@prefix vivo: . @prefix edm: . @prefix ns0: . @prefix dcterms: . @prefix skos: . vivo:departmentOrSchool "Science, Faculty of"@en, "Resources, Environment and Sustainability (IRES), Institute for"@en ; edm:dataProvider "DSpace"@en ; ns0:degreeCampus "UBCV"@en ; dcterms:creator "Morato Gomes, Telmo Alexandre Fernandes"@en ; dcterms:issued "2011-02-03T21:59:47Z"@en, "2006"@en ; vivo:relatedDegree "Doctor of Philosophy - PhD"@en ; ns0:degreeGrantor "University of British Columbia"@en ; dcterms:description "This thesis explores some fundamental questions about seamount ecology and fisheries. Initially, I characterized the seamount distribution on the Azores Exclusive Economic Zone using two bathymetry datasets. The algorithm developed was able to map and describe 63 large and 332 small seamount-like features in the EEZ of the Azores. The distribution suggests that large proportion of seamounts occur in chains along the Mid Atlantic Ridge, however, few isolated seamounts are also present in the Azores. In clarifying how seamounts affect primary productivity, I show that primary production enhancement is not sufficient to support often-observed large aggregations of fish. My work supports an alternative hypothesis, that a horizontal flux of prey is the key factor in sustaining rich communities living on seamounts. Additionally, the importance of seamounts to some large pelagic fish, marine mammals and seabirds is also demonstrated. In the case of skipjack and bigeye tuna, common dolphin and Cory's shearwater, these species were significantly more abundant in the vicinity of some seamount summits. I found that seamounts may act as feeding stations for some of these visitors. The methodology developed, however, failed to demonstrate seamounts' association for bottlenose dolphins, spotted dolphin, sperm whale, terns, yellow-legged gull, and loggerhead sea turtles. Fisheries exploitation is a major threat to seamount ecosystems, but I am not presently able to quantify the amount of catches taken from seamounts around the world. Instead, 1 demonstrate that global landings of demersal marine fishes have shifted to deeper water species over the last 50 years, an indirect indication that seamounts have also increased in importance. Moreover, I show that 'deep-water', 'seamount' and 'seamount-aggregating' fish stocks may be at serious risk of depletion, since their life histories render them highly vulnerable to overfishing with little resilience to overexploitation. Finally, ecosystem modelling analyses indicate that sustainable seamount fisheries with tolerable ecosystem impacts can be found only by maximizing an 'ecological' objective function. This suggests that industrial fisheries are not viable on seamounts. However, regulated small-scale artisanal fishing fleets could be sustained by seamount ecosystems."@en ; edm:aggregatedCHO "https://circle.library.ubc.ca/rest/handle/2429/31091?expand=metadata"@en ; skos:note "E C O L O G Y A N D F I S H E R I E S O F S E A M O U N T E C O S Y S T E M S by TELMO Alexandre Fernandes MORATO Gomes M.Sc., Universidade de Coimbra, Portugal, 2001 B.Sc. (Hons), Universidade do Algarve, Portugal, 1995 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR III hi DEGREE OF DOCTOR OF PHILOSOPH Y in THE FACULTY OF GRADUATE STUDIES (Resource Management and Environmental Studies) THE UNIVERSITY OF BRITISH COLUMBIA February 2007 © TELMO Alexandre Fernandes MORATO Gomes, 2006 ABSTRACT This thesis explores some fundamental questions about seamount ecology and fisheries. Initially, I characterized the seamount distribution on the Azores Exclusive Economic Zone using two bathymetry datasets. The algorithm developed was able to map and describe 63 large and 332 small seamount-like features in the EEZ of the Azores. The distribution suggests that large proportion of seamounts occur in chains along the Mid Atlantic Ridge, however, few isolated seamounts are also present in the Azores. In clarifying how seamounts affect primary productivity, I show that primary production enhancement is not sufficient to support often-observed large aggregations of fish. My work supports an alternative hypothesis, that a horizontal flux of prey is the key factor in sustaining rich communities living on seamounts. Additionally, the importance of seamounts to some large pelagic fish, marine mammals and seabirds is also demonstrated. In the case of skipjack and bigeye tuna, common dolphin and Cory's shearwater, these species were significantly more abundant in the vicinity of some seamount summits. I found that seamounts may act as feeding stations for some of these visitors. The methodology developed, however, failed to demonstrate seamounts' association for bottlenose dolphins, spotted dolphin, sperm whale, terns, yellow-legged gull, and loggerhead sea turtles. Fisheries exploitation is a major threat to seamount ecosystems, but I am not presently able to quantify the amount of catches taken from seamounts around the world. Instead, 1 demonstrate that global landings of demersal marine fishes have shifted to deeper water species over the last 50 years, an indirect indication that seamounts have also increased in importance. Moreover, I show that 'deep-water', 'seamount' and 'seamount-aggregating' fish stocks may be at serious risk of depletion, since their life histories render them highly vulnerable to overfishing with little resilience to overexploitation. Finally, ecosystem modelling analyses indicate that sustainable seamount fisheries with tolerable ecosystem impacts can be found only by maximizing an 'ecological' objective function. This suggests that industrial fisheries are not viable on seamounts. However, regulated small-scale artisanal fishing fleets could be sustained by seamount ecosystems. TABLE OF CONTENTS Abstract ii Table of Contents iii List of Tables vi List of Figures viii Acknowledgements xii Dedication xiii Co-authorship statement xiv CHAPTER 1 - Introduction: seamounts as hotspots of marine life 1 1.1 What are seamounts? 1 1.2 Currents and primary production around seamounts 3 1.3 Secondary production on seamounts: zooplankton communities 4 1.4 Seamount benthic communities 6 1.5 Seamount fishes 6 1.6 Seamount visitors: large pelagic fish, cephalopods, marine mammals, and seabirdsl2 1.7 Seamount fisheries and other threats 14 1.8 Thesis theme and objectives 17 1.9 References 20 CHAPTER 2 - Abundance and distribution of seamounts in the Azores 33 2.1 Introduction 33 2.2 Methods 34 2.3 Results 38 2.3.1 Peaks dataset 38 2.3.2 Small and large seamounts dataset 39 2.4 Discussion 45 2.5 References 47 CHAPTER 3 - Modelled effects of primary and secondary production enhancement by seamounts on local fish stocks 49 3.1 Introduction 49 3.2 Methods 53 3.2.1 Modelling approach 53 3.2.2 Generic model for seamounts in the North-Atlantic ...•' 54 3.2.3 Impact of primary production enhancement 61 3.3.4 The \"advection model\" 61 3.3 Results . 63 3.3.1 The \"closed seamount model\". : 63 3.3.2 Impact of primary production enhancement 65 3.3.3 The \"advection model\" 65 3.4 Discussion 68 3.5 References 73 CHAPTER 4 - Testing a seamount effect on aggregating visitors 80 4.1 Introduction 80 4.2 Methods .' 81 4.2.1 Study area .-....:...: 81 4.2.2 Data collection: POPA Observer program 83 4.2.3 Species 83 4.2.4 Data analyses 85 4.3 Results •..\". 86 4.3.1 Tuna ! 86 4.3.2 Other visitors 89 4.3.3 Seamounts 93 4.4 Discussion 95 4.5 References 98 CH APTER 5 - Fishing down the deep 104 5.1 Introduction 104 5.2 Methods 105 5.3 Results 109 5.3.1 Global trends 109 5.3.2 Atlantic Ocean I l l 5.3.3 Pacific Ocean ; I l l 5.3.4 Indian Ocean 116 5.3.5 Antarctic 116 5.3.6 Mean longevity of the catch 116 5.4 Discussion 118 5.5 References 120 CHAPTER 6 - Vulnerability of seamount fish to fishing: fuzzy analysis of life history attributes 124 6.1 Introduction 124 6.2 Methods 125 6.2.1. Compilation of species list 125 6.2.2. Comparisons of biological characteristics and vulnerabilities 125 6.2.3. Responses to fishing 128 6.3 Results 128 6.4 Discussion 134 6.5 References 137 CHAPTER 7 - Ecosystem simulations of management strategies for data-limited seamount fisheries 141 7.1 Introduction 141 7.2 Methods 143 7.2.1. Trophic model of seamount ecosystems 143 7.2.2. Model analyses 144 7.3 Results 146 7.3.1 Optimal fishing scenarios 146 7.3.2 Trade-offs 149 7.4 Discussion 154 7.5 References 156 CHAPTER 8 - Conclusions 160 8.1 Introduction 160 8.2 Main results 161 8.3 Conclusions 166 8.4 Future research 167 APPENDICES 1 Compilation offish species recorded on seamounts 168 2 List of large and small seamounts identified in the Azores EEZ 191 3 Details of the generic seamount model ....205 LISTS OF TABLES Table 1.1 - Examples of literature supporting or opposing the main hypotheses about seamount ecosystem functioning. Note this is not a complete list but some examples only 5 Table 1.2 - List of species considered as \"seamount-aggregating\" fishes 11 Table 3.1 - Input parameters and estimates (in parentheses) from the theoretical \"closed seamount\" (closed) and \"advection\" (adv.) models of a seamount. P/B is production to biomass ratio, Q/B is consumption to biomass ratio, EE is ecotrophic efficiency, and TL is trophic level of the groups. P/B, Q/B, TL and catch were the same in the immigration and no-migration models. Bold numbers show those groups with increased biomass as a result some type of seamount effect 57 Table 3.2 - Diet matrix in weight proportions for a generic seamount model in the Northeast Atlantic. Columns stand for predators while rows stand for prey. Numbers in columns headings represent the predator groups as defined in different rows of the first two columns 58 Table 3.3- Catch (t km\"2 year_i) and total catch (t-year1) estimated for the different fisheries considered in the theoretical seamount. DL is demersal longline; DWL is deep-water longline; SP is small pelagics fishery; T is tuna pole-and-line fishery; SW is-swordfish longline fishery; and DWT is deep-water trawl 60 Table 3.4 - Effect of primary production (PP) enhancement on orange roughy biomass at a generic seamount in the Northeast Atlantic. Estimated primary productivity for the Northeast Atlantic (PPNEA) region was 2 0 3 0 t km\"2 year\"' (SeaWIFS data set) . . . . .66 Table 3.5 - Total immigration rate required (t-km~2-year\"'), estimated as a function of potential biomass of predators as: / = a-Bj+b; where B, is the biomass of the predatory, a is the Q/B ratio of i and b is roughly the total consumption of i in the balanced \"closed seamount model\" ; 66 Table 4.1 - Species included in the present study, the number of observations by species and summary of the data on distance from observed data points to closest seamount (km).... 84 Table 5.1 - Summary of the likelihood ratio test (Hilborn and Mangel, 1997) used to compare the goodness-of-fit of different models where the more complex equation fits better than the simple equation. Where n is the number of parameters estimated in each model; d.f. the degrees of freedom; SS the sum-of-squares. L stands for Linear models, Q for quadratic, LL for the biphase linear-linear models, C for cubic models and F for fourth order models 108 Table 5.2 - Rate of increasing depth of fishing per decade before and after the breaking point (BP) estimated using a two phase model (linear-linear) as described in Hintze (1998). Coefficient of determination (r 2) for regressions also presented 111 Table 6.1 - Number of fish species considered as \"non-seamount fishes\", \"seamount fishes\" and \"seamount-aggregating\" fishes, and number of available estimates of life history attributes and intrinsic vulnerability. Sample sizes for fish species grouped by preferred habitat is also shown. NS are \"non-seamount\" fish species while S are fish occurring on seamounts 127 Table 6.2 - Intrinsic vulnerability weighted by the Log(catch) for \"seamount\" and \"seamount-aggregating\" species reported explicitly in FAO catches ...: 132 LISTS OF FIGURES Figure 1.1 - Number of seamounts large seamount (> 1km height) estimated by various authors and methods, where a) is for the Pacific Ocean, b) for the Atlantic Ocean, c) for the Indian Ocean, and d) for world's oceans. Colours refer to the.methods used: yellow is contour maps; orange is wide-beam soundings; green is multi-beam soundings; and blue is satellite altimetry 2 Figure 2.1 - Height (h) frequency distribution of all identified peaks. Solid circles are actual counts while open circles are the cumulative counts. The grey circle data point was excluded from the exponential model fit. The relationship can be expressed as A^ = 4107.6-e\"2'897', with h in km; r2=0.99. If expressed by unit area (km-2), v(H)= 4.31 •e\"2'89 /l 38 Figure 2.2 - Distribution of seamounts in the Azores Exclusive Economic Zone (black line). Green circles show large seamounts while black dots show small seamount-like features. Scale goes from dark blue (deep water; about 5000m) to dark red (shallow water) 39 Figure 2.3 - Depth of the summit frequency distribution of small (left) and large (right) seamounts-lilce features 40 Figure 2.4 - Distribution of large seamounts in the Azores EEZ by mean depth of summit: a) <250m; b) 250-500; c) 501-1000; d) >1000m. Black dots indicate seamounts of the re ferred category 41 Figure 2.5 - Distribution of small seamount-like features in the Azores EEZ by mean depth of summit: a) <250m; b) 250-500; c) 501-1000; d) >1000m. Black dots indicate seamounts of the referred category 42 Figure 2.6 - Histogram of the predicted height distribution of small (light grey bars) and large (black bars) seamounts-like features. Bin size is 100m 43 Figure 2.7 - Histogram of the seamounts height to radius ratio for small (grey bars) and large (black bars) seamount-like features. Bin size is 0.010 44 Figure 2.8 - Slope angle (tf>) and seamount height (h) linear relationship for small (open circles) and large (filled circles) seamount-like features. For all seamounts the relationship can be expressed as = 0.003-h + 0.86; r2 = 0.53 44 Figure 3.1 - Flow diagram of a seamount ecosystem. The size of the nodes is proportional to the biomass of each group 64 Figure 3.2 - Potential biomass of orange roughy (t-km\"2) that could be sustained by different rates of advected micronekton (t-km\"2-year\"1) and oceanic current velocities (cm s-1). Scale goes from white and light blue (low rates of advection) to dark red (high rates of advection) 68 Figure 4.1 - Map of the Azores archipelago and its seamounts 82 Figure 4.2 - Frequency distribution of the distances to seamount summit of the dataset of tuna fishing events and of a set of an equal number of randomly selected locations. Black bars indicate observed data while light grey bars indicate randomly selected locations 87 Figure 4.3 - Tuna catch per square kilometre per year (±95%CL) in relation to the distance to the nearest seamount summit in the Azores, a) skipjack, b) bigeye tuna. Bin size is 10 km. Light grey circles are those significantly higher (Dunnett test) than the overall mean (light grey line) 88 Figure 4.4 - Marine mammals sightings per square kilometre per hour (±95%CL) in relation to the distance to the nearest seamount summit in the Azores, a) common dolphin, b) bottlenose dolphins, c) spotted dolphin and d) sperm whale. Bin size is 10 km. Light grey circles are those significantly higher (Dunnett test) than the overall mean (light grey line) 90 Figure 4.5 - Loggerhead turtles sightings per square kilometre per hour (±95%CL) in relation to the distance to the nearest seamount summit in the Azores. Bin size is 10 km 91 Figure 4.6 - Seabirds sightings per square kilometre per hour (±95%CL) in relation to the distance to the nearest seamount summit in the Azores, a) Cory's shearwater, b) terns, c) yellow-legged gull. Bin size is 10 km. Light grey circles are those significantly higher (Dunnett test) than the overall mean (light grey line) 92 Figure 4.7 - Catches and sightings (±95%CL) above seamounts of various depths for those species with a significant association with seamounts. a) skipjack, b) bigeye tuna, c) common dolphin, d) Cory's shearwater. Bin size is 10 km. Light grey circles are those significantly higher (Z test) than the overall mean (light grey line)'.;' 94 Figure 5.1 - (a) Global trend of mean depth of world marine fisheries catches from 1950 to 2001 for all marine fishes including pelagics (dark grey dots) and for bottom marine fishes only (light grey squares). Open symbols are estimates for high seas areas only (beyond countries EEZs). Trend lines are fitted using the piecewise-polynomial model linear-linear (Hintze, 1998) or simple linear regression, (b) Time series of world marine bottom fisheries catches by depth strata. Catch in tonnes are logio transformed 110 Figure 5.2 - Trend of mean depth of marine bottom fisheries catches for: (a) North Atlantic; (b) Central Atlantic; (c) South Atlantic; (d) North Pacific; (e) Central Pacific; (f) South Pacific; (g) the Indian Ocean; and (h) Antarctic. Trend lines are fitted using the piecewise-polynomial model linear-linear (Hintze, 1998) or simple linear regression 113 Figure 5.3 - Time series of marine bottom fisheries catches by depth strata for: (a) North Atlantic; (b) Central Atlantic; (c) South Atlantic; (d) North Pacific; (e) Central Pacific; (f) South Pacific; (g) the Indian Ocean; and (h) Antarctic. Catch in tonnes are loge transformed 114 Figure 5.4 - (a) Global trend of mean fish longevity of the catches for all marine fishes including pelagics (dark grey dots), and for bottom marine fishes only (light grey squares), (b) Global trend of mean longevity of the 2001 world bottom marine fisheries catch by depth. Line is the least squares fit through points by using the logarithmic equation (r2 = 0.75). Mean age at maturity shows a similar pattern 117 Figure 6.1 - Comparison of some life-history characteristics of \"non-seamount\" fish species (NS), fish occurring on seamounts (S), and \"seamount-aggregating\" species (AGG). a) longevity (Jiviax); b) age at maturity (T m); c) natural mortality (M); d) Von-Bertalanffy growth parameter (K). In the graphs the middle point is the mean, the box is the Mean ± S.E., and the whisker is the Mean ± 95% CL 129 Figure 6.2 - Intrinsic vulnerability (Vi) index for fish species no-occurring on seamounts (NS), occurring on seamounts (S), and \"seamount-aggregating\" species (AGG). a) including all fish species; b) for species reported in FAO official landing statistics. In the graphs the middle point is the median, the box the 25%-75% percentiles, and the whisker is the range 130 Figure 6.3 - Intrinsic vulnerability (Fi) index for fish species of different habitats no-occurring on seamounts (NS), occurring on seamounts (S). Vulnerability for \"seamount-aggregating\" species (AGG) also presented. In the graphs the middle point is the Median, the box the 25%-75% percentiles, and the whisker is the range. ** indicates significant differences between medians (Mann-Whitney test; Pelagic: p= 0.471; Demersal: p= 0.003; Reef-Associated: p= 0.076; Benthopelagic: p= 0.001; Bathypelagic: p= 0.806; Bathydemersal: p= 0.833) 132 Figure 6.4 - Proportion of biomass change over time for fish groups with different intrinsic vulnerabilities (V\\). Biomass change was estimated by a generic seamount ecosystem model (Morato and Pitcher, 2002) and simulating the effect of a 0.3 fishing mortality rate for each group over a 20 years period 133 Figure 7.1 - Optimized fishing rates (F), expressed as proportions of the base model, obtained by maximizing 'economy' and 'ecology' objectives. Note differences in scale 146 Figure 7.2 - Changes in group biomasses (percent change of biomass from base model) under the three different fishing scenarios: no fishing, maximizing the 'ecology' objective, and maximizing the 'economy' objective 147 Figure 7.3 - Catches (t-km\"2 y\"') for the different fishing fleets under the base model and two fishing scenarios: maximizing the 'ecology' objective function, and maximizing the 'economy' objective function 148 Figure 7.4 - Value of the catches (relative value) for the different fishing fleets under the base model and two fishing scenarios: maximizing the 'ecology' objective, and maximizing the 'economy' objective 149 Figure 7.5 - Surface plots showing optimal scenario solutions for a range of weightings of ecological and economic objective functions: a) performance of 'economy' objective, maximizing net economic value; b) performance of'social' objective, maximizing number of jobs; c) performance of 'ecology' objective, maximizing ecological 'stability' of the ecosystem. Scale goes from light blue (low performance) to dark red (high performance). Smooth surface is interpolated 151 Figure 7.6 - Surface plots from EwE model showing the resulting fishing rates, as proportion of base model rates, of the optimal scenario solutions for a range of weightings of ecological and economic objective functions: a) deepwater longline; b) deepwater trawl; c) pelagic longline; d) small pelagic fishery; e) tuna fishery; f) demersal (bottom) longline. Scale goes from light blue (low proportion of base model rates) to dark red (high proportion of base model rates) 152 Figure 7.7 - Surface plots showing the resulting ecosystem indicators and total fisheries catch for the optimal scenario solutions for a range of weightings of ecological and economic objective functions: a) total catch; b) biodiversity index (Q75), please note that this figures is shown from a different viewpoint; c) mean trophic level of the catch. Scale goes from light blue (low) to dark red (high) 153 ACKNOWLEDGEMENTS I acknowledge the financial support received from the \"Fundaipao para a Ciencia e Tecnologia\" (Portugal, BD/4773/2001) and European Social Fund -through the Third Framework Programme. I am very grateful to my supervisors Tony J. Pitcher and Ricardo S. Santos for providing extremely valuable guidance, advices, ideas and support. Also to my supervisory committee members Daniel Pauly, Amanda Vincent and Les Lavkulich who provided extremely helpful assistance, discussions and ideas. I also wish to acknowledge the contribution of other Fisheries Centre and RMES members for providing tremendous help, namely Reg Watson, Maria (Deng) Palomares, Hadi Dowlatabadi, Sylvie Guenette, Adrian Kitchingman and Sherman Lai. A special thank to several people from the Department of Oceanography and Fisheries of the University of the Azores, for help and support during my entire career. Many thanks to Janice Doyle, Ann Tautz, Gerard O'doherty and Rosalie Casison for making my life much easier by dealing with extensive paper work and by solving many computer problems. Finally, 1 have to thank several anonymous reviewers of the submitted papers for effective editorial work and many helpful comments I was very lucky to be part of a group of very enthusiastic fellow students and other FC members. I have to thank all of them for the support given during my stay in Vancouver and for the discussions we had during those times. Xll DEDICATION I'm delighted and excited having the opportunity to dedicate this work to my grandparents Joaquim and Margarida for everything what they mean to me and for what this moment represents for them. I also want to dedicate it to Patricia and little Matilde, with sincere apologies for the time I have been absent. And to my parents Manuel and Odete and brother Filipe, with no further words. Let me share with the non-Portuguese speaking readers a poem from Fernando Pessoa (1888-1935). \"E com emogcio que cledico este trabalho ao meu avo Joaquim e avo Margarida, por tudo o que eles significant para mini e pelo que este momento representa para eles. Uma dedicagdo muito especial a Patricia e a pequeninct Matilde, com um pedido sincero cle desculpa pelo tempo que estive ausente. E aos mens pais Manuel e Odete e ao irmao Filipe, sent mais palavras.\" Fernando Pessoa Mar portugues O mar salgado, quanta do teu sal Sao lagrimas de Portugal! Por te cruzarmos, quantas maes choraram, Quantos filhos em vao rezaram! Quantas noivas ficaram por casar Para que fosses nosso, o mar! Valeu a pena? Tudo vale a pena Se a alma nao e pequena. Quern querer passar alem do Bojador Tern que passar alem da dor. Deus ao mar o perigo e o abismo deu, Mas nele e que espelhou o ceu. Fernando Pessoa Portuguese Sea Salt-laden sea, how much of all your salt Is tears of Portugal! For us to cross you, how many sons have kept Vigil in vain, and.mothers wept! Lived as old maids how many brides-to-be Till death, that you might be ours, sea! Was it worth? It is worth while, all, If the soul is not small. Whoever means to sail beyond the Cape Must double sorrow - no escape. Peril and abyss has God to the sea given And yet made it the mirror of heaven Translated into English by J.Griffin Xlll CO-AUTHORSHIP STATEMENT CHAPTER 2 Morato, T., M. Machete, A. Kitchingman, F. Tempera, S. Lai, G. Menezes, R.S. Santos and T.J. Pitcher (submitted) Abundance and distribution of seamounts in the Azores. Marine Ecology Progress Series. Co-authorship statement- my contribution to this paper was: 1) identification of research program, 2) performing the research, 3) data analyses, 4) manuscript preparation CHAPTER 3 Morato, T., C. Bulman and T.J. Pitcher (submitted) Impact of primary production enhancement by seamounts on local fish stocks. Deep-Sea Research II. Co-authorship statement- my contribution to this paper was: 1) identification and design of research program, 2) performing the research, 3) data analyses, 4) manuscript preparation CHAPTER 4 8- Morato, T., D.A. Varkey, C. Damaso, M. Machete, M. Santos, R. Prieto, R.S. Santos and T.J. Pitcher (submitted) Testing a seamount effect on aggregating visitors. Marine Ecology Progress Series. Co-authorship statement- my contribution to this paper was: 1) identification and design of research program, 2) performing the research, 3) data analyses, 4) manuscript preparation CHAPTER 5 9- Morato, T., R. Watson, T.J. Pitcher and D. Pauly (2006) Fishing down the deep. Fish and Fisheries 7: 24-34. Co-authorship statement- my contribution to this paper was: 1) identification and design of research program, 2) performing the research, 3) data analyses, 4) manuscript preparation CHAPTER 6 Morato, T., W.W.L. Cheung and T.J. Pitcher (2006) Vulnerability of seamount fish to fishing: fuzzy analysis of life history attributes. Journal of Fish Biology 68(1): 209-221. Co-authorship statement- my contribution to this paper was: 1) performing the research, 2) data analyses, 3) manuscript preparation CHAPTER 7 Morato, T. and T.J. Pitcher (2005) Ecosystem simulations in support of management of data-limited seamount fisheries. In: G.H. Kruse, V.F. Gallucci, D.E. Hay, R.I. Perry, R.M. Peterman, T.C. Shirley, P.D. Spencer, B. Wilson and D. Woodby (eds.) Fisheries assessment and management, in data-limited situations. Alaska Sea Grant, University of Alaska Fairbanks, Lowell Wakefield Fisheries Symposium Series 21, pp. 467-486. Co-authorship statement- my contribution to this paper was: 1) identification and design of research program, 2) performing the research, 3) data analyses, 4) manuscript preparation C H A P T E R 1 INTRODUCTION: SEAMOUNTS AS HOTSPOTS OF MARINE LIFE 1 1.1 WHAT ARE SEAMOUNTS? Seamounts are undersea mountains (usually of volcanic origin) rising from the seafloor and peaking below sea level (Epp and Smoot, 1989). Underwater mountains of heights above 1000 m are considered to be seamounts, those between 500-1000 m as knolls, and those below 500 m as hills (Rogers, 1994). Typically, seamounts are formed by volcanic activity over hotspots in the earth's crust (Epp and Smoot, 1989). Spreading of the sea floor away from these hotspots via plate tectonic movements means that seamounts are often arranged in long chains or clusters that radiate out from such spreading zones (Menard and Dietz, 1951; Menard, 1964). Seamounts can have slopes of up to 60°, much greater than anywhere else in the deep sea, and are often much younger than the surrounding sea floor. A seamount tall enough to break the sea surface is called an oceanic island, e.g., the islands of Hawaii, the Azores and Bermuda were all underwater seamounts at some point in the past. : y • Though most people may be unaware of it, underwater seamounts are fairly common. Estimates vary largely (Figure 1.1), but studies suggest that there may be from 1.9 to 130 thousand large seamounts, those of heights over 1000 m, in the Pacific Ocean (Viau and Cailleux, 1971; Cailleux, 1975; Jordan et al., 1983; Smith and Jordan, 1988; Abers et ai, 1988; Wessel and Lyons, 1997), from 1 to 2.8 thousand the Atlantic Ocean (Viau and Cailleux, 1971; Litvin and Rudenko, 1972; Smith and Cann, 1990) and from 500 to 900 large seamounts in the Indian Ocean (Viau and Cailleux, 1971; Cailleux, 1975). 1 A version of this chapter has been published. Morato, T. 2003. Seamounts - hotspots of marine life. ICES Newsletter 40: 4-6. http://www.ices.dk/marineworld/seamounts.asp 1,900 (Viau and Caileux. 1971) 2,400 (Caileux, 1975) •32;O0Oi (Smithand Jordan: 1988) 8,800:(Wessel lind Uyohs»l997) 9;200:(Kilchingman and Lai. 2004) 10.000 (Menard, 1959) I K 33,000 (Jordanefa/S\"l983) 70;000: (WesseUnaiiyoBsSl 997) 130,000 (.J/it'i.? el al liM) y ; 1,000 10,000 1,000 (Viau and Cailleux. 1971) i 1,000 (Utviri aral:Riidenkfi;»1972) 2,800 (Smi(h and Cann>!99o) | '3v300:(Rjtehingman and Lai: 2004) 100,000 1,000,000 ,000 i500:';(Cailleiix, 1975) 900 (Viau and Cailleux, 197l) 10,000 100,000 I :1 ;900;(Kilchingman and l,ai, 2004) 1,000 10,000 3,800 (Viau and Cailleux. 1971) 5,500s(Cailleiix, 1975) 8,600i(Gniig and Sandwell:4988) andl.ai. 2004) 15 ,000 (Wessel 2001) 100,000 k 100,000 (Wcssel,200!) 1,000 1,000,000 ,000,000 10,000 100,000 Numbers of large seamount (> 1km height) Figure 1.1 - Number of seamounts large seamount (> 1km height) estimated by various authors and methods, where a) is for the Pacific Ocean, b) for the Atlantic Ocean, c) for the Indian Ocean, and d) for world's oceans. Colours refer to the methods used: yellow is contour maps; orange is wide-beam soundings; green is multi-beam soundings; and blue is satellite altimetry. 1,000,000 The world's number of large seamounts is still unknown, but estimates vary from 3.8 to over 100 thousand (Viau and Cailleux, 1971 Cailleux, 1975; Craig and Sandwell, 1988; Marova, 2000; Wessel, 2001). Global seamount datasets containing information on world's seamount positions are rare, and most larger datasets often only contain data for single oceans (e.g., Fornari et al., 1987; Smith and Jordan, 1988; Epp and Smoot, 1989; Smith and Cann, 1990; Wessel and Lyons, 1997). Scientific knowledge on seamounts is still very poor and fundamental questions such as how many are out there are still hard to answer. In fact, only a small fraction of the world's seamounts have actually been mapped bathymetrically (Wessel and Lyons, 1997). 1.2 CURRENTS AND PRIMARY PRODUCTION AROUND SEAMOUNTS Seamounts are said to be hotspots of marine life in the relatively empty open ocean (Rogers, 1994). They tend to enhance water currents (Genin et al., 1986; Boehlert, 1988) and can have their own localized tides, eddies and upwellings (Lueck and Mudge, 1997) where cold deepwater moves up from the deep along the steep sides of the seamount. These patterns may enhance primary production (PP) over and around seamounts due either to uplifting of isotherms into the euphotic zone and introducing biogenes into nutrient-poor water (Genin and Boehlert, 1985; Dower et al., 1992; Odate and Furuya, 1998; Mourino et al., 2001), or to stabilization of the water column above the seamount, maintaining phytoplankton cells in a suitable light regime, promoting the growth of diatoms, and increasing growth rates and PP (Comeau et al., 1995). For example, Mourino et al. (2001) showed that local increase in chlorophyll a, enhanced carbon incorporation rates and changes in phytoplankton species composition were associated with a seamount. Although some investigations have failed to demonstrate persistent high chlorophyll a patches over seamounts (Pelaez and McGowan, 1986), Mourino et al. (2001) demonstrated that production enhancement effects were subjected to a large degree of spatial and temporal variability both at seasonal and shorter time scales. The effects of seasonality on upwelling may be partially responsible for the large variation in the results and conclusions of seamount studies. Nevertheless, the hypothesis that seamounts enhance PP is not yet well tested (Table 1.1) and a general procedure to test this hypothesis is still lacking. 1.3 SECONDARY PRODUCTION ON SEAMOUNTS: ZOOPLANKTON COMMUNITIES The biomass of zooplankton is often high over seamounts, but, as for PP, evidence concerning zooplankton features over seamounts is conflicting (Table 1.1). Huskin et al. (2001) concluded that mesoscale structures (such as seamounts) influence zooplankton distribution and abundance, although more detailed temporal and spatial studies were said to be required to determine the real influence of them. Fedosova (1974) reported increases in zooplankton abundances over seamounts of 2 to 8 fold, while Huskin et al. (2001) reported a 1.6 fold increase. On the other hand, an absence of zooplankton above seamounts due to grazing or other effects were detected in several studies (Genin et al., 1994; Haury et al., 2000), while other studies reported no differences in zooplankton biomass either on or off seamounts (Voronina and Timonin, 1986; Dower and Mackas, 1996). Sime-Ngando et al. (1992) reported an increase of ciliate biomass (micro-zooplankton) over seamounts, probably related to seamount-induced physical forcing, which likely generates microhabitats favourable to the growth of opportunistic or physiologically-adapted populations. Regarding pelagic crustaceans over and around seamounts, two main features have been observed (Vereshchaka, 1994): 1) the rise of lines of equal size, abundances and biomass of the pelagic animals, and 2) the decrease in abundance, biomass and sizes of pelagic animals near the bottom water layer. One of the possible important causes of the decrease in abundance and biomass of pelagic shrimps near the bottom is that they are consumed by benthic and benthopelagic predators. Vereshchaka (1996) concluded that the abundance of pelagic animals decreases while the concentration of benthopelagic predators increases near the seafloor and the role of the former in planktonic communities falls in the near-bottom layer. Table 1.1 - Examples of literature supporting or opposing the main hypotheses about seamount ecosystem functioning. Note this is not a complete list but some examples only. Hypotheses Supporting Opposing Judgment evidence evidence Enclosed circulation cells around seamounts 17, 22, 32, 34, 52 35 Tested and (Taylor columns) supported by data Increased phytoplankton biomass and 7, 19,26, 40, 48 10, 54 Not fully tested primary productivity Increased zooplankton biomass (micro and 4, 5, 20, 35, 37, 47, 11, 14, 24, 29, 42 Not fully tested meso) 53,55,58 Increased fish larvae biomass 46 21, 56 Not fully tested Increased micronekton biomass 13, 31 25,28, 57 Not fully tested Increased demersal and pelagic fish biomass 6 Tested and supported by data Tuna aggregations 18,41,43,45 Not fully tested Swordfish aggregations 49 Not tested Increased occurrence of sharks 16, 38 Not tested Increased occurrence of cephalopods 52 Not fully tested Increased occurrence of marine mammals 22 Not tested Increased occurrence of seabirds 27, 30 Not tested Increased occurrence of sea-turtles 60 Not tested Increased occurrence of corals and other 9, 15 15 Not fully tested epibenthic megafauna High endemism 12,36,39, 44,61 50,59 Not fully tested Increased demersal and pelagic fish biomass are supported by: bottom trapping of migrating zooplankton 3,24,31,33,51 . Not fully tested horizontal flux of non-migrating 8,9,33 • Not fully tested . zooplankton v.. locally enhanced primary production 1, 2, 6 Not fully tested 1) Uda and Ishino, 1958; 2) Hubbs, 1959; 3) Isaacs and Schwartzlose, 1965; 4) Simpson and Heydorn, 1965; 5) Fedosova, 1974; 6) Uchida and Tagami, 1984; 7) Genin and Boehlert, 1985; 8) Tseitlin, 1985; 9) Genin et al., 1986; 10) Pelaez and McGowan, 1986; 11) Voronina and Timonin, 1986; 12) Wilson and Kaufman, 1987; 13) Bohelert, 1988; 14) Genin et al., 1988; 15) Kaufman et al., 1989; 16) Klimley et al., 1988; 17) Brink, 1990; 18) Fonteneau, 1991; 19) Dower et al., 1992; 20) Sime-Ngando et al., 1992; 21) Boehlert and Mundy, 1993; 22) Reeves and Mitchell, 1993; 23) Freeland, 1994; 24) Genin et al., 1994; 25) Vereshchaka, 1994; 26) Comeau et al., 1995; 27) Haney et al., 1995; 28) Vereshchaka, 1995; 29) Dower and Mackas, 1996; 30) Monteiro e/ al., 1996; 31) Vereshchaka, 1996; 32) Goldner and Chapman, 1997; 33) Koslow, 1997; 34) Lueck and Mudge, 1997; 35) Mullimeaux and Mills, 1997; 36) Parin et al., 1997; 37) Saltzman and Wishner, 1997; 38) Hazin et al., 1998; 39) Koslow and Gowlett-Holmes, 1998; 40) Odate and Furuya, 1998; 41) Holland et al., 1999; 42) Haury et al., 2000; 43) Itano and Holland, 2000; 44) Richer de Forges et al., 2000; 45) Sibert et al., 2000; 46) Dower and Perry, 2001; 47) Huskin et al., 2001; 48) Mourino et al., 2001; 49) Sedberry and Loefer, 2001; 50) Fock et al., 2002a; 51) Fock et al., 2002b; 52) Diekmann and Piatkowski, 2004; 53) Fock et al., 2004; 54) Genin, 2004; 55) Martin and Nellen, 2004; 56) Nellen and Ruseler, 2004; 57) Pusch et al., 2004; 58) Schnack-Schiel and Henmng, 2004; 59) Tracey et al., 2004; 60) Dellinger, 2005; 61) Vereshchaka, 2005. Another example of high zooplankton abundance over seamounts was reported by Dower and Perry (2001) who found a high abundance of larval rockfish over Cobb Seamount (SW of Vancouver Island, Canada). They suggested that a persistent clockwise eddy, consistent with a stratified Taylor cone, plays a critical role in retaining larval rockfish and may contribute to the process of self-recruitment. 1.4 SEAMOUNT BENTHIC COMMUNITIES On the seamount floor there are often rich communities dominated by suspension feeders, e.g., gorgonians and other corals (Richer de Forges et al., 2000; Ohkushi and Natori, 2001; Koslow et al., 2001), that may be particularly susceptible and sensitive to disturbance by trawling (Probert et al., 1997; Koslow et al., 2001). Enhanced currents and steep slopes expose the volcanic rocks and favour the growth of suspension feeders in these benthic seamount communities (Genin et al., 1986; Grigg et al., 1987; Wilson and Kaufmann, 1987; Rogers, 1994), in contrast to the deposit feeders typical of most deep-sea benthos. The abundance and biomass of benthic organisms on some seamounts was, however, observed to be very low when compared to other hard bottom habitats at similar depths (Grigg et al., 1987; Gillet and Dauvin, 2000). Though the diversity and exceptionally localized distribution of species living in these communities are acknowledged (Richer de Forges et al., 2000), their biology and life history remain poorly studied, except for some indications that some of these species may be extremely long-lived, e.g., up to maximum ages of over 100 years (Grigg, 1993). 1.5 SEAMOUNT FISHES Seamounts have received much attention mainly because of the presence of substantial aggregations of forage fishes in mid- and deep-water (Boehlert and Sasaki, 1988; Rogers, 1994; Koslow, 1996, 1997; Koslow et al., 2000), which became the prime target of a highly technological fishery. What are \"seamount fishes\"? This is a simple question, yet the answer remains elusive. The designation of \"seamount fishes\" or seamount species has been widely employed (e.g. Koslow, 1996; Probert et al., 1997; Probert, 1999; Koslow et al., 2000; Fock et al., 2002a; Tracey et al., 2004), but the criteria used in identifying those taxa are rarely defined. Pioneering work on seamounts focused on the intriguing question: what species inhabit individual banks and seamounts? Since then, a large number of studies have described the fish fauna inhabiting these features. The results -of early studies have been summarised in a number of reviews (e.g., Wilson and Kaufman, 1987; Rogers, 1994; Froese and Sampang, 2004). The question is how can we appropriately classify those fishes that live in association with seamounts from those more typical of other habitats, or that span both. Most species appear to occupy a range of habitats. Many fish species occur on seamounts or congregate over their summits to feed due to enhanced levels of planktonic production, hydrographic retention mechanisms such as eddies, or being able to remain close to the bottom yet reach shallower depths (Tseytlin, 1985; Genin et al., 1988; Koslow, 1997). This may be the case for some commercially important species of deepwater fish, such as orange roughy, Hoplostethus atlanticus, pelagic armorhead, Pseudopentaceros wheeleri, oreosmatids, e.g. Allocyttus niger and Pseudocyttus maculatus, and alfonsinos, Beryx spp., as well as for some sharks (Klimley et al., 1988; Hazin et al., 1998), tunas (Holland et al., 1999; Itano and Holland, 2000; Sibert et al., 2000) and other large pelagic predators (Ward et al., 2000; Sedberry and Loefer, 2001). A range of fish species sporadically aggregate around shallow seamounts mainly for spawning; for instance, reef-associated fish like serranids (Mycteroperca rosacea, Paranthias colonus) and jacks (Caranx sexfasciatus, Seriola lalandi) (Sala et al., 2003). Recently, Tsukamoto et al. (2003) found that the spawning site of the Japanese eel (Anguilla japonica) in the western North Pacific appears to be near three seamounts, 2000-3000 km away from their freshwater habitats. Further examples are the deep-bodied species of the orders Zeiformes (mainly the genera Antigonia, Capros, Zenopsis and Cyttopsis) and Syngnathiformes (in particular the genus, Macroramphosus), which are. the dominant fishes (<500 m depth) of the Great Meteor seamount, a large, isolated, flat-topped feature in the central eastern Atlantic (Fock et al., 2002a). These fish are also the main prey of large demersal predators inhabiting the slopes of the Azores islands and seamounts (Morato et al., 1999, 2000, 2001, 2003). However, as well as occurring on seamount features, in some areas they are among the most abundant fishes from adjacent continental shelves. Coral reef scientists faced exactly the same problem when trying to provide a definition of \"reef fishes\" (see Choat and Bellwood, 1991; Bellwood, 1996; Bellwood, 1998; Robertson, 1998). They first tried to find potential taxonomic and ecological characteristics that could distinguish coral fish assemblages from other fish assemblages (Choat and Bellwood, 1991). They also proposed a consensus list of fish families that would better describe, not define, a coral reef assemblage (Bellwood, 1996). They concluded from this list that most reef fishes are characteristic of, but not restricted to, coral reefs (Bellwood, 1996). Coral reef scientists are still debating the meaning of reef fishes, and apparently they can only agree on the tautological definition of reef fishes: those that live on coral reefs. The definition of \"seamount fishes\" may be similar and involve the same redundancy with trying to define a functional type of label that applies only in part to the ecology of the species: seamount fishes are those individual fishes that live on seamounts. There is a group of fish species, however, living on (or visiting) seamounts that have raised much attention because of their high abundance and good flesh quality: they include orange roughy, pelagic armorhead and alfonsinos. These fish aggregate on top and around seamounts and have been object of intense exploitation since the late 1970's. The discovery of these commercially important aggregations of deepwater fish species on seamount features have changed the idea that significant commercial fisheries would never develop in the deep sea due to scarcity at those depths and poor palatability of fish flesh. Koslow (1996, 1997) explored the differences of fish species aggregating on seamounts from those generally considered typical of the deep-sea environment. By addressing this problem in an energetic perspective (see below), he concluded that fish species that aggregate around seamounts appeared to form a distinct guild. He found that these fishes differ markedly from other deep-water species in their relatively high levels of food consumption and energy expenditure, low growth and productivity and a robust body composition and body plan suited for strong swimming currents. Koslow called them \"seamount-associated fishes\" or \"seamount-aggregating fishes\" (Koslow, 1996; Koslow et al., 2000). For the propose of this thesis we will consider: 1) \"seamount fishes\" as those individual fishes that live on seamounts; and 2) \"seamount-aggregating fishes\" as those species that form large aggregations around these features and that are the main target for fisheries that develop around seamounts. Numerous studies have described the species richness and diversity of fish fauna on seamounts. Wilson and Kaufman (1987) reviewed seamount biota worldwide and reported about 450 fishes collected from more than 60 seamounts. Rogers (1994) provided a list of 77 commercial species fished on seamounts. Since then, more detailed studies of certain seamounts and seamount chains provide more comprehensive species lists, especially with an increase in exploratory fishing in the last two decades (e.g., Parin et al., 1997; Koslow and Gowlett-Holmes, 1998; Grandperrin et al., 1999; Fock et al., 2002a; Moore et al., 2003; Clark and Roberts, 2003; Tracey et al., 2004). Froese and Sampang (2004) compiled a list of fish that have been reported on seamounts, and found 535 fish species recognized as seamount fishes. Based on the best available information, I collated species lists for fishes that occur or aggregate in and around seamounts. \"Seamount fishes\" are defined as fish that have been reported as occurring on seamounts, even if rare. A total of 798 species of marine fishes were classified as \"seamount fishes\" (see Appendix 1). I now have the most comprehensive checklist of seamount fishes, even if incomplete. The number of known seamount fishes represents about 2.8% of the total number of known fish species and belong to 165 families (32% of the 515 known). Although the number of known seamount fishes is comparatively small, because they encompass a third of fish families, about half of the orders and many unique adaptations, they represent a relatively large and unique portion of fish biodiversity (Froese and Sampang, 2004). Currently recognized seamount fishes have different habitat preferences (associations). Forty-three species are pelagic, 94 are reef-associated, 118 demersal, 68 benthopelagic, 223 bathypelagic, and 252 bathypelagic. A large portion of the seamount fish community is composed by deep-sea fishes, but many shallow water species are also known to occur on these features. According to Froese and Sampamg (2004), only 6 seamount fishes are included in the 2000 IUCN Red List (Hilton-Taylor, 2000): Sebastes paucispinis is listed as 'critically endangered', Sphoeroides pachygaster and paucipinis are listed as 'vulnerable', and Squalus acanthias, Dalatias licha and Prionace glauca are listed as 'lower risk, near threatened.' Other seamount fishes have not been evaluated so far. The second category of fish species living on seamounts considered for the purpose of this chapter is the \"seamount-aggregating fish\". A list of 23 fishes was compiled (Table 1.2). I acknowledge that this list is preliminary and its completeness and accuracy will improve as we gain more knowledge about the ecology of seamount and deepwater fish species. Some of the most well known representatives of this group include the deep-water fishes: orange roughy, alfosinos (Beryx splendens and B. decadactylus), Patagonian toothfish {Dissostichus eleginoides), oreos, pelagic armourhead, several species of rockfishes (Sebastes spp.) (Koslow, 1996; Koslow et al., 2000) and probably roundnose grenadier (Coryphaenoides rupestris) (Vinnichenko, 2002a). These species are the main target of the large-scale fisheries that occurs on top and around seamounts. These fish aggregations are supported in the otherwise food-poor deep sea by the enhanced flux of prey organisms past the seamounts and the interception and trapping of vertical migrators by the uplifted topography (Tseytlin, 1985; Genin et al., 1988; Koslow, 1997). It has long been held, however, that the high biomass of fish on seamounts result, at least in part, from locally enhanced primary production and the subsequent bottom-up transfer of this energy to higher trophic levels in seamount food chains (Uda and Ishino, 1958; Hubbs, 1959; Uchida and Tagami, 1984). Table 1.2 - List of species considered as \"seamount-aggregating\" fishes. Species Aggregation Reference Alepocephalus bairdii Maybe 6, 11 Allocyttus niger a True 3,4 Allocyttvs verrucosus a Maybe 12 Aphanopus carbo b True 10 Beryx decadactylus . . ; True 4,9 Beryx splendens True ' 3 ,4 ,7 ,9 Coryphaenoides rupestris True 8, 5 Dissostichus eleginoides. True -•-4 Epigonus telescopus * True 10, 5 Hoplostethus atlanticus True 3 ,4 ,8 ,5 Hoplostethus mediterraneus Maybe 6 Lepidion eques * Maybe 6 Mora moro Maybe 6 Neocyttus rhomboidalis *'a Maybe 11 Pseudocyttus maculatus a True 3,4 Pseudopentaceros richardsoni True 9 Pseudopentaceros wheeleri * True 2 ,3 ,4 Sebastes entomelas *' c Maybe 1 Sebastes helvomaculatus *' c Maybe 1 Sebastes marinus True 5 Sebastes mentella True 8 Sebastes paucispinis c Maybe 1 Sebastes ruberrimus c Maybe 1 * intrinsic vulnerability index not estimated due to the lack of sufficient parameters, a) forming large shoals over rough ground near pinnacles and canyons; b) not a typical \"seamount-aggregating\" fish (sensu Koslow, 1996); c) juveniles form large schools. References: 1) Parker and Tunnicliffe, 1994; 2) Rogers, 1994; 3) Koslow, 1996; 4) Koslow et al., 2000; 5) Hareide and Games, 2001; 6) Pineiro et al., 2001; 7) Ramos et al., 2001; 8) Shibanov et al., 2002; 9) Vinnichenko, 2002a; 10) Vinnichenko, 2002b; 11) Allain et al., 2003; 12) Fishbase - Froese and Pauly, 2003. Studies of fish composition on seamounts have often reported high levels of endemism, exceeding 40% in one case (e.g. Wilson and Kaufmann, 1987; Parin et al., 1997; Richer de Forges et al., 2000; Froese and Sampang, 2004). However, estimates can be variable, and some studies have found little evidences of endemic seamount fish species. For example, none of the fish species recorded by Tracey et al. (2004) on New Zealand seamounts were regarded as endemic to any seamount, seamount chain, or even the region. However, the data were from fish trawls designed to capture relatively large-sized fishes, and these fish will generally tend to have wider distributions. Small sampling gear used off Tasmania revealed previously unknown, and probably endemic, species of Paralaemonena (Family Moridae), and Cataetyx (Family Bythitidae) (Koslow and Gowlett-Holmes, 1998). The number of seamount endemic species or the number of fish that lives only on seamounts is still not known. Froese and Sampang (2004) speculated that of the 535 seamount fishes they identified, 62 species are reported from only one seamount, suggesting a high rate of endemism. Generally, species accounts from seamounts focus on just the samples collected from the seamount features, and studies have not considered how specific the species composition is to seamounts. 1.6 SEAMOUNT VISITORS: LARGE PELAGIC FISH, CEPHALOPODS, MARINE MAMMALS, AND SEABIRDS While the importance of seamounts for bottom fishes is very well documented (see above), the importance for large pelagic or visiting organisms has been poorly tested (see Table 1.1). However, it has been hypothesised that there are higher abundances of some \"visiting\" animals, such as tuna, sharks, billfishes, marine mammals, sea-turtles and even seabirds, over seamounts but this has been based on sparse records, warranting further examination. Sharks appear to be attracted to seamounts as demonstrated by Klimley et al. (1988), who showed that hammerhead sharks remained grouped at a seamount in the Gulf of California (Mexico) during the day and moved separately into the surrounding pelagic environment at night. Hazin et al. (1998) showed that catches of gray sharks were significantly higher around seamounts, mainly in those with summits of about 300m and low-sloping depth profiles. The reasons for these aggregations are not clear, but Hazin et al. (1998) assumed that seamounts were used by some sharks as feeding stations. It is known by fishermen and researchers that large biomasses of tuna are sometimes concentrated on seamounts (Fonteneau, 1991; Holland et al., 1999; Itano and Holland, 2000; Sibert et al., 2000). Several thousand tons of tuna can be taken yearly on some remote seamounts, while other seamounts closer to land are apparently always poor in tuna, even when they are located in regular fishing areas (Fonteneau, 1991). It is possible that seamounts act both as feeding stations and as orientation points in the larger-scale movement patterns of these fish (Holland et al., 1999). The navigation role might explain why remote seamounts aggregate more tuna than seamounts located closer to land masses, as noticed by Fonteneau (1991). Swordfish and other billfishes appear also to be attracted to complex high-relief bottom structures. For example, swordfish that moved away from the Charleston Bump were frequently found associated with seamounts, submarine canyons, and with thermal fronts of the northern wall of the Gulf Stream (Sedberry and Loefer, 2001). Cephalopods may drift passively over and around seamounts and be subject to predation (Nesis, 1986). The author suggested that this link might be one of the reasons for the high abundance of benthic and demersal fish species on certain seamounts. Cephalopod fauna of seamounts may consist of four main components (Nesis, 1986): 1) bottom and near-bottom species that reside there permanently (e.g., genera Froekenia, Danoctopus and Scaeurgus)\\ 2) pelagic species that descend to or near the bottom to spawn, and including either those that maintain themselves constantly in midwater above or near seamounts, or those that migrate actively to seamounts for sexual maturation (e.g., genera Ornithoteuthis, some Todarodes, Lycoteuthis); 3) mesopelagic species that may migrate vertically and descend to the bottom during the daytime (e.g., Enoploteuthidae and Octopoteuthidae); and 4) non-migrating pelagic species that permanently inhabit the water column (Mastigoteuthidae, Vampyroteuthidae, Cranchiidae). Although several works have correlated marine mammals occurrence with complex and steep topographies (e. g. Schoenherr, 1991; Balcomb, 1989; Canadas et al., 2002; Hooker et al., 2002; Hastie et al., 2004; Yen et al., 2004) the literature addressing their association with seamounts is scarce. Reeves and Mitchell (1993) noticed that when in pelagic areas Baird's beaked whales (Berardius bairdii) are observed close to submarine escarpments and seamounts. Seabird density and biomass has been reported to be higher around seamounts when compared to adjacent areas (Haney et al., 1995; Monteiro et al., 1996). Haney et al. (1995) showed that seabird biomass was eight times higher within a 30-km radius centred on a seamount summit. The authors attributed seabird aggregation observed at the seamount to be related to an increase of food availability. 1.7 SEAMOUNT FISHERIES AND OTHER THREATS The steady and steep decline of global catches since the 1980s (Watson and Pauly, 2001) alludes to the fact that the world's fisheries resources are in serious danger of depletion (e.g. Pauly and Christensen, 1995; Pauly et al., 1998; Pitcher, 2001; Pauly et al., 2002), undoubtedly due to poor management practices and increased fishing pressure (Ludwig et al., 1993). Unsustainable fishing practices along with an excessive level of investment in fishing capacity have resulted in serious stock depletion on most continental shelves, thus creating new pressures on alternative fishing grounds (Pauly et al., 2002). Fisheries are evidently expanding offshore (e.g., Christensen et al., 2003; Myers and Worm, 2003; Pauly et al., 2003) and into deeper waters (Koslow et al., 2000; Garibaldi and Limongelli, 2003; FAO, 2004; Gianni, 2004). The expansion into offshore areas has been well documented, (for example, fisheries targeting oceanic tuna, billfishes and their relatives covered the world ocean by the early 1980s; Myers and Worm, 2003), but the extension into deeper waters is less well analysed, While many local examples of fisheries expansion into deeper waters have been reported (e.g., some European, Soviet, U.S.A., Canada, New Zealand and Australian fishing fleets: see references in Hopper, 1995; Moore, 1999; Koslow et al., 2000; Roberts, 2002), we still lack a global quantitative analysis. Seamounts are also among those \"newly\" targeted ecosystems that, since the second half of the 20th century, have been intensively fished (Rogers, 1994; Koslow et al., 2000). Deepwater fisheries in general and seamounts fisheries in particular usually exhibit a boom and bust sequence, crashing within about ten years of their initial development. This was the case of the orange roughy {Hoplostethus atlanticus) fisheries off New Zealand (Clark, 1995; Clark, 1999; Clark et al., 2000), Australia (Wayte and Bax, 2001; Lack et al., 2003), Namibia (Boyer et al, 2001; Branch,;2001) and even in the North Atlantic (Branch, 2001), the pelagic armourhead {Pseudopentaceros wheeleri) fisheries over seamounts in international waters off Hawaii (Boehlert and Genin, 1987), and the blue ling {Molva dipterygia) fisheries in the North Atlantic (Bergstad et al., 2003; Devine et al., 2006). As seamounts are rapidly depleted, the continued existence of a fishery depends upon the continuing discovery of unexploited seamounts with large fish aggregations. The species targeted by fisheries at seamounts have a very low overall abundance, but aggregate at seamounts as part of their life cycle strategy, e.g., for spawning (Clarke et al., 1996). They are often long-lived (some species to over 100 years), slow growing, late maturing (at about 30 years), and have low reproductive potential (Koslow, 1997). When they have been fished out it is estimated that it will take decades for these localised stocks to recover, as they are thought to have limited exchange with other seamounts (Koslow, 1997). This makes these fish communities very vulnerable to overfishing and the problem is even more pronounced in seamounts located in international waters where management strategies and agreements are absent. However, information on seamounts fisheries is very sparse, and it is difficult to make a distinction between deep-water fishing activities in general and those occurring on seamounts (Koslow et al., 2000). Moreover, fish species living on seamounts are also known to occur on other habitats, such as the continental slope, and landings statistics are not spatially allocated, making it difficult to make an estimate of the total fisheries occurring on seamounts worldwide. Depletion of fish stocks is not the only concern. Extensive trawling activities on seamounts are damaging benthic (bottom living) communities, particularly for dominant communities of corals and other suspension feeders (Koslow et al., 2001). The impact of trawling on complex seamount reefs appears to be dramatic, with the coral substrate and associated community largely removed from the most heavily fished seamounts (Koslow et al., 2001). Such massive removal of natural and structural components to the ecosystem has negative consequences on seamount biodiversity (Probert, 1999). There is a rising concern about the threats to seamount ecosystems in the Economic Exclusive Zones of coastal states and on the High Seas. Overfishing, even depletion, of the often slow-growing and late-reproducing fish populations, and the destructive impact of trawling activities on the benthic communities of seamounts, poses an immediate risk to these isolated ecosystems. Consequently, Canada, Australia, Portugal and New Zealand have begun to take the first steps towards protecting seamounts. In the Atlantic, no such protective measures have been established, but the Oslo and Paris Commission (OSPAR) is considering this issue at the moment within the framework of Annex V of the Convention on the Protection of the Marine Environment in the North-East Atlantic (OSPAR Convention). Seamounts are on an initial draft list of habitats and species that require conservation action, and the developing OSPAR Marine Protected Areas programme may provide one of the possible mechanisms. In addition, seamounts dominated by hard substrata in the waters of the European Community qualify for site protection under the European Habitats Directive (1992, Natura 2000 code 1170 \"reefs\" in the Interpretation Manual of European Union Habitats EUR 15/2). 1.8 THESIS THEME AND OBJECTIVES The presence of numerous seamounts in the world's oceans has only become known to the scientific community during the last 50 years (Rogers, 1994). The potential importance of these steep-sided undersea mountains to biogeography and diversity was only recognized after Hubbs (1959) work, but this environment has remained very poorly investigated (Forges et al., 2000). Hence, the most fundamental questions remain incompletely answered (see Table 1.1). The general objective of this PhD was to explore some fundamental questions about seamount ecology and fisheries: 1) Estimate seamount numbers and locations around the Azores islands; The main goal of Chapter 2 is to infer potential seamount locations and thus to generate estimates of the actual number of seamounts in the Azores. I will also describe seamount population according to location, depth of the summit, height, basal area, height to radius ratio, the average slope, and distance to nearest seamount. The output of this chapter will be used later (Chapter 4) to test some hypothesis related to seamount ecosystem functioning. 2) Examine the impact of a potential increase of local primary production on higher trophic levels; In an effort to better understand seamount ecosystem functioning, Chapter 3 will address how complex seamount food web structures are sustained. A generic seamount ecosystem model from the Northeast Atlantic will be used to test the impact of a potential increase of local primary production on higher trophic levels. 3) Quantify the amount of advected prey necessary to sustain a \"typical\" seamount fish community and to explore if the necessary prey can be supported with food provided by local oceanographic conditions (also in Chapter 3); 4) Test if the reported high abundances of seamount \"visitors\" on top and around seamounts are true; Some previous studies have focused on analysing the auto-ecology of some organisms in relation to seamounts. In Chapter 4, I will use data from a fishery observer program to explicitly test if the abundances of tuna, marine mammals, sea turtles and seabirds observed at Azores seamounts are higher than expected by chance. This chapter will also use the seamount dataset produced in chapter 2. 5) Test if a reported historical expansion into deeper-waters can be detected in global landings datasets; Whereas previous studies on global trends of fisheries have focused on catch or biomass changes over time, in Chapter 5 I will analyse changes in the mean depth of fishing to test if the predicted expansion into deeper-waters can be detected in global landings datasets. I will also test for the predicted higher vulnerability of deep-water fisheries resources, using longevity as the main proxy for vulnerability. 6) Test the hypothesis that \"seamount fishes\" generally have a higher than average vulnerability to fishing exploitation; Previous studies have found that vulnerability of fishes to exploitation is correlated with their life history characteristics. However, no attempt has been made to review, summarize and compare the life-history of seamount species with species typical from other habitats. Therefore, Chapter 6 will test the generalization that \"seamount fishes\" possess specific life history characteristics that render them more vulnerable than other species. Vulnerability was estimated quantitatively by analysis of life-history characteristics using a fuzzy-logic algorithm. 7) investigate if whole-ecosystem simulations can help in understanding the impact of fishing on pristine seamounts and provide guidelines for sustainable fisheries. Using ecosystem modeling loosely structured on North Atlantic case studies, data gathered from elsewhere, and optimization niethods for policy search, in this Chapter I will explore the types of fisheries that might be sustainable on seamount ecosystems. 1 will also investigate if ecosystem simulations can help in understanding the impact of fishing on pristine seamounts. 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Suzuki (2003) Seamounts, new moon and eel spawning: the search for the spawning site of the Japanese eel. Environmental Biology of Fishes 66: 221-229. Uchida, R.N. and D.T. Tagami (1984) Groundfish fisheries and research in the vicinity of seamounts in the North Pacific Ocean. Marine Fisheries Reviews 46: 1-17. Uda, M. and M. Ishino (1958) Enrichment pattern resulting from eddy systems in relation to fishing grounds. Journal of the Tokyo University of Fisheries 1-2: 105-119. Vereshchaka, A. L. (1994) The distribution of pelagic macroplankton (mysids, euphausiids, and decapods) over the continental slope and seamounts of the western Indian Ocean. Oceanology 34(1): 81-86. Vereschchaka, A.L. (1995) Macroplankton in the near-bottom layer of continental slopes and seamounts. Deep-Sea Research I 42(9): 1639-1668 Vereshchaka, A.L. (1996) Distribution of benthopelagic shrimps over the continental slopes and seamounts of the Western Indian Ocean. Oceanology 35(4): 528-531. Vereshchaka, A.L. (2005) New species of Galatheidae (Crustacea: Anomura: Galatheoidea) from volcanic seamounts off northern New Zealand. Journal of Marine Biological Association of the UK 85: 137-142. Viau, B. and A. Cailleux (1971) Frequence de monts sous-marins dans une parties de l'Ocean Indien et du Pacifique. Zeitschrift fur Geomorphologie 15: 471-478. Vinnichenko, V.I. (2002a) Prospects of fisheries on seamounts. ICES CM2002/M32. Poster. Vinnichenko, V.I. (2002b) Russian investigations and fishery on seamounts in the Azores area. In: Anonymous (ed.) Relatorio das XVIII e XIX Semana das. Pescas dos Agores, Secretaria Regional da Agricultura e Pescas, Horta,'pp. 115-129. Voronina, N.M. and A.G. Timonin (1986) Zooplankton of the region of seamounts in the western Indian Ocean. Oceanology 26: 745-748. Ward, P., J.M. Porter and S. Elscot (2000) Broadbill swordfish: status of established fisheries and lessons for developing fisheries. Fish and Fisheries 1: 317-336. Watson, R. and D. 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Hyrenbach (2004) Marine bird and cetacean associations with bathymetric habitats and shallow-water topographies: implications for trophic transfer and conservation. Journal of Marine Systems 50: 79-99. C H A P T E R 2 ABUNDANCE AND DISTRIBUTION OF SEAMOUNTS IN THE AZORES1 2.1 INTRODUCTION Seamounts have been recently recognized as highly important habitats for fisheries and biodiversity, and a target for conservation as they support often isolated but rich underwater ecosystems (Morato and Pauly, 2004). However, only a few seamount datasets containing information on positions are available (e.g., Fornari et al., 1987; Smith and Jordan, 1988; Epp and Smoot, 1989; Smith and Cann, 1990; Wessel and Lyons, 1997; Wessel, 2001). In fact only a small fraction of seamounts have actually been mapped (Wessel and Lyons, 1997). Recently, Kitchingman and Lai (2004) have conducted a global analysis with the goal of generating a spatial dataset of points across the world's oceans that indicate large peaked bathymetric anomalies with a high probability of being seamounts. In that study 14,287 potential large seamounts were identified in the world's oceans. Seamounts are thought to be common topographic features in the Azorean archipelago sub-area of the Portuguese Economic Exclusive Zone (EEZ), hereafter named Azores EEZ. However, their numbers and locations are poorly known. The Azores archipelago is a group of nine volcanic islands and many small islets that are parts of the Mid-Atlantic Ridge in the Northeast Atlantic Ocean (an irregular area within 33.5-43° N, 21-35.5° W). Relatively shallow seabed less than 600 m deep cover less than 1 % of the 953,633 km2 of the Azorean EEZ. This reflects the narrowness of the island shelves and means that most fishing grounds are scattered (Santos et al., 1995; Menezes, 2003). The only easily accessible seamount datasets that include the Azores are those of Wessel (2001) and Kitchingman and Lai (2004), with the later describing 58 seamounts in the Azores EEZ. Few other studies have been conducted in the Mid Atlantic Ridge, where estimates vary from 58 (Jaroslow et al., 2000) to 1 A version of this chapter has been submitted for publication. Morato, T.; M. Machete; A. Kitchingman; F. Tempera; S. Lai; G. Menezes; R.S. Santos; and T.J. Pitcher (submitted). Abundance and distribution of seamounts in the Azores. Marine Ecology Progress Series. 80 (Smith and Cann, 1990) seamounts per 1000 km2. These estimates are about 1000 times higher than the numbers presented by Kitchingman and Lai (2004) because their study accounts only for those topographic features with a relief larger than 1000m. Seamounts are important areas for conservation and fisheries in the Azores (Santos et al., 1995) and the knowledge of their locations are highly important for choosing and implementing management measures. In this study, seamounts are defined as any topographically distinct seafloor feature that is at least 200 meters high but which does not break the sea surface. The 200 meters threshold was chosen for being the smallest peak size that fit the negative exponential model (see Results). I classify seamounts as being large or small, depending on whether the height exceeds 1000 meters (regardless of depth). For standard cone like seamounts height and width are highly correlated (Smith, 1988). Thus, this height separation is useful in isolating large seamounts, whose global distribution is well resolved by satellite altimetry, from small seamounts the distribution of which must be inferred from local acoustic mapping and therefore remain poorly sampled. The main goal of this paper is to infer potential seamount locations and thus to generate estimates of the actual number of seamounts in the Azores. I will also describe seamount population according to location, depth of the summit, height, basal area, height to radius ratio, the average slope, and distance to nearest seamount. 2 . 2 METHODS I used an automated methodology adapted from Kitchingman and Lai (2004) to identified topographic structures with high probability of being seamounts. For this I used two bathymetric datasets with different resolutions. The MOMAR (Monitoring the Mid Atlantic Ridge) mid-resolution bathymetric map (Lourengo et al., 1998; http://www.motnar.org) was the finest available bathymetrical grid for the Azores region at the date of the analysis (February 2006) and was used for the area comprised by the parallels 36°N and 41°N and the meridians 24°W and 32°W. The dataset is supplied at a 1 minute cell resolution (approx. 1.8 km in length), thus allowing a reasonable scale at which to perform an analysis for large seamount like features. The \"Global seafloor topography from satellite altimetry and ship depth soundings\" database (Smith and Sandwell, 1997; http://topex.ucsd.edu/sandwe-J 1/sandwel 1 .html; also called S&S dataset) was used as the bathymetry dataset for the remaining area. This dataset is at a 2 minute cell resolution (approx. 3.7 km in length). This dataset contains some artefacts such as a spurious deep trough in the NE region of the study area. The methodology followed three succeeding steps: 1) identifying all detectable peaks in the bathymetry dataset; 2) isolating peaks with .heights greater than 200m and displaying an approximately circular or elliptical shape; and 3) isolating large seamount-like features. The datasets produced after step 1 will be called the peaks dataset, while the dataset produced after step 3 will be used as the Azores seamount dataset. The dataset produced after step 2 minus those produced after step 3 will be called the small seamounts dataset. The initial process of determining the locations of all detectable peaks (local maxima) in the elevation data was performed with the ESRI ArcGIS (ESRI, 1999-2004) software flow direction and sink algorithms. Both bathymetry datasets were used in an ESRI grid format for the cell-by-cell analysis. The ESRI flow direction algorithm was first used on the bathymetry data. This algorithm produces a grid in which each cell is allocated a flow direction value determined by the steepest descent from the immediate surrounding cells. There are eight valid flow direction values. Cells determined to have an undefined flow direction are given a value equal to the sum of the possible flow direction values. Undefined flow directions occur when all surrounding cells are higher than the focus cell or when two adjacent cells flow into each other. The ESRI sink algorithm is used on the resulting flow direction grid to identify all flow direction cells that have undefined flow directions. The resulting sink (seafloor peak) grid can then be overlaid with the depth grid to indicate all identifiable peaks on the seafloor. The bathymetry data was prepared by first eliminating all land cells (any elevation above 0) and then converting negative values (known as depths) to absolute numbers. This allowed the ESRI hydrology algorithms, designed to detect downhill flow direction and sinks, to identify the uphill flow directions and peaks. The next step of the analysis will isolate those the detected peaks that have a significant rise from the ocean floor and that have an approximate circular or elliptical base in an effort to eliminate small peaks found along the ridges or island slopes. The raw peak grid dataset was compared with the bathymetry data. An algorithm was developed that scanned depths around each peak, along 8 radii of 20 km each at 45° intervals. The lowest and highest depths over the radii and the cells where those values were obtained were then recorded. Subsequently, I isolated a dataset of potential seamounts and then extracted the large seamounts. A peak was considered to be a potential seamount when the following conditions were met: 1 Each and all of the 8 radii included depths differing by at least 200 m. This helped eliminate all peaks of insignificant rises. 2 No more than one of the 8 radii has the highest depth shallower than the depth of the peak and if the distance between these two cells is greater than 10 km. This helped eliminate peaks that were part of a larger structure and peaks close to island slopes. 3 If 2 radii included depths between 200 and 1000 m with the shallowest point being closer to the peak than to the deepest point, and if the radii formed an angle of less than 135°. This condition was created to help separate ridges from seamounts. 4 At least 5 of the 8 radii around a peak included depths with a difference of at least 1000 m, with the shallowest point being closer to the peak than to the deepest point. 5 The average height of the peak is greater than 1000 m. Peaks that met all five conditions were considered large seamounts while those that met the first three conditions but failed to meet the fourth and fifth were considered small seamounts. For the detected small and large seamounts several characteristics were recorded: 1) location; 2) depth of the summit (m); 3) seamount height (h in m); 4) basal area (a/, in km ); 5) height to radius ratio (£,.); 6) the average slope ( in degrees); and 7) distance to nearest seamount (km). The location of the seamounts was recorded as the latitude and longitude of the centroid of the detected peak or seamount. The depth of the summit was recorded as the depth of the cell where the peak was located and must be interpreted as the average depth of the cell, not the absolute minimum depth of the seamount. The seamount height was estimated as the average height of the 8 radii of the seamount, where each radius height was estimated as the difference between the summit and the deepest record. The area of the base of the seamount was approximated by the area of the octagon formed by the location of the deepest cell in each radius. The slope of the seamount was estimated as the average steepness of the 8 radii of the seamount calculated by the slope algorithm of ArcGIS software. The ArcGIS 'Slope' function calculates the maximum rate of change between each cell and its neighbours using the average maximum technique. Finally, the distance to the nearest seamount was calculated by identifying the closest feature and then estimating its distance. Seamount size distribution is well characterized by a negative exponential model that considers the cumulative numbers of seamounts having heights greater than a certain value (Jordan et al., 1983; Smith and Jordan, 1988). This distribution is expressed as v(H)=v„-exp(-P'H), where v(H) is the number of peaks per unit area with height greater than H, v0 is the total number of peaks per unit area and P is the negative of the slope of a line fitting ln(v(//)) and H. 2 .3 RESULTS 2.3.1 Peaks dataset A total of 3177 peaks were identified by the ArcGIS flow direction and sink algorithms, yielding an average density of 3.3 peaks per 1000 km . Of these peaks, 1104 were found with the MOMAR Azores dataset (24°W 36°N to 32°W 41°N), whereas 2073 were found with the S&S dataset used to cover the rest of the EEZ. The peaks dataset adequately identified topographic structures with heights larger than 100m (Figure 2.1). The resolution of the bathymetry data seems to be inadequate for peaks smaller than 100m, leading to an underestimation of the counts. Thus, this data point was excluded from the fit. The exponential model adequately fits the Azores peaks counts with vG= 4.31 peaks per 1000 km2 and /?= 2.89 km\"1, yielding a characteristic height (/?\"') of -350 m. According to this exponential model there are about 4100 potential peaks in the Azores where 1000 km contain an average of ~4 peaks of all sizes. 10000 3 0.1 1 1 . 1 , , , 0 500 1000 1500 2000 2500 3000 3500 Predicted peak height (m) Figure 2.1 - Height (h) frequency distribution of all identified peaks. Solid circles are actual counts while open circles are the cumulative counts. The grey circle data point was excluded from the exponential model fit. The relationship can be expressed as N = 4107.6-e\"2'89/i, with h in km; r2=0.99. If expressed by unit area (km\"2), v(H)= 4.31 -e\"2 8 9 /'. 2.3.2 Small and large seamounts dataset Figure 2.2 shows the location of 461 potential small and large seamount-like features in the Azores EEZ. Detailed tables of results for each seamount with location, depth of the summit, base area and average slope are shown in Appendix 2 2. Our methodology identified a total of 398 small features, which represents only 12% of the 3177 identified peaks. This discrepancy shows that our methodology successfully eliminated peaks of insignificant rises as well those that were part of larger structures or were in ridges or island slopes. Of the 398 potential small seamounts, 151 were identified around the islands with the Lourengo et al. (1999) bathymetrical dataset while 247 were identified offshore with the Smith and Sandwell (1997) dataset. I have also detected 63 large potential seamounts, which is only 2% of the identified peaks. Of these, 52 were identified with the Lourengo et al. (1999) dataset while 11 were identified with S&S dataset. The mean abundance of small and large seamounts in the Azores EEZ is 0.42 and 0.07 per 1000 km2, respectively. Figure 2.2 - Distribution of seamounts in the Azores Exclusive Economic Zone (black line). Circles show large seamounts while black dots show small seamount-like features. Scale goes from dark grey (deep water; about 5000m) to light grey (shallow water). 2 A digital version of this table is provided in the webaddress http://www.horta.uac.pt/ppl/tmorato/pdf/Appendix2_Morato_PhD.pdf Most of the seamounts in the Azores have deep summits (Figure 2.3) with a strong predominance of summit depths of 800-1500m.Only 4 large seamounts have a mean depth of the peak shallower than 250m while 14 lay in the depth range 250-500m. The other 45 large seamounts have their summits deeper than 500m. Small seamount-like features show a similar pattern with only 6 shallower than 500 m. Location of large and small seamounts by depth of the summit is presented in Figures 2.4 and 2.5. Small seamounts numbers 0 10 20 30 40 1000 -2 0 0 0 -3000 CL Q 4000 -5000 -6000 Large seamounts numbers 0 5 10 15 1000 -2000 • 3000 4000 i 5000 6 0 0 0 • Figure 2.3 - Depth of the summit frequency distribution of small (left) and large (right) seamounts-like features. d. srwsr 4 ««« >4 i\"1 *^^- S^VW JI^ W^ \"^jHf\"®!* . . • i , ! ^ - . •*•„. ...i,..,. A -msfymt Figure 2.4 - Distribution of large seamounts in the Azores EEZ by mean depth of summit: a) <250m; b) 250-500; c) 501-1000; d) > 1000m. Black dots indicate seamounts of the referred category. '.iKirv I / 1 / •. il^ flL- 1 * - ii^ V Figure 2.5 - Distribution of small seamount-like features in the Azores EEZ by mean depth of summit: a) <250m; b) 250-500; c) 501-1000; d) > 1000m. Black dots indicate seamounts of the referred category. to The distribution of small and large seamounts heights (h) distribution is shown in Figure 2.6. Small seamounts had a mean height of 612 m (S.D. = 210) while the mean height for large seamounts was 1267 m (S.D. = 272). Seamounts with small heights are much more abundant than larger seamounts. 90 80 70 CO u 60 -O E % 50 I 40 E 8 30 V) 20 10 0 0 500 1000 1500 2000 Predicted seamount height (m) Figure 2.6 - Histogram of the predicted height distribution of small (light grey bars) and large (black bars) seamounts-Iike features. Bin size is 100m. I J H T E Shapes of seamounts as characterized by basal radius (r h), by the height to radius ratio (£) and by the slope (= 0.003 h + 0.86; r1 = 0.53. 2 . 4 DISCUSSION This work is the first attempt to identify the seamounts in the Azorean EEZ. It must be emphasized that there are some potential sources of uncertainty in this study. First, the bathymetry of the Azores EEZ is not perfectly known and has not been, to our knowledge, extensively surveyed. Both bathymetry datasets used (Smith and Sandwell, 1997; Lourenfo et al., 1999) may lack resolution and thus preclude the identification of some small seamount-like features. For this reason, our references to seamounts should be interpreted as potential seamounts. A better but very costly solution would be to perform extensive multi-beam surveys that would provide not only excellent bathymetric data for mapping seamounts and estimating depths, areas and slopes but also backscatter data for mapping the nature of the seafloor. Our methodology of identification shows that peaks and seamounts are common features in the Azorean EEZ. The average density of 3.3 peaks of all sizes per 1000 km2 is in the same order of magnitude of that obtained in some studies in the Mid Atlantic Ridge (Batiza et al., 1989) but is an order of magnitude lower than obtained by Smith and Cann (1990) and by Jaroslow et al. (2000). The observed discrepancies are due to the facts that the later studies focused only on the MAR region, an area with a higher abundance of topographic structures (Smith and Cann, 1990), and that the resolution of. our datasets. is inadequate to detected peaks smaller than 100m height. The difference of about 1000 peaks between the real counts and the exponential model estimates (n= -4100) are due to underestimation of the counts for peaks smaller than 100m, suggesting that our methodology successfully identified small and large seamount-like features (/7>200m height). In this study, I was able to map and describe 63 large and 398 small seamount-like features in the whole EEZ of the Azores. The total area where seamounts are found is much larger than previous thought. However, most of the summits are in waters deeper than 1000m. Etnoyer (2005) presented some evidences that small features with deep peaks predicted by some bathymetry datasets can actually be large seamounts with shallower summits. Therefore, our estimates of seamount abundance may perhaps be biased by underestimating seamounts heights and overestimating depth of summits. For that reason, the large seamount abundance in the Azores may be even higher than presented here. Also, the depth of the summits may be shallower than I have estimated. The distribution suggests that a large proportion of the seamounts occur in chains along the Mid Atlantic Ridge. However, isolated seamounts are also present in the Azores. Seamounts showed a wide range of sizes (heights), depth of summits, slopes, and areas being difficult to make generalization about the seamounts in the Azores. Our data suggests that seamounts provide a large diversity of habitats that can be suitable for different type of faunal associations. Similar findings and suggestion were made by Rowden et al. (2004) for New Zealand seamounts. The fact that most of the seamounts have small heights and deep summits has strong implications for fisheries exploitation in the region where most of the bottom longlining occurs at depths of up to 600 m (Morato et al., 2001). Our data suggests that only 29 of the 461 seamounts (small and large) are available for this type of fisheries. Thus, our work supports the idea that fishing grounds for existing fisheries are limited in the Azores. It should be noticed that deep-water trawl operates at deeper waters and thus the potential fishing grounds for this fishery could be slightly larger. Recently, the EU regulation 1568/2005 (European Commission, 2005) banned deep water trawling in a large area of the Azorean EEZ. According to our distribution of seamounts, this regulation protects 58 large and 207 small seamounts. Thus, 57% of the potential Azores seamounts are protected against deep-water trawling. 2 .5 REFERENCES Batiza, R., P.J. Fox, P.R. Vogt, S.C. Cande, and N.R. Grindlay (1989) Abundant Pacific-type near-ridge seamounts in the vicinity of the Mid-Atlantic Ridge 26 deg S. Journal of Geology 97: 209-220. Environmental Systems Research Institute (ESRI) (1999-2004) ArcGIS: Release 9.0 [software]. Redlands, California, USA. Epp, D., and N.C. Smoot (1989) Distribution of seamounts in the North Atlantic. Nature 337: 254-257. Etnoyer, P. (2005) Seamount resolution in satellite-derived bathymetry. Geochemistry Geophysics Geosystems 6(3), 8pp. European Commission (2005) Council Regulation No. 1568/2005 of 20 September 2004. amending Regulation (EC) No 850/98 as regard the protection of deep-water coral reefs from the effect of fishing in certain areas in the Atlantic Ocean. OJ L252/2, 28.09.2005. Fornari, D.J., R. Batiza and M.A: Luckman (1987) Seamount abundances and distribution near the East Pacific Rise 0°-24°N based on seabeam data. In: B.H. Keating, P. Fryer, R. Batiza and G.W. Boehlert (eds.) Seamounts, Islands, and Atolls. American Geophysical Union, Washington, D.C., pp. 13-21. Jaroslow, G.E., D.K. Smith and B.E. Tucholke (2000) Record of seamount production and off-axis evolution in the western North Atlantic Ocean, 25°25' - 27°10'N. Journal of Geophysical Research 105: 2721-2736. Jordan, T.H., W. Menard and D.K. Smith (1983) Density and size distribution of seamounts in the Eastern Pacific inferred form wide-beam sounding data. Journal of Geophysical Research 88(B12): 10508-10518. Kitchingman, A. and S. Lai (2004) Inferences on potential seamount locations from mid-resolution bathymetric data. In: T. Morato and D. Pauly (eds.) Seamounts: biodiversity and fisheries. Fisheries Centre Research Report 12(5), pp. 7-12. Lourenfo, N., J.M. Miranda, J.F. Luis, A. Ribeiro, L.A. Mendes-Victor, J. Madeira and H.D. Needham (1998) Morpho-tectonic analysis of the Azores volcanic plateau from a new bathymetric compilation of the area. Marine Geophysical Researches 20: 141-156. Menezes, G.M.M. (2003) Demersal fish assemblages in the Atlantic archipelagos of the Azores, Madeira, and Cape Verde. Ph.D. Thesis, Universidade dos Agores, Horta, Portugal, 228pp. Morato, T. and D. Pauly (2004) Seamounts: biodiversity and fisheries. Fisheries Centre Research Report 12(5), 78 pp. Morato, T., S. Guenette and T. Pitcher (2001) Fisheries of the Azores, 1982-1999. In: D. Zeller, R. Watson, T. Pitcher and D. Pauly (eds.) Fisheries impacts on North Atlantic Ecosystems:- Catch, effort and national/regional data sets. Fisheries \"Centre Research Reports, University of British Columbia, 9(3), pp. 214-220. Rowden, A.A., M.R. Clark and I.C. Wright (2005) Physical characterisation and a biologically focused classification of \"seamounts\" in the New Zealand region. New Zealand Journal of Marine and Freshwater Research 39: 1039-1059. Santos, R.S., S. Hawkins, L.R. Monteiro, M. Alves and E.J. Isidro (1995) Case studies and reviews. Marine research, resources and conservation in the Azores. Aquatic Conservation: Marine and Freshwater Ecosystems 5: 311-354. Smith, D.K. (1988) Shape analysis of Pacific seamounts. Earth and Planetary Science Letters 90: 457-466. Smith, D.K. and J.R. Cann (1990) Hundreds of small volcanoes on the median valley floor of the Mid-Atlantic ridge at 24-30° N. Nature 348:152-155. Smith, D.K. and T.H. Jordan (1988) Seamount statistics in the Pacific Ocean. Journal of Geophysical Research 93(B4): 2899-2918. Smith, W.H.F. and D.T. Sandwell (1997) Global seafloor topography from satellite altimetry and ship depth soundings. Science 277: 1957-1962. Wessel, P. (2001) Global distribution of seamounts inferred from gridded Geosat/ERS-1 altimetry. Journal of Geophysical Research 106(B9): 19431-19441. Wessel, P. and S. Lyons (1997) Distribution of large Pacific seamounts from Geosat/ERS-1: Implications for the history of intraplate volcanism. Journal of Geophysical Research, 102(B10): 22459-22475. C H A P T E R 3 MODELLED EFFECTS OF PRIMARY AND SECONDARY PRODUCTION ENHANCEMENT BY SEAMOUNTS ON LOCAL FISH STOCKS 1 3 .1 INTRODUCTION It has long been recognized that many seamounts may harbour large aggregations of demersal or benthopelagic fish (Rogers, 1994; Boehlert and Sasaki, 1988; Koslow, 1996, 1997; Koslow et al., 2000) such as orange roughy, Hoplostethus atlanticus, pelagic armorhead, Pseudopentaceros wheeleri, and alfonsinos, Beryx spp. (Morato et al., 2006). However, the mechanisms under which these aggregations are sustained are still under debate. Three hypotheses have been presented to explain how large aggregations of fish found on seamounts are energetically supported (see Genin, 2004 for a review). The first hypothesis (7) proposes that the high biomass of fish results, at least in part, from locally enhanced primary production (PP) and the subsequent bottom-up transfer of this energy to higher trophic levels in seamount food chains (Uda and Ishino, 1958; Hubbs, 1959; Uchida and Tagami, 1984). It is unlikely, however, that water could be retained around a seamount for the several months needed for production to work its way through the food-web to the higher trophic level fish residing on the seamount itself. Thus, is not surprising that evidence for enhanced primary production leading to concentrations of fish over seamounts is sparse (Rogers, 1994). Moreover, evidence of increased primary production over conical seamounts located in the path of marine currents is still contradictory. While upwelling that drives patches of high primary production has been detected using field sampling over certain well-studied seamounts (Genin and Boehlert, 1985; Dower et al., 1992; Comeau et al., 1995; Odate and Furuya, 1998; Mourino et al., 2001, 2005), many studies have failed to demonstrate persistent high chlorophyll a patches over seamounts (e.g., Pelaez and 1 A version of this chapter has been submitted for publication. T. Morato, C. Bulman and T.J. Pitcher (submitted) Impact of primary production enhancement by seamounts on local fish stocks. Deep-Sea Research McGowan, 1986). In general, seamounts reaching close to the surface enhance local primary production, but its contribution to primary production enhancement is generally low (Mourino et al., 2005). The second hypothesis (2) proposes that fish aggregations are sustained by the enhanced horizontal flux of prey organisms past the seamount (Tseytlin, 1985; Dower and Mackas, 1996; Koslow, 1997), named as the \"feed-rest\" hypothesis by Genin (2004). Enhanced fluxes of prey in regions of amplified currents augment feeding by site-attached fish but at the same time may have extremely high energetic costs. The \"feed-rest\" hypothesis suggests that the fish rest motionless in quiescent shelters during non-feeding intervals and, when conditions are right, the fish emerge from shelter, feed quickly, and then retreat back to rest. Another possible mechanism (hypothesis 3) is the interception and trapping of vertical migrators - both descending and ascending (Isaacs and Schwartzlose, 1965; Genin et al., 1988, 1994; Williams and Koslow, 1997; Fock et al., 2002), named as the \"topographic blockage\" hypothesis by Genin (2004). Most of the studies supporting hypotheses 2 and 3 are based on observations of potential prey (micronekton) rather than on the predatory fish themselves. Only two studies have examined fish energetic requirements and food availability on seamounts (Tseytlin, 1985; Koslow, 1997). On seamounts off southern Tasmania, Australia, Koslow (1997) assumed an orange roughy biomass of 100 t-km\"2, with a consumption of 1% of its body weight, a trophic level of four, a trophic efficiency of 10%. Net primary production in the region was 200 gC-m\"2-year\"'; enough to sustain only about one-tenth of the estimated biomass, or about 11 t-km\"2 of orange roughy, assuming a conversion of carbon as 5% of wet weight. Using a different approach, Koslow (1997) took into account the annual contribution to the fourth trophic level of the particle flux from the surface (sinking of ungrazed phytoplankton) and vertical migrators. He estimated that local production available to the fourth trophic level was about 1.25 gC-m\" 2 1 2 •year\" which I estimate could only sustain about 7 t-km\" of orange roughy. Moreover, after an extensive field sampling program off southern Tasmania, Williams and Koslow (1997) estimated a biomass of micronekton migrating to about 900m depth between 0.94 to 3.36 2 I gC-m\" -year\" . Using the conversion ratios presented by Koslow (1997) this micronekton could sustain only between 5 to 18 t-km\"2 of orange roughy. All of these values are much less than orange roughy biomass estimates for Tasmania seamounts (50-125 t-km\"2; Koslow, 1997). From these calculations Koslow (1997) concluded that local productivity (hypothesis 1) and vertical migrators (hypothesis 3) are not sufficient to sustain known aggregations of seamount fishes on seamounts. Alternatively, they could be supported by advected sources (hypothesis 2). On the other hand, Tseytlin (1985) modelled the biomass of predatory fish that could be sustained at a seamount by food sources explained in hypothesis 2 and 3. With several assumptions, including a summit depth of 500m, a known mesozooplankton biomass of 50 mg-m3, a mean current velocity of 0.1 m-s\"1, and an average fish wet weight of 200g, he concluded that a maximum of 40 t km\"2 of fish could be supported by the horizontal flow of prey, while an extra 75 t-km\"2 of fish could be supported by vertical migrators, leading to a total of about 115 t-km\"2, in the range of known orange roughy biomasses. All of these studies strengthen hypothesis that imported food supplies support large fish aggregations on seamounts. However, these estimates account only for a single species and do not take into account the multispecies complexity of seamount ecosystems in which there may be more predators as well as additional sources of carbon and nutrients entering the food web from mesopelagic fish, visiting fish, squid and crustaceans, and, in some cases, detritus (Pitcher and Bulman, in press). On the south-eastern Australian and New Zealand seamounts, for instance, several species of oreo dories (e.g., Pseudocyttus maculatus and Allocyttus niger) are also aggregating in high densities (Koslow, 1997; Bulman, 2002) and other members of these communities such as the squalids and macrourids are also abundant (Koslow et al., 1994; Bulman, 2002). Many of these species consume similar prey as orange roughy and are therefore adding to the predation pressure on these prey (Bulman et al., 2001; Bulman et al., 2002a). Ecosystem-based modelling approaches can help in understanding the complex nature of ecosystem function. In seamount ecosystems, in particular, the complexity of the ecosystem is heightened by the apparent trophic inequities. However, there have been few attempts to fully model seamount ecosystems. Trophic models of the Tasmanian seamounts and large aggregations of orange roughy and oreos living on them were constructed using Ecopath with Ecosim to explore the hypothesis of Koslow (1997) that these large fish aggregations were supported by advection of prey past the seamounts (Bulman, 2002; Bulman et al., 2002b). Among the many tools for modelling marine ecosystems, the Ecopath with Ecosim (EwE) modelling approach and software has proved to be one of the most successful. It has seen widespread use and has generated helpful insights (Whipple et al., 2000; Robinson and Frid, 2003). The development of Ecopath in the early 1980s (Polovina, 1984) and its evolution in the following years into a dynamic modelling tool (Walters et al., 1997, 1999, 2000; Christensen and Walters, 2004a, b), has allowed us to address ecological questions, evaluate ecosystem effects of fishing, explore management policy options, analyse impact and placement of marine protected areas, and model the effect of environmental changes (e.g., Christensen and Pauly, 1993; Jarre-Teichmann, 1998; Pitcher et al., 2000; Shannon et al., 2000;. Watson et al., 2000; Guenette et al., 2001; Watson and Pauly, 2001; Walter et al., 2002; Christensen and Maclean, 2004; Morato and Pitcher, 2005; Pitcher et al., 2005). In an effort to better understand seamount ecosystem functioning, this paper will address how complex seamount food web structures are sustained. I used a generic seamount ecosystem model from the Northeast Atlantic to examine 1) the impact of a potential increase of local primary production on higher trophic levels, 2) to quantify the immigration of micronekton that would be required to maintain a \"typical\" seamount community, and 3) to quantify if the necessary immigration ratios could be supported by local oceanographic conditions. 3 . 2 METHODS 3.2.1 Modelling approach The modelling approach used in this study was Ecopath with Ecosim (EwE), including Ecospace (Christensen and Walters, 2004a). The parameterisation of an Ecopath model is based on satisfying two 'master' equations. The first describes how production for each group can be divided up, and the second is based on the principle of conservation of matter. As a trophic mass-balanced model Ecopath assumes that, for each functional group i in an ecosystem, mass balance should occur over a given time period. In the first 'master' equation biomass production of a compartment (/>,) is balanced by catches (y,-), predation mortality (Bj.M2j, where B, is the biomass of the group and M2, is the total predation rate for the group), biomass accumulation (BAj), net migration (£, = emigration-immigration) and other mortality (MO,), such that: (1) Pi = 7. + B,. • Ml, + E, + BAj + MO, Production is usually estimated from the production/biomass ratio (P/B) and the average annual biomass (B) and can be expressed as (P, = Bi -(P/B),). Predation mortality can be expressed as the sum of consumption by all predators (J) preying upon group (/'), i.e.: (2) B,-M2,=f jBj-(Q/B) j-DC j, where (Q/B)j is the consumption/biomass ratio of the predator (J) and DC/, is the fraction of the prey (i) in the'average diet of the predator (/'). The other mortality can also be expressed as: (3) MO, = /> • (1 - EE,) where EE is the ecotrophic efficiency, or the proportion of the production that is utilized in the system. Substituting (2) and (3) into the equation (1) means it can be re-expressed as: . (4) B• (P/B), • EE, = Y, + £Bj • (Q/B) . • DC.. + E, + BA, 7=1 The second 'master' equation is: (5) Qi=Pi+Ri+U i where Rj is respiration and U-, is unassimilated food. Ecosim uses the Ecopath model to estimate its initial parameters. It then uses a system of differential equations of the form given in (6) to calculate the biomass fluxes between pools through time: JT> (6) = QP I, - (M, B, at j j where dB/dt represents the growth rate of / during the time interval dt, g, is the net growth efficiency, Mj the natural mortality rate, F, is fishing mortality rate, e, is emigration rate, /, is immigration rate. The Q terms refer to consumption by group i (Qj,) and predation on i (Q,j), and are calculated using the 'forage arena' concept. For further details regarding the equations and their solutions see Walters et al. (1997, 2000) and Christensen and Walters (2004a). Ecospace represents biomass dynamics (as in eq. 6) over a two-dimensional rectangular grid space (x, y) and time (t). Such representations involve very complex sets of partial differential equations. For each cell the immigration rate /, of eq. (6) is assumed to consist of up to four immigration flows from the neighbouring cells in the grid. Similarly, the emigration flows e, in eq. (6) are represented as instantaneous movement rates to nearby cells. For further details see Walters et al. (1999). 3.2.2 Generic model for seamounts in the North-Atlantic A model of a hypothetical isolated seamount in the North Atlantic was built. The depth of the summit below the surface was set to be at around 300 m and the base at around 2000m. The area of the model was assumed to be 30 km radius from the summit, in order to include the theoretical area of its influence (see Chapter 4). As a result the total area under consideration was equal to 2827 km . A total of 37 functional groups were included in the seamount EwE model, stratified by depth of habitat (see Appendix 3 for details). The models included three marine mammal groups (toothed whales, baleen whales and dolphins), seabirds, turtles, seven invertebrate groups (benthic filter feeders such as corals or gorgonians, benthic scavengers, benthic crustaceans, pelagic crustaceans, seamount resident cephalopods, small and large drifting cephalopods), three zooplankton groups (gelatinous, shallow and deepwater zooplankton), primary producers (phytoplankton), detritus and twenty fish groups. Fish groups were divided based on environment use (depth and habitat - e.g., benthic, pelagic or benthopelagic), size, energetics (Childress et al., 1980; Koslow, 1996) and life-history. A general, stratification by depth was used: epipelagic, between of 0-200 m depth; mesopelagic, the region of the oceanic zone from 200 m to 1000 m; and bathypelagic, between 1000 m to 4000 m depth. Seamount associated fishes were divided into three different groups. Two groups contained species that are targeted by the north Atlantic fishery (Hoplostethus atlanticus and Beryx spp.), and a third group had the other seamount associated species. The deep scattering layer (DSL; a 50-200 m thick, sound-reflecting layer in ocean waters, consisting of a stratified, dense concentration of zooplankton and fish) was not considered as a single group, but as several groups that take part in its formation (i.e. shallow and gelatinous zooplankton, pelagic crustaceans, and small mesopelagic migrating fishes). The model parameters (Table 3.1) production to biomass ratios (P/B), consumption to 1 2 biomass ratios (Q/B) were calculated on a yearly basis (year\" ). Biomass (t-km\" ) and catch 2 1 (t-km\" -year\" ) were expressed in wet weight. The Q/B ratios for fish groups were estimated using an empirical equation (Palomares and Pauly, 1998). Temperature values (Celsius degrees) were established as being 18°C for the epipelagic region (0-200m), 8°C for the mesopelagic region (200-1000m), and 6°C for the bathypelagic region (1000-4000m). For some groups Q/B values were taken from other models. For most groups P/B ratios Were extracted from previously constructed models or were estimated assuming production and consumption ratio equal to 0.3. The proportion of food consumed and not assimilated was taken as 0.2. When no biomass estimate was available, this parameter was estimated by Ecopath using a value of 0.95 for Ecological Efficiency (EE). A preliminary diet matrix was assembled using published data, unpublished local information, and empirical knowledge (Table 3.2). Unidentified diet categories were excluded from the diet matrix and data were re-scaled to 100%. The theoretical seamount was assumed to have a low initial level of exploitation. Modelled seamount fisheries were loosely based on those operating at the Azores / North Atlantic ridge, and thus divided in 6 fleets (Morato et al., 2001): demersal longline (targeting shallow water demersal and benthic fish species; operating mainly up to 600m depth); deepwater longline (targeting bathypelagic and bathybenthic; operating mainly at 800-1200m); small pelagics fishery (for small pelagic fishes); tuna fishery; swordfish fishery; deepwater trawl (targeting seamount associated species, including orange roughy and alfonsinos; operating mainly at 800-1200). Landings (Table 3.3) were assumed to be small and varied from 218 t-year\"1 for shallow benthic fishes to 2.7 t-year\"1 for billfishes. Initially, I assumed the seamount to be a closed system with no advection of micronekton from outside the system or biomass accumulation. Thus, this model was named \"closed seamount model\". Another model was built which estimated rates of advection of micronekton into the system required to sustain various levels of fish biomass. This later model was named \"advection seamount model\" (described in section 3:2.4). Table 3.1 - Input parameters and estimates (in parentheses) from the theoretical \"closed seamount\" (closed) and \"advection\" (adv.) models of a seamount. P/B is production to biomass ratio, Q/B is consumption to biomass ratio, EE is ecotrophic efficiency, and TL is trophic level of the groups. P/B, Q/B, TL and catch were the same in the immigration and no-migration models. Bold numbers show those groups with increased biomass as a result some type of seamount effect . Group name Biomass (t-km\"2) closed Biomass (t-km\"2) adv. P/B Q/B (year\"1) (year\"1) EE closed EE adv. TL Catch (t-km\"2 •year\"1) Toothed whales 0.0001 0.0001 0.020 10.270 (0.514) 0.514 5.03 Baleen whales 0.123 0.123 0.060 5.563 (0.024) 0.024 3.45 Dolphins 0.040 0.100 0.070 11.410 (0.049) 0.020 4.31 Sea turtles 0.001 0.001 0.150 3.500 (0.900) 0.900 3.83 Seabirds 0.0001 0.00025 0.040 84.390 (0.257) 0.103 4.25 Tunas 0.032 0.191 0.742 16.291 (0.706) 0.118 4.34 0.011 Bill fishes 0.020 0.020 0.500 4.200 (0.101) 0.101 4.53 0.001 Pelagic sharks 0.011 0.011 0.300 3.100 (0.916) 0.914 4.57 0.002 Benthopelagic sharks 0.030 0.030 0.510 6.900 (0.154) 0.154 4.33 0.002 Rays and skates 0.020 0.020 0.170 1.500 (0.678) 0.678 3.84 0.002 Large oceanic planktivores (0.003) 0.003 0.112 2.066 0.100 (0.100) 3.50 Small epipelagic fish 0.859 0.859 2.053 19.867 (0.734) 0.734 3.07 0.050 Medium epipelagic fish 0.113 0.113 1.080 10.750 (0.982) 0.983 3.53 0.010 Large epipelagic fish 0.014 0.014 0.690 5.095 (0.870) 0.870 4.10 Small mig. mesopelagic fish 2.000 2.000 1.980 8.000 (0.974) 0.974 3.30 Large mig. mesopelagic fish (0.970) 0.970 0.600 3.550 0.950 (0.950) 3.98 Non-mig. mesopelagic fish (3.974) 3.974 0.500 1.570 0.950 (0.950) 3.17 Shallow benthic fish (0.723) (0.723) 0.590 4.700 0.950 0.950 3.64 0.080 Shallow demersal fish (0.215) (0.215) 0.660 5.200 0.950 0.950 3.98 0.020 Seamouts-associated fish (0.592) 0:592 0.060 2.200 0.950. (0.945) 4.08 0.011 Hoplostethus atlanticus (0.780) 41.930 0.048 2.000 0.850 (0.040) 4.19 0.010 Beryx spp. (0.531) 5.313 0.060 2.000 0.950 (0.095) 3.87 0.010 Bathypelagic fihes (0.796) 0.796 0.500 1 All 0.950 (0.950) 3.82 0.006 Bathybenthic fishes (1.264) (1.265) 0.200 0.500 0.950 (0.950 3.27 0.003 Bathydemersal Fishes (1.009) (1.010) 0.200 0.600 0.950 0,950 3.86 0.002 Benthic invert, filter feeders (0.755) 0.755 0.800 9.000 0.950 (0.950) 2.00 Benthic invert, scavengers (2.869) (2.870) 1.830 13.567 0.950 0.950 2.37 Benthic crustaceans (3.425) (3.426) 1.600 10.000 0.950 0.950 2.22 Pelagic crustaceans (6.094) 6.094 1.450 9.667 0.950 (0.950) 2.69 Cephalopods resident (0.120) (0.119) 2.890 10.000 0.950 0.950 3.39 Cephalopods drifting small (0.349) 0.349 4.450 16.863 0.950 (0.950) 3.60 Cephalopods drifting large (0.006) 0.006 2.500 10.000 0.950 (0.951) 4.15 Gelatinous zooplankton (9.428) 9.428 0.850 2.000 0.800 (0.800) 2.84 Shallow zooplankton 16.684 16.684 (11.214) 37.379 (0.710) 0.710 2.11 Deep zooplankton 6.849 6.849 (8.700) 29.000 (0.718) 0.718 2.11 Phytoplankton 7.160 7.160 283.500 - (0.261) 0.267 1.00 Detritus 100.000 100.000 - - (0.160) 0.162 1.00 Table 3.2 - Diet matrix in weight proportions for a generic seamount model in the Northeast Atlantic. Columns stand for predators while rows stand for prey. Numbers in columns headings represent the predator groups as defined in different rows of the first two columns. Prey \\ Predator 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 1 Toothed whales 0.001 2 Baleen whales 0.001 0.005 3 Dolphins 0.10 0.001 4 Sea turtles 0.001 5 Seabirds 0.001 6 Tunas 0.10 0.140 0.01 7 Billfishes 0.007 8 Pelagic sharks 0.08 0.01 0.005 9 Benthopelagic sharks 0.01 10 Rays and skates 0.007 0.002 11 Large oceanic planktivores 0.001 12 Small epipelagic fish 0.01 0.100.30 0.25 0.42 0.30 0.20 0.08 0.10 0.20 0.40 0.03 0.05 13 Medium epipelagic fish 0.05 0.08 0.35 0.05 0.20 14 Large epipelagic fish 0.10 15 Small mig. mesopelagic fish 0.100.100.050.050.04 0.05 0.01 0.09 0.05 0.01 0.167 0.20 0.15 16 Large mig. mesopelagic fish 0.05 0.01 17 Non-mig. mesopelagic fish 0.06 0.05 0.05 0.10 0.03 0.02 0.1560.01 18 Shallow benthic fishes 0.10 0.25 0.05 0.05 0.10 0.07 0.15 19 Shallow demersal fishes 0.10 0.10 0.100.01 0.150.05 0.015 20 Seamouts-associated fishes 0.05 0.05 0.005 0.001 21 Hoplostethus atlanticus 0.10 0.05 22 Beryx spp. 0.05 0.05 0.001 23 Bathypelagic fihes 0.08 0.05 0.056 24 Bathybenthic fishes 0.10 0.03 25 Bathydemersal Fishes 0.100.05 0.08 0.009 26 Benthic invert, filter feeders 0.05 0.08 0.07 0.05 27 Benthic invert, scavengers 0.02 0.05 0.18 0.150.15 28 Benthic crustaceans 0.01 0.01 0.05 0.07 0.25 0.05 29 Pelagic crustaceans 0.06 0.05 0.01 0.10 0.180.09 0.100.050.200.111 0.090.100.15 30 Cephalopods resident 0.20 0.05 0.10 0.100.08 0.050.10 0.01 0.05 31 Cephalopods drifting small 0.050.100.01 0.150.150.10 0.01 0.09 0.05 0.10 0.167 0.10 32 Cephalopods drifting large 0.20 0.05 0.06 0.04 33 Gelatinous zooplankton 0.05 0.89 0.03 0.03 0.158 0.1000.050.100.100.111 0.200.10 34 Shallow zooplankton 0.25 0.05 0.07 0.350.8000.550.100.35 0.90 35 Deep zooplankton 0.35 0.28 0.35 0.222 36 Phytoplankton 0.05 0.100 37 Detritus 0.05 38 Import Table 3.2 - cont. Prey \\ Predator 20 21 22 23 24 25 26 27 28 29' 30 31 32 33 34 35 1 Toothed whales 2 Baleen whales 3 Dolphins 4 Sea turtles 5 Seabirds 6 Tunas 7 Billfishes 8 Pelagic sharks 9 Benthopelagic sharks 10 Rays and skates 11 Large oceanic planktivores 12 Small epipelagic fish 0.05 0.05 13 Medium epipelagic fish 14 Large epipelagic fish 15 Small mig. mesopelagic fish 0.35 0.35 0.100.10 0.10 0.05 0.150.10 16 Large mig. mesopelagic fish 0.05 0.10 0.10 0.05 17Non-mig. mesopelagic fish 0.15 0.05 0.10 0.10 0.05 0.100.100.10 18 Shallow benthic fishes 0.05 19 Shallow demersal fishes 20 Seamouts-associated fishes 0.005 0.005 21 Hoplostethus atlanticus 0.005 0.01 22 Beryx spp. 0.01 23 Bathypelagic fihes 0.10 0.005 0.05 24 Bathybenthic fishes 0.045 0.05 0.15 0.20 25 Bathydemersal Fishes 0.05 0.05 0.01 0.30 26 Benthic invert, filter feeders 0.20 0.05 27 Benthic invert, scavengers 0.10 0.10 0.20 0.05 0.10 28 Benthic crustaceans 0.05 0.25 0.10 0.10 0.10 29 Pelagic crustaceans 0.05 0.25 0.500.10 0.10 0.05 0.150.050.100.10 30 Cephalopods resident 0.05 0.05 0.05 31 Cephalopods drifting small 0.050.15 0.04 0.15 0.01 0.05 32 Cephalopods drifting large 33 Gelatinous zooplankton 0.15 0.05 0.05 0.05 0.05 0.05 0.15 0.20 34 Shallow zooplankton 0.05 0.10 0.375 0.15 0.10 0.35 0.10 0.05 35 Deep zooplankton 0.20 0.015 0.050.10 0.25 0.200.200.25 0.05 36 Phytoplankton 0.25 0.10-0.25 0.100.800.10 37 Detritus 0.055 0.185 0.15 0.75 0.70 0.70 0.125 0.20 0.10 0.20 0.10 0.80 38 Import Table 3.3- Catch (t km^ year\"1) and total catch (t-year\"1) estimated for the different fisheries considered in the theoretical seamount. DL is demersal longline; DWL is deep-water longline; SP is small pelagics fishery; T is tuna pole-and-line fishery; SW is swordfish longline fishery; and DWT is deep-water trawl. _ 1 p Landings by fleet (t-km\" -year\" ) t-year\" Group name DL DWL SP T SW DWT Total Tunas 0.011 30.0 Billfishes 0.001 2.7 Sharks Pelagic 0.001 0.001 5.5 Sharks Benthopelagic 0.001 0.001 5.5 Rays and Skates 0.002 5.5 Epipelagic S 0.050 136.4 Epipelagic M 0.010 27.3 Shallow Benthic Fishes 0.080 218.2 Shallow Demersal fishes 0.020 54.6 Seamouts-associated Fishes 0.001 0.010 30.0 Hoplostethus atlanticus 0.010 27.3 Beryx spp. 0.005 0.005 27.3 Bathypelagic 0.005 0.001 16.4 Bathybenthic fishes 0.002 0.001 8.2 Bathydemersal Fishes 0.001 0.001 5.5 Total (t-km\"z-year\"') 0.110 0.008 0.060 0.011 0.002 0.029 3.2.3 Impact of primary production enhancement I explored the potential impact of heightened levels of primary production on higher trophic levels, using the \"closed model\". The estimated primary productivity for the Northeast 2 j Atlantic region was 2030 t-km\" -year (SeaWIFS data set). Pauly et al. (pers. comm.) found that seamounts whose peak reaches to within 100 m of the surface would generate an increase in local primary production of between 50 to 70% of the surrounding areas, while those with summits at 300m depth would generate an increase of only between 2 and 5%. I used these values to estimate the biomasses of orange roughy that could be sustained under these conditions. Also, I quantified the amount of primary production required (PPR) to sustain different levels of orange roughy biomasses, as reported for several geographical areas. The \"closed\" seamount model was balanced by providing the biomasses of ten top predators: toothed whales, baleen whales, dolphins, sea-turtles, seabirds, tunas, billfishes, pelagic sharks, benthopelagic sharks, rays and skates. The Ecopath model was then run with different biomasses of orange roughy and the primary production required to sustain the new system was calculated. The linear relationship between PPR and orange roughy biomass was then computed and the levels of orange roughy sustained by different levels of local PP estimated. 3.2.4 The \"advection model\" Using the EwE approach, advection of prey can be modelled in two ways. The first approach, which is used only when there-is a permanent addition of organisms from'other areas outside the system, assumes treatment of migratory flows as dispersal (immigration / emigration) rates across the system boundaries (Christensen et al., 2005). Dispersal rates allow quantification of the amount of imported resources needed to maintain the system and simulation of the impact of different levels of immigration, due to inter-annual changes in climate, primary production, and currents. The second approach, which I did not use, involves the assignment of a high diet proportion as \"import\" in the Ecopath diet composition matrix (Christensen et al., 2005). I simulated and quantified the immigration of prey organisms that would be required to maintain a \"hypothetical\" seamount community. Total immigration rates required were estimated as a function of potential biomass of predators as: (7) I — a-Bj+b where the slope a is the Q/B ratio of predator j and the intercept b is roughly the consumption of the predator in the balanced \"closed seamount model\". To estimate the total immigration required to sustain a \"typical\" seamount community in the northeast Atlantic, I assumed that the standing biomasses estimated by the \"closed seamount model\" would roughly represent the biomasses of most of the groups, except for seamount aggregating fish, orange roughy, Beryx spp., and some pelagic fish groups that are attracted to seamounts (i.e., seabirds, tunas, billfishes, and pelagic sharks). P/B, Q/B, EE, and the estimated standing biomasses were re-entered in the new model and the following assumptions were made: 1) biomass estimates for orange roughy in the North Atlantic are not available and so southern hemisphere data from Bulman (2002) were used. Biomass was re-scaled to the total area, assuming that part of it is open water where seamount associated 2 I species are unlikely to occur (41.9 t-km\" -year\" ); 2) biomasses alfonsinos were estimated to 2 1 * be 10 times higher than in the \"closed seamount model\" (5.3 t-km\" -year\" ); 3) biomass of groups attracted to seamounts was increased based on data from Chapter 4, and thus considered greater than in the open ocean, i.e. abundances of marine mammals, seabirds and tuna were assumed to be 2.5, 2.5 and 6 times higher than in the \"closed seamount model\". No changes were made to the benthic filter feeding group. In order to check if the necessary immigration ratios could be supported by local oceanographic conditions I estimated the available micronekton in t-km\"2-year\"' that could be advected to the system. Ocean current velocity (V c) was derived from local measurements at the Sedlo seamount North of the Azores island of Graciosa while the standing biomass (B) of migrating groups (j) were assumed to be those of the \"closed seamount model\". The immigration (7) of micronekton can thus be estimated as: (8) I rV c-BrW' where W is the average width of the area covered by the model. Sedlo seamount (40° 20'N, 26° 54'W) current patterns have been described by White et al. (2006). Sedlo is an elongated, multi-peaked seamount located within the sub tropical north Atlantic gyre. Current residual flow at Sedlo seamount may average 5 cm-s\"1 in the upper 800m while in deeper waters (>800m) may average 1 cm-s\"1. Tidal currents appeared to be amplified by about 2-4x the surrounding ocean values. Since most of the orange roughy aggregations are found at 800m or deeper, and in order to have conservative estimates of immigration rates, I assumed V c of 1 cm-s\"1. 3 . 3 RESULTS 3.3.1 The \"closed seamount model\" The initial model was built with no special features in the seamount ecosystem, in order to estimate baseline standing biomasses for the different groups. This \"closed model\" was balanced after small changes in the diet matrix and the resulting parameter set is given in Tables 3.1 and 3.2; trophic structure is shown in Figure 3.1. The total biomass of the modeled ecosystem, excluding detritus, was estimated as 68 t-km\" . Primary producers account for 10.5% of the total biomass of our \"closed model\", whereas the highest trophic levels (7X>4.30), i.e., toothed whales, pelagic sharks, billfishes, tunas, benthopelagic sharks and dolphins, account for 0.14%) of the total biomass. Aggregating fishes account for only 2.8% of the total biomass of the system. The estimated biomasses were 0.78 t-km\"2 of orange 2 2 roughy, 0.53 t-km\" of alfonsinos, and 0.59 t-km\" of other seamount-associated fish species. Sharks Pelagic jdphins T^ephsrfopocis Dri a^ttydemersal F V \\ f^tytoFtanktcn ~ \" -Figure 3.1 - Flow diagram of a seamount ecosystem. The area of the nodes is proportional to the biomass of each group. ON 3.3.2 Impact of primary production enhancement In our system the biomass of orange roughy (Borh) can be expressed as a function of the primary production ( P P ) as: Bo r h (t-km'2) = 0 . 0 0 1 8 - P P R - 2 . 9 6 5 (or P P R (t-km\"2-year_1) = 5 4 2 . l B O R H + l 6 0 7 . 6 ) . The average primary productivity for the Northeast Atlantic (PPNEA) could sustain only about 0.78 t-km\"2-year' of orange roughy for our generic seamount model. This analysis is based on the assumption of a linear relationship between PP and biomass of higher trophic levels, which may not always be the case, thus warranting further examination mainly through dynamic simulations modelling. The effect of primary production (PP) enhancement on orange roughy biomass at the \"closed seamount model\" in the Northeast Atlantic is presented in Table 3.4. A 50-70% increase in local PP, typical for seamounts reaching to about 100 m of the surface (Pauly et al., pers. comm.), could sustain about 3.0-7.3 t-km\" of orange roughy biomass, which represents a four to nine fold increase in biomass when compared to the non PP enhancement scenario. Our results suggested that seamounts with summits at about 300m depth (depth of the summit in the generic seamount model), and which generate an increase in PP between 2 and 5%, could 2 sustain only 0.8-1.0 t-km\"' of orange roughy. As previously suggested, the values of PP required to sustain the levels of orange roughy biomass reported from different seamounts around the world are much higher than the observed values. For example, to sustain 100 2 * • t-kiri of orange roughy (similar to that observed off Tasmania; Koslow, 1997; Bulman, 2002) the PPR is about 55800 t-km\"2-year_1, 28 times higher than the observed values for the Northeast Atlantic. 3.3.3 The \"advection model\" Total immigration biomass was estimated as a function of the potential biomass of different predatory groups that are somehow attracted to seamounts (Table 3.5). Taking as an example 2 I the orange roughy, the total immigration rate required (t-km\" -year\" ) was estimated as: I = 2.00\\S„,/,-l .560. Thus, a potential biomass of orange roughy of 100 t-km\" will require a total 2 I immigration rate of 198.4 t-km\"-year\" , which represents an import of 119%) the total standing biomass of the system (excluding detritus). Prey groups with higher immigration 2 I rates were: small (vertical) migrating mesopelagic fish (69.5 t km\" -year\" ), pelagic 2 1 2 1 crustaceans (49.6 t-km\" -year\" ), mesopelagic non-migrating fish (29.8 t-km\" -year\" ), small drifting cephalopods (29.8 t-km\"2-year\"'), gelatinous zooplankton (9.9 t-km\"2-year\"') and large (vertical) migrating mesopelagic fish (9.9 t-km\"2-year\"1). Table 3.4 -Effect of primary production (PP) enhancement on orange roughy biomass at a generic seamount in the Northeast Atlantic. Estimated primary productivity for the Northeast Atlantic (PPNEA) region was 2030 t-km\"2-year\"' (SeaWIFS data set). PP Enhancement PP Biomass (orange roughy) 2 1 (t-km\" -year\" ) (t-km\"2) 1 .00 2030 0.780 1.02 2071 0.855 1.05 2171 1.046 1.50 3261 3.052 1.70 5544 7.263 Table 3.5 - Total immigration rate required (t-km\"2-year\"'), estimated as a function of potential biomass of predators as: I = a-Bj+b\\ where Bj is the biomass of the predator j, the slope a is the Q/B ratio of i and the intercept b is roughly the consumption of i in the balanced \"closed seamount model\". Predator Group a b Orange roughy 2.000 -1.560 Alfonsinos 2.000 -1.062 Dolphins 11.410 -0.456 Seabirds 84.390 -0.008 Tuna 16.291 -0.519 To enable the model to estimate the total immigration required to sustain a \"typical\" seamount community in the northeast Atlantic, abundances of dolphins, seabirds and tuna were assumed to be 0.1, 0.00025 and 0.191 t-km\"2, respectively. Using these abundances to estimate the prey immigration rates required to sustain these predatory groups, I calculated that marine mammals (dolphins only) require 0.7 t-km\"2-year\"', seabirds 0.01 t-km\"2-year\"', 2 1 2 and tuna 2.6 t-km\" -year\" . Additionally, 1 assumed biomasses of orange roughy of 41.9 t-km\" and of alfonsinos of 5.3 t-km\"2 requiring immigration rates of 82.3 t-km\"2-year\"' and 9.6 t-km\" 2 1 •year\" , respectively. In this case, the total immigration rate required to sustain this \"typical\" 9 I seamount community would be 95.2 t-km\" -year\" . If I assumed an average current velocity (V c) of 1 cm-s\"1 (or 315 km-year\"1), a model width (W) of 53 km and a standing biomass (B) of migrating groups of 23.787 t-km\"2 (i.e. small epipelagic fish, medium epipelagic fish, small migrating mesopelagic fish, large migrating mesopelagic fish, non-migrating mesopelagic fish, pelagic crustaceans, drifting small cephalopods, and gelatinous zooplankton), the total immigration rates available to the system 2 1 ' was about 141.5 t-km\" -year\" , about 50% more than what may be needed by the \"typical\" 2 1 seamount ecosystem (95.2 t-km\" -year\" ). Higher current velocities, typical from seamount summits, generated even higher biomasses of advected organisms. I also tested the extent to which current velocities and advected rates of organisms may influence the potential standing biomass of orange roughy. For most current velocities there was enough food to sustain high standing biomasses of orange roughy (Figure 3.2). Only when the biomass of advected micronekton and current velocity were both very low (below 2 I 10 t-km\" and 5 cm-s\" , respectively), may food be a limiting factor for orange roughy. Otherwise, it seems that food is not the limiting factor for orange roughy abundance. Z axis; Potential biomass of orange roughy (t-km\"2) 0 1 1 1 j 1 1 1 1 1 1 r 1 1 1 1 1 T 1 1 1 \"1' v I '— I I n I —I I I | I I I t I I I I I | I I—\" r r'' T rTTT . 1 0 0 2 4 6 8 10 15 20 2 5 3 0 35 4 0 4 5 50 Advection rate of micronekton (t-km 2-year\"1) 2 Figure 3.2 - Potential biomass of orange roughy (t-km\" ) that could be sustained by different 2 1 rates of advected micronekton (t-km\" -year\" ) and oceanic current velocities (cm-s\" ). Scale goes from white and light blue (low rates of advection) to dark red (high rates of advection). 3 . 4 DISCUSSION The \"closed seamount model\" was built to estimate baseline standing biomasses for the different groups. Compared to a general \"open ocean\" model built for the North Atlantic (Vasconcellos and Watson, 2004) the biomasses estimated for the pelagic groups were very similar. Such a result was expected because I did not take into account any ecological processes such as dense fish aggregations around seamounts, the hydrological trapping of small organisms or horizontal flux of micronekton. In contrast, estimated biomasses for benthic and demersal groups of the present study were higher than in the open ocean model. For example, Vasconcellos and Watson (2004) estimated biomass of small and large bathydemersal fish in the slope and abyss zones of the North Atlantic as being 0.45 t-km\" while in the seamount models the biomass of bathydemersal and bathybenthic fishes summed to almost five times as much, 2.06 t-km\"2. These differences are due to greater habitat availability at suitable depths for these groups on seamounts. On the other hand, our biomass estimates of benthic organisms were very similar to the estimates for a deepwater flat bottom model off Tasmania (Bulman, 2002). The estimated biomasses of seamount associated species (orange roughy, alfonsinos, and other seamount associated fishes) in the \"closed model\" were very low (<2 t-km\" ) when compared to reported abundances found in the literature (Koslow, 1997; Bulman, 2002). For example, off Tasmania Bulman (2002) estimated biomasses of different seamount 2 2 aggregating fish as 106.7 t-km\" of orange roughy, 4.11 t-km\" of oreos (Pseudocyttus maculatus, Neocyttus rhomboidalis and Allocyttus niger) and 8.21 t-km\" of warty dory (Allocyttus verrucosus). These estimates were based on many assumptions and were later found to be conservative. Using commercial catch data from Tasmania seamounts Bulman et al. (2002b) found the biomass of oreos to be 300 t-km\" . Thus, these results suggest lack of resources in the system to support such amounts of seamount aggregating fish. In other words, local seamount production may be responsible for sustaining only a small amount of its total biomass. The question of whether increased primary production could explain the occurrence of large aggregations of seamount fishes was thus tested. The second part of this work was to obtain more accurate estimates of the primary production required (PPR) to sustain large aggregations of fish around seamount ecosystems. Our study supports the idea that local primary productivity enhancement cannot sustain large aggregations of seamount fishes. However, our results differ markedly from, those of the previous study based on a simpler approach. Koslow (1997), estimated that the PPR to 2 2 1 sustain 100 t-km\" of orange roughy was about 18000 t-km\" -year\" , while in our study the 2 i PPR to sustain the same biomass would be t 565 t- - r . The difference is due toour use of an ecosystem approach instead of single species perspective, i.e., not all PP will be available to orange roughy as in Koslow (1997) assumptions. Instead, there will be many other predators feeding on same prey as roughy that will also require some PP. From these calculations, ! can conclude that aggregations of seamount fishes on seamounts cannot likely be sustained by local biological productivity. This finding refutes the first hypothesis and supports Koslow (1997). Therefore, how are large aggregations of fish sustained around seamounts? Both local production and enhanced primary production (Uda and Ishino, 1958; Hubbs, 1959; Uchida and Tagami, 1984) are clearly not sufficient sources of energy to these aggregations. Our simulations using the \"advection\" model clearly show that these aggregations might be sustained by the horizontal flux of prey organisms that past seamounts, thus supporting the \"feed-rest\" hypothesis (term used by Genin, 2004) originally proposed by Tseytlin (1985). I suggest that the required flux of micronekton to an intermediate seamount can be measured as I = 2.00-i?,„./j-l .560 (where / the immigration to the system and Borh is the biomass of orange roughy). This seamount model, which took into account high abundances of fish, marine mammals, seabirds and tuna, required a total immigration of micronekton of 95.2 t-km\"2-year\"', which is less than the potential available biomass after considering currents (141.5 t-km\"2-year\"'). Therefore, I suggest that the horizontal flux of prey may be sufficient to sustain the rich communities living on seamounts. Problems with these calculations may arise from over-estimation of standing biomasses of micronekton (23.8 t-km\"2 in our example); or from over-estimation of current velocities (0.1 cm s\"1 in our example). However, using acoustic and trawling methods, the standing biomasses of mesopelagic fishes and pelagic crustaceans off southern Tasmania were estimated to be about 100 t-km\"2 and 5 t-km\"2 respectively (Koslow et al., 1997; Williams and Koslow, 1997). These values are four times higher than the values I used in our model. Furthermore, the current velocity I used is a very conservative value. Current velocities measured in different intermediate or shallow seamounts in northeast Pacific Ocean range from about 12 cm-s\"1 at Cobb seamount (Freeland, 1994) to 20-40 cm-s\"1 at Fieberling seamount (Eriksen, 1991) or 15-35 cm-s\"' at Emperor Seamount (Roden, 1987). In the north Atlantic current velocities at Great Meteor Seamount measured 12-15 cm-s\"1 (Mohn and Beckmann, 2002) and at Gorringe seamount measured 15-20 cm-s\"' (Serra and Ambar, 2002). These results suggest that food may not be a limiting factor for orange roughy at some seamounts. Only when the biomass of advected micronekton and current velocity are both extremely low (below 10 t-km\"2 and 5 cm-s\"', respectively) will the flux of prey be insufficient to sustain large aggregations of orange roughy (Figure 3.2). The question now is what can influence seamount fish abundance and explain the high variability of abundances from one seamount to the other (e.g., Clark et al., 2001; McClatchie and Coombs, 2005; Rowden et al., 2005). There is a considerable literature dealing with the composition of fish assemblages and their association with various environmental factors. Aspects of bottom depth, latitude, longitude, sediment type, bottom temperature, and oceanographic water masses are frequently recorded as important in determining fish species composition and abundance (e.g., Haedrich and Merrett, 1990; Koslow, 1993; Koslow et al., 1994; Francis et al, 2002). Clark et al. (2001) examined relationships between physical variables of seamounts around New Zealand, and the estimated size of orange roughy populations from those seamounts. They used multiple regression procedures to model the effects of the physical variables. Seamount location, depth of the peak, slope of the seamount flanks, and geological association (continental or oceanic) were significant factors in determining stock size in various analyses. Since I have shown that food may not be a limiting factor, other factors may explain the high variability of abundances between seamounts (Melo and Menezes, 2002). For large aggregations of seamount fish the availability of sheltered or resting areas may be a determinant factor in explaining intra-seamount differences in abundance. This is probably the reason why Clark et al. (2001) found the slope of the seamount flanks a significant factor.. For example, orange roughy and some oreos have a relatively high metabolic rate compared to many other deep-sea fishes as they in order to maintain position in highly dynamic current regimes require strong locomotory performance (Koslow, 1996; Bulman, 2002). However, they will also need quiescent areas for resting between feeding events. Some observational studies support this hypothesis (Lorance et al., 2002), showing that perhaps the fish has evolved to efficiently utilize enhanced horizontal fluxes in strong currents and to effectively reduce their metabolic expenditure by resting motionless in topographic shelters (Genin, 2004). 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C H A P T E R 4 TESTING A SEAMOUNT EFFECT ON AGGREGATING VISITORS 1 4.1 INTRODUCTION Seamounts have been recently recognized as highly important for fisheries, biodiversity and conservation as they often support isolated but rich underwater ecosystems (Morato and Pauly, 2004). One important characteristic of seamounts is that they tend to enhance water currents (Genin et al., .1986; Boehlert, 1988) thus enhancing the flux of prey organisms that pass the seamounts (Chapter 3; Tseitlin, 1985; Genin et al., 1986; Koslow, 1997). This characteristic has been used to explain the occurrence of large aggregations of bottom fishes around seamounts. While the importance of seamounts for bottom fishes is very well documented (Boehlert and Sasaki, 1988; Koslow, 1996; Koslow, 1997; Koslow et al., 2000), the importance for large pelagic or visiting organisms has been poorly examined. However, it has been hypothesised that there are higher abundances of some \"visiting\" animals, such as tuna, sharks, billfishes, marine mammals, sea-turtles and even seabirds, over seamounts but this has been based on sparse records, warranting further examination. It is known from tagging studies that some seamounts host transient populations of bigeye tuna Thunnus obesus and yellowfin tuna T. albacares (Holland et al., 1999; Itano and Holland, 2000; Sibert et al., 2000; Klimley et al., 2003; Musyl et al., 2003; Sibert et al., 2003), consequently some fisheries have taken advantage of these and other tuna aggregations (like albacore T. alalunga and skipjack Katsuwonus pelamis) to increase their yields (Yasui, 1986; Fonteneau, 1991; Adam et al., 2003). Other pelagic fishes such as billfishes (Ward et al., 2000; Sedberry and Loefer, 2001) and pelagic sharks (Hazin et al., 1 A version of this chapter has been submitted for publication. Morato, T.; M. Machete; A. Kitchingman; F. Tempera; S. Lai; G. Menezes; R.S. Santos; and T.J. Pitcher (submitted). Abundance and distribution of seamounts in the Azores. Marine Ecology Progress Series. 1998; Klimley et al., 1988) appear also to be attracted to complex high-relief bottom structures where they may be subject to local depletion. The reasons for these aggregations are still under debate but most seamounts act both as feeding stations and as orientation points in the larger-scale movement patterns of these visitors (e.g., Freon and Dagorn, 2000). Although several works have correlated cetacean occurrence with complex and steep topographies (e. g. Schoenherr, 1991; Balcomb, 1989; Canadas et al., 2002; Hooker et al., 2002; Hastie et al., 2004; Yen et al., 2004) the literature addressing their association with seamounts is scarce. Santos et al. (in press) concluded that seamounts are \"hotspots\" for sea-turtles because they found that tracked loggerhead Caretta caretta move toward seamounts where they have increased residence time. As to seabirds, Cory's shearwater Calonectris diomedea, yellow-legged gull Larus cachinnans atlantis, Madeiran storm petrel Oceanodroma castro (Monteiro et al. 1996), Cassin's auklet Ptychoramphus aleuticus (Yen et al., 2004, 2005), and black-footed albatross Diomedea nigripes (Haney et al., 1995) have also been observed aggregating above seamounts summits where they feed on zooplankton, small fish and small cephalopods. Whereas previous studies have focused on analysing the auto-ecology of some organisms, in this paper, using data from a fishery observer program, I explicitly test if the abundances of tuna, marine mammals, sea turtles and seabirds observed at Azores seamounts are higher than expected by chance. 4 . 2 METHODS 4.2.1 Study area The Azores archipelago is a group of nine volcanic islands and many small islets that are parts of the Mid-Atlantic Ridge in the Northeast Atlantic Ocean (an irregular area within 33.5-43° N, 21-35.5° W). Shallow seabed less than 600 m deep cover less than 1% of the 953,633 km of the Azorean EEZ. This reflects the narrowness of the island shelves and supports scattered fishing grounds. This study was conducted in a small rectangular area of the Azores EEZ (36.20-40.20° N, 24.20-32.00° W) chosen because it includes most of the seamounts, has better bathymetry data, and because all the tuna fishing effort and thus the fishery observer program was conducted within this area. In this study, I used seamounts mapped (using an automated technique -Chapter 2) in the designated area of the Azores EEZ as described above which contains 39 large and 151 small seamount-like features (Figure 4.1) with different shapes, heights and depth of summits. The seamounts included in this study are those with numbers 1, 3-28, 30, 31, 33-39, and 50-52 in Appendix 2. Data from island shores (<30 km) were excluded from the analyses because they may be biased by the Island Mass Effect and because I wanted to test the effect of offshore seamounts. J W * f w w 3HWW jbwv 5«reTf nsyttm >1 }H 1 i II - ~ •• t m . mmmm 5r:i rr'f.y i • -WW WW r w t Figure 4.1 - Map of the Azores archipelago and its seamounts. r 4.2.2 Data collection: POPA Observer program The observer program for fisheries in the Azores (POPA, http://www.horta.uac.pt/projectos/popa) was launched in 1998 with the main goal of gathering data on the pole-and-line tuna fisheries in order to certify the fisheries as 'dolphin safe'. The program covers about 50% of the fleet of around 20 vessels and often covers over 50% of the tuna catches. The program operates with observers onboard tuna fishing vessels recording data on fishing activities and gathering other scientifically-relevant information. Geo-referenced data collected onboard includes: fishing effort, tuna catch, sighting effort for different species, and sighting of named species of marine mammals, sea turtles, and seabirds. Fishing effort is estimated as the amount of time fishermen spent looking for tuna schools. Sighting effort is defined as the time the observer spent looking at the sea searching for species other than tuna. Marine mammals, sea-turtles and seabirds were quantified by counting individuals (or estimation when in large groups) that were up to 300 meters from the vessel. The average speed of the boats when searching was 8 knots 15 km-h\"1). 4.2.3 Species The present study focused on different species of tuna, marine mammals, sea turtles and seabirds (Table 4.1). Tuna are present in the Azores EEZ only during summer months: our study focuses on adult bigeye and skipjack, the most common tuna species caught in the Azores. The first species is present during April to June, while the second is usually caught from June to October. Albacore, yellowfin and bluefin tuna (Thunnus thynnus) are also caught in the Azores in small quantities. Tunas are caught with pole-and-line, usually with water spray and live bait. Only about 20 medium-sized Azorean boats (28-32 m long) fish within the EEZ. In fact, tuna fishery catches are relatively small and can be around 5000 tons in a good year. Table 4.1 - Species included in the present study, the number of observations by species and summary of the data on distance from observed data points to closest seamount (km). observations Dist. to seamount (km) Species n Min - max Mean ± SD Tuna Skipjack, Katsuwonus pelamis 1675 0.4->100 50.7±49.2 Bigeye tuna, Thunnus obesus 1497 2.0->100 28.4±36.3 Marine mammals Common dolphin, Delphinus delphis 2008 0.2->100 30.U33.0 Spotted dolphin, Stenella frontalis 528 0.7->100 44.3±53.9 Bottlenose dolphins, Tursiops truncatus 303 0.3->100 29.7±33.1 Sperm whale, Physeter macrocephalus 233 1.6->100 44.1±35.8 Sea Turtles * ' Loggerhead turtle, Caretta caretta- 566 0.6->100 52.5±65.6 Seabirds Cory's shearwater, Calonectris diomedea borealis 1681 0.4->100 37.0±46.8 Yellow-legged gull, Larus cachinnans atlantis 329 1,7->l 00 43.2±52.5 Terns, Sterna hirundo and S. dougalli 134 3.2->100 54.2±58.9 Over twenty-three species of cetaceans have been reported for the Azores (Gon9alves et al., 1992, 1996; Silva et al., 2003). From these only a part is recorded yearly and from those the most common species are the spotted dolphin Stenella frontalis, common dolphin Delphinus delphis, bottlenose dolphin Tursiops truncatus, sperm whale Physeter macrocephalus, Risso's dolphin Grampus griseus, stripped dolphin Stenella coeruleoalba, short-finned pilot whale Globicephala macrorhynchus (Silva et al., 2003). Our study will focus on only the first four species because of the lack of data for the others. The sea-turtles occurring in the Azores are loggerhead, leatherback Dermochelys coriacea, and green turtles Chelonia mydas, the first being the most commonly sighted species. Juvenile loggerhead turtles are transported by the North Atlantic Gyre current and live a pelagic life for about 8 years in the Eastern Atlantic, including around the Azores (Bjorndal et al., 2000). This study will focus only on the loggerhead turtle. Several seabird species are found in the Azores. However, the most commonly-sighted species are Cory's shearwater, yellow-legged gull, terns Sterna hirundo and S. dougalli, Madeiran storm petrel Oceanodroma castro, greater shearwater Puffmus gravis, and the little shearwater Puffmus assimilis. The petrels feed on vertical migrating fish while Cory's shearwater feeds often in association with marine predators such as dolphins and tuna (Monteiro et al., 1996). This study will focus only on Cory's shearwater, the yellow-legged gull and the terns. 4.2.4 Data analyses I used spatial data from the POPA program to test if tuna catches and sightings of sea-turtles, marine mammals and seabirds were higher closer to seamount summits. Data was available for the period 1998 to 2004 for tuna and marine mammals and for the period 2000-2004 for seabirds and sea turtles. A preliminary analyses showed no inter annual differences in the tuna catch or sighting effort. Thus, data for all years was pooled. I built grids of tuna catch, sighting effort, and sightings of different species by allocating each data point to a 0.05 x 0.05 degree cell (~5.5 x 5.5 km). This procedure produced two grids of tuna catch per 30.9 km2, one grid of sighting effort in hours per 30.9 km2, and several grids of numbers of individuals per 30.9 km . The latter grids where then divided by the effort grid to produce grids of numbers of individuals per 30.9 km2 per hour of search. These grids included cells with zero values, i.e., those cells with fishing or sighting effort but zero catch or observations. Null data cells were those with no fishing or sighting effort. Distances of observation cell to nearest seamount summit were separated into 10 classes of 10 km intervals from 0 to 100 km. Each cell with data was then allocated to one of the distance classes and the average catch and sightings were calculated for each class. The estimated averages were transformed in catches or sightings per km . I test if fishing locations were randomly selected by comparing the frequency distribution of the distances to seamount summit of the dataset of tuna fishing events and of a set of an equal number of randomly selected locations using the G statistic for the log-likelihood ratio goodness of fit. One-way analysis of variance (ANOVA) was used to test for significant differences between Log (x+1) transformed mean abundances (catch or sightings) at different distances from seamount summits. When significant F tests from the ANOVA were found, I looked for those means contributing to the effect using a post-hoc comparison. In this case, I used Dunnett's multiple comparison test to determine the significant differences between a control group mean and the treatment group means in the analysis of variance setting (Zar, 1999). The overall mean value was used as the control group mean, since I wanted to compare each distance to seamount to the overall mean (C.W. Dunnett, pers. comm.). Additionally, I have tested for seamounts that produced a significant effect on species association. Firstly, for those species that showed significant associations with seamounts I have estimated its abundance in the first 10 km for each seamount and compared these values with the overall mean for the 10 km bin. These analyses were preformed with a common Z test on log (x+1) transformed data. Finally, I have estimated species abundance in relation to seamount summits depth. Mean values of abundance for each seamount depth interval were then compared with the overall mean abundance to test if seamounts depth could be an important factor on explaining species abundances. These analyses were also preformed with a common Z test (Zar, 1999). 4 . 3 RESULTS 4.3.1 Tuna Observers recorded over four thousand tuna fishing events during the period from 1998 to 2004. The most common species caught with observers onboard during this period were skipjack and bigeye tuna with about 1500 and 962 tons, respectively. The frequency distribution of the distances to seamount summit of the dataset of tuna fishing events and of a set of an equal number of randomly selected locations is shown in Figure 4.2. Calculation of the G statistic for the log-likelihood ratio goodness of fit test show significant differences (G = 2344, df = 25, /?«0.001) between both distributions. This test allows us to assume that fishing locations were not randomly selected and thus to explore the influence of seamounts on tuna catches. 600 500 400 >> o C 0) 2- 300 CT 0) S-i 200 100 0 1 III I... I l l in i o in O (N in m • o m in o m m i o in >n l o in r--i o >n oo i o 00 in ON in o o , O o in m —• 0.05). a) 0.20 0.15 0.10 0.05 0.00 -0 9 b) 0.25 0.20 0.15 0.10 0.05 -0.00 II 0 10 20 30 40 50 60 70 80 90 100 Distance to seamount summit (Km) 0 10 20 30 40 50 60 70 80 90 100 Distance to seamount summit (Km) Figure 4.3 - Tuna catch per square kilometre per year (±95%CL) in relation to the distance to the nearest seamount summit in the Azores, a) skipjack, b) bigeye tuna. Bin size is 10 km. Light grey circles are those significantly higher (Dunnett test) than the overall mean (light grey line). 4.3.2 Oth er visitors A total of 3910 sightings of marine mammals were recorded from 1998 to 2004. The most commonly sighted species were common dolphin, spotted dolphin, bottlenose dolphin and sperm whale. Common dolphins showed significant different sightings at different distances from seamounts (ANOVA p« 0.001) whereas all other species showed no significant differences (ANOVAs bottlenose dolphin, p= 0.278; spotted dolphin, p= 0.392; sperm whale, p= 0.233). The common dolphin (Figure 4.4a) showed some association with seamounts where the highest observations per square kilometre per hour being recorded close to their summits (Dunnett (10km) p<0.02; Dunnett (20km) /?<0.01; Dunnett (30km) /?>0.05). On the other hand, bottlenose dolphins (Figure 4.4b), spotted dolphins (Figure 4.4c), and sperm whales (Figure 4.4d) showed no association with seamount and were not more abundant in the vicinity of the features. From 2000 to 2004 there were 566 valid observer sighting records of loggerhead turtle. The analyses of the number of loggerheads per square kilometre per hour (Figure 4.5) show no differences with distance to seamount summits (ANOVA sea turtles, p= 0.403). On the other hand, the distance to seamount summit influenced the abundance of Cory's shearwater (ANOVA p« 0.001). The abundance of Cory's shearwater (Figure 4.6a) was higher in the first 20 km from the seamount summit. However, only the second distance bin (10-20km) was significant higher than the overall mean (Dunnett 10km /?>0.05; Dunnett (20km)/K0.01; Dunnett (30km) /?>0.05): Terns (Figure 4.6b) and yellow-legged gull (Figure 4.6c) abundances where not different at different distances from seamounts (ANOVA yellow-legged gull,/?=0.364; terns,p= 0.998). . . . a\") 0.05 0.04 O JG 0.03 _ o e Jul ^ 0.02 -c 0 03 1 0.01 x> O 0.00 b) 0.012 0.010 0 .008 -0.006 0.004 0.002 1 0.000 0 10 20 30 40 50 60 70 80 90 100 Distance to seamount summit (Km) -i-0 10 20 30 40 50 60 70 80 90 100 Distance to seamount summit (Km) c) 0.040 0.035 - P 0.030 M 0.025 -^ 0.020 c cn C O '-s > S-i (-1 . Joao de Castro\" (# 25, Z^O.044), showed significant higher catches than the overall mean. Common dolphin showed higher abundances on top of \"Agores\" (#51, />=0.002), \"Princesa Alice\" (# 50, P=0.014), \"Agulhas do Sul do Gigante\" (# 36, P=0.044), whereas Cory's shearwater showed higher sightings on top of \"Princesa Alice\" (# 50, .P=0.003) and \"Agores\" (#51, /M3.006). a) Catch (kg-km\"2-year\"') 0.0 0.2 0.4 0.6 O.J c P o 6 03 0.00 0.02 0.04 0.06 0.08 0.10 G P o C3 (D xn 0.01). By combining mean depths and catch series, time series of the mean depth of the catch of marine bottom fishes (excluding pelagic) and for all marine fishes were estimated for the world and for different groupings of FAO statistical areas (ocean basis). The mean depth of the fisheries catch by year and ocean basis was estimated as the average depth of occurrence of the species (or group caught) weighted by the logarithm of their catch. Visual inspection of different datasets suggested an inflection point such that a single regression line would not suffice. I therefore fitted simple linear biphase regression models, using the algorithm described in Hintze (1998). I then compared biphase regression models to other simpler and more complex models. For this, I have fitted simple linear regression models as well as quadratic, cubic and fourth order models to the data. If the simpler model fits better (has a smaller sum-of-squares) than the more complex model (more parameters), then no statistical analysis was preformed and the simpler model was accepted. Since this rarely occurred, I used the likelihood ratio test (Hilborn and Mangel, 1997) to compare the goodness-of-fit of two models, where the more complex equation fits better than the simple equation. For most of the cases (8 out of 10) biphase regression models fitted the data significantly better than any other tested model (Table 5.1). Thus, biphase regression models were preferred. The only cases where biphase regression models were not preferred were for the time series data of mean depth of the fisheries catch for the whole world, where quadratic models fitted the data significantly better. Additionally, I estimated a time series of the mean longevity of fish in the world catch by combining data on fish longevity from FishBase with fish landings from FAO. The mean longevity of landings for each year and FAO area were estimated as the mean of the longevity of each species or group, weighted by the logarithm of their catch. The mean fish longevity of the catch was also estimated as a function of depth of fish occurrence. Since this has to be done in a yearly basis, I used year 2001 in FAO dataset. Table 5.1 - Summary of the likelihood ratio test (Hilborn and Mangel, 1997) used to compare the goodness-of-fit of different models where the more complex equation fits better than the simple equation. Where n is the number of parameters estimated in each model; d.f. the degrees of freedom; SS the sum-of-squares. L stands for Linear models, Q for quadratic, LL for the biphase linear-linear models, C for cubic models and F for fourth order models. Ocean basis Model n d.f. SS Comparisons Ratio (F) P Best Fit N. Atlantic Linear 2 50 2378.2 Quadratic j 49 777.6 L/Q 100.86 >0.001 Quadratic Linear-Linear 4 48 351.7 Q/LL 58.12 >0.001 Linear-Linear Cubic 4 48 429.5 LL/C Fourth 5 47 334.1 LL/F 2.48 0.122 Linear-Linear C. Atlantic Linear 2 50 184.5 Quadratic j 49 148.8 L/Q 11.76 0.001 Quadratic Linear-Linear 4 48 129.0 Q/LL 7.35 0.009 Linear-Linear Cubic 4 48 130.1 LL/C Fourth 5 47 126.1 LL/F 1.10 0.300 Linear-Linear S. Atlantic Linear 2 50 2928.9 Quadratic 3 49 2002.3 L/Q 22.67 >0.001 Quadratic Linear-Linear 4 48 1270.6 Q/LL 27.64 >0.001 Linear-Linear Cubic 4 48 1125.9 LL/C Fourth 5 47 1105.1 LL/F 7.04 0.011 Linear-Linear N. Pacific Linear 2 50 1501.5 Quadratic j 49 1258.0 L/Q 9.48 0.003 Quadratic Linear-Linear 4 48 876.9 Q/LL 20.86 >0.001 Linear-Linear Cubic 4 48 800.1 LL/C Cubic Fourth 5 47 791.6 LL/F 5.07 0.029 Linear-Linear C. Pacific Linear 2 50 1287.7 Quadratic 3 49 672.6 L/Q 44.81 >0.001 Quadratic Linear-Linear 4 48 426.7 Q/LL 27.67 >0.001 Linear-Linear Cubic 4 48 476.7 LL/C Fourth 5 47 381.3 LL/F 5.59 0.022 Linear-Linear S. Pacific Linear 2 50 13764.4 Quadratic 3 •'49; 6441.9 L/Q '. ' 37.51 >0.001 Quadratic Linear-Linear 4 48 3645.6 Q/LL ' 24.54 >0.001 Linear-Linear Cubic 4 48 4576.5 LL/C Fourth 5 47 4568.7 LL/F Indian Ocean Linear 2 50 3135.4 Quadratic 3 49 1124.9 L/Q 87.57 >0.001 Quadratic Linear-Linear 4 48 946.8 Q/LL 9.03 0.006 Linear-Linear Cubic 4 48 921.2 LL/C Fourth 5 47 823.0 C/F 7.07 0.011 Linear-Linear Antarctic Linear 2 50 48560.6 Quadratic j 49 37051.8 L/Q 15.22 >0.001 Quadratic Linear-Linear 4 48 32769.0 Q/LL 6.27 0.016 Linear-Linear Cubic 4 48 36762.0 LL/C Fourth 5 47 36415.9 LL/F 5 .3 RESULTS 5.3.1 Global trends Our results (Figure 5.1a) show that, for bottom marine fishes, the overall trend over the past 50 years has been a 42 m increase in the mean depth of the catch, from around 103 m in the early 1950's to 145 m in 2001. The biphase linear regression fitting the data (overall r2= 0.94) suggests two periods with different trends: a period of slow increase in the mean depth of fishing from 1950 to 1978 with a slope of about 2 mdecade\"1, followed by a period of marked increase in the mean depth of fishing at a rate of about 13 m- decade\"1 (Table 5.2). If I include pelagic fishes in the analysis (Figure 5.1a), the increase-in mean depth of the catch is lower but still considerable, with two distinguishable periods (overall r2= 0.93). In both cases, the early plateau and the estimated break point can be attributed to either a real increase in the fishing deeper trend, or to a lack of taxonomic resolution in the FAO landing statistics before the 1970's. Application of our method to catches from high seas areas only (i.e., beyond countries' EEZ) showed a more dramatic decline in the mean depth of fishing, at a rate of 22 m decade\"1 for bottom fishes only and 9.0 m decade\"1 when considering pelagic fishes. In general, fishing began to operate deeper from the late 1960's. Since the taxonomic resolution in the FAO landing statistics improved after the 1970's, this increase in depth could be caused by, 1) a proportional decrease in the catches of shallow water species (resulting from collapse of coastal resources); 2) a proportional increase in the landings of deep-water species (from the expansion of fisheries into deep water); or 3) both. Figure 5.1b helps elucidate this by showing that, at a global level, the increase in the mean depth of fishing has been caused by an increase in landings of deep-water species such as the orange roughy (Hoplostethus atlanticus, Trachichthyidae), grenadiers (Macrouridae), alfonsinos (Beryx spp., Berycidae) and several deepwater sharks. The steepest rates of depth increase match the development of most of the deepwater fisheries around the world (Hopper, 1995; Merrett and Haedrich, 1997; Moore, 1999; Koslow et al., 2000; Roberts, 2002; Garibaldi and Limongelli, 2003). Similar trends of increased mean depth of fishing were observed for all oceans, with rates ranging from 1 m decade\"1 deeper for the North Pacific to 180 m-decade\"1 for Antarctic fisheries (Table 5.1). Figure 5.1 - (a) Global trend of mean depth of world marine fisheries catches from 1950 to 2001 for all marine fishes including pelagics (dark grey dots) and for bottom marine fishes only (light grey squares). Open symbols are estimates for high seas areas only (beyond countries EEZs). Trend lines are fitted using the piecewise-polynomial model linear-linear (Hintze, 1998) or simple linear regression, (b) Time series of world marine bottom fisheries catches by depth strata. Catch in tonnes are logio transformed. Table 5.2 - Rate of increasing depth of fishing per decade before and after the breaking point (BP) estimated using a two phase model (linear-linear) as described in Hintze (1998). Coefficient of determination (r ) for regressions also presented. Linear-linear two model Slope (mdecade\"1) BP Slope (mdecade\"1) 1950-B.P Year SE B.P.-2001 2 r All fish 1.06 1978 2.0 8.80 0.93 Demersal fish 2.13 1978 2.0 13.17 0.94 Atlantic, North 5.49 1989 1.8 32.05 0.97 Central 8.36 1985 5.6 5.05 0.98 South -3.05 1966 1.9 15.17 0.92 Pacific, North 20.03 1959 1.4 0.58 0.59 Central -1.80 1992 6.0 21.76 0.67 South -7.64 1968 1.3 35.55 0.95 Indian Ocean -0.09 1986 2.4 22.54 0.83 Antarctica 99.84 1985 4.6 180.13 0.96 5.3.2 Atlantic Ocean In the Atlantic Ocean, the mean depth of the catch has increased steadily over the last decades at a rate of 32, 5 and 15 metres per decade for the North, Central and South Atlantic, respectively. In the North Atlantic (Figure 5.2a), the simplest biphase linear regression that fit the data suggests two periods with different trends: a period with a small rate of fishing deeper from 1950 to 1989 (±1.8 S.E.), with a slope of 5.5 mdecade1, followed by a period of marked increase in fishing deeper at a rate of 32.1 m decade\"1. The first period corresponds to the relative increase in the reported landings of some deep-water species (Figure 5.3a) such as the roundnose grenadier (Coryphaenoides rupestris, Macrouridae), alfonsinos, ling (Molva molva, Lotidae), blue ling (M. dypterygia, Lotidae), and tusk (Brosme brosme, Lotidae). The steepest increase observed for the second period, after 1989, matches the development of most of the deepwater fisheries in the North Atlantic (Hopper, 1995; Merrett and Haedrich, 1997; Moore, 1999; Koslow et al., 2000; Gianni, 2004). In Figure 5.3a one can clearly see some new deepwater species, with an average depth of about 1000m, being reported for the first time after 1989. These were orange roughy, bulls-eye (Epigonus telescopus, Epigonidae), and deepwater sharks (Centroscymnus coelolepis, Dalatiidae; Dalatias licha, Dalatiidae; Centrophorus squamosus, Centrophoridae; Deania calcea, Centrophoridae). Similar trends are apparent throughout the time series for the Central (Figure 5.2b) and South Atlantic (Figure 5.2c), although in the Central Atlantic fishing operates in more shallow waters (Figure 5.3b). In the South Atlantic (Figure 5.2c), the mean depth of fishing time series suggests two periods with different depth of fishing trends: a period of fluctuating mean depth of fishing from 1950 to 1966 (±1.9 S.E), with a slope of -3.0 m-decade\"', followed by a period of marked increase in the mean depth of fishing at a rate of 15.2 m decade\"1. In the South Atlantic basis, some deepwater fisheries have developed since the 1970's on the Patagonian shelf, Western South Atlantic (Catarci, 2004), and on the deeper continental shelf and slope of the Eastern South'Atlantic (Boyer et al., 2001). The highest rate of fishing deeper corresponds with increased landings of species with average depths at about four, seven and eleven hundred meters (Figure 5.3c). a) North Atlantic Year 1950 1975 2000 120 t j 140 - -ro o Q. O) •O 160 • • I • I • I • I • I # S 180+ 200 -L d) North Pacific Year 1950 1975 2000 100 tJ 120 •• ro u CL tu \"O V 140 •• I • I • I • I S 1 6 0 + 180 J-g) Indian Ocean Year 1950 1975 2000 60 | i I • I • I • I • I ro u CL (U \"O c ro cu 8 0 - -1 0 0 - • 120 - -140 J-b) Central Atlantic Year 1950 1975 2000 • I • I • I • I • I 40 60 8 0 • • 1 0 0 • • 120 -L e) Central Pacific Year 1950 1975 2000 40 6 0 • • 80 - -I . I . I V h) 100 120 J-Antarctic Year 1950 1975 2000 0 | • I • I • I . I • I 2 0 0 - -400 --600 -L \\ Aw c) South Atlantic Year 1950 1975 2000 80 100 • • 120 - • 140 •• 160 J-I • I • I • I • I \\ \\ « « «\\ S> m \\ f) South Pacific Year 1975 I . I . I . 1950 100 150 • • 200 -• 250 -• 300 J-2000 i— Figure 5.2 - Trend of mean depth of marine bottom fisheries catches for: (a) North Atlantic; (b) Central Atlantic; (c) South Atlantic; (d) North Pacific; (e) Central Pacific; (f) South Pacific; (g) the Indian Ocean; and (h) Antarctic. Trend lines are fitted using the piecewise-polynomial model linear-linear (Hintze, 1998) or simple linear regression. a) North Atlantic b) Year Central Atlantic Year 1950 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000 0 North Pacific Year e) Central Pacific Year 1950 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000 Or m i •WHS Q-0) o 1000 1500 Indian Ocean Year h) Antarctic Year 1950 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000 or * sssBm\"\" - T - i WSSSm CL OJ Q 500 1000 1500 2000 wm^w^m IMflpi*® MfMSm c) South Atlantic Year 1950 1960 1970 1980 1990 2000 u^smm f) South Pacific Year 1950 1960 1970 1980 1990 2000 14 12 10 8 6 4 2 0 Figure 5.3 - Time series of marine bottom fisheries catches by depth strata for: (a) North Atlantic; (b) Central Atlantic; (c) South Atlantic; (d) North Pacific; (e) Central Pacific; (f) South Pacific; (g) the Indian Ocean; and (h) Antarctic. Catch in tonnes are loge transformed. 5.3.3 Pacific Ocean The Pacific Ocean shows somewhat contrasting trends. The North Pacific (Figure 5.2d) has a steep increase in the mean depth of fishing (20 m-decade\"') for the period from 1950 to 1959 (±1.4 S.E), but no significant change after that. Large-scale deepwater fisheries have a long history in the North Pacific Ocean with some of the targeted species been fished since the early 1900s (Moore, 1999). There are also established deepwater fisheries for dover sole (Microstomus pacificus, Pleuronectidae), thornyheads (Sebastolobus spp., Sebastidae), other rockfishes (Seabastes spp., Sebastidae), pelagic armorhead (Pseudopentaceros wheeleri, Pentacerotidae) and alfonsinos. The lack of a clear trend in the mean depth of fishing in the North Pacific may be attributed to the time scale used in the analysis, because most of the deepwater fisheries started before 1950s or early 1960s (Moore, 1999; Koslow et al., 2000). Nevertheless, Figure 5.3d clearly shows the start of fisheries of some deep-water species, observed in the early 1960s and the late 1970s. The overall trend for Central Pacific Ocean (Figure 5.2e) is not clear. Until early 1990's the mean depth of the catch got shallower. This trend may be explained by three alternative hypotheses: 1) a problem with the official landings statistics mainly.by assigning catches to broad categories; 2) a proportional increase in shallow water fish landings greater than for deeper water species; and 3) a real lack of fisheries expansion into deeper waters. The last is not likely to be true, because it is clear from Figure 5.3b that in early 1970's some deeper water fish species like sablefish were being reported in the official landing statistics. After 1992 (±6.0 S.E.) the mean depth of fishing in the central Pacific increased at a rate of 21.8 m-decade\"1, with the increase in importance of some deeper water fish species (Figure 5.3e), such as the dover sole. In the South Pacific (Figure 5.2f) the mean depth of fishing has increased rapidly since 1968, at a rate of 36 m-decade\"1, coinciding with the start of orange roughy and other deepwater fisheries around New Zealand and Australia (Koslow et al., 2000). Figure 5.3f clearly shows that some deepwater species with average depths of fishing at about four, seven and nine hundred meters start being reported during the 1970s. 5.3.4 Indian Ocean The Indian Ocean (Figure 5.2g) shows no clear trend until 1986, but a steep increase in depth afterwards, at a rate of 22m per decade. The second period matches the appearance of deepwater species, such as the silver gemfish, Rexea solandri, orange roughy and Patagonian toothfish in landing statistics (Figure 5.3g). 5.3.5 Antarctic Finfish fisheries in Antarctica began only during the mid 1960s (Koch, 1992). This region (Figure 5.2h) exhibits the most dramatic increase in mean depth of the catch, from about 100m in the mid 1960's to 600m in 2001, a rate of more than 100 metres per decade. The observed trend in Antarctica clearly reflects the collapse and the implementation of fisheries restrictions for some shallower water fishes (Figure 5.3h) such as marble rockcod (Notothenia rossii, Nototheniidae) and other Nototheniidae species in the late 1980s (CCAMLR, 2004). It also reflects the increase landings of the deepwater Patagonian toothfish (Dissostichus eleginoides, Nototheniidae) during late 1980s (Koch, 1992; Constable etal., 2000). 5.3.6 Mean longevity of the catch The mean longevity of the catch (Figure 5.4a) has increased during the past 50 years, but most dramatically since the early 1.990's. Mean longevity of the catch by depth (Figure 5.4b) in landings from shallow waters has a lower mean longevity (about 15 years) when compared to intermediate (about 40 years) or deeper waters (over 100 years). Hence, fishing deeper means fishing for increasingly longer-lived and thus more vulnerable species. 17 1950 1975 2000 # Year 1500 Figure 5.4 - (a) Global trend of mean fish longevity of the catches for all marine fishes including pelagics (dark grey dots), and for bottom marine fishes only (light grey squares), (b) Global trend of mean longevity of the 2001 world bottom marine fisheries catch by depth. Line is the least squares tit through points by using the logarithmic equation (r2 = 0.75). Mean age at maturity shows a similar pattern. b ) Mean longevity (years) 0 50 100 150 5 . 4 DISCUSSION I have shown that global landings of fishes have shifted in the last 50 years from shallow to deeper water species, and also that, as a likely consequence, the mean longevity of the fish species caught has increased dramatically. This trend is a serious concern because species with larger body size, longer life span, later sexual maturity, and slow growth are more vulnerable to overfishing and pseudo-extinction (Jennings et al., 1998; Dulvy et al., 2003; Dulvy et al., 2004; Chueng et al., 2005). Deep-water fishes are thus highly vulnerable to overfishing and potentially have little resilience to overexploitation (Koslow et al., 2000; Clark, 2001; Morato et al., 2006). Moreover, deep waters act as the last refuge for some coastal stocks with an extensive vertical distribution where no fishing was occurring some decades ago (Caddy, 1993). With a fisheries expansion to deeper waters those refuges will no longer operate. There is a recent tendency in fisheries development to argue for a diversification of target fish species, mainly through the exploitation of 'under-utilised' deepwater species (see Moore, 1999). In fact we are already seeing the well-documented declines observed for shallow water fish stocks repeated in deepwater stocks (see Roberts, 2002 for some examples). Because of their life-history characteristics (Merrett and Haedrich, 1997; Morato et al., 2006) this phenomenon will be much faster with a smaller likelihood of recovery after collapse. Hence, deep-sea fisheries cannot be seen as a replacement for declining shallow-water resources; instead, deep-water habitats should be considered as the new candidates for conservation. This work is based on the FAO catch statistics and on the reported average depth range of fish from FishBase. The reliability the FAO catch statistics is of some concern (for more details see FAO, 2002; Pauly et al., 2002; Watson et al., 2004) and the lack of taxonomic resolution can be a problem when drawing general conclusions at a global scale (Watson et al., 2004). However, I have demonstrated global and regional trends towards fishing deeper in the oceans in spite of a large portion of the world's landings being assigned to broad categories. This is especially true for newly-developed or undocumented fisheries as is the case of many deep-water demersal fisheries. As an example, the dropline fishery around the Madeira Islands for the deepwater black scabbardfish (Aphanopus carbo, Trichiuridae) is known to have operated since the early 19th century (Martins and Ferreira, 1995), but the first official record of landings of this species is in 1986. As in this case, landing statistics may include a great proportion of deepwater species in broader categories, and because many deep-sea fishes are not very well known, the likelihood of having them aggregated in broader categories is higher when compared to well-known shallow coastal bottom fishes. Assigning catches to broad categories is often the case in tropical developing countries with strongly multispecies fisheries (Pauly et al., 1998b) and I did, in fact, find trends in the tropics less evident. In both cases I believe that, if better taxonomic resolution were to be available, the effect would be stronger because more deepwater fish species would be taken into account. I used the average depth range of fish distribution as an indicator of fishing depths because fisheries will mainly operate at depths where higher abundances of target species occur. 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C H A P T E R 6 VULNERABILITY OF SEAMOUNT FISH TO FISHING: FUZZY ANALYSIS OF LIFE HISTORY ATTRIBUTES 1 6.1 INTRODUCTION Seamounts are biologically distinctive habitats of the open ocean exhibiting a number of unique features (see Rogers, 1994). Seamounts have received much attention mainly because of the presence of substantial aggregations of forage fishes in mid- and deep-water (Boehlert and Sasaki, 1988; Rogers, 1994; Koslow, 1996, 1997; Koslow et al., 2000), which became the prime target of a highly technological fishery. Based on life history and ecological characteristics, several authors have placed \"seamount fishes\" at the extreme end of the vulnerability spectrum (Koslow, 1997; Branch, 2001; Boyer et al., 2001; Clark, 2001). However, with the exception of works by Koslow (1996) and Froese and Sampang (2004), few attempts have been made to review, summarize and compare the life-history of seamount species. Therefore, this paper tests the generalization that \"seamount fishes\" possess specific life history characteristics that render them more vulnerable than other species. A detailed description of what are seamount fishes is given in Chapter 1.5. Responses of a fish species to exploitation may be partly determined by life history and ecological characteristics (Adams, 1980; Roff, 1984;-'Stokes et al., 1993; Kirkwood et al., 1994). Fish that are large in size, mature late, have slow growth and low mortality rates, are likely to have higher vulnerability to fishing (Jennings et al., 1998, 1999; Russ and Alcala, 1998; Musick, 1999; Denney et al., 2002). In addition, species that display social aggregation behaviours such as shoaling, schooling (Pitcher and Parrish, 1993) or shoal spawning may have higher vulnerability because of increased catchability when their abundance declines, often referred to as hyperstability of catch rates (Hilborn and Walters, 1992; Pitcher, 1995, 1 A version of this chapter has been published. Morato, T., W.W.L. Cheung and T.J. Pitcher. (2006) Vulnerability of seamount fish to fishing: fuzzy analysis of life history attributes. Journal of Fish Biology 68(1): 209-221. 1997; Walters, 2003), and their spawning behaviour may also be disrupted by fishing (Johannes, 1998; Sala et al., 2001; Sadovy and Domeier, 2005). This paper attempts to test the hypothesis that \"seamount fishes\" generally have a high vulnerability to fishing exploitation. Previous studies have found that vulnerability of fishes to exploitation is correlated with their life history characteristics. Vulnerability was estimated quantitatively by analysis of life-history characteristics using a fuzzy-logic algorithm (Cheung et al., 2005). 6 . 2 METHODS 6.2.1 Compilation of species list Based on the best available information, I collated species lists for fishes that occur or aggregate in and around seamounts. \"Seamount fishes\" are defined as fish that have been reported as occurring on seamounts, even if rare. A total of 798 species of marine fishes were classified as \"seamount fishes\" and 23 as \"seamount-aggregating\" fishes (Appendix 1). 6.2.2 Comparisons of biological characteristics and vulnerabilities I compiled estimates of life history and ecological attributes for over 14,000 marine fish species based on information available on Fishbase (Froese and Pauly, 2004). These attributes include longevity (T Ma x), age at maturity (T m), asymptotic length (L m), total fecundity (FT), von-Bertalanffy growth parameter (AT), natural mortality rate (M), together with information on spatial behaviour and preferred habitat (pelagic, demersal, reef-associated, bethopelagic, bathypelagic and bathydemersal). Only those parameters directly estimated from empirical studies were used, while those that were calculated from empirical relationships between life history parameters were excluded. If more than one estimate was available for a particular life history parameter of a particular species, the arithmetic mean was used. I used a fuzzy expert system developed by Cheung et al. (2005) to predict 'intrinsic vulnerability to fishing. This index was demonstrated to provide vulnerability estimates that correlate better with observed data than existing alternatives (Musick, 1999; Hilton-Taylor 2000; ICES, 2001; Froese and Pauly, 2004). Cheung et al. (2005) defined intrinsic vulnerability as inherent capacity to respond to fishing that relates to the fish's maximum rate of population growth and strength of density dependence. The fuzzy expert system classifies fishes into different levels of vulnerability based on basic life history and ecological characteristics. The input variables include maximum length, age at first maturity, longevity, von Bertalanffy growth parameter' K, natural mortality rate, fecundity, strength of spatial behaviour, and geographic range. Heuristic rules were incorporated to describe the relationships between these biological traits and fish's intrinsic vulnerability, through which the latter can be predicted. Intrinsic vulnerability was expressed on an arbitrary scale from 1 to 100, with 100 being the most vulnerable. Comparisons with empirical data showed that the fuzzy expert system successfully predicted the intrinsic vulnerability of fishes to fishing (Cheung et al., 2005). Here, I have predicted the intrinsic vulnerability for 1,600 species of fish for comparison between seamount and non-seamount associated fishes (Table 6.1). To increase robustness, I excluded species for which total length was the only available life-history parameter. Biological characteristics and the estimated fuzzy intrinsic vulnerabilities were compared between \"non-seamount fishes\", \"seamount fishes\" and \"seamount-aggregating\" fishes. To test if commercially-targeted seamount fish species were also more vulnerable than other target fish species, intrinsic vulnerability was also estimated for those species reported in the Food and Agriculture Organization (FAO) official landing statistics. Additionally, to correct for the confounding factor that deep-sea and bathydemersal fish may have higher vulnerability in general, multiple regression analysis was used to explore the relationship between depth (log transformed median of depth range), habitats (seamount fishes, seamount-aggregating), and environmental preferences (pelagic, demersal or bathypelagic) with vulnerability. Differences between the biological characteristics estimates were tested by one-way ANOVA, while multiple comparisons were preformed by Tukey test with unequal sample sizes. Differences between intrinsic vulnerability estimates were tested with Mann-Whitney (U) non-parametric statistics (see Zar, 1999). Table 6.1 - Number of fish species considered as \"non-seamount fishes\", \"seamount fishes\" and \"seamount-aggregating\" fishes, and number of available estimates of life history attributes and intrinsic vulnerability. Sample sizes for fish species grouped by preferred habitat is also shown. NS are \"non-seamount\" fish species while S are fish occurring on seamounts. Number of Species T^vlax T M M K LOO F T VI* \"Non-Seamount\" 14924 430 460 176 1087 11900 482 1407 \"Seamount\" 798 92 85 38 150 726 77 193 \"Seamount-aggregating\" 23 19 16 10 18 22 11 18 Pelagic NS 1013 76 84 45 165 871 74 193 Pelagic S 40 11 13 7 18 36 9 18 Demersal NS 6047 200 201 66 417 4538 220 596 Demersal S 118 23 23 9 31 96 21 39 Reef-associated NS 4159 99 109 45 365 3828 111 421 Reef-associated S 94 11 13 8 29 92 10 35 Benthopelagic NS 831 39 54 18 92 646 39 114 Benthopelagic S 67 15 12 6 20 60 7 23 Bathypelagic NS 1051 7 4 1 26 736 5 31 Bathypelagic S 223 11 9 3 25 214 9 33 Bathydemersal NS 1827 11 10 2 24 1284 34 54 Bathydemersal S 252 19 13 4 25 225 20 43 Where: r M a x is the longevity, T m the age at maturity, Mis the natural mortality, K is the Von-Bertalanffy Growth function K, Lx is the Total Length infinity, Fj is total fecundity, and V\\ is the intrinsic vulnerability. * Even though vulnerability was estimated for almost all fish species (n= 14148), this table only shows the number of species included in further analysis, i.e. excluding those species from which total length was the only available life-history parameter. / - 127 r 6.2.3 Responses to fishing The relationship between vulnerability estimates and biomass change over time caused by fishing was evaluated using a simulation model. A mass-balanced ecosystem model (Ecopath with Ecosim, see Christensen and Walters, 2004 for details) developed for a theoretical isolated North Atlantic seamount (Morato and Pitcher, 2002) was used. This model included 37 functional groups, of which twenty were fish groups assembled according to environmental preference (i.e., depth and habitat: e.g., benthic, pelagic or benthopelagic), body size, energetics and life-history characteristics (see Morato and Pitcher, 2002 for a complete description of the model). The seamount fisheries were loosely based on those operating at the Azores / Mid Atlantic Ridge, and thus divided into six fleets (see Morato and Pitcher, 2002). Biomass changes over 20 years were simulated by assuming a fishing mortality rate of 0.3 for one fish group at a time. 6 . 3 RESULTS Significant differences in longevity (ANOVA, d.f.= 2; /?<0.001) and age at maturity (ANOVA, d.f.= 2; /?<0.001) were found among \"seamount\", \"non-seamount\" and \"seamount-aggregating\" fishes (Figure 6.1). The longevity (Figure 6.1a) of seamount fishes was significantly higher than \"non-seamount\" fishes (mean T Ms x- 41.6 years and 16.9 years respectively; Tukey test; d.f.= 519; p< 0.001). \"Seamount-aggregating\" fishes have the highest longevity among the three categories (mean 7iviax= 62.7 years), although the difference is significant only when comparing with \"non-seamount\" fishes (Tukey test; d.f.= 519;p< 0.001). Accordingly, age at maturity (Figure 6.1b) of both seamount and \"seamount-aggregation\" fishes were significantly higher (mean T m= 6.5 years and 15.6 years respectively) than the \"non-seamount fishes\" (mean T m= 3.9 y) (all Tukey test; d.f.= 542; p< 0.001). \"Seamount-aggregating\" fishes also have a significantly higher age at maturity than \"seamount fishes\" (Tukey test; p< 0.001). NS AGG b) 24 _ 20 11 C3 S 16 p 3 12 cd 2 a 8 v bO < d) 1.0 U 0.8 (U 0 1 0.6 S a. | 0.4 o a ^ 0.2 0 . 0 NS AGG NS S AGG NS S AGG Figure 6.1 - Comparison of some life-history characteristics of \"non-seamount\" fish species (NS), fish occurring on seamounts (S), and \"seamount-aggregating\" species (AGG). a) longevity (rMax); b) age at maturity (I'm); c) natural mortality (A/); d) Von-Bertalanffy growth parameter (AT). In the graphs the middle point is the mean, the box is the Mean ± S.E., and the whisker is the Mean ± 95% CL. Comparisons of natural mortality rate (Figure 6. lc) and the von Bertalanffy growth parameter K (Figure 6.Id) among the three categories of fishes show similar, but reciprocal, trends as longevity and age at maturity. \"Seamount-aggregating\" fishes have the lowest natural mortality and von Bertalanffy growth parameter (mean M= 0.16 and mean K= 0.10) while \"non-seamount fishes\" have highest among the three fish categories (mean M= 1.05 and mean K= 0.57). Although, paired comparisons (Tukey test) were only significantly different at the 5% confidence level for \"seamount fishes\" and \"non-seamount fishes\". Significant differences in the estimated intrinsic vulnerabilities were observed between \"seamount\", \"non-seamount\" and \"seamount-aggregating\" fishes. Median intrinsic vulnerabilities (Figure 6.2a) for \"non-seamount fishes\", \"seamount\" and \"seamount-aggregating\" fishes were estimated to be 45.0, 51.8 and 68.2 respectively. The differences in intrinsic vulnerabilities are significant when comparing both \"non-seamount\" and \"seamounts fishes\" (£7; p< 0.001), and comparing \"seamount fishes\" and \"seamount-aggregating\" fishes (U,P< 0.007). a) j? 3 > 100 90 80 70 60 50 40 30 20 10 0 •ill NS AGG b) j5 3 > 100 90 80 70 60 50 40 30 20 10 0 NS AGG Figure 6.2 - Intrinsic vulnerability (V\\) index for fish species no-occurring on seamounts (NS), occurring on seamounts (S), and \"seamount-aggregating\" species (AGG). a) including all fish species; b) for species reported in FAO official landing statistics. In the graphs the middle point is the median, the box the 25%-75% percentiles, and the whisker is the range. Vulnerabilities offish reported as catches in the FAO landings statistics were also higher for \"seamount fishes\", and significantly different from the median for \"non-seamount fishes\" (U; p< 0.001). However, there were no significant differences between the vulnerabilities of \"seamount fishes\" and \"seamount-aggregating\" species (U; p< 0.111), even though the median was higher for the later. Additionally, the mean of vulnerability weighted by volume of catch (log-transformed) (Table 6.2) was estimated. Similarly, vulnerability was higher for \"seamount-aggregation\" species and lower for \"non-seamount fishes\". The differences in vulnerability between \"seamount\" and \"non-seamount fishes\" were mainly due to benthopelagic and demersal fishes (Figure 6.3), which were found to have significantly different medians of intrinsic vulnerability (U; Demersal p= 0.003; Benthopelagic p= 0.001). For all other fish groups paired comparisons of medians were not significantly different. Bathydemersal fishes, benthopelagic and demersal fishes were among the most vulnerable fish groups, with \"seamount-aggregating\" fishes having the highest intrinsic vulnerability. Vulnerabilities of the \"deep-sea fish\" group (bathydemersal fishes not occurring on seamounts) (median Vf= 64.0) were not significantly different from seamount demersal fishes (median Vf= 61.0; U, p= 0.194), seamount benthopelagic fishes (median V\\= 64.0; U, p= 0.819) and seamount bathydemersal fishes (median V\\= 63.5; U, p= 0.833). \"Seamount-aggregating\" fishes (median V\\= 68.2) were the only group having higher vulnerability estimates than the \"deep-sea fish\" group, but this difference was not statistically significant {U,p= 0.335). Multiple regression analysis showed that \"seamount-aggregating\" was a significant factor (/?=0.011) in explaining the variance in vulnerability between species, when differences in depth range (log-transformed) were accounted for in the regression model. However, the \"seamount-aggregating\" factor was significant only when the lower vulnerability of bathypelagic fishes was accounted for in the regression model. Table 6.2 - Intrinsic vulnerability weighted by the Log(catch) for \"seamount\" and \"seamount-aggregating\" species reported explicitly in FAO catches. \"Non-seamount\" \"Seamounts\" \"Seamount-aggregating\" Number of Species 508 102 13 Vulnerability 39.9 47.9 64.5 XI c3 u e \"3 > 100 90 80 70 60 50 40 30 20 10 0 1 U • • T I NS S NS S NS S NS S NS S NS S AGG Pelagic Demersal** Reef-Ass. Benthopel.**Bathypel. Bathydem. Figure 6.3 - Intrinsic vulnerability (VI) index for fish species of different habitats no-occurring on seamounts (NS), occurring on seamounts (S). Vulnerability for \"seamount-aggregating\" species (AGG) also presented. In the graphs the middle point is the Median, the box the 25%-75% percentiles, and the whisker is the range. ** indicates significant differences between medians (Mann-Whitney test; Pelagic: p= 0.471; Demersal: p= 0.003; Reef-Associated: p= 0.076; Benthopelagic: p= 0.001; Bathypelagic: p= 0.806; Bathydemersal: p= 0.833). The intrinsic vulnerabilities estimated from the fuzzy system were significantly related (R2= 0.738, p= 0.007) to the simulated population declines of marine fish groups (Figure 6.4) caused by fishing. Groups of species with higher vulnerabilities had larger biomass declines than species with lower vulnerabilities. Simulations showed that even at modest levels of fishing, seamount species were depleted, not sustained. l.o -0.9 I 0.! C3 O 1.5 1.0 0.5 0.0 Demersal Longliie DeepWater Small Pelagics Tuna Swordfish DeepWater trawl Total Longline Fisheries Figure 7.4 - Value of the catches (relative value) for the different fishing fleets under the base model and two fishing scenarios: maximizing the 'ecology' objective, and maximizing the 'economy' objective. 7.3.2 Trade-offs Surface plots of optimal scenario solutions for a range of weightings of ecological and economic objective functions are shown in Figures 7.5 to 7.7. They show that it was not possible to maximize the performance of all three objectives (i.e., net economic value, number of jobs, and ecological 'stability') simultaneously. This is true because net economic value (Figure 7.5a) and number of jobs (Figure 7.5b) reach a maximum with a high weighting factor on the economy objective and a small weight on ecology. This results in a decrease in the stability of the system. On the contrary, to maximize 'ecosystem stability' a high weighting was assigned to the 'ecology\" objective (Figure 7.5c). Assigning a low weighting factor to 'economy' and a high weighting factor to 'ecology' resulted in a decrease of net economic value and number of jobs, with a corresponding increase in the system's stability. Intermediate weightings produced, in general, intermediate performances for the three objective functions. • ECOLOGY The fishing rates required to achieve different performances of the objective functions (i.e., fishing policies) are presented in Figure 7.6. In order to maximize the net economic value of the system all fisheries, except deepwater longline (Figure 7.6a), required an increase in their fishing rates. These increases were of approximately: 50 times the base model rate for the deepwater trawl fishery (Figure 7.6b); 16 times for the swordfish fishery (Figure 7.6c); 15 times for the small pelagic fish fishery (Figure 7.6d); 3.5 times for the tuna fishery (Figure 7.6e); and 2.5 times for the bottom longline fishery (Figure 7.6f). To achieve ecological stability in the system, however, a decrease in the fishing rates of most of the fisheries was required with the exception of the tuna, swordfish and, to a lesser extent, the bottom longline fisheries. The latter, along with the small pelagic fisheries, reached their highest fishing rates when a high weighting factor was assigned to the 'ecology' objective and an intermediate weight to 'economy'. Ecosystem indicators (i.e., mean trophic level of the catch and biodiversity) and total catches derived from the optimal fishing strategies for the overall range of weighting factors for 'ecology' and 'economy' are presented in Figure 7.7. Total catches were maximized when weighting was high for 'economy' and low for 'ecology' objective functions (Figure 7.7a). In contrast, the biodiversity index (Figure 7.7b) was high only when a very small weight was assigned to 'economy'. The mean trophic level of the catch (Figure 7.7c), in general, decreased with a corresponding increase in the weighting of 'economy' and a decrease in the weighting of 'ecology' objective functions. However, maximum trophic level was achieved with a high weighting of 'ecology' and an intermediate weighting of 'economy'. OJ 1ET O do _o o o UJ c o •4—» bp '5 £ 0.00 0.01 0.02 0.03 0.04 0.05 0 06 0.07 0.08 0.09 0.10 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 C 1.0 0.9 0 8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 . 0 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 Weight on Economy Objective Function Figure 7.5 - Surface plots showing optimal scenario solutions for a range of weightings of ecological and economic objective functions: a) performance of 'economy' objective, maximizing net economic value; b) performance of 'social' objective, maximizing number of jobs; c) performance of 'ecology' objective, maximizing ecological 'stability' of the ecosystem. Scale goes from light blue (low performance) to dark red (high performance). Smooth surface is interpolated. c c o -t—> o c p F-L, u > o > bO _o o o W e o £ W) 'u £ 1.0 0.9 0.8 0.7 0 .6 0.5 0.4 0.3 0.2 0.1 0.0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 01 0.0 a 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 ®mm e) 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 i 1.0 0 9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0 .1 0 0 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 L. ) 0.00 0.01 0 02 0.03 0.04 0.05 0.06 0.07 0.08 0 09 0.10 d) 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 f 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0 09 0.10 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 Weight on Economy Objective Function Figure 7.6 - Surface plots from EwE model showing the resulting fishing rates, as proportion of base model rates, of the optimal scenario solutions for a range of weightings of ecological and economic objective functions: a) deepwater longline; b) deepwater trawl; c) pelagic longline; d) small pelagic fishery; e) tuna fishery; f) demersal (bottom) longline. Scale goes from light blue (low proportion of base model rates) to dark red (high proportion of base model rates). O 1.0 DO O o 0.9 o OJ 08 e o 0.7 -C bO • >—< n* V £ 0.5 0.4 0.3 0 2 0.1 0.0 000 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 c) 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 Weight on Economy Objective Function Figure 7.7 - Surface plots showing the resulting ecosystem indicators and total fisheries catch for the optimal scenario solutions for a range of weightings of ecological and economic objective functions: a) total catch; b) biodiversity index (Q75), please note that this figures is shown from a different viewpoint; c) mean trophic level of the catch. Scale goes from light blue (low) to dark red (high). 7 . 4 DISCUSSION The analyses in this paper are not meant to describe actual fisheries for seamounts, but rather as an exercise to explore the overall responses of seamount ecosystems to various multi-species management strategies. The use of 'open loop policy' search procedures can be unrealistic because it can entrust a fishery to fishing rates calculated at some time in the past and from data available from that time (Walters et al., 2002; Christensen and Walters, 2004). Fisheries management needs to be implemented using 'feedback policies' in which harvest .goals are adjusted over time as new information becomes available and in response to unpredicted ecological changes (Christensen and Walters, 2004). However, 'open loop policy' calculations can give insights and directional guidance to where the system might be heading. In this study, this method appeared to be appropriate due to both the exploratory characteristic of the study and the data-limited situation of the N. Atlantic seamounts. Different extreme policy objectives for seamount fisheries may require different fleet configurations. Simulations that maximize economic performance favour deepwater trawling and require an increase in the fishing rates of all other fishing fleets, the only exception being the deepwater longline. On the other hand, maximization of ecological performance is achieved by favouring the operation of small pelagic and bottom longline fisheries. At the same time, a decrease in the fishing rates of all other fishing fleets is necessary. Different fishing rates and fleet configurations produced different impacts on catches and consequently in the whole ecosystem. Optimizing for economics yielded six times the amount of landed catch and money than the base model scenario and, 17 and 23 times the amount of landed catches and money yielded by the scenario where ecology was maximized. This would, however, have implications to the whole ecosystem. While maximizing ecology produces an overall increase in biomass of most functional groups in the model, maximizing economics leads to a decrease and further collapse of some groups such as tuna, seamount associated fishes, alfonsinos, as well as some charismatic species such as sea turtles and sharks. This point was well illustrated some time ago by Clark (1973) who pointed out that for populations that are economically valuable but possess low reproductive capacities (such as seamount associated fishes, alfonsinos and sharks), common property competitive exploitation and private property maximization of profits may lead to overexploitation and even to extinction of the population. It is interesting to note that major collapses in deepwater fisheries, for example off New Zealand (Clark, 1999; Clark et al., 2000), Tasmania (Koslow, 1997) and Namibia (Boyer et al., 2001), and habitat degradation (Probert et al., 1997; Koslow et al., 2001) were attributed to extensive deepwater trawling. In the Azores where no deepwater trawling is known to occur, seamount fisheries are mainly longline, handline and pole-and-line, and are believed to be more sustainable. However, signs of stock decline are becoming apparent even in these systems (Santos et al., 1995; Menezes, 2003). Thus, the question of whether deepwater (mainly trawl) fisheries are sustainable in the long term remains open (Clark, 2001). Some authors (e.g. Probert, 1999; Roberts, 2002), agencies (WWF; IUCN), and governments strongly advocate an urgent need for fishing regulations and/or the establishment of marine reserves in such areas. It is apparent that major conflicts among stakeholders might emerge when different optimization scenarios result in completely different fishing policies (Figure 7.5, 7.6). In addition, our. results illustrate that maximizing 'economy' affects biodiversity in the ecosystem and probably the trophic level of the catch, while maximizing the total landed catches (see Figure 7.7). The opposite is true when 'ecology' is favoured; the total catch and the number of jobs are decreased in order to achieve high biomass of long-lived species and increased biodiversity in the ecosystem. In conclusion, sustainable seamount fisheries with minimal ecosystem effects appear to be achieved when the 'ecology' objective is maximized. However, more information for these fragile ecosystems and the long-term impacts of fishing and other human activities needs to be acquired. 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C H A P T E R 8 CONCLUSIONS: ECOLOGY AND FISHERIES OF SEAMOUNT ECOSYSTEMS 8.1 INTRODUCTION The general objectives of this PhD were to explore some fundamental questions about seamount ecology and fisheries, namely to: a) examine the impact of a potential increase of local primary production on higher trophic levels; b) quantify the amount of advected prey necessary to sustain a \"typical\" seamount fish community and to explore if the necessary prey can be supported with food provided by local oceanographic conditions; c) test if the reported high abundances of seamount \"visitors\" such as tuna, marine mammals, sea turtles and seabirds on top and around seamounts are true; d) analyse changes in the mean depth of fishing to test if reported historical expansion into deeper-waters can be detected in global landings datasets. Also to test the hypothesis that deep-water fisheries resources are vulnerable; e) test the hypothesis that \"seamount fishes\" generally have a higher than average vulnerability to fishing exploitation; f) investigate if whole-ecosystem simulations can help in understanding the impact of fishing on pristine seamounts and provide guidelines for sustainable fisheries. Additionally, and in collaboration with the \"Sea Around Us Project\" I participated in small ad-hoc projects that helped: a) estimating of the number of seamounts in the world's oceans by inferring potential seamount locations; b) estimating seamount numbers and locations around the Azores islands; c) detecting primary production enhancement by world's oceanic seamounts using remotely sensed data on primary production d) estimating potential yield from seamounts and determining how catches have changed in recent years. 8 . 2 MAIN RESULTS # 1 Introduction: Seamounts as hotspots of marine life Seamounts are underwater volcanoes occurring throughout the world's oceans. In Chapter # 1 I introduced the topic highlighting the main characteristics of seamount ecosystems. Additionally, I have tried to compile a list of fish species living on seamounts. This is because the designation of \"seamount\" species has been widely employed, but rigorous criteria used in identifying these taxa have not been defined. The definition of \"seamount fishes\" may involve a redundancy as we try to define a functional type of label that applies only in part to the ecology of the species: seamount fishes are those individual fishes that live on seamounts. Species that aggregate in association with seamounts and other topographic features have been called \"seamount-aggregating\" fishes. I compiled a total of 798 species of marine fishes that fall under the classification of seamount fishes and a total of 23 potential seamount-aggregating species. This list is preliminary and its accuracy will improve as we gain more knowledge about the ecology of seamount and deepwater fish species. #2 Abundances and distribution of seamounts in the Azores Seamounts are important areas for conservation and fisheries in the Azores and the knowledge of its location is highly important for implementing management options and for testing hypothesis about ecosystem functioning. In Chapter #2, I characterized the seamount distribution on the Azores Economic Exclusive Zone using two bathymetry datasets. I identified seamount locations from local maxima in the bathymetry grid. The algorithm developed showed that peaks and seamounts are common features in the Azores EEZ. The real count average density of 3.3 peaks of all sizes per 1000 km2 is in the same order of magnitude than obtained in some studies in the Mid Atlantic Ridge. Nevertheless, I was able to map and describe 63 large and 332 small seamount-like features in the EEZ of the Azores. The distribution suggests that large proportions of seamounts occur in chains along the Mid Atlantic Ridge, however, few isolated seamounts are also present in the Azores. Our distribution of seamounts predicts that about 63% of the potential Azores seamounts are protected against deep water trawling by the European Commission Council Regulation No. 1568/2005. #3 Modelled effects of primary and secondary production enhancement by seamounts on local fish stocks Chapter #3 addresses how large aggregations of fish found on many seamounts are sustained. I used a generic seamount ecosystem model from the Northeast Atlantic to examine the impact of a potential increase of local primary production on higher trophic levels, to quantify the immigration of micronekton that would be required to maintain a \"typical\" seamount community, and to quantify if the necessary immigration ratios could be supported by local oceanographic conditions. The results indicate lack of resources in the system to support large amounts of seamount aggregating fish. In other words, local seamount production may be responsible for sustaining only a small amount of its total biomass. Additionally, our study supports the idea that enhancement of primary productivity can also not sustain large aggregations of seamount fishes. The seamount model, which took into account high abundances of fish, marine mammals, seabirds and tuna, required a total immigration of micronekton of 95.2 t-km\"2-year\"' less than the potential available biomass after considering currents of 141.5 t-km\"2-year\"'. Therefore, I suggest that the horizontal flux of prey is sufficient to sustain the rich communities living on seamounts. Additionally, the simulation results suggest that food may not be a limiting factor for fish aggregations at intermediate seamounts. I discussed what could influence seamount fish abundance and explain the high variability of abundances from one seamount to the other.. # 4 Testing a seamount effect on aggregating visitors The importance of seamounts for large pelagic or visiting organisms has been poorly tested. However, it has been hypothesised higher abundances of some \"visiting\" animals, such as tuna, sharks, billfishes, marine mammals, sea-turtles and even seabirds, over seamounts. Surprisingly, this has been based on some sparse records, warranting further examination. In Chapter #4 I tested-if the predicted high abundances of tuna, marine .mammals, sea turtles and seabirds on top and around the Azores seamounts are true. This study has demonstrated that some marine predators are associated with shallow waters seamounts. This was the case of tuna species skipjack and bigeye, common dolphin and Cory's shearwater. These species were significantly more abundant in the vicinity of some seamount summits than far from these features. The methodology developed, however, failed to demonstrate seamounts' association for bottlenose dolphins, spotted dolphin, sperm whale, terns, yellow-legged gull, and loggerhead sea turtles. Seamounts play a major role in localizing pelagic prey and thus attracting some pelagic fish, seabirds and marine mammals. Therefore, some seamounts in the Azores may act as feeding stations for some of these visitors. Not all seamounts, however, seemed to be equally important for these associations. Only seamounts shallower than 400m depth showed significant aggregation effects. The important seamounts in the Azores for these visitors are \"Princesa Alice\" and \"Acres'' for all four species, also \"D. Joao de Castro\" for both tuna species, \"Formigas and Dollabarat\" and \"Pico Leste\" of the Princesa Alice for skipjack, and \"Agulhas do Sul do Gigante\" for common dolphin. These seamounts should be considered hotspots of marine life in the Azores and a special effort should be made in order to ensure a sustainable management of these habitats. # 5 Fishing down the deep With the decline of shallow coastal waters resources, increasing demand, and new technology, fisheries are evidently expanding offshore and into deeper waters. In particular, seamounts are among those \"newly\" targeted ecosystems. However, information on seamounts fisheries is very sparse, and it is difficult to make a distinction between deep-water fishing activities in general and those occurring on seamounts. Moreover, fish species living on seamounts are also known to occur on other habitats, such as continental slope, and landings statistics are not spatially allocated, making it difficult to make an estimate of the total fisheries occurring on seamounts worldwide. The expansion into offshore areas has been well documented, but the extension into deeper waters is less well analysed. Whereas previous studies on global trends of fisheries have focused on catch or biomass changes over time, in Chapter #5 I have analysed changes in the mean depth of fishing to test if the predicted expansion into deeper-waters can be detected in global landings datasets. I also tested for the predicted higher vulnerability of deep-water fisheries resources, using longevity as the main proxy for vulnerability. Global landings of demersal marine fishes are demonstrated to have shifted to deeper water species over the last 50 years. Our analysis suggests deep-water fish stocks may be at serious risk of depletion, since their life histories render them highly vulnerable to overfishing with little resilience to overexploitation. Deep-sea fisheries are exploiting the last refuges for commercial fish species and should not be seen as a replacement for declining resources in shallower waters. Instead, deep-water habitats are new candidates for conservation. # 6 Vulnerability of seamount fish to fishing: fuzzy analysis of life history attributes Chapter #6 attempts to test the hypothesis that \"seamount fishes\" generally have a high vulnerability to exploitation and that this is correlated with their life history characteristics. Despite rather broad definitions, global analysis shows that \"seamount fishes\", particularly \"seamount-aggregating\" fishes, have higher intrinsic vulnerability than other groups of fishes. The pattern is similar when considering only commercially exploited species. Biological characteristics leading to greater vulnerability include a longer lifespan, later sexual maturation, slower growth and lower natural mortality. This research supports the contention that \"seamount fishes\", especially those that aggregate on seamounts, are highly vulnerable to exploitation and that fishing on seamounts may not be sustainable at current levels and with current methods. A number of seamount populations have already been depleted; more depletion, extirpations, and even species extinctions may follow if fishing on seamounts is not reduced. # 7 Ecosystem simulations of management strategies for data-limited seamount fisheries Traditional fisheries stock assessment requires large amounts of information, mainly from long-term data series, a requirement that is hard to apply to new or poorly-documented fishing grounds. In Chapter #7 I investigate if ecosystem simulations can help to understand the impact of fishing on pristine seamounts. Using ecosystem modelling tools, data gathered from elsewhere, and methods that search for optimal fishing policies, I explored what types of fisheries might be sustainable on seamounts. Although the analyses in this paper are not meant to describe actual fisheries for seamounts, some generalizations can be made. Simulations with policy objectives that maximize economic performance favour fleet configurations based on deepwater trawling, but entail a cost to biodiversity. Maximizing ecological performance favours fleets on based on small pelagic and bottom longline fisheries, and maximizes biomass of long-lived species and biodiversity, but sacrifices total catches and jobs. The overall study suggested that sustainable seamount fisheries with tolerable ecosystem impacts appear to be closer to those found by maximizing an 'ecological' objective function. 8 . 3 CONCLUSIONS In conclusion, this PhD have shown that the primary production enhancement by seamounts is not sufficient to support the well-documented large aggregations of fish; my work supports the alternative hypothesis that the horizontal flux of prey is the key factor sustaining the rich communities living on seamounts. This PhD has also demonstrated the importance of seamounts to some large pelagic fish, marine mammals and seabirds. Fisheries exploitation is a major threat to seamount ecosystems, however I was not able to quantify the amount of catches taken from the seamounts around the world. Instead, I was able to demonstrate that global landings of demersal marine fishes have shifted to deeper water species over the last 50 years. This result is an indirect indication that seamounts have also increased in commercial importance over the last years. I have also demonstrated that \"deep-water\", \"seamount\" and \"seamount-aggregating\" fish stocks may be at serious risk of depletion, since their life histories render them highly vulnerable to overfishing, and provide with little resilience to overexploitation. Finally, ecosystem modelling analyses showed that sustainable seamount fisheries with tolerable ecosystem impacts appear to be closer to those found by maximizing an 'ecological' objective function. This suggested that industrial-scale fishery cannot be supported by seamount. However, some regulated small-scale artisanal like fleet could be supported by seamounts ecosystems. 8 . 4 FUTURE RESEARCH Despite considerable recent research effort, including that presented in this thesis, our knowledge on seamounts ecosystems is still limited when compared to coastal / shallow ecosystems. A landmark on seamounts research was the launch in 2005 of a Global Census of Marine Life on Seamounts (CenSeam). This program was build towards \"a global understanding of seamount ecosystems, and the roles they have in biogeography, biodiversity, productivity, and evolution of marine organisms.\" The CenSeam project along with SeamountsOnline (an online information system for seamount biology) will hopefully help in the development of new understandings on seamount ecosystems. In my view, three goals should guide the future work on seamounts: 1 - Assess and monitor biodiversity both at global and local scales A detailed catalogue of species occurring on seamounts should be the top priority when assessing these habitats. This should 1) produce exhaustive lists of species living seamounts; 2) help findings new species to science; 3) identify the fraction of endemic species living on seamounts. This task could involve 1) the synthesis of seamount studies that have been conducted and 2) promote field efforts to improve our knowledge on the species inhabiting worldwide seamounts. 2- Increase understanding of seamount ecosystem functioning in terms of productivity and the underlying biological characteristics Extensive experimental fieldwork should be carried in different seamounts around the world to examine particular aspects of seamount ecosystem functioning that are not yet well understood. Topics to be addressed should include 1) examine zooplankton biomass over and around seamounts, with emphasis to fish larvae biomass; 2) using tagging techniques to better examine tuna, swordfish, sharks, aggregations on seamounts; 3) understand how cephalopods interact with seamounts; 4) better examine the importance of seamounts for marine mammals, seabirds and sea-turtles; 5) better understanding the importance of corals and other epibenthic megafauna to the seamount biota; 6) to validate the assumption that seamount fish biomass are supported by bottom trapping of migrating zooplankton and by the horizontal flux of non-migrating zooplankton. 2 - Assess the human impacts on seamount communities both at global and local scales Describing the catch of fishes from worldwide seamounts presents a major challenge, because information on seamounts fisheries is very sparse, and it is difficult to make a distinction between deep-water fishing activities in general and those occurring on seamounts. We should be able 1) to quantify total catches from world's seamounts and 2) to quantify catches from selected individual seamounts. The first goal could be achieved by automated mapping techniques as the ones developed by the Sea Around Us Project at the University of British Columbia. This algorithm could then be run to analyses the development of fishing catches from seamounts. The second goal could only be achieved with information from fishing vessels, i.e., where individual fishing vessels were operating. This could be accomplished by analyzing satellite tracking of fishing vessels along with data from landings. This could produce the first dataset with real information of catches from individual seamounts and would help identifying those seamounts that are under most threats. 3 - Long term monitoring of seamount communities There is also an urgent need for the establishment of long-term programmes to assess population trends of selected species in seamounts as a whole or in reference sites subject to different ecological and exploitation regimes. For the later, focus could be on seamounts where exploitation is more intense. Selected species should be those more vulnerable and those that are likely to be heavily damaged by fishing activities. The methodologies involved in assessing the status of the communities and species should be standardized and involve in situ observations using advanced technology such as ROV's and submersibles. A P P E N D I X 1 COMPILATION OF FISH SPECIES RECORDED ON SEAMOUNTS Table 1 - List of species considered as S \"seamount fishes\" and AGG \"seamount-aggregating\" fishes. Intrinsic Vulnerability (Vf) index and * V-, excluding species from only Total length infinity is available, are also presented. Species S vs AGG Habitat Vi Vi* Acanthocybium solandri S Pelagic 50.22 50.22 Acantholabrus palloni . S Reef-associated 16.29 Acanthurus olivaceus s Reef-associated 32.23 Acromycter perturbator s Bathydemersal 76.17 Adelosebastes I a tens s Bathydemersal 21.45 Ahliesaurus berryi s Bathypelagic 18.25 Albatrossia pectoralis s Bathydemersal 90 90 Aldrovandia affin is s Bathydemersal 40 Aldrovandia oleosa s Bathypelagic 40 Aldrovandia phalacra s Bathydemersal 40 Aldrovandia rostrata s Bathypelagic 64.7 Aiepisaurus brevirostris s Pelagic 60 Alepocephalus agassizii s Bathydemersal 52.62 Alepocephalus australis s Bathydemersal 44.89 Alepocephalus bairdii AGG Bathydemersal 71.14 76.97 Alepocephalus productus s Bathydemersal 32.77 Alepocephalus rostratus s Bathydemersal 10 10 Alepocephalus tenebrosus s Bathydemersal 49.55 49.55 Allocyttus niger AGG Bathypelagic 40 62.35 Allocyttus verrucosus AGG Bathypelagic 40 65.75 Amphichaetodon howensis S Reef-associated 10 Anarhichas denticulatus s Benthopelagic 82.43 82.43 Anarrhichthys ocellatus s Demersal 90 90 Anatolanthias apiomycter s Pelagic Anoplogaster cornuta s Bathypelagic 42 42 Anoplopoma fimbria s Bathydemersal 62.24 62.24 Anthias anthias s Reef-associated 18.35 Antigonia aurorosea s Bathydemersal Species S vs AGG Habitat v, Vj* Antigonia capros s Demersal 21.97 Antigonia eos s Bathydemersal Antigonia malayana s Demersal Antigonia rubescens s Bathydemersal 10 Antimora microlepis s Bathypelagic 51 Antimora rostrata s Bathypelagic 49.89 49.89 Aphanopus capricornis s Bathypelagic 52.13 Aphanopus carbo AGG Benthopelagic 74.48 54.09 Aphanopus intermedins s Bathypelagic 73.91 Aphareus furca s Reef-associated 29.48 Aprion virescens s Reef-associated 40.73 40.73 Apristurus brwmeiis s, Demersal 48.15 Apristurus laurussonii s Bathydemersal 48.15 Apristurus manis s Bathydemersal 55.06 Apristurus profundorum ' s Bathydemersal Aptocyclus ventricosus s Benthopelagic 32.26 Arctozenus risso s Bathypelagic 34.41 Argentina sphyraena s Bathydemersal 37.09 37.09 Argyripnus atlanticus s Benthopelagic 50 Argyripnus electronus s Bathydemersal Argyripnus iridescens s Bathypelagic 10 Argyropelecus aculeatus s Bathypelagic 10 Argyropelecus afjinis s Bathypelagic 17.57 17.57 Argyropelecus gigas s Bathypelagic 10 Argyropelecus hemigymnus s Bathypelagic 10 10 Argyropelecus lychnus s Bathypelagic 10 Ariomma bondi s Demersal 55.73 Ariomma lurida s Bathypelagic 26.6 Ariomma melanum s Bathydemersal 21.45 Ariosoma balearicum s Reef-associated 26.6 Ariosoma marginatum s Demersal 29.69 Aristostomias tittmanni s Pelagic 10 Arnoglossus imperialis s Demersal 16.29 Arnoglossus multirastris s Demersal 76.67 Arnoglossus rueppelii s Demersal 10 A rnoglossus sept em ventral is s Bathydemersal 80.42 Species S vs AGG Habitat Vi V* Arothron firmamentum s Demersal 26.6 Aspitrigla cucuius s Demersal 32.41 32.41 Assurger anzac s Benthopelagic 90 Astronesthes gemmifer s Bathypelagic 10 Astronesthes ijimai s Bathypelagic 10 Astronesthes macropogon s Bathypelagic 10 Atheresthes stomias s Demersal 54.65 54.65 Aulopus filamentosus s Demersal 40 Aulopus japonicus s Demersal 90 Aulostomus chinensis s Reef-associated 53.03 Avocettina bowersii s Bathypelagic Avocettina infans s Bathypelagic 50.79 Bajacalifomia megalops s Bathypelagic 31.74 Batistes capriscus s Reef-associated 27.73 27.73 Banjos banjos s Demersal 10 Barbantus curvifrons s Bathypelagic 10 Bassogigas gillii s Bathydemersal 55.06 Bathophilus flemingi s Bathypelagic 29.17 29.17 Bathophiius longipinnis s Bathypelagic 10 Bathophilus pawneei s Bathypelagic 10 Bathygadus favosus s Bathydemersal 40 Bathygadus melanobranchus s Bathydemersal 40 Bathylagus euryops s Bathypelagic 40 Bathylagus pacijicus s Bathypelagic 43.12 43.12 Bathymicrops regis s Bathydemersal 10 Bathypterois atricolor s Bathydemersal 10 Bathypterois dubius s Bathydemersal 32.77 32.77 Bathypterois longipes s Bathydemersal 16.18 Bathypterois phenax s Bathydemersal 10 Bathypterois quadrifilis s Bathydemersal 10 Bathyraja shuntovi \" s Bathydemersal 81.35 Bathysaurus ferox s Bathydemersal 46.52 Bathytroctes oligoiepis s Bathypelagic ' 23.82 Bathytyphlops marionae s Bathydemersal 29.69 Beilottia apoda s Bathydemersal 34.18 Bembradium furici s Bathydemersal Bembradium roseum Bembrops fdifera Benthalbella dentata Benthodesmus elongatus Benthodesmus tenuis Benthosema glaciate Beryx decadactylus Beryx splendens Bodianus bilumilatus Bodianus cy/indriatus Bolinichthys photothorax Bonapartia pedaliota Borostomias antarcticus Bothrocara brunneum Bothrocara molle Brama brama Brosme brosme Brotulotaenia brevicauda Brotulotaenia crassa Caelorinchns bollonsi Caelorinchus caelorhincus caelorhincus Caelorinchus celaenostomus Caelorinchus fasciatus Caelorinchus immaculatus Caelorinchus innotabilis Caelorinchus kaiyomaru Caelorinchus labiatus Caelorinchus matamua Caelorinchus multifasciatus Caelorinchus nazcaensis Caelorinchus spilonotus Caelorinchus trachycarus Callanthias parini Callanthias ruber Callionymus maculatus Cantliigaster callisterna S-. Bathydemersal 10 s Bathydemersal 10 s Bathypelagic 10 Bathydemersal - 64.72 64.72 s Bathydemersal 90 s Pelagic 28.72 28.72 AGG Bathydemersal 60 74.07 AGG Bathydemersal 59.53 66.84 S Reef-associated 40 s Reef-associated 10 s Bathypelagic 10 s Bathypelagic 10 s Bathydemersal 21.45 ' s Bathydemersal 47.33 s Bathydemersal 40 s Bathypelagic 53.16 53.16 s Demersal 54.22 54.22 s Bathypelagic 52.74 s Bathypelagic 55.46 s Benthopelagic 60 s Benthopelagic 50.49 50.49 s Bathydemersal 51.76 s Bathydemersal 40 s Bathydemersal 50.09 s Bathydemersal 23.51 s Bathydemersal 40 s Bathydemersal 40 s Bathydemersal 46.92 s Bathydemersal 10 s Bathypelagic 31.74 s Bathydemersal 10 s Bathydemersal 40 s Reef-associated 43.33 s Demersal 44.89 s Demersal 10 s Demersal 10 Canthigaster coronata Canthigaster epilampra Canthigaster rivulata Caprodon longimanus Caprodon schlegelii Capros aper Carangoides orthogrammus Caranx ignobilis Caranx lugubris Caranx melampygus Careproctus melanurus Caristius maderensis Cataetyx laticeps Centracanthus cirrus Centroberyx affinis Centrodraco acanthopoma Centrodraco nakaboi Centrodraco otohime Centrodraco striatus Centrolophus niger Centrophorus granulosus Centrophorus squamosus Centroscyllium fabricii Centroscyllium ritteri Centroscymnus coelolepis Centroscymnus crepidater Centroscymnus cryptacanthus Centroscymnus owstoni Centroscymnus plunketi Ceratoscopelus maderensis Ceratoscopelus warmingii Cetonurus crassiceps Cetorhinus maximus Cetostoma regani Chaetodon fremblii Chaetodon kleinii s Reef-associated 10 s Reel-associated 10 s Reef-associated 10 s Reef-associated 40 s Benthopelagic 26.6 s Demersal 55.73 s Reef-associated 30.5 s Reef-associated 61.4 61.4 s Reef-associated 53.38 53.38 s Reef-associated 45.28 45.28 s Bathydemersal 29.26 29.26 s Pelagic 56.76 s Bathydemersal 26.6 s Demersal 25.57 s Benthopelagic 61.77 61.77 s Bathydemersal 10 s Bathydemersal s Bathydemersal 10 s Bathydemersal 43.33 s Bathypelagic 50 s Bathydemersal 90 s Benthopelagic 90 90 s Bathydemersal 79.32 79.32 s Demersal .49.37 s Bathydemersal 80.4 80.4 s Bathydemersal 83.94 83.94 s Bathydemersal 71.92 s Bathydemersal 74.44 s Bathydemersal 78.83 78.83 s Bathypelagic 50 s Bathypelagic 10 s Bathydemersal 40 s Pelagic 60.53 60.53 s Bathypelagic 18.02 18.02 s Reef-associated 10 s Reef-associated 10 Species S vs AGG Habitat Vi V,* Chaetodon miliaris s Reef-associated 51.89 51.89 Chascanopsetta megagnatha s Bathydemersal 10 Chascanopsetta prorigera s Bathydemersal 10 Chauliodus macouni s Bathypelagic 31.05 31.05 Chauliodus sloani s Bathypelagic 26.6 Chaunax fimbriatus s Bathydemersal 10 Chaunax latipunctatus s Bathydemersal Chaunax pictus s Bathydemersal 31.74 Chaunax umhrinus s Demersal Chelidonichthys gurnardus s Demersal 37.07 37.07 Chelidoperca lecromi s Demersal 10 Chiasmodon niger s Bathypelagic 16.29 Chimaera lignaria s Bathydemersal 90 Chimaera monstrosa s Bathydemersal 50 Chimaera owstoni . s • Bathydemersal 60 Chirostomias pliopterus s Bathypelagic 10 Chlamydoselachus anguineus s Bathydemersal 90 90 CMopsis bicolor s Demersal 33.8 Chlorophthalmus agassizi s Bathydemersal 60.87 Chlorophthalmus albatrossis s Demersal 10 Chlorophthalmus ichthyandri s Demersal 76.67 Chlorophthalmus zvezdae s Demersal 76.67 Chrionema pallidum s Bathydemersal 10 Chromis verater s Reef-associated 32.22 Coccorella atlantica s Bathypelagic 10 Conger conger s Demersal 81.18 81.18 Conger oligoporus s Reef-associated Conocara macropterum s Bathypelagic 25.57 Cookeolus japonicus s Reef-associated 48.15 48.15 Coris ballieui s Reef-associated 21.45 Coryphaena hippurus s Pelagic • 50 50 Coryphaenoides acrolepis s Bathydemersal 79.66 79.66 Coryphaenoides alateralis s Bathydemersal 17.84 Coryphaenoides armatus s Bathydemersal 60 Coryphaenoides carapinus s Bathydemersal 40 Coryphaenoides cinereus s Bathydemersal 40 Species S vs AGG Habitat V; V* Coryphaenoides guentheri S Bathydemersal 36.18 36.18 Coryphaenoides longifilis S Bathydemersal 48.96 Coryphaenoides murrayi s Bathydemersal 28.66 Coryphaenoides rudis s Bathypelagic 68.38 Coryphaenoides rupestris AGG Bathypelagic 75.4 76.25 Coiyphaenoides serruiatus S Bathydemersal 40 Coryphaenoides subserrulatus s Bathydemersal 28.66 Cottunculus thomsonii s Bathydemersal 26.6 Cryptopsaras couesii s Bathypelagic 35 35 Cubiceps pauciradiatus s Bathypelagic 10 Cyciothone braueri s Bathypelagic 10 10 Cyciothone microdon s Bathypelagic 10 10 Cyciothone pallida s Bathypelagic 27.5 27.5 Cyttomimus stelgis s Bathydemersal 76.67 Cyttopsis rosea s Bathypelagic 56.24 Dactylopsaron dimorphicum s Bathydemersal 43.33 Dalatias licha s Bathydemersal 72.94 72.94 Dasyatis pastinaca s Demersal 67.52 67.52 Deania calcea s Bathydemersal 78.24 78.24 Deania profundorum s Bathydemersal 71.31 71.31 Decapterus macarellus s Pelagic 43.85 43.85 Decapterus maruadsi s Reef-associated 29.34 29.34 Decapterus muroadsi s Pelagic 65.51 Decapterus russelli s Reef-associated 14.75 14.75 Dendrochirus barberi s Reef-associated 10 Derichthys serpentinus s Bathypelagic 31.74 Diaphus adenomus s Bathypelagic 10 Diaphus brachycephalus s Bathypelagic 10 Diaphus confusus s Bathydemersal Diaphus dumerilii s Pelagic 10 10 Diaphus lucidus ' s Bathypelagic 10 Diaphus parini s Benthopelagic Diaphus perspicillatus s Bathypelagic 10 Diaphus rafmesquii s Bathypelagic 10 Diaphus splendidus s Bathypelagic 10 Diaphus theta s Bathypelagic 10 Species S vs AGG Habitat Vi Vi* Diastobranchus capensis s Bathydemersal 76.86 Dibranchus tremendus • • • s • Bathydemersal 10 Dicrolene introniger s Bathydemersal 26.6 Dicrolene nigra s Bathydemersal 45.22 Diodon holocanthus' . ' s . Reef-associated 24.69 Diplospinus multistriatus s Bathypelagic 35.98 35.98 Dipturus batis s Demersal 79.58 79.58 Dipturus oxyrinchus s Bathydemersal 90 Diretmichthys parini s Bathypelagic 31.74 Diretmus argenteus s Bathypelagic 17.58 Dissostichus eleginoides AGG Pelagic 78.84 68.94 Dolicholagus longirostris s Bathypelagic 10 Dolichopteryx longipes s Bathypelagic 25 25 Echinorhinus cookei s Demersal 90 Echiodon dentatus s Demersal 10 Echiostoma barbatum s Bathypelagic 28.45 Ectreposebastes imus s Bathypelagic 10 Einara macrolepis s Bathypelagic 34.74 Emmelichthys elongatus s Demersal Emmelichthys nitidus cyanescens s Bathydemersal 51.22 Emmelichthys nitidus nitidus s Bathydemersal 65.51 Emmelichthys struhsakeri s Demersal 21.45 Engyprosopon regani s Demersal 76.67 Enigmapercis acutirostris s Bathydemersal 76.67 Eopsetta jordani s Demersal 64.92 64.92 Epigonus atherinoides s Bathydemersal 10 Epigonus denticulatus s Bathydemersal 10 Epigonus elegans s Bathydemersal Epigonus notacanthus s Bathydemersal 10 Epigonus robustus s Bathydemersal 10 Epigonus telescopus AGG Bathydemersal 51 51 Epinephelus quernus S Demersal 75.05 Epinephelus septemfasciatus S Reef-associated 90 Erilepis zonifer S Bathydemersal 90 Etelis carbunculus S Reef-associated 70.9 70.9 Etelis coruscans S Reef-associated 63.22 63.22 Etmopterus baxteri Etmopterus gracilispinis Etmopterus litvinovi Etmopterus lucifer Etmopterus princeps Etmopterus pusillus Etmopterus pycnolepis Etmopterus spinax Eurypharynx pelecanoides Eurypleuron owasianum Eustomias obscurus Eustomias schmidti Euthynnus afjinis Evistias acutirostris Facciolelta castlei Fistularia commersonii Fistularia petimba Flagellostomias boureei Foetorepus kanmuensis Foetorepus kinmeiensis Gadella maraldi Gadella norops Gadella obscurus Gadiculus argenteus thori Gadomus aoteanus Gadomus arcuatus Gadomus dispar Gadomus melanopterus Gadus macrocephalus Gaidropsarus argentatus Gaidropsarus ensis Gaidropsarus granti Gaidropsarus macrophthalmus Gaidropsarus mediterraneus Gaidropsarus parini Galeorhinus galeus S Bathydemersal 51 S Benthopelagic 26.6 S Bathydemersal S Bathydemersal 65.46 S Bathydemersal 51 S Bathydemersal 40 S Bathydemersal S Bathydemersal 66.56 66.56 S Bathypelagic 60 S Bathydemersal 10 S Bathypelagic 10 S Bathypelagic 10 S Reef-associated 59.78 59.78 S Reef-associated 33.04 S Demersal 76.67 S Reef-associated 90 S Reef-associated 90 S Bathypelagic 23.72 S Bathydemersal 10 S Bathydemersal 10 S Benthopelagic 21.45 S Benthopelagic 18.15 S Benthopelagic 10 S Pelagic 16.38 16.38 S Benthopelagic 60 S Bathypelagic 44.07 S Bathydemersal 77.28 S Bathydemersal S Demersal 54.58 54.58 S Bathydemersal 26.6 S Benthopelagic 31.74 S Demersal 27.63 S Demersal 16.29 S Demersal 23.75 23.75 S Demersal S Benthopelagic 72.2 72.2 Galeus melastomus S Bathydemersal 51 Galeus murinus S Bathydemersal 68.05 Gephyroberyx darwinii S Benthopelagic 44.89 Gephyroberyx japonicus S Benthopelagic 10 Gigantura indica s Bathypelagic 15.1 Glossanodon danieli s Bathydemersal 10 Glossanodon leioglossus s Bathydemersal 10 Glossanodon nazca s Bathydemersal Glyptocephalus cynoglossus s Demersal 60.18 Gnathophis andriashevi s Bathydemersal 76.67 Gnathophis cinctus s Demersal 33.8 Gnathophis codoniphorus s Demersal Gnathophis mystax s . Demersal • 44.89 Gnathophis parini s Bathydemersal 43.33 Gnathophis smithi s Demersal 54.77 Gonichthys cocco . s • Bathypelagic 10 Goniistius vittatus s Reef-associated 32.77 Gonostoma bathyphilum s Bathypelagic 10 Gonostoma denudatum s Bathypelagic 10 Gonostoma elongatum s Bathypelagic 18.87 Grammatonotus laysanus s Demersal 75.18 Grammatostomias dentatus s Bathypelagic 50 Grammicolepis brachiusculus s Bathypelagic 46.52 Gymnothorax flavimarginatus s Reef-associated 90 Gymnothorax hepaticus s Reef-associated 60 Gymnothorax maderensis s Demersal . • 60 Gymnothorax punctatofasciatus s Reef-associated 40 Gymnothorax steindachneri s Reef-associated 60 Halargyreus johnsonii s Bathypelagic 40 Halosauropsis macrochir s Bathydemersal 60 Haplomacrourus nudirostris s Bathypelagic 40 Harriotta raleighana s Bathydemersal 73.84 Helicolenus avius s Bathydemersal Helicolenus dactylopterus dactylopterus s Bathydemersal 59.44 Helicolenus fedorovi s Bathydemersal 18.35 Helicolenus lengerichi s Bathydemersal 34.58 Hem ilep ido tus hem Hep idotus S Demersal 40 Hemilepidotus spinosus S Demersal 20.42 Heniochus diphreutes s Reef-associated 51.48 Heptranchias perlo s Bathydemersal 90 90 Heterophotus ophistoma s Bathypelagic 27.22 Heteropriacanthus cruentatus s Reef-associated 24.58 Hexanchus griseus s Reef-associated 77.65 77.65 Himantolophus albinares s Bathypelagic 50.04 Hippoglossoides platessoides s Demersal 65.86 65.86 Hippoglossus hippoglossus s Demersal 76.46 76.46 Hippoglossus stenolepis s Demersal 79.18 79.18 Hollardia goslinei s Demersal Holtbyrnia anomala s Bathypelagic 52.87 Holtbyrnia macrops s Benthopelagic 10 Hoplichthys citrinus s Demersal 60 Hoplichthys gilberti s , Demersal 10 Hoplostethus atlanticus AGG Bathypelagic 63.79 68.65 Hoplostethus crassispinus S Bathypelagic 34.97 Hoplostethus mediterraneus mediterraneus AGG Benthopelagic 45.11 66.22 Howella brodiei S Bathypelagic 10 10 Hozukius guyotensis S Bathydemersal 40 Hydrolagus afjinis S Bathydemersal 79.89 Hydrolagus bemisi s Bathydemersal 67.09 Hydrolagus mirabilis s Bathydemersal 29.69 Hydrolagus pallidus s Bathydemersal 83.27 Hydrolagus trolli s Bathydemersal 68.69 Hygophum hygomii s Bathypelagic 10 Hygophum taaningi s Bathypelagic 10 Hymenocephalus aterrimus s Bathypelagic 10 Hymenocephalus gracilis s Bathypelagic 10 Hymenocephalus italicus s Benthopelagic 36.96 36.96 Hymenocephalus longibarbis s Benthopelagic 60 Hymenocephalus longiceps s Benthopelagic Hymenocephalus neglectissimus s Bathydemersal 10 Hymenocephalus semipellucidus s Bathydemersal 10 Hymenocephalus striatulus s Bathypelagic 10 Hyperoglyphe japonica Hyperoglyphe perciformis Icichthys australis Icosteus aenigmaticus Idiacanthus fasciola ldiolychnus urolampus Ilyophis brunneus Kali indica Katsuwonus pelamis Kentrocapros flavofasciatus Kuronezumia pallida Lactoria fornasini Laemonema longipes Laemonema rhodochir Laemonema yarrellii Laemonema yuvto Lampadena luminosa Lampadena speculigera Lampadena urophaos atlantica Lampadena urophaos urophaos Lampanyctus photonotus Lappanella fasciata Lepidion eques Lepidion guentheri Lepidion inosimae Lepidion microcephalus Lepidion schmidti Lepidocybium flavobrunneum Lepidophanes guentheri Lepidopsetta bilineata Lepidopus calcar Lepidopus caudatus Lepidorhombus boscii Lepidorhombus whifjiagonis Lepidorhynchus denticulatus Leptostomias haplocaulus s Benthopelagic 60 s Pelagic 36.11 s Pelagic 53.43 s Bathypelagic 79.44 79.44 s Bathypelagic 40 s Pelagic 19.2 . s Bathypelagic 44.07 s Bathypelagic 17.47 s Pelagic 48.31 48.31 s . Demersal 10 s Bathydemersal 40 s Reef-associated 10 s Bathydemersal 50.79 s Benthopelagic 10 s Bathydemersal 50 s Bathydemersal s Bathypelagic 10 s Bathypelagic 10 s Bathypelagic 10 s Pelagic 10 s Bathypelagic 10 s Reef-associated 50 AGG Benthopelagic 40 S Benthopelagic 55.87 S Bathydemersal 45.09 s Bathydemersal 40 s Bathydemersal 55.06 s Bathypelagic 90 s Pelagic 10 10 s Demersal 66.75 66.75 s Bathydemersal 52.62 s Bathydemersal 78.61 78.61 s Demersal 41.98 41.98 s Bathydemersal 58.43 58.43 s Bathypelagic 40 s Pelagic 36.18 Leptostomias longibarba Lestrolepis intermedia Leucoraja circuiaris Leucoraja fullonica Leuroglossus schmidti Lobianchia dojleini Lobianchia gemellarii Lophiodes miacanthus Lophius piscatorius Lucigadus microlepis Lutjanus kasmira Lycodes esmarkii Lycodes terraenovae Lyconus brachycolus Macropinna microstoma Macroramphosus gracilis Macroramphosus scolopax Macrorhamphosodes uradoi Macroaroides inflaticeps Macrourus berglax Macrourus carinatus Macruronus magelianicus Macruronus novaezelandiae Magnisudis atlantica Malacocephalus laevis Malacosteus niger Malthopsis annulifera Malthopsis lutea Malthopsis tiarella Manducus maderensis Marukawichthys ambulator Marukawichthys pacificus Mataeocephalus acipenserinus Maulisia argipalla Maulisia mauli Maulisia microlepis s Bathypelagic 45.64 s Bathypelagic 16.29 s Demersal 73.84 s Bathydemersal 73.84 s Bathypelagic 10 s Bathypelagic 10 10 s Bathypelagic 10 s Bathydemersal 31.09 s Bathydemersal 68.48 68.48 s Bathydemersal 10 s Reef-associated 43.94 43.94 s Bathydemersal 51 s Bathydemersal 40 s Bathydemersal 36.49 s Bathypelagic 10 s Pelagic 42 42 s Demersal 27.97 27.97 s Demersal 10 s Bathypelagic 53.06 s Benthopelagic 78.08 78.08 s Bathydemersal 60 60 s Benthopelagic 71.19 71.19 s Benthopelagic 69.18 69.18 s Pelagic 40 s Bathydemersal 44.89 s Bathypelagic 10 s Bathydemersal 10 s Bathydemersal 10 s Demersal 10 s Bathypelagic 19.28 s Bathydemersal 31.55 s Bathydemersal 10 s Bathypelagic 10 s Bathypelagic 33.08 s Bathypelagic 10 s Bathypelagic 44.55 Maurolicus muelleri Maurolicits rudjakovi Maurolicus weitzmani Melamphaes lugubris • Melamphaes microps Melamphaes suborbitalis Melanocetus murrayi Melanogrammus aeglefmus Melanolagus bericoides Melanonus zugmayeri Melanostigma atlanticum Melanostomias bartonbeani Merluccius australis Mesobius antipodum Metavelifer multiradiatus Microcanthus strigatus Micromesistius poutassou Microstomus bathybius Microsiomus kitt Microstomus pacificus Microstomus shuntovi Mirognathus normani Mitsukurina owstoni Mola mola Molva dypterygia Molva macrophthalma Molva molva Monocentris reedi Mora moro Muraena helena Myctophum affine Myctophum selenops Myripristis murdjan Nannobrachium atrum Nannobrachium cuprarium Nannobrachium lineatum s Bathypelagic 10 10 s Bathypelagic 10 s Bathypelagic 10 s Bathypelagic 10 K s • Bathypelagic 10 s Bathypelagic 10 s Bathypelagic 10 s Demersal 43.31 43.31 s Bathypelagic 10 s Bathypelagic 19.39 s Bathypelagic 10 s Bathypelagic 17.53 s Benthopelagic 72.28 72.28 s Bathypelagic 47.33 s Benthopelagic 19.39 s Reef-associated 32.22 s Pelagic 34.1 34.1 s Bathydemersal 40 s Demersal 40.41 40.41 s Demersal 64.75 64.75 s Bathydemersal s Bathypelagic 31.13 s Bathydemersal 90 s Pelagic 71.33 71.33 s Demersal 78.39 64.99 s Demersal 66.56 s Demersal 75.34 75.34 s Demersal 49.47 49.47 AGG Bathypelagic 53.03 47.36 s Reef-associated 90 s Bathypelagic 10 s Bathypelagic 10 s Reef-associated 19.86 19.86 s Bathypelagic 10 s Bathypelagic 50.82 s Bathypelagic 10 Nannobrachium regale Nannobrachium ritteri Nansenia ardesiaca Nansenia Candida Narcetes stomias Naso brevirostris Naso hexacanthus Naso maculatus Naso unicornis Nealotus tripes Nemadactylus gayi Nemichthys scolopaceus Neobythites zonatus Neocyttus helgae Neocyttus rhomboidalis Neomerinthe procurva• Neoscopelus macrolepidotus Neoscopelus microchir Nesiarchus nasutus Nessorhamphus ingolfianus Nettastoma falcinaris Nettastoma parviceps Nezumia aequalis Nezumia convergens Nezumia longebarbata Nezumia propinqua Nezumia sclerorhynchus Normichthys operosus Notacanthus bonaparte Notacanthus chemnitzii Notacanthus sexspinis Notopogon fernandezianus Notopogon lilliei Notopogon xenosoma Notoscopelus caudispinosus Notoscopelus resplendens s Bathypelagic 10 s Bathypelagic 10 s Benthopelagic 10 s Bathypelagic 10 s Bathypelagic 40 s Reef-associated 44.89 s Reef-associated 51 s Reef-associated 44.89 s Reef-associated 41.21 s Bathypelagic 16.29 s Demersal 76.67 s Bathypelagic 56 s Bathydemersal 50.13 s Bathypelagic 55.98 AGG Bathypelagic 31.74 s Demersal s Bathypelagic 16.29 s Bathypelagic 21.97 s Bathydemersal 79.89 s ' Bathypelagic 44.8 s Bathydemersal 76.67 s Bathydemersal 53.84 s Benthopelagic 27.63 s Bathydemersal 21.45 s Bathypelagic 32.26 s Bathydemersal 18.35 s Bathypelagic 44.02 s Bathypelagic 10 s Bathypelagic 60 s Benthopelagic 73.84 s Bathydemersal 44.89 s Bathydemersal 10 s Demersal 18.35 s Bathydemersal 10 s Bathypelagic 10 s Bathypelagic 10 Odontaspis ferox Odontomacrurus murrayi Omosudis lowii Oneirodes macrosteus Oneirodes thompsoni Osopsaron karlik Ostichthys kaiamts Ostracion cubicus Oxycheilinus unifasciatus Oxynotus bruniensis Pagellus bogaraveo Pagrus pagrus Parabothus amaokai Parabothus coarctatus Parabrotula plagiophlhalmus Paraconger macrops Paralepis coregonoides Parapercis dockinsi Parapercis roseoviridis Parapristipomoides squamimaxillaris Paraulopus Jilamentosus Parupeneus chrysonemus Parupeneus multifasciatus Parupeneus pleurostigma Parupeneus porphyreus Penopus microphthalmus Pentaceros decacanthus Pentaceros japonicus Pentaceros quinquespinis Phenacoscorpius eschmeyeri Photonectes dinema Photostomias guernei Photostylus pycnopterus Phycis blennoides ' Phycis phycis Physiculus dahvigki s Bathydemersal 90 s Bathypelagic 46.52 s Bathypelagic 10 s Bathypelagic 83.27 s Bathypelagic 10 s Bathydemersal 43.33 s Bathydemersal 27.63 s Reef-associated 40 s Reef-associated 40 s Bathydemersal 67.44 67.44 s Benthopelagic 54.73 54.73 s Reef-associated 38.85 38.85 s Demersal 76.67 s Bathydemersal 10 s Bathypelagic 10 s Demersal 40 s Pelagic 40 s Demersal 43.33 s Demersal 31.09 s Reef-associated 39.4 s Bathydemersal 31.09 s Demersal s Reef-associated 37.06 s Reef-associated 24.54 s Reef-associated 47.85 s Bathydemersal 26.6 s Bathydemersal 10 s Benthopelagic 16.29 s Pelagic 76.67 s Demersal 10 s Pelagic 16.29 s Bathypelagic 10 s Bathypelagic 10 s Benthopelagic 48.86 48.86 s Benthopelagic 52.3 52.3 s Benthopelagic 21.45 Physiculus hexacytus Physiculus japonicus Physiculus longicavis Physiculus luminosus Physiculus parini Physiculus sazonovi Physiculus therosideros Plagiogeneion geminatum Plagiogeneion unispina Plagiopsetta glossa Platyberyx opalescens Plectranthias exsul Plectranthias kelloggi Plectranthias parini Plectrogenium barsukovi Plectrogenium nanum Pollachius virens Polyacanthonotus challengeri Polyipnus clarus Polyipnus inermis Polyipnus kiwiensis Polyipnus matsubarai Polymetme andriashevi Polymetme corythaeola Polymixia berndti Polymixia japonica Polymixia lowei Polymixia nobilis Polymixia salagomeziensis Polymixia yuri Polyprion americanus Pontinus kuhlii Pontinus macrocephalus Pontinus tentacularis Porogadus miles Poromitra capito s Bathydemersal 43.33 s Bathydemersal 40.69 s Benthopelagic 10 s Bathydemersal 21.45 s Bathypelagic 43.33 s Bathypelagic 43.33 s Bathypelagic 10 s Demersal 19.39 s Bathydemersal 10 s Demersal 10 s Pelagic 69.99 s Demersal 43.33 s Demersal 10 s Bathypelagic 76.67 s Bathydemersal s Bathydemersal 10 s Demersal 63.3 s Bathypelagic 44.89 s Bathydemersal 10 s Bathypelagic 43.33 s Benthopelagic 10 s Benthopelagic 31.09 s Bathydemersal 10 s Benthopelagic . 17.32 s Reef-associated 40 s Bathypelagic 21.45 s Bathydemersal . 10 s Bathydemersal 44.76 s Bathydemersal 10 s Bathydemersal 34.81 s Bathydemersal 90 s Bathydemersal 40 s Demersal 28.15 s . Demersal 60 s Bathydemersal 21.45 s Bathypelagic 22.97 Poromitra crassiceps Poromitra megalops Priacanthus macracanthus Priacanthus meeki Prionace glauca Pristipomoides a rgyro gram mic lis Pristipomoides auriciUa Pristipomoides filamentosus Pristipomoides multidens Pristipomoides sieboldii Pristipomoides zonatus Promethichthys prometheus Protogrammus sousai Protomyctophum thompsoni Psenes cyanophrys Psenes maculatus Psenopsis anomala Pseudanthias thompsoni Pseudobathylagus milleri Pseudocaranx dentex Pseudocetomtrus septifer Pseudocyttus maculatus Pseudopentaceros pectoral is Pseudopentaceros richardsoni Pseudopentaceros wheeleri Pseudotriakis microdon Pteroplatytrygon violacea Pterycombus brama Pterygotrigla picta Pycnocraspedum armatum Pyramodon parini Pyramodon ventralis Raja brachyura Raja clavata Raja maderensis Raja rhina s Bathypelagic 10 s ' Bathypelagic- 10 s Reef-associated 26.71 26.71 s Reef-associated 24.54 s ' Pelagic 75.01 75.01 ' s Reef-associated 31.74 . s Reef-associated 31.21 31.21 s Reef-associated 45.39 45.39 s Demersal 66.85 66.85 s Reef-associated 45.69 45.69 s Reef-associated 27.39 27.39 s Benthopelagic 55.06 55.06 s Bathydemersal 10 s Bathypelagic 10 s Bathypelagic 10 s Pelagic 19.75 s Benthopelagic 37.71 s Reef-associated 32.47 s Bathypelagic 10 s Reef-associated 47.2 47.2 s Bathydemersal 30.72 AGG Bathydemersal 57.31 72.9 s Pelagic 44.91 AGG Pelagic 40 78.25 AGG Benthopelagic 60 S Bathydemersal 79.18 79.18 S Reef-associated 78.77 78.77 S Pelagic 40 S Bathydemersal 25.99 s Benthopelagic s Benthopelagic 21.97 s Benthopelagic 10 s Demersal 70.35 70.35 s Demersal 75.51 75.51 s Bathydemersal 70.94 s Bathydemersal 60 60 Species S vs AGG Habitat Vi V* Rajella bigelowi S Bathydemersal 40 Regalecus glesne s Pelagic 90 Rexea antefurcata s Benthopelagic 49.13 Rexea brevilineata s Benthopelagic 60.07 60.07 Rhadinesthes decimus s Bathypelagic 32.77 Rhinochimaera atlantica s Bathydemersal 47.09 Rhinochimaera pacijica s Bathydemersal 79.89 Rondeletia loricata s Bathypelagic 28.33 28.33 Rostroraja alba s Demersal 90 90 Rouleina attrita s Bathypelagic 40 Rouleina maderensis s Bathypelagic 23.51 Ruvettus pretiosus s Benthopelagic 90 Saccopharynx ampullaceus s Bathypelagic 77.15 Sagamichthys abei s Bathypelagic 18.57 18.57 Sagamichthys schnakenbecki s Bathypelagic 18.35 Sarda sarda s Pelagic 48.46 48.46 Satyrichthys engyceros S • Demersal 38.91 Satyrichthys quadratorostratus s Bathydemersal 50.21 Schedophilus medusophagus s Pelagic 40 Schedophilus ovalis , • s Benthopelagic 60 Schindleria praematura s Reef-associated. 10 Scomber japonicus s Pelagic 46.46 46.46 Scombrolabrax heterolepis s Benthopelagic 21.45 Scopelarchus guentheri s Bathypelagic 10 Scopeloberyx opisthopterus s Bathypelagic 10 Scopelogadus beanii s Bathypelagic Scopelosaurus harryi s Bathypelagic 17.9 17.9 Scopelosaurus mauli s Bathypelagic 10 Scorpaena azorica s Demersal 10 Scorpaena loppei s Demersal 29.61 Scorpaena neglecta s Demersal 29.17 Scorpaena notata s Demersal 10 Scorpaena scrofa s Demersal 60.76 60.76 Scorpaena uncinata s Demersal 76.67 Scorpaenopsis oxycephala s Reef-associated 27.63 Scymnodalatias garricki s Bathypelagic Scynmodon obscurus Scynmodon ringens Searsia koefoedi Sebastes aleutianus Sebastes alutus Sebastes aurora Sebastes babcocki Sebastes borealis Sebastes brevispinis Sebastes crameri Sebastes diploproa Sebastes elongatus Sebastes entomelas Sebastes flammeus Sebastes flavidus Sebastes hehomaculatus Sebastes iracundus Sebastes maliger Sebastes marinus Sebastes mentella Sebastes miniatus Sebastes nebulosus Sebastes nigrocinctus Sebastes paucispinis Sebastes pinniger Sebastes proriger Sebastes reedi Sebastes ruberrimus Sebastes variegatus Sebastolobus alascanus Sebastolobus altivelis Seriola dumerili Seriola lalandi Serranus atricauda Serranus cabrilla Serrivomer beanii s Benthopelagic 48.96 s Bathypelagic 67.78 s Bathypelagic 10 s Bathydemersal 60 s Bathydemersal 70.24 70.24 s Bathydemersal 32.77 s Demersal 46.52 46.52 s Bathydemersal 66.56 66.56 s Demersal 61.17 61.17 s Demersal 61.99 61.99 s Bathydemersal 50.36 50.36 s Demersal 30.72 30.72 AGG Pelagic 63.15 63.15 s Bathydemersal 48.64 s Reef-associated 67.15 67.15 AGG Demersal 32.77 32.77 S Bathydemersal 50.79 S Demersal 45.29 45.29 AGG Pelagic 68.01 68.01 AGG Bathypelagic 67.81 67.74 S Reef-associated 60 60 s Reef-associated 40 40 s Reef-associated 45.29 45.29 AGG Reef-associated 55.24 60.87 • S Reef-associated 46.3 46.3 s Bathydemersal 42.54 42.54 s Bathydemersal 56.28 56.28 AGG Reef-associated 70.9 65.31 S Demersal 29.69 29.69 s Bathydemersal 53.03 53.03 s Bathydemersal 30.72 30.72 s Reef-associated 54.2 54.2 s Reef-associated 43.62 43.62 s Demersal 58.57 s Demersal 33.64 33.64 s Bathypelagic 52.22 Setarches guentheri Sladenia remiger Sladenia shaefersi Somniosus microcephalus Sonmiosus pacificus Somniosus rostratus Spectrunculus grandis Sphagemacrurus grenadae Sphagemacrurus hirundo Sphoeroides pachygaster Sphyraenops bairdianus Spiniphryne gladisfenae Squaliolus laticaudus Squalogadus modificatus Squalus acanthias Squalus blainville Stenobrachius leucopsarus Sternoptyx diaphana Sternoptyx pseudobscura Stethopristes eos Stomias afjinis Stomias boa ferox Styiephorus chordatus Sudis atrox Sudis hyalina Sujjlamen jraenatum Symbolophorus veranyi Symphysanodon maunaloae Synagrops japonicus Synagrops philippinensis Synaphobranchus ajjinis Synaphobranchus brevidorsalis Synaphobranchus kaupii Synchiropus phaeton Synodus doaki Synodus synodus s Bathydemersal 16.29 s Bathydemersal 31.74 s Bathydemersal s Benthopelagic 90 90 s Benthopelagic 90 90 s Bathydemersal 90 s Bathydemersal 78.07 s Bathypelagic 16.29 s Bathydemersal 53.92 s Demersal 32.26 s Pelagic 60 s Bathypelagic s Bathypelagic 10 s Bathypelagic 26.6 s Benthopelagic 79.42 79.42 s Demersal 77.21 77.21 s Bathypelagic 36.51 36.51 s Bathypelagic 10 s Bathypelagic 10 s Bathypelagic 76.67 s Bathypelagic 10 s Bathypelagic 21.45 s Bathypelagic 19.39 s Bathypelagic 10 s Bathypelagic 60 s Reef-associated 29.69 s Bathypelagic 10 s Demersal 10 s Bathydemersal 26.6 s Bathydemersal 10 s Bathydemersal 90' s Bathydemersal 68.38 s Bathydemersal 60 •S. • Demersal 10 s Reef-associated 19.39 s Reel-associated 24.54 Species S vs AGG Habitat Vi Vi* Taaningichthys batliyphilus S Bathypelagic 10 Tactostoma macropus s Bathypelagic 38.52 38.52 Talismania antiUarum s Bathypelagic 10 Talismania longifilis s Bathypelagic 40 Taractichthys longipinriis ' s . Pelagic 60 Thalassobathia pelagica s Bathypelagic 52.31 Thamnaconus analis s Reef-associated Thamnaconus tessellatus s Bathydemersal 10 Theragra chalcogratnma s Benthopelagic 42.36 42.36 Thunnus albacares s Reef-associated 51.79 51.79 Torpedo fairchildi s Bathydemersal 60 Torpedo microdiscus s Demersal 76.67 Torpedo nobiliana s Reef-associated 76.67 76.67 Torpedo semipelagica s Demersal 76.67 Trachipterus trachypterus s Bathypelagic 90 Trachonurus sulcatus s Bathypelagic 40 Trachonurus villosus s Bathypelagic 44.89 Trachurus picturatus s Benthopelagic 67.44 Trachurus symmetricus s Pelagic 60.41 60.41 Trachyrincus longirostris s Bathydemersal 44.89 • Trachyrincus murrayi s Benthopelagic 90 Trachyrincus scabrus s Bathydemersal 48.04 48.04 Trachyscorpia capensis s Bathydemersal 26.6 Trachyscorpia cristulata echinata s Bathydemersal 40 Triodon macropterus s Reef-associated 40 Tripterophycis svetovidovi s Bathypelagic 10 Trisopterus minutus s Benthopelagic 27.89 27.89 Tubbia tasmanica s Bathypelagic 47.74 Valenciennellus tripunctulatus s Bathypelagic 10 Venefica procera s Bathydemersal 67.05 Ventrifossa johnboborum s Bathydemersal 57.76 Ventrifossa macrodon s Bathydemersal 31.74 Ventrifossa macropogon s Bathydemersal 40 Ventrifossa obtusirostris s Bathydemersal 21.45 Ventrifossa teres s Bathydemersal 10 Vinciguerria nimbaria s Bathypelagic 10 Species S us AGG Habitat V; V* Xanthichthys mento s Reef-associated 55.21 Xenodermichthys copei s Bathypelagic 10 Xenolepidichthys dalgleishi s Benthopelagic 10 Xiphias gladius s Pelagic 73.5 73.5 Yarrella blackfordi s Bathypelagic 35.98 Zanclus cornutus s Reef-associated 10 Zaprora siiemts s Demersal 60 Zenion hololepis s Bathydemersal 10 Zenion leptolepis s Bathypelagic 10 Zenopsis conchifera s Benthopelagic 50.44 Zenopsis nebulosa s Bathydemersal 49.79 49.79 Zenopsis oblongus s Demersal Zeus faber s Benthopelagic 46.85 46.85 Zu cristatus s Bathypelagic 72.63 A P P E N D I X 2 LIST OF LARGE AND SMALL SEAMOUNTS IDENTIFIED IN THE AZORES ECONOMIC EXCLUSIVE ZONE Table 1 - Seamounts characteristics: number; category (large and small); location; depth of the summit (m); seamount height (h in m); basal area (at, in km ); height to radius ratio ($.); the average slope ((/> in degrees); distance to nearest large seamount (dL in km, for large seamounts only); and distance to nearest seamount (d in km). N Category Location Depth h Ub £ d L N d N Longitude Latitude (m) (m) Km2 degrees km km 1 Large -24.4850 37.3610 821 1252 1291 0.10 3.41 21.4 15 21.4 15 2 Large -25.8794 37.6032 265 1050 974 0.09 4.71 48.4 19 18.2 100 3 Large -29.5534 37.9873 364 1161 895 0.11 6.57 33.3 28 27.9 111 4 Large -26.1300 38.5718 1154 1038 964 0.08 3.43 31.6 33 31.6 33 5 Large -29.8791 39.6824 886 1020 796 0.10 3.23 60.9 39 19.7 142 6 Large -26.9316 39.7074 756 1125 1159 0.09 3.84 69.4 40 29.5 146 7 Large -25.7292 36.6179 1957 1144 1180 0.11 3.09 62.7 11 13.9 84 8 Large -26.1049 37.1022 1048 1721 1004 0.15 8.00 50.8 11 12.6 93 9 Large -28.1840 37.1690 1646 1141 654 0.10 3.80 18.0 12 18.0 12 10 Large -31.4322 37.1774 413 1305 1295 0.09 2.10 71.1 57 25.0 174 11 Large -25.6540 37.1773 270 2040 916 0.16 9.27 50.8 8 21.7 95 12 Large -28.0504 37.2609 1478 1148 1114 0.08 4.09 18.0 9 18.0 9 13 Large -24.7605 37.2609 65 1754 1054 0.12 8.32 32.6 1 32.6 1 14 Large -25.0945 37.3945 195 1835 1022 0.13 7.99 40.0 13 40.0 13 15 Large -24.3764 37.5197 1063 1086 1114 0.09 3.68 21.4 1 21.4 1 16 Large -26.5725 37.5281 1265 1079 947 0.10 5.47 40.3 19 19.1 97 17 Large -28.9689 37.7368 309 956 1062 0.08 2.74 35.7 23 35.7 23 18 Large -30.6723 37.7535 735 1088 596 0.11 5.07 18.8 20 18.8 20 19 Large -26.2886 37.7535 816 1448 1264 0.13 . 7.05 40.3 16 12.2 107 20 Large -30.8394 37.7786 679 1096 568 0.10 6.20 18.8 18 18.8 18 21 Large -31.4990 37.8287 824 22 Large -31.2151 37.8621 707 23 Large -28.6851 37.8871 408 24 Large -30.3384 38.2044 592 25 Large -26.6143 38.2211 390 26 Large -26.2301 38.2462 650 27 Large -30.1046 38.2712 730 28 Large -29.4366 38.2629 549 29 Large -27.7832 38.2629 473 30 Large -30.6389 38.2963 485 31 Large -31.0231 38.3213 712 32 Large -29.0357 38.5468 261 33 Large -25.8460 38.5885 1264 34 Large -26.6143 38.6887 468 35 Large -30.2298 38.6971 671 36 Large -29.9876 38.7305 380 37 Large -29.8875 38.9893 438 38 Large -31.8831 39.3818 556 39 Large -29.3363 39.6072 603 40 Large -26.6894 40.2835 705 41 Large -30.1714 40.3587 661 42 Large -29.7121 40.3671 958 43 Large -26.9315 40.3838 804 44 Large -29.3698 40.4255 570 45 Large -27.0234 40.5174.. 985 46 Large -28.8938 40.5424 896 47 Large -29.5034 40.7261 888 48 Large -29.7038 . 40.7428 75 f 49 Large -28.4346 40/8764 1230 1129 1009 0.11 3.88 31.8 22 13.5 181 1454 903 0.14 7.55 31.8 21 18.6 181 1297 557 0.12 7.24 35.7 17 23.4 182 1354 1150 0.11 3.86 27.0 27 9.3 118 1760 964 0.14 7.81 42.8 26 30.6 110 1747 1156 0.14 4.97 37.9 4 10.4 119 1166 1013 0.11 4.78 27.0 24 17.1 188 1134 1144 0.10 3.91 33.3 3 32.4 111 1103 1075 0.10 5.81 108.6 23 16.4 190 1414 1009 0.15 5.14 34.9 24 26.2 118 1197 1106 0.09 3.91 42.8 30 25.2 185 1269 718 0.11 7.35 39.2 51 23.3 189 1260 1015 0.09 5.47 31.6 4 31.6 4 968 1065 0.09 4.53 52.0 25 52.0 25 1055 798 0.11 4.24 27.2 36 15.0 191 1251 945 0.11 5.48 27.2 35 13.3 191 1069 1079 0.10 4.55 30.8 36 14.7 192 1223 906 0.09 6.21 151.7 31 43.6 128 1128 1144 0.09 5.24 60.9 5 25.8 144 1716 1095 0.12 6.11 29.1 43 29.1 43 1183 799 0.17 4.41 51.0 42 20.8 209 1447 839 0.13 5.93 38,6 44 34.0 209 1297 994 0.09 5.37 18.0 45 18.0 45 1884 790 0.23 5.61 36.6 47 35.3 207 1227 991 0.11 5.89 18.0 43 18.0 43 1247 891 0.13 7.61 54.5 44 12.6 211 1511 921 0.17 5.57 22.3 48 22.3 48 1562 990 0.15 ' 5.54 22.3 47 22.3 47 1250 766 0.09 5.46 63.1 46 29.5 163 N Category Location Depth h ah £ * d L N d N Longitude Latitude (m) (m) Km 2 degrees km km 50 Large -29.2361 37.9873 206 625 796 0.06 2.86 35.3 3 7.7 111 51 Large -28.9940 38.1961 204 876 961 0.07 4.06 35.5 50 17.8 116 52 Large -26.7562 36.5260 . 2226 1344 940 0.10 7.64 96.6 8 12.6 75 53 Large -27.5112 .34.8413 1241 1858 1048 0.17 ' . 5.75 107.8 54 37.1 239 54 Large -27.0457 35.6925 2668 1220 832 0.14 8.13 98.0 52 24.5 266 .55 Large -24.7980 35.9452 3176 1130 515 0.15 5.28 127.6 7 30.1 267 56 Large -33.0307 36.7831 1146 1247 883 0.17 3.91 66.5 62 51.6 282 57 Large -32.0598 37.3018 1057 1088 1040 0.12 3.67 51.9 58 37.4 176 58 Large -32.0997 37.7673 989 1202 921 0.14 3.46 51.9 57 29.6 105 59 Large -34.5469 . 38.3259 2402 1062 1059 0.10 3.86 169.3 62 22.4 311 60 Large -30.9426 41.5312 1080 1181 1045 0.11 5.14 155.9 41 29.7 365 61 Large -29.1870 42.4090 1306 1103 758 0.13 4.37 189.7 49 29.1 390 62 Large -33.4829 37.2353 606 1017 1083 0.10 4.39 51.4 63 51.4 63 63 Large -33.0440 37.3816 377 1114 959 0.11 5.64 51.4 62 50.2 299 64 Small -31.7161 36.1085 2377 466 606 0.04 1.99 20.0 68 65 Small -28.5515 36.1503 2927 446 672 0.04 2.09 44.9 71 66 Small -29.9877 36.2505 2798 414 535 0.04 1.47 39.1 76 67 Small -24.3681 36.2505 3135 751 1004 0.06 2.25 38.6 269 68 Small -31.7578 36.2839 2459 443 258 0.06 1.90 20.0 64 69 Small -29.1193 36.4008 2949 578 487 0.06 2.46 34.9 72 70 Small -29.6036 36.4175 3023 330 633 0.04 ' 1.57 22.1 72 71 Small -28.3010 -36.4676 2777 506 917 0.04 1.76 22.4 78 72 Small -29.4198 36.4927 3019 383 518 0.07 1.82 22.1 70 73 Small -26.3137 36.551 1 2249 1214 1001 0.09 5.57 36.3 169 74 Small -30.8393 36.5678 2337 495 1264 0.04 2.15 39.4 89 75 Small -26.8647 36.5595 2530 1012 1054 0.09 5.08 12.6 52 76 Small -29.9543 36.6012 2666 540 1096 0.04 2.24 30.1 172 77 Small -27.0151 36.6012 2621 967 567 0.09 5.75 17.3 75 78 Small -28.3845 36.6513 2828 476 689 0.05 2.33 22.4 71 N Category Location Depth h ab £ d L N d N Longitude Latitude (m) (m) Km2 degrees km km 79 Small -27.1987 36.6513 3080 544 455 0.06 2.51 21.2 77 80 Small -27.4826 36.6597 2918 659 576 0.06 3.19 31.6 79 81 Small -30.3133 36.6763 2683 320 820 0.03 1.76 17.0 172 82 Small -27.8167 36.6847 2749 655 912 0.06 3.25 32.2 83 83 Small -28.1005 36.7432 2595 648 602 0.06 4.07 15.9 173 84 Small -25.7208 36.7432 1991 918 1174 0.09 4.44 7.1 86 85 Small -29.1026 36.7682 3155 332 345 0.04 1.62 32.9 88 86 Small -25.6958 36.8016 1881 982 839 0.12 4.17 7.1 84 87 Small -30.3551 36.8350 2574 452 500 0.04 2.62 16.2 91 88 Small -28.8187 36.8517 2659 547 676 0.07 2.51 32.9 85 89 Small -30.6807 36.8851 2364 414 382 0.05 2.49 21.4 91 90 Small -25.7292 36.8851 2088 866 472 0.11 4.89 10.0 86 91 Small -30,4887 36.8935 2492 373 611 0.04 2.57 16.2 87 92 Small -31.7579 37.0271 915 902 747 0.08 '6.14 15.0 174 93 Small -26.1968 37.1690 1209 1406 1019 0.13 6.27 12.6 8 94 Small -30.0545 37.2191 1900 688 771 0.06 2.48 50.5 102 95 Small -25.7208 37.3610 393 1203 1114 0.11 6.03 16.0 100 96 Small -31.2235 37.3861 678 1142 883 0.09 6.42 10.0 99 97 Small -26.4890 37.3778 1618 842 1000 0.07 4.62 19.1 16 98 Small -30.9730 37.4028 816 1047 995 0.09 4.52 18.6 99 99 Small -31.1400 37.4195 710 1132 797 0.11 6.46 10.0 96 100 Small -25.8377 37.4445 393 1288 1175 0.12 5.02 16.0 95 101 Small -26.7979 37.5364 1577 857 711 0.08 4.02 25.1 16 102 Small -30.3217 37.5865 994 790 1298 0.07 2.20 26.4 106 103 Small -28.1507 37.6032 1986 463 504 0.08 2.64 18.2 177 104 Small -27.0986 37.6199 1729 707 600 0.08 3.15 34.7 101 105 Small -31.8414 37.7034 1205 915 621 0.10 5.56 13.9 179 106 Small -30.1380 37.7368 881 730 1020 0.09 3.80 26.4 102 107 Small -26.1800 37.7368 919 1377 993 0.15 4.63 12.2 19 N Category Location Depth h ah £ d L N d N Longitude Latitude (m) (m) Km2 degrees km km 108 Small -29.7539 37.7535 804 639 640 0.09 2.60 34.2 3 109 Small -31.8079 37.8537 1014 959 1067 0.09 4.33 17.1 105 110 Small -26.6226 37.9456 1303 818 1230 0.06 3.20 30.6 25 111 Small -29.3030 38.0040 131 756 837 0.08 3.86 7.7. 50 112 Small -27.4576 37.9957 1047 767 1174 0.06 2.60 29.6 117 113 Small -28.4262 38.0124 1203 751 993 0.07 2.75 19.9 184 114 Small -27.1653 38.0708 1068 812 905 0.07 4.29 13.7 186 115 Small -31.6326 38.1126 1182 843 449 0.10 4.24 34.7 109 116 Small -29.1360 38.1209 234 727 639 0.07 3.76 17.8 51 117 Small -27.6580 38.1710 644 902 1012 0.09 5.15 17.3 29 118 Small -30.4219 38.2044 866 1208 1060 0.10 4.36 9.3 24 119 Small -26.1466 38.2044 960 1374 1082 0.13 6.12 10.4 26 120 Small -28.8520 38.2796 502 613 344 0.09 4.37 13.9 189 121 Small -31.5992 38.4382 962 635 1084 0.05 1.99 36.4 115 122 Small -27.3741 38.4633 607 850 631 0.07 5.08 12.6 123 123 Small -27.4659 38.5301 674 820 646 0.09 4.01 12.6 122 124 Small -24.0759 38.5885 3302 389 521 0.04 2.21 15.8 316 125 Small -29.3531 38.6971 1427 485 764 0.04 2.45 38.6 129 126 Small -31.1483 38.8307 1149 740 1129 0.05 2.94 58.3 31 127 Small -30.4803 38.8474 1140 735 738 0.11 3.50 19.7 193 128 Small -31.7245 39.0227 965 502 1234 0.04 2.13 43.6 38 129 Small -29.4950 39.0144 1514 489 510 0.06 2.40 24.7 196 130 Small -27.5161 39.0561 753 831 688 0.08 4.70 26.5 131 131 Small -27.3240 39.1981 882 527 1166 0.04 1.56 26.5 130 132 Small -29.2862 39.2231 1437 568 544 0.06 2.37 14.9 196 133 Small -28.5013 39.2231 1064 966 837 0.07 5.39 34.4 137 134 Small -29.6119 39.2816 1067 909 684 0.12 5.10 13.2 199 135 Small -29.7538 39.3066 967 881 690 0.14 4.29 10.5 199 136 Small -29.3698 39.3316 1552 468 660 0.07; • 2:46 13.3 196 137 Small . . -28.6433 39.4987 1217 777 706 0.06 3.67 34.4 133 138 Small -30.5471 39.5320 1403 526 911 0.04 2.38 42.8 202 139 Small -29.0023 39.6323 1226 730 611 0.08 3.19 37.2 39 140 Small -29.6203 39.6657 1200 784 779 0.08 4.47 12.1 203 141 Small -24.3180 39.6657 3195 537 1204 0.04 2.16 46.9 149 142 Small -30.0545 39.7074 985 734 643 0.09 3.94 15.4 202 143 Small -30.2131 39.7659 1210 636 720 0.07 2.63 13.5 202 144 Small -29.4533 39.8076 1177 718 551 0.12 2.82 13.4 145 145 Small -29.3865 39.9078 1242 615 253 0.09 3.45 13.4 144 146 Small -27.0401 39.9496 1122 825 974 0.06 4.03 18.8 206 147 Small -29.2612 40.0414 1245 503 632 0.06 3.17 15.8 207 148 Small -25.9630 40.0581 2809 293 560 0.03 1.31 60.0 154 149 Small -24.1844 40.0665 3260 420 985 0.03 1.58 46.9 141 150 Small -30.0043 40.1082 1257 472 529 0.07 2.20 18.6 209 151 Small -25.2115 40.1082 2893 391 1013 0.03 1.52 44.8 154 152 Small -24.6270 40.1917 3249 370 1015 0.03 1.59 36.5 157 153 Small -28.0504 40.3086 1574 579 755 0.06 2.64 16.4 208 154 Small -25.5204 40.3671 2730 385 1100 0.04 1.29 44.8 151 155 Small -28.6600 40.4840 1554 793 488 0.08 4.70 21.4 156 156 Small -28.4679 40.4923 1943 505 531 0.05 2.72 21.4 155 157 Small -24.5852 40.5174 3217 387 1055 0.03 1.65 36.5 152 158 Small -30.1046 40.5424 1497 641 739 0.07 2.93 21.7 41 159 Small -29.0524 40.5424 1062 1153 512 0.10 7.60 17.6 46 160 Small -30.3884 40.5758 1455 784 880 0.07 ' 2.48 20.0 212 161 Small -30.6724 40.5925 1792 590 517 0.06 2.43 12.4 162 162 Small -30.6974 40.7010 1918 561 415 0.06 . ; 2.66 12.4 161 163 Small -28.1757 40.8180 1926 798 921 0.06 4.45 29.5 49 164 Small -30.3217 40.9432 1607 , 891 387 0.11 4.92 38.6 359 165 Small -25.4870 36.1586 3286 574 1073 0.04 ' 1.75 , 49.0 268 166 Small . -24.7522 36.2422 3063 681 1264 0.05 1.64 33.4 55 167 Small -27.3324 36.4008 2960 430 1114 0.05 1.30 31.5 79 168 Small -28.7519 36.5177 3174 294 612 0.03 1.61 37.8 88 169 Small -25.9880 36.5761 2591. 843 867 0.06 ' 3.80 ' 29.1 7 170 Small -24.9777 36.6680 2352 939 500 0.09 5.68 40.4 171 171 Small -24.6186 36.7264 2903 584 669 0.05 2.99 40.4 170 172 Small -30.1797 36.7515 2788 341 574 0.04 1.55 17.0 81 173 Small -28.1172 36.8851 2774 626 303 0.08 3.24 15.9 83 174 Small -31.6410 37.0939 935 774 592 0.10 4.05 15,0 92 175 Small -27.0401 37.1940 2225 336 593 0.03 1.82 46.6 101 176 Small -31.7245 37.2776 1140 832 490 0.09 4:52 22.4 174 177 Small -28.1089 37.4445 2105 315 734 0.04 1.96 18.2 103 178 Small -30.7141 37.5281 923 811 1045 0.07 2.13 25.5 18 179 Small -31.7161 37.7118 1265 776 593 0.10 4.61 13.9 105 180 Small -24.7355 37.7118 1564 826 701 0.10 4.06 45.2 15 181 Small -31.3821 37.8621 1090 862 900 0.10 4.04 13.5 21 182 Small -28.8020 38.0625 623 670 670 0.07 3.41 23.4 23 183 Small -29.8206 38.1209 1570 542 590 0.06 2.51 19.7 188 184 Small -28.5682 38.1209 930 641 479 0.06 3.49 19.9 113 185 Small -30.8895 38.1376 1350 842 351 0.11 4.66 25.2 31 186 Small -27.0568 38.1293 941 901 701 0.08 4.91 13.7 114 187 Small -31.2402 38.2044 1434 682 933 0.09 3.75 27.4 31 188 Small -29.9542 38.2378 1261 777 656 0.10 3.95 17.1 27 189 Small -28.9523 38.3547 488 797 451 0.10 4.79 13.9 120 190 Small -27.8834 38.3714 376 1011 774 0.11 6.22 16.4 29 191 Small -30.0962 38.6804 819 855 1155 0.07 3.71 • 13.3 36 192 Small -29.9960 38.9141 787 644 805 0.08 3.21 14.7 37 193 Small -30.4553 39.0227 1520 346 151 0.07 2.01 19.7 127 194 Small -27.7498 39.1396 1052 566 584 0.06 3.00 27.6 130 N Category Location Depth h «/> £ d L N d N Longitude Latitude (m) (m) Km2 degrees km km 195 Small -30.3634 39.1814 1330 608 725 0.06 3.67 20.4 193 196 Small -29.4199 39.2231 1472 572 915 0.06 1.83 13.3 136 197 Small -28.8604 39.2315 1358 566 868 0.04 2.80 38.3 137 198 Small -29.8457 39.3316 1218 672 569 0.07 3.71 10.6 135 199 Small -29.6870 39.3734 1136 812 724 0.11 5.06 10.5 135 200 Small -27.0234 39.4319 1001 689 889 0.05 3.52 32.3 6 201 Small -30.0377 39.5320 1154 797 297 0.10 5.43 19.6 142 202 Small -30.1797 39.6489 1309 561 537 0.10 3.10 13.5 143 203 Small -29.5785 39.7659 1245 654 992 0.09 4.18 12.1 140 204 Small -26.7645 39.9579 1807 555 614 0.05 1.88 30.6 146 205 Small -29.8791 39.9663 1381 623 822 0.13 1.68 21.0 150 206 Small -27.1821 40.0414 1420 548 746 0.05 2.83 18.8 146 207 Small -29.3865 40.1082 1227 682 747 0.09 2.09 15.8 147 208 Small -28.0087 40.1667 1852 242 795 0.02 1.30 16.4 153 209 Small -30.0043 40.2752 1089 585 580 0.06 2.96 18.6 150 210 Small -30.8978 40.4255 1874 350 773 0.03 1.46 31.2 161 211 Small -28.8604 .40.4338 1392 824 , 976 0.08 2.97 12.6 46 212 Small -30.5555 40.6426 2009 394 742 0.05 2.05 14.1 161 213 Small -29.0942 40.7511 1552 797 794 0.08 2.44 23.7 159 214 Small -25.6540 40.8513 3009 382 679 0.03 1.98 46.3 361 215 Small -24.4522 33.7374 4727 660 664 0.07 2.82 28.2 216 216 Small -24.6384 33.9103 4722 522 834 0.07 2.04 11.9 218 217 Small -26.8462 33.9635 3775 749 393 0.10 3.92 36.2 219 218 Small -24.5852 34.0034 4683 547 871 0.07 2.19 11.9 216 219 Small -26.5270 34.0300 4266 467 313 0.07 1.91 36.2 217 220 Small -24.0000 34.0300 4624 551 948 0.06 2.18 34.5 221 221 Small -24.2660 34.1896 4688 611 803 0.06 2.57 33.0 230 222 Small -22.8562 34.2428 4713 591 800 0.09 3.71 58.0 237 223 Small -24.8512 34.2960 4843 539 528 0.08 3.64 29.1 232 224 Small -26.2477 34.3359 3959 742 708 0.08 4.30 46.0 219 225 Small -25.3965 34.3625 4501 794 966 0.07 4.19 28.0 228 226 Small -24.5586 34.4290 4596 762 498 0.11 4.06 23.8 232 227 Small -23.6276 34.4157 5005 241 541 0.03 1.29 18.3 229 228 Small -25.1571 34.4423 4953 398 555 0.05 3.02 28.0 225 229 Small -23.4680 34.4556 4722 507 606 0.06 2.49 18.3 227 230 Small -24.2128 34.4822 4428 785 926 0.08 3.12 14.0 233 231 Small -24.0000 34.5088 4751 474 781 0.07 3.19 23.8 230 232 Small -24.7448 34.5354 4664 618 644 0.08 3.49 23.8 226 233 Small -24.3325 34.5221 4714 558 618 0.07 3.30 14.0 230 234 Small -23.2818 34.5487 4502 702 736 0.09 3.51 23.1 229 235 Small -26.7930 34.6152 3760 541 512 0.06 2.60 68.1 224 236 Small -25.8620 34.6684 4313 651 449 0.09 3.58 44.7 240 237 Small -23.0291 34.7349 4203 999 539 0.12 • 4.35 34.9 234 238 Small -22.0050 34.8280 4587 573 1002 0.09 3.62 39.8 244 239 Small -27.8437 34.8679 1945 878 905 0.08 2.50 37.1 53 240 Small -25.5295 34.8945 4125 574 1002 0.06 1.92 44.7 236 241 Small -22.6434 34.9344 4528 519 836 0.05 1.94 48.3 237 242 Small -24.5320 35.0408 4036 841 1014 0.07 3.37 60.9 232 243 Small -23.8670 35.0275 4146 737 697 0.11 4.15 47.3 248 244 Small -21.7656 35.0940 4604 505 948 0.06 3.28 39.8 238 245 Small -25.1970 35.2802 4021 576 831 0.05 2.44 29.1 249 246 Small -26.8595 35.3201 3664 501 747 0.05 2.70 19.3 251 247 Small -22.1114 35.3334 4620 368 919 0.03 1.62 23.8 254 248 Small -23.6010 35.3600 3971 895 1038 0.08 4.53 47.3 243 249 Small -24.9576 35.3866 3838 745 755 0.08 4.06 24.5 257 250 Small -22.6567 35.4265 4218 668 514 0.08 3.02 44.4 263 251 Small -26.9526 35.4664 3440 635 665 0.07 2.79 19.3 246 252 Small -29.6658 35.5196 2912 564 385 .0.08 3.20 46.0 272 253 Small -26.4073 35.5329 3290 709 729 0.08 3.30 20.9 259 254 Small -22.1380 35.5462 4541 427 591 0.05 2.21 23.8 247 255 Small -26.7531 35.5595 3255 757 603 0.09 4.36 21.5 260 256 Small -26.0748 35.5994 3688 403 943 0.04 1.68 37.7 253 257 Small -24.8645 35.5861 3710 636 964 0.06 2.40 24.5 249 258 Small -27.5378 35.6526 3075 484 810 0.07 2.17 27.3 265 259 Small -26.4339 35.7191 3333 767 657 0.12 3.66 10.5 261 260 Small -26.6999 35.7457 3136 749 522 0.10 4.38 21.5 255 261 Small -26.5004 35.7856 3105 883 653 0.12 4.26 10.5 259 262 Small -23.0823 35.7723 3686 924 546 0.10 4.84 19.9 271 263 Small -22.6833 35.8255 4258 383 406 0.06 1.88 23.4 275 264 Small -29.4131 35.8521 2779 515 924 0.05 2.31 25.3 272 265 Small -27.4846 35.8920 3004 672 520 0.07 2.73 27.3 258 266 Small -27.1388 35.8920 3156 765 499 0.11 3.61 24.5 54 267 Small -24.5320 35.8920 3810 555 599 0.09 2.26 22.2 269 268 Small -25.1172 35.9186 3561 615 910 0.06 2.69 35.6 55 269 Small -24.3325 35.9053 3963 527 703 0.07 2.35 22.2 267 270 Small -23.5212 35.9186 4037 487 916 0.06 2.11 31.1 274 271 Small -23.2020 35.9053 3976 549 802 0.08 2.56 19.9 262 272 Small -29.6259 35.9319 2800 497 884 0.05 1.92 25.3 264 273 Small -24.0665 35.9319 3950 599 1018 0.06 2.66 29.6 274 274 Small -23.8005 35.9319 3955 644 644 0.08 3.16 29.6 273 275 Small -22.7498 36.0250 3782 903 921 0.08 3.54 23.4 263 276 Small -22.9626 36.0782 4088 498 639 0.06 2.30 24.4 275 277 Small -21.8720 36.0782 4026 767 762 0.08 3.45 33.7 281 278 Small -21.1006 36.1314 4137 791 878 0.11 3.32 22.5 280 279 Small -32.2726 36.1580 2179 455 596 0.07 2.55 58.9 68 280 Small -21.2868 36.2112 4422 ^ 386 1010 0.04 2.10 22.5 278 281 Small -21.6060 36.2245 4296 461 406 0.06 2.45 33.7 277 282 Small -32.9243 36.3309 1823 383 646 0.06 1.39 51.6 56 283 Small -23.6276 36.3309 3682 732 568 0.09 3.82 27.0 284 284 Small -23.3882 36.3708 3866 486 671 0.11 1.97 23.4 287 285 Small -23.1488 36.3841 3750 703 8.43- 0.07 2.9.7 , 26.6 284 286 Small -22.7498 36.4506 3841 657 ' 897 0.07 2.98 44.9 285 287 Small -23.4547 36.5703 3840 347 494 0.06 1.84 23.4 284 288 Small -23.6941 36.6501 3454 717 900 0.07 2.61 28.0 287 289 Small -23.1488 36.7964 3691 627 632 0.07 3.20 37.3 294 290 Small -22.4971 36.9161 3742 679 563 0.11 3.69 56.8 296 291 Small -21.8720 36.9294 3448 748 730 0.09 4.49 24.4 292 292 Small -21.6592 36.9826 3443 651 805 0.08 2.57 24.4 291 293 Small -20.9410 37.0092 3595 708 932 0.08 2.87 25.8 297 294 Small -23.3350 37.0757 2898 898 788 0.09 3.42 37.1 295 295 Small -23.6675 37.1023 2956 959 890 0.17 1.23 37.1 294 296 Small -22.0981 37.2353 3627 525 851 0.05 1.88 29.6 298 297 Small -20.8878 37.2353 3086 935 671 0.11 4.20 25.8 293 298 Small -22.2577 37.4481 3890 463 698 0.07 2.49 29.6 296 299 Small -32.7115 37.6875 •s. 1196 689 702 0.10 3.12 50.2 63 300 Small -34.2144 .37.8604 2202 862 1158 0.07 2.70 45.2 302 301 Small -21.9784 37.8870 3641 775 548 0.09 4.45 36.1 306 302 Small -34.6134 37.9402 2451 892 821 0.09 4.11 28.4 304 303 Small -33.6558 37.9402 2098 481 636 0.06 2.74 50.5 312 304 Small -34.8661 37.9801 2756 959 392 0.17 7.17 28.4 302 305 Small -23.2552 38.0200 3551 419 756 0.06 1.83 27.2 307 306 Small -21.7124 38.0732 3866 592 720 0.06 2.86 36.1 301 307 Small -23.4813 38.1131 3423 360 507 0.05 1.46 27,2 305 308 Small -21.0607 38.1397 3973 658 588 0.07 3.44 39.9 313 309 Small -35.1986 38.2860 3420 635 730 0.09 2.56 50.2 304 310 Small -34.1346 38.2860 2294 873 878 0.09 3.45 23.7 311 311 Small -34.3474 38.2993 2551 807 980 0.08 2.10 22.4 59 312 Small -33.8154 38:3658 2260 484 859 0.06 1.78 36.6 310 313 Small -21.3400 38.3658 3900 739 791 0.08 3.31 39.9 308 314 Small -23.4680 38.4190 3480 368 697 0.05 1.67 34.0 307 315 Small -21.6725 38.6451 3757 706 951 0.09 2.92 48.3 313 316 Small -23.9601 38.6717 3437 344 503 0.05 1.25 15.8 124 317 Small -22.2976 38.8313 3846 415 960 0.05 1.65 40.4 320 318 Small -35.1321 38.9111 3605 307 910 0.03 1.02 43.1 326 319 Small -23.6010 38.9643 3175 684 632 0.08 3.34 28.7 322 320 Small -22.6301 38.9776 3575 710 1158 0.08 3.16 34.1 328 321 Small -34.3873 39.0175 2935 562 699 0.07 2.63 65.6 326 322 Small -23.8537 39.0175 3311 481 714 0.07 2.16 28.4 327 323 Small -22.9892 39.1106 3399 697 741 0.07 2.70 34.5 328 324 Small -32.8046 39.1904 1617 422 • 458 0.05 2.19 71.2 332 325 Small -23.4813 39.2037 3314 648 686 0.08 3^70 28.4 327 326 Small -34.9326 39.2436 3456 486 828 0.09 2.02 43.1 318 327 Small -23.7340 39.2436 3305. 601 797 0.06 2.94 28.4 322 328 Small -22.7232 39.2702 3393 766 750 0.07 3.34 34.1 320 329 Small -22.1646 39.4830 3832 645 795 0.08 3.30 25.9 331 330 Small -35.4646 39.6160 3890 355 831 0.03 1.44 33.7 335 331 Small -21.9651 39.6027 4009 415 476 0.06 2.47 25.9 329 332 Small -32.3790 39.6692 1523 419 1078 0.04 1.92 63.7 38 333 Small -34.1213 39.7357 3314 383 700 0.04 1.72 ' . ' 63.2 338 334 Small -22.4040 39.7357 3591 821 681 0.12 3.32 15.9 336 335 Small -35.2252 39.8022 3682 618 738 0.07 2.97 33.7 330 336 Small -22.2710 39.7889 3627 801 483 0.10 4.67 9.0 337 337 Small -22.1912 39.8022 3647 878 169 0.22 4.52 9.0 336 338 Small -33.5627 39.8421 2909 615 572 0.07 2.60 27.3 340 339 Small -22.0582 39.8554 3791 708 718 0.10 4.42 15.9 337 340 Small -33.3233 39.8953 3071 351 448 0.05 1.55 27.3 338 341 Small -23.6409 39.9485 3237 590 916 0.07 3.48 51.6 350 342 Small -22.6301 40.0150 3747 539 959 0.06 2.33 13.4 343 343 Small -22.5104 40.0017 3770 476 843 0.05 1.87 13.4 342 344 Small -23.0424 40.0549 3646 563 977 0.11 2.81 19.2 345 345 Small -22.8828 40.1214 3468 780 625 0.10 3.93 19.2 344 346 Small -33.6159 40.1347 3070 589 621 0.07 3.48 33.0 338 347 Small -33.3366 40.3608 2831 592 781 0.07 2.35 39.9 346 348 Small -32.8844 40.3608 2514 569 943 0.05 2.44 50.2 347 349 Small -34.7730 40.3874 3779 423 968 0.04 1.78 38.9 351 350 Small -23.7473 40.4007 3665 521 832 0.05 2.25 51.6 341 351 Small -35.1188 40.4406 3690 674 589 0.07 3.50 38.9 349 352 Small -34.5868 40.7066 3533 471 844 0.05 2.15 41.1 349 353 Small -33.3366 40.7598 3055 491 608 0.05 2.70 44.3 347 354 Small -31.3948 41.0258 2031 456 614 0.05 2.18 42.3 363 355 Small -24.0798 41.0258 3472 338 905 0.04 1.32 78.7 350 356 Small -24.8911 41.0657 3200 384 840 0.04 1.21 69.8 157 357 Small -28.2294 41.1056 2096 655 670 0.08 3.06 31.1 358 358 Small -27.9501 41.0923 2029 639 1034 0.06 2.21 31.1 357 359 Small -30.5702 41.1854 2109 491 457 0.07 1.76 38.6 164 360 Small -29.5860 41.2120 1429 599 905 0.08 1.90 45.9 362 361 Small -25.7689 41.2519 2937 551 905 0.05 1.94 46.3 214 362 Small -29.1870 41.3184 1414 645 859 0.08 3.56 45.9 360 363 Small -31.1687 41.3317 1612 723 832 0.07 3.47 33.5 60 364 Small -26.3408 41.4780 2598 439 456 0.05 2.01 41.9 370 365 Small -30.6766 41.5578 1506 766 894 0.08 3.55 29.7 60 366 Small -34.6134 41.6110 3667 412 789 0.04 2.41 24.5 369 367 Small -25.6758 41.6642 3055 422 802 0.05 2.85 43.5 370 368 Small -28.3092 41.6908 2046 553 849 0.06 2.62 50.3 375 369 Small -34.4139 41.7041 3612 347 723 0.04 1.44 24.5 366 370 Small -26.0615 41.7307 2316 841 925 0.08 3.75 40.6 376 371 Small -30.4638 41.7573 2004 519 565 0.06 2.70 30.5 374 372 Small -28.8279 41.8371 1699 545 202 0.12 3.01 20.6 373 373 Small -29.0008 41.9036 1602 501 953 0.06 1.87 20.6 372 374 Small -30.6367 41.9701 2072 534 627 0.06 2.58 30.5 371 375 Small -28.6018 42.0366 1670 739 591 0.10 3.66 23.6 379 376 Small -26.2610 42.0366 2303 776 438 0.10 3.01 40.6 370 377 Small -28.8678 42.1696 1645 650 500 0.12 3.80 30.9 379 378 Small -26.8595 42.1696 2514 511 792 0.05 2.59 68.1 376 379 Small -28.6018 42.2494 1713 768 607 0.10 3.42 23.6 375 380 Small -33.9218 42.2760 3582 488 769 0.05 2.41 83.8 369 381 Small -32.4455 42.2627 3088 260 836 0.02 1.04 32.7 384 382 Small -29.5993 42.2627 1520 771 679 0.09 4.09 19.3 387 383 Small -27.5777 42.2627 2347 439 887 0.05 2.27 80.5 378 384 Small -32.7248 42.3558 3153 428 706 0.05 1.75 32.7 381 385 Small -31.2352 42.3558 2680 332 932 0.03 1.30 36.1 392 386 Small -30.2510 42.4356 2049 567 937 0.06 2.31 67.2 374 387 Small -29.5860 42.4356 1461 827 912 0.09 2.68 19.3 382 388 Small -28.3624 42.4356 1791 886 688 0.10 4.56 33.7 379 389 Small -28.7880 42.4888 1705 619 380 0.10 4.08 18.0 390 390 Small -28.9476 42.5154 1531 602 457 0.15 3.74 13.2 393 391 Small -32.1529 42.5287 2957 323 700 0.03 1.64 43.9 381 392 Small -31.5012 42.5420 2833 322 999 0.03 1.22 36.1 385 393 Small -29.0008 42.6218 1414 723 401 0.13 3.73 13.2 390 394 Small -30.7298 42.8346 2502 612 403 0.09 2.96 69.3 386 395 Small -32.5652 42.8612 3010 381 898 0.03 1.42 58.9 391 A P P E N D I X 3 DETAILS OF THE GENERIC SEAMOUNT MODEL ECOLOGICAL GROUPS OF THE GENERIC SEAMOUNT MODEL A model of a theoretical isolated seamount in the North Atlantic was built. The depth of the summit was set to be at around 300 m and the base at around 2000m. The area of the model is assumed as 30 km radius from the summit, in order to include the theoretical area of its influence. As a result the total area under consideration was equal to 2827 km2. A total of 37 functional groups were included in the seamount model, stratified by depth of habitat. The models included three marine mammals groups (toothed whales, baleen whales and dolphins), seabirds, turtles, seven invertebrate groups (benthic filter feeders such as corals or gorgonians, benthic scavengers, benthic crustaceans, pelagic crustaceans, seamount resident cephalopods, small and large drifting cephalopods), three zooplankton groups (gelatinous, shallow and deepwater zooplankton), primary producers (phytoplankton), detritus and twenty fish groups. 1 Primary Producers In this model we assumed that the primary production was the same as in the surrounded areas, expecting that the upwelling generated by the model increased phytoplankton biomass over the seamount. Phytoplankton biomass was estimated based on the Azores model (Guenette and Morato, 2001). The authors used information taken at the Azores front (south of Azores), in early October (Li, 1994) and obtained a biomass of 7.16 g-m\"2. Based on the SeaWIFS data set (www.me.sai.jrc.it), Guenette and Morato (2001) estimated a primary productivity of phytoplankton at 2030 g-m\" -year . The P/B was estimated at 283.5 year\" , based on the primary production and phytoplankton biomass. 2 Zooplankton The shallow and deep-water zooplankton groups included both small and large size organisms. The small zooplankton was defined as small herbivores, while the large zooplankton included, among others, mysids, euphausiids, chaetognaths, decapods' larvae. The gelatinous zooplankton consisted mainly of Thaliacea (salps, pyrosomes), Hydrozoa (siphonophores, hydroids) and Scyphozoa (jellyfish). 2.1 Shallow Zooplankton Huskin et al. (2001) found that about 90% of the small zooplankton of the Subtropical Atlantic near the Azores was composed by copepods, mainly small calanoids. Using the data presented by Huskin et al. (2001), we estimated small zooplankton biomass (until 200 m) of 2 1 6.071 t-km and a Q/B of 43.285 year\" . Large shallow water zooplankton biomass was estimated from profiles of the zooplankton of the Azores front (Angel, 1989) from 0 to 1100 meters to be equal to 10.613 t-km\"2. Q/B estimate (i.e., 34 year\"1) was taken from the Azores model (Guenette and Morato, 2001; salps were not considered). Total biomass was then equal to the sum of small and large shallow water zooplankton, while for the final Q/B a weighted average was calculated (Q/B= 37.379 year\"1). Finally, production over consumption (P/Q) ratio was.considered equal to 0.3 (Christensen, 1996). 2.2 Deep-water Zooplankton Biomass estimated for deep large zooplankton from 0 to 1100 meters was 4.357 t-km\"2 (based on Angel, 1989). Assuming the same ratio large/small zooplankton as in shallow water, the estimate of deep small zooplankton biomass was equal to 2.492 t-km\"2. In addition, total biomass of deepwater zooplankton was 6.849 t-km\"2. Finally, Q/B value was equal to 29.0 year\"1, while P/Q of 0.3 (Christensen, 1996) was also used to estimate P/B. 2.3 Gelatinous Zooplankton Biomass estimation of gelatinous zooplankton was derived from data presented by Angel (1989). Using a conversion factor of 1 ml of displacement volume to 0.8 g of jellyfish we obtained a total biomass of 14.960 t-km\"2. This value was extremely high when compared to other models, such as the Eastern Bering Sea (0.048 t-km\" ; Trites et al., 1999) and the Barents Sea (6.47 t-km\"2; Dommasnes et al., 2001). We decided to enter an EE of 0.8 and let the model estimate the biomass for this group. P/B (0.85 year\"') and Q/B (2.0 year\"') values were adopted from Trites et al. (1999). 3 Cephalopods We used Nesis (1986) faunal components to originate two cephalopod groups. The Resident (benthic) Cephalopods includes groups 1 and 2 from Nesis (1986), while the group Drifting (pelagic) Cephalopods includes groups 3 and 4. 3.1 Resident (benthic) Cephalopods This group included mainly deep-sea octopus (Order Octopoda) and some species of squids that maintain themselves constantly above seamounts (e.g. Todarodes, Ornitoteuthis, Lycoteuthis, etc.). The occurrence of cuttlefish (Order Sepiida) and little cuttlefish like (Order Sepiolida) species over or around seamounts was not confirmed. At this stage these Orders were excluded form the model. P/B and Q/B values for this group were taken from the Azores model (Guenette and Morato, 2001) and were equal to 2.89 year'1 and 10.0 year\"1 respectively. Biomass was estimated by the model assuming an EE of 0.95. 3.2 Drifting (pelagic) Cephalopods This group included mainly squids (Order Teuthida) and vampire squid (Order Vampyromorphida) and was divided in two sub-groups depending on the size of the species. The small drifting cephalopods (mantle length < 50 cm) included species of the families Loliginidae, Histioteuthidae, as well as some small members of the families Onychoteuthidae and Ommastrephidae. Daily feeding rates were estimated at 4.09% of body weight for Loligo forbesi (Porteiro et al., 1995) and from 3.6% to 6.7% for Illex sp. (O'Dor, 1980). An average feeding rate of 4.62% was used to estimate the Q/B for small drifting cephalopods and was found equal to 16.863 year\"'. P/B ratio was inferred from daily growth rate for the same species and was equal to 4.45 year\"'. Biomass was estimated by the model assuming an EE of 0.95. The large drifting; cephalopods (mantle length > 50 cm) included species of the families Architeuthidae and Lepidoteuthidae as well as the large members of the families Onychoteuthidae and Ommastrephidae. P/B and Q/B values for this group were also taken from the Azores model (Guenette and Morato, 2001) and were equal to 2.5 year\"1 and 10.0 I 2 year\" respectively. Biomass was set at a very small value of 0.001 t-km\" . 4 Crustacea Crustaceans were considered separately from the other benthic or pelagic groups because of their importance in the seamount food web. The easiest way of sub-dividing crustaceans is to split them in two groups: pelagic (mainly shrimps) and benthic (mainly crabs). Two main features have been observed for pelagic crustaceans over and around seamounts (Vereshchaka, 1994): 1) the rise of lines of equal size, abundances and biomass of the pelagic animals, and 2) the decrease in abundance, biomass and sizes of pelagic animals near the bottom water layer. One of the possible important causes of the decrease in abundance and biomass of pelagic shrimps near the bottom is that they are consumed by benthic and benthopelagic predators. Many fish and invertebrates dwelling over seamounts are known to live mainly on pelagic macroplankton brought there by the ocean currents. Vereshchaka (1996) concluded that the abundance of pelagic animals decreases while the concentration of benthopelagic predators increases near the seafloor and the role of the former in planktonic communities falls in the near-bottom layer. 4.1 Pelagic Crustacea This group included mainly pelagic shrimps, such as Sergestidae, Penaeidae, Oplophoridae, Pandalidae, etc. P/B and Q/B values were taken from Bundy et al. (2000) and were equal to 1.45 year\"1 and 9.667 year\"1, respectively. An EE of 0.95 was used and the biomass was estimate by the model. 4.2 Benthic Crustacea This group included the benthic crab species (including deep-water species such as Chaceon affmis) as well as some benthic shrimps. P/B, Q/B and EE parameters were taken from the Azores model (Guenette and Morato, 2001) and were equal to 1.6 year\"1, 10.0 year\"1 and 0.95 respectively. 5 Benthic invertebrates The seamount benthic environment is typically dominated by corals and other suspension feeders, rather than the deposit feeders that are typical of most of the deep-sea benthos. 5.1 Benthos filter feeders This group consisted of benthic filter feeders, such as hard corals, gorgonian and anthipatharian corals, suspension feeding ophiuroids and polychaetes, etc. P/B and Q/B values were taken from Optiz (1993) and were equal to 0.8 year\"1 and 9.0 year\"1 respectively. These values were estimated based on sponges and corals. An EE of 0.95 was adopted. 5.2 Benthos scavengers This group consisted mainly of echinoderms (such as brittle stars Ophiuroidea, seastars Asteroidea, sea cucumbers Holothuroidea, and sea urchins Echinoidea), worms (Annelida), bivalves (Bivalvia), etc. P/B and Q/B values were estimated as the average of the groups \"worms\", \"mollusks\", \"echinoderms\" from Okey et al. (2001) and Ainsworth et al. (2001), and were equal to 1.83 year\"1 and 13.57 year\"1 respectively. An EE of 0.95 was adopted. 6 Epipelagic fishes 6.1 Epipelagic Small This group included species small (< 25 cm) Clupeidae (e.g. Sardina pilchardus), Atherinindae (e.g. Atherina presbyter), Scomberesocidae (e.g. Nanychthys simulans), Exocoetidae (e.g. Exocoetus volitans), Macroramphosidae (e.g. Macroramphosus scolopax), Caproidae (Capros aper), etc. These last two species are known as benthopelagic, but since they are preyed in the water column up to 200 m depth we opted to include them in the epipelagic group. 6.2 Epipelagic Medium This group included epipelagic fishes with more than 25 cm and less than 100 cm of total length (TL), such as the Scomberesocidae (e.g. Scomberesox saurus), Carangidae (e.g. Caranx sp., Trachurus sp., Trachinotus sp.), Scombridae (e.g. Scomber sp.), Centrolophidae (e.g. Schedophilus sp.), Balistidae (e.g. Balistes sp.), etc. 6.3 Epipelagic Large This group included epipelagic fish greater than 100 cm TL, such as Coryphaenidae (e.g. Coryphaena sp.), Carangidae (e.g. Seriola sp., Acanthocybium solandri), Sphyraenidae (e.g. Sphyraena sp.), etc. The average Q/B values for each group were estimated using the empirical equation of Palomares and Pauly (1998) and were equal to 19.867 year\"1 for small epipelagic fish, 10.750 year\"1 for medium, and 5.095 year\"1 for large. P/B values were taken from the model of the Oceanic ecosystems of the Atlantic (Vasconcellos and Watson, 2004), and were equal to 2.053, 1.080, 0.690 .for small, medium and large epipelagic groups, respectively. The Biomasses for the epipelagic groups were assumed to be same as those estimated for the North Atlantic by Vasconcellos and Watson (2004). The values were 0.859 t-km\" for small 2 2 epipelagic, 0.112 t-km\" for medium, and 0.014 t-km\" for the large group. 7 Mesopelagic and Deep-water fish groups Childress et al. (1980) showed that the estimated food intake of migratory fishes was greater than that of non-migratory ones. This food was channelled into activity, development of energy stores, and earlier reproduction. As a result, the growth rate of migratory fish was significantly greater than in non-migratory species. Thus, the Q/B and P/B values of migratory species were greater than in non-migratory species. Demersal deep water species are generally classified as either benthic (Bathybenthic) or benthopelagic (bathydemersal). Benthic fishes include those that sit and wait for prey or forage slowly over the bottom, while benthopelagic fishes move to the water column to feed (Childress and Somero, 1990). It has been suggested that benthic fishes have lower metabolic rates than benthopelagic fishes. Those fishes found in association with seamounts and other topographic features form another distinct group (Koslow, 1996). These fishes are typically robust-bodied and capable of strong swimming performances (Koslow et al., 1995). These species are known to have high metabolic rates (Koslow, 1996). Since it is required to maintain position in highly dynamic current regimes, such as at seamounts, with frequent movements and strong locomotory performance, these species require greater metabolic expenditure than benthic or benthopelagic ones. These fishes usually exhibit delayed age of first maturity, low growth, and low mortality (Roff, 1984). 7 Mesopelagic migrating fishes This group contained mesopelagic (200-1000 m depth) species that migrate diurnally into near-surface, and was sub-divided into two groups based upon body size. The small group (< 25 cm TL) consisted of Gonostomatidae (e.g. Bonapartia pedaliota, Cyclothone sp., Gonostoma sp.), Sternoptychidae (e.g. Argyropelecus sp., Maurolicus muelleri), Melanostomiidae (e.g. Bathophilus sp.), Myctophidae (e.g. Electrona rissoi, Hygophum hygomii, Lampanyctus sp., Lobianchia dofleini, Myctophum sp.), Diretmidae (Diretmus argenteus). P/B and Q/B values were assumed to be 2.053 year\"1 and 8.0 year\"1 (Koslow, 1996; Williams, et al., 2001) respectively. A biomass of 2.0 t-km\"2 was taken from the Azores model (Guenette and Morato, 2001), which was similar to the biomass estimated (i.e., 1.720 t/km2) by Vasconcellos and Watson (2004). The large (> 25 cm TL) mesopelagic migrating fish group included a large range of sizes (25 up to 250 cm TL) and consists of Melanostomiidae (e.g. Echiostoma barbatum, Eustomias Obscurus, Leptostomias haplocaulus, Photonectes dinema), Paralepididae (e.g. Macroparalepis affinis, Paralepis coregonoides borealis), Melanonidae (e.g. Melanomus zugmayeri), Macrouridae (e.g. Odontomacrurus murrayi), Regalecidae (e.g. Regalecus glesne), Gempylidae (e.g. Gempylus serpens), Alepisauridae (Alepisaurus brevirostris), Centrolophidae (Centrolophus niger). P/B and Q/B values were assumed to be 0.600 year\"1 and 3.550 year\"1 respectively. An EE value of 0.95 was used to let the model estimate the biomass of this group. 8 Mesopelagic non-migrating fishes The mesopelagic non-migrating fishes group consisted of species that remain at depth (until 1000 m), including some Gonostomatidae, Myctophidae species. For this group, P/B, Q/B and EE values were assumed to be 0.500, 1.507 (from Williams et al., 2001), and 0.95 respectively. 9 Bathypelagic fishes This group included non-migratory deepwater (> 1000m) pelagic species that remain at depth and have a reduced metabolic rate and \"poor\" condition (Koslow, 1996). This group includes some species of Saccopharyngidae (e.g. Saccopharynx ampullaceus), Alepocephalidae (e.g. Herwigia kreffti, Photostylus pycnopterus), Gonostomatidae (e.g. Gonostoma bathyphilum), Melamphaidae (e.g. Melamphaes microps), Trichiuridae (Aphanopus carbo). For this group, P/B and Q/B values were assumed to be 0.5 year\"1 and 1.477 year\"1 respectively. 10 Bathybenthic fishes This group included non-aggregating deep-water (>1000m) benthic species that do not feed in the water column. Some examples are: Apogonidae (e.g. Epigonus telescopus), Chlorophthalmidae (e.g. Bathypterois sp.), Synodontidae (e.g. Bathysaurus ferox), Aphyonidae (e.g. Aphyonus gelatinosus), Scorpaenidae (e.g. Trachyscorpia cristulata echinata). For this group, P/B and Q/B values were assumed to be 0.2 year\"1 and 0.5 year\"1 respectively. 11 Bathydemersal (Deep-sea Benthopelagic) This group included non-aggregating deep-water (>1000m) benthopelagic species that feed in the water column, thus having higher metabolic rates. Some of these species are: Alepocephalidae (e.g. Alepocephalus rostratus, A. bardii), Halosauridae (e.g. Halosauropsis sp.), Notacanthidae (e.g. Polyacanthonotus rissoanus), Synaphobranchidae (e.g. Synaphobranchus kaupi), Bythitidae (e.g. Cataetyx laticeps), Moridae (e.g. Mora moro, Lepidion guentheri), Macrouridae (e.g. Bathygadus melanobranchus, Cetonurus globiceps, Chalinura sp., Nezumia sp.,' Trachonurus villosus, Coelorhynchus labiatus), Gempylidae (e.g. Nesiarchus nasutus). For this group, P/B and Q/B values were assumed to be 0.2 year\"1 and 0.6 year\"1 respectively. 12 Seamount-associated fishes Seamount associated fishes were divided into three different groups. Two groups containing species that are targeted by the north Atlantic fishery (Hoplostethus atlanticus and Beryx spp.), and a third one with the other seamount associated species. Parameters for these three groups were taken from Bulman (2002; P/B=0.048 year' 1 and Q/B=2.0year~l for Hoplostethus atlanticus). For the other two groups, \"Beryx spp.\" (B. splendens and B. decadactylus) and \"Other Seamount-associated fishes\" (including some Oreosomatidae species inhabiting the North Atlantic waters, such as Allocyttus verrucosus, Neocyttus helgae, and some aggregating Macrouridae such as Coryphaenoides rupestris), we used the values estimated by Bulman (2002) for oreos (i.e., P/B= 0.06 year\"1 and Q/B= 2.2 year\"1). 13 Shallower Fish groups Shallower water fish groups included those fishes with benthic or benthopelagic affinities occurring between 200 and 1000m depth. This group was divided further into\"Shallow Benthic fishes\", and \"Shallow Benthopelagic fishes\". The first group included benthic fishes, such as Helicolenus dactylopterus, Ponthinus kuhlii, Molva macrophthalma, Conger conger Phycis phycis, Lophius piscatorius, etc. The second group included species such as Pagellus bogaraveo. P/B and Q/B values were taken from the Azores model (Guenette and Morato, 2001) and were equal to 0.59 year\"1 and 4.70 year\"1 respectively (based on the group of demersal large predators) for benthic fishes and 0.66 year\"1 and 5.20 year\"1 respectively for benthopelagic fishes (based on P. bogaraveo). 14 Large Oceanic Planktivores This group consisted of large oceanic planktivorous fish, such as the whale shark (.Rhincondon typus), basking shark (Cetorhinus maximus), manta rays (Mobida spp., Manta spp.), and sunfish (Mola mola, Masturus spp.). There is some anecdotic information (e.g. fishermen and observers onboard of tuna vessels) suggesting that these species may stop over and around seamount to feed during their migrations routes. An average Q/B was estimated from the empirical equation of Palomares and Pauly (1998) and was equal to 2.06 year\"'. The other parameters for this group were taken from Vasconcellos and Watson (2004; P/B= 0.112 year\"1 and EE= 0.1). Biomass was estimated by the model assuming no fishing mortality but allowing Pelagic Sharks to feed upon this group. 15 Tunas Three species are usually caught on the seamounts (Fonteneau, 1991; Holland et al., 1999): yellowfin (Thunnus albacares), skipjack (Katsuwonus pelamis) and bigeye tuna (T. obesus). The Tuna group also includes other species, such as bluefin tuna (T. thynnus) and albacore (T. alalunga). Vasconcellos and Watson (2004) estimated the biomass for these five species in the North Atlantic as being 0.029 t-km\" , similar to that estimated by Guenette and Morato (2001) for the Azores area (0.032 t-km\"2). We used the first value as a general estimation for the North Atlantic. For the same reason P/Band Q/B values were taken from Vasconcellos and Watson (2004), as the weighted mean of P/B and Q/B values for the five tuna species (i.e., P/B= 0.742 year\"' and Q/B= 16.291 year\"'). Comparing these values with the ones used by Guenette and Morato (2001) in the Azores model we found that the estimated P/B were very similar, while Q/B was much greater. This is probably due to that Vasconcellos and Watson (2004) estimated their Q/B values based on daily rations while Guenette and Morato (2001) used the empirical equation from Palomares and Pauly (1998). 16 Billfishes This group consisted of swordfish (Xiphias gladius) and several billfishes: blue marlin, (Makaira nigriscans), white marlin (Tetrapturus albidus), and longbill spearfish (T. pfluegeri). P/B value was equal to 0.5 year\"1 based on an average F value of 0.3 year\"1 and an average M of 0.2 year\"1. The biomass for this group was taken from Guenette and Morato (2001; 0.02 t-km\" ), while an average Q/B was estimated from the empirical equation from Palomares and Pauly (1998; 4.2 year\"1). 17 Rays and Skates This group consisted of all Rajiformes species (except Manta rays) such as Torpedo spp., Raja spp., Dasyatis spp., Myliobatis spp. 18 Pelagic Sharks Pelagic shark consisted of Carcharodon carcharias, Prionace glauca, Isurus oxyrinchus, Sphyrna spp., Alopias spp., Lamna nasus. P/B, Q/B and biomass for this group were taken from the Azores model (Guenette and Morato, 2001), based on P. glauca, L. nasus and Galeorhinus galeus. 19 Benthopelagic Sharks Benthopelagic sharks consisted mainly of deep-water species such as Squaliformes (Centroscymnus spp., Etmopterus spp., Daenia spp., Dalathia spp., Oxynotus spp., etc.) or Hexanchiformes. The Ecopath parameters came from the Azores model (Guenette and Morato, 2001), based on D. licha and Galeus melastomus. 20 Sea-Turtles Turtles occurring in the North Atlantic included the loggerhead (Caretta caretta), leatherback (Dermochelys coriacea), and green turtles (Chelonia mydas), whereas other species have been considered rare. Juvenile loggerhead turtles are transported by the North Atlantic Gyre current and live a pelagic life for about 8.2 years in the Eastern Atlantic (Bjorndal et al., 2000) where they seem to feed mainly on jellyfish (Bjorndal, 1997). P/B, Q/B and biomass values were taken from the Azores model (Guenette and Morato, 2001) and were equal to 0.15 year\"1, 3.50 year\"1, and 0.001 t-km\"2 respectively. Turtles have been found in sharks stomach (H. Rost Martins, unpublished data) and a considerable by-catch by the pelagic longline swordfish fishery has been reported (Ferreira et al., 2001). 21 Seabirds Seabirds occurring around this theoretical seamount were assumed to be the same occurring around the Azores (Guenette and Morato, 2001). This is not totally true because we assumed to simulate an isolated seamount without islands nearby. However, the values presented for the Azores were very similar to those presented for the North Atlantic (Vasconcellos and Watson, 2004). P/B, Q/B and biomass were assumed to be 0.04 year\"1, 84.39 year\"1, and 0.0002 t/km2 respectively. 22 Marine Mammals Marine mammals were separated in three groups based on their diets. Baleen whales that feed mainly on zooplankton and small fish included minke whale (Balenoptera acutorostrata), sei whale (Balaenoptera borealis), blue whale (Balaenoptera musculus), fin whale (Balaenoptera physalus), and humpback whales (Megaptera novaeangliae). 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"@en ; edm:hasType "Thesis/Dissertation"@en ; edm:isShownAt "10.14288/1.0074904"@en ; dcterms:language "eng"@en ; ns0:degreeDiscipline "Resource Management and Environmental Studies"@en ; edm:provider "Vancouver : University of British Columbia Library"@en ; dcterms:publisher "University of British Columbia"@en ; dcterms:rights "For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use."@en ; ns0:scholarLevel "Graduate"@en ; dcterms:title "Ecology and fisheries of seamount ecosystems"@en ; dcterms:type "Text"@en ; ns0:identifierURI "http://hdl.handle.net/2429/31091"@en .