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Large Marine Indicators global maps, 2015

http://onesharedocean.org/lmes

LMEs, Indicators global maps Transboundary Water Assessment Programme, 2015 Large Marine Ecosystems Indicators global maps, 2015

THE LARGE MARINE ECOSYSTEMS ...... 3 WHAT ARE LMES? ...... 3 SOCIO-ECONOMIC IMPORTANCE OF LMES ...... 4 CHANGING STATUS OF LME HEALTH ...... 5 THE GLOBAL ENVIRONMENT FACILITY’S SUPPORT FOR LMES...... 5 TWAP LMES ASSESSMENT METHODOLOGY ...... 5 LMES CONCEPTUAL FRAMEWORK ...... 6

PRODUCTIVITY ...... 7 PRIMARY ...... 7 Chlorophyll-A ...... 8 Chlorophyll-A (% Change)...... 8 Primary productivity group ...... 9 Primary productivity (% change) ...... 9 SEA SURFACE TEMPERATURE ...... 10 Sea Surface Temperature ...... 12

FISH AND FISHERIES ...... 13 Fishing subsidy ...... 14 ...... 15 Marine Trophic Index ...... 15 Fishing-in-Balance ...... 16 Stock status (number) ...... 16 Stock status () ...... 17 Catch form bottom impacting gear ...... 17 Fishing effort ...... 18 Change in catch potential 2030 ...... 18 Change in catch potential 2050 ...... 19 Percent change in catch potential 2050 ...... 19

POLLUTION ...... 20 NUTRIENTS ...... 20 Nutrient ratio (ICEP) 2000 ...... 22 Nutrient ratio (ICEP) 2030 ...... 22 Nutrient Ratio (ICEP) 2050 ...... 23 Nitrogen load 2000 ...... 23 Nitrogen load 2030 ...... 24 Nitrogen load 2050 ...... 24 Nutrient risk 2000 ...... 25 Nutrient risk 2030 ...... 25 Nutrient risk 2050 ...... 26 PLASTICS ...... 26

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Micro plastics ...... 27 Macro plastics ...... 28 POPS ...... 28 DDT score ...... 29 HCHs score ...... 30 PCBs score ...... 30

ECOSYSTEM HEALTH ...... 31 EXTENT ...... 31 CORAL REEFS AT RISK ...... 32 CHANGE IN EXTENT OF MARINE PROTECTED AREAS ...... 33 CUMULATIVE HUMAN IMPACT ON MARINE ECOSYSTEMS ...... 34 HEALTH INDEX ...... 35 Mangrove extent ...... 37 Coral extent ...... 37 Reefs at risk ...... 38 MPA extent change ...... 38 Cumulative impact ...... 39 Ocean Health Index ...... 39

GLOBAL SOCIOECONOMIC PROFILE OF LARGE MARINE ECOSSYTEMS ...... 40 Coastal (2010) ...... 41 Coastal Poor ...... 42 Fisheries revenues (landed value) ...... 42 Tourism revenues ...... 43 HDI (2009-2013) ...... 43 HDI (2100, SSP1) ...... 44 HDI (2100, SSP3) ...... 44 Climate threat index (Present day) ...... 45 SLR Threat 2100 (SSP1) ...... 45 SLR Threat 2100 (SSP3) ...... 46

GOVERNANCE ...... 47 Integration ...... 48 Engagement ...... 48 Completeness ...... 49

IDENTIFYING PATTERNS OF RISK AMONG LARGE MARINE ECOSYSTEMS USING MULTIPLE INDICATORS ...... 50 INTRODUCTION ...... 50 APPROACH ...... 50 RESULTS ...... 50

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The Large Marine Ecosystems

What are LMEs? Between the world’s continental margins and the open ocean are 66 Large Marine Ecosystems or LMEs (map). These are vast regions of coastal ocean space of 200,000 km2 or more, extending from river basins and seaward to the break or slope or to the outward margins of major current systems. Unique defining ecological criteria of LMEs include bottom depth contours, currents and water mass structure, marine productivity, and food webs. A common feature, however, is that all LMEs are transboundary in nature by virtue of interconnected currents, movement and migration of living resources, and pollution that straddle political boundaries. Since Dr. Kenneth Sherman and colleagues developed the LME concept in 1991, the LME has been widely adopted as the geographical unit for -based management of coastal marine areas and their living resources. In fact, LMEs have become a rallying point for countries to cooperate in addressing problems relating to the utilization of transboundary resources. In addition to LMEs, the TWAP LMEs component has also assessed the Western Pacific Warm Pool (WPWP). This is an immense area of open-ocean warm water in the Western Pacific Ocean north of Papua New Guinea with dimensions that fluctuate annually as this warm water body expands and contracts (map). The WPWP is not an LME due to its open ocean geographic location and physical characteristics that differ from LME defining criteria.

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1: East Bering Sea 22: 45: Northwest Australian Shelf 2: 23: 46: New Zealand Shelf 3: 24: Celtic-Biscay Shelf 47: 4: 25: Iberian Coastal 48: 5: 26: 49: 6: Southeast U.S. Continental Shelf 27: 50: 7: Northeast U.S. Continental Shelf 28: 51: 8: Scotian Shelf 29: Benguela Current 52: 9: Labrador - Newfoundland 30: 53: West Bering Sea 10: Insular Pacific-Hawaiian 31: Somali Coastal Current 54: Northern Bering - Chukchi Seas 11: Pacific Central-American 32: 55: Coastal 33: 56: 12: 34: 57: 13: 35: 58: 14: Patagonian Shelf 36: 59: Iceland Shelf and Sea 15: South Brazil Shelf 37: Sulu-Celebes Sea 60: Faroe Plateau 16: East Brazil Shelf 38: Indonesian Sea 61: 17: North Brazil Shelf 39: North Australian Shelf 62: 18: Canadian Eastern Arctic - West 40: Northeast Australian Shelf 63: Complex Greenland 41: East Central Australian Shelf 64: Central Arctic 19: Greenland Sea 42: Southeast Australian Shelf 65: Aleutian Islands 20: 43: South West Australian Shelf 66: Canadian High Arctic - North 21: Norwegian Sea 44: West Central Australian Shelf Greenland

Socio-economic importance of LMEs With some of the planet’s most productive waters and biodiverse such as coral reefs and , LMEs provide important ecosystem goods and services (pop-up definition here) that translate into livelihoods, income, food security, and other benefits for millions of people around the world. For example, more than 80 percent of the world’s marine catch comes from LMEs. These water bodies are the focus of the vast majority of ocean-related sectoral activities, including fisheries, tourism, shipping, and oil and gas exploitation, contributing an estimated $12 trillion annually to the global economy.

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Changing status of LME health A potent combination of anthropogenic and natural stressors is jeopardizing the health and productivity of LMEs and threatening their sustainability. Over-fishing, increasing land and marine- based pollution, invasive alien species, and degradation and loss are now all too common in coastal areas. It is within the boundaries of the LMEs that overfishing and the degradation of is most severe, coastal pollution is concentrated, and levels of are increasing. Exacerbating these conventional stressors are more recent threats in the form of climate change and ocean acidification. In the majority of cases these threats are accelerating, and without concerted action their impacts could become irreversible. Despite a range of mitigating actions, the health of many LMEs remains in decline.

The Global Environment Facility’s support for LMEs The Global Environment Facility (GEF) remains the world’s largest supporter of transboundary water projects through which countries are provided with financial, scientific, and technical assistance to address the negative trends evident in LMEs and to achieve the ecosystem-specific targets of the 2002 World Summit on Sustainable Development Plan of Implementation to which nearly 200 countries have agreed (pop up or link here- see below). Since the early 1990s, the GEF has provided grants totaling around $380 million to 20 LME projects involving 122 countries in Asia, Africa, Latin America, and Eastern Europe (map showing LME projects). This is supplemented by an additional $2.35 billion in cofinancing from the project countries and diverse partners. Related WSSD targets:

 Achievement of substantial reductions in land-based sources of pollution by 2006  Introduction of an ecosystems approach to marine assessment and management by 2010  Designation of a network of marine protected areas by 2012  Maintenance and restoration of fish stocks to maximum sustainable yield levels by 2015)

TWAP LMEs Assessment Methodology In 2010 a Working Group of institutional partners and experts, coordinated by the IOC, developed an indicator-based methodology for assessment of LMEs. The methodology report can be downloaded from the geftwap website1. This methodology built on the existing approach for assessment and management of LMEs that is based on five modules: Productivity, Fish and Fisheries, Pollution and , Socioeconomics, and Governance. A conceptual framework was developed that shows the link between the human and natural systems, to help facilitate the assessment of the impacts of human and natural stressors on LME health and provision of ecosystem goods and services, and consequences for humans and implications for governance of these water bodies.

1 http://www.geftwap.org/project-results-and-reports/methodologies-for-the-gef-transboundary-assessment- programme-1/methodologies-for-the-gef-transboundary-assessment-programme

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LMEs Conceptual Framework

Key framing questions that the assessment sought to answer included:

 What are the current trends in LMEs and the main drivers?  Which LMEs are most heavily impacted (for each theme and overall)?  Which ecosystem services are most at risk?  What are the implications for humans?  Where is human dependency greatest on LME ecosystem services?  Where are humans most vulnerable to changes in LME condition?  What is the status of the governance architecture in transboundary LMEs?  What are the emerging issues?

The assessment focused on a global comparative assessment of LMEs using a suite of indicators, with results presented in the LMEs Assessment Report and Summary for Policy Makers (link to reports). Results for individual LMEs are presented in electronic fact sheets on this web portal.

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Productivity

Primary productivity John E. O’Reilly and Kenneth Sherman Primary productivity (PP) is the rate of organic matter production by plants. Marine supports ocean food webs and can be related to the of marine ecosystems for supporting and fisheries resources. High primary productivity is generally regarded as beneficial except when stimulated by excessive nutrient loads (eutrophication). The bulk of marine primary production is carried out by , which can be seen from space due to their photosynthetic pigments (mainly chlorophyll). A 16-year (1998-2013) time-series of data from satellite ocean color sensors and satellites from the NASA Goddard Space Flight Centre were used to examine average levels of PP and chlorophyll a in LMEs and the Western Pacific Warm Pool. Among the seven LMEs with the highest PP are the Baltic Sea, Yellow Sea, and North Brazil Shelf. Episodes of , a symptom of eutrophication and excess production, have been reported in several LMEs with high and medium levels of production.

“ „

No large-scale consistent pattern of either increase or decrease in CHL was observed with most CHL trends near zero and weakly correlated with latitude. The four LMEs with significantly increasing CHL trends were Scotian Shelf, Patagonian Shelf, Labrador Newfoundland, and Southeast Australian Shelf. The three LMEs with significantly decreasing CHL trends were Indonesian Sea, Oyashio Current, and Celtic Biscay Shelf. “ „

Accurate assessments of the status and trends in CHL and productivity were not feasible for eight LMEs. In these LMEs, ocean color measurements from aircraft, for example, or in situ measurements would be required for more accurate indices of their productivity, phenology and trends. “ „

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Chlorophyll-A

Long term mean Chlorophyll-A concentration (mg.m-3) Very low Low Medium High Very high

0.05 - 0.20 0.20 – 0.40 0.40 – 0.60 0.60 – 0.80 > 0.8

Chlorophyll-A (% Change)

Percentage Change of the Chlorophyll-A concentration, over the period 1996 – 2014 (mg.m-3) < -40% -40% – -20% -20% – -10% -10% – -5% -5% – 5% 5% – 10% 10% – 20% > 20%

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Primary productivity group

Primary productivity group, from 1 to 5 1 2 3 4 5

Primary productivity (% change)

Percentage of change of the Primary productivity, in the period 1996 – 2014 (g.C.m-2.year-1) < -40% -40% – -20% -20% – -10% -10% – -5% -5% – 5% 5% – 10% 10% – 20% > 20%

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Sea Surface Temperature Igor M. Belkin Global warming is already having major effects on marine ecosystems, although there is no direct link between potential ecological risks and SST trends. For many LMEs, the ongoing warming is beneficial, while it is detrimental to other LMEs. The global warming signal can affect marine biota directly, as it translates down to the ocean-scale, basin-scale, and LME-scale signals that affect relevant ecosystems through ambient temperature. SST is the only oceanic variable measured worldwide since the 19th century, providing the longest instrumental record of ocean climate change. Long- term consequences of global warming will be LME-specific, and therefore regional estimates and forecasts of SST warming/cooling rates are especially important. The United Kingdom Met Office Hadley Centre global climatology allowed construction of long-term SST time series (1957 to 2012) in the 66 LMEs and the Western Pacific Warm Pool. Key messages: Between 1957 and 2012, all except two LMEs warmed, with three regions warming very fast: Northwest Atlantic off the U.S.-Canada Northeast; Eastern North Atlantic: European Seas; and East/Southeast Asian Seas. The East China Sea LME warmed at the maximum rate “ „

The long-term warming in 1957-2012 was not steady, especially in the North Atlantic and North Pacific that alternated between cooling and warming epochs separated by abrupt regime shifts. Since 1998, many LMEs in the North Pacific experienced slowdowns and even reversals of the late 20th century warming “ „

There is no direct link between ecological risks and SST trends. For many LMEs, the ongoing warming is beneficial, while the same warming is detrimental to other LMEs. The global warming signal can affect marine biota directly, as it translates down to the ocean-scale, basin-scale, and LME-scale signals that affect relevant ecosystems through ambient temperature “ „

Belkin, I.M., 2004. Propagation of the "Great Salinity Anomaly" of the 1990s around the northern North Atlantic. Geophysical Research Letters 31 (8), L08306. DOI: 10.1029/2003GL019334. Belkin, I.M., 2009. Rapid warming of Large Marine Ecosystems. Progress in Oceanography 81 (1-4), 207-213. DOI: 10.1016/j.pocean.2009.04.011. Belkin, I.M., Lee, M.A., 2014. Long-term variability of sea surface temperature in Taiwan Strait. Climatic Change 124(4), 821-834. DOI 10.1007/s10584-014-1121-4. Belkin, I.M., Levitus, S., Antonov, J., Malmberg, S.-A., 1998. "Great Salinity Anomalies" in the North Atlantic. Progress in Oceanography 41 (1), 1-68. Belkin, I., Krishfield R., Honjo, S., 2002. Decadal variability of the North Pacific Polar Front: Subsurface warming versus surface cooling. Geophysical Research Letters 29 (9), DOI: 10.1029/2001GL013806. Déry, S.J., Stieglitz, M., McKenna, E.C., Wood, E.F., 2005. Characteristics and trends of river discharge into Hudson, James, and Ungava Bays, 1964–2000. Journal of Climate 18(14), 2540–2557.

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DFO [Department of Fisheries and Canada], 2007. State of the Ocean 2006: Physical Oceanographic Conditions on the Scotian Shelf, Bay of Fundy and Gulf of Maine. DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2007/028, 10 pp. Dickson, R. R., J. Meincke, S.-A. Malmberg, and A. J. Lee, 1988. The “Great Salinity Anomaly” in the northern North Atlantic, 1968–1982, Progress in Oceanography, 20, 103–151. EEA [European Environment Agency], 2007. Europe's Environment — The Fourth Assessment. Copenhagen, Denmark, 452 pp. Fidel, Q. and O’Toole, M.J. (2007). Changing State of the Benguela LME: Forcing, Climate Variability and Ecosystem Impacts. Presentation to the 2nd Global Conference on Large Marine Ecosystems,11-13 September 2007,Qingdao, PR China. Ginzburg, A.I., Kostianoy, A.G., Sheremet, N.A., 2008. Sea surface temperature variability. In: Kostianoy, A.G., Kosarev, A.N. (Eds.), The Black Sea Environment. Springer, Berlin/Heidelberg, pp. 255-275. Hardman-Mountford, N.J., McGlade, J.M., 2002. Variability of physical environmental processes in the Gulf of Guinea and implications for fisheries . An investigation using remotely sensed SST. In: McGlade, J.M., Cury, P., Koranteng, K.A., Hardman-Mountford, N.J. (Eds.), The Gulf of Guinea Large . Elsevier, Amsterdam, pp. 49-66. Hare, S.R., Mantua, N.J., 2000. Empirical evidence for North Pacific regime shifts in 1977 and 1989. Progress in Oceanography 47 (2-4), 103-145. DOI: 10.1016/S0079-6611(00)00033-1. He, Y.-H., Guan, C.-H., Yamagata, T., 2000. The climate features of the South China Sea Warm Pool. Journal of Tropical Meteorology 6(1), 86-93. HELCOM, 2007. Climate Change in the Baltic Sea Area – HELCOM Thematic Assessment in 2007. Baltic Sea Environment Proceedings No. 111, 49 pp. Hughes, S.L., Holliday, N.P. (Eds.), 2007. ICES Report on Ocean Climate 2006, ICES Cooperative Research Report No. 289, 55 pp. Li, N., Shang, S.P., Shang, S.L., Zhang, C.Y., 2007. On the consistency in variations of the South China Sea Warm Pool as revealed by three sea surface temperature datasets. Remote Sensing of Environment 109(1), 118-125. DOI: 10.1016/j.rse.2006.12.012. Mackenzie, B.R., Schiedek, D., 2007. Daily ocean monitoring since the 1860s shows record warming of northern European seas. Global Change Biology 13 (7), 1335–1347. Mantua, N.J., Hare, S.R., Zhang, Y., Wallace, J.M., Francis R.C., 1997. A Pacific decadal climate oscillation with impacts on salmon. Bulletin of the American Meteorological Society 78(6), 1069- 1079. Matishov, G., Moiseev, D., Lyubina, O., Zhichkin, A., Dzhenyuk, S., Karamushko, O., Frolova, E., 2012. Climate and cyclic hydrobiological changes of the Barents Sea from the twentieth to twenty-first centuries. Polar Biology 35, 1773–1790. DOI: 10.1007/s00300-012-1237-9. McGregor, H.V., Dima, M., Fischer, H.W., Mulitza, S., 2007. Rapid 20th-century increase in coastal off Northwest Africa. Science 315 (5812), 637-639. DOI: 10.1126/science.1134839. Minobe, S., Sako, A., Nakamura, M., 2004. Interannual to interdecadal variability in the Japan Sea based on a new gridded upper water temperature dataset. Journal of Physical Oceanography 34 (11), 2382-2397. National Weather Service/Climate Prediction Center, 2007. Cold and warm episodes by seasons [3- month running mean of SST anomalies in the Niño 3.4 region (5°N-5°S, 120°-170°W)] [based on the 1971-2000 base period], http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ensoyears.shtml

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Petrie, B., Pettipas, R.G., Petrie, W.M., 2007a. Overview of meteorological, sea ice and sea surface temperature conditions off eastern Canada in 2006. DFO Can. Sci. Advis. Sec. Res. Doc. 2007/023. Petrie, B., Pettipas, R.G., Petrie, W.M., Soukhovtsev, V., 2007b. Physical oceanographic conditions on the Scotian Shelf and in the Gulf of Maine during 2006. DFO Can. Sci. Advis. Sec. Res. Doc. 2007/022. Ridgway, K.R., Condie, S.A., 2004. The 5500-km-long boundary flow off western and southern Australia. Journal of Geophysical Research 109 (C4), C04017. DOI: 10.1029/2003JC001921. Stein, M., 2005. North Atlantic Subpolar Gyre warming – Impacts on Greenland offshore waters. Journal of Northwest Atlantic Fishery Science 36, 43–54. Stein, M., 2007. Climatic conditions around Greenland – 2006. NAFO SCR Doc. 07/ 05, 26 pp.

Sea Surface Temperature

Net SST change (°C) between 1957 and 2012 <0.0°C 0.0°C – 0.4°C 0.4°C – 0.8°C 0.8°C – 1.2°C 1.2°C – 1.6°C No data

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Fish and Fisheries Daniel Pauly and Vicky W.Y. Lam LMEs contribute over 70% of global marine fisheries catches. Unsustainable fishing practices have resulted in the and collapse of many fish stocks in LMEs as well as in impacts at the ecosystem level. Fisheries catch data from national and other sources (mainly FAO) were assembled and mapped onto ½ degree latitude-longitude spatial cells (Watson and others 2004). The catches in these spatial cells were regrouped to produce the annual catches by fish taxa for LMEs and the Western Pacific Warm Pool (WPWP) for 1950 - 2010. The data were then used to evaluate a number of indicators: Fishing capacity-enhancing subsides, Primary production required (PPR) to sustain the landings within the LME (Ecological Footprint), Marine Trophic Index (MTI) and Fishing-in-balance Index (FIB), Stock status, Catch from bottom impacting gear types, Effective fishing effort, and Change in catch potential under global warming by 2050. Based on the results, LMEs were grouped into five colour-coded risk categories according to their relative level of ecological degradation or potential risk from fisheries (lowest, low, medium, high and highest risk). The LMEs rank very differently on different indicators, although certain of the indicators have high values in almost all the LMEs as well as the WPWP.

Key messages: No country reports fisheries statistical data at the LME scale. Thus, fisheries and other statistics for LMEs are always uncertain composites and the derived indicators may not represent any specific country and policy as some of the bordering countries may be rebuilding their exploited stocks and have different fisheries policies. “ „

Certain of the indicators have high values in almost all the LMEs as well as the Western Pacific Warm Pool, with ecosystem degradation increasing in some cases. Although the number of collapsed stocks is increasing in LMEs, the number of rebuilding stocks is also increasing, an encouraging sign. Overall, 50% of global stocks within LMEs are deemed overexploited or collapsed, and only 30% fully exploited. “ However, the fully exploited stocks still provide 50% of the globally reported landings, with the remainder produced by overexploited, and collapsed, developing and rebuilding stocks. This appears to confirm the common observation that fisheries tend to affect biodiversity (as reflected in the taxonomic composition of catches) even „ more strongly than they affect biomass (as reflected in the landed quantities).

Those parts of LMEs that are beyond the EEZs of coastal states are subjected to a management regime that is essentially open-access. The parts of LMEs that are under national jurisdiction should do better, as both domestic and foreign fishing within EEZs can, in principle, be regulated by the coastal countries concerned. However, a few countries are fully using the governance tools available to them to rebuild “ overfished stock and to mitigate the impact of fishing and between local „ and foreign fleets in their EEZs, and hence in the LMEs that they belong to.

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As climate change affects marine ecosystems and is expected to affect fisheries and other ecosystem services, the predicted change in the productivity of living marine resources under climate change may have significant implications for fishing industries, economies and livelihoods of many countries. “ „

Accurate catch data needed for fisheries assessments are not available for LMEs or for some countries’ EEZs, because the fisheries statistics supplied from member countries to the FAO usually fail to account for small-scale fisheries (artisanal, subsistence, and recreational). Catch reconstruction data accounting for small-scale fisheries at the national level are needed to improve the accuracy of LME catch time series and hence

“ the quality of the indicators. „

Bhathal, B. and D. Pauly. (2008). "Fishing down marine food webs" and spatial expansion of coastal fisheries in India, 1950-2000. Fisheries Research 91: 26-34. Cheung, W., Watson, R., Morato, T., Pitcher, T. and Pauly, D. (2007). Change of intrinsic vulnerability in the global fish catch. Marine Progress Series 333: 1-12. Pauly, D., V. Christensen, J. Dalsgaard, R. Froese and F.C. Torres Jr. (1998). Fishing down marine food webs. Science 279: 860-863. Pauly, D., Christensen, V. and Walters, C. (2000). , Ecosim and Ecospace as tools for evaluating ecosystem impact of fisheries. ICES Journal of Marine Science 57: 697-706. Watson, R., Kitchingman, A., Gelchu, A. and Pauly, D. 2004. Mapping global fisheries: sharpening our focus. Fish and Fisheries 5: 168-177.

Fishing subsidy

Ratio value of fishing subsidy to landed value Very low Low Medium High Very high 0 – 0.19 0.19 – 0.30 0.30 – 0.46 0.46 – 0.73 0.73 – 0.80

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Ecological footprint

Ecological footprint or Primary Production Required (PPR) Very low Low Medium High Very high 0 – 0.012 0.012 – 0.06 0.06 – 0.14 0.14 – 0.25 0.25 – 1.3

Marine Trophic Index

Marine Trophic Index (MTI): change in the average value of MTI in the 2000s from that in the 1950s Very low Low Medium High Very high 0.04 – 0.8 -0.028 – 0.04 -0.12 – -0.028 -0.35 – -0.12 -1.5 – -0.35

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Fishing-in-Balance

Fishing in Balance index (FiB): change in the average value of FIB in the 2000s from that in the 1950s Very low Low Medium High Very high -5 – -0.85 -0.85 – 0.39 0.39 – 0.9 0.9 – 1.8 1.8 – 4.2

Stock status (number)

Stock status: number of stocks in the collapsed and overexploited stages as a % of the total number of stocks between 2000-2010 Very low Low Medium High Very high 0 – 34 34 – 46 46 – 51.5 51.5 – 59 59 – 100

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Stock status (biomass)

Stock status: catch biomass of stocks in the collapsed and overexploited stages as % of the total catch biomass between 2000-2010 Very low Low Medium High Very high 0 – 10 10 – 18 18 – 31.5 31.5 – 47.8 47.8 – 100

Catch form bottom impacting gear

Catch from bottom impact gear as % of total catch Very low Low Medium High Very high 0 – 10.5 10.5 – 15 15 – 20 20 – 32.3 32.3 – 100

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Fishing effort

Rate of change of effective fishing effort (kW) from the mean of the 1980s to the mean of the 2000s Very low Low Medium High Very high -20 – 0.2 0.2 – 1.8 1.8 – 5.7 5.7 – 10 10 – 130

Change in catch potential 2030

Projection of the fish catch for decade 2030, as a percentage of catch in decade 2000. [LEGEND TO BE INSERTED]

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Change in catch potential 2050

Projection of the fish catch for decade 2050, as a percentage of catch in decade 2000. [LEGEND TO BE INSERTED]

Percent change in catch potential 2050

Percent (%) change in catch potential in the 2050s [LEGEND TO BE INSERTED]

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Pollution Marine and land-based pollution is of major concern in many LMEs. Pollution is often transboundary as hydrological interlinkages between river basins, marine ecosystems, and the atmosphere often result in effects far from the source of the emissions. The risk of transboundary impacts tends to be highest particularly for substances that readily migrate between water and air (such as DDT and mercury). In many coastal areas, pollution and eutrophication have been important driving forces of change. Under the pollution sub-module, three major issues were assessed: floating plastic debris, the concentration of Persistent Organic Pollutants (POPs) in beached plastic resin pellets, and nutrient input to coastal areas from watersheds. These substances can affect the ecological status of marine ecosystems, impairing their health and that of living marine resources and in some cases can result in harmful consequences for humans. Increase in the use of plastics, use of persistent chemicals including pesticides, and application of agricultural fertilizers and release of untreated sewage, among others, have resulted in high levels of these substances in some LMEs, especially with high coastal human .

Nutrients Sybil P. Seitzinger and Emilio Mayorga Excess nutrients—nitrogen (N), phosphorus (P), silica (Si)—entering coastal waters (eutrophication) can result in algal blooms, leading to reduced oxygen conditions, increased turbidity, and changes in composition, among other effects. Some of these blooms can be toxic to human and and impair ecosystem health. Among the major anthropogenic sources of river nutrient loading to LMEs are runoff from fertilizer use and livestock production, sewage, and atmospheric nitrogen deposition. A merged indicator of coastal eutrophication, based on 2 sub-indicators: N loading rate, which is the amount of nitrogen carried by rivers as they enter the LME; and ratio of dissolved Si to N or P (Index of Coastal Eutrophication Potential or ICEP), was developed for 63 LMEs for contemporary (approximately year 2000) conditions and for a future “current trends” scenario for the years 2030 and 2050 using results of the Global NEWS (Nutrient Export from Watersheds) model (Beusen and others 2009, Mayorga and others 2010, Seitzinger and others 2010). The merged indicator focuses attention on LMEs with relatively high N loads where the ICEP is also moderate or higher. Based on the results, LMEs were assigned to five colour-coded risk categories (lowest, low, medium, high and highest risk).

Key messages: Contemporary conditions and future trends in the coastal eutrophication index are associated with large urban populations, intense agricultural production supported by high fertilizer use, and/or large numbers of livestock. Sixteen percent of the 63 LMEs are at high or very high risk for coastal eutrophication; most of these are in Western Europe and southern and eastern Asia as well as the Gulf of Mexico. The majority of

“ LMEs, however, are in the very low or low risk category for coastal eutrophication. „

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In many watersheds around the world river nutrient loads are projected to increase in the future due to further increase in human activities. The risk for coastal eutrophication will increase in twenty-one percent of LMEs by 2050 based on “current trends”. The proportion of LMEs in the high or very high risk category for coastal eutrophication will increase to 19% in 2030 and 23% of LMEs by 2050. Most of the “ increase is in LMEs in southern and eastern Asia, but also some in South America and Africa. Only two LMEs (Iberian Coastal and Northeast US Continental Shelf LMEs) are „ projected to lower their coastal eutrophication risk by 2050 based on current trends.

To reduce current and future risk, reductions in nutrient inputs to specific watersheds are required. This can include, for example, increased nutrient-use efficiency in crop production, reduction in livestock and better management of manure, and increase treatment level (increased N and P removal) of human sewage. In order to develop appropriate reduction strategies for an LME, the relative contribution and location of “ nutrient sources within river basins and across the LME would be needed, and could „ be developed based on further analysis.

As illustrated in the BOBLME, there can be considerable variation in the nutrient loads and sources as well as in eutrophication potential among the various river basins within an LME. This kind of information is important in identifying the spatial variation of nutrient effects and their sources for reduction within LMEs. “ „

Beusen, A. H. W., Bouwman, A. F., Dürr, H. H. and others. 2009. Global patterns of dissolved silica export to the coastal zone: Results from a spatially explicit global model. Global Biogeochem. Cycles, 23, GB0A02, doi:10.1029/2008GB003281. Mayorga, E., Seitzinger, S. P., Harrison, J. A. and others. 2010. Global Nutrient Export from WaterSheds 2 (NEWS 2): Model development and implementation. Environmental Modelling & Software, 25, 837-853. Seitzinger, S. P., Mayorga, E., Bouwman and others. 2010. Global River Nutrient Export: A Scenario Analysis of Past and Future Trends. Global Biogeochemical Cycles, doi:10.1029/2009GB003587.

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Nutrient ratio (ICEP) 2000

Nutrient ratio (ICEP) 2000, categorized from lowest (1) to highest (5) values.

Nutrient ratio (ICEP) 2030

Nutrient ratio (ICEP) 2030, categorized from lowest (1) to highest (5) values

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Nutrient Ratio (ICEP) 2050

Nutrient ratio (ICEP) 2050, categorized from lowest (1) to highest (5) values

Nitrogen load 2000

Nitrogen load, base year 2000, categorized from the lowest (1) to the highest (5) values

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Nitrogen load 2030

Nitrogen loading projected to 2030, categorized from the lowest (1) to the highest (5) values

Nitrogen load 2050

Nitrogen load projected to 2050, categorized from the lowest (1) to the highest (5) values

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Nutrient risk 2000

Merged Nutrient Risk Indicator, base year 2000, categorized from the lowest (1) to the highest (5) values

Nutrient risk 2030

Merged Nutrient Risk Indicator projected to 2030, categorized from the lowest (1) to the highest (5) values

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Nutrient risk 2050

Merged Nutrient Risk Indicator projected to 2050, categorized from the lowest (1) to the highest (5) values

Plastics Peter J. Kershaw and Laurent C.-M. Lebreton Floating plastic is now ubiquitous in the global ocean, including the remotest parts of the Southern Ocean, as a result of the durability of plastic and the overall ocean circulation. The occurrence of plastic in an LME may be due to sea and land-based activities. A proportion of the plastic entering an LME is likely to be transported by wind and currents into an adjoining LME or the Open Ocean, making plastic pollution a classical transboundary issue. Larger plastic debris can have a significant impact on marine organisms, mainly due to entanglement and ingestion. Plastic can also cause major economic loss and pose a threat to navigation and human safety. There are insufficient empirical estimates of abundances of floating micro- or macro-plastics for all LMEs. The relative abundances of floating micro- (<4.75 mm in diameter) and macro-plastics (>4.75 mm) in each LME were estimated through a model that uses coastal population density, shipping density and the level of urbanization within major watersheds, to develop proxy sources of plastics. Based on the results, LMEs were assigned to five colour-coded risk categories (lowest, low, medium, high and highest risk). The estimated abundances of both floating micro-plastics and macro-plastics vary by over four orders of magnitude between the lowest value (Antarctica) and the highest (Gulf of Thailand). Very little is known about the actual effects of micro-plastics on marine organisms. Larger plastic debris has been shown to have a significant impact on many species of marine organisms, mainly due to entanglement and ingestion. Plastic can also cause major economic loss and may pose a threat to navigation and human safety. Once plastic enters the ocean it can become widely dispersed by ocean currents and winds “ and its impacts on the marine environment may occur at a considerable distance from „ the point(s) of entry.

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Plastic enters the marine environment from a wide variety of land-based and sea- based activities, and there are no reliable estimates of the nature and quantities of material involved. The general lack of reliable and consistent observational monitoring data on floating plastics prevents reliable quantitative estimates of the amount of micro and macro-plastics in both space and time for most LMEs. This poses “ difficulties in designing and implementing cost-effective measures to reduce inputs. In most cases, solutions will need to be multi-agency, multi-sector and trans-national to „ be fully effective.

While model estimates of plastic concentration are imperfect, they do provide a means for focusing future efforts, to improve predictive capacity, assessing potential socio-economic consequences and targeting mitigation measures. Further improvement should be made if data become available on key sources of plastics such as fishing, aquaculture and coastal tourism as well as the actual quantities entering “ the ocean, and how this may be influenced by the state of economic development in „ different countries.

Micro plastics

Micro plastics, count density (counts.km-2). Number of floating micro-plastics – estimated from model simulations, using 3 proxy sources (shipping density, coastal population density and area of urban impervious catchment. Count density (counts.km-2):

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Macro plastics

Macro plastics, weight density (g.km-2) Mass of floating macro-plastics – estimated from model simulations, using 3 proxy sources (shipping density, coastal population density and area of urban impervious catchment. Weight density (g.km-2):

POPs Hideshige Takada Most of the POP analyses in pellets were financially supported by The Mitsui & Co., Ltd. Environment Fund Persistent organic pollutants (POPs) are used in industrial and agricultural applications, and not only are they persistent, they are also bioaccumulative and toxic. They are regulated by the Stockholm Convention on POPs. Three classes of POPs were assessed: polychlorinated biphenyls (PCBs), dichlorodiphenyltrichloroethylene and its metabolites (DDTs), and hexachlorocyclohexane isomers (HCHs). Plastic resin pellets were used as passive samplers of POPs in coastal waters. The pellets, which are found stranded on beaches all over the world, sorb and concentrate POPs from the surrounding seawater. Pellets from 193 locations in 37 LMEs were collected by volunteers through the International Pellet Watch Programme between 2005 and 2014, and analyzed for levels of POPs. Based on the results, LMEs were assigned to five colour-coded risk categories (lowest, low, medium, high and highest risk). Within each LME, POPs levels were highly variable, and POPs were detected in all the samples including from remote islands. Several LMEs showed relatively high contamination levels (higher than category 3) for multiple POPs and a number of hotspots (categories 4 and 5) were found. While the International Pellet Watch Programme has proved to be an excellent sentinel to assess the pollution status of POPs in coastal waters, areal coverage is still limited and increased monitoring in LMEs is necessary. Also, time-series sampling is needed to understand the temporal trends of POPs pollution, to evaluate the effectiveness of regulatory measures, and to identify emerging pollution sources. “ Conventional monitoring approaches by using sediment, water, and biological samples should be conducted in hot spots to confirm the pollution levels and to „ identify the types/sources of pollution for effective regulation.

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Several POPs hot spots were identified and actions to reduce the levels of POPs are required. For example, in hot spots of PCBs and DDTs derived from secondary pollution sources in bottom sediment, some remediation actions such as dredging and/or capping of the contaminated bottom sediment should be considered. “ „

Current emissions of PCBs were suggested in some developing countries (Ghana in the Guinea Current and the Philippines in the Sulu-Celebes Sea). Source control, especially proper management and regulation of electronic waste, is recommended.

“ „

Intensive recent emission of DDTs was revealed in the South China Sea coasts and other locations such as Brazil, Ghana, Athens and Sydney. The contribution of DDT pesticide used in Malaria control was suggested for some tropical and subtropical regions, whereas illegal application of DDT pesticides and antifouling agents were of concern for other regions. “ „

Recent use of Lindane pesticide, which is globally banned for agricultural use, was suggested in HCH hot spots in the Agulhas Current and the New Zealand Shelf LMEs. Continuous monitoring of HCHs using plastic pellets and conventional means in these LMEs is required to identify the sources as well as spatial and temporal trends. “ „

DDT score

POPs: dichlorodiphenyltrichloroethylene and its metabolites (DDTs) score

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HCHs score

POPs: hexachlorocyclohexane isomers (HCHs) score

PCBs score

POPs: polychlorinated biphenyls (PCBs) score

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Ecosystem health Decline in the health of marine ecosystems is already having severe consequences for the millions of people who depend on them for food, coastal protection, building materials and tourism, among other goods and services. Marine ecosystems in general and coastal ecosystems in particular experience a wide range of stressors associated with human activities as well as natural changes. Under the Ecosystem Health sub-module, the assessment examined mangrove and extent (providing a baseline for future monitoring), Coral reefs at risk from human and natural pressures, Cumulative Human Impacts, and the Ocean Health Index. A widely implemented response to protect valuable marine habitats is Marine Protected Areas (MPAs), and temporal changes in their extent were also assessed.

Mangrove extent Miranda Jones Jan-Willem van Bochove, Simon Blyth, Emma Sullivan and Chris McOwen Mangroves provide at least US$ 1.6 billion each year in ecosystem services, including enhancing fisheries and filtering pollutants and contaminants from coastal waters and coastal protection from storms, floods and erosion. These habitats are also recognized as one of the key blue carbon habitats, capturing and storing carbon. Over the last century there has been extensive loss and degradation of mangrove habitats as a result of both human and natural pressures such as coastal development, pollution, aquaculture, logging for timber and fuel wood, and sea level rise. For the effective management and conservation of mangroves, baseline data on their extent and the threats they face is required to enable monitoring of changes over time and to inform management decisions. This first global baseline of mangrove extent by LME and the Western Pacific Warm Pool is based on the US Geological Survey’s Global Distribution of Mangroves dataset (Giri and others 2011). Key messages Globally, around 20% of total mangrove area has been lost between 1980 and 2005 due to a number of human activities such as coastal development, aquaculture expansion and timber extraction. The impact of coastal development has widespread, and increasing, importance. The relative impact of climate change on mangroves is largely unknown, but is predicted to increase in the future. “ „

Although mangrove habitat continues to decline at an estimated 1% annually, actual rates and key drivers of loss vary between regions. Overexploitation for timber, fuel wood and charcoal was found to be the greatest driver of mangrove loss, in particular in Africa and South and Southeast Asia, although the future impacts of this driver are largely unknown. “ „

Due to the high rates of mangrove deforestation in many areas, current calculations are likely to be overestimates of mangrove cover. Future mangrove assessments in LMEs can be improved with the availability of a more recent baseline mangrove layer and its frequent ground-truthing, which will also allow change in cover to be estimated. The assessment of relative impacts of key drivers of mangrove loss will “ benefit from the incorporation of surveys from a greater number of experts and at the „ LME scale.

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Giri, C., Ochieng, E., Tieszen, L.L. and others. 2011. Global distribution of mangroves. A product of US Geological Survey, in collaboration with ARSC Research and Technology Solutions, UNEP, NASA and the University of Queensland. Cambridge (UK): UNEP World Conservation Monitoring Centre. http://data.unep-wcmc.org/datasets/21

Coral reefs at risk Miranda Jones, Jan-Willem van Bochove, Simon Blyth, Emma Sullivan and Chris McOwen Coral reefs are some of the most economically valuable ecosystems on Earth, with their declines likely to have severe consequences for hundreds of millions of people who depend on them for food, coastal protection, building materials, and tourism. Coral reefs are one of the most endangered habitats on the planet, threatened by anthropogenic pressures such as warming waters, ocean acidification, pollution, overfishing, and extraction. This first assessment of the threats faced by coral reefs within LMEs and the Western Pacific Warm Pool used the Global Distribution of Coral Reefs 2010 (http://data.unep-wcmc.org/datasets/13) and the indices calculated by the Reefs at Risk Revisited Project (Burke and others 2010). Coral reefs are assessed using a present day integrated threat score, incorporating local threats from overfishing and destructive fishing, coastal development, pollution, and damage; and a global threat score, incorporating warming sea temperatures and ocean acidification projected to 2030 and 2050.

Key messages: LMEs containing coral reefs under the highest local threat (those that are anthropogenic and have direct, localized impacts) at present include the Somali Coastal Current, Kuroshio Current, Sulu-Celebes Sea, Gulf of California, and the East China Sea. The North Brazil Shelf faces the lowest level of local integrated threat. Among the local threats, overfishing and destructive fishing practices are of greater

“ threat to coral reefs than coastal development and . „

One quarter of LMEs have over 50% of their coral reef extent under high to very high threat based on local present day threats.

“ „

Global ocean warming and acidification will further increase the threats faced by coral reefs in the future. By 2050, only four LMEs have any reef area left under low threat. Multiple local threats likely reduce the ability of coral reefs to respond and adapt to ocean warming and acidification. Projected increases in these threats may impact human societies through changes in fishery resources, tourism and coastal protection. “ The extent of this negative impact will depend on the resilience of coral reefs to predicted threats as well as the implementation of measures to manage and protect „ coral reefs and their associated biodiversity.

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Implementing measures such as marine protected areas may enhance ecosystem resilience in the face of increasing global threats, while monitoring coral reef health is important to assess the actual impact to this threatened ecosystem from both local and global threats. “ „

Burke, L., Reytar, K., Spalding, M., Perry, A. 2010. Reefs at Risk Revisited, World Resources Institute, Washington, DC.

Change in extent of marine protected areas Miranda Jones, Simon Blyth and Chris McOwen The oceans are home to an estimated 50-80% of all life on Earth, and provide vital ecosystem goods and services to human populations. However, marine and coastal ecosystems are facing increasing threat from pollution, extractive infrastructure, fisheries, coastal development and climate change. Marine Protected Areas (MPAs) are vital to conserve the ocean’s biodiversity and productivity. Aichi Target 11 of the Convention on Biological Biodiversity (CBD) aims to effectively conserve 10% of the world’s coastal and marine areas by 2020. The first estimates of the change in extent of the world’s MPAs (between 1983 and 2014) for LMEs and the Western Pacific Warm Pool are presented, using data from the latest version (2014) of the World Database on Protected Areas (available at www.protectedplanet.net). Based on the percentage change in MPA coverage, LMEs were arranged into five categories: 1 (red) to 5 (blue), corresponding to highest, high, medium, low and lowest level of relative risk of potential biodiversity degradation, respectively. This analysis does not provide an assessment of the likely effectiveness of the MPAs, as countries may vary in their interpretation and classification of particular types of MPAs and the degree of implementation and enforcement may vary. Coverage of protected areas with marine components has increased from 340,230 km2 in 1982 to 5,161,225 km2 in 2014, with the greatest increases observed in the Australian Shelf Seas LMEs.

Key messages: The continued designation of MPAs between 1983 and 2014 has led to a 15-fold increase in global MPA coverage. LMEs with the highest percentage change in area of MPAs include three of the Australian Shelf LMEs, Gulf of California, and Red Sea. The increase in global MPA coverage indicates progress towards Aichi Target 11 of the CBD. “ „

The global distribution of MPA coverage indicates areas where potential threats to marine biodiversity may be reduced by further implementation of MPAs.

“ „

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This analysis does not provide an assessment of the likely effectiveness or impacts of MPAs on marine biodiversity, as countries may vary in their interpretation and classification of particular types of MPAs and the degree of implementation and enforcement may vary. Monitoring the effectiveness of designated MPAs and analysing how increasing coverage relates to the conservation of ocean biodiversity

“ and productivity remains of high importance. „

Cumulative human Impact on marine ecosystems Benjamin S. Halpern and Melanie Frazier The ocean is impacted by the cumulative impact of multiple human and natural stressors. Because LMEs are coastal regions of the ocean, the confluence of human impact is generally even more intense, with most coastal waters experiencing significant impact from land-based pollution, coastal small-scale and commercial fishing, climate change, , oil and gas development, coastal modification and , and many more. Recognition of the ubiquitous role of multiple stressors in these ecosystems motivates management to focus on ecosystem-based management and marine spatial planning. Cumulative human impact (CHI) assessments provide a tool for transparently and quantitatively informing such policy processes and decisions. This assessment draws on data for 19 stressors and 20 marine habitats from a variety of sources. Full details on data sources and processing are provided in extensive supplementary information in Halpern and others (2008; in prep.). In general, LMEs adjacent to heavily populated coastlines, particularly in developed countries that encompass large watersheds, have the highest CHI scores, while polar regions tend to have the lowest scores. LMEs were assigned to five risk categories (lowest to highest risk) based on the rank order of values across all LMEs. Eleven of the 13 LMEs with the highest average cumulative human impact score are those surrounding Europe and China (Kuroshio Current and Canary Current are the other two). Key messages: Stressors associated with climate change, most notably ocean acidification and increasing frequency of anomalously high sea surface temperature, are the top stressors for nearly every LME. However, this result in part emerges from the scale of the assessment. At smaller scales, particularly along coastlines, many other stressors, such as land-based pollution and fishing, play a dominant role. “ „

Commercial shipping and demersal commercial fishing are the other two main stressors at the scale of LME. Stressors associated with these activities tend to affect different parts of the ecosystem, such that where they overlap in space, cumulative impacts are likely to directly affect the entire . “ „

The most heavily impacted LMEs are all adjacent to China and Europe. These regions also contain most of the highest cumulative impact scores at small scales. The least impacted LMEs are all in the Arctic and Antarctica. Because this indicator does not project into the future, these results do not reflect any of the changes that are expected to occur in the near future that will heavily impact these regions. “ „

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Efforts to manage marine ecosystems at the scale of LMEs will require not only coordination among countries bordering the LME, but also among sectors. Coordination at the sector scale is critical to successful management because the key stressors are global in nature, and therefore, are beyond the scope of what can be identified and addressed through single-sector management. “ „

Halpern, B.S., S. Walbridge, K.A. Selkoe and others. 2008. A global map of human impact on marine ecosystems. Science 319: 948-952. Halpern, B.S., J. Potapenko, B.D. Best and others. in prep. Recent spatial and temporal changes in cumulative human impacts on the world’s ocean.

Ocean Health Index Benjamin S. Halpern, Melanie Frazier, Benjamin D. Best, Catherine Longo, and Julie S. Stewart Lowndes People value ocean ecosystems for the many benefits they provide, such as food, aesthetic beauty, supporting livelihoods, and the vast diversity of species within them. LMEs represent the confluence of these values. Coastal regions are extremely productive, contain a vast majority of marine species, house the coastal habitats that protect our shores and sequester carbon, and are where a majority of people on the planet live, work and play (Agardy and others 2005). To fairly assess the health of LMEs, one must therefore measure the status of all of these benefits provided by coastal marine ecosystems. The Ocean Health Index (OHI) measures the performance of 10 widely-held public goals for healthy oceans, including food provision, carbon storage, coastal livelihoods and economies, and biodiversity. Each goal is assessed against an ideal state, defined as the optimal and sustainable level that can be achieved for the goal. The Index was first calculated for 221 different EEZs (Halpern and others, in review), and then the proportion of overlap of EEZs in each LME was used to calculate an area-weighted average of the EEZ scores to produce an LME-specific score. As such, LMEs were indirectly assessed via their component EEZs. Nearly 80 different global datasets spanning ecological, social, economic, and governance measures were used for the OHI assessment. Extensive details on how each goal is measured and the data used to calculate the goal scores are provided in Halpern and others (2012), Halpern and others (in review) and at www.ohi-science.org. OHI scores for the 66 LMEs ranged from 57 to 82 out of 100, with two-thirds of all LMEs scoring between 65 and 75 (average 70.6). The lowest scoring LMEs were along the Equator, in particular in western Africa, while the highest scores were around Australia and in the sub-polar North Atlantic. For nearly three quarters of all LMEs the scores remained unchanged or improved since the previous year, although several others had significant declines in overall Index scores.

Key messages: For nearly all LMEs, there remains substantial opportunity to improve food provision by increasing the sustainable harvest of wild caught fisheries and production of mariculture. Achieving these outcomes would have important benefits to food security and local economies. “ „

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Coastal habitats play a key role in protecting coastal communities, storing carbon to help mitigate climate change, and supporting biodiversity. Overall ocean health tends to score lower where coastal habitats are degraded or destroyed. Habitat restoration and protection offers a key way to improve ocean health. “ „

Nearly all of the LMEs that lie along the equator have the lowest Index scores and are thus in the highest risk category. Priority should be given to improving the health of the ocean in these regions.

“ „

Many aspects of ocean health remain poorly monitored, hindering the precise tracking of ocean health across space and through time. Improving data reporting standards from all UN member states would significantly aid assessments of ocean health and decision making based on those assessments. “ „

Management of LMEs is intended to be comprehensive and span the ecological, economic and social aspects of communities and their interactions with ocean ecosystems. Such management is challenged when faced with disparate data and information focused on different aspects of the human and natural system; integration is left to the individual and is often ad hoc. The Ocean Health Index “ provides a framework for combining this disparate information into a single, comparable, quantitative and transparent measure of ocean health and its many component factors. „

Agardy T., Alder J., Dayton P. and others. 2005. Chapter 19, Coastal Systems. In: Ecosystems and Human Well-being: Volume 1: Current State and Trends. Baker J., Moreno Casasola P., Lugo A., Suárez Rodrıǵ uez A., Ling Tang, L.D. Eds. Pp. 513-549. Halpern B.S., Longo C., Hardy D. and others. 2012. An index to assess the health and benefits of the global ocean. Nature 488: 615-620. Halpern, B.S., C. Longo, J.S.S. Lowndes and others. in review. Patterns and emerging trends in global ocean health. PLoS ONE.

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Mangrove extent

Extent of mangrove habitat, as percentage (%) of LME cover.

Coral extent

Fraction (%) of the LME area covered with corals

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Reefs at risk

Total integrated present day threat score combining threat from overfishing and destructive fishing, watershed-based and marine-based pollution and damage, for each LME containing coral reefs.

MPA extent change

Change in (MPA) coverage (%) between 1982 and 2014.

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Cumulative impact

Cumulative Human Impact (CHI)

Ocean Health Index

Ocean Health Index (OHI), the higher the lower the risk.

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Global Socioeconomic profile of Large Marine Ecossytems Liana Talaue McManus and Maria Estevanez Coastal societies worldwide are direct consumers of goods and ecosystem services derived from Large Marine Ecosystems (LMEs) by virtue of their proximity to shore while non-coastal residents access the same via trade and tourism. The sheer numbers of coastal inhabitants, types of life styles (e.g. consumption patterns) and livelihoods along the rural to urban spectrum, and levels of human development defined by societal achievements in health, education and income, profoundly influence the manner with which they collectively use and govern their coastal and marine resources. In the socioeconomic assessment of LMEs, demographic, economic, human development and vulnerability indicators were examined and used in comparing 66 LMEs, 64 of which have resident inhabitants. Such an assessment provides a people-centered context that is crucial in understanding how human populations both exacerbate and mitigate the degrading environmental states of LMEs globally, and the consequences reduced ecosystem services have on livelihoods and wellbeing. Indeed, it is becoming increasingly clear that sustaining human wellbeing is predicated on maintaining the health and productivity of natural ecosystems including those of LMEs.

Key messages: Coastal populations exert intense pressure on LMEs. At around 2.7 billion coastal inhabitants in 2010, these can nearly double to 4.7 billion in a scenario of high growth rates and outstrip the natural rates at which LMEs can provide ecosystem services such as fish production. The 10 most populous LMEs in 2010 are the Bay of Bengal, South China Sea, Mediterranean Sea, Arabian Sea, Indonesian Sea, Yellow Sea, East “ China Sea, Kuroshio Current, Caribbean Sea and the Sulu–Celebes Sea. Together, the coastal population of these LMEs total 1.8 billion or 65% of the global total in 2010; „ and climbing to 2.4 billion in 2100.

Coastal economies such as those built on fishing and tourism depend on healthy ecosystems for long–term viability. Yearly, an average of $88 billion (2013 US$) worth of fish is landed at ex–vessel prices for the period 2001–2010. In the case of marine tourism, annual net revenues from all 66 LMEs are valued at almost $4 trillion (2013 US$) over the period 2004–2013. Where ecosystems are experiencing collapsing or “ overexploited fish stocks or degrading water quality and widespread habitat conversion, among other factors, sustaining coastal livelihoods and protecting food „ security may become much constrained.

The wellbeing of coastal societies may be gauged using the Human Development Index (HDI) as underpinned by metrics of health, education status and income levels. Of these, education has the potential to shape reproductive, other health and economic choices that impact the environment. The gap between the highest possible HDI (theoretically equal to 1.0) and the current level of wellbeing, also called the HDI “ GAP, is a measure of overall vulnerability to external shocks including disease, natural disasters, and extreme environmental events, include climate–related phenomena. Present–day vulnerability to climate related events such as storms, flooding and droughts, using the HDI Gap and coupled with losses in life and property in the last 20 years (1994–2013) as exposure metrics, is highest among tropical and cyclone-prone LMEs such as the South China Sea, the Sulu–Celebes Sea, the Caribbean Sea, the „ Pacific–Central American Coastal LME, the East China Sea, Indonesian Sea and the

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Yellow Sea LMEs, in addition to the five LMEs with the lowest HDI scores.

Development pathways can either increase or reduce the HDI Gap, and thus determine future vulnerabilities of coastal dwellers to projected environmental states such as sea level rise in 2100. In a sustainable scenario with low population growth rates, threat scores from exposure to sea level change ranges from 10 to 54% with a median of 31%. In a high population growth rate scenario, the median threat score “ increases to 56%, with a range of 20 to 78%. Larger population sizes and wider HDI Gaps in the latter development mode both cause threat levels to increase under the „ same conditions of sea level rise.

Coastal population (2010)

Coastal population (2010), in the 100km to the coast [missing legend]

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Coastal Poor

Coastal poor population, 2010: [missing population]

Fisheries revenues (landed value)

Fisheries average annual landed value, between 2001 and 2010, in 2013 USD. [missing legend]

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Tourism revenues

Annual tourism revenues in USD, for period 2004–2013 [missing legend]

HDI (2009-2013)

Human Development Index (2009-2013) [missing legend]

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HDI (2100, SSP1)

Human Development Index 2100 (Sustainable Pathway SSP1) [missing legend]

HDI (2100, SSP3)

Human Development Index 2100 (Fragmented World Pathway SSP3) [missing legend]

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Climate threat index (Present day)

Climate Threat Index (Present-Day) The contemporaneous Climate Threat Index (1993-2012) takes into account mortality and property losses due to flooding, storms and extreme temperatures (such as extreme heat and cold events, prolonged drought). Very low Low Medium High Very high No data

SLR Threat 2100 (SSP1)

Sea Level Rise Threat Index 2100 (Sustainable Pathway SSP1) For the 2100 sea level rise threat indices at sustainable development and fragment world development pathways, only the projected maximum sea level change data at RCP 8.5 scenario for 2100 was available and taken into account in the absence of projected data on flooding, storms and extreme temperature-caused mortality and damages for the same year.

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SLR Threat 2100 (SSP3)

Sea Level Rise Threat Index 2100 (Fragmented World Pathway SSP3) For the 2100 sea level rise threat indices at sustainable development and fragment world development pathways, only the projected maximum sea level change data at RCP 8.5 scenario for 2100 was available and taken into account in the absence of projected data on flooding, storms and extreme temperature-caused mortality and damages for the same year.

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Governance Lucia Fanning, Robin Mahon, Kimberly Baldwin, and Selicia Douglas The LME governance assessment focused on the transboundary governance arrangements and their associated architecture (the set of commonly-shared principles, institutions and practices that affect decision-making) relevant to fisheries, pollution, biodiversity and habitat destruction in 50 transboundary LMEs (two or more coastal countries). Three indicators were evaluated to monitor progress towards "good" governance in the LME, as viewed from an architectural design perspective: (i) the level of completeness of the structure of arrangements in terms of the completeness of the stages of the policy cycle to address a given issue(s); (ii) the level of integration of institutions involved in addressing the suite of identified transboundary issues; and (iii) the level of engagement of countries participating in arrangements that address the identified transboundary issues within the LME. The assessment does not evaluate the performance of the arrangements. This would require indicators that determine whether governance processes are working, stressors are being reduced, ecosystems are sustainable, and ultimately whether human well-being is secured or improved. The Mediterranean Sea LME shows the lowest level of risk across the three indicators, largely due to the nature and presence of an overarching integrating mechanism to address transboundary issues. Based on the assessed scores for the three indicators, the LMEs were assigned to five categories reflecting the perceived level of risk (lowest to highest). Key messages: There is considerable room for improvement in the design of governance arrangements in terms of the completeness of the policy cycle to address key transboundary issues. It is important to ensure that current and new agreements have policy cycle mechanisms in place that include a wide array of data and information providers, facilitates a strong knowledge-based/policy interface, hold decision-makers “ and those responsible for implementation accountable and ensure monitoring and evaluation mechanisms are in place and followed so as to facilitate appropriate „ adaptive management responses.

Over half of the assessed LMEs show very high risk levels for integration across arrangements to address transboundary issues, which poses a potentially very high risk to the adoption of EBM in these LMEs. This is primarily due to the significant disconnect between organizations involved with fisheries issues and those involved in pollution and biodiversity issues in these LMEs. Consequently, there is a need to “ ensure better collaboration between these arrangements if EBM is to be effectively implemented. For LMEs assigned the highest risk, existing arrangements for addressing tranboundary issues shared few organizations across similar stages of their „ policy cycles.

There is a high level of commitment by the countries towards participation in agreements addressing transboundary issues. This is positive, but does not guarantee follow-through actions on the part of the countries, especially if there is little to no repercussion for failing to comply with the terms of the agreement. This is of concern since the nature of agreements, binding versus non-binding, influences the level of

“ commitment demonstrated by countries. „

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Integration

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Engagement

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Completeness

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Identifying patterns of risk among Large Marine Ecosystems using multiple indicators K.M. Kleisner, L. Talaue-McManus , B.S. Halpern, P.J. Kershaw, V.W.Y. Lam, K. Sherman

Introduction One of the two goals of the Transboundary Waters Assessment Programme (TWAP) is to conduct a baseline global assessment of five transboundary water system categories: (1) Large Marine Ecosystems (LMEs), (2) Open Oceans, (3) Aquifers, (4) River Basins, and (5) Lakes & Reservoirs, with the aim of providing guidance to the GEF and other stakeholders for prioritization of interventions within these water systems. With the exception of the Open Oceans, these assessments are comparative and group the transboundary water systems into five risk categories (from very high to very low) based on a suite of indicators. Risk is broadly defined as the probability of adverse consequences for humans and the environment in relation to the changing states of transboundary waters. The LMEs assessment is based on indicators under each of the five LME modules (Productivity, Fish & Fisheries, Pollution & Ecosystem Health, Socioeconomics, and Governance), for which global data sets are available. Results of the individual indicators are presented by module on this website. Triggers of risk are usually multiple factors, which may be biophysical, socioeconomic, or governance-related in some combination. To identify patterns of risk among LMEs using multiple indicators, a number of indicators with strong directionality in indicating "good" or "bad" ecosystem states are used to identify groups of LMEs based on their similarities across the modules.

Approach The simultaneous use of multiple indicators allows for the classification of large marine ecosystems into thematic clusters, and along axes defined by a combination of dominant indicators, and for the ranking of LMEs using risk scores. Statistical classification and ordination techniques were used to identify clusters of LMEs subject to similar pressures and impacts; Indicators that were strongly directional in indicating ‘good’ or ‘bad' ecosystems states, and which were assessed for at least 60 of the 66 LMEs, were chosen for this ranking analysis and used to cluster the LMEs: ‘Demersal Non-destructive Low-Bycatch Fishing’, ‘Pelagic Low Bycatch’, ‘Proportion of Collapsed and Overexploited Stocks’, ‘Capacity enhancing subsidies as a fraction of the value of fisheries’, ‘Proportion of Catch from Bottom Impacting Gear’, ‘Index of Coastal Eutrophication Potential’, ‘Plastic Debris Density’, ‘Percentage Change in Area of MPAs’, ‘Shipping Pressure’, ‘Percentage Rural Population within 100km of the Coast’, and the ‘Night Light Development Index’; The Human Development Index was used as a measure of the socioeconomic status of an LME as well as a weighting factor in determining an overall risk score for each LME. The scores took into account the average of all fisheries indicators, and all metrics for pollution and ecosystem health used in the study. This approach is one of a number of ways to rank LMEs;

Results Figure 1 below illustrates how the LMEs cluster according to these indicators and lists the indicator(s) that were dominant in driving the clusters: Figure 1. The main LME clusters and associated indicators

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The nature of these clusters informs the identification of LMEs of potential interest for policy and management interventions. The risk categorization provides an interpretation of the priority status of the LMEs within a human developmental framework.

The dominant risks associated with combinations of indicators can be illustrated spatially, as shown in the following maps (Figures 2 and 3). Shipping pressure (brown colors) and coastal rural population density (blue colors) are two of the strongest indicators defining LME groupings (Figure 2). Heavily developed regions such as the North Sea, East China Sea and Northeast U.S. Continental Shelf LMEs have higher risks associated with shipping pressure. LMEs that have higher risk due to vulnerable rural populations in coastal areas include the High Arctic LMEs.

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Figure 2. Shipping pressure (highly positive/brown colors) and coastal rural population density (highly negative/blue colors).

High risk associated with pressures from catch from bottom impacting gear types (brown colors) and pressures due to demersal non-destructive low bycatch fishing (blue colors) were also strong indicators in defining the groupings (Figure 3). LMEs with high pressure from destructive bottom gears include the Southeast U.S. and the East Central Australian Shelf, Southwest Australian Shelf and Southeast Australian Shelf LMEs. Those with more pressure due to demersal non-destructive low bycatch fishing include LMEs in Asia like the Sulu-Celebes Sea, the Indonesian Sea and the South China Sea LMEs, as well as European LMEs like the Norwegian Sea, the Baltic Sea, and the Icelandic Shelf and Sea LMEs.

Figure 3. Catch from bottom impacting gear types (highly positive/brown colors) and pressures due to demersal non-destructive low bycatch fishing (highly negative/blue colors)

In order to provide an illustrative linear ranking of assessed units, which is often required by managers as a basis for setting priorities, the Human Development Index (HDI) was used to weight the LME risk scores. Other approaches could be taken, but the use of the HDI follows the premise that coastal populations of LMEs with lower socioeconomic development status will be at a higher risk for the same levels of environmental status, and may indicate situations where an LME population will have a limited ability to cope with degraded transboundary waters due to increased

52/53 LMEs, Indicators global maps Transboundary Water Assessment Programme, 2015 pressures and impacts on the coastal ecosystem and/or the increased dependence on ecosystem services. Low levels of human development based on educational attainment, life expectancy and per capita gross national income indicate few livelihood options and limited resources for basic existence, and much less for dealing with the impacts of natural disasters, climate change and degraded ecosystems. The results are shown in Figure 4, where LMEs with very high scores are colored red; those with medium risk scores in yellow, and LMEs with very low risk scores in blue. The LMEs that were at highest risk were those with the lowest HDI and included the Somali Coastal, Guinea and Canary Current LMEs (GEF-eligible LMEs). These LMEs had low fisheries related risks, but incurred high risks from pollution and low proportions of MPA Area coverage. LMEs with the lowest risk had high HDI status, but were also subject to pressures from pelagic low bycatch fishing levels and capacity-enhancing fisheries subsidies.

It should be noted that the LME rankings could change based on the indicators used as well as on the weighting or ranking scheme adopted. Results relate to the scale of the entire LME and do not reflect on any individual country's management of its coastal waters.

Figure 4. Risk levels of LMEs based on multivariate indicators weighted by the Human Development Index.

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