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Small but mighty : a global reconsideration of small-scale fisheries. Govender, Rhona 2013

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  SMALL BUT MIGHTY: A GLOBAL RECONSIDERATION OF SMALL-SCALE FISHERIES  by Rhona Govender  B.Sc., The University of British Columbia, 2008  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Zoology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  October 2013  ? Rhona Govender, 2013 ii  Abstract Small-scale (SS) fisheries have sustained people for millennia and are pervasive in coastal communities across the globe. Now, the future of what was once believed to be an endless supply of resources remains uncertain given current conditions. The small-scale fisheries sector employs over 34 million fishers, which is at least 24 times more than industrial fisheries. The vast majority of these small-scale fishers reside in developing countries, and strongly rely on these resources for food security and poverty alleviation. Despite their significance, global marine fisheries have been deeply troubled in recent history due to overfishing and inadequate management practices. It is imperative that policy makers base their decisions on reliable data in order to adequately manage this troubling situation, however, current information regarding the small-scale fisheries sector is dubious at best. After compiling data as to what constitutes a small-scale fishery and the associated catch, by country, a multiple linear regression was used to predict data for countries where none was obtained. Human development index (HDI), inshore fishing area (IFA), and whether or not the data came from the FAO, can be used to explain the variance in catch, and predict catch where countries are missing data. The multiple linear regression in Chapter 3 provided the global SS fisheries catch estimate of 25 million tonnes, which is 19% higher than the previous estimates. It is crucial to note that this catch is almost equivalent to the estimated 29 million tonnes bound for human consumption from the industrial sector. In addition, it was seen that data originating from the FAO underestimates the catch in this sector, which is congruent with qualitative information obtained from the literature search in Chapter 1. Lastly, countries with a low HDI were found to catch more (5.29 t?km2) per unit area than those that are highly developed (1.76 t?km2). iii  Preface All research, compilation of data, and analyses necessary to produce this thesis were completed by myself. Several colleagues have contributed to the methodology, project design, and provision of data. Dr. Daniel Pauly, my supervisor, has provided key suggestions in regards to data sources, and the design and content of the generalized linear model found in Chapter 3. Valuable catch data were provided by the Sea Around Us project, which is a global collaboration of many talented researchers. This thesis was composed by myself, with helpful revisions from Dr. Daniel Pauly, and my committee members Drs. Rashid Sumaila and William Cheung.  iv  Table of contents  Abstract .......................................................................................................................................... ii Preface ........................................................................................................................................... iii Table of contents .......................................................................................................................... iv List of tables.................................................................................................................................. vi List of figures ............................................................................................................................... vii List of abbreviations .................................................................................................................. viii Acknowledgements ...................................................................................................................... ix Chapter 1: Introduction ................................................................................................................1 1.1 Problem statement ........................................................................................................... 1 1.2 Background and literature review ................................................................................... 3 1.2.1 Defining small-scale fisheries ..................................................................................... 3 1.2.2 Extent of small-scale fisheries .................................................................................... 5 1.2.3 Current catch, species, and valuation knowledge ..................................................... 10 1.3 Research objectives ....................................................................................................... 11 Chapter 2: A global database on key small-scale fisheries statistics .......................................13 2.1 Introduction ................................................................................................................... 13 2.2 Materials and methods .................................................................................................. 17 2.2.1 Defining small-scale fisheries ................................................................................... 17 2.2.2 Inshore fishing area ................................................................................................... 17 2.2.3 Review of small-scale catch data .............................................................................. 18 v  2.3 Results ........................................................................................................................... 20 2.3.1 Existing definitions ................................................................................................... 20 2.3.2 Catch ......................................................................................................................... 22 2.3.3 Inshore fishing area ................................................................................................... 23 2.4 Discussion ..................................................................................................................... 24 2.4.1 General ...................................................................................................................... 24 2.4.2 Data limitations ......................................................................................................... 25 2.5 Concluding remarks ...................................................................................................... 26 Chapter 3: Global re-estimation of small-scale catch ...............................................................28 3.1 Introduction ................................................................................................................... 28 3.2 Materials and methods .................................................................................................. 30 3.2.1 Independent and dependent variables ....................................................................... 30 3.2.2 Models and calculations ............................................................................................ 33 3.3 Results ........................................................................................................................... 35 3.4 Discussion and conclusions .......................................................................................... 41 Chapter 4: Conclusion .................................................................................................................44 4.1 Discussion ..................................................................................................................... 44 4.2 Strengths, weaknesses, and future work ....................................................................... 47 References .....................................................................................................................................51 Appendices ....................................................................................................................................60 Appendix A. Supplementary data for Chapter 2 ................................................................ 60 Appendix B. Supplementary data for Chapter 3 ................................................................ 76 vi  List of tables  Table 1.1. The varied descriptors of a small-scale fishery. ............................................................ 4 Table 2.1. Summary of small-scale fisheries definitions .............................................................. 21 Table 2.2. Summary statistics on the inshore fishing area (IFA) .................................................. 23 Table 2.3. Estimates of small-scale fisheries catches by HDI category. ...................................... 23 Table 3.1. Descriptive statistics of variables included in the study. ............................................. 31 Table 3.2. Top 5 models and the corresponding AIC values. ....................................................... 36 Table 3.3. Summary statistics for model 3. .................................................................................. 37 Table A.1. Small-scale fisheries definitions by country. .............................................................. 60 Table A.2. Inshore fishing area (IFA), catch (tonnes), and the sources by country ..................... 69 Table A.3. Sources for ?Other? categories in Table A.2 ............................................................... 71 Table B.1. Values of independent and dependent variables ......................................................... 76 Table B.2. Correlation matrix of predictor variables .................................................................... 86 vii  List of figures  Figure 1.1. The cycle of marginalization of small-scale fisheries . ................................................ 2 Figure 1.2. Relationship between SS fisheries subsectors. ............................................................. 5 Figure 1.3. The dual nature of the large and small-scale fisheries sectors  .................................... 9 Figure 2.1. Countries with and without SS fisheries catch values. ............................................... 22 Figure 3.1. Relationship between small-scale fishery catches, IFA, and HDI ............................. 34 Figure 3.2. Relative importance of predictors .............................................................................. 37 Figure 3.3. Model assumptions for Model 3.  ............................................................................... 38 Figure 3.4. Observed versus predicted catch ................................................................................ 40 Figure 3.5. Residual versus fitted values. ..................................................................................... 40  viii  List of abbreviations AIC  Akaike information criterion GDP  Gross domestic product GLM  Generalized linear model GRT  Gross register tonnage HP  Horse power IUU  Illegal, unreported, and unregulated LS  Large-scale MAM  Minimum adequate model RF  Random forest model SS  Small-scale SSF  Small-scale fishery SSF?s  Small-scale fisheries  ix  Acknowledgements First and foremost, I would like to thank my parents, Chris and Sylvia, and brother, Shaun for their endless love and support. Thank you, mom and dad, for nurturing my adoration of learning and marine life, and for making such big sacrifices so that I could have every opportunity to follow my dreams. Thank you Shaun, for teaching me so many important lessons during childhood. Thank you to my dear friend Emily for your unparalleled love, understanding, and drive. I am privileged to have you in my life. Thank you, Paul, for encouraging me so very much, and for reinforcing my belief in the goodness of people. Your boundless talent continuously surprises me. I would also like to thank the amazing students at the Fisheries Centre. You are some of the kindest people and inspire me to reach higher every day. Thank you for the endless laughter, fortitude, and sharing of expertise. I offer my gratitude to Shannon, who has helped me through some of my toughest challenges. One can?t help but admire both your professional skill, and vast compassion. Thank you Brett, for so graciously helping me to further my work. Special thanks are owed to my supervisor, Dr. Daniel Pauly, who has been a continual source of expertise. I am grateful to you for providing me with such a great opportunity, and for being so courageous in what you do. The indomitable spirit that you have for your work has influenced me profoundly. Thank you, Dr. Rashid Sumaila. You eternally positive attitude and depth of wisdom will never cease to amaze me. Thank you, Dr. William Cheung for the very insightful comments and suggestions that you have provided me with. I am grateful to both of you for finding time to help me, even during your busiest periods. I am indebted to the Pew Environment group for making this research possible.  1  Chapter 1: Introduction 1.1 Problem statement Human reliance on the ocean?s resources dates back at least 125, 000 years (Richter et al. 2008), beginning with what we now refer to as small-scale fisheries (SSFs), and at present continues to be a vital component of modern society. Currently, over 34 million fishers are considered small-scale, with 90 percent living in the developing world (B?n? et al. 2005). Therefore, the status, productivity, and resilience of fish stocks are fundamentally important for not only global food security and poverty alleviation, but also a multitude of factors ranging from environmental to social and cultural values.  Despite their significance, global marine fisheries have been deeply troubled in recent history, as exemplified by the considerable decline in fish stocks around the world, due to overfishing (UNEP 2012) and inadequate management practices (Clark 2006; FAO 2010). It is imperative that policy makers base their decisions on reliable data in order to adequately manage this present situation, and potential future crisis. However, current information regarding the small-scale fisheries sector is dubious at best (Berkson et al. 2009).   It is commonplace that fisheries of high economic value are the ones that are well studied and managed (Honey et al.2010). However, in the case of small-scale fisheries, a large proportion of stocks remain un-assessed and unmanaged, partly due to their relatively small size, and hence low revenue, and, added to this, an assumption of low importance (Honey et al. 2010; Pauly 2  2006). A current mode of operation regarding the collection of small-scale fisheries catch data exists in many countries, where an initial assumption of low or no importance leads to little or no monitoring by government officials (Figure 1.1). This low level of monitoring (FAO and World Fish Center 2008) leads to low catch being observed, or to low catches being assumed, and eventually, to low catches being reported to national (usually the Department of Fisheries) and international (Food and Agriculture Organization of the United Nations - FAO) statistical collection agencies. These underreported values do not reflect the true magnitude of the catch, and this cycle of marginalization is perpetuated when harried external consultants, such as those from the World Bank or FAO assess the small-scale fisheries of a region, and ?confirm? their negligible role due to the same biased local statistics being provided to them.   Figure 1.1. The cycle of marginalization of small-scale fisheries (SSFs).  3  The often numerous and remote nature of small-scale landing sites make it an arduous task to obtain complete estimates of catches and related statistics (Munro 1979), further compounding the chronic underestimation of catch in this sector. Together with the marginalization of the fisheries and the issues associated with the landing sites, a dearth of resources (ranging from financial constraints to lack of human capacity) in many countries render them unable to adequately monitor their small-scale fisheries sector (Berkson et al. 2009; Honey et al. 2010). These aforementioned factors lead to an underrepresentation in official statistics, and coupled with the belief that the small-scale (SS) sector is insignificant, masks the significant value this sector generates thereby severely affecting management policies that are based on their catches (FAO/FishCode-STF 2005; Pauly 1997).   1.2 Background and literature review 1.2.1 Defining small-scale fisheries Small-scale fisheries are highly variable, from the type of gear or boat used, to the distance from shore or nature of the activity. Also, as opposed to the prototypical view which sees men as the sole actors in SSFs, women - who actually account for approximately half the total workforce (FAO and World fish Center 2008) and children may be the key harvesters, and in some cases, they may define the traditional usage in the region (Levine and Allen 2009; Harper et al. 2013). Thus, it may be argued that one static definition cannot adequately encapsulate such a diverse and fluid concept as that of small-scale fishing (FAO 2006). Instead, a SS fishery can be viewed as a point on a continuum, defined variously in different countries, with several key elements 4  linking neighboring categories. Given the varied nature of SSFs (Table 1.1), a reasonable approach may be to accept the definition a country provides as to what constitutes a small-scale fishery within its waters1 (Bogason 2009; Chuenpagdee et al. 2006).  Though this may seem to be variable, it was shown that 65% of countries studied use boat size to define their SSF, with the majority of countries using a limit of less than 15 m (Chuenpagdee et al. 2006). Thus, while there are differences between countries as to what SSFs are, they are likely to be minor, particularly if we compare countries after grouping them by income (or per capita GDP) classes, or, as done by Chuenpagdee et al. (2006), who grouped the SSF of countries with similar Human Development Index (HDI2).         Table 1.1. The varied descriptors of a small-scale fishery (Chuenpagdee et al. 2006). Key features Common definition (range) Boat size From 5-7 m; less than 10, 12 or 15 m (2 to 24 m) Boat GRT Less than 10 GRT (3 to 50 GRT) Size of engine Less than 60 HP; between 40-75 HP (15 to 400 HP) Boat type Canoe, dinghy, non-motorized boat, wooden boat, boat with no deck, traditional boat Gear type Coastal gathering, fishing on foot, beach seine, small ring net, handline, dive, traps  Distance from shore Between 5-9 km, within 13 km, up to 22 km Water depth Less than 10, 50, or 100 m in depth Nature of activity Subsistence, traditional to ethnic group, local, artisanal Number of crew 2-3, 5-6 Travel time 2-3 hours from landing sites  In this work, small-scale fisheries are defined as consisting of an artisanal, subsistence, and recreational subsectors. The artisanal component of SSF catch is sold commercially in local                                                  1 Note, however, two exceptions to the criterion that it is countries which define SSFs: (i) vessels, however small, which pull an active gear such as a trawl or a dredge are not considered small-scale (in accordance with Mart?n 2012), and (ii) vessels operating outside of their national EEZ, or in the High Seas, are not considered small-scale. 2 Human Development index is one measure of socio-economic well-being. It is comprised of life expectancy, education, and income indices that are used to rank countries. It ranges from 0-1, where values closest to 1 indicate more highly developed countries (Stanton 2006). 5  markets (Singh 2005), whereas the catch of subsistence fishers are consumed by the fisher or kin and not sold commercially (FAO 2012a). Lastly, a recreational fishery occurs primarily for enjoyment, where the primary motivation is not consumption or selling the catch (Cisneros-Montemayor and Sumaila 2010). Within the spectrum alluded to above, these fisheries types may blend into each other, such as in the case of recreational or subsistence fishers selling part of their catch, or artisanal fishers directing part of their catch for consumption by their families (Figure 1.2).    Figure 1.2. Relationship between small-scale fisheries subsectors, illustrating their overlap.  Lastly, due to the assumption that small-scale fishers are limited by the distance they can travel in a day, these fisheries overwhelmingly take place within the inshore fishing area (IFA). The IFA is defined as ?the shelf area ranging from shoreline to 50km in distance or 200m in depth, whichever comes first? (Chuenpagdee et al. 2006). 1.2.2 Extent of small-scale fisheries Small-scale fisheries have persisted for millennia and are pervasive in coastal communities across the globe (Sahrhage and Lundbeck 1992). The industrialization of the sector, which began 6  in the late 19th century, flourished in the 20th century (Pauly et al. 2002). As a result, what was once believed to be an endless supply of resources that had sustained many human populations over time was heavily impacted (Jackson et al. 2001). The FAO estimates that the proportion of marine stocks that are ?overexploited, depleted, and recovering? increased from 10% in the 1970s to above 30% in 2008 (FAO 2010). Moreover, recent work indicates that catch per effort is continuing to decline (Watson et al. 2012), and that 70% of global fish populations are classified as unsustainably overexploited (FAO 2012b).Though these numbers may be contested (Pauly et al. 2013; Pitcher and Cheung 2013; Worm et al. 2009), this trend is troubling, at best. Small-scale fisheries are integral to many communities and appropriate attention must be paid due to their significance.  If we take the FAO landing statistics at face value, approximately half of the world?s haul of fish destined for human consumption is supplied by the small-scale sector. In addition, this sector provides the main source of protein to 1 billion people around the world (FAO 2005a) and for every fisher, three other people are engaged in fisheries related jobs (Avenda?o 2006). A great number of people depend on these resources and the benefits derived from them, so the stability of the stocks has widespread implications. As the majority of SS fishers are in the developing world, the nutrition fish provide is a necessity for food security and for maintaining a healthy diet (UNEP 2012; Srinivasan et al. 2010; Srinivasan et al. 2013).   Small-scale fisheries also contribute substantially to local economies and tend to be chronically and largely undervalued (Honey et al. 2010; Zeller et al. 2007). They also proportionately create more wealth than the commercial sector in the developing world (Avenda?o 2006). In the 7  developing world, barriers to entry into occupations other than fishing tend to be high (B?n? 2003; Pauly 1997) and many fishers are drawn from the ranks of landless farmers and hence are very poor (Dunn 1989). This phenomenon is the ?Malthusian overfishing? of Pauly (2006), wherein landless farmers migrate onto the coast and start fishing, and thus compete with the local fishers and challenge existing management systems. This consequently affects the income of all fishers and the sustainability of the stocks.   Currently, 87% of stocks are classified as fully exploited, overexploited, or recovering by the FAO (2012b) and management quality is poor in both the developed and developing world. Management is considered to be better in the developed world due to the use of stock assessment models, however, it is still considered poor when both efficacy and adherence to regulations such as the FAO (UN) Code of Conduct for Responsible Fisheries are taken into account (Pitcher and Cheung 2013). The future of the sustainability of fisheries remains uncertain given the inadequate management across the globe and current status of the majority of fish stocks. Notably, there is increasing competition for decreasing resources between the small and large-scale sectors (Pauly 2006; FAO 2012b), with the large scale being usually favoured by politicians, as measured, e.g., by their allocation of subsidies (Jacquet and Pauly 2008). This creates a tenuous situation for those that rely on fisheries resources the most. In the developing world, many of the fishers lack political power due to their low socioeconomic status (Pauly 1997) and tend to be left out of decision-making and political plans (Friedman 1992), thereby leaving those most vulnerable without adequate security to a necessary resource.  8  It is important to note that being small-scale does not necessarily mean that a fishery is sustainable. SS fishers can be quite efficient and sometimes utilize destructive fishing practices such as dynamite fishing (UNEP 2012; FAO 2005b). Even though the catch of the small-scale sector is underestimated and some fishers partake in poor fishing practices, it has the potential to be much more sustainable than the industrial sector due to beneficial aspects such as the highly selective gear used and traditional knowledge of the fishers (Figure 1.3).  Figure 1.3 illustrates some of the major differences between the large-scale (or industrial) and small-scale sectors (Pauly 2007). Not only do SS fishers catch 4 times more fish per litre of fuel used (Jacquet and Pauly 2008), but the overwhelming bulk of the catch goes to human consumption, which is not the case for industrial fishing (notably because of ?reduction fisheries?, i.e., fishing for fishmeal). It will be shown later how and why these ratios actually underestimate the contribution of small-scale fisheries. Thus, if small-scale fisheries were adequately studied and managed (both economically and politically), they would have the potential to be the sustainable future of fisheries - in combination with sustainable practices in the industrial sector.  9   Figure 1.3. The dual nature of the large and small-scale fisheries sectors (Pauly 2007; Sumaila et al. 2001), based on data now shown to underestimate the small-scale sector (see text).  FISHERY  BENEFITS LARGE SCALE   SMALL SCALE   Number of fishers employed  about ? million  over 12 millions Annual catch of marine fish for human consumption  about 29 million tonnes  about 24 million tonnes Capital cost of each job on fishing vessels  $30,000 - $300,000  $250 - $2,500 Annual catch of marine fish for industrial reduction to meal and oil, etc.                          about 22 million tonnes  Almost none Annual fuel oil consumption  14 ? 19 million tonnes  1 ? 3 million tonnes Fish caught per tonne of fuel consumed   =   2 ? 5 tonnes  =  10 ? 20 tonnes Fishers employed  for each $1 million invested in fishing vessels   5 - 30   500 ? 4,000 Fish and invertebrates discarded at sea    10-20 million tonnes  Little  10  1.2.3 Current catch, species, and valuation knowledge Currently, the primary source of information in regards to key fisheries statistics such as catch, landings, species breakdown, and the value of landings is through the FAO, which, among other things, summarize these data in the form of ?country profiles?. The FAO began collecting fisheries data in 1950 in an attempt to monitor the status of global fisheries (FAO and World Fish Center 2008). Member countries routinely provide these statistical data to the FAO on a voluntary basis. However, key components of catch statistics (i.e., the unregulated component; the contribution of women to catch) are often missing, or the SS sector is buried within the industrial component (FAO/FishCode?STF 2005; Chuenpagdee et al. 2006; Johnson 2006). As previously mentioned, many countries do not have the resources to adequately monitor their SSF, with the result that these fisheries tend to be marginalized and, due to the trouble associated with collecting data from remote landing sites, catch data are either missing the small-scale component, or it is severely underrepresented. There are many examples of small-scale social science studies aimed at collecting very localized data, such as village catches and number of fishers, through such means as household surveys (Kronen et al. 2008; Merlijn 1989), but again, these data are at the small community level and are usually not raised to the national level, and thus do not enter international statistic databases.  Chuenpagdee et al. (2006) created the first detailed estimate of global small-scale catch at the country level and then aggregated globally catch, which yielded a global estimate of 21 million tonnes per year. However, this estimate was based on data with many gaps, and broad assumptions had to be made to fill in these gaps.   11  The Sea Around Us Project (, which was created in 1999 at the Fisheries Centre of the University of British Columbia, has developed a method for ?reconstructing? time series of fisheries catch data, including the small-scale sector, from the bottom up (Pauly 1998; Zeller et al. 2007). Thus, detailed small-scale fisheries catch data currently (June 2013) exist in various forms for nearly 200 countries and island territories (D. Pauly, Sea Around Us Project, pers. comm.), although a smaller number of these reconstructions were available for this analysis (see below).  Recently, small-scale fisheries have been thrust into the political and academic limelight, and a strong focus on establishing the data gaps and the next avenues of research is in place. Piecing together all currently available data will provide a platform that would form the most comprehensive estimate of key SSFs statistics to date.  1.3 Research objectives A consensus exists among fisheries scientists that small-scale fisheries play a vital role in numerous communities and historically, not enough political attention has been placed on this sector, with the majority of resources focused on the expansion and management of industrial fisheries (Andrew et al. 2007). Though some may believe sustainability and food security issues are only ?buzzwords? used to garner attention, the reality of the situation is that millions of people would have few or no alternative employment opportunities or sources of animal protein if fisheries were to collapse. Many regulations have been put in place, and studies undertaken to try and remedy this situation (Worm et al. 2009) but the data on which these regulations are 12  based are incomplete and thus often unreliable (Honey et al. 2010; Zeller et al.2007; FAO and World Fish Center 2008)   With a strong understanding of the current data poor situation, this work aims to form a basis upon which more effective fisheries management policies can be based. The second chapter of this thesis details the global database of catch statistics that was assembled for this work. In the third chapter, the methods and model (linear multiple regression) used to fill in data gaps where none of these values were found or existed are presented. These data will be used to test conclusions from various agencies (i.e., the FAO) that the catch in this sector is underestimated. The fourth chapter ties the whole thesis together, provides a summary of key finding, and offers some suggestions for future direction.   13  Chapter 2: A global database on key small-scale fisheries statistics  2.1  Introduction  Small-scale fisheries have historically been important to numerous communities across the globe (B?n? 2006), and continue to be an integral component of many societies, as this subsector has major implications for food security and poverty alleviation, as well as significant cultural value. These fisheries provide more than 1 billion people with a source of protein and employ 22-28 million people directly through fishing, or in secondary sectors such as fish processing (Alfaro-Shigueto 2010; Teh and Sumaila 2011). It is also estimated that 160 million family units rely on these resources and income (McGoodwin 2001).  The SSF sub-sector is diverse and dynamic, often evolving as the taxonomic composition and/or relative abundance of the underlying resources change. These fishers can adapt to variable seasonal conditions by changing gear types and targeting different species (Johnson 2006). They tend to be remote and dispersed (FAO 2004), and operate in closer proximity to the shoreline than industrial fisheries, as they use smaller vessels (McConney and Charles 2008). The decentralized nature of the fisheries can provide the added bonus of generating employment and ensuring the food security of households, and allowing resource rent to remain within communities (FAO 2004).These fisheries also have higher opportunities for employment and are less expensive to operate than industrial operations (Johnson 2005). It is also important to note that when compared to large-scale fisheries (LSF), small-scale fisheries proportionally create 14  more wealth (Avenda?o 2006). It is clear that SSF?s play a vital role in the communities in which they are based, and thus this sector should receive due consideration.   Humans have relied on marine resources for millennia (Kronen 2007). Studies have shown that the taxonomic composition of some small-scale fisheries catches is generally similar to that of earlier periods (Levine and Allen 2009), indicating the potential for sustainability in this sector. This potential is likely due to the fishing methods and gear characteristics that are inherent to SSFs, which in many fisheries are the more sustainable method. Now that fisheries, worldwide, have been largely industrialized and followed with rapid increase in fishing effort, we find ourselves heading toward a potential crisis in fisheries; yet small-scale fishing still remains, and will likely continue to be a key factor for food security, contribution to local economies and employment, as well as traditional practices (Levine and Allen 2009).   In the last 50 years, massive resources have been devoted to make the industrial sector more efficient in fishing, notably through the use of advanced technology such as GPS and side-scan sonars. Also, industrial fisheries have been expanding geographically (Levine and Allen 2009; Swartz et al. 2010). This increase in efficiency and expansion, however, does not come without a cost. These two factors when combined with inadequate management practices and excess catch in many fisheries, have led to the instability in global fisheries. In recent decades, overfishing, due to the aforementioned factors, has led to the collapse and depletion of many key fish stocks. It is estimated that seventy-five percent of industrially important stocks are overfished, or on their way to being overfished (Levine and Allen 2009). It is possible that we have lost sight of what sustainability means, and how we can achieve it. Vast financial resources, in the form of 15  capacity enhancing or negative subsidies, have been dedicated to the LS sector. In addition to making industrial fleets more efficient through increased technology, support has been allocated to increasing and renewing the fleet, improving fish landing site infrastructure, and marketing support (Sumaila et al. 2010). Troublingly, the majority of these subsidies are allocated to the LS sector (Jacquet and Pauly 2008), but in a small-scale fishery, these negative subsidies could have positive outcomes and therefore be considered to be good ones. For example, many small-scale fleets are in need of repair or renewal, and if infrastructure and marketing was enhanced, it would create a situation where these fishers would be more competitive in the market (Jacquet and Pauly 2008). Greater investment in these elements in the SS sector can alleviate the problems associated with the future viability of global fisheries, if we refocus on the principle that small-scale fisheries can form the basis of sustainable fisheries (Avenda?o 2006).  In order for the true potential of SSFs to be realized, more resources need to be invested in the sector, and we need a better understanding of these fisheries and the role they play in communities. Due to several factors, small-scale fisheries have historically and continue to be undervalued at both the international and national levels (Graaf et al. 2011). These fishers are often marginalized due to the belief that the fisheries are insignificant (Figure 1.1) and due to their lack of political power (Pauly 1997). Additionally, many countries lack the resources and personnel to adequately monitor what they perceive as lower value small-scale fisheries (Honey et al. 2010). This creates a situation where national governmental agencies underestimate, and therefore underreport the catch and related statistics of their small-scale fisheries to international organizations, especially to the FAO. The marginalization of this sector is responsible for its underdevelopment and undervaluation (Graaf et al. 2011), as well as leaving a large group of 16  vulnerable people without security ? in regards to both access and sustainability of the stocks (Avenda?o 2006). One of the key repercussions is that with this lack of recognition, there has been a failure to estimate the actual and substantive role that this sub-sector plays in the economic growth and stability at the country level, as well as the community and household levels (FAO 2004).   In order to create more effective policies to manage and assess SSFs, their magnitude must be known, as well as other key fisheries and social metrics. Although millions of people rely on this sector, with the majority residing in the developing world (B?n? et al. 2006), there are still large gaps in data about SSF. It is widely recognized that more attention needs to be paid to this sector (FAO 2004, FAO 2005a, Alfaro-Shigueto 2010), and attempts have been made to try and rectify some of the problems. FAO had projects related to small-scale fisheries valued at 80 million dollars in 2004 (FAO 2004), and that number is now likely to be larger. However, focus needs to be put on gathering relevant fisheries metrics such as catch and biological information before more meaningful management policies can be put in place.   To establish the importance of the sector in the eyes of political leaders, more research will have to go into SSFs. At the outset, an initial desktop assessment based on collecting available small-scale data, such as catch and effort, needs to be undertaken (FAO 2004). In the small-scale fisheries sector, more and better disaggregated data must be available for more effective social, environmental, and economic policies to be designed. This desktop study provides a centralized and accessible database, using the most reliable data to date, thereby forming a basis from which key local and global analysis can be done. 17  2.2 Materials and methods 2.2.1 Defining small-scale fisheries A literature review was conducted in order to aggregate the definitions of small-scale fisheries, as defined by the country in question, for every maritime country. These data were primarily obtained from FAO country profiles3. Additional information was collected from Chuenpagdee et al. (2006) to supplement the definitions that were not found from the FAO. Lastly, information from primary and secondary literature, as well as independent governmental and non-governmental reports, was used where available. It was assumed that all maritime countries and territories that are inhabited and engage in fishing participate in either artisanal, subsistence, or recreational small-scale fishery.  2.2.2 Inshore fishing area Small-scale fishing activities were assumed to take place within the inshore fishing area (IFA) of a country, because SS fishers tend to perform day trips and thus the distance over which they fish is limited, as is, in most cases, the water depth that they can access. Thus, IFA is defined as ?the area of its shelf within the Exclusive Economic Zone of a country from the shoreline to 50 km offshore or 200 m in depth, whichever comes first4? (Chuenpagdee et al. 2006; see also Appendix A1). These inshore fishing areas were calculated in ArcGIS using bathymetric data                                                  3; Varying levels of information are available for 174 countries. 4 Note that this definition implies that if what otherwise looks like a small-scale fishery is conducted outside of one?s EEZ, it is not considered to be small-scale (see also footnote 1). For example, the fisheries in Guinea-Bissau and Guinea by long-distance ?pirogues? from Senegal is not considered a small-scale fishery, despite their boats being smaller than industrial vessels and their artisanal fishing methods.   18  (Amante and Eakins 2009) from the Sea Around Us project world parameters table (?World Table?). The World Table is based, among other things, on the Land-Ocean Interactions in the Coastal Zone Project data cell system (LOICZ 1993). The cell matrix consists of 259,200 individual cells of 30? by 30?, i.e., 360 rows and 720 columns. This World Table contains physical oceanographic data such as temperature and primary productivity, as well as demographics such as population numbers. 2.2.3 Review of small-scale catch data Various sources were reviewed in order to collect catch data of the small-scale fisheries for 216 countries and territories included in this study. Data were compiled from FAO country profiles, reconstructed country totals from the Sea Around Us project, sources detailed in Chuenpagdee et al. (2006), the Big Numbers Project (2008), and a thorough search of primary publications, governmental and NGO reports. Data from such a large variety of sources are likely to be variable in quality (Rosenberger and Stanley 2006), and thus a detailed list of sources and values maybe found in Tables A2 and A3, from which the reliability of the data used here can be assessed.  It is important to differentiate between the terms ?catch? and ?landings?. Catch includes the discarded and unreported fish component, and while landings do not. Data in this database are from sources that are a combination of landings (i.e., FAO data) and catch (Sea Around Us and Big Numbers projects). The term catch will be used hereon in, as the majority of these sources aim to include these missing components (Zeller and Pauly 2005). 19  The FAO is required to collect data on food and nutrition, which includes fisheries related information, in order to assess the level of hunger and malnutrition by country (Ward 2004). Member countries volunteer to provide fisheries statistics to FAO on an annual basis, which is then analyzed and disseminated by the FAO (Jacquet et al. 2010). However, these statistics are likely to be an underestimate, as detailed in Chapter 1. The majority of independent reports considered here also do not include components such as subsistence catches and are thus likely to underestimate SS fisheries catches. The Sea Around Us Project aims to reconstruct the fisheries of a country (from 1950 to present), from the bottom up, to include illegal, unreported, and unregulated (IUU) catches, which are often missing from official statistics. Data are collected from numerous sources (such as peer-reviewed and grey literature and governmental and governmental reports), and are likely to be more reflective of the true catch in a country, given that these missing components are explicitly accounted for (Zeller et al. 2007).   Another important source of reconstructed information was found in the Big Numbers Project, a joint project of the FAO, the World Fish Center, and the World Bank. This project aimed to reassess fisheries in key countries in order to address the inaccurate nature of a lot of data regarding small and large-scale fisheries, through the use of case studies (FAO and World Fish 2008).   Chuenpagdee et al. (2006) aimed to reassess the small-scale fisheries of every maritime country. Data were collected from sources similar to those relied upon here, and a method was developed to estimate catch data for countries where no data were available. This consisted in grouping countries by high, medium, or low ?human development index? (HDI), and computing average 20  catches per km2 within these three groups, which were then applied to the IFA of countries with missing data to estimate their catch (Chuenpagdee et al. 2006).   A base year of 2005 was used where possible, and the closest year available was used in situations where data for 2005 were lacking. Additionally, offshore territories of metropolitan countries (e.g. Pitcairn Islands, a U.K. territory) were treated as separate entities when compiling the data. Lastly, countries spanning a large area (e.g., the U.S.) were split into more meaningful and manageable subareas, as environmental variables (e.g., sea surface temperature) are not likely to be consistent when representing very large areas. Mean catch density, which is the average catch (in tonne) per km2 of IFA, was calculated by dividing catch by the IFA of a given country. Lastly, China was identified as the only significant outlier, thus the analysis was done with and without the inclusion of this value as this value appears to be unreasonable.  2.3 Results 2.3.1 Existing definitions Small-scale fishing was found to take place in 216 maritime countries and territories (henceforth: ?countries?), and definitions for 115 of them were found. Of the countries where a definition was found, one major similarity is that 65% (75 countries) use the length of the boats as a key indicator in defining their small-scale fisheries. The boats range from 4-16 m in length, with 15 m being most common. SS fisheries were also defined based on gross registered tonnage (GRT), and were classified as less than 50 GRT in all countries for which size data were available. 21  Additionally, horsepower (HP) is also a key factor, ranging from 5 HP in Syria to 400 HP in the Congo (Brazzaville). Not as commonly, definitions included the distance from shore (up to 22 km), type of boat used (dugout canoe in Madagascar), and whether it is a traditional activity in the country (e.g., subsistence fishing in Australia), or use traditional methods (e.g., in Tanzania) or not (Table 2.1, Appendix A.1).  Table 2.1. Summary of small-scale definitions by key features. Total number of countries using each key feature is included, as well as the respective percent of total countries. n = 115 observations.  Key Features Range of Definitions Total % of total Boat size < 10 m in length, 2 to 35 m in length, up to 2.10 m wide  71 62 Boat type open planked wooden boats, with outboard or inboard engines, traditional, beam trawlers, canoe, day boats, sail boats, outriggers, pirogues, diesel 30 26 Location near shore, inshore, coastal, up to 40m or 30 miles from shore, shallow waters, intertidal zone, up to 100 m in depth, continental shelf, 2-3 hours from landing sites 28 24 Boat volume 10 tons, < 50 GRT, holds less than 33 metric tonnes  24 20 Engine size < 75 HP, small to medium engine 20 17 Method trap, hand lining, gill netting, collecting, diving, beach seining, trammel net, spearing, traditional 17 145 Descriptors low cost, labour intensive, unsophisticated gear, supplementary activity, produce small catches, species targeted, knowledge intensive, family scale 7 6 Type of fishery subsistence, artisanal, traditional, coastal or island ethnic groups 4 3.5 Crew size 2 to 16 3 2.6  22  2.3.2 Catch Catch data were found for 119 of the 216 countries (Figure 2.1) in this study (55%), of which 31 are classified as highly developed (HDI of 0.8 or higher), 57 have a medium HDI (0.5-0.79), and 31 are low on the development scale (<0.50). This leaves 97 countries where no quantitative data were found. Of the countries studied, 73% had data from a source other than that of the FAO, and in total, data for 44 countries (37%) came from Sea Around Us reconstructions.   Figure 2.1. Countries with and without small-scale fisheries catch values. Green = high HDI; yellow = medium HDI; red = low HDI; grey = no data; white = inland countries  Catches ranged from the maximum of 11,483,000 t in China to 13.8 t in the Pitcairn Islands (Table 2.2, Appendix A.2). On average, countries catch 227,000 t of fish, but the median is only 12,000 t.  23  Table 2.2. Summary statistics on the inshore fishing area (IFA) of countries with catch data. Summary Catch (t) IFA (km2) % of Total Area Average 227,000 50,000 0.63 Median 12,000 15,000 0.11 Maximum 11,483,000 1,089,000 7.8 Minimum 13.8 8.6 6.2E-05   The sum of the catch for all countries with ?hard? data points (i.e., not estimated from the model to be presented in Chapter 3) in the database is 27 t when China is included and 15.5 t when it is not included. Mean catch density was found to be 1.76, 4.59, and 5.29 t?km2 in high, medium, and low HDI categories, respectively (Table 2.3).   Table 2.3. Estimates of small-scale fisheries catches by HDI category. HDI Category High Medium Low No. of countries with data  31 57 31 Mean catch density (t per km2) 1.76 4.59 5.29 Average HDI 0.854 0.676 0.411  2.3.3 Inshore fishing area The area available for countries to fish within (IFA) widely fluctuated and ranged from 8.6 km2 in Nauru to the largest value of 1,089,000 km2 in Indonesia (Table 2.2). The average inshore fishing area was seen to be 50,000 km2, with a median value of 15,000 km2. 24  2.4 Discussion 2.4.1 General The range of definitions of small-scale fisheries in different countries revealed in this thesis is consistent with the results of Chuenpagdee et al. (2006). The definition of a small-scale fishery is not static (FAO 2006), but instead varies according to the current conditions of the fisheries, and is meaningful to the country in question. As there is often confusion about what constitutes a SS fishery (Bogasun 2009), aggregating the definitions of this sector enables us to better understand what it means to be small-scale where no definition is formally provided. It is seen (in Tables 2.1 and A.1) that SS fishers generally utilize boats that are smaller than the industrial sector in a given country, and operate closer to shore. They often use less sophisticated technology, and are able to use multiple gears and methods such as diving and reef gleaning. The activities are commonly described as ?traditional?, as they employ methods historically used by small-scale fishers and their ancestors. This information is also commonly reflected in the literature related to this sector (McConney and Charles 2008; Johnson 2005; FAO 2004).  A wide range of countries were included in this database. As with all estimates, it is important for the sample set to be reflective of the population which is being studied (Walker and Joseph 1953). This database includes coverage across a wide geographic range, i.e., a range of countries across most latitudes were included (Figure 2.1). Also, socio-political factors are included, which allowed distinguishing 31, 57, and 31 countries with high, medium, and low Human Development Index (HDI), respectively. In addition, countries with a spectrum of IFAs ranging from Pitcairn Islands (small IFA) to Indonesia (large IFA) were included. A greater number of 25  more complete and therefore more accurate sources were used when compared to previous studies (i.e., Chuenpagdee et al. 2006).   The annual catches ranged from a low of 13 tonnes in Pitcairn, which is likely, in part, due to the limited area available to fish within (IFA = 78 km2). Conversely, it is seen that the SS sector of China, with a moderate IFA of 358,000 km2 was able to catch 11.5 million t of fish. IFA does not fully explain the variability in catch, and other variables will be explored in Chapter 3 of this thesis. Mean catch density was found to be inversely related with HDI level. Catch per km2 was lowest in countries that were categorized as having a high human development index (0.85 on average), and lowest in counties that have a low human development index (0.41 on average). This shows that fishers in countries that are more socioeconomically challenged fish more than countries that are better off.  2.4.2 Data limitations It was seen in numerous cases that only a part of the small-scale catch was included (e.g., only the artisanal catch, that is the catch that is marketed, but not subsistence catch, which are directly consumed by the fishers and their family), or only data from one village, or province, rather than the whole country. Additionally, it was often hard to decipher if a fishery was considered small-scale, especially in the FAO profiles. Numerous terms, i.e. ?artisanal?, ?small-scale?, ?small-scale commercial?, ?traditional?, ?coastal?, ?subsistence?, ?recreational? are used, often without definition. Indeed, some of the terms used to define small-scale fisheries in some countries 26  defined industrial component in other countries. Therefore, data was used only if it was clearly identifiable as being small-scale in nature.   Additionally, it has been shown that fisheries catch data from China is often unreliable, as there is a tendency towards over reporting (Watson and Pauly 2001). The value of 11.5 million t from the FAO and World Fish Center (2008) appears to be quite large. Further inspection of this estimate must be undertaken in subsequent work.  The Sea Around Us and Big Numbers projects both recognize that vast gaps exist in current data (FAO and World Fish Center 2008; Zeller et al. 2007) and both aim to obtain more complete and accurate estimates of the fisheries catch of the maritime countries of the world. As only 41% of the catch data comes from these more complete sources (44 catch data points are from the Sea Around Us reconstructions and 5 from the Big Numbers Project), it is likely that the present work will still underestimate the true catch of small-scale-fishers around the world. 2.5 Concluding remarks In the fall of 2013, catches from all maritime countries of the world and their territories (over 250 entities) will be covered by the Sea Around Us Project, which will allow testing the reconstructed SSF catch then obtained, against the data included in the database describe here.    As the current database stands, it suggests a global small-scale fisheries catch of 27 million t?year-1 in the mid-2000s when China is included and 15.5 million t?year-1 when it is excluded. Either way, we see that the catch of small-scale fishers is not negligible. This sector does indeed 27  provide a significant source of protein to millions of families, as well as contributing greatly to the rural economies of many countries around the world (see, e.g., Zeller et al. 2007).   Given that a varied definition of small-scale fisheries has historically, and is currently being used (Bogason 2009; Chuenpagdee et al. 2006) due to reasons discussed in Chapter 1, it will be informative to see if a more static definition will provide any benefits or disadvantages to the fishers. The European Union (EU), has a common fisheries policy, and also recently mandated a common SS fisheries definition which includes boats less than 12 m that use non-trawl gear (Martin 2012). The stipulation that vessels must have a length less than 12 m may seem reasonable, as this would overlap with the majority of small-scale definitions. However, an argument can be made that the EU is comprised of 28 states that vastly differ in their fisheries fleets and practices. Most important is the exclusion of trawlers, regardless of size, from the definition. Many fishermen will be excluded by this rigid definition, e.g., Finnish trawl fishermen (Salmi 2008).  Chapter 3 will explore the multiple linear regression statistical model that was developed to predict SSF catches for countries for which no or insufficient data were found, using several independent variables. As will be shown, this model lends further support to the fact that the catches of small-scale fisheries are not negligible, and that we must break the cycle of marginalization presented in Chapter 1 (Figure 1.1), by challenging the assumption of low catches in this important sector. 28  Chapter 3: Global re-estimation of small-scale catch 3.1 Introduction Fishing has been occurring for millennia (Richter et al. 2008), ranging from the catch of small inshore species to apex predators. Due to the industrialization of fisheries, the spread of fisheries into new areas (Pauly et al. 2003), and an increasing human population, managing fisheries, which requires that the catch and effort of fisheries be monitored, is more important than ever. Monetary resources in fisheries tend to be directed toward fisheries with substantial levels of output (Honey et al. 2012), therefore often ignoring the small-scale fisheries sector (see Chapter 1).   There are often dire consequences for having incomplete data. Firstly, this may mask true contribution of this sector to numerous communities. If the magnitude of SS fisheries continues to be underestimated, it is inevitable that these fishers will be left out of political and management decisions, time and time again. Additionally as hundreds of millions of people rely on the SS sector, for both food and income, the sustainability of the resource is irreplaceable (McGoodwin 2001). As fisheries resources have long been an important contributor to society, the Food and Agriculture Organization of the United Nations (FAO) began, since the mid-1940s, to collect and publish fisheries data on the fisheries of the world, and issuing annual compendia of world fisheries statistics since 1950, based on reports by member countries (FAO and World Fish Center 2008; Pauly and Froese 2012). However, the small-scale sector is often missing or underreported in the statistics due to a variety of reasons (Johnson 2006). This may be due to the 29  assumption of insignificance and therefore lack of monitoring of the SS sector (See Figure 1.1). Also, this may also be due to the fact that the majority of small-scale fishers reside in the developing world (B?n? 2006), where resources to monitor SSFs tend to be scarce In order to properly manage a fishery, one must know how much is being caught, at the very least.   The present study aimed, firstly, to test for a relationship between several independent variables (latitude, inshore fishing area, human development index, sea surface temperature, and primary production) and small- scale fisheries catch by country. Secondly this study aimed to develop a model that could be used to predict catch in countries where no catch data was available, given a set of predictor variables. Two of the key variables examined here are inshore fishing area (IFA), which sets the boundaries in which fishing can occur (Chuenpadgee et al. 2006, Chapter 1) and primary production (PP). PP has been, for obvious reasons, closely linked to fisheries catch, i.e., it has been shown in the LS sector that it constrains maximum catch both at the regional scale, and in large marine ecosystems (Chassot et al. 2010), and is likely to have the same effect on SS catch within the IFA.   It is imperative to collect existing catch data as a baseline to understand their relationships with other variables, from which generalizable inferences can be drawn. Though there is a debate about the role catch data plays in fisheries science (Branch et al. 2011; Kleisner et al. 2012), at the very least, it can aid to ending the cycle of marginalization, by contradicting the initial assumption that the catch of SS fisheries is insignificant.  30  3.2 Materials and methods 3.2.1 Independent and dependent variables  Catches were assembled from various sources, including the FAO country profiles (data from Chuenpagdee et al. 2006, FAO and World Fish Center 2008), the Sea Around Us Project catch reconstructions, and various other sources detailed in Chapter 2 (Tables A2 and A3). The catch of China was estimated to be about 11,500,000 (FAO and World Fish Center 2008), which is a significant outlier. This point was removed due to the dubious nature of this extreme value, but could be added later, following further inquiry into its validity.  Indicators of small-scale (SS) fisheries catch were calculated for all countries in which SS fishing takes place. These indicators are: 1) inshore fishing area (IFA), 2) latitude, 3) ?reefiness? of the fishing ground (% of the IFA that is considered coral reef), 4) sea surface temperature, 5) Human Development Index (HDI) and 6) primary productivity (PP) per area. Two dummy variables were also included: 7) whether the data was from the FAO or not and 8) from the Sea Around Us reconstructions or not. All numeric values except for latitude and HDI were calculated in ArcGIS, within the IFA, i.e., from the shoreline to 50 km in distance or 200 m in depth, whichever came first (Chuenpagdee et al. 2006). They were also calculated and averaged on a per cell basis, with one spatial cell equal to a half degree longitude by half degree latitude. (Initial model construction included estimated data from Chuenpagdee et al. (2006). However, because these data were based on both HDI and IFA, they were subsequently removed in order 31  to avoid circularity). All of the data for independent and dependent variables may be found in Table B.1 and are summarized in Table 3.1, below.  Table 3.1. Descriptive statistics of independent and dependent variables included in the study (n=223 observations). The two dummy variables are not included (see text). *Standard deviation for catch was calculated after removing the missing values. Variable (units) Mean Median Minimum Maximum Std. dev. Catch (t?year-1) 138,882 9,924 13.8 2,169,557 345,432* Inshore Fishing Area (km2) 62,5450 13,986 3.70 1,901,344 191,126 Latitude (oN or S) 14.196 15.18 -54.62 81.82 28.03 Human Development Index 0.714 0.766 0.231 0.985 0.18 Temperature (0Celsius) 20.8 24.6 -1.1 28.8 7.91 Primary prod. (mgCm-2d-1) 869 638 123 37202 6440 Reefiness (%) 0.003036 0.000176 0 0.162374 0.01   Inshore fishing area was calculated in ArcGIS. Firstly, a 50 km buffer from the basemap's shore line was created. Then, the bathymetric map was overlaid to determine the location of 200 m depth. A polygon was subsequently created to define the IFA area. Using the ?Calculate Polygon Area? function of ArcGIS, the area of the IFA in square kilometers was calculated for each country and offshore territory. Bathymetric data was obtained from the Sea Around Us world parameters table (?World Table?), and is mainly based on the Land-Ocean Interactions in the Coastal International Project (available at cell system. The cell matrix consists of 259,200 individual cells of 30? by 30?, i.e., 360 rows and 720 columns.  32  Latitudes were determined from, which displays the central latitude of a country. Upon visual inspection, those countries with distributions skewed north or south were adjusted to correct for asymmetry.  Reefiness, the percentage of a cell or area that is ?reefy? originates from data derived from the coral reef occurrence model of Kleypas (1997); it does not indicate absolute coral cover, as this is difficult to standardize worldwide, and had led to frequent disagreements. Equation 3.1 was used in ArcGIS to calculate overall reefiness values within an IFA, where i is a cell within a country?s EEZ and n is the total number of cells within that country.  ?                        (                                            )                                                                                (3.1)  Climatological average (2002-2012) sea surface temperature was calculated using the MODIS sea surface temperature dataset (, based on the average values of 0 m and 10 m in depth. Values were calculated in ArcGIS, and averaged over the entire IFA.  Human development index (HDI) was obtained for the year 2005 from the United Nations Development Program (UNDP), available at  Primary productivity (PP) was downloaded from which came from the NASA SEAWiFS website at http://seawifs.gsfc.nasa.gove/SEAWIFS.html. These data (expressed in 33  mgCm-2d-1) were averaged over the 1998-2007 period. For every country, data was averaged using Equation (3.2), where i is a cell within the IFA, P is the primary productivity of that cell, A is the area of the cell, and PT is the average primary productivity of a cell within a given IFA.      ?          ?                                                                                               (3.2)  This weighted average was used (because the area of a cell varies depending on latitude) for each cell within the inshore fishing area to calculate an average PP value per cell. Calculations were done through ArcGIS. If a cell bordered both the exclusive economic zone (EEZ) and IFA, only the portion within the IFA was considered.  Population data for the year 2000 was taken from Center for International Earth Science Information Network (CIESIN), available from Coastal populations were defined as people living within 50 km of the shoreline of a given country. 3.2.2 Models and calculations  The relationships between IFA, HDI, and catch can be seen in Figures 3.1a and 3.1b. The values of variables were inspected to ensure that the data was imported correctly and the correlations between variables were investigated (Table B.2). The catch and IFA variables were log-transformed to linearize the data. All variables were first included in a generalized linear model (GLM; Sokal and Rohlf 1995). Variables that showed no significance (P > 0.05) were removed from the model one by one, starting with those that had the highest P value, until the minimum 34  adequate model (MAM) was found, in which all remaining predictors were significant. The variables were only included in the final model if a logical connection could be found between catch and the variables in question. Model selection was repeated using the step AIC (Akaike information criterion) function in R.   Figure 3.1. Relationship between small-scale fishery catches and explanatory variable. 3.1a: Relationship between catch (log(tonnes?year-1)) and Inshore fishing area (log(km2)); 3.1b. Relationship between catch (log(tonnes?year-1)) and human development index by country.  This ?backwards? step AIC procedure starts with a full model including all relevant data, and the variable that improves AIC the least is removed, until the model cannot be improved. An improvement in the model is shown by a reduction in the AIC, as this indicates a higher likelihood. Models with the lowest AIC values include both latitude and HDI as independent variables. However, HDI and latitude are highly correlated (Pendergast et al. 2008). There is no proposed theory that supports a direct relationship between latitude and countries? SS Fisheries catches. Thus, latitude was not included in the final models. The relative importance of variables (the percentage they contribute to the R2) was calculated using the ?relaimpo? package in R and 2 4 6 8 10 12 14468101214Inshore Fishing Area log km2Catchlogtonnesyear0.4 0.6 0.8 1.0468101214Human Development IndexCatchlogtonnesyear35  the ?lmg? method. The ?lmg? method enters the regression variables one at a time, and in all possible orders, and then it averages the consequent increment to R2 (Gr?mping. 2007).  Data were only available for 119 out of 216 countries included in this study (Chapter 2). Catch levels (t?year-1) for the remaining 97 countries were extrapolated using model 3 (equation 3.5; Table 3.2) from the (GLM) described above using the statistical software R (available from A ?smearing estimate? correction (Duan 1983; Costello et al. 2013) was used when predicted catches for countries where this data was missing, as both the dependent variable (catch) and independent variable (IFA) were log transformed (Equations 3.3 and 3.4). Without this estimate, catch would be underestimated. The equations use the sum of the untransformed residuals (Sigma in equation 3.3) and the fitted values (beta in equation 3.4) to correct for this bias. Further information may be found in Costello et al. 2013).                                                                                                                       (3.3)                                                                                                                    (3.4)  3.3 Results A total of 223 countries and territories were included in this study. Of these, observed catch was available for 119 countries. A summary of statistics related to this analysis may be found in 36  Table 3.1, and all of the observations are available in Appendix B.1. Model 1 (Table 3.2) was initially selected, as it had the lowest AIC of 113.3. However, this was dropped due to the inclusion of both latitude and HDI. Model 3 (Table 3.2; Model 3.5) was selected because it had a low AIC while having the lowest number of variables (i.e. the most parsimonious). The best model in statistical terms was model 1 in table 3.2, however, which includes latitude. The association between SSF catch and latitude should be studied in subsequent investigation, as latitude ? which is correlated with the amount of sunlight, might be a proxy for primary production.                   Table 3.2. Top 5 models and the corresponding AIC values. Smaller values                   indicate a higher likelihood. Model Independent Variables  AIC 1 lat + log(ifa) + hdi + fao 113.3 2 lat + log(ifa) + hdi + fao + reef1 114.17 3 log(ifa) + hdi + fao 115.13 4 lat + log(ifa) + hdi + tempK + fao + reef1 115.32 5 lat + log(ifa) + hdi + tempK + log(pp) + fao + reef1 116.07  The summary statistics for model 3, which explains 67% of the variance in catch, are shown in Table 3.3 and in the equation below.     (     )              (   )      (   )      (   )                                          (3.5)  37  The model suggests that the area available for fishing (IFA) is positively related to the catch. Moreover, high HDI correlates inversely with catches (Table 3.3). Finally, FAO catch estimates tend to be lower than others. The correlation matrix in Table B.2 shows that temperature, latitude and HDI are all moderately cross correlated, with the correlation between HDI and temperature being the strongest (-0.409). Table 3.3. Summary statistics for model 3.  Estimate  Std. Error  t value Pr(>|t|)    Intercept  4.98685 1.03889 4.8 5.03e-06 *** log(ifa)  0.87482 0.06586 13.283 < 2e-16 *** hdi  -3.28241 0.9272 -3.54 0.000587 *** fao  -1.17531 0.36861 -3.188 0.001863 ** Mult R2:  0.67 Adj R2: 0.661  p < 2.2e-16  -- --  The bar-plot in Figure 3.2 shows the relative importance for all predictors in model 3. The log of the IFA is seen to have the highest value, contributing over 80% to the R2.  Figure 3.2. Relative importance for log(catch) using the LMG method in R.  IFA HDI FAOMethod LMG % of R2020406080100Relative importances for CatchR266.75%, metrics are normalized to sum 100%.38  The assumptions of the linear regression (linear relationship between the independent and dependent variables, normality in the error distribution, constant variance of the errors), were be verified by visual examination of the plots in Figures 3.3, 3.4, and 3.5. The second plot is a normal quantile-quantile plot of the residuals, and it shows that a strong deviation from normality does not occur. The top left graph in Figure 3.3 shows the residuals versus fitted values for Model 3. The residuals appear to be randomly distributed around the 0 line, which suggests that the assumption that the relationship is linear is reasonable to make. The residuals also form a horizontal band around the 0 line which indicates that the variances of the error terms are equal.   Figure 3.3. Summary statistics used to check the assumptions for Model 3. Top left: residuals vs. fitted values, bottom left, Normal Quantile-Quantile plot, top right: Scale-location, and bottom right: Residuals vs. Leverage. 4 6 8 10 12-6-4-2024Fitted valuesResidualsResiduals vs Fitted14924130-2 -1 0 1 2-4-3-2-10123Theoretical QuantilesStandardized residualsNormal Q-Q149241304 6 8 10 valuesStandardized residualsScale-Location149241300.00 0.04 0.08-4-202LeverageStandardized residualsCook's distance 0.5Residuals vs Leverage1026314939   Three data points are significant outliers (Namibia, Panama, and Eritrea; figure 3.5). These catch values are lower than the model would predict, and is due to their several factors. The artisanal catch in Panama is known to be severely underestimated and therefore not reflective of reality (FAO 2013b). The low value for Namibia may be explained by the fact that nobody lives, and fishes, along the coast, except near the port city of Swakopmund. Further inspection for Eritrea is necessary to understand the cause of this underestimation. Additionally, the Shapiro-Wilk normality test was carried out on untransformed IFA, catch, and HDI with p-values 2.2e-16, 2.2e-16, and 4.037e-09, respectively. Therefore the null hypothesis that the data originate from a normally distributed population is rejected. Normality was also tested via the residuals of Model 3 (Figure 3.5) and the same conclusion was reached that the data are not from a normally distributed sample. However, given the large sample size, the central limit theorem states that the distribution of means have an approximate normal distribution (Sokal and Rohlf 1995). Observed versus fitted (predicted) values for log(catch) are displayed in Figure 3.4. These values follow a linear trend. By using the observed catches where data were available, and the corrected regression predictions for cases where no data existed, a global small-scale fisheries catch of approximately 25,071,223 million t?year-1 was estimated, or roughly 25 million t?year-1 (Appendix B.1 and B.3).  40   Figure 3.4. Observed versus predicted catch; log scale, with 95% confidence intervals by country.   Figure 3.5. Residuals versus fitted values showing 95% confidence intervals (see text about outliers). 1 2 3 4 5 601234567Observed CatchPredicted CatchR-Squared = 0.672 3 4 5-2-1012Fitted ValueResiduals41  3.4 Discussion and conclusions The results of the study show that IFA, HDI, and whether the data originate from FAO are the best predictors of SS fisheries catch, for any given country, explaining 67% of the variance in the model (Figure 3.4). The cumulative estimate of 25 million t?year-1 is approximately 19% higher than the estimate of 21 million t?year-1 reported in Chuenpagdee et al. (2006), thereby showing that the small-scale sector is in fact underestimated. This is to be expected, as the latter study was based nearly exclusively on FAO data, which the dummy variable for this study identifies as providing significantly lower catches than other sources.   It is also interesting to note that countries with lower HDI - which is based on life expectancy at birth, mean years of schooling, expected years of schooling, and gross national income per capita (Sagar and Najam 1998) - catch significantly more fish per area of IFA. This may be because highly developed countries engage in more industrial fisheries, and more of the catch share is allocated to them. Therefore the SS sector is smaller, and this is reflected in the catch. This trend could also be due to a stronger dependence on fishing as a source of livelihood in countries that are less developed.   Primary productivity was shown to be insignificant in explaining the variation of SSF catches between countries. This is in contrast to other studies that have shown that primary productivity constrains maximum catch (Chassot et al. 2010). One possible hypothesis may be that the catches from many SS fisheries are below carrying capacity and thus primary productivity as a limiting factor of catches becomes less important. Alternately, the reliability of primary 42  production estimates may be questioned as satellite-derived estimate of primary production are known to be unreliable inshore (i.e., in the IFA), where the chlorophyll signal become difficult to disentangle from other biological pigments and from terrigenous substances (Dierssen 2010). Future analysis including effort data will help to elucidate the relationship between primary productivity and small-scale catch. Alternatively, such analysis could follow up on latitude correlating, if weakly, with SSF catches (see above).   The error associated with the observed versus predicted plot (Figure 3.4) appears to be quite large. This is in congruence with the knowledge that data for small-scale fisheries is incomplete (Berkson et al. 2009). This desktop study provides a starting point, from which we can say that SS fisheries are not insignificant.   More work needs to be done to improve the validity of the model. Notably, a more accurate catch value for China needs to be re-estimated, as the currently available, extremely large value, when included, had undue influence on the regression. Model 3 predicts the catch of China to be 654,900 t, which 18 times lower than the previous FAO and World Fish (2008) estimate of 11,483,000 t. Thus, either the catch for China needs to be investigated further, especially given that China has been shown to overestimate landing data (Watson and Pauly 2001), or a more robust model should be used. It is also noted that currently no measure of effort is included in the model. Variables that would act as a proxy for effort include the number of fishers per country (Teh and Sumaila 2011) and coastal population density.  43  It was seen that the catch of small-scale fishers is underestimated in official statistics collected by the FAO and that small-scale fishers catch and rely on catch that is not trivial, both in quantity and importance. This study aims to provide more evidence that the small-scale sector is important, and more quantitative work needs to be undertaken so that the cycle of marginalization is halted, and fishers have a sustainable resource to rely on in the future. 44  Chapter 4: Conclusion 4.1 Discussion From the beginning of human existence we have relied on the environment to provide us with sustenance for our survival (Jackson et al. 2001); yet we currently find ourselves in a position where the future sustainability of our marine resources is at risk (Jackson et al. 2001; Pauly et al. 2003). 34 million people, the vast majority of fishers, operate within the small-scale sector, and 90% of those live in the developing world (B?n? et al. 2005). Despite this, the SS sector is poorly studied in many countries, as it is not a mandate of many governments to focus on this sector in addition to the more profitable industrial one.  The findings of this study confirm what numerous experts (Pauly 1997; Berkes 2003; FAO 2013a) have claimed over the last few decades: that-small-scale fisheries are not insignificant. They have been cyclically marginalized (Figure 1.1) and catch a larger quantity than previously assumed. A worldwide assumption that SS fisheries catch is marginal often leads to this false belief, which is then often reflected in official statistics, and then ultimately to the FAO, which assembles this information for every country. Just as Charles Darwin (1881) wrote about earthworms, i.e., about the large impact such small and seemingly lowly organisms can have on the world, such is the also case with small-scale fishers. A better understanding of what defines their impacts and its magnitude will provide a basis from which more effective management policies can be based.  45  A lot of work on small-scale fisheries is decidedly qualitative, and where quantitative information is available (i.e. social scientists studying small and remote villages), it is claimed that these estimates cannot be scaled up due to the specialness of the small area in question (Pauly 2006). And while each member state of the FAO volunteers key fisheries metrics so that the United Nations can monitor the status of fisheries at the global level, the data these countries provide have been repeatedly shown to underestimate the true magnitude of SS fisheries (Zeller et al. 2007, FAO/FishCode STF 2005).   Previous attempts to gather country-level data were based on information that was often incomplete (as a large percentage came from the FAO database), and/or missing key components such as the subsistence potion of the catch or the inshore gleaning by women (Chuenpagdee et al. 2006). Thus, the results presented in this thesis contain the most complete data to date, and are likely to be the most accurate estimates currently available at the global scale.  Results for Chapter 2 show that 115 out of 216 maritime countries considered provided definitions for their SS fisheries. This may seem troubling at first glance, but in most cases where no definition was found, the small-scale can be differentiated from the industrial sector. The data presented here supported by findings by Chuenpagdee et al. (2006), i.e., showed that the majority of countries (65%) define their SSF by boat size, with the industrial vessels being obviously larger. Small-scale fishers were also seen to operate closer to shore and use less technologically advanced gear. Similarly, catch data was available for 119 of those countries, with high, medium, and low human development index (HDI) countries sufficiently included. 73% of countries included were from sources other than FAO, which we know to contain chronic 46  underestimates. Using this data to build a multiple linear regression in Chapter 3 provided the global SS fisheries catch estimate of 25 million tonnes, which is 19% higher than the previous estimate by Chuenpagdee et al. (2006), and on par with Pauly (2007). It is crucial to note that this catch is almost equivalent to the estimated 29 million t bound for human consumption from the industrial sector (Chapter 1, Figure 1.3). An entire sector that is often brushed off as insignificant has been shown to generate catches equivalent to those of large-scale fisheries that provide fish for human consumption.  Delving deeper into these results, we see that the human development index (HDI), inshore fishing area (IFA), and whether the data came from the FAO or not, can be used to explain the variance in catch, and predict catch where countries are missing data. Obviously, the larger area one has to fish, the lager the potential catch will be, and this is reflected in the positive relationship between catch and IFA size. Of importance is the negative relationship between HDI (composed of education, life expectancy, and income indices; Stanton 2006) and catch. It is seen that countries that have a lower HDI fish more. This is congruent with the fact that the majority of fishers are in the developing countries, and that some of the most vulnerable people on the planet rely the most on fisheries resources.   Unexpectedly, environmental independent variables such as primary productivity were not significantly related to the catch, which is contrary to the results of previous research involving the industrial sector (Cheung et al. 2008). This possibly speaks to the vastly different nature of the SS sector, where socio-economic factors drive trends. 47  4.2 Strengths, weaknesses, and future work In Chapters 2 and 3, I have provided a database and estimations at the global and country levels, of the catch of small-scale fisheries. Analyses with more complete data will undoubtedly better reflect reality in all countries considered. To the best of our knowledge, no global study has been completed to date using reconstructed (estimates including missing components such as the contribution of women and children, or correcting for underestimation) data to estimate the SSF catch. As 44% of data (Chapter 2, Tables A2 and A3) came from sources considered to be more complete (the Sea Around Us Project, FAO and World Fish Centre 2008, and Gillett 2009) this study is likely to represent the most accurate representation of this sector to date. Additionally, all methods used have been detailed, and sources of information have been included in the Appendices of Chapters 2 and 3 so that results may be reproduced, or analyses re-run when new data becomes available. Key predictor variables, also listed in the Appendices, were drawn from several major areas that could impact these fisheries, including both socio-economic variables and environmental ones. Moreover, the results of the multiple regression showed that 67% of the variance in the data was explained by the model. This appears to be a good starting point, from which more robust models can be built when more reliable data becomes available. Even though the best data that was available and/or accessible was used, there can be many weaknesses that come when conducting desktop studies  A major weakness is a lack of ground-truthing of the data. This is not likely to skew the results significantly as a large fraction of the data came from the aforementioned more complete sources, which have reassessed the small-scale fisheries of those countries by aiming to include 48  unreported and underestimated portions of the catch. Additionally, if the data were to be skewed, it would err on the more conservative side, because it is known that data reported to FAO and even independent reports are often underestimated (FAO and World Fish Centre 2008; Zeller et al. 2007; Chapter 3). However, if the database consisted solely of reconstructed catch data, this would decrease the error associated with the multiple regression, and provide a more robust assessment, and potentially stronger relationships between independent and dependent variables may be found, thereby increasing the predictive power of the model.  In order for this to be achieved, either of two scenarios could take place in the future. The first, most likely and feasible option, is that when projects such as the Sea Around Us and Big Numbers Project (FAO and World Fish Centre 2008) complete the work they have undertaken for select countries, the catch values they will obtain are input into the multiple regression. This will be accomplished by the Sea Around Us by the end of 2013; it is unknown when the final FAO and World Fish Centre (2008) final report will be available. It will be imperative that the numbers between these two projects are compared to each other, as well as to the results of this analysis to evaluate how accurate these estimates are. The second, and evidently long-term option, is for SSF?s to be re-assed by experts within a country. This may be achieved through changes in existing governmental monitoring schemes, or the use of external sources such as FAO consultants, or regional biologists or anthropologists where even rudimentary monitoring systems do not exist. Reconstructing a fishery from within a country is likely to be the better option, as several key components can be incorporated, that might be missing from other methods.  49  It is well known that SS fishers possess a wealth of knowledge, and usually have the most detailed information in regards to what occurs in their fishery (Berkes 2003). Additionally, it may empower a country to take hold of what is likely an important sector to the local economies and food security. Ultimately, when these data from different studies are compared (i.e., 25 million t from the model in Chapter 3, versus 21 million t from Chuenpagdee at el. 2006), these differences may be considered to be trivial when the big picture is considered, because neither of these are small. They are all likely to confirm that the SS fisheries sector is not insignificant, and rivals the industrial sector in many ways.  It is also important to note that the independent variables considered may not all remain constant over time. We know that the climate is changing and this is likely to have great impacts on both large and small-scale fisheries (Cheung et al. 2008; 2010; 2013). Change in temperature is also likely to affect primary productivity (Sarmiento et al. 2004), and may have large impacts on the survival and production of fish (Chassot et al. 2010; Cheung et al. 2008). Additionally, Hoegh-Guldberg and colleagues (2008) described the deleterious effects of increasing temperatures and ocean acidity on coral reefs. Furthermore, fishing effort is likely to increase if SSFs are following the same path as the industrial side, where stocks are being depleted and effort increases (Anticamara et al. 2011; Watson et al. 2012) in response to this loss. This will only be known if resources are available and willingly directed towards estimating the status of key species or species complexes that are important to small-scale fishers, as opposed to the current approach where resources are mainly focused on industrial fisheries that are economically important (Honey et al. 2010).  50   Certain models, such as the Random Forest (RF) model, have benefits over others, and are being adapted and utilized in new fields with positive outcomes from disciplines in which their use is commonplace. This model has strong predictive capabilities, and is able to model interactions between predictor variables quite well (Cutler et al. 2007; Knudby et al. 2010). The RF has been shown to outperform other methods in some cases, as estimates can be cross validated for accuracy, the predictive power for missing data points is often more accurate, the importance of the independent variable can be measured directly (as opposed to indirectly in the case of regressions), and clearer relationships can be seen between variables (Cutler et al. 2007). I believe that the application of this model to the SS catches and related country data presented here would be informative in terms of explaining variable relationships as well as predicting catch for countries where no data is available. However, without better data, it is difficult to disentangle whether any patterns identified from alternative statistical methods stem from data or model uncertainties. 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Good news, bad news: global fisheries discards are declining, but so are total catches. Fish and Fisheries 6(2):156-159.  60  Appendices Appendix A. Supplementary data for Chapter 2  Table A.1. Small-scale fisheries definitions by country. Country Definition Source Albania Boat size less than 12 m in length ?obani, M. 2003. Small-scale fisheries in Albania. Ministry of Agriculture and Food. Sheshi Skenderbej, Tirana, Albania Algeria Boat size 5-12 m in length, small motorized boats, crew size 2-3, 1-10 tons, trap fishing common FAO 2013 Antigua Glass-reinforced plastic GRP launches powered by inboard diesel engines and range up to 18 m, hand lining and gill netting common FAO 2013 Am. Samoa Near shore activities (rod and reel, handline, free diving, gill netting, low tide collecting, and throw netting), occasional use of boats NOAA, 2009. American Samoa as a Fishing Community. NOAA Technical Memorandum NMFS-PIFSC-19 Angola Boat up to 10 m IPA 2013. Artisanal fisheries in Angola. Argentina Coastal gatherers, commercial divers, beach seiners and small boats, usually less than 10 m in length, and small inshore vessels (10-18 m) known as "rada/ria" El?as, I. Carozza, C., Di Gi?como, E.E., Isla, M. S., Orensanz, J. M., Parma, A.M., Pereiro, R.C., Perier, M. R., Perrotta, R.G., R?, M. E., Ruarte, C. 2006. Profile of Coastal Fisheries from Latin American and the Caribbean ? Argentina. In: Salas et al. (eds). Coastal Fisheries of Latin America and the Caribbean. COASTFISH Conference Proceedings. Merida, Mexico. October 2004 Australia Fishing for subsistence by coastal or island ethnic groups using traditional methods Caton, A., McLoughlin, K. 2005. Fisheries Status Report 2004: Status of fish stocks managed by the Australian Government. Bureau of Rural Sciences. Australian Government. 243 p. Bahrain Boat size 10-15 m in length; 94% under 11 m FAO 2013 61  Country Definition Source Bangladesh Fishing area up to 40 m from shore Khan, S. M., Haqu, M.S. 2003. A Socioeconomic and Bioeconomic Analysis of Coastal Fisheries of Bangladesh. In: Silvestre et al. (eds.). Assessment Management and Future Directions for Coastal Fisheries in Asian Countries. WorldFish Center Conference Proceedings 67. WorldFish Centre, Penang (Malaysia), 1120 p. Barbados Boat size less than 12 m in length FAO 2013 Belgium Boat size less than 10 m in length FAO 2013 Brazil Boat size less than 10 m in length FAO 2013 Belize Open boats, boat size between 4.3-7.6 m in length, outboard engines, sloops up to 10 m, outboard engines, small engines CARICOM 2005.  Brunei Darussalam Open planked wooden boats, outboard engines, operating in shallow waters Fisheries Department. 2005. General Information. Ministry of Industry and Primary Resources. Government of Brunei Darussalam Myanmar Fishing in onshore area (intertidal zone) and Inshore (9-18 km from coast) FAO 2013 Eritrea Boat size less than 16 m in length FAO 2013 Cambodia Dinghies and day boats, boat size between 7-11 m in length, engine range <50 HP, operating up to 30 miles offshore to a depth of 20 m; family scale FAO 2013 Cameroon Beam trawlers, small to medium engine up to 30 HP FAO 2013 Canada Boat size less than 10 m in length FAO 2013 Cape Verde Mainly use hooks, lines, and seines Blaha, F. 2009. Review fishery status and prospective Module I interventions in lusophone and hispanophone SFP beneficiary ACP states/OCT: CA031GEN. all/files/projects/technicalpapers/TP066%2009%2003%20CA031GEN.pdf Chile Boat size less than 18 m in length, less than 50 GRT Jalil ?lvarez, A. 2003. Estudio sobre el Impacto Socioecon?mico de la Pesca Artesanal en los Estados Miembros de la Comisi?n Permanente del Pac?fico. Comision Permanente del Pacifico Sur (CPPS) Colombia, Chile, Ecuador, Peru. 62  Country Definition Source Colombia Wooden or fibreglass boats with outboard motors of 15, 40, or 75 HP FAO 2013 Comoros Islands Traditional boats (Galawa) and motorised fibre-glass boats from 6.3 to 7.1m in length, < 25 HP Abdoulhalik, M.F. 1996. Marine Science Country Profiles for Comores. Report prepared for IOC/UNESCO and WIOMSA. 35pp. Congo (Brazzaville) Small, medium capacity, engine less than 400 HP, produce small catches, operate near coasts, up to 14 m in length FAO 2013 Congo (Kinshasa) Fishing using beach seine and canoe FAO 2013 Costa Rica Fishing area up to 10 m from shore Departamento de Comisiones Legislativas. 2004. Asamblea Legislativa de la Rep?blica de Costa Rica Ley de Pesca Y Acuicultura Informe Sobre la Redacci?n Final del Texto Aprobado en Primer Debate.  Cuba Boats between 10 and 23 m; referred to as 'coastal fleet' FAO 2013 Cyprus Boat size between 6-12 m in length FAO 2013 Denmark Boat size less than 50 GRT Mathiesen, C. 2003. Analytical framework for studying fishers? behaviour and adaptation strategies. In Myers and Reschny (eds). ?Beyond boom and bust in the Circumpolar North". Circumpolar Arctic Social Sciences Ph.D. Network. Dominica Boat size between 5-8 m in length CARICOM 2005.  Dominican Republic Fishing area within 100 m from shore Office of the United States Trade Agreements. 2005. ANNEX I: Schedule of the Dominican Republic Ecuador Boat size up to 15m in length and 50 GRT FAO 2013 El Salvador Boat size between 5-7 m in length, and less than 10 m FAO 2013 Equatorial Guinea Boat size less than 11 m in length FAO 2013 Estonia Boat size less than 12 m in length, referred to as "inshore" FAO 2013 63  Country Definition Source Fiji Boat size between 6.3-7.1 m in length FAO 2013 Finland Boat size between 8-15 m in length, referred to as "inshore"  France Boat size between 5-6 m in length, 2-3 crew (Vili), Boat size < 16 m in length, 5-6 crew FAO 2013 Djibouti Boat size less than 14 m FAO 2013 Gambia Fishing area within 10m from shore; low cost and labour intensive; traditional fishing crafts  Mendy, A.B., 1999. National Report ? The Gambia.  Georgia 4.5 m average boat; use of hook line, trammel nets, seine nets, gillnets FAO 2013 Ghana Small canoe with no engines and all crafts with outboard motors Bennett, E. 2001. The Challenges of Managing Small Scale Fisheries in West Africa: Analytical Appendix 2.  Greece Seiners, small ring netters, small drifters and liners fishing near the shore, referred to as inshore Sayas, J.P., Papadopoulos, A.J. and Kasimis, C., 1999. Coastal Areas, Islands and the Fisheries Sector in Greece: Some Remarks Regarding Marginal Fishing Communities. Paper presented in the First INDICCO Workshop, University of Seville, Seville, Spain, 17 Grenada 16-30 ft, day boats, less than 16 ft operate on a subsistence basis Baldeo, R. 2006. Profile of Coastal Fisheries from Latin American and the Caribbean ? Grenada. Guatemala Boat size less than 10 GRT; up to 35 ft in length; within 5 nm of coast FAO 2013 Guinea Low water line to 12 miles offshore FAO 2013 Guinea Bissau Up to 60 HP FAO 2013 Guyana Boat size between 4.5-18 m in length; propelled by sail or inboard or outboard engines FAO 2013 Honduras Mostly outboard motors between 15-25 HP FAO 2013 Iceland Boat size less than 10 GRT Ministry of Fisheries. 2000. Responsible Fisheries. Information Centre of the Icelandic Ministry of Fisheries.  India Boat size between 16-20 m in length (small-scale fisheries), artisanal fisheries boat size is less than 12 m, boat size less than 25 GRT, rudimentary dugout canoes to motorized boats Mathew, S. 2002. Small-scale Fisheries Management in India: Need for a paradigm shift. In: Seilert, H.E.W., ed. Interactive mechanisms for small-scale fisheries management: Report of the regional consultation. FAO Regional Office for Asia and the Pacific 64  Country Definition Source Indonesia Fishing units without boat, or with boats powered by sail or outboard engines FAO 2013 Iran Boat size of 2-11 m in length FAO 2013 Ireland Boat size less than 15 m in length Mhara, B.I., 1999. Irish Sea Fisheries Board. Irish Inshore Fisheries Sector, Review & Recommendations.    Israel Boat size less than 15 m in length FAO 2013 Italy Boat size less than 12 m in length; less than 6 GRT, on continental shelf OECD, 2002. Country Note on National Fisheries Management Systems -- Italy  Ivory Coast Boat size less than 8 m in length or about 20 GRT FAO 2013 Japan Boat size less than 12 m in length, or less than 10 GRT FAO 2013 Kenya Small, non-motorized boats such as outriggers, dhows, cataracts, and planked pirogues OECD, 2002. Country Note on National Fisheries ( South Korea Boat size less than 10 GRT FAO2013 Kuwait Boat size less than 12 m in length FAO 2013 Latvia Boat size less than 14 m in length, but majority are less than 12m FAO 2013 Libya Boat size < 10 m the majority; some > 10 m; 10-35 HP Lamboef, M., Abdallah, A.B., Amer, A., Turki, A.,  El-Gushti, A., Fituri, A. El-Srif, E., Shugman, E., Ghebli, H., Coppola, R., Germoni, A. and  Spinelli, M. 1994. Artisanal Fisheries in Libya: Census of Fishing Vessels and Inventory of Artisanal Fishing  Madagascar Dugout canoes using oars or sails FAO 2013 Malaysia Small, non-mechanised or outboard powered crafts, mostly less than 10 GRT, operating in shallow waters with traditional gear, from shore to 5nm Abu Talib, A. and Alias, M., 1997. Status of Fisheries in Malaysia- An Overview. In: Silvestre and Pauly (eds). Status and Management of Tropical Coastal Fisheries in Asia. ICLARM Conference Proceedings 53. 65  Country Definition Source Malta Boat size less than 15 m in length De Leiva, I., Busuttil, C., Darmanin, M. and Camilleri, M. 1998. Artisanal Fisheries in the Western Mediterranean: Malta Fisheries. The Department of Fisheries and Aquaculture of Malta. PROJECT: FAO COPEMED Marshall Islands Paddling and sailing canoes, crafts 4.5-6 m; outboard motors 15-40 HP FAO 2013 Mauritiana 1-5 GT; made of wood, aluminum, fibreglass; to 20 m in depth; within 6 miles of coast; no bigger than 26 m FAO 2013 Mauritius Boat size less than 10 m in length; 8-25 HP FAO 2013 Mexico Boat size between 10-13.5 m in length FAO 2013 Micronesia Spearing, trolling from 5-6 m outboard-powered skiffs, hand-lining FAO 2013 Morocco Boats under 2 GRT FAO 2013 Mozambique Boat size between 3-8 m in length FAO 2013 Oman Boats less than 10 m in length, except launches (wooden vessels 12 m or more in length with inboard diesel engines, 3 percent of artisanal vessels); skiffs, houris, shashas, and launches FAO 2013 Namibia Boat size less than 12 m in length FAO 2013 Netherlands Boat engine less than 60 HP FAO 2013 Vanuatu Boat size less than 15 m in length FAO 2013 New Zealand Boat size between 6-18 m in length FAO 2013 Nicaragua Boat size between 5-10 m in length FAO 2013 Nigeria Artisanal canoes of 3-13 m in size, 15-45 HP; up to 25 m; exploit coastal waters up to 5nm from shore FAO 2013 Norway Boat size less than 13 m in length FAO 2013 66  Country Definition Source Pakistan < 10 GRT (except vessels fishing sea bream, dusky grouper, pompano, dolphin fish, and sharks with more than 10 GRT); using nets called katra; depths less than 20 m FAO 2013 Palau Hook and line, boats 4.8-7.6 m with outboard motors FAO 2013 Panama < 10 GRT; outboard motors, low mechanisation FAO 2013 Papua New Guinea Boat size between 3-10 m in length, small engine; knowledge intensive with unsophisticated  gear FAO 2013 Peru Boat size will hold capacity of less than 32.6 metric tonnes FAO 2013 Philippines Boat size less than 3 GRT; within 15km from coastline  Poland Boats of overall length up to 15 m, operating in waters up to12nm FAO 2013 Portugal Boat size less than 5 GRT FAO 2013 Guinea Bissau Boat engine less than 60 HP Country profile INFOPECHE ( Qatar Boat size less than 11 GRT FAO 2013 Russia  Small vessels of 24.34 m in length FAO 2013 Saint Kitts Nevis Boat size between 3-6 m in length FAO 2013 Sao Tome Pr. Boat size less than 10 GRT;  5-15 m in length, 0.9-2.10 m wide FAO 2013 Seychelles Boats 5-13 m, 25-40 HP, handlining  Payet, R. 2001. Illegal Fishing in Seychelles: A Review of its Implications for a Small Island Developing State Rondolph Payet, Seychelles Fishing Authority. International Collective in Support of Fishworkers (ICSF) and International Ocean Institute (IOI), India's conference "Forging Unity: Coastal Communities and the Indian Ocean?s  Sierra Leone Canoes fishing within 5-9 km from shore (Inshore Exclusive Zone, IEZ); 1-16 men FAO 2013 67  Country Definition Source Viet Nam Boat size less than 10 GRT FAO 2013 Suriname Boat size less than 15 m in length, fishing in water < 10m in depth, Guyana boats, korjaal canoes, and small canoes FAO 2013 Sweden Boat size less than 12 m FAO 2013 Syria Boat size between 4-10 m in length; within 12 nm of coast (mostly 1-5 nm); engines 5-150 HP FAO 2013 Tanzania Within 12 nm of coast, low technology Mochii, A., Guard, M. and Horril, C., 1998. Tanzania Coastal Zone. (cited in: Berachi, I.G. Bioeconomic Analysis of Artisanal Marine Fisheries of Tanzania (Mainland).  Thailand Boat size less than 5 GRT; traditional fishing gears Pimoljinda, J., 2002. Small-Scale Fisheries Management in Thailand. In: Seilert, H.E.W., ed. Interactive mechanisms for small-scale fisheries management: Report of the regional consultation. FAO Regional Office for Asia and the Pacific, Bangkok, Thailand. Togo Fishing on foot or using different non-motorized boat FAO 2013 Trinidad & Tobago Boat size of 7-10 m, with 40-75 HP outboard engines (Trinidad), and 6.7-12.1 m, with 15-100 HP outboard engines (Tobago) Potts, A.C., Thomas, A.D. and Nichols, E. 2002. An economic and social assessment of the flyingfish (pelagic) fishery of Trinidad and Tobago. pp. 133-145 In: FAO/Western Central Atlantic Fishery Commission. Report  of the second meeting of the WECAFC Ad Hoc Flying fish Working Group of the Eastern Caribbean; Mohammed et al. 2006.  United Arab Em. Boats less than 10 m in length FAO 2013 Turkey Boat size 10 m or less FAO 2013 Ukraine Fishing no farther than 2-3 hours' motor boat travel from landing sites FAO 2013 Egypt Boat size < 12 m in length FAO 2013 United Kingdom Fishing on foot or using non-motorized boat FAO 2013 Timor Leste No more than a supplementary activity FAO 2013 68  Country Definition Source Turkey Boats 8-10 m, 10-25 HP, basic gear FAO 2013 Tuvalu Collection, 4-5 m skiffs FAO 2013 United Arab Emirates Vessels 8-15 m in length FAO 2013 Uruguay Vessels less than 10 GRT, fishing within 13 km of the coast Defeo, O., Puig, P., 2006. Coastal Fisheries Profile of Latin America and the Caribbean: Uruguay. In: Salas et al. (eds). Coastal Fisheries of Latin America and the Caribbean. COASTFISH Conference Proceedings. Merida, Mexico. October 2004. Samoa Boats less than 15 m in length King, M. and Fa?asili, U. 1998. Community-Based Management of Subsistence Fisheries in Samoa. Fisheries Division, Ministry of Agriculture, Forests Yemen Boat size between 5-15 m in length, fish in areas less than 100 m deep; 15-250 HP FAO 2013 69  Table A.2. Inshore fishing area (IFA), catch (t), and the sources by country, 1) FAO; 2) Big Numbers Report; 3) Gillett 2009; 4) Sea Around Us Data; 5) Chuenpagdee et al. (2006), 6) Other data Country Catch (t) Source IFA (km2) Country Catch (t) Source IFA (km2) Am. Samoa 145.3 4 437 Fr. Guiana 3127.9 4 16238 Angola 50420.0 6 35363 Fr. Polynesia 12930.5 4 10767 Antigua Barbuda 2527.0 6 3144 Djibouti 350.0 1 2525 Bahrain 9847.0 6 6721 Gabon 22223.8 1,5 27154 Bangladesh 454552 6 31145 Gambia 29743.0 6 4065 Barbados 2133.0 6 320 Gaza Strip 1814.1 4 905 Brazil 353350.0 6 376117 Ghana 231681.0 1,2 16699 Belize 5877.7 4,5 11808 Gibraltar 20700.0 1 22 Solomon Is. 18250 3,4,5,6 55003 Greece 79000.0 6 178060 Myanmar 478241.3 4,5 124280 Guadeloupe 12221.5 4 4653 Eritrea 1300.0 1,6 55493 Guam 210.3 4 339 Cambodia 115000.0 6 22431 Guatemala 74611875 1,5 12618 Cayman Is. 153.5 4 649 Guinea 50000.0 6 17761 Sri Lanka 123855.6 4,5 27193 Haiti 22900.4 4,5 7081 Chile 1000000 6 211070 Iceland 65000.0 6 67328 China 11483051 2,5 358425 India 1500000.0 6 207278 Christmas Is. 24.2 4 161 Indonesia 2169557.0 1 1089191 Cocos Is. 196.7 4 178 Iran 42045.0 1,5 80305 Colombia 5224.0 1 39460 Iraq 13400.0 1 606 Comoros Is. 14000.0 6 1553 Israel 1839.8 1 3168 Mayotte 2308.3 4 1141 Italy 55600.0 6 94054 Rep. du Congo 15000.0 6 7044 Jamaica 39184.5 4,5 4512 Cook Is. 1216.0 4 479 Japan 1577000.0 1 259634 Costa Rica 11002.4 4,5 15371 Jordan 170.0 4 27 Cyprus 1649.0 6 3343 Kenya 8000.0 1,5 8759 Benin 34650.0 6 1899 South Korea 512600.0 4,5 97246 Dominica 1477.6 4,5 606 Kuwait 4455.0 1 10297 Dominican Rep. 13000.0 6 7274 Lebanon 3646.0 6 526 Ecuador 50000.0 1 23894 Latvia 3500.0 1 14186 El Salvador 11038.0 1 12856 Macau 299.3 4 41 Eq. Guinea 18400.0 1 7544 Madagascar 101823.6 1,4 83191 Fiji 27919.2 1,4,5 49425 Malaysia 294564.1 4 85199 70  Country Catch (t) Source IFA (km2) Country Catch (t) Source IFA (km2) France 453.0 1 85367 Sabah 223763.0 4 47678 Maldives 74453.3 4,5 32702 Saudi Arabia 39028.7 1 86254 Sarawak 50348.5 4 36476 Senegal 406980.0 1,2 16943 Malta 887.0 6 2384 Seychelles 5000.0 1,6 16699 Martinique 7604.4 4 1576 Sierra Leone 55000.0 5,6 16600 Mauritania 80000.0 1,5 24596 Viet Nam 1458783.0 2,5 164775 Mauritius 5875.5 1,4,5 2222 Sudan 5000.0 1 24652 Montserrat 81.9 4 127 Suriname 29614.7 4,5,6 18182 Morocco 765241.0 6 49033 Thailand 188216.0 2,5 122330 Mozambique 96000.0 1,5 73307 Togo 23013.0 1,5 950 Oman 88560.0 1 51403 Tokelau 420.9 4 144 Namibia 365.0 1 53325 Tonga 6500 3,4 8479 Nauru 650.0 3,4 9 UAE 8184.0 1 52678 New Caledonia 7637.6 4 28666 Tuvalu 1215 3,4 509 Vanuatu 3368.0 3,5 13986 Tanzania 50000.0 6 23557 Nicaragua 4308.0 1 49756 Ascension 32.5 4 94 Nigeria 320000.0 1,6 32959 Tr. da Cunha 54.2 4 241 Niue 150 3,4 144 Uruguay 3500.0 6 25838 N. Mariana Is. 161.1 4 741 Venezuela 386129.0 6 109426 Micronesia 12600.0 3 8293 W. & Futuna 962.4 4 514 Marshall Is. 3750.0 3 14885 Samoa 12558.4 1,4,5 2675 Palau 2115 3,4 1989 Yemen 105191.0 6 25435 Panama 1585 1,5 46652 Cameroon 65000 6 4560 PNG 35700.0 1,5 170599 Cape Verde 4645.2 6 5697 Peru 374196.0 1,6 55340     Philippines 988240 5,6 328592     Pitcairn 13.8 4 78     Poland 7150.0 1 19427     Guinea Bissau 20400 1,5,6 24440     Qatar 8685.7 1,5 23989     Saint Helena 228.5 4 122     St. Vincent  1120.0 1 2080     Sao Tome  4025.1 1,6 1499      71  Table A.3. Sources for ?Other? categories in Table A.2 Country Source Angola Anon. 2013. Artisanal Fisheries in Angola. Antigua Barbuda Tietze, U., Thiele, W., Lasch, R., Thomsen, B. and Rihan, D. 2005.  Economic performance and fishing efficiency of marine capture fisheries. FAO siheries technical paper 482. Bahrain INFOSAMAK. Centre for Marketing Information & Advisory Services For Fishery Products in the Arab Region. 1998. The State of Bahrain: Fisheries Data /english/documents/bahrain.html?CFID=831875&CFTOKEN=95357148  Bangladesh Islam, Md. Shahidul. 2012. Poverty in small-scale fishing communities in Bangladesh: Barbados McConney, P., 2006. Coastal Fisheries Profile of Latin America and the Caribbean: Reef fish, pelagic fish and sea urchin coastal fisheries in Barbados. In: Salas et al. (eds). Coastal Fisheries of Latin America and the Caribbean. COASTFISH Conference Proceedings. Merida, Mexico. October 2004. Benin SFLP, 2002. Integrating small scale fisheries into poverty reduction planning for West Africa. Report of the Consultation, November, 2002. Sustainable Fisheries Livelihoods Programme, Benin. Brazil Diegues, A.C., 2002. Sea Tenure, Traditional Knowledge and Management Among Brazilian Artisanal Fishermen. NUPAUB. Research Center on Population and Wetlands. Sao Paulo Cambodia Try, I., Vansereyvuth, S., Somony T. 2002. Small-Scale Fisheries Management in Cambodia. In: Seilert, H.E.W., ed. Interactive mechanisms for small-scale fisheries management: Report of the regional consultation. FAO Regional Office for Asia and the Pacific, Bangkok, Thailand. RAP Publication 2002/10, 153 pp. Cameroon Folack, J. 1994. Industrial catch of small coastal pelagic fish in Cameroon. Scientia Marina. 59(3-4):549-555. Cape Verde Blaha, F. 2009. Review fishery status and prospective Module I interventions in lusophone and hispanophone SFP beneficiary ACP states/OCT: CA031GEN. all/files/projects/technicalpapers/TP066%2009%2003%20CA031GEN.pdf 72  Country Source Chile Jalil ?lvarez, A. 2003. Estudio sobre el Impacto Socioecon?mico de la Pesca Artesanal en los Estados Miembros de la Comisi?n Permanente del Pac?fico. Comision Permanente del Pacifico Sur (CPPS) Colombia, Chile, Ecuador, Peru. /espa /estudioimpactodelapescaartesanalenpaisesmiembrosdelacppsversion3.pdf Comoros Is. Abdoulhalik, M.F., 1996. Marine Science Country Profiles for Comores: Report prepared for IOC/UNESCO and WIOMSA. 35pp. Cyprus Republic of Cyprus Planning Bureau. 2004. Single Programming Document for the Fisheries Sector 2004-2006. FISHERIES/$FILE/SPD_fisheries_final.pdf Dominican Rep. Herrera, A., Betancourt, L., Silva, M., Lamelas, P., Melo, A., 2006. Coastal Fisheries Profile of Latin America and the Caribbean: Dominican Republic. In: Salas et al. (eds). Coastal Fisheries of Latin America and the Caribbean. COASTFISH Conference Proceedings. Merida, Mexico. October 2004. Eritrea Habteyonas, M. Z. and Scrimgeour, F., 2003. An Economic Analysis Artisanal Fisheries in Eritrea: Identifying the ConstraintsNew Zealand Association of Economists Conference. 25-27 June 2003. Gabon Fossi, A. 2009. Evaluation des conditions sanitaires actuelles du secteur de la p?che artisanale au Gabon et propositions pour de futures interventions. Gambia African Development Bank Group. 2009. The Gambia Artisanal fisheries development project. Supplementary loan project appraisal report. uploads/afdb/Documents/Project-and-Operations/AR%20En%20gamb1.pdf Greece Sayas, J.P., Papadopoulos, A.J. and Kasimis, C., 1999. Coastal Areas, Islands and the Fisheries Sector in Greece: Some Remarks Regarding Marginal Fishing Communities. Paper presented in the First INDICCO Workshop, University of Seville, Seville, Spain, 17-20th November 1999 73  Country Source Guinea Bah, T.S., 2001. Incursions by industrial trawlers into Guinea's coastal zone at last a sigh of relief from the small-scale fishers of Bongolon. RTG - Conakry, Guinea Guinea Bissau Blaha, F. 2009. Review fishery status and prospective Module I interventions in lusophone and hispanophone SFP beneficiary ACP states/OCT: CA031GEN. all/files/projects/technicalpapers/TP066%2009%2003%20CA031GEN.pdf Iceland Ministry of Fisheries. 2000. Responsible Fisheries. Information Centre of the Icelandic Ministry of Fisheries. India Mathew, S. 2002. Small-scale Fisheries Management in India: Need for a prardigm shift. In: Seilert, H.E.W., ed. Interactive mechanisms for small-scale fisheries management: Report of the regional consultation. FAO Regional Office for Asia and the Pacific, Bangkok, Thailand. RAP Publication 2002/10, 153 pp.  Italy OECD, 2002. Country Note on National Fisheries Management Systems -- Italy Kenya Odido, M. 1997. Marine Science Country Profiles: Seychelles.  Intergovernmental oceanographic commission and Western Indian ocean marine science association. Lebanon Earthtrends Country Profile coa_cou_422.pdf Malta Anon. 2006.  Malta?s National Strategic Plan For Fisheries 2007-2013. fisheries/cfp/eff/national_plans/list_of_national_strategic_plans/malta_en.pdf Morocco Anon., 2003. Modernization programme for the fish industry. Eurofish Magazine, Issue 2 / 2003, April. Mozambique Blaha, F. 2009. Review fishery status and prospective Module I interventions in lusophone and hispanophone SFP beneficiary ACP states/OCT: CA031GEN. all/files/projects/technicalpapers/TP066%2009%2003%20CA031GEN.pdf Nigeria SFLP, 2002. Integrating small scale fisheries into poverty reduction planning for West Africa. Report of the Consultation, November, 2002. Sustainable Fisheries Livelihoods Programme, Benin. 74  Country Source Peru Jalil ?lvarez, A. 2003. Estudio sobre el Impacto Socioecon?mico de la Pesca Artesanal en los Estados Miembros de la Comisi?n Permanente del Pac?fico. Comision Permanente del Pacifico Sur (CPPS) Colombia, Chile, Ecuador, Peru. Philippines Green, S., White, A.,  Flores, J.Carreon III, M., Asuncion, S., 2003. Philippine Fisheries in Crisis: A Framework for Management. Coastal Resource Management Project of the Department of Environment and Natural Resources, Cebu City, Philippines. 77p. Rep. du Congo SFLP, 2002. Integrating small scale fisheries into poverty reduction planning for West Africa. Report of the Consultation, November, 2002. Sustainable Fisheries Livelihoods Programme, Benin. Sao Tome  Blaha, F. 2009. Review fishery status and prospective Module I interventions in lusophone and hispanophone SFP beneficiary ACP states/OCT: CA031GEN. all/files/projects/technicalpapers/TP066%2009%2003%20CA031GEN.pdf Seychelles Shah, N.J. 1998. Marine Science Country Profiles: Seychelles.  Intergovernmental oceanographic commission and Western Indian ocean marine science association. Sierra Leone Ward, A. 2010. Capacity building in improved handling practices at new landing facilities in Sierra Leone as follow up to mission ART001SLE 29/6/10 to 17/7/10. Module 4 - Strengthening Small Scale Fisheries. 10_07_28_ART034SLE_Final_Report.pdf Solomon Islands do Porto, O. and Buckley, K. 2006.  Assessment of Fishery Products Sanitary Conditions in the Pacific Region Comprising Marshall Islands, Micronesia, Papua New Guinea, Solomon Islands and Vanuatu. Surinam Blaha, F. 2009. Review fishery status and prospective Module I interventions in lusophone and hispanophone SFP beneficiary ACP states/OCT: CA031GEN. all/files/projects/technicalpapers/TP066%2009%2003%20CA031GEN.pdf Tanzania Francis, J. 1996. Marine Science Country Profiles: Tanzania. Intergovernmental oceanographic commission and Western Indian ocean marine science association. 75  Country Source Tonga Watson, I. And Crozier, B. 2006. Assessment of Fishery Products Sanitary Conditions in the Pacific Region comprising Cook Islands, Fiji, Kiribati, Western Samoa, Tonga and Tuvalu Uruguay Defeo, O., Puig, P., 2006. Coastal Fisheries Profile of Latin America and the Caribbean: Uruguay. In: Salas et al. (eds). Coastal Fisheries of Latin America and the Caribbean. COASTFISH Conference Proceedings. Merida, Mexico. October 2004. Papua New Guinea Blaha, F. 2010 Country profiles and fishery product hygiene/ food safety situation analysis of Papua New Guinea and Dominican Republic. 10_10_01_IND088GEN_Report.pdf Venezuela Museo Marino de Magarita. Las Pesquerias, 2000. Esciencia No. 8, Ano 4. Fundacion Polar, Venezuela. Yemen PERSGA. 2002. Strategic Action Programme for the Red Sea and Gulf of Aden Status of the Living Marine Resources in the Red Sea and Gulf of Aden and Their Management. Regional Organization for the Conservation of the Environment of the Red Sea and Gulf of Aden. %2520LMR%2520in%2520RSGA.pdf 76   Appendix B.  Supplementary data for Chapter 3 Table B.1. Untransformed values of independent and dependent variables: 1) Latitude[?N or S], 2) inshore fishing area [km2] 3) human development index, 4) temperature in Kelvins, 5) primary production[mgCm-2d-1], 6) FAO value or not, 7) Sea Around Us Project reconstruction value or not, 8) reefiness value [%], 9) observed catch [t], 10) predicted catch [t], 11) upper limit of predicted catch [t] and 12) lower limit of predicted catch [t] 13) Difference between observed and predicted catch [%] Country Independent Variables  Dependent Variables   Lat IFA HDI Temp PP FAO recon reef Obs Catch Predicted Upper Lower Diff Alaska 64.2 512831 0.899 276.23 773 1 1 0.00E+00 NA 385719.1 11054582.7 13458.6  Albania 41.14 5297 0.71 289.84 488 1 1 0.00E+00 NA 12791.1 332632.1 491.9  Algeria 30.83 10263 0.664 290.8 491 1 1 0.00E+00 NA 26617.7 692558.3 1023.0  America_Samoa -14.31 437 0.827 301 183 1 2 3.97E-04 145 971.0 25798.7 36.5 85.0 Ams. Is. St. Paul -34 151 0.865 287 461 1 1 0.00E+00 NA 336.3 9105.7 12.4  Andaman & Nico 12.12 27648 0.502 301 591 1 1 2.20E-02 NA 108225.6 2864806.7 4088.5  Angola -12.08 35363 0.392 295.12 1938 1 1 0.00E+00 50420 192669.6 5225313.5 7104.2 73.8 Anguilla 18.22 1601 0.865 300 333 1 1 2.04E-03 NA 2685.5 71082.3 101.5  Antigua_Barbuda 17.29 3144 0.86 300 331 1 1 3.34E-02 2527 4945.6 130653.1 187.2 48.9 Argentina -42.84 166486 0.764 282.64 1374 1 1 0.00E+00 NA 222916.2 6033472.5 8236.0  Aruba  12.52 2886 0.908 299.29 547 1 1 5.41E-04 NA 3920.3 104577.8 147.0  Ascension -7.93 94 0.797 298 321 1 2 0.00E+00 33 277.0 7517.4 10.2 88.3 Australia -28.57 978016 0.932 295.03 721 1 1 1.54E-03 NA 611139.9 18043250.4 20699.8  Azores 38.72 3161 0.785 291 420 1 1 0.00E+00 NA 6352.2 166050.6 243.0  Bahamas 25.03 62227 0.781 299 484 1 1 2.19E-03 NA 88668.0 2365225.7 3324.0  Bahrain 26.05 6721 0.801 297.75 1108 1 1 2.34E-02 9847 11710.2 306950.2 446.7 15.9 Bangladesh 23.73 31145 0.452 300.04 1995 1 1 3.09E-04 248000 141562.2 3783438.4 5296.7 -75.2 77  Country Independent Variables  Dependent Variables   Lat IFA HDI Temp PP FAO recon reef Obs Catch Predicted Upper Lower Diff Barbados 13.15 320 0.784 300 384 1 1 4.15E-04 2133 848.7 22552.9 31.9 -60.2 Belgium 50.6 2637 0.863 284 1762 1 1 0.00E+00 NA 4195.6 110904.3 158.7  Belize 17.13 11808 0.693 300.04 624 1 2 1.45E-02 5878 27385.9 713196.2 1051.6 78.5 Benin 9.31 1899 0.426 298.5 864 1 1 0.00E+00 34650 13139.9 355669.3 485.4 -62.1 Bermuda 32.33 699 0.95 295 371 1 1 2.09E-02 NA 980.2 26513.6 36.2  Br. Ind. Ocean Terr -6.17 10835 0.846 300.67 344 1 1 3.72E-02 NA 15386.2 407177.0 581.4  Brazil -15.88 376117 0.687 297.28 1246 1 1 2.50E-04 353350 587717.3 16042282.7 21531.3 39.9 British Virgin Is. 18.38 2387 0.945 300 323 1 1 1.06E-02 NA 2938.7 79099.1 109.2  Brunei_Daruss. 4.52 6438 0.804 301 1256 1 1 4.03E-03 NA 11165.3 292745.4 425.8  Bulgaria 42.63 9334 0.736 285.54 1066 1 1 0.00E+00 NA 19342.2 504291.9 741.9  Cambodia 11.76 22431 0.483 301.02 662 1 1 4.28E-04 115000 95809.2 2541514.9 3611.8 -20.0 Cameroon 4.43 9771 0.449 299.82 2117 1 1 8.53E-04 65000 51531.9 1374489.8 1932.0  Canada 58 1901344 0.885 276.56 553 1 1 0.00E+00 NA 1279668.1 38024991.9 43065.1  Canary_Islands 28.29 7230 0.857 292.46 518 1 1 0.00E+00 NA 10395.2 275034.0 392.9  Cape_Verde 16 5697 0.527 296.4 738 1 1 6.70E-04 4645.2 24826.5 652924.9 944.0  Cayman_Islands 19.51 649 0.968 300 277 1 2 1.85E-03 154 865.7 23534.7 31.8 82.3 Channel_island 49.21 9148 0.985 285 1123 1 1 0.00E+00 NA 8410.1 230330.7 307.1  Chile -33.54 211070 0.773 280.72 1165 1 1 0.00E+00 1000000 266717.0 7264285.5 9792.8 -73.3 China 35.86 358425 0.641 296.44 1203 1 1 1.91E-04 NA 654865.9 17807869.1 24082.0  Christmas_Island -10.45 161 0.621 300 401 1 2 3.41E-03 24 791.9 21389.8 29.3 96.9 Cocos_Islands -12.16 178 0.621 300 258 1 2 5.58E-04 197 865.2 23322.1 32.1 77.3 Colombia 4.57 39460 0.676 299.18 1171 2 1 1.35E-04 5224 25454.2 691544.4 936.9 79.5 Comoros_Islands -12.21 1553 0.425 299.25 394 1 1 5.65E-03 14000 11044.4 299665.9 407.0 -26.8 Cook_Islands -21.24 479 0.822 298 197 1 2 5.74E-04 1216 1069.4 28370.7 40.3 -13.7 78  Country Independent Variables  Dependent Variables   Lat IFA HDI Temp PP FAO recon reef Obs Catch Predicted Upper Lower Diff Costa_Rica 9.75 15371 0.719 299.68 684 1 2 1.27E-03 11002 31722.8 828289.8 1215.0 65.3 Croatia 45.1 38763 0.762 289.2 475 1 1 0.00E+00 NA 62207.7 1644601.9 2353.0  Crozet_Islands -46.41 5471 0.865 277.92 509 1 1 0.00E+00 NA 7923.4 209724.7 299.3  Cuba 21.52 59411 0.839 299.75 488 1 1 6.48E-03 NA 70403.9 1896245.2 2614.0  Cyprus 35.13 3343 0.804 293.3 350 1 1 0.00E+00 1649 6271.2 164277.1 239.4 73.7 Dem. RepCongo -4.04 1088 0.231 295.5 3720 1 1 0.00E+00 NA 15242.3 447456.9 519.2  Denmark 55.72 57838 0.863 281.02 1400 1 1 0.00E+00 NA 63564.5 1720366.8 2348.6  Desventuradas Is. -26.32 265 0.773 291 295 1 1 0.00E+00 NA 744.6 19828.7 28.0  Djibouti 11.83 2525 0.394 300.54 1505 2 1 2.11E-03 350 5700.2 161463.1 201.2 93.9 Dominica 15.41 606 0.814 300 384 1 2 5.97E-04 1478 1349.5 35688.3 51.0 -9.5 Dominican_Rep 18.74 7274 0.652 299.59 313 1 1 1.26E-03 13000 20448.3 531723.7 786.4 36.4 Easter_Island -27.12 269 0.773 295 123 1 1 0.00E+00 NA 754.3 20082.0 28.3  Ecuador -1.83 23894 0.687 295.69 1138 2 1 0.00E+00 50000 15788.8 428514.7 581.7 -68.4 Egypt 26.71 58139 0.604 296.19 457 1 1 5.62E-03 NA 149090.3 3933504.2 5650.9  El_Salvador 13.79 12856 0.651 300.77 683 2 1 0.00E+00 11038 10294.6 278861.0 380.0 -7.2 Equatorial_Guinea 1.65 7544 0.525 299.44 1744 2 1 3.30E-04 18400 9726.8 265597.7 356.2 -89.2 Eritrea 15.18 55493 0.466 301.18 1284 1 1 1.57E-02 1300 224826.5 6017342.7 8400.2 99.4 Estonia 58.6 28217 0.812 278.84 2045 1 1 0.00E+00 NA 39936.0 1059543.5 1505.3  Faeroe_Islands 61.89 13656 0.95 280.97 414 1 1 0.00E+00 NA 13418.8 364832.8 493.6  Falkland_Islands -51.8 43836 0.933 280.14 1260 1 1 0.00E+00 NA 39602.9 1087171.9 1442.6  Fiji -17.71 49425 0.667 299.62 366 1 2 2.05E-02 27919 105147.3 2768201.5 3993.9 73.4 Finland 64.86 58137 0.869 277.54 1967 1 1 0.00E+00 NA 62612.4 1697127.4 2310.0  Fr. Moz. Ch. Is. -10 314 0.838 299.3 460 1 1 3.82E-04 NA 699.3 18675.0 26.2  France 46.23 85367 0.865 287.09 984 2 1 0.00E+00 453 27041.9 763901.7 957.3 98.3 79  Country Independent Variables  Dependent Variables   Lat IFA HDI Temp PP FAO recon reef Obs Catch Predicted Upper Lower Diff French_Guiana 3.93 16238 0.811 299.4 2002 1 2 0.00E+00 3128 24635.5 649994.7 933.7 87.3 French_Poly -17.68 10767 0.895 299.24 259 1 2 1.03E-03 12930 13033.1 348481.6 487.4 0.8 Gabon -0.8 27154 0.637 296.99 1791 2 1 0.00E+00 20507 20813.2 563722.0 768.4 1.5 Galapagos_Islands -0.67 11424 0.687 293.97 543 1 1 1.18E-04 NA 27129.7 706312.5 1042.1  Gambia 13.44 4065 0.376 295.88 2313 1 1 0.00E+00 29743 30240.2 825501.6 1107.8 1.6 Gaza_Strip 31.95 905 0.729 295 435 1 2 0.00E+00 1814 2537.8 66517.3 96.8 28.5 Georgia 42.19 1536 0.692 286 1093 1 1 0.00E+00 NA 4564.3 119119.5 174.9  Germany 51.24 29918 0.883 281.48 1463 1 1 0.00E+00 NA 33326.5 897757.5 1237.1  Ghana 7.95 16699 0.457 297.85 934 1 1 1.28E-03 231681 80459.4 2143081.9 3020.8 -65.3 Gibraltar 36.14 22 0.961 289.5 1372 1 1 0.00E+00 NA 44.2 1265.1 1.5  Greece 39.07 178060 0.849 290.98 409 1 1 0.00E+00 79000 179053.1 4932888.6 6499.2 55.9 Greenland_arctic 81.82 40461 0.869 272.05 402 1 1 0.00E+00 NA 45509.1 1226831.8 1688.2  greenland_ne_atl 76.23 196303 0.869 272.73 402 1 1 0.00E+00 NA 182739.6 5070177.7 6586.3  Greenland_nw_atl 76.23 148089 0.869 273.43 402 1 1 0.00E+00 NA 142590.0 3932184.7 5170.6  Grenada 12.26 943 0.812 300 665 1 1 1.32E-03 NA 2005.7 52837.7 76.1  Guadeloupe 16.27 4653 0.839 300 344 1 2 1.63E-03 12221 7480.6 196999.5 284.1 -63.4 Guam 13.44 339 0.901 301 165 1 2 1.44E-03 210 609.6 16418.2 22.6 65.5 Guatemala 15.78 12618 0.549 300.68 819 1 1 4.60E-05 7127 46521.0 1219719.8 1774.3 84.7 Guinea 10.7 17761 0.334 297.77 1559 1 1 3.71E-06 50000 127060.0 3499431.9 4613.4 60.6 Guinea_Bissau 11.8 24440 0.283 296.98 1646 2 1 0.00E+00 20400 60448.6 1732829.3 2108.7 91.1 Guyana 4.86 22695 0.596 299.76 1524 1 1 0.00E+00 NA 66867.1 1750164.8 2554.7  Haiti 18.97 7081 0.406 300 313 1 2 7.10E-04 22900 44678.1 1205458.4 1655.9 48.7 Hawaii 20.63 12504 0.899 297.63 241 1 1 1.00E-03 NA 14674.1 393201.0 547.6  Heard & Mc Is. -53 2787 0.932 275 355 1 1 0.00E+00 NA 3514.2 94282.6 131.0  80  Country Independent Variables  Dependent Variables   Lat IFA HDI Temp PP FAO recon reef Obs Catch Predicted Upper Lower Diff Honduras 15.2 24300 0.594 300 474 1 1 1.68E-03 NA 71478.9 1871924.2 2729.4  Hong_Kong 22.25 2084 0.854 296 1621 1 1 4.75E-05 NA 3512.3 92727.8 133.0  Iceland 64.83 67328 0.877 278.32 564 1 1 0.00E+00 65000 69405.6 1889481.3 2549.4 6.3 India 21.78 207278 0.502 300.18 1464 1 1 6.49E-04 1500000 637421.6 17253232.1 23549.6 -57.5 Indonesia -3.26 1089191 0.582 300.95 908 2 1 2.35E-03 2169557 642336.0 18123154.9 22766.2 -70.4 Iran 33.57 80305 0.685 298.37 1355 2 1 1.67E-03 42045 46194.8 1261988.9 1690.9 9.0 Iraq 32.18 606 0.583 295.29 1939 1 1 0.00E+00 134000 2877.3 76397.3 108.4 -97.9 Ireland 53 58678 0.893 283.94 1023 1 1 0.00E+00 NA 58354.4 1591482.9 2139.7  Israel 31.53 3168 0.868 295 353 2 1 8.56E-06 1840 1474.5 41217.1 52.7 -24.8 Israel_red 29.77 28 0.868 298.61 353 1 2 3.35E-08 75 74.6 2092.2 2.7 -0.8 Italy 41.87 94054 0.847 289.81 473 1 1 0.00E+00 55600 102763.1 2794186.4 3779.4 45.9 Ivory Coast 7.54 8332 0.4 297.26 1266 1 1 3.06E-04 NA 52582.5 1420417.2 1946.6  J.Fern_Felix_Ambr -25 283 0.773 289.5 407 1 1 0.00E+00 NA 789.3 20999.4 29.7  Jamaica 18.11 4512 0.683 300 308 1 2 4.96E-03 39185 12133.0 315381.3 466.8 -69.0 Jan_Mayen 70.97 1615 0.936 274.55 662 1 1 0.00E+00 NA 2146.0 57649.9 79.9  Japan 36.2 259634 0.879 292.11 639 2 1 2.47E-04 1577000 68760.6 1983284.3 2383.9 -95.6 Johnston_Atoll 16.74 136 0.899 299 224 1 1 1.25E-03 NA 274.3 7476.9 10.1  Jordan 30.28 27 0.668 298.61 338 2 1 1.76E-04 170 42.7 1288.5 1.4 -74.9 Kenya -0.02 8759 0.457 299.07 672 1 1 2.67E-03 NA 45591.2 1214002.0 1712.2  Kerguelen Is -49.25 27254 0.865 275.87 405 1 1 0.00E+00 NA 32563.7 872558.7 1215.3  Kiribati 1.33 6550 0.515 300.5 352 1 1 2.73E-03 NA 29198.1 769154.6 1108.4  Kuwait 29.31 10297 0.767 295.29 1579 2 1 1.93E-03 4455 5791.9 158421.6 211.8 23.1 Latvia 56.88 14186 0.771 279.98 1923 2 1 0.00E+00 3500 7579.0 207429.0 276.9 53.8 Lebanon 33.85 526 0.8 295 349 1 1 0.00E+00 3646 1247.4 32992.0 47.2 -65.8 81  Country Independent Variables  Dependent Variables   Lat IFA HDI Temp PP FAO recon reef Obs Catch Predicted Upper Lower Diff Liberia 6.43 14176 0.281 299.34 968 1 1 1.71E-03 NA 123930.3 3479650.3 4413.9  Libya 26.34 50980 0.741 292.98 340 1 1 0.00E+00 NA 84806.9 2244485.8 3204.4  Lithuania 55.17 2758 0.783 281.08 1911 1 1 0.00E+00 NA 5671.4 148253.7 217.0  Lord Howe Is -31.56 461 0.932 294 333 1 1 5.12E-04 NA 720.5 19468.2 26.7  Macau 22.2 41 0.933 296 2126 1 2 0.00E+00 299 85.0 2382.8 3.0 -71.6 Macquarie Is. -54.62 256 0.932 278.14 290 1 1 0.00E+00 NA 430.3 11697.4 15.8  Madagascar -18.37 83191 0.431 298.65 710 1 2 2.12E-03 101824 360069.1 9739919.5 13311.2 71.7 Madeira 32.65 901 0.785 292 321 1 1 0.00E+00 NA 2104.9 55329.4 80.1  Malaysia 4.21 85199 0.735 301.46 1042 1 2 8.16E-04 294564 135918.2 3621733.8 5100.8 -53.9 Maldives 4.17 32702 0.584 301 547 1 2 2.95E-02 74453 95922.5 2519147.7 3652.5 22.4 Malta 35.94 2384 0.81 291.88 391 1 1 0.00E+00 887 4565.6 119736.5 174.1 80.6 Marshall_Islands 7.13 14885 0.563 300.98 275 1 1 8.38E-03 NA 51393.6 1346017.6 1962.3  Martinique 14.64 1576 0.904 300 406 1 2 3.72E-03 7604 2332.2 62198.9 87.5 -69.3 Mauritania 18.66 24596 0.424 293.05 3301 2 1 0.00E+00 10000 38313.1 1057403.2 1388.2 73.9 Mauritius -20.35 2222 0.694 298.5 276 1 2 8.05E-03 5876 6273.5 163389.2 240.9 6.3 Mayotte -12.83 1141 0.865 299.5 561 1 2 1.44E-02 2308 1993.0 52825.9 75.2 -15.8 Mexico 23.63 251122 0.741 296.31 835 1 1 2.27E-04 NA 345117.9 9390494.4 12683.7  Micronesia 6.93 8293 0.614 298.84 274 1 1 5.36E-03 NA 25987.0 676898.2 997.7  Monaco 43.73 4 0.865 290.22 546 1 1 0.00E+00 NA 12.8 380.4 0.4  Montenegro 42.71 3508 0.765 289.5 505 1 1 0.00E+00 NA 7432.6 193917.2 284.9  Montserrat 50.37 127 0.821 300 351 1 2 0.00E+00 82 333.4 9010.7 12.3 75.4 Morocco 31.79 49033 0.553 290.63 849 1 1 0.00E+00 765241 151649.8 4006770.4 5739.7 -80.2 Mozambique -18.67 73307 0.273 298.36 803 2 1 1.99E-03 100000 164253.2 4715414.6 5721.5 39.1 Myanmar 19.39 124280 0.432 300.94 1360 1 2 5.30E-03 478241 510987.0 13882668.0 18808.2 6.4 82  Country Independent Variables  Dependent Variables   Lat IFA HDI Temp PP FAO recon reef Obs Catch Predicted Upper Lower Diff Namibia -22.96 53325 0.594 287 2700 2 1 0.00E+00 365 43397.1 1177940.1 1598.8 99.2 Nauru -0.52 9 0.663 300.6 507 1 2 2.11E-05 437 52.1 1515.9 1.8 -88.1 Neth_antilles 18.03 1609 0.886 300 451 1 1 5.76E-04 NA 2519.3 66937.4 94.8  Netherlands 52.13 19651 0.885 283.43 1513 1 1 0.00E+00 NA 22870.0 613369.4 852.7  New_Caledonia -20.9 28666 0.869 297.58 381 1 2 1.26E-02 7638 33601.1 901714.6 1252.1 77.3 New_Zealand -40.9 193838 0.902 286.96 628 1 1 0.00E+00 NA 162211.3 4540170.2 5795.5  Nicaragua 12.87 49756 0.556 299.63 449 2 1 2.61E-03 4308 46238.6 1256138.6 1702.0 90.7 Nigeria 9.08 32959 0.414 299.61 1262 1 1 0.00E+00 320000 168509.5 4543219.4 6250.1 -89.9 Niue -19.05 144 0.774 299 142 1 2 2.04E-04 166 433.1 11647.1 16.1 61.8 Norfolk_Island -29.04 2654 0.958 294 304 1 1 0.00E+00 NA 3091.2 83499.9 114.4  North_Korea 40.2 30115 0.766 283.15 1263 1 1 0.00E+00 NA 49163.9 1296648.2 1864.1  N. Mariana Is. 15.18 741 0.875 301 181 1 2 2.24E-05 161 1320.1 35142.4 49.6 87.8 Norway 60.47 110640 0.936 279.62 730 1 1 0.00E+00 NA 88589.6 2475056.6 3170.9  Oman 21.51 51403 0.836 298.05 1742 2 1 1.42E-03 88560 19026.4 530677.1 682.2 -78.5 Pakistan 28.07 30958 0.48 298.09 2300 1 1 0.00E+00 NA 128479.9 3415529.8 4833.0  Palau 7.51 1989 0.861 301 281 1 2 6.76E-03 2501 3293.9 87072.7 124.6 24.1 Palmyra_Atoll 5.88 943 0.899 300 327 1 1 2.73E-03 NA 1508.7 40280.0 56.5  Panama 8.53 46652 0.741 299.38 817 1 1 7.08E-04 93 78435.6 2073486.1 2967.1 99.9 Papua_New_G -6.31 170599 0.418 300.82 559 1 1 7.41E-03 NA 706984.3 19342758.5 25840.5  Peru -9.19 55340 0.71 290.43 1666 1 1 0.00E+00 374196 100896.3 2665799.7 3818.8 -73.0 Philippines 12.88 328592 0.629 300.28 509 1 1 7.73E-03 793824 630948.3 17119908.3 23253.4 -25.8 Pitcairn -25.07 78 0.6 297 126 1 2 0.00E+00 14 448.5 12343.6 16.3 96.9 Poland 51.92 19427 0.785 280.5 1716 2 1 0.00E+00 7150 9547.8 262052.8 347.9 25.1 Portugal 39.4 18437 0.785 288.54 1047 1 1 0.00E+00 NA 29996.2 789294.5 1140.0  83  Country Independent Variables  Dependent Variables   Lat IFA HDI Temp PP FAO recon reef Obs Catch Predicted Upper Lower Diff Prince_Edward_Is. -46.77 832 0.591 278 391 1 1 0.00E+00 NA 3701.0 97775.4 140.1  Qatar 25.35 23989 0.799 298.47 1134 1 1 7.67E-03 NA 36124.0 954763.1 1366.8  Rep du Congo -0.23 7044 0.478 295.5 3127 1 1 0.00E+00 15000 35136.5 931804.0 1324.9 57.3 Reunion -21.11 563 0.799 298 230 1 1 1.58E-04 NA 1327.9 35093.9 50.2  Romania 45.94 8440 0.754 284.74 1661 1 1 0.00E+00 NA 16689.9 435586.9 639.5  Russia 64.77 1348718 0.708 276.29 1088 1 1 0.00E+00 NA 1688415.1 47759262.3 59689.9  S. Georg Sandwich -54.28 14950 0.846 273.99 415 1 1 0.00E+00 NA 20426.5 541874.0 770.0  Sabah 5.42 47678 0.735 301 824 1 2 8.44E-03 223763 81537.7 2154791.7 3085.4 -174.4 Saint_Helena -15.95 122 0.797 295 271 1 2 0.00E+00 228 348.0 9397.1 12.9 34.3 Saint_Kitts_Nevis 17.28 551 0.831 300 343 1 1 4.46E-03 323 1174.4 31146.0 44.3 72.5 Saint_Lucia 13.91 416 0.817 300 441 1 1 6.39E-04 NA 960.4 25506.8 36.2  Samoa -13.76 2675 0.59 301 201 1 2 2.36E-03 12558 10383.8 271650.4 396.9 -20.9 Sao Tome Prin. 0.32 1499 0.479 299.5 740 1 1 6.37E-04 3150 8969.9 240133.4 335.1 64.9 Sarawak 2.56 36476 0.735 300.99 895 1 2 2.08E-04 50349 64414.7 1696826.1 2445.3 21.8 Saudi_arabia 24.16 86254 0.743 299.37 945 1 1 8.15E-03 NA 133847.1 3570421.8 5017.6  Senegal 14.5 16943 0.4 295.62 2261 1 1 1.43E-04 406980 98207.3 2651662.4 3637.2 -75.9 Seychelles -4.63 16699 0.838 299.62 363 1 1 3.44E-03 5000 23113.7 612820.7 871.8 78.4 Sierra_Leone 8.46 16600 0.306 298.63 1120 2 1 1.11E-03 522000 39885.4 1137036.1 1399.1 -92.4 Singapore 1.37 814 0.837 301 1783 1 1 4.79E-03 NA 1623.7 42949.4 61.4  Slovenia 46.15 185 0.823 287.5 514 1 1 0.00E+00 NA 461.6 12401.3 17.2  Solomon Is. -9.27 55003 0.491 301.82 345 1 2 4.29E-03 18878 205549.0 5478152.3 7712.5 90.8 Somalia 5.15 50990 0.284 298.87 1072 1 1 2.65E-04 NA 378673.2 10642670.8 13473.4  South_Africa -30.56 86916 0.591 292.8 1457 1 1 1.50E-05 NA 221638.9 5879848.5 8354.6  84  Country Independent Variables  Dependent Variables   Lat IFA HDI Temp PP FAO recon reef Obs Catch Predicted Upper Lower Diff South_Korea 36.47 97246 0.865 287.95 1123 1 2 0.00E+00 512600 99770.4 2725883.3 3651.7 -80.5 Spain 40.46 24170 0.857 289.24 618 1 1 0.00E+00 NA 30074.4 803349.5 1125.9  Sri_Lanka 7.87 27193 0.647 300.74 920 1 2 3.40E-03 123856 66348.6 1736005.5 2535.8 -86.7 St. Pierre Miq. 46.78 4917 0.865 277.5 926 1 1 0.00E+00 NA 7212.0 190832.9 272.6  St. Vincent Gren 13.19 2080 0.763 300 505 2 1 5.57E-03 1120 1436.0 39577.4 52.1 22.0 Sudan 16.97 24652 0.37 300.78 751 2 1 1.76E-02 5000 45813.5 1279881.5 1639.9 89.1 Suriname 3.91 18182 0.641 299 1144 1 2 0.00E+00 29615 47478.0 1238831.9 1819.6 37.6 Svalbard_Is 78.22 92864 0.936 273.14 420 1 1 0.00E+00 NA 75933.1 2114048.9 2727.4  Sweden 60.3 100596 0.885 278.62 1756 1 1 0.00E+00 NA 96274.9 2645397.7 3503.8  Syria 34.85 469 0.583 294.5 368 1 1 0.00E+00 NA 2296.2 61187.9 86.2  Taiwan 23.7 45529 0.931 297.45 610 1 1 9.92E-04 NA 41215.4 1131436.4 1501.4  Tanzania -6.37 23557 0.383 299.55 612 1 1 6.62E-03 50000 138775.6 3766915.5 5112.6 64.0 Thailand 15.87 122330 0.643 301.07 769 1 1 2.57E-03 188216 252559.5 6729881.3 9478.1 25.5 Timor_Leste -8.87 3023 0.473 301 649 1 1 8.51E-04 NA 16962.1 451989.7 636.5  Togo 7.6 950 0.421 298.5 855 2 1 0.00E+00 10146 2206.5 63098.1 77.2 -78.3 Tokelau -9.17 144 0.75 302 222 1 2 7.43E-04 421 470.8 12653.3 17.5 10.6 Tonga -21.18 8479 0.671 298.56 236 1 2 3.79E-03 4395 21990.5 571858.9 845.6 80.0 Trinidad_Tobago 10.38 18804 0.727 299.36 2476 1 1 3.27E-04 NA 36902.7 965407.9 1410.6  Tristan_da_Cunha -37.11 241 0.797 285 519 1 2 0.00E+00 54 633.0 16902.7 23.7 91.4 Tunisia 33.89 41389 0.667 291.82 478 1 1 0.00E+00 NA 89943.3 2363209.4 3423.2  Turkey 39.2 52808 0.67 288.14 755 1 1 0.00E+00 NA 110367.9 2908195.1 4188.5  Turks_Caicos_Is 21.51 9060 0.873 299.75 274 1 1 1.62E-01 NA 12032.1 319759.8 452.7  Tuvalu -8.21 509 0.583 302 222 1 2 5.87E-04 3532 2465.5 65623.6 92.6 -43.3 Ukraine 48.38 55044 0.706 284.09 1124 2 1 0.00E+00 8700 30927.7 844118.4 1133.2 71.9 85  Country Independent Variables  Dependent Variables   Lat IFA HDI Temp PP FAO recon reef Obs Catch Predicted Upper Lower Diff United_Arab_Emir. 25.09 52678 0.807 299.24 1137 2 1 8.03E-03 8184 21377.1 592544.0 771.2 61.7 United_Kingdom 55.38 216763 0.846 282.89 1020 1 1 0.00E+00 NA 214998.0 5944656.6 7775.7  Uruguay -32.52 25838 0.75 287.34 2547 1 1 0.00E+00 3500 45272.9 1190024.7 1722.3 92.3 US_ECoast_C_atl. 40.53 73716 0.899 296.2 1277 1 1 3.41E-04 NA 69946.3 1918145.6 2550.6  US_Ecoast_NW_atl. 40.58 108533 0.899 283.21 1277 1 1 0.00E+00 NA 98319.2 2715831.0 3559.4  US_Gulf_of_Mex. 30.9 138484 0.899 295.89 1277 1 1 9.35E-04 NA 121842.9 3382369.7 4389.1  US_west_coast 40.58 54109 0.899 284.49 1277 1 1 0.00E+00 NA 53279.5 1453467.4 1953.1  Vanuatu -15.38 13986 0.425 300.46 287 1 1 6.45E-03 NA 76436.2 2050273.9 2849.6  Venezuela 6.42 109426 0.687 298.52 1369 1 1 2.60E-04 386129 198241.1 5282211.8 7440.0 -94.8 Viet_Nam 14.05 164775 0.556 298.73 638 1 1 7.57E-04 1458783 436439.5 11710501.3 16265.7 -70.1 Virgin_islands_us 18.34 1536 0.894 300 318 1 1 3.64E-03 NA 2355.7 62699.0 88.5  Wallis_and_Futuna -13.77 514 0.793 301 200 1 2 5.02E-03 962 1251.3 33083.2 47.3 23.1 Western_Sahara 24.21 39543 0.553 290.97 1754 1 1 0.00E+00 NA 125491.2 3309154.3 4758.9  Yemen 15.55 25435 0.422 298.71 1406 1 1 1.66E-03 105191 130670.7 3512258.3 4861.5 19.5 86  Table B.2. Correlation matrix of untransformed variables.   Latitude IFA HDI Temperature Catch PrimProd Reef Latitude 1.000 0.133 0.215 -0.331 NA 0.135 -0.031 IFA 0.133 1.000 0.060 -0.212 NA 0.017 -0.042 HDI 0.215 0.060 1.000 -0.409 NA -0.311 0.037 Temperature -0.331 -0.212 -0.409 1.000 NA -0.132 0.185 Catch NA NA NA NA 1.000 NA NA PrimProd 0.135 0.017 -0.311 -0.132 NA 1.000 -0.137 Reef -0.031 -0.042 0.037 0.185 NA -0.137 1.000   


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