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Changing jellyfish populations : trends in large marine ecosystems 2011

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      CHANGING JELLYFISH POPULATIONS: TRENDS IN LARGE MARINE ECOSYSTEMS  by  Lucas Brotz  B.Sc., The University of British Columbia, 2000   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE  in  The Faculty of Graduate Studies  (Oceanography)   THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  October 2011   © Lucas Brotz, 2011  ii  Abstract Although there are various indications and claims that jellyfish have been increasing at a global scale in recent decades, a rigorous demonstration to this effect has never been presented.  As this is mainly due to scarcity of quantitative time series of jellyfish abundance from scientific surveys, an attempt is presented here to complement such data with non- conventional information from other sources.  This was accomplished using the analytical framework of fuzzy logic, which allows the combination of information with variable degrees of cardinality, reliability, and temporal and spatial coverage.  Data were aggregated and analysed at the scale of Large Marine Ecosystem (LME).  Of the 66 LMEs defined thus far, which cover the world’s coastal waters and seas, trends of jellyfish abundance (increasing, decreasing, or stable/variable) were identified (occurring after 1950) for 45, with variable degrees of confidence.  Of these 45 LMEs, the overwhelming majority (31 or 69%) showed increasing trends.  Recent evidence also suggests that the observed increases in jellyfish populations may be due to the effects of human activities, such as overfishing, global warming, pollution, and coastal development.  Changing jellyfish populations were tested for links with anthropogenic impacts at the LME scale, using a variety of indicators and a generalized additive model.  Significant correlations were found with several indicators of ecosystem health, as well as marine aquaculture production, suggesting that the observed increases in jellyfish populations are indeed due to human activities and the continued degradation of the marine environment.    iii  Table of Contents Abstract ............................................................................................................................................... ii Table of Contents .............................................................................................................................. iii List of Tables ..................................................................................................................................... vi List of Figures................................................................................................................................... vii Acknowledgments.......................................................................................................................... viii 1. Introduction................................................................................................................................ 1 1.1 Definition of ‘Jellyfish’ ...................................................................................................... 1 1.2 Problem Statement ............................................................................................................. 2 1.3 Challenges of Studying Jellyfish Populations ................................................................ 2 1.4 Impacts of Jellyfish Blooms .............................................................................................. 4 1.5 Anthropogenic Causes of Increasing Jellyfish Populaitons .......................................... 6 1.5.1 Invasive Species ......................................................................................................... 7 1.5.2 Warming ..................................................................................................................... 7 1.5.3 Eutrophication .......................................................................................................... 10 1.5.4 Overfishing ............................................................................................................... 15 1.5.5 Bottom-Trawling ...................................................................................................... 17 1.5.6 Increased Substrate .................................................................................................. 18 1.5.7 Aquaculture .............................................................................................................. 20 1.5.8 Other Factors ............................................................................................................ 20 1.5.9 Synergistic Effects .................................................................................................... 22 1.6 Regime Shifts .................................................................................................................... 24 1.7 The Jellyfish Spiral ........................................................................................................... 25 2 The Jellyfish Index ................................................................................................................... 29 2.1 Methods ............................................................................................................................ 29 2.1.1 Large Marine Ecosystem Approach ...................................................................... 29 2.1.2 1950 Baseline ............................................................................................................. 29 2.1.3 The Jellyfish Chronicles ........................................................................................... 30 2.1.4 Data Not Included ................................................................................................... 31 2.1.5 Abundance Trend .................................................................................................... 32 2.1.6 Scoring Chronicles ................................................................................................... 33 2.1.7 Invasive Species ....................................................................................................... 34 2.1.8 Fuzzy Expert System ............................................................................................... 35 2.1.9 Uncertainty ............................................................................................................... 39 2.2 Results ............................................................................................................................... 40 2.2.1 The Jellyfish Index ................................................................................................... 40 2.2.2 Effects of Invasive Species ...................................................................................... 44 2.2.3 Effects of Jellyfish Overexploitation ...................................................................... 46 2.3 Discussion ......................................................................................................................... 48 2.3.1 Defining an ‘Increase’ .............................................................................................. 48 2.3.2 Invasive Species ....................................................................................................... 51 2.3.3 Taxonomic Concerns ............................................................................................... 51 iv  2.3.4 LME #1 – East Bering Sea ........................................................................................ 52 2.3.5 LME #2 – Gulf of Alaska ......................................................................................... 53 Invasive Species in LME #2 – Gulf of Alaska .................................................... 58 2.3.6 LME #3 – California Current .................................................................................. 58 Invasive Species in LME #3 – California Current ............................................. 65 2.3.7 LME #4 – Gulf of California .................................................................................... 67 2.3.8 LME #5 – Gulf of Mexico ......................................................................................... 67 Invasive Species in LME #5 – Gulf of Mexico ................................................... 69 2.3.9 LME #6 – Southeast U.S. Continental Shelf .......................................................... 70 Invasive Species in LME #6 – Southeast U.S. Continental Shelf ..................... 71 2.3.10 LME #7 – Northeast U.S. Continental Shelf .......................................................... 72 Invasive Species in LME #7 – Northeast U.S. Continental Shelf ................ 76 2.3.11 LME #8 – Scotian Shelf ............................................................................................ 76 2.3.12 LME #9 – Newfoundland-Labrador Shelf ............................................................. 77 2.3.13 LME #10 – Insular Pacific-Hawaiian...................................................................... 77 Invasive Species in LME #10 – Insular Pacific-Hawaiian ............................ 78 2.3.14 LME #11 – Pacific Central-American Coastal ....................................................... 79 Invasive Species in LME #11 – Pacific Central-American Coastal ............. 80 2.3.15 LME #12 – Caribbean Sea ........................................................................................ 80 Invasive Species in LME #12 – Caribbean Sea .............................................. 81 2.3.16 LME #13 – Humboldt Current ................................................................................ 82 Invasive Species in LME #13 – Humboldt Current ...................................... 83 2.3.17 LME #14 – Patagonian Shelf ................................................................................... 83 Invasive Species in LME #14 – Patagonian Shelf .......................................... 84 2.3.18 LME #15 – South Brazil Shelf .................................................................................. 84 Invasive Species in LME #15 – South Brazil Shelf ........................................ 85 2.3.19 LME #16 – East Brazil Shelf .................................................................................... 86 Invasive Species in LME #16 – East Brazil Shelf ........................................... 87 2.3.20 LME #18 – West Greenland Shelf ........................................................................... 87 2.3.21 LME #21 – Norwegian Sea ...................................................................................... 88 Invasive Species in LME #21 – Norwegian Sea ............................................ 88 2.3.22 LME #22 – North Sea ............................................................................................... 89 Invasive Species in LME #22 – North Sea ..................................................... 94 2.3.23 LME #23 – Baltic Sea ................................................................................................ 94 Invasive Species in LME #23 – Baltic Sea ...................................................... 95 2.3.24 LME #24 – Celtic-Biscay Shelf................................................................................. 96 2.3.25 LME #25 – Iberian Coastal ...................................................................................... 98 Invasive Species in LME #25 – Iberian Coastal ............................................. 99 2.3.26 LME #26 – Mediterranean Sea ................................................................................ 99 Invasive species in LME #26 – Mediterranean Sea ..................................... 103 2.3.27 LME #28 – Guinea Current ................................................................................... 105 2.3.28 LME #29 – Benguela Current ................................................................................ 105 2.3.29 LME #30 – Agulhas Current ................................................................................. 107 v  2.3.30 LME #31 – Somali Coastal Current ...................................................................... 107 2.3.31 LME #32 – Arabian Sea.......................................................................................... 108 2.3.32 LME #34 – Bay of Bengal ....................................................................................... 109 2.3.33 LME #35 – Gulf of Thailand .................................................................................. 112 2.3.34 LME #36 – South China Sea .................................................................................. 113 2.3.35 LME #40 – Northeast Australian Shelf ................................................................ 114 2.3.36 LME #41 – East Central Australian Shelf ............................................................ 115 2.3.37 LME #42 – Southeast Australian Shelf ................................................................. 116 Invasive Species in LME #42 – Southeast Australian Shelf ....................... 117 2.3.38 LME #47 – East China Sea ..................................................................................... 117 Invasive Species in LME #47 – East China Sea ........................................... 119 2.3.39 LME #48 – Yellow Sea ........................................................................................... 119 Invasive Species in LME #48 – Yellow Sea .................................................. 120 2.3.40 LME #49 – Kuroshio Current ................................................................................ 121 Invasive Species in LME #49 – Kuroshio Current ...................................... 123 2.3.41 LME #50 – Sea of Japan ......................................................................................... 124 2.3.42 LME #51 – Oyashio Current ................................................................................. 124 2.3.43 LME #52 – Sea of Okhotsk .................................................................................... 125 2.3.44 LME #53 – West Bering Sea................................................................................... 126 2.3.45 LME #60 – Faroe Plateau ....................................................................................... 127 2.3.46 LME #61 – Antarctic............................................................................................... 127 2.3.47 LME #62 – Black Sea .............................................................................................. 128 Invasive Species in LME #62 - Black Sea ..................................................... 130 2.3.48 LME #63 – Hudson Bay ......................................................................................... 130 3 Anthropogenic Impacts and Increasing Jellyfish Populations ......................................... 132 3.1 Methods .......................................................................................................................... 132 3.1.1 Indicators ................................................................................................................ 132 Warming ............................................................................................................. 132 Eutrophication .................................................................................................... 133 Overfishing ......................................................................................................... 133 Bottom-Trawling ................................................................................................ 133 Increased Substrate and Coastal Development .............................................. 133 Aquaculture ........................................................................................................ 134 Cumulative Factors ............................................................................................ 134 3.1.2 Generalized Additive Model ................................................................................ 135 3.2 Results ............................................................................................................................. 136 3.2.1 Generalized Additive Model ................................................................................ 139 3.3 Discussion ....................................................................................................................... 145 4 Conclusions ............................................................................................................................ 149 References ....................................................................................................................................... 150 Appendices ..................................................................................................................................... 187 Appendix A:  Jellyfish Chronicles ........................................................................................... 187 Appendix B:  Belief Indexes ..................................................................................................... 198 vi  List of Tables Table 2.1 Abundance Trend Rule Set ............................................................................................ 33 Table 2.2 Time Score Rule Set ........................................................................................................ 33 Table 2.3 Space Score Rule Set ....................................................................................................... 33 Table 2.4 Reliability Score Rule Set................................................................................................ 33 Table 2.5 Invasive Reliability Score Rule Set ................................................................................ 35 Table 2.6 Score Combination Matrix ............................................................................................. 37 Table 2.7 Membership Rule Set ...................................................................................................... 37 Table 2.8 Belief Profile Selection Rule Set ..................................................................................... 38 Table 2.9 Results including native and invasive species ............................................................ 41 Table 2.10 Results with native species only (effects of invasive species excluded; only those LMEs that had invasive species are shown) ............................................................ 45 Table 2.11 Results with effects of jellyfish overfishing added (only those LMEs with jellyfish overharvesting are shown) .................................................................................... 47 Table 3.1 Anthropogenic factors suspected to cause more jellyfish ........................................ 132 Table 3.2 Impacts and associated indicators tested using a generalized additive model (GAM) ................................................................................................................................... 136 Table 3.3 Significant indicators included in final GAM ............................................................ 140   vii  List of Figures Figure 2.1 Schematic diagram of the fuzzy expert system with the North Sea LME as an example ............................................................................................................................. 36 Figure 2.2 Fuzzy Input Profiles ...................................................................................................... 38 Figure 2.3 Map of results including native and invasive species of jellyfish ........................... 43 Figure 2.4 Jellyfish populations off southern California (CalCOFI 2010) ................................. 60 Figure 2.5 Jellyfish populations off central California (CalCOFI 2010)..................................... 61 Figure 3.1 Pair plots of the Jellyfish Index and non-fishing related indicators.  The first row of plots shows the relationship between the Jellyfish Index and each indicator, with the axes transposed in the first column of plots.  The rest of the plots show the relationships between the various indicators.  The units vary according to the indicator being plotted .......................................................................... 137 Figure 3.2 Pair plots of the Jellyfish Index and indicators related to fishing.  The first row of plots shows the relationship between the Jellyfish Index and each indicator, with the axes transposed in the first column of plots.  The rest of the plots show the relationships between the various indicators.  The units vary according to the indicator being plotted .......................................................................... 138 Figure 3.3 Pair plot of the Jellyfish Index and the indicator for cumulative impacts.  The top plot shows the relationship with the Jellyfish Index on the y-axis, while the bottom plot has the axes transposed ................................................................................. 139 Figure 3.4 Additive effect of the stock status indicator resulting from the GAM.  The plot illustrates the relationship between the Jellyfish Index and the indicator when other indicators are held constant.  The shaded region represents the 95% confidence interval .............................................................................................................. 141 Figure 3.5 Additive effect of the mariculture indicator resulting from the GAM.  The plot illustrates the relationship between the Jellyfish Index and the indicator when other indicators are held constant.  The shaded region represents the 95% confidence interval .............................................................................................................. 142 Figure 3.6 Additive effect of the cumulative impacts indicator resulting from the GAM.  The plot illustrates the relationship between the Jellyfish Index and the indicator when other indicators are held constant.  The shaded region represents the 95% confidence interval ............................................................................................... 143 Figure 3.7 Additive effect of the GDP indicator resulting from the GAM.  The plot illustrates the relationship between the Jellyfish Index and the indicator when other indicators are held constant.  The shaded region represents the 95% confidence interval .............................................................................................................. 144 Figure 3.8 Plot of the observed Jellyfish Index (from Section 2) versus predicted Jellyfish Index (resulting from the GAM) showing the relative success of the model................ 145   viii  Acknowledgments First and foremost, I would like to thank my principle supervisor Dr. Daniel Pauly, who inspired this research and my pursuit of a career in marine science.  I would also like to thank my co-supervisor, Dr. Evgeny Pakhomov, who provided guidance and support throughout the entire process.  Thanks are also due to Dr. Villy Christensen, who graciously agreed to serve on my committee.  Funding provided by the Pew Environment Group and the Sea Around Us Project is gratefully acknowledged. Several colleagues provided invaluable assistance with the methodology, especially Dr. William Cheung, without whom this analysis would have suffered.  Thanks are also owed to Drs. Kristin Kleisner and Ashley McCrea Strub, who were always helpful and patient with my questions.  I am indebted to Ganapathiraju Pramod, who provided a wealth of valuable information on the jellyfish of India.  I must also thank numerous students for their assistance with translation, including Wilf Swartz, Andrés Cisneros Montemayor, Lingo Li, and Laura Tremblay-Boyer.  I also thank the numerous marine professionals and scientists who contributed their knowledge of jellyfish to this study – they are too numerous to mention here, but are cited throughout this review. I would also like to thank the students and staff of the Fisheries Centre, all of whom made this work more enjoyable.  Finally, I would like to thank my family and friends for their continued support. 1  1. Introduction 1.1 Definition of ‘Jellyfish’ Throughout this analysis, the term jellyfish (used interchangeably with jellies), refers to gelatinous zooplankton including medusae of the phylum Cnidaria (scyphomedusae, hydromedusae, cubomedusae, and siphonophores) and planktonic members of the phylum Ctenophora.  Thaliaceans of the order Salpida - pelagic tunicates known as salps - will also be included due to their gelatinous nature, pulsed life cycles, and apparent response to changing oceanic conditions (Loeb et al. 1997; Atkinson et al. 2004; Lee et al. 2010). Pyrosomes and doliolids could arguably be included in such a definition of jellyfish as well. However, time-series data on these organisms are especially sparse, and this dearth of information prevented their inclusion in the analysis. Other gelatinous zooplankton such as appendicularians, molluscs, and chaetognaths are not included due to their different size, life history, ecological role, carbon-to-weight ratio, and the fact that they are generally not considered jellyfish (e.g., definitions in Mianzan & Guerrero 2000; Graham & Bayha 2007; Richardson et al. 2009). Pleustonic jellyfish, such those belonging to the genera Physalia, Porpita, and Velella, have also been excluded because their local distribution is heavily influenced by wind patterns (Mackie 1974).  As such, locations reporting these species are frequently implicated in claims of “unprecedented” blooms and mass beach strandings lacking a historical context. The term jellyfish will be used to refer to both single and multiple species.  This is a common practice in the literature, as opposed to the less colloquial, but possibly more accurate jellyfishes.  Combined, there are roughly 1500 species of pelagic cnidarians, ctenophores, and thaliaceans that have been described (Purcell et al. 2007), and likely thousands more that have yet to be discovered. 2  1.2 Problem Statement While many jellyfish populations fluctuate with climatic cycles (Purcell 2005), recent evidence suggests that jellyfish may also be synanthropic, i.e., benefiting from human interactions with the oceans, and thus may be increasing globally (Mills 2001; Purcell et al. 2007; Pauly et al. 2009b; Richardson et al. 2009).  However, a lack of long-term datasets in most ecosystems makes abundance trends uncertain, and the links with human impacts tend to be correlative or anecdotal.  While previous global reviews of jellyfish populations (e.g., Mills 2001; Chudnow 2008) show evidence of numerous localized increases, the perceived global, or even widespread, increase in jellyfish still lacks a rigorous foundation. As such, the goal of this study will be to establish an analytical framework that will facilitate the comparison of trends in jellyfish populations around the globe in order to determine whether or not they are indeed changing, as well as the extent of any such changes.  To compensate for the lack of available scientific datasets on jellyfish, this framework must be designed in such a way to include a wide variety of information that covers different temporal and spatial scales.  In addition, this information will have variable degrees of reliability, and as such, the relative strength of any conclusions stemming from the analysis must be captured. If jellyfish populations are indeed showing signs of change, these changes will be evaluated and discussed in relation to anthropogenic impacts and other causal mechanisms in the marine environment. 1.3 Challenges of Studying Jellyfish Populations Establishing abundance trends for jellyfish is difficult due to a number of factors. There is a dearth of historical information on jellyfish, as they were usually damaged or not recorded when caught in routine bottom trawl or zooplankton surveys (Pugh 1989; Hay 2006).  In fact, the latter often used gear designed to either exclude jellyfish from plankton samples (e.g., Heinle 1965) or were based on methodologies which explicitly recommended their removal before analysis (e.g., Dovel 1964; Burrell & van Engel 1970).  For example, a classic manual on zooplankton sampling published by UNESCO (1968) mentions jellyfish 3  only once, to dismiss them, i.e., “Gelatinous organisms and other animals […] will occur in the catches and these must be considered separately from the main sample.” Moreover, jellyfish are difficult to sample even when targeted (Omori & Hamner 1982; Pierce 2009).  As a results of their neglect in routine surveys and marine samples in general, jellyfish were perceived as a bothersome, but unimportant component of marine ecosystems (Pauly et al. 2009b), which then justified their further neglect.  Despite recent advances in research and understanding of jellyfish ecology at local scales, such knowledge is rarely used to evaluate possible causes or consequences of jellyfish blooms at larger scales, or to make predictions (Purcell 2009). Jellyfish are also understudied due to their peculiar life cycles, which can result in extremely high variability in abundance, peaking in the form of ‘blooms’ (Mills 2001; Purcell et al. 2007; Boero et al. 2008; Dawson & Hamner 2009; Hamner & Dawson 2009).  All cubozoans, as well as many hydrozoans and scyphozoans have a bipartite life history, consisting of a sessile polyp phase and a planktonic mesdusa phase.  Herein, many polyps reproduce asexually through the process of strobilation, producing multiple ephyrae which join the zooplankton community (Arai 1997) and rapidly grow to become medusae (Palomares & Pauly 2009).  For some species, the polyps may asexually bud more polyps or form dormant cysts capable of resisting harsh environmental conditions (Arai 2009).  These characteristic life history traits make jellyfish uniquely suited to high variability environments as they can survive when conditions are unfavourable and rapidly reproduce when conditions become favourable again (Boero et al. 2008; Richardson et al. 2009). Siphonophores, ctenophores, and salps lack a polyp phase, but can also reproduce rapidly under favourable conditions (Purcell et al. 2007; Alldredge & Madin 1982).  Such varied reproductive strategies make it extremely difficult to assess jellyfish populations.  Indeed, even if few surveys have been conducted to quantify medusa abundance, very little, if anything, is known about their polyps (Mills 2001). Although jellyfish have become increasingly popular in public aquariums in recent years, most jellies are difficult to culture due to the fact that they will not survive in 4  traditional fish tanks, but rather require different tank designs and equipment (Widmer 2008).  As such, jellyfish have also been understudied in the laboratory. The lack of jellyfish population datasets that cover large temporal and spatial scales limits the conclusions that can be drawn about jellyfish on a global scale.  To compensate for this in the present analysis, methods were adopted and designed to allow for the inclusion of a wide variety of input types, including anecdotal data. 1.4 Impacts of Jellyfish Blooms In recent years, more attention is being paid to jellyfish, especially as they directly interfere with human activities (reviewed by Purcell et al. 2007; Richardson et al. 2009), i.e., through: stings (beach closures, tourism impacts, injuries, deaths); clogging intakes (coastal power plants, mining operations, shipping, military operations, aquaria); interference with fishing (clogged and split nets, spoiled catch, stung fishers, damaged gear, capsized boats); aquaculture (fish deaths, pens fouled by polyps); and marine biological surveys (e.g., interference with trawls and acoustic surveys).  Although such events are generally not included in the analysis (see Section 2.1.4), they may results in untold millions of dollars in losses (e.g., Graham et al. 2003; Kawahara et al. 2006; Anonymous 2007b).  Preventative measures are also increasing, and threats from jellyfish stings have spawned the development of new products for prevention and treatment of envenomations, including swim nets (, Provenzano et al. 1983), stinger suits (, post-sting gel (, and first aid kits (  A lotion combined with sunscreen to prevent jellyfish stings has also been developed ( which has been shown to be effective for some species (Kimball et al. 2004; Boulware 2006) but not others (Burnett 2005). Jellyfish can also have ecosystem impacts that are difficult to quantify, such as indirect effects on fisheries resources via: predation of zooplankton, predation on fish eggs and ichthyoplankton, and as a vector for parasites (reviewed by Purcell & Arai 2001).  These effects may be dramatic, such as in the Black Sea, where intense competition by the invasive 5  ctenophore Mnemiopsis leidyi has been blamed for the collapse of the anchovy (Engraulis encrasicolus) fishery (Kideys 1994), although overfishing is more likely (Niermann 2004).  In fact, it was probably a combination of these two factors, neither of which are likely to be able to effect such a severe stock collapse individually (Oguz et al. 2008).  In any case, mesozooplankton, ichthyoplankton, and fish eggs all declined following the M. leidyi blooms, and the collapse of the anchovy stocks affected higher trophic levels of the Black Sea food web (Shiganova et al. 2004a).  Consumption rates support the notion that blooms of jellyfish may have massive predation potential, making them capable of collapsing entire zooplankton populations (Niermann 2004).  Similar effects have also been observed in Kiel Bight, where mesozooplankton and larval herring abundance were reported to be significantly lower during years of high Aurelia abundance (Möller 1984; Schneider & Behrends 1994).  Examples such as the Baltic and Black Seas illustrate that jellyfish blooms have the potential to modify the entire zooplankton community and trophic structure of ecosystems (Behrends & Schneider 1995; Purcell et al. 2001b; Shiganova et al. 2004a). Predation effects due to large blooms of jellyfish can also ripple through multiple trophic levels, affecting primary production (e.g., Pitt et al. 2007; Kideys et al. 2008) and microbial processes (e.g., Hansson & Norrman 1995; Condon et al. 2011b).  The role of jellyfish blooms in biogeochemical cycles is uncertain, but may be very significant (e.g., Billett et al. 2006; Condon et al. 2011b).  In addition, jellyfish are potentially good indicators of ecosystem and climatic changes (Hays et al. 2005; Hay 2006). Despite recent alarm, jellyfish are a natural presence in healthy ecosystems and have been blooming for hundreds of millions of years (Hagadorn et al. 2002; Young & Hagadorn 2010).  Jellyfish have many important ecological functions.  Recent investigations and calculations demonstrate that jellyfish likely play an important role in the mixing of ocean layers (Katija & Dabiri 2009; Leshansky & Pismen 2010).  In addition, jellyfish are food for more than 100 fish species, as well as dozens of species of sea birds, sea turtles, and parasitic amphipods (Pauly et al. 2009b).  Medusae may also serve as a food source for benthic and even sessile animals, including adult crabs (Towanda & Thuesen 2006) and solitary corals (Alamaru et al. 2009).  Jellyfish can also serve as refugia habitat for many species of fish 6  (Purcell & Arai 2001), and may carry a variety of associated organisms.  Such associations may be parasitic (amphipods), commensal (crabs and shrimp), or even mutualistic (crabs) (Towanda & Thuesen 2006).  Dolphins have also been observed using jellyfish for several playful behaviours (e.g., Turner 2009; Edwards 2011), presumably for skill development; however, such activities are not well understood.  Regardless, it is clear that these relationships provide only a glimpse of the complex interactions between jellyfish and other fauna in the marine realm. Some jellyfish also benefit humans, notably as food (Hsieh et al. 2001), and potentially for use in drugs (Sugahara et al. 2006; Ohta et al. 2009).  Products derived from jellyfish have also been used for animal feed, fertilizers, cement additives, processed foods, and cosmetics. However, it can be a challenge to make these products economically viable, and the use of jellyfish for such purposes is still very small compared to the harvesting of jellyfish for direct human consumption.   The discovery, isolation, and development of a fluorescent protein from jellyfish led to a revolution in biotechnology (Zimmer 2005) and a Nobel Prize (Coleman 2010).  However, such proteins are now synthesized in the laboratory.  A deeper investigation of jellyfish mechanics has also inspired the development of new medical devices (Anonymous 2010f) and has informed the design of more efficient underwater vehicles (Dabiri 2011), with possible applications for the U.S. Navy (Walter 2011). Unfortunately, it appears such benefits are minor compared to the direct and indirect negative consequences of jellyfish blooms. 1.5 Anthropogenic Causes of Increasing Jellyfish Populaitons As mentioned, jellyfish have bloomed for hundreds of millions of years (Hagadorn et al. 2002; Young & Hagadorn 2010) and are a natural presence in healthy ecosystems.  Many jellyfish populations are known to fluctuate with oceanic climate (review in Purcell 2005). However, there are more recent suggestions that jellyfish are synanthropic, and may therefore benefit from anthropogenic pressures on the marine environment (Mills 1995, 2001; Purcell et al. 2007; Pauly et al. 2009b; Richardson et al. 2009).  While these mechanisms will be discussed in detail, it should be noted that, with the exception of translocation, no 7  anthropogenic activities have actually been demonstrated to cause any blooms, and the limited evidence for such inferences are based on correlations (Purcell et al. 2007). 1.5.1 Invasive Species Invasive species of jellyfish have been reported in numerous locations around the globe.  In some cases, their presence has dramatic ecological and economic consequences.  In addition, it is likely that far more invasions have occurred than have been documented due to incomplete treatment, unusual life histories, and species crypsis (Holland et al. 2004; Dawson et al. 2005; Graham & Bayha 2007).  The unique life history of many jellyfish is important to their invasiveness in a variety of ways (Graham & Bayha 2007).  Inconspicuous benthic stages make detection and monitoring of invasions difficult.  Without prior detection, a large bloom of invasive jellyfish could appear suddenly, but may actually represent an established polyp colony.  As well, invasions can occur though either ballast water (planulae, medusae) or hull fouling (polyps).  Many jellyfish polyps and cysts are resistant to harsh conditions (Boero et al. 2008; Arai 2009) and may therefore have an increased chance of surviving transport to new ecosystems.  The ability of numerous jellyfish to reproduce asexually suggests that a successful invasion could occur from a single benthic organism.  Indeed, examples exist where representative samples taken from large blooms of invasive jellyfish are all the same sex (e.g., Graham et al. 2003), potentially indicating just such an event.  Invasive species of jellyfish will be identified and discussed throughout this analysis, which will help to illuminate the global extent of invasions. 1.5.2 Warming The vast majority of the world’s coastal ecosystems and seas have experienced an increase in temperature over the last 50 years (Belkin 2009), and global temperatures are forecast to continue increasing (IPCC 2007).  Temperature is known to influence jellyfish in many ways, especially during reproductive phases.  Purcell et al. (2007) list the small number of temperate species of jellyfish that have been studied, and note that the majority (18 of 24) increase with warmer temperatures.  In some cases, warmer temperatures may increase reproductive output, trigger reproductive phases earlier, increase distribution, or 8  increase survival.  Conversely, warmer temperatures may be detrimental for other jellyfish species living near their thermal maxima.  Unfortunately, little information exists on jellyfish populations in equatorial or Arctic regions, so there may be undetected decreases of some species in these areas.  Moreover, warmer temperatures may result in the expansion of other jellyfish populations into waters that were previously inhospitable. Warmer temperatures appear to be linked to increased reproductive output for several jellyfish species.  The sea nettle Chrysaora quinquecirrha produces more eggs when temperatures are warmer (Purcell & Decker 2005).  This species also appears to be tolerant of very high temperatures.  In Chesapeake Bay, medusae of C. quinquecirrha have been known to survive temperatures up to 30°C, and their encysted polyps up to 32°C (Purcell et al. 1999a).  Laboratory studies also indicate that temperature appears to influence asexual reproduction of moon jellyfish in the genus Aurelia.  Han & Uye (2010) found that the number of buds produced by each polyp increased significantly with temperature; however, increased light intensity may reverse this effect (Liu et al. 2009).  Warmer temperatures also appear to increase strobilation of Aurelia spp., including a higher proportion of polyps strobilating, earlier onset of strobilation, increased number of cycles of strobilation, and more ephyrae produced per polyp (Purcell 2007; Purcell et al. 2009; Liu et al. 2009).  A. labiata ephyrae also exhibit greater growth in the laboratory under warmer temperatures (Widmer 2005).  Another scyphomedusan, Cotylorhiza turberculata, also showed similar responses to warmer temperatures, including quicker planulae settlement, faster reproduction, and a higher proportion of polyps strobilating (Prieto et al. 2010). Some jellyfish populations may suffer high mortality if exposed to cold temperatures for prolonged periods, suggesting increased survival with warmer temperatures.  For example, young ephyrae of the notorious stinger Pelagia noctilua show reduced survival at temperatures below 8°C (Rottini-Sandrini 1982; Avian et al. 1991), an important fact given the holoplanktonic life history of this species.  P. noctiluca appears to have increased in the Mediterranean Sea (see Section 2.3.26), potentially due to increasingly mild winters (Molinero et al. 2005; Yahia et al. 2010).  Survival of other jellyfish species with benthic stages may also be sensitive to low temperatures, as is the case for Cotylorhiza tuberculata which has 9  increased in Mar Menor, Spain (Prieto et al. 2010, also see Section 2.3.26).  Using a simple- predator prey model, Legović (1987) demonstrated that a jellyfish population will indeed increase if there is increased winter survival due to warmer temperatures. Increased temperatures may also allow some jellyfish populations to increase in distribution.  Warmer waters may explain the increased presence of cubomedusae observed in the southeastern U.S.A. (see Section 2.3.9) and northeastern Australia (see Section 2.3.35). In the Antarctic, it appears that salps are increasing, possibly due to the negative effects of decreased sea ice on krill populations (see Section 2.3.46). Temperature has also been shown to trigger strobilation for numerous species (e.g., Calder 1973; Hofmann et al. 1978; Lotan et al. 1994; Lucas 2001; Miyake et al. 2002; Kawahara et al. 2006; Prieto et al. 2010), which could mean that jellyfish in seasonal environments will appear earlier.  In the Dutch Wadden Sea, the day of first appearance is dramatically earlier than it was decades ago for numerous species of jellies (see Section 2.3.22).  In Narragansett Bay, Rhode Island, the ctenophore Mnemiopsis leidyi increased through the 1980s and 1990s, with the timing of first occurrence and peak blooms shifting earlier by a staggering two months (see Section 2.3.10).  This species also appears to thrive in newly invaded environments during warmer years (Faasse & Bayha 2006; Hansson 2006).  In the southern North Sea, two ctenophore species showed a dramatic phonological shift to permanent earlier appearances, correlated with an increase in winter and spring temperatures (Schlüter et al. 2010). Jellyfish may also persist longer with warmer waters, as the onset of a cooler season signals the decline of adult medusae in many temperate areas.  An increase in the Aurelia population in the western Seto Inland Sea in Japan may be due to warmer temperatures, as the duration of occurrence for these jellies has increased considerably, with occasional observations of over-wintering medusae (Uye & Ueta 2004). Not all species of jellies thrive in warmer temperatures.  Cold-water species will presumably decrease with warmer waters, and most jellyfish are expected to have upper temperature thresholds.  Indeed, Aurelia polyp mortality increases under continuous high temperatures (Liu et al. 2009), and medusae of the tropical Mastigias sp. declined 10  dramatically in response to warmer temperatures in a marine lake (Dawson et al. 2001).  In the late 1980s, numerous species of jellyfish increased in the northwestern Mediterranean Sea (see Section 2.3.26).  However, the ctenophore Pleurobrachia rhodopsis, which shows an affinity for lower temperatures, declined during the same time period (Molinero et al. 2008b).  In the Bering Sea, the population of the sea nettle Chrysaora melanaster increased dramatically during a period of warmer temperatures, but then appeared to decline somewhat under even hotter conditions (see Section 2.3.4).  Confounding effects of temperature are also likely for the giant jellyfish, Nemopilema nomurai in East Asia.  For this species, warmer temperatures may increase polyp production (Whiteman 2011) and trigger strobilation (Kawahara et al. 2006).  However, the large medusae appear to show some affinity for cooler water masses (Ding & Cheng 2007). While the effects of warming on jellyfish are not universal, it appears that in many cases, moderate warming may lead to increased jellyfish populations.  This suggests that jellyfish may increase under scenarios of global warming.  In addition, localized sources of warming, such as thermal effluents, could have similar effects.  Nonetheless, the mechanisms involved are not always straightforward.  For example, warmer temperatures may strengthen stratification, which has been shown to decrease the probability of finding large aggregations of salps in the western Mediterranean Sea (Ménard et al. 1994).  Not surprisingly, there is still much to be learned about the potentially important effects of temperature on jellyfish, both from experiments in the laboratory and in situ observations. 1.5.3 Eutrophication Eutrophication is most often described as an increase in the input of nutrients (primarily nitrogen and phosphorus) to a body of water (Arai 2001).  This may occur naturally (e.g., upwelling, river input); however, modern discussion of ‘cultural’ eutrophication concerns nutrient sources of anthropogenic origin.  Such sources include excess fertilizer from agriculture, dumping of sewage, and effects of logging.  The deposition of reactive nitrogen as a result of fossil fuel burning is also a significant contributing factor (Nixon 1995).  If nutrients are limiting, such increased input will result in 11  more phytoplankton, often in the form of algal blooms.  The subsequent decay of these blooms often leads to reduced concentrations of dissolved oxygen, and in extreme cases, hypoxic and anoxic ‘dead zones’.  There are now over 400 of these regions worldwide, and they have been increasing exponentially since the 1960s (Diaz & Rosenberg 2008). Eutrophication may benefit jellyfish in a number of ways.  Increased nutrients can simply create more primary and secondary production, increasing the amount of food available to jellyfish polyps and medusae.  Rates of asexual reproduction for some species of jellyfish are influenced by nutrition (Arai 1997; Purcell et al. 1999b), and therefore increased food availability could lead to an increased abundance of jellies.  Aurelia polyps have been shown to asexually bud more polyps at higher food concentrations (Han & Uye 2010), possibly leading to larger blooms.  In Mar Menor, a Spanish coastal lagoon, new populations of the large scyphomedusa Rhizostoma pulmo appear to be thriving due to their unusual ability to feed directly on diatoms, and therefore benefit from the increasing eutrophication of the lagoon (Pérez-Ruzafa et al. 2002; Lilley et al. 2009, also see Section 2.3.26).  Using a simple predator-prey model, Legović (1987) concluded that jellyfish populations can increase due to higher nutrient input. Eutrophication can also decrease water clarity.  As jellyfish are mainly tactile predators, they may have an advantage over visual predators (such as fish) in turbid waters. Perhaps the most tangible example of this effect is from several Norwegian fjords, where jellyfish now dominate systems that were once occupied by fish (see Sections 2.3.21 and 2.3.22).  In the most severe cases in these fjords, the change appears to be due to decreased visibility, potentially from cultural eutrophication (Eiane et al. 1999; Sørnes et al. 2007). As mentioned, eutrophication can also result in lower concentrations of dissolved oxygen (DO).  Although jellyfish are generally less abundant in hypoxic waters, many types of jellies are tolerant to low DO concentrations (reviewed in Purcell et al. 2001a; Thuesen et al. 2005).  Such conditions are typically detrimental to other animals, and may reduce survival or impair function, such as the ability to escape from predators (e.g., Breitburg et al. 1997).  Therefore, jellyfish may have an advantage over fish and other organisms in regions with periodic or persistent hypoxia (Purcell et al. 1999a; 2001a; Arai 2001).  Laboratory 12  experiments under low DO conditions have begun to confirm such hypotheses, including increased predation by jellyfish on ichthyplankton due to a diminished prey escape response (Shoji et al. 2005b), as well as jellyfish out-competing fish for prey (Shoji et al. 2005a). Eutrophication appears to be linked to the increase in Aurelia populations observed in the Seto Inland Sea, Japan (see Section 2.3.40).  In Hiroshoma Bay, medsuae were more abundant in coastal areas with lower concentrations of DO in bottom layers, compared to other zooplankton (Shoji et al. 2010).  Hypoxic conditions may also explain the success of the ctenophore Mnemiopsis leidyi in Chesapeake Bay, which appears to be much more tolerant of low DO concentrations than the rest of the zooplankton community (Keister et al. 2000). Other species of jellyfish also appear to show an affinity for eutrophic waters.  The hydromedusae Aglantha digitale and Rathkea octopunctata showed increasing population gradients towards the more polluted inner waters of Oslofjord, Norway (Beyer 1968), which may be due to the distribution of their copepod prey (Smedstad 1972).  In these areas, bottom waters are anoxic, and the success of the two species of hydromedusae may be facilitated by the fact that they are holoplanktonic and do not require substrate for reproduction (Purcell et al. 2001a). In the northern Gulf of Mexico, excess nutrients from the Mississippi River and numerous other estuaries results in widespread seasonal hypoxia (Rabalais et al. 1994). Jellyfish in this region are monitored using trawls (see Section 2.3.8), and the sea nettle Chrysaora quinquecirrha appears to have expanded into hypoxic waters in the 1990s.  The frequency of hypoxia explained almost 60% of the variation in the catch rate (Purcell et al. 2001a).  Despite this correlation, a full understanding of the mechanisms involved in this ecosystem remains elusive.  When jellyfish were viewed in situ using a video profiler, they were rarely found in the hypoxic bottom layer (Purcell et al. 2001a).  As Graham (2001) notes, while hypoxia does not appear to directly cause distributional shifts in C. quinquecirrha, it may create an environment that promotes success of this species.  As such, the continued intensification of seasonal hypoxia in the Gulf of Mexico may forecast further increases in jellyfish abundance and distribution. 13  Eutrophication has also been blamed, at least in part, for some of the changes that have occurred in the Black Sea.  The northwestern shelf receives discharge from two of Europe’s largest rivers – the Danube and the Dnieper.  Including several other rivers, the Black Sea receives discharge from a drainage basin over 5 times the area of the sea itself, including industrial and agricultural areas of 9 countries (Mee 1992).  The Black Sea is anoxic below 150-200m, making roughly 90% of its volume virtually devoid of oxygen (Sorokin 1983).  While the large northwestern shelf lies above 200 m and the surface waters of the Black Sea are biologically extremely productive, continued eutrophication has resulted in widespread hypoxic events.  This eutrophication, combined with several other stressors (see Section 1.5.9), has been blamed for the increased Aurelia populations observed in the 1960s, 1970s, and 1980s (Kideys 1994).  In the open area of the Black Sea, Aurelia planulae were found in high densities in the hypoxic waters where almost no other plankton was present (Vinogradov et al. 1985).  The ctenophore Pleurobrachia pileus also appears to tolerate relatively low oxygen conditions in the Black Sea (Mutlu & Bingel 1999; Vinogradov et al. 1985). Eutrophication also been linked to increasing jellyfish populations in other parts of the world.  In the East China Sea and the Yellow Sea, some blame increased scyphomedusae populations on eutrophication, and countermeasures have been proposed, including wetland conservation and seaweed planting (Guan et al. 2007).  A similar situation exists in Barnegat Bay, New Jersey, U.S.A., where populations of sea nettles (Chrysaora sp.) have increased in recent decades (see Section 2.3.10).  Eutrophication is also suspected of playing a role in the increased populations of Aurelia observed in Limfjorden, Denmark (see Section 2.3.22). Like medusae, jellyfish polyps may have a competitive advantage over other organisms due to a tolerance for low DO concentrations.  Polyps of Chrysaora quinquecirrha have survived and reproduced asexually during prolonged exposure to hypoxic conditions in laboratory experiments (Condon et al. 2001).  In Tokyo Bay, notable increases in the Aurelia population first occurred during the 1960s, when industrial and civic pollution caused increased eutrophication (Nomura & Ishimaru 1998; Ishii 2001; Han & Uye 2010). 14  The bottom layer of Tokyo Bay is now hypoxic in summer, which may eliminate competition for habitat with other sessile organisms.  Indeed, Ishii et al. (2008) found that polyps cultured from this population will grow and bud more polyps under low DO or even hypoxic conditions.  The authors note that most suitable substrate in all areas except the bottom layer of the innermost part of the bay is occupied by other organisms, such as mussels, barnacles, and ascidians.  As such, they conclude that the hypoxic conditions in Tokyo Bay provide polyps with otherwise unavailable habitat, with a consequent increase in the medusae population.  Similar processes may be occurring in Korea, where increasing Aurelia populations are causing escalating problems for power plants and fisheries in several eutrophic bays and inlets (Han & Uye 2010). Not all jellyfish respond to hypoxia in the same way.  The benthic stages of many hydrozoans are sensitive to low oxygen concentrations, and hypoxia has been blamed for the reduced diversity of hydromedusae in the Adriatic Sea (Benović et al. 1987; 2000).  As Arai (2001) concludes in a review of jellyfish and eutrophication, typically the diversity of jellyfish decreases, but the biomass of select species may increase. Eutrophication can impact the phytoplankton community directly, with potentially significant consequences for the rest of the food chain.  If a shift takes place from a diatom- dominated community to a non-diatom community (e.g., flagellates), energy could be shunted from a diatom-copepod-fish food chain to a flagellate-mesozooplankton-jellyfish food chain (Parsons & Lalli 2002; Sommer et al. 2002; Nagai 2003).  Testing such hypotheses should be a priority, including analyses of current data sets, as well as ecosystem models and future research.  Of course, there will also be exceptions to such trophic interactions, as jellyfish are an exceptionally diverse group of animals (see Section 1.1), with a similarly diverse range of prey.  Some jellyfish also have algal symbionts (e.g., jellies Cassiopea and Phyllorhiza), or may even consume phytoplankton directly, including both flagellates (e.g., salps; Chrysaora hysoscella) and diatoms (e.g, Rhizostoma pulmo in Mar Menor). Jellyfish of the genus Cassiopea are commonly called ‘upside-down jellyfish’, as they spend much of their time inverted on the sea floor.  These jellies harbour photosynthetic zooxanthellae, and are generally found in shallow lagoons.  There are several examples 15  where Cassiopea spp. appear to have increased with eutrophication, including Florida (see Section 2.3.8), the Bahamas (see Section 2.3.9), and Hawaii (see Section  While these increases may be due to eutrophication, they could also be as a result of dredging, whereby sea grasses are removed, thus creating available habitat for Cassiopea medusae (Arai 2001). Despite the numerous examples of jellyfish occurring and increasing in eutrophic waters, the relationships between eutrophication and jellyfish remain poorly understood. Causal links involving eutrophication are difficult to demonstrate, due primarily to confounding impacts and a lack of historical data.  Nonetheless, it appears that in many cases, eutrophication may result in an increase in jellyfish biomass, albeit with an associated reduction in jellyfish species diversity (Arai 2001). 1.5.4 Overfishing Fishing and overfishing can cause profound changes in marine ecosystems, including alterations to faunal communities, trophic structure, production, habitat, biodiversity, as well as ecosystem function and stability (Pauly et al. 2002). In some cases, it appears that overfishing may also lead to increased jellyfish populations.  Many fish are predators and/or competitors of jellyfish, and therefore removing large amounts of fish from a system may release jellyfish from predation and/or competition (Purcell & Arai 2001; Purcell et al. 2007; Pauly et al. 2009b).  Using a simple predator-prey model, Legović (1987) concluded that jellyfish populations may increase as a result of reducing the number of jellyfish predators and competitors (i.e., through fishing). Fishing down marine food webs is a phenomenon whereby the largest, high trophic level fish are reduced initially, followed by a gradual shift toward smaller, lower trophic level fish and invertebrates (Pauly et al. 1998).  Indeed, ‘fishing down’ appears to be widespread (Pauly & Palomares 2005), and the mean trophic level of all fisheries catch is considered to be a good indicator of ecosystem health (Pauly & Watson 2005).  The extreme result of this process is a near absence of profitable fisheries, and a dominance of planktonic organisms, including jellyfish (Pauly et al. 2009a). 16  Although the cause and effect relationships between the dramatic changes in the Black Sea in recent decades remain contentious, an increasing amount of evidence suggests that the collapse of the anchovy fishery (Engraulis encrasicolus) and numerous other fish stocks were due to overfishing, and subsequently may have facilitated the dramatic blooms of the invasive ctenophore Mnemiopsis leidyi (Niermann 2004).  Fisheries in the Black Sea show a long history of fishing down the food web.  Numerous fish species, especially those of high trophic level, have become rare or absent in recent decades, and current fisheries are dramatically different from those of the 1970s.  In fact, the mean tropic level of commercial fisheries catches in Romanian waters declined from 3.21 in 1965-1970 to 2.96 in 2001-2005 (Bǎnaru et al. 2010).  Mackerel (Scomber scombrus) are known predators of jellyfish in the Black Sea, and their stock collapses in the 1970s due to overfishing may have released Aurelia from predation (Zaitsev 1992).  Along the Turkish coast, the use of purse seines increased dramatically after 1970, rapidly depleting stocks of large, valuable, pelagic species such as bonito and mackerel (Gücü 2002).  As a result, increasing effort shifted to the anchovy fishery, which, aided by government subsidies, had also collapsed by 1990 (Gücü 2002).  It was precisely at this time that the massive blooms of M. leidyi occurred, despite the observation of specimens for several years prior (Shiganova 1998).  Thus, it would seem that overfishing may have played a significant role in creating conditions that facilitated increasing jellyfish populations in the Black Sea over the last several decades.  This hypothesis has been corroborated by ecosystem model simulations, which suggest that fishing down the food web and overfishing of small pelagic species cleared fish from their ecological niche, thus allowing jellyfish to thrive (Gücü 2002).  An eerily similar tale appears to be playing out in the Caspian Sea, where the recent invasion and large blooms of M. leidyi coincided with a collapse of the kilka (Clupeonella sp.) fishery (Roohi et al. 2010). Ecosystems in Asia also show signs of fishing down the food web and increasing jellyfish populations.  Overfishing has been blamed for the increase of jellyfish observed in the Seto Inland Sea of Japan (Nagai 2003; Uye & Ueta 2004; Uye 2010).  In the East China and Yellow Seas, increases in jellyfish occurred simultaneously with declines in fisheries (Hong et al. 2008).  Using an ecosystem model of the East China Sea, Hong et al. (2008) 17  identify a positive feedback loop, or “jellyfish spiral” (see Section 1.7) whereby jellyfish come to dominate the ecosystem.  In the model, the spiral is triggered by trawl fishing for Stromatidae - predators and competitors of jellyfish.  Indeed, the large 2003 bloom of Nemopilema nomurai coincided with changes in fish species dominance and depressed stocks of Stromatidae (Dong et al. 2010; Ding & Cheng 2005).  Peng & Zhang (1999) also note that fish were replaced by the jellyfish Cyanea nozakii in major fishing grounds of the East China Sea in the late 1990s. Impacts due to overfishing are also suspected of playing a role in the observed increases of jellyfish populations in the Benguela Current LME (see Section 2.3.28).  Fishing- down effects and the collapse of the sardine and hake fisheries in the 1960s appear to have forecast a new regime for this ecosystem, where jellyfish now outnumber fish (Lynam et al. 2006; Bakun & Weeks 2006; Pauly 2010; Utne-Palm et al. 2010).  Indeed, this ecosystem now appears to have ‘switched’ to an alternate state, with a dramatically different faunal community (see Section 1.7 for further discussion). As with most factors that have been proposed to lead to increased jellyfish populations, linkages are difficult to demonstrate and there are numerous exceptions. Many regions of the world have experienced dramatic overfishing, evidenced by a reduced mean trophic level and the collapse of stocks, without a concurrent increase in jellyfish populations (Bilio & Niermann 2004).  Thus, it would seem that the mechanisms involved are highly complex and inter-related, and what occurs in one ecosystem is not necessarily reflected in another. 1.5.5 Bottom-Trawling Bottom-trawling has also been suggested as a factor that could lead to increased jellyfish populations, through mechanisms similar to eutrophication (see Section 1.5.3).  As discussed, jellyfish may benefit from eutrophic waters due to increased availability of production at low trophic levels, increased turbidity, and lower levels of dissolved oxygen. Bottom-trawling may lead to similar conditions, as it can destroy benthic communities composed of filter feeders and deposit feeders.  These communities tend to keep the water 18  column oligotrophic by suppressing phytoplankton and detritus, as well as preventing re- suspension of sediments.  With the loss of these communities, the water column may become increasingly eutrophic and the sediments less consolidated, facilitating the re- suspension of particles and nutrients by storms and by trawling itself, further aggravating the problem (Pauly et al. 2009b). The dramatic ecological changes observed in the Black Sea over the last several decades have primarily been attributed to eutrophication, overfishing, and species invasions.  However, there is some evidence to suggest that bottom-trawling should also be on the list.  The coastal waters of Romania, near the productive Danube Delta, were only fished using stationary nets in shallow waters through the 1960s and 1970s (Bǎnaru et al. 2010).  In 1980, bottom-trawling began in this region, and was much more efficient at extracting fish from the sea.  Not surprisingly, trawl catches increased steadily through the 1980s, but declined drastically after 1990, along with a dramatic change in species composition (Bǎnaru et al. 2010).  Consistent with hypotheses about how bottom-trawling can lead to more jellyfish, some of the many ecological changes observed in the Black Sea include increased episodes of hypoxia and anoxia, increased turbidity, and the decline of benthic species diversity (Zaitsev & Mamaev 1997). In all likelihood, there was no single cause for the changes observed in the Black Sea, but rather it was a combination of anthropogenic impacts and meterological factors that led to the dramatic sequence of events observed in recent decades (also see Sections 1.5.9 and 2.3.47).  Indeed, it seems likely that bottom-trawling also played a role in forcing these ecological changes. 1.5.6 Increased Substrate Many jellyfish have a sessile polyp phase of their life history (see Section 1.3), which attaches to a hard substrate.  In the natural environment, such substrate can include rocks, submerged trees, or the shells of benthic molluscs and other animals.  In marine environments that have been developed by humans, the amount of hard substrate increases dramatically, and can include boats, docks, wharfs, marinas, breakwaters, sea walls, 19  platforms, artificial reefs, debris, and many other structures.  If habitat for polyps is a limiting factor for jellyfish populations, then increased substrate provided by humans could result in an increase in medusae abundance.  Polyps from numerous species appear to show an affinity for anthropogenic materials, such as plastic and glass, over natural materials (Holst & Jarms 2007; Hoover & Purcell 2009).  Many jellyfish polyps also appear to show a preference for the shaded side of structures, possibly to avoid burial by sedimentation, as well as mortality due to solar irradiation (Svane & Dolmer 1995; Holst & Jarms 2007). Natural shaded structures are relatively rare in marine environments, whereas anthropogenic structures are often floating, and therefore provide a multitude of shaded habitat.  In addition, these floating structures often have no connection with the benthos, potentially allowing settled polyps to escape predation from benthic predators such as nudibranchs. One spectacular example comes from a marina in Washington State, U.S.A., where there is an estimated population of 100 million Aurelia labiata polyps living on the underside of dock floats (Purcell et al. 2009).  As these polyps can produce an average of 10 ephyrae each, this is a potential addition of hundreds of millions of jellyfish to the ecosystem that presumably would not be there otherwise (see Section 2.3.5 for further discussion). In the Gulf of Mexico, both C. quinquecirrha and A. aurita have sessile polyp stages that attach to hard substrates.  While natural substrates, such as oyster reefs, are expected to be relatively constant over time, artificial substrates have increased dramatically with the expansion of oil and gas development.  There are over 6000 operating and discontinued oil and gas structures in the northern Gulf, with the highest concentrations on the Louisiana shelf (Graham 2001).  As most of these structures extend throughout the entire water column, they likely provide suitable habitat for thriving polyp colonies, potentially contributing to increases in medusae populations.  Despite this hypothesis, no polyps have been identified or documented attached to oil and gas structures in the Gulf of Mexico to date. Jellyfish polyps are typically very small, and are therefore likely underreported. Nonetheless, jellyfish polyps have been documented on anthropogenic structures in 20  numerous locations around the world, including Japan, Australia, the Mediterranean, and North America (Duarte et al. 2011, also see Section 2.3).  In many of these locations, jellyfish are perceived to have increased in abundance. 1.5.7 Aquaculture Aquaculture, or more specifically mariculture, may benefit jellyfish in several ways. Operations typically provide increased nutrients to the surrounding waters due to excess feed and excrement, and the previously discussed effects of eutrophication may result (see Section 1.5.3).  In addition, the nets and pens of many aquaculture operations may provide suitable substrate for jellyfish polyps (see Section 1.5.6). By far the most convincing example of aquaculture operations affecting jellyfish abundance comes from Tapong Bay in Taiwan.  Extensive aquaculture operations over several decades led to increasing eutrophication of this coastal lagoon, with concurrent blooms of Aurelia sp.  However, in 2002, the removal of all aquaculture operations resulted in the total absence of jellyfish blooms thereafter, suggesting that aquaculture may be solely responsible for the jellyfish blooms in this bay (Lo et al. 2008, also see Section 2.3.34 for further discussion). There are also aquaculture operations and hatcheries that intentionally attempt to increase jellyfish production for harvesting and human consumption.  In Liaodong Bay, China, hundreds of millions of juvenile Rhopilema esculentum are released from hatcheries each year, in the hopes of harvesting them as adults (Dong et al. 2009).  While some economic success has been achieved with such programs, recent blooms tend to be of less desirable species, and efforts are shifting toward pond culture of jellyfish (You et al. 2007). 1.5.8 Other Factors Other factors that can be influenced by human activity are also likely to have important impacts on jellyfish populations, such as salinity and ocean acidification.  While many jellyfish species appear to be tolerant of a wide range in salinity, they may also be highly influenced by it.  In years of low precipitation, increased salinity may result in large populations of Chrysaora quinquecirrha medusae in Chesapeake Bay (Cargo & King 1990). 21  Based on data collected from 2003-2005, Ding & Cheng (2007) conclude that various water masses in the East China Sea and Yellow Sea, identified by temperature and salinity, may affect the abundance and distribution of Nemopilema nomurai.  For some species, such as Aurelia labiata, changes in salinity may have significant influence reproduction, especially when the effects of temperature are also taken into account (Purcell 2007).  In addition, evidence suggests that some scyphozoan polyps are able to survive a wide range of salinities (Prieto et al. 2010; Holst & Jarms 2010).  Changes in salinity due to climate change scenarios are expected to be highly localized and difficult to predict.  As the effects of salinity on most jellyfish species are not well understood, it remains to be seen how salinity will affect jellyfish populations in the future. Ocean acidification is occurring as a result of increasing anthropogenic CO2 emissions, with a reduction of ocean pH occurring at a rate not seen in millions of years (Feely et al. 2004).  This change is likely to portend serious consequences for marine biota, especially organisms that build shells and skeletons of calcium carbonate (Orr et al. 2005).  If organisms that are competitors of jellyfish are negatively affected by ocean acidification, jellyfish may benefit as a result.  Attrill et al. (2007) found a significant correlation between jellyfish abundance and lower pH in the North Sea; however, this relationship disappeared when the analysis was extended over a larger region (Richardson & Gibbons 2008). The direct effects of lower pH waters on jellyfish are not well understood.  The polyps of some species, including a scyphozoan and a cubozoan, appear to be tolerant of low pH conditions (Anonymous 2009a; Winans & Purcell 2010).  Many jellyfish medusae have statocysts - sensory organs which can detect gravity and therefore assist in orientation (Arai 1997).  These organs contain statoliths, which are crystals that contain calcium (Becker et al. 2005).  Winans & Purcell (2010) found that the statoliths of Aurelia labiata ephyrae were not reduced in number, but were significantly smaller when the parent polyps were reared in low pH conditions.  However, it is unknown how such changes will affect medusa fitness or survival, and the overall effects of ocean acidification on jellyfish populations remain uncertain. 22  1.5.9 Synergistic Effects In many locations with increasing jellyfish populations, a combination of factors may be to blame.  For example, a typical harbour can provide numerous advantages for jellyfish, including eutrophic conditions, increased substrate, warm thermal effluents, and a continuous supply of invasive species.  Compounded by the effects of overfishing, these factors may operate synergistically (Purcell et al. 2007).  Depending on the environment in question, a specific combination of factors may be required to create conditions for more jellyfish, rather than any particular stressor in isolation.  Further complicating these effects is the fact that different life stages of jellyfish will likely be influenced by different factors.  For example, jellyfish polyps are likely to be impacted by temperature, hypoxia, and habitat modification.  Alternatively, medusae are more likely to be affected by the effects of overfishing and eutrophication.  As such, the mechanisms involved with anthropogenic impacts and jellyfish populations may be highly inter-related and therefore difficult to isolate. The Black Sea ecosystem has been dramatically impacted by human activity, especially since the 1960s (Mee 1992).  Anthropogenic activities including shipping, mining, damming of rivers, increased nutrient loading, and the dumping of toxic wastes have led to the Black Sea being called “one of the most polluted and mismanaged semi-enclosed seas in the world” (Oguz & Velikova 2010).  Compounded by overfishing (Daskalov 2002) and bottom-trawling (Bǎnaru et al. 2010), these impacts led to dramatic changes in the Black Sea ecosystem, including increased eutrophication, the collapse of numerous fish stocks, and extreme shifts in zooplankton communities.  The large blooms and successful establishment of the invasive ctenophore Mnemiopsis leidyi in the 1980s have been implicated in both causing the dramatic ecological changes as well as being a consequence of these changes (Niermann 2004).  Furthermore, it remains possible that climatic changes were responsible for the observed shifts in the zooplankton community; the same year M. leidyi formed unprecedented blooms, a regime shift was evident in the Black Sea which saw a dramatic shift to warmer temperatures. 23  The Black Sea may be more vulnerable to human impacts than other seas due to a very high enclosure index (99.6%) and the influence of a large catchment area (Bǎnaru et al. 2010). Indeed, it appears that there is no single cause of the dramatic changes observed in the Black Sea ecosystem, but rather a combination of factors including pollution, eutrophication, overfishing, species invasions, and climatic events are all inter-related.  This system represents an important case study of the just how severe the consequences of anthropogenic impacts on marine systems can be.  Instead of an ecosystem ‘recovery’, as suggested by some authors (e.g., Shiganova 1998; Kideys 2002), others propose that the Black Sea shelf ecosystem has exhibited three alternative states since the 1960s, and that the current ‘post-eutrophication’ state “differs considerably from the pristine state and does not truly represent a recovery” (Oguz & Velikova 2010). In Chesapeake Bay, eutrophication coincided with a collapse of the oyster population due to overharvesting and disease, as well as the collapse of zooplantivorous fish stocks (e.g., menhaden) - all factors which have been blamed for ecosystem degradation and increasing ctenophore populations in the region (Purcell et al. 1999a; Jackson 2008; Pauly et al. 2009b).  Model results suggest a recovery of the oyster population would dramatically reduce gelatinous biomass in Chesapeake Bay (Ulanowicz & Tuttle 1992).  Interestingly, the decline in abundance of the sea nettle Chrysaora quinquecirrha may have been due to the collapse of the oyster population, as oyster shells provide substrate for C. quinquecirrha polyps (Breitburg & Fulford 2006).  Consequently, ctenophore populations, which lack a polyp phase and are prey of C. quinquecirrha, have dramatically increased in Chesapeake Bay (see Section 2.3.10). Examples such as the Black Sea and Chesapeake Bay illustrate the fact that the mechanisms affecting jellyfish populations can be highly varied.  In addition, different ecosystems and different species of jellyfish may not necessarily respond to impacts in the same way.  As such, disaggregating the effects of temperature, eutrophication, climatic variations, coastal development, and fishing on jellyfish populations may remain untenable for most ecosystems. 24  1.6 Regime Shifts Outbreaks of jellyfish are often reported alongside of other ecological changes.  While the cause and effect relationships among these changes are debated, the influence of environmental conditions should not be ignored.  In the Black Sea, numerous ecological changes have taken place in recent decades.  The extreme blooms of the invasive ctenophore Mnemiopsis leidyi coincided with the collapse of the anchovy (Engraulis encrasicolus) fishery in the late 1980s.  Interestingly, M. leidyi had been observed in the Black Sea since 1982, but its population did not explode until 1989 (Shiganova 1998), perhaps because environmental conditions were not suitable in the mid-1980s.  1989 marked a dramatic shift to warmer temperatures from a cold regime in the Black Sea.  Cold winter temperatures in 1988/1989 resulted in increased mixing and more nutrients in the surface waters (Niermann 2004).  The transition to warm temperatures in 1989 coincided precisely with the outbreak of M. leidyi and the collapse of the anchovy fishery.  Indeed, it appears that while the changes observed in the Black Sea ecosystem appear linked to anthropogenic impacts, the influence of meteorological conditions cannot be denied. Many neighbouring regions also experienced dramatic changes at similar time, including the North Sea, the Baltic Sea, and the north Atlantic, suggesting the influence of widescale meterological phenomena (Niermann et al. 1999).  Indeed, the North Atlantic Oscillation (NAO) index has shown highly positive values since the regime shifts in the late 1980s. Multiple regime shifts also coincide with the dramatic changes observed in the jellyfish population of the Bering Sea.  The change from the 1980s regime to the 1990s was marked by warmer temperatures, and coincided with increased jellyfish biomass and distribution (Brodeur et al. 2008a).  The subsequent shift to another regime after 1999 showed even warmer temperatures, but jellyfish biomass dropped.  In the case of the Bering Sea, it would seem that warmer temperatures do not necessarily mean more jellyfish, but regime shifts may cause dramatic changes in the jellyfish population (Brodeur et al. 2008a, also see Section 2.3.4).  Regime shifts have also been linked to changing jellyfish populations in the Irish Sea, but the effects are difficult to isolate from other factors, such as overfishing 25  (Lynam et al. 2011, also see Section 2.3.24).  Not surprisingly, it appears that climatologically-induced regime shifts can strongly influence jellyfish populations; however, these effects are difficult to separate from other confounding factors. As mentioned, anthropogenic impacts can dramatically alter ecosystems.  In some cases, it appears that these impacts and their associated results may actually be the trigger for a regime shift, rather than climate forcing.  In addition, these shifts may represent an alternate stable state, maintained through mechanisms such as positive feedback loops. These phenomena will be discussed in reference to jellyfish in the next section. 1.7 The Jellyfish Spiral As discussed, there are numerous anthropogenic activities that may lead to conditions that are more favourable for jellyfish than fish.  In some cases, these conditions may allow jellyfish populations to grow, even over short time periods.  Many jellyfish have the ability to prey directly upon fish eggs and ichthyoplankton (Cowan & Houde 1993; Purcell et al. 1999a; Purcell & Arai 2001), and consumption may increase under hypoxic conditions (Shoji et al. 2005b).  This predation can be dramatic, and, in combination with competition, may result in reduced fish survival and recruitment (e.g., Möller 1984; Lynam et al. 2005b). Therefore, a population of jellyfish that has increased due advantageous conditions from anthropogenic stressors may continue to dominate and prevent the recovery of fish populations, even if such stressors diminish.  These phenomena have been referred to as the “jellyfish joyride” (Richardson et al. 2009) and the “jellyfish spiral” (Uye & Ueta 2004). An ecosystem model from the East China Sea confirms this hypothesis, whereby overfishing may trigger a positive feedback loop, resulting in dominance by jellyfish (Hong et al. 2008).  In the Seto Inland Sea, there has been an increase of Aurelia in recent decades, which may be due to both overfishing and warmer winter temperatures (Uye & Ueta 2004). In turn, the jellyfish are thought to exert top-down control on the ecosystem, resulting in an apparently alternate state that is dominated by jellyfish. Other ecosystems also appear to have “switched” to a jellyfish phase.  In the Benguela Current upwelling system, the collapse of abundant fish stocks has coincided with a 26  dramatic increase in jellyfish populations (also see Section 2.3.28).  Bakun & Weeks (2006) discuss a predator-prey function, whereby prey may “breakout” of a predator pit and increase in abundance beyond the point where the prey’s population size is controlled by top-down forcing.  For such a breakout to occur, the prey must increase its production or decrease its mortality; or both.  The 1960s witnessed the collapse of the sardine fishery in this highly productive region, followed also by declines in hake and mackerel.  This may have facilitated an explosion in the herbivorous zooplankton population due to releases from competition and predation, and hence an explosion in the jellyfish population which preys on zooplankton, ultimately allowing jellyfish to escape from their own predator pits. An increased abundance of jellies then consumes fish eggs and larvae, holding them in a new predator pit, and resulting in an alternative ecosystem state with high jellyfish biomass and few fish.  In addition, excess primary production exported to the benthos may provide additional food items for jellyfish (Flynn & Gibbons 2007), as well as an increased frequency of sulphur eruptions and hypoxic events (Bakun & Weeks 2004; Weeks et al. 2004). Combined, these mechanisms may serve to maintain conditions suitable for jellyfish to dominate. Breitburg & Fulford (2006) discuss another possible jellyfish spiral in Chesapeake Bay, involving oysters, ctenophores and scyphozoans.  In recent decades, Chrysaora quinquecirrha abundance has declined, possibly due to overfishing and disease of oysters, which are important substrate for C. quinquecirrha polyps.  The decline of C. quinquecirrha may have potentially released the ctenophore Mnemiopsis leidyi from predation, which has increased in abundance.  In turn, M. leidyi prey upon oyster larvae, and may therefore inhibit recovery of the oyster population.  To add to the situation, the lack of oysters in the bay reduces the filtering capacity, ultimately reinforcing the eutrophic and hypoxic conditions. In the Black Sea, it is likely that the anchovy fishery collapsed due to overfishing rather than from predation or competition with Mnemiopsis leidyi (Gücü 2002; Niermann 2004).  However, the presence of M. leidyi may have suppressed the recovery of fish stocks through these mechanisms (Bilio & Niermann 2004).  Regardless, reduced fishing pressure eventually resulted in a partial recovery of the anchovy stock in the Black Sea in the 1990s 27  (Bilio & Niermann 2004), suggesting that the jellyfish spiral was not so strong that recovery of fish stocks was impossible.  It is assumed that, under natural conditions, a stock of small pelagic fish can successfully compete with jellyfish for the same resources, given that fish are quicker and more active (Gücü 2002).  Thus, reducing fishing pressure has the potential to not only help fish stocks recover, but also to suppress jellyfish abundance, and should therefore be considered as an important management tool in controlling jellyfish populations (Bilio & Niermann 2004). Trophic cascades involving jellyfish can also result in positive feedback loops. Limfjorden is a system in Denmark which connects the North Sea with the Kattegat.  This system became highly eutrophic through the 1960s and 1970s, largely due to agricultural runoff.  This has led to widespread hypoxia in bottom waters and the loss of large mussel beds and demersal fish populations, as well as a concurrent increase in the Aurelia population (see Section 2.3.22).  The large population of jellyfish now consumes extensive amounts of herbivorous zooplankton, with a concomitant increase in phytoplankton.  In turn, phytoplankton blooms reinforce the oxygen depletion in this system, resulting in high mortality of mussels, which further exacerbates the problem (Møller & Riisgård 2007a; Riisgård et al. 2011). There is also evidence of similar trophic cascades in other areas, whereby jellyfish reduce the standing stock of herbivorous zooplankton, resulting in increased phytoplankton.  Examples include a fjord in British Columbia (Huntley & Hobson 1978), Narragansett Bay in Rhode Island (Deason & Smayda 1982), and the Kiel Bight in the western Baltic Sea (Schneider & Behrends 1998).  This process has also been demonstrated in mesocosm experiments, with a resultant increase in a noxious photoplankton.  Pitt et al. (2007) conducted field-based experiments in Australia, where results suggested that predation by Catostylus mosaicus on copepods subsequently released the heterotrophic dinoflagellate Noctiluca scintillans from competition.  N. scintillans has a global distribution and is notorious for forming noxious blooms known as ‘red tides’.  Thus, it would appear that in certain cases, jellyfish may promote the formation of red tides. 28  Although these impacts of jellyfish on ecosystems are not well understood, the examples discussed above suggest that jellyfish may have significant influence over ecosystem processes, and can trigger trophic cascades and positive feedback loops that result in alternate ecosystem states.  Developing a better understanding of these mechanisms and relationships should be a priority for researchers and modellers alike, as it appears that jellyfish have the potential to be “ecosystem engineers” (Breitburg et al. 2010). 29  2 The Jellyfish Index 2.1 Methods 2.1.1 Large Marine Ecosystem Approach In order to examine and compare changes in jellyfish populations, data were stratified by Large Marine Ecosystem (LME).  First introduced at the 1984 annual meeting of the American Association for the Advancement of Science (AAAS), the LME framework defines boundaries based on ecological criteria rather than economic or political criteria (Sherman & Hempel 2009).  LMEs may extend from nearshore areas, including river basins and estuaries, out to the seaward boundaries of continental shelves or coastal currents (Sherman & Tang 1999).  When defining the physical extent of the LME boundaries, four ecological parameters are considered: i. bathymetry; ii. hydrography; iii. productivity; iv. trophic relationships. These ecological measures are highly inter-related and each LME is defined by a distinct combination of factors.  LMEs can range from 150,000 km2 to more than 5 million km2.  To date, 66 LMEs have been described in terms of these parameters (see  As the majority of recently reported changes in jellyfish populations around the globe occur in coastal waters or semi-enclosed seas (Mills 2001; Purcell et al. 2007), the LME framework provides a suitable stratification scheme for examining these changes. 2.1.2 1950 Baseline In order to examine changes in jellyfish populations, a baseline must be selected.  For the purposes of this analysis, changes were only considered if they occurred after 1950, notably because this was the first year for which the newly founded Food and Agriculture Organization of the United Nations (FAO) published its annual compendium of global 30  fisheries catches (which now include jellyfish), part of an effort by the United Nations to “quantify the world” (Ward et al. 2004).  The past 60 years have also seen a dramatic increase in the global human population, along with concomitant impacts on marine environments around the globe (Halpern et al. 2008).  In addition, many of the changes reported in jellyfish populations are from recent decades (Mills 2001; Purcell et al. 2007) and thus a 1950 baseline provides the contrast required for comparison and testing of such reports.  Finally, many of the anthropogenic factors that have been suggested as causes of recent increases in jellyfish populations are quantifiable after 1950, notably because many are derived from FAO data (e.g., Watson et al. 2004) and have been re-expressed at the LME scale (see Section 0). 2.1.3 The Jellyfish Chronicles The data used in this analysis were aggregated into chronicles.  Each chronicle consists of one or more pieces of supporting evidence and has an associated Abundance Trend and Confidence Index (which is calculated from scores for spatial, temporal, and reliability components).  These chronicles were aggregated by LME and then combined using rule sets and a fuzzy expert system to generate a Jellyfish Index for each LME.  Details for all chronicles included in the analysis are found in Appendix A. Multiple pieces of evidence covering similar temporal and spatial scales were included as one chronicle.  Only data that referred to changes (or lack thereof) over several years or greater were included.   Therefore, isolated references to “lots of jellyfish” or “more jellyfish than last year” would not qualify for inclusion due to low temporal coverage, whereas a reference to “more jellyfish in recent years” would be included.  The same rationale applied for decreases, relatively stable populations, or those showing high variability. Increasing or decreasing trends were reported to occur only if they were sustained. Thus, a population of jellies showing a prolonged increase followed by a similar decrease was classified as “stable/variable” (see Section 2.1.5).  Chronicles with no recent data (post- 31  2000) were given a lower temporal score to reflect the uncertainty of whether the identified trend has continued or not (see Section 2.1.6). 2.1.4 Data Not Included While all direct commentary or measurements indicating changes (or lack thereof) in jellyfish populations over several years or more were included in the analysis, indirect evidence was not.  Such indirect evidence includes impacts of jellyfish on human activities such as sting events, clogging of intake pipes for power generation, shipping or mining operations, as well as interference with aquaculture operations.  Although changes in the frequency of these events may indicate changes in jellyfish populations (Purcell et al. 2007), they can also be a consequence of changes in sampling effort.  For example, a jellyfish bloom that interferes with an industrial operation may actually represent a stable jellyfish population if the industrial operation is new to the region, rather than an actual increase in the jellyfish population (Mills 2004).  Therefore, isolated interference events with industrial operations have been excluded from the analysis. Individual events related to direct interference of fishing activities were also excluded. However, an exception was made for information that referred to the changing frequency of such events, as it is assumed to be a strong indication of a change in jellyfish abundance. For example, fishers in some locations reported catching an increasing amount of jellyfish bycatch over years or decades.  As fishers generally have a keen understanding of the marine environment, such statements are assumed to be reliable.  In addition, it is unlikely that these fishers have dramatically increased their effort over these time frames.  In fact, it is expected they would improve their ability to avoid catching jellyfish over time (e.g., Kendall 1990; Matsushita & Honda 2006; Nagata et al. 2009), and therefore any increases in bycatch are likely the result of increased jellyfish populations. As mentioned, sting data were generally not included in the analysis, as it is also problematic due to a number of factors.  An increase in the number of people participating in marine activities can be assumed to increase encounter rates (Macrokanis et al. 2004).  In addition, data showing an increase in sting events may simply be a reflection of increased 32  reporting (Gershwin et al. 2010).  As such, an increase in sting events may not necessarily represent an increase in the amount of jellyfish present.  Conversely, awareness and education campaigns, as well as the use of jellyfish deterrents or countermeasures, can lead to a decrease in sting events without a concomitant reduction of the jellyfish population (Gershwin et al. 2010).  Therefore, sting data has been excluded from the analysis, except where it may reveal temporal changes (e.g., increase in the stinger season) or spatial changes (e.g., increased distribution of jellyfish). 2.1.5 Abundance Trend Each chronicle was assigned an Abundance Trend of increasing (+1), decreasing (-1), or stable/variable (0).  This was identified by considering changes of integrated biomass, which is affected by both abundance and presence.  Therefore, increases (or decreases) in any of overall biomass, frequency of occurrence, or duration of occurrence were all considered to be indications of an increase (or decrease).  As such, more frequent blooms, larger blooms, longer-lasting blooms, and range expansions (and their converses) are all included.  When there was knowledge of multiple species over similar scales, the overall biomass of jellyfish within the ecosystem was considered.  In addition, small, non-abundant hydromedusae were scored lower (see section 2.1.6) due to the fact that they are less likely to affect the overall biomass of jellyfish in the ecosystem. Supporting evidence for each chronicle consisted of either qualitative or quantitative information.  Chronicles with qualitative data as their primary source were classified based on the description of the jellyfish population in question (Table 2.1).  For chronicles with quantitative records, such as multi-year datasets with values for relative abundance or biomass, a general linear regression analysis was performed.  If the slope of the linear regression (abundance against time) was positive and significantly different from zero (p < 0.05), the dataset was considered to represent an increase.  Conversely, a significant negative slope constituted a decrease.  If the slope of the linear regression was not statistically significant, the dataset was classified as stable/variable.  For further discussion regarding the classification of abundance trends, see Section 2.3.1. 33  Table 2.1 Abundance Trend Rule Set Abundance Trend Change Definition -1 Decrease Decrease in  overall  biomass, relative abundance, frequency of occurrence or duration of occurrence 0 Stable  / Variable  Stable or no obvious trend +1 Increase Increase in  overall  biomass, relative abundance, frequency of occurrence or duration of occurrence 2.1.6 Scoring Chronicles Each chronicle was scored according to a set of rules based on temporal coverage (Time Score, Table 2.2), spatial coverage (Space Score, Table 2.3), and reliability (Reliability Score, Table 2.4).  Reliability for invasive species was scored differently (see Section 2.1.7). These scores were used as inputs for calculating the overall Confidence Index of each chronicle (see Section 0). Table 2.2 Time Score Rule Set Time Score Definition Low Multiyear trend < 5 years; recent and unrepeated bloom that has not occurred previously; unclear timeframe; n o recent data (post -2000) Medium Short term (5-9 years) High Medium term (10-14 years) Very High Long term (>=  15 years)  Table 2.3 Space Score Rule Set Space Score Definition Low Singular location or small  region within LME  (< 200 km wide) Medium Large region or two disparate locations within LME (> 200 km apart) High Three or more disparate locations within LME ; widescale sampling in at least  half of LME Very High Widescale sampling of LME  Table 2.4 Reliability Score Rule Set Reliability Score Definition Low Lifeguard or NGO commentary; species unlikely to contri bute significantly to biomass; high uncertainty; d ocumented anthropogenic polyp habitat Medium Marine professional  commentary (e .g . ,  fisher) High Marine scientist  commentary; synthesized knowledge ;  ‘bookend’ (i .e . , non-continuous) scientific  data Very High Scientific  data of numerous or dominant species; well-documented frequency of blooms 34  2.1.7 Invasive Species Invasive species were considered to represent those that have been declared as non- indigenous by experts.  The presence of invasive species of jellyfish was assumed to represent an increase in jellyfish biomass (Abundance Trend = 1).  With this assumption, it is clearly important to understand if an invasive species is truly established, as some invaders can appear briefly in a particular area and may not be detected thereafter.  Knowledge of such events was assumed to represent no change in a jellyfish population (Abundance Trend = 0), rather than an increase, as the excess biomass due to the invader presumably vanishes if the species is no longer detected.  However, it should be noted that the possibility of repeated detection persists in these cases due to potential establishment by cryptic polyps or successive invasions, as is likely with Phyllorhiza sp. in the South Brazil Shelf LME (see Section 2.3.18). The possibility also exists that invasive species of jellies could cause a reduction in native jellyfish biomass.  However, no evidence of such an event was found.  Even in the case of the Black Sea, where invasion by the ctenophore Mnemiopsis leidyi coincided with a decline of the native Aurelia population, there still appears to be a long-term increase in Aurelia abundance (see Section 2.3.47). Chronicles that pertained to invasive species were scored similarly to other chronicles on the basis of time and space, but differently for reliability.  The contribution to an increase in jellyfish biomass due to an invader was weighted by the Invasive Reliability Score in order to provide a more accurate estimate of the total change in jellyfish biomass (Table 2.5).  The assumptions and weighting factors were designed with the intention of avoiding an overemphasis due to invasive species.  Nonetheless, it should be noted that the invasive jellyfish accounted for in this review represent a conservative estimate, as it is likely that far more invasions have occurred than have been documented due to incomplete treatment, unusual life histories, and species crypsis (Holland et al. 2004; Dawson et al. 2005; Graham & Bayha 2007). 35  Invasive species were separated for analysis, allowing assessment of their contribution to the results.  Consistent with the baseline selected for the analysis, species that invaded regions prior to 1950 were excluded. Table 2.5 Invasive Reliability Score Rule Set Reliability Score Definition Low Uncertainty of invasiveness or species is unlikely to contribute significantly to biomass (e.g., small hydromedusae) Medium Documented invasive species or newly-blooming species (without knowledge of other species in ecosystem) or unsuccessful establishment* High Thriving invasive species Very High Known dominant species * Note: Abundance Trend = 1 in all invasive cases except for unsuccessful establishment (where Abundance Trend = 0 and Invasive Reliability Score = Medium)  2.1.8 Fuzzy Expert System Scores and chronicles were combined using a series of rule sets and fuzzy logic (see Figure 2.1 for a schematic diagram of the fuzzy expert system used in the analysis, using the North Sea LME as an example).  Fuzzy set theory, originally developed by Zadeh (1965), is now firmly established in engineering and science (e.g., Lee 1990; van der Werf & Zimmer 1998; Cheung et al. 2007).  Fuzzy models are increasingly being used for ecological applications (Jørgensen 2008), and a review of such models in ecosystem studies is available in Adriaenssens et al. (2004).  Fuzzy set theory allows the representation of variables according to a gradation or degree of membership, rather than the classic true and false membership of conventional Boolean sets.  In addition, fuzzy logic allows a conclusion to be reached with an associated gradation or degree of belief.  As such, fuzzy set theory and logic provide an ideal system for combining information of variable cardinality and confidence. 36  Figure 2.1 Schematic diagram of the fuzzy expert system with the North Sea LME as an example  Variables with differing degrees of confidence were combined using the ‘MYCIN’ method, an asymptotic accumulation of the degree of belief, after Buchanan & Shortliffe (1984).  This knowledge accumulation method is not affected by the order in which evidence is combined, and can be defined as: Degree of beliefn+1 = Evidencen + ((1-Evidencen) * Evidencen-1) where Degree of beliefn+1 is the membership in the conclusion after combining the membership from Evidencen and Evidencen+1.  The membership for any number of pieces of evidence can thus be combined to yield a final membership (i.e., degree of belief) in the conclusion. The 3 scores for each jellyfish chronicle (Time Score, Space Score, and Reliability Score) were combined using a fuzzy rule set, or combination matrix, to yield a Confidence Index (Table 2.6).  The combination matrix used treats all 3 scores equally, and therefore represents all possible combinations of scores.  Thus, each chronicle has an associated Abundance Trend representing the direction of change for the jellyfish population in question, and a Confidence Index representing the degree of belief.  For details for all chronicles included in the analysis, see Appendix A. 37  Table 2.6 Score Combination Matrix Score A Score B Score C Confidence Index Low Low Low Low Low Low Medium Low Low Low High Medium-Low Low Low Very High Medium-Low Low Medium Medium Medium-Low Low Medium High Medium Low Medium Very High Medium Low High High Medium Low High Very High Medium-High Low Very High Very High Medium-High Medium Medium Medium Medium Medium Medium High Medium-High Medium Medium Very High Medium-High Medium High High Medium-High Medium High Very High High Medium Very High Very High High High High High High High High Very High High High Very High Very High Very High Very High Very High Very High Very High  Within each LME, chronicles that had the same Abundance Trend were combined to yield a Belief Index.  This was derived by converting the Confidence Indexes for each chronicle into a membership (degree of belief) according to Table 2.7, and subsequently combining these memberships using MYCIN. Table 2.7 Membership Rule Set Confidence Index Degree of Belief (per chronicle) Low 0.0156 Medium-Low 0.0313 Medium 0.0625 Medium-High 0.1250 High 0.2500 Very High 0.5000  The resulting Belief Indexes for each Abundance Trend were used to select an appropriate Belief Profile according to Table 2.8. 38  Table 2.8 Belief Profile Selection Rule Set Belief Index Belief Profile 0 None 0.01 – 0.09 Low 0.10 – 0.19 Medium-Low 0.2 – 0.34 Medium 0.35 – 0.49 Medium-High 0.50 – 0.59 High 0.60 – 1 Very High  The Belief Profiles used in the fuzzy expert system are membership functions designed to represent the degree of belief over a continuous scale of -100 to +100, with negative scores representing declining jellyfish populations and positive scores representing increasing populations (see Figure 2.2).  These asymmetrical Belief Profiles therefore provide a representation of the accumulated evidence for each particular trend, including both the quantity and the relative certainty of the evidence.  Within each LME, one profile was selected for each Abundance Trend, as long as there was supporting evidence (i.e., Belief Index > 0).  Thus, an LME could have 1, 2, or 3 profiles as inputs for the fuzzy expert system, depending on whether or not there were chronicles supporting each Abundance Trend. Figure 2.2 Fuzzy Input Profiles   The Belief Profiles were combined using the MYCIN method to yield a final Degree of Belief Profile for each LME.  This profile contains information about the evidence within each 39  LME over all Abundance Trends.  To calculate a final Jellyfish Index, the centroid-weighted method (Cox 1999) was used to ‘defuzzify’ the final profile. 2.1.9 Uncertainty The confidence in the Jellyfish Index was quantified by the degree of belief at the centroid value (the Confidence Factor) and the associated values at Degree of Belief = 0.25 (the Confidence Limits).  The difference between the Confidence Limits is defined as the Confidence Interval.  If a particular profile did not reach a Degree of Belief = 0.25 due to lack of evidence (e.g., Gulf of California LME), the upper and lower limits of the confidence interval were selected where the degree of belief falls to zero.   Using these two measures of uncertainty (the Confidence Factor and the Confidence Interval) provides information about both the strength of the data within an LME and how consistent the observed trend is (if any).  In a sense, these can be interpreted similar to measures of ‘accuracy’ and ‘precision’.  That is to say, a high Confidence Factor represents a robust conclusion, and hence can be interpreted as accurate.  Similarly, a small Confidence Interval would indicate that the chronicles included in a particular LME exhibit similar trends, and are therefore precise.  The combination of these two measures ultimately defines the overall confidence in the Jellyfish Index for each LME, and thus we can define a Confidence Quotient, equal to the Confidence Factor divided by the Confidence Interval.  Conclusions with a Confidence Quotient > 1 were classified as high certainty, while those with a Confidence Quotient < 1 were classified as low certainty. Based on the Belief Profiles used in the analysis (see Figure 2.2), Jellyfish Indexes could range from a minimum of -70 to a maximum of +70.  LMEs with a Jellyfish Index of greater than +10 were classified as increases, while those with a Jellyfish Index less than -10 were classified as decreases.  LMEs with a Jellyfish Index between -10 and +10 were classified as stable/variable, indicating they did not show an increasing or decreasing trend.  These thresholds were chosen in order to ensure there was sufficient evidence to suggest a trend. 40  2.2 Results 2.2.1 The Jellyfish Index In total, 133 chronicles were included in the analysis, spread over 45 LMEs.  Of the 45 LMEs, 31 (69%) showed increasing trends, while only 2 (4%) showed decreasing trends.  The remaining 12 LMEs (27%) were classified as stable/variable, showing neither increasing nor decreasing trends. Out of the 31 LMEs exhibiting increases, 12 were classified as high certainty (Confidence Quotient > 1), and 19 were classified as low certainty (Confidence Quotient < 1).  Of the 12 LMEs with stable/variable trends, 3 were of high certainty and 9 were of low certainty.  The Humboldt Current LME was the only system to exhibit a decrease associated with a high certainty. The results are similar when normalized by area.  Accounting for the size of the LMEs, 24% of the total area included represented regions with increases of high certainty, while increases of low certainty represented 46%.  Stable/variable regions represented 25% of the total area included, while the remaining 5% was associated with decreases. Results for all LMEs, including invasive species and excluding overfishing of jellyfish are shown in Table 2.9 and a map of the results is presented in Figure 2.3.  The Belief Indexes used for each LME in the analysis are included in Appendix B.  When examined separately, these Belief Indexes illustrate the degree of evidence for chronicles based on native species compared to those based on invasive species, as well as those representing overharvesting of jellyfish. 41  Table 2.9 Results including native and invasive species LME ID LME Name Trend Conclusion Conclusion Certainty Jellyfish Index Confidence Quotient Confidence Factor Lower Limit Upper Limit Interval 1 East Bering Sea Increase High 61.84 1.47 0.83 34.50 91.00 56.50 2 Gulf of Alaska Stable/variable Low 7.06 0.80 0.58 -35.00 37.24 72.24 3 California Current Increase Low 25.55 0.63 0.73 -31.25 85.00 116.25 4 Gulf of California Increase Low 35.87 0.13 0.13 0.00 100.00 100.00 5 Gulf of Mexico Increase Low 14.13 0.75 0.65 -35.00 51.25 86.25 6 Southeast US Continental Shelf Increase Low 14.13 0.75 0.65 -35.00 51.25 86.25 7 Northeast US Continental Shelf Increase High 52.52 1.58 0.83 43.75 96.25 52.50 8 Scotian Shelf Stable/variable High 0.00 1.07 0.67 -31.25 31.25 62.50 9 Newfoundland-Labrador Shelf Stable/variable High 0.00 1.54 0.83 -27.00 27.00 54.00 10 Insular Pacific-Hawaiian Increase High 54.84 1.13 0.67 25.63 85.00 59.37 11 Pacific Central-American Coastal Increase Low 41.74 0.77 0.30 12.50 51.25 38.75 12 Caribbean Sea Increase Low 13.60 0.81 0.31 3.00 41.26 38.26 13 Humboldt Current Decrease High -42.80 1.26 0.71 -91.00 -34.50 56.50 14 Patagonian Shelf Increase Low 47.90 0.87 0.50 17.50 75.00 57.50 15 South Brazil Shelf Stable/variable Low 7.06 0.80 0.58 -35.00 37.24 72.24 16 East Brazil Shelf Increase Low 35.87 0.13 0.13 0.00 100.00 100.00 18 West Greenland Shelf Decrease Low -35.87 0.13 0.13 -100.00 0.00 100.00 21 Norwegian Sea Increase Low 41.74 0.70 0.27 12.50 51.25 38.75 22 North Sea Increase Low 35.89 0.22 0.30 -40.67 96.25 136.92 23 Baltic Sea Increase Low 14.13 0.75 0.65 -35.00 51.25 86.25 24 Celtic-Biscay Shelf Increase Low 36.94 0.44 0.56 -37.50 91.00 128.50 25 Iberian Coastal Stable/variable Low 7.06 0.80 0.58 -35.00 37.24 72.24 26 Mediterranean Sea Increase Low 43.95 0.22 0.30 -37.50 96.25 133.75 28 Guinea Current Increase Low 35.87 0.13 0.13 0.00 100.00 100.00 29 Benguela Current Increase High 54.84 1.15 0.67 26.63 85.00 58.37 30 Agulhas Current Stable/variable Low 0.00 0.71 0.50 -35.00 35.00 70.00 31 Somali Coastal Current Stable/variable Low 0.00 0.44 0.33 -37.50 37.50 75.00 32 Arabian Sea Increase Low 14.13 0.75 0.65 -35.00 51.25 86.25 34 Bay of Bengal Increase Low 14.57 0.52 0.58 -37.24 75.00 112.24 42  LME ID LME Name Trend Conclusion Conclusion Certainty Jellyfish Index Confidence Quotient Confidence Factor Lower Limit Upper Limit Interval 35 Gulf of Thailand Increase Low 35.87 0.13 0.13 0.00 100.00 100.00 36 South China Sea Stable/variable Low 8.86 0.56 0.44 -37.50 40.67 78.17 40 Northeast Australian Shelf Increase Low 35.87 0.13 0.13 0.00 100.00 100.00 41 East Central Australian Shelf Stable/variable Low 0.00 0.71 0.50 -35.00 35.00 70.00 42 Southeast Australian Shelf Stable/variable Low 8.86 0.56 0.44 -37.50 40.67 78.17 47 East China Sea Increase High 70.00 1.90 1.00 43.75 96.25 52.50 48 Yellow Sea Increase High 61.84 1.47 0.83 34.50 91.00 56.50 49 Kuroshio Current Increase High 35.34 1.13 0.67 25.63 85.00 59.37 50 Sea of Japan Increase High 61.84 1.47 0.83 34.50 91.00 56.50 51 Oyashio Current Increase Low 32.81 0.38 0.48 -35.00 91.00 126.00 52 Sea of Okhotsk Increase High 61.84 1.47 0.83 34.50 91.00 56.50 53 West Bering Sea Increase High 61.84 1.47 0.83 34.50 91.00 56.50 60 Faroe Plateau Stable/variable High 0.00 1.54 0.83 -27.00 27.00 54.00 61 Antarctic Increase High 61.84 1.47 0.83 34.50 91.00 56.50 62 Black Sea Increase High 70.00 1.90 1.00 43.75 96.25 52.50 63 Hudson Bay Stable/variable Low 0.00 0.44 0.33 -37.50 37.50 75.00  43  Figure 2.3 Map of results including native and invasive species of jellyfish  Red = increase (high certainty); orange = increase (low certainty); green = stable/variable; blue = decrease; grey = no data.  Circles represent discrete chronicles with relative sizes reflecting the Confidence Index.  Circle locations are approximate, as some were shifted to avoid overlap; the circle for the Antarctic LME summarizes circumpolar observations 44  2.2.2 Effects of Invasive Species Invasive species were separated from the analysis in order to examine their impact on the results (for results with native species only, see Table 2.10).  Invasive species of jellyfish were reported in 21 LMEs.  In 8 of these LMEs, the inclusion of invasive species had a negligible contribution to the results, and did not affect the Jellyfish Index. The inclusion of invasive species can be considered responsible for the conclusion of low certainty increases in 4 LMEs (Gulf of Mexico, Southeast U.S. Continental Shelf, Caribbean Sea, and Baltic Sea), as the exclusion of invaders changed the classification of these LMEs from increasing to stable/variable.  Similarly, invaders can be considered responsible for the low certainty increase reported in the East Brazil Shelf LME, as there were no data for native species.  The Insular Pacific-Hawaiian LME exhibited an increase due to native species; however, the inclusion of invasive species increased the certainty of the conclusion to high.  In the remaining LMEs, the inclusion of invasive species increased the Jellyfish Index by variable amounts, but did not alter the conclusions. Several invasive species of jellyfish from disparate taxonomic groups were reported in numerous locations, including scyphozoans Aurelia spp. and Phyllorhiza punctata; hydrozoans Blackfordia virginica, Maeotias marginata, and Turritopsis spp.; as well as the infamous ctenophore Mnemiopsis leidyi. 45  Table 2.10 Results with native species only (effects of invasive species excluded; only those LMEs that had invasive species are shown) LME ID LME Name Trend Conclusion Conclusion Certainty Jellyfish Index Confidence Quotient Confidence Factor Lower Limit Upper Limit Interval 3 California Current Increase Low 19.82 0.73 0.78 -31.25 75.00 106.25 5 Gulf of Mexico Stable/variable Low 7.06 0.80 0.58 -35.00 37.24 72.24 6 Southeast US Continental Shelf Stable/variable Low 7.06 0.80 0.58 -35.00 37.24 72.24 7 Northeast US Continental Shelf Increase High 52.52 1.58 0.83 43.75 96.25 52.50 10 Insular Pacific-Hawaiian Increase Low 47.90 0.87 0.50 17.50 75.00 57.50 11 Pacific Central-American Coastal Increase Low 35.87 0.09 0.09 0.00 100.00 100.00 12 Caribbean Sea Stable/variable Low 0.00 0.17 0.17 -50.00 50.00 100.00 13 Humboldt Current Decrease High -61.84 1.47 0.83 -91.00 -34.50 56.50 14 Patagonian Shelf Increase Low 47.90 0.87 0.50 17.50 75.00 57.50 15 South Brazil Shelf Stable/variable Low 0.00 0.71 0.50 -35.00 35.00 70.00 16 East Brazil Shelf No data 21 Norwegian Sea Increase Low 41.74 0.70 0.27 12.50 51.25 38.75 22 North Sea Increase Low 35.89 0.22 0.30 -40.67 96.25 136.92 23 Baltic Sea Stable/variable Low 0.00 0.71 0.50 -35.00 35.00 70.00 25 Iberian Coastal Stable/variable Low 0.00 0.71 0.50 -35.00 35.00 70.00 26 Mediterranean Sea Increase Low 31.02 0.54 0.66 -37.50 85.00 122.50 42 Southeast Australian Shelf Stable/variable Low 8.86 0.56 0.44 -37.50 40.67 78.17 47 East China Sea Increase High 70.00 1.90 1.00 43.75 96.25 52.50 48 Yellow Sea Increase High 61.84 1.47 0.83 34.50 91.00 56.50 49 Kuroshio Current Increase High 35.34 1.13 0.67 25.63 85.00 59.37 62 Black Sea Increase High 61.84 1.47 0.83 34.50 91.00 56.50 46  2.2.3 Effects of Jellyfish Overexploitation Interestingly, several of the chronicles that were classified as decreases in the analysis (Abundance Trend = -1) concerned jellyfish species that have been harvested for food, science, or unique proteins, and have subsequently declined, possibly as a result of overfishing. However, only 4 chronicles had a primary source of evidence that directly attributed a decrease to overexploitation.  As such, these chronicles were treated separately in the analysis (Table 2.11).  In the Arabian Sea LME, the inclusion of overfishing of jellies reduced the Jellyfish Index sufficiently to alter the trend conclusion from increasing to stable/variable (both conclusions of low confidence).  Including of overfishing of jellyfish for the Bay of Bengal LME resulted in no change to the Jellyfish Index.  The South China Sea and East Central Australian Shelf LMEs showed a reduced Jellyfish Index when overfishing of jellies was included; however, this reduction was not sufficient enough to classify these LMEs as decreases, and are therefore still classified as stable/variable (Abundance Trend = 0).  Thus, it can be said that in the majority of locations where overfishing of jellyfish could be identified, it did not alter the conclusions of the analysis. 47  Table 2.11 Results with effects of jellyfish overfishing added (only those LMEs with jellyfish overharvesting are shown) LME ID LME Name Trend Conclusion Conclusion Certainty Jellyfish Index Confidence Quotient Confidence Factor Lower Limit Upper Limit Interval 32 Arabian Sea Stable/variable Low 7.19 0.61 0.54 -37.24 51.25 88.49 34 Bay of Bengal Increase Low 14.57 0.52 0.58 -37.24 75.00 112.24 36 South China Sea Stable/variable Low 0.00 0.41 0.33 -40.67 40.67 81.34 41 East Central Australian Shelf Stable/variable Low -7.06 0.69 0.50 -37.24 35.00 72.24 48  2.3 Discussion This study represents the first rigorous demonstration that jellyfish populations appear to be increasing in coastal ecosystems worldwide, as previously suspected (Mills 2001; Purcell et al. 2007; Pauly et al. 2009b; Richardson et al. 2009).  Of the 45 LMEs included in the analysis, 31 (69%) showed increasing trends, while only 2 (4%) showed decreasing trends.  The remaining 12 LMEs (24%) were classified as stable/variable, with no obvious trend.  These results suggest that while increases of jellyfish populations are not universal, they are both numerous and widespread.  Regarding the 21 LMEs that were not included, most were from the Arctic (11), Australia (4), and the South Pacific (3).    Therefore, the results represent extensive spatial coverage of the world’s coastal ecosystems.  While only 36% of the conclusions are of high certainty, the majority of these (12 of 16) were in LMEs that showed increasing trends.  In addition to demonstrating that jellyfish populations are increasing in numerous ecosystems around the world, this also underscores the fact that information on jellyfish abundance is poor over much of the globe.  As such, we must strive to learn more about these fascinating and important creatures, especially given the fact that they seem to be one of the few groups of organisms that appear to be benefiting from the continued anthropogenic impacts on the world’s biosphere. 2.3.1 Defining an ‘Increase’ Information used in the analysis was weighted by time, space, and reliability in order to reflect the relative contribution to a change in jellyfish populations within each LME.  As a consequence of the methods used and the inclusion of anecdotal data, the results reflect the degree of belief that any particular jellyfish population has changed or not, rather than the magnitude of those changes.  Therefore, observations of ‘more’ jellyfish may not necessarily mean there are truly ‘more jellyfish’ if the observations are not normalized by effort.  Nonetheless, it is expected that these factors are correlated, as changes of larger magnitude are assumed to be more noticeable and thus have more supporting evidence. Only after accepting this assumption should conclusions from this analysis be identified as ‘increases’ and ‘decreases’ in colloquial language. 49  Jellyfish populations are extremely variable on both temporal and spatial scales due to their peculiar ecology (see Section 1.3).  Thus, even LMEs showing pronounced increases in jellyfish populations with ‘high certainty’ may also experience dramatic declines over short timescales.  For example, the trend in the East Bering Sea LME is classified as an increase due to the results of a regression analysis, but jellyfish in the Bering Sea declined dramatically after 2000 (Brodeur et al. 2008b).  Despite this decline, jellyfish abundance in this LME appears sustained above the levels observed in the 1980s, and the increase remains significant (see Section 2.3.4).  Other long-term studies also show high variability, such as the 38-year dataset from Peru (Quiñones et al. 2010b).  Jellyfish populations in that system appear tightly correlated with El Niño events; however, the data appear to exhibit a decline (see Section 2.3.16).  Even the well-documented increase in blooms of the giant jellyfish (Nemopilema nomurai) in East Asia is not persistent, as blooms do not occur every year (Uye et al. 2010).  With these cyclical patterns of jellyfish populations, trends may turn out to be ephemeral, and an apparent long-term pattern may collapse with updated data.  An example is the population of cannonball jellyfish (Stomolophus meleagris) in the southeastern United States, which appeared to show a decline over the last decade, but then rebounded strongly in recent years (see Section 2.3.9).  Clearly then, increases or decreases may actually represent a trend during only part of a cycle, and may reverse over a longer timeframe. Also, with such high variability, poor sampling frequency could dramatically affect the results.  To account for these concerns, attempts were made to ensure chronicles used in the analysis were up to date wherever possible, thus reflecting changes to present day.  As well, all chronicles were scored based on the time-span covered, and therefore information covering longer timescales had more influence on the results.  On the contrary, those chronicles without up-to-date information were also given the lowest possible temporal score.  Nonetheless, datasets of jellyfish abundance spanning multiple decades are sparse, and therefore the results only represent a rough estimate of true jellyfish population dynamics. Secondly, the fact that jellyfish are typically part of the zooplankton makes them vulnerable to changes in oceanic current patterns.  The presence or absence of a bloom may 50  be simply due to relocation, and thus an increase observed in one location may be concomitant with a decrease in another location.  If the increase is observed but the decrease is not, one comes to the false conclusion that jellyfish have increased.  Whenever there was evidence of such an explanation, the chronicle was not included.  An example would be a recent quote from of a fisher in Florida who said he was seeing more sea nettles (Chrysaora sp.) than in the preceding decades.  However, it is possible that this was due to the relocation of a population that is normally observed elsewhere in the Gulf of Mexico (Spinner 2010).  Even without knowledge of such events, the analysis is not overly sensitive to this pitfall, as only multi-year data from the same location were used.  As chronicles were either up-to-date or scored with low reliability, increases due to spatial redistributions would have to be sustained.  In addition, chronicles based on information over short time periods or from single locations were also scored lower, thereby minimizing the effect on the results. There is also the possibility of a reporting bias, whereby newsworthy blooms or increases in jellyfish are reported, but absences and stable or declining populations are not. While this understandable bias is likely to overestimate increases, the methods used in this analysis were designed to minimize this effect.  Episodic blooms were not included unless a temporal component of at least several years could be identified.  In addition, these temporal components were scored based on their relative duration, ensuring that information covering longer time-spans had more influence on the results than information that spanned less than a decade.  Interference events with human activities, which are typically newsworthy, were also not included unless the information was in a clear historical context (see Section 2.1.4).  In addition, all information was up to date wherever possible, ensuring any apparent trends were sustained.  If updated and recent material could not be found, the chronicle in question was scored lower and therefore had less influence on the results.  Finally, much of the anecdotal information used in the analysis was gleaned from targeted interviews.  As numerous responses in these interviews indicated stable populations, they are assumed to represent a relatively unbiased source of information where scientific data are lacking. 51  2.3.2 Invasive Species Invasive species of jellyfish were reported in 21 of 45 LMEs found in this analysis (47% of the systems included).  For the most part, invasive species were not responsible for the observed increases reflected in the results.  However, the widespread detections demonstrate that jellyfish are truly global invaders of significant concern.  Thriving populations of invasive jellyfish in systems like the Mediterranean and Black Seas should serve as warnings for other ecosystems around the globe, and it is likely that far more invasions have occurred than are reported (see Section 1.5.1). 2.3.3 Taxonomic Concerns The term ‘jellyfish’, according the definition used here (see Section 1.1), refers to specimens from several phyla (Cnidaria, Ctenophora, and Chordata).  Such organisms are obviously extremely distant phylogenetic relatives, and therefore grouping them under an umbrella term such as ‘jellyfish’ is problematic.  Firstly, the use of such an idiom ignores proper taxonomy.  The changes evident in the results of this analysis should not only be viewed in their entirety, but also in the context of ecology and evolution.  Without proper taxonomic resolution, a deeper and more meaningful understanding of the mechanisms and consequences involved may be unattainable (Haddock 2004).  Secondly, using a broad category also runs the risk of inferring attributes of a larger group of organisms based only on a handful of species.  Such ‘errors of commission’ (Dawson 2010) could inhibit robust conclusions if they are not made in the light of evolution.  Generalizations concerning such a broad group of organisms will certainly have exceptions (Bayha & Dawson 2010), and we must be careful not to ignore these differences by focusing only on commonalities. Despite these concerns, there is also value in generalized results.  Notwithstanding their phylogenetic diversity, jellyfish share many similarities.  As demonstrated in this analysis, one such similarity is a recent trend of increasing abundance.  If such trends are indeed caused by anthropogenic factors (see Sections 1.5 and 3), raising awareness of the issues and developing a deeper understanding of the mechanisms involved should be priorities.  Wherever possible, jellyfish included in this analysis were identified to species. 52  Hopefully, this will facilitate analysis and discussion from both specific and general perspectives. 2.3.4 LME #1 – East Bering Sea1 Knowledge of jellyfish abundance in this LME is unique, due to a long-term dataset from the Resource Assessment and Conservation Engineering (RACE) Division of the Alaska Fisheries Science Center (Brodeur et al. 1999; Brodeur et al. 2002; Brodeur et al. 2008a). This time series consists of bottom-trawl surveys beginning in 1975, with continuous annual measurements since 1979.  Data were collected for 356 stations each summer, producing total biomass estimates for six geographic regions.  Jellyfish were not identified to species for most of the record.  However, recent taxonomic survey data suggest the biomass is primarily composed of Chrysaora melanaster, which is indigenous to the Bering Sea (Brodeur et al. 2002; Brodeur et al. 2008a).  During the day, this species is found within a narrow depth range of 30-40 m (Brodeur 1998; Brodeur et al. 2002), suggesting that bottom-trawls would only catch jellyfish during deployment and retrieval.  As such, this sampling method vastly underestimates the number of jellyfish present and does not allow for the calculation of estimates of absolute biomass.  However, as sampling has been consistent throughout most of the study, the record does provide an index of relative abundance for which trends can be identified (Brodeur et al. 2002; Brodeur et al. 2008a). The Bering Sea time series shows a dramatic increase in jellyfish biomass throughout the 1990s with a peak in 2000, followed by a sharp decline (Brodeur et al. 2008a).  Between 2001 and 2009, the population was variable but appeared to somewhat stabilize at levels below those of the 1990s, but above those of the 1970s and ‘80s.  However, 2009 showed another increase to levels comparable to the mid-1990s (Decker et al. 2009).  Linear regression over the entire dataset shows a significant increase (R2 = 0.23, p = 0.006), indicating a variable but sustained long-term increase in jellyfish abundance in this LME since the 1970s.  1 Numbers preceding each LME denote the identity (ID) used by the Sea Around Us Project database 53  The onset of both the rapid increase and decrease coincide with recent regime shifts in the Bering Sea (Brodeur et al. 2008a).  In addition to the increase in biomass, the jellyfish from this dataset exhibited a significant range expansion beginning in 1991 (Liu et al. 2011). Distribution expanded northwesterly to the middle of the shelf in the 1990s and into the shallow inner shelf during the year 2000 peak (see Fig. 4 in Brodeur et al. 2008a).  The shift to the 1990s regime was characterized by warmer summertime SST than the 1980s regime (Hunt et al. 2002), coinciding with a steep increase in jellyfish biomass.  However, after the 1999 regime shift, the eastern Bering Sea showed even warmer summer SST and reduced winter ice coverage, which coincided with the precipitous decline in jellyfish (Brodeur et al. 2008a).  Thus, it should be noted that warmer temperatures do not necessarily mean an increase in jellyfish (Brodeur et al. 2008a), as has been demonstrated for the majority of species investigated (reviewed in Purcell et al. 2007). Correlations with biotic and abiotic factors varied over time and space, highlighting the fact that jellyfish population dynamics can be complicated and regional.  As usual, virtually nothing is known about the field ecology of the polyps (Mills 2001; Brodeur et al. 2008a). As the Bering Sea is relatively unpolluted, Brodeur et al. (2008a) suggest that the only probable causes for changes in jellyfish populations are climate and fishing.  More specifically, the authors hypothesize that complex bottom-up changes affected the reproduction, survival, and growth of large jellyfish.  While a correlation with regime shifts is evident, the association with fish and other marine populations is indiscernible (O'Harra 2004), and the current understanding of the ecosystem dynamics remains limited.  With continued warming predicted for this LME, further northward range expansion of the jellyfish population is expected (Brodeur et al. 2008a). 2.3.5 LME #2 – Gulf of Alaska Small-mesh trawls have been conducted by the National Marine Fisheries Service and the Alaska Department of Fish and Game in the Gulf of Alaska since 1953.  These trawls were conducted mainly along the Alaskan Peninsula to identify concentrations of 54  commercially exploitable shrimp.  The standardization of trawling gear and methods in 1972 allows comparison of annual data.  Anderson & Piatt (1999) compared catch per unit effort (CPUE) data for a wide variety of zooplankton from 6812 trawls over the period 1973- 1996.  Data presented for jellyfish (scyphozoa) show low to moderate levels in the 1970s, a dramatic increase in biomass in the mid to late 1980s, and a moderate decline in the 1990s. With the fact that CPUE levels from the 1990s are on the order of 2 to 10 times higher than those of the 1970s, it can be said that jellyfish have increased in the region over this time period.  Linear regression over the time series also reveals a significant, positive slope. However, without recent data it cannot be concluded that this trend has continued so this chronicle is assigned a Time Score = Low.  Due to a tight coupling between populations of many taxa with physical oceanographic conditions, the authors of this study argue that the Gulf of Alaska ecosystem is regulated to a large degree by ‘bottom-up’ processes.  They also point out that the coherence of population trends for many ecologically disparate taxa (including both commercially exploited and non-targeted species) supports a common cause such as climate change.  This powerful dataset, yet another from fisheries research, sheds light on the complex nature of jellyfish ecology and ecosystem dynamics.  An updated version of the dataset should be of great interest to medusologists. Canada’s Department of Fisheries and Oceans (DFO) has been collecting zooplankton data off the British Columbia coast for decades.  Regions studied include a survey line known as ‘Line P’, which consists of 26 stations extending from the mouth of Juan de Fuca Strait to Ocean Station Papa, located at 50°N and 145°W.  Zooplankton tows are collected from 150 m to the surface, typically three times per year.  Surveys are also conducted over and offshore of the continental shelf of Vancouver Island, and in the Strait of Georgia (see below).  However, most of these surveys will intentionally avoid large scyphomedusae, and may throw out samples that have chance encounters with large jellies (M. Galbraith, DFO, pers. comm., Nov. 2010).  Combined measurements of ctenophores, hydromedusae, scyphomedusae, and siphononphores show large interannual variations, but no obvious trend (Abundance Trend = 0) over the period 1983-2010 in all B.C. regions, including Line P, 55  stations along the continental shelf off Vancouver Island, and further north toward Hecate Strait (data provided by Galbraith 2010). Prince William Sound is the site of the 1989 ‘Exxon Valdez’ oil spill, and is home to a diverse ecosystem that depends largely on forage fish.  Several projects were launched after the 1989 spill to assess the impact on forage fish, which included aerial and acoustic surveys, as well as seining and underwater video for target verification.  Aggregations of Aurelia labiata were visible during these surveys conducted over three consecutive summers in the mid-1990s.  Purcell et al. (2000) present their results for surveys from 1995, 1996, and 1997, where a total of 995 jellyfish aggregations were observed.  Abundance of jellyfish in the aggregations followed a seasonal pattern, generally peaking in July and August of each year.  Dramatic interannual variation in relative abundance was observed, with moderate density levels in 1995, a 2-fold increase in 1996, followed by low densities in 1997.  The authors note that 1996 was characterized by deep mixing and also had high densities of zooplankton and hydromedusae.  In contrast, 1997 was unusually warm due to a strong El Niño, and showed low densities of zooplankton and hydromedusae.  This dataset was extended by an additional two years, when it was noted that in 1998, an even larger number of Aurelia aggregations were observed than for any of the three previous years (see Purcell 2003 and references therein) but were virtually absent in 1999 (Purcell et al. 2000).  Further studies in Prince William Sound also showed high interannual variation in jellyfish populations (Purcell 2003), although may not be useful for identifying abundance trends as they did not include large aggregations of medusae. At the southern end of this LME is a region known as the Salish Sea, consisting of the Georgia Strait, Puget Sound, and the Juan de Fuca Strait.  Knowledge of jellyfish in this region is reasonably high, and provides an important example of the high variability of jellyfish populations across space and taxa.  Mills (1981) presents seasonal distributions more than 50 species of jellyfish from Friday Harbor on San Juan Island, compiled over 4 years (1976-1980).  No reference is made to interannual variations in abundance; however, seasonal distributions were noted to show “surprisingly little” variation between years.  The author also notes numerous species of hydromedusa-producing polyps on the underside of 56  dock floats and boat hulls around Friday Harbor, including Bougainvillia ramosa, Sarsia spp., and Obelia spp. , which suggests artificial structures could be contributing to increased habitat for jellyfish polyps in this region.  This idea is supported by a study from Purcell et al. (2009) of Aurelia labiata polyps under the floating docks at Cornet Bay Marina on Whidbey Island, Washington.  This small marina has approximately 60 slips for recreational vessels.  Studies from 15 sites within the marina over 3 years (2004-2006) show an average coverage of 58% by A. labiata polyps, with an average density of 9.3 cm-2.  Extrapolating this density over the entire 685 m2 of the floating docks suggests a colony of roughly 100 million individual polyps.  In the first year of observation (2004), the number of discs per strobilating polyp averaged about 10, indicating that polyps have the potential to produce numerous ephyrae.  The fraction of polyps that strobilated each year varied by site and date; however strobilation was observed during all years.  As the Cornet Bay Marina was originally constructed in the 1950s and expanded numerous times since, it can be said that this location has produced an increasing amount of artificial habitat since its creation.  With a colony of 100 million polyps, many of which can produce an average of 10 ephyrae, this anthropogenic habitat likely now contributes hundreds of millions of jellyfish to the ecosystem each year that otherwise would not be there.  With the exception of a few aquaculture operations in other ecosystems, this is perhaps the most quantitative and well- defined example to date of how coastal development may contribute to increases in jellyfish populations.  Such artificial habitat may be especially well suited for jellyfish polyps as they have been shown to prefer artificial plastic substrates both in the laboratory (Holst & Jarms 2007; Hoover & Purcell 2009) and in situ at this site (Hoover & Purcell 2009).  Floating docks may also provide a refuge from benthic predators of polyps as they are unable to reach the floating substrate.  During the course of the study, no large predators, such as nudibranchs, were observed under the floats. Ctenophores, siphonophores, and medusae have been recorded by Fisheries and Oceans Canada zooplankton surveys in Georgia Strait since 1991.  Except for a couple of notable peaks in the late 1990s, jellyfish biomass data show no obvious trend between 1991 and 2006 in the surface layer (L. Li, UBC, pers. comm., Nov. 2010). 57  Hydromedusae were sampled in the spring over a 5 year period in 2 locations on the east coast of Vancouver Island in British Columbia (also a part of the Salish Sea).  While the intent was to study the impact of Aequorea victoria on larval herring, densities of jellyfish were calculated for each year.  Summarized in Purcell & Arai (2001), the data again suggest extreme interannual variation, with densities varying by up to three orders of magnitude from one year to the next.  Physical data are also available from one of the sampling sites, with mean jellyfish densities showing a significant positive correlation with both temperature and salinity (Purcell 2005). Another jellyfish dataset of jellyfish abundance comes from Roscoe Bay, British Columbia, where measurements of the Aurelia labiata population have been taken periodically for 10 years.  An index of relative abundance shows extreme interannual variation and no obvious trend (D. Albert, UBC, pers. comm., July 2010). There are some indications that medusae populations have recently increased near Vancouver, British Columbia.  Residents reported seeing unprecedented strandings of Cyanea capillata along beaches in 2008 (Sutherland 2008) and others have noticed apparent increases of C. capillata and Aequorea sp. in 2008 and 2009 (M. Neale, Van. Aqua., pers. comm., Oct. 2009).  However, a dramatic change in jellyfish populations was observed in Indian Arm in summer 2010.  In contrast to the previous two years, virtually no C. capillata were seen.  However, Aurelia labiata were at their highest abundance in at least 10 years. Then, in 2011, an unusually cold spring resulted in a later-than-usual plankton bloom, and virtually no jellyfish were seen until very late in the season, when A. labiata and C. capillata began to increase in abundance. Aequorea victoria was heavily harvested in Friday Harbor, Washington in order to extract and purify the proteins aequorin and green fluorescent protein (GFP).  From the 1960s to the 1990s, between 25,000 and 125,000 Aequorea medusae were harvested nearly every year (Mills 2001).  Aequorea sp. have since declined steadily in the region throughout the last two decades, with current abundances barely in the hundreds (Mills 2001; 2004). However, this does not appear to be the case in the southern part of Puget Sound, where populations of Aequorea victoria may actually be increasing (E. Thuesen, ESC, pers. comm., 58  Nov. 2010).  Mills (2001) has also noted a decline in Polyorchis pencillatus in this region, another species which has been collected for research purposes. Such diverse examples of jellyfish population changes in the Salish Sea demonstrate the complexity of jellyfish population dynamics, even over small spatial scales.  Changes over the last decade include increases in anthropogenic polyp habitat, declines of both harvested and non-harvested jellyfish, and recent blooms of other species.  As this evidence does not demonstrate a consistent trend, this chronicle is labeled variable (Abundance Trend = 0) with high uncertainty (Reliability Score = Low). Invasive Species in LME #2 – Gulf of Alaska While not included as a separate chronicle due to the additional knowledge from the region and the likely small contribution to biomass of this species, established populations of the hydrozoan Cladonema radiatum have been reported from the Salish Sea (USGS 2011). 2.3.6 LME #3 – California Current Plankton samples along the California coast have been collected and analyzed by the California Cooperative Oceanic Fisheries Investigations (CalCOFI) since 1949, and sampling continues today.  Lavaniegos & Ohman (2007) present detailed results of select taxa for both the Central California (CC) stations and the Southern California (SC) stations for the period 1951-2005.  Calycophoran siphonophores showed a significant increase in both CC and SC, whereas salps showed a significant decrease in SC only.  To gain an overall picture of the gelatinous biomass from this dataset, the analysis was modified to include all members of Ctenophora, hydromedusa, siphonophora, and Salipida.  As per Lavaniegos & Ohman (2003), pooled data from springtime night tows were used.  Updated to 2006, data from CalCOFI (2010) show a significant increase in jellyfish density in SC, with 5 of the 6 highest abundances occurring in the last decade (see Figure 2.4).  Pooled samples for the Central Station were also available, although there is a gap in data collection from 1986 to 2002.  In this region, salps show extreme peaks in 1961, 1980, and 1982.  However, there is no trend in the overall data, as densities for 2003-2006 are on the order of those prior to 1986 (see Figure 59  2.5).  As such, the two CalCOFI stations were treated as separate chronicles for the purposes of this analysis, with differing Abundance Trends. 60  Figure 2.4 Jellyfish populations off southern California (CalCOFI 2010)    61  Figure 2.5 Jellyfish populations off central California (CalCOFI 2010)  62  Similar research has been conducted further south in this LME, off Baja California. Lavaniegos (2009) presents results for cruises conducted by the Mexican Research of the California Current program (IMECOCAL) for the period 1997-2007.  Results were divided into 2 geographic areas – Northern Baja California (NBC) and Central Baja California (CBC). Results reveal that both tunicates (appendicularians, doliolids, salps, and pyrosomes) and “carnivores” (chaetognaths, siphonophores, medusae, ctenophores, and heteropods) showed a significant increase in NBC, whereas “carnivores” showed a significant increase in CBC and tunicates showed no significant trend.  While these groups include many organisms that are classified as jellyfish in this analysis, they also include organisms that are not (see Section 1.1).  Therefore, the results must be included with caution, as organisms such as chaetognaths are shown to be highly abundant taxa in this LME (Lavaniegos & Ohman 2007), and are possibly responsible for the observed trends.  As such, data from this study are classified as an increase (Abundance Trend = 1) over the entire study area (Space Score = Medium), albeit with high uncertainty (Reliability Score = Low). Numerous studies have also been conducted in Monterey Bay, located along the central Californian coast.  The abundance and distribution of two hydromedusae were analyzed using data from the Monterey Bay Aquarium Research Institute’s (MBARI) remotely operated vehicle (ROV).  Presented by Raskoff (2001), the study encompasses 9 years of data (1990-1998) and includes two El Niño events (1991/1992 and 1997/1998).  The El Niño events were clearly evident through a visualization of water masses entering the bay, using the calculation of spiciness2.  Both of these events strongly affected the abundance and distribution of both hydromedusan species.  Mitrocoma cellularia showed a sudden increase in abundance, as well as an increased depth range, during both El Niño events, while its abundance and range was much more limited in the time between events.  Conversely, the abundance of Colobonema sericeum dropped to very low levels during the events, while it was found in high numbers between events.  While the responses from these two species to  2 The measurement of spiciness combines the dynamic interaction of temperature and salinity.  Low temperature, low salinity water has a low spiciness, whereas warm, salty water has a high spiciness 63  the El Niño events are clearly dramatic, it remains unclear whether they are due to tolerance, reproduction, or transport. A number of siphonophores have also been studied in Monterey Bay.  Robison et al. (1998) present results for 257 ROV transects including over 10,000 observations of the physonect siphonophore Nanomia bijuga.  The time series encompassed only three years of data and the authors found no significant differences in abundance between years.  A similar sample of 295 ROV transects included 1755 calycophoran siphonophores.  Again, there was no significant difference in overall abundance over the three year study period (Silguero & Robison 2000).  While these surveys show no obvious trend for hydromedusa and siphonophore populations through the 1990s, other populations of jellyfish in Monterey Bay appear to be changing.  Irregular seasonality and increased duration of occurrence has been noted for several species in Monterey Bay (Tucker 2010).  There is also suspicion that Aurelia populations have increased in Monterey Bay in recent decades (W.M. Graham, DISL, pers. comm., May 2010).  Unfortunately, the data to verify such suspicions have not been published or released by MBARI, and the anecdotal data were offered with considerable caution and uncertainty.  Given the high uncertainty and somewhat contrasting information, Monterey Bay is not included as a separate chronicle in the analysis, and jellyfish abundance in this LME is probably better estimated using the more robust datasets. Hopefully, the powerful dataset maintained by MBARI will see more analysis and publication of jellyfish data in the near future. A series of sampling cruises were conducted in the spring and summer of 1981 off of the coasts of Oregon and southern Washington.  While the primary goal of these cruises was to study juvenile salmonids, large volumes of jellyfish were also collected and analyzed. Results are presented by Shenker (1984).  A total of 263 purse seine sets were collected. However, several sets contained so many medusae that the net was split and the samples were lost.  Scyphomeduse were “very abundant” throughout the survey, dominated by Chrysaora fuscescens which occurred in over 82% of all samples.  Other scyphomeduse included Aurelia spp., Cyanea capillata, and Phacellophora camtschatica, which were all reported as “widely distributed, but abundant only in discrete locations.”  Hydromedusae, 64  including Aequoria spp. and Eutonina indicans, were also collected but were not quantified due to the fact that they were generally extruded through the seine mesh during net retrieval.  C. fuscescens was found in relatively low densities in May, with much higher concentrations in June, July, and August, especially nearer to inshore stations.  Calculated densities of collected samples ranged as high as 1.8 L of medusae per 100 m3 and 50 mgCm-3, suggesting that C. fuscescens is a major component of the summer neritic plankton community in the northern California Current LME. Two decades later, scyphomeduse were again sampled in the northern California Current LME.  Suchman & Brodeur (2005) present results for cruises from Newport, Oregon to Crescent City, California as part of the US Global Ocean Ecosystems (GLOBEC) program. A total of 365 successful trawls were made over four cruises in June and August of both 2000 and 2002.  Large medusae were widely distributed throughout the study area, with Chrysaora fuscescens being the dominant species.  Other species of varying abundance included Aurelia labiata, Phacellophora camtschatica, and Aequorea sp.  As usual, distribution was not even across sampling stations and varied by month and year.  Highlighting the difficulty in quantifying abundance estimates for jellyfish, a large proportion of the total catch of each species was usually concentrated in relatively few stations.  Actual abundance estimates were likely higher than those calculated (discussed in Suchman & Brodeur 2005). Correlation analysis of a variety of environmental variables showed that latitude, and either water depth or distance from shore explained the distribution of medusae on all cruises.  As C. fuscenens is most abundant close to shore during the summer, it was suspected that it would inhabit primarily cold, upwelled water.  While this was the case for the largest catches, C. fuscenens was actually found across a wide range of temperatures and salinities, with both variables proving to be poor predictors of habitat. Calculations of maximum biomass concentrations for C. fuscenens were 64 mgCm-3 in 2000 and 28 mgCm-3 in 2002, comparable with Shenker’s (1984) estimate of 50 mgCm-3 in 1981.  The authors also note that triennial groundfish trawl surveys off the U.S. west coast since 1980 show no consistent trend in jellyfish abundance, other than declines during El Niño years.  With no apparent trend from the fisheries surveys, as well as the similarity of 65  the aforementioned biomass concentrations, the authors conclude that there is “no evidence that abundance or distribution of C. fuscescens has substantially changed over the past 20 years off the coast of Oregon.”  As such, this chronicle is rated as stable/variable (Abundance Trend = 0).  However, Brodeur et al. (2008b) have suggested that habitat dynamics are changing to favour jellyfish over fish in this region due to the expansion of anoxic and hypoxic zones along the Oregon shelf.  Chan et al. (2008) note that a five-decade dataset shows little evidence of hypoxia prior to 2000, but there have been recent increases in both the frequency and severity of hypoxic events along the shelf due to changes in winds that drive upwelling.  In 2006, anoxia was observed at inner-shelf stations associated with the complete absence of all fish and near-complete mortality of macroscopic benthic invertebrates (Chan et al. 2008).  As many jellyfish are tolerant of low-oxygen conditions and may outcompete fish in these situations (Purcell et al. 2001a), the California Current LME could see an increase in jellyfish in the years to come (Brodeur et al. 2008b). Invasive Species in LME #3 – California Current There are reports from several locations in the California Current LME of invasions by jellyfish, likely due to translocation from shipping activities.  Three species of invasive hydromedusae, all native to the Black Sea, have been observed in the San Francisco Estuary system (Mills & Rees 2000).  Maeotias marginata was first collected in 1992, with medusae populations in the thousands in 1993, indicating a probable establishment of at least several years prior (Mills & Sommer 1995).  Other reports suggest that M. marginata may have been present in the San Francisco Bay estuary system as far back as 1959 (Mills & Rees 2000). While this species may be confined near river systems due to intolerance of high salinities, it has been found in more than one river in the San Francisco Estuary and has the potential to expand its range by transport of sessile polyps (Rees & Gershwin 2000).  Initial invasion may have been by a solitary polyp, as all early collections of medusae were male.  The inability for these medusae to reproduce sexually appears inconsequential due to the prolific asexual reproduction by polyps (Mills & Sommer 1995).  Interestingly, a few female medusae were found in the Napa River in 1998 (Rees & Gershwin 2000). 66  Blackfordia virginica was first collected in the San Francisco Estuary in 1970, with the discovery of polyps densely covering living, invasive barnacles in 1997 (Mills & Rees 2000). Collections in 1993 included both male and female medusae (Mills & Sommer 1995).  Larger females were found to be producing eggs, and size distributions suggested hydroids were continuously budding medusae throughout the summer.  This species has also been found in one other location in the California Current LME.  First observed in Coos Bay, Oregon in 1998, thousands were seen in 1999 (Mills & Rees 2000).  Unidentified species of Moerisia have also been found in the San Francisco Estuary since at least 1993 (Mills & Rees 2000). While the presence of this species was more sporadic, it does seem to have impressive reproductive capacity through asexual budding of polyps.  In laboratory culture, budding polyps grew to dense colonies within months (Rees & Gershwin 2000).  Another culture grew to over 200 individuals from a singular polyp (originally misidentified) and its budded offspring (Mills & Sommer 1995). Evidence suggests that a number of cryptic species have also invaded San Francisco Bay.  Greenberg et al. (1996) note that Aurelia sp. have been blooming annually in Foster City lagoon since they were first noticed in 1988.  Based on morphological and allozyme evidence, the authors conclude that the Aurelia sp. found in Foster City are an isolated population of an introduced species, possibly originating from Japan.  The hydrozoan Cladonema sp. may have also been introduced from East Asia.  Specimens were not directly collected from the field; however, medusae from two different aquaria in 1979 and 1981 lead Rees (1982) to conclude that a nonindigenous species of Cladonema may have invaded the Bay area. Another invasive hydrozoan was noted in nearby Bodega Harbor in the late 1990s.  A hydroid of Amphinema sp. was collected from floats in the harbor and was cultured to produce medusae which were raised to maturity (Rees 2000).  This species was not detected during numerous intensive surveys of the area between 1971 and 1980 (Rees 1975, 2000). Phyllorhiza punctata also appears to be a successful invader in this LME.  Collected in Mission Bay in 1981, this species now appears established in San Diego Bay (Larson & Arneson 1990; Graham & Bayha 2007). 67  2.3.7 LME #4 – Gulf of California Knowledge of jellyfish in this LME is sparse; however, a fishery for the cannonball jellyfish Stomolophus meleagris has developed here over the last decade, as fishers sought additional income due to lower revenues from the shrimp and squid fisheries (Ocampo et al. 2010).  This fishery may harvest anywhere from 1,000 to 15,000 tonnes of jellyfish each year in the state of Sonora (Ocampo et al. 2010).  Despite this, populations of S. meleagris around Sonora appear to have increased in recent years (L. Ocampo, CIBNOR, pers. comm., Sept. 2010). 2.3.8 LME #5 – Gulf of Mexico Data from the United States National Marine Fisheries Service (NMFS) Southeast Area Monitoring and Assessment Program (SEAMAP) were analyzed by Graham (2001).  The data contained biannual (spring and summer) shrimp and groundfish trawl surveys, divided into ten statistical regions in the northern Gulf of Mexico.  In total, over 10,000 individual trawls were included in the analysis.  Two species of scyphomedusae were presented in the study, both of which are abundant and widely distributed in the region. Chrysaora quinquecirrha typically peak in the summer months, whereas Aurelia aurita peak in the fall.  Thus, the biannual seasonal trawl data are well-suited for analyzing the abundance of these two species.  Numerical trawl data were normalized, allowing comparison between years as a standardized catch.  As jellyfish were not the target species and bottom-trawls do not sample the entire water column evenly, the data provide an index of relative abundance, rather than estimates for absolute biomass. Data for C. quinquecirrha and A. aurita spanned 11 and 13 years respectively. Interannual variations in regional densities up to an order of magnitude occurred for both species, and were frequent for A. aurita.  Statistically significant long-term increases were found for 2 of the 10 regions for C. quinquecirrha; however, additional regions showed dramatic cycling between presence and absence.  Shifts in distribution for this species were also significant in some areas, with a trend toward offshore waters in 4 of the 10 regions. While there was no significant temporal correlation between hypoxia and C. quinquecirrha 68  populations in the two regions of numerical increase, there was substantial spatial overlap (see Section 1.5.3 for further discussion). Long-term increases in A. aurita were more widespread, occurring in 6 of the 10 regions.  Several neighbouring regions showed increases up to an order of magnitude through the 1990s.  Distributional shifts offshore were less widespread, occurring in 3 of 10 regions.  However, this dataset was visited more recently and updated through 2006, and abundance of A. aurita has returned to pre-increase levels comparable to the 1980s (W.M. Graham, DISL, pers. comm., May 2010).   Therefore, this chronicle was classified as stable/variable (Abundance Trend = 0).  Although the details were not available at the time of writing, data since 2007 are again showing signs of increasing abundances (K. Robinson, DISL, pers. comm., Feb. 2011). There is anecdotal evidence to suggest that populations of Cassiopea spp. have increased in areas with intensive coastal development in this LME.  Fitt & Costley (1998) note that while there has been little monitoring of populations over time, “many canals and near-shore areas in the Florida Keys have become filled with adult medusae during the past ten years where apparently few if any existed before.”  Anecdotal reports indicate the continued spread of this epibenthic jellyfish in developed areas of the keys, with distributions showing high spatial variability (L. Chiaverano, DISL, pers. comm., Nov. 2010).  It now appears there are at least 3 different species of Cassiopea in the Florida Keys, with considerable morphological variation among locations (Chiaverano et al. 2010). Abundance of Cassiopea spp. may have also increased in a coastal lagoon in Cancun, Mexico, that is highly impacted from coastal development (Arai 2001 and references therein). Florida’s Gulf Coast has also been witness to unusually large blooms of Pelagia noctiluca over the last several years (Alvarez 2011; Kuo 2011; Taylor 2011).  Although this species is more common in the eastern Atlantic and Mediterranean Sea, it is unclear if the recent blooms in the Gulf of Mexico are a new phenomenon, as there were also blooms of this species in the Gulf of Mexico in the late 1990s (Raines 2011). Another unusual jellyfish event took place in this LME in 2000.  Williams et al. (2001) report a “population explosion” of Drymonema sp. from Louisiana to Florida that persisted 69  for several months.  This jellyfish had not been noted previously in the Gulf of Mexico and the authors consider it an “invader” of tropical origin.  However, the taxonomy of this group has recently been investigated and these specimens are now described as belonging to the species D. larsoni, which is distributed in the Caribbean (Bayha & Dawson 2010).  As such, its presence in the Gulf of Mexico likely represents a range expansion or shift, rather than a true invasion.  In addition, these jellies are known to disappear for decades before blooming again (Bayha & Dawson 2010), so they may have historically escaped detection in many locations.  While specimens have been observed in the Gulf of Mexico almost annually since 2000, there were none seen in 2010, despite considerable effort (K. Bayha, DISL, pers. comm., Nov. 2010).  Therefore, this species has been excluded from the analysis until a more complete understanding of its population dynamics can be developed. Invasive Species in LME #5 – Gulf of Mexico Graham et al. (2003) report an invasion of a large population of Phyllorhiza punctata in 2000 along the northern Gulf of Mexico.  Sampling of the population included 13 small-boat trips, six aerial surveys, and one large vessel survey.  The large vessel survey examined an aggregation in Lake Borgne, spanning 150 km2.  Estimated abundance for this aggregation alone was over 5 million medusae weighing nearly 35,000 tonnes.  In total, an estimated 10 million large medusae occupied the Mississippi Sound region in 2000 (Bolton & Graham 2004).  While 2000 marked this spectacular bloom, confirmed reports indicate that this species may have been present since 1993 (Graham et al. 2003).  This species has subsequently been observed in Louisiana in several years since 2000, including 2001 (Graham et al. 2003), 2004 (Johnson et al. 2005), and 2007 (Britt 2007), as well as 2006 in Texas (Barord et al. 2007).  These repeat observations suggest additional invasions or a newly- established population of P. punctata in the Gulf of Mexico.  Interestingly, the invaders possessed no photosymbiotic zooxanthallae and had a bleached appearance, unlike their native counterparts in the tropic western Pacific.  Despite the lack of photosynthetic symbionts, P. punctata collected in the northern Gulf were considerably larger than described populations from other parts of the world.  This adaptation is especially 70  concerning, as it suggests that this species can move between vastly different productivity regimes (Graham et al. 2003).  In addition, meduase analyzed for sex were all male, suggesting the initial bloom may be the result of an invasion by a solitary individual.  This is potentially an example of how invasive jellyfish can thrive in a new environment from the introduction of a single polyp or cyst. 2.3.9 LME #6 – Southeast U.S. Continental Shelf Numbers of the cubomedusae Carybdea marsupialis have apparently “skyrocketed” in North Carolina waters over the past few years (Anonymous 2009a).  This is potentially the same species that has been blooming in increasing numbers and areas along the Florida coast over the last decade (Anonymous 1998, 2010g).  Other species, such as sea nettles of the genus Chrysaora, may also be blooming unusually in North Carolina (James 2010) and Florida (Asuaje 2010).  However, the identity of the species in question has not been confirmed (P. Anderson, WLMB, pers. comm., Dec. 2010).  Species identification is often problematic in the popular media.  For example, a bloom responsible for over 2,000 stings in Florida’s Volusia County over the 2011 Independence Day holiday weekend were often reported as moon jellyfish (Aurelia sp.) (e.g., Anonymous 2011d).  However, photographs and other media reports identify them as sea nettles (Chrysaora sp.) (e.g., Burbank 2011). Interestingly, there was an unusually large bloom of Pelagia noctiluca along central Florida’s Brevard County in 2011, which stung over 1800 people around the Memorial Day holdiay (Kuo 2011; Alvarez 2011).  This species is rarely found in the coastal waters of the eastern U.S.A., but may have been transported there from the Gulf of Mexico, where populations have been observed for the past two years (Taylor 2011). The cannonball jellyfish, Stomolphus meleagris, has been identified as a major component of overall biomass in this LME and is the target of a developing fishery.  Starting in 2001, S. meleagris have been recorded separately from other species by the SEAMAP South Atlantic Coastal Survey, which consists of over 100 tows per season from North Carolina to Florida (SCDNR 2005).  As the entire survey area is sampled only once per season, aggregations of S. meleagris can obviously escape detection due to their patchy 71  variability over time.  However, the consistent sampling methods and the wide spatial coverage of the surveys provide a useful index of relative abundance.  Hendrix & Boylan (2010) and Petersen (2011) present annual density measurements of S. meleagris for 2001- 2010, which are high at the beginning and end of the decade, but depressed through the middle years (Abundance Trend = 0).  The data for 2011 had not been published at the time of writing.  However, abundances were reportedly some of the highest on record, at times clogging the sampling nets (Petersen 2011).  As mentioned, this species is the target of a developing fishery, with harvests over the past 10 years averaging a modest 300 tonnes (FAO 2011). There is interesting evidence to suggest that populations of Cassiopea spp. have increased in this LME, which also points to anthropogenic impacts as the cause.  Stoner et al. (2011) compared populations of Cassiopea spp. at sites adjacent to areas of dense human population with those of uninhabited sites on Abaco Island, Bahamas.  Although the analysis does not contain a time series of abundance data, it is some of the most convincing evidence to date that the populations of a species of jellyfish are directly affected by anthropogenic impacts.  The results showed a significant increase in both abundance and individual size for Cassiopea spp. that were adjacent to areas populated by humans.  There was also a significant correlation with total phosphorus in the water, suggesting that the mechanism involved may include increased nutrient input from anthropogenic sources supporting symbiotic zooxanthellae found in these jellyfish (Stoner et al. 2011). Invasive Species in LME #6 – Southeast U.S. Continental Shelf The invasive Phyllorhiza punctata has been reported in more than one location in this LME.  Graham et al. (2003) note this species was first detected in Florida’s Indian River Lagoon in 2001.  It was detected in the same location again in 2002 and 2009, as well as several other locations in Florida (Anonymous 2009c; Waymer 2009).  This species has also been reported as far north as North Carolina (Britt 2007). Turritopsis dohrnii has been present in Fort Pierce, Florida, since at least 2006 (Miglietta & Lessios 2009).  This invasion is not noted as a separate chronicle as it overlaps with the 72  chronicle used for P. punctata.  However, the presence of this species is noteworthy, as Turritopsis spp. have become increasingly invasive around the globe.  This is possibly due to their unique ability to reverse their life cycle through the process of transdifferentiation, potentially increasing the chances of being transported through ballast water (Miglietta & Lessios 2009; Miglietta et al. 2007).  There is also reportedly an established population of the invasive Blackfordia virginica in South Carolina (USGS 2011). The hydromedusa Phialella falklandica may also be invasive in this LME.  Kramp (1970a) notes that this jellyfish was collected in the late 1960s near the Bahamas.  Previously, this species was only known to occur in New Zealand and South America.  However, the author only describes the new distribution as “interesting”, and also notes that species may have been collected nearby in Florida in the late 19th century.  The invasiveness and establishment of this jellyfish remains unclear in this LME, and therefore it has been excluded from the analysis. 2.3.10 LME #7 – Northeast U.S. Continental Shelf Link & Ford (2006) analyzed stomach content data of the spiny dogfish (Squalus acanthias) from the Northeast Fisheries Science Center (NEFSC) bottom trawl survey program.  Standardized sampling methods in 1981 allow the comparison of over 40,000 spiny dogfish stomachs from a wide range of locations within the Northeast U.S. Continental Shelf LME.  At least 1000 stomachs were analyzed throughout each year. Stomach contents were examined immediately after the catch was sorted on deck, eliminating concerns surrounding preservation and rapid digestion.  As S. acanthias do not appear to masticate ctenophores, but rather ingest them whole, these prey items are readily identifiable in the stomach contents.  While Ctenophora prey items were not identified to species, they are assumed to be any combination of Mnemiopsis leidyi, Pleurobrachia pileus, or Bolinopsis infundibulum. There was a significant increase by a factor of 2 to 8 in the frequency of occurrence of ctenophores over the study period.  This increase was also widespread, showing expansion both northward and southward in the LME.  There was no significant trend over the study 73  period for either percent composition or gross consumption of ctenophores in the stomach contents.  Consistent with the knowledge that spiny dogfish are omnivores and opportunistic feeders, this suggests that feeding on ctenophores is a routine, non-selective process that is primarily dependent on encounter probability.  Therefore, examining frequency of occurrence in stomach contents should provide a good indicator for relative abundance of Ctenophora (Link 2004; Link & Ford 2006).  This long-term and widespread dataset is a unique and important component of knowledge regarding jellyfish populations. It provides an extremely strong indication that ctenophore populations have increased in this LME.  As will be discussed, this finding is supported by other studies in bays located in the region. Chesapeake Bay is the largest estuary in the United States.  It is home to the sea nettle Chrysaora quinquecirrha and the ctenophore Mnemiopsis leidyi, both of which can form large populations in the summer.  Cargo & King (1990) present an index of relative abundance derived from daily summer sight counts of C. quinquecirrha.  The 26 year dataset (1960-1986) shows dramatic interannual variation with count densities varying from 0 to 320 m-2.  There was no obvious long-term trend over the study.  Purcell & Decker (2005) present densities for the same species as well as M. leidyi from 1987-1990 and 1995-2000.  During the study period, either the scyphomedusan (C. quinquecirrha) or the ctenophore (M. leidyi) dominated the bay.  Over the course of the 10 years with data, C. quinquecirrha dominated half of the time (1987-1990, 1995) and M. leidyi dominated the other half (1996-2000).  As ctenophore densities showed a significant increase during the second half of the study period, and since ctenophore densities were typically one to four orders of magnitude higher than medusae densities, it can be said that jellyfish biomass increased over the course of the study. Up the coast from Chesapeake Bay is Narragansett Bay, where there is further evidence that ctenophore populations are increasing.  Sullivan et al. (2001) present data from a 1999 study on M. leidyi abundance, combined with 14 years of data from both published and unpublished reports, dating back to 1950.  Comparison with these historical accounts showed that peak densities of M. leidyi greater than 1 cm in length were 2-3 times higher in 1999 than in the early 1970s.  In addition, dates of first appearance and peak blooms had 74  shifted earlier by an alarming 2 months.  It appears that these changes in phenology can be attributed to climatic warming, especially in the shallow embayments of Narragansett Bay (Costello et al. 2006).  Recent data show more variability in this M. leidyi population, with high abundances of individuals (>1 cm) until 2005, when abundances appeared to decline precipitously (Rynearson 2010).  The year 2007 had a summer with virtually no ctenophores, potentially due to predation by Beroe spp. the previous fall (Rynearson 2010).  Abundances appear to have remained relatively low since 2005, with the exception of a large spike of specimens less than 1 cm in size in 2008, which reached a peak density of over 877 individuals per m3.  Despite the apparent decline since 2005, this chronicle was still classified as an increase (Abundance Trend = 1) due to the increases sustained through the 1980s, 1990s, and early 2000s, as well as the dramatic shift to peak abundances occurring earlier in the year.  However, to account for the recent decline, the Reliability Score was reduced to Low. Although it was not included as a separate chronicle, there is evidence to suggest an increase in the population of M. leidyi in the estuaries of Long Island, New York. McNamara et al. (2010) note that ctenophore abundances measured in 2006 in Great South Bay and Peconic Bay were two to five times higher than in the 1970s and 1980s.  In addition, peak abundances occurred an astounding two months earlier than in the prior studies.  In contrast with these findings, Nuttall et al. (2011) compiled ecological data for Great South Bay and note a decline in ctenophore biomass of about 50% from the 1980s to the 2000s.  It remains unclear why the information from these two studies is in disagreement. Interestingly, it appears there was an even more dramatic decline in ctenophore biomass in Great South Bay from the 1880s to the 1930s (Nuttall et al. 2011). Barnegat Bay is a highly developed and polluted bay in New Jersey.  Sea nettle jellyfish (Chrysaora sp.) were “unheard of” prior to 2000 (Dutzik & O'Malley 2010), but they have become increasing problematic in recent years, stinging swimmers and causing beach closures (APP 2010; Campbell 2010).  Other reports suggest these jellyfish were present in the Bay more than a century ago, but echo the rapid increase over the past decade (e.g., Nee 2011).  Recent government bills and plans have been approved in an attempt to mitigate the 75  perceived causes, including limiting the amount of nutrients flowing into the bay, upgrading storm-water basins, and closing the Oyster Creek Nuclear Generating Station 10 years ahead of schedule (Parry 2010).  There have also been suggestions that the sea nettles are proliferating due to increased anthropogenic habitat for polyps (Nee 2011).  Regardless of the causes, the sea nettle infestation in Barnegat Bay continues, with 2011 being one of the worst years on record (Nee 2011; Moore 2011). Several sources also point to an increase in Cyanea capillata around New York State in recent years (e.g., Gaskell 2008; Parry 2008; Grossman 2010), including new observations during winter months (Beeler 2011).  While some of the reported locations are within 200 km of Barnegat Bay, the trend appears to extend further.  In addition, this increase is a different species and occurs on a different timescale, and was therefore recorded as a separate chronicle, albeit with lower confidence (Confidence Index = Low).  Large blooms of Aurelia sp. have also been reported further north, such as in Boston Harbor (Ryan 2007; O'Neil 2011).  While these events were not included as a separate chronicle, there are indications that the frequency and intensity of these blooms may be increasing, and that the size of individual medusae may also be increasing (R. Dicker, UMB, pers. comm., Feb. 2011). One station from the Atlantic Zooplankton Monitoring Program (AZMP) is in this LME.  The AZMP was implemented in 1998 with the aim of increasing the capacity to understand, describe, and forecast the state of the marine ecosystem, as well as to quantify changes (Harrison et al. 2009).  The Prince-5 station, located near the mouth of the Bay of Fundy, was sampled on at least a monthly basis.  Unfortunately, data on jellyfish abundance are only presented pooled with appendicularians, so the Reliability Score was reduced to Low.  Despite this, there appears to be no obvious trend in the jelly+appendicularia group relative to other zooplankton groups from 1999 to 2008 (Harrison et al. 2009).  The only anomaly is a large “pulse of jellies and appendicularia” observed in the summer of 2004 (Harrison et al. 2005). 76 Invasive Species in LME #7 – Northeast U.S. Continental Shelf Several species of invasive hydromedusae have also been reported from Chesapeake Bay.  Maeotias marginata was collected in 1968 in Virginia and South Carolina, and again in 1993 in Chesapeake Bay (USGS 2011).  Additional invasive hydromedusae from Chesapeake Bay include Blackfordia virginica (Mills & Sommer 1995) and Moerisia lyonsi (Purcell et al. 1999a; Ruiz et al. 2000). 2.3.11 LME #8 – Scotian Shelf Data from this LME are exclusively from Canada’s Department of Fisheries and Oceans zooplankton measurements.  Numerous transect lines are investigated twice a year in this LME as part of the Atlantic Zooplankton Monitoring Program (see Fig. 1 in Harvey & Devine 2009).  Most transects show high interannual variability between 2001 and 2009, and while some stations appear to show modest increases in recent years (e.g., TASO transect in fall), there is no consistent trend across space or time (M. Harvey, DFO, pers. comm., Aug. 2010).  As such, this chronicle was classified as stable/variable (Abundance Trend = 0).  A separate, fixed station known as Halifax-2 is also in this LME, and has been monitored for even longer.  As jellyfish abundance is only presented pooled with appendicularians, this chronicle is assigned a Reliability Score = Low.  Nonetheless, this station shows no obvious trend over the course of the dataset (see Fig. 27 in Harrison et al. 2009).  A notable spike in abundance was observed in 2005, where total zooplankton was higher than other years, apparently due to jellyfish and/or appendicularians. Annual zooplankton surveys are also conducted in the Gulf of St. Lawrence, with numerous transects in and around the estuary (see Fig. 14 in Harvey & Devine 2009). Average abundances and biomass for data collected from 1995 to 2008 are relatively stable, with the obvious exception of a large peak of Aglantha digitale in 2004 (see Fig. 16 in Harvey & Devine 2009).  Average biomass is larger after 2004 than it was in the preceding years; however, the overall trend is not significant.  2003 also marks a change in sampling methods from collection in September to October-November.  Thus, the time-periods are not directly 77  comparable, and within each time-period, abundances are relatively stable.  Therefore, this chronicle is classified as stable/variable (Abundance Trend = 0). 2.3.12 LME #9 – Newfoundland-Labrador Shelf Jellyfish are not the focus of any studies in this region.  However, the Atlantic Zooplankton Monitoring Program (AZMP) of Fisheries and Oceans Canada provides some useful data on jellyfish populations.  As part of the AZMP, 4 large transects are monitored in this LME: 3 off of Newfoundland (Southeast Grand Banks, Flemish Cap, and Bonavista) and 1 off of Labrador (Seal Island) (see Fig. 1 in DFO 2008).  These transects are investigated between 1 and 3 times per year, and collectively provide good temporal and spatial coverage of this LME.  Pepin et al. (2009) present seasonally-adjusted relative abundance data for Aglantha digitale and the much less abundant Pelagia noctiluca for the period 1999- 2008.  Data for both species from all transects show no obvious trends over the time period. Interestingly, the Seal Island and Southeast Grand Banks transects show a spikes of A. digitale in 2004; however, these peaks are not evident in data from the other transects.  While A. digitale can be abundant at times, and Aurelia spp. occasionally form large blooms, jellyfish abundance in this LME is generally assumed to be insignificant compared to other taxa, and no long-term trends are evident (P. Pepin, DFO, pers. comm., Dec. 2009). 2.3.13 LME #10 – Insular Pacific-Hawaiian There is considerable evidence to suggest that the box jellyfish Carybdea alata has increased in Hawaiian waters.  Although this species has been present since at least the late 19th century, abundances around the main Hawaiian Islands increased dramatically in the late 1980s (Thomas et al. 2001).  Since 1994, box jellyfish have been sighted every year on Oahu (Crow et al. 2010), and are now a major concern for tourism at many Hawaiian beaches due to an irritating sting.  Interestingly, large influxes of these jellies tend to show a strong correlation with the lunar cycle, peaking 9 or 10 days after the full moon, making management of stings possible (Thomas et al. 2001). 78 Invasive Species in LME #10 – Insular Pacific-Hawaiian At least two species of Cassiopea have invaded Hawaii (Holland et al. 2004) and these jellies are now found throughout the main Hawaiian Islands (Eldredge & Smith 2001). Members of this unique genus of scyphomedusa typically rest bell-down on the ocean floor with oral arms and tentacles pointing upward.  Mucous containing nematocysts may also be released to help ensnare zooplankton prey.  Medusae will swim if disturbed and may release nematocysts during avoidance (Daoust 2009).  In conjunction with their semi-sessile lifestyle, Cassiopea host photosymbiotic zooxanthellae and are therefore generally restricted to shallow environments.  Cassiopea spp. are thought to have spread to Hawaii via ships, first to Pearl Harbour in the 1940s, and then to Honolulu Harbour by 1950 (Devaney & Eldredge 1977).  An invasion prior to 1950 would normally be excluded from this analysis (see section 2.1.2); however, the continued spread of these jellies and possible subsequent invasions after 1950 permit inclusion.  Polyps of this species have been observed on discarded plastic in Hawaii, and in some rare cases, the medusae can actually be hermaphroditic (Hofmann & Hadfield 2002).  Molecular analysis of Cassiopea samples from Oahu showed two distinct lineages, geographically separated on the windward and leeward shores of the island (Holland et al. 2004).  These divergent lineages are thought to represent two distinct invasions, one suspected from the Indo-Pacific region and the other from the Red Sea or western Atlantic Ocean.  While Cassiopea can clearly colonize new nearshore areas, it is not a strong natural disperser (Holland et al. 2004) and removal of this species has been successful at other locations in Hawaii (Hofmann & Hadfield 2002; Kelsey 2009).  Thus, management of these species may be possible. The combination of molecular analysis and ocean circulation modelling has identified an invasive species of Aurelia in Hawaii, hereafter referred to as Aurelia sp. 4.  Dawson et al. (2005) note that Aurelia sp. 4, which is endemic to Borneo and Palau, also occurs in Hawaii. However, a current model reveals “there is no available ocean pathway that naturally connects these zones of occurrence…”  In addition, the genus Aurelia was not reported from Hawaii prior to the 1950s, despite surveys of jellyfish starting in the 1900s.  Therefore, the 79  authors conclude that the occurrence of Aurelia sp. 4 in Hawaii “is most likely due to anthropogenic translocation”.  The scyphomedusa Anomalorhiza shawi also appears to be invasive in Hawaii.  Cooke (1984) notes that this jellyfish, which is endemic to the Philippines, “was not seen before 1983” in Hawaii.  Specimens of this jellyfish were collected in 1983 and 1984 in Kaneohe Bay, and despite infrequent observations since, the observation of an adult specimen in the same location in 2001 suggest an established, reproducing population (Lum 2001). The highly invasive Phyllorhiza punctata also appears to be established in Hawaiian waters.  However, in their study of Juvenile Attraction Devices (JADs) on carangid fish, Clarke & Aeby (1998) note the disappearance of this jellyfish from Kaneohe Bay, Oahu.  The primary author notes that Phyllorhiza punctata formed high densities several times during the period 1968-1970, which was followed by a sudden disappearance in the early 1970s. Despite this disappearance, P. punctata is present in several other locations on Oahu (Eldredge & Smith 2001).  However, it is likely that invasion occurred prior to 1950 (Eldredge & Smith 2001), and therefore this jellyfish has been excluded from the analysis. Numerous species of cryptogenic hydrozoa have also been reported from Kaneohe Bay (Coles et al. 2002). 2.3.14 LME #11 – Pacific Central-American Coastal Information on jellyfish from this LME is sparse, especially in English.  Aside from a study linking hydromedusa blooms to upwelling events (Miglietta et al. 2008), the only reported change for a native species concerns the rhizostome Stomolophus meleagris (cannonball jellyfish).  Ocaña-Luna & Gómez-Aguirre (1999) report that this jelly has colonized and thrives in a pair of coastal lagoons in Mexico.  The authors suggest that S. meleagris occurs at lower abundances in lagoons to the north due to a strong oscillation of the water temperatures.  However, they suggest that in Oaxaca’s Lagunas Superior and Inferior, reduced runoff due to irrigation has reduced temperature fluctuations, thereby allowing the cannonball population to thrive.  As there are no recent data on this population, a Time Score of Low was used. 80 Invasive Species in LME #11 – Pacific Central-American Coastal There are several reports of invasive jellyfish in this LME, all concerning small hydrozoans (Invasive Reliability Score = Low).  Blackforia virginica is reported to have invaded the lagoons of Chantuto-Pensacola, Chiapas in the 1990s (Álvarez-Silva 1999; Álvarez-Silva et al. 2003).  The invasive Turritopsis dohrnii has also been reported from Panama Bay since at least 2006 (Miglietta & Lessios 2009).  As mentioned, this tiny jellyfish has successfully invaded a number of locations around the globe, possibly facilitated the unique ability to reverse its life cycle through the process of transdifferentiation (Miglietta et al. 2007; Miglietta & Lessios 2009).  Several other small hydromedusae were recently reported from this LME for the first time, including Amphinema dinema, Sarsia coccometra, and Clytia mccradyi (Segura-Puertas et al. 2010).  These jellies occur at such low abundances relative to others that they likely make no detectable contribution to the gelatinous biomass at present, and were therefore not included in the analysis.  However, these species may become more abundant in this LME in the future, as C. mccradyi is the most abundant jellyfish in some coastal systems of the Caribbean (Segura-Puertas et al. 2010 and references therein). 2.3.15 LME #12 – Caribbean Sea There is very little information on jellyfish populations in the Caribbean.  Williams et al. (2001) report an unprecedented “population explosion” of Drymonema sp. around all coasts of Puerto Rico throughout the summer and fall of 1999.  Previously, this jellyfish was only observed around Puerto Rico in small numbers in the 1970s.  Unfortunately, it is unclear if the blooms experienced in 1999 have repeated in the last decade, and therefore this event was not included in the analysis.  In addition, jellies from this genus are known to disappear for decades at a time before blooming again (Staisny 1940 in Williams et al. 2001; Bayha & Dawson 2010).  As species of Drymonema can prey on other jellyfish, larger populations of jellyfish at lower trophic levels will be required to sustain it.  Therefore, sustained blooms of this species may indicate large blooms of other jellyfish species.  In addition, Drymonema may be important in regulating these blooms (Williams et al. 2001). Recent investigations of this genus have resulted in improved taxonomy, with the 81  description of a new family (Drymonematidae) and new species descriptions (Bayha & Dawson 2010).  Such investigations greatly improve the knowledge of this group of enigmatic jellies, and should help with understanding of their population dynamics in the future. Williams et al. (2001) also report on other jellyfish blooms in the Caribbean.  Annual blooms of Aurelia aurita are noted around Puerto Rico; however, there is no information to suggest if these blooms are either changing or stable.  A “population explosion” of unidentified jellyfish is also reported from the region around Bocas del Toro in Panama, indicating such outbreaks have occurred several times in this region since 1997.  While it is unclear if these events occurred previous to 1997, a local marine expert notes there have been no major changes in jellyfish abundance in this region over the last 10 years (G. Jacome, STRI, pers. comm., Jan. 2011). Measurements of the gelatinous community in Lime Cay, Jamaica were reported from 1985/1986 by Clarke (1988) and then again from 1992/1993 by Persad et al. (2003). Abundance, biomass, and production were recorded for a number of hydromedusae and ctenophores, although larger scyphomedusae were present but not sampled.  Despite the large fluctuations in samples within both studies, overall abundance and biomass measurements were remarkably similar between the two, suggesting the gelatinous community in Lime Cay may be relatively stable (Persad et al. 2003).  Interestingly, there was a dramatic spatial decline in abundance of the hydromedusae population from the harbour to Lime Cay, while this was not the case with ctenophores.  This suggests that the nearshore habitat of the harbour may be important for jellyfish with a meroplanktonic life cycle (Persad et al. 2003). Invasive Species in LME #12 – Caribbean Sea The invasive Phyllorhiza punctata was first reported from Jamaica in the late 1950s (Vannucci 1964 in Kramp 1970b).  Cutress (1973) also noted that this species occurs in Jamaica, but a specific location was not reported.  A healthy specimen of this species was observed and photographed off the cliffs of Negril, Jamaica in October 2010 (pers. obs.). 82  This species is also reported from Laguna Joyunda, Puerto Rico; however, it likely invaded this location prior to 1950 (Graham & Bayha 2007), and therefore only the Jamaican location was included in the analysis. The globally invasive Turritopsis dohrnii has also been reported in this LME from at least two locations Panama: Galeta and Bocas del Toro (Miglietta & Lessios 2009).  As discussed, this jellyfish has the unique ability to reverse its life cycle through transdifferentiation, potentially increasing the probability that it will be successfully transported through ballast water (Miglietta & Lessios 2009; Miglietta et al. 2007).  Despite the ability of T. dohrnii to occur in high abundance, it is relatively small in size and unlikely to contribute significantly to the gelatinous biomass (Invasive Reliability Score = Low). 2.3.16 LME #13 – Humboldt Current While jellyfish populations in the majority of this large LME remain understudied, there is one excellent dataset of jellyfish abundance, once again from fisheries surveys.  A 38-year record of the large scyphozoan Chrysaora plocamia from the coast of Peru was presented by Quiñones et al. (2010b).  This valuable time-series is the result of extensive sampling performed at least twice per year along the coastline from 4° S to below 18°S.  The population of jellyfish showed high variability throughout the dataset, and there was a strong correlation with the Peruvian Oscillation Index (POI).  As the POI is the best representation of ENSO pulses along the Peruvian coast (Quiñones et al. 2010a), jellyfish abundance appears highly influenced by climatic oscillations in this LME, which is also one of the most heavily fished ecosystems in the world due to the massive catches of Peruvian anchoveta (Engraulis ringens).  Despite the dramatic variation in jellyfish abundance, a linear regression of the dataset reveals a significant negative trend, albeit with a poor fit (R2 = 0.12, p = 0.04), and has therefore been classified as a decrease (Abundance Trend = -1). Interestingly, abundance of C. plocamia may also influence populations and behaviour of the endangered green turtle Chelonia mydas.  A time-series of landings of C. mydas showed a tight correlation with ENSO events, and large populations of these turtles were observed 83  feeding extensively on abundant jellyfish in Peruvian waters during the 1987 El Niño event (Quiñones et al. 2010a). There have been several other studies of jellyfish in this region (e.g., Pagès & Orejas 1999; Pagès et al. 2001; Palma et al. 2007), but they lack a temporal component.  Palma et al. (2011) note an increase in abundance for numerous jellyfish species from 2004-2006 in the Chiloé Interior Sea of Chile.  However, it is unclear if these increases are sustained.  The authors also document several species new to Chilean waters.  Unfortunately, the scarcity of records in the region prevents recognition of non-indigenous species. Jellyfish have also interfered with aquaculture operations in Chile, with high mortality of farmed salmon in 2002 (Palma et al. 2007) and again in 2009/2010 (H. Mianzan, INIDEP, pers. comm., Jan. 2010).  Recent initiatives to catalogue the jellyfish of South America should help to improve the knowledge of jellyfish dynamics in this LME (Oliveira et al. 2010). Invasive Species in LME #13 – Humboldt Current A species of the genus Aurelia was detected in the South East Pacific for the first time in 2005.  Information presented by Häussermann et al. (2009) from the Chilean fjord region describes observations of several medusae and numerous scyphozoan polyps. Morphological and molecular evidence indicate the meduase and polyps are Aurelia aurita, which the authors infer to be invasive.  This species likely arrived via offloaded ballast water, but surprisingly, this jellyfish has not been observed in major ports nearby. The hydromedusa Heterotiara minor was also recently identified in Chilean waters for the first time (Palma et al. 2007).  However, it is unclear if this species is invasive. 2.3.17 LME #14 – Patagonian Shelf Data from over 1,000 plankton samples from the Buenos Aires coast were analyzed by Genzano et al. (2008).  Spanning 13 years, the dataset revealed a large bloom of Obelia longissima in 2003.  A tiny hydromedusa, Obelia was found at very low densities between 1993 and 2002, never surpassing 5 individuals per m3.  In October of 2003, a bloom of this species increased the frequency of occurrence to more than 4 times previously recorded levels, and densities increased more than 2 orders of magnitude.  Interestingly, this bloom 84  was followed a few months later by large shoreline accumulations of the hydroid phase of the same species.  Medusae abundance declined in the following years (2004-2006). However, it remained higher than in years preceding the bloom.  A linear regression of the mean densities over time recorded in this study does not result in a significant relationship. Nonetheless, a linear regression applied to the frequency of occurrence does yield a significant trend over time, suggesting that although the densities encountered may not be increasing significantly, this jellyfish is definitely being encountered more often in the latter part of the dataset.  Thus, we can conclude that Obelia has increased over the course of the study. Recent initiatives to catalogue the diversity of jellyfish around South America will further the understanding of jellyfish dynamics in this LME (Oliveira et al. 2010).  In addition, the abundant scyphomedusa Lychnorhiza lucerna, is now being considered for commercial exploitation in this region (Schiariti 2008). Invasive Species in LME #14 – Patagonian Shelf The highly invasive Blackfordia virginica was detected in the Río de la Plata estuary for the first time in 2000.  Genzano et al. (2006) report no detection of this species from 1983 to 1999; however, in 2000 it was found in abundance with thousands of medusae per sample. Specimens of both sex and different size classes suggest local reproduction and an established population.  This species was found again in the same estuary in 2005 and 2006, also at very high abundances. 2.3.18 LME #15 – South Brazil Shelf Historical knowledge of jellyfish from Brazil is poor.  However, recent surveys are rapidly cataloguing the diversity of species (e.g., Cornelius & Silveira 1997; Silveira & Cornelius 2000; Nogueira & Haddad 2006; Nogueira et al. 2010; Oliveira et al. 2010). Unfortunately, the lack of baseline data in this LME makes identifying abundance trends difficult.  Jellyfish researchers in the area suggest that Brazil is at least 100 years behind Europe in understanding local jellyfish populations (Marques et al. 2003).  Nonetheless, there are some indications that native jellyfish populations may be stable in this LME, 85  thanks to a study that interviewed local fishermen.  Nagata et al. (2009) interviewed 48 fishers from Paraná and Santa Catarina states who regularly use trawl nets to target shrimp. As half of the fishermen interviewed had more than 30 years of experience, a Time Score of Very High was used.  Although the communities that the interviewees called home are not more than 200 km apart, the fishermen are assumed to fish in a large region, and thus a Space Score of Medium was used.  Interviewed between 2003 and 2007, the fishers were asked about the nuisance of jellyfish to their trawl fishing activities.  The majority of interviewees regarded jellyfish as a nuisance to their fishing activities and more than 70% claimed economic losses caused by jellyfish interference.  Although all respondents reported interannual fluctuations of jellyfish abundance, “they did not report any recent frequency increase in massive occurrences of medusae.”  Interestingly, one species which is blamed for major interference with trawling activities, Lychnorhiza lucerna, may soon be the target of an experimental fishery in nearby Argentina (Schiariti 2008). An unusual number of Aurelia sp. was detected off of the coast near the Paraná/Santa Catarina state border in 2005 (M. Nogueira, UFP, pers. comm., Sept. 2010).  This came as a surprise both to scientists and local fishermen, as this species had not been documented in high abundance in the region before.  However, large aggregations have not been reported since, and thus this unique event has not been included in the analysis. Invasive Species in LME #15 – South Brazil Shelf While the invasive Phyllorhiza puncata has been noted in this LME on more than one occasion, it seems to disappear after several years of detection, at least at the medusa stage. The possibility remains that polyp colonies have successfully established in this LME. However, the sudden disappearance of medusae after several consecutive years of presence, as well as the long time frame between new detection events, suggests multiple invasions. The first Brazilian specimens were recorded in the 1950s around Rio de Janeiro, São Paulo State, and Paraná State (Kramp 1970b; Moreira 1961 in Haddad & Nogueira 2006).  Briefly very abundant, this population disappeared a few years later and medusae were not seen again in this region until 2001, when they began to be reported annually (Haddad & 86  Nogueira 2006).  Abundant until 2005, this population then began to decline and no medusae have been detected since 2007 (M. Nogueira, UFP, pers. comm., Sept. 2010).  While these fluctuations are categorized as stable/variable (Abundance Trend = 0) for analysis purposes, the probability of subsequent invasions remains high due to the history of invasions in the region, high shipping traffic, and the presence of this species in the nearby East Brazil Shelf LME.  In fact, Haddad & Nogueira (2006) suggest that P. punctata is likely occurring along the entire coast of Brazil, but scattered records make the distribution uncertain.  Interestingly, recent molecular evidence suggests that the invasive populations found in Brazil may be a separate species from other invasive Phyllorhiza populations, and that populations in Brazil may have originated in Indonesia (Bayha et al. 2010). Medusae of both Moerisia inkermanica and Blackfordia virginica were collected in the Paranaguá Bay system in 2004 and 2005 (Nogueira & de Oliveira 2006).  Due to a lack of previous samples, the authors consider these species cryptogenic.  However, it is likely they are invasive given that they are native to the Black Sea, are well-known invaders, and the area is subject to high port traffic (Nogueira & de Oliveira 2006).  B. virginica was collected in this area again in 2007 and 2008, as well as a number of other estuaries along the coast, including the Cananéia complex, Guaratuba Bay, and Babitonga Bay (Bardi & Marques 2009b).  While it is unclear if this species persists in all of these locations, individuals of both sexes and a variety of size classes have been collected in Paranaguá Bay and Babitonga Bay, suggesting established populations.  The discovery of medusae in these new locations provides additional support for invasions, as this species was not detected in earlier extensive plankton studies of southern Brazil (Bardi & Marques 2009b and references therein).  Combined with the fact that it is now found only in estuaries with high shipping traffic, it is considered invasive. 2.3.19 LME #16 – East Brazil Shelf As mentioned, knowledge of jellyfish in Brazil is poor, and is estimated to be at least 100 years behind jellyfish knowledge in Europe (Marques et al. 2003).  While recent surveys 87  are cataloguing the diversity of jellyfish in this LME (e.g., Morandini et al. 2006; Oliveira et al. 2010), there are no sources of data to identify trends in native jellyfish populations. Invasive Species in LME #16 – East Brazil Shelf The invasive Phyllorhiza punctata is reported from two disparate locations in this LME. The first population, in Todos os Santos Bay in Bahia State, was reported as early as 1991 and appears to be established (Silveira & Cornelius 2000).  Medusae have also been seen further north in Fortaleza, Ceará State, apparently since 2003 (Haddad & Nogueira 2006).  It is unclear if these populations have disappeared like those further south in Brazil (see Section, but as they represent separate populations, they are assumed to be established.  As stated, it is possible that P. punctata is occurring along the entire Brazilian coast (Haddad & Nogueira 2006). The invasive hydromedusan Blackfordia virginica was also reported in this LME prior to 1963 (Parananguà 1963 in Moore 1987).  However, it remains unclear if it is established. 2.3.20 LME #18 – West Greenland Shelf Pedersen & Smidt (2000) present the results of oceanographic surveys conducted off of southwestern Greenland by the Greenland (Danish) Fisheries Research Institute from 1950 to 1984.  The only jellyfish reported (assumed dominant) was the hydromedusa Aglantha digitale, for which a “red type” and “white type” were enumerated.  An index of abundance for A. digitale is presented from 1956 to 1982 for section S3 (southernmost section).  While there were variations in abundance across sections, the trend observed for section S3 is assumed to apply to the entire study area.  Over the course of the study, A. digitale shows a decreasing trend through the 1960s and an increasing trend thereafter.  Although this increasing trend continues through the 1970s and into the 1980s, abundance levels did not return to levels seen in the 1960s by the end of the study period.  Therefore, the overall trend is classified as a significant decrease (Abundance Trend = -1).  Without recent data, it is unknown whether the increasing trend has continued in recent decades and if this population has recovered to historic levels.  This LME has experienced major regime shifts in recent decades, driven by interactions between climate and anthropogenic influences 88  (Buch et al. 2004).  As such, it is likely that jellyfish populations in this region have also experienced significant changes.  Due to the low spatial resolution of the data presented, the lack of data from the past three decades, and the likelihood of recent changes, this chronicle is classified with high uncertainty (Reliability Score = Low). 2.3.21 LME #21 – Norwegian Sea The mesopelagic Periphylla periphylla appears to have increased in several Norwegian fjords, where it now occurs in very high abundances.  After Lurefjorden (see Section 2.3.22) became dominated by Periphylla in the 1970s (Fosså 1992), it was followed by Halsafjorden in the 1980s (Sørnes et al. 2007).  Periphylla are normally deep-ocean jellyfish with an entirely holopelagic lifestyle (Jarms et al. 1999), and their success in Norwegian fjords is likely due to a combination of retention and light attenuation (Sørnes et al. 2007).  Retention is achieved by sills of the fjord basins, as well as vertical migration.  As light has lethal effects on Periphylla (Jarms et al. 2002), sufficient optical depths must be achievable for these jellyfish to survive.  The precipitous increase of this species in Norwegian fjords may be due to decreasing light levels mediated by climatic changes and cultural eutrophication in the North and Baltic Seas (Eiane et al. 1999; Sørnes et al. 2007).  Populations of this species continue to be discovered in fjords where it was not previously observed (Hosia 2007). Perhaps not surprisingly, this species of jellyfish, as well as others, are now being considering for exploitation as a commercial food fishery in Norway (Wang 2007). There is circumstantial evidence that siphonophores may also be increasing in the Norwegian Sea (Båmstedt et al. 1998; Fosså et al. 2003; Hosia 2007).  However, the scope of the events is unclear and will be discussed as part of the North Sea LME. Invasive Species in LME #21 – Norwegian Sea The invasive ctenophore Mnemiopsis leidyi was recently documented in this LME. Underwater photographs from 2008 confirmed the identity of M. leidyi in Trondheimsfjorden, which is at 64 °N (A. Hosia, IMR, pers. comm., Oct. 2010).  As such, it appears that the range of this highly invasive jellyfish continues to expand. 89  2.3.22 LME #22 – North Sea The Continuous Plankton Recorder (CPR) survey is one of the most temporally and spatially extensive plankton datasets in the world.  Recorders are towed behind ships of opportunity at a depth of approximately 10 m (see Batten et al. 2003).  Although the entrance aperture for sampling is only 1.27 cm2, the survey records samples of cnidarian tissue and nematocysts.  While this device is clearly not designed to sample jellyfish, the consistent sampling methods over time and space allow for an index of presence of Cnidaria sampled. In the North Sea, the most common species sampled is assumed to be the hydromedusan Aglantha digitale (Attrill & Edwards 2008).  Licandro et al. (2010) analyzed CPR data from the North Sea from 1958-2007 and found an increase in frequency of Cnidaria since the early 1980s.  This is consistent with another analysis of this dataset over similar scales (Attrill et al. 2007; Attrill & Edwards 2008).  While CPR data do not provide a true index of relative abundance, and despite concerns surrounding collection and analysis methods (Haddock 2008), the increase in the frequency of occurrence of Cnidaria sampled represents an increase in the integrated gelatinous biomass, at least for the species sampled. An extensive survey of jellyfish abundance in the North Sea comes from yet another fisheries dataset.  Hay et al. (1990) present data on scyphomedusae bycatch collected during the ICES International 0-group Gadoid Surveys in June and July from 1971-1986.  Trawls were fished for one hour using a standard depth profile of 20 minutes near the sea bottom, 20 minutes at the thermocline or in mid-water, and 20 minutes near the surface (5-10 m). While this method underestimates total jellyfish biomass, the consistent methods used throughout the study allow for comparison and an index of relative abundance.  In total, over 2000 trawls were made, catching more than 430,000 jellyfish.  While much of the northern area of the North Sea was sampled extensively, 4 sub-areas were established based on consistent sampling and occurrence of dominant jellyfish species.  Populations of jellyfish showed high variability in all of the sub-areas over the time-series, with no significant linear temporal trends (Lynam et al. 2004).  While the findings from this dataset do not necessarily agree with the CPR data discussed above, the surveys are thought to sample different gelatinous communities (Haddock 2008; Lynam et al. 2010a).  The trawl 90  dataset was also analyzed by Lynam et al. (2004; 2005a; 2010a) who demonstrated that jellyfish abundance was significantly correlated with several climate indices including the North Atlantic Oscillation Index (NOAI), as well other hydrographic measurements.  These results suggest that the dominant jellyfish species in the North Sea may be highly influenced by climatic changes. Jellyfish populations appear to be changing in the Dutch Wadden Sea.  Catch data from a fish fyke off Texel Island in the Netherlands have been collected on a daily basis (except during winter and mid-summer) for 50 years by the Royal Netherlands Institute for Sea Research (NIOZ).  The fyke works much like a set net, and although it was designed for monitoring fish populations, jellyfish are also counted.  Findings from the analysis on jellyfish have not yet been published, but van Walraven (2010) presented some preliminary results.  While there has been no consistent trend in abundance trends for all jellyfish, the ctenophore Pluerobrachia pileus appeared with greater frequency in recent decades.  The most dramatic finding from the dataset is revealed when jellyfish phenology is examined. Numerous scyphomedusae, including Aurelia aurita, Chrysaora hysoscella, Cyanea capillata, and Cyanea lamarcki have all shown a dramatic shift in the time of first appearance in latter decades.  In most cases, this shift is on the order of weeks or even months.  This remarkable shift in phenology may be due to water temperatures which have increased significantly in the Wadden Sea in recent decades (Martens & van Beusekom 2008).  Rhizostoma pulmo did not show the same trend, and actually declined in abundance over the course of the study, but this could be because it is predominantly a summer species.  The last two years of data show extreme variability, with 2009 showing low densities for most native species and a precipitous spike in the population of the invasive ctenophore Mnemiopsis leidyi.  After a very cold winter, 2010 resulted in fewer M. leidyi and a return to higher abundances for most native species. As discussed in the Norwegian Sea LME (see Section 2.3.21), populations of the holopelagic Periphylla periphylla appear to have increased in several Norwegian fjords.  This species has been reported from Lurefjorden in varying amounts since at least the 1940s, but 1973 marked a “population explosion” that was a nuisance to fishers (Fosså 1992).  Since 91  then, the problem has become increasingly worse (Fosså 1992), and recent measurements show even higher abundances of Periphylla in this fjord (Youngbluth & Båmstedt 2001; Sørnes et al. 2007).  As mentioned, Periphylla is normally a mesopelagic species.  Its success in Norwegian fjords is likely due to a combination of retention and light attenuation in the fjord basins (Sørnes et al. 2007).  Climatic changes and cultural eutrophication in the Baltic and North Seas may have increased light attenuation in these fjords in recent decades, facilitating the increase of this unique and fascinating jellyfish (Eiane et al. 1999; Sørnes et al. 2007).  In addition, Periphylla populations have recently been discovered in several fjords where it was not observed previously (Hosia 2007).  Norway is now considering a commercial harvest of this jellyfish, as well as other species (Wang 2007). There is circumstantial evidence to suggest that siphonophores may be increasing in the northern part of this LME.  Although the evidence did not meet the qualifications to be included as a chronicle in the analysis, the trend is worthy of discussion.  Båmstedt et al. (1998) note a mass occurrence of Apolemia uvaria in Norwegian waters in 1997 which caused high mortality to fish in aquaculture operations.  While fishers report that the high abundance of siphonophores is not a new phenomenon, confusion with salps may have occurred (Båmstedt et al. 1998).  More impacts on farmed salmon due to this species were reported in 2001 (Fosså et al. 2003).  A similar event involving Muggiaea atlantica occurred in 2002, the first time a mass occurrence has been described from Norway (Fosså et al. 2003). Reports of stings came from swimmers in Denmark and Norway, and more than 1,000 tonnes of farmed salmon died as a result of lesions and suffocation (Fosså et al. 2003).  While it is unclear if these events involving siphonophores are indications of increased populations, it seems likely that more southerly species will continue to appear as waters warm and currents are affected (Hosia 2007). There is also evidence to suggest that jellyfish have increased in Limfjorden, a highly eutrophicated system in Denmark that connects the North Sea with the Kattegat.  Riisgård et al. (2011) tell a compelling story that begins with increasing nutrient input through the 1960s and 1970s, due primarily to runoff from agricultural activities.  This caused increased eutrophication in Limfjorden, which now suffers from annual summer hypoxia that can 92  cover up to 40% of the bottom.  These events result in the release of toxic hydrogen sulphide from the sediments and mass mortality of zoobenthos, including dense mussel beds and demersal fish.  The dramatic reduction in demersal fish through the 1980s was associated with a concurrent increase in jellyfish, primarily the scyphomedusa Aurelia aurita.  Predation by these jellies on zooplankton likely reduces grazing on phytoplankton, contributing to a positive feedback loop whereby eutrophication is exacerbated (Møller & Riisgård 2007a, also see Section 1.7).  Large blooms of jellies interfere with fisheries research trawls in Skive Fjord, a section of Limfjorden.  Hoffmann (2005) has calculated the percentage of trawls that are not completed due to overloading of the fishing gear by jellyfish.  This ‘Hoffmann -index’ (Møller & Riisgård 2007a) helps to demonstrate the large interannual variability of medusae populations, as they can be virtually absent in some years, whereas in other years they can interfere with more than half of the research trawls conducted. Other factors are also important in regulating the jellyfish community in Limfjorden, including hydrodynamics.  In 2004 and 2005, intrusions of large volumes of high-salinity waters from the North Sea coincided with an absence of Aurelia and the high abundances of the hydromedusa Aequorea vitrina, a jelly which had not previously been recorded in Limfjorden (Møller & Riisgård 2007b; Riisgård et al. 2011).  Limfjorden has also recently been invaded by the prolific ctenophore Mnemiopsis leidyi (see below). The Helgoland Roads dataset is unique in that it represents a long time-series from a single offshore station that actively samples gelatinous zooplankton, typically three times per week (Greve et al. 2004).  Analyses of interannual changes in mean annual abundance and phenology were presented by Greve et al. (1996) covering the period from 1974-1994. The hydromedusae Aglantha digitale and Obelia spp. both showed no obvious trend.  Further information was presented for the ctenophore Pleurobrachia pileus by Greve et al. (2004), and additional details on the seasonality of P. pileus and Beroe gracilis can be found in Schlüter et al. (2010).  P. pileus and B. gracilis both showed dramatic phenological changes, including a shift to permanent earlier appearances starting in 1987-1989 of 4-10 weeks, and expansions of peak abundance into spring and summer.  While this may indicate an increase in integrated jellyfish biomass, more recent abundances of P. pileus are still well below the 93  peak abundance recorded in 1984.  Information is also presented for Muggiaea atlantica, which did not appear in the zooplankton until 1989, when it was observed in extraordinary abundance in the German Bight (Greve 1994).  M. atlantica has been part of the local fauna since then, but only sporadically and never in abundances comparable to those in 1989 (Greve et al. 2004).  This detailed, long-term dataset highlights the challenges of identifying trends in jellyfish populations (see Section 1.3).  Despite the phonological changes observed in P. pileus and B. gracilis, as well as the recent occurrences of M. atlantica, this dataset is conservatively classified as variable (Abundance Trend = 0), due to the aforementioned abundance of P. pileus.  However, due to the possible increase of the other two species, the Reliability Score of this chronicle is reduced to Low.  A more robust statistical analysis including all jellyfish in this valuable dataset is required before definitive conclusions can be drawn regarding a change in gelatinous biomass. One of the few observed declines in a species of jellyfish occurs in this LME.  Attrill & Thomas (1996) report on jellyfish recorded from samples taken at the West Thurrock Power Station in the Thames Estuary.  Samples were taken every two weeks between 1977 and 1992, until the power station was closed in 1993.  The ctenophore Pleurobrachia pileus was recorded consistently in summer over the course of the study, often occurring in blooms so dense as to rupture sampling nets.  Despite the high seasonality and some interannual variability, P. pileus showed no obvious trend over the period sampled.  In contrast, large blooms of Aurelia aurita were observed in the first half of the dataset, but abundances were dramatically reduced after 1984, with many years showing a virtual absence of this species. While this rapid decline of a conspicuous scyphozoan is alarming, the lack of data after 1992 prevents robust conclusions.  This decline may also simply represent a change in the hydrodynamics of the estuary; A. aurita typically peak later in the season at this location compared to nearby areas, which could reflect a period required for medusae to move up the estuary (Attrill & Thomas 1996). Although it was not included as a separate chronicle because the details were not available at the time of writing, there is an interesting dataset from this LME that will hopefully be available soon.  Daily visual counts of Aurelia spp. and Cyanea spp. have been 94  made from a quay in Arenal, Norway from 1992 to the present.  The data show high variability, thus making analysis difficult.  However, there appears to be a small decrease in Aurelia spp., with no change for Cyanea spp. (T. Falkenhaug, IMR, pers. comm., March 2011). Invasive Species in LME #22 – North Sea The highly invasive ctenophore Mnemiopsis leidyi appears to have successfully invaded the North Sea and is rapidly expanding its range.  This species was first observed in 2005 in southeast Norway (Oliveira 2007) and western Denmark (Tendal et al. 2007), and then in several locations in 2006, including the Netherlands (Faasse & Bayha 2006), Sweden (Hansson 2006), Helgoland (Boersma et al. 2007), and many additional locations in Danish waters (Tendal et al. 2007).  It has also since been observed in western Norway (Hosia 2007). While 2005 marked the first confirmed observation, it is likely that M. leidyi has been present in the North Sea for much longer (Faasse & Bayha 2006; Hansson 2006; Boersma et al. 2007) and it continues to be detected (Selander et al. 2010).  In 2007, Denmark’s Limfjorden was witness to populations of M. leidyi with densities exceeding 800 individuals m-3 and bio- volumes up to 300 ml m-3, eclipsing the peak bio-volumes from the Black Sea (Riisgård et al. 2007). 2.3.23 LME #23 – Baltic Sea Barz & Hirche (2005) report on scyphomedusae abundance and biomass in the Bornholm Basin and compare their measurements to those published from earlier years.  An examination of abundance in August allows comparison of data back to 1994, as well as inclusion of additional data from 2003 reported by Barz et al. (2006).  Thus, four separate abundance measurements over a 10-year span can be examined for Aurelia aurita and the less abundant Cyanea capillata.  Such a comparison reveals a stable/variable trend for these species, with abundances in 1998 and 2002 being roughly double what they were in 1994 and 2003.  The different patterns observed in 2002 and 2003 highlight the large potential for interannual variability in this system (Barz et al. 2006; Barz & Hirche 2005).  In addition, the possible of absence of ephyrae combined with the late-arrival of medusae point to advection 95  as the controlling factor for medusae in the central Baltic - a hypothesis supported by circulation models (Barz et al. 2006). Schneider & Behrends (1994) similarly report interannual variation for A. aurita in Kiel Bight.  Median abundance and biomass was measured and compared with earlier studies, allowing comparisons from 1978-1993.  Both abundance and biomass varied by an order of magnitude, sometimes between consecutive years.  More recent data on jellyfish populations would allow further comparison and would be useful for fisheries managers, as A. aurita appear to be a major predator in this region, potentially exerting top-down control on larval herring (Möller 1984), as well as copepods and other ichthyoplankton (Möller 1979; Behrends & Schneider 1995).  In these years of high A. aurita abundance, such predation may result trophic cascades (Schneider & Behrends 1998). Invasive Species in LME #23 – Baltic Sea Several species of invasive jellyfish have been observed in the Baltic Sea.  The highly invasive ctenophore Mnemiopsis leidyi was first observed in this LME in the Kiel Bight in 2006, where it increased to very high abundances (Javidpour et al. 2006).  The range of M. leidyi appeared to spread further in 2007, when it was detected in several additional locations (Kube et al. 2007; Tendal et al. 2007) including the Bornholm Basin (Haslob et al. 2007).  While the population in the Bornholm Basin is likely not self-sustaining, average autumn abundance increased from 2007 to 2009 (Schaber et al. 2011).  There are also reports that M. leidyi has spread further into the central and northern Baltic (e.g., Lehtiniemi et al. 2007).  However, there is some doubt as to whether the species detected is truly M. leidyi, as no molecular evidence has confirmed presence in more northerly locations (Gorokhova et al. 2009; Gorokhova & Lehtiniemi 2010).  Therefore, a Space Score = Medium has been conservatively assigned to this chronicle. The species that was potentially misidentified as M. leidyi in the northern and central Baltic is more likely Mertensia ovum.  Gorokhova et al. (2009) confirmed the presence of this species from various samples taken from numerous locations in the northern half of the Baltic Sea, based on molecular evidence.  This jellyfish is known to have a broad distribution 96  at high latitudes, but had not previously been reported from the Baltic.  As such, this jellyfish is assumed invasive; however, a Reliability Score = Low was used to reflect the uncertainty concerning the invasion (Anonymous 2009b). The invasive hydromedusa Maeotias marginata was first detected in the Väinameri Archipelago of the northern Baltic Sea near Estonia in 1999, with ballast water suggested as a potential vector (Väinölä & Oulasvirta 2001).  Medusae were not observed in 2000, but sampling from 2002-2005 did reveal a couple of individuals in 2002 and 2003 (Ojaveer & Kotta 2006).  While this chronicle is scored with Confidence Index = Low due to the confirmation of only a few individuals, the presence of this species cannot be ignored as it has been shown to occur at high abundances in other non-indigenous locales (Mills & Sommer 1995). The hydromedusa Gonionemus vertens is likely also invasive in the Baltic.  However, this species was detected there well before 1950 (Leppäkoski et al. 2002) and was therefore not included in the analysis. 2.3.24 LME #24 – Celtic-Biscay Shelf While the Continuous Plankton Recorder (CPR) is certainly not designed to sample jellyfish, it can be useful for identifying trends in frequency of occurrence (see Section 2.3.22 for further discussion).  Licandro et al. (2010) analyzed CPR data for presence of cnidarian tissue and nematocysts in an area spanning this entire LME.  Data were divided into two periods, 1958-2001 and 2002-2007.  Mean spatial distributions were calculated for each period, along with the associated anomaly.  Waters in this LME showed an increase in the frequency of Cnidaria in the latter period (i.e., since 2002).  While this trend was evident throughout the LME, the region of greatest increase was located off southwest Ireland (see Fig. 2a in Licandro et al. 2010).  Genetic analysis of Cnidaria samples from 2007 and 2008 revealed the dominant species was Pelagia noctiluca (Baxter et al. 2010; Licandro et al. 2010), while four species of siphonophore were also identified.  P. noctiluca lacks a polyp stage and has a wide distribution across ocean basins (Arai 1997; Purcell 2005).  This species was also involved in a major fish kill at an aquaculture operation in 2007, resulting in the death of 97  more than 250,000 fish and losses in the millions of dollars (Anonymous 2007b; Doyle et al. 2008). Lynam et al. (2010b) analyzed annual spring survey data for juvenile gadid fish in the Irish Sea from 1994 to 2009.  Double-oblique tows were conducted and jellyfish were separated from the catch and weighed.  Ctenophores were also separated from the catch. However, no data are presented on their abundance or biomass.  Since 2007, jellyfish catch has been separated by species and has been dominated by Aurelia aurita, Cyanea capillata, and Cyanea lamarckii.  Several other species were also detected at low abundances.  Analysis revealed an increasing linear trend in annual catch weights of jellyfish (R2 = 0.26, n = 16, p = 0.03).  Frequency of occurrence data from the CPR survey showed a significant positive correlation to jellyfish catch weights, lending strength to the validity of both datasets.  The authors conclude that “in the Irish Sea, an increase in jellyfish abundance was evident”. Recent anecdotal reports indicate that the populations of Cyanea spp. have remained high in 2010 and 2011 around Ireland (Gittens 2011). Lilley et al. (2009) report on abundance of the conspicuous Rhizostoma octopus over three years (2003-2006) from aerial surveys of two “hotspots” in the Irish Sea.  A third hotspot had only two years of data, but showed a similar trend.  All stations showed high average abundances (>25 jellyfish·m-2) during 2003, followed by much lower abundances in 2004 and 2005 (2 stations only).  While this could be evidence of a decline in jellyfish biomass over the short 3 year time-period, the authors also compiled historical reports of Rhizostoma spp. in European waters.  The authors note that the reports appear to suggest an increased frequency of large blooms in the late 20th century.  However, the sporadic nature of the reports must cast some doubt on this conclusion.  Recently, there have been reports of large blooms of this species and other jellyfish in the Irish Sea (Anonymous 2011f, 2011h), including blooms that extend into cooler seasons than usual (Murphy 2011). The Plymouth Marine Laboratory has been collecting weekly data on zooplankton abundance at a coastal station known as “L4” since 1998.  Located in the Western English Channel, the L4 station is sampled using vertical net hauls from the sea floor (~55 m) to the surface.  The 20-year time-series reveals a significant decline in average abundance for 98  hydromedusae (WCO 2011).  However, if siphonophores are also included in the analysis, there is no significant trend.  Therefore, this chronicle is classified as stable/variable (Abundance Trend = 0). There is also limited knowledge of jellyfish populations in Southampton Water (C. Lucas, NOC, pers. comm., Jan. 2011), an estuary in southern England.  Abundances of Aurelia sp. were relatively high in the 1980s and appeared to decline in the 1990s.  No Aurelia medusae were spotted in 2006 and 2007; however, in recent years the population appears to have returned to high abundances.  In addition, ctenophores of the genus Pleurobrachia have been observed in high abundances in recent years, and appear to be in the water column for longer periods.  While the sampling for jellies in this region has not been consistent over the years, the recent observations of high abundances suggest populations may have increased in this location.  Aurelia polyps were also discovered on mussel shells attached to the underside of floating pontoons in the estuary in 2009 (Duarte et al. 2011).  As such, this chronicle has been classified as an increase (Abundance Trend = 1) albeit with Confidence Index = Low. 2.3.25 LME #25 – Iberian Coastal The Instituto Español de Oceanografía (I.E.O.) has been monitoring plankton communities and other variables at numerous stations along the north and northwest coast of Spain for over a decade (Valdés et al. 2007).  Stations Vigo and Coruna report on siphonophores and salps, as does Santander with the addition of medusae.  While the sampling gear used in the surveys is not designed for jellyfish and therefore must be interpreted with caution (A. Bode, IEO, pers. comm., June 2010), consistent methods allow for comparison of relative abundance.  All stations show high variability in the density of these groups from the mid-1990s into the mid-2000s (IEO 2010).  Peak years for salps and siphonophores are obvious, but show no significant trends over the time period (Abundance Trend = 0). 99 Invasive Species in LME #25 – Iberian Coastal Medusae of the globally invasive Blackfordia virginica were found in high densities in the Guadiana Estuary in 2008 (Chícharo et al. 2009).  Comprehensive surveys of this estuary from 1999 to 2003 did not detect this species, suggesting a recent invasion.  While it may be too early to indicate that this species has successfully established itself here, specimens have been found of both sexes over a wide range of size classes.  Combined with the high abundances observed, this would suggest local reproduction (Chícharo et al. 2009).  This species was also detected nearby in the Mira Estuary in 1984 (Moore 1987).  However, it is unclear if that population has persisted. 2.3.26 LME #26 – Mediterranean Sea Jellyfish population changes are perhaps better documented in the Mediterranean than anywhere else, thanks primarily to a combination of long-term datasets and high public interest due to tourism impacts. By far the most notorious jellyfish in the Mediterranean is the mauve stinger, Pelagia noctiluca.  This scyphomedusa lacks a polyp phase, and therefore has a holoplanktonic lifestyle that does not restrict it to coastal waters.  Nonetheless, blooms of this jellyfish are often found near shorelines, inflicting painful but non-fatal stings to tens of thousands of seabathers each year (Anonymous 2010h).  The population dynamics of this species can be depicted as “presence-absence” (UNEP 1984, 1991), whereby blooms occur for several consecutive years followed by periods lacking major outbreak events.  A long-term dataset constructed by Goy et al. (1989) from various sources has records of P. noctiluca dating back to the 18th century, indicating bloom years and non-bloom years.  Although some observations were made from single locations (such as the extensive records from the Station Zoologique in Villefranche-sur-Mer, France), most blooms that are described with accuracy indicate a trend that appears to cover the entire western basin of the Mediterranean (Goy et al. 1989).  The analysis from 1875-1986 indicated that episodes of bloom years showed a significant period of about 12 years.  However, blooms of P. noctiluca began to stray from this pattern in the late 1990s, and persistent blooms have occurred in the 100  western Mediterranean almost annually for the last decade (Yahia et al. 2010; Anonymous 2008b, 2010c).  Although the number of jellyfish observed along Spain’s Catalan coast do not show an obvious trend over the last decade (Atienza et al. 2010), there are reportedly increases of P. noctiluca in recent years further to the west along Costa Blanca (Anonymous 2010h).  There are also reports of Rhizostoma pulmo polyps attached to concrete columns in Badalona, Spain (Duarte et al. 2011). When other gelatinous groups are considered - such as hydromedusae, siphonophores, and ctenophores - the patterns of jellyfish abundance in the western Mediterranean become more complicated.  Abundance of the small, holoplanktonic hydromedusae Liriope tetraphylla showed considerable seasonal, interannual, and decadal variation from 1966-1993 at Villefranche-sur-Mer, but there was no overall increasing or decreasing trend apparent in the dataset (see Fig. 2 in Buecher et al. 1997).  Interestingly, the abundance of L. tetraphylla appeared to correspond negatively to that of P. noctiluca, with the strongest years for L. tetraphylla occurring during periods when P. noctiluca was absent (Buecher et al. 1997).  It remains unclear whether this correlation is due to competition, predation, or environmental conditions (Buecher et al. 1997; Legović 1987).  García-Comas et al. (2011) analyzed the seasonality and abundance of numerous zooplankton groups at Villefranche-sur-Mer, using ZooScan technology which facilitated the processing of large samples.  Data from 1974 to 2003 were included, and gelatinous zooplankton were divided into carnivorous medusae and siphonophores.  Both of these groups showed relatively low abundances through the 1970s along with increases through the 1980s.  These results are consistent with those of Molinero et al. (2005; 2008a; 2008b), where only select species of jellyfish were examined. However, the increases observed in the early 1990s were not observed in the analysis of the entire jellyfish community.  Rather, the medusae and siphonophore populations continued a near-decadal cycle by exhibiting relatively low abundance through the 1990s, followed by returns to higher abundances in the last few years of the dataset (see Figs. 3 and 4 in García- Comas et al. 2011).  In addition, the ctenophore Pleurobrachia rhodopis appeared to decrease in the late 1980s (Molinero et al. 2008a), and salps showed periodic blooms but no consistent trends (Licandro et al. 2001).  This dataset highlights some of the differences between 101  examining individual species versus considering entire community groups, such as jellyfish and zooplankton.  As stated by García-Comas et al. (2011), “...the analysis of broad groups […] does not substitute but efficiently complements the species level approach…” The population of jellyfish appears to have increased in Mar Menor, a Spanish coastal lagoon.  This hypersaline lagoon is rather shallow (~3.5 m average depth) and is separated from the Mediterranean by a sandy barrier with several inlets.  Traditionally, this lagoon was a singular ecosystem that supported important artisanal fisheries, as well as a small population of Aurelia spp. (Pagès 2001).  However, the lagoon has been subject to major environmental changes due to anthropogenic disturbances, starting in the 1970s, which have dramatically changed the ecosystem.  The disturbances began with the enlargement of several inlets to facilitate the passage of recreational boats, and have continued to include the construction of new harbours, dredging and dumping of sand for artificial beaches, mining operations, changes in runoff, increased eutrophication, and intensive coastal development (Pérez-Ruzafa et al. 1991; Pagès 2001).  In the mid-1980s, two new scyphomedusae (Cotylorhiza tuberculata and Rhizostoma pulmo) were recorded in the lagoon and began forming large blooms in the mid-1990s (Pérez-Ruzafa et al. 2002).  These large blooms have been problematic for the tourist industry ever since, and there are now efforts to capture and remove thousands of tonnes of jellyfish from this lagoon (Pagès 2001; Conesa & Jiménez-Cárceles 2007; Prieto et al. 2010).  Nonetheless, it appears that both of these species have completed their life cycle in Mar Menor (Fuentes et al. 2011) and continue to thrive there.  Curiously, R. pulmo directly consumes diatoms in Mar Menor, and may benefit from increased production due to eutrophication (Pérez-Ruzafa et al. 2002; Lilley et al. 2009). Polyps of Aurelia spp. and C. tuberculata have been reported from this lagoon attached to artificial dock structures and marine debris (Duarte et al. 2011).  As polyps of C. tuberculata appear highly influenced by temperature, it is suspected that blooms of this jellyfish will be increasingly recurrent in Mar Menor under global warming scenarios (Prieto et al. 2010). The recent changes in Pelagia noctiluca populations in the western basin are not consistent with the rest of the Mediterranean, but rather demonstrate different dynamics in recent decades.  Blooms in the Aegean Sea appear to be maintaining the aforementioned 12- 102  year periodicity (Yahia et al. 2010).  In the Adriatic Sea, P. noctiluca was relatively rare until 1977, when it began blooming frequently (Zavodnik 1987).  Blooms continued for about 10 years, until 1987, when P. noctiluca blooms in the Adriatic appeared to subside and virtually disappear for more than a decade.  However, in 2004, blooms began in this region again (Yahia et al. 2010) and continued until 2007 (Kogovšek et al. 2010). In addition to the periodic appearances of P. noctiluca, the Adriatic Sea shows other signs of increasing jellyfish populations.  Kogovšek et al. (2010) performed a wavelet analysis of jellyfish blooms in the Adriatic over the last 200 years, and found that blooms have been occurring more frequently in recent decades.  Several scyphozoans were included in the analysis, with Aurelia spp. being the most frequently reported.  This species showed periodic blooms throughout the dataset, but the frequency of these events increased during the 1990s.  While increased observations of this species may be partially due to improved sampling techniques, major blooms of Aurelia appear to be on the rise in the Adriatic, and have occurred annually since 2004.  Rhizostoma pulmo also showed similar dynamics, with an increased recurrence of blooms over the last two decades.  However, the abundance of this species appears to have decreased since 2006.  Perhaps playing a role in the increase of Aurelia medusae, polyps have been discovered on anthropogenic habitat in this region. Scyphopolyps were reported in Koper Harbor, Gulf of Trieste, on the undersides of oyster shells attached to piers (Duarte et al. 2011).  Di Camillo et al. (2010) also recorded scyphopolyps on underside portions of an iron shipwreck near Ancona, Italy, but did not find polyps in the proximate natural environment, which includes rocky cliffs.  Polyps on the wreck were monitored at densities up to 45 polyps·cm-2 and the authors estimate 780,000 to 2,600,000 ephyrae could be released per m2. Information presented by Malej (2001) also appears to confirm a possible increase in “irregular events” involving jellyfish in the Northern Adriatic.  With the exceptions of P. noctiluca (discussed above) and Cotylorhiza tuberculata, numerous species of jellyfish appeared to show an increase in the frequency of blooms through the 1970s, 1980s, and/or 1990s.  These included Aurelia spp., Aequorea forskalea, Chrysaora hysoscella, Rhizostoma pulmo, and Ctenophora.  Despite the apparent increase in large scyphomedusae in the Northern 103  Adriatic, Benović et al. (1987; 2000) discuss a decline in the hydromedusan community.  The authors point to increased hypoxic and anoxic events due to anthropogenic disturbance as a cause for decreased abundance and species diversity, primarily for meroplanktonic species. While it is presumed that the overall biomass of jellyfish in this system has increased (see above), the decline in meroplanktonic hydromedusan species highlights the fact that different groups of jellyfish will respond differently to anthropogenic impacts, and some groups may be affected negatively (Purcell et al. 1999a).  Polyps of Aurelia spp. have also been observed in the Gulf of Trieste, found on the undersides of oyster shells attached to piers (Duarte et al. 2011). There have been many reports of jellyfish around Malta in recent years, thanks primarily to a recent citizen science campaign entitled “Spot the Jellyfish” (see  Not surprisingly, this program has revealed large variations in abundance, with sizeable blooms reported in 2009 and fewer sightings in 2010 (Anonymous 2010e) and 2011 (Anonymous 2011g).  As problems with jellyfish have been reported from Malta every decade since the 1950s (Deidun 2011), trends in jellyfish populations in Maltese waters remain unclear. Invasive species in LME #26 – Mediterranean Sea Numerous species of invasive jellyfish appear to be thriving in the Mediterranean Sea. The highly invasive ctenophore Mnemiopsis leidyi was first recorded in the Mediterranean in the 1990s in the Aegean Sea near Greece (Shiganova et al. 2001; Shiganova et al. 2004b) and Turkey (Kideys & Niermann 1994), as well as in the eastern Mediterranean near Syria (Shiganova 1997), and in the Marmara Sea (Isinibilir et al. 2004), where a number  of jellyfish species appear to have been introduced (Isinibilir et al. 2010).  M. leidyi was subsequently discovered in the northern Adriatic in 2005 (Shiganova & Malej 2009) and in Spain in 2008 (Fuentes et al. 2010).  Then, in 2009, large blooms of this invader spanned many disparate locations in the Mediterranean, including Israel (Galil et al. 2009a), Italy (Boero et al. 2009), and Spain (Fuentes et al. 2010).  The species identity of M. leidyi from the Mediterranean has been confirmed using molecular techniques, and given the widespread occurrence of 104  simultaneous blooms, it is likely that this species has been well distributed and established in the Mediterranean for some time (Fuentes et al. 2010).  Although the abundances of many other jellyfish were unusually low during the 2009 blooms of M. leidyi (Fuentes et al. 2010), the large aggregations of M. leidyi suggest that the gelatinous biomass in the Mediterranean may be increasing due to this infamous invader. Interestingly, the reliable predator of M. leidyi - Beroe ovata - has also been found in the Mediterranean; initially in 2004 in the Aegean Sea (Shiganova et al. 2007), then in 2005 in the Adriatic (Shiganova & Malej 2009), and in 2011 off the coast of Israel (Galil et al. 2011). Currently, it remains unclear to what extent B. ovata is established in the Mediterranean, and whether it has significantly reduced the abundance of M. leidyi, as is the case for the Black Sea (see Section The conspicuous scyphomedusa Rhopilema nomadica first appeared along the coast of Israel in the mid-1970s (Galil et al. 1990) and blooms have continued to increase there ever since (Lotan et al. 1992; 1994; Waldoks 2010; Marshall 2010).  This species appears to have extended its range to Lebanon and Syria (Lotan et al. 1994), Egypt, Turkey (Kideys & Gücü 1995), Greece (Siokou-Frangou et al. 2006), and on two occasions, even Malta (Anonymous 2011i).  Blooms of R. nomadica directly interfere with fisheries, industry, and tourism, resulting in significant economic losses (Lotan et al. 1992; Galil et al. 2010). The invasive Phyllorhiza punctata also appears to have established a growing population in the Mediterranean.  A solitary specimen was observed in Mediterranean waters in 1965 (Galil et al. 1990), but there have been reports of individual medusae and large blooms from the coast of Israel since 2005 (Galil et al. 2009b).  A reproducing population of this invader also exists in a bay on a Greek island in the Ionian Sea, where it has occurred for roughly a decade (Abed-Navandi & Kikinger 2007).  In 2009, a single P. punctata specimen was also observed near Sardinia, Italy (Boero et al. 2009), and in 2010 a bloom of this species forced the closure of six different beaches in Spain’s Costa Brava, after more than 100 swimmers were stung (Anonymous 2011a). Cassiopea spp. are also suspected to be invasive in the eastern Mediterranean, having been detected in Israel (Spanier 1989) and Lebanon (references in Galil et al. 1990).  However, 105  there is at least one report of Cassiopea spp. in the eastern Mediterranean before 1950 (Galil et al. 2010), so these invasions may have occurred prior to their reporting.  Regardless, it appears the range expansion of Cassiopea spp. will continue, with recent sightings from Malta (Schembri et al. 2010 in Galil et al. 2010) and Turkey (Çevik et al. 2006). Yet another invasive species of jellyfish was recently discovered off the coast of Israel, which resulted in the description of an entirely new genus.  Marivagia stellata was first found in 2006 and several recent detections suggest an established a population (Galil et al. 2010). The cubomedusan Carybdea marsupialis has also invaded the Mediterranean Sea.  First observed in the Adriatic in the mid-1980s (Boero & Minelli 1984), this jellyfish “is now an obnoxious stinger” in the Mediterranean (CIESM 2008) and has recently been reported from Spain (Bordehore et al. 2011), France (Cuneo 2009), and Malta (Schembri 2010; Anonymous 2011b). In addition to recent range expansions of invasive species, introductions of new species continue to be documented.  In 2010, the first sighting of the large scyphomedusan Catostylus tagi, which is normally restricted to Atlantic waters, was reported from the Sicily Channel in Italy (Nastasi 2010). 2.3.27 LME #28 – Guinea Current No scientific data on jellyfish populations were available for the Guinea Current. However, there are anecdotal reports suggesting jellyfish have been increasing in the region (B. Asiedu, U. Ghana, pers. comm., Jan. 2010), and that fishers have been catching more jellyfish in their nets over the last decade (F.K.E. Nunoo, U. Ghana, pers. comm., Feb. 2010). 2.3.28 LME #29 – Benguela Current The Benguela Current LME is arguably the most highly productive upwelling system in the world (Carr 2001), close to the Humboldt Current LME.  The high variability associated with this ecosystem can result in similar variability in the gelatinous community (Gibbons & Buecher 2001).  There is evidence to suggest that the Benguela Current LME has experienced a large increase in jellyfish over the last half-century, but a lack of baseline data inhibits drawing definitive conclusions.  This historical data deficiency has led some 106  scientists to question the perceived increase of jellies in this LME (e.g., Mills 2001).  However, the circumstantial evidence supporting a large increase in jellyfish appears to outweigh a lack evidence to the contrary. The most convincing indication of an increase is the absence of large jellyfish from the reports of comprehensive surveys conducted in the 1950s and 1960s (Hart & Currie 1960; Fearon et al. 1992 and references therein).  While it is true that jellyfish have historically been ignored, avoided, and discarded from plankton studies (Mills 2001), these early surveys from the Benguela ecosystem documented small jellyfish, including hydromedusae and ctenophores.  Thus, it seems highly unlikely that conspicuous jellyfish would have been omitted from the reports, especially if they had occurred at high levels of abundance.  Today, two large jellyfish are present in this ecosystem at very high abundances.  Chyrsaora hysoscella and Aequorea forskalea now dominate the gelatinous biomass of the Namibian coast (Fearon et al. 1992), and it has been suggested that the gelatinous biomass now eclipses that of fish in this ecosystem (Lynam et al. 2006). More evidence that jellyfish have increased in this region comes from reports of interference with fisheries.  Venter (1988) notes that jellyfish have become an “increasingly irritating nuisance” for fishers since the 1970s.  An increase of jellyfish in this region now appears to be accepted by most scientists (e.g., Brierley et al. 2001; Sparks et al. 2001; Bakun & Weeks 2006; Utne-Palm et al. 2010).  However, it is curious that this shift was not discussed in papers that examined the ecosystem through the 1980s (e.g., Shannon et al. 1992; Gibbons et al. 1992).  Nonetheless, evidence suggests the increase in jellyfish continued in the 1990s (Heymans et al. 2004). Formal studies to identify changes in jellyfish populations in this LME are rare, likely due to a lack of funding (Sparks et al. 2001).  Buecher & Gibbons (2000) document a decline in jellyfish species diversity through the 1990s in St. Helena Bay.  However, there is no indication of how this trend affected the overall gelatinous biomass and it was therefore excluded from the analysis.  There have also been suggestions of an increase in box jellyfish off Namibia (Hartman 2011), but the details are unclear.  Recent interest in cataloguing the 107  gelatinous plankton of this region will undoubtedly help to identify future changes (e.g., Pagès et al. 1992; Gibbons & Thibault-Botha 2002; Gibbons et al. 2010). 2.3.29 LME #30 – Agulhas Current While there are no scientific data from this LME that examine jellyfish populations over time, limited anecdotal evidence suggests that populations are stable.  The inshore waters of KwaZulu-Natal are periodically witness to large blooms of conspicuous medusae every three to five years.  However, there are no indications that these blooms have become larger or more frequent in at least the past 35 years (R. van der Elst, ORI, pers. comm., Jan. 2010). An informal survey of delegates was conducted at the Marine Biodiversity workshop of the South West Indian Ocean Fisheries Project (SWIOFP) held in Mauritius (van der Elst 2010).  Respondents were asked about jellyfish “outbreaks” in their region, and were notified of the survey in advance so they could deliberate with colleagues at their institutes. While there were reports of periodic blooms, representatives from Mozambique, Comoros, and Madagascar all reported that there have been no noticeable, recent changes in jellyfish populations.  Interestingly, there was a large bloom of jellyfish in Mauritius in October 2009 that had not been seen previously.  A photograph of these jellies revealed that they were mostly likely a species of Cassiopea.  However, they were not included in the analysis as Mauritius is not part of any LME. 2.3.30 LME #31 – Somali Coastal Current As with the neighbouring Agulhas Current LME, there are no scientific data on jellyfish from eastern Africa.  However, anecdotal data suggest jellyfish populations may be stable.  As mentioned, an informal survey of delegates was conducted at the Marine Biodiversity workshop of the South West Indian Ocean Fisheries Project (SWIOFP) held in Mauritius (van der Elst 2010).  While there were reports of periodic blooms, representatives from Tanzania and Kenya both reported that there have been no noticeable, recent changes in jellyfish populations. 108  2.3.31 LME #32 – Arabian Sea Interviews with 90 fishers were conducted by Ganapathiraju Pramod in September 2008 in 4 Indian states within this LME (Pramod 2010).  Small-scale fishers and mechanized trawler operators were asked about interannual and decadal changes jellyfish populations. In three of the states (Gujarat, Maharashtra, and Karnataka) the majority of interviewees responded that jellyfish populations were stable (G. Pramod, UBC, pers. comm., Sept. 2010). In the state of Kerala, the majority of interviewees indicated that jellyfish had increased, as more jellyfish were being caught in fishing nets as bycatch and more jellyfish were washing up on shore, as well as over longer distances (G. Pramod, UBC, pers. comm., Sept. 2010). Other evidence also supports an increase in some regions of India, with fishers from the west coast reporting losses due to interference with jellyfish (B. Ingole, NIO, pers. comm., Oct. 2010), and more jellies washing up on beaches (Anonymous 2010b).  The results of the interviews by G. Pramod were included in the analysis for this LME as two separate chronicles, each with a differing Abundance Trend and Space Score. In 2002, a massive bloom of the scyphomedusa Crambionella orsini occurred throughout the Persian Gulf and the Gulf of Oman.  This bloom interfered with fisheries and clogged intake pipes of desalination plants and power stations.  It also resulted in a massive flux of organic matter to the sea bed as the jellies died, exceeding the average annual flux of organic carbon by more than an order of magnitude (Billett et al. 2006). Dryanabard & Dawson (2008) discuss data from demersal trawls that have been conducted since 1998 along the coast of Iran.  They note that C. orsini typically occurs at very low abundances in these trawls.  With the obvious exception of 2002, there have been no recent blooms of this species (Daryanabard & Dawson 2008; Billett et al. 2006).  In addition, it would appear that while blooms of this species are not annual, they have been occurring for at least half a century.  Erdman (1950) reports a bloom of jellyfish in the Persian Gulf containing “more individual jellyfish […] than one could possibly count.”  His description of the jellyfish in question fits well with C. orsini.  As there are no indications that C. orsini is blooming more frequently in recent years or decades, this chronicle is classified as variable (Abundance Trend = 0). 109  Additional anecdotal reports from the Persian Gulf suggest that jellyfish populations are increasing.  Erftemeiger & Langenberg (2010) state that “outbursts of large numbers of jellyfish are observed in what appears to be increasing quantities throughout the Gulf.” They suggest an increase over the last 7-10 years, and point to evidence from internet blogs, sting reports, clogged intake pipes, and interference with fisheries.  While the evidence supporting an increase is circumstantial, there are certainly reports of complaints by both sport fishers (Picow 2010) and commercial fishers (Al-Rubiay et al. 2009), who may even haul jellyfish to shore and dump them on beaches (Nazzal 2006).  There is also a report of a new species occurring in Dubai, but it may have been a singular event due to a hurricane (Bardsley & Landais 2007).  The species in question was reported as belonging to the genus Aurelia, but the photographs and content of the article suggest otherwise. While the majority of evidence seems to support an increase of jellyfish in the Gulf (Erftemeiger & Langenberg 2010), catches of jellyfish in Bahrain tell a different story. Established in 2003, the jellyfish fishery in Bahrain annually harvests hundreds of tonnes of jellyfish for export to Asia (Erftemeiger & Langenberg 2010).  However, catch rates in 2007 were so low that the processor estimated they had fallen by 90% (Mohammed 2008).  The decline was blamed on jellyfish moving further offshore, due to land reclamation and the large amount of sediment used.  Therefore, this decline is excluded due to the fact that it is a suspected spatial relocation (see Section 2.3.1), in addition to the potential effects of jellyfish harvesting.  In fact, there are suggestions that overfishing of jellyfish has reduced jellyfish populations around Karachi, Pakistan over the last 5 years (Roghay 2011). 2.3.32 LME #34 – Bay of Bengal Anecdotal data from this LME show conflicting reports of both stable and increasing populations.  Interviews with over 100 fishers were conducted by Ganapathiraju Pramod in September 2008 in 5 Indian states within this LME (Pramod 2010).  Small-scale fishers and mechanized trawler operators were asked about interannual and decadal changes jellyfish populations.  In three of the states (Tamil Nandu, Andhra Pradesh, Andaman & Nicobar Islands) the majority of interviewees responded that jellyfish populations were stable (G. 110  Pramod, UBC, pers. comm., Oct. 2010).  In the states of Orissa and West Bengal, the majority of interviewees indicated that jellyfish had increased, as more jellyfish were being caught in fishing nets as by-catch and more jellyfish were washing up on shore (G. Pramod, UBC, pers. comm., Oct. 2010).  Orissa has always experienced jellyfish blooms, but reports indicate that these blooms have been increasing in both abundance and frequency - from every 3-4 years previously to almost annually now (G. Pramod, UBC, pers. comm., Oct. 2010).  The results of these interviews are included in the analysis for this LME as two separate chronicles, each with a differing Abundance Trend and Space Score. Other anecdotal evidence points to an increase of jellyfish on the east coast of India, but the timescale is unclear, as increases have been reported in each of the past several decades.  Marine scientists point to an increase of numerous jellyfish in recent years, especially tropical species including Limnocnida spp., Crambionella spp., and Dactylometra quinquicirrha (B. Ingole, NIO, pers. comm., Oct. 2010).  Jellyfish are also reportedly being caught by fishers in increasing numbers (B. Ingole, NIO, pers. comm., Oct. 2010).  James et al. (1985) present catch rates of jellyfish for a 5-year period (1981-1985) and state that their “analysis clearly shows that the medusae of Crambionella stulhmanni are becoming more abundant year after year.”  However, the authors also note that “swarms of jellyfish are a common sight off Madras” and a linear regression performed over the short time-series does not reveal a significant increase.  Interference with power plants in this region was also reported in the 1980s and 1990s, due primarily to D. quinquicirrha and Crambionella spp. (Rajagopal et al. 1989; Masilamoni et al. 2000).  Reports of interference and high jellyfish abundance from earlier decades cast some doubt on more recent perceptions of increases, and the possibility of the shifting baselines syndrome exists (Pauly 1995), especially due to the pulsed nature of jellyfish populations (Condon et al. 2011a).  Nonetheless, the opinion of contemporary marine scientists should not be ignored, as they are often aware of the shifting baselines syndrome.  In addition, marine scientists are keenly tuned to changes in the oceanic environment and they are often the best source of information in the absence of scientific data.  Thus, the opinions of Dr. Baban Ingole were included in the analysis with an associated Confidence Index = Medium-Low. 111  Scattered reports of jellyfish fisheries from the east coast of India confuse the issue further.  Jellyfish harvesting has existed in India for some time.  However, the scale of this harvesting is unclear, as catches are not reported to the Food and Agriculture Organization of the United Nations (FAO).  Harvesting of jellyfish along India’s east coast likely began in the 1980s, mainly for the purpose of exporting Crambionella stuhlmanni (Kuthalingam et al. 1989).  The jellyfish fishery in India is still small compared to other Asian countries. However, India reportedly had a very large catch of C. stuhlmanni in 2003 (CMFRI 2009), when there was also an apparent expansion, as well as harvesting of Lobonema smithii (Murugan & Durgekar 2008).  This expansion is said to be due to dwindling catches of more valuable fish species, and provides an example of fishing down the food web (Murugan & Durgekar 2008).  The rapid expansion is also said to have caused major conflict as merchants and fishers scrambled to compete in the fishery, and concerns over pollution from jellyfish processing huts exacerbated the problem (Magesh & Coulthard 2004).  The 2004 tsunami reportedly caused severe damage to jellyfish fishery infrastructure in India (CMFRI 2009), and this, in combination with declining catch rates, instigated a scaling back of the jellyfish fishery in Tamil Nadu in 2005 (Manickaraja & Balasubramanian 2006).  It appears the fishery has expanded eastward in recent years, with catches reported from Andhra Pradesh in recent years (CMFRI 2009).  In addition, there are complaints that overharvesting of jellyfish off the state of Orissa has resulted in a decline, a concern due to the perceived importance of jellyfish in the diet of Olive Ridley sea turtles (Lepidochelys olivacea)  (Anonymous 2007a, 2008a).  While there appear to be no empirical data to back up this claim, catches of jellyfish in Orissa may be much larger than in other parts of India (G. Pramod, UBC, pers. comm., Oct. 2010).  While the reported decline of jellyfish due to overharvesting in Orissa conflicts with the results of the aforementioned interviews by G. Pramod, it was included in the appropriate phase of the analysis (see Section 2.2.3). Government officials in Malaysia are becoming increasingly concerned with high numbers of jellyfish in Penang waters, and the associated effects on tourism (Kwang & Yahya 2010).  Evidence of trends in jellyfish populations in this region are rare; however, recent reports of unusual blooms in both Malaysia (e.g., Lau 2010) and Thailand (e.g., 112  Morison 2009) suggest that jellyfish may be increasing in this region.  As well, there appears to be a general consensus from interviews with locals that there are more intense and frequent blooms in recent years along the western coast of the Malay Peninsula (M.R.B. Idid, IBS, Jan. 2011), and recent blooms near the Straits of Malacca are surprising fishers and causing them problems (Anonymous 2011c). Populations of jellyfish studied at Penang National Park appear stable, but data are only available for the past several years (S.Y. Kwang, CMCS, pers. comm., Jan. 2010). Adding to the inconsistent trends in this region, there are also reports of declines in harvested jellyfish species.  In 2005, Malaysian jellyfish harvesters reported a “dramatic drop in catch in recent years, believed to be due to increasing pollution” (Heng 2005).  Not surprisingly, FAO statistics add further confusion, as there was a decline in the Malaysian harvest at this time, but Thailand recorded massive harvests in 2005 and 2006, before dropping to zero again in 2008 (FAO 2011). Interestingly, possible declines of harvested jellyfish species were reported in the Andaman Sea as far back as the 1970s (Soonthonvipat 1976), while FAO has no record of catches in this area before 1978 (FAO 2011). Recent stings and tourist deaths due to box jellies in this area have also received media attention (e.g., Wipatayotin 2008), and there are some suggestions that these events are due to recent range expansions (e.g., Morison 2008) or increases in abundance (Anonymous 2010a).  However, it is more likely that these species of jellies have long been present in the region (Fenner et al. 2010; Phattrasaya & Morison 2008), and recent attention is more likely due to increased media exposure (Lippmann et al. 2011).  In addition, the anecdotal evidence suggesting increases appears to refer more to the Gulf of Thailand (Suntrarachun et al. 2001). Therefore, these events have been excluded from this LME but are discussed further in the Gulf of Thailand LME. 2.3.33 LME #35 – Gulf of Thailand As mentioned, the Gulf of Thailand has recently received media attention for increased sightings of box jellyfish and the severe and sometimes fatal stings due to these venomous jellies (Fenner & Lippmann 2009).  While these reports are outside the previously 113  known distribution for many of these species (e.g., de Pender et al. 2006) and may indicate a range expansion of box jellyfish, anecdotal reports suggest an increase in abundance and species diversity (Suntrarachun et al. 2001).  However, it may be the case that these jellies have occurred in this region for a long time (Fenner et al. 2010).  Serious and fatal jellyfish stings have occurred historically in Thailand, with the vast majority of cases likely being unreported (Fenner & Lippmann 2009).  The apparent recent increase in reports is probably linked to the attention by foreign media due to cases involving tourists, as well as the viral nature of the internet (Fenner & Lippmann 2009).  While this suggests anecdotal reports of increases should be questioned, they also should not be ignored.  Therefore, the reports suggesting increases of jellies in this region are included, albeit with the highest degree of uncertainty (Confidence Index = Low). 2.3.34 LME #36 – South China Sea One of the most interesting and convincing examples that aquaculture operations can lead to increased jellyfish populations comes from this LME.  Lo et al. (2008) report on an “experiment” in Tapong Bay, a tropical lagoon in Taiwan.  For decades, this bay has been used extensively for aquaculture, primarily oyster raft and fish pen operations.  As a result, the bay became highly eutrophic due to increased nutrient input and reduced water circulation.  In addition, Tapong Bay was subject to frequent blooms of Aurelia aurita.  In 2002, the aquaculture rafts and pens were completely removed from the bay, which resulted in the complete disappearance of jellyfish thereafter.  This remarkable correlation suggests that the aquaculture operations were wholly responsible for the abundance of jellyfish in the bay, and the study of variables both before and after the removal of the structures permits exploration of the mechanisms involved.  As the rafts and pens likely provided ideal habitat for jellyfish polyps, their removal as substrate is likely the main cause for the absence of jellyfish.  The authors also note significant increases in water clarity and circulation after removal, suggesting that the hydrodynamic effects of the rafts may have also influenced the proliferation of jellyfish.  Copepod abundance increased sixfold after removal, suggesting that predation by fish and jellies, as well as competition from oysters may have controlled 114  copepod populations.  The fact that jellyfish were absent even with this increased food source, further supports the hypothesis that the aquaculture operations were responsible for influencing jellyfish populations.  With such a tight correlation between jellyfish abundance and aquaculture production, it seems unlikely that jellyfish were present in this bay prior to any aquaculture operations.  As such, this chronicle is scored as stable/variable (Abundance Trend = 0), rather than as a decrease.  This in situ experiment provides convincing support for the hypothesis that increased aquaculture production around the globe may contribute to increased jellyfish populations (Purcell et al. 2007; Lo et al. 2008; Duarte et al. 2011). The massive power outage that affected much of the Philippines in December 1999 was apparently attributed to jellyfish clogging the intake screen of a coastal power plant (Anonymous 1999).  While this event would not normally be included as evidence of an increase (see section 0), a report of the incident was accompanied by anecdotal evidence that the jellyfish population in Lingayen Gulf had dramatically increased due to overfishing (Anonymous 1999).  As this claim was from an unidentified environmental group, and since there are no recent reports on increased jellyfish populations from the region, it was included in the analysis with a Confidence Index = Low. In Malampaya Sound, the Philippines, Lobonema spp. are harvested and processed for export to Korea and Japan.  In this region, more than 10% of the population may directly benefit from the jellyfish fishery.  However, evidence suggests that intensive harvesting has contributed to a decrease in jellyfish populations, at least for targeted species (PCAMRD 2008). 2.3.35 LME #40 – Northeast Australian Shelf Quantitative information on jellyfish populations is lacking in this LME; however, investigations of stings by cubomedusae provide some insight into changes.  While a change in the frequency of sting events does not warrant inclusion in the analysis (see Section 2.1.4), there is evidence of both temporal and spatial increases of the stinger season in this LME. Box jellyfish expert Dr. Jamie Seymour has commented on the dramatic change in the length of the season for Irukandji syndrome, suggesting that it has increased by 3-4 months over 115  the last several decades in Queensland, possibly due to warmer water temperatures (Anonymous 2010d).  Analysis of data from nearby Darwin Harbour (located in the neighbouring Northern Australia Shelf LME) revealed a strong correlation between the number of Chironex fleckeri stings and the average daily minimum SST (Jacups 2010).  With predicted warming, the length of the stinger season is likely to increase in this and neighbouring LMEs (Jacups 2010). Spatial distributions have also reportedly increased, with increasing cubomedusae reports from further south in Queensland over the last 5 years (Donaghey 2009).  Changes in the seasonal and distributional patterns of cubomedusae in this LME are echoed by other experts who suggest the dynamics in recent years are exceptionally unusual (Smail 2010). While these trends are especially concerning from a public health perspective and may be indicative of ecosystem changes, the contribution of cubomedusae to overall jellyfish biomass in this LME is uncertain (Reliability Score = Low). 2.3.36 LME #41 – East Central Australian Shelf Although it is not included in the analysis as a separate chronicle, the spatial expansion of cubomedusae noted in the Northeast Australian Shelf LME may also be extending into this LME.  Recent reports of box jellyfish as far south as Coffs Harbor have been called “very strange” by experts, but a lack of funding has so far prevented formal study (Anonymous 2011e). Surveys conducted in this LME by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) in conjunction with the University of New South Wales revealed a large increase in the salp Thalia democratica in the waters off Sydney in 2008, with abundances more than an order of magnitude higher than surveys dating back 70 years (Strong 2008; Henschke 2009).  However, measurements from 2009 indicate that abundances have returned to levels closer to those measured from the 1940s, and the recent data do not support a significant increase over historical values (K. Pitt, GU, pers. comm., May 2010). Rather, the high biomass measured in 2008 was associated with a very large cold core eddy, 116  and salp abundances outside of such formations are expected to be much lower (J. Everett, UNSW, pers. comm., Feb. 2011). The presence of the conspicuous scyphomedusa Catostylus mosaicus has been monitored in a large region around Moreton Bay since 2002 as part of the Ecosystem Health Monitoring Program (EHMP).  Over the course of the dataset, C. mosaicus showed a low presence for three years, increased presence over the next three years, followed by a subsequent return to low abundances in recent years (K. Pitt, GU, pers. comm., May 2010). There is also evidence that some jellyfish populations in this LME have been eradicated due to collection for science.  The upside-down jellyfish Cassiopea used to be present in Myora Drain, an artificial tidal channel.  However, numerous collections in 2007 (e.g., Bouchet 2007; Templeman & Kingsford 2010) led to the complete eradication of this species in this location, with no specimens present since 2007 (K. Pitt, GU, pers. comm., May 2010).  A similar story seems to have played out in the nearby tidal lagoon known as Pelican Waters, where further collection for science (e.g., Mortillaro et al. 2009) has eradicated the population (K. Pitt, GU, pers. comm., May 2010). 2.3.37 LME #42 – Southeast Australian Shelf The conspicuous scyphomedusa Catostylus mosaicus has been monitored in Port Phillip Bay for nearly two decades.  Port Phillip Bay is a large, nearly-enclosed embayment important for recreational and industrial uses, as well as fishery resources (DPI 2010). Annual trawl surveys reveal pulses of C. mosaicus in 1995, 1997, 2004, 2008 and 2009, with lower abundances or near absence in other years (Coleman 2004; K. Pitt, GU, pers. comm., May 2010).  While there is no significant trend over the course of the dataset, it will be interesting to see if the high abundances seen in the last two years continue.  A developmental fishery has recently been established for C. mosaicus in Port Phillip Bay (Coleman 2004; DPI 2006); however, annual catches remain small or nil. Wilcox et al. (2008) studied polyps of Aurelia sp. on anthropogenic structures in Tasmania.  The polyps were monitored starting in 2002 at two nearby sites – the underside of a cement breakwater in Hobart and the underside of a floating marina in Kettering.  The 117  proportion of polyps strobilating showed large variation between sites and years.  While there is no evidence to suggest that Aurelia medusae populations have increased in this area, large blooms have caused significant interference and economic losses to aquaculture operations (Willcox et al. 2008).  In addition, Aurelia medusae in this region form intense blooms in some years but not others (Willcox et al. 2008; Naidoo 2009), and therefore increases in favourable polyp habitat due to anthropogenic structures may facilitate persistence and growth of medusae populations (Duarte et al. 2011). Invasive Species in LME #42 – Southeast Australian Shelf Several species of hydromedusae or their associated hydroids have been reported from Port Phillip Bay.  While many detections occurred prior to 1950, new species continued to be reported in the 1970s and 1980s including Turritopsis nutricula, Bougainvillea muscus (ramose), Clytia hemisphaerica, and Obelia dichotoma (australis) (Hewitt et al. 2004).  While most of these hydromedusae have a cosmopolitan distribution and are cryptogenic, they are included here as some of these species are highly invasive (e.g., Turritopsis spp.) and the detailed historical monitoring of Port Phillip Bay suggests they are new additions to the ecosystem. 2.3.38 LME #47 – East China Sea The East China Sea is one of only two LMEs with the maximum possible Jellyfish Index, and evidence suggests that several species are increasing over a large spatial scale (Cheng et al. 2004).  Due to these changes, recent initiatives have been announced that allocate millions of dollars to study jellyfish dynamics in this region (Stone 2010; Sun et al. 2011).  Yan et al. (2004) state that fisheries surveys from 1990-2003 show an increase in jellyfish biomass in recent years compared with the 1990s for a region extending from 28°30’ N to 34° N and from the coast to 127° E.  The trend applies to both Nemopilema nomurai3 and Cyanea spp., and appears to be correlated with declines in fish stocks (Yan et al. 2004; Ding & Cheng 2005).  Causal links are speculative, but it appears there may be a positive feedback loop  3 Reports of Stomolophus meleagris from Chinese literature are assumed to be Nemopilema nomuraii (Omori & Kitamura 2004). 118  with increasing jellyfish and declining fish, possibly triggered by overfishing (Hong et al. 2008).  The increase in N. nomurai is also being observed in the northern part of this LME near Korea, with dramatic increases since 2003 (Ding & Cheng 2005; Rahn 2009; Yoon et al. 2008).  Recent evidence from DNA analyses suggests that the population of N. nomurai in the East China Sea is distinct from the population in the Yellow Sea (Hanzawa et al. 2010). Increasing jellyfish blooms have also been reported from the Yangtze Estuary.  Xian et al. (2005) note that Rhopilema esculentum used to be the common jellyfish in this region, but overharvesting of this species has resulted in a gradual replacement by Cyanea spp. since 1997.  Then in 2004, Sanderia malayensis, which was previously only recorded in the South China Sea, began blooming in the estuary.  S. malayensis dominated the jellyfish catch, and the authors point to possible links with declining fisheries catches.  Although R. esculentum has most certainly declined in this region, it has been harvested there for decades or possibly even centuries.  Therefore, it is unlikely that the biomass of this targeted species has declined as precipitously as the increases in the other species, especially given the fact that recent blooms of S. malayensis can almost completely cover the surface in parts of the estuary (Xian et al. 2005).  Interestingly, S. malayensis began blooming only one year after the Three Gorges Dam filled the first one-third of its storage capacity, resulting in a dramatic reduction of river flow to the Yangtze estuary and the East China Sea.  Within months, ecological changes were observed in the microbial food web (Jiao et al. 2007)  and more profound changes are forecast (Wu et al. 2004). Eutrophication has also been blamed for increases of jellyfish in this region, and countermeasures such as erosion prevention, wetland conservation, and seaweed planting have all been proposed in an attempt to prevent increased jellyfish blooms (Guan et al. 2007). In addition, anthropogenic habitat for polyps has been documented in this LME.  Miyake et al. (1997) documented polyps found on the undersurface of floating polystyrene piers in an engineered canal in Kagoshima Bay, Japan.  These polyps were observed again several years later, and it is assumed that they persist in this location.  Notably, polyps were also observed attached to a discarded cellophane cigarette package (Miyake et al. 2002). 119 Invasive Species in LME #47 – East China Sea Evidence suggests that Aurelia populations have been spreading throughout this part of the world.  There is no record of these jellyfish in Korean waters prior to 2000, but since then, at least two dense blooms have occurred.  Ki et al. (2008) found that Aurelia medusae from Korea have the same genotype as those from California, and only slightly different than specimens from Japan.  With such a broad distribution and the inability to attribute this distribution to natural dispersion patterns, it would appear Aurelia sp. 1 is invasively spreading due to anthropogenic translocation (Dawson et al. 2005; Ki et al. 2008). 2.3.39 LME #48 – Yellow Sea Jellyfish dynamics in the Yellow Sea are very similar those in the East China Sea, and several major species appear to be increasing throughout this LME (Cheng et al. 2004).  The giant jellyfish, Nemopilema nomurai, has been blooming more frequently in the Yellow Sea near Korea (Yoon et al. 2008), in Jiaozhou Bay, China (Sun et al. 2010), and in the Bohai Sea (Dong et al. 2010).  N. nomurai tend to drift over a large region of this LME, as well as into and out of other LMEs, so reports from different areas could represent the same populations. However, recent genetic analysis suggests that populations of N. nomurai in the Yellow Sea are distinct from those in the East China Sea (Hanzawa et al. 2010). Problems with jellyfish along the south coast of Korea have prompted officials to release filefish at beaches in Busan (Jun-shik 2009).  Filefish, which are also targeted by the Korean fishery, are predators of jellyfish and it is hoped that releasing hundreds of thousands of filefish at beaches will result in fewer jellyfish.  While there was indeed a reduction of stings along Busan beaches in 2007 and 2008 (Jun-shik 2009), jellyfish along the south coast of Korea continue to be a problem (Rahn 2009).  As mentioned for the East China Sea LME, other countermeasures have also been proposed to combat eutrophication, including erosion prevention, wetland conservation, and seaweed planting (Guan et al. 2007). Again similar to the East China Sea LME, Rhopilema esculentum appears to have declined in the region, while Cyanea nozakii has increased.  Dong et al. (2006) note increases 120  in C. nozakii in the Yellow Sea and the Bohai Sea, which have been more of a problem since at least 1997 (Zhong et al. 2004).  Interference with fishing activities has been reported, including broken nets and shortened seasons (Dong et al. 2006).  Blooms of C. nozakii have also been blamed for the reduced harvest of R. esculentum, such as the dramatically low catch in Liaodong Bay in 2004, which represented an 80% decline and a loss of US$70 million (Ge & He 2004; Zhang et al. 2005).  This is despite attempts to restock waters with R. esculentum by means of hatcheries, whereby hundreds of millions of juvenile medusae are released in the spring with the hopes of harvesting them in the fall (Dong et al. 2009).  While such hatchery methods achieved economic success in the 1990s through the release of billions of young medusae (You et al. 2007), it remains unclear if these programs continue to be successful, and efforts appear to be shifting towards pond culturing of jellyfish (You et al. 2007).  In an attempt to adapt to the decline in R. esculentum and the shift in species composition, C. nozakii has been successfully processed into jellyfish food since the 1980s. However, the poor quality of the product has resulted in values only one hundredth of those for R. esculentum (Lu et al. 2003; Zhong et al. 2004).  The story with attempts to process and sell Nemopilema nomurai is all too similar (Dong et al. 2010). Invasive Species in LME #48 – Yellow Sea Populations of Aurelia spp. appear to be increasing in parts of this LME.  Interference with fishing and cooling water intakes are reported in numerous locations in the Yellow and Bohai Seas (Dong et al. 2010; Ki et al. 2008).  These reports are not necessarily indications of increasing abundance (see Section 2.1.4),  but Aurelia populations appear to be invasive in at least Korean waters, where no records are found prior to 2000 (Ki et al. 2008).  The species in Incheon has the same genotype as other parts of the world, a fact which cannot be explained by natural oceanic dispersal (Dawson et al. 2005; Ki et al. 2008).  Han & Uye (2010) also note that chronic blooming of Aurelia populations is now common in eutrophic bays and inlets in Korea, such as Shihwa Lake.  This artificial lake is a failed experiment that now contains hypoxic sea water (Han & Park 1999).  This suggests that other factors, such as cultural eutrophication, may assist in the establishment of invasive species. 121  2.3.40 LME #49 – Kuroshio Current While there is evidence for a decline of one species in this LME, the majority of species showing changes in abundance appear to be increasing.  The most obvious example concerns the giant jellyfish, Nemopilema nomurai.  Records of this jellyfish blooming extend back to the early part of the century, and “bloom years” are clearly distinct from “non- bloom years”.  Historically, this species would bloom every 35-40 years, with blooms occurring in 1920, 1958, and 1995 (Uye 2008).  However, these blooms have become increasingly frequent, occurring in 2002, 2003, 2005, 2006, 2007, and 2009 (S. Uye, HU, pers. comm., May 2010).  While 2010 and 2011 were not bloom years (Uye 2011), 2009 was perhaps the largest bloom of this species ever witnessed (S. Uye, HU, pers. comm., May 2010).  These jellyfish appear to originate along the coasts of China and Korea, and then drift into the Sea of Japan LME (Uye 2008, 2010; Uye et al. 2010; Hanzawa et al. 2010).  In the fall, many jellyfish are then carried through the Tsugaru Strait into this LME, where they can interfere with fisheries in a variety of ways, causing substantial economic losses (Kawahara et al. 2006). Jellyfish also appear to be increasing in and around the Seto Inland Sea.  Uye & Ueta (2004) surveyed over one thousand fishers, each with at least 20 years of experience.  65% of respondents indicated that populations of Aurelia sp. had increased in the last 20 years. While 65% is not overwhelming support for an increase, the authors note that in widespread areas of the Inland Sea “an elongation of the period of occurrence of medusae is obvious.” Other sources of information also point to increased jellyfish populations in the area. Monthly reports of fishing and oceanographic conditions in the Seto Inland Sea indicate an increase in jellyfish in the late 1990s (Nagai 2003).  In addition, polyps of this species have been observed on the underside of floating docks and on pier pilings in the region, suggesting increased anthropogenic habitat (Miyake et al. 2002; Duarte et al. 2011). Combinations of eutrophication, increased polyp habitat, and overfishing have been suggested as the mechanism for increased jellyfish populations in this region (Shoji et al. 2010; Uye 2010).  Supporting evidence for increased Aurelia populations is also found from studies along western Shikoku, near the entrance to the Seto Inland Sea.  An unusually large 122  aggregation was observed in 2000, likely caused by a rapid intrusion of offshore waters (Uye et al. 2003; Takahashi et al. 2010).  The cause of this bloom might suggest that jellyfish populations in other years were not necessarily smaller, but rather just more sparsely distributed.  However, indications are that while populations have been variable over the last 7-8 years, there was an apparent increase in the late 1990s, prior to which no medusa aggregations were found despite frequent field surveys (S. Uye, HU, pers. comm., Oct. 2010). Starting in 1998, wet weights of jellyfish (predominantly Aurelia spp.) were recorded daily from the screens of the Ikata Nuclear Power Station near Seto, and were analysed by Kaneda et al. (2007).  The 7-year dataset reveals interannual variations, but no obvious trend (see Fig. 2 in Kaneda et al. 2007).  The lack of an increase in this dataset is in contrast with the majority of the anecdotal data from the Seto Inland Sea (see above).  However, most of the increases were reported to occur in the 1990s, with variable or stable populations thereafter. Therefore, the temporal scale of the data from the Ikata Power Station may not extend back far enough to capture the increase.  As discussed above, there are numerous anecdotal sources suggesting an increase the jellyfish population of the Seto Inland Sea in recent decades.  While the dataset from the Ikata Nuclear Power Station does not add to this evidence, it also does not preclude an increase in the 1990s.  Therefore, the chronicle for the Seto Inland Sea is included as an increase (Abundance Trend = 1), with the Reliability Score reduced to Low. Numerous jellyfish species appear to have increased in the highly eutrophic bays of coastal Japan, including Tokyo Bay and Mikawa Bay.  The most conspicuous species is Aurelia, which used to be found only in low abundances throughout most of the 20th century (Nomura & Ishimaru 1998).  However, with increased eutrophication through the 1960s, a shift in zooplankton coincided with increasingly larger Aurelia blooms in Tokyo Bay (Omori et al. 1995; Nomura & Ishimaru 1998; Ishii 2001) and Mikawa Bay (Toyokawa et al. 2011). These blooms have been interfering with human activities for decades, such as blocking seawater intakes at coastal power stations (Kuwabara et al. 1969; Toyokawa et al. 2000). Aurelia polyps have been observed to colonize artificial substrates in both locations (Watanabe & Ishii 2001; Toyokawa et al. 2011), and they appear to benefit from the hypoxic 123  conditions as they can out-compete other sessile organisms for habitat due to a tolerance for low oxygen (Ishii et al. 2008). The populations of other jellyfish species in Tokyo Bay, such as the ctenophore Bolinopsis Mikado, appear to have increased through the 1980s (Nomura & Ishimaru 1998), while the 1990s showed high variability (Kinoshita et al. 2006; Kasuya et al. 2000).  Species composition has also changed over the past several decades, including new appearances of jellies such as Beroe cucumis (Arai 2001; Nomura & Ishimaru 1998). While increasing jellyfish populations appear to be common in this LME, it is not the case with all species.  The hydromedusae Spirocodon saltatrix appears to have decreased throughout much of its range in coastal Japan (Mills 2001).  Ironically, some of the factors that have been blamed for increased populations of jellyfish in this region, such as eutrophication and coastal development, may be responsible for the decline of S. saltatrix (Mills 2001; S. Uye, HU, pers. comm., Oct. 2010).  The most dramatic decline of S. saltatrix populations took place in the Seto Inland Sea.  There has been some recovery of this population, and the overall decline is assumed to be small relative to the increases of other jellyfish in this region.  Decreases have also been noted in other locations, such as the Misaka Marine Biological Station, where harvesting of this species for research has likely contributed to its decline (C. Mills, WU, pers. comm., Oct. 2010).  While this species has most certainly been negatively impacted, the extent of the decline is unclear, and therefore a Spatial Score = Low has been used.  While S. saltatrix may be abundant at times, it is not considered a dominant species.  In addition, the few areas where decreases have been documented also show dramatic increases in other species, and therefore a Reliability Score = Low was used. Invasive Species in LME #49 – Kuroshio Current As mentioned, hydromedusae belonging to the genus Turritopsis have a unique ability to reverse their life cycle through transdifferentiation.  During this process, starving or damaged medusae revert to a benthic cyst.  These cysts can subsequently produce a new polyp colony that is capable of releasing new medusae.  This remarkable ability has led to 124  these jellyfish being acclaimed as the world’s only “immortal” organism (e.g., Than 2009), and it may increase the probability of Turritopsis spp. being transported to new locations through ballast water (Miglietta et al. 2007; Miglietta & Lessios 2009).  At least one species, T. dohrnii, appears to be invasive in Okinawa Island and possesses a revealing Mediterranean haplotype (Miglietta et al. 2007).  While blooms of this species may occur at high abundance, the medusae are relatively small and may not contribute significantly to the gelatinous biomass (Invasive Reliability Score = Low). 2.3.41 LME #50 – Sea of Japan As with neighbouring LMEs, the Sea of Japan has suffered from recent increases of the giant jellyfish, Nemopilema nomurai.  Large blooms of this species have increased dramatically in the last decade, causing interference with fisheries and millions of dollars in economic losses (Kawahara et al. 2006).  The medusae appear to originate from coastal Korea and China, and are then transported through the Tsushima Strait into this LME, where they spend much of the summer (Uye 2008, 2010; Uye et al. 2010).  While the medusae from these blooms do not appear to originate in this LME, they may have the potential to colonize it (Kawahara et al. 2006). Anthropogenic substrate for Aurelia polyps has also been identified in this LME. Matsumura et al. (2005) observed large and dense polyp colonies in three harbours in Wakasa Bay.  As with other documented anthropogenic habitat, the colonies were found on the shadowed undersurfaces of quays and pontoons.  While there are no direct data that suggest Aurelia medusae have increased in this LME, there have been problems with medusae at a nearby power plant.  Molecular evidence suggests that the medusae invading this plant originated from polyps on the anthropogenic structures in Wakasa Bay (Matsumura et al. 2005). 2.3.42 LME #51 – Oyashio Current Knowledge of jellyfish in this region comes from recent publications concerning a Russian dataset that spans three LMEs.  This extensive dataset, yet another of fisheries origin, is based on trawls conducted by the Pacific Fisheries Research Center (TINRO) 125  between 1984 and 2006.  Over 9,000 plankton samples were taken during more than 100 surveys.  While the methods were designed to sample nekton, the data provide an index of relative abundance for jellyfish larger than ~3 cm (Zavolokin 2010).  Volkov (2008) summarizes the zooplankton characteristics from this dataset, and reports biomass trends grouped by time periods and regions.  Although the boundary of the “Northwestern Pacific” reported by the author differs somewhat from the boundary separating this LME from the West Bering Sea LME, the trends reported are consistent across both LMEs, and therefore results can be interpreted for the purposes of the present analysis.  The trends show a separation between what the author labels biotopes - “Outer shelf” waters, where jellyfish populations appear to be reasonably stable, and the “Deepwater area”, where jellyfish appear to be increasing.  As such, this study was included in this LME as two separate chronicles with differing Abundance Trends.  The study regions classified as “Deepwater” have a mean annual biomass density more than twice that as those classified as “Outer shelf” (19.6 and 9.2 mg·m-3 respectively).  In addition, “Deepwater” areas are slightly larger in aggregate than those classified as “Outer shelf”.  Therefore, the Confidence Index used for the “Deepwater” chronicle is Very High, while High is used for the “Outer shelf” chronicle.  The “Deepwater” regions show an increase in “Coelenterata” in recent years, with the average biomass after 1999 being three to four times that prior to 1999.  Comparatively, overall gelatinous biomass in the “Outer shelf” region appears relatively stable. Interestingly, the observed increases in particular regions appear due to species other than the abundant Aglantha digitale, which showed relatively stable populations, along with some declines (Zavolokin 2010). 2.3.43 LME #52 – Sea of Okhotsk The aforementioned Russian dataset also covers this entire LME.  Based on the data presented by Volkov (2008), there has been a large increase in jellyfish in recent years. Average biomass of “Coelenterata” from 1999-2006 was up to an order of magnitude higher compared to the period 1984-1999 in the “Inner shelf” and “Outer shelf” regions.  The trend was less pronounced for the “Deepwater” region of this LME.  However, this region is 126  relatively smaller, contains proportionately less jellyfish in general, and still showed an increase in jellies of more than 60%.  As such, this dataset was represented in this LME as a chronicle with an Abundance Trend = 1 and a Confidence Index = Very High.  These trends appear to be due to species other than the abundant Aglantha digitale, which showed relatively little variation in biomass over the dataset in this region.  The increases interpreted from the methods of Volkov (2008) are more likely due to large blooms in particular years after 1999.  Evidence of such blooms can been seen in reports relating to the same data at higher resolution, which show a high biomass of mesopelagic scyphozoans in 2005, and high numbers for mesopelagic hydrozoans in several years after 1999 (Zavolokin 2010).  Higher resolution data from the epipelagic show high interannual variability (Il'inskii & Zavolokin 2007; Zavolokin 2010), with extreme peak years for certain groups or species, such as Chrysaora spp. in 1999 (Il'inskii & Zavolokin 2007). The various papers and citations listed above are a good illustration of some of the problems that arise when trying to identify trends in jellyfish data (see Section 2.3.1).  That is to say, differing trends can be identified over different taxonomic, spatial, and temporal scales.  For example, based on data presented by Il’inskii & Zavolokin (2007), one could conclude that total winter biomass of epipelagic scyphozoans has declined since 1999. However, data presented by Zavolokin (2010) from the very same dataset show the biomass of mesopelagic scyphozoans demonstrates an altogether different trend.  As such, conclusions should be drawn from datasets over the largest possible taxonomic, temporal, and spatial scales – in this case Volkov (2008). There is also evidence to suggest that blooms of the giant jellyfish, Nemopilema nomurai, are appearing in this LME.  According to a report by the Japanese Fisheries Service Center, these jellies have been observed in the Sea of Okhotsk along the coast of Hokkaido (Anonymous 2009d), but their abundance and distribution in this LME remains unclear. 2.3.44 LME #53 – West Bering Sea As with the Oyashio Current and Sea of Okhotsk LMEs, the Russian dataset based on Pacific Fisheries Research Center (TINRO-Center) trawl surveys reveals increases in jellyfish 127  biomass in this region.  Volkov (2008) reports changes in biomass of “Coelenterata”, which show dramatic increases for “Inner shelf” regions and the comparatively large “Deepwater” areas after 1999.  This trend is not as evident in the smaller “Outer shelf” regions, due to the large biomass of Aglantha digitale reported in 1997-1998.  However, an increase is still evident in these regions upon comparing average values from 1999-2006 compared to those of 1984-1996.  In contrast with the two aforementioned LMEs, the reported increases in this region appear to be due in part, at least in the “Deepwater” areas, to the abundant hydromedusan Aglantha digitale.  Examination of the epipelagic data at higher taxonomic resolution reveals a dramatic increase in the hydromedusa Aequorea forskalea and various scyphozoans in the southern part of this LME in the early 2000s, whereas biomass of the abundant scyphomedusa Chrysaora melanaster appears relatively stable in the northern part of this LME over the same time period (Zavolokin et al. 2008).  As discussed, conclusions from this dataset are based on the broadest taxonomic, temporal, and spatial scales – in this case Volkov (2008). 2.3.45 LME #60 – Faroe Plateau Continuous Plankton Recorder (CPR) data spanning this LME were presented by Licandro et al. (2010).  While CPR surveys are clearly not designed to sample jellyfish, they can provide an index of occurrence for the species sampled (see discussion in Section 2.3.22). Roughly one half of this LME (the northeast) appears to show an increase in the jellyfish anomaly from 1958-2007, while the southwest section shows a decline.  Therefore, this dataset was classified as variable (Abundance Trend = 0). 2.3.46 LME #61 – Antarctic Jellyfish are a major component of the plankton in this LME, which can include large salp blooms and several abundant ctenophores and medusae (Pagès et al. 1996).  Like many populations, jellyfish in this LME show high interannual variability, with populations changing from high abundance to near absence in consecutive years (Larson & Harbison 1990; Pagès et al. 1996).  In the Antarctic, there is a negative correlation between salps and krill, whereby one group or the other tends to dominate in a particular year.  This 128  relationship appears to be mediated by sea ice, as ice algae provide food for krill in winter, hence promoting larval recruitment.  In contrast, salps occupy open waters with lower primary production, and therefore sea ice extent may dictate whether krill or salps dominate  (Loeb et al. 1997; Moline et al. 2004).  Salp populations monitored at Elephant Island over the past several decades appear to be highly variable, with alternating regimes of salp and krill dominance (Lee et al. 2010).  A similar trend was evident for Prydz Bay through the 1980s (Perissinotto & Pakhomov 1998).  While these examples would lead one to conclude that salp populations do not show significant changes, a larger scale analysis suggests otherwise.  Atkinson et al. (2004) compiled data for krill and salp populations back to the 1920s, covering an extensive area of this LME.  Abundance data from 1976-2003 were compared with that from 1926-1939, revealing a significant decline in krill and a concurrent increase in salps.  This pattern was evident for several disparate locations around the Antarctic continent.  While data prior to 1950 were generally not included in this analysis (see Section 2.1.2) an exception was made in this case due to the extensive temporal and spatial coverage of the database, which comprises nearly 12,000 net hauls.  In addition, the significant decline in krill shown from 1976 to 2003 may indicate a similar trend for salps over the same time period.  If warming trends continue in this LME, salp populations may continue to increase (Loeb et al. 1997; Moline et al. 2004). 2.3.47 LME #62 – Black Sea The Black Sea has a unique and fascinating history of changing jellyfish populations, linked primarily to a number of anthropogenic impacts including eutrophication (see Section 1.5.3), overfishing (see Section 1.5.4), and species invasions (see Section The history of this ecosystem must also be viewed in light of climatic conditions, which likely played a significant role in driving the ecological changes (Niermann 2004). The scyphomedusa Aurelia aurita is suspected to be invasive here (Dawson et al. 2005); however, the timeline concerning this invasion is unclear.  As Aurelia has been present here since at least the 1950s, it was therefore classified as a native species for the purposes of this analysis.  Abundances of Aurelia were relatively low in the 1950s and 1960s, with a total 129  biomass somewhere between 1 million tonnes (Niermann 2004) and 30 million tonnes (Shushkina & Musaeva 1983 in Bat et al. 2009).  Abundance of Aurelia increased dramatically through the 1970s and 1980s due to eutrophication and other anthropogenic stressors (Niermann 2004), ultimately reaching a peak biomass on the order of 400-600 million tonnes (Gomoiu 1981; Flint et al. 1989).  The invasion of the ctenophore Mnemiopsis leidyi in 1988 (see below) resulted in a significant reduction of Aurelia thereafter (Shushkina & Vinogradov 1991), possibly due to the fact that Aurelia populations are constrained by a sessile polyp phase requiring suitable substrate within the oxygenated zone, whereas ctenophores release gametes directly into the water column (Gücü 2002; Niermann 2004). The population of Aurelia in the Black Sea now appears to be on the order of 100 million tonnes (Mutlu et al. 1994; Bat et al. 2009).  As this is still much higher than the biomass reported from the 1950s and 1960s, Aurelia is considered to have increased over this time period (Abundance Trend = 1).  While the fluctuations of Aurelia biomass tend to be negatively correlated with biomass of M. leidyi, it would appear that the overall gelatinous biomass of the Black Sea shelf has certainly increased since the 1950s and 1960s due to the presence of both species (Oguz & Velikova 2010). The ctenophore Pleurobrachia pileus also occurs in the Black Sea in high abundances. While no major population changes are suspected over the past several decades (Mutlu et al. 1994), there is a suggestion that the P. pileus population has been reduced since 1999 due to predation by the invasive ctenopore Beroe ovata.  However, this decline does not appear to be significant, as a lower biomass of P. pileus was also observed in the mid-1990s, prior to the invasion of B. ovata (Shiganova et al. 2004a), and P. pileus abundance continues to be high in the southern Black Sea (Mutlu 2009). Interestingly, the scyphozoan Rhizostoma pulmo used to be one of the most common jellyfish in coastal areas of the southern Black Sea, but was not observed in 2006/2007 (Mutlu 2009).  The sudden disappearance of this conspicuous species is noteworthy, especially if its absence continues.  Nonetheless, the decline of R. pulmo is surely not sufficient to compensate for the increased biomass due to the other jellyfish in the Black Sea. 130 Invasive Species in LME #62 - Black Sea The highly invasive ctenophore Mnemiopsis leidyi is native to the eastern U.S.A. and was accidentally introduced to the Black Sea in the 1980s, presumably via ballast water from cargo vessels (Shiganova 1998).  M. leidyi was first detected in the inshore waters of Sudak Bay in 1982, and began to spread throughout the Black Sea in 1988 (Shiganova 1998 and references therein).  The population of M. leidyi in the Black Sea quickly grew to astounding abundances in 1989 and 1990, subsequently declining thereafter.  This pattern appears to have repeated, with additional peaks in the population occurring in the mid-1990s and early-2000s, both of which were followed by years of variable abundance (Shiganova 1998; Mutlu 2009). In 1997, another invasive ctenophore - Beroe ovata - was detected in the Black Sea, again presumably due to ballast water from shipping (Finenko et al. 2001and references therein).  Ironically, this jellyfish preys almost exclusively on other ctenophores, and intentional introduction was being considered in the Black Sea as a possible means of controlling M. leidyi blooms (Shiganova & Malej 2009).  B. ovata quickly established a large population, subsequently suppressing M. leidyi abundance (Shiganova et al. 2004a). Laboratory experiments suggest that B. ovata will regulate its own population size by adjusting reproduction to suit prey availability (Shiganova et al. 2004a), and indications are that it can control populations of M. leidyi effectively.  While it appears that the populations of these invasive ctenophores have declined since their peaks in the 1990s, both species are considered successful invaders and appear established in the Black Sea (Mutlu 2009; Oguz & Velikova 2010). 2.3.48 LME #63 – Hudson Bay Canada’s Department of Fisheries and Oceans (DFO) has conducted annual surveys in Hudson Bay since 2003.  Zooplankton data for summer (August or September) are collected from numerous stations for a large transect (~600 km) inside Hudson Bay, as well other transects in Foxe Basin and Hudson Strait.  Dominant jellyfish are small hydromedusae, including Aglantha digitale and the less abundant Aeginopsis laurentii.  Jellyfish abundance 131  data for the period 2003-2006 show variability for Hudson Bay, with a notable peak of A. digitale in 2004 (M. Harvey, DFO, pers. comm., Aug. 2010).  The other transects also show variability over several years of data.  Interestingly, the peak of A. digitale is not evident in Foxe Basin, where jellyfish abundance was actually lower in 2004.  132  3 Anthropogenic Impacts and Increasing Jellyfish Populations 3.1 Methods 3.1.1 Indicators Numerous anthropogenic impacts on the marine environment have been hypothesized to lead to increases in jellyfish abundance (Mills 1995, 2001; Purcell et al. 2007; Pauly et al. 2009b; Richardson et al. 2009).  Indicators for some of these impacts can be estimated quantitatively at the LME scale.  Nine individual indicators, as well as an additional indicator of cumulative impacts (see Table 3.1 for all indicators), were tested for correlations with the Jellyfish Index calculated in Section 2. Table 3.1 Anthropogenic factors suspected to cause more jellyfish Impact Indicator(s) Time Source Warming SST change 1957-2006 Belkin (2009) Eutrophication Estimated nitrogen input from fertilizer 1995 Maranger et al. (2008) Overfishing Marine Trophic Index (MTI) 1990-2005 average Sea Around Us (2011) Mean maximum length (MML) of all catch 1990-2005 average Sea Around Us (2011) Catch intensity of small pelagics 1990-2005 average Sea Around Us (2011) Stock status (fraction of stocks overexploited and collapsed) 1990-2005 average Sea Around Us (2011) Bottom-trawling Catch from trawling and dredging per unit area of shelf (<200 m depth) 1990-2005 average Sea Around Us (2011) Increased substrate and coastal development GDP per capita 2008 UNSTATS (2011) Aquaculture Mariculture production 1990-2005 average Campbell (2011) Cumulative Factors Cumulative impact score per area Circa 2000 Halpern et al. (2008) Salinity NOT TESTED ――― ――― Ocean Acidification NOT TESTED ――― ――― Warming Due to the influence of temperature on jellyfish reproduction, increasing temperatures may ultimately lead to more jellyfish (see Section 1.5.2).  Warming of LMEs was analysed by Belkin (2009), using sea surface temperature (SST) from the U.K. Meterological Office Hadley Centre SST climatology.  The calculated degree of warming used for the indicator in this analysis was reported as a linear regression of SST from 1957-2006. 133 Eutrophication The increasing use of fertilizer for agriculture is one of the main sources of cultural eutrophication, which may provide advantages for jellyfish (see Section 1.5.3).  Fertilizer input by LME was modeled by Maranger et al. (2008) for the year 1995.  Data were tested as absolute values (input·year-1), as well as normalized by the area of each LME (input·year·km-2). Overfishing As discussed, overfishing of both the predators and competitors of jellyfish can lead to increases in jellyfish abundance (see Section 1.5.4).  Overfishing was measured using four indicators for each LME (averaged 1990-2005) from the Sea Around Us Project database: i. Marine Trophic Index (MTI; see Pauly & Watson 2005); ii. average mean maximum length (MML) of total catch, weighted by the catch each stock; iii. fraction of the catch of small pelagic fish to the total catch; iv. fraction of overexploited and collapsed stocks compared to all stocks. Bottom-Trawling Bottom-trawling and dredging may also result in conditions that provide advantages to jellyfish (see Section 1.5.5).  Bottom-trawling was measured using the total catch from trawling and dredging, normalized according to the amount of shelf area in each LME (defined as depth < 200 m) from the Sea Around Us Project database. Increased Substrate and Coastal Development As many jellyfish have a sessile polyp phase as part of their life cycle (see Section 1.3), the addition of anthropogenic structures to the marine environment may provide additional habitat for jellyfish populations (see Section 1.5.6).  As there is no direct measurement of anthropogenic structures by LME, a proxy representing coastal development was calculated.  This proxy was calculated by first obtaining the Gross Domestic Product (GDP) per capita in US Dollars for each coastal country in 2008, from the United Nations Statistics Division Main Aggregates Database (UNSTATS 2011).  Each country occupies a particular 134  portion of the coastline within an LME, which was calculated by comparing the number of coastal cells4 for each country with the total number of coastal cells in each LME.  Finally, a weighted average GDP per capita value was then calculated for each LME. Aquaculture As discussed, aquaculture may benefit jellyfish in a number of ways, including eutrophication and increased substrate (see Section 1.5.7).  In this case, we are mainly concerned with the effects of mariculture, and freshwater aquaculture production has been excluded from the analysis.  Mariculture production was obtained from the Global Marine Production Database (GMPD) that was created by Campbell (2011).  This dataset presents mariculture production at a finer geographic and taxonomic resolution than statistics reported by the Food and Agriculture Organization of the United Nations (FAO).  However, the observed trends are similar in both datasets (Campbell 2011), and can be considered comparable at some scales.  Mariculture production for each LME was average over the period 1990-2005. Cumulative Factors As discussed, several anthropogenic impacts on the marine environment may individually lead to conditions that are advantageous to jellyfish.  However, many of these impacts may work in concert; whereby one mechanism alone may have no effect, but the combined effects of several factors create conditions that ultimately lead to more jellyfish (see Section 1.5.9). Halpern et al. (2008) analysed 17 different anthropogenic drivers of impacts on the marine environment within the last decade, including various indicators of pollution, population density, overfishing, oil and gas development, invasive species, shipping, warming, UV light, and ocean acidification.  Each driver was evaluated on 20 different types of ecosystems, including coral reefs, seagrass beds, mangroves, rocky reefs, as well as various bottom types.  The results of the analysis provided impact weights that were  4 GIS data cells in the Sea Around Us Project database are 0.5° latitude by 0.5° longitude.  Coastal cells are defined as those cells containing both land and water. 135  combined to develop a single comparable estimate of recent anthropogenic impacts on marine ecosystems. The final results of the cumulative impacts were reported by ‘ecoregion’, a classification of coastal and shelf areas based on species composition and biogeography (Spalding et al. 2007).  With 232 described ecoregions, this system provides higher spatial resolution than the LME framework.  However, many of the boundaries are the same in both systems, and conversion between the two systems is possible.  Using the established borders for both systems, the results of Halpern et al. (2008) were converted to estimates for each LME by combining the effects of the ecoregions within each particular LME.  Where boundaries of the different systems overlapped, an estimate was made for how much of the ecoregion was inside the LME, and an appropriate weighting factor was used. 3.1.2 Generalized Additive Model The computing package ‘R’ was used for all statistical analysis.  Multiple regression was analysed using a generalized additive model (GAM), with non-linear parametric smoothing functions applied to all indicators. Three LMEs were removed prior to the final analysis: the Humboldt Current, West Greenland Shelf, and Antarctic.  The Humboldt Current and West Greenland Shelf LMEs were removed as these are the only two LMEs to show decreases, and may potentially distort the results.  This is due to the fact that the hypotheses being tested are whether or not more intense anthropogenic impacts correlate with increasing jellyfish populations, rather than what might cause a decline in jellyfish.  In addition, the Humboldt Current LME has a disproportionately large catch of small pelagic fish due to harvesting of the Peruvian anchoveta (Engraulis ringens), which could also distort any potential correlations with the overfishing indicators.  The Antarctic LME was removed as it is the only polar region included in the study, and the information on jellyfish populations is dominated by salps, unlike all other LMEs.  Current evidence suggests the observed increase in salps may be due to warming via the loss of sea ice (see Section 2.3.46).  However, the warming calculated by 136  Belkin (2009) for this LME is only 0.11°C, much less than most other LMEs.  As such, this LME can be considered an outlier, and should be considered separately. 3.2 Results The indicators used (see Section 3.1.1) were plotted against the Jellyfish Index (see Section 2.2.1).  Most indicators appeared to be non-linear, and are therefore plotted with a non-parametric smoothing function.  These are presented as pair plots and are separated into indicators of overfishing (Figure 3.1), those that did not include overfishing (Figure 3.2), and the indicator used for cumulative impacts (Figure 3.3). Table 3.2 Impacts and associated indicators tested using a generalized additive model (GAM) Impact Indicator(s) Warming SST change Eutrophication Estimated nitrogen input from fertilizer Overfishing Marine Trophic Index (MTI) Mean maximum length (MML) of all catch Catch intensity of small pelagics Stock status (fraction of stocks overexploited and collapsed) Bottom-trawling Catch from trawling and dredging per unit area of shelf (<200 m depth) Increased substrate and coastal development GDP per capita Aquaculture Mariculture production Cumulative Factors Cumulative impact score per area  137  Figure 3.1 Pair plots of the Jellyfish Index and non-fishing related indicators.  The first row of plots shows the relationship between the Jellyfish Index and each indicator, with the axes transposed in the first column of plots.  The rest of the plots show the relationships between the various indicators.  The units vary according to the indicator being plotted  138  Figure 3.2 Pair plots of the Jellyfish Index and indicators related to fishing.  The first row of plots shows the relationship between the Jellyfish Index and each indicator, with the axes transposed in the first column of plots.  The rest of the plots show the relationships between the various indicators.  The units vary according to the indicator being plotted  139  Figure 3.3 Pair plot of the Jellyfish Index and the indicator for cumulative impacts.  The top plot shows the relationship with the Jellyfish Index on the y-axis, while the bottom plot has the axes transposed  3.2.1 Generalized Additive Model Numerous combinations of indicators were used to create a variety of different generalized additive models (GAM).   Final model selection was based on the Akaike’s Information Criterion (AIC), which is a useful measure of the fit of a model as it will penalize the incorporation of any superfluous variables (Crawley 2007). The final best-fit GAM included four indicators of significance (p < 0.05): stock status, mariculture production, cumulative effects, and GDP (Table 3.3).  The value of R2 in the final model was 71%.   140  Table 3.3 Significant indicators included in final GAM Indicator p value Stock status 0.0019 Mariculture production 0.0062 Cumulative effects 0.0130 GDP 0.0489  The additive effects for the significant covariates are shown in Figures 3.4-3.7.  The shaded regions represent 95% confidence limits.  The success of the model can also be examined by plotting the differences between the predicted values from the model and the observed values from the analysis (Figure 3.8).  141  Figure 3.4 Additive effect of the stock status indicator resulting from the GAM.  The plot illustrates the relationship between the Jellyfish Index and the indicator when other indicators are held constant.  The shaded region represents the 95% confidence interval  142  Figure 3.5 Additive effect of the mariculture indicator resulting from the GAM.  The plot illustrates the relationship between the Jellyfish Index and the indicator when other indicators are held constant.  The shaded region represents the 95% confidence interval  143  Figure 3.6 Additive effect of the cumulative impacts indicator resulting from the GAM.  The plot illustrates the relationship between the Jellyfish Index and the indicator when other indicators are held constant.  The shaded region represents the 95% confidence interval  144  Figure 3.7 Additive effect of the GDP indicator resulting from the GAM.  The plot illustrates the relationship between the Jellyfish Index and the indicator when other indicators are held constant. The shaded region represents the 95% confidence interval  145  Figure 3.8 Plot of the observed Jellyfish Index (from Section 2) versus predicted Jellyfish Index (resulting from the GAM) showing the relative success of the model  3.3 Discussion Significant relationships between the Jellyfish Index and four indicators were incorporated into the final GAM.  This suggests that increasing jellyfish populations are highly influenced by these four factors: stock status, mariculture production, GDP, and cumulative anthropogenic impacts.  The final model was a reasonably good fit at R2 = 71%. As can be seen in the plot of observed versus predicted values (Figure 3.8), the model appears to slightly underestimate the effects at low Jellyfish Indexes, and slightly overestimates them at high Jellyfish Indexes. 146  The relationships between the Jellyfish Index and the significant covariates are clearly non-linear, and appear to contain certain thresholds.  Stock status, defined as the proportion of totals stocks within each LME that are overexploited or collapsed, was the most significant covariate (p = 0.0019).  This suggests that overfishing may be an important factor for determining the fate of jellyfish populations, either through the direct effects of overfishing on jellyfish (see Section 1.5.4), or through more complicated trophic and ecosystem structure interactions (also see Section 1.7). Mariculture production was also a highly significant covariate (p = 0.0062), suggesting that such operations can influence jellyfish abundance through various mechanisms (see Section 1.5.7).  Cumulative anthropogenic impacts and the GDP indicator were also found to be significant covariates, albeit to a lesser extent (p = 0.0130 and 0.0489 respectively).  These results suggest that the observed increases in coastal jellyfish populations around the globe are likely due to unhealthy ecosystems, and that the causes of ecosystem decline are largely anthropogenic in origin.  Thus, it would seem that coastal development and its associated effects on the marine environment are linked to increasing jellyfish populations. Despite the fact that many of the indicators included in the analysis were not found to have significant relationships with the Jellyfish Index, this does not necessarily exclude these impacts as causal factors for increasing jellyfish populations.  First, the LME framework may be too coarse of a spatial scale to account for the localized effects of some anthropogenic impacts.  The sources of many stressors can be highly concentrated, such as near cities, harbours, and estuaries.  Other impacts, such as fishing, may also have highly uneven distributions.  As such, the dynamics of certain regions may not be accurately represented by a single indicator estimated as an average over the entire LME.  To compensate for this, future analyses could evaluate effects at multiple spatial scales. Second, the analysis treats each LME as if it will be affected in the same way, and does not take into account how each system will respond to particular impacts.  For example, an open coastal ecosystem can presumably handle a certain amount of nutrient input without becoming eutrophic, whereas a semi-enclosed sea may have a much lower threshold. Therefore, the effects of anthropogenic impacts may be difficult to isolate, and it is likely 147  that each system will respond differently depending on physical, chemical, and ecological characteristics.  To overcome this effect, the analysis could be expanded to include a vulnerability index for each ecosystem, representing the likelihood of response to each particular impact. In addition, improved indicators could be designed that may better capture the mechanisms being tested.  For example, nitrogen input due to fertilizer use was used as a proxy for eutrophication.  However, the implications of excess fertilizer input may not necessarily result in the effects that are hypothesized to create advantageous conditions for jellyfish.  As discussed, these effects include increased production, hypoxia, and turbidity, as well as potential shifts in phytoplankton community structure.  As such, designing indicators that more accurately capture these effects may help to improve the model. Many of the impacts that appear linked to increasing jellyfish populations started occurring prior to 1950, such as pollution, historical overfishing, and previous habitat destruction.  As such, it is possible that the observed increases in jellyfish in recent decades are a response to a long history of anthropogenic impacts that are not entirely captured by the timescale of indicators used. Similarly, the indicators used do not account for the detailed history of an ecosystem, even since 1950.  For example, the scaling up of a fishery and the resulting demise of a stock can occur within a short timeframe.  Such a stock may even recover, and therefore may not be detected by an indicator that is calculated only for the end of a particular period.  In the case of warmer temperatures, a simple linear regression of SST does not accurately capture shorter periods of warmer waters, whereby a polyp population may increase over a single season or several years (see Section 1.5.2).  As such, examining indicators at more detailed temporal scales may help to better identify significant covariates.  For example, the number of “warm winters” could be examined by comparing annual winter SST to a mean value, thereby helping to identify years when polyp populations may have increased. As mentioned, there was a significant covariation between the Jellyfish Index and the analysis by Halpern et al. (2008), which estimates cumulative anthropogenic impacts.  While this indicator accounts for multiple impacts and the vulnerability of various ecosystem 148  types, it still does not take potential synergistic effects into account.  If such an indicator were to be estimated, the significance of the relationship may improve further. Although there are several improvements that could be made to a second iteration of the model, the observed trends and correlations are convincing.  Indeed, the reasonable success of the model underscores the fact that the widespread increases observed in jellyfish populations are likely due to the continued degradation of the marine environment by human activities.   149  4 Conclusions Jellyfish populations appear to be increasing in the majority of the world’s coastal ecosystems and seas.  While these increases are conspicuous in several locations, even basic knowledge of jellyfish populations is most regions is poor.  As jellyfish populations can have important and profound impacts on human activities and marine ecosystems, it is of paramount importance that we rapidly increase our understanding of these enigmatic creatures. There is evidence to suggest that the observed increases in jellyfish populations are due to anthropogenic impacts on the marine environment.  Indeed, several indicators of such impacts were found to be significant covariates with changing jellyfish populations using a generalized additive model.  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Prometheus Books, Amherst, New York.    187  Appendices Appendix A:  Jellyfish Chronicles CI – Confidence Index; AT – Abundance Trend; TS – Time Score; SS – Space Score; RS – Reliability Score; Invasive Species; Harvested Species; AT - overharvesting LME ID CI AT TS SS RS Country Location Dates Species Main Reference Additional References 1 Very High 1 Very High Very High Very High USA (Alaska) Bering Sea 1975-2009 Primarily Chrysaora melanaster (Brodeur et al. 2008a) (Decker et al. 2009) 2 High 0 Very High Medium Very High Canada & USA Line P, Vancouver Island shelf, Hecate Strait, Salish Sea 1980s-2010 Numerous (data provided by Galbraith 2010) See discussion 2 Medium 1 Low Medium Very High USA (Alaska) Alaskan Peninsula 1973-1996 Scyphozoa (Anderson & Piatt 1999)  2 Medium -Low 0 Low Low Very High USA (Alaska) Prince William Sound 1995-1999 Aurelia labiata (Purcell et al. 2000) (Purcell 2003) 3 High 1 Very High Medium Very High USA California coast (Southern Station) 1951-2006 Ctenophora, Hydromedusae, Salpida, Siphonophora (CalCOFI 2010) (Lavaniegos & Ohman 2003, 2007) 3 High 0 Very High Medium Very High USA California coast (Central Station) 1951-2006 Ctenophora, Hydromedusae, Salpida, Siphonophora (CalCOFI 2010) 3 Medium 1 High Medium Low Mexico Baja California 1998-2007 Numerous (Lavaniegos 2009) 3 High 0 Very High Medium High USA Oregon 1981; 2001/2002 Chrysaora fuscescens (Suchman & Brodeur 2005) (Shenker 1984) 3 Medium -High 1 Very High Low High USA San Francisco Bay Since 1980s and 1990s Numerous invasive spp. (Mills & Rees 2000; Rees & Gershwin 2000) (Mills & Sommer 1995; Greenberg et al. 1996) 3 Medium 1 High Low Medium USA Coos Bay, Oregon Since 1998/1989 Blackfordia virginica (Mills & Rees 2000)  3 Medium 1 Very High Low Medium USA Mission Bay & San Diego Bay Since 1981 Phyllorhiza punctate (Larson & Arneson 1990) (Graham & Bayha 2007) 188  LME ID CI AT TS SS RS Country Location Dates Species Main Reference Additional References 4 Medium -Low 1 Low Low High Mexico Sonora Last several years Stomolophus meleagris (L. Ocampo, CIBNOR, pers. comm., Sept. 2010) (Ocampo et al. 2010) 5 High 0 Very High Medium Very High USA Northern Gulf of Mexico 1985-2006 Aurelia aurita, Chrysaora quinquecirrha (Graham 2001) (W.M. Graham, DISL, pers. comm., May 2010) 5 Medium 1 High Low High USA Florida Keys Since 1990s Cassiopea spp. (Fitt & Costley 1998) (Chiaverano et al. 2010) 5 Medium -High 1 High Medium Medium USA Northern Gulf of Mexico Since 2000 (possibly since 1993) Phyllorhiza punctata (Graham et al. 2003) See Discussion 6 Medium 1 Medium Medium Medium USA North Carolina & Florida Recent years and decade Carybdea marsupialis (Anonymous 2009a) (Anonymous 1998, 2010g) 6 Low 1 Low Low Low Bahamas Abaco Island 2009 Site comparison Cassiopea spp. (Stoner et al. 2011) 6 Medium 1 Medium Medium Medium USA North Carolina & Florida Since 2001 Phyllorhiza punctata (Turritopsis dohrnii & Blackfordia virginica also noted) (Britt 2007; Waymer 2009) (Miglietta & Lessios 2009; USGS 2011) 6 High 0 Medium High Very High USA North Carolina to Georgia 2001-2009 Stomolophus meleagris (Hendrix & Boylan 2010) (SCDNR 2005; Petersen 2011) 7 Very High 1 Very High Very High Very High USA Entire LME 1981-2000 Ctenophora (Link & Ford 2006) 7 Medium -High 1 High Low Very High USA Chesapeake Bay 1987-2000 Mnemiopsis leidyi & Chrysaora quinquecirrha (Blackfordia virginica, Maeotias marginata, & Moerisia lyonsi also noted) (Purcell & Decker 2005) (Cargo & King 1990; Mills & Sommer 1995; USGS 2011; Ruiz et al. 2000) 7 Medium -Low 1 Very High Low Low USA Narragansett Bay 1971-2009 Mnemiopsis leidyi (Sullivan et al. 2001) (Rynearson 2010) 7 Medium 1 High Low High USA Barnegat Bay Since 2000 Chrysaora sp. (Dutzik & O'Malley 2010) (APP 2010; Campbell 2010) 7 Low 1 Low Low Low USA New York State Since 2008 Cyanea capillata (Grossman 2010) (Gaskell 2008; Parry 2008)  189  LME ID CI AT TS SS RS Country Location Dates Species Main Reference Additional References 7 Medium -Low 0 High Low Low Canada Prince-5 Fixed Station 1999-2008 Jelly + Appendicularia (Harrison et al. 2009) (Harrison et al. 2005) 8 High 0 Medium High Very High Canada Numerous transects 2001-2009 Numerous (primarily Aglantha digitale & Diomphyes arctica) (M. Harvey, DFO, pers. comm., Aug. 2010) (Harvey & Devine 2009) 8 Medium -High 0 High Medium High Canada Gulf of St. Lawrence 1995-2008 Primarily Aglantha digitale (Harvey & Devine 2009)  8 Medium -Low 0 High Low Low Canada Halifax-2 fixed station 1999-2008 Jelly + Appendicularia (Harrison et al. 2009)  9 Very High 0 High Very High Very High Canada Widescale sampling 1999-2008 Aglantha digitale & Pelagia noctiluca (Pepin et al. 2009) (P. Pepin, DFO, pers. comm., Dec. 2009; DFO 2008) 10 High 1 Very High Medium High USA Main Hawaiian Islands Since 1980s Carybdea alata (Thomas et al. 2001; Crow et al. 2010)  10 Medium -High 1 Very High Medium Medium USA Main Hawaiian Island Increased distribution since 1950s Cassiopea spp. (Holland et al. 2004; Eldredge & Smith 2001; Devaney & Eldredge 1977) (Daoust 2009; Hofmann & Hadfield 2002; Kelsey 2009) 10 Medium 1 Very High Low Medium USA Oahu, Hawaii Since 1953 Aurelia sp. 4 (Dawson et al. 2005)  10 Medium -Low 1 Very High Low Low USA Kaneohe Bay, Oahu, Hawaii Since 1983 Anomalorhiza shawi (Cooke 1984) (Lum 2001) 11 Medium -Low 1 Low Low High Mexico Lagunas Superior & Inferior, Oaxaca Since at least 1988 Stomolophus meleagris (Ocaña-Luna & Gómez-Aguirre 1999)  11 Medium 1 Very High Low Low Mexico Lagoons of Chantutuo- Pensacola, Chiapas Since at least 1997 Blackfordia virginica (Álvarez-Silva 1999) (Álvarez-Silva et al. 2003) 11 Low 1 Low Low Low Panama Panama Bay Since at least 2006 Turritopsis dohrnii (Miglietta & Lessios 2009)  12 Medium -Low 0 Low Low High Jamaica Lime Cay 1992/1993 & 1985/1986 Numerous (Persad et al. 2003) (Clarke 1988)   190  LME ID CI AT TS SS RS Country Location Dates Species Main Reference Additional References 12 Medium 0 High Low Medium Panama Bocas del Toro Since 2000 Unknown (G. Jacome, STRI, pers. comm., Jan. 2011) (Williams et al. 2001) 12 Medium 1 Very High Low Medium Jamaica Negril Since at least 1973 Phyllorhiza punctata (Cutress 1973) pers. obs. 12 Medium -Low 1 Medium Medium Low Panama Galeta & Bocas del Toro Since at least 2006 Turritopsis dohrnii (Miglietta & Lessios 2009)  13 Very High 0 Very High High Very High Peru From Puerto Pizarro to beyond Mollendo 1972-2010 Chrysaora plocamia (Quiñones et al. 2010b) (Quiñones et al. 2010a) 13 Medium -Low 1 Medium Low Medium Chile Chilean fjord region Since at least 2005 Aurelia aurita (Häussermann et al. 2009)  14 High 1 High Medium Very High Argentina & Uruguay Buenos Aires coast 1993-2006 Obelia longissima (Genzano et al. 2008)  14 Medium 1 High Low Medium Argentina Río de la Plata Since 2000 Blackfordia virginica (Genzano et al. 2006)  15 High 0 Very High Medium High Brazil States of Paraná & Santa Catarina Interviews conducted 2003-2007 Numerous (esp. Lychnorhiza lucerne) (Nagata et al. 2009) 15 Medium -High 0 Very High Medium Medium Brazil States of São Paulo, Paraná, & Santa Catarina History back to 1950s Phyllorhiza punctata (Haddad & Nogueira 2006) (M. Nogueira, UFP, pers. comm., Sept. 2010) 15 Medium 1 Low Medium Medium Brazil Numerous locations Since 2004 in Paranaguá Bay; Since 2007 in other locations Blackfordia virginica (Nogueira & de Oliveira 2006) (Bardi & Marques 2009a) 16 Medium 1 Medium Medium Medium Brazil States of Bahia & Ceará Since 2003 at latest Phyllorhiza punctate (Haddad & Nogueira 2006)  18 Low -1 Low Medium Low Greenland (Denmark) SW Greenland 1956-1982 Aglantha digitale (Pedersen & Smidt 2000)  21 Medium -High 1 Very High Low High Norway Halsafjorden Since 1980s Periphylla periphylla (Sørnes et al. 2007) 21 Low 1 Low Low Medium Norway Trondheimsfjor den Since 2008 Mnemiopsis leidyi (A. Hosia, IMR, pers. comm., Oct. 2010)  191  LME ID CI AT TS SS RS Country Location Dates Species Main Reference Additional References 22 Very High 1 Very High Very High High Numerous Entire LME Since 1980s Likely Aglantha digitale (Licandro et al. 2010; Attrill et al. 2007) (Haddock 2008; Attrill & Edwards 2008) 22 Medium -High 0 Low High Very High Numerous Widescale sampling in half of LME 1971-1986 Numerous (Hay et al. 1990) (Lynam et al. 2004, 2005a) 22 Medium -High 1 Very High Low Very High Netherlands Texel Island Recent decades Numerous (van Walraven et al. 2010)  22 Medium -High 1 Very High Low High Norway Lurefjorden Since 1970s Periphylla periphylla (Fosså 1992) (Sørnes et al. 2007; Youngbluth & Båmstedt 2001) 22 Medium -High 1 Very High Low High Denmark Limfjorden Since 1980s Aurelia aurita (& others) (Riisgård et al. 2011) (Hoffmann 2005; Møller & Riisgård 2007a, 2007b) 22 Medium -Low 0 Very High Low Low Germany Helgoland, German Bight 1975-1993; 1975-2002 Numerous (Greve et al. 2004; Greve et al. 1996) (Greve 1994; Schlüter et al. 2010) 22 Medium -Low -1 Low Low High United Kingdom Thames Estuary Since 1985 (data 1977- 1992) Aurelia aurita (Pleurobrachia pileus is variable) (Attrill & Thomas 1996)  22 Medium -High 1 Medium High Medium Numerous Numerous Since at least 2005 Mnemiopsis leidyi (Oliveira 2007; Tendal et al. 2007) (Boersma et al. 2007; Faasse & Bayha 2006; Hansson 2006; Hosia 2007; Riisgård et al. 2007) 23 High 0 High Medium Very High Sweden & Poland Bornholm Basin 1994-2003 Aurelia aurita & Cyanea capillata (Barz & Hirche 2005; Barz et al. 2006)  23 Medium -Low 0 Low Low Very High Germany Kiel Bight 1978-1993 Aurelia aurita (Schneider & Behrends 1994) (Möller 1979, 1984; Behrends & Schneider 1995; Schneider & Behrends 1998) 192  LME ID CI AT TS SS RS Country Location Dates Species Main Reference Additional References 23 Medium 1 Medium Medium Medium Numerous Southwestern Baltic Since at least 2006 Mnemiopsis leidyi (Javidpour et al. 2006; Kube et al. 2007; Haslob et al. 2007; Tendal et al. 2007) (Gorokhova et al. 2009; Gorokhova & Lehtiniemi 2010) 23 Medium -Low 1 Low High Low Numerous Northern and central Baltic Since at least 2007 Mertensia ovum (Gorokhova et al. 2009) (Anonymous 2009b) 23 Low 1 Low Low Low Estonia Väinameri Archipelago Since at least 1999 Maeotias marginata (Ojaveer & Kotta 2006; Väinölä & Oulasvirta 2001)  24 Very High 1 Very High Very High High Numerous Entire LME 1958-2007 Presumably Pelagia noctiluca (Licandro et al. 2010) (Baxter et al. 2010) 24 Medium -High 1 Very High Low Very High Ireland Irish Sea 1994-2009 Mainly Aurelia aurita & Cyanea spp. (Lynam et al. 2010b) (Gittens 2011) 24 Medium -High 0 Very High Low Very High United Kingdom L4 station 1998-2007 Hydromedusae & siphonophores (WCO 2011) 24 Low 1 Low Low Low United Kingdom Southampton Water Recent years Aurelia sp. & Pleurobrachia sp. (C. Lucas, NOC, pers. comm., Jan. 2011) (Duarte et al. 2011) 25 High 0 High Medium Very High Spain Vigo, Coruña, & Santander stations Mid-1990s- mid-2000s Salps & siphonophores (IEO 2010) (Valdés et al. 2007) 25 Low 1 Low Low Medium Portugal / Spain Guadiana Estuary Since 2008 Blackfordia virginica (Chícharo et al. 2009)  26 Medium -High 0 Very High Low Very High France Villefranche-sur- Mer 1974-2003 Entire medusae & siphonophore communities (García-Comas et al. 2011) (Buecher et al. 1997; Licandro et al. 2001; Molinero et al. 2005; 2008a; 2008b) 26 High 1 Very High High High Numerous Western Mediterranean Since 1990s (records back to 1800s) Pelagia noctiluca (Yahia et al. 2010; Anonymous 2008b, 2010c) (Goy et al. 1989; Molinero et al. 2005; 2008a; Anonymous 2010h; Licandro et al. 2010)   193  LME ID CI AT TS SS RS Country Location Dates Species Main Reference Additional References 26 Medium -High 1 Very High Low Very High Spain Mar Menor Since 1990s Cotylorhiza tuberculata & Rhizostoma pulmo (Pagès 2001; Pérez-Ruzafa et al. 2002) (Conesa & Jiménez- Cárceles 2007; Prieto et al. 2010; Fuentes et al. 2011; Duarte et al. 2011) 26 Medium -High 1 Very High Low Very High Slovenia, Italy, Croatia Gulf of Trieste Since 1960s (records back to 1800s) Aurelia sp. & other scyphomedusae and ctenophores (Kogovšek et al. 2010) (Malej 2001; Di Camillo et al. 2010; Duarte et al. 2011) 26 Medium -High 1 Low Very High High Numerous Numerous Since 1990s; large blooms in 2009 Mnemiopsis leidyi & Beroe ovata (Fuentes et al. 2010) (Boero et al. 2009; Galil et al. 2009a; Galil et al. 2011) 26 High 1 Very High Medium High Israel, Lebanon, Syria, Greece Numerous Since 1970s Rhopilema nomadica, (Phyllorhiza punctata, & Marivagia stellata also noted) (Galil et al. 1990; Lotan et al. 1992; 1994) (Galil et al. 2009b; 2010; Waldoks 2010; Marshall 2010) 26 Medium -Low 1 Low Medium Medium Greece, Spain Numerous Since early 2000s (Greece), 2010 (Spain) Phyllorhiza punctata (Abed-Navandi & Kikinger 2007) (Anonymous 2011a) 26 Medium -Low 1 Low Medium Medium Italy, Spain, France, Malta Numerous Recent years Carybdea marsupialis (CIESM 2008) (Boero & Minelli 1984; Cuneo 2009; Schembri 2010; Bordehore et al. 2011) 26 Low 1 Low Low Low Italy Sicily Channel 2010 Catostylus tagi (Nastasi 2010) 28 Medium 1 High Low High Ghana  2000s Unknown (B. Asiedu, U. Ghana, pers. comm., Jan. 2010) (F.K.E. Nunoo, U. Ghana, pers. comm., Feb. 2010) 29 High 1 Very High High High Namibia Coastal and shelf waters Since 1960s Chrysaora hysocella & Aequorea forskalea (Lynam et al. 2006) (Fearon et al. 1992; Hart & Currie 1960) 29 High 1 Very High Medium High Namibia Coastal and shelf waters Since 1970s Chrysaora hysocella & Aequorea forskalea (Venter 1988) 194  LME ID CI AT TS SS RS Country Location Dates Species Main Reference Additional References 30 Medium -High 0 Very High Low High South Africa KwaZulu-Natal 1975-2010 Unclear (R. van der Elst, ORI, pers. comm., Jan. 2010)  30 Medium -High 0 Medium Medium High Mozambiqu e, Madagascar, Comoros  Unclear (assume min. 5 years)  (van der Elst 2010) 31 Medium -High 0 Medium Medium High Tanzania & Kenya  Unclear (assume min. 5 years)  (van der Elst 2010) 32 High 0 Very High High High India States of Gujarat, Maharashtra, & Karnataka Interviews conducted in 2008 Unclear (G. Pramod, UBC, pers. comm., Sept. 2010) (Pramod 2010) 32 Medium -High 1 Very High Low High India State of Kerala Interviews conducted in 2008 Unclear (G. Pramod, UBC, pers. comm., Sept. 2010) (Pramod 2010; Anonymous 2010b) 32 Medium -High 0 Medium High High Iran Gulf of Oman 1998-2006 Crambionella orsini (Daryanabard & Dawson 2008) (Billett et al. 2006) 32 Medium 1 Medium Low High Numerous Persian Gulf Last 7-10 years Unclear (Erftemeiger & Langenberg 2010) Numerous (see Discussion) 32 Low -1 Low Low Medium Pakistan Kerachi Last 5 years Rhizostoma pulmo and/or Catostylus mosaicus? (Roghay 2011) 34 High 0 Very High High High India States of Tamil Nadu, Andhra Pradesh, Andaman & Nicobar Islands Interviews conducted in 2008 Unclear (G. Pramod, UBC, pers. comm., Sept. 2010) (Pramod 2010) 34 High 1 Very High Medium High India States of Orissa & West Bengal Interviews conducted in 2008 Unclear (G. Pramod, UBC, pers. comm., Sept. 2010) (Pramod 2010) 34 Medium -Low 1 Low Low High India Madras Unclear (recent years or decades) Numerous (incl. Cambionella stulhmanni) (B. Ingole, NIO, pers. comm., Oct. 2010) (James et al. 1985; Rajagopal et al. 1989; Musilamoni et al. 2000)  195  LME ID CI AT TS SS RS Country Location Dates Species Main Reference Additional References 34 Medium -Low -1 Low Low High India Orissa Since 2007 Unknown (Anonymous 2008a) (Anonymous 2007a) 34 Medium 1 Low Medium High Malaysia & Thailand Langkawi, MY & Phuket, TH 2009 & 2010 Unknown; possibly Cephea cephea in Thailand (M.R.B. Idid, IBS, Jan. 2011) (Lau 2010; Morison 2009; Kwang & Yahya 2010) 34 Medium -Low -1 Very High Low Low Thailand Segenting 2005 Catostylus mosaicus (Heng 2005) 35 Low 1 Medium Low Low Thailand Gulf of Thailand beaches Past decade Unknown (likely cubomedusae) (Suntrarachun et al. 2001) (Anonymous 2010a) 36 Medium -High 0 Very High Low Very High Taiwan Tapong Bay Before/after 2002 Aurelia aurita (Lo et al. 2008) 36 Low 1 Low Low Low Philippines Lingayen Gulf December 1999 Unknown (Anonymous 1999)  36 Medium -Low -1 Low Low High Philippines Malampaya Sound 2008 Lobonema spp. (PCAMRD 2008) 40 Medium -Low 1 Very High Low Low Australia Northern Queensland Over last 30- 40 years Cubomedusae (Anonymous 2010d) (Smail 2010) 40 Medium -Low 1 Medium Medium Low Australia Gold Coast Last 5 years Cubomedusae (Donaghey 2009) 41 Medium -High 0 Very High Low High Australia New South Wales 2008-2009 (& 1940s) Thalia democratica (K. Pitt, GU, pers. comm., May 2010) (Strong 2008; Henschke 2009) 41 Medium -High 0 Medium Medium High Australia Around Moreton Bay Since 2002 Catostylus mosaicus (K. Pitt, GU, pers. comm., May 2010)  41 Medium -Low -1 Low Low Very High Australia Myora Drain & Pelican Waters Since 1997 (MD) & 1999 (PW) Cassiopea sp. (K. Pitt, GU, pers. comm., May 2010) (Bouchet 2007; Templeman & Kingsford 2010; Mortillaro et al. 2009) 42 Medium -High 0 Very High Low Very High Australia Port Phillip Bay 1991-2009 Catostylus mosaicus (K. Pitt, GU, pers. comm., May 2010) (Coleman 2004; DPI 2006) 42 Low 1 Medium Low Low Australia Tasmania Since at least 2002 Aurelia sp. polyps (Willcox et al. 2008) (Naidoo 2009) 42 Medium -Low 1 Very High Low Low Australia Port Phillip Bay Since 1970s and 1980s Numerous hydromedusae (Hewitt et al. 2004) 47 Very High 1 Very High High Very High China >Half of LME 1990-2003 (and after) Cyanea spp. & Nemopilema nomurai (Yan et al. 2004) (Dong et al. 2010) 196  LME ID CI AT TS SS RS Country Location Dates Species Main Reference Additional References 47 High 1 Very High Medium Very High South Korea & China Near Jeju Island Since 2003 Nemopilema nomurai (Yoon et al. 2008) (Ding & Cheng 2005; Rahn 2009) 47 Medium -High 1 High Low Very High China Yangtze Estuary Since at least 1997 Numerous (Xian et al. 2005) 47 Medium -Low 1 Very High Low Low Japan Kagoshima Bay Since at least 1993 Aurelia aurita (Miyake et al. 1997) (Miyake et al. 2002) 47 Medium 1 High Low High Korea Busan & Geoje- do Since 2000 Aurelia sp. 1 (Ki et al. 2008) 48 Very High 1 Very High High Very High China & South Korea Western Korea; Bohai Sea; Jiaozhou Bay Since 2003 Nemopilema nomurai (Yoon et al. 2008) (Dong et al. 2010; Sun et al. 2010; Rahn 2009) 48 Medium -High 1 High Medium High China Yellow Sea & Bohai Sea Since 1997 Cyanea nozakii (R. esculentum declined) (Dong et al. 2006) (Dong et al. 2010; Ge & He 2004) 48 Medium 1 High Low High Korea Incheon Since 2000 Aurelia sp. 1 (Ki et al. 2008) (Han & Uye 2010) 49 High 1 Very High Medium Very High Japan NE coast of Honshu Since 2002 (records back to 1920s) Nemopilema nomurai (Uye 2008) (Kawahara et al. 2006) 49 Medium 1 High Medium Low Japan Seto Inland Sea Since 1990s Aurelia sp. (Uye & Ueta 2004) (Nagai 2003; Takahashi et al. 2010; Uye et al. 2003; Kaneda et al. 2007) 49 High 1 Very High Medium High Japan Tokyo Bay & Mikawa Bay Since 1960s and 1970s Aurelia sp. & others (Omori et al. 1995; Nomura & Ishimaru 1998; Toyokawa et al. 2011) (Kinoshita et al. 2006; Kasuya et al. 2000; Arai 2001) 49 Medium -Low -1 Very High Low Low Japan Unclear (see Discussion) Since 1980s Spirocodon saltatrix (Mills 2001) (C. Mills, UW, pers. comm., Oct. 2010; S. Uye, HU, pers. comm., Oct. 2010) 49 Low 1 Medium Low Low Japan Okinawa Island Since at least 2003 Turritopsis dohrnii (Miglietta et al. 2007) (Miglietta & Lessios 2009)  197  LME ID CI AT TS SS RS Country Location Dates Species Main Reference Additional References 50 Very High 1 Very High High Very High Japan Entire west coast of Honshu Since 2002 (records back to 1920s) Nemopilema nomurai (Kawahara et al. 2006; Uye 2008) (Uye 2010; Uye et al. 2010) 50 Low 1 Medium Low Low Japan Wakasa Bay Since 2002 Aurelia sp. 1 (Matsumura et al. 2005)  51 Very High 1 Very High High Very High Russia “Deepwater” area 1984-2006 Coelenterata (Volkov 2008) 51 High 0 Very High High High Russia “Outer shelf” area 1984-2006 Coelenterata (Volkov 2008) 52 Very High 1 Very High Very High Very High Russia Entire LME 1984-2006 Coelenterata (Volkov 2008) (Zavolokin 2010; Il'inskii & Zavolokin 2007) 52 Low 1 Low Low Medium Japan Northern coast of Hokkaido 2009 Nemopilema nomurai (Anonymous 2009d)  53 Very High 1 Very High Very High Very High Russia Entire LME 1984-2006 Aglantha digitale & other Coelenterata (Volkov 2008) (Zavolokin et al. 2008) 60 Very High 0 Very High Very High High Numerous Entire LME 1958-2007 Presumably Aglantha digitale (Licandro et al. 2010)  61 Very High 1 Very High High Very High Antarctica Several disparate locations 1926-2003 Salpa thompsoni (Atkinson et al. 2004) (Perissinotto & Pakhomov 1998; Loeb et al. 1997; Lee et al. 2010) 62 Very High 1 Very High Very High High Numerous Widespread sampling within LME Since 1960s Aurelia aurita (Bat et al. 2009; Mutlu et al. 1994; Oguz & Velikova 2010) (Flint et al. 1989; Gomoiu 1981; Niermann 2004; Shushkina & Vinogradov 1991) 62 Very High 1 Very High Very High Very High Numerous Widespread sampling within LME Since 1988 Mnemiopsis leidyi & Beroe ovata (Shiganova 1998) (Finenko et al. 2001; Shiganova et al. 2004a) 63 Medium -High 0 Low High Very High Canada Large transect in Hudson Bay; also Foxe Basin & Hudson Strait 2003-2006 Aglantha digitale & Aeginopsis laurentii (M. Harvey, DFO, pers. comm., Aug. 2010)   198  Appendix B:  Belief Indexes LME ID LME Name Abundance Trend: Belief Index Native Species Only Invasives Only Overharvest -1 -1 0 +1 0 +1 1 East Bering Sea    50.00 2 Gulf of Alaska   27.34 6.25  ~ 3 California Current   43.75 29.69  23.10 4 Gulf of California    3.13 5 Gulf of Mexico   25.00 6.25  12.50 6 Southeast US Continental Shelf   25.00 7.72  6.25 7 Northeast US Continental Shelf   3.13 60.89  ~ 8 Scotian Shelf   36.43 9 Newfoundland-Labrador Shelf   50.00 10 Insular Pacific-Hawaiian    25.00  20.53 11 Pacific Central-American Coastal    3.13  7.71 12 Caribbean Sea   9.18   9.18 13 Humboldt Current  50.00    3.13 14 Patagonian Shelf    25.00  6.25 15 South Brazil Shelf   25.00  12.50 6.25 16 East Brazil Shelf      6.25 18 West Greenland Shelf  1.56 21 Norwegian Sea    12.50  1.56 22 North Sea  3.13 15.23 66.50  12.50 23 Baltic Sea   27.34   10.60 24 Celtic-Biscay Shelf   12.50 56.93 25 Iberian Coastal   25.00   1.56 26 Mediterranean Sea   12.50 42.58  53.06 28 Guinea Current    6.25 29 Benguela Current    43.75 30 Agulhas Current   23.44 31 Somali Coastal Current   12.50 32 Arabian Sea 3.10  34.38 17.97 34 Bay of Bengal 3.13 3.13 25.00 31.88 35 Gulf of Thailand    1.56 36 South China Sea 3.13  12.50 1.56 40 Northeast Australian Shelf    6.15 41 East Central Australian Shelf 3.13  23.44 42 Southeast Australian Shelf   12.50 1.56  3.13 47 East China Sea     68.21  6.25 48 Yellow Sea    56.25  6.25 49 Kuroshio Current  3.13  47.27  1.56 50 Sea of Japan    50.78 51 Oyashio Current   25.00 50.00 52 Sea of Okhotsk    50.78 53 West Bering Sea    50.00 60 Faroe Plateau   50.00 61 Antarctic    50.00 62 Black Sea    50.00  50.00 63 Hudson Bay   12.50 


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