towards increasing fisheries' contribution to food security · the global fisheries crisis has...

Towards increasing fisheries' contribution to food security

Part 1: Brazil, Chile, India &

the Philippines

The Pew Charitable Trusts

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Pauly, D, K. Kleisner, B. Bhathal, L. Boonzaier, K. Freire, K. Greer, C. Hornby, V. Lam, M.L.D. Palomares, A. McCrea- Strub, L. van der Meer and D. Zeller. 2012. Towards increasing fisheries’ contribution to food security. Part I: The potentials of Brazil, Chile, India and the Philippines. A report of the Sea Around Us Project to Oceana and the Bloomberg and Rockefeller Foundations. Vancouver, 109 p.

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Towards increasing fisheries’ contribution to

food security Part I: The potentials of Brazil, Chile, India and the Philippines

By

Daniel Pauly, Kristin Kleisner, Brajgeet Bhathal, Lisa Boonzaier, Kátia de Meirelles Felizola

Freire, Krista Greer, Claire Hornby, Vicky Lam, Maria Lourdes Palomares, Ashley McCrea Strub,

Liesbeth van der Meer, and Dirk Zeller

A Report of the Sea Around Us Project submitted to Oceana

and the Bloomberg and Rockefeller Foundations

30 September 2012

Fisheries for food security

Sea Around Us Project Page 1

Executive summary This report reviews the status of fisheries in four countries: Brazil, China, India and the Philippines. The specific features are identified, which, for each country, have contributed to the fisheries being in the state that they are currently in (i.e., mostly over-exploited). The marine protected areas (MPAs) created in these four countries are also reviewed, with the conclusion that they as a whole, do not sufficiently protect the marine biodiversity of the species occurring in these countries. Additionally, various indicators are presented, which provide a picture of the status of the stocks, economy, and state of management. These indicators allow a comparison of the fisheries management performance of these four countries among themselves, and with other maritime countries. The status of the ten species which - in each of our four countries - contribute most to the catch is assessed using a recently-developed stock-assessment method with minimum data requirements. The results suggest that the 40 species in question are all strongly exploited and that reduction in fishing effort would allow these species to increase their biomass, and thus their sustainable levels of yield. If the current analyses, derived from a subset of taxa, are extrapolated to other species, it might allow total fisheries yield in the four countries to be increased by an overall average of 37%, with country-specific percentages being: Brazil (79%), Chile (35%), India (5%) and the Philippines (28%). It is not certain that such substantial levels of increase could be achieved sustainably, even under optimum management, given complex predator-prey interactions, global warming, and other ecosystem-level effects. However, this work highlights the potential for substantial improvements in (sustainable) yield and biodiversity protection, if the abundance of these stocks was allowed to increase through better management and the establishment of effective marine reserves.



Contents Executive summary ............................................................................................................ 1

List of tables ....................................................................................................................... 3

List of figure ...................................................................................................................... 3

Introduction ........................................................................................................................ 4

Materials and Methods ....................................................................................................... 6

Fisheries catch data ........................................................................................................ 6

Biomass and MSY estimates ......................................................................................... 7

Between-country comparisons ....................................................................................... 8

Results .............................................................................................................................. 10

Brazil ............................................................................................................................ 10

Chile ............................................................................................................................. 17

India ............................................................................................................................. 25

The Philippines ............................................................................................................ 35

Between-country comparisons ..................................................................................... 46

Discussion ........................................................................................................................ 51

References ........................................................................................................................ 54

APPENDIX 1 ................................................................................................................... 67

APPENDIX 2 ................................................................................................................... 71



List of tables

Table 1. Catch/MSY ratios for the top 10 species for Brazil. Table 2. Catch/MSY ratios for the top 10 species for Chile. Table 3. Catch/MSY ratios for the top 10 species for India. Table 4. Catch/MSY ratios for the top 10 species for the Philippines. Table 5. Summary statistics for MPAs in India, Brazil, Chile and the Philippines. Table 6. Indicator scores for India, Brazil, Chile and the Philippines Table 7. ‘Good’, ‘bad’, and ‘ugly’ subsidies for India, Brazil, Chile and the

Philippines.

List of figure Figure 1. Map of Brazil showing the 200 nm EEZ. Figure 2. Time series of total, industrial, and artisanal landings for Brazil Figure 3. Biomass trajectories for ten important species landed in Brazil. Figure 4. Map of Chile showing the 200 nm EEZ. Figure 5. Time series of total, industrial, and artisanal landings for Chile. Figure 6. Biomass trajectories for ten important species landed in Chile. Figure 7. Map of India showing the 200 nm EEZ. Figure 8. Time series of total, industrial and artisanal marine fish catch for India. Figure 9. Total Indian catches for all species, and the corresponding fishing effort. Figure 10. Biomass trajectories for the ten species in India. Figure 11. Map of the Philippines showing the 200 nm EEZ. Figure 12. Philippine marine fisheries catches reported by different agencies. Figure 13. Surplus-yield models for Manila Bay and the West coast of Luzon. Figure 14. Yield per recruit analyses of three resource species in Lingayen Gulf. Figure 15. Surplus production models of two Philippine-wide fisheries. Figure 16. Yield-per-recruit models for 112 stocks studied in the early 1980s. Figure 17. Biomass trajectories for ten important species in the Philippines. Figure 18. Size distribution of MPAs in Brazil, Chile, India and the Philippines. Figure 19. Fishing capacity growth in Brazil, Chile, India and the Philippines.



Introduction The global fisheries crisis has been illustrated by numerous examples: catches are declining worldwide in spite of increasing fishing effort (Anticamara et al. 2011; Watson et al. 2012); fisheries that would otherwise not be profitable are kept afloat by government subsidies (Sumaila et al. 2010a; Sumaila et al. 2010b); and the state of stocks – except for a few areas with prudent management – is abysmal (Jackson et al. 2001; Coll et al. 2008). This occurs in the face of an increasing world population, more than ever in need of the protein that seafood can provide (Garcia and Rosenberg 2010; Srinivasan et al. 2010; LeManach et al. 2012; Sumaila et al. 2012). There are now over a billion people that rely on marine resources for livelihoods (Teh and Sumaila 2011). Fisheries overexploitation also manifests itself in the form of a marine biodiversity crisis, with an increasing number of species of large fishes, seabirds and marine mammals registered on the IUCN Red List of Endangered Species (IUCN 2011). Additionally, research has illustrated that the rate of marine biodiversity decline has not been reduced in the last decade (Butchart et al. 2010; Veitch et al. 2012). It is common, in the world of marine fisheries and biodiversity, to frame approaches that attempt to mitigate and overcome this fisheries crisis in the form of a zero-sum game, where increased fisheries yields are seen as incompatible with maintaining marine biodiversity. This view is best exemplified by the notion, spread by Japanese officials, that ‘whales eat our fish’ (Tamura and Ohsumi 1999, 2000), and that, hence, large-scale culling (i.e., eradication) of whales and other marine mammals would make immense quantities of fish available for commercial fisheries. However, this zero-sum view is not only wrong as an approach to increasing fisheries yields (see e.g., Kaschner and Pauly 2005; Gerber et al. 2009; Morissette et al. 2012), but reflects a deeper problem: an erroneous framing of the issues at hand. We can look at this framing issue by comparing the situation in the ocean to the situation on land. For example, in countries such as Brazil, which is known for both its productivity and biodiversity, we may find either a productive soya field or a biodiverse tropical forest – we can’t have both. On the contrary, in the aquatic realm, if the mostly depleted stocks (see e.g., Tremblay-Boyer et al. 2011) were allowed to rebuild, they would produce more in terms of fisheries yield and contribute to increased biodiversity in the marine ecosystems in question. That we have the potential, in the sea, for a win-win situation was stressed, e.g., in the keynote address of the 4th World Fisheries Congress held in 2004 (Pauly 2008). Unfortunately, this is counterintuitive to those with a mindset shaped by the conservation debates on land, and also to fisheries managers who still believe that increasing fishing effort in order to ‘out-fish’ the other guys is the way to go. But, the win-win situation is a fact that logically follows from the basic principles of both fisheries science and marine conservation science (Hilborn and Walters 1992; Odum and Barrett 2005): in situations where stocks have been (or are being) overfished, allowing stocks to rebuild will, after a transition period, lead to potentially higher, and if managed well, sustainable catches. Additionally, the rebuilt



biomass will also accommodate a wider array of top predators, among them many species that are now considered threatened (Worm and Myers 2003; Sibert et al. 2006; Ainley and Blight 2008). In this preliminary report, we will document the potential for catch increases in Brazil, Chile, India and the Philippines, countries which currently lack strong fisheries management systems, and the control and surveillance that is required for enforcement of quotas and regulations (Pitcher et al. 2006; Mora et al. 2009; Pitcher et al. 2009). We will do this using a variety of methods, including an assessment-type approach that relies on a time series of landings to estimate a reference point, Maximum Sustainable Yield (MSY), and biomass time series. We will also present a host of indicators that are currently available to assess the status of each of these four countries in terms of biological health, management effectiveness, economic well-being, and the level of protection currently and potentially afforded to marine species. The biomass assessments and indicator evaluations are presented separately for each country. Furthermore, for each of the indicators presented, these countries are also placed in a global context through comparison with the top, bottom, and middle-scoring countries.



Materials and Methods

Fisheries catch data Currently, global reported landings data represent the basic data available for assessing a fishery. Landings data from 1950 to 2006, as reported to the Food and Agriculture Organization of the United Nations (FAO) and other sources by country, are spatialized by the Sea Around Us project (Watson et al. 2004). The process of spatialization relies on information on the mapped distributions of all commercially exploited species and information on fishing access agreements, which determine which countries are permitted to operate in other countries’ EEZ waters. Landings refer to fish caught and kept, and differ from ‘catch’ data, which include both the fish which are kept and those that are discarded, and may include landings that are unreported. Therefore, the basic input to the analysis presented here are catch data, recently criticized as inadequate to deal with issues of fisheries status and stock assessments (Branch et al. 2011; Daan et al. 2011), but which are indeed the key to any fisheries research (Froese et al. 2012; Kleisner et al. 2012). There are three kinds of catch data:

a) Locally precise catch data, often collected by researchers, and used to answer questions pertaining to local fisheries (such data are not considered here);

b) Official national data, assembled and published by national governments, and also submitted to the FAO, where they are combined with the data from other countries to become the only available set of international ‘FAO statistics’;

c) ‘Reconstructed’ catch statistics, which include the total catch and discards from all fisheries, including those that are usually ignored in official statistics.

Presently (September, 2012), the Sea Around Us Project and its global collaborators, are engaged in completing catch reconstructions for all maritime countries and territories of the world, including Brazil, Chile, India and the Philippines. Thus, we will occasionally shift, in the text and analyses below, between official catches and the reconstructed catches that are, at present, partially available for the four countries of interest (but not without mentioning the catch type). Also note that in this report, all catches are expressed in tonnes (t = metric ton = 1000 kg) The rationale for reconstructions stems from a need to have a better quantification of what is removed from marine systems (Pauly 1998b; Zeller and Pauly 2007). Currently, the catch data that are available on a global basis are the ‘FAO statistics’ referred to in (b) above. However, there are typically three components of catch data: (i) nominal landings, (ii) discarded by-catch, and (iii) unreported catch, which is typically catch from small-scale fisheries and illegal catches (Pauly and Zeller 2003). Catches of type (i), i.e., nominal landings, are typically all that is reported to FAO, although there may



be temporal and taxonomic gaps in these data. Additionally, landings from small-scale artisanal fisheries are generally underreported to FAO by member countries, particularly among developing countries. The catch reconstruction approach (detailed in Zeller et al. 2007) attempts to complement the FAO landings data that has been spatialized by the Sea Around Us project (Watson et al. 2004) with more inclusive catch statistics, in particular of type (i) and (iii). These data can provide a more complete picture of the total fish biomass that is extracted from marine systems.

Biomass and MSY estimates The majority of commercially exploited species have never been formally assessed and there are no traditional estimates of Maximum Sustainable Yield (MSY) for these species. Determining MSY typically requires, at minimum, time series information on historical removals (e.g., catch and discards) information on trends in abundance (e.g., catch per unit of effort or CPUE), and a model that describes the underlying production function (e.g., a surplus-production model; Schaefer 1954). Parameters for the model, the carrying capacity of the stock (k), and the maximum rate of population increase (r), are normally estimated by fitting the model to the relative abundance data. While catch data are available for most species, abundance estimates are more difficult to obtain. However, a recent method developed by Martell and Froese (2012) enables preliminary stock assessments to be performed without a time series of fishing effort being available. While the method is new, it rests on a sound foundation of population dynamics principles, explained in the following paragraphs. The method of Martell and Froese (2012) requires a time series of annual catches, extracted from a population (B). This population has an initial abundance (B0), which can be treated as a proxy for carrying capacity k. If B0 was reduced by successive catches, this would logically result in a decline in abundance, (partly) offset by population growth. If the initial value of B0 was small and/or its growth rate r was low1, the population would have crashed early, and we would not have the time series of catches that we do have. Conversely, if the initial value of B0 was very large and/or its growth rate was very high, the catches would not have been able to noticeably reduce the population, and, with time, the population would exceed its carrying capacity. Thus, the novelty of this method rests on the finding that if one has a relatively long time series of annual catches and is willing to assume that the population has not collapsed or exceeded carrying capacity, it is possible to identify a relatively narrow range of carrying capacity k (and consequently initial population size, B0) and population growth rate r compatible with the available catch time series. From these ‘viable’ r-k pairs, MSY can then be calculated for each species of interest and an associated biomass time series developed based on an assumed level of depletion. For each country discussed here, we compute the MSY and biomass time series for the ten species that represent the bulk of the reported landings data. We discuss the general 1 The growth rate, i.e., the rate of instantaneous population growth, is defined as the rate at which a population increases in size if there are no density-dependent forces regulating the population.



trends in the biomass trajectories, and, for the stocks which were in biomass equilibrium during the last years for which catch and biomass time series were available (i.e., where the biomass trajectories went ‘flat’), we compute the ratio of average catch relative to the MSY estimated for the species in question2.. Then, we use the average of these ratios to obtain an idea of the potential for increased yields for the fisheries given optimal management and conservation. This approach was inspired by the fisheries Food Provision model recently designed for the ‘Ocean Health Index’ (Halpern et al. 2012; Kleisner et al. in preparation), which compares estimates of multispecies MSY (mMSY) derived for all fished stocks in a country or region to the current level of total landings in 2006 (the latest year for which the Sea Around Us project has spatialized the FAO reported landings).

Between-country comparisons Various indicators are available to compare the fisheries management of maritime countries, or to rank them according to their success in managing their Exclusive Economic Zone (EEZ), notably using the multiple criteria in Alder et al. (2010), the implementation of FAO’s Code of Conduct for Responsible Fisheries (Pitcher et al. 2006), the disbursing of subsidies to national fisheries (Sumaila et al. 2010a), or the newly proposed ‘Ocean Health Index’ (Halpern et al. 2012). These published indices have evaluated the four countries covered here as part of global analyses, and we will use several of the most pertinent indicators from these studies to further describe their fisheries, and the prospects for increasing the biomass of their marine resources and their overall catch within the foreseeable future. The six indicators are presented in detail in Appendix 1. The Exclusive Economic Zones of these 4 countries are all part of well-defined Large Marine Ecosystems (LMEs, see Pauly et al. 2008; Sherman and Hempel 2008; and www.seaaroundus.org), and reference will be made to these LMEs where appropriate. Time series trends of fishing effort To illustrate how fishing effort has changed over time, time series of fishing capacity (i.e., the cumulative power of the engines powering the vessels of national fleets) were assembled, assuming that capacity tracks the fishing effort that is actually deployed.

2 The reason why we couldn’t use all catch data to estimate the ratios of ‘current’ catch to MSY is because very high catches that are extracted from a relatively small biomass drive it further down, i.e., the stock is not in equilibrium, and not even a case of “sustainable overfishing” (which, incidentally, is not an oxymoron, see Hilborn 2005).

http://www.seaaroundus.org/



Marine protected areas To better understand the level of marine protection within the EEZs of Brazil, Chile, India and the Philippines, information regarding the marine protected areas (MPAs) established by each of these countries was analyzed. The Sea Around Us Project maintains a global database of MPAs (see individual country pages at www.seaaroundus.org) from which data for this report were gathered. This database includes information describing MPA size, location, year of establishment, as well as governance and management. To ensure that this information is current and accurate for each country, this database is continually updated using data compiled from peer-reviewed and grey literature, including government documents and websites. In most countries, MPAs are predominately located adjacent to the coast. Therefore, it was necessary to include MPAs designated within the territorial waters extending up to 12 nm from the shoreline, in addition to MPAs situated offshore within the EEZ waters, which are generally defined as extending from the outer limit of territorial waters of a country out to a maximum distance of 200 nm from shore. For simplicity, we treat territorial waters as part of a country’s EEZ in the present context. Recently, countries have begun establishing MPAs in waters surrounding islands separated from the mainland, thus meeting protection targets while avoiding potential conflicts with local stakeholders. While this is a positive conservation action, we excluded these MPAs from our analysis as they do not offer protection to the marine resources within the main EEZs. However, we make reference to MPAs created within the EEZs of offshore territories to provide context for the current and potential future state of MPA development in each country. This serves, in some cases, to illustrate the difference between MPA coverage in the main EEZ versus the EEZs of island territories. For each country, the total area encompassed by MPAs was computed. However, not every ‘MPA’ is entirely marine, as boundaries of some MPAs may encompass both land and sea. To determine the proportion of the EEZ that is protected, we estimate the marine portion of each MPA. When available documentation for an MPA only indicates total area, we estimate the marine area using the median fraction of marine area relative to total area for those MPAs for which this quantity was known. MPAs are established for a variety of reasons and provide differing levels of protection for the species and habitats that occur within their boundaries. Not all MPAs are created for the purpose of improving the sustainability of fisheries (Cullis-Suzuki and Pauly 2008, 2010); in fact, relatively few restrict or prohibit fishing activities. In addition to estimating the total area and marine area covered by MPAs in each country’s EEZ, this study lists the area in which fishing is prohibited within MPA boundaries, i.e., the ‘no-take’ area. An assessment of coverage alone cannot provide a complete picture of protection; one needs to also consider effectiveness (Spalding et al. 2008). Therefore, we also discuss, for each of the countries covered here, a brief overview of the current level of effectiveness of the MPAs in the region.




Results

Brazil The Brazilian EEZ spans the northeast and central eastern coast of South America from approximately 5°N, at the border with French Guyana, to about 33°S, at the border with Uruguay (Figure 1). There are 17 maritime states in Brazil. In northern Brazil, the states are Amapá and Pará (Figure 1, 1-2). There are nine states in northeastern Brazil:

Maranhão, Piauí, Ceará, Rio Grande do Norte, Paraíba, Pernambuco, Alagoas, Sergipe, and Bahia (Figure 1, 3-11). In southeastern Brazil, the maritime states include Espírito Santo, Rio de Janeiro, and São Paulo (Figure 1, 12-14). Finally, in the south, we find the states of Paraná, Santa Catarina, and Rio Grande do Sul (Figure 1, 15-17). The Brazilian EEZ encompasses three LMEs, the North Brazil Shelf (in part), the East Brazil Shelf, and the South Brazil Shelf. Also, the EEZ of Brazil includes oceanic islands, mainly Trindade and Martin Vaz Islands, which are located 1200 km off the coast and support fisheries (Pinheiro et al. 2010), but we refer only to the mainland Brazilian fisheries for the analyses presented here. Within the Brazilian EEZ, there are a wide range of ecosystem types resulting in large differences between the fisheries exploiting the array of marine resources. In the southern and southeastern states of Brazil, these

Figure 1. Map of Brazil showing the 200 nm EEZ adjacent to the mainland, all maritime states, and the EEZ of Trindade and Martin Vaz Islands. The Brazilian EEZ is part of three LMEs: the North Brazil Shelf, the East Brazil Shelf, and the South Brazil Shelf. Numbers correspond to maritime states: Amapá, Pará, Maranhão, Piauí, Ceará, Rio Grande do Norte, Paraíba, Pernambuco, Alagoas, Sergipe, Bahia, Espírito Santo, Rio de Janeiro, São Paulo, Paraná, Santa Catarina, and Rio Grande do Sul, 1-17 respectively.



fisheries tend to concentrate on a smaller number of temperate species, notably the much diminished, but once very abundant Brazilian sardinella (Sardinella brasiliensis)3. Conversely, in the tropical northeastern states of Brazil, the fisheries exploit a diverse array of tropical species, most of which have not been assessed as to their status (Freire et al. 2007). One of the key issues plaguing Brazilian fisheries is the fact that there are many national and state fisheries agencies suffering greatly from institutional instability. This has resulted, among other things, in the absence, in Brazil, of a standardized list of common names for the fish whose catches they report. This results in national catch statistics that are even more unreliable than catch statistics typically are from biodiverse tropical/subtropical countries (Freire and Pauly 2003, 2005).

To address this issue of taxonomic inaccuracy and other problems associated with reported landings data mentioned above (e.g., missing data, erroneous reporting, estimation of discarding, etc.), a reconstruction of Brazilian catches is underway. Currently, however, the best available data remain the FAO reported landings that have been spatialized by the Sea Around Us project. Therefore, the analyses and indicators presented here must be viewed with caution and be used only tentatively to estimate the status of Brazilian fisheries.

3 The scientific name of this species has changed a few times in the last decades. We stick here to the original name, which seems to be the ‘right’ one anyway.

Figure 2. Time series of total, industrial, and artisanal landings for Brazil for 1980 to 2000 (from Freire, 2003).



Moreover, while there are numerous publications on the marine biodiversity of Brazil, there is a scarcity of fish stock assessments, except for the Brazilian sardine (Sardinella brasiliensis). This species has received considerable attention because of the strong fluctuations in biomass and catches (Cergole et al. 2002), and because this stock occurs in the southeastern of the country, off the coast of São Paulo state, where living standards are higher than along the more northern/northeastern shores of Brazil, with the resulting effects on fisheries research. Because of this socio-economic gradient, the fisheries of north and northeastern Brazil are understudied. This situation is further aggravated by the large number of species being exploited in the northern waters, which is typical in tropical regions of the world. The situation is slowly being resolved through an improvement of catch statistics (Freire 2003), including the nomenclatural problems associated with these statistics (Freire and Pauly 2003, 2005). This has allowed the occurrence of the 'fishing down' phenomenon to be detected in northeastern Brazil (Freire and Pauly 2010). Furthermore, the construction of ecosystem models (Freire et al. 2008) has allowed identification of elements for future ecosystem-based management plans for the fisheries of northeastern Brazil (Freire et al. 2007). Catch-MSY method for the main Brazilian stocks Brazilian sardinella (Sardinella brasiliensis), whitemouth croaker (Micropogonias furnieri), Argentine hake (Merluccius hubbsi), Atlantic seabob (Xiphopenaeus kroyeri), chola guitarfish (Rhinobatos percellens), chub mackerel (Scomber japonicus4), southern red snapper (Lutjanus purpureus), Caribbean spiny lobster (Panulirus argus5), Brazilian menhaden (Brevoortia aurea), and Argentine croaker (Umbrina canosai) represent the ten species caught in the greatest abundance in Brazil, according to officially reported landings. The biomass trajectories for 6 of these species (Figure 3) show a declining trend, while Argentine hake, chub mackerel, Brazilian sardinella and Chola guitarfish, appear to have stabilized, albeit at low biomasses. The ratios of current catches relative to the estimated MSY are higher than 1 for 6 of the 10 species covered here (Table 1). For the 4 other species, whose current catches may be sustainable, the mean catch/MSY ratio was 0.56, suggesting, if these 4 species can be taken as representative, that the average sustainable total catch of Brazil could be up to 79 % higher than the present catch given optimal management6. However, this would not account for species interactions, i.e., the fact that some of these species prey on each other. Also, this estimate, being based on only 4 species, is highly uncertain, and should be recomputed using a larger number of species.

4 This is probably Scomber colias, but we shall here follow the name used in Brazilian statistics. 5 The catch statistics pool this species with other, less abundant lobsters species, notably P. laevicauda 6 Because 1/0.56 = 1.79, i.e., an increase of 79%.



Figure 3. Biomass trajectories for the ten species that comprise the bulk of the marine landings in Brazil. Species are ordered alphabetically, not according to contribution to the catch, as in the text. Nine trajectories are displayed for each species, corresponding to different levels of initial depletion assumed in the first year of the time series.



Table 1. Catch/MSY ratios for the top 10 species for Brazil, sorted by percentage contribution to the total catch. Species in bold have a relatively stable biomass trajectory in the last years of the time series (see also Figure 3), suggesting that catches are at equilibrium. Only ratios for these species were averaged. The corresponding biomasses (BCurrent/BMSY) are also given. Species BCurrent/BMSY Catch/MSY Brazilian sardinella 0.41 0.64 Whitemouth croaker 0.42 1.59 Argentine hake 0.46 0.23 Atlantic seabob 0.14 1.62 Chola guitarfish 0.38 0.57 Chub mackerel 0.35 0.80 Southern red snapper 0.39 1.57 Caribbean spiny lobster 0.40 1.54 Brazilian menhaden 0.74 2.47 Argentine croaker 0.42 1.71 Mean ratios (bold spp. only)_ 0.40 0.56

Protecting marine biodiversity in Brazil In Brazil, marine (and terrestrial) protected areas have been established and governed at federal, state and municipal levels under the National System of Conservation Units (SNUC), which recognises two main groupings of protected areas: those for ‘sustainable use’ and those that are no-take. Within these groupings, there are a number of different protected area designations, including biological reserve, ecological station, extractive reserve and sustainable development reserve. Although Brazil has an extensive terrestrial system of protected areas, covering around 17% of the country’s continental area (Ministry of the Environment 2010b), marine habitats remain poorly represented. Available information indicates that there are currently 104 MPAs in Brazil’s EEZ. Collectively, these MPAs encompass a total area of over 91,000 km2, of which an estimated 98.5% are actual marine waters. When compared to the total area of the Brazilian EEZ, an estimated 2.8% of the Brazilian EEZ is protected (Table 2). While this figure compares poorly to terrestrial protected area coverage in the country, it is the highest proportion of the four countries surveyed here.7 Furthermore, this number could be an underrepresentation. While there are records available for the federally created protected areas in Brazil, there is less comprehensive information available for state and municipal protected areas (Ministry of the Environment 2010a; Kalikoski and Vasconcellos 2011), and exact numbers can be difficult to determine (Gerhardinger et al. 2011). In addition, the Marine and Coastal Zone in Brazil is considered to be the area extending 12 nm from the coastline and includes coastal municipalities (Ministry of the Environment 2010b; Szlafsztein 2012). Coastal and marine areas are not often distinguished, making it difficult to determine which protected areas include a marine 7 Recall that we consider here only MPAs in the EEZs adjacent to the mainland, which thus exclude, in Chile, the large Salas y Gomez MPA (see section on Chile).



component (intertidal, subtidal or both). Whatever the exact percentage protected, it is recognised as insufficient, and the need to increase this amount, as well as the no-take proportion, has been made a national priority (Ministry of the Environment 2010a). Incidentally, there are no MPAs currently established within the EEZs of the offshore Martin Vaz Islands or Trindade Islands.

Table 2. Summary statistics for marine protected area (MPA) numbers and coverage for Brazil. EEZ area (km2) 3,192,376 Number of MPAs 104 Fraction incl. no-take 0.01 MPA coverage (km2) Total 91,731 Marine 90,088 No-take 362 Percentage of EEZ Total 2.8 No-take 0.01

Of the 104 Brazilian MPAs, at least 54 are for ‘sustainable use’, according to the SNUC groupings. The no-take area closed to extractive activities amounts to 362 km2, which is a very small proportion of the country’s 3 million km2 EEZ (<0.01%; Table 2). This lack of no-take coverage could result from flaws in SNUC’s process for establishing no-take MPAs, which excludes involvement of local people (Kalikoski and Vasconcellos 2011). However, the recently created Brazilian Ministry of Fisheries and Agriculture, which has the responsibility of increasing fisheries yields, has recognized the importance of no-take areas as a tool for fisheries management (Gerhardinger et al. 2011). Most of the 104 Brazilian MPAs (~40%) are between 100 and 1,000 km2 in extent; there are few very large (>10,000 km2) or very small (<0.1 km2) MPAs (Figure 4). With a greater representation of relatively large MPAs, countries such as Brazil are able to protect a higher proportion of their EEZ with greater efficiency relative to a country whose EEZ is dominated by small MPAs, such as the Philippines.



Figure 4. Size distribution of marine protected areas in Brazil. MPAs were grouped into size classes according to the total area (a) of each MPA, including both terrestrial and marine components. The size classes were defined using a logistic scale due to the predominance of small MPAs, (i.e., Size Class A: a ≤ 0.1 km2; B: 0.1 < a ≤ 1 km2; C: 1 < a ≤ 10 km2; D: 10 < a ≤ 100 km2; E: 100 < a ≤ 1,000 km2; F: 1,000 < a ≤ 10,000 km2; G: 10,000 < a ≤ 100,000 km2; H: a > 100,000 km2). Histograms represent the proportion of the total number of MPAs in each size class.

The effectiveness of Brazil’s current system of marine protected areas is unclear, as no comprehensive assessment has been conducted and made publicly available (Gerhardinger et al. 2011; Kalikoski and Vasconcellos 2011). However, the results of a general analysis of the issues associated with implementing a system of protected areas in Brazil reveals a number of gaps, including inadequate financial and human resources, and a lack of infrastructure, management plans, and institutional support and coordination (Gerhardinger et al. 2011). These authors go on to suggest that establishment of additional MPAs in Brazil would have no greater effect than helping to realize goals of marine protection only “on paper,” unless these issues are addressed. Amaral and Jablonski (2005) echo some of the same concerns. Therefore, the designation of additional MPAs to increase percentage coverage is likely insufficient to ensure protection of Brazil’s marine resources and biodiversity, and should be coupled with efforts to deal with these on-the-ground shortfalls.



Chile The Republic of Chile is a long but narrow country, with the Andes flanking the eastern border and the Southern Pacific and Antarctic Oceans to the west (Figure 5). This geography has resulted in a population with strong ties to the sea. Although fishing

accounts for only 0.4% of the GDP of Chile, and is dwarfed by mining, Chile’s overall fisheries landings in 2010 were the seventh largest in the world (OEDC 2012). In addition to the mainland EEZ, which encompasses approximately 2 million km2, Chile owns several oceanic islands: the Desventuradas Islands 850 km from the coast (EEZ area: 449,805 km2), the Juan Fernandez, Felix and Ambrosio Islands 890 km from the coast (EEZ area: 502,490 km2), and Easter Island over 3,500 km from the mainland (EEZ area: 720,395 km2), which is known as the most remote inhabited island in the world. For this report, Chile’s oceanic islands are not considered, except with reference to their MPAs.

Mainland Chile is divided into 15 administrative ‘regions’, all but one of which are coastal. The northern regions include Arica, Tarapaca, Antofagasta, Atacama, Coquimbo, and Valaparaiso (Figure 5, 1-6), while the southern states include Libertador, Maule, Blobio, Araucania, Los Rios, Los Lagos, Aisen, and Magallanes (Figure 5, 7-14). In terms of biology and biodiversity, Chile’s EEZ consists of four main zones: the north, central, southern and austral zones, each characterized by specific environmental and biological conditions (Peña-Torres 1997). The mainland EEZ component largely overlaps with the southern half of the Humboldt Current LME. The Eastern Boundary Humboldt Current (EBHC) is recognized as one of the largest and most productive marine ecosystems in the world (Mann and Lazier 1991), and is highly variable due to El Niño events. The EBHC is a classical eastern boundary zone (Parrish et al. 1983; Werner et al. 2008), where strong coastal winds

Figure 5. Map of Chile showing the 200 nm EEZ adjacent to the mainland, all maritime regions, and the EEZs of the Desventuradas, Juan Fernandez and Ambrosia, and Easter Islands. (The shaded area indicates an area also claimed by Peru). Numbers correspond to maritime regions: Arica, Tarapaca, Antofagasta, Atacama, Coquimbo, Valaparaiso, Libertador, Maule, Blobio, Araucania, Los Rios, Los Lagos, Aisen, and Magallanes, respectively, 1-14 respectively.



drive water northward and off the coast, resulting in upwelling of deeper nutrient-rich waters and thereby allowing for an extraordinarily strong primary production (Carr and Kearns 2003). The large amount of plankton in this region allows, in turn, for a high abundance of zooplankton, which eventually translates to fish and other vertebrates, notably seabirds and marine mammals. Thus Chile, similarly to Peru, which occupies the northern part of the Humboldt Current LME, is one of the richest countries in the world in terms of marine fisheries resources. The high fish catches that this allows, however, are concentrated on a few species, notably forage fish, sardine and anchoveta, as well as chub and horse mackerel – most of which are fed to reduction plants, i.e., turned into fishmeal and related products8. Pelagic species represent 85% of the total catch, with anchoveta and South American pilchard comprising 65% of the total catch. Demersal species account for only 3.6% of the total catch and include species such as Pacific hake and Patagonian grenadier, which are of higher value and are exported as frozen or chilled seafood products. Fisheries in Chile consist of large-scale industrial fisheries and small-scale artisanal fisheries. Industrial fisheries operate vessels greater than 18 m in length, and correspondingly, small-scale (or artisanal) fisheries refer to landings from vessels under 18 meters in length. Both industrial and artisanal fishers must be registered with the National Registry of Industrial Fisheries (NRIF) and National Registry of Artisanal Fisheries (NRAF), respectively. Over the last decade, an average of 4.76 million t per year were landed in Chile. While artisanal fisheries have increased their catch, the catches of the industrial fisheries have declined, such that overall landings have decreased by an estimated 17% in the last decade (SONAPESCA 2008; CENDEC 2010). In terms of bulk, the major Chilean industrial fisheries are for pelagic species, both in the north and central part of the country. In the northern regions, anchoveta account for most of the landings, followed by jack mackerel and American mackerel (OEDC 2009). The largest quantities of mackerel and sardine are caught in central and southern Chile. Up to 80% percent of the industrial landings are used by the local fishmeal industry to produce fishmeal and fish oil for salmon aquaculture, while the rest is exported chilled or frozen. In 1994, industrial landings reached a record 7.5 million t (Figure 6) and have declined since, reaching 3.6 million t in 2010. Inca scad (Trachurus murphyi) and chub mackerel (Scomber japonicus) are also caught in increasing quantities outside the Chilean EEZ, a relatively recent development, which has required large vessels with substantial refrigeration capacities. The rest of the industrial fleet is composed of several factory vessels, which are allowed to fish only in the Austral zone and in international waters, and which target South Pacific hake, conger eels and Patagonian toothfish (i.e., ‘Chilean seabass’) for local consumption and export (OEDC 2012; Hugo Arancibia, pers. comm.).

8 As required by a large (and problem-ridden) salmon-farming industry, which is not covered here.



Figure 6. Time series of total, industrial, and artisanal landings for Chile for 1960-2010 Data extracted from SERNAPESCA statistics database by Dr. Hugo Arancibia (Universidad de Concepción, Chile; pers. comm.).

Artisanal fisheries are widely practiced along the Chilean coastline, with participation having substantially increased in the past 10 years. Today, these fisheries contribute to almost half (46%) of the fish and crustacean landings in the country. Artisanal fisheries land their products in coastal villages (‘caletas’) or at wharfs, most of the latter located in rural areas where most livelihoods depend directly on fishing (CENDEC 2010). Historically, artisanal fisheries have targeted shell-fish such as ‘Chilean abalone’ or ‘loco’ (Concholepas concholepas, a snail species), mussels, and demersal fish (Gelcich et al. 2005). Most of the artisanal landings are used for local consumption as the caletas generally lack freezing capacity. The remaining fraction of the artisanal landings are directly sold to seafood exporters. Artisanal fishers are required to register with the NRAF in the particular area where they reside and can only operate in that area. They are allocated exclusive rights to waters up to 5 nautical miles from shore. In the most southern regions, artisanal fishers are also allowed to fish in ‘interior marine waters’, i.e., waters out to 12 nm, where industrial fisheries are not permitted to operate. Artisanal fishers are typically allocated free access to these zones, but once the stock is considered ‘fully exploited’, access can be limited (OEDC 2009). As a result of the overexploitation of benthic resource, such as Chilean abalone or loco, an area-based cooperative system was introduced after the fishery was officially closed in 1989. This new form of management was established in 1997 and established the Management Areas for the Exploitation of Benthic Resources (MAERB). Through this policy, the Undersecretary of Fisheries (SUBPESCA) gives formal property rights to



certain natural resources in defined geographical areas of the seabed to registered syndicates. This includes the right to exclude non-members from exploiting that area of the seabed (Gelcich et al. 2005). After this measure was established, the stocks recovered, and now provide steady incomes for some 50,000 artisanal fishers (Anon. 2012). This policy model is now a global example of successful property rights management in fisheries.

However, the current fishery and aquaculture legislation (‘Ley General de acuicultura y pesca’) expires at the end of 2012, and the government has yet to decide on a more permanent solution. Moreover, as artisanal fisheries have grown in importance, the government is realizing the need to regulate the artisanal fleet. As an initial step, an official distinction is being made between medium-sized boats (those between 12 and 18 meters in length) and boats that are less than 12 meters long. The medium-sized boats represent only 10% of the artisanal fleet, but account for 90% of its catch. Other measures include the mandatory installation of satellite transponders in the vessels at the owner’s expense (Long 2012). The new laws will create scientific committees, which will intervene in the decision-making process of marine resource quota allocations. The inclusion of scientific committees in the decision-making process was part of a proposal by Oceana to the Ministry of Economy in 2010. Oceana also proposed a new mechanism for quota allocation: (1) scientific recommendations must be respected when quotas are allocated; (2) the setting of country-wide quotas must not be influenced by any fishing actors; (3) there must be transparency in the decision making process; and (4) scientific committees must include participants such as universities, NGOs and any competent organization that could enhance the knowledge about the stocks and the overall biology of the resource being assessed (www.pescaaldia.cl/entrevistas/?doc=458). This proposal represents a major step forward for Chilean fisheries. There have been several other successful policy and environmental campaigns in Chile over the past decade in which Oceana played a key role. In July 2001, a national ban on shark-finning was implemented. A multi-year campaign to raise the awareness about the overfishing of jack mackerel resulted in a considerable quota reduction in October 2010. Also, several marine reserves were established, including the world’s fourth largest marine reserve around Salas y Gomez Islands in the Pacific and a reserve in northern Chile to protect endangered Humboldt penguins, which was established in an area where a power plant was to be built.

http://www.pescaaldia.cl/entrevistas/?doc=458



Catch-MSY method for the main Chilean stocks Anchoveta (Engraulis ringens), South American pilchard (Sardinops sagax), Inca scad (Trachurus murphyi), Araucanian herring (Strangomera bentincki), Patagonian grenadier (Macruronus magellanicus), chub mackerel (Scomber japonicus), South Pacific hake (Merluccius gayi gayi), Chilean sea urchin (Loxechinus albus), Taca clam (Protothaca thaca), and southern hake (Merluccius australis) are the ten species contributing most to total Chilean reported landings. The biomass trajectories for each of these species (Figure 7) show a generally declining trend, with the exception of South American pilchard, South Pacific hake, and southern hake, which show relatively stable trends since the 1990s, or in the case of South Pacific hake, since the 1970s. Incidentally, these species also show the greatest variability in the trends between assumed depletion levels.

Figure 7. Biomass trajectories for the ten species that comprise the bulk of the landings for Chile. Species are ordered alphabetically, not according to contribution to the catch. Nine trajectories are displayed for each species, corresponding to different levels of initial depletion assumed in the first year of the time series.



The ratios of current catches relative to the estimated MSY are higher than 1 for 7 of the 10 species covered here (Table 3). For the 3 other species, whose current catches may be sustainable, the mean catch/MSY ratio was 0.74, suggesting, if these 3 species can be taken as representative, that the average sustainable total catch of Chile could be up to 35 % higher than the present catch, given optimal management9. However, this would not account for species interactions, i.e., the fact that some of these species prey on each other. Also, this estimate is highly uncertain, as it is based on only 3 species, and should be recomputed using a larger number of species.

Table 3. Catch/MSY ratios for the top 10 species for Chile, ordered by percentage contribution to the total catch. The 3 species in bold have a relatively stable biomass trajectory in the last years of the time series (see also Figure 7), suggesting catches are at equilibrium. Only ratios for these species were averaged. The corresponding biomasses (BCurrent/BMSY) are also given. Species BCurrent/BMSY Catch/MSY Anchoveta 0.65 1.86 South American pilchard 0.36 0.36 Inca scad 0.48 1.29 Araucanian herring 0.53 2.13 Patagonian grenadier 0.30 1.18 Chub mackerel 0.71 2.42 South Pacific hake 0.47 0.92 Chilean sea urchin 0.46 2.05 Taca clam 0.42 1.27 Southern hake 1.20 0.95 Mean ratios (bold spp. only) 0.68 0.74

Protecting marine biodiversity in Chile Chile has a long history of marine protection, beginning in 1935 when the first MPAs were established. At present, there exist a number of legal instruments for protection of marine and coastal areas, including national reserves, national parks, marine concessions, marine parks, and natural sanctuaries (Fernandez and Castilla 2005). The regulations governing each of these instruments vary greatly. For example, some MPAs, such as La Rinconada Marine Reserve in northern Chile, have been established to protect only single populations of exploited species (Rovira et al. 2008). Overall, the processes of MPA establishment, implementation and enforcement are complex, requiring input from several administrative agencies (Fernandez and Castilla 2005). Therefore, adoption of extensive MPA networks has been slow.

9 Because 1/0.74 = 1.35, i.e., an increase of 35%.



There are currently 29 MPAs in the waters off mainland Chile, covering over 52,200 km2 of marine and coastal habitats (Table 4). With the exception of the largest MPA in the main Chilean EEZ, Cabo de Hornos, Chilean MPAs are typically 100% marine. Using this information, it was estimated that 32,200 km2 (i.e., 1.6%) of the EEZ is protected (Table 4). Approximately 50% of the MPAs are less than 1 km2 in extent (Figure 8), with the smallest (Qunitay Marine Concession) occupying only 0.002 km2. Seven of the Chilean MPAs are small ‘marine concessions’, assigned to universities and private companies mainly for research purposes (Fernandez and Castilla 2005). When one considers the current extent of MPA coverage in the country, the most conspicuous deficiency lies with no-take coverage: a meagre 4 km2, equating to <0.01% of the Chilean EEZ (Table 4).

Table 4. Summary statistics for marine protected area (MPA) numbers and coverage for Chile. EEZ area (km2) 2,006,482 Number of MPAs 29 Fraction incl. no-take 0.07 MPA coverage (km2) Total 52,222 Marine 32,216 No-take 4 Percentage of EEZ Total 1.6 No-take <0.01

Figure 8. Size distribution of marine protected areas in Chile. MPAs were grouped into size classes according to the total area (a) of each MPA, including both terrestrial and marine components. The size classes were defined using a logistic scale due to the predominance of small MPAs, (i.e., Size Class A: a ≤ 0.1 km2; B: 0.1 < a ≤ 1 km2; C: 1 < a ≤ 10 km2; D: 10 < a ≤ 100 km2; E: 100 < a ≤ 1,000 km2; F: 1,000 < a ≤ 10,000 km2; G: 10,000 < a ≤ 100,000 km2; H: a > 100,000 km2). Histograms represent the proportion of the total number of MPAs in each size class.



In addition to the protection instruments mentioned above, Chile employs tools termed ‘ancillary’ marine conservation measures by the Convention on Biological Diversity (Secretariat of the Convention on Biological Diversity 2004) – most notably, management and exploitation areas for benthic resources (MEABRS), which explicitly restrict the extraction of benthic resources, and thus, by association, offer some level of protection to other species groups. MEABRS vastly outnumber other legal instruments for marine conservation in the country; there are hundreds, covering an area of more than 1,000 km2 (SERNAPESCA 2005). Although these co-management areas have the potential to complement the biodiversity conservation objectives of other protected areas in the region (Gelcich et al. 2008; Gelcich et al. 2012), there is clearly a dearth of coverage dedicated to conserving biodiversity comprehensively and preventing extractive activities. A notable advancement in Chile’s current system of MPAs is the recent establishment of the 150,000 km2 Sala y Gomez Marine Park within the Easter Island EEZ, which represented a two-fold expansion of the country’s MPA coverage (Anon. 2010). This new area is entirely no-take, and had it been included in this report, no-take coverage would have increased more than twofold. However, this MPA is thousands of miles from the mainland EEZ, from which it vastly differs in terms of the biodiversity that it protects. Indeed, the mainland EEZ remains largely unprotected from extractive activities although it is exposed to numerous anthropogenic threats (Miethke et al. 2007). For example, the marine environment along the central Chilean coast, adjacent to the largest portion of the country’s human population, is underrepresented in the country’s MPA network. Competition for different uses, such as aquaculture, and management and exploitation areas, is suggested as the primary constraint to MPA establishment (Fernandez and Castilla 2005; Tognelli et al. 2009). Fernandez and Castilla (2005) assert that the current system of MPAs in Chile is “promising,” but there are failings that need to be addressed, among them the complex administrative system, a lack of governmental support for marine conservation initiatives (as opposed to exploitation), and insufficient funding. Poor enforcement is also an issue that could preclude the establishment of effective no-take MPAs (Tognelli et al. 2009).



India The Republic of India is located in South Asia and shares land borders with Pakistan on the west, China, Nepal, and Bhutan to the north-east, and Burma and Bangladesh to the east (Figure 9). India is the second most populous country in the world, with

approximately 1.2 billion people in 2011, representing 17.5% of the world population10. India covers a total land area of about 3.3 million km2, with 28 States and 7 Union Territories, the latter under the direct authority of the central government (Arora and Grover 1996; Bhathal 2005). The west coast of India has five maritime states: Gujarat, Maharashtra, Goa, Karnataka, Kerala (Figure 9, 1-5) and two Union Territories, Daman & Diu and Lakshadweep. The east coast of India has four maritime states: Tamil Nadu,

Andhra Pradesh, Orissa, and West Bengal (Figure 9, 6-9). The Union Territories include Pondicherry and the Andaman and Nicobar Islands. The marine waters of India encompass two LMEs, the Arabian Sea along the west coast and the Bay of Bengal along the east coast. India’s EEZ covers a total area of 1.63 million km2 (including the Lakshadweep Islands on the west coast). In the Bay of Bengal, the EEZ of the Andaman and Nicobar Islands covers a total area of 660,000 km2 (www.seaaroundus.org) and represents about 30% of the total Indian EEZ. For the purposes of this study, we concentrate only on the fisheries of the mainland EZZ and do not evaluate the fisheries

10 http://www.censusindia.gov.in/2011-prov-results/data_files/india/Final_PPT_2011_chapter3.pdf

Figure 9. Map of India showing the 200 nm EEZ adjacent to the mainland, all maritime States and Union Territories, and the territorial EEZ of the islands of Andaman and Nicobar. The Indian EEZ is part of two LMEs: the Arabian Sea LME to the west and the Bay of Bengal LME to the east. Numbers correspond to maritime States: Gujarat, Maharashtra, Goa, Karnataka, Kerala, Tamil Nadu, Andhra Pradesh, Orissa, and West Bengal, 1-9 respectively.


http://www.censusindia.gov.in/2011-prov-results/data_files/india/Final_PPT_2011_chapter3.pdf



of the Andaman and Nicobar Islands (but see Bhathal and Pauly 2008). As with most developing countries with long coastlines, the rich resources of the surrounding ocean play an important role in the national economy, diet, and culture of the Indian people. The Indian Ocean is the warmest ocean in the world, resulting, via a strong, semi-permanent stratification, in low primary productivity in most regions. Despite this low productivity, the marine fishing sector in India has shown steady growth since India’s independence in 1947. India declared its EEZ in 1976, and divided this zone into three regions: territorial waters, which extends out to 12 nm, the contiguous zone, which extends out to 24 nm, and the Exclusive Economic Zone, which extends out to maximum of 200 nm (Bhathal 2005). The west coast of India, also known as the ‘Malabar coast’, has a wider continental shelf and a relatively high primary production, and supports over 75% of India’s total fish landings (Bhathal 2005). The east coast of India, also known as ‘Coromandel coast’, has a much narrower shelf and primary and secondary production in the Bay of Bengal is much lower than the Arabian Sea. Still, there are nearly 4,000 fishing villages and 2,000 traditional landing centers along this coast (FAO 2004). The waters off India host a wide diversity of marine resources targeted by artisanal fishers, some operating with century-old methods, and by large-scale industrial fishing operations which are disrupting coastal communities and their way of life, notably through intense competition for the same resources. In general, marine resources in India are targeted by four groups, operating various types of fishing vessels and gears: (1) artisanal fishers operating non-mechanized vessels, (2) artisanal fishers operating vessels with outboard motors (less than 50 hp) in inshore waters, (3) industrial fishers using vessels with inboard motors, and (4) industrial deep-sea vessels. Overall, there are approximately 1.45 million fishers in India and the bulk of marine fish landed (68%) is taken by mechanized vessels (Funge-Smith et al. 2005). Trawling has emerged as the most important method of exploiting demersal resources and accounts for 50% of the total Indian catch. Valuable species such as Indian oil sardine (Sardinella longiceps), penaeid and non-penaeid shrimp, Indian mackerel (Rastrelliger kanagurta), Bombay duck (Harpadon nehereus), and croakers (Micropogonias spp.) are the preferred targets, although various types of commercial finfish are often caught as bycatch (Gordon 1991). Among the multitude of species contributing to the catch, one species, the Indian oil sardine (Sardinella longiceps) contributes the majority of the yields, although they fluctuate strongly (Longhurst and Pauly 1987; Bhathal 2005). The marine fisheries in India are regulated both by the Central and State Governments. Deep-sea fishing within the EEZ by domestic and foreign fleets is managed by the Central Government; however, there is no comprehensive fisheries legislation for fisheries within the EEZ (Rajagopalan 2011). Fisheries within the 12 nautical mile territorial zone fall under the jurisdiction of the States, which are responsible for managing and collecting official fisheries statistics under the Marine Fishing Regulation Act (MFRA, Bhathal 2005; Rajagopalan 2011). Along with the State governments, the



Central Marine Fisheries Research Institute (CMFRI, http://www.cmfri.org.in) estimates the annual fish landings by state and compiles the data for the entire country. National catch statistics prior to 1994 were obtained through a rigorous stratified sampling procedure; however, since the mid-1990s, changes to the sampling program have caused the deterioration of India’s marine production statistics (Bhathal and Pauly 2008). The CMFRI national data were primarily used for the ongoing catch reconstruction work by the Sea Around Us project, as most data published by other institutes and departments are not readily available (details in Bhathal 2005). India regularly reports commercial landings from the artisanal sector. However, industrial landings have historically been unreported. Bhathal (2005) estimated total catch by industrial vessels from 1972-2000, since the first commercial trawlers arrived in Indian waters and began operation in 1972 (Devaraj 1996). Industrial and mechanized vessel discards were also estimated, as they are rarely reported (Bhathal and Pauly 2008). However, these estimates were considered to be conservative compared to previous reports on bycatch and discards in India (Gordon 1991; Davies et al. 2009; Dineshbabu et al. 2010). Furthermore, it was assumed that all bycatch was retained prior to 1970, as even low-value species had a market, resulting in negligible discarding (Bhathal 2005). Some studies exist of bycatch and discards in later years (George et al. 1981; Gordon 1991; Zacharia et al. 2006; Dineshbabu et al. 2010). The earliest survey found that 79% of the total landings in the shrimp trawl fishery consisted of non-shrimp catch (George et al. 1981). The practice of discarding the shrimp bycatch in India is associated with long distance, multi-day fishing, almost all of which is from Visakhapatnam, Andhra Pradesh (Gordon 1991; Zacharia et al. 2006). While discard estimates can vary among states, it has been estimated that overall, approximately one third of all bycatch is discarded (Davies et al. 2009). Other sources suggest the figure is closer to 20% of the bycatch, i.e., approximately 1.3 million t annually, although this should be interpreted as a conservative estimate (Chandrapal 2007). A more detailed estimate of fishery extractions, including illegal and unreported catches from India can be found in Ganapathiraju (2012). Estimates of illegal fishing by Indian and foreign vessels, discards by industrial trawlers, subsistence fishing, and underreporting by the artisanal sector, such as bait fish, dry fish landings and harvest of molluscs, were obtained during a 2008 field study. Ganapathiraju (2012) conducted interviews with fishers from the small-scale and mechanized sector in 9 out of 10 coastal states11, including the Andaman and Nicobar Islands. In addition to the reported catch, Ganapathiraju’s findings suggested that approximately 1.5 million t went unreported in 2008. The highest unreported catch (~1.2 million t) was contributed by discards from industrial trawlers and other vessels. Subsistence fishing, which are generally missing from the catch statistics (Zeller et al. 2007), totalled 149,000 t and underreported catch within the artisanal sector totalled 105,000 t (details in Ganapathiraju 2012).

11 Goa and the Union Territory of Daman and Diu were not visited on this study. Information from the Lakshawdeep

Islands is under review as of the time of writing and therefore not included.

http://www.cmfri.org.in/



Ganapathiraju (2012) also calculated discards by foreign-chartered trawlers and tuna longliners fishing intermittently within India’s EEZ from 1982-2009. Illegal catch by foreign fishing vessels was estimated to be 60,000 t per year, not counting an estimated 1,840 t per year and per vessel of discarded trash fish (Devaraj 1996; Ganapathiraju 2012). Observations from joint venture tuna fisheries suggested that only 20% of the catch is reported and bycatch is rarely reported. Recreational catch by part-time fishers and catches from remote fish landing centers, which are presently poorly monitored by government officials, were apparently incorporated into Ganapathiraju’s (2012) study; however, individual estimates for these sectors were not given. Through the 1970s, the non-mechanized sector fished primarily with hooks and lines, gillnets, seines, bag nets and traps, from catamarans, canoes and plank built boats. These vessels were gradually modified through the 1980s to hold outboard engines of 5-9 hp, in order to travel farther. Major endeavours were made to increase mechanization during the 1970s and 1980s, prompting the development of an industrial motorized fleets consisting of small trawlers, pair trawlers, purse seiners and gillnetters that could accommodate small inboard engines and readily fish to depths of 50 m. Additionally, chartered and joint venture deep-sea trawlers, tuna long-liners, and multi-purpose vessels, which have the capacity to target both prawns and fish, were introduced in 1972 and now make up the bulk of the industrial fleet (Devaraj 1996). This push for modernization of the vessels in India stemmed from a desire to promote the evolution of fisheries into more industrial (deep-sea) activities (Rao and Murty 1993). The resulting geographic expansion into deeper waters was the main reason for the growth and maintenance of Indian fisheries catches (Figure 10). However, this expansion must be accounted for when evaluating the health and productivity of Indian fisheries, as true trends in the status of fisheries (e.g., changes in mean trophic level and changes in mean size of fishes) may be masked when catch data are not disaggregated spatially. Overall, the push to expand has been fuelled mainly by the perception of Indian policy makers that the demersal fisheries could be by operating in deeper waters. However, the low oxygen levels in deeper water layers, especially on the West Coast (Banse 1959), constrain such expansion. Therefore, the new subsidized trawlers added to the Indian fleets since the 1980s tend to compete with small-scale fishers operating close inshore. This indeed is one of the reasons why the conflict between small- and large-scale fisheries is most pronounced in India.



Figure 10. Total, industrial and artisanal marine fish catch by India from 1950-2010. Data from historical reconstruction of Indian marine fisheries catches (Bhathal 2005). According to the FAO, Indian marine catches have increased from 530,000 t in 1950, and approached 3 million t in 2010 (Figure 10)12. At present, increased fishing pressure by a large number of fishing vessels (Figure 11; Bhathal and Pauly 2008), has depleted inshore resources and increased catch of juveniles and discards (Somvanshi 2001; Sathiadhas 2005). Overall, fishing effort is far in excess of that needed to sustainably fish India’s marine resources, as evidenced by the drastic decline in CPUE over time (Figure 11B), and catches, which for a long time appeared to increase steadily, are reaching a plateau (Figure 11).

12 Fishstat database: http://www.fao.org/fishery/statistics/en

0

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1950 1960 1970 1980 1990 2000

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03 )

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http://www.fao.org/fishery/statistics/en



Figure 11. Total catches of India for all species excluding tuna and billfish from 1950 to 2010, fishing effort in kW days, and catch-per-unit-effort; A: concurrent increase over time in both catch (solid line) and effort (dashed line); B: illustrating the serious decline in catch per unit effort (CPUE) over time; C: the levelling-off in the relationship between catch and effort, suggesting that further growth in effort will not increase catches (from B. Bhathal, draft PhD thesis).

Overall, fishing effort is far in excess of that needed to achieve mMSY (see below), but how to achieve this mMSY poses an immense equity and policy challenge, which, in terms of complexity, dwarfs the problem of estimating the elusive MSY and the associated optimum efforts. Catch-MSY method for the main Indian stocks Indian oil sardine (Sardinella longiceps), Bombay duck (Harpadon nehereus), Indian mackerel (Rastrelliger kanagurta), silver pomfret (Pampas argenteus), torpedo scad (Megalaspis cordyla), narrow-barred Spanish mackerel (Scomberomorus commerson), false trevally (Lactarius lactarius), kawakawa (Euthynnus affinis), black pomfret (Parastromateus niger), and Indo-Pacific king mackerel (Scomberomorus guttatus) are the ten species contributing most to total Indian catches. The biomass trajectories for each of these species show a generally declining trend (Figure 12). For most of the



species, with the exception of false trevally and Indian mackerel, the trends for each depletion level tend to converge, indicating potentially higher confidence in the trajectories.

Figure 12. Biomass trajectories for the ten species that comprise the bulk of the landings for India. Species are ordered alphabetically, not according to contribution to the catch. Nine trajectories are displayed for each species, corresponding to different levels of initial depletion assumed in the first year of the time series.

The ratios of the current catch (averaged for 2000-2006, or in the case of torpedo scad, silver pomfret, and black pomfret, from 1994-2000) relative to the estimated MSY illustrate that, for 8 species, yields are much too high to be sustainable (Table 5); indeed, this is the reason why CPUE in the Indian fisheries are declining so strongly (see Figure 11B). Only 2 species may have catches that are roughly in equilibrium with their current biomass, Indian mackerel and false trevally (see Figure 12). These two species yield a mean catch/MSY ratio of 0.96 (Table 5), suggesting, if these two species can be taken as representative, that the overall MSY of India could be about 5% higher than the present catch, given optimal management. However, this would be achieved at a much lower level of fishing effort, thus generating more income for smaller fisheries, especially as



the biomasses (and hence CPUE) would be much higher (see BCurrent/BMSY ratios in Table 5). Needless to say, this result is very uncertain, and will need to be verified by studying the dynamics of far more Indian species than presented here.

Table 5. Catch/MSY ratios for the top 10 species for India, ordered by percentage contribution to the total catch. The two species in bold have a relatively stable biomass trajectory in the last years of the time series (see also Figure 12), suggesting that their catch may have been at equilibrium. The corresponding biomasses (BCurrent/BMSY) are also given. Species BCurrent/BMSY Catch/MSY Indian oil sardine 0.90 1.92 Bombay duck 0.65 1.72 Indian mackerel 0.50 0.82 Silver pomfret 0.54 3.01 Torpedo scad 0.92 3.71 Narrow-barred Spanish mackerel 0.52 1.83 False trevally 0.42 1.10 Kawakawa 0.36 1.82 Black pomfret 0.58 4.84 Indo-Pacific king mackerel 0.51 1.52 Mean ratios (bold spp. only) 0.46 0.96

Protecting marine biodiversity in India Protected area legislation in India has been designed for the purpose of protecting terrestrial habitats and associated species. There is no formal legal framework for the designation and governance of marine protected areas (Rajagopalan 2011). Instead, national parks, wildlife sanctuaries, and tiger reserves that contain both marine and terrestrial components are categorized as ‘marine and coastal protected areas’ (MCPAs). Currently, 20 MCPAs have been declared along the Indian coastline under the Indian Wildlife Protection Act of 1972 (Table 6). Additionally, there are two, partially marine, biosphere reserves designated under the Man and Biosphere Programme of the United Nations Educational, Scientific, and Cultural Organization (UNESCO; Singh 2003; Rajagopalan 2008; Rajagopalan 2011). While most MCPAs were declared between 1975 and 1995, the most recent was established in 2000. All are located within the 12 nautical mile territorial waters of India.



Table 6. Summary statistics for marine protected area (MPA) numbers and coverage for India. EEZ area (km2) 1,629.182 Number of MPAs 22 Fraction incl. no-take 0.18 MPA coverage (km2) Total 24,011 Marine 8,384 No-take 1,111 Percentage of EEZ Total 0.5 No-take 0.07

While there are relatively few MCPAs in India, most are rather large in size (Figure 13). All but two MCPAs cover an area greater than 10 km2, and the majority are between 100 and 1,000 km2 in total area (including terrestrial and marine components; Figure 13). The largest, the Gulf of Mannar Biosphere Reserve, encompasses 10,500 km2. However, Indian MCPAs primarily protect terrestrial habitats. According to available information, the median marine proportion of the total area of Indian MCPAs is 0.4. Therefore, despite the fact that over 24,000 km2 of the coastal and marine environment is protected by MCPAs, we estimated that less than 1% (nearly 8,400 km2) of the Indian EEZ is protected (Table 6).

Figure 13. Size distribution of marine protected areas in India. MPAs were grouped into size classes according to the total area (a) of each MPA, including both terrestrial and marine components. The size classes were defined using a logistic scale due to the predominance of small MPAs, (i.e., Size Class A: a ≤ 0.1 km2; B: 0.1 < a ≤ 1 km2; C: 1 < a ≤ 10 km2; D: 10 < a ≤ 100 km2; E: 100 < a ≤ 1,000 km2; F: 1,000 < a ≤ 10,000 km2; G: 10,000 < a ≤ 100,000 km2; H: a > 100,000 km2). Histograms represent the proportion of the total number of MPAs in each size class.



MCPAs in India were not established for the purposes of fisheries management, though legislation dictates that fishing activity is prohibited within national parks and is often restricted within wildlife sanctuaries (Rajagopalan 2011). Unfortunately, there is no readily available information regarding fishing regulations in the wildlife sanctuaries. According to estimates of the marine area of each national park, approximately 1,100 km2 of the Indian EEZ is designated as no-take (Table 6). However, implementation and enforcement of fisheries management measures is inadequate in MCPAs (Rajagopalan 2011). Additionally, local compliance with fishing restrictions is unlikely, given the lack of community involvement in the creation and management of MCPAs (Rajagopalan 2011). In addition to the 22 MPAs in the main Indian EEZ, there are 16 MPAs in the EEZ surrounding the Andaman and Nicobar Islands. The majority of these MPAs are small (< 10 km2 total area), and 5 include no-take zones. Collectively, these MPAs cover over 1,550 km2, of which an estimated 730 km2 is marine and 300 km2 is no-take.



The Philippines The Philippines consist of over 7,000 islands of various sizes (Figure 14) and encompass most of the Sulu-Celebes Sea LME, a world hotspot of marine biodiversity (Randall 1998; Carpenter and Springer 2005; Hoeksema 2007; Carpenter et al. 2011). These islands cover a land area of 300,000 km2, while the EEZ that might be claimed by the

Philippines13. covers an area of 2.3 million km2 (ADB 1993), including parts of the heavily contested Spratly Islands group, Scarborough Shoal, and Miangas Island (Bautista 2008). About 12% of this sea area consists of continental shelf (to 200 m depth), hosting coral reefs, mangrove and algal ecosystems. It is theses ecosystems that form the habitats of the large number of valuable species supporting coastal fisheries. The Philippine islands are organized into 14 administrative regions covering 81 provinces (80% coastal) and 1,514 municipalities (65%

coastal; see ADB 1993). Fisheries are administered locally, i.e., by the municipal government, a form of micro-management which renders implementation of fisheries rules and regulations rather difficult, and produces very variable results (see e.g., Fabinyi and Dalabajan 2011), although it allows flexibility, a theme to which we shall return. 13 This tortuous wording is based on the fact that the Philippines claim is based not on UNCLOS, as might be expected, but on the 1898 Treaty of Paris, which formalized the transfer of colonial territories from Spain to the United States (Bautista 2008).

Figure 14. Map of the Philippines showing the 200 nm EEZ. The Philippine EEZ is part of the Sulu-Celebes Sea LME. Shaded area indicates parts of the EEZ being disputed by other countries.



Due to the archipelagic nature of the Philippines, and with monsoon seasons affecting its high biodiversity, no single (or small group of) species dominates its fisheries catches. In fact, even abundant and popular food fish, such as ‘galunggong’ (i.e., ‘round scads’, of the genus Decapterus) consist of different species and populations, caught in different parts of bays, gulfs and seas, depending on the season (Alix 1976). Thus, none of their unique populations, if it were the only one to be optimized in terms of biomass and effort, would noticeably affects the total catch (Ronquillo 1975; Calvelo and Dalzell 1987). Moreover, in the Philippines, which produces, publishes, and distributes annually immense amounts of extremely precise fisheries statistics (see BFAR 2012b) that are readily cited by various national and international NGOs, the real catch of the marine fisheries is essentially unknown, notably because, in the Philippines, the collection of fisheries statistics is connected with the taxation system. Commercial landing statistics were collected since 1954 by the Bureau of Fisheries and Aquatic Resources (BFAR) for ten fishery districts (Simpson 1979), based on monthly catch reports (by the operators of vessels >3 gross tons). It was determined that these landings were ‘inadequate’, and they were summarily ‘corrected’ by an expansion factor derived from monthly landings collected by enumerators from randomly sampled survey areas to estimate regional and national production values (PDNR 1976). Already then, underreporting of the catch and/or undervaluing of species caught by the few registered (and/or reporting) fishing vessels was a rampant form of tax evasion and as such, these statistics accounted for less than half of what was really caught (Simpson 1979). In some areas, underreporting may have been as much as 80% of the actual catch (Storer 1967). In addition, small-scale fisheries catches were estimated from only six municipal reports since 1951, which was later discontinued (FIDC 1979). The small-scale fisheries, called ‘municipal fisheries’ in the Philippines, are defined as using fixed gear or craft of less than 3 gross tons (Philippines 1933; Pauly 1982). Since the 1960s, the catch of municipal fisheries has been estimated from the same fixed ratio for the relationship between small-scale and industrial catches (FIDC 1979; see below). This ratio most likely originated from the projected increase of fisheries production to respond to domestic demand, i.e., 6-7%, needed for self-sufficiency in fish by 1976, and thus, for surplus production by 1977 (PDNR 1976). Thus, it appears that even before the Marcos regime, fisheries statistics were generated which showed politically convenient regular catch increases, a problem which has not been addressed since democracy was restored in 1986. Lack of funds and repeated reorganisations of the government divisions handling fisheries prevented the establishment of a comprehensive fisheries data collection system that included the catch of small-scale and subsistence fishers (PDNR 1976; FIDC 1979). It took more than seven decades since the creation of the Division of Fisheries created by the Philippine Commission under the Department of the Interior in 1901 (BFAR 2012a) before a fisheries statistics data collection system could be put in place (Chakraborty 1976). This was implemented after several training workshops for enumerators organized by the South China Sea Fisheries Development and Coordinating



Programme in the mid-1970s (Chakraborty and Wheeland 1976). The first of a series of annual fisheries statistics accounts for all sectors was published by BFAR only in 1977 (BFAR 2012b). Further changes in the governing institutions in the late 1980s transferred the responsibility of fisheries data collection from BFAR to the Bureau of Agricultural Statistics in 1988 (see BFAR 2012a). The continuous problem of funding allocation for data collection, which has beset this sector for decades, prevented regular/consistent data collection until the 2000s (see Itano and Williams 2009). Figure 15 illustrates officially reported total catch from ‘commercial’ and ‘municipal’ fisheries.

Figure 15. Philippine marine fisheries catches reported by the Philippine Fisheries Commission (1951-1976), the Bureau of Fisheries and Aquatic Resources (1977-1988) and the Bureau of Agricultural Statistics (1989-2010) in their annual fisheries statistics reports. Note that small-scale marine fisheries statistics, though available for the period 1951-1971 and 2006-2010, were not separately reported from the inland fisheries statistics and were thus not included in this graph.

Municipal fishing operators, however, remained a problem, notably because of the 1935 Fish and Game Administrative Order (No. 2-2) limiting vessels of more than 3 GT to operate outside municipal waters (see Philippines 1933). This led to the development of scaled-down industrial operations (‘baby trawlers’) which can be operated in the inshore waters, i.e., within 7 km of the coastline (amended to 15 km of the coastline in 1998 by the Philippine Fisheries Code; Philippines 1998), and in waters less than 12.8 m deep, which was traditionally reserved for artisanal fisheries (Tapiador 1978; Pauly and Smith 1983; Cruz-Trinidad 1997). Thus, the highly heterogeneous municipal sector, which is clearly suffering from dwindling resources, as indicated by a very low and declining catch per day per individual fisher (see Simpson 1979; Dalzell et al. 1987; Dickson 1987; Muñoz 1991; Sunderlin 1994; Shannon 2002; Stobutzki et al. 2006; Muallil et al.

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2012), and an ever increasing number of fishers, i.e., the ‘Malthusian overfishing’ of Pauly (2006) is linked to the ever-increasing industrial fleets, which obtain increasing catches from (mostly illegal) fishing in the waters of neighbouring countries, especially in Malaysia (Sabah) and Eastern Indonesia (see Lewis 2004). Therefore, the estimation of multi-species MSY (mMSY) such as would be obtained under conservative fisheries management is largely academic in the present situation. However, since we are academics, we will perform these analyses. However, we will complement them with a brief review of earlier assessments of the overall status of marine stocks in the Philippines. Numerous assessments of the status of fisheries in the Philippines have been conducted, especially in the 1980s, when the International Center for Living Aquatic Resources Management (ICLARM), then based in the Philippines, was very active. These analyses can be grouped into three categories: i) Surplus-yield models pertaining to the demersal and/or pelagic fish of a local

fishing ground, as illustrated in Figure 16; ii) Single- or multispecies yield-per-recruit analyses pertaining to a given fishing

ground, as illustrated in Figure 17; iii) Philippine-wide analyses based either on data such as used in (i), (ii), or other

approaches as illustrated in Figure 18 and 19. Though they tend to provide over-optimistic results (Pauly 1987), simple surplus-yield models (Schaefer 1954; Fox 1970) can be, and were used extensively in the Philippines to assess the status of multispecies stocks and the demersal or pelagic fisheries exploiting them (Dalzell et al. 1987; Silvestre and Pauly 1987; Campos et al. 1988; Culasing 1988; Silvestre and Pauly 1997). These models, in the aggregate, suggested that the majority of fishing grounds in the Philippines, which were extremely productive in the 1950s and 1960s (Butcher 2004), were overfished by the late 1970s and/or 1980s. This is confirmed by yield-per-recruit analyses, i.e., analyses of the ‘yield’ (or catch in weight) that could be obtained by letting individual fish grow to their optimum size, i.e., by regulating not only fishing intensity, but also mesh sizes, which determines size at first capture (Beverton and Holt 1957; see Figures 17 and 19). Analyses of this sort can be performed without detailed catch time series, given that the size composition of the catch is available (length-frequency data; Pauly 1998a). In fact, methods to analyze length-frequency data were developed throughout the 1980s by ICLARM (Pauly and Morgan 1985, 1987), and were applied to a vast number of stocks (see e.g., Floyd and Pauly 1984). Jointly, these analyses confirm that from the 1980s onwards, Philippine marine fishes were massively ‘growth overfished’ throughout the country.



Figure 16. Surplus-production models from two important fishing grounds of the Philippines: A) Manila Bay (Silvestre et al. 1987); B) West coast of Luzon (Simpson 1979).



Figure 17. Multispecies yield-per-recruit assessment of the demersal fisheries of the Philippines, based on vital statistics, relative recruitment rates and per kg prices of 28 representative groups of fish and invertebrates from three major fishing areas (West Sulu Sea, Lamon Bay and Visayan Sea). The method used relies on logistic selection ogives rather than the assumption of knife-edge selection. Dots on intersections of lines A (= optimal or ‘eumetric’ fishing) and B (= suboptimal or ‘cacometric’ fishing) show range of optimum mesh size and fishing mortality combinations. Note excessively high effort and low mesh sizes in Lingayen Gulf, leading to losses of up to 20% and 40% relative to maxima in yield and value, respectively (from Pauly et al. 1989).



Figure 18. Surplus production models of two Philippine fisheries; A: Philippine small pelagic fisheries; B: Philippine demersal fisheries. Adapted from Silvestre et al. (1987).

Figure 19. Summary of length-frequency-based yield-per-recruit models, pertaining to 112 stocks of exploited marine fishes (E< 0.5 = 26; E> 0.5 = 86), illustrating that the majority (77%) of stock in the Philippines were already heavily or over-exploited in the late 1970s and early 1980s. (Adapted from Ingles and Pauly 1984).

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Other approaches to study the status of Philippine fisheries have included the estimation of demersal biomass in the country (Silvestre et al. 1986), which by the early 1980s had declined to 30% of the 1947/1948 value, consistent with generalized overfishing, and the analysis of time-series of artisanal catch per effort, which dropped from over 4 t per year per fisher in 1900 to 1.4 t per year per fisher in 1977 (Pauly 2000). Catch-MSY method for the main Philippine stocks Indian mackerel (Rastrelliger kanagurta), short mackerel (Rastrelliger brachysoma), skipjack tuna (Katsuwonus pelamis), rainbow sardine (Dussumieria acuta), kawakawa (Euthynnus affinis), bigeye scad (Selar crumenophthalmus), blue swimming crab (Portunus pelagicus), yellowfin tuna (Thunnus albacares), narrow-barred Spanish mackerel (Scomberomorus commerson), and torpedo scad (Megalaspis cordyla) appear to be, in order of importance, the ten species contributing most to the catch of the Philippines. For most of the species, the trends for each depletion level tend to converge, indicating potentially higher confidence in the trajectories. The biomass trajectories for 7 of these species (Figure 20) show a generally declining trend, due to excessive catches, while for kawakawa, narrow-barred Spanish mackerel and rainbow sardine, current catches appear to be compatible with sustaining their (reduced) biomasses. . The ratios mean of the current catch to MSY of the 3 species that are currently ‘sustainably overexploited’ is 0.78 (Table 7), suggesting, if these species can be taken as representative, that average sustainable total catch of the Philippines could be up to 28% higher than the present catch, given optimal management. However, this is highly uncertain, being based on a very small number of species, and it is recommended to redo this, or a similar analysis with a much larger number of species. Also, this estimate does not account for species interactions, i.e., the fact that some of these species prey on each other.

Table 7. Catch/MSY ratios for the top 10 species for the Philippines, ordered by percentage contribution to total catch. The 3 species in bold have a relatively stable biomass trajectory in the last years of the time series, suggesting catches are at equilibrium. Only ratios for these species were averaged. Species BCurrent/BMSY Catch/MSY Indian mackerel 0.41 1.67 Short mackerel 0.58 1.56 Skipjack tuna 0.68 1.50 Rainbow sardine 0.42 0.76 Kawakawa 0.41 0.72 Bigeye scad 0.82 2.01 Blue swimming crab 0.41 1.94 Yellowfin tuna 0.46 1.73 Narrow-barred Spanish mackerel 0.42 0.85 Torpedo scad 0.52 2.05 Mean ratios (bold spp. only) 0.42 0.78



Figure 20. Biomass trajectories for the ten species that comprise the bulk of the landings for the Philippines. Species are ordered alphabetically, not according to contribution to the catch. Nine trajectories are displayed for each species, corresponding to different levels of initial depletion assumed in the first year of the time series.

Protecting marine biodiversity in the Philippines The national legal framework that exist for the establishment of MPAs in the Philippines consists of the National Integrated Protected Areas Systems Act of 1992, the Fisheries Code of 1998 and the Local Government Code of 1991 (White et al. 2006a; Alcala et al. 2008). Prior to 1970, establishment of MPAs was largely centralized at the national level, but the enactment of the Local Government Code contributed to devolution of responsibility to local governments. This, along with the enactment of the Fisheries Code, helped spur the designation of many new MPAs (White et al. 2006b; Weeks et al. 2010; Horigue et al. 2012). Recent marine conservation efforts in the Philippines have focused on scaling-up individual MPAs into coordinated networks of MPAs to enhance conservation and fisheries benefits (Lowry et al. 2009; Horigue et al. 2012).



There are at least 562 MPAs in the Philippines at present (Table 8); however, the majority of these (>85%) are less than 1 km2 in extent (Figure 21). Nearly all of the known Philippine MPAs are completely marine (i.e., the estimated median marine proportion of the total area was 1.0). Although the Philippines has the largest number of MPAs of the four countries analysed here (over five times more than the next highest, Brazil), this does not translate into the most expansive coverage. Together, the Philippines’ MPAs cover a marine area of slightly over 15,000 km2 (Table 8). Some researchers estimate the number of MPAs in the Philippines to be higher (e.g., 1,100; Lowry et al. 2009), and thus, these figures could be an underestimate of actual coverage.

Table 8. Summary statistics for marine protected area (MPA) numbers and coverage for the Philippines. EEZ area (km2) 2,267,479 Number of MPAs 562 Fraction incl. no-take 0.43 MPA coverage (km2) Total 17.090 Marine 15,164 No-take 3,225 Percentage of EEZ Total 0.7 No-take 0.14

Approximately 34% of Philippine MPAs include some area that is no-take, accounting for less than 0.14% of the country’s EEZ (Table 8). While this proportion is small, it represents the highest proportion of no-take coverage of the four countries analysed in this report, as well as the largest absolute area that is no-take (3,225 km2). With this low level of total coverage, it is unlikely that the Philippines MPA network is effectively conserving a representative portion of its marine biodiversity. This is supported by the results of Weeks et al. (2010), who assessed the conservation effectiveness of MPAs at the national scale. This network is also unlikely to be effective at ensuring the sustainability of fished stocks (Campos and Aliño 2008). A 2003 investigation of MPA management in the Philippines found that most (68%) of the 156 MPAs surveyed had yet to reach “enforced” status, with regulations and management activities implemented for at least two years (White et al. 2006a). Similarly, an assessment of no-take MPAs in the Visayas (i.e., the central Philippines) rated only 33% as “functional” – the most successful ranking in this particular study (Alcala et al. 2008). With these results in mind, it seems likely that less than 0.24% of the Philippines’ EEZ is effectively protected, and thereby garnering the maximum conservation and biodiversity benefits.



Figure 21. Size distribution of marine protected areas in the Philippines. MPAs were grouped into size classes according to the total area (a) of each MPA, including both terrestrial and marine components. The size classes were defined using a logistic scale due to the predominance of small MPAs, (i.e., Size Class A: a ≤ 0.1 km2; B: 0.1 < a ≤ 1 km2; C: 1 < a ≤ 10 km2; D: 10 < a ≤ 100 km2; E: 100 < a ≤ 1,000 km2; F: 1,000 < a ≤ 10,000 km2; G: 10,000 < a ≤ 100,000 km2; H: a > 100,000 km2). Histograms represent the proportion of the total number of MPAs in each size class.



Between-country comparisons MPA coverage In terms of overall reserve coverage, Brazil covers the greatest proportion of its MPA with close to 3% protected (Table 9). However in terms of no-take coverage, the Philippines protects the greatest proportion of its EEZ. India protects the least amount of its EEZ relatively, although they were found to have the highest proportion of protected area in the Bay of Bengal LME (Kleisner and Pauly 2011). Overall, the amount of area protected by each country, both overall and in terms of no-take reserves is quite low, much lower in fact than the CBD-stated conservation goal of protecting at least 10 percent of the world’s marine coastal and ecological regions by 2012 (CBD 2006). The Philippines has the greatest number of reserves, most of them small (Figure 22), which may preclude effective national management. With a greater representation of relatively large MPAs, countries such as Brazil and India are able to protect a higher proportion of their EEZ with greater efficiency relative to a country whose EEZ is dominated by small MPAs, such as the Philippines (Figure 22).

Table 9. Summary statistics for marine protected area (MPA) numbers and coverage for India, Brazil, Chile and the Philippines. Brazil Chile India The Philippines EEZ area (km2) 3,192,376 2,006,482 1,629.182 2,267,479 Number of MPAs 104 29 22 562 Fraction incl. no-take 0.01 0.07 0.18 0.43 MPA (km2) Total 91,731 52,222 24,011 17.090 Marine 90,088 32,216 8,384 15,164 No-take 362 4 1,111 3,225 Percentage of EEZ Total 2.8 1.6 0.5 0.7 No-take 0.01 <0.01 0.07 0.14



Figure 22. Size distribution of marine protected areas in Brazil, Chile, India and the Philippines. For each country MPAs were grouped into size classes according to the total area (a) of each MPA, including both terrestrial and marine components. The size classes were defined using a logistic scale due to the predominance of small MPAs, (i.e., Size Class A: a ≤ 0.1 km2; B: 0.1 < a ≤ 1 km2; C: 1 < a ≤ 10 km2; D: 10 < a ≤ 100 km2; E: 100 < a ≤ 1,000 km2; F: 1,000 < a ≤ 10,000 km2; G: 10,000 < a ≤ 100,000 km2; H: a > 100,000 km2). Histograms represent the proportion of the total number of MPAs in each size class.



Comparisons of indicators Table 10 provides a comparison of the indicator scores for each of the four countries presented here, as well as a global comparison to provide context to each of the indicator rankings (for details see Appendix 1, page 67). The use of destructive gears is greatest in Brazil and lowest in the Philippines. The highest scoring country globally (i.e., lowest use of trawling or dredging gears) is the Marshall Islands, a group of islands in the northern Pacific Ocean where fishing is conducted mainly with longlines. The lowest scoring country globally is Guatamala, which has a large shrimp trawl fishery. The taxonomic reporting quality indicator gives a sense of the number of taxa being reported at detailed level (i.e., species and genus) rather than at more aggregated level. Chile has the highest score, indicating a relatively high quality of fisheries statistics, likely due to their relatively well developed fisheries management system. Brazil scores second highest, although given the above mentioned inconsistent nomenclature, this should be interpreted with caution. Globally, Portugal and the United States, both highly developed countries with established management structures, score highest, while Myanmar scores the lowest for reporting quality. The number of stocks in the collapsed and overexploited stock-status categories gives a sense of the health of the resources. India scores highest, i.e., it has the lowest proportion of collapsed or overexploited stocks, while Chile has the greatest proportion. A reason for India’s higher score may be the significant geographic expansion, which has temporarily allowed India to maintain higher catches. Globally, Navassa Island, a small uninhabited island in the Caribbean has the lowest proportion of collapsed and overexploited stocks, due mainly to its status as a wildlife refuge. Conversely, the Cayman Islands and Anguilla, both also in the Caribbean have the highest proportion of collapsed and overexploited stocks, due to heavy fishing pressure. Chile has the best compliance scores with respect to the FAO Code of Conduct. Again, this is indicative of relatively strong fisheries management. Brazil and the Philippines scored lowest, indicating a lack of effective fisheries management. Globally, the highest scoring country (out of 54 analysed) is Norway, a highly developed country with a well-established fisheries management plan, and monitoring and enforcement. North Korea has the lowest scores for compliance globally. In terms of economics, the Economic Impact Factor (EIF) relative to each country’s GDP illustrates the importance of the fisheries sector. Of our four countries, Brazil scores highest, followed by Chile, the Philippines, and finally India. The lower score for India is not surprising, due to the fact that India’s economy is largely devoted to service and agriculture. Globally, the island nation of Vanuatu scores highest, and Jordan, a country with a tiny coastline, scores lowest. Chile had the lowest amount of subsidies relative to the value of its landings, while the Philippines had the highest subsidies relative to the landed value of its fisheries. Globally, Romania had the lowest subsidies relative to landed value, while Dominica, a poor Caribbean island country had the highest.



Table 10. Indicator scores for Brazil, Chile, India and the Philippines, where TRAPRESS = proportion of landing taken with trawling and dredging gears; TC = taxonomic reporting quality; SSPOC = proportion of stocks that are determined to be overexploited or collapsed according to a catch-based stock status plot (SSP); CODEFAO = average score based on 44 questions related to how well a country complies with the FAO Code of Conduct; SUBLV = the proportion of fisheries subsidies relative to the landed value of the catch; and EIFGDP = the economic impact of the fisheries sector relative to the country’s Gross Domestic Product. All indicators are scaled from 0 to 1, with a score of 1 indicating the ‘best’ score and 0 the ‘worst’. See Appendix 1 for details. Indicator Brazil Chile India Philippines TRAPRESS 0.60 0.84 0.80 0.86 TC 0.56 0.70 0.30 0.38 SSPOC 0.68 0.39 0.78 0.55 CODEFAO 0.36 0.67 0.44 0.38 EIFGDP 0.0047 0.0040 0.0010 0.0039 SUBLV 0.933 0.995 0.914 0.876

The picture for subsidies becomes different if we consider the subsidy contributions according to type, i.e., beneficial (‘good’), harmful (‘bad’), or ambiguous (‘ugly’) for each country (Table 11). In terms of beneficial subsidies, such as stock enhancement programs, fisheries research and development, and investment in new MPAs and enforcement of existing MPAs, the Philippines contributes the greatest amount. Chile contributes substantially less, which is surprising given the importance of the fisheries to the economy of the country. Harmful subsidies (i.e., capacity enhancing) include funds that are put towards fleet renewal and modernization, the building of new ports and harbours, marketing, and infrastructure, as well as tax exemptions and fuel subsidies. India has the largest contributions in this category, followed by the Philippines. Chile has the lowest amount of harmful subsidies, and no ambiguous or ‘ugly’ subsidies such as unemployment insurance, permit and license buyback programs, or small-scale development programs.



Table 11. Sources of beneficial (‘good’), harmful (‘bad’), and ambiguous (‘ugly’) subsidies for Brazil, Chile, India and the Philippines by subsidy type. Amounts shown are in 2003 real value (US$ 000), where brackets = estimated values. Subsidy Type Subsidy description Brazil Chile India Philippines

Good

Stock enhancement programs & other good subsidies (93,598.88) 41,028.29 175,289.70 (158,324.00) Research & development (66,582.41) 4,000.00 5,496.62 (112,625.20) MPAs -- 2,606.78 (1,926.98) 15,088.49 Total ‘Good’ 160,181.29 47,635.07 182,713.30 286,037.69

Bad

Financial support towards fleet renewal & modernization / state investments in firms, cooperatives & parastatals (52,903.95) -- 433,511.10 (307,262.20) Development grants for fishery projects 2,940.79 2,736.89 4,056.90 6,306.32 Port & harbour construction, renovation programs (29,849.15) -- 4,145.47 -- Marketing support, processing & storage infrastructure programs -- 43,371.34 4,844.95 2,428.27

Tax exemptions (57,699.05) -- (134,054.30) (97,598.84) Fuel 62,686.89 -- 271,255.90 (196,297.10) Total ‘Bad’ 206,079.83 46,108.23 851,868.62 609,892.73

Ugly

Income support programs -- -- 4,145.47 -- Unemployment insurance programs 26,183.68 -- -- -- Permit & license retirement 3,607.77 -- -- -- Rural fishers’ development programs -- -- -- (22,894.86) Total ‘Ugly’ 29,791.45 0.00 4,145.47 22,894.86

Total subsidies 396,052.57 93,743.30 1,038,727.39 918,825.28



Discussion The bulk of the work reported here occurred in September 2012, during the very period when the Sea Around Us Project was completing its ‘reconstructions’ of the historic catches of about 250 maritime countries and their overseas territories and/or islands. The over 120 reconstructions completed so far (as of September 2012) suggest that, as a rule, the reconstructed (total) catch of fisheries is 20-50% higher than officially reported data in developed countries, while it can be 100-500% higher in developing countries (Zeller et al. 2007; Zeller and Pauly 2007; Zeller and Harper 2009; Zylich et al. 2012). Unfortunately, the reconstruction work for Brazil, Chile, India and the Philippines is not completed, and thus cannot be reported upon here. However, as mentioned in the country-specific results sections, we do know enough about the fisheries of these countries to be able to infer that, whatever the actual amount they are currently catching, they could catch substantially more if they adjusted their overall level of effort to that required for optimizing the catch of their main target species. We cannot overemphasize that our estimates of potential increases of total catches (Brazil 79%; Chile 35%; India 5%, and the Philippine 28%) under ‘optimal management are extremely tentative, and need to be refined by extending our analysis to far more of the exploited species in these for countries. However, we are comforted by the fact that our analysis, with a mean suggested increase of 37% is in line with a recent analysis by Costello et al. (2012) who inferred, based on the life-history of ‘un-assessed stocks’ (mainly in developing countries), that better fisheries management could increase catches by up to 40%. However, such increases do not account for feeding interactions, i.e., for the fact that some of the fish stocks that would be recovering under prudent management would be consuming more food, and that this food would consist, at least in part, of other exploited species. For example, the present overabundance of lobsters in the Gulf of Maine is largely due to the near complete absence of cod and other large bottom fish in the Gulf of Maine (Steneck 1997). Hence, rebuilding the cod and bottom fish stocks in the Gulf of Maine, while enabling a more profitable bottom fish fishery, could decimate the lobster fishery. There are numerous examples of predatory interactions of this kind, suggesting that the sum of MSY, or in a sense, ‘the parts’, sum to more than can really be extracted from the ecosystem in a sustainable fashion (Gaichas et al. 2012; Link et al. 2012). This line of thought has led to suggested correction factors, such as the 75% of mMSY incorporated in the ‘Food Provision model’ of the Ocean Health Index (Halpern et al. 2012). However, the correct way to account for feeding interactions is to model them explicitly, as can be done using the widely-used Ecopath or EwE modelling software (Christensen and Pauly 1992; Christensen 1998; Pauly et al. 2000). Extensive comparative studies conducted by Walters et al. (2005) with EwE across a wide range of ecosystems suggest that a single correction factor may not be appropriate across the board. In fact, the



increase of predation that results from increased stock sizes can, under certain circumstances, increase the biomass available for exploitation Walters et al. (2005). We will investigate this in more detail in the second installment of this two-part report, to cover 25 countries, which should allow us to improve on both the fraction by which the total catch of different countries can be increased, and the fraction of the potential catch that may be lost due to increased within-ecosystem predation. Another related consideration is the need to retain sufficient forage fish in the ecosystem (Pikitch et al. 2012), notably to support marine mammals, and hence allow for the emergence of a whale-watching industry. The potential of such a non-extractive industry, including in the four countries analyzed here, was reported on by Cisneros-Montemayor et al. (2010). These technical considerations should not distract from the real difficulties facing us when identifying levers for change in the four countries analyzed here, and by extension in others, and which would allow their trajectory of fishing effort, currently up and up, to be reversed. Thus, two of these four countries, namely India and Brazil, were leading the charge against successive attempts by the World Trade Organization (WTO) to lower subsidies to fisheries (WTO 2008, 2010), although such subsidies are as harmful to their fisheries as they are elsewhere (Sumaila et al. 2010b). Common to all four countries analyzed here, is the difficult relationship between the small-scale (inshore, artisanal) sector and the large-scale industrial sector. Chile has squarely addressed this issue by creating a body of laws which effectively limits access to inshore fishery resources to the small-scale sector- and banning inshore trawling. This system works (Castilla et al. 2007). On the other end of the spectrum, in India, conflicts between the small-scale sector and large-scale sector are frequent, mainly due to the non-enforcement of legislation meant to prevent inshore incursion by industrial trawlers. Impunity in the fact of flagrant law-breaking thus seems to be a major issue to be addressed in India. Brazil and the Philippines are perhaps intermediate in this; thus, in the Philippines the decentralization of fisheries management enables maritime municipalities to set up near- and inshore marine protected areas. However, industrial vessels frequently raid the fish that accumulate in such reserves. Indeed, many of these reserves are unprotected, even though they are well within the coastal zone that is - by law - unavailable to industrial vessels. But most insidious is the fact that in the Philippines, the very definition of small-scale fisheries (or their local equivalent: “municipal fisheries”), works against a functional distinction of these two types of operations. Thus, ‘below 3 tons’, which defines a small-scale craft has led to the emergence of a gigantic fleet of 3 ton ‘baby trawlers’, which have access to inshore fishing grounds, and effectively sabotage measures such as implemented in Chile to separate small-scale from industrial fisheries. The potential for replacing this archaic definition of Philippine small-scale fisheries should be investigated.



Overall, we conclude that there is a real potential for rebuilding the fisheries of Brazil, Chile, India and the Philippines, and that Chile is most advanced along this path. Acknowledgments We wish to thank Dr. Michael Hirshfield, Chief Scientist at Oceana, for spotting a subtle, but serious error in the first draft of this report.



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APPENDIX 1 Description of the methodology and rationale behind the six indicators used Trawling pressure index (TRApress): Bottom trawling and dredging equipment has been described as the most destructive fishing gear in use today (Watson et al. 2006). This fishing method relies on large nets that are dragged along the bottom to collect fish and invertebrates in a non-selective manner. Trawling and dredging typically result in large amounts of bycatch and discards. Bottom habitat is significantly affected and damage can be long-lasting (Watling and Norse 1998), especially in cases where continuous trawling and dredging occur. In some cases, biodiversity is significantly reduced. Catch data from the Sea Around Us global catch database (Watson et al. 2004) are available by gear type, and a subset of catch from trawling and dredging gears was obtained by EEZ. The trawling pressure index was calculated as the amount of catch (tonnage) from trawling and dredging gears relative to ‘trawlable habitat’, defined as soft shelf and subtidal soft sediments. Habitat rasters based on benthic substrate point samples (see Halpern et al. 2008 for details) for both types of sediment were obtained from NCEAS (http://www.nceas.ucsb.edu/globalmarine/impacts) and scaled to a 0.5 degree grid cell to obtain the proportion of trawlable area per cell. This proportion was multiplied by the area of the cell, and trawlable area per EEZ was obtained by summing the trawlable area of all cells within an EEZ. This indicator was used in the Ocean Health Index (Halpern et al. 2012) and the Environmental Performance Index (http://epi.yale.edu/), which encompasses both marine and land-based indicators to evaluate the overall environmental performance of all countries globally. Taxonomic reporting quality index (TC): This indicator was introduced by Alder et al. (2010) and incorporated in the Ocean Health Index (Halpern et al. 2012). It was designed to account for the quality of each country’s reporting system for capture fisheries in a regional context through the proportion of reported commercial taxa to total commercial taxa occurring in an EEZ. The taxa that were presumed to occur in an EEZ were estimated based on the overlap of at least 10% of a country’s EEZ by the static species range maps of the Sea Around Us project (Pauly and Watson 2008). The assumption here is that the ecological distribution range map of a given reported commercial taxon (defined as a taxon included in the marine catch statistic by a least one FAO member country) will overlap with the EEZ of at least one country and will often overlap with the EEZs of other countries. The fact that different countries may report the same fish or invertebrates at different taxonomic levels is also accounted for, by using six levels of taxonomic resolution, from species (highest weight w = 6) to broad International Standard Statistical Classification of Aquatic Animals and Plants (ISSCAAP) groups (lowest weight w = 1). In other words, both the reported taxa and the taxa presumed to occur in an EEZ were weighed by the taxonomic resolution of the taxonomic group, where the weight was highest for fine-scale species-level classification (e.g., dusky grouper, Epinephelus marginatus) and lowest for broader taxonomic groupings (e.g., ‘marine molluscs’ or ‘groupers’).Therefore, the numerator is a weighted sum of the number of taxa reported (nr) at each taxonomic aggregation level (m)

http://www.nceas.ucsb.edu/globalmarine/impacts

http://epi.yale.edu/



compared to the weighted sum of the total number of commercial taxa distributions (nt) for each taxonomic aggregation level:

TC =nrm * wm

m =1

6

∑

ntm * wmm =1

6

∑ Eq. 1

Proportion of overexploited and collapsed taxa (SSPOC): Species that are being overfished are producing catches that are below the level that could be sustainably derived. As a result of intense exploitation, most fisheries generally follow sequential stages of development (i.e., developing, fully exploited, overfished, collapsed, and possibly recovering). Stock-status plots (SSPs) use catch time series to assign development stages to individual stocks based on catch levels in relation to the maximum or peak catch of the time series. For example, overexploited classifications occur after the time series peak and for catch levels that are between 10 and 50% of the peak catch. Collapsed classifications also occur after the time series peak, but for catch levels lower than 10% of the peak catch (see Kleisner et al. 2012 for details of the complete SSP algorithm). The algorithm can be applied to both the numbers of stocks and to catch tonnage to highlight the annual proportions of stocks and total catch in a particular category. Stocks that are classified as overexploited or collapsed are indicative of possible unsustainable catch, especially when the bulk of the catch tonnage is from taxa with these designations. Therefore, this indicator is defined as the product of the percentages of catch and numbers of stocks from the overexploited and collapsed categories. This value is subtracted from unity (i.e., 1) so that a lower number is indicative of a worse condition. Compliance with the FAO code of conduct (CODEFAO): Pitcher et al. (2006) assessed countries’ compliance with the FAO code of conduct. The evaluation of the data was based on an adaptation of the appraisal scheme of 44 management related questions, each scored on a scale of 0-10. The scores were based on published and unpublished literature, and expert opinion (Pitcher et al. 2006) and are used here directly as an indicator of management effectiveness. Fifty-four countries, including the countries presented here, have been evaluated with this scoring system. Subsidies relative to landed value (SUBLV): Information on the fisheries subsidies of Brazil, Chile, India and the Philippines was obtained from a global subsidies database developed by Sumaila et al. (2010a). Fishery subsidies are defined as financial transfers, direct or indirect, from public entities to the fishing sector, which help the sector make more profit than it would otherwise. Subsidy data are classified into categories based on their potential impact on the sustainability of the fishery resources. As fishery resources are considered to be a renewable natural capital, one can, within limits, engage in ‘investment’ in the natural capital assets, by refraining from fishing and allowing the resource to rebuild to a biological optimum. Similarly, one can also engage in ‘disinvestment’ in the natural resource, for example, through overfishing. Based on this, three broad categories of subsidies are identified: (i) beneficial or ‘good’; (ii) harmful or



‘bad’ (i.e., capacity-enhancing); and (iii) ambiguous or ‘ugly’ subsidies. Subsidies are identified and assessed using the following guidelines: (i) policy objective of the subsidy; (ii) the subsidy program descriptions; (iii) scope, coverage and duration; (iv) annual US$ amounts; (v) sources of funding; (vi) administering authority; (vii) subsidy recipients; and (viii) the mechanisms of transfer (FAO 2003; Westlund 2004). Fishery subsidies are further classified into 26 sub-types (Sumaila et al. 2010a). Subsidies data were obtained from the following major sources: (a) Organization for Economic Cooperation and Development (OEDC 2000, 2004, 2005a, 2005b, 2006); (b) Asian Pacific Economic Cooperation (APEC 2000); (c) FAO web resources on sections that concern ‘aid’ and ‘international cooperation’ under specific country profiles and ‘investment’ or ‘subsidies’ under the fisheries management information link for any given country; (d) a global MPA cost database (Cullis-Suzuki and Pauly 2008); and (e) various published literature and on-line resources including news articles and grey literature. Subsidy data on the 26 sub-types from the years 1989 to present were collected. As the database is a static analysis of the year 2003, the value was estimated based on the information within five years of 2003, if the subsidy value in 2003 is not known but a subsidy is known to exist. The data from years prior to or after 2003 are normalized to constant 2003 US dollars by applying the consumer price index (CPI), extracted from the World Development Indicators. Two approaches were adopted to fill the missing data. For non-fuel subsidies, we computed the subsidy intensity for each type of subsidy. The subsidy intensity was defined as the ratio of the known subsidies for a given subsidy type to a country’s total landed value. The mean subsidy intensity for two groups of countries, i.e., developed and developing countries, were computed. The mean subsidy intensity for each subsidy type and country group is used, along with the 2003 landed value for a given country, in cases where subsidies are reported but with unknown magnitude, to compute estimates of subsidies provided by each country. For fuel subsidies, we used fuel subsidies estimated in Sumaila et al. (2008) where data collected was expressed as a subsidy per litre of fuel usage or where the total fuel subsidy is not reported. In cases where total fuel subsidy is reported we use this data rather than estimates from Sumaila et al. (2008). Subsidies relative to landed value are computed from total subsidies relative to the value of the catch (Sumaila and Pauly 2006), expressed on a scale from zero to ten as detailed in Mondoux et al. (2008). Countries with higher levels of subsidies relative to the value of the landings have less incentive to manage their fisheries sustainably (Sumaila and Pauly 2006). Total subsidies data are derived for the year 2005 from Sumaila et al. (2010a) and landed values for 2005 (expressed in year 2005 USD) from Dyck and Sumaila (2010). This indicator was used in Alder et al. (2010) as a means of evaluating on a country basis, the level of reliance on subsidies for fisheries and is available for all coastal countries. Additionally we also present a breakdown of the ‘good’, ‘bad’, and ‘ugly’ subsidies provided to the fisheries in each country according to the main subsidy program that the funds were directed towards.



Economic impact factor relative to Gross Domestic Product (EIFGDP): The economic impact factor relative to GDP is expressed as the total output in an economy that is dependent (at least partially) on current fisheries output (the economic impact of the fisheries sector of a country) relative to its Gross Domestic Product (GDP). This indicator represents a modification of the landed value relative to GDP (LVGDP) economic indicator used by Alder et al. (2010). LVGDP was used previously as earlier studies found a general trend of well-managed fisheries when fisheries are a significant contributor to GDP as seen in developed countries (Hannesson 1993). However, Dyck and Sumaila (2010) have shown that generally, for many countries, the fishing industry contributes a relatively small amount to GDP, with most countries only reporting fisheries contributions of less than 1%. Because fisheries output affects a number of different resource and employment sectors, the importance of this industry to the economy may be understated when considering only the direct values obtained, for example, landed value (Sumaila et al. 2007; Willmann et al. 2009). The Economic Impact Factor (EIF) therefore builds on landed value, or the direct economic value of fisheries sector output, and reveals a more complete picture of the contribution of fisheries to the economy of a country. The EIF for each country for 2003 is calculated following the method of Dyck and Sumaila (2010), which is based on the input-output analysis technique (Leontief 1966). These values are divided by 2003 GDP estimates from the World Bank (www.worldbank.org), the International Monetary Fund (www.imf.org) and the World Factbook (https://www.cia.gov/library/publications/the-world-factbook/index.html).

http://www.worldbank.org/

http://www.imf.org/

https://www.cia.gov/library/publications/the-world-factbook/index.html

https://www.cia.gov/library/publications/the-world-factbook/index.html



APPENDIX 2 Figure description: Graphic output from the Catch-MSY method. From left to right, the upper panels show (1) the time series of catches with overlaid estimate of MSY (thick red line) and the limits (thin red lines) that contain about 95% of the estimates; (2) the frame of the prior uniform distribution of r and k; the black dots show the r-k combinations that are compatible with the time series of catches; and (3) a magnification of the viable r-k pairs in log space, with the geometric mean MSY estimate (thick red line) ± 2 standard deviations (thin red lines) overlaid. The lower panels read from left to right: (1-2) show the posterior densities of r, k, and MSY, respectively; and (3) the geometric mean MSY (thick red line) where ± 2 standard deviations (thin red lines) are indicated. A similar plot was produced for each species analyzed for each country. Species are listed in order of the relative percent contribution to total catch.

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