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Ecology and fisheries of seamount ecosystems 2006

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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   = 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 <f>  = 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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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 (<f>  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 *̂̂- ŜVW JÎ Ŵ "̂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 / •. il̂ flL- 1 * - iî 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 (</)) show marked differences  between small and large seamount like features.  Small seamount-like features  showed a mean basal radius of  rb= 9.4 km (SD=2.0) while large seamounts showed a mean basal radius of  r/,= 11.2 km (SD= 1.8). Accordingly, basal area is smaller on small seamounts (db= 742 km2; SD= 213) than on large seamounts (ah= 961 km2; SD= 171). The mean height to radius ratio increase from  £.= 0.07 (SD= 0.027) on small seamounts to 0.12 (SD= 0.030) on large seamounts (Figure 2.7). Average slope angles ranged from  <jr= 0.73 to (jj=  9.27. The sample mean slope angle was <ff=  2.90 for  small seamounts and 5.23 for  large seamounts. Slope angle and summit height relationship is presented in Figure 2.8. 0.00 0.05 0.10 0.15 0.20 Height to radius ratio 0.25 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. 10 0 I ' ' 1 ' <— -r- . "I • . . » I r— 0 500 1000 1500 2000 Height (m) Figure 2.8 - Slope angle 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 <f>=  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 T̂ephsrfopocis  Dri âttydemersal F V \ f̂tytoFtanktcn  ~ " - 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 to our 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). Another key element that can explain the high variability in abundance from  one seamount to the other is recruitment. Different  seamounts will have different  probabilities of  hosting large aggregations of  fish  due to different  probabilities of  receiving those fish  from  elsewhere, as juveniles or adults. Clark's et al. (2001) factors,  seamount location and geological association may play an important role in explaining recruitment variability. 3 . 5 REFERENCES Boehlert, G.W. and T. Sasaki (1988) Pelagic biogeography of  the armorhead, Pseudopentaceros  wheeleri,  and recruitment to isolated seamounts in the North Pacific Ocean. Fishery Bulletin 86: 453-466. Bulman, C.M. (2002) Trophic  ecology and  food  web modelling  of  midslope  demersal  fishes off  southern Tasmania,  Australia.  PhD thesis. University of  Tasmania, Hobart, Australia. Bulman, C., F. Althaus, X. He, N. Bax and A. 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Pelegri (2006) Dynamics at an elongated,  intermediate  depth  seamount in the north Atlantic  (Sedlo  seamount, 40 20'N,  27 SOW).  Preliminary Draft.  OASIS project publication, Hamburg, 32pp. Williams, A. and J.A. Koslow (1997) Species composition, biomass and vertical distribution of  micronekton over the mid-slope region off  southern Tasmania, Australia. Marine Biology 130: 259-276. 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 —• <N O Distance to seamount summit (km) 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. Tuna catches per square kilometre per year were significantly  different  (ANOVA p« 0.001 for  skipjack and bigeye tuna) at different  distances from  seamount summits (Figure 4.3). For skipjack (Figure 4.3a), catches occurring within 30 km from  seamount summits were significant  higher than the overall mean (Dunnett tests for  10km, 20km and 30km /?<0.01) with all other catches being smaller. Bigeye tuna catches per square kilometre per year (Figure 4.3b) were significant  higher within 20 km from  the summits (Dunnett tests for  10km and 20km /?<0.01; Dunnett tests for  30km p>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 <u X) o 0.015 0.010 - 0.005 - 0.000 d) 0.004 0.003 0.002 0.001 0.000 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 4.4 - 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). 0.000 0 10 20 30 40 50 60 70 80 90 100 Distance to seamount summit (Km) Figure 4.5 - Loggerhead turtles sightings per square kilometre per hour (±95%CL) relation to the distance to the nearest seamount summit in the Azores. Bin size is 10 km. 3 o "6 M C a) 0.07 - 0 . 0 6 - 0.05 0.04 0.03 e eS > (-1 <D M JO O 0.02 0.01 0.00 - ) f  1 ( ) ) • 0.004 0.003 0.002 0.001 0.000 -I- 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) c) 0.012 0.010 - o 0.008 (N 'g. 0.006 m .2 0.004 » t O. 0.002 0.000 - 0 0 0 10 20 30 40 50 60 70 80 90 100 Distance to seamount summit (Km) 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). 4.3.3 Seamounts Catches and sightings above seamounts of  various depths are showed in Figure 4.7, for  those species with a significant  association with seamounts. Skipjack and bigeye tuna showed significantly  higher catches on seamounts shallower than 400 m depth (Figure 4.7a,b), whereas common dolphin (Figure 4.7c) and Cory's shearwater (Figure 4.7d) showed significantly  higher sightings on seamounts with depths between 200 and 400 meters. In all cases, seamounts with deep summits had relatively low catches and sightings. I have also tested which seamounts produced a significant  effect  on species association. For skipjack, "Formigas  and  Dollabarat"  (seamount # 13; P=0.012), "Princesa Alice'''  (# 50, P=0.014), "D. Joao  de  Castro"  (# 25, P=0.026), "Agores" (#51, P=0.030) and "Pico Leste" of  the Princesa Alice (# 23, P=0.031), all showed significant  higher catches than the overall mean. For bigeye, only "Agores" (#51, P=0.004), "Princesa Alice" (# 50, P=0.009), and "£>. 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 <U C/5 tu J3 O .G Oh <D Q 0 500 1000 o o 1500 o o 2 0 0 0 - L l.o b) o.o 1 ' 0 Catch (kg-km"2-year"') 0.2 0.4 0.6 0.8 500 1000 o F o 1500 o o o 2 0 0 0 - L Observations (n-km"2-hour"1) c) 2 I Observations (nkm" hour" ) 0.00 0.02 0.04 0.06 0.08 0.10 cT> 0.00 0.02 0.04 0.06 0.08 0.10 G P o C3 (D xn <a -G o si <u 0 500 1000 - 1500 ? 2000 ,—1—_ - KM 1 , 1 , ! , 1 - HCH HCH o o 0 J_ 500 1000 - 1500 o 2000 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). 4 . 4 DISCUSSION This study has demonstrated that some marine predators are associated with seamounts with shallow summits. 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.  Our methodology, 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. Tuna species such as skipjack and bigeye have previously been acknowledged to occur on seamounts. However, this study was the first  to quantitatively demonstrate these associations. Common dolphins are know to feed  mostly on small pelagic fish  and squids (Silva, 1999) and may, therefore,  take advantage of  the localized abundance of  prey on seamounts. These species are also known to be comparatively more characteristic of  offshore  habitats than the bottlenose or spotted dolphins. Thus, the common dolphins may use seamounts as important feeding  areas (Lopez et al., 2004). Similarly, Monteiro et al. .(1996) inferred  from  stomach contents data that Cory's shearwaters feed  often  in association with seamounts, therefore,  supporting our results. Not all seamounts, however, seemed to be equally important for  these associations. For all species only seamounts shallower than 400m depth showed significant  aggregation effects. The important seamounts in the Azores for  these visitors are "Princesa Alice" and "Agores" 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. These seamounts are also known to be heavily exploited by local fishermen,  with the exception of  "Formigas and Dollabarat" which are a marine reserve. Reconciling fisheries  with conservation on these seamounts should be a priority for  the local management authorities. On the other hand, the area of  influence  of  the seamounts seemed to be about 20 to 30 km, since only the observations at these distances were significant  higher than the average. The lack of  association of  sperm whales with seamounts summit is not surprisingly since these animals prey mostly in deep waters on large squid (Clarke et al., 1993) by echo- locating deep prey patches in the mesopelagic environment (Watwood et al., 2006). However, the lack of  association with seamounts for  bottlenose and spotted dolphins is more difficult  to explain. Stomach contents data for  these two species in the Azores are not available. Bottlenose dolphins have a high degree of  foraging  plasticity (Spitz et al., 2006) but in coastal waters seem to prefer  benthic species. For instance, around Scotland (UK) this species feeds  mostly on benthic fish  such as cod (Gadus  morhua), saithe (Pollachius  virens) and whiting (Merlangius  merlangus),  and also cephalopods (Santos et al., 2001). Similar patterns were observed for  bottlenose dolphins from  the U.S. mid-Atlantic coast (Gannon and Waples, 2004). On the other hand, bottlenose dolphins off  Galicia (Lopez et al., 2004) and off  Gulf  of  Mexico (Jefferson  and Schiro, 1997) have been considered to be more common in coastal areas when compared to common dolphin. This pattern may also be true in the Azores since depth was found  to be a significant  but negative factor  explaining bottlenose dolphins' distribution (Seabra et al., 2005), and therefore  this species has been considered to be more associated with coastal habitats than common dolphin. Pelagic and mesopelagic prey may thus have less importance in bottlenose dolphins' diet as compared to common dolphin, meaning that the seamount effect  on enhancing secondary and tertiary production may not be important for  bottlenose dolphins. Spotted dolphins are known to associate with relatively low seafloor  relief  gradients (Davis et al., 1998; Mignucci-Giannoni, 1998; Perrin, 2002) and thus may display a stronger association with coastal habitats than with seamounts. Yellow-legged gulls feed  opportunistically on several prey such as fish,  barnacles and rats (Monteiro et al., 1996), therefore  there is no reason to expect an association of  this species with seamounts. However, the lack of  association with seamounts of  terns was somehow unexpected because in the Azores these species feed  mainly on small pelagic and mesopelagic fish  (Monteiro et al., 1996), which are expected to have higher abundances close to seamounts. Probably, the number of  observation available for  these species was not sufficient  to detect such associations. The presence of  large concentrations of  forage  fish  during the summer in the Azores originates mixed-species feeding  aggregations (Clua and Grosvalet, 2001). Tuna fish  may help prey rising to the surface  before  being concentrated by common dolphins, resulting in the formation  of  a compact 'ball' of  forage  fish  close to the. surface.  Tuna, common dolphins and Cory's shearwater collectively feed  on this concentration of  prey. Therefore,  an alternative explanation for  the association with seamounts of  commpn dolphins and Cory's shearwater and the lack of  association for  bottlenose and spotted dolphin is the interaction with tuna: I cannot exclude the hypothesis that the seamount association of  these species is an indirect effect  of  the seamount. The rational for  this would be seamounts attracting tuna species and, in turn, tuna species attracting common dolphins and Cory's shearwater but not bottlenose and spotted dolphins. Silva et al. (2002) have demonstrated that common dolphin is significantly  associated with tuna in the Azores and that bottlenose and spotted dolphins show lower degree of  interaction. Loggerhead sea turtle is widely distributed in the North Atlantic where it feeds  mainly on jellyfish.  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Fourth edition, Prentice Hall, New Jersey, USA. C H A P T E R 5 FISHING DOWN THE DEEP 1 5.1 INTRODUCTION A global crisis in marine fisheries  was regarded with scepticism by many fisheries  scientists as recently as ten years ago. Today, however, few  dispute worrying trends (Pitcher and Pauly, 1998; Pitcher, 2001; Pauly et al., 2002; Christensen et al., 2003; Hilborn et al., 2003; Pauly and Maclean, 2003; FAO, 2004). Historical data from  marine ecosystems clearly suggest that overfishing  has had, for  thousands of  years, a major impact on target species and have fundamentally  altered marine ecosystems (Jackson et al., 2001; Pitcher, 2001), including coral reefs  (Pandolfi  et al., 2003). A dramatic depletion of  large predators (Baum et al., 2003; Christensen et al., 2003; Myers and Worm, 2003) has triggered fisheries  to target species of  lower trophic levels in a process called "fishing  down marine food  webs" (Pauly et al., 1998a). More recently, fisheries  exploitation has spread from  coastal areas to the open ocean and a general decline in fish  biomass has been reported (Baum et al., 2003; Christensen et al., 2003; Myers and Worm, 2003): as a consequence, many marine species are of  serious conservation concern (Casey and Myers, 1998; Spotila et al., 2000; Baum et al., 2003; Sadovy and Cheung, 2003). Not surprisingly, there has been a decline in global fisheries  catches since the late 1980s (Watson and Pauly, 2001; Zeller and Pauly, 2005) at an approximate rate of  0.36 million tonnes per year. Nevertheless, a global increase of  fishing effort  and catching power has continued (Greboval, 2003). With the decline of  shallow coastal waters resources, increasing demand, and new technology, 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 1 A version of  this chapter has been published. Morato, T., R. Watson, T.J. Pitcher and D. Pauly. 2006. Fishing down the deep. Fish and Fisheries 7:24-34. 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 lack a global quantitative analysis. Deep-water fish  resources are generally considered to have high longevity, slow growth, late maturity, and low fecundity.  Thus, they have been considered more vulnerable to fishing  than most species exploited on the continental shelf,  upper continental slope or in open ocean pelagic ecosystems (Merrett and Haedrich, 1997; Koslow et al., 2000). Deep-water stocks can be rapidly depleted and recovery can be very slow, although this will not apply to a few deep-water species with life  history traits comparable to shallow water species (Large et al., 2003). Whereas previous studies on global trends of  fisheries  have focused  on catch or biomass changes over time (e.g., Christensen et al., 2003; Myers and Worm, 2003), in this paper 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. 5 . 2 METHODS I used three publicly-available databases; official  landings statistics from  the Food and Agriculture Organization (FAO) from  1950 to 2001, which are based on reports submitted annually by FAO member states; FishBase (htip://\vww.fishbase.or g), an information  system with key data on the biology of  fishes  (Froese and Pauly, 2003); and the Sea Around Us Project database (SAUP: http://www.seaaroundtis.org), which contains estimated maps of global fisheries  catches from  1950 to the present. The SAUP database includes data from  the Food and Agriculture Organization (FAO), International Council for  the Exploration of  the Seas (ICES), Northwest Atlantic Fisheries Organization (NAFO), and other sources (Watson et al., 2004) and was used to compile catch data for  high seas areas. In this study, depth range is defined  as the extremes of  the depths reported for  juveniles and adults (but not larvae), while common depth is the range where adults are most often  found, and is more precisely defined  as the range within which approximately 95% of  the biomass of  a species occurs (Froese and Pauly, 2003). For those taxa not reported to species level, the average for  the genus or family  was calculated using the species most likely to be present at that locality. FishBase was used to estimate the average depth of  occurrence for  most of  the 775 different species or groups of  marine fishes  included in the FAO landings statistics, and to gather data on their longevity. Average depth of  occurrence for  taxa identified  at species level in the landings statistics was estimated as the mean of  the common depth range or as 1/3 of  the total depth range. In the latter case, I assume fish  to have a lognormal distribution with depth, whose peak in abundance is at 1/3 of  their range (Alverson et al., 1964; Pauly and Chua, 1988). I have tested this assumption using FishBase data on full  depth ranges and common depth ranges for  136 fish  species; the only species with both ranges in the database. The average peak abundance was 0.302 of  the full  depth range (95% confidence  interval; 0.28- 0.33): this value is not significantly  different  from  a 1/3 assumption (t-test: P>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. Although this is probably not true for  non-target species and by-catch, I do not think it unduly affected  the analysis because a), the proportion of  non-target landings is smaller and thus will not have a significant  effect  on the general trends; or, b), by-catch species are not generally reported in FAO statistics. 5 . 5 REFERENCES Alverson, D.L., A.T. Pruter and L.L. 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Parin (2000) Continental slope and deep-sea fisheries:  implications for  a fragile  ecosystem. ICES Journal of  Marine Science 57: 548-557. Large, P.A., C. Hammer, O.A. Bergstad, J.D.M. Gordon and P. Lorance (2003) Deep-water fisheries  of  the northeast Atlantic: II. Assessment and management approaches. Journal of  Northwest Atlantic Fishery Science 31: 151-163. http://journal.nafo.int/archive22- 33/J3 l/session3/large.pdf Martins, R. and C. Ferreira (1995) Line fishing  for  Black Scabbardfish  (Aphanopus  carbo Lowe, 1839) and other deep water species in the eastern mid-Atlantic to the north of Madeira. In: A.G. Hooper (ed.) Deep-water fisheries  of  the North  Atlantic  oceanic slope. Kluwer Academic Publishers, Dordrecht, pp. 323-335. Merrett, N.R. and R.L. Haedrich (1997) Deep-sea demersal  fish  and  fisheries.  Chapman & Hall, London. Moore, J.A. (1999) Deep-sea finfish  fisheries:  lessons from  history. Fisheries 24: 16-21. 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. Myers, R.A. and B. Worm (2003) Rapid worldwide depletion of  predatory fish  communities. Nature 423: 280-283. Pandolfi,  J.M., R.H. Bradbury, E. Sala, T.P. Hughes, K.A. Bjorndal, R.G. Cooke, D. McArdle, L. McClenachan, M.J.H. Newman, G. Paredes, R.R. Warner and J.B.C. Jackson (2003) Global trajectories of  the long-term decline of  coral reef  ecosystems. Science 301: 955-958. Pauly, D. and J. Maclean (2003) In  a Perfect  Ocean: fisheries  and  ecosystems in the North Atlantic.  Island Press, Washington, D.C. Pauly, D. and T.E. Chua (1988) The overfishing  of  marine resources: socioeconomic background in Southeast Asia. AMBIO: a Journal of  the Human Environment 17: 200- 206. Pauly, D., V. Christensen, J. Dalsgaard, R. Froese and F.Jr. Torres (1998a) Fishing down marine food  webs. Science 279: 860-863. Pauly, D., R. Froese and V. Christensen (1998b) Response to Caddy et al. How pervasive is "fishing  down marine food  webs?" Science 282: 1384-1386. Pauly, D., V. Christensen, S. Guenette, T.J.P. Pitcher, U.R. Sumaila, C.J. Walters, R. Watson and D. Zeller (2002) Towards sustainability in world fisheries.  Nature 418: 689-695. Pauly, D., J. Alder, E. Bennett, V. Christensen, P. Tyedmers and R. Watson (2003) World fisheries:  the next 50 years. Science 302(5649): 1359-1361. Pitcher T.J. (2001) Fisheries managed to rebuild ecosystems: reconstructing the past to salvage the future.  Ecological Applications 11: 601-617. Pitcher, T.J. and D. Pauly (1998) Rebuilding ecosystems, not sustainability, as the proper goal of  fishery  management. In: T.J. Pitcher, P.J.B. Hart and D. Pauly (eds.) Reinventing fisheries  management.  Fish and Fisheries Series 23, Kluwer Academic Press, Netherlands, pp. 311-329. Roberts, C.M. (2002) Deep impact: the rising toll of  fishing  in the deep sea. Trends in Ecology and Evolution 17: 242-245. Sadovy, Y. and W.W.L. Cheung (2003) Near extinction of  a high fecund  fish:  the one that nearly got away. Fish and Fisheries 4: 86-99. Spotila, J.R., R.D. Reina, A.C. Steyermark, P.T. Plotkin and F.V. Paladino (2000) Pacific leatherback turtles face  extinction. Nature 405: 529-530. Watson, R. and D. Pauly (2001) Systematic distortion in world fisheries  catch trends. Nature 414:534-536. Watson, R., A. Kitchingman, A. Gelchu and D. Pauly (2004) Mapping global fisheries: sharpening our focus.  Fish and Fisheries 5: 168-177. Zeller, D. and D. Pauly (2005) Good news, bad news: global fisheries  discards are declining, but so are total catches. Fish and Fisheries 6(2): 156. 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 T̂vlax 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 <u <C 0.7 0.6 0.5 0.4 0.3 0.2 H 0.1 0.0 10 ® • 20 30 40 50 60 Intrinsic Vulnerability 70 80 90 Figure 6.4 - Proportion of  biomass change over time for  fish  groups with different  intrinsic vulnerabilities (Fj). 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. 6 . 4 DISCUSSION Despite rather broad definitions,  this global analysis of  over 1,600 species shows that "seamount fishes",  particularly "seamount-aggregating" fishes,  have higher intrinsic vulnerability than other groups of  fishes.  Similar patterns were found  when considering commercially exploited species only. Biological characteristics leading to greater vulnerability include a longer lifespan,  later sexual maturation, slower growth and lower natural mortality. However, as fish  vulnerability was strongly related to depth range, seamount-association (as in "seamount fishes"  group) may not be the proximal factor;  higher vulnerability of  fish  found  at seamounts may be confounded  because they include more deep- water species. These findings  are in agreement with life-history  qualities for  "seamount fishes"  proposed by Koslow (1996, 1997), although very few  complete case studies are available. In any case, seamounts provide physical and biological conditions for  aggregation of  vulnerable deep-water fishes  and thus unsustainable exploitation in such habitats should be a concern. Deep-water species were considered sensitive to exploitation because of  their vulnerable biological characteristics (Merrett and Haedrich, 1997; Roberts, 2002). The analysis presented here supports this theory. Particularly, bathydemersal fishes  were more vulnerable than any other "non-seamount" group of  fish;  only "seamount aggregating" fish  had higher vulnerabilities. As these species are often  targeted by deep-sea fishing  (trawling in particular), proper monitoring and management of  their exploitation should be a priority. Conservation concerns about the exploitation of  "seamount fishes"  were supported by results from  our simulation modelling. The simulation model confirmed  that the biomass of  fish  with higher vulnerabilities declined more rapidly under exploitation. Although data limitations prevent prevents us from  validating the modelling results using empirical data, evidence from other species assemblages suggests a significant  positive correlation between vulnerability and population decline (Cheung et al., 2005). Considering that seamount fishes  are increasingly being targeted by fishing  (Watson and Morato, 2004), populations of  highly vulnerable seamount species such as orange roughy, alfonsinos  and other "seamount- aggregating" fishes  may be under considerable risk of  local extinction (extirpation) under only moderate fishing  intensity (F=0.3 in simulation model). In the light of  this analysis, one may ask whether seamount fisheries  may be sustainable in the long term (Clark, 2001). Examples from  all over the world have showed the "boom and bust" characteristic of  seamount trawling fisheries,  with rapid stock reduction and serial depletion of  successively-exploited new seamounts. The case of  the orange roughy, a "seamount-aggregating" fish,  is well known. In Namibian waters, orange roughy has been fished  down to 10% of  its virgin biomass in six years (Branch, 2001), while in Australia biomass levels dropped to 7-13%) in about 15 years (Lack, 2003). The orange roughy stock in New Zealand was fished  down to 15-20%) of  virgin biomass in less than 15 years (Clark, 2001) while annual sustainable levels of  fishing  have been estimated to be less than 2% of virgin biomass (Francis et al., 1995), which may not be economically viable. Another example is Russian fishing  on seamounts at the Mid Atlantic Ridge. Vinnichenko (2002) showed that the total catch (mainly of  alfonsino  and scabbardfish  Lepidopus  caudatus Euphrasen 1788) at 9 seamounts in the South Azores area and in three seamounts at the Corner Rising area declined, in each area, from  12000t to below 2000t in just two years. In a larger area of  the ridge that included 34 seamounts, catches declined from  30000t to below 2000t in about 15 years (mainly roundnose grenadier and orange roughy). The high vulnerability of  "seamount fishes"  should encourage more precaution in managing seamount resources. Collapse of  seamount fisheries  have been partially attributed to the lack of  management. However, even in places where detailed research programmes where in place when exploitation by trawl fisheries  commenced, and where scientific  recommendations for management were followed  and the fisheries  controlled, catches declined unexpectedly fast and stocks have been depressed well below Bmsv  (Boyer et al., 2001). Our research supports the conclusion that fishing  on seamounts is not sustainable, at current levels and with current methods. A number of  seamount populations have already been depleted. More will be depleted and some will be extirpated if  fishing  on seamounts continues at current or even more moderate levels. The fuzzy-logic  life-history  attributes method of  estimating intrinsic vulnerability to depletion by fishing,  followed  by evaluation of  sensitivity and local extinction risk using simulation, is a relatively new technique, but it has proved more robust than previous methods (Cheung et al., 2005). It provides a quantitative basis for  more precautionary management of  fisheries  for  "seamount" and "seamount-aggregating" fish  in the future. 6 . 5 REFERENCES Adams, P.B. (1980) Life  history patterns in marine fishes  and their consequences for  fisheries management. Fishery Bulletin 78: 1-12. Boehlert, G.W. and T. Sasaki (1988) Pelagic biogeography of  the armorhead, Pseudopentaceros  wheeleri,  and recruitment to isolated seamounts in the North Pacific Ocean. Fishery Bulletin 86: 453-466. Boyer, D.C., C.H. Kirchner, M.K. McAllister, A. Staby and B.I. Staalesen (2001) The orange roughy fishery  of  Namibia: Lessons to be learned about managing a developing fishery. In: A.I.L. Paine, S.C. Pillar and R.J.M. Crawford  (eds.) A decade  of  Namibian  fisheries science. South African  Journal of  Marine Science 23, pp. 205-211. Branch, T.A. (2001) A review of  orange roughy Hoplostethus  atlanticus  fisheries,  estimation methods, biology and stock structure. In: A.I.L. Paine, S.C. Pillar and R.J.M. Crawford (eds.) A decade  of  Namibian  fisheries  science. South African  Journal of  Marine Science 23, pp. 181-203. Cheung, W.W.L., T. J. Pitcher and D. Pauly (2005) A fuzzy  logic expert system to estimate intrinsic extinction vulnerabilities of  marine fishes  to fishing.  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Morato and D. Pauly (eds.) Seamounts:  Biodiversity  and  Fisheries.  'University of  British Columbia, Vancouver, Fisheries Centre Research Report 12(5), pp. 25-31. Hilborn, R. and C.J. Walters (1992) Quantitative  fisheries  stock  assessment: choice, dynamics  and  uncertainty.  New York: Chapman and Hall. Hilton-Taylor, C. (2000) (compiler) 2000 IUCN  Red  List of  Threatened  Species.  IUCN, Gland, Switzerland and Cambridge, UK. ICES (International Council for  the Exploration of  the Sea) (2004) Report of  the ICES advisory committee on fishery  management, 2001. ICES Cooperative Research Report 246, 895 pp. Jennings, S., J.D. Reynolds and S.C. Mills (1998) Life  history correlates of  responses to fisheries  exploitation. Proceedings of  the Royal Society of  London: Biological Science 265: 333-339. Jennings, S., S.P.R. Greenstreet and J.D. Reynold (1999) Structural change in an exploited fish  community: a consequence of  differential  fishing  effects  on species with contrasting life  histories. Journal of  Animal Ecology 68: 617-627. Johannes, R.E. (1998) The case for  data-less marine resource management: examples from tropical nearshore fin  fisheries.  Trends in Ecology and Evolution 13: 243-246. Kirkwood, G.P., J.R. Beddington and J.A. Rossouw (1994) Harvesting species of  different lifespans.  In: P.J. Edwards, R.M. May and N.R. Webb (eds.) Large-Scale  Ecology  and Conservation  Biology.  Blackwell Science, Oxford,  pp. 199-227. Koslow, J.A. (1996) Energetic and life-history  patterns of  deep-sea benthic, benthopelagic and seamount-associated fish.  Journal of  Fish Biology 49(Supplement A): 54-74. Koslow, J.A. (1997) Seamounts and the ecology of  deep-sea fisheries.  American Scientist 85: 168-176. Koslow, J.A., G.W. Boehlert, J.D.M. Gordon, R.L. Haedrich, P. Lorance and N. Parin (2000) Continental slope and deep-sea fisheries:  implications for  a fragile  ecosystem. ICES Journal of  Marine Science 57: 548-557. Lack, M., K. Short and A. Willock (2003) Managing risk and uncertainty in deep-sea fisheries:  lessons from  orange roughy. Traffic  Oceania and WWF Endangered Seas Programme, Australia. Merrett, N.R. and R.L. Haedrich (1997) Deep-sea demersal fish  and fisheries.  Chapman and Hall, London. Morato, T. and T. Pitcher (2002) Challenges and problems in modelling seamount ecosystems and their fisheries.  ICES (International Council for  the Exploitation of  the Sea) 2002/M:8. Musick, J.A. (1999) Criteria to define  extinction risk in marine fishes.  Fisheries 24: 6-14. Pitcher, T.J. and J. Parrish (1993) The functions  of  shoaling behaviour. In: T.J. Pitcher (ed.) The  behaviour of  teleost  fishes.  Chapman and Hall, London, pp. 363-439. Pitcher, T.J. (1995) The impact of  pelagic fish  behaviour on fisheries.  Scientia Marina 59: 295-306. Pitcher, T.J. (1997) Fish shoaling behaviour as a key factor  in the resilience of  fisheries: shoaling behaviour alone can generate range collapse in fisheries.  In: D.A Hancock, D.C. Smith, A. Grant and J.P. Beumer (eds.) Developing and  Sustaining  World Fisheries  Resources: the State  of  Science and  Management.  CSIRO, Collingwood, Australia, pp. 143-148. Roberts, C.M. (2002) Deep impact: the rising toll of  fishing  in the deep sea. Trends in Ecology and Evolution 17: 242-245. Roff,  D.A. (1984) The evolution of  life  history parameters in teleosts. Canadian Journal of Fisheries and Aquatic Sciences 41: 898-1000. Rogers, A.D. (1994) The biology of  seamounts. Advances in Marine Biology 30: 305-350. Russ, G.R. and A.C. Alcala (1998) Natural fishing  experiments in marine reserves 1983- 1993: roles of  life  history and fishing  intensity in family  responses. Coral Reefs  17: 399-416. Sadovy, Y. and M. Domeier (2005) Are aggregation-fisheries  sustainable? Reef  fish  fisheries as a case study. Coral Reefs  24: 254-262 Sala, E., E. Ballesteros and R.M. Starr (2001) Rapid decline of  Nassau grouper spawning aggregations in Belize: fishery  management and conservation needs. Fisheries 26: 23- 30. Stokes, T.K., J.M. McGlade and R. Law (eds.) (1993) The  Exploitation  of  Evolving Resources. Springer-Verlag, Berlin. Vinnichenko, V.I. (2002) Prospects of  fisheries  on seamounts. ICES CM2002/M32. Poster. Walters, C. (2003) Folly and fantasy  in the analysis of  spatial catch rate data. Canadian Journal of  Fisheries and Aquatic Science 60: 1433-1436. Watson, R. and T. Morato (2004) Exploitation patterns in seamount fisheries:  a preliminary analysis. In: T. Morato and D. Pauly (eds.) Seamounts:  Biodiversity  and  Fisheries. Fisheries Centre Research Report 12(5), pp 61-66. Zar, J.H. (1999) Biostatistical  Analysis, 4th edition. Prentice Hall, New Jersey. C H A P T E R 7 ECOSYSTEM SIMULATIONS OF MANAGEMENT STRATEGIES FOR DATA-LIMITED SEAMOUNT FISHERIES 1 7 .1 INTRODUCTION With global catches declining since the late 1980's (Watson and Pauly, 2001), the world's fisheries  resources have been characterized as seriously depleted or in danger of  depletion (e.g. Jackson et al., 2001; Pauly et al., 2002; Baum et al., 2003; Christensen et al., 2003; Myers and Worm, 2003), with very little evidence for  any recovery (Hutchings, 2000). What has caused this phenomenon has been the subject of  serious debate (Pitcher, 2001). Indeed, poor management practices and increased fishing  pressure (Ludwig et al., 1993), along with an excessive level of  investment in fishing  capacity, have resulted in serious stock depletion on continental shelves and have created serial depletion from  new pressures on alternative fishing  grounds (Pauly et al., 2002). Seamounts are among those "newly" targeted ecosystems that have been intensively fished  since the second half  of  the 20th century (Rogers, 1994; Koslow et al., 2000). Deep-water fisheries  in general, and seamounts fisheries  in particular, are usually characterized by a boom and bust sequence (Koslow et al., 2000; Watson and Morato, 2004), with the targeted fish  stocks showing signs of  overexploitation within a short period after  the beginning of  the fishery.  For example, this has been the case with the orange roughy (Hoplostethus  atlanticus)  fishery  off  the waters of  New Zealand (Clark, 1999; Clark et al., 2000), Australia (Koslow, 1997), Namibia (Boyer et al., 2001), and the North Atlantic (Branch, 2001); the pelagic armourhead (Pseudopentaceros  wheeleri)  fishery  over seamounts 1 A version of  this chapter has been published. Morato, T. and T.J. Pitcher (2005). Ecosystem simulations in support of  management of  data-limited seamount fisheries.  Pp: 467-486 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. in international waters off  Hawaii (Sasaki, 1986); and the blue ling (Molva  dipterygia) fishery  in the North Atlantic (Bergstad et al., 2003). Seamount aggregating fish  stocks are long-lived, slow growing species, with late maturity and low recruitment rates (Koslow, 1997; Rico et al., 2001; Morato et al., 2004), often  forming  highly localized aggregations (Clark, 1996). Thus, seamount fish  stocks are rapidly depleted and maintenance of  seamount fisheries  depends on the discovery of  unexploited seamounts. Moreover, many seamounts are located in international waters where no management is applied., Once depleted, seamount populations likely require decades to recover (Koslow, 1997). Side effects  caused by overfishing  or extensive trawling on seamounts raise serious concerns: for  example, damage to benthic communities dominated by corals and other fragile  suspension feeders  is common (Richer de Forges, 2000; Koslow et al., 2001) and impacts on transient migratory species whose life  histories rely on seamount food  webs (Haney, 1995; Holland et al., 1999; Weimerskirch et al., 2002). The prevention of  further  negative impacts on these sensitive ecosystems is now an important policy objective (Probert, 1999; Roberts, 2002). There is rising concern about threats to seamount ecosystems in the Exclusive Economic Zones of  coastal states and the high seas; several countries, such as Canada, Australia, New Zealand and Portugal have begun to take action for  the protection of  such 'fragile' ecosystems. However, seamount ecosystems remain one of  the worst cases of  data-limited situations, comprising a true challenge for  fishery  scientists and managers who are urged to develop new fisheries  under the precautionary approach of  the Code of  Conduct for Responsible Fisheries (FAO, 1995). Little is known about seamount ecosystems in the NE Atlantic and elsewhere, or the impact of  human activities upon them. A recent attempt to tackle this global lack of  information  has been made by the European Commission, which has funded  the first  European Seamount Project to integrate physical, biogeochemical and biological research - the 'OASIS project' (OceAnic Seamounts: an Integrated Study). In this paper I investigate if  ecosystem simulations can help in understanding the impact of fishing  on pristine seamounts. By using ecosystem modeling loosely structured on North Atlantic case studies, data gathered from  elsewhere, and optimization methods for  policy search, 1 explore the types of  fisheries  that might be sustainable on seamount ecosystems. 7 . 2 METHODS 7.2.7 Trophic  model  of  seamount ecosystems In this study, I used a general ecosystem model developed for  North Atlantic seamounts (Morato and Pitcher, 2002) based on the 'Ecopath with Ecosim' approach (EwE; http://www.ecopath.org), a software  for  ecosystem trophic mass-balance analysis (Ecopath), with a dynamic modeling capability (Ecosim) (for  details see Christensen and Walters, 2004). This model was developed for  a theoretical isolated seamount. Habitat covered by the model was defined  by the summit, set at around 300 m, down to the base of  the seamount at around 2000 m. The model covered a small area of  about 3000 km2 and included 37 functional  groups: three marine mammal groups (i.e., toothed whales, baleen whales and dolphins), seabirds, turtles, seven invertebrate groups (i.e., 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 (i.e., gelatinous, shallow and deepwater zooplankton), primary producers (i.e., phytoplankton), detritus and twentyfish  groups assembled according to their environmental preferences  (i.e., depth and habitat: e.g. benthic, pelagic or benthopelagic), body size, energetics and life- history characteristics (see Appendix 3 and Morato and Pitcher, 2002 for  a complete description of  the model). The theoretical seamount was assumed to have a low initial level of  exploitation and its fisheries  were loosely based on those operating at the Azores / Mid Atlantic Ridge comprising 6 fleets  (see Appendix 3 and Morato et al, 2001): (a) demersal longline (targeting shallow water demersal and benthic fishes);  (b) deepwater longline (targeting bathypelagic and bathybenthic fishes);  (c) small pelagics fishery  (for  small pelagic fishes); (d) tuna fishery;  (e) swordfish  fishery;  and (f)  deepwater trawl (targeting seamount associated fishes,  including orange roughy and alfonsinos,  Beryx splendens  and B. decadactylus). Landings, prices, and job estimates were loosely based on the Azores case study (Morato et al, 2001; Morato and Pitcher, 2002). Ecopath outputs are known to be very sensitive to the vulnerability parameters (see Walters et al, 1997; Pitcher and Cochrane, 2002). In this study a standard value of  0.3 representing mixed predator/prey control was used. A brief  sensitivity analysis of  the policy simulations to different  vulnerability settings was conducted by repeating simulations with vulnerabilities of 0.2, 0.3 and 0.5. Results from  simulations were generally consistent between different vulnerabilities. 7.2.2  Model  analyses The impacts of  alternative time patterns of  fishing  mortalities were explored using an optimization method (Walters et al, 2002; Christensen and Walters, 2004) to search for patterns of  relative fishing  effort  by fishing  fleets  which would maximize one or more of  the considered objectives: 1) 'Economy', or net present economic value (i.e., total present value of  the catch); 2) 'Jobs', or employment (i.e., a social indicator, assumed proportional to gross landed value of  catch for  each fleet  with a different  jobs/landed value ratio for  each fleet); 3) 'Ecology', or ecological 'stability' (i.e., measured by assigning a weighting factor  to each group based on their longevity, and optimizing for  the weighted sum). Net present economic value of  landed catches was calculated as the discounted sum over all fleets  and times of  catches multiplied by the prices of  landed fish  species. A discount rate of 0.04 was used. The ecological criterion component is based on Odum's (1971) definition  of 'maturity', with mature ecosystems being dominated by large, long-lived organisms. Thus, it is intended to identify  the fleet  structure that maximizes biomass of  long-lived organisms, defined  by the inverse of  their production/ biomass ratios (P/B). The objective function  can be thought of  as a 'multi-criterion objective function',  represented as a weighted sum for  the three above-mentioned criteria indicators: OBJ  = W y -X-W,  + W,  •£./,,  + W E • + * where W=  weighting factors;  V=  value; J=  jobs; E= ecology; z'= fleets;  y—species caught; t= time in years; NPV= net present value; and B/P= biomass production ratio, assumed to be proportional to species longevity and thus ecological stability, with s a normally distributed error term. The Davidon-Fletcher-Powell (DFP) non-linear optimization procedure was used to iteratively optimize the three above-mentioned objectives by changing relative fishing  rates (F) (Walters et al., 2002). This search procedure results in what control systems analysts call an 'open loop policy'; a recommendation for  what to do at different  future  times without reference  to what the system actually ends up doing along the way (Christensen and Walters, 2004). The resulting 'optimum' fishing  rates by year/fleet  served as input for  the dynamic simulation, 'Ecosim', where they replaced the baseline relative efforts  by fleet/gear  type. Ecosim, was then run for  a 50-year-period to simulate the effect  of  the optimized fishing rates and to estimate the biomass, catch and value variation. These two scenarios were compared with a 'no fishing'  scenario where all the fishing  rates were set to 0. Non-linear optimization methods, such as DFP, can be difficult  to use and can be misleading. In particular, the method can 'hang up' on a local maximum and can give extreme answers due to an inappropriate objective function  (Walters et al., 2002). To check for  false convergence to local maxima, random starting F's were used. To search for  trade-offs  among objective functions,  optimal scenario solutions for  a range of  weightings of  ecological and economic objectives were accessed. Additionally, at the end of  each run, ecosystem indicators such as the mean trophic level of  the catch (see equation in Pauly et al., 1998) and biodiversity index (modified  from  Kempton and Taylor, 1976, Q75; Ainsworth and Pitcher, 2006) which resulted from  the suggested fishing  effort  in each range of  weighting, were estimated. 7 .3 RESULTS 7.3.1 Optimal  fishing  scenarios The optimized fishing  rates (F) for  the 'economy' and 'ecology' objectives, expressed as proportions of  the base model fishing  rates are summarized in Figure 7.1. Maximizing net economic value led to an increase in fishing  rate in all fisheries  (deepwater trawl, Ffj na|/FbaSe= 97.6; swordfish  fisheries  (Fnnai/Fbase= 18.1); small pelagic fisheries,  Ffi na|/Fbase= 16.2), except for  the deepwater longline, where the fishing  rate was reduced to 0.86 of  the base model value. In contrast, maximizing 'ecosystem' stability led to a large decrease in all fishing  rates (swordfish  fisheries.  (Ffi nai/Fbase= 0.01; demersal longline, Ffi nai/FbaSe= 0.16; deepwater trawl, F f i n a | / F b a s e = 0.32). Economy 0.0 ' Ecology 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. The effects  of  the optimized fishing  rates on biomass (i.e., percentage of  biomass change from  base model) after  a 50-year simulation are presented, together with a 'no fishing' scenario, in Figure 7.2. Not surprisingly, the 'no fishing'  scenario produced a general increase in biomass for  most of  the groups, but particularly for  sea turtles, rays and skates, and pelagic sharks. However, this was not the case for  the most important prey groups of  the system: mesopelagic fish  and benthic invertebrates. The optimized fishing  rates for  the 'ecology' objective function  produced very similar results when compared with the 'no fishing'  scenario, producing a large increase in groups that have slow turnover and higher trophic levels. When economic value was maximized, a general decrease in biomass was observed associated with collapse of  ten functional  groups (pelagic sharks, tunas, benthopelagic sharks, seamount-associated fishes,  bathypelagic fishes,  sea-turtles, rays and skates, alfonsinos,  medium-sized epipelagic fish,  and benthic invertebrate filter  feeders,  e.g. deep-sea corals). 150 w 100 < to E .£ 50 • NO FISHING • ECOLOGY ECONOMY -50 -100 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. The effects  of  the different  fishing  policies on the total landed catches are shown in Figure 7.3. Maximizing the 'economy' objective led to an increase in landings when compared to the base model. In this scenario, the deepwater trawl fishery  was favoured  and had the highest contribution to the total catch. In contrast, maximizing the 'ecology' objective required an overall decrease in catches and fishery  operations conducted mostly by small pelagic and bottom longline fishing  fleets. Comparing the total value of  the catches for  the three scenarios (i.e., base model, maximizing 'economy', and maximizing 'ecology'; Figure 7.4), maximizing 'economy' generated more money than the base model and the 'ecology' scenario. In all cases, deepwater trawl and bottom longline fishing  fleets  contributed the most to the total value. 1 . 6 • ECOLOGY • BASE B ECONOMY U 0.4 0.2 | Demersal Longline DeepWater Small Pelagics Longline Tuna Fisheries Swordfish  Deep Water trawl Total 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. 3.5 3.0 2.5 2.0 u > 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 <u x? 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 £ <t> 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 £ <t> 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 «/> £ <t> 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).  Toothed whales included killer whale (Orcinus  orca), false  killer (Pseudorca  crassidens),  pilot whale (Globicephala  sp.), northern bottlenose whale (Hyperoodon  ampullatus),  Gervais' beaked whale (Mesoplodon  europaeus), Sowerby's beaked whale (Mesoplodon  bidens),  and the sperm whale (Physeter  macrocephalus).  Dolphins included the common dolphin (Delphinus delphis),  striped dolphins (Stenella  coeruleoalba),  spotted dolphins (Stenella  frontalis), Risso's dolphins (Grampus  griseus), bottlenose dolphins (Tursiops  truncatus).  Ecopath parameters were taken from  the Azores model (Guenette and Morato, 2001). References Ainsworth, C., B. Ferris, E. Leblond and S. Guenette (2001) The Bay of  Biscay, France; 1998 and 1970 models. In: S. Guenette, V. Christensen and D. Pauly (eds). Fisheries impacts on North  Atlantic  ecosystems: models  and  analyses. Fisheries Centre Research Reports 9(4), pp. 271-313. Angel, M.V. 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