Large Marine Ecosystems Indicators global maps,...
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Large Marine Ecosystems Indicators global maps, 2015
http://onesharedocean.org/lmes
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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 PRODUCTIVITY .................................................................................................................... 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
Ecological footprint ........................................................................................................................... 15
Marine Trophic Index ........................................................................................................................ 15
Fishing-in-Balance ............................................................................................................................. 16
Stock status (number) ....................................................................................................................... 16
Stock status (biomass) ....................................................................................................................... 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 MANGROVE EXTENT ........................................................................................................................ 31 CORAL REEFS AT RISK ....................................................................................................................... 32 CHANGE IN EXTENT OF MARINE PROTECTED AREAS .................................................................................. 33 CUMULATIVE HUMAN IMPACT ON MARINE ECOSYSTEMS .......................................................................... 34 OCEAN 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 population (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 estuaries seaward to the continental shelf 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 ecosystem-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
2: Gulf of Alaska
3: California Current
4: Gulf of California
5: Gulf of Mexico
6: Southeast U.S. Continental Shelf
7: Northeast U.S. Continental Shelf
8: Scotian Shelf
9: Labrador - Newfoundland
10: Insular Pacific-Hawaiian
11: Pacific Central-American
Coastal
12: Caribbean Sea
13: Humboldt Current
14: Patagonian Shelf
15: South Brazil Shelf
16: East Brazil Shelf
17: North Brazil Shelf
18: Canadian Eastern Arctic - West
Greenland
19: Greenland Sea
20: Barents Sea
21: Norwegian Sea
22: North Sea
23: Baltic Sea
24: Celtic-Biscay Shelf
25: Iberian Coastal
26: Mediterranean Sea
27: Canary Current
28: Guinea Current
29: Benguela Current
30: Agulhas Current
31: Somali Coastal Current
32: Arabian Sea
33: Red Sea
34: Bay of Bengal
35: Gulf of Thailand
36: South China Sea
37: Sulu-Celebes Sea
38: Indonesian Sea
39: North Australian Shelf
40: Northeast Australian Shelf
41: East Central Australian Shelf
42: Southeast Australian Shelf
43: South West Australian Shelf
44: West Central Australian Shelf
45: Northwest Australian Shelf
46: New Zealand Shelf
47: East China Sea
48: Yellow Sea
49: Kuroshio Current
50: Sea of Japan
51: Oyashio Current
52: Sea of Okhotsk
53: West Bering Sea
54: Northern Bering - Chukchi Seas
55: Beaufort Sea
56: East Siberian Sea
57: Laptev Sea
58: Kara Sea
59: Iceland Shelf and Sea
60: Faroe Plateau
61: Antarctica
62: Black Sea
63: Hudson Bay Complex
64: Central Arctic
65: Aleutian Islands
66: Canadian High Arctic - North
Greenland
Socio-economic importance of LMEs With some of the planet’s most productive waters and biodiverse habitats such as coral reefs and
mangroves, 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 fish 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 habitat 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 marine
habitats is most severe, coastal pollution is concentrated, and levels of eutrophication 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 resource 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
Ecosystem Health, 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 primary
production supports ocean food webs and can be related to the carrying capacity of marine
ecosystems for supporting biodiversity 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 phytoplankton, 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 hypoxia, 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 Oceans 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 recruitment. 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 Marine Ecosystem. 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
upwelling 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 overexploitation 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 competition 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 Ecology 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). Ecopath, 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 populations.
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
community composition, among other effects. Some of these blooms can be toxic to human and
marine life 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 coral reef 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 marine pollution.
„
“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, invasive species, oil and gas development,
coastal modification and habitat destruction, 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 food web. „
“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.,
Suarez Rodrıguez 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 Marine Protected Area (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
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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.
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Tourism revenues
Annual tourism revenues in USD, for period 2004–2013
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HDI (2009-2013)
Human Development Index (2009-2013)
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HDI (2100, SSP1)
Human Development Index 2100 (Sustainable Pathway SSP1)
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HDI (2100, SSP3)
Human Development Index 2100 (Fragmented World Pathway SSP3)
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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
[missing legend]
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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
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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.