorded as exhibiting this behaviour has grown, which prompted a review of symbiotic behaviour between small fish and jellyfish by Mansueti (1963). In the review, Mansueti

(1963) identified a total of 66 different fish species from nine different families involved in fish-jellyfish associations. However, despite suggesting that these fish may only represent a small number of families that produce young that could be potentially

31 symbiotic, Mansueti (1963) made light of the importance of these symbioses, stating that they are probably of no great significance to the long-range ecology and biology of the fishes involved. Castro et al. (2001) counter argued this assertion from Mansueti

(1963), suggesting that such behaviour has evolved to safeguard the survival of eggs, larvae and juvenile stage of fishes during vulnerable stages of their development.

Although it is now widely accepted that the motivation for fish to associate with jellyfish is multi-faceted, the two main hypotheses genrally accepted as driving the behaviour are protection from predators (Gooding & Magnuson, 1967; Hunter &

Mitchell, 1968; Feigenbaum et al., 1989) and availability of food (Gooding & Magnuson,

1967; Purcell & Arai, 2001).

Despite the classic example of fish-jellyfish association involving the man-of-war-fish

(Nomeus gronovii) with the pleustonic siphonophore (Physalia physalia), the most common associations are among juvenile fish and scyphomedusae (Purcell & Arai,

2001). Young walleye pollock (Gadus chalcogrammus) are frequently associated with large scyphomedusae (predominantly Chrysaora melanaster) in the Bering Sea

(Brodeur, 1998). Brodeur (1998) suggested that juvenile pollock are symbiotic with jellyfish until they reach a size at which they can escape most gape-limited predators and suggested that the amount of suitable ‘medusae habitat’ available to them may be a determinant of recruitment success. Although large carnivorous jellyfish such as

Chrysaora melanaster may affect earlier stages of pollock, either by directly consuming them or by competing with them for limited food resources, Brodeur (1998) hypothesised some benefit (i.e. shelter from predators) could be derived by juvenile pollock through association with jellyfish.

In the North Sea, Lynam and Brierley (2006) observed gadoid fish sheltering and finding refuge from predators beneath jellyfish umbrellas and found considerable spatial distribution overlaps between young gadoid fish and scyphomedusae jellyfish in the same area. Subsequent analysis revealed significant positive correlations between whiting (Merlangius merlangus) survival and jellyfish abundance, suggesting that jellyfish may be an important factor influencing the mortality of whiting in the North

Sea. Similarly, in the North Pacific, Sassa et al. (2006) reported the abundance of jack mackerel (Trachurus symmetricus) juveniles was positively correlated with that of

Pelagia noctiluca medusae and suggested that the fish may profit from the drift of medusae into convergences where planktonic food often accumulates. An increase of food availability is widely accepted as the other principal motivator for juvenile fishes to associate with jellyfish. Moreover, there are a range of pathways by which juvenile fish could increase their food availability when associating with jellyfish beyond that of associating with floating material. Indirectly, juvenile fish can increase their access to food resources by feeding on i) zooplankton entrained by jellyfish swimming pulses or captured in the tentacles of the jellyfish before host ingestion; and (ii) amphipod parasites present on the jellyfish host (Mansueti, 1963; Purcell & Arai, 2001; Fleming et al., 2014). What is more, jellyfish hosts can also provide a direct means of food resource to associating fish. For example, D’Ambra et al. (2014) used stable isotope analysis to highlight how juvenile Atlantic bumper (Chloroscombrus chrysurus) fed directly on their jellyfish hosts (Aurelia sp. and Drymonema larsoni) which contributed up to 100% of their assimilated diet in some instances. This tendency was also observed by Mansueti (1963) who noted how juvenile harvestfish (Peprilus paru) use their medusa hosts initially for protection from predators but, as they grow, the dynamics of

33 the relationship change to a point where the fish consume parts of the jellyfish, as well as possibly stealing food caught by the jellyfish.

1.4.3 The knock-on effect on humans and global food security

In 2015, fish protein accounted for about 17% of animal protein consumed by the global population (FAO, 2018). More specifically, fish provided approximately 3.2 billion people with almost 20% of their average per capita intake of animal protein whilst the fishing and aquaculture industry assured the livelihoods of 10-12% of the world’s population (FAO, 2018). Therefore, failing to account for ecological interactions, including fish-jellyfish interactions, will have large socio-economic impacts as a result of positively or negatively influenced fish survivorship and mortality. (Tupper &

Boutilier, 1995b; Brodeur et al., 2002; Lynam et al., 2005; Lynam & Brierley, 2006).

Studying the impact of jellyfish on fish communities has increased salience given the stressors fish populations are already experiencing, including, but not limited to, overfishing, climate change and habitat loss (FAO, 2014).

1.5 Knowledge gaps and research objectives

There is good evidence that jellyfish have paradoxical (i.e both negative and positive) effects on fish communities (Fig 1.4). However, the main challenge for researchers is to quantify the relative importance of these effects and to understand the consequences of these contrary interactions on fish communities when they co-occur.

This is likely to be context dependent (i.e. it varies spatially, temporally and depending on community species composition). This work offers a major contribution to understand this balance in an Irish Sea context.

Figure 1.4. An overview of the contrary impacts fish-jellyfish interactions can have on fish, in what I have termed “The Jellyfish Paradox”

1.5.1 Addressing positive impacts of fish-jellyfish interactions

Within marine systems, phylogenetically-based comparative analysis has been used to address key questions including speciation rates and body size evolution in fishes and the evolutionary origins and ancestral state reconstruction of cephalopods (Lindgren et al., 2012; Rabosky et al., 2013). Fish-jellyfish interactions such as competition, predation and association are important factors contributing to fish stock success

(Lynam & Brierley, 2006; Sassa et al., 2006). Jellyfish can compete with fish for food resources or feed on fish eggs and larvae, which works to reduce survivorship and recruitment of fish species (Shiganova, 1998; Lynam et al., 2006). Nevertheless, jellyfish also provide habitat and space for developing larval and juvenile fish which use their host as means of protection from predators and for feeding opportunities (Fig. 1.5), helping to reduce fish mortality and increase recruitment (Bonaldo et al., 2004; Lynam

& Brierley, 2006). As pressure on fin-fish stocks continues to mount year on year, it is important to consider all potential positive and negative impacts jellyfish can have on fish communities. Relatively little is known about the evolutionary dynamics and drivers of such associations which would allow for their more effective consideration in ecosystem models. This thesis adopted a phylogenetically based comparative analysis approach to investigate the evolution of a long-standing paradox in jellyfish

35 ecology, namely, how gelatinous species that are routinely portrayed as solely negative stressors of fish communities can serve as habitat providers during their early life history. I explored the evolutionary history of fish-jellyfish associations and tested hypotheses on the potential selective forces that have promoted the evolution of such associations. Given the wide-spread occurrence and diversity of fish species involved in these associations, they provided a valuable model to explore the evolutionary origins of a fish-jellyfish interaction occurring on a global scale.

1.5.3 Producing reliable trophic position estimates for jellyfish

Reliable trophic discrimination factors (TDFs - the ratio at which a consumer is enriched in certain element isotopes compared to its food) are vital for trophic studies including determination of trophic position (Vander Zanden & Rasmussen, 2001; Post, 2002;

Bastos et al., 2017). However, determining TDFs can be extremely challenging for marine taxa such as jellyfish that are difficult to culture due to a lack of knowledge of life cycle and diet, or where their morphology precludes maintenance in tank studies.

Currently, there is no consensus as to whether standard TDF values are appropriate for jellyfish, which differ remarkably in morphology from other taxa for which the standard

TDF values were generated via captive feeding studies with prey of known isotopic ratios (Fleming et al., 2011; D’Ambra et al., 2013),. This study aims to circumnavigate the need for tank-based feeding studies to establish TDFs for four different species of scyphozoan jellyfish and one hydrozoan using a combination of Bulk Stable Isotope

Analysis (BSIA) and Compound Specific Isotope Analysis (CSIA). BSIA centres on the comparison of consumer and prey stable isotope ratios adjusted for the isotopic shifts

Figure 1. 5. Compass jellyfish (Chrysaora hysoscella) with associating juvenile fish. The ability of juvenile fish to hide in among the bell and oral arms of jellyfish is well demonstrated in (a) where it is difficult to locate the fish (see red arrows). Furthermore, (b) highlights the potential for single jellyfish to host large numbers of juvenile fish.

associated with assimilation (TDFs) (Post, 2002; Fry, 2006; Middelburg, 2014). Whereas,

CSIA is based on how heavily fractionating “trophic” amino acids (AAs) provide a robust indicator of trophic transfer between consumer and prey while minimally fractionating

“source” AAs closely reflect the δ15N value at the base of the food web all from a single

37 sample of consumer tissue (e.g. Batista et al., 2014; Chikaraishi et al., 2014; Choy et al.,

2015). The premise of using these methods in tandem is that if there is a common TDF for jellyfish, acting like a sensitivity analysis, one trophic position estimated using a range of different TEF values on the BSIA data will converge on the results gathered using the CSIA method, providing an indirect means to estimate jellyfish TDFs.

1.5.4 Trophic fish-jellyfish interactions in the Irish Sea

The ecological relevance of jellyfish is often neglected beyond their role as stressors of the marine environment (Richardson et al., 2009). The result of this can be, at best, the inclusion of jellyfish in ecosystem and fisheries models in the literature as a single functional group, feeding at the same trophic level over time (Pauly et al., 2009). At its worst, jellyfish are not included in marine food webs, ecosystem or fisheries models at all (Pauly et al., 2009). However, it is well established now that as a diverse polyphyletic group in marine ecosystems, this is a gross over-simplification of the true role of jellyfish, yet the link between field biologists and modellers remains limited (Pauly et al., 2009; Doyle et al., 2014; Brodeur et al., 2016).

Here we used BSIA with resolved jellyfish TDF factors to investigate trophic interactions occurring in the Irish Sea between jellyfish and commercial fish communities. The overarching aim of this study was to explore the importance of incorporating jellyfish into Irish Sea food web, ecosystem and fisheries-based models in the future.

1.6 References Acha, E.M., Uye, S., Mianzan, H., Jellyfish, W.M.G., Robinson, K., Ruzicka, J., Decker, M.B., Brodeur, R.D., Hernandez, F., Quiñones, J., Acha, M., and Graham, W.M. (2014). Jellyfish, Forage Fish, and the World’s Major Fisheries. Oceanography 27, 104–115.

Addad, S., Exposito, J.Y., Faye, C., Ricard-Blum, S., and Lethias, C. (2011). Isolation, characterization and biological evaluation of jellyfish collagen for use in biomedical applications. Marine Drugs 9, 967–983.

Alaimo, S. (2013). Jellyfish Science, Jellyfish Aesthetics: Posthuman Reconfigurations of the Sensible. Thinking with Water 139–164.

Anonymous. (2005). Millennium Ecosystem Assessment. Ecosystem and human well- being: biodiversity synthesis. World Resources Institute, Washington, DC.

Arai, M.N. (1988). Interactions of fish and pelagic coelenterates. Canadian Journal of Zoology 66, 1913–1927.

Arai, M.N. (2005). Predation on pelagic coelenterates: a review. Journal of the Marine Biological Association of the UK 85, 523–536.

Ates, R.M.L. (1988). Medusivorous Fishes, A Review. Zoologische Mededelingen 62, 29–42.

Ates, R.M.L. (2017). Benthic scavengers and predators of jellyfish, material for a review. Plankton and Benthos Research 12, 71–77.

Baird, D., and Ulanowicz, R.E. (1989). The Seasonal Dynamics of The Chesapeake Bay Ecosystem. Ecological Monographs 59, 329–364.

Bakun, A., and Weeks, S.J. (2006). Adverse feedback sequences in exploited marine systems: are deliberate interruptive actions warranted? Fish and Fisheries 7, 316–333.

Balamurugan, E., Reddy, B.V., and Menon, V.P. (2010). Antitumor and antioxidant role of Chrysaora quinquecirrha (sea nettle) nematocyst venom peptide against ehrlich ascites carcinoma in Swiss Albino mice. Molecular and Cellular Biochemistry 338, 69– 76.

Bastian, T., Haberlin, D., Purcell, J.E., Hays, G.C., Davenport, J., McAllen, R., and Doyle, T.K. (2011). Large-scale sampling reveals the spatio-temporal distributions of the jellyfish Aurelia aurita and Cyanea capillata in the Irish Sea. Marine Biology 158, 2639–2652.

Bastos, R.F., Corrêa, F., Winemiller, K.O., and Garcia, A.M. (2017). Are you what you eat? Effects of trophic discrimination factors on estimates of food assimilation and trophic position with a new estimation method. Ecological Indicators 75, 234–241.

Baxter, E.J., Rodger, H.D., McAllen, R., and Doyle, T.K. (2011). Gill disorders in marine- farmed salmon: Investigating the role of hydrozoan jellyfish. Aquaculture Environment Interactions 1, 245–257.

Beneden, P.J. (1876). Animal parasites and messmates. HS King & Company.

Bonaldo, R.M., Krajewski, J.P., and Sazima, I. (2004). Does the association of young fishes with jellyfishes protect from predation? A report on a failure case due to damage to the jellyfish. Neotropical Ichthyology 2, 103–105.

Boreo, F. (2013). Review of Jellyfish Blooms in the Mediterranean and Black Sea. Studies and Reviews. General Fisheries Commission for the Mediterranean.

Bosch-Belmar, M., Azzurro, E., Pulis, K., Milisenda, G., Fuentes, V., Kéfi-Daly Yahia, O., Micallef, A., Deidun, A., and Piraino, S. (2017). Jellyfish blooms perception in Mediterranean finfish aquaculture. Marine Policy 76, 1–7.

Bowman, R., Eppi, R., and Grosslein, M. (1984). Diet and consumption of spiny dogfish in the Northwest Atlantic. ICES CM 27, 16.

Brodeur, R.D. (1998). In situ observations of the association between juvenile fishes and scyphomedusae in the Bering Sea. Marine Ecology Progress Series 163, 11–20.

Brodeur, R.D., Hunsicker, M.E., Hann, A., and Miller, T.W. (2019). Effects of warming ocean conditions on feeding ecology of small pelagic fishes in a coastal upwelling ecosystem: a shift to gelatinous food sources. Marine Ecology Progress Series 617, 149–163.

Brodeur, R.D., Link, J.S., Smith, B.E., Ford, M.D., Kobayashi, D.R., and Jones, T.T. (2016). Ecological and Economic Consequences of Ignoring Jellyfish: A Plea for Increased Monitoring of Ecosystems. Fisheries 41, 630–637.

Brodeur, R.D., Sugisaki, H., and Jr, G.L.H. (2002). Increases in jellyfish biomass in the Bering Sea : implications for the ecosystem. Marine Ecology Progress Series 233, 89– 103.

Brotz, L. (2016). Jellyfish Fisheries of the World - Thesis.

Brotz, L., and Pauly, D. (2016). Studying jellyfish fisheries: toward accurate national catch reports and appropriate methods for stock assessments. Jellyfish: Ecology, Distribution Patterns and Human Interactions. Nova Publishers, Hauppauge.

Bryden, H.L., and Imawaki, S. (2001). Chapter 6.1 Ocean heat transport. International Geophysics 77, 455–474.

Buesseler, K.O., Antia, A.N., Chen, M., Fowler, S.W., Gardner, W.D., Gustafsson, O., Harada, K., Michaels, A.F., Rutgers van der Loeff, M., Sarin, M., Steinberg, D.K., and Trull, T. (2007). An assessment of the use of sediment traps for estimating upper ocean particle fluxes. Journal of Marine Research 65, 345–416.

Canadell, J.G., and Raupach, M.R. (2008). Managing forests for climate change mitigation. Science 320, 1456–1457.

Cardona, L., de Quevedo, I.Á., Borrell, A., and Aguilar, A. (2012). Massive consumption of gelatinous plankton by Mediterranean apex predators. PLoS ONE 7.

Castro, J.J., Santiago, J. a., and Santana-Ortega, A.T. (2001). A general theory on fish aggregation to floating objects: An alternative to the meeting point hypothesis. Reviews in Fish Biology and Fisheries 11, 255–277.

Cegolon, L., Heymann, W.C., Lange, J.H., and Mastrangelo, G. (2013). Jellyfish stings and their management: A review. Marine Drugs 11, 523–550.

Condon, R.H., Duarte, C.M., Pitt, K.A., Robinson, K.L., Lucas, C.H., Sutherland, K.R., Mianzan, H.W., Bogeberg, M., Purcell, J.E., Decker, M.B., Uye, S., Madin, L.P., Brodeur, R.D., Haddock, S.H.D., Malej, A., Parry, G.D., Eriksen, E., Quiñones, J., Acha, M., Harvey, M., Arthur, J.M., and Graham, W.M. (2013). Recurrent jellyfish blooms are a 41 consequence of global oscillations. Proceedings of the National Academy of Sciences of the United States of America 110, 1000–5.

Condon, R.H., Graham, W.M., Duarte, C.M., Pitt, K.A., Lucas, C.H., Haddock, S.H.D., Sutherland, K.R., Robinson, K.L., Dawson, M.N., Beth, M., Mills, C.E., Purcell, J.E., Malej, A., Mianzan, H., and Uye, S. (2012). Questioning the Rise of Gelatinous Zooplankton in the World’s Oceans. BioScience 62, 160–169.

Condon, R.H., Steinberg, D.K., del Giorgio, P.A., Bouvier, T.C., Bronk, D.A., Graham, W.M., and Ducklow, H.W. (2011). Jellyfish blooms result in a major microbial respiratory sink of carbon in marine systems. Proceedings of the National Academy of Sciences 108, 10225–10230.

Cox, P.M., Betts, R.A., Jones, C.D., Spall, S.A., and Totterdell, I.J. (2000). Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408, 184–187.

Currie, B.J., and Jacups, S.P. (2005). Prospective study of Chironex fleckeri and other box jellyfish stings in the ‘Top End’ of Australia’s Northern Territory. Medical Journal of Australia 183, 631–636.

D’Ambra, I., Carmichael, R.H., and Graham, W.M. (2013). Determination of δ13C and δ15N and trophic fractionation in jellyfish: implications for food web ecology. Marine Biology 161, 473–480.

D’Ambra, I., Graham, W.M., Carmichael, R.H., and Hernandez, F.J. (2014). Fish rely on scyphozoan hosts as a primary food source: evidence from stable isotope analysis. Marine Biology 162, 247–252.

Dawson, M.N., Martin, L.E., and Penland, L.K. (2001). Jellyfish swarms, tourists, and the Christ-child. Hydrobiologia 451, 131–144.

De Donno, A., Idolo, A., Bagordo, F., Grassi, T., Leomanni, A., Serio, F., Guido, M., Canitano, M., Zampardi, S., Boero, F., and Piraino, S. (2014). Impact of stinging jellyfish proliferations along south Italian coasts: Human health hazards, treatment and social costs. International Journal of Environmental Research and Public Health 11, 2488–2503.

42 de Groot, R.S., Wilson, M.A., and Boumans, R.M.J. (2002). A typology for the classification, description and valuation of ecosystem functions, goods and services. Ecological Economics 41, 393–408.

Dean, B., Gudger, E.W., and Henn, A.W. (1923). A Bibliography of Fishes: Anonymous titles no. 651-712. Addenda to v. 1-2 (p. 4-203) Pre-Linnaean publications. General bibliographies which include references to fishes. Voyages and expeditions. Periodicals relating to fish and fish-culture. Errata and c. The Museum.

Deibel, D. (1998). The abundance, distribution, and ecological impact of doliolids. The Biology of Pelagic Tunicates 171–186.

Dias, R.M., and Almeida, D.F. (2015). First record of predation on jellyfish by an aggregation of French Angelfish Pomacanthus paru on Brazilian rocky reefs. Marine Biodiversity Records 8, 8–10.

Doyle, T.K., De Haas, H., Cotton, D., Dorschel, B., Cummins, V., Houghton, J.D.R., Davenport, J., and Hays, G.C. (2008). Widespread occurrence of the jellyfish Pelagia noctiluca in Irish coastal and shelf waters. Journal of Plankton Research 30, 963–968.

Doyle, T.K., Hays, G.C., Harrod, C., and Houghton, J.D.R. (2014). Ecological and Societal Benefits of jellyfish. In Kylie A. Pitt & C. H. Lucas (Eds.), Jellyfish Blooms (pp. 105–127). Dordrecht: Springer Netherlands.

Doyle, T.K., Headlam, J.L., Wilcox, C.L., and Yanagihara, A.A. (2017). Evaluation of Cyanea capillata Sting Management Protocols Using Ex Vivo and In Vitro Envenomation Models. Toxins 9.

Dunlop, K.M., Jones, D.O.B., and Sweetman, A.K. (2017). Direct evidence of an efficient energy transfer pathway from jellyfish carcasses to a commercially important deep-water species. Scientific Reports 15–18.

FAO. (2018). The State of World Fisheries and Aquaculture 2018 - Meeting the sustainable development goals.

Feigenbaum, D., Friedlander, A., and Bushing, M. (1989). Determination of the feasibility of fish attracting devices for enhancing fisheries in Puerto Rico. Bulletin of Marine Science 44, 950–959.

Fenner, P.J. (1999). Irukandji envenomation in far north Queensland. The Medical Journal of Australia 170, 512.

Fleming, N.E.C., Harrod, C., Griffin, D.C., Newton, J., and Houghton, J.D.R. (2014). Scyphozoan jellyfish provide short-term reproductive habitat for hyperiid amphipods in a temperate near-shore environment. Marine Ecology Progress Series 510, 229– 240.

Fleming, N.E.C., Harrod, C., and Houghton, J.D.R. (2013). Identifying potentially harmful jellyfish blooms using shoreline surveys. Aquaculture Environment Interactions 4, 263–272.

Fleming, N.E.C., Harrod, C., Newton, J., and Houghton, J.D.R. (2015). Not all jellyfish are equal: isotopic evidence for inter- and intraspecific variation in jellyfish trophic ecology. PeerJ 3: e1110.

Fleming, N.E.C., Houghton, J.D.R., Magill, C.L., and Harrod, C. (2011). Preservation methods alter stable isotope values in gelatinous zooplankton: implications for interpreting trophic ecology. Marine Biology 158, 2141–2146.

Food and agriculture Organisation of the United Nations. (2014). Fisheries, Aquaculture and Climate Change.

Fossette, S., Gleiss, A.C., Casey, P., Lewis, A.R., and Hays, G.C. (2012). Does prey size matter? Novel observations of feeding in the leatherback turtle (Dermochelys coriacea) allow a test of predator – prey size relationships 351–354.

Gershwin, L.A., de Nardi, M., Winkel, K.D., and Fenner, P.J. (2010). Marine stingers: Review of an under-recognized global coastal management issue. Coastal Management 38, 22–41.

Gibbons, M.J., and Richardson, A.J. (2013). Beyond the jellyfish joyride and global oscillations: Advancing jellyfish research. Journal of Plankton Research 35, 929–938.

González Carman, V., Botto, F., Gaitán, E., Albareda, D., Campagna, C., and Mianzan, H. (2014). A jellyfish diet for the herbivorous green turtle Chelonia mydas in the temperate SW Atlantic. Marine Biology 161, 339–349.

Gooding, R.M., and Magnuson, J.J. (1967). Ecological Significance of a Drifting Object to Pelagic Fishes. Pacific Science 21, 486–497.

Gorsky, G. (1998). The role of Appendicularia in marine food webs. The Biology of Pelagic Tunicates.

Graham, W.M., and Bayha, K.M. (2008). Biological invasions by marine jellyfish. In Biological Invasions (Vol. 193, pp. 239–255).

Graham, W.M., Gelcich, S., Robinson, K.L., Duarte, C.M., Brotz, L., Purcell, J.E., Madin, L.P., Mianzan, H., Sutherland, K.R., Uye, S.I., Pitt, K.A., Lucas, C.H., Bøgeberg, M., Brodeur, R.D., and Condon, R.H. (2014). Linking human well-being and jellyfish: Ecosystem services, impacts, and societal responses. Frontiers in Ecology and the Environment 12, 515–523.

Graham, W.M., Martin, D., and Martin, J. (2003). In situ quantification and analysis of large jellyfish using a novel video profiler. Marine Ecology Progress Series 254, 129– 140.

Gregg, W.W., Ginoux, P., Schopf, P.S., and Casey, N.W. (2003). Phytoplankton and iron: Validation of a global three-dimensional ocean biogeochemical model. Deep-Sea Research Part II: Topical Studies in Oceanography 50, 3143–3169.

Guan, S., Zhang, P.G., and Liu, C.Y. (2004). Pond cultivation state, existing problem and successful example of Rhopilema esculenta of Liaoning Province. Fish Science 23, 30–31.

Hamilton, G. (2016). The Secret Lives of Jellyfish. Nature 432–434.

Hansson, L.J., and Norrman, B. (1995). Release of dissolved organic carbon (DOC) by the scyphozoan jellyfish Aurelia aurita and its potential influence on the production of planktic bacteria. Marine Biology 121, 527–532.

Hay, S., and Murray, A. (2008). Jellyfish problems faced by the aquaculture industry. Fish Farmer 5, 40–41.

Hays, G.C., Doyle, T.K., and Houghton, J.D.R. (2018). A Paradigm Shift in the Trophic Importance of Jellyfish? Trends in Ecology & Evolution.

Heaslip, S.G., Iverson, S.J., Bowen, W.D., and James, M.C. (2012). Jellyfish Support High Energy Intake of Leatherback Sea Turtles (Dermochelys coriacea): Video Evidence from Animal-Borne Cameras 7, 1–7.

Heckmann, R. (2004). What else can happen? Other problems for fish production. Aquaculture Magazine.

Henschke, N., Bowden, D.A., Everett, J.D., Holmes, S.P., Kloser, R.J., Lee, R.W., and Suthers, I.M. (2013). Salp-falls in the Tasman Sea: A major food input to deep-sea benthos. Marine Ecology Progress Series 491, 165–175.

Hernroth, L., and Grondahl, F. (1985). On the Biology of Aurelia aurita (L.): Major factors regulating the occurrence of ephyrae and younf medusae in the Gullmar Fjord, Western Sweden. Bulletin of Marine Science 37, 567–576.

Hoover, R.A., Armour, R., Dow, I., and Purcell, J.E. (2012). Nudibranch predation and dietary preference for the polyps of Aurelia labiata (Cnidaria: Scyphozoa). Hydrobiologia 690, 199–213.

Houghton, J.D.R., Doyle, T.K., Davenport, J., and Hays, G.C. (2006). Develping a simple, rapid method for identifying and monitoring jellyfish aggegations from the air. Marine Ecology Progress Series 314, 159–170.

Houghton, J.D.R., Doyle, T.K., Davenport, J., Lilley, M.K.S., Wilson, R.P., and Hays, G.C. (2007). Stranding events provide indirect insights into the seasonality and persistence of jellyfish medusae (Cnidaria: Scyphozoa). Hydrobiologia 589, 1–13.

Hunter, J.R., and Mitchell, C.T. (1968). Field experiments on the attraction of pelagic fish to floating objects. ICES Journal of Marine Science 31, 427–434.

Johnson, P. (2002). Jellyfish, algae take toll on Scottish salmon. Fish Information Service, Europe.

Johnston, B.D., Irvine, H., Sötje, I., Pierce, G.J., and Secombes, C.J. (2005). Ecological toxicology and effects of mass occurrences of gelatinous zooplankton on teleosts in enclosed coastal ecosystems. AM Soc Limnol Oceanogr Meet.

Katija, K. (2012). Biogenic inputs to ocean mixing. Journal of Experimental Biology 215, 1040–1049. 46

Kideys, A.E. (2002). The comb jelly Mnemiopsis leidyi in the Black Sea. In Invasive Aquatic Species of Europe. Distribution, Impacts and Management (pp. 56–61). Springer.

Lamb, P.D., Hunter, E., Pinnegar, J.K., Creer, S., Davies, R.G., and Taylor, M.I. (2017). Jellyfish on the menu: mtDNA assay reveals scyphozoan predation in the Irish Sea. Royal Society Open Science 4, 171421.

Lamb, P.D., Hunter, E., Pinnegar, J.K., van der Kooij, J., Creer, S., and Taylor, M.I. (2019). Cryptic diets of forage fish: jellyfish consumption observed in the Celtic Sea and western English Channel. Journal of Fish Biology.

Lebrato, M., and Jones, D.O.B. (2009). Mass deposition event of Pyrosoma atlanticum carcasses off Ivory Coast (West Africa). Limnology and Oceanography 54, 1197–1209.

Lebrato, M., Pitt, K.A., Sweetman, A.K., Jones, D.O.B., Cartes, J.E., Oschlies, A., Condon, R.H., Molinero, J.C., Adler, L., Gaillard, C., Lloris, D., and Billett, D.S.M. (2012). Jelly-falls historic and recent observations: A review to drive future research directions. Hydrobiologia 690, 227–245.

Li, J., and Hsieh, Y.P. (2004). Traditional Chinese food technology and cuisine. Asia Pacific Journal of Clinical Nutrition 13, 147–155.

Lindgren, A.R., Pankey, M.S., Hochberg, F.G., and Oakley, T.H. (2012). A multi-gene phylogeny of Cephalopoda supports convergent morphological evolution in association with multiple habitat shifts in the marine environment. BMC Evolutionary Biology 12, 129.

Lo, W., Purcell, J.E., Hung, J., Su, H., and Hsu, P. (2008). Enhancement of jellyfish (Aurelia aurita) populations by extensive aquaculture rafts in a coastal lagoon in Taiwan. ICES Journal of Marine ScienceJournal of Marine Science 453–461.

Lüthi, D., Le Floch, M., Bereiter, B., Blunier, T., Barnola, J.M., Siegenthaler, U., Raynaud, D., Jouzel, J., Fischer, H., Kawamura, K., and Stocker, T.F. (2008). High- resolution carbon dioxide concentration record 650,000-800,000 years before present. Nature 453, 379–382.

Lynam, C.P., and Brierley, A.S. (2006). Enhanced survival of 0-group gadoid fish under jellyfish umbrellas. Marine Biology 150, 1397–1401.

Lynam, C.P., Gibbons, M.J., Axelsen, B.E., Sparks, C.A.J., Coetzee, J., Heywood, B.G., and Brierley, A.S. (2006). Jellyfish overtake fish in a heavily fished ecosystem. Current Biology 16, 492–493.

Lynam, C.P., Hay, S.J., and Brierley, A.S. (2004). Interannual variability in abundance of North Sea jellyfish and links to the North Atlantic Oscillation. Limnology and Oceanography 49, 637–643.

Lynam, C.P., Heath, M.R., Hay, S.J., and Brierley, A.S. (2005). Evidence for impacts by jellyfish on North Sea herring recruitment. Marine Ecology Progress Series 298, 157– 167.

Madin, L.P. (1982). Production, composition and sedimentation of salp fecal pellets in oceanic waters. Marine Biology 67, 39–45.

Madin, L.P., Kremer, P., Wiebe, P.H., Purcell, J.E., Horgan, E.H., and Nemazie, D.A. (2006). Periodic swarms of the salp Salpa aspera in the Slope Water off the NE United States: Biovolume, vertical migration, grazing, and vertical flux. Deep-Sea Research Part I: Oceanographic Research Papers 53, 804–819.

Mansueti, R. (1963). Symbiotic Behavior Between Small Fishes and Jellyfishes, with New Data on That Between the Stromateid, Peprilus alepidotus and the Scyphomedusa, Chrysaora quinquecirrha. Copeia 1, 40–80.

Mariottini, G.L., and Pane, L. (2010). Mediterranean jellyfish venoms: a review on scyphomedusae. Marine Drugs 8, 1122–52.

McFarlane, G.A. (1984). Data for the biology and diet studies of spiny dogfish (Squalus acanthias) in Hecate Strait, BC, August 1977 and June 1978. Department of Fisheries and Oceans, Resource Services Branch, Pacific Biological Station.

Merceron, M., Le Fevre-Lehoerff, G., Bizouarn, Y., and Kempf, M. (1995). Fish and jellyfish in Brittany(France). Equinoxe. Nantes 6–8.

Morise, H., Shimomura, O., Johnson, F.H., and Winant, J. (1974). Intermolecular energy transfer in the bioluminescent system of Aequorea. Biochemistry 13, 2656– 2662.

Nickell, T., Davidson, K., Fox, C., Miller, P., and Hays, G.C. (2010). Developing the capacity to monitor the spatial and temporal distribution of jellyfish in western Scottish waters.

Ohba, Y., Fujioka, Y., Nakada, S., and Tsuda, M. (2013). Fluorescent protein-based biosensors and their clinical applications. Progress in Molecular Biology and Translational Science (1st ed., Vol. 113). Elsevier Inc.

Omori, M., and Nakano, E. (2001). Jellyfish fisheries in Southeast Asia. Hydrobiologia 451, 19–26.

Östman, C. (2000). A guideline to nematocyst nomenclature and classification, and some notes on the systematic value of nematocysts*. Scientia Marina 64, 31–46.

Pauly, D., Graham, W.M., Libralato, S., Morissette, L., and Deng Palomares, M.L. (2009). Jellyfish in ecosystems, online databases, and ecosystem models. Hydrobiologia 616, 67–85.

Pitt, K.A., Koop, K., and Rissik, D. (2005). Contrasting contributions to inorganic nutrient recycling by the co-occurring jellyfishes, Catostylus mosaicus and Phyllorhiza punctata (Scyphozoa, Rhizostomeae). Journal of Experimental Marine Biology and Ecology 315, 71–86.

Post, D.M. (2002). Using Stable Isotopes to Estimate Trophic Position: Models, Methods, and Assumptions. Ecology 83, 703–718.

Purcell, J.E. (2012). Jellyfish and ctenophore blooms coincide with human proliferations and environmental perturbations. Annual Review of Marine Science 4, 209–35.

Purcell, J.E., and Arai, M.N. (2001). Interactions of pelagic cnidarians and ctenophores with fish: a review. Hydrobiologia 451, 27–44.

Purcell, J.E., Bâmstedt, U., and Bâmstedt, A. (1999). Prey, feeding rates, and asexual reproduction rates of the introduced oligohaline hydrozoan Moerisia lyonsi. Marine Biology 134, 317–325.

Purcell, J.E., Uye, S., and Lo, W. (2007). Anthropogenic causes of jellyfish blooms and their direct consequences for humans: a review. Marine Ecology Progress Series 350, 153–174.

Rabosky, D.L., Santini, F., Eastman, J., Smith, S.A., Sidlauskas, B., Chang, J., and Alfaro, M.E. (2013). Rates of speciation and morphological evolution are correlated across the largest vertebrate radiation. Nature Communications 4, 1958.

Rajagopal, S., Nair, K.V.K., and Azariah, J. (1989). Some observations on the problem of jelly fish ingress in a power station cooling system at Kalpakkam, East coast of India. Mahasagar 22, 151–158.

Richardson, A.J., Bakun, A., Hays, G.C., and Gibbons, M.J. (2009). The jellyfish joyride: causes, consequences and management responses to a more gelatinous future. Trends in Ecology & Evolution 24, 312–22.

Rorke, R.O., Lavery, S., Chow, S., Takeyama, H., Tsai, P., Beckley, L.E., Thompson, P.A., Waite, A.M., and Jeffs, A.G. (2012). Determining the Diet of Larvae of Western Rock Lobster (Panulirus cygnus) Using High-Throughput DNA Sequencing Techniques 7.

Ruzicka, J.J., Richard, B.D., and Wainwright, T.C. (2007). Seasonal food web models for the Oregon inner-shelf ecosystem: investigating the role of large jellyfish. CalCOFI Rep 48, 106–128.

Sassa, C., Konishi, Y., and Mori, K. (2006). Distribution of jack mackerel (Trachurus japonicus) larvae and juveniles in the East China Sea, with special reference to the larval transport by the Kuroshio Current. Fisheries Oceanography 15, 508–518.

Shiganova, T.A. (1998). Invasion of the Black Sea by the ctenophore Mnemiopsis leidyi and recent changes in pelagic community structure. Fisheries Oceanography 7, 305– 310.

Smith, C.R., De Leo, F.C., Bernardino, A.F., Sweetman, A.K., and Arbizu, P.M. (2008). Abyssal food limitation, ecosystem structure and climate change. Trends in Ecology and Evolution 23, 518–528.

Stachowicz, J.J., and Lindquist, N. (2000). Hydroid defenses against predators: the importance of secondary metabolites versus nematocysts 280–288.

Step, E. (1924). Messmates: a book of strange companionships in nature. Hutchinson & Company.

Sweetman, A.K., Smith, C.R., Dale, T., Jones, D.O.B., and Sweetman, A.K. (2014). Rapid scavenging of jellyfish carcasses reveals the importance of gelatinous material to deep-sea food webs 1–8.

Symondson, W.O.C. (2002). Molecular identification of prey in predator diets. Molecular Ecology 11, 627–641.

Tomlinson, B., Maynou, F., Sabatés, A., Fuentes, V., Canepa, A., and Sastre, S. (2015). Systems approach modelling of the interactive effects of fisheries, jellyfish and tourism in the Catalan coast. Estuarine, Coastal and Shelf Science 201, 198–207.

Troell, M., Naylor, R.L., Metian, M., Beveridge, M., Tyedmers, P.H., Folke, C., Arrow, K.J., Barrett, S., Crépin, A.-S., Ehrlich, P.R., Gren, Å., Kautsky, N., Levin, S.A., Nyborg, K., Österblom, H., Polasky, S., Scheffer, M., Walker, B.H., Xepapadeas, T., and de Zeeuw, A. (2014). Does aquaculture add resilience to the global food system? Proceedings of the National Academy of Sciences 111, 13257–13263.

Tsikliras, A.C., Dinouli, A., Tsiros, V.Z., and Tsalkou, E. (2015). The Mediterranean and Black Sea fisheries at risk from overexploitation. PLoS ONE 10(3)

Tupper, M., and Boutilier, R.G. (1995). Effects of habitat on settlement, growth, and postsettlement survival of Atlantic cod (Gadus morhua). Canadian Journal of Fisheries and Aquatic Sciences 52, 1834–1841.

Turner, J.T. (2002). Zooplankton fecal pellets, mrine snow and sinking phytoplankton blooms. Aquatic Microbial Ecology 27, 57–102.

Vaidya, S.K. (2005). Jellyfish choke Oman desalination plants. Gulf News 05 May 2003.

Vander Zanden, M.J., and Rasmussen, J.B. (2001). Variation in delta δ15N and delta δ13Ctrophic fractionation: Implications for aquatic food web studies. Limnology and Oceanography 46, 2061–2066.

Vargas, C.A., Tönnesson, K., Sell, A., Maar, M., Møller, E.F., Zervoudaki, T., Giannakourou, A., Christou, E., Satapoomin, S., Petersen, J.K., Nielsen, T.G., and Tiselius, P. (2002). Importance of copepods versus appendicularians in vertical carbon fluxes in a Swedish fjord. Marine Ecology Progress Series 241, 125–138.

Weill, R. (1983). Contribution a l’étude des cnidaires et de leurs nématocystes: Recherches sur les nématocystes (morphologie, physiologie, développement) La valeur taxonomique du cnidome. Laboratoire d’évolution des êtres organisés.

West, E.J., Pitt, K.A., Welsh, D.T., Koop, K., and Rissik, D. (2009). Top-down and bottom-up influences of jellyfish on primary productivity and planktonic assemblages. Limnology and Oceanography 54, 2058–2071.

Wieczorek, A.M., Croot, P.L., Lombard, F., Sheahan, J.N., and Doyle, T.K. (2019). Microplastic Ingestion by Gelatinous Zooplankton May Lower Efficiency of the Biological Pump. Environmental Science & Technology.

Wunsch, C. (2017). Ocean Mixing. Oxford Research Encyclopedia of Climate Change.

You, K., Ma, C., Gao, H., Li, F., Zhang, M., Qiu, Y., and Wang, B. (2007). Research on the jellyfish (Rhopilema esculentum Kishinouye) and associated aquaculture techniques in China: Current status. Aquaculture International 15, 479–488.

Zimmer, M. (2002). Green fluorescent protein (GFP): Applications, structure, and related photophysical behavior. Chemical Reviews 102, 759–781.

Chapter 2 - Unravelling the macro-evolutionary ecology of fish- jellyfish associations: life in the ‘gingerbread house’

This chapter is published:

Griffin, D. C., Harrod, C., Houghton, J. D., & Capellini, I. (2019). Unravelling the macro- evolutionary ecology of fish–jellyfish associations: life in the ‘gingerbread house’.

Proceedings of the Royal Society B, 286(1899), 20182325.

2.1 Abstract

Fish-jellyfish interactions are important factors contributing to fish stock success.

Jellyfish can compete with fish for food resources, or feed on fish eggs and larvae, which works to reduce survivorship and recruitment of fish species. However, jellyfish also provide habitat and space for developing larval and juvenile fish which use their hosts as means of protection from predators and feeding opportunities, helping to reduce fish mortality and increase recruitment. Yet, relatively little is known about the evolutionary dynamics and drivers of such associations which would allow for their more effective incorporation into ecosystem models. Here, we found that jellyfish association is a probable adaptive anti-predator strategy for juvenile fish, more likely to evolve in benthic (fish living on the sea floor), benthopelagic (fish living just above the bottom of the seafloor) and reef-associating species than those adapted to other marine habitats. We also found that jellyfish association likely preceded the evolution of a benthic, benthopelagic and reef-associating lifestyle rather than its evolutionary consequence, as we originally hypothesised. Considering over two thirds of the associating fish identified here are of economic importance, and the wide-scale occurrence and diversity of species involved, it is clear the formation of fish-jellyfish associations is an important but complex process in relation to the success of fish stocks globally.

Keywords: anti-predator strategies; demersal fishes; early life stages; evolution; fisheries

2.2 Introduction

Over past decades, many studies have documented how jellyfish blooms (Phylum

Cnidaria, Class Scyphozoa) have pronounced consequences for human endeavour

(Purcell et al., 2007; Richardson et al., 2009). Be it impacts on coastal tourism, the clogging of fishing nets or the blocking of power station cooling-water intakes (Purcell et al., 2007), the result has been an overall negative perception of gelatinous species

(Doyle et al., 2014). While the scientific community has concentrated efforts on investigating the deleterious effects of large aggregations of jellyfish (Lynam et al.,

2005; Richardson et al., 2009), the counterbalancing positive roles of jellyfish have typically received less attention (Doyle et al., 2014; Hamilton, 2016). However, recent efforts to address this gap are gaining momentum and a more nuanced picture of jellyfish ecology is emerging (Hamilton, 2016).

Broadly, jellyfish contribute to the four main categories of ecosystem services defined by the Millennium Ecosystem Assessment: regulating, provisioning, supporting and cultural services (Doyle et al., 2014). Furthermore, the traditional view of jellyfish as trophic dead ends, i.e. energy and nutrients directed towards jellyfish are lost to taxa higher up the food chain, is now overturned thanks to numerous studies demonstrating how jellyfish are key prey for apex marine predators and species of commercial value

(Purcell & Arai, 2001). For example, predation on jellyfish by commercially important species in the Irish Sea is far from rare, with >20% of sampled Atlantic herring (Clupea harnegus) having jellyfish detected in their stomachs (Lamb et al., 2017). Opportunistic jellyfish predators also include species such as mallard ducks (Anas platyrhynchos)

(Phillips et al., 2017), albatross (McInnes et al., 2017), Adélie penguins (Pygoscelis adeliae) (Thiebot et al., 2017) and deep sea octopods (Hoving & Haddock, 2017).

However, jellyfish also provide habitat for juvenile fishes in what is generally considered a facultative symbiotic relationship (Fig. 2.1), and greater food acquisition opportunities for the fish is often cited as an important causal factor in the formation of such interactions (Gooding & Magnuson, 1967; Castro et al., 2001). Juvenile fish can feed on the zooplankton entrained by jellyfish swimming pulses or captured on their tentacles, or even on crustacean parasites on their jellyfish host (Purcell & Arai, 2001;

Riascos et al., 2012; Fleming et al., 2014). Moreover, stable isotope analysis has revealed that associating juvenile Atlantic bumper (Chloroscombrus chrysurus) feed directly on their jellyfish hosts (Aurelia sp. and Drymonema larsoni), constituting up to

100% of their diet during this life stage (D’Ambra et al., 2014). This study does not stand alone but reinforces previous evidence of how juvenile fish often feed on their jellyfish hosts (Lie, 1961; Mansueti, 1963; Janssen, 1981; Janssen et al., 1989). Beyond food acquisition, protection from predators is also believed to be a key driver behind fish- jellyfish associations (Castro et al., 2001). For example, 0-group (<12 months old) gadoid fish avoid predation by retreating among jellyfish tentacles which may improve survival during this critical time in their development (Lynam & Brierley, 2006).

Similarly, Sassa et al. (2006) reported correlational evidence that the abundance of jack mackerel (Trachurus japonicus) juveniles in the North Pacific was higher when increases in jellyfish Pelagia noctiluca were recorded. The ability of juvenile associative fishes to feed directly on the host under which they are sheltering, arguably sets fish- jellyfish associations apart from the straightforward predator-prey relationship described previously, where marine predators consume the entire jellyfish. However, the benefits gained by associating with jellyfish may be broader and differ to some extent among fish species with diverse life histories, ecology and/or behaviour.

Mansueti (1963) proposed that fish-jellyfish associations persist when the host provides protection from predators on a sustaining basis. Larger juveniles or adults of benthic, benthopelagic and reef-associating fish can achieve protection from predators by living on or close to the seabed and structurally complex habitats such as coral reefs.

However, these species often have an earlier pelagic developmental phase until they are of sufficient size to recruit into benthic or reef habitats (Kingsford & Choat, 1989).

For fully pelagic species, schooling behaviour is a common anti-predator adaptation which typically begins after fin formation is complete early in their development (Leis,

2010). There is currently little evidence that early life stages of demersal fish employ schooling behaviour in a similar way (Leis, 2010). Conversely, they are often found in association with jellyfish (Mansueti, 1963; Castro et al., 2001) or floating or static objects in the ocean. This tendency suggests that benthic and benthopelagic fish have evolved an alternative adaptive strategy against predation in the form of jellyfish association, where the jellyfish acts as a structured refuge in the pelagic habitat, before recruitment to other, e.g. benthic habitats. If anti-predator schooling behaviour in benthic, benthopelagic and reef associating fish is less common than in fully pelagic species, then the former species should gain a greater evolutionary advantage from displaying jellyfish association than pelagic species.

Here, we tested the hypothesis that jellyfish association was more likely to evolve in benthic, benthopelagic and reef-associating species (broadly defined here as demersal type fishes), than in species adapted to other marine habitats. To do this we compiled a global scale dataset of jellyfish-fish associations to date and used phylogenetic comparative approaches, better suited to unravel generality of patterns and processes

Figure 2. 1. Examples of juvenile fish associating with jellyfish; (a) juvenile Atlantic horse mackerel (Trachurus trachurus) around the oral arms of a large Rhizostoma pulmo jellyfish off the coast of Southern England, (b) juvenile gadoids shelter among the oral arms and tentacles of a compass jellyfish (Chrysaora hysoscella) in Irish coastal waters, (c) a single juvenile gadoid swimming above the bell of a blue jellyfish (Cyanea lamarckii) off the Isle of Man and, (d) three juvenile gadoids camouflaged against the frilly oral arms of a compass jellyfish (Chrysaora hysoscella) off the northeast coast of Ireland (photos courtesy of (a) Steve Trewhalla, (b) Sarah Tallon, (c) Sarah Bowen, and (d) Karen Patterson).

than studies based on one or few species (Harvey & Pagel, 1991; Garland & Adolph,

1994). The hypothesis predicted that association with jellyfish was more likely to involve demersal type fish than not (i.e. positively correlated). However, it was silent regarding how pelagic non-associating fish species have evolved into demersal type associating ones, and so whether they first evolved demersal type and next association with jellyfish, or the opposite. Our phylogenetic comparative approach specifically investigated which evolutionary pathway appeared more likely.

2.3 Methods

2.3.1 Data collection

Following Castro et al. (Castro et al., 2001), we defined fish-jellyfish association as a close spatial relationship between a larval or juvenile fish with gelatinous zooplankton species (‘jellyfish’) that span the Phyla Cnidaria (Class Scyphozoa, Cubozoa and

Hydrozoa), Chordata (Class Thaliacea), and Ctenophora. We conducted a literature search in Web of Science using English keywords such as ‘jellyfish fish association’ and

‘gelatinous zooplankton AND juvenile fish’ to collate a list of fish species observed as associating with jellyfish during early developmental stages. Information on associating species was then extracted from peer-reviewed primary and review publications, and supplemented with data from unpublished datasets, personal observations and museum collections. To test whether fish that associate with jellyfish were more likely to be demersal type, we also needed to include species in the dataset that are not known to associate with jellyfish and for which information on lifestyle was available.

To this end we collected data on lifestyle (see below) for a randomly selected sample

59 of fish species, which were not known to associate with jellyfish but that belong to the same families of those that do, leading to a total sample size of 145 fish species with and without associations with jellyfish. We extracted lifestyle data from online databases (Froese & Pauly, 2018) on whether each species in our dataset was benthic, benthopelagic or reef associating (‘demersal’) or fully pelagic (‘pelagic’).

The absence of an observed association between a given fish species and jellyfish in the literature may reflect either the true absence of such an association in nature, or the fact that it has not been observed yet, leading to a misclassification of some fish species. To account for this issue, we employed the commonly used procedure of using number of citations in WoS for a given species as a measure of the research intensity on that species (Nunn et al., 2003; West & Capellini, 2016; Halliwell et al., 2017), under the expectation that highly studied species should be more likely to be correctly classified as not associating with jellyfish. From our full dataset (n=145 fish species) we then excluded ‘non-associating’ fish species with fewer than 10 citations (remaining species: n=130 fish species) or fewer than 25 citations (remaining species: n=119) as potentially misclassified. Results of all analyses were highly consistent between the two reduced data sets, suggesting that they were robust to sampling. Here we present results from the larger dataset of 130 taxa (jellyfish associating and demersal n=43, associating and pelagic n=18, non-associating and demersal n=51, non-associating and pelagic n=18). The dataset is available as Supplementary File 1. Finally, we coded each fish species for two behavioural traits: association with jellyfish (Yes=1/No=0) and lifestyle (Pelagic=0/Demersal=1).

2.3.2 Evolutionary history of fish-jellyfish association

We first investigated the evolutionary history of the association with jellyfish and lifestyle separately to assess how frequently each trait evolved and was lost over time across the phylogeny. We thus ran ancestral state reconstructions for discrete data in maximum likelihood, using the R package ‘ape’ (Paradis et al., 2004) and a comprehensive fish phylogeny (Rabosky et al., 2013) (see Supplementary File 2). This analysis estimates the likely character states of ancestors in the phylogeny and the rates of transitions between states across the whole tree (i.e. the rate of gains and losses) (Harvey & Pagel, 1991; Pagel, 1994). We fitted two alternative evolutionary models to the data; one in which the rate of gain and rate of loss were the same (Equal

Rate model - ER), thus estimating one parameter (i.e. the rate of change), and the other in which the rates of gain and losses could differ, estimating two rate parameters (All

Rates Different model - ARD). We then assessed the fit to the data of these two alternative models using a likelihood ratio test with degrees of freedom (DF) equalling the difference in the number of estimated parameters of the two competing models

(here df = 1) (Pagel, 1994).

2.3.3 Independent and dependent models of evolution

We tested whether associating with jellyfish was evolutionarily correlated with lifestyle using maximum likelihood estimation and the programme BayesTraits V.3 (Pagel et al.,

2004). Specifically, we compared the fit to the data of two alternative evolutionary models: the Independent Model of evolution where jellyfish association and lifestyle evolve independently of each other, and the Dependent Model of evolution in which they evolve in a correlated fashion (Pagel, 1994). The independent model estimates four parameters (the rates of gain and losses for each of the two traits independently),

61 while the dependent model estimates eight parameters which are the transition rates among the four combination of character states that the two traits can jointly take (i.e. non-associating pelagic 0,0, non-associating demersal 0,1, associating pelagic 1,0, and demersal associating 1,1; see Figure 2.4). We used a likelihood ratio test with four degrees of freedom to assess which model fitted the data significantly better. If the LR test is significant, this indicates that the dependent model had a significantly better fit to the data, and so the two traits are evolutionarily correlated. The dependent model of evolution can also reveal the evolutionary pathway through which two traits have evolved together, and so whether the evolution of one trait precedes and facilitates the evolution of the other (Pagel, 1994). Specifically, should the dependent model provide a better fit to the data, the examination and comparison of the magnitudes of the transition rates between the four combinations of character states of the two traits can reveal whether associating with jellyfish in demersal fish species (condition 1,1, see

Figure 2.4) evolved from non-associating pelagic fish (condition 0,0) by gaining first a demersal lifestyle (transition rates q12 to condition 0,1, see Figure 2.4) and subsequently the association with jellyfish (transition rates q24), or the other way round

(transition rates q13 to condition 1,0, see Figure 2.4). Thus, if one evolutionary pathway is more likely, this indicates that the trait evolving first is more likely to promote the evolution of the other, which is evidence consistent with causation (Pagel, 1994).

Conversely, if the dependent model provides a better fit than the independent model but both evolutionary pathways exhibit transition rates of equal magnitude, we can infer that the two traits are evolutionary correlated but there is no specific evolutionarily (causal) pathway.

The analysis with dependent and independent models was run in triplicate, with all runs producing identical results to the third decimal place for the model fit and all parameter estimates, suggesting that the analysis consistently converged on the same maximum likelihood estimates and are robust.

2.4 Results

2.4.1 Fish-jellyfish associations in the literature

In the literature we found 173 instances of specific fish-jellyfish associations from across the globe, involving 86 species of fish spanning 24 families and 84 jellyfish taxa

(Fig 2.2b). Fish species from the Carangidae family were the most numerous (n=28) followed by the Centrolophidae, Nomeidae and Monacanthidae families (n=11, n=8 and n=7 respectively) (Fig. 2.2a).

Of all the jellyfish-fish associations, the Atlantic bumper (Chloroscrombrus chrysurus) and shrimp scad (Alepes djedaba), both from the family Carangidae, associated with the most diverse range of jellyfish species (both n=9), while Cyanea capillata and

Aurelia aurita were the most common jellyfish species for which fish-jellyfish associations were recorded (Fig. 2.2b). Indeed, fish associating with Cyanea sp. accounted for 12.5% of the total associations documented. Demersal type fish species were recorded in 57% (n=49) of associations, with the remainder classified as fully pelagic (43%, n=37). While Cyanea capillata, Aurelia aurita, Stomolophus meleagris and

Nemopilema nomurai are the most widely reported species involved in these associations around the world, together accounting for 33.1% of the individual instances of fish-jellyfish associations in the literature, most associations are from single observations of specific interactions. 63

Figure 2. 2. Bargraphs of (a) the carangids, a family of fish which includes the jacks, pompanos, jack mackerels, runners and scads, were the most reported in the literature as displaying associative behaviour with jellyfish, (b) a broad range of jellyfish, including medusa and non- true jellyfishes such as ctenophores and salps, are involved in fish-jellyfish associations (n=64).

2.4.2 Evolutionary history of fish-jellyfish association and lifestyle

An ARD model for the association with jellyfish did not improve the fit to the data relative to an ER model (LR=2.12, df = 1, p=0.15), thus gains and losses of the association with jellyfish occurred at equal rates (0.034±0.001) across the fish phylogeny. The ER model estimated at least two gains and seven losses of associations with jellyfish throughout the tree (Fig. 2.3a). Conversely, the ARD model for lifestyle better fitted the data relative to the ER model (LR=7.34, df = 1, p=0.007), and indicated that the transition rate from demersal to pelagic was significantly lower than the reverse (demersal to pelagic: 0.004±0.001; pelagic to demersal: 0.013±0.004). This model identified at least five evolutionary origins and nine losses of the demersal lifestyle among the recent ancestors of extant fish species (Fig. 2.3b).

2.4.3 Correlated evolution between fish-jellyfish association and lifestyle

The analysis of correlated evolution between lifestyle and association with jellyfish revealed that these two traits evolved in a correlated fashion for the sample of fish species of our dataset, as the dependent model of evolution fitted the data better than the independent model (LR = 9.72, df = 4, p=0.045). The dependent model also

Figure 2. 3. Evolutionary history of fish-jellyfish association (a) and demersal type versus pelagic lifestyle (b) in a sample of 130 fish species as estimated using Maximum Likelihood. In (a) the ancestral state reconstruction of fish associative behaviour based on the Equal Rate model 66 identifies at least 7 evolutionary losses and 2 evolutionary gains of association with jellyfish (associative behaviour with jellyfish is coded as black, non-associative as grey). In (b) the ancestral state reconstruction of lifestyle based on the All Rates Different model identifies at least 9 evolutionary events whereby the demersal lifestyle is likely lost and 5 gains (demersal is coded in black, pelagic in grey). In both (a) and (b) the area of the pie for the internal nodes is in proportion of the probability that a node takes either of the two alternative states for the tested trait.

estimated that from a condition of no association and pelagic lifestyle (0,0), the association with jellyfish was gained first while the gain of a demersal type lifestyle in the absence of association with jellyfish was estimated to be 0 (association first: q13=4.52; demersal first: q12=0; Figure 4). Once pelagic fish evolved an association with jellyfish (1,0), a demersal lifestyle was gained quickly (q34 = 8.13; Figure 2.4). This finding suggests that associating with jellyfish may be an important driver that facilitated the evolution of a demersal lifestyle for some fishes. Finally, the dependent model showed that a demersal lifestyle without association with jellyfish (0,1) was likely to evolve from associating demersal fish (1,1) by losing the association with jellies while retaining a demersal lifestyle (q42=9.78); however, this condition was highly likely to be reverted by regaining the association with jellyfish (q24=5.15) (Fig. 2.4). Thus, the combined ‘associating’ and ‘demersal’ character state (1,1) was relatively evolutionarily stable.

Figure 2. 4. Dependent model of correlated evolution for the combined traits of association with jellyfish lifestyle. The arrows indicate the direction of change between the 4-possible combination of character states, with the arrow thickness proportional to the magnitude of transition rates estimated by the model (also reported as number). Transition rates estimated to be equal to 0 are indicated with dotted lines. Sample size of species by combination of character states as used in the analysis (jellyfish association and demersal type or pelagic): (0,0) n=18, (0,1) n=51, (1,0) n=18 and (1,1) n=43. Sample size of species by combination of character states based on lifestyle; associating and pelagic n=18, non-associating and pelagic n=18, associating and benthic n=8, non-associating and benthic n=26, associating and reef associating n=21, non-associating and reef associating n=16, associating and benthopelagic n=14, non-associating and benthopelagic n=9.

2.5 Discussion

We tested the hypothesis that jellyfish association was more likely to evolve in benthic, benthopelagic and reef-associating species than species adapted to other marine habitats. We find support for this idea and show that both demersal type lifestyle and association with jellyfish traits have been gained and lost multiple times across the fish phylogeny. However, our analysis revealed that associating with jellyfish is more likely to be one evolutionary driver of adapting to a demersal lifestyle, rather than its evolutionary consequence as we found that fish-jellyfish association is very likely to precede, not follow, the evolution of a demersal lifestyle. This pattern is perhaps not surprising given that predation pressure is extremely high when larvae and juveniles are in the water column (Cowan et al., 1996; Le Pape & Bonhommeau, 2015). If the demersal fish lifestyle trait evolved first, but without the predatory defence mechanisms of jellyfish association or others such as schooling for example, they would presumably face a very high risk of mortality. Larval mortality in fish is strongly size- related: modelling studies suggest that a significant proportion (56%-99%) of total larval mortality occurs before a critical size is achieved (fish total length), after which mortality due to predation decreases sharply (Cowan et al., 1996). Thus, presettlement benthic or reef fish that lack schooling behaviour as anti-predator strategy

(Leis, 2010) should be under intense selection to evolve or retain alternative adaptations that allow them to survive the high predation levels in the upper water column. Our analysis showed that associating with jellyfish might play an important evolutionary role in this context. Jellyfish offer a complex three-dimensional structure that provides juvenile fish with a refuge in an environment that is otherwise remarkably devoid of physical habitat (Breitburg et al., 2010). The presence of such physical

69 structure has been linked strongly to increased larval recruitment in fishes (Tupper &

Boutilier, 1995a). Our findings suggest that it is more likely that the association with jellyfish evolves in pelagic species prior to the evolution of a demersal lifestyle.

Therefore, other evolutionary drivers, rather than antipredator strategies in non- schooling juvenile fish, have promoted the evolutionary origin of jellyfish association in pelagic fishes. To investigate jellyfish association evolutionary drivers further, future studies should explore how fully benthic, reef-associating and benthopelagic fish as individual groups evolved with regard to the association with jellyfish given the potential for different evolutionary pathways leading to jellyfish association, once more data become available for a larger number of species.

While Mansueti (1963) noted how only a very small proportion of pelagic fish globally are reported as displaying associative behaviour, the implication of dismissing the potential impact of such behaviours for the fishing industry may be great, considering over 70% of the jellyfish associating fish species in this study are of commercial value.

Unlike benthic fishes, pelagic fish can rely on schooling to reduce predation risk when they are juveniles. We suggest that one potential driver of jellyfish associations is the enormous potential as a food source that jellyfish represent for juvenile fish, especially considering that jellyfish can often form large aggregations (Condon et al., 2013).

Although jellyfish have a low calorific value compared to other prey items, their gonads can be very large, representing over 20% of their total body in some species and have higher energetic content than bell or oral arm tissues (Doyle et al., 2007). Indeed, a recent study has revealed that jellyfish represent a highly rewarding food source to commercial fish (Boops boops) (Milisenda et al., 2014). In our dataset, many jellyfish associating species are carangids, a large and diverse family considered among the

70 most economically important fishes in the world (Mukherjee et al., 2018). The ability to exploit jellyfish as trophic resource may therefore offer a huge advantage to the growth and survival of both demersal type and pelagic juvenile fishes.

Our ancestral state reconstruction showed that associating with jellyfish in extant fishes is likely to have independently evolved multiple times across the fish phylogeny.

We propose that the evolutionary cost of evolving the suite of adaptations required to associate with jellyfish is small (i.e. ability to locate and move close to jellyfish host for protection and realising opportunistic food acquisition opportunities). The immediate costs of associating with jellyfish (e.g. risk of injury/death from jellyfish nematocyst stings) are likely less than the consequences of not doing so; namely an increased predation risk and decreased food opportunities. Furthermore, possible mechanisms for juvenile fish to evade the potential negative effects of jellyfish stinging cells include avoiding contact, resistance to nematocyst toxin and reduced nematocyst discharge due to mucus on the fish skin (Arai, 1988). Whereas, even momentary disturbances in fish-jellyfish associations that caused juvenile scads (Trachurus lathami) to desert their jellyfish hosts, resulted in immediate predation by grouper (Mycteroperca acutirostris)

(Bonaldo et al., 2004).

Our study highlights how large-scale comparative approaches can be used to answer important questions on the evolutionary ecology of fish-jellyfish associations, at least from the perspective of the fish. To fully understand the evolution of these associations however, we need to also study how such associations evolved from the perspective of the jellyfish and their characteristics. Our study has revealed that some jellyfish taxa are in fact far more frequently involved in fish-jellyfish associations than others. Thus, future studies could investigate whether the frequency of associations of juvenile fish

71 with different jellyfish species reflects the relative abundance of different jellyfish species and distribution worldwide, or are determined by the jellyfish morphological characteristics, such as size, volume, tissue complexity or strength of nematocyst sting, that make it more likely for fish to associate with them. Jellyfish morphology varies hugely, from micro through to macro-zooplankton species weighing >200kg (e.g.

Nemopilmea nomurai), so their potential for providing shelter against predators and food resources should be very different (Rountree, 1989). Furthermore, jellyfish also differ in swimming mode, foraging and feeding strategies; traits that could elucidate the role and importance of food acquisition in fish-jellyfish associations. Specifically the two main foraging modes that jellyfish exhibit, ambush or cruise predators, result in interspecific dietary differences (Costello et al., 2008) and may influence the success of associating juvenile fish that take advantage of prey entrained in the pulse of the jellyfish or prey captured in the tentacles. To address these questions over large comparative scale and exploit powerful phylogenetic comparative methods to reveal generality of principles, we urgently need to build comprehensive jellyfish phylogenies and collect data on a variety of jellyfish characteristics, including whether juvenile associating fish also associate with non-gelatinous Floating Aggregating Devices (FADs) or floating objects. A number of jellyfish associating fish species identified by Castro et al. (2001) were also documented as associating with FADs or drift objects and it is possible that they may gain similar benefits from these types of association to some degree such as the redistribution of food and a change in the behaviour of predators

(Kingsford, 1993). However, jellyfish precede human flotsam and FADs by millions of years and could provide better or additional protection from predators by way of deterrence, as predators seek to avoid their nematocyst stinging cells (Mansueti, 1963).

Furthermore, jellyfish may provide a greater range of indirect feeding opportunities by actively hunting for food which is subsequently stolen by associating fish before ingestion as well as direct feeding opportunities via their energy rich gonads (Doyle et al., 2007). Thus, when appropriate data for a large number of fish species become available, we can explore intricate ecological and evolutionary questions such as whether jellyfish are a uniquely important habitat for juvenile fish, or whether they are just one of the many floating structures in the sea which act as potential shelter and source of food for juvenile fishes, using phylogenetic comparative approaches as shown in this study.

Together with recent studies (Dawson & Hamner, 2009; Pauly et al., 2009; Brodeur et al., 2016), our findings suggest that jellyfish have important evolutionary and ecological roles such as providing shelter from predators and trophic resources to juvenile fish, an ecological service with huge implications for the population dynamics and long term persistence of marine fish biodiversity. Here, we propose the

‘gingerbread house hypothesis’ from classic folklore (i.e. a house you can eat) to describe the specific coaction whereby juvenile fish gain both shelter and food shelter from jellyfish. Considering that pressure on fin-fish stocks is increasing globally, and that 72% of the associating fish species in our study are economically important, we advocate further research to quantify the overall benefit of jellyfish to fish recruitment over a range of scales.

2.6 References Arai, M.N. (1988). Interactions of fish and pelagic coelenterates. Canadian Journal of Zoology 66, 1913–1927.

Breitburg, D.L., Crump, B.C., Dabiri, J.O., and Gallegos, C.L. (2010). Ecosystem engineers in the pelagic realm: Alteration of habitat by species ranging from microbes to jellyfish. Integrative and Comparative Biology 50, 188–200.

Castro, J.J., Santiago, J. A., and Santana-Ortega, A.T. (2001). A general theory on fish aggregation to floating objects: An alternative to the meeting point hypothesis. Reviews in Fish Biology and Fisheries 11, 255–277.

Condon, R.H., Duarte, C.M., Pitt, K.A., Robinson, K.L., Lucas, C.H., Sutherland, K.R., Mianzan, H.W., Bogeberg, M., Purcell, J.E., Decker, M.B., Uye, S., Madin, L.P., Brodeur, R.D., Haddock, S.H.D., Malej, A., Parry, G.D., Eriksen, E., Quiñones, J., Acha, M., Harvey, M., Arthur, J.M., and Graham, W.M. (2013). Recurrent jellyfish blooms are a consequence of global oscillations. Proceedings of the National Academy of Sciences of the United States of America 110, 1000–5.

Costello, J.H., Colin, S.P., and Dabiri, J.O. (2008). Medusan morphospace: Phylogenetic constraints, biomechanical solutions, and ecological consequences. Invertebrate Biology 127, 265–290.

Cowan, J.H.J., Houde, E.D., and Rose, K.A. (1996). Size-dependent vulnerability of marine fish larvae to predation: an individual-based numerical experiment. ICES Journal of Marine Science 53, 23–37.

Dawson, M.N., and Hamner, W.M. (2009). A character-based analysis of the evolution of jellyfish blooms: Adaptation and exaptation. Hydrobiologia 616, 193–215.

Doyle, T.K., Hays, G.C., Harrod, C., and Houghton, J.D.R. (2014). Ecological and Societal Benefits of jellyfish. In K. A. Pitt & C. H. Lucas (Eds.), Jellyfish Blooms (pp. 105– 127). Dordrecht: Springer Netherlands.

Doyle, T.K., Houghton, J.D.R., McDevitt, R., Davenport, J., and Hays, G.C. (2007). The energy density of jellyfish: Estimates from bomb-calorimetry and proximate- composition. Journal of Experimental Marine Biology and Ecology 343, 239–252.

Froese, R., and Pauly, D. (2018). FishBase 2018. World Wide Web Electronic Publication Retrieved from Http://Www. Fishbase. Org.

Garland, T., and Adolph, S.C. (1994). Why Not To Do two-species comparative studies: limitations on inferring adaptation. Physiological Zoology 67, 797–828.

Gooding, R.M., and Magnuson, J.J. (1967). Ecological Significance of a Drifting Object to Pelagic Fishes. Pacific Science 21, 486–497.

Halliwell, B., Uller, T., Holland, B.R., and While, G.M. (2017). Live bearing promotes the evolution of sociality in reptiles. Nature Communications 8, 2030.

Hamilton, G. (2016). The Secret Lives of Jellyfish. Nature 432–434.

Harvey, P.H., and Pagel, M. (1991). The comparative method in evolutionary biology. Oxford: Oxford university press (Vol. 239).

Hoving, H.J.T., and Haddock, S.H.D. (2017). The giant deep-sea octopus Haliphron atlanticus

Janssen, J. (1981). Fish in Salps: the association of Squaretails (Tetragonurus Spp.)

Janssen, J., Gibbs, R.H., Pugh, P.R., and Warkentine, E. (1989). Association of Caristius Sp. (Pisces: Caristiidae) with a siphonophore, Bathyphysa conifera. American Society of Ichthyologists and Herpetologists 1, 198–201.

Kingsford, M.J. (1993). Biotic and abiotic structure in the pelagic environment: importance to small fishes. Bulletin of Marine Science 53, 393–415.

Kingsford, M.J., and Choat, J.H. (1989). Horizontal distribution patterns of presettlement reef fish: are they influenced by the proximity of reefs? Marine Biology 101, 285–297.

Le Pape, O., and Bonhommeau, S. (2015). The food limitation hypothesis for juvenile marine fish. Fish and Fisheries 16, 373–398.

Leis, J.M. (2010). Ontogeny of behaviour in larvae of marine demersal fishes. Ichthyological Research 57, 325–342.

Lie, U. (1961). On the growth and food of 0-group coalfish, Pollachius virens (L.), in Norwegian waters. Sarsia 3(1), 1–36.

Lynam, C.P., and Brierley, A.S. (2006). Enhanced survival of 0-group gadoid fish under jellyfish umbrellas. Marine Biology 150, 1397–1401.

Lynam, C.P., Heath, M.R., Hay, S.J., and Brierley, A.S. (2005). Evidence for impacts by jellyfish on North Sea herring recruitment. Marine Ecology Progress Series 298, 157– 167.

Mansueti, R. (1963). Symbiotic behavior between small fishes and jellyfishes, with new data on that between the Stromateid, Peprilus alepidotus and the Scyphomedusa, Chrysaora quinquecirrha. Copeia 1, 40–80.

McInnes, J.C., Alderman, R., Lea, M.A., Raymond, B., Deagle, B.E., Phillips, R.A., Stanworth, A., Thompson, D.R., Catry, P., Weimerskirch, H., Suazo, C.G., Gras, M., and Jarman, S.N. (2017). High occurrence of jellyfish predation by black-browed and

Campbell albatross identified by DNA metabarcoding. Molecular Ecology 26, 4831– 4845.

Milisenda, G., Rosa, S., Fuentes, V.L., Boero, F., Guglielmo, L., Purcell, J.E., and Piraino, S. (2014). Jellyfish as prey: Frequency of predation and selective foraging of boops boops (vertebrata, actinopterygii) on the mauve stinger Pelagia noctiluca (cnidaria, scyphozoa). PLoS ONE 9(4), e94600.

Mukherjee, M., Karna, S.K., Suresh, V.R., Manna, R.K., and Panda, D. (2018). New records of Carangids (Perciformes: Carangidae) from Chilika Lagoon , East coast of India. Fish Taxa 2, 226–231.

Nunn, C.L., Altizer, S., Jones, K.E., and Sechrest, W. (2003). Comparative Tests of Parasite Species Richness in Primates. The American Naturalist 162, 597–614.

Pagel, M. (1994). Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters. Proceedings: Biological Sciences 255, 37–45.

Pagel, M., Meade, A., and Barker, D. (2004). Bayesian estimation of ancestral character states on phylogenies. Systematic Biology 53, 673–684.

Paradis, E., Claude, J., and Strimmer, K. (2004). APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290.

Pauly, D., Graham, W.M., Libralato, S., Morissette, L., and Deng Palomares, M.L. (2009). Jellyfish in ecosystems, online databases, and ecosystem models. Hydrobiologia 616, 67–85.

Phillips, N., Eagling, L., Harrod, C., Reid, N., Cappanera, V., and Houghton, J.D.R. (2017). Quacks snack on smacks: mallard ducks (Anas platyrhynchos) observed feeding on hydrozoans (Velella velella). Plankton and Benthos Research 12, 143–144.

Purcell, J.E., and Arai, M.N. (2001). Interactions of pelagic cnidarians and ctenophores with fish: a review. Hydrobiologia 451, 27–44.

Purcell, J.E., Uye, S., and Lo, W. (2007). Anthropogenic causes of jellyfish blooms and their direct consequences for humans: a review. Marine Ecology Progress Series 350, 153–174. 77

Riascos, J.M., Vergara, M., Fajardo, J., Villegas, V., and Pacheco, A.S. (2012). The role of hyperiid parasites as a trophic link between jellyfish and fishes. Journal of Fish Biology 81, 1686–1695.

Rountree, R.A. (1989). Association of fishes with fish aggregation devices: Effects of structure size on fish abundance. Bulletin of Marine Science 44, 960–972.

Thiebot, J.B., Arnould, J.P.Y., Gómez-Laich, A., Ito, K., Kato, A., Mattern, T., Mitamura, H., Noda, T., Poupart, T., Quintana, F., Raclot, T., Ropert-Coudert, Y., Sala, J.E., Seddon, P.J., Sutton, G.J., Yoda, K., and Takahashi, A. (2017). Jellyfish and other gelata as food for four penguin species – insights from predator-borne videos. Frontiers in Ecology and the Environment 15, 437–441.

Tupper, M., and Boutilier, R.G. (1995). Effects of conspecific density on settlement, growth and post-settlement survival of a temperate reef fish. Journal of Experimental Marine Biology and Ecology 191, 209–222.

West, H.E.R., and Capellini, I. (2016). Male care and life history traits in mammals. Nature Communications 7, 1–10.

Chapter 3 - Inter-tissue isotopic variation in three scyphozoan

jellyfish

Collaborators: Donal C. Griffin1, Nicholas Fleming2, Steven E. Beggs3, Jonathan D.R.

Houghton1,4, Jason Newton5, Chris Harrod6,7

1 School of Biological Sciences, Queen’s University Belfast, 19 Chorine Gardens, Belfast,

BT9 7DL

2 Department of Biosciences, Wallace Building, Swansea University, Swansea, SA2 8PP

3 Agri-Food and Biosciences Institute, Belfast, UK

4 Queen’s Marine Laboratory, Portaferry, UK

5 NERC Life Sciences Mass Spectrometry Facility, Scottish Universities Environmental

Research Centre, East Kilbride, UK

6 Universidad de Antofagasta Stable Isotope Facility, Universidad de Antofagasta, Chile

7 Instituto de Ciencias Naturales Alexander von Humboldt, Universidad de Antofagasta,

Chile

3.1 Abstract

The known diversity of jellyfish predators is increasing owing to advances in various technologies, not least, stable isotope analysis. Such species serve as prey for obligate and opportunistic predators, from deep sea scavengers through to migratory seabirds, but the way jellyfish are ingested is not uniform and selective feeding on different bodyparts has been observed. Thus, if jellyfish tissue is not biochemically consistent then a single integrated estimate of their tissue composition may mask important selective foraging patterns by predators. From this vantage point we sampled three species of scyphozoan jellyfish (Aurelia aurita n=43, Cyanea lamarckii n=37, and

Cyanea capillata n=50) from a North East Atlantic sea lough and explored whether isotopic inter-tissue variation is a factor worth considering when interpreting jellyfish isotopic nitrogen data. More broadly, we investigated whether tissue specific δ15N could provide a useful parameter or functional group by which to order jellyfish trophic information in large-scale ecosystem-based studies. Using Friedman’s and Nemenyi post hoc pairwise comparison tests, we found evidence for inter-tissue δ15N variation within and across scyphozoan jellyfish species. Furthermore, we showed how δ15N is influenced by jellyfish size, whereby larger jellyfish have higher δ15N values, which, in certain instances, may be useful in accounting for the nuances of jellyfish trophic ecology. While further research is needed, appropriately assigned jellyfish functional groups are required for effective inclusion into ecosystem and fishery models in the future.

Keywords: jellyfish, stable isotope analysis, tissue variation, trophic ecology

3.2 Introduction

Pauly et al. (2009) highlighted the lack of consideration jellyfish taxa receive in online databases and ecosystem models, often reflecting the scarcity (or adequate consolidation) of available dietary information. Faced with such data gaps, modellers are often left to integrate gelatinous zooplankton at a single trophic level which fails to encompass their true trophic complexity, either as predators or prey. Such an approach for any vertebrate group encompassing >1400 species with body mass scaling by several orders of magnitude would be inconceivable and considered not fit for purpose.

So, the challenge falls to empirical biologists to gather and pass adequate information up the ‘analytical food chain’ to move understanding forward.

Stable isotope analysis (SIA) is increasingly used to characterise consumer diet and food web structure (Fry, 2006; Al-Habsi et al., 2008; Kürten et al., 2013; Nagata et al., 2015).

Trophic structure estimates founded on stable isotope analysis (SIA) underpin understanding of many food webs today, surpassing (but not substituting) alternative approaches such as traditional gut content analysis (Ben-David et al., 2001; Middelburg,

2014; Sweetman et al., 2016). The common use of SIA by ecologists, facilitated by decreases in cost and increase in accessibility (Vinson & Budy, 2011), is revealing previously unknown transfer processes within ecological systems (Post, 2002; Fry,

2006; Middelburg, 2014). Stable isotope analysis has become a common approach in understanding the trophic ecology of gelatinous zooplankton, a polyphyletic assemblage that inhabits a wide range of marine systems from coastal areas to the deep-ocean and employs a wide variety of foraging strategies (Costello et al., 2008).

Indeed, because such species are difficult to detect visually in gut contents (Symondson,

2002), SIA-based studies have helped markedly in detecting cryptic or

81 underappreciated aspects of jellyfish ecology. For example, Fleming et al. (2015) presented isotopic evidence for inter- and intraspecific variation in jellyfish trophic structure whereby differences in δ15N (trophic position) were evident between all three species, with size-based and temporal shifts in δ15N apparent in A. aurita and C. capillata. Furthermore, the isotopic niche width for all species combined, increased throughout the season, reflecting temporal shifts in trophic position. Similarly, Graham

& Kroutil (2001) found both quantitative and qualitative changes in diet as a function of medusa size concluding that growth to a large size by scyphomedusae can provide a means to enhance diet which in turn, may positively influence growth and reproduction.

While further research is needed, taken together, these finding helps facilitate their inclusion in trophic size-based analyses first outlined by Jennings et al. (2002).

Size-spectral information coupled with δ15N estimates of trophic level can be used to estimate a range of ecologically important parameters such as trophic transfer efficiency, mean predator-prey body-mass ratios and the mean predators-to-prey species ratio (Jennings et al., 2002). Furthermore, SIA suggests that jellyfish most likely can form an important nutritional component of the diet for a range of fish species including Atlantic bumper (Chloroscombrus chrysurus), bluefin tuna (Thunnus thynnus), little tunny (Euthynnus alletteratus), spearfish (Tetrapturus belone), and swordfish

(Xiphias gladius) (Luis Cardona et al., 2012). Others have used SIA to show the importance of jellyfish for green sea turtles (Chelonia mydas) in some regions, a species traditionally thought to be mainly herbivorous when living in coastal environments

(González Carman et al., 2014). However, thanks to a range of new technologies and advancement of methods, not only in SIA but also in animal borne cameras and use of gut DNA, the perception that jellyfish are trophic dead ends is now thoroughly rejected.

Jellyfish are an integral component of marine food webs (Hamilton, 2016; Lamb et al.,

2017, 2019; Hays et al., 2018).

At present, many jellyfish SIA studies sub-sample jellyfish (Phylum Cnidaria, Class

Scyphozoa) using bell tissue as a proxy whole organism (D’Ambra et al., 2014; Fleming et al., 2015; Nagata et al., 2015; Javidpour et al., 2016). However, it is not clear whether bell isotopic values are the same as other tissues such as gonads and oral arms.

Conversely, other studies sub-sample jellyfish incorporating tissue from all tissue parts of the jellyfish (e.g. Fleming et al., 2014). In this instance, important inter-tissue isotopic variation could be masked by a sub-sampling method which, while strategically and financially necessary, leads to a reduction in resolution of results concerning jellyfish

SIA and subsequent trophic position (TP) calculation. In contrast, sub-sampling the bell only may be misrepresenting important tissue specific predator-prey interactions occurring on potentially large scales all around the world. For example, there is growing evidence that some predators, including many fish species, feed selectively on specific jellyfish tissues (e.g. Mansueti, 1963; Milisenda et al., 2014; Nakamura et al., 2015). In the Strait of Messina (NE Italy), Boops boops selectively fed according to jellyfish

(Pelagia noctiluca) gender and body part (Milisenda et al., 2014). Furthermore, by employing SIA on bell tissue only, D’Ambra et al. (2014) revealed scyphozoan jellyfish contributed up to 90% of the diet of associating juvenile (Chloroscombrus chrysurus) in the Mediterranean and suggested further inter-tissue analyses could help to resolve whether the juvenile fish analysed in their study also showed selective predation, as highlighted by Milisenda et al. (2014). Similarly, Nakamura et al. (2015) presented video evidence of ocean sunfishes (Mola mola) feeding on jellyfish gonads and oral

83 arms, leaving the intact bell behind and suggested these fish may only feed on energy- rich parts of jellyfish.

In the current study, we build on the research carried out by D’Ambra et al. (2013) which showed δ15N values did not differ among tissue types of wild caught Aurelia sp., yet in laboratory reared specimens inter-tissue δ15N variation was observed. To explore further, we investigated the inter-tissue isotopic variation of three of the most commonly found scyphozoan jellyfish found in the NE Atlantic to answer three specific questions: (i) Is there a difference in isotopic δ15N values between body parts within individual jellyfish species and if so does the same pattern of variation exist for all species considered here? (ii) Is there a uniform pattern of inter-tissue δ15N isotopic variation when body parts from different species are combined? (iii) Is δ15N isotopic tissue variation influenced by jellyfish size? Following the work of Fleming et al. (2015) on size structured shifts of δ15N within species, we discuss whether jellyfish tissue and/or size based isotopic grouping are suitable parameters for inclusion in ecosystem/fisheries models and food webs in the future.

3.3 Methods

3.3.1 Sampling and SIA preparation

Three species of jellyfish (Phylum Cnidaria, Class Scyphozoa) (Fig. 3.1) were sampled from Strangford Lough in Northern Ireland (54° 28' 20.98"N 5° 35' 10.60"W) during the summer of 2012 (Fig. 3.2). Aurelia aurita, Cyanea lamarckii and Cyanea capillata medusae were collected opportunistically in the top metre of the water column from a small boat using dip nets (mesh size 1 mm) and transported back to the laboratory

Figure 3. 1. Jellyfish in the Irish Sea. (a) A bloom of mostly moon jellyfish (Aurelia aurita) in the Irish Sea; (b) lion’s mane jellyfish (Cyanea capillata) which has ensnared moon jellyfish in its tentacles and (c) a stranded blue jellyfish (Cyanea lamarckii) on a beach.

85 in 5L buckets. Jellyfish were weighed and measured (wet mass: ± 1g; bell diameter: ±

1cm) as whole individuals then dissected into their main components (bell, gonad and oral arms). All body components were dried to a constant mass at 60°C as per Fleming et al. (2011), desiccated for 48 hours then ground to a fine powder using an agate pestle and mortar in preparation for SIA.

Figure 3. 2. Three species of scyphozoan jellyfish (A. aurita, C. lamarckii and C. capillata) with a typical complex lifecycle involving a pelagic (swimming) medusa stage and a bottom-living polyp stage, were collected from semi-enclosed Strangford Lough (a) which opens out into the Irish Sea (b).

Samples were weighed into tin cups prior to stable isotope analysis; preliminary analyses to check isotopic peaks were large enough to register sufficient δ15N and δ13C readings were then performed. Optimal sample mass for mass spectrometry varied

86 between taxa i.e. Aurelia aurita ≈ 12 mg; Cyanea lamarckii ≈ 2.4 mg, Cyanea capillata

≈ 5.1 mg. Samples were analysed for δ15N at the East Kilbride Node of the Natural

Environment Research Council Life Sciences, Mass Spectrometry Facility via continuous flow isotope ratio mass spectrometry using an ECS 4010 elemental analyser (Costech,

Milan, Italy) interfaced with a Delta XP mass spectrometer (Thermo Electron, Bremen,

Germany). Drift correction using a 3-point normalization was performed using three in- house standards (GEL, ALAGEL and GLYGEL) encompassing a range of isotope compositions, run every ten samples, and a suite of GELs of different sizes were used to correct samples for linearity (Werner & Brand, 2001). GEL is dried from a solution of gelatin, ALAGEL from an alanine-gelatine solution, and GLYGEL from a glycine-gelatine solution. Four differently sized USGS 40 glutamic acid standards (Qi et al., 2003; Coplen et al., 2006) were used as independent checks of accuracy and to acquire the calibration for N and C content. All data are reported with respect to the international standard of AIR for δ15N and V-PDB for δ13C. Results are reported in δ notation

(McKinney et al., 1950). Experimental precision was 0.07‰ for carbon and 0.09‰ for nitrogen (standard deviation of laboratory standard replicates of GEL).

3.3.2 Statistical analyses

After checking for data normality, I employed non-parametric Friedman tests to examine the isotopic intra and inter-specific differences between jellyfish body parts.

To investigate individual comparisons, I then conducted pairwise Nemenyi with Chi-

Squared distribution post-hoc tests. To investigate the effect of size on isotopic variation of jellyfish body parts I ran a generalised linear model (GLM) for continuous data and included body part and species as factors and δ15N and jellyfish size (bell

87 diameter) as dependent variables. All statistical analyses were done in the statistical software R version 1.1.414 (R Core Development Team, 2018).

3.4 Results

Separate Friedman tests were carried out to compare the δ15N values for different body parts of Aurelia aurita, Cyanea lamarckii and Cyanea capillata. There was no significant difference among body part δ15N values in Aurelia aurita samples (Friedman’s Chi-

Squared = 2.28, df=2, p=0.32), nor was there a significant difference among the tissues of Cyanea lamarckii despite a marginal probability value (Friedman’s Chi-Squared =

6.24, df=2, p=0.05) (Fig. 3.3). Further Nemenyi post hoc tests indicated that while bell and gonad C. lamarckii tissue δ15N values were not significantly different (p=0.34), bell tissues were δ15N depleted relative to oral arms (p<0.001) and furthermore, oral arm tissues were N enriched relative to gonads (p<0.005). Similarly, a Friedman test found there was a significant difference among the bodyparts in δ15N values of Cyanea capillata (Friedman’s Chi-Squared = 16.84, df=2, p <0.001). While Nemenyi post hoc tests indicated there was no significant difference between bell and gonad tissues

(p=0.64), inter-tissue variation was driven by N enriched oral arm tissues compared to both bell (p<0.001) and gonad tissues (p=0.019).

Table 3.2. Mean bell diameter, wet mass, and δ15N of different body parts for different scyphozoan jellyfish sampled from Strangford Lough, Northern Ireland.

Mean bell Mean wet Mean Mean Mean oral Species N diameter mass (± SD) bell δ15N gonad δ15N arms δ15N (± SD) (cm) (g) (± SD) (± SD) (± SD) Aurelia 43 18.94 381.47 9.86 10.21 9.91 aurita (7.62) (424.54) (1.78) (2.52) (1.94)

Cyanea 37 10.66 114.02 11.14 11.68 12.53 lamarckii (4.29) (114.23) (1.91) (1.62) (3.00)

Cyanea 50 27.13 2328.94 13.46 13.12 14.52 capillata (14.18) (3911.35) (3.19) (2.87) (3.06)

When bell, gonads and oral arm isotopic values were combined for the three species sampled in this study, we found significant δ15N inter-tissue variation (Friedman’s Chi-

Squared = 14.59, df=2, p=<0.001). Consistent with the results when looking at individual species, when combined, Nemenyi post hoc tests revealed that there was no significant difference in δ15N values between bell and gonad. Rather, this difference was driven by oral arms having higher δ15N values than with bell (p<0.0001) and gonad tissue (p=0.0045).

Figure 3. 3. Box plots showing mean and ± SD δ15N isotopic variation in bell, gonad and oral arms tissue for (a) Aurelia aurita, (b) Cyanea lamarckii and (c) Cyanea capillata. Box plot (d) illustrates inter-tissue δ15N isotopic variation when all jellyfish are combined.

The GLM output suggested that δ15N values among jellyfish bodyparts differ when species data are combined, mimicking the results from when we foucsed on individual species only. However, it also tentatively suggested that size may have an effect on

δ15N of jellyfish but was not dependent on body part, rather species has a greater and significant influence, although with a marginal significance value (df = 2, deviance =

34.35, p<0.044).

Figure 3. 4. Bell, gonad and oral arm tissue δ15N versus jellyfish bell diameter linear regressions (coloured lines) and with low associated R2 valuesfor (a) Aurelia aurita, (b) Cyanea lamarckii and (c) Cyanea capillata and (d) all jellyfish combined.

3.5 Discussion

Jellyfish gonad and bell tissue had consistently similar δ15N values when considered as individual species or collectively. This finding suggests that the turnover and fractionation rates amongst these tissues are similar. From a pragmatic perspective, sub-sampling the gonads or the bell in particular for SIA is often the easiest and quickest method and together represent the majority of the jellyfish in terms of their wet and dry mass (Doyle et al., 2007; D’Ambra et al., 2013). Our results indicate that using homogenised whole jellyfish samples for SIA for eventual inclusion into food webs and ecosystem modelling may be appropriate for some species (e.g. Aurelia aurita and Cyanea lamarckii), given that there was no evidence of inter-tissue isotopic variation for these species. D’Ambra et al. (2013) found a similar pattern whereby δ15N values did not differ among tissue types for wild caught Aurelia sp. and concluded that, in jellyfish which reached a steady-state in their diet, the bell is the most suitable body part to accurately determine stable isotope composition in Aurelia sp.. In contrast,

D’Ambra et al. (2013) also found that laboratory-fed, rather than wild caught Aurelia sp. displayed inter-tissue δ15N variation whereby oral arms had significantly higher δ15N when compared to either bell or gonads. We also detected this pattern of δ15N inter- tissue variation between tissues for Cyanea capillata and when all species were combined. These results demonstrate how it is possible to mask important jellyfish trophic information due to species specific differences in inter-tissue isotopic variation.

Ultimately this could lower the resolution of subsequent SIA interpretation, mixing models and TP calculation for these taxa. Although further investigation is needed, our results reflect and build on those of D’Ambra et al. (2013) adding weight and confidence to our findings. While beyond the scope of the present study, our findings

92 also raise interesting questions about the mechanisms which cause jellyfish tissue to assimilate differently and whether inter-tissue isotopic variation is related to readily identifiable morphological features, such as the structure or size of the tissue driving the inter-tissue variation.

The importance of these isotopic subtleties for trophic ecology and specifically jellyfish research, is not insignificant considering the continued reporting of tissue specific predation on jellyfish from across the globe. For example, Arai (2005) collated literature reports of marine animals feeding on jellyfish, of which amphipods (Hyperia spp.) (Dittrich, 1988, 1992; Buecher et al., 2001), goose barnacles (Alepas pacifica)

(Tabachnik, 1986; Pagès, 2000) and seabirds (Ates, 1991) were all observed feeding selectively on the gonads of jellyfish. Furthermore, D’Ambra et al. (2014) also showed how juvenile fish (Chloroscombrus chrysurus) associated with jellyfish fed specifically on their host’s gonads. Incorporating δ15N isotopic inter-tissue variation may be warranted, if not essential, in some instances considering the specific nature of some of these trophic interactions, or at the very least a broader understanding of how these differences could influence results. In instances outlined by Nakamura et al. (2015) where ocean sunfish (Mola mola) fed preferentially on the gonads and the oral arms of jellyfish, accounting for inter-tissue isotopic variation is key for fully interpreting and assigning predator prey relationships within food webs. The usefulness of exploring such inter-tissue variation within jellyfish is not without precedent. For example, Doyle et al. (2007) investigated the inter-tissue calorific content variation of scyphozoan jellyfish and found that oral arms and gonads were much more energy rich than the bell, findings which have since been supported by subsequent work on different scyphozoan jellyfish (Milisenda et al., 2014).

For all jellyfish combined, the resultant patterns of δ15N inter-tissue variation was the same as that for the two Cyanea sp. whereby gonad and bell were not significantly different and oral arms were slightly, but significantly, higher in δ15N. While currently restricted to these three N.E. Atlantic scyphozoans, these methods could help form a pragmatic rule of thumb for scyphozoan jellyfish in regard of inter-tissue variation and provide the means for researchers to account for these differences once validated more broadly. However, as well as tissue, I exploredthe importance of addressing size based δ15N isotopic variation. Broadly, I found a weak signal that larger jellyfish have a higher δ15N which can reflect their higher trophic position in food webs. Considering the diversity of gelatinous zooplankton as a whole, the range of different feeding strategies they exhibit (Costello & Colin, 1994) and the range of individual growth throughout their lifespan (Fleming et al., 2015), it is unsurprising that they exhibit such isotopic variation akin to fish (Javidpour et al., 2016; Shipley et al., 2017). Further research is required, but our results warrant further investigation into whether size offers a pragmatic approach to incorporating jellyfish inter-tissue trophic complexity into models across scyphozoan species. Our findings also suggest the size-based differentiation of δ15N for the jellyfish investigated here is species specific which may reflect the different maximum size estimates for these species. For example, Aurelia aurita grows to 30 cm bell diameter, and Cyanea lamarckii usually grows to 15cm bell diameter in British waters, but exceptionally can grow to >30cm (Russel, 1970). By contrast, Cyanea capillata usually grows to <50cm bell diameter, but can grow much larger to >90cm (Russel, 1970); the largest specimen sampled in this study was 85cm.

Consequently, it is logical to suggest that as jellyfish grow, their isotopic niche broadens to include bigger sized prey items and, in some cases, including small fish which are

94 higher up the food chain, which gives rise to bigger jellyfish having an increased δ15N value and trophic position (Graham & Kroutil, 2001; Fleming et al., 2015). As jellyfish grow, there are shifts in their prey field in terms of zooplankton succession which may contribute to this phenomenon of jellyfish gaining a larger δ15N value over time

(Graham & Kroutil, 2001; Fleming et al., 2014, 2015).

3.5.1 Conclusions

This study is a small step in providing data to modellers so that the lack of representation of jellyfish in ecosystem, fishery and food web models may be addressed. Our preliminary results indicate that there is significant inter-tissue isotopic variation among the three scyphozoan jellyfish presented here. More significantly, accounting for such variation is vital to our broader understanding and interpretation of SIA studies. Further research is needed, and we suggest investigating isotopic inter- tissue variation in a greater range of species, which may help more obvious functional groupings to be ascertained either in relation to species, species specific size (or maximum size), feeding strategy or ideally by the presence/absence of readily identifiable morphological features.

3.6 References Arai, M.N. (2005). Predation on pelagic coelenterates: a review. Journal of the Marine Biological Association of the UK 85, 523–536.

Ates, R.M.L. (1991). Predation on Cnidaria by vertebrates other than fishes. Hydrobiologia 216/217, 305–307.

Ben-David, M., Schell, D.M., and Phillips, D.L. (2001). Mixing models in analyses of diet using multiple stable isotopes: A response. Oecologia 127, 180–184.

Buecher, E., Sparks, C., Brierley, A.S., Boyer, H., and Gibbons, M. (2001). Biometry and size distribution of (Chrysaora hysoscella) (Cnidaria, Scyphozoa) and Aequorea aequorea (Cnidaria, Hydrozoa) off Namibia with some notes on their parasite Hyperia medusarum. Journal of Pl 23, 1073–1080.

Cardona, L., de Quevedo, I.Á., Borrell, A., and Aguilar, A. (2012). Massive consumption of gelatinous plankton by Mediterranean apex predators. PLoS ONE 7.

Costello, J.H., and Colin, S.P. (1994). Morphology, fluid Motion and Predation by the Scyphomedusa Aurelia Aurita. Marine Biology 121, 327–334.

Costello, J.H., Colin, S.P., and Dabiri, J.O. (2008). Medusan morphospace: Phylogenetic constraints, biomechanical solutions, and ecological consequences. Invertebrate Biology 127, 265–290.

D’Ambra, I., Carmichael, R.H., and Graham, W.M. (2013). Determination of δ13C and δ15N and trophic fractionation in jellyfish: implications for food web ecology. Marine Biology 161, 473–480.

D’Ambra, I., Graham, W.M., Carmichael, R.H., and Hernandez, F.J. (2014). Fish rely on scyphozoan hosts as a primary food source: evidence from stable isotope analysis. Marine Biology 162, 247–252.

Dittrich, B. (1988). Studies on the life cycle and reproduction of the parasitic amphipod Hyperia galba in the North Sea. Helgoländer Meeresuntersuchungen 42, 79–98.

Dittrich, B. (1992). Functional morphology of the mouthparts and feeding strategies of the parasitic amphipod Hyperia galba (Montagu, 1813). Sarsia 77, 11–18. 96

Fry, B. (2006). Stable isotope ecology. New York: Springer (Vol. 521).

Graham, W.M., and Kroutil, R.M. (2001). Size-based prey selectivity and dietary shifts in the jellyfish, Aurelia aurita. Journal of Plankton Research 23, 67.74.

Hamilton, G. (2016). The Secret Lives of Jellyfish. Nature 432–434.

Hays, G.C., Doyle, T.K., and Houghton, J.D.R. (2018). A Paradigm Shift in the Trophic Importance of Jellyfish? Trends in Ecology & Evolution.

Javidpour, J., Cipriano-Maack, A.N., Mittermayr, A., and Dierking, J. (2016). Temporal dietary shift in jellyfish revealed by stable isotope analysis. Marine Biology 163, 1–9.

Jennings, S., Warr, K.J., and Mackinson, S. (2002). Use of size-based production and stable isotope analyses to predict trophic transfer efficiencies and predator-prey body mass ratios in food webs. Marine Ecology Progress Series 240, 11–20.

Middelburg, J.J. (2014). Stable isotopes dissect aquatic food webs from the top to the bottom. Biogeosciences 11, 2357–2371.

Nagata, R., Moreira, M., Pimentel, C., and Morandini, A. (2015). Food web characterization based on δ15N and δ13C reveals isotopic niche partitioning between fish and jellyfish in a relatively pristine ecosystem. Marine Ecology Progress Series 519, 13–27.

Nakamura, I., Goto, Y., and Sato, K. (2015). Ocean sunfish rewarm at the surface after deep excursions to forage for siphonophores. Journal of Animal Ecology 84, 590–603.

Pagès, F. (2000). Biological associations between barnacles and jellyfish with emphasis on the ectoparasitism of Alepas pacifica (Lepadomorpha) on Diplulmaris malayensis (Scyphozoa). Journal of Natural History 34, 2045–2056.

Pauly, D., Graham, W.M., Libralato, S., Morissette, L., and Deng Palomares, M.L. (2009). Jellyfish in ecosystems, online databases, and ecosystem models. Hydrobiologia 616, 67–85.

Post, D.M. (2002). Using Stable Isotopes to Estimate Trophic Position: Models, Methods, and Assumptions. Ecology 83, 703–718.

Russel, F.R. (1970). The Medusae of the British Isles Vol II.

Shipley, O. N., Polunin, N. V., Newman, S. P., Sweeting, C. J., Barker, S., Witt, M. J., & Brooks, E. J. (2017). Stable isotopes reveal food web dynamics of a data-poor deep- sea island slope community. Food Webs, 10, 22-25.

Sweetman, A.K., Chelsky, A., Pitt, K.A., Andrade, H., van Oevelen, D., and Renaud, P.E. (2016). Jellyfish decomposition at the seafloor rapidly alters biogeochemical cycling and carbon flow through benthic food-webs. Limnology and Oceanography 61, 1449– 1461.

Symondson, W.O.C. (2002). Molecular identification of prey in predator diets. Molecular Ecology 11, 627–641.

Tabachnik, K.R. (1986). Parasitism of Alepas Pacifica (Cirripedia, Thoracica) on scyphozoan jellyfish. Zoologische Mededelingen 65, 1731–1733.

Team, R.C. (2018). R: A language and environment for statistical computing. R Core Development Team, Vienna.

Vinson, M.R., and Budy, P. (2011). Sources of variability and comparability between salmonid stomach contents and isotopic analyses: study design lessons and recommendations. Canadian Journal of Fish and Aquatic Science 151, 137–151.

Chapter 4 - Regional and individual variation in jellyfish trophic

position shown using bulk and amino acid δ15N

Collaborators: Donal C. Griffin1, Steven E. Beggs2, Victoria Dominguez3, Jonathan D.R. Houghton1,4,5, Jason Newton6, Alison Kuhl7, Natasha D. Phillips1, Chris Harrod8,9,10

1 School of Biological Sciences, Queen’s University, Belfast, UK

2 Agri-Food and Biosciences Institute, Belfast, UK

3 School of Biological and Chemical Sciences, Queen Mary University of London, London, UK

4 Queen’s Marine Laboratory, Portaferry, UK

6 NERC Life Sciences Mass Spectrometry Facility, Scottish Universities Environmental Research Centre, East Kilbride, UK

7 NERC Life Sciences Mass Spectrometry Facility School of Chemistry, University of Bristol, UK

8 Universidad de Antofagasta Stable Isotope Facility, Universidad de Antofagasta, Chile

9 Instituto de Ciencias Naturales Alexander von Humboldt, Universidad de Antofagasta, Chile

10 Núcleo Milenio INVASAL, Concepción, Chile

100

4.1 Abstract

Often, due to the lack of available data, jellyfish (Phylum Cnidaria, Class Scyphozoa) taxa are underrepresented in ecosystem and fisheries research. Meanwhile, in relation to predator prey interactions, a more nuanced view of their ecological function in marine systems is needed. To address this inconsistency, trophic position (TP) is a standard metric included in ecosystem models and may prove useful for exploring complex ecological groups, such as jellyfish. Trophic position is commonly estimated for consumers by using nitrogen stable isotope ratios (δ15N) measured from consumers and an isotopic baseline, accounting for diet-consumer trophic fractionation, commonly referred to as trophic discrimination factor (TDF). However, it is currently unclear whether standard TDF values estimated for other aquatic consumers are appropriate for jellyfish studies and therefore it is equally unclear whether estimates of jellyfish TP based on bulk isotopes are reliable.

Towards this goal, we compare TP based on bulk and amino acid δ15N for four species of scyphozoan jellyfish and one hydrozoan captured from the N.E. Atlantic, Mediterranean and S.E. Pacific. We estimated bootstrapped median ±97.5 percentile TP by comparing values for “trophic” and “source” amino acids, using TDFs of 7.6 and 6.2‰. Median ±97.5% credibility-intervals of bulk TP were also estimated, using two commonly used TDFs in the literature (2.9±0.3 and 3.4±1‰). Whilst median jellyfish TP varied considerably between methods, estimates based on a bulk TDF of 2.9‰ and an amino-acid TDF of 7.6‰ overlapped for all comparisons. This indicates that these TDF values may be appropriately representative of jellyfish, allowing for their inclusion in isotopic food web studies in the future.

Keywords: jellyfish, trophic discrimination factor, trophic position, amino acid, stable isotope analysis

101

4.2 Introduction

Jellyfish (Cnidarian medusae and associated gelatinous zooplankton such as ctenophores and pelagic tunicates) are important components of marine food webs

(Gibbons & Richardson, 2013; Brodeur et al., 2016; Hays et al., 2018). However, due to their low energy content and the perception that they had few predators, jellyfish were traditionally considered trophic dead ends (Houghton et al., 2007; Riascos et al., 2012;

Hosia et al., 2014). This was perceived as negative because limiting the flow of nutrients to higher trophic levels impacts on food web dynamics and nutrient recycling (Lynam et al., 2006). Whilst fish feeding on jellyfish has been well documented for decades

(Arai, 1988; Ates, 1988), there has been a shift in how the roles of these organisms in marine food webs are viewed. Many other species have been discovered to feed opportunistically or predominantly on jellyfish (e.g. ; Hoving and Haddock, 2017;

Phillips et al., 2017). For instance, in the Irish Sea, Lamb et al. (2017, 2019) identified many fish species previously unknown as jellyfish predators in the Irish Sea while jellyfish serve as food for four penguin species in the Southern Ocean (Thiebot et al.,

2017). Considering such revelations, Hays et al. (2018) echoed calls from other researchers stating that more careful consideration is needed to fully parameterise the role of jellyfish in marine systems.

Categorising groups of species based on their trophic position (TP) is an effective way to incorporate morphological and functional diversity into ecosystem models and food webs (Post, 2002). More specifically, TP derived from bulk stable isotope analysis (BSIA) has become a routine means of food web investigation for many ecologists (Post, 2002;

McMahon & McCarthy, 2016). The comparison of consumer and prey stable isotope ratios of carbon (C) and nitrogen (N), adjusted for the isotopic shifts associated with

102 assimilation (trophic discrimination factors TDFs), are commonly used to elucidate trophic structure and energy transfer processes (Post, 2002; Fry, 2006; Middelburg,

2014). TDFs account for the small natural variations in stable isotope ratios resulting from physical, chemical and biological processes that cause isotope fractionation: the stepwise accumulation of the heavier isotope (15N or 13C) with each trophic level in a consumer compared to its diet (McCutchan et al., 2003; McMahon and McCarthy,

2016; Post, 2002). Ordinarily, consumer TP is derived from the difference in consumer and baseline δ15N values when TDFs are known. However, efforts by Fleming et al.

(2015) to estimate jellyfish TP and consumption patterns (via mixing models) were constrained by a lack of reliable TDFs and complicated further by the finding that jellyfish δ15N is temporally variable (Fleming et al., 2015). D’Ambra et al. (2014) suggested that TDFs for nitrogen in jellyfish may be very low in comparison to other marine species (e.g. Aurelia spp. Δ15N = 0.1 ‰). However, Fleming et al. (2015) found that when applied to δ15N of Aurelia aurita collected from the N.E Atlantic, use of this low TDF resulted in unfeasibly high TP estimates for Aurelia aurita (estimated TP range:

2.3 – 78.7) and other jellyfish they examined (C. capillata & C. lamarckii). Critically,

D’Ambra et al. (2014) estimated that nitrogen TDF for Aurelia sp. is also low, compared to that estimated for other aquatic consumers (e.g. 2.9‰ - McCutchan et al. (2003), or

3.4‰ – Post (2002)), and as such, it is difficult to know whether estimates of jellyfish

TP based on commonly used standard TDFs are reliable. The variability in determined

TDFs to-date could reflect differences between productive temperate systems (N.E.

Atlantic & Irish Sea) and oligotrophic systems (Mediterranean Sea) or be the result of inherent physiological differences in nitrogen metabolism within, and/or between, species. A lack of reliable TDFs is creating a bottleneck in jellyfish trophic ecology which

103 precludes the reliable calculation of TP for a taxon which is already overlooked by ecosystem and fisheries modellers (Pauly et al., 2009).

Compound specific isotope analysis (CSIA) of individual amino acids (AAs) has recently emerged as a powerful analytical tool for ecologists similar to BSIA (Chikaraishi et al.,

2014; Nielsen et al., 2015; McMahon & McCarthy, 2016). Crucially, the CSIA AA approach first identified by McClelland & Montoya (2002) potentially increases the accuracy and precision of TP estimates. This method centres on how heavily fractionating “trophic” AAs such as glutamine acid (Glu), provide a robust indicator of trophic transfer between consumer and prey while minimally fractionating “source”

AAs such as phenylalanine (Phe) closely reflect the δ15N value at the base of the food web (McMahon & McCarthy, 2016), all from a single sample of consumer tissue (e.g.

Batista et al., 2014; Chikaraishi et al., 2014; Choy et al., 2015). Ultimately, what

McClelland and Montoya (2002) first suggested, which later studies confirmed, is that differential amino acid δ15N fractionation with trophic transfer could yield independent information on both trophic level and δ15N baseline, allowing ecologists to calculate internally referenced consumer TP.

Subsequent feeding and field studies using the CSIA approach seemed to converge on consistent trophic fractionation values of approximately 7.6 ‰, which became the widely adopted TDF value for calculating TP from CSIA methods across multiple taxa and environments (Lorrain et al., 2009, 2015; Dale et al., 2011; Choy et al., 2012; Miller et al., 2013; Nakatomi et al., 2013). However, a number of studies challenged the idea that a single constant TDF value can accurately represent true fractionation values among many consumer-resource relationships in complex systems (Lorrain et al., 2009;

Dale et al., 2011; Matthews & Ferguson, 2014; Bradley et al., 2015). In a study

104 investigating the diet of orcas (Orcinus orca), Matthews and Ferguson (2014) proposed that a multi-TDF calculation of TP yielded more accurate results because it accounted for differences between consumers N cycling. Other studies reach a similar conclusion whereby the adoption of a constant 7.6‰ value often resulted in an underestimation of TP in comparison to other methods including gut content analysis and visual observation (Dale et al., 2011; Matthews & Ferguson, 2014; Bradley et al., 2015).

With these issues in mind, we conducted BSIA and amino acid CSIA on four species of scyphozoan jellyfish and one hydrozoan from the N.E. Atlantic, Mediterranean and S.E.

Pacific. While incorporating TDF variability, TP estimates from each method were calculated, the rationale being that if estimates converge towards a consistent TDF range then we can start to develop more reliable estimates of jellyfish TP. This would ultimately allow for the inclusion of these important consumers into isotopic food web studies in the future.

4.3 Methods

To estimate trophic position using BISA and CSIA, we sampled four different species of scyphozoan jellyfish and one hydrozoan commonly found in each of the three marine systems. Aurelia aurita, Cyanea lamarckii and Cyanea capillata were collected during annual fisheries surveys conducted in the Irish Sea by the Agri-Food and Biosciences

Institute, Northern Ireland. The Irish Sea is a semi-enclosed, dynamic and ecologically important system located between Great Britain and Ireland (Horsburgh et al., 2000;

Gowen & Stewart, 2005). Pelagia noctiluca and Velella velella samples were collected opportunistically from fishing boats or the shore at Santa Margherita Ligure

(44o20’1.1”N, 9o12’50.7”E) in the Mediterranean coast of northern Italy, an 105 oligotrophic system characterised by low primary production (Bethoux et al., 1998;

Thingstad et al., 2005). Pelagia noctiluca samples were also collected from a highly- productive coastal upwelling area of the South East Pacific in Northern Chile (Thiel et al., 2007). These sample locations represent three distinct marine systems across the globe (Fig. 4.1) allowing for intra-specific inspection of results based on environmental differences.

4.3.1 Sample collection and processing for SIA

Between April 2015 and September 2016, jellyfish specimens were collected using

Methot Isaacs Kidd (MIK) nets (Irish Sea samples) and 1 cm mesh dip nets

(Mediterranean and S.E. Pacific Pelagia noctiluca samples). Freshly stranded Velella velella from the Mediterranean were collected directly from the shoreline immediately after stranding. Medusae bell diameter, measured as inter-rhopalia distance (±0.5 cm), was recorded along with whole specimen wet mass (±1.0 g). Velella velella size was taken as a measurement of its maximum diameter. Separated scyphozoan bell and whole Velella velella were thoroughly washed in filtered sea water to remove unwanted metabolic nitrogenous compounds as per Mackenzie et al. (2017) and oven dried at 60oC until they reached a constant mass, or initially preserved by freezing before being oven dried later. Following methods outlined in Doyle et al. (2007) once dried to a constant mass, samples were homogenised to a fine powder using a mortar and pestle.

106

Figure 4. 1. Locations of jellyfish sampling from the (b) Irish Sea (Aurelia aurita, Cyanea lamarckii and Cyanea capillata), (c) Mediterranean (Pelagia noctiluca and Velella velella) and (d) South East Pacific (Velella velella).

4.3.2 Bulk Stable Isotope Analysis (BSIA)

After preliminary analysis to check isotopic peaks were large enough to register sufficient δ15N and δ13C readings, an optimal sample mass was found to be ≈3-4 mg for all the sampled jellyfish species. Powdered jellyfish samples of ≈3-4 mg were weighed into tin capsules and continuous flow isotope ratio mass spectrometry (CF-IRMS) was performed using an ECS 4010 elemental analyser (Costech, Milan, Italy) coupled to a

Delta XP mass spectrometer (Thermo Electron, Bremen, Germany). All samples were analysed for δ13C, δ15N, %C and % N at the East Kilbride Node of the Natural

Environment Research Council Life Sciences Mass Spectrometry Facility. Drift correction using a 3-point normalization was performed using three in-house standards

107

(GEL, ALAGEL and GLYGEL) encompassing a range of isotope compositions, run every ten samples, and a suite of GELs of different sizes were used to correct samples for linearity (Werner & Brand, 2001). GEL is dried from a solution of gelatin, ALAGEL from an alanine-gelatine solution, and GLYGEL from a glycine-gelatine solution. Four differently sized USGS 40 glutamic acid standards (Qi et al., 2003; Coplen et al., 2006) were used as independent checks of accuracy and to acquire the calibration for N and

C content. All data are reported with respect to the international standard of AIR for

δ15N and V-PDB for δ13C. Results are reported in δ notation (McKinney et al., 1950).

Experimental precision was 0.07‰ for carbon and 0.09‰ for nitrogen (standard deviation of laboratory standard replicates of GEL).

4.3.3 Compound Specific Isotope Analysis (CSIA)

All samples were prepared for compound specific nitrogen isotope analysis of amino acids by acid hydrolysis followed by derivatization to produce N-acetyl isopropyl esters

(Corr et al., 2007) at the NERC Life Sciences Mass Spectrometry Facility School of

Chemistry, University of Bristol, UK.

4.3.4 Liberation of free amino acids by acid hydrolysis

Approximately 2 mL 6M hydrochloric acid (HCl) was added to aliquots of each dried homogenised sample (2mg) in addition to 40 μg (50 μl of a 200 μg mL-1 solution) of norleucine (internal standard); the headspace was filled with oxygen-free nitrogen gas before sealing the tube and heating at 100°C for 24 h. Acid hydrolysis was performed under nitrogen to minimise the oxidative degradation of amino acids under these harsh conditions. After cooling, the hydrolysates were blown down to dryness under a stream of nitrogen at 60°C and stored in 1 mL 0.1 M HCl at -25°C.

108

4.3.5 Purification of amino acids by cation exchange column chromatography

Dowex 50X-W8 cation exchange resin (Sigma FchatAldrich, Dorset, UK) was prepared as follows to ensure all cation exchange sites were occupied by H+ ions. Approximately

5g resin was soaked for at least 12 h in a 3M sodium hydroxide solution (NaOH). The liquid was then poured away and any excess NaOH was removed by washing five times with double distilled water (DDW).The washed resin was then soaked in 6M HCl for at least 24 h and stored under 6M HCl until required. 1 mL of the prepared Dowex resin was transferred to a flash column and washed again by passing 3 x 2 mL DDW through it. The hydrolysates in 1 mL 0.1 M HCl were added to the top of the column and allowed to run through the column until the top of the solution was just above the level of the resin. The salts were eluted with 2 x 2 mL DDW before eluting the amino acids with 2 x

2 mL 2M ammonium hydroxide solution (NH4OH). The amino acid fraction was blown down to dryness under N2 at 60°C.

4.3.6 Preparation of N-acetyl, O-isopropyl derivatives

Due to their dual-ionic character, amino acids require derivatisation before analysis by techniques involving separation by gas chromatography. Amino acids were derivatised by conversion to their isopropyl esters before subsequent N acetylation, as per Corr et al. (2007). A 4:1 v/v mixture of isopropanol and acetyl chloride was created by cooling a vial of isopropanol in an ice bath and adding acetyl chloride dropwise once the isopropanol was ice cold; 0.25 mL of this mixture was added to the hydrolysed amino acid fraction of each sample in a culture tube. The tubes were sealed and heated at

100°C for 1 h before quenching the reaction in a freezer at -25°C for a further hour.

Excess reagents were removed by evaporation under a gentle stream of nitrogen at no more than 40°C. Dichloromethane (DCM) (2 x 0.25 mL) was added and gently blown

109 down each time to ensure removal of all reagents. A mixture of acetone, triethylamine and acetic anhydride (5:2:1 v/v/v) was added to the tube (1 mL), which was then sealed and heated for 10 min at 60°C. The sample was allowed to cool before being blown down under a stream of nitrogen at room temperature, then re-dissolved in 2 mL ethyl acetate. A 1 mL volume of saturated aqueous sodium chloride (NaCl) solution was added and the mixture vortex-mixed. A phase separation was carried out, and the organic phase was evaporated under a gentle stream of nitrogen in an ice bath. Any remaining water was removed with the addition of 3 successive 1 mL aliquots of DCM, blowing down each time. The vials were capped and stored in a freezer at -25°C until required for analysis.

4.3.7 Gas chromatography with Flame ionisation Detection (GC-FID) analyses

GC analyses were performed using a Hewlett-Packard 7890 Gas Chromatograph equipped with an Agilent DB-35 capillary column (dimensions 30 m x 0.32 mm internal diameter; 0.5μm film thickness). Samples were introduced using on-column injection with helium as the carrier gas at a flow rate of 2 mL per minute. Oven temperature

70°C was initially held for 2 minutes, then heated by 15°C min-1 to 150°C, then by 2°C min-1 to 210°C and finally 8°C min-1 to 270°C at which point it was held for 15 min.

Detection of eluant compounds was achieved using a flame ionisation detector (FID).

Peaks were identified by comparison of retention times with those of derivatised amino acid standards.

4.3.8 Gas chromatography-combustion-isotope ratio mass spectrometry

These analyses were performed using a ThermoFisher Scientific Trace GC connected to a Delta V IRMS via a GC Isolink and Conflow IV interface. The GC was again equipped with a DB-35 capillary column (dimensions 30 m x 0.32 mm internal diameter; 0.5μm

110 film thickness). Samples were introduced using a programmable temperature vaporisation (PTV) injector with the initial oven temperature at 40°C for 5 minutes before raising it by 15°C min-1 to 120°C, by 3°C min-1 to 180°C, by 1.5°C min-1 to 210°C and finally by 5°C min-1 to 270°C at which point it was held for 8.5 mins. Helium was used as the carrier gas at a flow rate of 1.4ml per minutes. A Nafion dryer removed water and a cryogenic trap was employed in order to remove CO2 from the oxidised and reduced sample. Peaks were identified by comparison of retention times with those of derivatised amino acid standards. Isotopic compositions are expressed using

15 15 14 the delta scale as follows: δ N = Rsample/ Rstandard - 1, where R is the N/ N ratio, and the standard is atmospheric N2 (air). Peaks were identified by comparison of retention times with those of derivatised amino acid standards. Isotope ratios were computed by comparing with reference N2 of known isotopic composition, introduced directly into the ion source in four pulses at the start and end of each run. Isotopic compositions

15 are expressed using the delta scale as follows: δ N = Rsample/ Rstandard - 1, where R is

15 14 the N/ N ratio, and the standard is atmospheric N2 (air).

4.3.9 Estimation of trophic position using BSIA and CSIA

The trophic position of jellyfish from BSIA data was estimated using the R package tRophicPosition (version 0.7.5; Quezada-Romegialli et al., 2018) using the equation:

15 15 15 13 TPJellyfish = + (δ NJellyfish – [δ Nbaseline1 x (1 -)])/ Δ Nwhere  = (δ CJelly –

13 13 13 15 δ Cbaseline2)/(δ Cbaseline1 – δ Cbaseline2), Δ N = TDF and  = trophic level of baseline indicator.

The tRophicPosition package incorporates a Bayesian model of analysis using consumer

δ15N values, TDFs and baseline values for a known trophic position (filter feeding bivalves collected at each location). More specifically, we calculated median ±97.5% credibility intervals for TP from bulk δ15N values using the Onebaseline model option.

111

We estimated TPs using two commonly used TDFs for bulk δ15N. The first is a TDF based on mean ± SD observed diet-consumer shift for a wide variety of consumers (Post,

2002: 3.4 ± 1.0 ‰) and one based on aquatic carnivores (2.9 ± 0.3 ‰ McCutchan et al.

2003). We examined overlap in 97.5% credibility intervals for TP to indicate statistically significant differences in TP for the different species as well as for the two different

TDFs.

Following a similar approach, we estimated TP from CSIA-AA from Δ15N glutamine- phenylalanine data using two commonly used TDFs using the equation: TPCSIA =

15 15 1+(δ NGlu-δ NPhe-β/TDFGlu-Phe). As reported in McMahon and McCarthy (2016), the most common formulation for TPCSIA is based on the offset between the trophic amino

15 15 acid glutamine and source amino acid phenylalanine, where δ NGlu and δ NPhe represent the stable nitrogen isotope values of the consumer glutamine and phenylalanine, respectively. β represents the difference in δ15N between these same amino acids in primary producers at the base of the food web, and TDFGlu-Phe represents the trophic discrimination factor (TDF) between diet and consumer, calculated by

15 15 normalizing trophic fractionation of glutamine (Δ NGlu) to phenylalanine (Δ NPhe).

Many researchers using AA δ15N to estimate TP of consumers from a wide range of taxa and habitats have used a canonical TDF value of 7.6 ‰ (McMahon & McCarthy, 2016).

However, this value has been questioned, and McMahon & McCarthy (2016) provided an estimate of 6.2 ‰ based on a wide range of taxa, food types and nitrogen excretion.

We used a βGlu-Phe value of 3.4‰, calculated bootstrapped median (± 97.5 % confidence interval) TP values (n replacements = 1000) and examined overlap between these values as evidence for differences between species, locations, TDFs and type of isotope analysis.

112

4.4 Results

4.4.1 Bulk Trophic Position Estimation

15 2 Jellyfish bulk δ N values were significantly different between systems (χ 2=33.4,

2 p<0.001) and species (χ 5=19.12 p<0.001). Kruskal Wallis post-hoc Nemenyi tests showed that there was no difference in δ15N between the two Mediterranean jellyfish

(p<0.01) and both had distinctly lower δ15N values in comparison to the other jellyfish sampled (Fig. 4.2). As an assemblage, Irish Sea jellyfish were not significantly different from each other in terms of δ15N (p>0.05) and had significantly different δ15N values than those from the Mediterranean (p<0.001) and S.E. Pacific (p<0.001)

Pelagia noctiluca sampled from the S.E. Pacific had significantly higher δ15N (p<0.01) than those sampled from the Mediterranean. Bulk stable isotope analysis data (Table

4.1) indicated that jellyfish TP ranged from 1.93 (±95% credibility interval 1.65 - 2.21) to 3.54 (±95% credibility interval 3.23 - 3.90). Cyanea capillata had the highest median

TP using both TDF values (3.03 ([±95% credibility interval 2.87 - 3.20]) and 3.54 ([±95% credibility interval 3.23 - 3.90]) of all jellyfish sampled, followed broadly by the other jellyfish collected in the Irish Sea, Cyanea lamarckii and Aurelia aurita. Velella velella and P. noctiluca from the Mediterranean had lower TP estimates, however, P. noctiluca from the S.E. Pacific had the lowest TP estimate of all using the Post (2002) TDF value

(1.93 (±95% credibility interval 1.65 - 2.21). Median TP estimates were lower in each case using the Post (2002) TDF but, in all but one case, credibility intervals overlapped with estimates based on the McCutchan et al. (2003) TDF. The 95% credibility interval for C. capillata TP based on the Post (2002) TDF fell below those based on the

McCutchan et al. (2003) TDF value.

113

Using the Post (2002) TDF (Δ15N=3.4±1‰), median TP (±95% credibility intervals) was

3.03 (±95% 2.87 – 3.20), while the McCutchan et al. (2003) TDFs (Δ15N=2.9±0.3‰) indicates median TP of 3.54 (±95% 3.23 – 3.90). All TPs were estimated to be over 2.0 except that of P. noctiluca from the S.E Pacific using the Post (2002) TDF with a TP of

1.93 (±95% 1.65 – 2.21).

Table 4. 1. Jellyfish trophic position estimates from Bayesian mixing models using tRophicPosition incorprating isotopic variation in baselines (filter-feeding bivalves, TP = 2) and consumer δ15N as well as in TDFs.

Bulk δ15N Bulk δ15N Δ15N=2.9 ±0.3‰ Δ15N=3.4 ±1‰ Location Species (± 95 % CI) (± 95 % CI)

Mediterranean P. noctiluca 2.27 (2.01 – 2.95) 2.18 (2.00 – 2.63) V. velella 2.17 (2.00 – 2.69) 2.11 (2.00 – 2.45) Irish Sea A. aurita 2.40 (2.24 – 2.58) 2.27 (2.16 – 2.38) C. lamarckii 2.71 (2.44 – 3.00) 2.48 (2.31 – 2.66) C. capillata 3.54 (3.23 – 3.90) 3.03 (2.87 – 3.20) S.E. Pacific P. noctiluca 2.39 (1.97 – 2.86) 1.93 (1.65 – 2.21)

4.4.2 Amino Acid Trophic Position Estimation

Phenylalanine δ15N varied among the three marine areas (Table 4.3). Mediterranean samples had very low phenylalanine δ15N values, while the Irish Sea scyphozoans had a similar spread of δ15N values. However, the S.E Pacific P. noctiluca were distinct from their Mediterranean conspecifics with much higher δ15N values, although the

Pphenylalanine δ15N reflects differences in baseline sources.

114

Figure 4.2. Raw bulk δ15N and δ13C isotopic values of jellyfish from three different systems around the world.

Table 4.2. Mean glutamine and phenylalanine amino acid δ15N ±SD for four scyphozoan and one hydrozoan jellyfish.

Mean Mean Location Species Glutamine ± SD Phenyalaline ± SD δ15N δ15N Mediterranea P. noctiluca 13.81343 2.365204 -1.04686 1.421498 n V. velella 12.75193 0.960601 0.207667 1.191811 Irish Sea A. aurita 23.60379 1.429466 5.937143 1.3797 C. lamarckii 25.82 0.644012 6.249429 1.125297 C. capillata 27.70992 1.036637 5.999333 0.918546 S.E. Pacific P. noctiluca 29.0325 1.162398 9.914214 1.360602

The difference in δ15N between amino acids (Glutamine-Phenylalanine) indicated the baseline corrected trophic position of individual samples (Fig. 4. 3). Here, the

Mediterranean P. noctiluca had a greater spread of δ15N values with a higher mean

δ15N than the phenylalaine δ15N, while glutamine-phenylalanine δ15N for Velella velella

115 has remained low. Conversely, S.E. Pacific P. noctiluca displayed a much higher isotopic glutamine-phenyalanine δ15N difference than their Mediterranean counterparts.

However, baseline corrected estimates revealed a slight overlap in their isotopic trophic position. The Irish Sea jellyfish glutamine-phenylalanine δ15N showed that after correcting for baseline differences, the A. aurita had a relatively large TP range (Fig.

4.4). In contrast, while both Cyanea sp. had retained their narrower isotopic niche, C. capillata were at a higher TP than the other two Irish Sea jellyfish.

Trophic positions estimated using glutamine-phenylalanine δ15N values and TDFs of

6.2‰ and 7.6‰ revealed a similar pattern to eachother (Fig. 4.5). Mediterranean P. noctiluca and Velella velella are feeding at the lowest TP of all the groups examined here while the Irish Sea jellyfish has higher TPs, most notably Cyanea capillata which had a isotopically distinct range from C. lamarckii and A. aurita. South East Pacific P. noctiluca had a similarly broad isotopic range to the other jellyfish with a varied estimated TP and a slight overlap with Mediterranean P. noctiluca.

4.4.3 Bulk vs amino acid trophic position comparisons

Comparison of bulk and amino acid methods to estimate TP (Bayesian median ±95% credibility interval and Bootstrapped median ±95% confidence interval) using different

TDFs reveals the closest agreement in median values is between the lower TDF estimates for bulk N (2.9‰) and amino acid N (7.6‰) TDFs (Table 4.3). However, the different estimates produced for the S.E Pacific P. noctiluca do not overlap in any combination of bulk vs amino acid derived TP estimates.

116

Figure 4.3. Variation in phenylalanine δ15N values for three different scyphozoan jellyfish in three different marine areas.

117

Figure 4.4. Trophic glutamine δ15N minus source phenyalaline amino acid δ15N for jellyfish samples collected in the Mediterranean, Irish Sea and the S.E Pacific indicating δ15N trophic position corrected for baseline differences in productivity from those areas..

118

Figure 4.5. Amino acid derived jellyfish TP using two different TDF values (6.2 & 7.6‰).

119

Table 4.3. Bulk (with Bayesian median median ± 95 % credibility intervals) versus amino acid (with bootstrapped median ± 95 % credibility intervals) estimates of TP using a range of different TDFs.

Bulk Amino acid Δ15N=2.9‰ Δ15N=3.4‰ Δ15N=6.2‰ Δ15N=7.6‰ Location Species (± 95 % CI) ( ± 95 % CI) (± 95 % CI) (± 95 % CI) Mediterranean Pelagia noctiluca 2.27 (2.01 – 2.95) 2.18 (2.00 – 2.63) 2.85 (2.37 – 3.26) 2.51 (2.12 – 2.85) Velella velella 2.17 (2.00 – 2.69) 2.11 (2.00 – 2.45) 2.48 (2.14 – 2.75) 2.21 (1.93 – 2.43) Irish Sea Aurelia aurita 2.40 (2.24 – 2.58) 2.27 (2.16 – 2.38) 3.29 (2.88 – 3.73) 2.88 (2.53 – 3.23) Cyanea lamarckii 2.71 (2.44 – 3.00) 2.48 (2.31 – 2.66) 3.61 (3.43 – 3.76) 3.13 (2.99 – 3.24) Cyanea capillata 3.54 (3.23 – 3.90) 3.03 (2.87 – 3.20) 3.95 (3.85 – 4.05) 3.41 (3.31 – 3.49) S.E. Pacific Pelagia noctiluca 2.39 (1.97 – 2.86) 1.93 (1.65 – 2.21) 3.53 (3.3 – 4.01) 3.01 (2.88 – 3.46)

120

4.5 Discussion

Stable isotope analysis as an investigative tool in ecology has revolutionised the way in which we attribute energy flow in marine systems (e.g. Nagata et al., 2015; Morais et al.,

2017; Tilves et al., 2018). Here, estimates of bulk TP based on two different TDF values ranged from 1.93 (1.65-2.21) (S.E. Pacific Pelagia noctiluca) to 3.54 (3.23-3.90) (Irish Sea C. capillata). Curiously, the median TP for S.E Pacific P. noctiluca using the Post (2002) TDF was very low (TP =1.93), indicating that there may be a photosynthetic contribution whereby jellyfish symbionts are producing nutrients via photosynthesis. Whilst there is little evidence that P. noctiluca gain nutrients from primary production via zooxanthellae symbionts, such relationships do occur among other species of jellyfish (e.g. upside-down jellyfishes Cassiopeia sp., Papuan jellyfish Mastigias papua and thimble jellyfish Linuche unguiculate) (Ohtsuka et al., 2009). Rädecker et al. (2015) suggested that the symbiosis between jellyfish hosts and endosymbiotic algae sustains primary production in the absence of other major nutrient sources for the jellyfish host. However, in this instance, given that these samples were collected from a highly productive upwelling region off the coast of Chile (Thiel et al., 2007), potential photosynthetic contributions are unlikely to be driven by a lack of nutrient resources within the system. TDFs in aquatic consumers are typically lower than the typical 3.4‰ proposed by Post (2002). Accordingly, BSIA based on a lower TDF of 2.9±0.3‰ (McCutchan et al., 2003) revealed subsequent estimates of TP more consistent across P. noctiluca collected from both the Mediterranean and S.E. Pacific

(2.27 (2.01-2.95) and 2.39 (1.97-2.86) respectively). This suggests that while P. noctiluca may be feeding low in the food web, they are not necessarily making gaining energy photosynthetically through symbiotic relationships. CSIA derived estimates of TP also 121 revealed P. noctiluca from the Mediterranean occupied the lowest trophic position of species considered in this study, corroborating Syvaranta et al., 2012) even though other studies have found P. noctiluca in the NW Mediterranean to be important predators of summer ichthyoplankton (Sabatés et al., 2010). The amino acid TP estimates also indicate that S.E Pacific P. noctiluca occupy a higher median TP, albeit narrowly overlapping with the TP of the Mediterranean samples. This may reflect the more productive upwelling environment of S.E. Pacific in comparison to the relatively oligotrophic, Mediterranean

(Bethoux et al., 1998; Thingstad et al., 2005). More generally, it may be that P. noctiluca can display trophic plasticity with respect to the ambient prey field whereby in oligotrophic areas they can revert to feeding at a very low trophic level. Conversely, in productive areas,

P. noctiluca can adapt their feeding habits to incorporate potentially larger items which typically had a higher δ15N ratio - potentially employing their stinging cells to do so.

Graham and Kroutil (2001), amongst others, previously presented evidence for size-based prey selectivity and dietary shifts in A. aurita and C. capillata.

By contrast, V. velella host photosynthetic zooxanthellae and, discounting the single P. noctiluca TP estimate based on TDF for the S.E Pacific, they consistently occupy the lowest

TP in this study across method and TDFs. Additionally, V. velella TP estimates are also lower than estimates presented by Cardona et al. (2007) despite samples originating in the

Balearic archipelago, one of the most oligotrophic areas in the Western Mediterranean

(Cardona et al., 2007). If V. velella trophic ecology is similar to that of P. noctiluca in being capable of feeding at exceedingly low trophic levels when ambient resources dictate, then it is possible that primary production in NW Italy is even lower than that of the Balearic

122 archipelago. Cardona et al. (2007) suggested that zooxanthellae are the main source of C for V. velella in the western Mediterranean; this may provide an alternative strategy of production in resource-scarce regions.

C. capillata consistently occupied the highest TP of species in this study and also the largest maximum size among these species; it is an important consumer of zooplankton prey in many coastal communities, including other jellyfish (Robison, 2004; Titelman et al., 2007;

Hosia & Titelman, 2011). Although intra-guild predation (when potential competitors eat each other) is likely common among many larger jellyfish species, the evidence that C. capillata specifically feed on other jellyfishes is overwhelming (Båmstedt et al., 1994;

Hansson, 1997; Martinussen & Bamstedt, 1999). C. capillata TP estimates derived from amino acid CSIA are also distinguishable from the other Irish Sea jellyfish by feeding at a higher trophic level than both C. lamarckii and A. aurita. However, both Cyanea sp. had a relatively narrow TP breadth while A. aurita had wider TP range, perhaps because it is a generalist consumer (Sullivan et al., 1994). Critically, A. aurita isotopic breadth may increase as it widens its potential prey field, retaining the ability to feed at lower trophic level while also capable of feeding higher up the food web. (Dietary shift & Fleming ref and others to add in here). In contrast, Cyanea spp. may undergo a shift of feeding habits onto larger prey items at a higher trophic level. In other words, they appear to be more selective in their feeding habits, opting not to feed at lower trophic levels once the option to feed at higher trophic levels is available, which may be governed by jellyfish size for example.

The best agreement of jellyfish TP among methods presented here is between those calculated using the lower (2.9 ‰) TDF value for bulk N and the higher (7.6 ‰) TDF value

123 for amino acid N corroborating McCutchan et al. (2003) and Chikaraishi et al. (2009). Using these values, the TP of jellyfish examined here ranged from 2 to ca. 3.5. Taken together, these estimates are indicative of a range of species feeding at a range of different trophic levels in three very different systems but which all support large aggregations of jellyfish annually. From feeding on phytoplankton to zooplankton, it highlights the trophic diversity among even a small subset of jellyfish. However, despite considerable inter and intraspecific variation, there was broad agreement between these two assumed TDF values which can now be used to inform food-web based studies or indeed experimental methodological studies to further investigate jellyfish TDF.

4.5.1 Conclusions

TDF values of 2.9‰ for BSIA and 7.6‰ for CSIA may be used in trophic studies and included in mixing models, now with a greater degree of confidence that they accurately account for the trophic fractionation of jellyfish. These findings may go some way in releasing the current bottleneck created by the lack of appropriate TDFs and the inability to derive reliable TP for jellyfish species. This helps address calls by Pauly et al. (2009) and others to include a more nuanced view of jellyfish trophic complexity into future models. It is surprising that this abundant and diverse taxonomic group such as jellyfish, often heralded as having the capability of greatly influencing local prey resources (e.g. Shiganova, 1998;

Lynam et al., 2005, 2011; Lynam & Brierley, 2006), is not better accounted for in marine food webs and ecosystem based studies.

124

4.6 References Arai, M.N. (1988). Interactions of fish and pelagic coelenterates. Canadian Journal of Zoology 66, 1913–1927.

Arai, M.N. (2005). Predation on pelagic coelenterates: a review. Journal of the Marine Biological Association of the UK 85, 523–536.

Ates, R.M.L. (1988). Medusivorous Fishes, A Review. Zoologische Mededelingen 62, 29– 42.

Båmstedt, U., Martinussen, M.B., and Matsakis, S. (1994). Trophodynamics of the two scyphozoan jellyfishes, Aurelia aurita and Cyanea capillata, in western Norway. ICES Journal of Marine Science 51, 369–382.

Batista, F.C., Ravelo, A.C., Crusius, J., Casso, M.A., and McCarthy, M.D. (2014). Compound specific amino acid δ15N in marine sediments: A new approach for studies of the marine nitrogen cycle. Geochimica et Cosmochimica Acta 142, 553–569.

Bethoux, J.P., Morin, P., Chaumery, C., Connan, O., Gentili, B., and Ruiz-Pino, D. (1998). Nutrients in the Mediterranean Sea, mass balance and statistical analysis of concentrations with respect to environmental change. Marine Chemistry 63, 155–169.

Bradley, C.J., Wallsgrove, N.J., Choy, C.A., Drazen, J.C., Hetherington, E.D., Hoen, D.K., and Popp, B.N. (2015). Trophic position estimates of marine teleosts using amino acid compound specific isotopic analysis. Limnology and Oceanography: Methods 13, 476– 493.

Cardona, L., Revelles, M., Sales, M., Aguilar, A., and Borrell, A. (2007). Meadows of the seagrass Posidonia oceanica are a significant source of organic matter for adjoining ecosystems. Marine Ecology Progress Series 335, 123–131.

125

Chikaraishi, Y., Ogawa, N.O., Kashiyama, Y., Takano, Y., Suga, H., Tomitani, A., Miyashita, H., Kitazato, H., and Ohkouchi, N. (2009). Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnology and Oceanography: Methods 7, 740–750.

Chikaraishi, Y., Steffan, S. a., Ogawa, N.O., Ishikawa, N.F., Sasaki, Y., Tsuchiya, M., and Ohkouchi, N. (2014). High-resolution food webs based on nitrogen isotopic composition of amino acids. Ecology and Evolution 4, 2423–2449.

Choy, C.A., Davison, P.C., Drazen, J.C., Flynn, A., Gier, E.J., Hoffman, J.C., McClain-Counts, J.P., Miller, T.W., Popp, B.N., Ross, S.W., and Sutton, T.T. (2012). Global Trophic Position Comparison of Two Dominant Mesopelagic Fish Families (Myctophidae, Stomiidae) Using Amino Acid Nitrogen Isotopic Analyses. PLoS ONE 7.

Choy, C.A., Popp, B.N., Hannides, C.C.S., and Drazen, J.C. (2015). Trophic structure and food resources of epipelagic and mesopelagic fishes in the north pacific subtropical Gyre ecosystem inferred from nitrogen isotopic compositions. Limnology and Oceanography 60, 1156–1171.

Corr, L.T., Berstan, R., and Evershed, R.P. (2007). Optimisation of derivatisation procedures for the determination of δ13C values of amino acids by gas chromatography/combustion/isotope ratio mass spectrometry. Rapid Communications in Mass Spectrometry 21, 3759–3771.

Dale, J.J., Wallsgrove, N.J., Popp, B.N., and Holland, K.N. (2011). Nursery habitat use and foraging ecology of the brown stingray Dasyatis lata determined from stomach contents, bulk and amino acid stable isotopes. Marine Ecology Progress Series 433, 221–236.

Doyle, T.K., Houghton, J.D.R., McDevitt, R., Davenport, J., and Hays, G.C. (2007). The energy density of jellyfish: Estimates from bomb-calorimetry and proximate-composition. Journal of Experimental Marine Biology and Ecology 343, 239–252. 126

Fry, B. (2006). Stable isotope ecology. New York: Springer (Vol. 521).

Gibbons, M.J., and Richardson, A.J. (2013). Beyond the jellyfish joyride and global oscillations: Advancing jellyfish research. Journal of Plankton Research 35, 929–938.

Gowen, R.J., and Stewart, B.M. (2005). The Irish Sea: Nutrient status and phytoplankton. Journal of Sea Research 54, 36–50.

Graham, W.M., and Kroutil, R.M. (2001). Size-based prey selectivity and dietary shifts in the jellyfish, Aurelia aurita. Journal of Plankton Research 23, 67.74.

Hansson, L.J. (1997). Capture and digestion of the scyphozoan jellyfish Aurelia aurita

by Cyanea capillata and prey response to predator contact. Journal of Plankton Research 19, 195–208.
Hays, G.C., Doyle, T.K., and Houghton, J.D.R. (2018). A Paradigm Shift in the Trophic Importance of Jellyfish? Trends in Ecology & Evolution.
Horsburgh, K.J., Hill, A.E., Brown, J., Fernand, L., Garvine, R.W., and Angelico, M.M.P. (2000). Seasonal evolution of the cold pool gyre in the western Irish Sea. Progress in Oceanography 46, 1–58.
Hosia, A., Augustin, C.B., Dinasquet, J., Granhag, L., Paulsen, M.L., Riemann, L., Rintala, J.M., Setälä, O., Talvitie, J., and Titelman, J. (2014). Autumnal bottom-up and top-down impacts of Cyanea capillata: A mesocosm study. Journal of Plankton Research 37, 1042– 1055.
Hosia, A., and Titelman, J. (2011). Intraguild predation between the native North Sea jellyfish Cyanea capillata and the invasive ctenophore Mnemiopsis leidyi. Journal of Plankton Research 33, 535–540.
127
Houghton, J.D.R., Doyle, T.K., Davenport, J., Lilley, M.K.S., Wilson, R.P., and Hays, G.C. (2007). Stranding events provide indirect insights into the seasonality and persistence of jellyfish medusae (Cnidaria: Scyphozoa). Hydrobiologia 589, 1–13.
Hoving, H.J.T., and Haddock, S.H.D. (2017). The giant deep-sea octopus Haliphron atlanticus
Lamb, P.D., Hunter, E., Pinnegar, J.K., Creer, S., Davies, R.G., and Taylor, M.I. (2017). Jellyfish on the menu: mtDNA assay reveals scyphozoan predation in the Irish Sea. Royal Society Open Science 4, 171421.
Lamb, P.D., Hunter, E., Pinnegar, J.K., van der Kooij, J., Creer, S., and Taylor, M.I. (2019). Cryptic diets of forage fish: jellyfish consumption observed in the Celtic Sea and western English Channel. Journal of Fish Biology.
Lorrain, A., Graham, B., Ménard, F., Popp, B., Bouillon, S., Van Breugel, P., and Cherel, Y. (2009). Nitrogen and carbon isotope values of individual amino acids: A tool to study foraging ecology of penguins in the Southern Ocean. Marine Ecology Progress Series 391, 293–306.
Lorrain, A., Graham, B.S., Popp, B.N., Allain, V., Olson, R.J., Hunt, B.P.V., Potier, M., Fry, B., Galván-Magaña, F., Menkes, C.E.R., Kaehler, S., and Ménard, F. (2015). Nitrogen isotopic baselines and implications for estimating foraging habitat and trophic position of yellowfin tuna in the Indian and Pacific Oceans. Deep-Sea Research Part II: Topical Studies in Oceanography 113, 188–198.
Lynam, C.P., and Brierley, A.S. (2006). Enhanced survival of 0-group gadoid fish under jellyfish umbrellas. Marine Biology 150, 1397–1401.
Lynam, C.P., Gibbons, M.J., Axelsen, B.E., Sparks, C.A.J., Coetzee, J., Heywood, B.G., and Brierley, A.S. (2006). Jellyfish overtake fish in a heavily fished ecosystem. Current Biology 16, 492–493.
Lynam, C.P., Heath, M.R., Hay, S.J., and Brierley, A.S. (2005). Evidence for impacts by jellyfish on North Sea herring recruitment. Marine Ecology Progress Series 298, 157–167.
128
Lynam, C.P., Lilley, M.K.S.S., Bastian, T., Doyle, T.K., Beggs, S.E., and Hays, G.C. (2011). Have jellyfish in the Irish Sea benefited from climate change and overfishing? Global Change Biology 17, 767–782.
Martinussen, M.B., and Bamstedt, U. (1999). Nutritional ecology of gelatinous planktonic predators. Digestion rate in relation to type and amount of prey. Journal of Experimental Marine Biology and Ecology 232, 61–84.
Matthews, C.J.D., and Ferguson, S.H. (2014). Spatial segregation and similar trophic-level diet among eastern Canadian Arctic/north-west Atlantic killer whales inferred from bulk and compound specific isotopic analysis. Journal of the Marine Biological Association of the United Kingdom 94, 1343–1355.
McClelland, J.W., and Montoya, J.P. (2002). Trophic relationships and the nitrogen isotopic composition of amino acids in plankton. Ecology 83, 2173–2180.
McCutchan, J.H., Lewis, W.M., Kendall, C., and McGrath, C.C. (2003). Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102, 378–390.
McMahon, K.W., and McCarthy, M.D. (2016). Embracing variability in amino acid δ15N fractionation: Mechanisms, implications, and applications for trophic ecology. Ecosphere 7(12), e01511. 10.1002/ecs2.1511.
Middelburg, J.J. (2014). Stable isotopes dissect aquatic food webs from the top to the bottom. Biogeosciences 11, 2357–2371.
Miller, R.J., Page, H.M., and Brzezinski, M.A. (2013). δ13C and δ15N of particulate organic matter in the Santa Barbara Channel: drivers and implications for trophic inference. Marine Ecology Progress Series 474, 53–66.
Morais, P., Dias, E., Cruz, J., Chainho, P., Angélico, M.M., Costa, J.L., Barbosa, A.B., and Teodósio, M.A. (2017). Allochthonous-derived organic matter subsidizes the food sources of estuarine jellyfish. Journal of Plankton Research 39, 870–877.
Nagata, R., Moreira, M., Pimentel, C., and Morandini, A. (2015). Food web characterization based on δ15N and δ13C reveals isotopic niche partitioning between
129 fish and jellyfish in a relatively pristine ecosystem. Marine Ecology Progress Series 519, 13–27.
Nakatomi, N., Hirahara, M., Natori, N., Toda, T., and Yamamoto, S. (2013). Change in metabolism and nitrogen isotopic composition of amino acids through egg production of the calanoid copepod Acartia steuer. Researches in Organic Geochemistry 29, 61–64.
Nielsen, J.M., Popp, B.N., and Winder, M. (2015). Meta-analysis of amino acid stable nitrogen isotope ratios for estimating trophic position in marine organisms. Oecologia 631–642.
Ohtsuka, S., Koike, K., Lindsay, D., Nishikawa, J., Miyake, H., Kawahara, M., Mujiono, N., Hiromi, J., and Komatsu, H. (2009). Symbionts of marine medusae and ctenophores. Plankton and Benthos Research 4, 1–13.
Pauly, D., Graham, W.M., Libralato, S., Morissette, L., and Deng Palomares, M.L. (2009). Jellyfish in ecosystems, online databases, and ecosystem models. Hydrobiologia 616, 67– 85.
Phillips, N., Eagling, L., Harrod, C., Reid, N., Cappanera, V., and Houghton, J.D.R. (2017). Quacks snack on smacks: mallard ducks (Anas platyrhynchos) observed feeding on hydrozoans (Velella velella). Plankton and Benthos Research 12, 143–144.
Post, D.M. (2002). Using Stable Isotopes to Estimate Trophic Position: Models, Methods, and Assumptions. Ecology 83, 703–718.
Quezada-Romegialli, C., Jackson, A.L., Hayden, B., Kahilainen, K.K., Lopes, C., and Harrod, C. (2018). tRophicPosition, an r package for the Bayesian estimation of trophic position from consumer stable isotope ratios. Methods in Ecology and Evolution 9, 1592–1599.
Rädecker, N., Pogoreutz, C., Voolstra, C.R., Wiedenmann, J., and Wild, C. (2015). Nitrogen cycling in corals: The key to understanding holobiont functioning? Trends in Microbiology 23, 490–497.
130
Riascos, J.M., Vergara, M., Fajardo, J., Villegas, V., and Pacheco, A.S. (2012). The role of hyperiid parasites as a trophic link between jellyfish and fishes. Journal of Fish Biology 81, 1686–1695.
Robison, B.H. (2004). Deep pelagic biology. Journal of Experimental Marine Biology and Ecology 300, 253–272.
Sabatés, A., Pagès, F., Atienza, D., Fuentes, V., Purcell, J.E., and Gili, J.M. (2010). Planktonic cnidarian distribution and feeding of Pelagia noctiluca in the NW Mediterranean Sea. Hydrobiologia 645, 153–165.
Shiganova, T.A. (1998). Invasion of the Black Sea by the ctenophore Mnemiopsis leidyi and recent changes in pelagic community structure. Fisheries Oceanography 7, 305–310.
Sullivan, B.K., Garcia, J.R., and Klein-MacPhee, G. (1994). Prey selection by the scyphomedusan predator Aurelia aurita. Marine Biology 121, 335–341.
Syvaranta, J., Harrod, C., Kubicek, L., Carpanera, V., and Houghton, J.D.R. (2012). Stable isotopes challenge the perception of ocean sunfish Mola mola as obligate jellyfish predators. Journal of Fish Biology 225–231.
Thiebot, J.B., Arnould, J.P.Y., Gómez-Laich, A., Ito, K., Kato, A., Mattern, T., Mitamura, H., Noda, T., Poupart, T., Quintana, F., Raclot, T., Ropert-Coudert, Y., Sala, J.E., Seddon, P.J., Sutton, G.J., Yoda, K., and Takahashi, A. (2017). Jellyfish and other gelata as food for four penguin species – insights from predator-borne videos. Frontiers in Ecology and the Environment 15, 437–441.
Thiel, M., Macaya, E.C., Acuna, E., Arntz, W.E., Bastias, H., Brokordt, K., Camus, P.A., Castilla, J.C., Castro, L.R., and Cortes, M. (2007). The Humboldt Current System of Northern and Central Chile. Oceanography and Marine Biology: An Annual Review 195– 344.
Thingstad, T.F., Krom, M.D., Mantoura, R.F.C., Flaten, G.A.F., Groom, S., Herut, B., Kress, N., Law, C.S., Pasternak, A., Pitta, P., Psarra, S., Rassoulzadegan, F., Tanaka, T., Tselepides, A., Wassmann, P., Woodward, E.M.S., Riser, C.W., Zodiatis, G., and Zohary, T. (2005).
131
Nature of P limitation in the ultraoligotrophic Eastern Mediterranean. Supporting Material. Science 309, 1068–1071.
Tilves, U., Fuentes, V.L., Milisenda, G., Parrish, C.C., Vizzini, S., and Sabatés, A. (2018). Trophic interactions of the jellyfish Pelagia noctiluca in the NW Mediterranean: evidence from stable isotope signatures and fatty acid composition. Marine Ecology Progress Series 591, 101–116.
Titelman, J., Gandon, L., Goarant, A., and Nilsen, T. (2007). Intraguild predatory interactions between the jellyfish Cyanea capillata and Aurelia aurita. Marine Biology 152, 745–756.
Zanden, M.J. Vander, Cabana, G., and Rasmussen, J.B. (1997). Comparing trophic position of freshwater fish calculated using stable nitrogen isotope ratios ( d 15 N ) and literature dietary data. Canadian Journal of Fish and Aquatic Science.
132
Chapter 5 - The jellyfish paradox: are trophic interactions with
fishes a two-way street?
Collaborators: Donal C. Griffin1, Steven E. Beggs2, Victoria Dominguez3, Jonathan D.R. Houghton1,4,5, Jason Newton6, Natasha D. Phillips1, Chris Harrod7,8,9
1 School of Biological Sciences, Queen’s University, Belfast, UK
2 Agri-Food and Biosciences Institute, Belfast, UK
3 School of Biological and Chemical Sciences, Queen Mary University of London, London, UK
4 Queen’s Marine Laboratory, Portaferry, UK
6 NERC Life Sciences Mass Spectrometry Facility, Scottish Universities Environmental Research Centre, East Kilbride, UK
7 Universidad de Antofagasta Stable Isotope Facility, Universidad de Antofagasta, Chile
8 Instituto de Ciencias Naturales Alexander von Humboldt, Universidad de Antofagasta, Chile
9 Núcleo Milenio INVASAL, Concepción, Chile
133
5.1 Abstract
Gelatinous zooplankton contribute in a range of ways to energy flow in ecosystems.
Despite a recent paradigm shift in how scientists perceive the trophic importance of gelatinous zooplankton, their ecological relevance has often been neglected beyond their role as stressors of the marine environment. Here we used bulk stable isotope analysis
(BSIA) with resolved jellyfish TDFs to investigate fish-jellyfish trophic interactions occurring in the Irish Sea. More specifically we assessed the trophic relevance of jellyfish to fish diets and vice versa in order to characterise the interplay of these interactions in our study area.
We found that trophic interactions with fishes are a two-way street whereby fish are predators of and prey for gelatinous zooplankton. Furthermore, we presented evidence of a complex food web with multiple interactions between gelatinous and fish species constituting a fish-entwined ‘jellyweb’ in which ctenophores and hydrozoans feature prominently as well as scyphozoan jellyfish.
Keywords: fish-jellyfish interactions, ‘jellyweb’, gelatinous zooplankton, commercial fish
134
5.2 Introduction
Jellyfish are an important component of marine food webs (Pauly et al., 2009; Hamilton,
2016; Hays et al., 2018) which interact with fish communities in a range of ways (Brodeur et al., 2002; Lynam et al., 2004; Lynam & Brierley, 2006; Doyle et al., 2014). Whether they are competing with fish for food, feeding directly on small fish, eggs and larvae or providing shelter and food to developing juvenile fish, it is clear the role of jellyfish is nuanced, complex and likely to differ temporally and spatially as environmental conditions vary
(Bastian et al., 2011; Condon et al., 2013). It is this potential for jellyfish to impact on fish communities both negatively and positively, which we have termed ‘The jellyfish paradox’ representing the complex and long standing role of jellyfish in marine food webs which requires adequate ecological description and quantification (Boero et al., 2008; Pauly et al., 2009; Doyle et al., 2014). For example, Robison (2004) suggested that gelatinous zooplankton may operate within semi-closed food webs alongside well-established fish models with primary, secondary, and tertiary consumers, constituting its own ‘jellyweb’.
Furthermore, Fleming et al. (2013) demonstrated how in a temperate sea lough three species of jellyfish (Aurelia aurita, Cyanea capillata and Cyanea lamarckii) displayed greater isotopic variability than the sympatric fish community (n=23 sp.) with a low level of trophic overlap between the two groups. Conversely, Nagata et al. (2015) demonstrated pronounced overlap of the isotopic niche between co-occurring jellyfish and fish suggesting that the extent of interplay can vary markedly between locations. Either way, given that gelatinous zooplankton pre-date fishes by millions of years and have co-existed since the Cambrian period (Boero et al., 2008; Dunn et al., 2008) these trophic interactions are long standing and have far from hindered the evolution and expansion of fishes 135 throughout the oceans. Where the difference lies is in the perception of these two faunal groups whereby fish are treated as complex and nuanced predators, whilst jellyfish are viewed as one-dimensional and heavily constrained in their feeding habits despite ranging in mass by several orders of magnitude. Likewise, understanding of jellyfish as prey has shifted vastly in recent years as techniques such as stable isotopes, fatty acids and animal mounted cameras have revealed that opportunistic and episodic predation on gelatinous fauna is widespread (Hays et al., 2018). This encompassing view of jellyfish in ecosystems
(i.e. one that accounts for both positive and negative impacts) has prompted a paradigm shift in how jellyfish are viewed (Hamilton, 2016; Hays et al., 2018). The onus is now to gain a better understanding of gelatinous zooplankton to facilitate their effective inclusion into ecosystem and fisheries-based models (Pauly et al., 2009; Brodeur et al., 2016; Hays et al., 2018). However, the bottleneck in doing this to date has not been the analytical capacity of modellers, rather a lack of empirical data linking jellyfish to their prey given the inherent problems of sampling such soft-bodied taxa (Fleming et al., 2011; Doyle et al.,
2014). To address this issue techniques such as stable isotope analysis (SIA) and compound specific isotope analysis (CSIA) are being employed, primarily as a means to determine trophic position (TP) of jellyfish consumers in food webs, but more broadly as a means to understand the ecology of individuals, populations, species, communities and ecosystems
(Middelburg, 2014). Typically, SIA is performed on bulk δ13C and δ15N. For example, bulk stable isotope analysis (BSIA) suggested up to 90% of the diet of a juvenile commercially important forage fish (Chloroscombrus chrysurus) in the Gulf of Mexico was provided by the jellyfish host (D’Ambra et al., 2014), while that a range of species in the Mediterranean discovered that many apex predators such as bluefin tuna (Thunnus thynnus), little tunny 136
(Euthynnus alletteratus), spearfish (Tetrapturus belone) and swordfish (Xiphias gladius), feed on jellyfish (Cardona et al., 2012).
Here we used BSIA and jellyfish specific TDFs derived using both BSIA and CSIA to investigate fish-jellyfish trophic interactions in the Irish Sea. Building on Lamb et al. (2017,
2019) which showed predation fo jellyfish in the Irish Sea may be more common than previously acknowledged, we considered whether some of the most commonly found jellyfish in the Irish Sea and co-occurring fish species and larvae have similar δ15N values and whether they show a similar isotopic width and complexity to co-occurring fish.
Secondly, using a Bayesian mixing model approach, we assessed the dietary contribution of jellyfish to fish diets and vice versa to assess whether there is a two-way interplay in this specific system. Accounting for the trophic nuances of fish-jellyfish interactions on a localised level such as the Irish Sea is an essential step in fully understanding the processes contributing to healthy commercial and non-commercial fish stock success. Furthermore, considering the socio-economic importance of commercial fisheries worldwide (FAO,
2018) and that over 70% of fish reported to associate with jellyfish for food and shelter were of commercial value (Chapter 2; Griffin et al., 2019),there is need to account for all factors influencing fish stock health in a holistic and balanced manner so that sustainable fisheries management can be achieved.
137
5.3 Methods
5.3.1 Study site
The Irish Sea is a semi-enclosed temperate shelf region, separating the islands of Ireland and Great Britain. It contains both a diverse array of both gelatinous species (Bastian et al.,
2011; Lynam et al., 2011) and fin fish species (Bentley et al., 2018) which made it an ideal study system to investigate fish-jellyfish trophic interactions.
5.3.2 Sample collection
Jellyfish bycatch samples were collected on board annual fisheries surveys carried out by the Agri-Food and Biosciences Institute (AFBI) in the northern Irish Sea. Methot Isaacs Kidd
(MIK) net (Methot, 1986) surveys, carried out between May and June on board the RV
Corystes from 2015 to 2016, were used primarily to sample larval fish but often yield large abundances of gelatinous zooplankton (Bastian et al., 2010, 2011). Samples of the following gelatinous species were collected: Aurelia aurita, Cyanea lamarckii, Cyanea capillata and Bolinopsis infundibulum and Staurostoma mertensii. Adult fish samples
(Scomber scombrus, Melanogrammus aeglefinus, Sprattus sprattus, Merlangius merlangus and M. merlangus larvae) were collected during separate fisheries surveys carried out between July and August of 2015 and 2016 using a beam trawl, whereas fish larvae and P. pileus samples were collected using a Gulf VII high-speed plankton sampler (Nash and
Dickey Collas, 1998). Individual Pleurobrachia pileus were combined into one sample to ensure enough dried sample was collected for SIA. All jellyfish and fish samples were frozen at -20oC following Hobson et al. (1997) until samples were processed further in the laboratory.
138
5.3.3 Sample processing for SIA
Intact jellyfish were collected from the net and sorted into species. Medusa bell diameter was measured as inter-rhopalia distance (±0.5 cm) and was recorded along with whole specimen mass (±1.0 g). Sample specimens were thoroughly washed in filtered sea water to remove unwanted metabolic nitrogenous compounds following methods described in
Mackenzie et al. (2017) after which a sample of bell-tissue was taken and frozen before laboratory preparation (-20oC). Once in the laboratory, samples were dried at 60oC until they reached a constant mass. Once dried to a constant mass, samples were homogenised to a fine powder using a mortar and pestle as per Doyle et al. (2007). Sub-samples of muscle tissue were removed, whereas whole larval fish from individual sampling efforts were combined to ensure enough sample for SIA was obtained. All samples were then dried and homogenised as described for jellyfish samples.
5.3.4 Bulk stable isotope analysis
After preliminary analysis to check isotopic peaks were large enough to register sufficient
δ15N and δ13C readings, optimal sample masses were found to be 1-7mg for different jellyfish species. Powdered jellyfish samples were then weighed into tin capsules and continuous flow isotope ratio mass spectrometry (CF-IRMS) was performed using an ECS
4010 elemental analyser (Costech, Milan, Italy) coupled to a Delta XP mass spectrometer
(Thermo Electron, Bremen, Germany). All samples were analysed for δ13C, δ15N and C:N at the East Kilbride Node of the Natural Environment Research Council Life Sciences Mass
Spectrometry Facility and at Queen Mary University of London (QML) School of Biological and Chemical Sciences. Drift correction using a 3-point normalization was performed using
139 three in-house standards (GEL, ALAGEL and GLYGEL) encompassing a range of isotope compositions, run every ten samples, and a suite of GELs of different sizes were used to correct samples for linearity (Werner & Brand, 2001). GEL is dried from a solution of gelatin,
ALAGEL from an alanine-gelatine solution, and GLYGEL from a glycine-gelatine solution.
Four differently sized USGS 40 glutamic acid standards (Qi et al., 2003; Coplen et al., 2006) were used as independent checks of accuracy and to acquire the calibration for N and C content. All data are reported with respect to the international standard of AIR for δ15N and V-PDB for δ13C. Results are reported in δ notation (McKinney et al., 1950).
Experimental precision was 0.07‰ for carbon and 0.09‰ for nitrogen (standard deviation of laboratory standard replicates of GEL).
5.3.5 Statistical analysis
To examine the differences in BSIA-derived δ15N for the species groups focused on here, I first accounted for preservation effects on δ15N following Fleming et al. (2011). We used a Kruskal-Wallis one-way analysis of variance and pairwise Nemenyi with Chi-Squared distribution post-hoc tests to gather information on individual pairwise comparisons. The trophic position of jellyfish and fish groups using BSIA data was estimated using the R package tRophicPosition (version 0.7.5; Quezada-Romegialli et al, (2018) which incorporates a Bayesian model of analysis using the equation: TPJellyfish = + (δ15NJellyfish
15 15 13 13 13 13 15 – [δ Nbaseline1 x (1 -)])/ Δ N where  = (δ CJelly – δ Cbaseline2)/(δ Cbaseline1 – δ Cbaseline2), Δ N
= TDF (which in this case we used McCutchan et al. (2003) TDF value of 2.9 ‰ following conclusions from ‘chapter 4’), and  = trophic level of baseline indicator following (Zanden et al., 1997) (bivalve filter feeders collected from each location). More specifically, median
140
±97.5% credibility intervals for TP from bulk δ15N values using the Onebaseline model option were determined.
I then estimated the contribution of gelatinous zooplankton to the assimilated diet of four species of adult fish and one larval fish sample using the Bayesian R package ‘Stable Isotope
Mixing Models in R’ (Parnell, 2019) and TDFS for δ13C ( 1.3‰ ±0.3) and δ15N (2.9‰ ±0.3) following (McCutchan et al., 2003) and the conclusions of ‘Chapter 4’. This mixing model assumes that the variability associated with sources and the uncertainty associated with
TDFs is normally distributed although this may not always be the case and that the model assumes that no isotopic routing occurs within the body of the consumer and that all isotopes are assimilated equally.
5.4 Results
5.3.1 δ15N variation between fish and jellyfish species
Three species of scyphozoan jellyfish, two species of ctenophore, one hydrozoan and four species of fish (including one sample of fish larvae) sampled from the Irish Sea and revealed
15 13 2 significant differences between the species groups on δ N and δ C, (χ 11=247.18, p<0.001). Pairwise Nemenyi-tests with chi-squared distribution showed that the adult fish were not significazntly different and nor were the gelatinous zooplankton (Table 5.2), except for Aurelia aurita and Cyanea capillata which were significantly different (p<0.001).
141
Table 5.1. Sample number and mean bulk δ15N and δ13C with ± SD for the gelatinous zooplankton and fish species groups sampled from the Irish Sea.
Jellyfish species n Mean δ15N ± SD Mean δ13C ± SD Aurelia aurita 154 8.96 1.88 -16.82 2.26 Cyanea lamarckii 74 9.70 2.49 -17.77 2.06 Cyanea capillata 40 11.55 1.66 -17.29 1.80 Bolinopsis infundibulum 3 11.72 0.34 -14.33 0.91 Staurostoma mertensii 27 10.65 1.14 -19.89 2.27 Pleurobrachia pileus 11 10.57 2.45 -16.21 2.16 Fish species Scomber scombrus 13 13.20 1.09 -20.07 0.74 Melanogrammus aeglefinus 19 14.35 0.51 -17.759 1.62 Merlangius merlangus 33 14.96 0.56 -17.75 0.99 Sprattus sprattus 21 12.19 0.91 -18.10 0.90 Merlangus merlangus larvae 4 11.70 0.28 -21.14 0.20
Generally, the scyphozoan jellyfish showed a wider δ15N isotopic range than the other gelatinous zooplankton and fish species considered (Fig. 5.2). Cyanea capillata was significantly different from M. merlangus while Cyanea lamarckii was similar to all the other gelatinous zooplankton and only S. sprattus of the fish considered here. The ctenophores Pleurobrachia pileus and Bolinopsis infundulum were not significantly different from the fish or larvae in δ15N (Table 5.2) yet Staurostoma mertensii tended to differ from M. aeglefinus (p=0.044) and M. merlangus (p<0.005) but not from M. merlangus larvae (p>0.05).
142
Figure 5.2. Mean δ15N of the fish and jellyfish species sampled in the Irish Sea.
143
5.4.2 Irish Sea fish and jellyfish trophic position estimates
The ‘Onebaseline’ Bayesian multi-species TP model returned posterior estimates of median(95% credibility intervals) TP for each species group (Fig. 5.4, Table 5.3). Gelatinous zooplankton show considerable isotopic overlap with eachother and Sprattus sprattus while Scomber scombrus, Merlangius merlangus and Merlanogrammus aeglefinus samples have higher δ15N values. Merlangius merlangus larve occupy a similar isotopic space with other gelatinous zooplankton in regards to δ15N but have a lower mean δ13C.
Median TP of the gelatinous zooplankton varied from 2.022 (2.001-2.105 95% CIs) for A. aurita to 2.686 (2.258-3.392 95% CIs) for the ctenophore B. infundulum. Median TP of the fish group varied from 2.815 (2.641-3.003 95% CIs) for S. sprattus to 3.448 (3.285-3.632) for M. aeglefinus. Larval whiting was 2.674 (2.451-2.919 95% CIs).
Finally, we statistically compared the posterior sample distributions of TP between species groups using pairwise comparisons (Table 5.4). A. aurita had an equal or lower TP than all of the other jellyfish and fish groups (Table 5.4). Similarly, C. lamarckii displayed an equal or lower TP than all the other species groups apart from the A. aurita and the ctenophore
B. infundulum which had higher median TPs than C. lamarckii (p>0.05 & p>0.05 respectively). C. capillata had a higher TP than all other gelatinous zooplankton. Scomber scombrus, M. aeglefinus, S. sprattus, M. merlangus and M. merlangus larvae revealed higher TP estimates than all of the gelatinous zooplankton species. However, while M. merlangus larvae also had a higher TP than the gelatinous species, they also had a higher
TP than S. sprattus (p>0.05) yet a lower TP than all the other fish species.
144
Table 5.2. δ15N pairwise comparisons using Nemenyi-test with Chi-squared approximation for independent samples for all the species in
this study including three scyphozoan jellyfish, two ctenophore species, one hydrozoan and 4 adult and one larval fish species. * denotes
a significant difference between groups whereas ** denotes a marginal significant difference between groups.
Melanogrammus Scomber Aurelia Bolinopsis Cyanea Cyanea Pleurobrachia Sprattus Staurostoma Merlangius aeglefinus scombrus aurita infundulum capillata lamarckii pileus sprattus mertensii merlangus S. scombrus P>0.05 A. aurita P<0.001* P>0.05 B. infundulum P>0.05 P>0.05 P>0.05 C. capillata P>0.05 P>0.05 P<0.001* P>0.05 C. lamarckii P<0.001* p=0.0466** P>0.05 P>0.05 P>0.05 P. pileus P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 S. sprattus P>0.05 P>0.05 P<0.001* P>0.05 P>0.05 P>0.05 P>0.05 S. mertensii p=0.0442** P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 M. merlangus P>0.05 P>0.05 P<0.001* P>0.05 p=0.0443** P<0.001* P>0.05 P>0.05 P<0.001* M. merlangius larvae P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05
145
Figure 5.3. Mean δ15N and δ13C with ± SD for gelatinous zooplankton (scyphozoan, ctenophores and hydrozoan) and fish (Sprattus sprattus while Scomber scombrus, Merlangius merlangus and Merlanogrammus aeglefinus) sampled from the Irish Sea.
146
Figure 5.4. Posterior estimates (median and 95% credibility intervals) of trophic position for jellyfish and fish sampled in the Irish Sea.
147
Table 5.3. Numerical summary of posterior estimates (median and 95% credibility intervals) of trophic position for jellyfish and fish sampled in the Irish Sea.
Aurelia Cyanea Cyanea Bolinopsis Pleurobrachia Staurostoma Scomber Melanogrammus Sprattus Merlangius M. merlangus aurita lamarckii capillata infundulum pileus mertensii scombrus aeglefinus sprattus merlangus larvae 2.5% 2.001 2.007 2.437 2.258 2.038 2.19 2.881 3.285 2.641 3.467 2.451 50% 2.022 2.109 2.626 2.686 2.369 2.366 3.117 3.448 2.815 3.637 2.674 97.5% 2.105 2.298 2.83 3.392 2.876 2.542 3.371 3.632 3.003 3.812 2.919
Table 5.4. Comparison of posterior estimates of TP across species. Probabilities that consumers in row 1 have posterior TP estimates less than or equal to consumers in column 1. * denotes a significant difference between groups whereas ** denotes a marginal significant difference between groups.
Row 1 Aurelia Cyanea Cyanea Bolinopsis Pleurobrachia Staurostoma Scomber Melanogrammus Sprattus Merlangius M. merlangus Column 1 aurita lamarckii capillata infundulum pileus mertensii scombrus aeglefinus sprattus merlangus larvae A. aurita - P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 C. lamarckii P>0.05 - P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 C. capillata 0.0* 0.0* - P>0.05 P>0.05 0.027* P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 B. infundulum 0.002* 0.012* P>0.05 - P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P. pileus 0.021* P>0.05 P>0.05 P>0.05 - P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 S. mertensii 0.0* 0.022* P>0.05 P>0.05 P>0.05 - P>0.05 P>0.05 P>0.05 P>0.05 P>0.05 S. scombrus 0.0* 0.0* 0.001* P>0.05 0.006* 0.0* - P>0.05 0.025* P>0.05 0.011* M. aeglefinus 0.0* 0.0* 0.0* 0.022* 0.0* 0.0* 0.016* - 0.0* P>0.05 0.001* S. sprattus 0.0* 0.0* P>0.05 P>0.05 0.049** 0.0* P>0.05 P>0.05 - P>0.05 P>0.05 M.merlangus 0.0* 0.0* 0.0* 0.016* 0.0* 0.0* 0.001* P>0.05 0.0* - 0.0* M. merlangus larvae 0.0* 0.003* P>0.05 P>0.05 P>0.05 0.019* P>0.05 P>0.05 P>0.05 P>0.05 - 148
5.4.3 Jellyfish contributions to fish diet
Our results showed that C. capillata accounted for nearly half (0.46) and over one third
(0.361) of M. merlangus and M. aeglefinus diets respectively (Table 5.5, Fig. 5.5). Similarly, the hydrozoan S. mertensii also contributed considerably to the diets of these two fish species (0.294 & 0.233), while the two ctenophore species represented relatively small proportions of fish diet for all the species considered. Mesoplankton contributeed considerably to the diets of S. sprattus, S. scombrus and M. merlangus larvae (0.244, 0.457 and 0.273 respectively) as does microplankton to M. merlangus only (0.265).
149
Table 5.5. Estimated mean (95% credibility limits) proportional diet of gelatinous zooplankton contributed to by commercial fish species in the Irish Sea.
Prey item Aurelia Cyanea Cyanea Bolinopsis Pleurobrachia Staurostoma Meso- Micro- Consumer aurita lamarckii capillata infundulum pileus mertensii plankton plankton 0.038 0.088 0.066 0.057 0.070 0.089 0.273 0.265 M. merlangius (0.009- (0.009- (0.008- (0.008- (0.008- (0.009- (0.016- (0.018- larvae 0.327) 0.308) 0.221) 0.186) 0.239) 0.298) 0.708) 0.581) 0.032 0.043 0.361 0.095 0.054 0.294 0.085 0.037 Melanogrammus (0.005- (0.005- (0.029- (0.009- (0.006- (0.019- (0.008- (0.006- aeglefinus 0.092) 0.134) 0.630) 0.307) 0.175) 0.681) 0.252) 0.118) 0.023 0.033 0.464 0.051 0.037 0.233 0.129 0.030 Merlangius (0.004- (0.004- (0.136- (0.006- (0.005- (0.019- (0.014- (0.005- merlangus 0.067) 0.105) 0.692) 0.178) 0.117) 0.509) 0.270) 0.084) 0.048 0.066 0.083 0.052 0.057 0.160 0.457 0.077 Scomber (0.007- (0.008- (0.009- (0.007- (0.007- (0.013- (0.195- (0.009- scombrus 0.145) 0.202) 0.246) 0.148) 0.177) 0.455) 0.635) 0.224) 0.208 0.152 0.062 0.052 0.082 0.098 0.224 0.122 Sprattus (0.032- (0.017- (0.009- (0.008- (0.011- (0.012- (0.064- (0.019- sprattus 0.404) 0.370) 0.174) 0.147) 0.236) 0.278) 0.369) 0.247)
150
Figure 5.5 Estimated (95% credibility limits) proportional diet consumption including gelatinous zooplankton by commercial fish species in the Irish Sea; (a)Merlangius merlangus, (b) Sprattus sprattus, (c) Melanogrammus aeglefinus, (d) Merlnagius merlangus larvae, (e)
Scomber scombrus commercial fish species in the Irish Sea. 151
5.4.4 Fish contribution to jellyfish diet
The 95% credibility limits for the proportional fish diets of gelatinous zooplankton were(Table 5.6, Fig. 5.7), however, the data indicated that S. sprattus contributed considerably to the diets of the three scyphozoan jellyfish A. aurita, C. lamarckii and C. capillata (0.404, 0.337, 0.448 respectively). Aside from S. sprattus, the other fish M. merlangus, M. aeglefinus and S. scombrus contributed relatively smaller but not inconsiderable proportions 10-15% (0.118-0.144) to the diets of gelatinous zooplankton in the Irish Sea. Microplankton was prominent in all the gelatinous zooplankton diet estimates ranging from 0.169 for P. pileus to over three quarters for S. mertensii (0.755).
Mesoplankton contributed less to the diets of gelatinous zooplankton species ranging from
<1% for A. aurita to 10% for S. mertensii. Similarly, M. merlangus larvae contributed a similarly small proportion to the diet of gelatinous zooplankton but was most prominent in the diet of B. infundulum representing over 12% of its assimilated diet.
152
Table 5.6. Estimated mean (95% credibility limits) proportional diet consumption of commercial fish species by gelatinous zooplankton in the Irish Sea.
Prey item Scomber Sprattus Merlangius M_merlangius Melanogrammus Meso- Micro- Consumer scombrus sprattus merlangus larvae aeglefinus plankton plankton Aurelia 0.013 0.404 0.012 0.014 0.014 0.017 0.527 aurita (0.001- (0.002- (0.001- (0.001- (0.001- (0.001- (0.029- 0.052) 0.874) 0.045) 0.049) 0.054) 0.064) 0.979) Bolinopsis 0.118 0.144 0.118 0.122 0.128 0.145 0.224 infundulum (0.011- (0.012- (0.010- (0.011- (0.011- (0.011- (0.016- 0.431) 0.536) 0.427) 0.430) 0.484) 0.546) 0.702) Cyanea 0.043 0.337 0.039 0.042 0.046 0.047 0.446 capillata (0.005- (0.020- (0.005- (0.006- (0.006- (0.006- (0.065- 0.137) 0.723) 0.125) 0.129) 0.145) 0.147) 0.770) Cyanea 0.035 0.506 0.033 0.034 0.040 0.037 0.316 lamarckii (0.004- (0.304- (0.004- (0.005- (0.005- (0.005- (0.147- 0.109) 0.665) 0.100) 0.107) 0.135) 0.121) 0.462) Pleurobrachia 0.124 0.256 0.121 0.095 0.142 0.093 0.169 pileus (0.011- (0.017- (0.009- (0.010- (0.011- (0.010- (0.012- 0.452) 0.749) 0.459) 0.332) 0.536) 0.348) 0.640) Staurostoma 0.025 0.035 0.022 0.036 0.023 0.105 0.755 mertensii (0.003- (0.004- (0.003- (0.004- (0.003- (0.006- (0.456- 0.007) 0.117) 0.064) 0.119) 0.071) 0.352) 0.908)
153
Figure 5.7 Estimated (95% credibility limits) proportional diet consumption including commercial fish species by gelatinous zooplankton in the Irish Sea; (a) Aurelia aurita, (b) Pleurobrachia pileus, (c) Cyanea lamarckii, (d) Cyanea capillata, (e) Bolinopsis infundulum and (e)
Staurostoma mertensii. 154
5.5 Discussion
Although described as “arguably the most important predators in the sea” (Pauly et al.,
2009), efforts to include jellyfish in ecosystem models have been hindered by a lack of empirical dietary information from the field (Hansson & Norrman, 1995; Lynam et al.,
2006; Ruzicka et al., 2007; Condon & Steinberg, 2008; Condon et al., 2011). Our findings represent one small step in addressing this gap in the data. We found evidence for a complex food web in the Irish Sea suggesting the ‘jellyfish paradox’ holds true for this system with fish feeding on gelatinous zooplankton and vice versa.
5.5.1 δ15N variation between fish and jellyfish species
Unsurprisingly, the gelatinous zooplankton and fish species were significantly different in their δ15N values, driven primarily by individual differences between these two taxa but interestingly Aurelia aurita and Cyanea capillata were also significantly different in δ15N values. C. capillata can feed higher up the food web than other gelatinous zooplankton
(Javidpour et al., 2016) and this species feeds on A. aurita is well documented
(Martinussen & Båmstedt, 1995; Hansson, 1997; Titelman et al., 2007). C. capillata are able to feed at a higher trophic level akin to fishes using their well-developed stinging cells to capture larger prey items (Titelman et al., 2007).
We also found that the scyphozoan jellyfish had a broader isotopic niche (i.e. δ15N) even when compared to other ctenophore and hydrozoan gelatinous zooplankton considered here as well as fish. This broadly supports Fleming et al. (2013) who found that for the same scyphozoan jellyfish species sampled from Strangford Lough, Northern Ireland (A. aurita, C. lamarckii and C. capillata), were far more isotopically variable than the sympatric
155 fish community, occupying an isotopic niche area almost double that of 28 sympatric species of fish. However, further investigation should be carried out to determine whether size is a contributing factor to bredth of scyphozoan trophic niches (utilising the refined
TDF from Chapter 3). This approach could provide modellers with means to more accurately incorporate gelatinous zooplankton into ecosystem and food web models in a similar manner to fishes whereby the larger the individual, the higher the δ15N or TP
(Jennings et al., 2002a,b).
5.5.2 Irish Sea fish and jellyfish trophic position estimates
A. aurita had the lowest TP (2.1) estimate of the gelatinous zooplankton while the ctenophore B. infundulum had the highest TP estimate (3.4); this was similar to that of the sympatric fishes. Much research has focused on the invasive ctenophore Mnemiopsis leidyi due to its large predatory impact on ecosystems and ultimately commercial fisheries first highlighted after its introduction to the Black Sea in the 1980s (Shiganova, 1998). The native ctenophore B. infundulum is unlikely to have such negative impacts in the Irish Sea, yet our results showed that B. infundulum feeds at a trophic level similar to that of fish and above that of other gelatinous zooplankton. This finding highlights the trophic relevance of ctenophores as well as scyphozoans in Irish Sea food webs and demonstrates why such species should be given due consideration in the same terms as scyphozoans in ecosystem, food web and fisheries models.
5.5.3 Are fish eating jellyfish?
Robison (2004) proposed the existence of the ‘jellyweb’ with two potentially separate food webs, whereby gelatinous zooplankton occupy an equally complex trophic space, largely
156 separate to but operating alongside fish. While δ15N gives a good indication of trophic complexity, mixing models of proportional contributions to gelatinous zooplankton and fish diets here allowed investigation of the existence of a ‘jellyweb’ in the Irish. C. capillata and S. mertensii accounted for a considerable proportion (46% and 36%) of M. merlangus and M. aeglefinus diet. To our knowledge this is the first time TP for the hydrozoan S. mertensii (2.5) has been idenitified in fish diets despite being relatively common in the
Irish Sea, North Sea and N.W Atlantic (Halsband et al., 2018; Record et al., 2018). Using molecular gut contents analysis, Lamb et al. (2017, 2019) found that M. merlangus feed on jellyfish prey more often than previously acknowledged in the Irish Sea. In contrast to this study, M. aeglefinus was not identified as feeding on jellyfish in either of the molecular studies carried out by Lamb et al. (2017, 2019), which suggests a multidisciplinary approach may be best to resolve complicated trophic interactions such as gelatinous zooplankton consumption by fish. In part, SIA may help overcome some caveats associated with traditional and molecular gut content analysis owing to fast deterioration of gelatinous prey items in the gut of consumers (Båmstedt & Martinussen, 2000; Ishii &
Tanaka, 2001; Pitt et al., 2009). Interestingly, M. merlangus and M. aeglefinus are both known to associate with jellyfish as juveniles (O’Connor & McGrath, 1978; Purcell & Arai,
2001; 'Chapter 2': Griffin et al., 2019) which demonstrates the complexity of fish-jellyfish interactions and the benefits jellyfish can have on fish by helping to increase not only juvenile but also adult fish survival and recruitment.
5.5.4 Are jellyfish eating fish?
Our results suggest Sprattus sprattus is an important contributor to the diet of the three scyphozoans (A. aurita, C. lamarckii and C. capillata), but less so for the other gelatinous 157 zooplankton considered. S. sprattus are small clupeoid fish which grow to a maximum of
16cm but the typically 8-12cm in length (Froese & Pauly, 2018) whereas A. aurita and
C.capillata can grow much larger (Weisse et al., 2000). The fact that gelatinous zooplankton, especially medusae and siphonophores, eat fish has been known for many years however the records reviewed by Arai (1988) and subsequently by Purcell & Arai,
(2001) focused on larval fish whereas our results suggest that this predator-prey relationship may occur across a greater range of fish developmental stages and pesist into adulthood. We found that larval M. merlangus contributed relatively low (1-4%) proportions to the assimilated diets of all the gelatinous zooplankton apart from the two ctenophore species where it contributed more than twice that to their assimilated diets
(9-12%). Considering the ability of other ctenophore species to substantially influence fish larvae and egg numbers in discrete areas where there is sufficient temporal and spatial overlap (Shiganova, 1998; Cao et al., 2015; Gibbons et al., 2016), further research exploring the dietary contributions of larvae from other fish species to the diets of gelatinous zooplankton is recommended.
5.5.5 Conclusions
Increasing awareness of the trophic and functional complexity of gelatinous zooplankton
(Cardona et al., 2012; Doyle et al., 2014; Jaspers et al., 2014; Nakamura et al., 2015; Hays et al., 2018) indicates greater knowledge of specific fish-jellyfish interactions is desirable.
Here we make some comment on the two-way interactions where fish were predators of jellyfish and vice versa. We provide evidence of trophic interactions involving ctenophores and hydrozoans which equally should be addressed in ecosystem models in the same way many studies have aimed to incorporate scyphozoan jellyfish. Gelatinous zooplankton 158 food webs, or ‘jellywebs’ are intertwined and synonymous with that of fish, maintained via a two-way street of fish-jellyfish interaction involving the predation of gelatinous zooplankton by fish and the predation of fish by gelatinous zooplankton. Both these fish- jellyfish interactions have the potential to impact on the success of commercial fish stocks in the Irish Sea. Increasingly, the basis on which we manage fisheries and strive for sustainability is reliant on ecosystem and fisheries models underpinned by accurate and comprehensive data. Including fish-jellyfish interactions into these models will ultimately result in better model outputs which will better inform fisheries management measures.
159
5.6 References Arai, M.N. (1988). Interactions of fish and pelagic coelenterates. Canadian Journal of Zoology 66, 1913–1927.
Båmstedt, U., and Martinussen, M.B. (2000). Estimating digestion rate and the problem of individual variability, exemplified by a scyphozoan jellyfish. Journal of Experimental Marine Biology and Ecology 251, 1–15.
Bastian, T., Haberlin, D., Purcell, J.E., Hays, G.C., Davenport, J., McAllen, R., and Doyle, T.K. (2011). Large-scale sampling reveals the spatio-temporal distributions of the jellyfish Aurelia aurita and Cyanea capillata in the Irish Sea. Marine Biology 158, 2639–2652.
Bastian, T., Stokes, D., Kelleher, Jane E., Hays, G.C., Davenport, J., and Doyle, T.K. (2010). Fisheries bycatch data provide insights into the distribution of the mauve stinger (Pelagia noctiluca) around Ireland. ICES Journal of Marine Science 68, 436–443.
Bentley, J.W., Serpetti, N., Fox, C., Reid, D., and Heymans, J.J. (2018). Modelling the food web in the Irish Sea in the contect of a depleted commercial fish community. Part 1: Ecopath Technical Report. Scottish Association for Marine Science, Oban, U.K. 147.
Boero, F., Bouillon, J., Gravili, C., Miglietta, M.P.P., Parsons, T., and Piraino, S. (2008). Gelatinous plankton: irregularities rule the world (sometimes). Marine Ecology Progress Series 356, 299–310.
Brodeur, R.D., Link, J.S., Smith, B.E., Ford, M.D., Kobayashi, D.R., and Jones, T.T. (2016). Ecological and Economic Consequences of Ignoring Jellyfish: A Plea for Increased Monitoring of Ecosystems. Fisheries 41, 630–637.
Brodeur, R.D., Sugisaki, H., and Jr, G.L.H. (2002). Increases in jellyfish biomass in the Bering Sea: implications for the ecosystem. Marine Ecology Progress Series 233, 89–103.
Cao, L., Liu, J., Yu, X., Zhao, B., Shan, X., Zhuang, Z., and Dou, S. (2015). Size-dependent predation of fish larvae by jellyfish: an experimental evaluation exemplified with the flounder Paralichthys olivaceus larvae and the moon jellyfish Aurelia aurita medusae. Hydrobiologia 754, 135–146.
160
Cardona, L., de Quevedo, I.Á., Borrell, A., and Aguilar, A. (2012). Massive consumption of gelatinous plankton by Mediterranean apex predators. PLoS ONE 7.
Condon, R.H., Duarte, C.M., Pitt, K.A., Robinson, K.L., Lucas, C.H., Sutherland, K.R., Mianzan, H.W., Bogeberg, M., Purcell, J.E., Decker, M.B., Uye, S., Madin, L.P., Brodeur, R.D., Haddock, S.H.D., Malej, A., Parry, G.D., Eriksen, E., Quiñones, J., Acha, M., Harvey, M., Arthur, J.M., and Graham, W.M. (2013). Recurrent jellyfish blooms are a consequence of global oscillations. Proceedings of the National Academy of Sciences of the United States of America 110, 1000–5.
Condon, R.H., and Steinberg, D.K. (2008). Development, biological regulation, and fate of ctenophore blooms in the York River estuary, Chesapeake Bay. Marine Ecology Progress Series 369, 153–168.
Condon, R.H., Steinberg, D.K., del Giorgio, P.A., Bouvier, T.C., Bronk, D.A., Graham, W.M., and Ducklow, H.W. (2011). Jellyfish blooms result in a major microbial respiratory sink of carbon in marine systems. Proceedings of the National Academy of Sciences 108, 10225– 10230.
D’Ambra, I., Graham, W.M., Carmichael, R.H., and Hernandez, F.J. (2014). Fish rely on scyphozoan hosts as a primary food source: evidence from stable isotope analysis. Marine Biology 162, 247–252.
Doyle, T.K., Hays, G.C., Harrod, C., and Houghton, J.D.R. (2014). Ecological and Societal Benefits of jellyfish. In Kylie A. Pitt & C. H. Lucas (Eds.), Jellyfish Blooms (pp. 105–127). Dordrecht: Springer Netherlands.
Doyle, T.K., Houghton, J.D.R., McDevitt, R., Davenport, J., and Hays, G.C. (2007). The energy density of jellyfish: Estimates from bomb-calorimetry and proximate-composition. Journal of Experimental Marine Biology and Ecology 343, 239–252.
Dunn, C.W., Hejnol, A., Matus, D.Q., Pang, K., Browne, W.E., Smith, S. a, Seaver, E., Rouse, G.W., Obst, M., Edgecombe, G.D., Sørensen, M. V, Haddock, S.H.D., Schmidt- Rhaesa, A., Okusu, A., Kristensen, R.M., Wheeler, W.C., Martindale, M.Q., and Giribet, G.
161
(2008). Broad phylogenomic sampling improves resolution of the animal tree of life. Nature 452, 745–9.
FAO. (2018). The State of World Fisheries and Aquaculture 2018 - Meeting the sustainable development goals.
Fleming, N.E.C. (2013). Gelatinous zooplankton in the North East Atlantic: Distribution, seasonality and trophic ecology.
Fleming, N.E.C., Harrod, C., and Houghton, J.D.R. (2013). Identifying potentially harmful jellyfish blooms using shoreline surveys. Aquaculture Environment Interactions 4, 263– 272.
Fleming, N.E.C., Houghton, J.D.R., Magill, C.L., and Harrod, C. (2011). Preservation methods alter stable isotope values in gelatinous zooplankton: implications for interpreting trophic ecology. Marine Biology 158, 2141–2146.
Froese, R., and Pauly, D. (2018). FishBase 2018. World Wide Web Electronic Publication Retrieved from http://www. Fishbase. org.
Gibbons, M.J., Boero, F., and Brotz, L. (2016). We should not assume that fishing jellyfish will solve our jellyfish problem. ICES Journal of Marine Science 73, 1012–1018.
Griffin, D.C., Harrod, C., Capellini, I., and Houghton, J.D.R. (2019). Unravelling the macro- evolutionary ecology of fish-jellyfish associations: life in the ‘gingerbread house’. Proceedings of the Royal Scociety B 286(1899), 20182325.
Halsband, C., Majaneva, S., Hosia, A., Emaus, P.A., Gaardsted, F., Zhou, Q., Nøst, O.A., and Renaud, P.E. (2018). Jellyfish summer distribution, diversity and impact on fish farms in a Nordic fjord. Marine Ecology Progress Series 591, 267–279.
Hamilton, G. (2016). The Secret Lives of Jellyfish. Nature 432–434.
Hansson, L.J. (1997). Capture and digestion of the scyphozoan jellyfish Aurelia aurita by Cyanea capillata and prey response to predator contact. Journal of Plankton Research 19, 195–208.
162
Hansson, L.J., and Norrman, B. (1995). Release of dissolved organic carbon (DOC) by the scyphozoan jellyfish Aurelia aurita and its potential influence on the production of planktic bacteria. Marine Biology 121, 527–532.
Hays, G.C., Doyle, T.K., and Houghton, J.D.R. (2018). A Paradigm Shift in the Trophic Importance of Jellyfish? Trends in Ecology & Evolution.
Hobson, K.A., Gloutney, M.L., and Gibbs, H.L. (1997). Preservation of blood and tissue samples for stable-carbon and stable-nitrogen isotope analysis. Canadian Journal of Zoology 75, 1720–1723.
Houghton, J.D.R., Doyle, T.K., Davenport, J., Lilley, M.K.S., Wilson, R.P., and Hays, G.C. (2007). Stranding events provide indirect insights into the seasonality and persistence of jellyfish medusae (Cnidaria: Scyphozoa). Hydrobiologia 589, 1–13.
Ishii, H., and Tanaka, F. (2001). Food and feeding of Aurelia aurita in Tokyo Bay with an analysis of stomach contents and a measurement of digestion times. Hydrobiologia 451, 311–320.
Jaspers, C., Acuña, J.L., and Brodeur, R.D. (2014). Interactions of gelatinous zooplankton within marine food webs. Journal of Plankton Research 37, 985–988.
Javidpour, J., Cipriano-Maack, A.N., Mittermayr, A., and Dierking, J. (2016). Temporal dietary shift in jellyfish revealed by stable isotope analysis. Marine Biology 163, 1–9.
Jennings, S., Pinnegar, J.K., Polunin, N.V.C., and Warr, K.J. (2002a). Linking size-based and trophic analyses of benthic community structure. Marine Ecology Progress Series 226, 77–85.
Jennings, S., Warr, K.J., and Mackinson, S. (2002b). Use of size-based production and stable isotope analyses to predict trophic transfer efficiencies and predator-prey body mass ratios in food webs. Marine Ecology Progress Series 240, 11–20.
Lamb, P.D., Hunter, E., Pinnegar, J.K., Creer, S., Davies, R.G., and Taylor, M.I. (2017). Jellyfish on the menu: mtDNA assay reveals scyphozoan predation in the Irish Sea. Royal Society Open Science 4, 171421.
163
Lamb, P.D., Hunter, E., Pinnegar, J.K., van der Kooij, J., Creer, S., and Taylor, M.I. (2019). Cryptic diets of forage fish: jellyfish consumption observed in the Celtic Sea and western English Channel. Journal of Fish Biology.
Lynam, C.P., and Brierley, A.S. (2006). Enhanced survival of 0-group gadoid fish under jellyfish umbrellas. Marine Biology 150, 1397–1401.
Lynam, C.P., Gibbons, M.J., Axelsen, B.E., Sparks, C.A.J., Coetzee, J., Heywood, B.G., and Brierley, A.S. (2006). Jellyfish overtake fish in a heavily fished ecosystem. Current Biology 16, 492–493.
Lynam, C.P., Hay, S.J., and Brierley, A.S. (2004). Interannual variability in abundance of North Sea jellyfish and links to the North Atlantic Oscillation. Limnology and Oceanography 49, 637–643.
Lynam, C.P., Lilley, M.K.S.S., Bastian, T., Doyle, T.K., Beggs, S.E., and Hays, G.C. (2011). Have jellyfish in the Irish Sea benefited from climate change and overfishing? Global Change Biology 17, 767–782.
Mackenzie, K.M., Trueman, C.N., Lucas, C.H., Bortoluzzi, J., Trueman, C.N., and Lucas, C.H. (2017). The preparation of jellyfish for stable isotope analysis. Marine Biology 164, 1–9.
Martinussen, M.B., and Båmstedt, U. (1995). Diet, estimated daily food ration and predator impact by the scyphozoan jellyfishes Aurelia aurita and Cyanea capillata. Ecology of Fjords and Coastal Waters 1, 127–145.
McCutchan, J.H., Lewis, W.M., Kendall, C., and McGrath, C.C. (2003). Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102, 378–390.
Middelburg, J.J. (2014). Stable isotopes dissect aquatic food webs from the top to the bottom. Biogeosciences 11, 2357–2371.
Nagata, R., Moreira, M., Pimentel, C., and Morandini, A. (2015). Food web characterization based on δ15N and δ13C reveals isotopic niche partitioning between fish and jellyfish in a relatively pristine ecosystem. Marine Ecology Progress Series 519, 13–27.
164
Nakamura, I., Goto, Y., and Sato, K. (2015). Ocean sunfish rewarm at the surface after deep excursions to forage for siphonophores. Journal of Animal Ecology 84, 590–603.
O’Connor, B., and McGrath, D. (1978). On the occurrence of the Scyphozoan Rhizostoma octopus (L .) around the Irish Coast in 1976. Irish Naturalists’ Journal Ltd 19, 261–263.
Parnell, A. (2019). A Stable Isotop Mixing Model.
Pauly, D., Graham, W.M., Libralato, S., Morissette, L., and Deng Palomares, M.L. (2009). Jellyfish in ecosystems, online databases, and ecosystem models. Hydrobiologia 616, 67– 85.
Pitt, K.A., Connolly, R.M., and Meziane, T. (2009). Stable isotope and fatty acid tracers in energy and nutrient studies of jellyfish: a review. Hydrobiologia 616, 119–132.
Purcell, J.E., and Arai, M.N. (2001). Interactions of pelagic cnidarians and ctenophores with fish: a review. Hydrobiologia 451, 27–44.
Quezada-Romegialli, C., Jackson, A.L., Hayden, B., Kahilainen, K.K., Lopes, C., and Harrod, C. (2018). tRophicPosition, an r package for the Bayesian estimation of trophic position from consumer stable isotope ratios. Methods in Ecology and Evolution 9, 1592–1599.
Record, N.R., Tupper, B., and Pershing, A.J. (2018). The jelly report: Forecasting jellyfish using email and social media. Anthropocene Coasts 1, 34–43.
Robison, B.H. (2004). Deep pelagic biology. Journal of Experimental Marine Biology and Ecology 300, 253–272.
Ruzicka, J.J., Richard, B.D., and Wainwright, T.C. (2007). Seasonal food web models for the Oregon inner-shelf ecosystem: investigating the role of large jellyfish. CalCOFI Rep 48, 106–128.
Shiganova, T.A. (1998). Invasion of the Black Sea by the ctenophore Mnemiopsis leidyi and recent changes in pelagic community structure. Fisheries Oceanography 7, 305–310.
165
Titelman, J., Gandon, L., Goarant, A., and Nilsen, T. (2007). Intraguild predatory interactions between the jellyfish Cyanea capillata and Aurelia aurita. Marine Biology 152, 745–756.
Weisse, T., Gomoiu, M.-, and Traian. (2000). Biomass and size structure of the scyphomedusa Aurelia aurita in the northwestern Black Sea during spring and summer. Journal of Plankton Research 22, 223–239.
Zanden, M.J. Vander, Cabana, G., and Rasmussen, J.B. (1997). Comparing trophic position of freshwater fish calculated using stable nitrogen isotope ratios (δ15N) and literature dietary data. Canadian Journal of Fish and Aquatic Science.
166
Chapter 6 - General discussion
167
6.1 Synthesis of main findings
As ecologists discover more about the true role of gelatinous zooplankton in marine systems, a more balanced view of such species appears to be emerging (Hamilton, 2016;
Hays et al., 2018). This does not mean that detrimental impacts were over-exaggerated, simply that they were only part of the story and often amplified by human degradation of the systems themselves (e.g. overfishing or eutrophication). This thesis aligns with this new philosophy within the context of jellyfish-fish interaction. From the four ‘experimental’ chapters, six salient points emerge: (i) fish-jellyfish associations are long standing and potentially predate the evolution of benthic, benthopelagic or reef-associating lifestyles;
(ii) the majority of reported fish-jellyfish associations involve commercial fish species; (iii) inter-tissue and inter-specific δ15N variation among jellyfish could mask selective feeding by predators; (iv) further investigation is needed to determine whether size scaled δ15N in jellyfish provides a means to conduct size-based analysis akin to that routinely carried out on fish; (v) jellyfish specific TDF estimates opens the door for their effective inclusion in ecosystem and fisheries-based models in the future; (vi) jellyfish are predators of, and prey for, commercial fish in the Irish Sea. Here we discuss each key finding in relation to its wider significance for jellyfish ecology including suggested avenues for future research.
6.1.1 Evolution of jellyfish-fish associations
Chapter 2 suggested that fish association with jellyfish is more likely to be one evolutionary driver of adapting to a demersal lifestyle, rather than its evolutionary consequence.
Considering the high level of predation pressure juvenile fish experience (Stewart & Jones,
2001; Almany et al., 2006), in an otherwise barren environment, jellyfish offer structurally
168 complex habitat which may aidincreased juvenile survival through increased shelter availability and decreased predator efficiency (Tupper & Boutilier, 1995b). However, through ontogeny and growth in size, the protection from predators may become less effective driving a shift towards demersal habitats. For example, Brodeur (1998) suggested that juvenile pollock were symbiotic with jellyfish until they reached a size at which they can escape most gape-limited predators. However, our findings also suggested that it is more likely that the association with jellyfish evolves in pelagic species prior to the evolution of a demersal lifestyle. Additional work is needed to explore other evolutionary drivers which have promoted the evolutionary origin of jellyfish association with pelagic fishes which have additional mechanisms to gain protection for predators such as schooling behaviour (Leis, 2010). Building on the methods presented in ‘Chapter 4’, future research could tease out the different habitats in the demersal type grouping we investigated here. While grouping these fish was necessary to present robust statistical analysis here, with more phylogenetic information on a greater range of species, this may soon be possible.
6.1.2 Majority of fish-jellyfish associations involve commercial fish species
An increasing number of taxa, including fish species, are reported as feeding on jellyfish, contributing to new perception and paradigm shift in how scientists view jellyfish (Hays et al., 2018). However, the number of fish species recorded as being involved in fish-jellyfish associations has also steadily increased since the first comprehensive review by Mansueti
(1963). This research demonstrates th potential increase in fish stock recruitment gained through fish-jellyfish association is not insignificant considering recruitment is critical in establishing adult population levels (Fogarty et al., 1991). This point is particularly relevant 169 given that 72% of fish species reported as exhibiting jellyfish associative behaviour in this study were of commercial importance. Considering that an estimated 33.1% of fish stocks globally are currently overfished and 1/3 of the world’s human population rely on fish for sustenance (FAO, 2018), marine scientists have a responsibility to account for all factors influencing fish stock health in a holistic and balanced manner. Furthermore, the scale of potential habitat offered by large blooms of jellies around the world to large numbers of young and vulnerable pelagic and demersal fish not yet settled in the seabed is enormous
(Purcell, 2005; Brotz et al., 2012; Bastian et al., 2014). Even if only availed of opportunistically, the impacts on juvenile fish survival by associating with jellyfish could be considerable. For example, Mansueti (1963) reported that up to 100 juvenile Atlantic horse mackerel (Trachurus trachurus) were found sheltered under one jellyfish while 427 juvenile shrimp scad (Alepes djedaba) were observed under four edible jellyfish
(Rhopilema hispidum) in the Indian ocean (Panikkar & Prasad, 1952). Hays et al. (2018) also highlighted how jellyfish as a form of habitat and food for marine taxa may be particularly important given that jellyfish compose a large fraction of the pelagic biomass and, in some regions, have oscillated or increased significantly in their abundance (Condon et al., 2013;
Duarte et al., 2013). Taken together there is now evidence that the co-occurrence of jellyfish in juvenile fish nursery grounds has the potential to support rather than detract from the productivity of commercial fisheries. Indeed, Riascos et al. (2018) stated that jellyfish represent an “economically defendable resource” and argued that scyphozoan jellyfish may be viewed as floating nurseries for juvenile fish. Accounting for interactions which are potentially beneficial for fish stocks will have far reaching implications for fisheries management, both in theory and practice. With ecosystem-based approaches 170 becoming increasingly recognised as the best way to achieve healthy and susatianble fisheries, it would be informative for fisheries scientists to include jellyfish in regular monitoring regimes, in much the same way they do so for fish. This information can then be used to feed into food web, ecosystem and fisheries modelling in the future. Ultimately, building a more complete picture of how fish are affected by jellyfish at a regional or seas level will help improve the status of commercial fish stocks in those areas.
Targeted research is needed to expand knowledge on the extent and diversity of fish species involved in jellyfish associations. Often, fisheries research centres upon the data gathered from fish caught in a net (Cadrin et al., 2013). Whilst these are critical for assessing fish stock status, by the undiscerning nature of net sampling, it is impossible to determine fish-jellyfish association when fish and jellyfish are hauled up together. A different approach is needed to identify the true extent of fish species which exhibit this behaviour. The use of high-definition cameras in the marine environment has already aided our understanding of how ocean sunfish (Mola mola) selectively feed on jellyfish
(Nakamura et al., 2015) and similar technology employed strategically on fishing nets or other scientific equipment deployed in the marine environment (e.g. CTD profilers, BRUVs, sampling buoys) may garner equal success in exploring the true extent of this behaviour in marine coastal systems.
6.1.3 Inter-tissue and inter-specific δ15N variation among jellyfish could mask selective feeding by predators
Jellyfish are a biologically and physiologically diverse group which include micro through to macro-zooplankton species weighing >200kg (e.g. Nemopilmea nomurai). It is difficult
171 to incorporate jellyfish into ecosystem, fisheries and food web studies (Pauly et al., 2009;
Brodeur et al., 2016) because a single functional group often fails to represent the true role of different species of jellyfish in marine systems. However, we found for the scyphozoan jellyfish looked at in this study, gonad and bell tissue, which constitutes most of the wet and dry mass of the species considered here (Doyle et al., 2007; D’Ambra et al.,
2013), had similar δ15N values when considered as individual species or collectively. It is therefore encouraging that this method is often the standard procedure in many jellyfish
SIA studies (D’Ambra et al., 2014; Fleming et al., 2015; Nagata et al., 2015; Javidpour et al.,
2016). However, we also found that oral arm δ15N was significantly higher than other tissues in Cyanea capillata and when all species were combined. This demonstrates the complexity of jellyfish SIA and the potential pitfalls when trying to collect data which would help assign appropriate functional groups for inclusion in future ecosystem, fisheries or food web models. Whilst accounting for jellyfish δ15N by grouping species together may be appropriate (or pragmatically the only option available) in some instances, failing to be aware of inter-tissue isotopic patterns could result in the masking of important jellyfish trophic information and energy pathways. Considering that many predators feed selectively on different jellyfish tissues (e.g. Mansueti, 1963; Milisenda et al., 2014;
Nakamura et al., 2015), the result of neglecting inter-tissue isotopic variation could hinder rather than help to improve our broader understanding of the true tole of jellyfish in ecosystems. An investigation into inter-tissue isotopic variation in a greater range of scyphozoan and non-scyphozoan jellyfish species could better inform how effective functional groups could be assigned for jellyfish within, and between, species or based on alternative morphological features such as size or feeding strategy. 172
6.1.4 Size-scaled δ15N in jellyfish
More research is needed to conclusively state δ15N is influenced by jellyfish size, whereby larger jellyfish have higher δ15N values, reflecting higher trophic position in food webs.
However, in the future it may prove a useful tool in accounting for the nuances of jellyfish trophic ecology. Body size is an easily obtained metric in marine studies which has profound consequences for the function of organisms and underpins their inherent physiology, metabolism and ecology (Woodward et al., 2005). Furthermore, growing evidence shows that food webs in marine systems are strongly size structured (Reide et al., 2010) and advances in statistical modelling have helped to capture this complexity for effective inclusion into ecosystem/fisheries and food web models (Jennings et al., 2002a;
Jennings et al., 2002b; Cohen et al., 2003). These approaches use abundance-body mass relationships, where it is assumed that total biomass decreases in progressively heavier body-mass classes because there is inefficient energy transfer from prey to predators (Kerr
1974; Dickie et al., 1987; Boudreau et al., 1991). The benefit of such approaches would be that they could be applied applied to jellyfish in much the same way as fish diversity is assessed and grouped for ecosystem, fisheries and food web models currently. To our knowledge, such efforts have not been directed towards gelatinous zooplankton despite the diversity of the group (i.e. >1200 species) and their ubiquity in marine systems
(Haddock, 2004; Jennings & Blanchard, 2004; Richardson et al., 2009). The broader relevance here is that size-based models may provide a much-needed mechanism whereby seemingly competitive jellyfish and fish species can be considered concurrently whilst also conserving important information on trophic diversity. Seeing that gelatinous zooplankton pre-date the fishes and have co-existed since the Cambrian period (Boero et 173 al., 2008; Dunn et al., 2008), it is unsurprising that jellyfish have evolved a range of beneficial attributes enabling them to exploit resources through a range of different feeding strategies, resulting in a trophic range equal to, or greater than, some fish (Riisgård
& Larsen, 2010).
6.1.5 Jellyfish specific TDF estimates open the door for their effective inclusion in ecosystem and fisheries-based models in the future
Reliable trophic enrichment factors (the ratio at which a consumer is enriched in certain element isotopes compared to its food) are vital for trophic studies in order to determine trophic position (Vander Zanden & Rasmussen, 2001; Post, 2002; Bastos et al., 2017).
However, currently there is no consensus as to whether standard TDF values are appropriate for jellyfish, which differ remarkably in morphology from other taxa for which the standard TDF values were generated (Fleming et al., 2011; D’Ambra et al., 2013).
While median jellyfish TP varied considerably for both methods looked at here, estimates based on a bulk TDF of 2.9‰ and an amino-acid TDF of 7.6‰, as suggested by McCutchan et al. (2003) and Chikaraishi et al. (2009) respectively, overlapped for all comparisons indicating that these TDF values may be appropriately representative of the jellyfish.
However, the advantage of this using this twin BSIA and CSIA method to determine appropriate TDFs, is that it can be easily repeated so that a greater range of species or geographical specific TDF variability for example, can be accounted for, resulting in more robust modelling and conclusions. Considering ecological modelling provides the best option to understand the role of jellyfish in large fisheries-based ecosystems, and is consistent with ecosystem based management practices (Pauly et al., 2009), jellyfish
174 specific TDFs will open the door for their effective inclusion in the future, as well as being able to be applied retrospectively to SIA datasets around the world.
6.1.6 Two-way trophic interactions with fishes
This study adds to the evidence that jellyfish have paradoxical (i.e both negative and positive) effects on fish communities. Due to the historically high level of fishing in the
Irish Sea, catch-quotas and net mesh size regulations were brought in during the 1970s to halt the decline of commercial species in the Irish Sea (Briggs, 1987). Currently catch quotas are updated on an annual basis to help regulate fish stock to try and maintain sustainability (Roughgarden, 1996). However, the main challenge for researchers is to quantify the relative importance of these effects and to understand the consequences of these contrary interactions on fish communities when they co-occur. Fisheries scientists and modellers need detailed information on a range of different variables which are likely to be context dependent (i.e. it varies spatially, temporally and depending on community species composition), including the trophic position and dietary contributions of gelatinous zooplankton to commercial fish and vice versa. Building on our findings that fish are prey for and predators of gelatinous zooplankton in the Irish Sea, future research should investigate a broader range of species and how competing fish-jellyfish interactions and the jellyfish paradox develops and changes temporally.
6.2 Concluding comment on the jellyfish paradox
As pressure on fin-fish stocks continues to mount year on year (FAO, 2018), it is important to consider all potential impacts jellyfish have on fish communities, including both negative
175
(competition, predation) and positive (prey and commensalism) (Fig. 6.2). Further understanding of the net impact of fish-jellyfish interactions over spatial and temporal scales, i.e. the jellyfish paradox, are also essential to fully account for the true role of jellyfish in our oceans.
As fisheries advice and management moves towards a more integrated ecosystem approach, the need for improved knowledge of trophic interactions between key species is required (Fig. 6.2). Recent work in the Irish Sea has increased our awareness of the ecological role jellyfish play in ecosystems (Lamb et al., 2017, 2019) and Pauly et al. (2009) stressed that gelatinous taxa require more robust inclusion in marine fisheries and ecosystem models. Taken together, the key findings of this thesis go some way in addressing these issues and are targeted at fisheries, marine ecosystem scientists and, ultimately, policy makers. However, ecological-modelling and fisheries management communities cannot be expected to consider gelatinous zooplankton in adequate detail if the required data are not provided by other researchers (Doyle et al., 2014) and this thesis is just one step towards addressing that data and knowledge deficit. While it is clear that jellyfish should not be considered as a single functional group or an ‘average’ group of animals feeding on the same prey throughout their life history (Boero et al., 2008; Pauly et al., 2009), future research should focus on providing useful information on the effect of jellyfish on fish communities. The complex and nuanced aspects of jellyfish ecology and biology should be given due consideration considering Pauly et al. (2009) described jellyfish ‘as arguably the most important predators in the sea’. Pauly et al. (2009) added that a description of jellyfish with a set of appropriate functional groups will certainly help in better understanding their role in food webs and their effects on ecosystems; making it 176 possible to integrate jellyfish, in spite of their strange biology, into the mainstream of marine biology and fisheries in the future. It is hoped that the current thesis makes a meaningful contribution in this regard.
177
6.3 References
Almany, G.R., Webster, M.S., Foundation, B.M., and Francisco, S. (2006). The predation gauntlet: early post-settlement mortality in reef fishes. Coral Reefs 25, 19–22.
Bastian, T., Lilley, M.K.S., Beggs, S.E., Hays, G.C., and Doyle, T.K. (2014). Ecosystem relevance of variable jellyfish biomass in the Irish Sea between years, regions and water types. Estuarine, Coastal and Shelf Science 149, 302–312.
Bastos, R.F., Corrêa, F., Winemiller, K.O., and Garcia, A.M. (2017). Are you what you eat? Effects of trophic discrimination factors on estimates of food assimilation and trophic position with a new estimation method. Ecological Indicators 75, 234–241.
Boero, F., Bouillon, J., Gravili, C., Miglietta, M.P.P., Parsons, T., and Piraino, S. (2008). Gelatinous plankton: irregularities rule the world (sometimes). Marine Ecology Progress Series 356, 299–310.
Boudreau, P.R., Dickie, L.M., and Kerr, S.R. (1991). Body-size spectra of production and biomass as system-level indicators of ecological dynamics. Journal of Theoretical Biology 152, 329–339.
Briggs, R. P. (1987). A review of the factors influencing landing size in the Northern Ireland Nephrops fishery. ICES CM.
Brodeur, R.D. (1998). In situ observations of the association between juvenile fishes and scyphomedusae in the Bering Sea. Marine Ecology Progress Series 163, 11–20.
Brodeur, R.D., Link, J.S., Smith, B.E., Ford, M.D., Kobayashi, D.R., and Jones, T.T. (2016). Ecological and Economic Consequences of Ignoring Jellyfish: A Plea for Increased Monitoring of Ecosystems. Fisheries 41, 630–637.
Brotz, L., Cheung, W.W.L., Kleisner, K., Pakhomov, E., and Pauly, D. (2012). Increasing jellyfish populations: trends in Large Marine Ecosystems. Hydrobiologia 690, 3–20.
Cadrin, S.X., Kerr, L., and Mariani, S. (2013). Stock Identification Methods: Applications in Fishery Science: Second Edition.
Chikaraishi, Y., Ogawa, N.O., Kashiyama, Y., Takano, Y., Suga, H., Tomitani, A., Miyashita, H., Kitazato, H., and Ohkouchi, N. (2009). Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids. Limnology and Oceanography: Methods 7, 740–750.
Cohen, J.E., Jonsson, T., and Carpenter, S.R. (2003). Ecological community description using the food web, species abundance, and body size. Proceedings of the National Academy of Sciences of the United States of America 100, 1781–6.
178
Condon, R.H., Duarte, C.M., Pitt, K.A., Robinson, K.L., Lucas, C.H., Sutherland, K.R., Mianzan, H.W., Bogeberg, M., Purcell, J.E., Decker, M.B., Uye, S., Madin, L.P., Brodeur, R.D., Haddock, S.H.D., Malej, A., Parry, G.D., Eriksen, E., Quiñones, J., Acha, M., Harvey, M., Arthur, J.M., and Graham, W.M. (2013). Recurrent jellyfish blooms are a consequence of global oscillations. Proceedings of the National Academy of Sciences of the United States of America 110, 1000–5.
D’Ambra, I., Carmichael, R.H., and Graham, W.M. (2013). Determination of δ13C and δ15N and trophic fractionation in jellyfish: implications for food web ecology. Marine Biology 161, 473–480.
D’Ambra, I., Graham, W.M., Carmichael, R.H., and Hernandez, F.J. (2014). Fish rely on scyphozoan hosts as a primary food source: evidence from stable isotope analysis. Marine Biology 162, 247–252.
Dickie, L.M., Kerr, S.R., and Boudreau, P.R. (1987). Size-Dependent Processes Underlying Regularities in Ecosystem Structure. Ecological Monographs 57, 233–250.
Doyle, T.K., Hays, G.C., Harrod, C., and Houghton, J.D.R. (2014). Ecological and Societal Benefits of jellyfish. In K. A. Pitt & C. H. Lucas (Eds.), Jellyfish Blooms (pp. 105–127). Dordrecht: Springer Netherlands.
Doyle, T.K., Houghton, J.D.R., McDevitt, R., Davenport, J., and Hays, G.C. (2007). The energy density of jellyfish: Estimates from bomb-calorimetry and proximate-composition. Journal of Experimental Marine Biology and Ecology 343, 239–252.
Duarte, C.M., Pitt, K.A., Lucas, C.H., Purcell, J.E., Uye, S., Robinson, K., Brotz, L., Decker, M.B., Sutherland, K.R., Malej, A., Madin, L., Mianzan, H., Gili, J.-M., Fuentes, V., Atienza, D., Pagés, F., Breitburg, D., Malek, J., Graham, W.M., and Condon, R.H. (2013). Is global ocean sprawl a cause of jellyfish blooms? Frontiers in Ecology and the Environment 11, 91–97.
Dunn, C.W., Hejnol, A., Matus, D.Q., Pang, K., Browne, W.E., Smith, S. a, Seaver, E., Rouse, G.W., Obst, M., Edgecombe, G.D., Sørensen, M. V, Haddock, S.H.D., Schmidt- Rhaesa, A., Okusu, A., Kristensen, R.M., Wheeler, W.C., Martindale, M.Q., and Giribet, G. (2008). Broad phylogenomic sampling improves resolution of the animal tree of life. Nature 452, 745–9.
FAO. (2018). The State of World Fisheries and Aquaculture 2018 - Meeting the sustainable development goals.
Fleming, N.E.C., Harrod, C., Newton, J., and Houghton, J.D.R. (2015). Not all jellyfish are equal: isotopic evidence for inter- and intraspecific variation in jellyfish trophic ecology. PeerJ 3: e1110. 179
Fleming, N.E.C., Houghton, J.D.R., Magill, C.L., and Harrod, C. (2011). Preservation methods alter stable isotope values in gelatinous zooplankton: implications for interpreting trophic ecology. Marine Biology 158, 2141–2146.
Fogarty, M.J., Sissenwine, M.P., and Cohen, E.B. (1991). Recruitment variability and the dynamics of exploited marine populations. Trends in Ecology & Evolution 6, 241–246.
Haddock, S.H.D. (2004). A golden age of gelata: past and future research on planktonic ctenophores and cnidarians. Hydrobiologia 530–531, 549–556.
Hamilton, G. (2016). The Secret Lives of Jellyfish. Nature 432–434.
Hays, G.C., Doyle, T.K., and Houghton, J.D.R. (2018). A Paradigm Shift in the Trophic Importance of Jellyfish? Trends in Ecology & Evolution.
Javidpour, J., Cipriano-Maack, A.N., Mittermayr, A., and Dierking, J. (2016). Temporal dietary shift in jellyfish revealed by stable isotope analysis. Marine Biology 163, 1–9.
Jennings, S., and Blanchard, J.L. (2004). Fish abundance with no fishing: predictions based on macroecological theory. Journal of Animal Ecology 73, 632–642.
Jennings, S., Pinnegar, J.K., Polunin, N.V.C., and Warr, K.J. (2002). Linking size-based and trophic analyses of benthic community structure. Marine Ecology Progress Series 226, 77–85.
Jennings, S., Warr, K.J., and Mackinson, S. (2002). Use of size-based production and stable isotope analyses to predict trophic transfer efficiencies and predator-prey body mass ratios in food webs. Marine Ecology Progress Series 240, 11–20.
Kerr, S.R. (1974). Theory of Size Distribution in Ecological Communities. Journal of the Fisheries Research Board of Canada 31, 1859–1862.
Lamb, P.D., Hunter, E., Pinnegar, J.K., Creer, S., Davies, R.G., and Taylor, M.I. (2017). Jellyfish on the menu: mtDNA assay reveals scyphozoan predation in the Irish Sea. Royal Society Open Science 4, 171421.
Lamb, P.D., Hunter, E., Pinnegar, J.K., van der Kooij, J., Creer, S., and Taylor, M.I. (2019). Cryptic diets of forage fish: jellyfish consumption observed in the Celtic Sea and western English Channel. Journal of Fish Biology.
Leis, J.M. (2010). Ontogeny of behaviour in larvae of marine demersal fishes. Ichthyological Research 57, 325–342.
Mansueti, R. (1963). Symbiotic Behavior Between Small Fishes and Jellyfishes, with New Data on That Between the Stromateid, Peprilus alepidotus and the Scyphomedusa, Chrysaora quinquecirrha. Copeia 1, 40–80.
180
McCutchan, J.H., Lewis, W.M., Kendall, C., and McGrath, C.C. (2003). Variation in trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos 102, 378–390.
Milisenda, G., Rosa, S., Fuentes, V.L., Boero, F., Guglielmo, L., Purcell, J.E., and Piraino, S. (2014). Jellyfish as prey: Frequency of predation and selective foraging of boops boops (vertebrata, actinopterygii) on the mauve stinger Pelagia noctiluca (cnidaria, scyphozoa). PLoS ONE 9(4), e94600.
Nagata, R., Moreira, M., Pimentel, C., and Morandini, A. (2015). Food web characterization based on δ15N and δ13C reveals isotopic niche partitioning between fish and jellyfish in a relatively pristine ecosystem. Marine Ecology Progress Series 519, 13–27.
Nakamura, I., Goto, Y., and Sato, K. (2015). Ocean sunfish rewarm at the surface after deep excursions to forage for siphonophores. Journal of Animal Ecology 84, 590–603.
Panikkar, N.K., and Prasad, R.R. (1952). On an interesting association of Ophiuroids fish and crab with the jellyfish Rhopilema hispidum. Journal of Bombay Natural History Society 51, 1–2.
Pauly, D., Graham, W.M., Libralato, S., Morissette, L., and Deng Palomares, M.L. (2009). Jellyfish in ecosystems, online databases, and ecosystem models. Hydrobiologia 616, 67– 85.
Post, D.M. (2002). Using Stable Isotopes to Estimate Trophic Position: Models, Methods, and Assumptions. Ecology 83, 703–718.
Purcell, J.E. (2005). Climate effects on formation of jellyfish and ctenophore blooms: a review. Journal of the Marine Biological Association of the UK 85, 461–476.
Reide, J.O., Rall, B.C., Banasek-Richter, C., Navarrete, S.A., Wieters, E.A., Emmerson, M.C., Jacob, U., and Brose, U. (2010). Scaling of Fod-Web Properties with Diversity and Complexity Across Ecosystems. Advances in Ecological Research (Vol. 42).
Riascos, J.M., Aguirre, W., Hopfe, C., Morales, D., Navarette, A., and Tavera, J. (2018). Floating nurseries? Scyphozoan jellyfish, their food and their rich symbiotic fauna in a tropical estuary. PeerJ Preprints 6, e26497v1.
Richardson, A.J., Bakun, A., Hays, G.C., and Gibbons, M.J. (2009). The jellyfish joyride: causes, consequences and management responses to a more gelatinous future. Trends in Ecology & Evolution 24, 312–22.
Riisgård, H., and Larsen, P. (2010). Particle capture mechanisms in suspension-feeding invertebrates. Marine Ecology Progress Series 418, 255–293.
181
Roughgarden, J. (1996). Why fisheries collapse and what to do about it. Proceedings of the National Academy of Sciences 93, 5078–5083.
Stewart, B.D., and Jones, G.P. (2001). Associations between the abundance of piscivorous fishes and their prey on coral reefs: implications for prey-fish mortality. Marine Biology 138, 383–397.
Tupper, M., and Boutilier, R.G. (1995). Effects of habitat on settlement, growth, and postsettlement survival of Atlantic cod (Gadus morhua). Canadian Journal of Fisheries and Aquatic Sciences 52, 1834–1841.
Vander Zanden, M.J., and Rasmussen, J.B. (2001). Variation in delta δ15N and delta δ13Ctrophic fractionation: Implications for aquatic food web studies. Limnology and Oceanography 46, 2061–2066.
Woodward, G., Ebenman, B., Emmerson, M., Montoya, J.M., Olesen, J.M., Valido, A., and Warren, P.H. (2005). Body size in ecological networks. Trends in Ecology & Evolution 20, 402–9.
182
Appendix I List of papers documenting jellyfish-fish associations in the literature used to create phylogenetic comparative analysis dataset in ‘Chapter 2: Unravelling the macro-evolutionary ecology of fish- jellyfish associations: life in the ‘gingerbread house’’
No. Fish-jellyfish association source papers 1 ALVARINO, A. 1985. Predation in the plankton realm; mainly with reference to fish larvae. Invest. Mar. CICIMAR, 2: 1 – 122 2 ANDRIASHEV, A. P. 1954. Fishes of the northern seas of U.S.S.R. Zool. Inst. Acad. Sci. U.S.S.R. Moscow 566 pp. (In Russian), ill. 3 Auster, P. J., C. A. Griswold, M. J. Youngbluth & T. G. Bailey, 1992. Aggregations of myctophid fishes with other pelagic fauna. Envir. Biol. Fishes 35: 133–139. 4 BAUGHMAN, J.L. 1950. Random notes on Texas fishes. Parts 1 and 2. Tex. J. Sci. 2(1):117-38; 2(2):242-63 5 BEEBE, W. 1928. Beneath tropic seas, a record of diving among the coral reefs of Haiti. G. P. Putnam's Sons, N. Y. xiii + 234, ill. 6 BEEBE, W. 1936. A West Indian grand tour. Bull. N. Y. Zool. Soc. 39(4):127-41, ill. 7 BERRY, F. H. 1959. Young jack crevalles (Caranx species) off the south eastern coast of the United States. U. S. Fish & Wildl. Serv. 59(152):417-535, ill. 8 BESEDNOV, L. N. 1960. Some data on the ichthyofauna of the Pacific Ocean driftwood. Acad. Sci. U.S.S.R. Trud. Inst. Oceanogr. 41: 192-7. (In Russian), ill. 9 BIGELOW, H. B. AND W. W. WELCH. 1924. Fishes of the Gulf of Maine. Bull. U. S. Bur. Fish. 40(Pt. 1):1-567, ill. 10 Bonaldo RM, Krajewski JP, Sazima I (2004) Does the association of young fishes with jellyfishes protect from predation? A report on a failure case due to damage to the jellyfish. Neotrop Ichthyol 2: 103– 105. 11 Brodeur, R. D. (1998). In situ observations of the association between juvenile fishes and scyphomedusae in the Bering Sea. Marine Ecology Progress Series, 163, 11-20. 12 Cevik, C., Derici, O. B., & Cevik, F. (2011). First record of Phyllorhiza punctata von Lendenfeld, 1884 (Scyphozoa: Rhizostomeae: Mastigiidae) from Turkey. 13 Coleman N (1977) A field guide to Australian marine life. Rigby, Adelaide 14 COLLETT, R. 1896. Poissons provenant des Campagnes du yacht 1'Hirondelle (1885-1888). In Resultats des campagnes Scientifiques . . . Yacht Albert I, Prince . . . de Monaco. Govt. of Monaco. Fasc. (10):1-198, ill. 15 COLLINGWOOD, C. 1868. Rambles of a naturalist on the shores and waters of the China Sea . ..in 1866 and 1867. London xiii + 445, ill. 16 COLTON, J. B., JR. AND R. F. TEMPLE. 1961. The enigma of Georges Bank spawning. Limnol. & oceanogr. 6(3):280-91, ill. 17 CUNNINGHAM, J. T. 1886. The eggs and larvae of Teleosteans. Trans. Roy. Soc. Edinburgh 33(Pt. 1):97-136, ill. 1896. The natural history of the marketable marine fishes of the British Islands. Macmillan and Co., Ltd., London xvi+375, ill. 18 Davenport, J., & Rees, E. I. S. (1993). Observations on neuston and floating weed patches in the Irish Sea. Estuarine, Coastal and Shelf Science, 36(4), 395-411. 19 Drazen, J. C., & Robison, B. H. (2004). Direct observations of the association between a deep-sea fish and a giant scyphomedusa. Marine and Freshwater Behaviour and Physiology, 37(3), 209-214.
183
20 Duffy, D.C. 1988. Predator-prey interactions between Common Terns and butterfish. Ornis Scandinavica 19: 160-163 21 EIGENMANN, C. H. 1902. Description of a new oceanic fish found off Southern New England. Bull. U. S. Fish Comm. 21:35-6, ill. 22 FITCH, J. E. 1949. Some unusual occurrences of fish on the Pacific Coast. Calif. Fish & Game 35(1):41-9, ill. 23 FOWLER, H. W. 1945. A study of the fishes of the southern Piedmont and Coastal Plain. Monogr. Acad. Nat. Sci. Phila. (7):1-408, ill 24 GOSLINE, W. A. AND V. E. BROCK. 1960. Hand- book of Hawaiian fishes. Univ. Hawaii Press, Honolulu ix + 372, ill. 25 GUNTER, G. 1935. Records of fishes rarely caught in shrimp trawls in Louisiana. Copeia (1):39-40 26 Gunter G (1938) Seasonal variations in abundance of certain estuarine and marine fishes in Louisiana, with particular reference to life histories. Ecol Monogr 8: 313–346 27 GiJNTHER, A. 1880. An introduction to the study of fishes. A. & C. Black, Ltd., Edin- burgh xvi + 720, ill. 28 HARDY, A. C. 1959. The operf sea: its natural history; Part II. Fish and fisheries, with chapters on whales, turtles and animals of the sea floor. Collins, London xiv +322, ill. 29 HARGITT, C. W. 1905. The medusae of the Woods Hole Region. Bull. U. S. Bur. Fish. 24:21-79, ill. 30 Hay, S. J., Hislop, J. R. G. and Shanks, A. M. (1990) North Sea Scyphomedusae; summer distribution, estimated biomass and significance particularly for 0-Group gadoid fish. Neth. J. Sea Res., 25, 113–130. 31 HEINCKE, FR. 1900. Eier und Larven von Fischen der Deutschen Bucht; II. Die Bestimmung der Schwimmenden Fischeier und die Methodik der Eimessungen. Komm. Untersuch. deutsch. Meere in Kiel und Biolog. Anstalt Helgoland. Neue Folga 3(2):129-332, ill. 32 HICKSON, S. J. 1906. Coelenterata and ctenophora. In The Cambridge Nat. Hist. Vol. 1:243-424, ill. 33 Hildebrand, S. F. (1941). An annotated list of salt and brackish water fishes, with a new name for a menhaden, found in North Carolina since the publication of" The Fishes of North Carolina" by Hugh M. Smith in 1907. Copeia, 1941(4), 220-232. 34 Janssen, J., & Harbison, G. R. (1981). Fish in salps: the association of squaretails (Tetragonurus spp.) with pelagic tunicates. Journal of the Marine Biological Association of the United Kingdom, 61(4), 917-927 35 JONES, S. 1960. Notes on animal associations. 2. The scyphomedusa Acromitus flagellatus Stiasny and young Seleroides leptolepis (Cuvier & Valenciennes) with the latter forming a vanguard. J. Mar. Biol. Assn. India 2(1):51-2, ill. 36 JORDAN, D. S. 1923. Note on Icichthys lockingtoni Jordan and Gilbert, a pelagic fish from California. Proc. U. S. Nat. Mus. 63(4):1-3, ill. 37 JORDAN, D. S. AND B. EVERMANN. 1896-1900. The fishes of North and Middle America. Four volumes. Bull. U. S. Nat. Mus. 96:1-3313, ill. 38 KENNEDY, M. 1954. The sea angler's fishes. Hutchinson, London xiv + 524, ill. 39 KISHINOUYE, K. 1910. Some medusae of Japanese waters. Coll. Sci. Univ. Tokyo 27(9):1- 35, ill.
184
40 Kingsford, M.J., Pitt, K.A. and Gillanders, B.M. (2000) Management of jellyfish fisheries, with special reference to the order Rhizostomeae. Oceanography and Marine Biology Annual Review 38, 85–156. 41 KOBAYASHI, K. 1961. Primary record of Psenes maculatus Liitken, from the North Pacific. Bull. Fac. Fish. Hokkaido Univ. Hakodate, Japan 11(4):191-4. 42 Koeller, P. A., P. C. F. Hurley, P. Perley & J. D. Neilson, 1986. Juvenile fish surveys on the Scotian Shelf: implications for year- class size assessments. J. Cons. int. Explor. Mer. 43: 59–76. 43 Kondo, Y., Ohtsuka, S., Nishikawa, J., Metillo, E., Pagliawan, H., Sawamoto, S., ... & Urata, M. (2014). Associations of fish juveniles with rhizostome jellyfishes in the Philippines, with taxonomic remarks on a commercially harvested species in Carigara Bay, Leyte Island. Plankton and Benthos Research, 9(1), 51-56. 44 Larson RJ (1987) Daily ration and predation by medusae and ctenophores in Saanich Inlet, B.C., Canada. Neth J Sea Res. 45 LAGORCE, J. O. 1924. Fishes and fisheries of our north Atlantic seaboard. In The Book of Fishes. Nat. Geo. Soc. Wash. 76 pp., ill. 46 LE DANOIS, E. 1949. Vie et moeurs des poissons. Payot, Paris 335 pp., ill.Cooco (1834), Padoa 47 Limbaugh, C. (1955) Fish life in the kelpbeds and effects of da harvesting. Univ. Calif. Inst. Mar. Resources.Ref. 55–9, 1–58. 48 LUNEL, G. 1884. Sur un cas de commensalisme d'un Caranx et d'une Crambessa. Rec. Zool. Suisse 1:65-74. Also 1893. Ann. Mag. Nat. Hist. Ser. 5, 12:264-70. 49 MALM, A. W. 1852. Ichthyologiska anmlirkningar. Ofvers. Vetensk. Akad. F6rhandl. Stockholm 9:223-34 50 MALM, A. W. 1877. Goteborgs och Bohuslans Fauna, ryggradsajuren. Goteborg. 51 Mansueti, R. (1963). Symbiotic behavior between small fishes and jellyfishes, with new data on that between the stromateid, Peprilus alepidotus, and the scyphomedusa, Chrysaora quinquecirrha. Copeia, 40-80. 52 Martin, J. W. & H. G. Kuck, 1991. Faunal associates of an un- described species of Chrysaora (Cnidaria, Scyphozoa) in the Southern California Bight, with notes on unusual occurrences of other warm water species in the area. Bull. South. Calif. Acad. Sci. 90: 89–101 53 López, M. J., & Rodríguez, R. J. (2008). First record of association of the blackfin jack Hemicaranx zelotes with the cannonball jellyfish Stomolophus meleagris in Kino Bay, Gulf of California. Hidrobiologica, 18, 173-176. 54 Masuda, R., Yamashita, Y., & Matsuyama, M. (2008). Jack mackerel Trachurus japonicus juveniles use jellyfish for predator avoidance and as a prey collector. Fisheries Science, 74(2), 276-284. 55 Masuda, R. (2008). Ontogenetic changes in the ecological function of the association behavior between jack mackerel Trachurus japonicus and jellyfish. In Jellyfish Blooms: Causes, Consequences, and Recent Advances (pp. 269-277). Springer, Dordrecht. 56 MATTHEWS, J. E. AND H. H. SHOEMAKER. 1952. Fishes using ctenophores for shelter. Copeia (4):270 57 MAYER, A. G. 1910. Medusae of the world. Vol. I and II. Hydromedusae; Vol III. Scyphomedusae. Carneg. Inst. Wash. Pub. (109):1-735, ill. 58 Moreira MGBS (1961) Soˆbre Mastigias scintillae sp. nov. das costas do Brasil. Boletim do Instituto Oceanogra`fico 11:5–30 (in Portuguese)
185
59 Morton, B. (1972). The occurrence of Caranx kalla and C. malabaricus in association with the jellyfish, Cyanea nozakii. Copeia, 1972(4), 873-875. 60 MUNRO, I. S. R. 1958. Handbook of Australian fishes. Austral. Fish. Newsletter 17(12):124, ill. 61 MUNRO, I. S. R. 1960. Ibid. Austral. Fish. Newsletter 19(5):17-20, ill. 62 Nagabhushanam, A. K. (1964). On the biology of the whiting, Gadus merlangus, in Manx waters. Journal of the Marine Biological Association of the United Kingdom, 44(1), 177-202 63 NICHOLS, J. T. 1937. Notes on carangin fishes. Am. 1938. On Caranx mate ludini. (Jordan and Seale). Copeia 1938(3):144-5 64 NICHOLS, J. T 1940. Notes on carangin fishes. V.-Young Trachurus in the Gulf of Mexico. Am. Mus. Novitates. (1067):1-4. 65 NICHOLS, J. T AND C. M. BREDER, JR. 1927. Marine fishes of New York and southern New Eng- land. Zoologica 9(1):1-192, ill. 66 NICHOLS, J. T. AND R. C. MURPHY. 1922. On a collection of marine fishes from Peru. Bull. Am. Mus. Nat. Hist. 46(9):501-16, ill. 67 Noble A (1963) On the association between the fish, Caranx malabaricus Cuv. & Val. and the siphonophore, Porpita pacifica Lesson. J Mar Biol Ass India 5: 142– 143. 68 O’Connor, B. & D. McGrath, 1978. On the occurrence of the scyphozoan Rhizostoma octopus (L.) around the Irish Coast in 1976. Ir. Nat. J. 19: 261–263. 69 Ohtsuka S, Hora M, Suzaki T, Arikawa M, Omura G, Yamada K (2004) Morphology and host-specificity of the apostome ciliate Vampyrophrya pelagica infecting pelagic copepods in the Seto Inland Sea, Japan. Mar Ecol Prog Ser 282: 129–142 70 Ohtsuka, S., Koike, K., Lindsay, D., Nishikawa, J., Miyake, H., Kawahara, M., ... & Komatsu, H. (2009). Symbionts of marine medusae and ctenophores. Plankton and Benthos Research, 4(1), 1-13. 71 Pagès, F., M. G. White & P. G. Rodhouse, 1996. Abundance of gelatinous carnivores in the nekton community of the Antarctic Polar Frontal Zone in summer 1994. Mar. Ecol. Prog. Ser. 141: 139–147 72 PANIKKAR, N. K. AND R. R. PRASAD. 1952. On an interesting association of Ophiuroids, fish and crab with the jellyfish Rhopilema hispidum. J. Bombay Nat. Hist. Soc. 51(1):295-6. 73 PARIN, N. V. 1858. Pelagic ichthyofauna of the northwestern Pacific Ocean. Priroda (Nature) 47(5):60-6. (In Russian), ill 74 PEACH, C. W. 1855. Notes on the habits of medusae and of small fishes. Proc. Linn. Soc. London 2:280-1. 75 PEARSON, J. C. 1941. The young of some marine fishes taken in lower Chesapeake Bay, Virginia, with special reference to the gray sea trout, Cynoscion regalis (Bloch). U. S. Fish & Wildl. Serv. Fish. Bull. 76 PELLEGRIN, J. 1905. Commensalisme des jeunes Caranx et de Rhizostomidae. C. R. Assoc. France av. Sci. Cherbourg 34(2):570-1 77 PHILLIPS, P. J., BURKE, W. D., and KEENER, E. J. 1969. Observa- tions on the trophic significance of jellyfishes in Mississippi Sound with quantitative data on the associative behaviour of small fishes with medusae. Trans. Am. Fish. Soc. 98: 703 -7 12 78 Purcell JE, Brown ED, Stokesbury KDE, Haldorson LH, Shirley TC (2000) Aggregations of the jellyfish Aurelia labiata: abundance, distribution, association
186
with age-0 walleye pollock, and behaviors promoting aggregation in Prince William Sound, Alaska, USA. Mar Ecol Prog Ser 195:145–158 79 Rees, W. J. (1966). Cyanea lamarcki Péron & Lesueur (Scyphozoa) and its association with young Gadus merlangus L.(Pisces). Annals and Magazine of Natural History, 9(100-102), 285-287. 80 Reese, D. C., & Brodeur, R. D. (2015). Species associations and redundancy in relation to biological hotspots within the northern California Current ecosystem. Journal of Marine Systems, 146, 3-16. 81 Riascos JM, Vergara M, Fajardo J, Villegas V, Pacheco AS (2012) The role of hyperiid parasites as a trophic link between jellyfish and fishes. J Fish Biol 81: 1686−1695 82 Risso, A. 1810. Ichthyologie de Nice. Chez F. Schoell, Paris xxxvi + 388, ill. 83 Robison, B. H., 1983. Midwater biological research with the WASP ADS. Mar. Tech. Soc. J. 17: 21–27. 84 Robison, B.H. (1984). Herbivory by the myctophid fish Ceratoscopelus warmingii. Mar. Biol., 84, 119–123 85 Rountree, R. A. (1983). The ecology of Stomolophus meleagris, the cannon ball jellyfish, and its symbionts, with special emphasis on behavior. Unpublished manuscript. Wilmington, NC, University of North Carolina. 86 SARS, G. O. 1879A. Report of practical and scientific investigations of the cod fisheries near the Loffoden Islands, . . . 1864-69. Translated by H. Jacobsen from Norwegian. Rept. Dept. Interior, Christiania, 1869. Rept. U. S. Fish Comm. 1877 (5):565-611. 87 Nakamura, I., Goto, Y., & Sato, K. (2015). Ocean sunfish rewarm at the surface after deep excursions to forage for siphonophores. Journal of Animal Ecology, 84(3), 590-603. 88 SCHEFFER, V. B. 1940. Two recent records of Zaprora silenus Jordan from the Aleutian Islands. Copeia (3):203. 89 SCHEURING, L. 1915. Beobachtung fiber den Parasitismus pelagischer Jungfische. Biol. Centralblatt. 35:181-90 90 SCHULTZ, L. P. 1962. [MS. addenda to "Fishes of the Marshall Islands"]. U. S. Nat. Mus. 91 SCORTECCI, G. 1957. Animali: come sono, dove vivono, come vivono. Volume 5. Anfibi, Pesci. Edizioni Labor, Milano 1021 pp.,ill 92 SHERRIN, R. A. A. 1886. Handbook of the fishes of New Zealand. Wilson & Horton, Auckland 307 pp.,ill. 93 Shojima Y (1962) On the postlarvae and juveniles of carangid fishes collected together with the jellyfishes. Contrib Seikai Regional Fish Res 147: 48–58. (in Japanese with English abstract) 94 Shojima Y (1963) Scyllarid phyllosomas’ habit of accompanying the jelly-fish. Bull Jap Soc Sci Fish 29: 349–353 95 SMITH, H. M. 1907. The fishes of North Carolina. N. C. Geol. Econ. Surv. Vol. 2:xi + 453, Ill. 96 SMITH, J. L. B. 1949. The stromateid fishes of South Africa. Ann. Mag. Nat. Hist. Ser. 12, 2(23):839-51. 97 SMITH, J. L. B. 1950. The sea fishes of South Africa. Central News Agency, Ltd., Capetown xvi + 550. Also Fourth Ed. 1961. Ibid. xvi +480, ill.
187
98 Southcott R V, Glover CJM. THE OCCURRENCE OF DESMONEMA-GAUDICHAUDI NEW-RECORD LESSON SCYPHOZOA SEMAEOSTOMEAE IN SOUTH AUSTRALIAN WATERS WITH RECORDS OF FISH-JELLYFISH SYMBIOSES. Transactions of The Royal Society of South Australia. 1987;111:131–2 99 Spanier, E., & Galil, B. S. (1991). Lessepsian migration: a continuous biogeographical process. Endeavour, 15(3), 102-106. 100 STEAD, D. G. 1906. Fishes of Australia; a popular and systematic guide to the study of the wealth within our waters. Wm. Brooks & Co.,Sydney xii + 278, ill. 101 Thiel, H., 1979. Assoziationen von Quallen und Fischen. Nat. Mus. 109: 353–360. 102 TOLLEY, S. G. 1987. Association of young Chloroscombrus chry- sur1i.s (Pisces: Carangidae) with the jellyfish Aurelia aurira. Copeia, 1987: 216-219. 103 TOMIYAMA, I. AND T. ABE. 1954. Figures and descriptions of the fishes of Japan (a continu- ation of Dr. Shigaho Tanaka's work). Kazamna Shobo, Tokyo 50:983- 1011, ill 104 VAN BENEDEN, J. P. 1876. Animal parasites and messmates. Kegan Paul, Trench & Co., London xxviii + 274, ill. (fourth ed. publ. in 1889 105 WHITLEY, G. P. 1947. The shepherd fish and its flock (popular account of stromateoid and allied fishes). Austral. Mus. Mag. Sydney 9: 194-200, ill. 106 Yasuda T (2003) Jellyfish: UFO of the sea. Koseisha- Koseikaku, Tokyo (in Japanese)
188
Appendix II Phylogenetic comparative dataset showing whether each species displays jellyfish association behaviour, and whether each fish is a demersal type (benthic, benthopelagic or reef-associated) or fully pelagic living. This data set was used to carry out the ancestral state reconstruction and the correlated evolution analyses in ‘Chapter 2: Unravelling the macro-evolutionary ecology of fish-jellyfish associations: life in the ‘gingerbread house’’.
Association Habitat Species name (1=associating, 0= non- (1=demersal type, 0=fully associating) pelagic) Acanthopagrus_australis 1 1 Alepes_djedaba 1 1 Alepes_kleinii 0 1 Aluterus_monoceros 1 1 Aluterus_scriptus 0 1 Anoplopoma_fimbria 0 1 Atule_mate 1 1 Auxis_rochei_rochei 0 0 Auxis_thazard_thazard 0 0 Balistes_capriscus 0 1 Balistes_polylepis 0 1 Boops_boops 0 1 Brosme_brosme 1 1 Canthidermis_maculata 0 1 Carangoides_bartholomaei 1 1 Carangoides_equula 1 1 Carangoides_malabaricus 1 1 Caranx_caballus 0 0 Caranx_caninus 0 0 Caranx_crysos 1 1 Caranx_hippos 1 1 Caranx_melampygus 1 1 Centrolophus_niger 1 0 Chaetodipterus_faber 1 1 Chloroscombrus_chrysurus 1 0 Ciliata_mustela 0 1 Ciliata_septentrionalis 0 1 Cubiceps_gracilis 1 0 Decapterus_kurroides 0 1 Decapterus_maruadsi 1 1 Diplodus_puntazzo 0 1 Epinephelus_areolatus 0 1
189
Euthynnus_affinis 0 0 Gadus_macrocephalus 1 1 Gadus_morhua 1 1 Gadus_ogac 0 1 Gaidropsarus_mediterraneus 0 1 Girella_nigricans 1 1 Girella_punctata 0 1 Gnathanodon_speciosus 1 1 Hemicaranx_zelotes 1 1 Hippocampus_abdominalis 0 1 Hippocampus_erectus 0 1 Hippocampus_zosterae 1 1 Icichthys_lockingtoni 1 0 Icosteus_aenigmaticus 1 0 Katsuwonus_pelamis 0 0 Leuroglossus_stilbius 1 0 Liza_argentea 0 1 Lutjanus_lutjanus 0 1 Macroramphosus_scolopax 1 1 Melanogrammus_aeglefinus 1 1 Merlangius_merlangus 1 1 Merluccius_albidus 0 1 Merluccius_australis 0 1 Merluccius_bilinearis 1 1 Merluccius_polli 0 1 Microgadus_tomcod 0 1 Micromesistius_poutassou 0 0 Mola_mola 0 0 Mullus_surmuletus 0 1 Naucrates_ductor 1 1 Ocyurus_chrysurus 0 1 Oplegnathus_fasciatus 1 1 Oplegnathus_punctatus 0 1 Pagellus_acarne 0 1 Pagrus_pagrus 0 1 Parablennius_sanguinolentus 0 1 Parablennius_tentacularis 0 1 Paralabrax_clathratus 0 1 Peprilus_simillimus 1 1 Peprilus_triacanthus 1 1 Pholis_nebulosa 0 1
190
Pollachius_pollachius 1 1 Pollachius_virens 0 1 Polyprion_americanus 0 1 Pomatomus_saltatrix 0 0 Pristigenys_alta 0 1 Psenes_arafurensis 1 0 Psenes_cyanophrys 1 0 Psenes_maculatus 1 0 Psenes_pellucidus 1 0 Psenopsis_anomala 1 1 Pseudocaranx_dentex 1 1 Rachycentron_canadum 0 1 Rhabdosargus_sarba 1 1 Schedophilus_medusophagus 1 0 Schedophilus_ovalis 1 1 Schedophilus_velaini 0 0 Scomber_scombrus 0 0 Sebastes_caurinus 0 1 Sebastes_inermis 0 1 Sebastes_thompsoni 0 1 Sebastes_vulpes 0 1 Selaroides_leptolepis 1 1 Seriola_lalandi 0 1 Seriola_rivoliana 0 1 Seriola_zonata 1 1 Seriolella_brama 1 1 Seriolina_nigrofasciata 0 1 Sphyraena_argentea 0 0 Sphyraena_barracuda 0 1 Stenobrachius_leucopsarus 1 0 Stenobrachius_nannochir 0 0 Stephanolepis_hispidus 1 1 Stigmatopora_argus 0 1 Stromateus_fiatola 1 1 Stromateus_stellatus 0 1 Syngnathus_floridae 0 1 Syngnathus_typhle 0 1 Tetragonurus_cuvieri 1 0 Thamnaconus_modestus 1 1 Theragra_chalcogramma 1 1 Thunnus_alalunga 0 0
191
Thunnus_albacares 0 0 Thunnus_thynnus 0 0 Trachurus_declivis 1 1 Trachurus_japonicus 1 0 Trachurus_lathami 1 1 Trachurus_mediterraneus 1 0 Trachurus_murphyi 0 0 Trachurus_novaezelandiae 1 0 Trachurus_picturatus 0 1 Trachurus_symmetricus 1 0 Trachurus_trachurus 1 0 Urophycis_chuss 1 1 Urophycis_regia 0 1 Urophycis_tenuis 0 1 Xiphias_gladius 0 0 Zaprora_silenus 1 1
192
Appendix III Bell diameter, wet mass, and δ15N of different body parts of jellyfish sampled from Strangford Lough, Northern Ireland. Data used in ‘Chapter 3: Inter-tissue isotopic variation in three scyphozoan jellyfish’
Species Bell diameter Wet mass Bell Gonad Oral arms (cm) (g) δ15N δ15N δ15N A. aurita 6.0 12 7.3 7.6 7.8 A. aurita 7.0 16.6 6.7 - - A. aurita 8.0 30.2 6.9 7.6 7.2 A. aurita 9.0 31.9 9.2 8.0 8.2 A. aurita 10.0 42.2 7.1 8.1 8.0 A. aurita 11.0 54.6 9.0 7.5 8.1 A. aurita 12.0 82.4 9.6 8.1 8.2 A. aurita 13.0 98.3 8.6 8.0 7.7 A. aurita 14.0 110.3 9.2 7.7 - A. aurita 15.0 124.1 8.9 8.8 8.3 A. aurita 16.0 172.1 9.1 8.6 8.6 A. aurita 17.0 191.3 10.2 - 8.1 A. aurita 18.0 206.3 8.6 7.3 8.8 A. aurita 19.0 191.5 9.8 9.7 8.9 A. aurita 20.0 290.8 7.4 6.8 7.3 A. aurita 22.0 400.1 8.0 7.4 7.1 A. aurita 10.0 42.01 7.8 11.0 10.3 A. aurita 11.0 65.2 8.5 12.0 12.7 A. aurita 13.0 100.9 9.0 10.5 11.1 A. aurita 15.0 135.40 9.7 9.3 9.3 A. aurita 16.5 169.9 9.9 11.7 11.0 A. aurita 17.5 186 8.6 10.7 10.6 A. aurita 18.5 244.1 10.8 10.0 8.4 A. aurita 20.0 279.90 10.2 11.9 11.7 A. aurita 21.0 417 10.3 15.8 8.0 A. aurita 22.0 370.20 11.0 - 8.1 A. aurita 23.5 391.1 9.9 - - A. aurita 25.0 504.60 12.1 11.2 10.8 A. aurita 27.5 722.9 10.3 10.9 10.2 A. aurita 29.5 1097 10.9 10.2 12.7 A. aurita 30.0 971.3 10.0 - - A. aurita 34.0 1399.6 10.3 - - A. aurita 35.0 1701.6 14.8 19.8 13.0 A. aurita 36.0 1510.8 10.9 11.5 11.0 A. aurita 14.5 118.480 8.2 10.8 11.0 A. aurita 17.5 214.400 12.4 11.5 12.4 193
A. aurita 18.5 290.230 12.5 12.4 13.9 A. aurita 20.0 266.780 11.4 10.0 12.4 A. aurita 26.5 732.140 10.2 10.5 11.0 A. aurita 28.0 802.870 12.3 10.7 9.5 A. aurita 29.0 1025.580 13.1 11.3 10.7 A. aurita 19.0 247.860 12.2 12.3 12.5 A. aurita 19.5 340.930 11.3 10.8 12.0 C. lamarckii 4.0 3.2 9.3 - 8.8 C. lamarckii 5.0 12.8 8.5 - 8.4 C. lamarckii 6.0 23 8.1 - 9.0 C. lamarckii 7.0 49.2 8.8 9.9 9.9 C. lamarckii 8.0 69.8 7.7 8.5 8.8 C. lamarckii 9.0 54.4 8.5 10.5 10.3 C. lamarckii 10.0 50.5 9.4 - 9.6 C. lamarckii 12.5 168.70 12.0 10.0 11.4 C. lamarckii 13.0 184.70 11.9 10.0 10.7 C. lamarckii 14.5 135.00 9.0 9.9 9.8 C. lamarckii 16.5 304.20 10.0 10.3 10.7 C. lamarckii 3.5 4.1 11.7 15.7 11.6 C. lamarckii 6.0 13.8 12.3 10.8 12.1 C. lamarckii 7.0 30.4 14.2 11.7 - C. lamarckii 7.5 69.30 11.3 - - C. lamarckii 8.0 39.30 10.0 11.5 12.1 C. lamarckii 9.0 49.60 11.7 11.5 12.1 C. lamarckii 9.5 57.4 10.9 - - C. lamarckii 10.0 69.3 11.5 13.3 17.5 C. lamarckii 11.0 100.10 10.9 11.6 12.7 C. lamarckii 12.0 94.40 14.2 11.9 14.1 C. lamarckii 13.0 152.20 13.4 13.3 17.7 C. lamarckii 14.0 130.50 10.4 - 14.9 C. lamarckii 15.0 195.30 9.0 11.5 11.0 C. lamarckii 16.0 249.70 12.7 16.4 14.5 C. lamarckii 17.5 230.90 11.4 11.1 13.2 C. lamarckii 18.0 324.60 13.0 11.5 20.4 C. lamarckii 19.0 384.80 15.8 11.5 12.1 C. lamarckii 20.0 492.80 9.4 12.2 19.0 C. lamarckii 6.5 19.39 12.1 12.3 - C. lamarckii 7.0 22.26 12.0 - - C. lamarckii 8.0 46.63 10.3 11.8 13.6 C. lamarckii 11.0 69.73 14.0 11.0 12.7 C. lamarckii 13.0 114.43 12.3 11.8 13.3 C. lamarckii 8.0 40.05 10.7 13.0 14.0 C. lamarckii 9.0 60.01 12.4 11.8 - 194
C. lamarckii 10.5 102.38 11.5 12.3 12.5 C. capillata 8.0 50.4 7.8 - 7.3 C. capillata 16.0 218.9 7.6 - 7.8 C. capillata 7.0 25.70 8.5 - 9.9 C. capillata 10.5 68.37 9.2 7.1 - C. capillata 25.0 1900.20 12.1 9.9 11.1 C. capillata 7.5 33.3 11.0 13.0 11.3 C. capillata 8.0 25.3 12.9 12.4 - C. capillata 12.5 102.2 17.5 - - C. capillata 22.0 1295.5 19.2 13.4 17.9 C. capillata 24.5 1023 10.8 11.8 11.9 C. capillata 26.0 995.1 12.1 - - C. capillata 27.5 1418 25.3 - 23.3 C. capillata 29.0 1630.7 13.4 11.4 12.1 C. capillata 31.0 1728.8 12.1 11.6 11.5 C. capillata 55.0 14533.5 13.7 11.6 13.2 C. capillata 85.0 23682.2 12.1 14.3 17.0 C. capillata 10.5 92.84 11.5 11.2 12.5 C. capillata 13.5 193.58 13.6 - 12.9 C. capillata 14.5 222.66 11.5 11.6 13.6 C. capillata 16.0 353.01 11.7 11.8 12.2 C. capillata 18.5 496.81 11.3 11.3 13.5 C. capillata 23.5 750.28 13.3 16.6 19.8 C. capillata 26.0 1175.8 19.0 22.1 17.6 C. capillata 27.5 1154.76 12.4 12.5 14.1 C. capillata 29.0 1950.2 14.1 11.5 15.0 C. capillata 30.0 2132.36 11.8 12.3 15.5 C. capillata 31.0 2044.91 13.3 12.1 14.0 C. capillata 32.0 2481.58 11.8 11.8 15.4 C. capillata 34.5 3706.53 14.6 12.9 16.1 C. capillata 36.0 4038.82 12.5 12.4 12.8 C. capillata 39.5 3964.95 15.6 13.8 17.4 C. capillata 43.0 6071.83 21.4 22.6 20.2 C. capillata 50.0 7556.7 18.7 19.3 18.8 C. capillata 6.0 19.42 11.9 12.1 12.8 C. capillata 12.0 152.04 12.4 11.5 14.0 C. capillata 14.0 241.49 11.8 11.3 17.6 C. capillata 25.0 681.5 12.8 12.9 13.8 C. capillata 25.5 1136.67 13.3 - 13.6 C. capillata 26.0 966.1 12.8 - 13.9 C. capillata 27.0 1329.55 13.3 12.3 16.8 C. capillata 30.5 1228.01 13.5 - 15.0 C. capillata 32.5 1791.44 12.7 12.9 13.3 195
C. capillata 32.5 1974.44 16.9 13.7 - C. capillata 33.5 2001.42 13.2 - 14.9 C. capillata 35.0 2411.42 16.1 12.7 15.0 C. capillata 35.5 3473.21 12.8 13.4 16.8 C. capillata 36.0 3529.46 14.9 14.7 14.7 C. capillata 37.0 2268.44 13.4 14.4 13.3 C. capillata 38.0 2797.2 13.3 13.8 16.8 C. capillata 41.0 3326.16 14.6 13.4 15.4
196
Appendix IV Glutamine and phenylalanine amino acid and bulk δ15N data from five jellyfish species collected from the North East Atlantic, Mediterranean and South East Pacific from ‘Chapter 4: Regional and individual variation in jellyfish trophic position shown using bulk and amino acid δ15N.’
Location Species Size(mm) Glutamine Phenylalaline Bulk δ15N δ15N δ15N North East Atlantic A. Aurita 170 21.26 7.01 11.76
North East Atlantic A. Aurita 185 24.14 4.16 11.14
North East Atlantic A. Aurita 165 24.80 5.50 12.41
North East Atlantic A. Aurita 100 24.99 4.30 11.56
North East Atlantic A. Aurita 185 23.88 6.50 12.11
North East Atlantic A. Aurita 180 24.19 7.90 12.31
North East Atlantic A. Aurita 190 21.97 6.19 11.05
North East Atlantic C. lamarckii 185 26.71 6.10 12.76
North East Atlantic C. lamarckii 105 25.53 5.21 11.79
North East Atlantic C. lamarckii 140 25.03 5.66 11.59
North East Atlantic C. lamarckii 115 25.64 5.30 11.02
North East Atlantic C. lamarckii 239 26.51 7.86 12.50
North East Atlantic C. lamarckii 243 26.13 7.82 12.74
North East Atlantic C. lamarckii 203 25.20 5.80 11.90
North East Atlantic C. capillata 190 27.96 5.81 12.65
North East Atlantic C. capillata 240 27.49 5.17 11.87
North East Atlantic C. capillata 200 27.88 6.90 12.59
North East Atlantic C. capillata 180 28.83 6.81 11.84
North East Atlantic C. capillata 230 28.29 6.60 13.68
197
North East Atlantic C. capillata 200 25.80 4.71 13.23
Mediterranean P. noctiluca 80 15.98 0.70 4.48
Mediterranean P. noctiluca 100 13.11 -3.70 4.47
Mediterranean P. noctiluca 85 14.37 0.10 3.82
Mediterranean P. noctiluca 95 11.05 -0.95 4.24
Mediterranean P. noctiluca 75 10.45 -1.35 0.48
Mediterranean P. noctiluca 65 16.46 -1.62 4.10
Mediterranean P. noctiluca 93 15.27 -0.50 2.06
South East Pacific P. noctiluca 45 30.72 7.51 13.72
South East Pacific P. noctiluca 70 27.22 9.10 15.24
South East Pacific P. noctiluca 67 30.26 9.32 15.13
South East Pacific P. noctiluca 45 28.52 10.50 16.25
South East Pacific P. noctiluca 55 28.73 11.00 13.67
South East Pacific P. noctiluca 90 28.69 10.50 16.38
South East Pacific P. noctiluca 70 29.10 11.48 15.72
Mediterranean V. velella 35 12.76 - 3.13
Mediterranean V. velella 44 12.84 0.30 3.67
Mediterranean V. velella 46 12.42 -1.75 3.49
Mediterranean V. velella 44 11.98 1.31 3.71
Mediterranean V. velella 25 12.91 -0.45 3.74
Mediterranean V. velella 32 11.68 1.46 2.62
Mediterranean V. velella 37 14.66 0.37 2.97
198
Appendix V Amino acid mean and ± SD δ15N for four scypyhozoan and one hydrozoan jellyfish sampled from the Irish Sea, Mediterranean and S.E. Pacific. (Ala – Alaninie, Gly - Glycine, Val - Valine, Leu - Leucine, Thr - threonine, Ser - Serine, Pro – Proline, Asp - Asparagine, Glu - Glutamine acid , Hyp - Hydroxyproline , Phe - Phenylalanine, Lys - lysine)
Species Location Me Me Me Me Me Me Me Me Me Me Me Me an an an an an an an an an an an an δ15 δ15 δ15 δ15 δ15 δ15 δ15 δ15 δ15 δ15 δ15 δ15 N N N N N N N N N N N N Ala Gly Val Leu Thr Ser Pro As Glu Hy Ph Lys p p e A. Irish Sea 21. 3.8 11. 17. - 6.5 19. 16. 23. 19. 5.9 4.3 aurita 15 5 56 25 4.5 8 76 15 60 17 4 3 3 ± SD 1.1 0.5 1.0 2.7 1.8 0.8 0.8 1.0 1.4 0.7 1.3 1.3 2 8 9 4 1 6 7 0 3 0 8 5 C. Irish Sea 22. 2.3 13. 21. - 5.8 19. 18. 25. 19. 6.2 5.8 lamarck 21 9 06 96 8.5 5 32 97 82 51 5 9 ii 7 ± SD 1.3 1.1 1.8 4.3 1.4 1.2 1.0 1.1 0.6 1.3 1.1 0.6 9 2 0 0 4 4 4 5 4 7 3 7 C. Irish Sea 25. 2.0 14. 21. - 7.1 21. 20. 27. 22. 6.0 6.1 capillat 68 6 10 43 10. 4 76 13 71 70 0 3 a 81 ± SD 1.7 0.6 2.8 2.7 1.0 1.3 1.4 1.3 1.0 1.2 0.9 1.1 2 9 9 7 0 1 6 9 4 4 2 7 P. Mediterr 14. - 9.2 8.5 - - 7.9 10. 13. 8.8 - - noctiluc anean 20 3.5 7 1 15. 1.6 1 67 81 2 1.0 1.1 a 5 68 6 5 4 ± SD 2.3 1.3 1.2 4.1 3.3 1.8 1.8 1.3 2.3 1.7 1.4 1.5 4 0 6 0 8 3 0 5 7 0 2 9 P. S.E. 26. 6.9 13. 22. - 10. 19. 23. 29. 20. 9.9 10. noctiluc Pacific 39 7 20 55 4.9 12 95 00 03 39 1 04 a 4 ± SD 2.6 1.4 1.0 4.8 2.2 1.4 2.1 1.4 1.1 1.3 1.3 1.2 5 4 8 7 3 1 9 2 6 0 6 4 V. Mediterr 10. 2.3 9.5 7.4 - 3.4 11. 9.7 12. 11. 0.2 0.0 velella anean 38 7 2 2 8.3 5 53 8 75 95 1 0 3 ± SD 0.7 0.5 0.7 1.0 1.6 0.5 0.7 0.6 0.9 1.9 1.1 0.9 4 9 8 6 1 9 3 1 6 1 9 2
199
Appendix VI Individual plots of raw and mean δ15N and δ13C for jellyfish and fish sampled from the Irish Sea and isotopic phytoplankton baselines. zooplankton in regards to δ15N but have a lower mean δ13C.
200
201
Appendix VII As a part of the field work and data collection carried out for this thesis, I contributed to a large collaborative project which investigated the role of ocean currents in the secondary spread of a marine invasive comb jelly across western Eurasia. Our findings were published: Jaspers, Cornelia, et al. "Ocean current connectivity propelling the secondary spread of a marine invasive comb jelly across western Eurasia." Global ecology and biogeography 27.7 (2018): 814-827.
Abstract Aim - Invasive species are of increasing global concern. Nevertheless, the mechanisms driving further distribution after the initial establishment of non‐native species remain largely unresolved, especially in marine systems. Ocean currents can be a major driver governing range occupancy, but this has not been accounted for in most invasion ecology studies so far. We investigate how well initial establishment areas are interconnected to later occupancy regions to test for the potential role of ocean currents driving secondary spread dynamics in order to infer invasion corridors and the source–sink dynamics of a non‐native holoplanktonic biological probe species on a continental scale. Location -Western Eurasia. Time period - 1980s–2016. Major taxa studied - ‘Comb jelly’ Mnemiopsis leidyi. Methods- Based on 12,400 geo‐referenced occurrence data, we reconstruct the invasion history of M. leidyi in western Eurasia. We model ocean currents and calculate their stability to match the temporal and spatial spread dynamics with large‐scale connectivity patterns via ocean currents. Additionally, genetic markers are used to test the predicted connectivity between subpopulations. Results - Ocean currents can explain secondary spread dynamics, matching observed range expansions and the timing of first occurrence of our holoplanktonic non‐native biological probe species, leading to invasion corridors in western Eurasia. In northern Europe, regional extinctions after cold winters were followed by rapid recolonizations at a speed of up to 2,000 km per season. Source areas hosting year‐round populations in highly interconnected regions can re‐seed genotypes over large distances after local extinctions. Main conclusions - Although the release of ballast water from container ships may contribute to the dispersal of non‐native species, our results highlight the importance of ocean currents driving secondary spread dynamics. Highly interconnected areas hosting invasive species are crucial for secondary spread dynamics on a continental scale. Invasion risk assessments should consider large‐scale connectivity patterns and the potential source regions of non‐native marine species.
202
Appendix VIII
List of publications and conferences presented in arising from the work detailed in this thesis.
Griffin, D. C., Harrod, C., Houghton, J. D., & Capellini, I. (2019). Unravelling the macro- evolutionary ecology of fish–jellyfish associations: life in the ‘gingerbread house’. Proceedings of the Royal Society B, 286(1899), 20182325. Jaspers, C., Huwer, B., Antajan, E., Hosia, A., Hinrichsen, H. H., Biastoch, A., ... & Griffin, D.C. (2018). Ocean current connectivity propelling the secondary spread of a marine invasive comb jelly across western Eurasia. Global ecology and biogeography, 27(7), 814- 827. International Council for the Exploration of the Sea (ICES) international conference in Spain (Sept 2014). Marine Biological Association (MBA) postgraduate conference poster presentation in QUB (May 2015) Agri-Food and Biosciences Institute (AFBI) postgraduate conference at Hillsborough AFBI (June 2015) Department of Agriculture, Environment and Rural Affairs (DAERA) postgraduate conference at Greenmount CAFRE campus (September 2015) Association for Study of Animal Behaviour (ASAB) postgraduate conference (Poster presentation- April 2016) Jellyfish Blooms Symposium, Barcelona (oral presentation-May 2016) DAERA postgraduate conference at Greenmount CAFRE campus (September 2016) AFBI postgraduate conference at Hillsborough AFBI (May 2017)
203