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The global network of marine protected areas: developing baselines and identifying priorities Wood, Louisa Jane 2007

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THE GLOBAL NETWORK OF MARINE PROTECTED AREAS: DEVELOPING BASELINES AND IDENTIFYING PRIORITIES by LOUISA JANE WOOD B.A. (Hons), University of Cambridge, 2000 M.Sc., University of Newcastle-Upon-Tyne, 2002  A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Geography)  THE UNIVERSITY OF BRITISH COLUMBIA October 2007  © Louisa Jane Wood, 2007  Abstract Recently adopted global marine protection targets aim to protect 10-30% of marine habitats within the next 3 to 5 years. However, these targets were adopted without prior assessment of their attainability. Moreover, our ability to monitor progress towards such targets has been constrained by a lack of robust data on marine protected areas (MPAs). In this thesis I present the results of the first explicitly marine-focused, global assessment of MPAs in relation to three global marine protection targets. Approximately 2.35 million km2, equivalent to 0.65% of the world’s oceans, are currently protected, and only 12% of that is ‘no-take’. Over the last two decades, the marine area protected globally has grown at ~5% per year. At this rate, even the most modest target is unlikely to be met for at least several decades.  The utility of large-scale conservation targets has been repeatedly questioned, although mainly on ecological grounds. However, if, as is suggested here, their primary role is to motivate behavioural change, then a more serious problem is that they seem to be failing in this regard, too. I explore possible reasons for this and suggest two main problems: firstly, an as yet unmet need to develop a hierarchical system of targets that reflects the multi-scale and pluralistic nature of ecological and political systems; and secondly, feedback mechanisms between political will, perceived attainability, and target formulation which may impede implementation of the targets.  Since the adoption of the global targets, no implementation strategy has been developed, which may also impede target attainment. In order to fill this gap, I applied a rarity-complementarity heuristic place prioritisation algorithm (PPA) to a dataset consisting of 1038 global species distributions with 0.5° latitude/longitude resolution, under ten scenarios devised to reflect the  ii  global targets. This is the first time that species distribution ranges of marine species have been used in a globally synthetic way, and is by far the largest application of a PPA to date. Global priority areas for protection are identified for each scenario, which may be used to identify where regional-scale protected areas network design efforts might be focused.  iii  Table of Contents Abstract................................................................................................................................ ii Table of Contents .................................................................................................................iv List of Tables .......................................................................................................................vi List of Figures .....................................................................................................................vii Acknowledgements ...............................................................................................................x Co-authorship Statement ....................................................................................................xiv 1. Introduction.....................................................................................................................1 Rationale ...............................................................................................................................2 Research Objectives ..............................................................................................................9 Thesis Outline .......................................................................................................................9 References...........................................................................................................................12 2. Assessing progress towards global marine protection targets: shortfalls in information and action.........................................................................................................18 Introduction.........................................................................................................................19 Methods ..............................................................................................................................20 Database..........................................................................................................................20 MPA network coverage...................................................................................................22 MPA network characteristics...........................................................................................23 Assessing global MPA network representativeness..........................................................24 Global MPA network growth and predicting feasibility of target attainment. ...................24 Results ................................................................................................................................25 The current global network of MPAs...............................................................................25 Feasibility of attaining global MPA targets......................................................................28 Discussion...........................................................................................................................30 Tables..................................................................................................................................34 Figures ................................................................................................................................36 References...........................................................................................................................47 3. Conceptual challenges to global monitoring of marine protected areas .....................51 Introduction.........................................................................................................................52 What is a definition and why define? ...................................................................................52 What is a ‘marine protected area’?.......................................................................................53 Conceptualisation of ‘protection’.........................................................................................54 Reconciling concepts of protection for more effective global monitoring of the oceans .......57 References...........................................................................................................................59 4. Global marine protection targets: how S.M.A.R.T are they?......................................61 Introduction.........................................................................................................................62 Global marine protection targets: are they Specific? ............................................................64 What is an MPA? ............................................................................................................65 What should MPAs be representative of? ........................................................................65 What is a ‘network’ of MPAs? ........................................................................................66 What is management effectiveness of MPAs? .................................................................66 Global marine protection targets: are they Measurable? .......................................................68 Targets must be quantitative ............................................................................................68 iv  What information needs to be collected? .........................................................................69 Capacity to collect, store, and report necessary information.............................................69 Global marine protection targets: are they Achievable? .......................................................70 Global marine protection targets: are they Realistic? ...........................................................70 Global marine protection targets: are they Time-bound?......................................................71 Discussion...........................................................................................................................71 References...........................................................................................................................77 5. GIS-based multicriteria evaluation and fuzzy sets to identify priority sites for marine protection..............................................................................................................................83 Introduction.........................................................................................................................84 Global marine resource conservation and extraction ........................................................86 MPA site selection tools ..................................................................................................87 MCE and fuzzy sets for identification of priority sites for marine protection....................88 Methods ..............................................................................................................................91 Study site ........................................................................................................................91 Data sources and software ...............................................................................................92 MCE procedure...............................................................................................................93 MCE procedure applied to Objective 1: Biodiversity conservation ..................................95 Scenario 1A: All species are equally important to protect ................................................96 Scenario 1B: some species are more (or less) important to protect than others. ................96 MCE procedure applied to Objective 2: Fisheries management .......................................97 Multi-objective space allocation analysis.........................................................................98 Results ................................................................................................................................99 Objective 1: Biodiversity Conservation ...........................................................................99 Objective 2: Fisheries Management ...............................................................................100 Multiobjective space allocation analysis ........................................................................100 Discussion .........................................................................................................................101 Tables................................................................................................................................105 Figures ..............................................................................................................................106 References.........................................................................................................................112 6. Priority areas for a global network of marine protected areas ................................118 Introduction.......................................................................................................................119 Methods ............................................................................................................................123 Data Sets.......................................................................................................................123 Place prioritisation protocols .........................................................................................124 Results ..............................................................................................................................127 Discussion .........................................................................................................................128 Tables................................................................................................................................135 Figures ..............................................................................................................................138 References.........................................................................................................................152 7. Conclusions..................................................................................................................159 References.........................................................................................................................166 8. Appendices...................................................................................................................168 Appendix 1........................................................................................................................169  v  List of Tables Table 2.1 Total and marine areas of the ten largest MPAs globally. ........................................34 Table 2.2 Proportion of the world’s MPAs by number and area that are within the size recommendations made by a) Halpern and Warner (2003) and b) Shanks et al (2003). .................................................................................................................................34 Table 2.3 Proportion of the world’s MPAs by number and area that meet minimum size requirements made by a) Halpern and Warner (2003) and b) Shanks et al (2003)......34 Table 2.4 Summary of the percentage of the world’s MPAs by number and area that meet both minimum size and inter-MPA distance recommendations made by a) Halpern and Warner (2003) and b) Shanks et al (2003), and c) Palumbi (2003). ...........................34 Table 2.5 Summary statistics for MPAs by number and area in high latitudes (>50° north and south) .......................................................................................................................35 Table 2.6 Summary of the annual rates of increase in global MPA coverage required to meet various marine protection targets on time, both at the time the targets were made and also currently. ...........................................................................................................35 Table 5.1 showing hypothetical criterion weighting schemes, which were applied in this MCE for the Biodiversity Conservation objective. ...........................................................105 Table 5.2 Table showing hypothetical multiobjective objective weighting schemes, which were used in the multiobjective space allocation analysis. ...............................................105 Table 6.1Taxonomic summary of species whose distributions were used in ResNet analyses. ...............................................................................................................................135 Table 6.2 Attributes of the ten scenarios investigated. WPC = World Parks Congress, CBD = Convention on Biological Diversity. .......................................................................136 Table 6.3 Summary statistics for solutions found for each of the 10 scenarios investigated. See Table 6.2 for more details on the scenarios. Values presented are means based on 100 solutions generated for each scenario......................................................................137 Table 6.4 Area of overlap between solutions including and excluding marine mammal distributions for each target scenario. Data are based on cells selected in 100% of solutions for each scenario, and are expressed as absolute area, as well as percentage of the mean solution area for that scenario, both including and excluding marine mammal distributions. ............................................................................................137  vi  List of Figures Figure 2.1 Global distribution of all MPAs designated by 31st December 2006......................36 Figure 2.2 Global distribution of no-take MPAs designated by 31st December 2006. Solid areas are MPAs that are entirely no-take; open areas are MPAs which include one or more no-take zones............................................................................................................37 Figure 2.3 Area-frequency distribution of the world’s MPAs, showing recommended MPA sizes using marine larval dispersal distances: a) 10-100km2 (Halpern and Warner, 2003); b) minimum 3.14km2, preferable 12.5-28.5km2 (Shanks et al., 2003).............38 Figure 2.4 Frequency (black dots) and area (grey bars) of MPAs exhibiting variable individual levels of ‘connectedness’, as measured by the number of MPAs occurring a) 10-20km away from each MPA and b) 20-150km away from each MPA.................................39 Figure 2.5 Marine area protected, as a function of distance from the coast. The world’s ten largest MPAs are shown separately (see Table 2.1). The limits for territorial sea (12nm) and Exclusive Economic Zone (200nm) are indicated for clarity. .................40 Figure 2.6 Distribution of MPAs (by number and area) as a function of distance from the Equator. Graph a) shows absolute area protected, with the world’s ten largest MPAs shown separately (see Table 2.1). Graph b) shows the proportion of the sea area within 20 degree latitudinal bands that is protected. ..................................................41 Figure 2.7 Estimated proportion of marine habitats protected within the current global MPA network, for habitat types where global distribution data are available. .....................42 Figure 2.8 Percentage of Large Marine Ecosystem that is protected........................................43 Figure 2.9 Percentage of 200nm Exclusive Economic Zones of maritime countries and territories that is protected. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown as grey lines. ......................................44 Figure 2.10 Growth in cumulative global marine area protected for: total (solid circles), log(total) (open circles) and no-take (squares) area. ..................................................45 Figure 2.11 Projection of the annual rate of increase (4.6%, r2 = 0.96) of global MPA area protected between 1984 and 2006 into the future, in relation to attainment of marine protection targets adopted by the Convention on Biological Diversity (CBD) and the World Parks Congress (WPC). .................................................................................46 Figure 5.1 Map showing the approximate location of study area, in the heavy box: the Pacific Canadian Exclusive Economic Zone (EEZ) ............................................................106 Figure 5.2 Hypothetical formulation of the decision problem of identifying priority sites for marine protection using the hierarchy defined by Malczewski (1999). The criteria listed here do not represent an exhaustive list..........................................................107 Figure 5.3 Monotonically increasing, sigmoidal fuzzy membership function used to standardize MCE criteria layers. a, b c, and d represent inflection points where the membership function rises above zero, approaches one, falls below one and approaches zero again, respectively. A monotonically increasing function rises to one and never falls again. Hence b, c, and d all have the same criterion value of 1. .........................................108  vii  Figure 5.4 Outputs of MCE for Objective 1 – Biodiversity Conservation. Maps show the 30% most suitable cells for protection from within the Pacific Canadian EEZ identified by the following scenarios: A) Equal weights for all species; B) Linear weighting regime for IUCN Red Listed species; C) Logistic weighting regime for IUCN Red Listed species; D) Logistic weighting regime for IUCN Red Listed species with Critically Endangered category removed. ...............................................................................109 Figure 5.5 Outputs of MCE for Objective 2 – Fisheries Management. Map A shows the catch value distribution across the entire Pacific Canadian EEZ. Map B shows the 30% most suitable cells for continued resource extraction from with the Pacific Canadian EEZ, as identified by catch value distribution. .................................................................110 Figure 5.6 Outputs of Multiobjective space allocation analysis, with biodiversity conservation and fisheries management as the two, conflicting objectives. Maps show the 30% most suitable cells for protection from within the Pacific Canadian EEZ, as identified by the following objective weighting schemes, listed biodiversity conservation : fisheries management: A = 0.3 : 0.7; B = 0.5 : 0.5; C = 0.7 : 0.3. ...........................111 Figure 6.1 Total richness of 1038 fish, invertebrate, and marine mammal species, calculated using expected occurrence data (see text). White areas of sea in the Arctic and Antarctic are where there are no data. .....................................................................138 Figure 6.2 Richness of 923 commercially targeted fish and invertebrate species. White areas of sea in high latitudes are areas where there are no data.............................................139 Figure 6.3 Richness of 115 marine mammal species. White areas of sea in the Arctic and Antarctic are where there are no data. .....................................................................140 Figure 6.4 Frequency of selection of cells over 100 iterations of ResNet for scenario WPC_20All. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black. ...............................................................................141 Figure 6.5 Frequency of selection of cells over 100 iterations of ResNet for scenario WPC_30All. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black. ...............................................................................142 Figure 6.6 Frequency of selection of cells over 100 iterations of ResNet for scenario CBD_All. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black. .................................................................................................143 Figure 6.7 Frequency of selection of cells over 100 iterations of ResNet for scenario CBD_GAll. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black. ...............................................................................144 Figure 6.8 Frequency of selection of cells over 100 iterations of ResNet for scenario Global_10All. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black......................................................................145 Figure 6.9 Frequency of selection of cells over 100 iterations of ResNet for scenario WPC_20Fi. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black. ...............................................................................146 Figure 6.10 Frequency of selection of cells over 100 iterations of ResNet for scenario WPC_30Fi. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black. ...............................................................................147  viii  Figure 6.11 Frequency of selection of cells over 100 iterations of ResNet for scenario CBD_Fi. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black. .................................................................................................148 Figure 6.12 Frequency of selection of cells over 100 iterations of ResNet for scenario CBD_GFi. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black. ...............................................................................149 Figure 6.13 Frequency of selection of cells over 100 iterations of ResNet for scenario Global_10Fi. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black. ...............................................................................150 Figure 6.14 Overlap between cells selected 100% of the time in scenarios WPC30_All and WPC30_Fi. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black. ...............................................................................151  ix  Acknowledgements This thesis was conducted as part of the Sea Around Us Project, an activity funded and initiated by the Pew Charitable Trusts. Funding was generously provided by WWF (known as World Wide Fund for Nature, except in the United States and Canada where it is known as World Wildlife Fund), and a Partial University Graduate Fellowship from the University of British Columbia. The work to update the MPA data was carried out under a formal collaboration between the Sea Around Us Project, WWF, and the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), and with the additional support of the World Conservation Union World Commission on Protected Areas (IUCN-WCPA). In addition, part of this research was enabled by the use of WestGrid computing resources, which are funded in part by the Canada Foundation for Innovation, Alberta Innovation and Science, BC Advanced Education, and the participating research institutions. WestGrid equipment is provided by IBM, Hewlett Packard and SGI.  I would first like to offer my deepest and most sincere thanks to my supervisor, Daniel Pauly. Thank you for pushing me to expand my vision, for pushing me to think deeper even when I didn’t want to, or felt I couldn’t any more. Thanks for showing me what I needed to learn and how to get there. More importantly, however, thank you for teaching me not just about the stuff you were supposed to, but for taking the time and investing the energy to be an inspiring, entertaining, and incredibly supportive mentor. My lasting memory of this whole process will be the peace with myself that you helped me to find.  I would also like to thank my co-supervisor, Brian Klinkenberg, and my committee members, Jackie Alder, Suzana Dragicevic, and Rashid Sumaila, for your commitment and support. You  x  have all been amazingly responsive, constructive and helpful throughout my thesis. In particular, thanks for putting in the time at the eleventh hour to provide feedback quickly as I scrambled to meet the submission deadline I set for myself. It means a huge amount to feel such support in achieving that goal.  Along the way, I have been fortunate enough to receive the much appreciated help from what is now a very long list of people….. so here goes! First of all I would like to thank Lucy Fish from UNEP-WCMC for providing the original MPA data from the World Database on Protected Areas, and for her help in processing it. Also Josh Laughren from WWF Canada for allowing me to use of surplus budget to develop the MPA Global website, without which my data collection efforts would likely have been much less effective. Thanks for sharing my vision! I would also like to thank Chris O’Grady for his great web-development skills, as well as his patience and generosity in ensuring that the MPA Global website did what I needed it to, and more. Much help with data entry was received from Mike Browne, Shalini Cawich, Rodrigo Donadi, Marianne Gilbert, Sue Gubbay, Saeko Kumagai, Vance Lockton, Suzanne Mondoux, Rojyar Yousefi, Apo Wai, Colette Wabnitz, and Sue Wells. Thanks also to Irina Fainberg for her much needed Russian skills, and to the many, many people from all over the world for being so responsive to my requests for data and clarifications of seemingly simple issues that often turned out to require hours of emails and/or phone conversations. From the Fisheries Centre, Adrian Kitchingman and Chris Close have both been veritable life-savers with GIS and data questions on many occasions. Chris I owe you a six pack or two!! Thanks to Kristin Kaschner for generously providing the marine mammal distribution data, and at short notice. From WestGrid, I would like to thank Roman Baranowski and Brent Gawryliuk for going well beyond their mandate and teaching this Linux novice how to use their facilities, as  xi  well as occasionally bumping my jobs to the head of the queue so I could finish on time. Thanks for being such approachable and unintimidating computer geeks! Thanks to Sherman Lai as well for helping me get set up with WestGrid. I would also like to express my sincere thanks to Sahotra Sarkar and Trevon Fuller from the University of Texas at Austin for the many hours of stimulating discussions and technical support you have both freely given to help me use ResNet appropriately and also to process the gargantuan dataset I was working with. I couldn’t have done it without your help.  And I’m still not done… in so many ways this PhD has pushed the limits of my patience and self-belief. It has been grueling and solitary and an immense test of will, stamina and determination. There were times when I literally would have preferred to have torn my hairs out one by one than keep working on or thinking about it. And at those times, my precious friends and family rescued me and reminded me to put on my perspective head and keep believing. To my Beany Blister, who is so different from me yet in some ways so similar, thanks for just being you, and loving me for me, and helping me stay on the path. To Pluffy, for much needed thathoo therapy. To Mum, Fairy Flatfeet and Lady Luck: I guess it worked! The pot’s pretty full now, so I can send them back to you now if you need ☺ To Poppy, thanks for all of our wonderfully silly conversations, mostly about gravy, beer, bikes, and Very Important Programmes, which always bring a smile back to my face. To Grandad, for continuing to inspire me to stay true to myself and follow my heart.  And my friends, my friends. Where would I be without my friends? Colette, I love you and I just can’t tell you how much I value your friendship, or how much you have helped me survive this last rollercoaster of a year in particular. Thank you for being there. Cheryl, with whom a  xii  good old whinge about everything and nothing over a cup of Dilmah and some salt n vinegar crisps was and always will be one of the most fulfilling and valuable ways of spending my time. And, so many more of my deeply cherished friends, both nearby and far away, but for whom distance and time has no bearing. I’m not going to make a list because you know who you are and you know how much you mean to me, so, quite simply, thankyou, from the bottom of my heart.  Finally, to Josh, to whom I owe more pages of appreciation, and apologies, than there are pages in this thesis. It was a great journey, and I will treasure it forever.  xiii  Co-authorship Statement Apart from Chapters 1 and 7, all chapters have been prepared as stand-alone manuscripts for submission to a peer-reviewed journal. They are currently all either published, accepted, or in review, with the exception of Chapter 6, which is in preparation for submission. I am the senior author on all papers, and I assumed primary responsibility for the design, implementation, analysis, and writing of co-authored papers. I am the sole author on Chapters 3 and 4, although Daniel Pauly provided guidance on both. The contributions of co-authors to Chapters 2, 5 and 6 are summarized below.  Chapter 2 is co-authored by Lucy Fish, Josh Laughren, and Daniel Pauly. Lucy Fish provided the original World Database on Protected Areas MPA data that were used to build the MPA database, and feedback on the manuscript. Josh Laughren provided feedback on the design of the research program and the manuscript. Daniel Pauly provided guidance on all stages of the work.  Chapter 5 is co-authored by Suzana Dragicevic, who provided guidance on multicriteria evaluation techniques and implementation, and provided feedback on the manuscript.  Chapter 6 is co-authored by Trevon Fuller, William Cheung, Chris Close, Kristin Kaschner, Reg Watson, and Daniel Pauly. Trevon Fuller and Sahotra Sarkar provided technical support with the ResNet software and data processing. William Cheung, Chris Close, Kristin Kaschner, Reg Watson and Daniel Pauly were responsible for developing the species distributions that were the input data to the ResNet analysis. Daniel Pauly also provided guidance on all stages of the work.  xiv  1. Introduction  1  Rationale The negative impacts of human activities, both exploitative and non-exploitative, on the world’s coastal and marine ecosystems are now widely documented. Marine habitats have undergone substantial declines over the last few decades – and, indeed, centuries (Jackson, 2001) – among the most well documented being coral reefs (Roberts et al., 2002; Wilkinson, 2002; Bellwood et al., 2004; Côté et al., 2005; Graham et al., 2006), seagrasses (Fortes, 1988; Duarte, 2002), and mangroves (Valiela et al., 2001; Alongi, 2002). Concerns are now also growing for offshore habitats, particularly seamounts and cold-water coral reefs (Koslow et al., 2000; Clark and O'Driscoll, 2001; Koslow et al., 2001; CBD, 2004b; Roberts and Hirshfield, 2004). Despite the difficulties in measuring marine extinction rates, there is growing evidence of rapid and profound declines and/or local extinctions for various marine species (Carlton, 1993; Gray, 1997; Casey and Myers, 1998; Roberts and Hawkins, 1999; Dulvy et al., 2003; Hutchings and Reynolds, 2004). Indeed, there are now 416 marine species listed as Vulnerable, Endangered, or Critically Endangered on the 2007 IUCN Red List of Threatened Species, including various new additions such as Galápagos coral and seaweed species (IUCN, 2007a). These numbers are likely to increase as more species are assessed under the Global Marine Species Assessment (currently, only 1372 of the 40177 species assessed in the IUCN Red List are considered marine species) (IUCN, 2007b).  Exploitation and habitat loss and/or degradation are considered to be responsible for the vast majority of observed declines and local marine extinctions, much of which is attributable to fishing, the biggest and most ubiquitous threat to marine ecosystems globally (Jennings and Kaiser, 1998; Sadovy, 2001; Dulvy et al., 2003; Myers and Ottensmeyer, 2005; Myers and Worm, 2005; Preikshot and Pauly, 2005). Worldwide, FAO reports that around 77% of the  2  world’s marine fisheries are either fully exploited, with no room for expansion, or overexploited (FAO, 2006, p29). Fisheries collapses have been observed at varying scales and from many parts of the world’s oceans, most notably in the North Atlantic, where extensive data are available (Baum et al., 2003; Christensen et al., 2003; Myers and Worm, 2003). Although 95% of fisheries catches have historically been made on the continental shelf (Pauly and Christensen, 1995), fisheries have also been shifting to deeper waters over the last 50 years (Morato et al., 2006), raising concerns for deep-sea fishes and habitats, which are generally vulnerable to, and slow to recover from, (over)exploitation and physical perturbation, respectively (Koslow et al., 2000; Devine et al., 2006; Davies et al., in press).  These declines have all been observed within the context of existing fisheries management and governance mechanisms, which have, as a consequence, been criticised for failing to prevent the collapses (Botsford et al., 1997; Browman and Stergiou, 2004). Common criticisms of traditional fisheries management mechanisms include the ‘roving bandit’ problem (Berkes et al., 2006), which has masked the decline of individual stocks by their sequential replacement with new ones. This apparently sustained high level of catches reinforced the impression of inexhaustible stocks that had been expressed by the likes of Lamarck and Huxley in the 19th century (Roberts and Hawkins, 1999), leading to substantial overinvestment in the structural capacity of fleets during the 1970s, resulting in overcapacity, currently estimated as being double or more of the optimal level (Pauly et al., 2002; Mace, 2004). Fisheries management has for the last 50 years been embedded in the neoclassical economic view, which considered the fish stock as the primary unit of management concern, and the fishing industry as the primary user of marine resources (Norse and Crowder, 2005). The development of natural resource economics and environmental economics did little to correct for the fundamental flaw  3  in neoclassical economic theory that the economy is a closed system of production and consumption that is not dependent on the state and/or quality of the broader environment (Minteer and Manning, 2003). This presiding view (along with increased disciplinary segregation of science at the time) led to the development of single species stock assessment models, which gave little consideration to the ecosystem from which the fish were taken, including the habitats and other species that may have been impacted directly or indirectly through the process of fishing. Nevertheless, these models were used to estimate maximum sustainable yield (MSY), which formed the basis of many management recommendations, such as quotas. However, the MSY concept has been challenged for the last two decades on multiple grounds: it doesn’t account for spatial variability; it is a high risk strategy (overshoot can have very serious consequences for stock persistence); it only considers the target species, and it fails to consider the costs of fishing (Botsford et al., 1997).  In response to these criticisms, a popular current trend in the fisheries management literature is to move away from single-species management approaches towards an ecosystem approach to fisheries (Browman and Stergiou, 2004; Pikitch et al., 2004). The underlying premise is that an ecosystem approach is spatially explicit, adaptive, considers uncertainties and external influences, and strives to balance societal objectives. Marine protected areas (MPAs) form an integral part of ecosystem-based management (Sumaila et al., 2000) because they have the potential to: maintain and restore ecosystems, biodiversity and ecological processes; manage conflicting uses of ocean space; buffer against natural and anthropogenic uncertainty; promote integrated management of marine resources; augment fisheries through spillover and larval replenishment; and maintain aesthetic and traditional values (Jones, 1994; Alder, 1996; Jones, 2002; Gerber et al., 2003; Lubchenco et al., 2003). This, along with a growing sense of  4  urgency about the need to mitigate and reverse the global declines in marine fisheries and habitats, has led to the adoption of various global marine protection targets in recent years.  The 2002 Plan of Implementation of the World Summit on Sustainable Development (WSSD) committed to establishing a representative global network of MPAs by 2012 (United Nations, 2002b, Section IV, paragraph 32(c)). At the Vth World Parks Congress (WPC) in 2003, the recommendation was made to “[g]reatly increase the marine and coastal area managed in marine protected areas by 2012; these networks should include strictly protected areas that amount to at least 20-30% of each habitat”. Most recently, at the Eighth Ordinary Conference of the Parties (COP8) to the Convention on Biological Diversity (CBD) in 2006, a target that “at least 10% of each of the world’s ecological regions [including marine and coastal be] effectively conserved [by 2010]” was adopted (CBD, 2006a).  The adoption of these targets automatically confers an explicit need to monitor progress towards their attainment. However, the only existing global database on protected areas, the World Database on Protected Areas (WDPA), maintained by the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), contained limited information for MPAs, enabling only very broad analyses on the total number and area of MPAs (Chape et al., 2005). Prior to the work presented here, the level of protection in the world’s oceans was estimated at around 0.5%, although these assessments suffered from data limitations (Kelleher et al., 1995a; Chape et al., 2005). The portion of the global marine area that was no-take, or strictly protected, was largely unknown, although guesstimates on the order of 0.01% have been published (Pauly et al., 2002; Roberts, 2003). Consequently, formal calls for better information on MPAs have been issued (CBD, 2004a). Unfortunately,  5  systematic collection of baseline data such as this is now widely perceived as a ‘filling-in’ activity rather than an innovative branch of marine science (Longhurst, 1998). Yet, it is the critical foundation to our understanding of the extent, distribution, and level of regulation of human activities in the world’s oceans, and our ability to monitor progress towards targets such as those under discussion here. The global targets are also ambitious: based on previously available information, they imply an 8- to 3000-fold increase in area protected, in less than a decade.  The process of increasing the global marine area protected is fundamentally a decision-making problem. Decision-making occurs at and amongst multiple, nested levels, ranging from individual to group to organisation to society. Over time, decision-making at any, or all, of these levels has become increasingly complex and multidimensional, due to increases in the range and availability of goods and services, and increasingly variable extent and magnitude of impacts of any one alternative on other stakeholders and/or use/non-use options for a given resource (Kleindorfer et al., 1993). Early decision theory described problems with a unique, determinate solution as ‘benign’, and problems with no determinate solution – instead, having only a negotiated and temporarily stable balance that will always be open to renegotiation and adjustment as context and power relations change in society – as ‘wicked’ (Minteer and Manning, 2003). The decision over what places to protect in the world’s oceans appears to be inherently wicked: measurement of marine ecosystems is practically difficult, making spatial representation of species, habitats, and ecological processes, as well as the location, intensity and impacts of human activities, a major challenge. Variability in the marine environment through space and time further confounds our ability to understand and manage the system effectively (Longhurst, 1998; Norse and Crowder, 2005). There are also many socio-political  6  objectives for use of marine systems, including fisheries extraction, biodiversity conservation, oil and gas exploration and extraction, substrate (e.g., sand and gravel) extraction, shipping, mining, tourism and recreation, and cultural uses (Duxbury and Duxbury, 1989; Norse and Crowder, 2005). The development of a global network of MPAs imposes opportunity costs on these other objectives, to varying degrees, and as such is a multicriteria decision problem (Moffett and Sarkar, 2006). Multicriteria decision-making (MCDM) provides a means to evaluate the trade-offs between different alternative solutions to a decision-making problem, on the basis of often conflicting criteria, and without necessarily requiring that all parameters under consideration be placed on the same scale (as is the case with cost-benefit analysis) (Carver, 1991; Faith and Walker, 1996; Malczewski, 1999).  In light of the multicriteria nature of the problem of creating a global network of MPAs, as well as the limited resources available to implement it (Wilson et al., 2006), it is clear that not all areas of biological interest can be protected (Sarkar et al., 2006). As a consequence, a science of selecting and designing protected area networks has been developing over the last 25 years (to a large extent from the theory of island biogeography), to identify, using place prioritisation algorithms, maximally spatially economical ways to achieve representation and persistence of biodiversity, within specified constraints (e.g., total area, surrogate representation targets) (Kirkpatrick, 1983; Kingsland, 2002; Sarkar et al., 2006). This emerging field of research is now known as systematic conservation planning (Margules and Pressey, 2000; Sarkar et al., 2006). Although these techniques take socio-political and economic considerations into account to some degree, for example, the cost criterion implemented in MARXAN (Stewart and Possingham, 2005), it is in a largely ad hoc manner, and so these methods do not represent formal MCDM techniques. Indeed, formal MCDM  7  techniques have either never, or very rarely, been used in conservation planning (Moffett and Sarkar, 2006).  It was suggested in the early 1990s that the most appropriate strategy for systematic conservation planning is to first develop global priority areas, then, in a subsequent process, design regional or national-level networks (Vane-Wright et al., 1991; Pressey et al., 1993). However, at that time, computational capacity was a significant limiting factor to the maximum size of tractable problems, so that although global prioritisation analyses were possible using, for example, WORLDMAP, this was only achievable with relatively small data matrices, for example, spatial resolution of 10° latitude/longitude, i.e., 864 cells, and using only 43 species (Vane-Wright et al., 1991; Williams, 1991). Similarly, appropriate data on the global spatial distributions of surrogates for marine diversity were largely unavailable. As a consequence, although a global marine prioritisation exercise has already been undertaken (Kelleher et al., 1995a), it did not use systematic conservation planning methods, and did not attempt to ensure representation of known biodiversity. Rather, it identified a number of regional and national priority areas using expert knowledge on a range of criteria: biogeography; ecology; naturalness; economic importance; social importance; international or national significance; and practicality or feasibility.  Since the work of Kelleher et al (1995a), three new global marine protection targets have been adopted, and systematic conservation planning techniques and computational capacity have improved dramatically. In addition, global distribution data for marine species have been developed, for example, through the Sea Around Us Project (Close et al., 2006; Kaschner et al., 2006; Sea Around Us, 2007). However, until this thesis, they have not been used together  8  to identify global marine protection priorities. In light of the ambitiousness and imminence of the target deadlines, therefore, this thesis provides very timely assessments of the current level of protection in the world’s oceans and the attainability of the global marine protection targets. It also identifies global priority areas for protection in the world’s oceans, based on the targets, which may be used as a strategic framework to guide subsequent stages of the planning process to develop global networks of MPAs.  Research Objectives The research I present in this thesis is framed within the context of two main objectives. The first objective is to develop a robust baseline for the world’s MPAs that enables progress towards the global targets to be monitored. The second is to identify priority areas for protection in the world’s oceans, based on the quantitative requirements imposed by the global targets. To address these problems, I posed the following questions: 1. What is the current global extent and distribution of the world’s marine protected areas? 2. What is the current growth rate of global marine area protected and is it sufficient for the global targets to be met on time? 3. Where in the world’s oceans should be prioritised for protection, according to the spatial distribution of known biodiversity, and as specified by the global targets?  Thesis Outline In total there are seven chapters in this thesis, consisting of five research chapters, opened with a general introductory chapter (Chapter 1). The next three chapters (Chapters 2, 3, and 4) relate to the first research objective, to develop a robust global baseline of MPAs. I approached this  9  by extracting MPA data already available in the World Database on Protected Areas, restructuring it and creating a new database, called MPA Global. The database is available online (Wood, 2007) and allows for registered users to review and edit existing data. The database also has field-level referencing to enable greater transparency of source data. Updating of the data is an ongoing process, but to date, over 200,000 edits have been made using over 1100 sources, and the current list of MPAs has changed by around 75% from the original dataset. In Chapter 2, I provide a global analysis of MPAs based on the new MPA data, including an assessment of the attainability of the global targets. In Chapter 3, I explore some of the conceptual and philosophical challenges associated with global monitoring of a human construct such as MPAs, which became apparent during the process of updating the global database. In Chapter 4, I assess the extent to which the formulation of the global targets may affect their attainability. I investigated the second thesis objective, to identify global priority areas for marine protection in two different ways. I present these two approaches in Chapters 5 and 6. In Chapter 5, I use a species-richness based approach within multicriteria evaluation (MCE) to investigate the variability in priority areas identified depending on how two resource use objectives, biodiversity conservation and fisheries profit maximisation, are weighted. The study area for this analysis was the Pacific Canadian Exclusive Economic Zone (EEZ). In Chapter 6, I use a place prioritisation algorithm to identify priority areas in the global oceans, based on distribution range maps for 923 fish and invertebrate species, developed by the Sea Around Us Project (Close et al., 2006) and 115 marine mammals (Kaschner et al., 2006). Five scenarios that reflect the various global marine protection targets were investigated, and each of these was investigated using (a) all species distributions, and (b) fish and invertebrate species distributions only, in order to assess the influence of the wide-ranging annual movements of many marine mammal (Kaschner et al., 2006) species on the size and  10  spatial configuration of the solutions. Finally, in Chapter 7, I provide a synthesis of the findings presented in this thesis, their limitations, as well as some recommendations for future work.  11  References Alder, J. (1996) Have tropical marine protected areas worked? An initial anaysis of their success. Coastal Management, 24(2), 97-114. Alongi, D.M. (2002) Present state and future of the world's mangrove forests. Environmental Conservation, 29, 331-349. Baum, J.K., Myers, R.A., Kehler, D.G., Worm, B., Harley, S.J. & Doherty, P. (2003) Collapse and conservation of shark populations in the northwest Atlantic. 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(2003) Our shifting perspectives on the oceans. Oryx, 37(2), 166-177. Roberts, C.M. & Hawkins, J.P. (1999) Extinction risk in the sea. Trends in Ecology and Evolution, 14(6), 241-246. Roberts, C.M., McClean, C.J., Veron, J.E.N., Hawkins, J.P., Allen, G.R., McAllister, D.E., Mittermeier, C.G., Schueler, F.W., Spalding, M., Wells, F., Vynne, C. & Werner, T.B. (2002) Marine biodiversity hotspots and conservation priorities for tropical reefs. Science, 295(5558), 1280-1284. Roberts, S. & Hirshfield, M. (2004) Deep-sea corals: out of sight, but no longer out of mind. Frontiers in Ecology and the Environment, 2(3), 123-130. Sadovy, Y. (2001) The threat of fishing to highly fecund fishes. Journal of Fish Biology, 59(Suppl. a), 90-108. Sarkar, S., Pressey, R.L., Faith, D.P., Margules, C.R., Fuller, T., Stoms, D.M., Moffett, A., Wilson, K.A., Williams, K.J., Williams, P.H. & Andelman, S. (2006) Biodiversity conservation planning tools: Present status and challenges for the future. 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Vane-Wright, R.I., Humphries, C.J. & Williams, P.H. (1991) What to protect? - Systematics and the agony of choice. Biological Conservation, 55, 235-254. Wilkinson, C., (ed.) (2002) Status of coral reefs of the world: 2002. Australian Institute of Marine Science, Townsville, Queensland, Australia. Williams, P.H. (1991) The bumble bees of the Kashmir Himalaya (Hymenoptera: Apidae, Bombini). Bulletin of the British Museum (Natural History, Entomology), 60(1), 1-204. Wilson, K.A., McBride, M.F., Bode, M. & Possingham, H.P. (2006) Prioritizing global conservation efforts. Nature, 440(7082), 337-340. Wood, L.J. (2007) MPA Global: a database of the world's marine protected areas, Sea Around Us Project, UNEP-WCMC & WWF. www.mpaglobal.org [accessed 5th July 2007].  17  2. Assessing progress towards global marine protection targets: shortfalls in information and action1  1  A version of this chapter has been accepted for publication. Wood, L. J., Fish, L., Laughren, J., and D. Pauly. Assessing progress towards global marine protection targets: shortfalls in information and action. Oryx (in press).  18  Introduction Marine protected areas (MPAs) are increasingly viewed as an important spatial management tool within a suite of policy alternatives to reduce, prevent and/or reverse, ongoing (and in some cases rapid) declines in marine biodiversity and fisheries (Agardy, 1994; Pauly et al., 2002; Hoyt, 2005; Roberts et al., 2005). This has led to their inclusion in several recent global marine protection targets. The 2002 Plan of Implementation of the World Summit on Sustainable Development (WSSD) committed to establishing a representative global network of MPAs by 2012 (United Nations, 2002b, Section IV, paragraph 32(c)). At the Vth World Parks Congress (WPC) in 2003, the recommendation was made to “[g]reatly increase the marine and coastal area managed in marine protected areas by 2012; these networks should include strictly protected areas that amount to at least 20-30% of each habitat”. Most recently, at the Eighth Ordinary Conference of the Parties (COP8) to the Convention on Biological Diversity (CBD) in 2006, a target that “at least 10% of each of the world’s ecological regions [including marine and coastal be] effectively conserved [by 2010]” was adopted (CBD, 2006a). However, these targets were adopted with very limited prior knowledge of the existing global MPA network (the most recent global assessment of MPAs is over 10 years old and had data limitations (Kelleher et al., 1995a)). Similarly, the targets were adopted without any assessment of the feasibility of the targets.  The World Database on Protected Areas (WDPA), maintained by the United Nations Environment Programme - World Conservation Monitoring Centre (UNEP-WCMC) is a global source of MPA data that has been widely used for monitoring MPAs, such as the United Nations List of Protected Areas. However, its coverage of MPAs has significant limitations (CBD, 2003), permitting only relatively broad-scale analyses on the total number and area of  19  MPAs (see, e.g. Chape et al., 2005). More complete information on individual MPAs has been largely unavailable. Consequently, formal calls for better information on MPAs have been issued (CBD, 2004a). In response, a collaboration was established between the Sea Around Us Project at the University of British Columbia, Canada, World Wildlife Fund, and UNEPWCMC, to extensively revise and update the MPA data in the WDPA.  The objective of this study was to collect data to enable more effective monitoring of MPAs that relate to four stated requirements of the three global targets: i) their distribution and coverage; ii) their network characteristics, as defined by available information on larval dispersal distances, iii) the representativeness of the global MPA network, and iv) growth of the network over time. I present a global review of the current status of the world’s MPAs, with explicit reference to the three global targets, as well as a preliminary quantitative assessment of the feasibility of meeting the targets. I discuss these results, their implications and their limitations, and the role of large-scale targets in advancing marine conservation.  Methods Database Spatial and descriptive data were extracted from the WDPA (version 6.2) for all sites that were listed as marine. This includes MPAs that have been designated using statutory and nonstatutory mechanisms operating at a range of scales, including individual MPA agreements, customary or traditional mechanisms, state/provincial legislation, national legislation, and international conventions. It also includes MPAs of variable designation status including designated, proposed, and degazetted. These data were restructured and used to create a new database, called MPA Global. Some new fields were added, including marine area (portion of  20  the total area which is below the mean high water mark), no-take area (portion of the marine area where extraction of resources – both living and, where information allowed, non-living – is prohibited), and regulatory information. The database is available online (Wood, 2007). Registered users can view, review, and submit edits to the database. Field-level referencing was built into the online editing process, to increase the transparency of the database as well as document discrepancies between source materials.  The criterion used for inclusion of an MPA in MPA Global is based on the IUCN definition:  “Any area of intertidal or subtidal terrain, together with its overlying water and associated flora, fauna, historical and cultural features, which has been reserved by law or other effective means to protect part or all of the enclosed environment” (IUCN, 1988).  This definition was applied by reviewing the legal boundary of the site. If it extended seaward of the mean high water mark, the site was left in, or added to, MPA Global. For sites designated under non-statutory mechanisms, or where the designating legislation did not specify the legal seaward boundary, eligibility for inclusion was assessed using multiple sources (see below). Protected areas in the Caspian Sea are not included on the grounds that they are saline but not marine. Sites whose only ‘marine’ area was lagoonal were included only if the lagoon has a permanent surface connection to the sea. A globally extensive (although not yet fully exhaustive), multi-pronged, site-level update and verification process was undertaken. MPAs whose boundaries appeared to fall completely inland, using the 1:3,000,000 countries coastline shapefile provided in the ESRI Data and Maps Media Kit 2003 (ESRI, 2003), were identified in a Geographic Information System (GIS) and individually assessed. Updates were made at regional, country, and sub-country levels, using multiple sources including: a range of existing MPA databases, legislation, websites, peer-reviewed and non-peer-reviewed literature, 21  and direct communications with regional/in-country experts. Finally, stratified sampling was undertaken to verify the data for the largest sites.  To date, over 1100 sources have been used to perform over 200,000 edits, pertaining to all countries with MPAs. Almost 1000 non-qualifying MPAs have been removed, 1000 MPAs have been added from the WDPA but that were not previously listed in the WDPA as marine, and almost 900 new sites have been added. These updates represent a ~75% change to the original WDPA list of ‘MPAs’. New spatial data (MPA boundary polygons) have been obtained for 1822 of 3061 MPAs with spatial boundary data.  MPA network coverage Global MPA coverage was estimated for all MPAs designated up to 31st December 2006. Sites listed under international conventions (e.g., UNESCO World Heritage Convention 1972, RAMSAR Convention 1971) were excluded due to near-complete overlap with nationally designated sites. Sites whose status was not designated or informally designated, were excluded. It was considered more accurate to estimate global MPA coverage by summing marine area estimates obtained through the editing process rather than through spatial analysis, due to a lack of spatial boundary data for ~31% of MPAs, and knowledge that some of the boundary data are out of date and substantially under- or over-sized. Of the total MPA area estimate, 92% was obtained from verified sources, and 8% estimated. For MPAs with unknown marine area, their total area was prorated according to the median proportion of total area for MPAs with known marine area and matching broad habitat types (intertidal only; intertidal and subtidal; subtidal only). Where total area was also unknown, the area assigned to the MPA was the global median marine area (4.6 km2). Double counting of area due to overlap  22  between MPAs was eliminated by subtracting the area of sites identified throughout the verification process as overlapping. Some overlap may remain, but this is negligible relative to the total area.  Information on no-take status was collected for MPAs on two levels: a qualitative status (all/part/none of the MPA is no-take) and a quantitative areal estimate where available. Notake data are currently available for 65% of the total MPA area. Total global no-take area was estimated by summing the areas stored in the attribute data; no overlap is known to exist between sites for which no-take data are available.  MPA network characteristics I assessed the ‘connectedness’ of MPAs globally in terms of recommendations for MPA size and inter-MPA spacing based on known marine larval dispersal distances. A size-frequency distribution was produced using marine area data to identify the number and combined area of the world’s MPAs that are large enough to be self-seeding for short-dispersing species. Sizes assessed were: >3.14 km2, 12.5-28.5 km2 (Shanks et al., 2003), and 10-100 km2 (Halpern and Warner, 2003). Recommended inter-MPA distances were used to create buffer ‘bands’ around MPAs in a GIS. Distances used were: 10-20 km (Shanks et al., 2003) and 20-150 km (adapted from Palumbi (2003) and Cowen et al (2006)). MPAs occurring within these bands were considered to be ‘connected’ to at least one other MPA. These two analyses were combined in order to identify MPAs that meet both size and spacing requirements.  23  Assessing global MPA network representativeness. Four measures of MPA network representativeness were investigated. Firstly, the distance of the central point of each MPA from the coast was estimated in GIS (ESRI, 2003), enabling both the frequency and area to be plotted as a function of distance from shore. Secondly, the same procedure was used to measure distance of MPAs from the Equator. The highly variable size and shape of individual MPA boundaries relative to the land and the equator mean that these distances may be an overestimate in some cases and an underestimate in others, but it represents a standard measure for all MPAs. Thirdly, the proportion of the following individual habitat types (for which a global distribution map is available) that is protected was estimated in GIS: estuaries (Alder, 2003); mangroves (UNEP-WCMC data); seagrass (UNEP-WCMC data); coral reef (UNEP-WCMC data); seamounts (Kitchingman and Lai, 2004). Finally, the proportion of two large scale political and/or broad marine habitat classifications that is currently protected was estimated in GIS: Large Marine Ecosystem (LME) (Sherman, 1991) and Exclusive Economic Zone (EEZ).  Global MPA network growth and predicting feasibility of target attainment. Designation dates were available for MPAs constituting 98% of the total global area protected. The remaining 2% of the area was distributed across all years, prorated according to the proportion of the total global MPA area (in sites of known marine area) designated in that year. Known chronological changes in the size of individual MPAs were incorporated into the cumulative growth data. Simple linear regression of the logged cumulative global MPA area was used to estimate annual growth rate with which to predict target attainment dates.  24  Results The current global network of MPAs Extent of the world’s MPAs By 31st December 2006, around 4435 MPAs had been statutorily or non-statutorily designated at national or more local levels, covering approximately 2.35 million km2, and occurring entirely within EEZs. This represents 0.65% of the world’s oceans, or 1.6% of the total global marine area within EEZs. Only 12.8% of those 2.35 million km2, representing 0.08% of the world’s oceans and 0.20% of the global marine area under national jurisdiction, is subject to no-take regulations (i.e, is ‘strictly protected’ in the wording of the World Parks Congress Recommendation (IUCN, 2003)) (Figures 2.1 and 2.2). This is the first estimate of global notake area that has been based directly on no-take data, and improves upon previous estimates which, due to a lack of such information, used IUCN management category as a proxy (e.g., Agardy et al (2003), and Jones (2006)).  Network characteristics of the world’s MPAs The mean size of MPAs is approximately 544 km2, with a median size of 4.6 km2. The substantial difference between mean and median MPA size is largely attributable to ten very large MPAs, which constitute 68% of the global MPA area (Table 2.1). Following size range suggestions derived from larval dispersal distances (Halpern and Warner, 2003; Shanks et al., 2003), 79% of MPAs, representing 98.6% of total marine area protected, appear to be either too small or too large (Figure 2.3 & Table 2.2), particularly the latter, due to the 10 largest MPAs. However, if the size recommendations are viewed as minima, between 35% and 60% of MPAs, representing over 99% of the total area protected, are large enough (Table 2.3).  25  A total of 2496 MPAs (56.3% of the world’s MPAs), covering 1.28 million km2 (54.5% of the world’s marine protected area) are ‘connected’ within 10-20 km of at least one other MPA. The vast majority of these (85% by number and 98% by area) are connected to a maximum of ten MPAs (Figure 2.4a). Using the larger connectedness distance of 20-150 km, 3487 MPAs (78.6% of the world’s MPAs), covering 1.88 million km2 (80.3% of the world’s marine protected area) are connected to at least one other MPA, and, as expected, are generally connected to more MPAs than under the previous scenario (Figure 2.4b).  Combining the minimum size and spacing requirements indicates that at best, 49% of MPAs (80% by area), and, at worst, only 18% of MPAs (54% by area) could be considered as part of a connected network (Table 2.4).  Representativeness of the global MPA network The distribution of the world’s MPAs is distinctly non-uniform, being heavily biased towards both coastal waters and the 10 largest MPAs referred to earlier (Figure 2.5, Table 2.1). The number of MPAs declines exponentially with distance from shore, as does the distribution of area protected with distance from shore, with the exception of some of the 10 largest MPAs. However, the boundaries of all of these very large MPAs do abut the coast. As such, the measured distance of their centroid from shore is high simply by virtue of their large size.  The majority of the global marine area protected (approximately 65%, representing 43% of all MPAs) is located within the tropical latitude belt, between 30°N and 30°S, suggesting that tropical coastal habitats may in fact be among the best protected of all marine habitat types, at least on paper (Figure 2.6a). However, most of the remaining global marine area protected  26  (31%, representing 26% of all MPAs and including 5 of the world’s ten largest MPAs) is located in latitudes higher than 50°, two-thirds of which is located in the northern hemisphere. These northern MPAs protect by far the highest proportion of sea surface area by latitude (Figure 2.6b). However, this may be largely attributable to the relatively small surface area of sea north of 50°N. Intermediate latitudes (30°-50°), and particularly southern temperate and polar latitudes, appear to be the least well protected.  Laurel and Bradbury (2006) suggest that based on larval dispersal distances, MPA size should increase with latitude. Using a subset of MPA Global, they conclude that this trend is not observed in the global network of MPAs. Using the complete dataset, I found a similar result. However, I also found that mean and median MPA size at latitudes greater than 50° is larger than the global values, and increases through the high latitude range (see Figure 2.6b and Table 2.5).  Proportional representation of habitat types within the global MPA network is shown in Figure 2.7. These are the only habitats for which global distributional data are known to be available, and mirrors the paucity of global data found for terrestrial habitats (Balmford et al., 2003). The accuracy of the proportions protected varies with habitat type, due to variable (and largely unknown) accuracy of the habitat distributions themselves, both in terms of their precision as well as the confounding problems of habitat loss and change through time.  Figures 2.8 and 2.9 show the proportions of large marine ecosystem (LME) and exclusive economic zone (EEZ) that are currently protected, respectively. LMEs are suggested by the CBD as an appropriate classification system for monitoring progress towards its target (CBD,  27  2005). However, this is problematic for Pacific Island countries and territories, none of which occur within a LME, but all of which (with the exception of overseas territories of the United States of America) are party to the Convention. Given this, and the largely national scale of implementation of the CBD target, I view the proportion of EEZ as the best current assessment of the representativeness of the existing global MPA network, despite the political basis of the boundaries. It indicates that the current global MPA network falls far short of target requirements. Over 87% of 226 coastal countries (including 69 overseas territories and the non-contiguous US states of Hawaii and Alaska, listed separately) have less than the global average of 1.6% of their EEZs protected (Appendix 1). Of the nine countries that currently have more than 10% of their EEZs protected, four have relatively small maritime territories, rather than a high absolute area under protection. The remaining five are overseas territories (including the non-contiguous US state of Hawaii), that include four of the ten largest MPAs in the world.  Feasibility of attaining global MPA targets Growth of the global MPA network The cumulative area of the world’s MPAs has grown steadily since the mid 1970s, coincident with the coming into force of various international conservation conventions (e.g., UNESCO Man and the Biosphere Program 1970, Ramsar Convention 1971, UNESCO World Heritage Convention 1972), and with some irregularities due to the creation of a few large MPAs (Figure 2.10, Table 2.1). Growth of no-take area is less steady, and has been very slow until recently, when the rezoning of the Great Barrier Reef Marine Park (GBRMP) in 2004 (Great Barrier Reef Marine Park Authority, 2004) increased the global no-take area by over 50% and 100,000 km2 (Figure 2.10). More recently still, on 15th July 2006, the Northwestern Hawaiian  28  Islands Coral Reef Ecosystem Reserve (341,362km2, originally designated in 2000) was redesignated as a Marine National Monument. Although it is not yet completely no-take, various habitat-damaging activities and all fishing is required to have ceased within 5 years (Establishment of the Northwestern Hawaiian Islands Marine National Monument: a Proclamation by the President of the United States of America, 2006).  Simple linear regression of the log-transformed cumulative area of MPAs indicates a 4.6% annual increase between 1984 and 2006, r2 = 0.96 (Figure 2.11). This timespan was selected as it represents a time of very steady growth and is representative of the recent political environment. As such it was considered an appropriate timeframe on which to base projections for target attainment. Subsequent to the designation of the majority of the GBRMP in 1984 (it was created through a series of extensions between 1978 and 1984 (GBRMPA, 2007)), seven of the 10 largest MPAs were designated, together covering 43% of the current global marine area protected and 67% of the combined area of the top 10 MPAs (Table 2.1). In spite of this substantial increase in area protected, the overall rate of global MPA growth has not shifted from what appears to be a very stable, but slow, trajectory.  Projected attainment dates of MPA targets I extrapolated the 4.6% growth into the future to assess the attainability of the WPC and CBD targets. It was not possible to assess attainability of the WSSD target using this method as it does not state quantitative areal targets. Results indicate that even the most modest targets will not be met for at least several decades (Figure 2.11). Furthermore, the growth rates required to meet these targets on time are at least an order of magnitude greater than observed (Table 2.6). In other words, a marine area at least three times the combined size of the ten largest MPAs  29  (i.e., ~4.5 million km2) would have to be designated every year until and including 2010 for timely attainment of the CBD target. These projections do not impose any of the additional requirements stated in the targets, including ‘strict protection’, habitat representation, and management effectiveness.  Discussion These results indicate that the current extent, distribution, sizing and spacing of MPAs globally is vastly inadequate, particularly for no-take MPAs, and especially in light of past, ongoing, and expected future impacts on the oceans. The coastal bias of existing MPAs may not be too serious a disadvantage, since the coastal shelves contribute most to the world’s primary production, known marine biodiversity and fisheries productivity (Pauly et al., 2002). However, other attributes of the existing MPA network may serve to reduce the ‘effective’ area and extent of the network. Between 20 and 46% of the global area protected occurs in small and isolated MPAs, which may thus not be effective at ensuring persistence of marine populations or form part of a coherent global network. At the other extreme, the majority of the total marine area protected globally is contained within a handful of extremely large MPAs. At least some very large MPAs are needed to protect highly migratory species such as large pelagic fish and marine mammals, as well as to offset the concentration of fishing effort outside them (Walters, 2000), particularly if (as is the current situation) fishing effort is high and not reduced in conjunction with the creation of MPAs (Pauly et al., 2002; Worm et al., 2003). However, the total marine area protected globally is currently so small that its concentration in a few MPAs means that much of the world’s oceans are essentially unprotected. This configuration of the world’s MPAs thus confers very low levels of representation of many marine habitats, as well as of various biophysical, geographical and  30  political regions. All of these factors may limit the resilience of the global MPA network to many external threats, as well as anticipated spatial shifts in species, communities and hydrological features in response to climate change (Carr et al., 2003; Perry et al., 2005; Parmesan, 2006; Simmonds and Isaac, 2007).  In addition, the results presented here are also best case scenarios, representing only the areas of the world’s oceans that are protected on paper. It should not be assumed that: i) the process that created these MPAs also provided mechanisms for regulating human activities in the marine area; ii) where regulatory mechanisms are in place, they are all being implemented; or that iii) they are implemented effectively. Indeed, in many, if not most, MPAs, the biodiversity and fisheries benefits that may accrue through protection are eroded or undermined by inadequate management resources (financial and human), poor compliance with regulations, and little- or un-managed external threats (Alder, 1996; McClanahan, 1999; Jameson et al., 2002). The best available information on management effectiveness is currently from largescale analyses that are either outdated or focused on a subset of the world’s MPAs. These assessments indicate very low rates of effective management (Kelleher et al., 1995a; Alder, 1996; Mora et al., 2006).  These results imply almost certain failure, at the very least in terms of attainment of global marine protection targets. Despite the designation of the 184,700 km2 Phoenix Islands Protected Area (PIPA) by the Government of Kiribati, a huge individual achievement, at least 76 more countries each need to create MPAs covering an area equivalent to PIPA before 2010 for the CBD target to be met on time. Unfortunately, I suspect that the negative connotations of these predictions may undermine the benefits and successes of positive results at smaller  31  scales, such as that of Kiribati. These results do, however, demand that the question be asked (again): can large scale conservation targets do more harm than good?  The utility of broad scale conservation targets has been questioned on numerous occasions. Targets have historically been justified in terms of political expediency rather than ecological knowledge (Soulé and Sanjayan, 1998; Agardy et al., 2003). Broad scale, uniform conservation targets may thus be inadequate for meeting biodiversity conservation objectives (Rodrigues et al., 2004), and may ultimately weaken the political process to create protected areas if the expected benefits are not observed, particularly within the electoral timeframe. However, the terrestrial protected area network has developed over more than a century, with at least half of the area designated (Chape et al., 2005) before quantitative global targets were first established in the early 1980s (Soulé and Sanjayan, 1998). Similarly, the first explicitly marine, quantitative global protection target was made in 2003 (IUCN, 2003), when over 95% of the current marine area protected had already been created. Therefore, the location and design of marine (and terrestrial) protected areas have, to date, been selected largely without explicit consideration of many of the recently formalised principles of ‘MPA (network) design theory’ (Lubchenco et al., 2003; Roberts et al., 2003a) or the application of systematic conservation planning tools which have developed over the last 25 years (Kirkpatrick, 1983; Kingsland, 2002). While it is important to understand the (in)adequacy of existing protected areas in meeting specific objectives to inform future conservation planning, it may be counterproductive (and perhaps irrelevant) politically to criticise the products of past processes in terms of current ones.  32  Perhaps a more pressing question is how to garner the political will required to motivate a rapid increase in marine protection, particularly in the face of wider policy concerns such as food security, human welfare, and health. In this regard, broad scale conservation targets can help mobilise support for, and schedule, conservation intervention in the face of limited resources, ongoing biodiversity losses, and inadequate protection (Margules and Pressey, 2000; Pressey et al., 2003). In particular, the CBD target demonstrates a commitment of the parties to the Convention (presently 188; Appendix 1) to translate their general obligations under the Convention into concrete action for conservation and sustainable use (Pauly and Watson, 2005). Nevertheless, given the mismatch between the resources available and the resources required to implement and monitor a global network of MPAs, it seems likely that the global MPA network developed by the time of the target deadlines will almost certainly be a compromise, between quantity (i.e., how closely the targets are met) and quality (i.e., how appropriately designed and effectively implemented the MPAs thus created are). Broad scale conservation targets are thus, perhaps, necessary but not sufficient for effective marine resource conservation and management.  The work presented here has substantially improved the global MPA baseline, and enhanced our ability to monitor various aspects of MPA targets. While the value of a ‘list’ of MPAs in terms of assessing the ‘effective’ level of protection has been questioned (Roff, 2005), it remains a fundamental prerequisite to any assessment of status or progress. This analysis has provided the first quantitative estimate of the rate of change needed for these targets to be met. While daunting, this new information arms decision-makers and conservation planners with a greater understanding of the magnitude of the task ahead, and the urgency with which they must tackle it.  33  Tables Table 2.1 Total and marine areas of the ten largest MPAs globally. Year Country MPA name Designation type designated Australia Great Barrier Reef Marine Park 1979 USA Northwestern Coral Reef Ecosystem 2000 Hawaiian Islands Reserve* Republic Phoenix Islands Protected Area 2006 of Kiribati Australia Macquarie Island Marine Park 1999 Ecuador Galapagos Marine Reserve 1996 Denmark Greenland National Park 1974 Colombia Seaflower Marine Protected Area 2005 Marine Reserve 2002 Australia Heard Island and McDonald Islands Zapovednik (Strictly 1993 Russia Komandorsky† Protected Nature Reserve) Russia Wrangel Island† Zapovednik (Strictly 1976 Protected Nature Reserve) Total: * This site was redesignated as a Marine National Monument in June 2006 † Total and marine areas for these sites include buffer zone areas.  Total area (’000 km2) 344.4 341.4  Marine area (’000km2) 344.4 341.4  184.7  184.7  162.0 133.0 972.0 65.1 64.6  162.0 133.0 110.6 65.0 64.6  58.3  55.8  54.7  46.7  2380.2  1,508.2  Table 2.2 Proportion of the world’s MPAs by number and area that are within the size recommendations made by a) Halpern and Warner (2003) and b) Shanks et al (2003). % of MPAs % of area Size recommendation (km2) 10 - 100a 21 1.4 12.5 - 28.5b 8 0.3  Table 2.3 Proportion of the world’s MPAs by number and area that meet minimum size requirements made by a) Halpern and Warner (2003) and b) Shanks et al (2003). % of MPAs % of area Size recommendation (km2) > 3.14 b 58 99.7 > 10 a 35 99.4 >12.5 b 33 99.4  Table 2.4 Summary of the percentage of the world’s MPAs by number and area that meet both minimum size and inter-MPA distance recommendations made by a) Halpern and Warner (2003) and b) Shanks et al (2003), and c) Palumbi (2003). Minimum size connected within 20-150kmc connected within 10-20kmb 2 % of MPAs % by area % of MPAs % by area (km ) >3.14b 34.1 54.6 49.1 80.3 >10a 19.9 54.4 29.9 80.1 >12.5b 18.4 54.4 27.6 80.0  34  Table 2.5 Summary statistics for MPAs by number and area in high latitudes (>50° north and south) Median Number of % of # of top Latitude % of world % of world Mean MPA size MPAs MPAs ten largest ocean MPA area MPA size (km2) MPAs (km2) World 100 100 544 5 4435 100 10 > 50° 33 31 699 4 1169 26 5 > 60° 21 17 1521 7 263 6 2 > 70° 11 14 7629 398 43 1 2  Table 2.6 Summary of the annual rates of increase in global MPA coverage required to meet various marine protection targets on time, both at the time the targets were made and also currently. Annual rate of increase in global MPA coverage MPA area (103 km2) Target Target Target required to meet target start deadline At target At target As of end End 2006 Target start start 2006 CBD 10% of EEZs 2006 2010 2,162 2,350 16,444 50.0 91.3 (CBD) 10% of 2006 2010 2,162 2,350 36,106 75.6 148.6 world ocean* WPC 20% of world 2003 2012 2,086 2,350 72,212 48.3 98.4 ocean WPC 30% of world 2003 2012 2,086 2,350 108,318 55.1 115.1 ocean * The CBD target does not explicitly include the high seas in its target, although states that the high seas should be urgently protected using international cooperation. The data presented here are based on an extension of the CBD 10% target to include the high seas as well, but should not be viewed as an official or adopted target of the CBD.  35  36  Figure 2.1 Global distribution of all MPAs designated by 31st December 2006.  Figures  37  Figure 2.2 Global distribution of no-take MPAs designated by 31st December 2006. Solid areas are MPAs that are entirely no-take; open areas are MPAs which include one or more no-take zones.  Figure 2.3 Area-frequency distribution of the world’s MPAs, showing recommended MPA sizes using marine larval dispersal distances: a) 10-100km2 (Halpern and Warner, 2003); b) minimum 3.14km2, preferable 12.5-28.5km2 (Shanks et al., 2003).  38  Figure 2.4 Frequency (black dots) and area (grey bars) of MPAs exhibiting variable individual levels of ‘connectedness’, as measured by the number of MPAs occurring a) 10-20km away from each MPA and b) 20-150km away from each MPA.  39  Figure 2.5 Marine area protected, as a function of distance from the coast. The world’s ten largest MPAs are shown separately (see Table 2.1). The limits for territorial sea (12nm) and Exclusive Economic Zone (200nm) are indicated for clarity.  40  Figure 2.6 Distribution of MPAs (by number and area) as a function of distance from the Equator. Graph a) shows absolute area protected, with the world’s ten largest MPAs shown separately (see Table 2.1). Graph b) shows the proportion of the sea area within 20 degree latitudinal bands that is protected.  41  Figure 2.7 Estimated proportion of marine habitats protected within the current global MPA network, for habitat types where global distribution data are available.  42  43  Figure 2.8 Percentage of Large Marine Ecosystem that is protected.  44  Figure 2.9 Percentage of 200nm Exclusive Economic Zones of maritime countries and territories that is protected. 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(2007) The impacts of climate change on marine mammals: early signs of significant problems. Oryx, 41(1), 19-26. Soulé, M.E. & Sanjayan, M.A. (1998) Conservation targets: do they help? Science, 279(5359), 2060-2061. United Nations (2002) Report of the World Summit on Sustainable Development, Johannesburg, South Africa, 26 August-4th September 2002. United Nations, Johannesburg, South Africa. Walters, C. (2000) Impacts of dispersal, ecological interactions, and fishing effort dynamics on efficacy of marine protected areas: how large should protected areas be? Bulletin of Marine Science, 66(3), 745-757. Wood, L.J. (2007) MPA Global: a database of the world's marine protected areas, Sea Around Us Project, UNEP-WCMC & WWF. www.mpaglobal.org [accessed 5th July 2007]. Worm, B., Lotze, H.K. & Myers, R.A. (2003) Predator diversity hotspots in the blue ocean. Proceedings of the National Academy of Sciences, 100, 9884-9888.  50  3. Conceptual challenges to global monitoring of marine protected areas1  1  A version of this chapter has been submitted for publication. Wood, L. J. Conceptual challenges to global monitoring of marine protected areas.  51  Introduction Various targets recently adopted to substantially increase the level of marine protection globally (United Nations, 2002a; IUCN, 2003; CBD, 2006a) have heightened the need for effective monitoring of marine protected areas (MPAs). A fundamental premise of any monitoring program is that, in order to be monitored, the target subject of that program must be readily identified, and distinguishable from its surroundings. In other words, it needs to be defined. However, in the process of recent efforts by the author, in collaboration with others, to improve the information available on MPAs (Wood et al., in press), it became apparent that the conceptual issues of what MPAs are, as well as if, how, and by whom any definition of MPA should be developed, are far from resolved. This imposes various practical limitations to monitoring MPAs at the global scale. The objective of this chapter is to explore these limitations, and consider the extent to which they affect our understanding of spatial and temporal patterns of human presence and activity in the oceans.  What is a definition and why define? According to the Oxford English Dictionary, a definition is ‘a statement of the meaning of a word or the nature of a thing’. To define something means, inter alia, ‘to give the exact meaning of’; ‘describe or explain the scope of’; or ‘mark out the boundary or limits of’ (Thompson, 1990). The purpose of defining thus seems to be to express concepts in words in a way that enables them to be understood by others who may be unfamiliar with them. Definitions also often undergo some degree of interpretation in order to become operational for a specific purpose and context (Mintzberg, 1992). To define or interpret is conscious, purposeful (i.e., goal-directed) human behaviour (Latham and Locke, 1991) . In the context of  52  monitoring MPAs globally, the purpose of defining MPA is to provide a means by which to identify what to include or exclude in a global database of MPAs.  What is a ‘marine protected area’? This is not an entirely new question – see, for example, Agardy et al (2003) - and initially, it might also appear to be a trivial one. Cognitively, most, if not all, of us, would be able to imagine what an MPA might be, if asked. But would we be able to translate such a cognitive image into words? Over the past few decades, various attempts have been made to develop written definitions of MPA. Possibly the first, and certainly the most widely used and cited, definition is that adopted by the World Conservation Union (IUCN, 1988):  “Any area of intertidal or subtidal terrain, together with its overlying water, and associated fauna, flora, historical, or cultural features, which has been reserved by law or other effective means to protect all or part of the enclosed environment.”  Given its wide application and acceptance in the literature, I considered this definition to be the most appropriate to use to update the global MPA baseline (Wood et al., in press). However, in trying to interpret this definition for global monitoring purposes, a profound conceptual problem arose: what does ‘protect all or part’ actually mean? What are we protecting the marine environment from? By how much? How much of the whole environment is enough to be considered part of it? In terms of scientific defensibility, one can simply avoid confronting these issues by stating assumptions and criteria explicitly. However, this has the unfortunate consequence of overlooking some critical conceptual debates.  Normatively, the phrase ‘protect all or part’ implies some sort of continuum of protection along which MPAs fall, based on the extent to which human activities within the area are restricted. 53  This notion is supported by the system developed by the IUCN to facilitate classification of protected areas. It consists of seven categories, which imply a gradation of human intervention from strict protection and preservation of species and genetic diversity, to sustainable use of natural ecosystems (IUCN, 1994). The concept of a continuum of protection is also indicated by available MPA data: globally there are some 350 different MPA designation types, which themselves reflect variation in regulations. Regulations known to apply in MPAs around the world range from complete exclusion of human access to near-zero regulation of human activities, most likely due to local circumstances and threats (Allison et al., 1998; Agardy et al., 2003; Wood, 2007). However, despite extensive consideration of the pluralism of MPAs, there has been scant discussion of the minimum level of regulation necessary for an area to be considered ‘protected’. The sustainable use tenet of the least protected IUCN category doesn’t clarify the situation, instead requiring further interpretation of ‘sustainable use’. However, this minimum boundary is critical to developing a global database of MPAs. How can a database be built without knowing how to identify what to include or exclude? For example, the minimum requirement for inclusion in FishBase (Froese and Pauly, 2007) is that the organism be a fish, the definition of which has been subjected to over two centuries’ worth of taxonomic classification work. No such body of work exists for MPAs.  Conceptualisation of ‘protection’ Much of the difficulty in defining and interpreting terms such as ‘protection’ and ‘sustainable use’ arises from them being normative concepts. Unlike fish, MPAs are not tangible entities with established (if imperfect) methods of measurement and classification. MPAs are human constructs, the product of a complex suite of social, political, cultural, economic, and often legal, processes, which exist almost exclusively within the human mind. Displaying them on  54  maps and marking their boundaries with buoys may lend some physical substance to MPAs, but for the most part they remain firmly within our psyches. As mental constructs, MPAs are goal-oriented, subjective, and fundamentally linked to individual values (Collet, 2006). The form that such values take is influenced by our philosophical attitude to the environment, which in turn affects our perceived responsibility to the environment, and therefore our conceptualisation of protection (Sarkar, 2005). Earlier formulations of environmental worldviews suggested a dichotomy between a resource conservation ethic and a preservation ethic (Callicott, 1991), borne of anthropocentric and biocentric philosophies, respectively (Sarkar, 2005). More recent thinking suggests a unified philosophy of conservation, with these worldviews constituting either extreme of a continuum (Callicott et al., 1999). Indeed, Sarkar (2005) discusses a ‘tempered anthropocentrism’ which seeks to retain anthropocentric values of biodiversity without a loss of reverence for nature. Rather than being narrow and intransigent, therefore, worldviews experienced by individuals may span part or all of this continuum, depending on the sector in which they are trained and work, the scale at which they work, and the specific context of the problem at hand (Callicott et al., 1999).  This presents various practical challenges to defining protection as a global concept. Firstly, the perceived need to develop a definition of protection is in itself value-laden and goaloriented; in this case, that global targets for marine protection are appropriate and that progress towards them should be monitored at the global scale using a single definition of protection. The process of developing a definition is thus embedded within, and bounded by, the worldview(s) held by those developing the definition. Secondly, in order to develop a definition, individualistic and changeable cognitive images must be somehow extracted from a global constituency (which also needs to be defined), translated into words, and amalgamated  55  into a single definition; this is an enormous process of social coordination. Coordination necessitates consensus-seeking through communication, the effectiveness of which is contingent upon the level of motivation within the global constituency to coordinate (Scheff, 1967), i.e., political will, as well as the diversity of worldviews held by both the constituency and the coordinators. Complete consensus occurs when there is an infinite series of reciprocating understandings between group members concerning the issue (i.e., I understand that he/she understands that I understand, and so on). Partial consensus occurs when this reciprocity breaks down. In general, more complex transactions, such as that under discussion here, require higher levels of consensus for social coordination to occur (Scheff, 1967). In planning, coordination is often synonymous with coercion (Mintzberg, 1992), particularly where political will for coordination is lacking. Consequently, there is potential for the worldviews held by those developing the definition to prevail in the final definition.  These challenges are particularly relevant in the context of marine protection, since spatial regulation of human activities has arisen in multiple sectors, notably fisheries management and biodiversity conservation. However, monitoring of MPAs has historically fallen to the conservation sector (hence the widespread use of the IUCN definition, even in the fisheries literature). Areas designated primarily for or by fisheries are seldom considered by conservationists as ‘protected areas’. They are seen, rather, as ‘area-based management tools’. The reasoning behind this linguistic dichotomy seems to be embedded within differences between actual and perceived environmental worldviews between the two sectors. In particular, much of the conservation community still leans towards preservationist worldviews (Callicott, 1991; Agardy et al., 2003), and tacitly assumes that the fisheries sector leans towards resource conservationist worldviews, which inherently impose less strict regulation of human activity.  56  This sectoral divide is thus often considered by the conservation community to be a best approximation of the minimum boundary of protection for MPAs. However, environmental worldviews held by different sectors may be more similar than perceived (Harms and Sylvia, 2001), suggesting that the reciprocity of understanding necessary for complete consensus on a global definition of protection is lacking. Indeed, available information does not support a sectoral basis for defining the minimum boundary of protection. If the regulations applied in space are reflective of environmental worldviews, then there appears to be considerable overlap between the range of worldviews held by the two sectors (Wood, 2007). This sectoral basis for defining a minimum boundary of protection thus causes substantial consistency problems in developing a global database of MPAs. In addition, it is difficult to uphold, due to the often opportunistic use of existing legislation (e.g., fisheries laws) to create MPAs, including for biodiversity conservation objectives. Furthermore, the functional overlap between MPAs designated for different primary objectives is poorly understood and so the empirical basis of this dichotomy is very limited (Hastings and Botsford, 2003). Such a dichotomy also seems to be outdated in light of the growing emphasis on ecosystem- (i.e., space- rather than sector-) based management of the oceans (Caddy, 1999; Sumaila et al., 2000; Agardy et al., 2003; Agardy, 2005; Edwards, in press).  Reconciling concepts of protection for more effective global monitoring of the oceans Fishing is currently the biggest threat to marine ecosystems, yet despite the long-running conflict between fisheries management and biodiversity conservation, their long-term goals are the same (Preikshot and Pauly, 2005). Global monitoring of MPAs using a conceptualisation of protection that excludes the fisheries sector thus seems to be the biggest shortcoming of the current system. Furthermore, the reasons for this exclusion seem to be driven by philosophical  57  differences and misperceptions, rather than empirical ones. Explicit consideration of the conceptual and philosophical basis for these difficulties is useful in gaining a better understanding of current limitations to global monitoring efforts, but it doesn’t necessarily get us any closer to solving the problem. Furthermore, given the depth and complexity of the problem, one must ask: is it even possible to achieve global consensus on individually-based, changeable philosophical views of the environment? However, given the political reality of global targets for protection, and mandates to monitor progress towards them, there remains a practical need to try to navigate these difficulties, and at least, to recognise and try to overcome some of the shortcomings of the current system. Perhaps the best solution might be to recognise that we may not be able to define a minimum boundary of protection, and embrace the concept of a continuum of protection from zero to absolute. Every location in the oceans is protected or exploited to some degree; isn’t it logical to measure this on a single scale? This implies a radical transformation to a global database of all spatial restrictions on human activity in the oceans, the resource requirements of which are indeed vast. The purpose of this article is not to blindly suggest that we ‘should just do this’. However, in line with Agardy et al (2003), we do need to think and talk more about trying to do this. Insufficient data and resources are a ubiquitous problem in conservation, but the consequences of not addressing these conceptual issues are far more profound in terms of our ability to understand, communicate about and manage our oceans.  58  References Agardy, T. (2005) Global marine conservation policy versus site-level implementation: the mismatch of scale and its implications. Marine Ecology Progress Series, 300, 242-248. Agardy, T., Bridgewater, P., Crosby, M.P., Day, J., Dayton, P.K., Kenchington, R., Laffoley, D., McConney, P., Murray, P.A., Parks, J.E. & Peau, L. (2003) Dangerous targets? Unresolved issues and ideological clashes around marine protected areas. Aquatic Conservation: Marine and Freshwater Ecosystems, 13(4), 353-367. Allison, G.W., Lubchenco, J. & Carr, M. (1998) Marine reserves are necessary but not sufficient for marine conservation. Ecological Applications, 8(1), S79-S92. Caddy, J.F. (1999) Fisheries management in the twenty-first century: will new paradigms apply? Reviews in Fish Biology and Fisheries, 9, 1-43. Callicott, J.B. (1991) Conservation ethics and fishery management. Fisheries, 16(2), 22-28. Callicott, J.B., Crowder, L.B. & Mumford, K. (1999) Current normative concepts in conservation. Conservation Biology, 13(1), 22-35. CBD (2006) Decisions adopted by the Conference of the Parties to the Convention on Biological Diversity at its eighth meeting (Decision VIII/15, Annex IV). Convention on Biological Diversity, Curitiba, Brazil. Collet, S. (2006) Values at sea, value of the sea: mapping issues and divides. Social Science Information, 46(1), 35-66. Edwards, S. (in press) Ocean zoning, first possession and Coasean contracts. Marine Policy. Froese, R. & Pauly, D. (2007) FishBase, www.fishbase.org [accessed 25th July 2007]. Harms, J. & Sylvia, G. (2001) A comparison of conservation perspectives between scientists, managers, and industry in the West Coast groundfish fishery. Fisheries, 26(10), 6-15. Hastings, A. & Botsford, L.W. (2003) Comparing designs of marine reserves for fisheries and for biodiversity. Ecological Applications, 13(1), S65-S70. IUCN (1988) Resolution 17.38 of the 17th General Assembly of the IUCN. IUCN, Gland, Switzerland and Cambridge, UK. IUCN (1994) Guidelines for protected area management categories. IUCN & UNEP-WCMC, Gland, Switzerland and Cambridge, UK. IUCN (2003) WPC Recommendation 5.22: Building a global system of marine and coastal protected area networks. IUCN, Durban, South Africa.  59  Latham, G.P. & Locke, E.A. (1991) Self-regulation through goal setting. Organizational Behavior and Human Decision Processes, 50, 212-247. Mintzberg, H. (1992) The rise and fall of strategic planning: reconceiving roles for planning, plans, planners. The Free Press, New York, USA. Preikshot, D. & Pauly, D. (2005) Global fisheries and marine conservation: is coexistence possible? In Marine conservation biology: the science of maintaining the sea's biodiversity (eds E.A. Norse & L.B. Crowder), pp. 185-197. Island Press, Washington DC, USA. Sarkar, S. (2005) Biodiversity and environmental philosophy: an introduction. Cambridge University Press, Cambridge, UK. Scheff, T.J. (1967) Toward a sociological model of consensus. American Sociological Review, 32(1), 32-46. Sumaila, U.R., Guénette, S., Alder, J. & Chuenpagdee, R. (2000) Addressing ecosystem effects of fishing using marine protected areas. ICES Journal of Marine Science, 57(3), 752-760. Thompson, D. (ed.) (1990) The Concise Oxford Dictionary of Current English, 9th edition. Clarendon Press, Oxford, UK. United Nations (2002) Draft plan of implementation of the World Summit on Sustainable Development. United Nations, Johannesburg, South Africa. Wood, L.J. (2007) MPA Global: a database of the world's marine protected areas, Sea Around Us Project, UNEP-WCMC & WWF. www.mpaglobal.org [accessed 5th July 2007]. Wood, L.J., Fish, L., Laughren, J. & Pauly, D. (in press) Assessing progress towards global marine protection targets: shortfalls in information and action. Oryx.  60  4. Global marine protection targets: how S.M.A.R.T are they?1  1  A version of this chapter has been submitted for publication. Wood, L. J. Global marine protection targets: how S.M.A.R.T. are they?  61  Introduction Large-scale, typically percentage-based, conservation targets have been criticised for being ecologically irrelevant, particularly because they are rarely sufficient to ensure persistence of populations. They may also be political hindrances to further conservation efforts (Soulé and Sanjayan, 1998; Agardy et al., 2003; Pressey et al., 2003; Rodrigues et al., 2004). As a consequence, some authors have suggested that ‘policy-driven’ or ‘data-independent’ conservation targets should be abandoned (Solomon et al., 2003; Svancara et al., 2005; Wiersma and Nudds, 2006). Worse still, however, is that even these apparently meagre and inadequate targets have rarely been attained. Large-scale targets are often considered to be over-ambitious, unattainable, and are thus frequently discredited and ignored (Roberts, 2005). Indeed, existing global marine protection targets seem extremely unlikely to be met on time, instead reaching only a fraction of the target by the deadlines (Wood et al., in press). Thus, despite the many declarations, resolutions, conventions and protocols adopted since the United Nations Conference on the Human Environment in Stockholm, 1972, the overall state of the environment has continued to deteriorate (Elliott, 1998). Although large-scale conservation targets are widely considered to provide overarching frameworks for, and motivate, action at smaller scales (Pressey et al., 2003), it seems that this rarely happens, which would appear to be a more immediate (and serious) problem than the question of their ecological relevance.  Goal-setting is a fundamental component of human behaviour: goals are expressions of values and needs, and motivate and direct behaviour and performance (Taylor, 1976; Latham and Locke, 1991; Locke and Latham, 2002), at both the individual and group levels (Erez, 2005; Latham and Pinder, 2005). As such, goals and targets have been incorporated into all scales of planning, in a range of sectors, from formal business plans and local systematic conservation  62  planning efforts, to health services planning, to broader public policy and international regimes (Rondinelli, 1976; Mintzberg, 1992; Margules and Pressey, 2000; Broadhead, 2002; Pressey et al., 2003; Thomas, 2003; United Nations, 2006). In broad terms, goals are generalised statements about an ideal state to be attained, and establish the tone for the planning process. Targets refer to specific outputs to be reached in support of goal attainment (Thomas, 2003).  Given that targets are integral to planning processes, then, it may be more appropriate in the case of large scale conservation targets to attempt to better understand the reasons for their a) general lack of uptake and subsequently b) ecological (ir)relevance, than to abandon them completely. In order to be effective, it is suggested that targets must be operationalisable, measurable, amenable to evaluation, and time-bound with clear deadlines (Thomas, 2003). These principles have been embodied in the SMART concept (Specific, Measurable, Achievable, Realistic, and Time-bound). The exact origin of the SMART concept is not known, although it has been applied in fields as varied as policy planning (HM Treasury, 2003), healthcare (van Herten and Gunning-Schepers, 2000; Busse and Wismar, 2002), financial management (Kawohl et al., 2003), education (Muncey and McGinty, 1998), climate data management (Plummer et al., 2005), global plant conservation (CBD, 2002), as well as the Millennium Development Goals (Roberts, 2005). Recently, it has been gaining traction in the marine conservation literature, particularly in relation to MPA objective and management effectiveness assessments (Jones, 2000; Manghubai, 2001; Day et al., 2002). At a larger scale, the SMART concept has been incorporated into marine conservation and planning objectives, and assessments thereof, in, for example, Canada (Stark, 2004), the Irish Sea (Lumb et al., 2004), Europe (Rice et al., 2005), and South Australia (Department for Environment and Heritage, 2006), as well as in relation to the Convention on Biological Diversity indicators on  63  sustainable use of biodiversity (Tucker, 2005). However, to date, the SMART concept has not been explicitly applied to global MPA targets.  The objective of this study is to assess three global marine protection targets using the SMART framework, and use this assessment in combination with a critical review of the current literature, and experiences in monitoring progress towards them (Wood et al., in press) to attempt to better understand the challenges to their implementation. The targets to be assessed are: 1) the World Summit on Sustainable Development Plan of Implementation commitment to “the establishment of marine protected areas consistent with international law and based on scientific information, including representative networks by 2012” (United Nations, 2002b, Section IV, paragraph 32(c)); 2) Recommendation 5.22 made at the Vth World Parks Congress (2003) to “Establish by 2012 a global system of effectively managed, representative networks of marine and coastal protected areas….. these networks should be extensive and include strictly protected areas that amount to at least 20-30% of each habitat” (IUCN, 2003, p2); and 3) Decision VIII/15 adopted by the Conference of the Parties to the Convention on Biological Diversity at its eighth meeting, that “at least 10% of each of the world’s ecological regions [including marine and coastal be] effectively conserved [by 2010]” (CBD, 2006a, p237).  Global marine protection targets: are they Specific? Specific targets are clear and easy to understand, i.e., well defined, and are therefore more readily accepted by those implementing them (van Herten and Gunning-Schepers, 2000). Specific targets also help to direct behaviour towards a reduced number of potential outcomes, such that behaviour between actors is more consistent, and ‘effective performance’ is more evident and measurable (Latham and Locke, 1991).  64  All three targets appear to be quite specific: they require ‘MPAs’ to be created in ‘representative networks’ and be ‘effectively managed’. However, definitions of these concepts themselves are still widely debated, which has the potential to hinder their implementation. Some of the ambiguities surrounding the conceptual requirements imposed by the targets are summarized below.  What is an MPA? The definition of an MPA varies in both the literature and in practice with such attributes as the objectives of the MPA; the activities regulated both within and outside it; and the effectiveness with which those regulations are implemented (Jameson et al., 2002; Jones, 2002; Agardy et al., 2003). However, perhaps the most critical concept to define in the context of the global targets is what the minimum level of regulation of human activities is required for that ocean space to be considered ‘protected’ – i.e., what is an MPA? What differentiates an MPA from ocean space adjacent to it? This is largely a philosophical question, based on individual conceptualisations of protection, and as such, it will always be open to debate (Wood, submitted).  What should MPAs be representative of? Representativeness is a key component of systematic conservation planning, and refers to the inclusion of samples of all of biodiversity within protected areas (Pressey et al., 1993; Sarkar et al., 2006). However, biodiversity is itself very difficult to define (Sarkar, 2005). Incomplete knowledge of the spatial distributions of many aspects of biodiversity means that surrogates must be used in conservation planning exercises. Surrogates may operate at a range of spatial scales and consist of a range of biological or non-biological features. The choice of surrogate  65  used to approximate and measure representativeness thus depends on the scale of the analysis as well as the available data for the area of interest (Margules and Pressey, 2000; Sarkar et al., 2006).  What is a ‘network’ of MPAs? MPA networks may be conceptualised in a multiplicity of ways. In ecological terms, they consist of MPAs that are connected oceanographically by larval dispersal and juvenile or adult migration (Ballantine, 1995; Gaines et al., 2003; Lubchenco et al., 2003; Palumbi, 2003; Norse and Crowder, 2005). However, as MPA boundaries are open to external influences, the design of MPA networks may also need to consider: the impacts of the MPA on the spatial redistribution of fishing effort and benefits of fishing (Walters, 2000; Sumaila and Armstrong, 2006); resilience of the MPAs (or network) over time to external factors and longer term catastrophes (Jameson et al., 2002; Wagner et al., 2006); the objectives of the MPA or network (Halpern and Warner, 2003; Hastings and Botsford, 2003); as well as the socio-cultural context of the area and the level of stakeholder support for the MPA or network (Sumaila et al., 2000; Walmsley and White, 2003).  What is management effectiveness of MPAs? MPA management effectiveness, rather than simple presence and size, is a more a meaningful measure of the ‘actual’ contribution made by MPAs to biodiversity conservation and other objectives because of the many threats to MPAs, including inadequate regulation of human activities, non-compliance, and a range of external threats (Boersma and Parrish, 1999; Jameson et al., 2002). Management effectiveness consists of multiple components: design issues of protected areas and networks, appropriateness of management systems and processes,  66  and delivery of protected area objectives (Hockings et al., 2000). In addition, many methodologies for measuring MPA management effectiveness exist, ranging in application from global (Pomeroy et al., 2004; Staub and Hatziolos, 2004) to regional (Wells and Mangubhai, 2004), to national or sub-national (Pollnac et al., 2001), as well as MPA programme-specific, for example, World Heritage Sites (Hockings et al., 2004).  These brief summaries point to considerable complexity surrounding various concepts embodied in the global marine targets. Under conditions of ambiguity such as this, there is a clear need for these concepts to be defined operationally, i.e., in practical terms that enable them to be implemented, and progress towards the targets to be monitored (Wood, submitted). However, the World Summit on Sustainable Development (WSSD) target does not provide any definition (conceptual or operational) of MPA, network, or representativeness, and it is unstated whether the WSSD target requires MPA networks to be effectively managed. The World Parks Congress (WPC) target is slightly more specific than the WSSD target in that it specifies ‘marine habitats’ as the features to be represented in MPA networks. However, it provides no definitions for MPA, strictly protected MPA, network, habitat, or effective management. The Convention on Biological Diversity (CBD) target is the most specific of the three targets. It provides a detailed definition of MPA:  “ ‘Marine and Coastal Protected Area’ means any defined area within or adjacent to the marine environment, together with its overlying waters and associated flora, fauna, and historical and cultural features, which has been reserved by legislation or other effective means, including custom, with the effect that its marine and/or coastal biodiversity enjoys a higher level of protection than its surroundings” (CBD, 2003, p11).  67  Additional documentation expands the scope of this definition considerably: “Other measures, such as fisheries management areas, well-functioning integrated marine and coastal area management regimes (which effectively manage land-based sources of marine pollution), prohibition of destructive practices (such as bottom trawling) may also contribute to effective protection”  (CBD, 2005, p3). Critically, however, these definitions do not specify what the minimum level of regulation of human activities is required for an area to be considered protected, and as such they are not immediately operational. While the CBD target does also specify the scale and type of biodiversity features that the representation requirement pertains to, it does not define network or management effectiveness.  Global marine protection targets: are they Measurable? Measurable targets are easily appraised (van Herten and Gunning-Schepers, 2000), and thus allow feedback on progress to be provided in a timely manner. Progress that is reported publicly conveys the message that progress is valued, which in turn can increase motivation and commitment to achieving the targets, as well as increase public demand for further progress (Latham and Locke, 1991; Roberts, 2005). There are various aspects to measurability, which are discussed below, along with an assessment of the extent to which the three global marine targets meet them.  Targets must be quantitative In order for a target to be measurable, it must be quantitative in some way. However, the WSSD target provides no quantification of the global representative networks of MPAs that it commits to achieve, and as such it is impossible to assess whether and/or when the target will be achieved. The WPC target is partially measurable, in that it requires 20-30% of the world’s  68  oceans to be ‘strictly protected’. However, the target also requires these strictly protected areas to be embedded within global representative networks of MPAs, the size and extent of which is not specified. The CBD target is fully measurable in that the full scope of the target is subject to a numerical target of 10% of all marine ecological regions under national jurisdiction.  What information needs to be collected? Measurability is inherently related to the specificity of the targets, in that unambiguous, operational definitions of target requirements render measurement information needs explicit. However, as discussed, operational definitions are lacking for the conceptual requirements of all three targets.  Capacity to collect, store, and report necessary information In addition to knowing what information is needed to monitor progress towards the targets, that information must also be available. If it is not, then there must be the capacity to collect it. There must also be sufficient capacity to store and report the information in a timely manner. The World Database on Protected Areas (WDPA), maintained by the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) has been widely used for global protected area monitoring. However, this database has significant limitations for reporting on MPAs (CBD, 2003; Chape et al., 2005) and formal calls were made for better information on MPAs (CBD, 2004a). A global MPA database was developed from the WDPA, which was then expanded and updated, to enable more effective monitoring of the three global targets, to the extent that available information – and specificity of the targets – allowed. This represents an improvement in measurement capacity, but there remain  69  important information gaps which require additional capacity and resources to fill (Wood et al., in press).  Global marine protection targets: are they Achievable? An achievable target is one that is action-oriented, and where those implementing it have the necessary knowledge, information, skills, and resources to do so (van Herten and GunningSchepers, 2000). However, the actions specified by the targets are, in essence, simply to ‘designate MPAs’, with little guidance on how this should be done, what information it should be based on, who should do it, and with no explicit recognition that much of the information needed to meet the targets (however their requirements are operationally defined) is currently unavailable. In terms of skills and resources, inadequate capacity and insufficient financial and technical resources, are all already well-cited as a major contributor to the failure of existing MPAs in meeting their management objectives (Kelleher et al., 1995b; Alder, 1996; McClanahan, 1999). Achievement of the targets, which represents a 6-375 fold increase in the current global marine area protected (depending on the target & level of protection required), is thus likely to be heavily limited by this lack of resources.  Global marine protection targets: are they Realistic? Targets are realistic when the level of change required to meet them is itself attainable. They should be ambitious enough to require commitment, motivation and effort to reach them (targets that are too low may lapse into formalities), but not so high that they cause frustration and complacency (van Herten and Gunning-Schepers, 2000). The extent to which a target is realistic is thus also related to the timeframe available to reach it. In the case of the global MPA targets, the current rate of growth of the global MPA network is on the order of 5% per  70  year, and has not changed from that prior to adoption of the targets. At this rate, the targets will not be met for at least several decades (Figure 2.11), and it needs to increase by at least an order of magnitude in order for the targets to be met on time (Wood et al., in press).  Global marine protection targets: are they Time-bound? All three global marine protection targets have explicitly stated deadlines. The deadline for the WSSD and WPC targets is 2012. The CBD target deadline is 2010, except for MPAs in small island developing states, where the deadline is 2012 (CBD, 2006a).  Discussion Overall, the three global marine protection targets provide a certain degree of specificity, which represents explicit attempts to address, at least in part, some of the ecological concerns expressed about earlier, purely percentage-based targets. However, this specificity is largely superficial in that the concepts embodied in the targets are not operationally defined. This reduces their measurability, which in turn reduces their achievability, because the actions and behaviours needed to meet them, as well as who is responsible for implementing them, are not readily apparent. Given these factors, in combination with the very short timeframe over which the targets were agreed to be implemented, they do not appear to be realistic, especially since additional time is necessary to gather the information, resources and capacity required to implement them. Although economies of scale are likely to occur at some point (James et al., 1999; Balmford et al., 2004), the disparity between the current situation and that needed to meet the targets (Wood et al., in press), is so large that as yet, there is little, if any, scale to economise on. Therefore, the three global marine protection targets largely fail to meet the SMART criteria.  71  However, while many of the difficulties facing the achievement of the targets may appear to stem from the apparent lack of operational definitions for the target requirements, it is not at all evident that the global targets themselves should provide them, or in fact that this is the true root problem. The most comprehensive MPA network is physically and socially connected, hierarchical in structure, and functions at multiple, complementary scales through that hierarchy (Agardy, 2005). The development of such networks is ultimately the consequence of political processes, which, like ecological systems, operate on multiple scales (Agrawal and Ostrom, 2006). Accordingly, one might expect a vertically integrated system of policies, goals and targets towards a global MPA network, applicable at multiple scales (both spatial and temporal) ranging from global to local (Latham and Locke, 1991; Roberts, 2005). Global targets thus provide the overarching framework at the top of this hierarchy, providing a common context for more local efforts (Roberts, 2005). However, there has been little recognition of the need for a hierarchical system of targets, or, in particular, of the need to translate large-scale targets into intermediate-scale ones, although they are now beginning to emerge (the CBD target explicitly describes itself as an intermediate, policy-driven goal (CBD, 2005)). Nevertheless, most assessments of the ecological relevance of large-scale targets have applied them directly to very local or specific contexts, without any interpretation or modification for the scale of application (Solomon et al., 2003; Wiersma and Nudds, 2006). As such the questionable ecological relevance of large-scale targets may be (at least partially) due to a mismatch between the scale at which the targets were intended to operate, and the scale at which they were assessed.  72  Thus, while large-scale targets have been criticised for being primarily the products of political processes (Soulé and Sanjayan, 1998; Solomon et al., 2003; Svancara et al., 2005), this is, in fact, inevitable in membership organizations where decisions are reached by negotiated consensus (Roberts, 2005). Furthermore, the political process through which large-scale targets are formulated also represents a public and formal commitment to action. Indeed, commitment to and belief in a particular issue (i.e., political will) is a fundamental pre-requisite for behaviour directed toward resolving it (Latham and Yukl, 1975; Routhe et al., 2005). As such it may in fact be more appropriate that the primary function of large-scale targets be viewed as psychological, rather than ecological. Psychology is a key, yet often overlooked, component of biodiversity conservation, since human behaviour is a major contributor to both causing and slowing or preventing biodiversity loss (Saunders et al., 2006). However, even if one accepts that the primary function of large-scale targets is to motivate action, the question remains: why do they appear to be failing in this regard?  From a psychological perspective, the formulation of the target is critical to its attainment, and the SMART concept can provide useful guidance in this regard, in particular highlighting the importance of developing operational definitions of concepts to be implemented. However, a hierarchical framework of targets as described above implicitly requires that global targets actually be flexible – and thus not overly specific – in order to retain relevance to the full diversity of contexts at smaller scales. It also implies that operational definitions of target requirements need to be developed at multiple scales. Under this framework, the responsibility for developing operational definitions must fall, not to the global targets, but to the organizations responsible for implementing them at that scale of function. As such, the process to translate global targets into operational and measurable action is fundamentally dependent  73  on political will, which both influences and is influenced by perceived attainability of targets, and which is also a product of target formulation.  Under-ambitious targets may not solicit a change in behaviour, while over-ambitious targets may inhibit progress towards the target, especially where confidence in ability to meet it is low. Challenging targets may also hurt performance if: a) little or no strategy is provided, but the target is adopted at an early stage of learning; b) the task is heuristic; and c) there is pressure to perform well immediately (Latham and Locke, 1991). These conditions mirror the current context for the global MPA targets, in that: a) the current level of protection is near-zero, and the increase required is substantial; in addition, systematic planning of MPA networks is still a relatively nascent field that typically requires more data than are currently available, and has to date been used patchily, at relatively small scales, and mostly in an academic context (Leslie et al., 2003; Sarkar et al., 2006; Wood et al., in press); b) the process to create global networks of MPAs is politically and ecologically complex; and c) the target deadlines are imminent.  The formal adoption of the targets does demonstrate a certain level of political will to increase the level of marine protection globally, but it is also important to recognise that this political will resides largely with those already interested in marine resource management and biodiversity conservation, and not necessarily those with the decision-making power to invest the resources necessary for the targets to be met. Arguably the most successful example of large-scale target attainment is the reduction and phase out of chlorofluorocarbons (CFCs) via the Convention for the Protection of the Ozone Layer (1985) and the Protocol on Substances that Deplete the Ozone Layer (1987). However, much of this success has been attributed to the fact that DuPont, a chemical giant which at the time accounted for about 25% of global CFC  74  production, had already developed alternatives to the substances that were to be phased out through the Montreal Protocol. As such, industry resistance to the reduction targets was lower because economically viable (and profitable) alternatives were already available (Elliott, 1998; Broadhead, 2002).  While concerns expressed about the ecological relevance of the targets are thus indeed valid, it is also critical to consider the psychological and political context of the target-setting process. If the three global targets assessed here had been formulated so that they met all of the SMART criteria, in particular the achievability and realistic criteria, then their ecological relevance may well have been even more heavily questioned. By the same token, while the incorporation of ecological considerations of MPA design into the targets is an improvement to earlier, purely percentage-based targets, the gains made in ecological relevance may have come at a cost to the attainability of the targets. Nevertheless, the global targets can only be viewed as very ambitious when compared to the current level of protection, resources available to implement them, and the time over which they should be implemented. Over time the targets have also become more specific, the WSSD target being the most general and the CBD target being the most specific and action-oriented, as well as being adopted by nation states, rather than primarily conservationists. This indicates a consolidation of broader political support for the notion of a global network of MPAs. Indeed, formal commitments have been made by the Government of Fiji, Micronesian countries, Indonesia and Grenada to protect substantial proportions of their marine waters, as explicit responses to the global targets (WWF, 2005; CBD, 2006b). 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(submitted) Conceptual challenges to global monitoring of marine protected areas. Wood, L.J., Fish, L., Laughren, J. & Pauly, D. (in press) Assessing progress towards global marine protection targets: shortfalls in information and action. Oryx. WWF (2005) Weaving a tapestry of protection and sustainability. WWF, Suva, Fiji.  82  5. GIS-based multicriteria evaluation and fuzzy sets to identify priority sites for marine protection1  1  A version of this chapter has been published. Wood, L. J., and S. Dragicevic (2007). GIS-based multicriteria evaluation and fuzzy sets to identify priority sites for marine protection. Biodiversity and Conservation, 16(9), 2539-2558.  83  Introduction Marine protected areas (MPAs) are most commonly defined as:  “any area of intertidal or subtidal terrain, together with its overlying water and associated flora, fauna, historical and cultural features, which has been reserved by law or other effective means to protect part or all of the enclosed environment” (IUCN, 1988).  MPAs have the potential to contribute to a range of ecosystem goods and services, particularly the protection of marine biodiversity and the sustainable management of fisheries (Boersma and Parrish, 1999; Botsford et al., 2001; Hastings and Botsford, 2003; Lubchenco et al., 2003). While MPAs represent one of a suite of policies considered necessary to stop the current decline in fish catches, biomass, and biodiversity as a whole (Ward et al., 2001), it is one that resonates through the recent literature and the global conservation community. For example, the World Summit on Sustainable Development (WSSD) Plan of Implementation (2002) committed to establishing a global representative network of marine protected areas by 2012 (United Nations, 2002a). In accordance with this, Recommendation 5.22 of the Vth World Parks Congress (WPC) (2003) was made to ‘[g]reatly increase the marine and coastal area managed in marine protected areas by 2012 …. includ[ing] strictly protected areas that amount to at least 20-30% of each habitat’ (IUCN, 2003).  The decision-making process of siting new MPAs is a highly complex one that requires the consideration of multiple factors including cost, the time frame over which the MPAs will be created, and the ecological, geological, hydrological, socio-economic, political and cultural environment of the area. There is also a growing emphasis on the development of networks of MPAs (United Nations, 2002a), but the definition of network itself remains unclear. Roff (2005) states that a “network of MPAs should capture and be able to sustain the regional 84  elements of marine biodiversity”, and that the current global configuration of MPAs represents only a ‘set’ of MPAs and not a true network. However, the criteria for achieving a true MPA network are complex and a method to assess the extent to which a set of MPAs constitutes a network is yet to be developed. Consequently, the ability of existing MPA site selection tools to identify ‘true’ MPA networks has not yet been fully established, and hence it might be more appropriate to refer here to a global set of MPAs rather than a global network. The entire process is further constrained by the uncertainty conferred on the results of any site selection model by the general lack of data covering genetic, species, ecosystem and ecological processes in marine systems (Ward et al., 1999). Underlying all of these complexities of the MPA site selection problem is the lack of coordination and consistent frameworks for marine conservation at national, regional and international levels (Roff, 2005; Wood, submitted).  In light of the growing momentum behind the drive for a rapid and substantial increase in the extent of marine protection at the global scale, there is a clear and urgent need for the development of a theoretical framework to guide the realisation of this global MPA goal. In order to address these issues in an objective manner, this research develops an integrated decision support framework based on geographic information systems (GIS), multicriteria evaluation (MCE) and fuzzy sets to objectively identify priority locations for future marine protection. The framework was implemented in the Pacific Canadian Exclusive Economic Zone (EEZ) using two objectives that are largely considered to be in conflict - biodiversity conservation and fisheries profit-maximisation. The chapter begins with an overview of marine protected areas and resource management, then describes the integrated decision support framework developed, implements the framework in a Western Canada case study, and then discusses its significance.  85  Global marine resource conservation and extraction Currently around 0.6% of the world’s marine habitats are subject to some level of protection, and only 0.08% are subject to strict ‘no-take’ protection (Wood et al., in press). This has often been related to the generally ad hoc basis on which many MPAs have been designated. In the designation process, there is rarely a systematic and comprehensive assessment or consideration of the conflicts that may arise from partially or completely closing an area that previously had been open to resource use (Jones, 2002; Roberts et al., 2003b). The development of fishing technologies over the last 50 years has made fishing essentially ubiquitous, such that previously inaccessible areas that once acted as natural refuges from fishing have now been eliminated (Agardy et al., 2003). MPAs essentially represent the recreation of these refuges, but with legal, customary, and voluntary access constraints rather than physical ones. Given that fisheries management has been conducted for the last 400 years considering fish as open access resources (Russ and Zeller, 2003), and maximising short term profits (Sumaila and Walters, 2005), fishing can reasonably be assumed to be a prolific source of conflict when imposing constraints on access to resource extraction through the designation of new MPAs. Nevertheless, the momentum to implement MPAs to assist fisheries management is also growing (Bohnsack, 1996; Ward et al., 2001; Lubchenco et al., 2003). Global fish catches, previously considered to be increasing, were recently shown to have been in decline since the 1980’s (Watson and Pauly, 2001). Biomass of high trophic level fishes has been shown to have declined by two-thirds since 1950 in the North Atlantic (Christensen et al., 2003), and of predatory fishes by 90% since industrial fisheries began globally (Myers and Worm, 2003). Fishing has been heavily implicated in these declines, and there are now 416 species from the marine biome listed as Vulnerable, Endangered, or Critically Endangered on the  86  IUCN Red List of Threatened Species (IUCN, 2007a). In 2002, for the first time, marine fish species were listed on Appendix II of the Convention for International Trade in Endangered Species (CITES, 2004). This creates a complex situation since one of the main drivers for increasing marine protection may also be a major challenge to realising it. While it has been suggested that the fishing sector’s attitudes towards MPAs may be changing (Agardy et al., 2003) and that there is a great deal of overlap between conservation goals and human (resource extraction) needs (Roberts et al., 2003b), smaller scale studies showing improved compliance must be placed within the context of developing a global network of MPAs affording strict protection to 20-30% of marine habitats. This represents a 31 to 375 fold increase in protection from the status quo, depending on the target and whether any level of protection, or only notake MPAs, are considered, respectively (Wood et al., in press). The ramifications of this scale of increase for current fishing practises can only be assumed to be substantial: conflicts are inevitable. The literature on contributing factors to MPA management success and failure emphasise the need to address resource use conflicts explicitly (e.g., Jones (2002)). Such conflicts should therefore be addressed explicitly in any analysis that seeks to identify priority marine areas for future protection.  MPA site selection tools At large scales, models provide a means to integrate diverse data and consider them simultaneously. Many techniques have been developed over the past 25 years to assist in the design of terrestrial protected area ‘sets’ (Kirkpatrick et al., 1983; Pressey et al., 1993; Pressey et al., 1996; Pressey et al., 1997; Possingham et al., 2000; McDonnell, 2002). More recently, attention has been focused to applying this to the marine biome. For example, MARXAN uses simulated annealing to design a ‘set’ of MPAs for a given region that is expected to be  87  optimally efficient (Ball, 2004). Efficiency is assumed to reflect minimal costs of implementation and is derived from the boundary length to surface area ratio. Efficiency is assumed to increase as this ratio decreases (Possingham et al., 2000; Ball, 2004). All of these site selection techniques, including MARXAN, have to date been only used at relatively local scales – mostly ranging from single site to a region within a country, and they have also generally focused on primarily achieving biodiversity conservation objectives, for example, Ardron et al (2002) and Lewis et al (2003). However, given the nature of the MPA site selection problem, it would be preferable to develop an approach that a) can function from national to international scales, and b) addresses multiple objectives simultaneously and explicitly. It was recently suggested that a comprehensive efficient MPA set design approach, such as MARXAN, may only produce optimal results when the entire MPA set is implemented immediately (Meir et al., 2004). There is also the suggestion that using a more simple suite of decision rules may result in an MPA set that approximates optimality more closely when the MPA set is being implemented over an extended period of time (Meir et al., 2004). This could indeed be expected to be the scenario for the implementation of a global set of MPAs.  MCE and fuzzy sets for identification of priority sites for marine protection Multicriteria evaluation (MCE), or multicriteria decision analysis (MCDA) is defined as the evaluation of a set of alternatives based on multiple criteria where the criteria are quantifiable indicators of the extent to which decision objectives are realized (Malczewski, 1999). Spatially explicit MCE requires data on the spatial distribution of criterion values. In MCE there is a one-to-one relationship between objective and criterion. Multi-objective evaluation is essentially a hierarchical extension of MCE, having a one-to-many relationship between objective and criteria. The most general objectives are at the top of the hierarchy and the most  88  specific criteria at the lowest level (Keeney and Raiffa, 1976; Pitz and McKillip, 1984; Malczewski, 1999). In this chapter, the term MCE will be used to refer to both multicriteria and multiobjective evaluation. In MCE, criterion map layers and decision-maker preferences are aggregated according to a decision rule that yields an optimal solution (Malczewski, 1999). When objectives are in conflict, an ‘optimal compromise’ solution is found (Eastman et al., 1993; 1995).  MCE was developed as a spatial decision support tool for land use planning when it was realized that spatial suitability analyses alone were fundamentally flawed due to their lack of consideration of decision-makers’ preferences. It facilitates the integration of social, political, environmental and economic requirements with suitability analyses (Jankowski and Richard, 1994). Its integration with geographic information systems (GIS) has further enhanced this capability (Carver, 1991; Eastman et al., 1995), and it has since been described as ‘perhaps the most fundamental of decision support operations in geographical information systems’ (Jiang and Eastman, 2000). MCE is noted for its capacity to ascribe varying importance to different criteria, according to stakeholder preferences (Ceballos-Silva and Lopez-Blanco, 2003), as well as its simplicity and its capacity to handle many different types of criteria (Jankowski and Richard, 1994). It also allows for decision-making under varying levels of uncertainty, from deterministic decisions (low uncertainty) to fuzzy decisions (high uncertainty attributable to the inherent imprecision of information used in decision-making) (Malczewski, 1999). The use of fuzzy set theory when developing criterion layers is considered to allow more flexible MCE operations, and explicitly take into account the continuity and uncertainty in the relation between the criteria and the decision set (Jiang and Eastman, 2000). For example, standardising criterion layers to fuzzy measures means that the criterion value for each cell is  89  standardised to a measure of the possibility of belonging to the set along a continuous scale from 0-1 (real number scale) or 0-255 (byte scale) (Eastman, 2003). This is a more realistic standardisation approach than a binary set membership rule as is used in Boolean analyses, especially when there is uncertainty inherent in the input data. Finally, when used with GIS, MCE also enables the outcomes to be visualized as maps. As a consequence of these various advantages to MCE, it has been used extensively in the resolution of terrestrial resource allocation problems, in fields as varied as: industrial development (Eastman et al., 1995); agricultural development (Janssen and Rietveld, 1990; Ceballos-Silva and Lopez-Blanco, 2003); route selection (Jankowski and Richard, 1994); risk analysis (Chen et al., 2003); habitat suitability modelling (Store and Kangas, 2001); environmental impact assessment (Janssen, 2001); forestry (Huth et al., 2004) and waste management alternatives (Carver, 1991; Chung and Poon, 1996).  The advantages of MCE described above indicate that it has high potential applicability to marine resource decision problems. The optimal compromise solutions derived from MCE are of particular relevance to a global set of MPAs being implemented over time within the context of multiple, potentially conflicting resource use objectives. However, in contrast to the widespread application of MCE to terrestrial decision making, MCE has rarely been used in spatial decision-making for marine natural resource management, including fisheries (Mardle and Pascoe, 1999), and even less so for MPAs. Through an extensive literature search, I identified only three studies applying MCE to MPAs and they were all applied at a local level. Brown et al (2001) used the decision support aspects of MCE as a means to facilitate stakeholder involvement in a trade off analysis in a Caribbean MPA, but did not make use of the integration of MCE with GIS. Killpack et al (2001) and Villa et al (2002) used MCE to  90  develop a zoning plan for a single MPA in the USA and Italy respectively. In the latter two examples, MCE was integrated with GIS to produce spatially explicit results in the form of maps. The work presented in this chapter differs from previous marine MCE analyses in four ways. First, it focuses on large scales. The feasibility of applying the MCE approach in ocean basin and global scale models of new MPA location is assessed for the Pacific Canadian Exclusive Economic Zone (EEZ). Second, MCE is applied in the context of identifying priority areas for future protection and to guide smaller scale analyses in the context of MPA networks, rather than fine-tuning the management of existing MPAs. Third, it uses fuzzy decisionmaking within the MCE to address the uncertainty associated with the coarse scale marine data and the global MPA ‘set’ design decision-making process. Further, it differs from many MARXAN applications in that it addresses both biodiversity conservation and fisheries management objectives explicitly to produce optimized trade off results.  Methods Study site Canada declared its EEZ under the 1996 Oceans Act (Oceans Act, 1996), and in accordance with the United Nations Convention on the Law of the Sea, which Canada ratified in 2003 (UNCLOS, 2004). The EEZ extends to 200 nautical miles from the coastline and on the Pacific Canadian coast the EEZ covers an area of approximately 458,000km2 adjacent to the province of British Columbia (Figure 5.1). The British Columbia provincial government mandated a Protected Areas Strategy to protect a minimum of 12% of the province, including its waters, by the year 2000 (British Columbia, 1993), cited in (Zacharias and Howes, 1998). However, five years after the deadline, this target still remains to be met; a recent assessment indicated that only 0.2% of the EEZ is protected, and only 0.0007% of the EEZ is protected by no-take  91  MPAs (Ardron et al., 2002). Furthermore, most MPAs in the Pacific Canadian EEZ have been, to date, designated on a relatively ad hoc basis, associated with human recreation values rather than ecosystem or species conservation per se (Jamieson and Levings, 2001). While there are efforts underway to identify potential networks of MPAs in regions of the Pacific Canadian EEZ (Ardron et al., 2002; Rumsey et al., 2004), none has addressed this issue at the scale of the entire EEZ. This situation is analogous to other locations around the world and is representative of the lack of a framework observed for marine conservation globally. The efforts of the various agencies in Canada that have some mandate for marine conservation (provincial and federal) are neither nationally nor regionally coordinated (Roff, 2005).  Data sources and software The Sea Around Us Project (SAUP), based at the Fisheries Centre, University of British Columbia, has spatially distributed global data on marine fisheries catches, fishing access agreements and a wide variety of marine fisheries and biodiversity-related data in a 0.5° latitude and longitude grid of spatial cells (Watson et al., 2005). Of the 259,200 cells that cover the world, more than 180,000 contain some marine area (Watson et al., 2004). The Pacific Canadian EEZ consists of 295 cells. As these cells are defined by decimal degrees, they vary in size, decreasing in surface area from low to high latitudes. The effect of this on spatial analyses is mitigated by pro-rating cell values according to the surface area of water contained within them. While 0.5° resolution might be considered coarse in analogous analyses in terrestrial ecosystems, and the preconception that oceans are homogenous and resilient has been refuted in recent times (e.g., Agardy (1994)), the high connectivity of the oceans can generally be said to aggregate the scale at which marine processes occur (Jones, 2002). A resolution of 0.5° is a compromise that seeks to address the complexity of the oceans while allowing for large scale  92  analyses. Furthermore, from a practical perspective, obtaining comprehensive data over such large areas at higher resolutions is extremely difficult. The Sea Around Us Project database, with its extensive spatially explicit marine data at relatively fine resolutions was used for this analysis. Data processing was performed in the ArcGISTM and IDRISI Version 14.0 (Kilimanjaro) GIS software systems.  MCE procedure The decision problem was formulated based on the guidelines suggested by Malczewski (1999). Figure 5.2 shows a general criteria structure that can be used to identify priority sites for MPAs. Due to the problem complexity, and that the goal of this research is to explore the utility of MCE to identify candidate areas for protection, the focus will be on a subset of realistic criteria available for each objective. Each objective was evaluated separately, before being assessed as a multiobjective evaluation during the analysis procedure.  The validity of the MCE outputs was maintained by standardising all criterion input layers such that their scales of measurement were commensurate. Linear scale transformation and fuzzy set membership functions are two available standardisation methods (Malczewski, 1999). The criterion layers in this study were based on continuous data with different units of measurement. They all possessed a level of uncertainty as is inherent in coarse scale marine data. Hence, standardisation of the criterion layers as fuzzy measures was considered to be the most appropriate standardisation technique. The fuzzy set membership function used was a monotonically increasing sigmoid membership model (Figure 5.3).  93  Once the criterion layers have been standardised, the user assigns weights to them. These weights enable the solution to reflect the importance (as perceived by the user) of the input criteria relative to each other. Similarly, different objectives (which themselves are comprised of multiple, individually weighted criteria) can also be weighted relative to each other. Various weighting schemes both within and between objectives were investigated and their details are presented below. Once weights have been specified, the criteria (or objectives) are combined according to a decision rule. The simple additive weighting method, also known as weighted linear combination, is the most common type of decision rule used in GIS-based decisionmaking (Malczewski, 1999). This type of rule is implemented by multiplying each criterion layer by its weight and then summing the results (Eastman, 2003) according to the following equation (Malczewski, 1999):  Ai = ∑ j w j xij where Ai is the final suitability score in each pixel, xij is the score of the ith pixel with respect to the jth criterion, and weight wi is a normalised weight so that Σwi = 1. The final result, Ai, is a layer of suitability scores of each pixel to fulfilling the objective under assessment. In this study, 30% of the most suitable cells were selected from the results of the procedure to reflect current directions in the international conservation community towards a global network of MPAs protecting up to 30% of the world’s oceans (IUCN, 2003), and to indicate how such a level of protection might be represented spatially on a map.  It is important to recognise that quantitative modelling with GIS, such as MCE, produces results in the form of maps that provide no indication of the level of error associated with those results. However, the robustness of such models is inherently dependent upon the quality of the spatial data used, the quality of the model, and the way the data and the model interact 94  (Burrough and McDonnell, 1998). Sensitivity analysis is considered to be a valid robustness assessment method, and is defined as “a procedure for determining how the recommended course of action is affected by changes in the inputs of the analysis” (Malczewski, 1999). Sensitivity analysis was performed at various stages of the analysis by varying the input parameters and changing the weighting regimes used within and between objectives.  MCE procedure applied to Objective 1: Biodiversity conservation Biodiversity conservationists, in the process of identifying new areas to protect, might generally be considered to seek to: (i) fully represent biodiversity, and (ii) ensure persistence of biodiversity (Soulé and Terborgh, 1999). This implies the consideration of all species, habitats and processes in the ecosystem(s) under study. A conceptualisation of this is shown in Figure 5.2. The sub-objectives of species representation and persistence were selected to represent the objective of biodiversity conservation, and the criterion of species distribution selected to represent these sub-objectives. The Sea Around Us Project database contains distribution data for all species that have been reported as having been caught in commercial fishing operations, globally. This includes targeted and by-caught species, and all marine mammals, thereby including species of both some and no economic value. Distributions for all species occurring in the Pacific Canadian EEZ, totalling 178, were obtained, as a binary layer of presence/absence by cell. These distributions were used to develop species richness criterion layers. Suitability to protection was assumed to increase with cell species richness. Two scenarios were investigated using these data.  95  Scenario 1A: All species are equally important to protect As all species were weighted equally, a total species richness layer was sufficient to represent this scenario (Table 5.1). This layer was developed by running an iterative model to union all species distributions. The layer was then fuzzy standardised according to the monotonically increasing sigmoid membership function illustrated in Figure 5.3, and the top 30% most suitable cells were selected as candidate priority sites for marine protection.  Scenario 1B: some species are more (or less) important to protect than others. The World Conservation Union (IUCN) Red List of Endangered Species (IUCN, 2007a) classifies species according to their risk of extinction into the following categories, listed in descending order of risk of extinction: Critically Endangered; Endangered; Vulnerable; Near Threatened; Lower Risk; Data Deficient; Not Evaluated. Twenty nine species occurring in the  Pacific Canadian EEZ are listed in one of the six categories from Critically Endangered to Data Deficient, although none was classified as Least Concern, so this category was excluded from the  analysis. The rest were categorised as Not Evaluated. Species richness per IUCN Red List category was evaluated using the same iterative union model described in scenario 1A, resulting in 6 input factors to the MCE. Each factor was then weighted according to the relative importance of each to the overall objective of biodiversity conservation. In general it may be reasonable to weight species more heavily as their vulnerability to extinction increases, but in reality the configuration of the weighting scheme can vary substantially, and the outputs of the MCE can be heavily influenced by this. A sensitivity analysis of the robustness of results to changes in criterion weights was performed by investigating the results generated by various user-defined weighting schemes; two of them are presented here. The first weighting scheme was linear, thus, the weight increased linearly with the risk of extinction, as defined by the  96  IUCN Red List categories. The second scheme sought to ascribe higher weights to species at higher risk of extinction, and lower weights to those at lower risk, than linear weighting allowed for. This was achieved by predicting weights according to a logistic growth function, similar to the curve illustrated in Figure 5.3. Actual weights used for all scenarios are shown in Table 5.1.  The two categories, Data Deficient and Not Evaluated, were weighted equally, because in both cases a categorisation of the risk of extinction was unavailable, either due to lack of data or lack of resources. A further sensitivity analysis of the results was conducted by removing one IUCN Red List group at a time and re-running the MCE.  MCE procedure applied to Objective 2: Fisheries management Fisheries have largely functioned to date under the concept of short-term profit maximisation (Sumaila and Walters, 2005) and the assumption of open access (Russ and Zeller, 2003). The identification of new locations for MPAs by fishing industry stakeholders can therefore be expected to be heavily influenced by the primary goal of minimising the costs experienced by fishers and fishing companies through loss of fishing grounds to increased area under protection. This primary goal is conceived here as the identification of locations of greatest value to the fishery, whose suitability to resource extraction is greatest and restriction of access to which, through the creation of MPAs, is least desirable. The cost minimisation sub-objective (Figure 5.2) was selected to represent the overall objective of identifying priority sites for marine protection for fisheries management.  97  For such an economic analysis, it was assumed that only species of commercial value should be considered, rather than all species, as for the biodiversity conservation objective. Catch data, by species and by cell, were obtained for the year 2000 from the SAUP database, 90 species in total. Catch data from 2000 were used to provide a current indication of the relative importance of areas to fisheries, and from one year only to reflect the generally short-term, profitmaximising approach to fisheries. In essence, the economic value of the species represents a weighting for that species. Ex-vessel landed price data for 2000 (DFO, 2004) was used to calculate catch value per cell for each species. Only fifteen species were found to constitute 80% of the total catch value, and it was assumed that using the criterion layers for these fifteen species would sufficiently represent the total catch value. The catch value distributions were then combined using the iterative union model used in previous analyses, and standardised as fuzzy measures using the same membership function as described above, to produce a total catch value distribution layer. The 30% most valuable cells were selected from this layer as being the most suitable for continued access to resource extraction.  Multi-objective space allocation analysis A multi-objective space allocation analysis was completed using the result from Scenario 1B.ii for the biodiversity conservation objective with the result for the fisheries management objective as inputs. The required output was set such that 30% of the EEZ would be allocated to the biodiversity conservation objective (i.e., it would be allocated as highly suitable for future protection). The remaining 70% of the EEZ would be allocated to fishing, and as such considered as more suitable for continued fishing than for protection. A sensitivity analysis of these results was performed by weighting the two objectives differently; this process also served as an exploration of how different stakeholders’ perceptions and needs might affect the  98  results of such an analysis, and to have the effects represented spatially. The weights used are summarised in Table 5.2.  Results Objective 1: Biodiversity Conservation The results of the MCE for the three scenarios for the biodiversity conservation objective are shown in Figure 5.4. The results are consistent, showing mainly inshore areas as the most suitable for protection based on species richness criteria. There are some subtle differences between these three results. Firstly, in comparison to Scenario 1A (Figure 5.4A), both weighting regimes of Scenario 1B (Figures 5.4B,C) result in a slight increase in the number of offshore pixels selected. These offshore pixels also have a higher suitability score than the few offshore pixels selected in Scenario 1A. This is most likely because many of the species listed in the ‘more’ endangered IUCN Red List categories have distributions that extend offshore, and so this region is prioritised more strongly in the MCE when IUCN Red Listed species are weighted according to their risk of extinction. However, these species’ distributions generally covered both inshore and offshore regions; hence the relatively slight increase in suitability offshore. Secondly, the two weighting regimes yield slightly different results, particularly in the distribution of suitability scores. The suitability scores resulting from the linear weighting regime (Figure 5.4B) are very similar to those of Scenario 1A (Figure 5.4A). This is most likely because the weights are not widely spread in this scenario (Table 5.1) – the heaviest weight is only around 3 times that of the lightest weight. The logistic weighting regime for Scenario 1B.ii generated an inshore region of uniformly high suitability score (Figure 5.4C), which in the previous two analyses was more heterogeneous, although still highly suitable. It is possible that this was caused by the high weight given to the Critically Endangered category,  99  which in this analysis contained only one species (Bocaccio rockfish, Sebastes paucispinus), and whose distribution overlaps almost perfectly with this inshore area of high suitability. However, the sensitivity analysis that excluded the Critically Endangered IUCN Red List category yielded a similar result (Figure 5.4D). In fact, this area remained of uniformly high suitability for protection in every sensitivity analysis of this weighting scheme, indicating that the results for this assessment are quite robust.  Objective 2: Fisheries Management The standardised catch value distribution is shown in Figure 5.5A, and the 30% most valuable cells are shown in Figure 5.5B. There are some areas in the Canadian EEZ that are of no commercial value to fisheries (blank cells within the EEZ), because nothing was caught there in 2000. Therefore, no immediate losses would be incurred by fisheries in the event of these areas becoming protected. As such, these cells currently represent areas of zero conflict for protection. However, very few of these cells, if any, overlap with the areas indicated as highly suitable for protection by any of the biodiversity conservation scenarios (Figures 5.3A-C). In fact, areas of the highest value to the fishery overlap substantially with the areas indicated as highly suitable for protection according to biodiversity conservation objectives, indicating a classic conflicting multiobjective decision problem.  Multiobjective space allocation analysis The multiobjective analysis was implemented using the result Scenario 1B.ii (Figure 5.4C) for the biodiversity conservation objective and the result for the fisheries management objective (Figure 5.5A) as inputs. The results of using the three different weighting schemes outlined in Table 5.2 are illustrated in Figure 5.6. The results in Figure 5.6 show absolute allocations of  100  each cell to one objective or the other, without an indication of suitability. Figure 5.6A shows the result of weighting biodiversity conservation at 0.3 and fisheries management at 0.7. The weighting of fisheries management is so heavy that very little of the area considered highly suitable for biodiversity conservation is allocated to that objective (Figure 5.4C). Figure 5.6B shows the results of weighting fisheries management and biodiversity objectives equally. Quite a large area that is highly suitable to biodiversity conservation and valuable to fisheries is selected for biodiversity conservation, and vice versa (compare Figure 5.4C with Figure 5.5). The weighting regime also imposes the selection of some areas for biodiversity conservation which are of no value to fisheries management i.e. zero conflict areas (Figure 5.5A), even though they are of lower suitability for biodiversity conservation. Figure 5.6C shows the results of weighting biodiversity conservation at 0.7 and fisheries management at 0.3. This solution is very similar to the biodiversity conservation solution shown in Figure 5.4C.  Discussion The focus of this study was not to develop policy recommendations, but to assess the utility of MCE to spatially identify marine locations for future protection using a reduced set of criteria. The results should therefore be interpreted in terms of how the MCE process functioned, the questions it raised, and how the MCE results can be investigated for their robustness.  The analysis presented here indicates that MCE does not offer prescriptive solutions to a given resource allocation problem, but instead offers a range of scenarios that address different decision makers’ preferences to varying extents. This is generally considered to be preferable as it enables decision-makers to explore different solutions (Possingham et al., 2000) or use it as an integral part of a spatial decision process (Balram et al., 2003). Furthermore, the  101  visualisation of these scenarios as maps can encourage stakeholder discussions. The importance of considering multiple objectives has been made evident, even when using a restricted data set, as was the case here. The high level of overlap between optimal outcomes for biodiversity conservation and fisheries management objectives illustrates that decisionmaking might easily cause conflicts with different resource users if the process fails to explicitly consider different objectives. The results also show that even with a limited amount of weighting of factors, MCE can explicitly incorporate different priorities, as expressed by decision-makers or stakeholders, and the effects of changing these priorities on the ability of other stakeholders to meet their own objectives are readily visible as a map. Presentation of a spatial resource allocation problem using MCE with GIS also enabled areas of zero conflict to be identified. When spatial resource allocation objectives are in conflict to the extent that biodiversity conservation and resource extraction appear to be in the Pacific Canadian EEZ, areas of no conflict are hard to envisage. This methodological framework has identified such areas and enabled their locations to be visualized. While it may seem most conciliatory to decision-makers to select areas of zero conflict for protection, the suitability scores enable different areas to be compared using a standardised, semi-quantitative scale, and as such inform the decision-maker as to the contribution of the area of zero conflict to both objectives. This improved interpretive ability is conferred to the decision maker by the use of fuzzy standardisation of criteria which would not be possible with the binary outcomes from Boolean analyses of ‘suitable’ or ‘unsuitable’ areas. Fuzzy standardisation of criteria also takes into account some of the uncertainty associated with the input data. Finally, the sensitivity analysis performed provided insights into the potential causes behind a particular outcome, as well as an indication of the robustness of the results of the MCE. The application of a sensitivity analysis  102  by changing the weighting regime used also shows that the results can differ depending on the views of the stakeholders in the decision process.  This chapter has shown that MCE has high transferability to the decision-making problem of developing a large scale theoretical framework that can guide the implementation of smaller scale MPA network design initiatives within the context of a global set of MPAs. One challenge that MCE faces with respect to its applicability to MPA site selection is how to model a true ‘network’, as defined by Roff (2005), rather than simply a set. However, this is a challenge that currently faces all site selection tools, particularly at very large scales. Similarly, while the success of this framework is heavily contingent on the appropriate selection of criteria and the use of reliable data and models by which to represent them, this is also a challenge common to all site selection models.  The MCE framework and implementation has shown substantial potential to support MPA planning and management. However, there are some aspects of the analysis that require further attention, and these provide the basis for future work. Firstly, the framework could be expanded to incorporate as many of the criteria and sub-objectives illustrated in the hierarchical structure of the problem (Figure 5.2) as available data permit. This is critical to obtaining results that are relevant to the scale of study and also reliable for decision-making. For example, the criterion for biodiversity conservation used here was species richness, which is considered to be a useful, but incomplete, measure of biodiversity (Gaston, 1996), cited in (Ward et al., 1999). However, there are various measures of diversity, which function at different geographical scales, and it would be preferable to better represent these in this framework. Community species richness is largely considered to be a measure of alpha  103  diversity (within-community scale), whereas information on variation in habitat types and its use by species can confer information about beta diversity (between-community scale) (Whittaker, 1972). There is an array of literature demonstrating the utility of additional information such as habitat type (e.g., Ward, et al. (1999) and Worm, et al (2003)), level of threat to habitats (e.g., Roberts, et al (2002), as well as biophysical parameters and processes such as latitude and productivity (Worm et al., 2003) to the identification of priority areas for protection of varying levels and scales of biodiversity. The incorporation of additional data sets including those suggested in Figure 5.2 can reasonably be expected to improve the ability of the analysis to capture diversity over multiple scales. Secondly, this framework could be developed to further extend the spatial scale of the model, specifically to the ocean basin or global scale. Thirdly, the results obtained from this framework could be compared to those obtained from using other site selection approaches, particularly complementarity-based place prioritization algorithms, such as MARXAN (Ball and Possingham, 2000) and ResNet (Garson  et al., 2002).  104  Tables Table 5.1 showing hypothetical criterion weighting schemes, which were applied in this MCE for the Biodiversity Conservation objective. IUCN Red List Category Equal Weighting Linear Weighting Logistic Weighting (Scenario 1A) (Scenario 1B.i) (Scenario 1B.ii) Critically Endangered 0.167 0.261 0.321 Endangered 0.167 0.217 0.309 Vulnerable 0.167 0.174 0.242 Lower Risk 0.167 0.130 0.093 Data Deficient 0.167 0.087 0.017 Not Evaluated 0.167 0.087 0.017  Table 5.2 Table showing hypothetical multiobjective objective weighting schemes, which were used in the multiobjective space allocation analysis. Objective Scenario A Scenario B Scenario C Biodiversity Conservation 0.3 0.5 0.7 Fisheries Management 0.7 0.5 0.3  105  Figures  Figure 5.1 Map showing the approximate location of study area, in the heavy box: the Pacific Canadian Exclusive Economic Zone (EEZ)  106  107  Figure 5.2 Hypothetical formulation of the decision problem of identifying priority sites for marine protection using the hierarchy defined by Malczewski (1999). The criteria listed here do not represent an exhaustive list.  Figure 5.3 Monotonically increasing, sigmoidal fuzzy membership function used to standardize MCE criteria layers. a, b c, and d represent inflection points where the membership function rises above zero, approaches one, falls below one and approaches zero again, respectively. A monotonically increasing function rises to one and never falls again. Hence b, c, and d all have the same criterion value of 1.  108  Figure 5.4 Outputs of MCE for Objective 1 – Biodiversity Conservation. Maps show the 30% most suitable cells for protection from within the Pacific Canadian EEZ identified by the following scenarios: A) Equal weights for all species; B) Linear weighting regime for IUCN Red Listed species; C) Logistic weighting regime for IUCN Red Listed species; D) Logistic weighting regime for IUCN Red Listed species with Critically Endangered category removed.  109  Figure 5.5 Outputs of MCE for Objective 2 – Fisheries Management. Map A shows the catch value distribution across the entire Pacific Canadian EEZ. Map B shows the 30% most suitable cells for continued resource extraction from with the Pacific Canadian EEZ, as identified by catch value distribution.  110  Figure 5.6 Outputs of Multiobjective space allocation analysis, with biodiversity conservation and fisheries management as the two, conflicting objectives. Maps show the 30% most suitable cells for protection from within the Pacific Canadian EEZ, as identified by the following objective weighting schemes, listed biodiversity conservation : fisheries management: A = 0.3 : 0.7; B = 0.5 : 0.5; C = 0.7 : 0.3.  111  References Agardy, T. (1994) Advances in marine conservation: the role of marine protected areas. Trends in Ecology and Evolution, 9(7), 267-270. Agardy, T., Bridgewater, P., Crosby, M.P., Day, J., Dayton, P.K., Kenchington, R., Laffoley, D., McConney, P., Murray, P.A., Parks, J.E. & Peau, L. (2003) Dangerous targets? Unresolved issues and ideological clashes around marine protected areas. Aquatic Conservation: Marine and Freshwater Ecosystems, 13(4), 353-367. Ardron, J., Lash, J. & Haggarty, D. (2002) Modelling a network of marine protected areas for the central coast of British Columbia. Version 3.1. Living Oceans Society, Sointula, Canada. Ball, I. 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(1972) Evolution and measurement of species diversity. Taxon, 21(2/3), 213251. Wood, L.J. (submitted) Global marine protection targets: how S.M.A.R.T. are they? Wood, L.J., Fish, L., Laughren, J. & Pauly, D. (in press) Assessing progress towards global marine protection targets: shortfalls in information and action. Oryx.  116  Worm, B., Lotze, H.K. & Myers, R.A. (2003) Predator diversity hotspots in the blue ocean. Proceedings of the National Academy of Sciences, 100, 9884-9888. Zacharias, M.A. & Howes, D.E. (1998) An analysis of marine protected areas in British Columbia, Canada, using a marine ecological classification. Natural Areas Journal, 18(1), 413.  117  6.  Priority areas for a global network of marine protected areas 1  1  A version of this chapter will be submitted to a journal. Wood, L. J., Fuller, T., Cheung, W., Close, C., Kaschner, K., Watson, R., and Pauly, D. Priority areas for a global network of marine protected areas.  118  Introduction Much of current activity in marine conservation is framed within the context of three global targets, adopted in the last five years. Firstly, the Johannesburg Plan of Implementation, adopted at the World Summit on Sustainable Development in 2002, committed to developing global representative networks of marine protected areas by 2012 (United Nations, 2002a). Secondly, the World Parks Congress adopted a recommendation in 2003 to strictly protect 2030% of each marine habitat by 2012 (IUCN, 2003). Finally, in 2006, the Convention on Biological Diversity made a decision to effectively conserve 10% of the world’s ecological (including marine) regions (within Exclusive Economic Zones (EEZs)) by 2010 (CBD, 2006a). However, none of these global targets are likely to be met on time, due to both the magnitude of increase in protection required as well as the imminence of the target deadlines (Wood et al., in press; Wood, submitted). Since the adoption of these targets, however, no global strategy or framework has been developed to guide the identification and selection of areas for protection. This is a substantial hindrance to the attainment of the global targets, not least because it is likely to result in continued ‘ad hoc’ creation of MPAs, which is widely considered to be inefficient and ineffective (Possingham et al., 2000; Agardy et al., 2003; Stewart, 2003). The objective of this study was to use an heuristic place prioritisation algorithm to identify priority areas for protection in the world’s oceans, under various scenarios that reflect the quantitative global marine protection targets adopted by the World Parks Congress and the Convention on Biological Diversity. This is by far the largest geographical area over which a place prioritization exercise has been conducted to date.  Reserve selection, more recently termed conservation area selection or place prioritization (Sarkar et al., 2004; Sarkar et al., 2006), is an explicit stage in systematic conservation  119  planning (Margules and Pressey, 2000). It is the identification of locations which aim to ensure that biodiversity is adequately represented within networks of conservation areas as effectively and efficiently as possible, such that biodiversity will persist into the future (Pressey et al., 1993; Margules and Pressey, 2000; Wilson et al., 2005b; Possingham et al., 2006; Sarkar et al., 2006). Place prioritization approaches to achieving these overall goals of representation and persistence have been evolving over the last twenty five years (Kirkpatrick, 1983; Kingsland, 2002), and are centred around the concept of complementarity, as well as a suite of additional guiding principles including irreplaceability, flexibility, vulnerability, spatial economy, and computational efficiency (Pressey et al., 1993; Reyers et al., 2000; Williams, 2001; Faith et al., 2003; Possingham et al., 2006; Sarkar et al., 2006).  Place prioritization algorithms have been broadly categorized as optimal, heuristic, or metaheuristic (Sarkar et al., 2006). Heuristic algorithms have been criticized because they are not guaranteed to find optimal (i.e. maximally spatially economical) solutions, and the degree of suboptimality is unpredictable, varying with the dataset and the algorithm (Underhill, 1994; Rodrigues et al., 2000; Rodrigues and Gaston, 2002). However, the level of sub-optimality can be very small and in some cases heuristic solutions may even match optimal solutions (Csuti et  al., 1997; Pressey et al., 1997; Sarkar et al., 2004). Furthermore, the nature of place prioritization problems is such that even relatively simple problems (which seldom have much practical relevance) may be intractable for optimal algorithms, a problem which is not necessarily alleviated by increasing computer processing speed. Heuristic algorithms, while sacrificing some spatial economy, are more computationally efficient (Sarkar et al., 2006). This is especially the case for large datasets and/or complex constraint scenarios (e.g., proportional representation targets), which are more commonly encountered in practice  120  (Pressey et al., 1996). Metaheuristics, such as simulated annealing (Kirkpatrick et al., 1983; Ball and Possingham, 2000; Possingham et al., 2000), may achieve greater spatial economy than heuristic algorithms, but are currently less computationally efficient (Kelley, 2002).  Place prioritization studies require that surrogates for biodiversity be used, because our knowledge of global biodiversity is incomplete, and more detailed data cannot typically be obtained in the timeframe within which landscape- or seascape-altering decisions are made (Margules and Pressey, 2000; Favreau et al., 2006; Possingham et al., 2006), or target deadlines have passed. Despite concerns about, and the difficulties of testing, the effectiveness of surrogates in representing total biodiversity (van Jaarsveld et al., 1998; Reyers et al., 2000; Favreau et al., 2006), they remain a practical necessity (Flather et al., 1997; Margules and Pressey, 2000). Possingham et al (2006) thus recommend that the best use of all available data should be made, and Beger et al (2003) recommend the use of surrogates with high levels of distribution heterogeneity. Surrogates can be either biological or non-biological features, or a combination of both, and must have distributions that can be easily assessed in the field or reliably modelled at a scale relevant to the scale of the planning operation (Zacharias and Howes, 1998; Ward et al., 1999; Margules and Pressey, 2000; Gladstone, 2002; Sarkar et al., 2004; Stevens and Connolly, 2004; Wilson et al., 2005b; Rondinini et al., 2006). While the global marine protection targets under consideration focus on the representation of habitats and/or ecological regions, there is as yet no habitat data or ecological classification available with both global extent and sufficiently high resolution to render a habitat-based global place prioritization exercise feasible.  121  Place prioritization can be formulated either as minimum-set problems or maximum-coverage problems. Minimum set problems identify the minimum area required to meet the specified targets for all surrogates, while maximum coverage problems maximize the level of surrogate representation for a given cost, for example, area (Williams, 2001; Sarkar et al., 2006). Formulation of place prioritization as a maximum coverage problem is sometimes considered to be more realistic than minimum-set formulation because conservation goals are usually more heavily constrained by financial and/or spatial budgets than by notions of representation (Underhill, 1994; Williams, 2001; Sarkar et al., 2006). Minimum set problems may also fail to achieve representation of biodiversity if more remote surrogates, such as habitat types or higher taxonomic groupings, are used (Williams, 2001). However, maximum coverage problems run the risk of representing some surrogates beyond that which is biologically relevant, while not achieving adequate representation of others. As such it is still advisable to set minimum representation targets for surrogates in a maximum-set problem (Sarkar et al., 2004).  The process of developing targets is a complex one. Large-scale, percentage-based targets, such as those under consideration here, have been criticized for being ecologically irrelevant and often insufficient to ensure persistence of biodiversity (Soulé and Sanjayan, 1998; Pressey  et al., 2003; Solomon et al., 2003). However, such conclusions have typically been drawn from applications of global targets directly to local situations. This is unlikely to be appropriate given the multiscale nature of both ecological systems and political organisation. Instead, a hierarchical system of targets that spans spatial and temporal scales of application is likely to be needed (Wood, submitted). In practical conservation planning, two broad rounds of analysis are recommended: firstly, identification of global priority areas, followed by regional reserve  122  network selection within global priority areas (Vane-Wright et al., 1991; Pressey et al., 1993). In this context, global targets would best be applied at the stage of identifying global priority areas. To date, systematic conservation planning techniques have not yet been used for either analysis.  Methods Data Sets This analysis makes use of the global spatial grid system adopted by the Sea Around Us Project, which consists of 259,200 cells of 0.5° latitude/longitude resolution, and whose area is prorated according to sea surface area (Watson et al., 2004; Sea Around Us, 2007). Cells containing no sea water, and cells whose percent mean ice cover between 1979 and 2002 was 100 (Cavalieri et al., 1996, updated 2006) were excluded, leaving a total of 176093 cells.  Global distributions have recently been developed through the Sea Around Us Project for 923 fish and invertebrate species that have been reported by the Food and Agriculture Organisation of the United Nations (FAO) as being part of commercial fisheries catches between 1950 and 2003. The distributions were developed using a rigorous rule-based revision of geographic ranges (Close et al., 2006) and are available online (Sea Around Us, 2007). Global distributions are also available for 115 species of marine mammal using the same 0.5° global grid (Kaschner et al., 2006). This makes a total of 1038 species with a broad taxonomic representation (Table 6.1). Both datasets provide relative probabilities of occurrence, i.e., the probabilities across all cells for each species summed to one. However, the use of relative probabilities of occurrence is problematic here because summing them (a step in implementing the place prioritization algorithm) is based on unrealistic assumptions about the independence  123  of probabilities of occurrence between surrogates and through space. Interpreting the relative probabilities as expectations, by dividing the relative probabilities by the maximum relative probability for that species, circumvents this problem (Sarkar et al., 2006). Relative probabilities were thus converted to expectation values and truncated to a precision of 0.1. Values less than 0.1 were truncated to zero. Richness of a) all species, b) fish and invertebrates, and c) marine mammals, based on these expectation values are shown in Figures 6.1-6.3.  Place prioritisation protocols This study made use of the rarity-complementarity heuristic algorithm encoded in the ResNet software package (Garson et al., 2002). Rarity-complementarity heuristics have generally been found to produce the most spatially economical solutions (Csuti et al., 1997; Pressey et al., 1997). Sarkar et al (2004) found a complementarity-only algorithm produced more spatially economical solutions than a rarity-complementarity algorithm with probabilistic surrogate data. However, I still selected the rarity-complementarity algorithm because the differences in economy were small (0.2%), and excluding rarity considerations from the algorithm may result in under-representation of locations that, on the basis of the data used, currently appear to be species-depauperate but, as more data come available, may in fact not be, as may be the case in the Antarctic (Brandt et al., 2007).  I developed five scenarios to investigate the WPC and CBD targets, and variations thereof, as minimum-set problems. I used minimum-set problems because I did not want to constrain the total area available for MPAs at this stage of the global planning process. Although the targets pertain specifically to habitat types or ecological regions, I used the 1038 global species distributions described earlier as surrogates for biodiversity in all scenarios. This is because of  124  both the lack of habitat data at sufficient resolution and with global extent, and also to avoid difficulties with minimum-set problems failing to represent biodiversity when using more remote surrogates (see Introduction). Two scenarios were configured as direct interpretations of the World Parks Congress (WPC) targets, with representation targets of 20% and 30% of all species’ global distributions, and a study area of the world’s oceans. Two scenarios addressed the Convention on Biological Diversity (CBD) target. The study area for both was the combined area contained within the EEZs of the maritime countries of the world, as specified by the target. The first scenario set a representation target of 10% of the combined within-EEZ distributions of all species, and is intended as a direct interpretation of the CBD target. The second set a target of 10% of the global distributions of all species. The purpose of this scenario was to investigate the possibility of representing 10% of species’ global distributions completely within EEZs. This is of interest because, although progress has been made to develop the legal framework necessary to implement MPA networks on the high seas (i.e., the area beyond EEZs, which constitutes around 56% of the surface area of the world’s oceans), it is not yet in place (Gjerde and Kelleher, 2005; Norse and Crowder, 2005; Sumaila et al., 2007). Finally, a fifth scenario was investigated, with a representation target of 10% of all species’ global distributions and a study area of the world’s oceans. This last scenario is intended as an extension of the CBD target to the world’s oceans, since the CBD target also recommends that areas beyond national jurisdiction be afforded urgent and increased protection through international cooperation and action (CBD, 2005). It is also intended as a precursor to the higher percentage targets recommended by the World Parks Congress. Each of the five scenarios was then also re-run using fish and invertebrate distributions only, i.e., excluding marine mammals, in order to investigate the effect of the wide-ranging annual movements of  125  many marine mammal species (Kaschner et al., 2006) on the size and spatial configuration of the solutions. This resulted in a total of ten scenarios (Table 6.2).  There are usually many solutions to a particular place prioritisation problem (Pressey et al., 1994), but there are often practical constraints to implementing some of them. Providing a range of flexible solutions can facilitate the decision-making process (Pressey et al., 1993; Possingham et al., 2000). However, heuristic solutions typically only produce a single solution (Leslie et al., 2003; Sarkar et al., 2006). Alternative solutions can be produced by the algorithm used here by using multiple, randomly re-ordered input files (Sarkar et al., 2002; Fuller et al., 2006). I produced 100 randomised input files in order to generate 100 alternative solutions for each scenario. Existing MPAs were not locked into the solutions for two reasons. Firstly, existing MPAs have largely been created in an ad hoc manner (Agardy et al., 2003; Agardy, 2005), and the inclusion of them in systematic conservation planning processes has been shown to result in less efficient solutions (Stewart, 2003; Stewart et al., 2007). Secondly, the vast majority of MPAs are much smaller than the cell size used here (Wood et al., in press), so locking in entire cells would be inappropriate, and may have a compounding effect on the inefficiency resulting from the ad hoc nature of their creation. Finally, the optional redundancy rule of ResNet was also invoked in all runs. This part of the algorithm iterates over the final selection of cells to check for redundancy, i.e., that cells selected earlier in the process were not made redundant in terms of the representation targets by subsequent cell selections. Redundant cells are removed from the solution, improving the spatial economy of the final solution (Garson et al., 2002).  126  Pilot tests indicated that the processing time required to generate 100 solutions for each of the 10 scenarios listed in Table 6.2 would be on the order of four years if run serially on a single PC. In order to generate solutions in a more timely manner, I ran the jobs on the Glacier cluster of 1680 computing nodes provided through WestGrid, a high performance computing, collaboration and visualization infrastructure for western Canada (WestGrid, 2007). This reduced the real-time processing to a few weeks. For each scenario, the 100 solutions were summed to produce maps showing frequency of selection of each cell, which is sometimes referred to as irreplaceability (Pressey et al., 1994). Finally, the degree of spatial overlap between cells selected 100% of the time for solutions developed including and excluding marine mammals was measured using Jaccard’s coefficient of similarity (Rice and Belland, 1982).  Results Table 6.3 provides summary statistics for the solutions identified under each scenario. Maps showing the frequency of cell selection for the 100 solutions generated for each scenario are shown in Figures 6.4-6.13 inclusive. The WPC targets to represent 20-30% of all species (WPC20_All and WPC30_All) can be met by protecting 73-110 million km2, or 20-30% of the world’s oceans, respectively. Under both scenarios, around half of the selected area falls inside EEZs, representing 26-36% of the global combined EEZ area (Figures 6.4 & 6.5). The CBD target of 10% of the distribution of species within EEZs (CBD_All) can be met for all species in 15 million km2, representing 4% of the world’s oceans and 10% of global combined EEZ area (Figure 6.6). The modified CBD target of 10% of species’ global distributions (CBD_GAll) can be met for 1022 species in 37 million km2, or 10% of the world’s oceans and 25% of the global combined EEZ area (Figure 6.7). This target could not be met for 16  127  Antarctic species (2 marine mammals, 2 invertebrates, and 12 fish), because less than 10% of their global distributions occur within EEZs. Finally, the target of 10% of all species’ global distributions in the world oceans (Global10_All) can be met within 37 million km2, or 10% of the world’s oceans (Figure 6.8). However, in contrast to CBD_GAll, where the entire solution fell within EEZs, only 57% of this solution occurred within EEZs, representing 14% of the global combined EEZ area.  Exclusion of marine mammal distributions from the scenarios resulted in significantly smaller solutions, on average, by about 27% (p<0.05, paired t-test; Figures 6.9-6.13). The overlap in the spatial configuration of solutions generated for a given target scenario using a) all species or b) fish and invertebrates is quite low, with Jaccard’s coefficients ranging from 0.32 to 0.42 (Table 6.4). Expressed differently, the average overlap area represents 45% of the mean solution area for scenarios including marine mammals, and 60% of mean solution area for scenarios excluding marine mammals (Table 6.4; see also Figures 6.4-6.13). To illustrate this, I combined and remapped the cells selected 100% of the time under scenarios WPC30_All and WPC30_Fi (i.e., Figures 6.8 and 6.9), but differentiating between the cells selected 1) for fish and invertebrates only, 2) for marine mammals only, and 3) all species (Figure 6.14).  Discussion This study is the largest area over which systematic conservation planning techniques have been applied to date, and the first time that they have been used to identify global priorities for protection in the world’s oceans. It is also the first globally synthetic test of the species distribution data used. The results generated for the World Parks Congress scenarios, using all species, provide the broadest indication of what a truly global network of MPAs might look  128  like, with around half of the area selected falling in the high seas. The solutions also make intuitive sense, in that they highlight some major oceanographic and bathymetric phenomena that broadly identify areas of high productivity, diversity, and ecosystem value, such as: coastal areas; reefs, such as the Coral Triangle; upwelling regions, e.g. the Benguela Current system; and seamounts, for example, the Azores (Shannon et al., 1988; Pauly and Christensen, 1995; Costanza, 1999; Probert, 1999; Roberts et al., 2002; Worm et al., 2003; Rogers, 2004). However, it is worth noting that selected areas are not necessarily species-rich, for example parts of the Sea of Okhotsk, the coast of Greenland, and south of French Polynesia. Similarly, not all species-rich areas are selected, such as the coastal area of the Great Australian Bight, and parts of the Mediterranean. This is attributable to the rarity-complementarity basis of the algorithm used, which selects areas that contain the next rarest surrogate and whose biodiversity content is most different from previously selected cells (Garson et al., 2002).  Despite the spatial restriction of CBD targets to areas within EEZs, even a target of 10% of species’ global distributions is largely attainable. However, Antarctic waters and many oceanic features are completely unrepresented in these solutions, meaning that the biodiversity associated with these habitats is also under-represented (Figures 6.6 & 6.7). Indeed, even based on the surrogate data set, it was not possible to represent all species by constraining the study area to EEZs. Furthermore, given the ocean-wide distances between EEZs, and therefore selected areas, solutions such as those shown in Figure 6.7 are unlikely to function as effectively as their global ocean equivalent (Figure 6.8) as a connected network of MPAs. To some extent, the possibility of protecting 10% of all species’ global distributions purely by designating MPAs within EEZs may be appealing, as it provides an alternative route to achieving higher representation targets more quickly while the legal complexities of creating  129  functional MPAs on the high seas are resolved. However, in light of the representation and connectivity challenges likely to be faced by a network of MPAs created exclusively within EEZs, high seas MPAs should still be actively pursued, through, for example, strengthening of international governance mechanisms such as Regional Fisheries Management Organisations (Foster et al., 2005; Gjerde and Kelleher, 2005; Grant, 2005). Furthermore, the costs to global high seas fisheries of creating MPAs are relatively low. Although the distribution of costs between countries is likely to be uneven, borne largely by countries that are already fishing the high seas (Sumaila et al., 2007), the costs of creating MPAs exclusively within EEZs is likely to be higher in absolute terms, borne by more countries, and potentially with even less equitable distribution (Figure 6.7).  The inclusion of marine mammals in the ResNet runs resulted in solutions that are significantly larger than solutions using fish and invertebrates only, and the overlap between the two is limited. Given the highly migratory nature of marine mammals, and therefore generally transitory occupancy of a given location (Kaschner et al., 2006), the substantial increase in area required to meet representation targets for marine mammals raises questions over the appropriateness of MPAs as a tool for their conservation, especially given the limited resource base for implementing them (Boersma and Parrish, 1999; Norse and Crowder, 2005). Detailed exploration of these issues is somewhat beyond the scope of this chapter; however, it is conceivable that overlap between solutions generated with and without marine mammals may serve as an alternative type of prioritization (see Figure 6.14 for an example of this). Although Figure 6.14 no longer shows an efficient solution to a specific minimum-set problem, it could be used to identify areas which are high priority for high levels of protection (areas of overlap between solutions), as well as candidate areas for different management or regulatory regimes,  130  including, for example, dynamic MPAs for areas selected primarily for marine mammals (and, indeed, other wide-ranging species) (Hyrenbach et al., 2000; Hoyt, 2005).  Setting these questions aside, however, the inclusion of marine mammals is important to this analysis in terms of addressing the broader goal of representing biodiversity in a global network of MPAs. Furthermore, the inclusion of marine mammals goes some way to offsetting obvious shortcomings of the fish and invertebrate distribution data, specifically the substantial data gaps in the Southern Ocean (Figure 6.2), and potential biases resulting from them being commercially caught species. The inclusion of marine mammals thus broadens the taxonomic and distributional range of biodiversity surrogates used, and makes this study a proactive, or strategic, approach to conservation planning, rather than a reactive one, which would tend to focus on individual species, for example those at imminent risk of extinction (Williams, 2001). By the same token, persistence of biodiversity into the future is related to its vulnerability to current or impending threatening processes (Pressey et al., 1996; Wilson et al., 2005a). Fishing is currently the biggest and most imminent threat to marine ecosystems (Preikshot and Pauly, 2005) and so the use of primarily commercially caught species in this study represents an explicit attempt to incorporate vulnerability considerations into the priority-setting process. Beyond this, ensuring persistence of biodiversity through time also means incorporating spatiotemporally dynamic ecological, evolutionary and socio-political processes into systematic conservation planning, a challenge which is at the frontier of research in this field, but which should largely be addressed at more local scales than this study (Vane-Wright et al., 1991; Pressey et al., 1993; Balmford et al., 1998; Nicholls, 1998; Araújo and Williams, 2000; Cabeza and Moilanen, 2001; Sarkar et al., 2006).  131  In all scenarios investigated, most cells were selected 100% of the time, i.e., there were relatively low levels of flexibility between the 100 solutions identified for each scenario. However, high flexibility may actually be undesirable in the early stages of a global planning process, where the goal is to identify priority areas within which MPA network design processes will be undertaken. Indeed, the fundamental purpose of prioritisation is to reduce, or at the very least, order, choices, but certainly not to increase them. Nevertheless, it is worth exploring the possible reasons for the low flexibility observed, because the number of possible solutions to systematic conservation planning problems typically increases with problem size (Pressey et al., 1993; Arthur et al., 1997; Csuti et al., 1997). However, this effect is weakened when the algorithms are using real numbers with many possible values such as selection unit (cell) area or number of features per unit (Pressey et al., 1997). In this case, cell area varied over 4 orders of magnitude from ~3km2 to >3000km2. In addition, a large number of surrogates were used, represented by expectations of occurrence (rather than presence/absence), which increases the range of possible biodiversity content scores for a given cell. Precision of surrogate expectation values during pilot tests was 10-8, but was reduced to 10-1 because solutions generated using the former were almost completely identical, and the use of such high levels of precision is hard to justify ecologically given the resolution of the data and uncertainties associated with the species distributions (Close et al., 2006). In addition, it is also possible that the relatively low flexibility observed may be at least partly attributable to the somewhat (albeit necessarily) coarse resolution of the species distributions used, which might best be viewed as refined extents of occurrence, as opposed to areas of occupancy (Gaston, 1991). Patterns of biodiversity, and therefore complementarity, are dependent on the spatial scale at which they are measured, and this in turn affects the selection of sites by the algorithm (Williams, 2001; Warman et al., 2004). For example, flexibility decreases as the number of  132  rare species increases (Cabeza and Moilanen, 2001). Decreasing resolution effectively increases the number of rare species, as well as decreasing the number of cells in which they occur (Pressey et al., 1993). This increases the number of cells that are required in all solutions to ensure their representation targets are met.  The various considerations discussed here pertaining to surrogate choice, data sources, scale, targets, and gaps, all point to the main conclusion that none of the solutions presented here should be viewed as definitive MPA network configurations. The maps simply illustrate hypothetical priorities identified at a global scale, designed to match the scale of the targets that they are addressing, and that represent known biodiversity efficiently. Furthermore, as alluded to earlier, these results make no assessment of the costs or political feasibility of implementing them, or how equitably the costs are distributed between countries. Political will is a critical requirement for the global targets to be implemented on time (Wood, submitted), but this can be reduced when broad objectives for resource are in conflict, as is the case for an up to 375-fold expansion of the current global MPA network, while maintaining or even increasing fishing activities (Wood and Dragicevic, 2007). Multicriteria decision-making (MCDM) techniques can be used to explore decision-makers preferences for different alternative solutions to a particular resource allocation problem (Malczewski, 1999; Wood and Dragicevic, 2007). They are becoming incorporated into the design of conservation area networks, although at generally smaller scales than global priority-setting exercises. (Fuller et  al., 2006; Moffett et al., 2006; Moffett and Sarkar, 2006).  Finally, it is worth noting that none of the products of these analyses necessarily result in conservation action. Indeed, despite the increase in use of conservation area selection  133  algorithms over the last 2 decades, only two conservation area networks designed using them have been implemented fully, in Tasmania (Kirkpatrick, 1983) and the Great Barrier Reef (Fernandes et al., 2005). This is analogous to the almost ubiquitous failure of formal planning in the business world (Wildavsky, 1973; Mintzberg, 1992), yet, ironically, the business model is currently viewed as the best model for conservation planning (Margules and Pressey, 2000; Possingham, 2001; Leslie et al., 2003). This is not to say that ad hoc approaches to MPA designation are necessarily better, particularly in terms of representing biodiversity efficiently (Stewart, 2003), but the limitations of the reductionist approaches inherent in formal planning need to be more explicitly recognised. The analytic approach provides not a solution, but a perspective, another way to look at the problem (Mintzberg, 1992). The critical, yet least well understood, part of planning processes is that where ideas are translated into behaviour and action (Wack, 1985; Mintzberg, 1992). The changes in behaviour needed for the global targets to be met on time is at least partly dependent on the perception that they are attainable, which is itself a function of political will and adequate information and resources (Wood, submitted). It is hoped that the analysis presented here will help to motivate some of the necessary behavioural changes, by illustrating how the 361 million km2 of the world’s oceans can be reduced to a more manageable area for focused MPA network design and implementation efforts.  134  Tables Table 6.1Taxonomic summary of species whose distributions were used in ResNet analyses. Fish No. species Pelagic fish 159 Demersal fish 231 Bathypelagic fish 17 Bathydemersal fish 34 Benthopelagic fish 117 Reef-associated fish 58 Sharks and rays 83 Total fish 699 Invertebrates Crustaceans 117 Molluscs 99 Echinoderms 4 Other invertebrates 4 Total invertebrates 224 Marine Mammals Baleen whales 14 Toothed whales 69 Pinnipeds 32 Total marine mammals 115 Total species 1038  135  Table 6.2 Attributes of the ten scenarios investigated. WPC = World Parks Congress, CBD = Convention on Biological Diversity. Official # # Target Scenario Study area Notes target cells surrogates (%) WPC20_All WPC World 176093 1038 20 All species WPC20_Fi  WPC  World  176093  923  20  Fish & invertebrates only  WPC30_All  WPC  World  176093  1038  30  All species  WPC30_Fi  WPC  World  176093  923  30  Fish & invertebrates only  10  All species Target is 10% of species distributions contained within EEZs  CBD_All  CBD  EEZs only  70305  1038  CBD_Fi  CBD  EEZs only  70305  923  10  Fish & invertebrates only Target is 10% of species distributions contained within EEZs  CBD_GAll  CBD  EEZs only  70305  1038  10  All species Target is 10% of global species distributions  CBD_GFi  CBD  EEZs only  70305  923  10  Fish & invertebrates only Target is 10% of global species distributions  Global10_All  n/a  World  176093  1038  10  All species  Global10_Fi  n/a  World  176093  923  10  Fish & invertebrates only  136  Table 6.3 Summary statistics for solutions found for each of the 10 scenarios investigated. See Table 6.2 for more details on the scenarios. Values presented are means based on 100 solutions generated for each scenario. Solution statistics (mean values) Scenario Area % of world % of solution % of EEZs in ocean in solution within EEZs solution (million km2) WPC20_All  73.4  20  52  26  WPC20_Fi  52.5  15  46  17  WPC30_All  109.5  30  48  36  WPC30_Fi  80.4  22  46  26  CBD_All  14.9  4  100  10  CBD_Fi  11.1  3  100  8  CBD_GAll  36.5  10  100  25  CBD_GFi  28.3  8  100  19  Global10_All  37.0  10  57  14  Global10_Fi  25.5  7  47  8  Table 6.4 Area of overlap between solutions including and excluding marine mammal distributions for each target scenario. Data are based on cells selected in 100% of solutions for each scenario, and are expressed as absolute area, as well as percentage of the mean solution area for that scenario, both including and excluding marine mammal distributions. Percent of mean solution area that overlaps Jaccard’s with the solution Scenario Overlap area coefficient (target) Scenario (species composition) (million km2) of similarity All species Fish & invertebrates WPC20 30.2 41.1 57.5 0.32 WPC30 47.1 43.0 58.6 0.33 CBD 6.5 43.5 58.4 0.33 CBD_G 19.3 52.8 68.1 0.42 Global10 16 43.3 62.8 0.34  137  138  Figure 6.1 Total richness of 1038 fish, invertebrate, and marine mammal species, calculated using expected occurrence data (see text). White areas of sea in the Arctic and Antarctic are where there are no data.  Figures  139  Figure 6.2 Richness of 923 commercially targeted fish and invertebrate species. White areas of sea in high latitudes are areas where there are no data.  140  Figure 6.3 Richness of 115 marine mammal species. White areas of sea in the Arctic and Antarctic are where there are no data.  141  Figure 6.4 Frequency of selection of cells over 100 iterations of ResNet for scenario WPC_20All. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black.  142  Figure 6.5 Frequency of selection of cells over 100 iterations of ResNet for scenario WPC_30All. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black.  143  Figure 6.6 Frequency of selection of cells over 100 iterations of ResNet for scenario CBD_All. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black.  144  Figure 6.7 Frequency of selection of cells over 100 iterations of ResNet for scenario CBD_GAll. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black.  145  Figure 6.8 Frequency of selection of cells over 100 iterations of ResNet for scenario Global_10All. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black.  146  Figure 6.9 Frequency of selection of cells over 100 iterations of ResNet for scenario WPC_20Fi. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black.  147  Figure 6.10 Frequency of selection of cells over 100 iterations of ResNet for scenario WPC_30Fi. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black.  148  Figure 6.11 Frequency of selection of cells over 100 iterations of ResNet for scenario CBD_Fi. Approximate boundaries of the 200nm Exclusive Economic Zone (EEZ) boundaries are shown in black.  149  Figure 6.12 Frequency of selection of cells over 100 iterations of ResNet for scenario CBD_GFi. 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Sea Around Us (2007) A global database on marine fisheries and ecosystems, Fisheries Centre, University British Columbia, Vancouver (British Columbia, Canada). http://www.seaaroundus.org [accessed 5th September 2007].  156  Shannon, L.V., Crawford, R.J.M., Brundrit, G.B. & Underhill, L.G. (1988) Responses of fish populations in the Benguela ecosystem to environmental change. Journal du Conseil pour l'Exploration de la Mer, 45, 5-12. Solomon, M., Van Jaarsveld, A.S., Biggs, H.C. & Knight, M.H. (2003) Conservation targets for viable species assemblages? Biodiversity and Conservation, 12(12), 2435-2441. Soulé, M.E. & Sanjayan, M.A. (1998) Conservation targets: do they help? Science, 279(5359), 2060-2061. Stevens, T. & Connolly, R.M. (2004) Testing the utility of abiotic surrogates for marine habitat mapping at scales relevant to management. Biological Conservation, 119(3), 351-362. Stewart, R.R., Ball, I.R. & Possingham, H.P. (2007) The effect of incremental reserve design and changing reservation goals on the long-term effliciency of reserve systems. Conservation Biology, 21(2), 346-354. Stewart, R.R., Noyce, T., Possingham, H. P. (2003) Opportunity costs of ad hoc marine reserve design decisions: an example from South Australia. Marine Ecology Progress Series, 253, 2538. Sumaila, U.R., Zeller, D., Watson, R., Alder, J. & Pauly, D. (2007) Potential costs and benefits of marine reserves in the high seas. Marine Ecology Progress Series, 345, 305-310. Underhill, L.G. (1994) Optimal and suboptimal reserve selection algorithms. Biological Conservation, 70, 85-87. United Nations (2002) Draft plan of implementation of the World Summit on Sustainable Development. United Nations, Johannesburg, South Africa. van Jaarsveld, A.S., Freitag, S., Chown, S.L., Muller, C., Koch, S., Hull, H., Bellamy, C., Kruger, M., Endrody-Younga, S., Mansell, M.W. & Scholtz, C.H. (1998) Biodiversity assessment and conservation strategies. Science, 279, 2106-2108. Vane-Wright, R.I., Humphries, C.J. & Williams, P.H. (1991) What to protect? - Systematics and the agony of choice. Biological Conservation, 55, 235-254. Wack, P. (1985) Scenarios: shooting the rapids. Harvard Business Review, 63(6), 139-150. Ward, T., Vanderklift, M.A., Nicholls, A.O. & Kenchington, R. (1999) Selecting marine reserves using habitats and species assemblages as surrogates for biological diversity. Ecological Applications, 9(2), 691-698. Warman, L.D., Sinclair, A.R.E., Scudder, G.G.E., Klinkenberg, B. & Pressey, R.L. (2004) Sensitivity of systematic reserve selection to decisions about scale, biological data, and targets: case study from southern British Columbia. Conservation Biology, 18(3), 655-666.  157  Watson, R., Kitchingman, A., Gelchu, A. & Pauly, D. (2004) Mapping global fisheries: sharpening our focus. Fish and Fisheries, 5, 168-177. WestGrid (2007) Western Canada Research Grid, http://www.westgrid.ca/ [accessed 11th September 2007]. Wildavsky, A. (1973) If planning is everything, maybe it's nothing. Policy Sciences, 4(2), 127153. Williams, P. (2001) Complementarity. In Encyclyopedia of Biodiversity (ed S.A. Levin), pp. 813-829. Wilson, K.A., Pressey, R.L., Newton, A., Possingham, H. & Weston, C. (2005a) Measuring and incorporating vulnerability into conservation planning. Environmental Management, 35(5), 527-543. Wilson, K.A., Westphal, M.I., Possingham, H.P. & Elith, J. (2005b) Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biological Conservation, 122(1), 99-112. Wood, L.J. (submitted) Global marine protection targets: how S.M.A.R.T. are they? Wood, L.J. & Dragicevic, S. (2007) GIS-based multicriteria evaluation and fuzzy sets to identify priority sites for marine protection. Biodiversity and Conservation, 16(9), 2539-2558. Wood, L.J., Fish, L., Laughren, J. & Pauly, D. (in press) Assessing progress towards global marine protection targets: shortfalls in information and action. Oryx. Worm, B., Lotze, H.K. & Myers, R.A. (2003) Predator diversity hotspots in the blue ocean. Proceedings of the National Academy of Sciences, 100, 9884-9888. Zacharias, M.A. & Howes, D.E. (1998) An analysis of marine protected areas in British Columbia, Canada, using a marine ecological classification. Natural Areas Journal, 18(1), 413.  158  7. Conclusions  159  In this thesis I have provided various contributions to the knowledge and understanding of the current level of protection in the world’s oceans, as well as some insights into the practical and theoretical challenges associated with global monitoring and data collection. I have also provided the first spatial visualisations of the extent of marine protection required to meet formally adopted targets. A concluding summary of the findings presented in this thesis, their limitations, as well as some suggestions for future work, is provided below.  For the first half of this thesis I investigated the current extent and distribution of the world’s marine protected areas (MPAs). I present the results of the first explicitly marine-focused, global assessment of MPAs, and the first quantitative assessment of the attainability of global marine protection targets, based on an extensive baseline data collection exercise (Chapter 2). Although some consider systematic collection of baseline data to be only a ‘filling in’ activity (see Longhurst, 1998), the extent and breadth of changes made to the original MPA dataset that I describe in Chapter 2 indicate that this is a misperception of the relationship between new insights and old data. Indeed, prior to this research, global maps of MPAs had never been produced. The use of a Geographic Information System (GIS) to visualise MPAs on maps has enabled knowledge about the extent of protection to be communicated in a more intuitive way. This can help to engage the public and political representatives in debates about if, how and where to increase the level of protection (Pauly et al., 2003). Similarly, the process of comparing targets to their expected achievement dates, and, in the case of the Convention on Biological Diversity (CBD) targets, the display on maps of countries’ level of protection compared to the targets, may help to mobilise support for the policy shifts and increased resources needed to increase the current level of marine protection.  160  The processes involved in developing a global MPA database posed profound practical and theoretical challenges associated with global monitoring of MPAs, notably because the MPA concept, within the concept of ‘protection’, has deep roots in pragmatics and ethics. In particular, the challenge is to develop from individual perceptions a global definition of the minimum level of regulation of human activity necessary for an area to be considered ‘protected’ (Chapter 3). These issues are still being debated and, consequently, the operational definition of MPA used for the work presented in Chapter 2 should not be assumed to reflect the full spectrum of spatial regulation of human activity in the oceans. There remain some data gaps, and updating of the database is an ongoing process, so the assessment presented in Chapter 2 will need to be updated as more data come available. Future work on the database could also include expanding it to enable more effective monitoring of additional aspects of the global marine protection targets, including management effectiveness and habitat representation.  To some extent, the results that I present in Chapter 2 validate concerns expressed over the relevance and utility of broad conservation targets (Soulé and Sanjayan, 1998; Solomon et al., 2003; Svancara et al., 2005; Wiersma and Nudds, 2006). However, in Chapter 4, I suggest that the primary role of large-scale targets may be psychological – to motivate behavioural change – rather than ecological. Nevertheless, large-scale targets have historically been relatively unsuccessful in this role. I explored the possibility that this may be attributable to the way in which large-scale targets are formulated, using the SMART framework as a means of assessment. Although a lack of operational definitions of target requirements may be a proximate cause of low measurability and perceived attainability of targets, I suggest that there are more fundamental problems. Firstly, both ecosystems and political systems are multi-tiered  161  and hierarchical in nature, implying a need for a pluralistic, systems approach to marine resource management and conservation (Berkes, 2007), and thereby a hierarchical system of targets to be developed for application at multiple scales in time and space. However, to date, there is a general lack of intermediate-scale targets that translate large-scale targets into locally relevant ones. Indeed, most criticisms of large-scale targets have been based on direct applications of large-scale targets to much more local scale contexts. Secondly, the political will required to achieve the targets does not necessarily reside with those who have the decision-making power to invest the resources necessary for the targets to be met. However, I propose that there seem to be some feedback mechanisms in place, whereby political will and perceived attainability are influenced both by each other as well as by target formulation. Furthermore, the whole process is likely to be undermined by challenging targets that have no clear implementation strategy, as is the case here. I conclude that the process of formulating targets is itself evolving, that the SMART concept may provide useful guidance in this regard, and that it may be premature to abandon the use of large-scale targets altogether.  In the second half of this thesis (Chapters 5 and 6) I therefore investigated the utility of decision support methods to identify global priorities for protection in the world’s oceans, which may provide some strategic context to inform and/or facilitate the decision-making process to create global networks of MPAs. The multicriteria nature of the decision problem lent itself to an application of multicriteria evaluation (MCE), which was used to assess the suitability of different cells within the Pacific Canadian EEZ based on two (largely conflicting) objectives: biodiversity conservation and fisheries profit-maximisation (Chapter 5). This was one of the first times that MCE had been used in spatial decision-making in the marine environment, and the first time that MCE had been used to identify priority areas for protection  162  in such a large area. The approach proved useful for investigating how different stakeholder preferences might influence the selection of different cells. However, the cell-level comparison required an aggregate measure of each criterion (in this case, species richness and ex-vessel value of the catch). The species-richness basis of the selection process did not guarantee representation of all species under consideration within the selected areas, which is a primary goal of systematic conservation planning (Margules and Pressey, 2000; Sarkar et al., 2006), as well as the global targets.  In Chapter 6 I used an heuristic place prioritisation algorithm encoded in the ResNet software package (Garson et al., 2007) to investigate various minimum set problems for species representation targets that reflect the global marine protection targets, using distribution range maps for 923 commercially caught (and hence, generally abundant) fish and invertebrate species, and 115 marine mammal species in a global grid of ~180,000 cells measuring 0.5° latitude/longitude. This was the first time that the species distribution ranges of marine species have been used in a globally synthetic way, and is by far the largest application of a place prioritisation algorithm to date. It is also the first time that the concept of a global network of MPAs has been visualised on a map (although this is not to say that the solutions presented are true ‘networks’; see Chapter 2). As such the results I present in Chapter 6 are both unique and novel, and bring a new level of context and tangibility to the magnitude of the change required for the targets to be met on time.  The solutions I present in Chapter 6 show locations in the world’s oceans that, based on available data, represent global priority areas where regional-scale protected area network design exercises might be undertaken. As such they may be understood to represent the first  163  step in a global planning process to implement global networks of MPAs. However, none of the solutions identified in Chapter 6 are claimed to be definitive MPA network configurations: they are hypothetical global priorities developed based on existing global targets and known spatial patterns of biodiversity. Furthermore, and partly because these solutions are the first of their kind, they are subject to various limitations that provide scope for further work. These are discussed below.  Although the species distributions used represent a broad taxonomic range, there are data gaps, especially in high latitudes. In addition, solutions varied in size and spatial location depending on the surrogate set that was used, which re-confirms that choice of surrogates can substantially influence the results generated by place prioritisation algorithms (van Jaarsveld et  al., 1998; Reyers et al., 2000). These analyses should thus be repeated as new species distribution data come available, both for additional species as well as refinements to existing distributions. In addition, since only one type of place prioritisation algorithm was used here, it would be interesting to repeat this study using a range of different algorithms, including heuristics using different rules (e.g., complementarity only), as well as optimal and metaheuristic algorithms.  Various related issues relating to spatial and temporal scale could be investigated as a follow up to this study. For example, the extent to which changes in species distributions resulting from climate change (Soto, 2002; Perry et al., 2005; Parmesan, 2006; Simmonds and Isaac, 2007) affect the location of priority areas for MPAs could be investigated by comparing solutions produced using species distributions modelled under different climate scenarios. Given the variation in solutions (and in some cases, lack of congruence between them)  164  produced by algorithms applied at different scales and resolutions (Pressey et al., 1993; Erasmus et al., 1999; Hopkinson et al., 2000; Warman et al., 2004), it is also important that protected area network design exercises are carried out in smaller areas and with higher resolution, to complement the results presented here. The implementation of MPA networks is unlikely to occur instantaneously, instead developing incrementally through space and time (Meir et al., 2004; Stewart et al., 2007). The interactions between, and impacts of, different spatial and temporal scales of MPA implementation on the characteristics and representation levels achieved (at various scales) could be explored by, for example, comparing solutions generated with areas of local, regional or global importance locked into them.  Finally, the scenarios investigated in Chapter 6 were formulated in order to identify priority areas based solely on their biodiversity content, but multicriteria decision-making techniques could be used to investigate the socio-political feasibility of implementing them. However, at the global scale, the development of meaningful weightings of different criteria is problematic, because the constituency from which the weighting should be derived is effectively the world population, which cannot realistically be censused. A more feasible approach might be to use formal scenario analysis (Peterson et al., 2003; Swart et al., 2004), where hypothetical scenarios of alternative futures (which reflect different policy preferences) are devised, and theoretical weightings developed based on those the policy preferences.  165  References Berkes, F. (2007) Community-based conservation in a globalized world. Proceedings of the National Academy of Sciences, 104(39), 15188-15193. Erasmus, B.F.N., Freitag, S., Gaston, K.J., Erasmus, B.H. & van Jaarsveld, A.S. (1999) Scale and conservation planning in the real world. Proceedings of the Royal Society of London. Series B: Biological Sciences, 266(1417), 315-319. Garson, J., Aggarwal, A. & Sarkar, S. (2007) ResNet Manual Ver 1.2. Hopkinson, P., Evans, J. & Gregory, R.D. (2000) National-scale conservation assessments at an appropriate resolution. Diversity and Distributions, 6(4), 195-204. Longhurst, A. (1998) Ecological geography of the sea. Oval Press, London, UK. Margules, C.R. & Pressey, R.L. (2000) Systematic conservation planning. Nature, 405, 243253. Meir, E., Andelman, S. & Possingham, H. (2004) Does conservation planning matter in a dynamic and uncertain world? Ecology Letters, 7, 615-622. Parmesan, C. (2006) Ecological and evolutionary responses to recent climate change. Annual Review of Ecology and Systematics, 37, 637-669. Pauly, D., Watson, R. & Christensen, V. (2003) Ecological geography as a framework for a transition toward responsible fishing. In Responsible fisheries in the marine ecosystem. (eds M. Sinclair & G. Valdimarson), pp. 87-101. CAB International, Perry, A.L., Low, P.J., Ellis, J.R. & Reynolds, J.D. (2005) Climate change and distribution shifts in marine fishes. Science, 308(5730), 1912-1915. Peterson, G.D., Cumming, G.S. & Carpenter, S.R. (2003) Scenario planning: a tool for conservation in an uncertain world. Conservation BIology, 17(2), 358-365. Pressey, R.L., Humphries, C.J., Margules, C.R., Vane-Wright, R.I. & Williams, P.H. (1993) Beyond opportunism: key principles for systematic reserve selection. Trends in Ecology and Evolution, 8(4), 124-128. Reyers, B., van Jaarsveld, A.S. & Kruger, M. (2000) Complementarity as a biodiversity indicator strategy. Proceedings: Biological Sciences, 267(1442), 505-513. Sarkar, S., Pressey, R.L., Faith, D.P., Margules, C.R., Fuller, T., Stoms, D.M., Moffett, A., Wilson, K.A., Williams, K.J., Williams, P.H. & Andelman, S. (2006) Biodiversity conservation planning tools: Present status and challenges for the future. Annual Review of Environment and Resources, 31, 123-159.  166  Simmonds, M.P. & Isaac, S.J. (2007) The impacts of climate change on marine mammals: early signs of significant problems. Oryx, 41(1), 19-26. Solomon, M., Van Jaarsveld, A.S., Biggs, H.C. & Knight, M.H. (2003) Conservation targets for viable species assemblages? Biodiversity and Conservation, 12(12), 2435-2441. Soto, C. (2002) The potential impacts of global climate change on marine protected areas. Reviews in Fish Biology and Fisheries, 11(3), 181-195. Soulé, M.E. & Sanjayan, M.A. (1998) Conservation targets: do they help? Science, 279(5359), 2060-2061. Stewart, R.R., Ball, I.R. & Possingham, H.P. (2007) The effect of incremental reserve design and changing reservation goals on the long-term effliciency of reserve systems. Conservation Biology, 21(2), 346-354. Svancara, L.K., Brannon, R., Scott, J.M., Groves, C.R., Noss, R.F. & Pressey, B. (2005) Policy-driven vs. evidence-based conservation: a review of political targets and biological needs. Bioscience, 55, 989-995. Swart, R.J., Raskin, P. & Robinson, J. (2004) The problem of the future: sustainability science and scenario analysis. Global Environmental Change, 14, 137-146. van Jaarsveld, A.S., Freitag, S., Chown, S.L., Muller, C., Koch, S., Hull, H., Bellamy, C., Kruger, M., Endrody-Younga, S., Mansell, M.W. & Scholtz, C.H. (1998) Biodiversity assessment and conservation strategies. Science, 279, 2106-2108. Warman, L.D., Sinclair, A.R.E., Scudder, G.G.E., Klinkenberg, B. & Pressey, R.L. (2004) Sensitivity of systematic reserve selection to decisions about scale, biological data, and targets: case study from southern British Columbia. Conservation Biology, 18(3), 655-666. Wiersma, Y.F. & Nudds, T.D. (2006) Conservation targets for viable species assemblages in Canada: Are percentage targets appropriate? Biodiversity and Conservation, 15(14), 45554567.  167  8. Appendices  168  Appendix 1 Table showing the proportion of the world’s maritime territories that is protected. Overseas territories and the non-contiguous US states of Alaska and Hawaii are shown separately. < 1.6% of maritime territory protected Alaska * Albania Algeria American Samoa * Andaman and Nicobar Islands * Angola Anguilla * Antigua and Barbuda Argentina Aruba * Ascension Island * Azores Islands * Bahamas Bahrain Baker and Howland Islands * Bangladesh Barbados Belgium Benin Bermuda * Bouvet Island * Brazil British Indian Ocean Territory * Brunei Darussalam Bulgaria Cambodia Canada Canary Islands * Cape Verde Cayman Islands * Channel Islands * Chile China Christmas Island * Clipperton Island * Cocos (Keeling) Islands * Comoros Congo, Democratic Republic of the Congo, Republic of the Cook Islands * Costa Rica Cote d'Ivoire Croatia Crozet Islands * Cuba Cyprus  † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † †  < 1.6% of maritime territory protected Desventuradas Islands * Djibouti Dominica East Timor Easter Island * Ecuador El Salvador Equatorial Guinea Eritrea Faeroe Is * Falkland Islands / Malvinas Islands * Fiji Finland France French Guiana * French Mozambique Channel Islands * French Polynesia * Gabon Gambia Gaza Strip * Georgia Ghana Gibraltar * Greece Grenada Guadeloupe * Guam * Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Jan Mayen * Japan (Pacific Ocean Coast & Sea of Japan) Jarvis Island * Johnston Island *  † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † †  169  < 1.6% of maritime territory protected Juan Fernandez Islands * Kenya Kerguelen Island * Kermadec Islands * Korea, Democratic People's Republic of Korea, Republic of Kuwait Latvia Lebanon Liberia Libyan Arab Jamahiriya Lord Howe Island * Madagascar Madeira Island * Malaysia Maldives Malta Marcus Island * Marshall Islands Martinique * Mauritius Mayotte * Mexico Micronesia, Federated States of Midway Islands * Monaco Montserrat * Morocco Myanmar Namibia Nauru Netherlands Netherlands Antilles * New Caledonia * New Zealand Nicaragua Nigeria Niue * Norfolk Island * Northern Mariana Islands * Norway Ogasawara Islands * Oman Pakistan Palau Palmyra Atoll * Papua New Guinea Peru Philippines Pitcairn Islands * Poland Portugal  † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † †  < 1.6% of maritime territory protected Prince Edward Island * Puerto Rico * Qatar Reunion * Ryukyu and Daitoshoto Islands * Saint Helena * Saint Kitts and Nevis Saint Lucia Saint Paul and Amsterdam Islands * Saint Pierre and Miquelon * Saint Vincent and the Grenadines Samoa Sao Tome and Principe Senegal Serbia Montenegro Seychelles Sierra Leone Singapore Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is * Spain Sri Lanka Sudan Suriname Syrian Arab Republic Taiwan, Province of China Tanzania, United Republic of Thailand Togo Tokelau * Tonga Trindade and Martin Vaz Island * Trinidad and Tobago Tristan da Cunha * Tromelin Island * Tunisia Turkey Turks and Caicos Islands * Tuvalu United Arab Emirates United Kingdom Uruguay Vanuatu Viet Nam Virgin Islands (British) * Virgin Islands (U.S.) * Wake Island * Wallis and Futuna Islands * Western Sahara *  † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † † †  † †  170  < 1.6% of maritime territory protected Yemen > 1.6% of maritime territory protected Australia Belize Colombia Denmark Egypt Estonia Greenland * Kiribati Lithuania Mauritania Mozambique Navassa Island * Panama Romania Russian Fed (All) Saudi Arabia (All) Sweden Ukraine United States of America Venezuela  > 10% of maritime territory protected Cameroon Dominican Republic Galapagos Islands * Germany Hawaii * Heard Island and McDonald Islands * Jordan Macquarie Island * Svalbard Island *  †  † † † † † † † † † † † † † † † † † †  † † † † † † † †  * overseas territory or non-contiguous US state † ratified / acceded to CBD  171  

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