ࡱ>  gi\]^_`abcdef @ bjbjPP h::Km@@@ J VVV$zxxxPy z24JJJqJ<       $Rd/V 1@q  /VVJJDR :VJVJ   |VVыJ& @g xFn]lq`  ыzzVVVVVыf$ ! g //z^d,L^z^,ANNEX III TWAP Marine Habitat Extent Working Group: Recommendations for Developing Marine Habitat Indicators for TWAP-LME UNEP-WORLD CONSERVATION MONITORING CENTRE Contents  TOC \o "1-3" \h \z \u  HYPERLINK \l "_Toc262747027" PART 1. Objectives and report outline  PAGEREF _Toc262747027 \h 2  HYPERLINK \l "_Toc262747028" PART 2. The value of marine habitats  PAGEREF _Toc262747028 \h 2  HYPERLINK \l "_Toc262747029" 2.1 Threats to marine habitats  PAGEREF _Toc262747029 \h 4  HYPERLINK \l "_Toc262747030" a. Climate variability and change  PAGEREF _Toc262747030 \h 4  HYPERLINK \l "_Toc262747031" b. Fragmentation and Habitat loss  PAGEREF _Toc262747031 \h 4  HYPERLINK \l "_Toc262747032" c. Overfishing and destructive fishing practices  PAGEREF _Toc262747032 \h 5  HYPERLINK \l "_Toc262747033" d. Marine pollution  PAGEREF _Toc262747033 \h 6  HYPERLINK \l "_Toc262747034" e. Invasive species  PAGEREF _Toc262747034 \h 6  HYPERLINK \l "_Toc262747035" 2.2 Emerging issues  PAGEREF _Toc262747035 \h 6  HYPERLINK \l "_Toc262747036" a. Acidification  PAGEREF _Toc262747036 \h 67  HYPERLINK \l "_Toc262747037" b. Carbon storage in the oceans  PAGEREF _Toc262747037 \h 7  HYPERLINK \l "_Toc262747038" c. Deep seabed mineral deposits  PAGEREF _Toc262747038 \h 78  HYPERLINK \l "_Toc262747039" d. Gas hydrates  PAGEREF _Toc262747039 \h 8  HYPERLINK \l "_Toc262747040" e. Disease  PAGEREF _Toc262747040 \h 8  HYPERLINK \l "_Toc262747041" PART 3. Marine Habitats as Indicators  PAGEREF _Toc262747041 \h 8  HYPERLINK \l "_Toc262747042" 3.1 Challenges  PAGEREF _Toc262747042 \h 9  HYPERLINK \l "_Toc262747044" 3.2 Methodology  PAGEREF _Toc262747044 \h 14  HYPERLINK \l "_Toc262747045" PART 4. Indicator templates  PAGEREF _Toc262747045 \h 16  HYPERLINK \l "_Toc262747046" 1. Extent of warm water coral habitat (km2)  PAGEREF _Toc262747046 \h 16  HYPERLINK \l "_Toc262747047" 2. Extent of mangrove habitat (km2)  PAGEREF _Toc262747047 \h 18  HYPERLINK \l "_Toc262747048" 3. Extent of seagrass habitat (km2)  PAGEREF _Toc262747048 \h 2021  HYPERLINK \l "_Toc262747049" 4. Number of Ramsar sites with estuarine waters, tidal/mud flats, lagoons, and kelp beds, recorded  PAGEREF _Toc262747049 \h 2223  HYPERLINK \l "_Toc262747050" 5. Number of observations of cold water coral habitat  PAGEREF _Toc262747050 \h 2425  HYPERLINK \l "_Toc262747051" 6. Number of observations of cold seep and hydrothermal vent habitat  PAGEREF _Toc262747051 \h 2627  HYPERLINK \l "_Toc262747052" 7. Number of seamount observations  PAGEREF _Toc262747052 \h 2829  HYPERLINK \l "_Toc262747053" 8. Number of large seamount areas  PAGEREF _Toc262747053 \h 3031  HYPERLINK \l "_Toc262747054" 9. Percentage habitat covered by Protected Area  PAGEREF _Toc262747054 \h 3233  HYPERLINK \l "_Toc262747055" PART 5: SUMMARY  PAGEREF _Toc262747055 \h 3435  HYPERLINK \l "_Toc262747056" 5.1 Recommendations for future indicator development and associated data needs  PAGEREF _Toc262747056 \h 3435  HYPERLINK \l "_Toc262747057" REFERENCES  PAGEREF _Toc262747057 \h 3637  HYPERLINK \l "_Toc262747058" ANNEX I: Potential data sources for the development of marine habitat indicators  PAGEREF _Toc262747058 \h 4041 PART 1. Objectives and report outline 2 PART 2. The value of marine habitats 2 2.1 Threats to marine habitats 4 a. Climate change 4 b. Fragmentation and Habitat loss 4 c. Overfishing and destructive fishing practices 5 d. Marine pollution 5 e. Invasive species 6 2.2 Emerging issues 6 a. Acidification 6 b. Carbon storage in the oceans 7 c. Deep seabed mineral deposits 7 d. Gas hydrates 7 e. Disease 8 PART 3. Marine Habitats as Indicators 8 3.1 Challenges 9 3.2 Methodology 9 PART 4. Indicator templates 12 1. Global extent of warm water coral habitat (km2) 12 2. Global extent of mangrove habitat (km2) 14 3. Extent of seagrass habitat (km2) 16 4. Number of Ramsar sites with estuarine waters, tidal/mud flats, lagoons, and kelp beds, recorded 18 5. Number of observations of cold water coral habitat 20 6. Number of observations of cold seep and hydrothermal vent habitat 22 7. Number of seamount observations 24 8. Number of large seamount areas 26 PART 5: SUMMARY 28 REFERENCES 29 ANNEX I: Potential data sources for the development of marine habitat extent indicators 33  PART 1. Objectives and report outline This report has been developed prepared by UNEP-WCMC, a by members of the Transboundary Water Assessment Project Programme (TWAP) Large Marine Ecosystems (LME) working group in order to recommend a set of indicators and associated methodologies for the assessment of marine habitat within Large Marine Ecosystems (LMEs), as defined by UNEP. The LME approach is aimed at assisting developing coastal countries to meet ecosystem-related targets, and the GEF recommends that ecosystem-based assessments and management strategies are implemented at the geographic scale of the LME. The main objectives of this chapter are as follows: To present the rationale for assessment of marine habitats, including their value in terms of ecosystem services and the broad range of current and emerging pressures to which they are exposed; To introduce the concept of indicators in relation to marine habitat extent, building on previous work undertaken by the Convention on Biological Diversitys 2010 Biodiversity Indicators Partnership (2010 BIP); To prioritize a number of critical marine habitats for inclusion in the TWAP-LME assessment based on a review of international conventions; To evaluate the challenges inherent in developing marine habitat indicators at the present time and to put forward recommendations on how to respond; To outline a methodology for the selection of indicators based on priority and emerging issues and available data for marine habitats; and To propose a number of potential indicators based on the defined methodology, with detailed rationale for their submission. PART 2. The value of marine habitats The oceans cover nearly 71 percent of the Earths surface and provide more than 90 percent of the habitable area for life (UNEP, 2006). With their remarkable diversity of environments, the oceans and coasts host a myriad of habitats ranging from the inter-tidal to the deep seabed. These marine habitats provide a broad range of ecosystem services with direct and indirect benefits on human wellbeing, including: food provisioning, coastline protection from weather and erosion events, recreation and tourism, water filtration, and soil formation (Nellemann et al. 2009; UNEP, 2008). In light of exponential human population growth, these valuable habitats are increasingly under threat and thus it is of vital importance that global indicators are developed to facilitate their effective assessment and monitoring to inform their sustainable ecosystem-based management. Coastal ecosystems form an extensive and highly biologically productive landscape that includes coral reefs, beaches and dunes, estuaries, lagoons, marshes, mangrove forests, and seagrass beds. Approximately half of the worlds cities with more than half a million inhabitants lie within 50 kilometers of the coast and represent direct beneficiaries of the ecosystem services provided (TEEB, 2009a; Agardy & Alder, 2005). The value of these ecosystem services, particularly for tropical coastal ecosystems, have been attributed high economic worth with annual per hectare values of US$4,290 for mangroves; US$73,900 for estuaries, lagoons and seagrass; and US$129,000 for coral reefs which, as home to between 1-3 million species, are among the most biodiverse and economically valuable of all ecosystems (Allsopp et al. 2009; TEEB, 2009a; TEEB, 2009b). In Hawaii alone, reef-related tourism and fishing generates $360 million per year (TNC, 2008). Sandy beaches and dunes provide both a natural buffer to coastal habitats and a source of recreation, as well as a number of indirect benefits including the provisioning of habitat for estuarine species and water quality services through water filtration, nutrient cycling, nutrient uptake and water storage (CCRM, 2009). Similarly positioned at the interface between land and sea, tidal marshes are uniquely suited to provide ecosystem services associated with waste treatment, biological productivity, and disturbance regulation (Craft et al. 2009). Moving further offshore, habitats become less familiar yet still play an essential role in maintaining the healthy functioning of the global ecosystem. Many deep sea habitats, such as cold water corals and sponge beds, are highly structural providing potentially vast three-dimensional secondary habitat, including feeding and nursery grounds, for a wide diversity of organisms including commercial fish and shellfish species, such as juvenile rockfish, cod, and ling (UNEP, 2008; UNEP, 2006). Some studies in the North Atlantic have found up to twice as many species in the vicinity of sponge fields than on the surrounding seabed (Levin & Dayton, 2009; UNEP, 2006). Seamounts, or underwater mountains formed through volcanic activity, exhibit a range of environmental conditions suitable for a number of other secondary habitats to form including cold-water corals, sponge beds, and hydrothermal vent communities. Some species of cold-water coral can form lush thickets or forests on seamounts or the seabed, generally in areas of strong current flow (UNEP, 2006). Seamounts also interact with the water column to induce localized areas of high productivity which results in aggregation sites for a wide range of fish, including commercial species, and large marine fauna (Morato et al., 2010; Corrigan & Kershaw, 2008). Cold seeps and hydrothermal vents are deep sea habitats that depend on chemosynthesis rather than photosynthesis for energy and food production, where communities of bacteria and microbes fix inorganic molecules in the sulphur-rich emissions into organic carbon and nutrients (Fulweiler, 2009; UNEP, 2006). The majority of species discovered in these habitats are new to science and over 75% are endemic to a single site. This has attracted interest from commercial bioprospectors searching for compounds to be used in a range of products, from pharmaceuticals to anti-freeze (UNEP, 2006). Recent research has also brought to light that their host communities play an important role in nitrogen fixation, delivering biologically useable nitrogen to deep-sea sediments and providing a link between the carbon, nitrogen and sulphur cycles (Dekas et al. 2009). The biodiversity value of marine resources, especially deep sea resources, is considered under-researched (TEEB, 2008) and there are still significant gaps in the scientific and valuation literature (TEEB, 2009a). It can therefore be considered that any estimations of the economic value of marine habitats to date, from coastal regions to the deep sea, will be an under-representation of the true value that these ecosystems represent to human health and well-being. 2.1 Threats to marine habitats The high value of the marine habitats discussed in section 2.1 is dependent on diverse and functioning systems. The oceans are being subjected to ever increasing pressure that is threatening the viability of theand sustained delivery of marine related ecosystem services. High diversity and abundance of marine life enhances the resilience of the oceans to these external perturbations and threats, increasing the potential for their sustainability (UNEP, 2006). The high value of the marine habitats discussed in section 2.1 is therefore dependent on diverse and functioning systems. Five major stressors on marine ecosystems were identified by Nellemann and colleagues in 2008: climate change, fragmentation and habitat loss, over-harvesting from fisheries, pollution (primarily coastal), and invasive species. Crucially, climate change has the potential to exacerbate impacts of other stressors further impairing ecosystem resilience (UNEP, 2008). a. Climate changevariability and change Sea level rise represents one of the most tangible threats posed byimpacts of anthropogenic climate change. In terms of its impacts on coastal habitats, coastal erosion is increasing and leading to losses in extent of coastal habitats as they become submerged or migrate landward, replacing freshwater and brackish systems (UNEP, 2008; Craft et al. 2006). Ecological modeling has demonstrated significant changes in habitat coverage., fFor example, simulations undertaken along the Georgia Coast in the United States using the IPCC mean and maximum estimates of sea level rise for the year 2100, suggest that salt marshes will decline in area by 20% and 45% respectively in this region. The area of tidal freshwater marshes will increase by 2% under a mean scenario, but decline by 39% under the maximum scenario (Craft et al. 2009). As the oceans warm, rises in sea watersurface temperature can result in catastrophic coral bleaching events, where the range of temperature tolerance of the corals symbiotic zooxanthellae is exceeded and they are expelled from the coral resulting in mass mortality. Regeneration of these coral communitiesCoral regeneration is hindered by human activities, such as overfishing or destructive fishing practices, poor water quality, and sedimentation, and in areas of significant human impact recovery from a mass bleaching event may be entirely absent (TEEB, 2009a). Coral reefs are estimated to have lost approximately 20 percent of their original area (Valiela et al. 2001) and a further 15 percent of coral reefs are seriously threatened with loss in the next two decades (Wilkinson, 2008). While the impact of sea water temperature extremes on corals are visibly detectable, . Tthese changes in the environmental conditions are also likely to be having invisible eaffects on the physiology and behavioral ecology of a broad range of other species groups. b. Fragmentation and Habitat loss Humans have historically populated the coastlines and altered and developed them to best suit their needs, making good use of the services, accessibility, and high productivity which that coastal ecosystems provide. However, as populations continue to expand, the utilization of the coastline through exploitation, land reclamation, coastal construction and engineering, and contamination/pollution (see section d)pollution, has become unsustainable and many habitats have become degraded and fragmented meaning their ecosystems services are no longer sufficient for many coastal communities (Gedan et al. 2009). This is likely to be compounded by ongoing concentration (and growth?) of human population within the coastal zone. In addition to the significant losses experienced by coral reefs, at least 35 percent of mangroves and 30 percent of seagrass have been lost globally in the last two decades (Waycott et al. 2009; Valiela et al. 2001). There is increasing recognition of the importance of the interdependence between marine and coastal ecosystems, with pathways and processes that generate ecosystem services flowing from one habitat to another, and the role of these linkages in overall ecosystem function. Habitat fragmentation may sever these links, disrupting the provision of ecosystem services by a marine habitat which occurs downstream of the habitat being directly impacted (Silvestri & Kershaw, 2010). c. Overfishing and destructive fishing practices Fishing activities are the most pressing direct threat to open ocean and deep seabed biodiversity (UNEP, 2006) and also have significant impacts on coastal and nearshore habitats. Many fishing operations have serious physical and biological impacts but bottom trawling is deemed the most damaging to seabed and seamount habitats (Clark & Koslow, 2007). Studies have suggested that the impact of bottom trawling equals or exceeds the impact of all other types of fishing combined (Eastwood et al. 2007) and this type of fishing is likely to increase in coming years as deep sea fish stocks within national jurisdiction are depleted and/or increasing restrictions are placed on them (UNEP, 2006). Deep seabed habitats are particularly vulnerable to damage as they are often large, fragile and long-lived (Probert et al. 1997), thus exhibiting less resilience to human disturbance and slower recovery rates than habitats in shallower waters (Clark et al. 2010). Seamounts, long known by fishermen as aggregation sites for commercial fish species, have been particularly targeted by bottom trawlers, a behavior enabled by advances in fishing technology (Clark et al. 2010). Catch and effort levels, and associated impacts, can be much greater and more concentrated on seamounts than on the continental slope where effort is spread over larger areas (Clark et al. 2010). Framework-building habitats, such as scleractinian corals, can form major components of the benthic community composition of seamounts (OHara et al. 2008) and losses of up to 98 percent of seamount coral cover as a result of deep sea bottom trawling have been reported (UNEP, 2006). In contrast to this large-scale, industrial threat, tropical coral reefs are also endangered by locally-driven, small scale destructive fishing practices, such as blast fishing, where dynamite or other explosive are used to kill or stun fish, and muroami fishing, where weighted nets are used to pound the reef to startle and herd reef fish, breaking live coral in the process (McClellan & Bruno, 2008). Blast fishing occurs as a result of the socioeconomic conditions of a region, seen as a last resort by most fishers to sustain their livelihood, and is facilitated by coral reef occurrence in tropical areas where poverty levels are high (Pet-Soede et al. 1999). Corals are slow to recover from such severe levels of physical damage even if the site is then protected from further harm, and is also likely to be hampered by other external anthropogenic stressors prevalent in coastal ecosystems (Fox et al. 2003). Shellfish reefs represent another coastal habitat that are under serious threat from intensive fishing efforts, exacerbated by other factors such as coastal degradation and predicted ocean acidification. Oyster and other shellfish reefs are near or past the point of functional extinction worldwide and can be considered the most imperiled marine habitat on earth, with oyster reefs suffering a 90 percent decline from historic levels and functional extinction (more than 99 percent loss) in 37 percent of estuaries and 28 percent of ecoregions (UNEP/CBD/SBSTTA/14/4, 2010). d. Marine pollution Marine pollutants have a wide range of sources, including land-based activities, oil spills, untreated sewage and agricultural run-off, heavy metals, and persistent organic pollutants (POPs), among others. Of these, agricultural, urban and industrial run-off is possibly the most significant threat to coastal habitats; nutrient enrichment leads to large-scale areas of eutrophication contributing toward toxic algal blooms and hypoxic dead zones in coastal regions, which also affect the deeper margin both directly and through nutrient transfer (Levin & Dayton, 2009; Diaz & Rosenberg, 2008). Evidence also indicates that a reduction in water quality leads to increases in marine disease, resulting in yet further habitat degradation (Hall-Spencer et al. 2007). Some 8 million items of marine litter are being dumped in the oceans daily, accumulating to 6.4 million tonnes per year. Over 48,000 pieces of plastic litter are floating on every square kilometer of the ocean, causing gradual and permanent build-up in all aspects of the marine and coastal environment (UNEP/OSPAR, 2009). Lost or abandoned fishing gear and other industrial debris can also smother or fracture seabed habitats (UNEP, 2006). e. Invasive species The number and severity of outbreaks and infestations of invasive species is growing, and invasions of marine habitats are occurring at an alarming rate (Ruiz et al. 1997) and have been documented to occur in 84 percent of the worlds marine ecoregions (Molnar et al. 2008). Shipping has been strongly linked to invasive species dispersal routes, and is therefore identified as the primary introduction factor. However, establishment of invasive appears to be linked to overall ecosystem health, and is strongly concurrent with fishing intensity, bottom-trawling, pollution and other stressors (UNEP, 2008). Invasive species can have severe detrimental impacts on marine habitats. A famous example is the zebra mussel, Dreissena polymorpha, native to the Black Sea and introduced to western and northern Europe, and eastern regions of North America. Individuals congregate in large clumps, encrusting all available hard substrate and displacing native aquatic life and alter habitat structure (GISP, 2008). Diseases? E.g. Coral diseases should also be included. Incidence of diseases should be considered for inclusion as an indicator 2.2 Emerging issues There are also a number of emerging threats to marine habitats as a result of unprecedented human population levels and anthropogenic climate change. There is little known about the nature of these threats or how marine ecosystems and habitats may respond to them. It is therefore important that reliable baselines for the current state of marine habitats are formed to ensure environmental impacts can be effectively monitored and assessed. A number of emerging threats that have been identified by the marine science community are discussed below: a. Acidification The ocean plays a critical role in the global carbon cycle, absorbing approximately one quarter of the carbon dioxide emitted to the atmosphere from human activity (Secretariat of the Convention on Biological Diversity, 2009a). The high levels absorbed by the oceans at ever increasing rates has resulted in changes to their chemical balance, naturally slightly alkaline, causing them to become more acidic (Doney et al. 2009; Canadell et al. 2007). Current research shows that ocean acidity has increased by 30 percent since the beginning of the industrial revolution 250 years ago (The Royal Society, 2005) and is predicted to continue to increase by 0.5-0.1 percent per year throughout the 21st century (Guinotte & Fabry, 2009; Secretariat of the Convention on Biological Diversity, 2009a; Kleypas et al. 2006). Increasing ocean acidification reduces the availability of carbonate minerals in seawater, important building blocks for marine plants and animals, including coral habitats formed through the deposition of a calcium carbonate skeleton (Secretariat of the Convention on Biological Diversity, 2009a). 70 percent of cold water corals are predicted to be exposed to corrosive waters by 2100 and tropical reefs are also expected to experience rapid declines in carbonate ions, reducing rates of net warm water coral reef accretion and leaving biologically diverse reefs outpaced by bioerosion and sea level rise (Secretariat of the Convention on Biological Diversity, 2009a). b. Carbon storage in the oceans As part of a portfolio of climate mitigation measures, scientists are attempting a variety of experiments to increase the potential of the ocean to absorb CO2. Ocean fertilization involves the addition of dissolved iron to specific areas to increase phytoplankton growth and thus increase the absorption of CO2. These phytoplankton blooms will ultimately be deposited on the seabed, altering marine food webs and potentially suffocating seabed habitats through oxygen depletion (UNEP, 2006). A recent scientific synthesis of the impacts of ocean fertilization on marine biodiversity undertaken by the Secretariat of the Convention on Biological Diversity concluded that, despite the amount of data and literature available on ocean fertilization, sound and objectively verifiable scientific data on the impacts of ocean fertilization on marine biodiversity are scarce (Secretariat of the Convention on Biological Diversity, 2009b). Despite the unknown dangers to both the environment and human health, there are currently no internationally agreed guidelines in place to govern experimental or industrial scale activities (UNEP, 2006). Direct CO2 storage injection into deep waters is another carbon storage option which is already feasible with existing technology. Artificially CO2 enriched water increases in density and sinks into cooler, slow moving waters deep in the ocean. As with ocean acidification, the addition of CO2 will increase ocean acidity and have the similar detrimental effects on calcareous habitats. It may also disrupt marine microbial processes and cause widespread degradation of the seafloor, causing all organisms underneath and within the vicinity of the high CO2 layer to perish (UNEP, 2006). Comparably, geological storage of carbon involves injecting unwanted CO2 into disused oil wells and saline aquifers. The risk of leakage from the seafloor into deep ocean waters is significant and drilling operations will require extreme care to prevent and monitor this hazard (UNEP, 2006). c. Deep seabed mineral deposits Underwater Seabed mining is emerging as a significant future ocean industry, responding to ever increasing global markets (Hoagland et al. 2010). Large-scale mining of manganese, metals, seafloor massive sulphides (SMS), metals, and other mineral from seamounts and the deep seabed may soon be commercially feasible and possibly common practice (Hoagland et al. 2010; Levin & Dayton, 2009). Little research has been undertaken to explore the effects of mining on seabed habitats, but direct physical disturbance and sediment plume generation have been likened to trawling effects (Clark et al. 2010), and it is speculated that mining activity may also disturb hydrothermal vent circulation systems (Hoagland et al. 2010). Indirect implications for marine habitats also include disturbance to ecosystem function, connectivity and persistence of endemic species (Levin & Dayton, 2009). d. Gas hydrates It is hypothesized that gas hydrates may be the largest reservoirs of carbon on earth, existing in crystalline form in a matrix of sediment and ice along continental shelves, slopes and margins to 2,000 meters depth (UNEP, 2006). There is increasing pressure to attempt to mine these resources as traditional sources of carbon from fossil fuels continue to be depleted. It is thought that gas hydrates may play a key role in maintaining seafloor stability and so their removal could trigger massive slides of underwater sediments posing the threat of both tsunamis, directly damaging coastal habitats, and thend enormous uncontrolled releases of methane, exacerbating climate change impacts, ocean acidification and sea level rise (UNEP, 2006). e. Disease Over the last 30 years there has been a rapid emergence of diseases in the marine environment affecting all major groups of organisms and humans. Disease outbreaks may alter the structure and function of marine ecosystems (Ward & Lafferty, 2004) in ways which impact service provision and human health. In terms of marine habitats, diseases can alter community structure and associated food webs, physically change habitats and their productivity, and cause extinction of species. Diseases affecting coral reefs have increased in frequency and severity in recent decades and combine with existing human-induced impacts to compromise their health and sustainability (GCDD, 2010). Thus, anthropogenic disturbances can increase the risk of an organism acquiring a disease directly, or indirectly through their impact on the biodiversity of infectious agents, reservoirs, and vectors (Chivian, 2002). Through this mechanism, diseases serve as indicators of declining ecological integrity in coastal marine systems (Heed, 1998). PART 3. Marine Habitats as Indicators Part 2 highlights the value of marine habitats in terms of their benefits and services to ecosystem function, resilience, and ultimately human health and well-being, and also makes clear the significant number and range of threats they face which that jeopardizesplaces their ability to continue theis continuation of this service provision in jeopardy. In order to realize successful ecosystem-based management of these habitats, to secure the sustainable provision of their ecosystem services, there is an urgent need to establish a baselines by which to measure and monitor environmental change, and indeed management intervention success or failure. Trend analysis on the global extent and quality of different biomes, ecosystems, and habitat-types is an essential component of understanding the status and trends of biodiversity as a whole, and for assessing and modeling the status of threatened species or populations (2010 BIP, 2010). This information is fundamental to ensuring that emerging environmental problems are given adequate consideration by policy and decision makers (UNEP, 2003). The ability to measure and quantify change in habitat extent and quality at the scale of large marine ecosystems, or even globally, can be made possible by the use of indicators. An indicator framework has been utilized by the Convention on Biological Diversity to assess international progress toward the 2010 Biodiversity Target, to achieve by 2010 a significant reduction of the current rate of biodiversity loss at the global, regional and national level... The Convention on Biological Diversity represents only one of the many conventions established by the international community which is concerned with the protection of critical marine habitats, i.e. those which provide essential ecosystem services and are also seriously threatened. As a basis for this chapter, a number of critical habitats have been identified through the review of biodiversity- and marine-related international conventions, and we recommend that they be adopted here as indicators for the TWAP-LME assessment, recognizing that doing so would still leave gaps in terms of ensuring total coverage of LME area. Table 1 represents the outcome of this work, illustrating which conventions are concerned with each critical habitat type. Table 1: Critical habitats identified through international conventions HabitatConventionCBDMEAWSSDRAMSARRamsarIMOCoral reefsxxxxxMangrovesxxxxxSeagrassesxxxxxBeaches and dunesxDeltasxEstuariesxxTidal/mud flatsxxxKelp forestsxxLagoonsxxSaltmarshesxx NOTEREF _Ref260213694 \h \* MERGEFORMAT 1Shellfish reefsxCold water coralsxCold seepsxHydrothermal ventsxSeamountsxSponge reefsx 3.1 Challenges The mapping of marine habitats has been identified as one of the first steps towards ecosystem-based management (Cogan et al. 2009), however it is becoming increasingly apparent that currently, the data sets required to underpin these large-scale assessments are of insufficient quantity and quality, and that there is a significant need to collaboratively integrate existing data with new information collected through standardized methodologies spanning multiple sectors in order to develop robust indicators of change. There are a number of challenges in developing global indicators for the assessment of marine habitats in the context of Large Marine Ecosystems, tThe majority of these challenges which stem from a general lack of available, standardized, and validated data for many habitats at the global and regional scales. Table 2 summarizes these challenges using a hierarchical framework where data availability, indicated by the arrow and associated score, declines through the list. Spatial extent Spatial resolution Habitat quality/condition Temporal resolution Table 2: Hierarchical framework of data availability gaps in marine habitat data ScoreData availability gapDescription1Spatial extentGeographic coverage of data: the majority of data sets do not provide comprehensive global coverage, with biases in the location of research and reporting. Additionally many reporting entities represent small-scale initiatives with no comparable data collection/reporting methodology.2 Spatial resolution Inconsistencies in resolution of data: global data, often derived from satellite or remote sensing techniques, tends to be relatively coarse with a higher probability of error. Data derived through small-scale activities tends to be much higher-resolution with lower probability of error, but is not conducted at the scale appropriate for global/regional assessment and international decision-making.3Habitat quality/conditionHabitat presence/absence (and therefore extent) data does not provide information on the quality or condition of the habitat being measured which would provide a more accurate method to assess ecosystem health. Studies to assess habitat condition are generally conducted on a local scale and the data are not typically integrated into broad-scale, global assessments. Examples include the percentage of diseased colonies on a coral reef, or the tree diversity of a mangrove forest. 4Temporal resolutionInconsistencies and evolution in data collection methodologies as well as time lags in data reporting have resulted in time series data being almost non-existent for marine habitat extent. It is critical to establish validated baselines and develop approaches which will allow the timely compilation of data in order to measure change in habitat extent over time. Spatial extent There is generally poor coverage of habitat data within LMEs globally and there are heavy biases towards particular habitats which preventimpede the ability to establish a comprehensive overview of current state and emerging trends. Only 0.0001 percent of the deep seafloor has been subject to biological investigations and it is generally agreed that mankind has more knowledge of the surface of the moon than the deep ocean (UNEP, 2006); the first hydrothermal vent was discovered eight years after man first set foot on the moon (UNEP, 2006). Deep sea habitats pose particular a challenge in relationdue to their relative inaccessibility; surveys are costly and require specialized equipment such as ROVs and submersibles, and someaning their implementation on large scales is often impractical. As an example, sSeamounts deeper than 2000 meters cant be easily sampled by trawls and thus the summits of seamounts are much more intensively researched (Clark et al. 2010; Stocks, 2009). The high resource requirements of deep sea research also results in geographic biases, with only developed nations being able to carry outprioritize these activitiesis research. Coastal habitats, in contrast, have received much more attention due to their natural accessibility and thus more cost-efficient research methods relative to areas further offshore. However, the majority of research has been conducted on charismatic habitats in tropical regions compared to temperate areas; 60 percent of all published research has been carried out on coral reefs, compared to 11-14 percent for each of salt marshes, mangrove forests and seagrass meadows (Duarte et al. 2008). These biases have led to significant gaps in data coverage for habitats such as beaches and dunes, tidal flats and salt marshes. For an indicator to accurately measure change, the data being used needs to be comparable across its entire extent. If this is not the case, it cannot be determined if any trend measured is actually due to a change in habitat or whether the result is an artifact of different sampling techniques. With the majority of data historically being derived at the national level or via small-scale NGO-led initiatives, there has to date been little coordination and standardization of data collection and reporting methodologies. Robust methods of integrating this existing information to provide standardized reporting at the regional and global scales are needed, and the development of internationally agreed frameworks and guidelines for future data collection is essential. A number of high-resolution habitat mapping technologies are becoming increasingly used over wide areas to provide information on habitat extent and in some cases habitat quality (Brock & Purkis, 2009), including multi-beam side scan sonar, airborne light detection and ranging (LiDAR), and certain types of habitat remote sensing (Kaplan et al. 2010). While representing significant advances in marine habitat mapping, the majority of these techniques whilst applicable to coastal submarine areas, are reliant on shallow, clear waters amenable to optical mapping techniques (Brock & Purkis, 2009). These constraints may further increase bias toward tropical regions, as they not only exclude deep-sea habitats but also those which reside in the turbid waters of temperate regions, for which there is still remains no large-scale accurate mapping methodology available. The emerging field of predictive habitat modeling (e.g. Dawson et al. 2009; Guinan et al. 2009; Guinotte et al. 2009; Tittensor et al. 2009) presents an approach for addressing these data gaps. There a many different modeling approaches that can be employed, of which maximum entropy modeling (Maxent; Philips et al. 2006) has proven a particularly robust methodology. The employment of habitat modeling to improve existing marine habitat data sets represents an important future area of work. Predictive modeling techniques may be used here. For an indicator to accurately measure change, the data being used needs to be comparable across its entire extent. If this is not the case, it cannot be determined if any trend measured is actually due to a change in habitat or whether the result is an artifact of different sampling techniques. With the majority of data historically being derived at the national level or via small-scale NGO-led initiatives, there has to date been little coordination and standardization of data collection and reporting methodologies. Robust methods of integrating this existing information to provide standardized reporting at the regional and global scales are needed, and the development of internationally agreed frameworks and guidelines for future data collection is essential. Spatial resolution Spatial scale is a primary consideration when interpreting the results of any study, especially in the marine environment where influencing factors take place at a multitude of scales, from the environmental conditions of a specific micro-habitat through to the global process of ocean circulation. For example, wWarm water coral habitat may appear the dominant habitat type within a 10 square kilometer study area, but at the 100 square kilometer scale they may in fact only represent a minor contribution to overall habitat coverage. Similarly, the scales at which international policy decisions are made are often of little relevance to those dealing directly with habitat management on the ground. Global datasets are often of a coarse resolution with relatively high rates of error, able to provide general insights and to highlight areas for of interest to be prioritized for future research, whilst remaining unsuitable for informing management decisions. Conversely, scales at which managers and field-workers operate are often of too fine a resolution to be of relevance to the global decision making processes., often describing a single area in detail resulting in data that is not necessarily transferable to the same habitat type in other geographic areas. As discussed above, the data sets which are available for marine habitats form a disparate resource resulting from a range of sampling methods taking place over a variety of spatial resolutions, mainly chosen as being the most appropriate for meeting the goals of that individual project at that time. This adds technical complexity to the task of consolidating this information into a comparable data set for the global assessment of Large Marine Ecosystems (LMEs), with implications for the validity and interpretation of results, as well as the time and resources required to undertake the analysis. Future data collection and habitat mapping efforts should aim to benefit both small- and large-scale decision-making processes via a multi-scale approach. The ability to aggregate and disaggregate data over a range of hierarchical scales would provide a comprehensive information base for all members of the marine community, encouraging participation and collaboration across different sectors in its development. Habitat quality/condition It is much more feasible to assess changes in habitat extent at large scales, such as for example through remote sensing techniques, than it is to assess changes in the quality of habitat in a given area. For example, over a ten year period, an area of mangrove forest may appear to remain unchanged in terms of spatial extent, however the diversity of tree species within the area may be greatly reduced, meaning that the habitats capacity for service provision may have become significantly degraded. The quantification of these qualitative changes in habitat is often impossible via using techniques such as remote sensing indirect techniques and requires on- the- ground field studies. This revisits the above-described issues of creating global data sets and indicators from small-scale studies that are not necessarily representative or comparable over larger areas. Some initiatives are working to tackle this issue. Coral disease incidence is generally accepted as a measure of coral reef health, with functioning reefs more able to resist infection than those that have been degraded by other stressors such as low water quality and rises in sea surface temperature. The Global Coral Disease Database (GCDD) has been developed by UNEP-WCMC in partnership with NOAA-NMFS and stores observations of coral diseases reported since the 1970s. This data suffers from the typical caveats described here, including lack of standardization in data collection methods and the naming of diseases and coral species, as well as the inability to validate specific records. UNEP-WCMC and NOAA are addressing these issues through the development of a reporting form, formulated in conjunction with coral disease experts, containing standardized fields with which all new coral disease observations submitted to the database must comply. Through this approach, a globally comparable indicator of coral reef health can be developed. Building on the above example, the continued development of agreed global standards and related reporting processes will contribute significantly to overcoming these challenges, and should be positioned as a high priority activity on the international assessment agenda. Additionally, the majority of past research has treated marine habitats as isolated systems and not fully taken their interdependence and connectivity into account. For example, an increase in the extent of saltmarsh does not necessarily indicate a healthy ecosystem but may be related to a reduction in an adjacent habitat. This single-system paradigm is now changing and the necessity to take a more holistic, ecosystem- based approach to assessment and management whichthat recognizes that ecosystem service provision is highly dependent onf the flow of these services from one habitat to another, is increasingly acknowledged (Silvestri & Kershaw, 2010). In spite of this evolution in attitude, such as that being undertakenas demonstrated by the Transboundary Waters Assessment Project, it will be some time until this is filtered down to available marine habitat data. Temporal resolution Data with a temporal dimension is considered in this chapter as one of the most significant gaps in available habitat data. Consistent data sets which are comparable over time are essential to developing indicators that can assess trends in environmental change, a need which corresponds directly to the international policy realm, in particular the Convention on Biological Diversitys 2010 target to reduce biodiversity loss. Converse to common perceptions, there have been a number of long-running initiatives to collect marine time-series data, the most famous of which is perhaps Sir Alistair Hardys (1896-1985) efforts to establish the Continuous Plankton Recorder (CPR) Survey in 1931 and which continues today as one of the longest records of sustained ocean observations (Ducklow et al. 2009; Hardy, 1935). However, these initiatives have not been systematically founded and as such currently provide an exceedingly fragmented portrait of change in the marine environment. Historically, time series data has been personality driven and taxonomically focused;. eExperts setting upestablishing long-term monitoring programs centered upon species that were of particular interest, which would often ending in conjunction with the experts career. Even today, much of the research being undertaken in the marine realm is taxonomically driven, in part due to the disproportionate lack of knowledge of marine compared to terrestrial ecosystems, and in part due to traditional academic constructs; it is much easierless difficult to become highly specialized in a particular species or taxa than it is to study and understand the entirety of the components of the ecosystem in which it resides. The paradigm shift toward ecosystem-based management corresponds to a shift toward collaboration between academic institutions, as well as government bodies, NGOs, and even the private sector, in order to consolidate expertise and gain holistic understanding of ecosystems to the subsequent benefit of all contributors. Establishing current baselines against which to measure trends in environmental change is of high priority for the majority of global and regional assessment initiatives, however, in doing so it is of great importance that existing data and knowledge is built upon rather than discarded in favor of a new data collection model. Considerable effort should be placed on reviewing historical data and developing methodologies for its integration with present-day data collection and management techniques in order to ensure that indicator development for baseline establishment, and ultimately analysis of trends, is building on as many previous efforts as possible. Validation A number of organizations have compiled global data sets for marine habitats from multiple data sources submitted by numerous reporting entities. In spite of the challenges outlined in points a-d, these data sets are considered some of the best available at the current time. Ideally, such data sets would be validated through ground-truthing, for example using surveys, in order to verify whether that habitat does in fact exist at the location identified by the reporting entity or not. Unfortunately, these types of validation processes are often not feasible due to costs, labor requirements, and time constraints. To ensure the most reliable data possible is being made available, these multiple-source data sets should continue be subject to standard expert- and peer-review processes, and the precautionary approach should, as always, be adopted in any decision-making process. Use of technologies such as GPS/spatial data Due to the data gaps for marine habitat indicators being so extensive in relation to the area covered by LMEs, we provide a detailed discussion of these data gaps and caveats, as well as priorities for future indicator development and associated data needs, in Annex 2 with the view to supporting the longer term objectives of the TWAP/LME assessment and international biodiversity conventions. Perhaps include something on the different classification schemes here under challenges as discussed. Related to this, we are limiting our discussions here to a limited number of habitats within the benthic zones and some coastal habitat, and we are not discussing anything to do with pelagic zones and habitats - perhaps we ought to specify why - and this may provide a cross over point with the open oceans group? 3.2 Methodology Reflecting on the challenges identified above and the evidence presented in Annex 2in Part 3.1, we conclude that the most feasible large-scale indicators for marine habitats in light of data availability are currently extent-based measures, i.e. related to the area covered by a particular habitat. This recommendation is supported by the previous work undertaken by the 2010 Biodiversity Indicator Partnership (2010 BIP) which prioritized the development of an extent of assorted habitats indicator to assess global progress toward the Convention on Biological Diversitys target to reduce biodiversity loss by the year 2010. This indicator is still in development and currently focuses on tropical ecosystems, namely coral reefs, mangroves and seagrass beds (2010 BIP, 2010). Spatial datasets are most applicable for assessing changes in habitat extent, facilitating relatively simple overlay analysis using GIS, the results of which can be aggregated at a range of scales, including the level of LMEs, and also enabling the simple visualization of results through maps and graphics. Non-spatial data and more qualitative research can be used to add value to these spatial layers by assisting the interpretation of findings. Table 2: Hierarchical framework of data availability gaps in marine habitat data ScoreData availability gapDescription1Spatial extentGeographic coverage of data: the majority of data sets do not provide comprehensive global coverage, with biases in the location of research and reporting. Additionally many reporting entities represent small-scale initiatives with no comparable data collection/reporting methodology.2 3Spatial resolution Classification schemesInconsistencies in resolution of data: global data, often derived from satellite or remote sensing techniques, tends to be relatively coarse wither a higher probability of error. Data derived through small-scale activities tends to be much higher-resolution with lower probability of error, but is not conducted at the scale appropriate for global/regional assessment and international decision-making.43Indicators of hHabitat quality/conditiondegradationHabitat presence/absence (and therefore extent) data does not provide information on the quality or condition of the habitat being measured which would provide a more accurate method to assessassessment of overall ecosystem health. Studies to assess habitat condition are generally conducted on a local scale and the data are not typically integrated into broad-scale, global assessments. Examples include the percentage of diseased colonies on a coral reef, or the tree diversity of a mangrove forest. Studies which undertake assessments of habitat condition are generally very localized/ fine-scale and cannot be integrated into broad-scale, global indicators.54Temporal resolutionInconsistencies and evolution in data collection methodologies as well as time lags in data reporting have resulted in time series data being almost non-existent for marine habitat extent. It is critical to establish validated baselines and develop approaches which will allow the timely compilation of data in order to measure change in habitat extent over time. We also propose maintaining a global focus for indicator development. Whilst an extensive number of regional and national initiatives exist and can provide important insights into the indicator development process, an emphasis at the global scale will reduce the possibility of inconsistency in results and conclusions in the assessment and comparison of LMELarge Marine Ecosystems. Prioritizing global data sets will also further underline the urgent need for an international data partnership, promoting inter-organizational cooperation in response to the inherent gaps and caveats outlined in this chapter. Based uponIn theconsideration of the current threats and emerging issues identified in Sections 2.1 and 2.2se recommendations, we undertook an extensive review of available global data sets which that may provide a useful basis for developing indicators of marine habitat extent,. the results of which can be found in Annex 1 detailing 17 data sets and their associated data partners. As part of this review, we assessed the data sets via a number of preliminary parameters related to their applicability and validity for inclusion as indicators in the TWAP-LME assessment. These parameters included an examination of their methodological approach, spatial and temporal resolutions, validation procedures, and any fundamental limitations or uncertainties which may influence their validity as indicators. Of the initial 197 data sets identified, Through this process a subset of eightnine data sets were identified (see Table 3) as highly suitable for further development as indicators, and are discussed in more detail in Part 43 of this chapter. These eight data sets were recognized as having the four essential properties: Global coverage; Spatial data; Publically availabileity; and a Validation process. Details of the remaining 101 data sets identified through the review process but which need not meet the selection criteria can be found in Annex 1 . Table 3 provides an overview of the data sets included in Part III. Table 3: Overview of data sets for which indicator templates were completed in Part 43 Template No.Organization(s)IndicatorsIndicator category1UNEP-WCMC (compiled from multiple sources)Global ePercentage of global extent of warm water coral habitat (%km2)State2UNEP-WCMC; International Society for Mangrove Ecosystems (ISME)Percentage of global eExtent of mangrove habitat (km2)State3UNEP-WCMC; Dr Frederick T. Short (Univ. New Hampshire, USA)Percentage of global eExtent of seagrass habitat (km2)State4Ramsar Secretariat; Wetlands International; Center for International Earth Science Information Network (CIESIN)Number of Ramsar sites with estuarine waters, tidal/mud flats, lagoons, and kelp beds, recordedState; Response5UNEP-WCMC; A. Friewald, Alex Rogers, Jason Hall-Spencer and other contributorsNumber of observations of cold water coral habitatState6Census of Marine Life (CoML); CHEss project members: National Oceanography Centre, Southampton, UK; Woods Hole Oceanographic Institute (WHOI), USA; Institut de Cincies del Mar, Spain.Number of observations of cold seep and hydrothermal vent habitatState76Census of Marine Life (CoML) CHEss project membersNumber of observations of cold seep and hydrothermal vent habitatState87Census of Marine Life (CoML) Censeam project membersNumber of seamount observationsState9UNEP-WCMC; IUCN.Percentage habitat covered by a Protected AreaResponse PART 4. Indicator templates 1. Global eExtent of warm water coral habitat (km2) Indicator Indicator NameGlobal Eextent of warm water coral habitat. Category (Pressure-State-Impact-ResponseStateDefinition of indicator / DescriptorEGlobal extent (area) of warm water coral habitat providing measurement of current state.Units of measurements (spatial and temporal)1km2 Raster dataset Indication of presencet state / absence no time series componentRelevance Rationale for Inclusion (why shortlisted)Tropical coral reef habitats have been identified as one of the most valuable on Earth in terms of ecosystem service provision, yet they are under significant threat globally due to a wide range of pressures, including irreversible damage due to ocean acidification. Warm water corals are included in the following international framework(s): CBD; MEA; WSSD; Ramsar; IMO (see Table 1) This indicator is derived from the most comprehensive global data set on warm water coral habitat extent currently available.Significance for inter-linkages with other water systemsWarm water coral reefs are closely linked and often functionally interdependent on the functioning of other tropical, coastal marine ecosystems including seagrass beds and mangrove forest. Linkage with other indicatorsGlobal eExtent of mangrove habitat; Global eExtent of seagrass habitat; Number of Ramsar sites with eEstuarine waters, Ttidal/mud flats, Llagoons, and Kkelp beds recorded.Suitability for inclusion in TWAPAdvantages: Considered the most comprehensive dataset currently available Global coverage Available for non-commercial use Spatial data available Disadvantages: In need of updating (update will be available in 2010) No time series Not globally ground-truthed Reporting biasesMethodologyDescription of measurement methods and calculation of the indicatorHierarchical approach used to map coral reef distribution, starting with a commercially available 1:1,000,000 global base map (Mundocart) which was produced through the digitization of information from Operational Navigation Charts (ONC). At too coarse a scale to accurately capture many reefs, a major data gathering exercise involving extensive evaluation by UNEP-WCMC and marine experts was undertaken, followed by digitization of data from numerous paper map sources. A wide range of data sources, digitized at various scales, were integrated. Many individuals and organizations contributed by providing their data in electronic form. The scale of input data sets ranges from 1: 1,000,000 to 1: 10,000.ScaleGlobal (tropical regions)Validation processesAlthough the UNEP-WCMC coral dataset is seen at present to be the best and most accurate global dataset of coral reefs in existence, there are limited resources available for systematic ground-truthing to take place. Limitations and uncertaintyBased on multiple data sources in various GIS formats and mapping software which can be challenging to harmonize together accurately. Largely dependent on voluntary reporting from countries and organizations; this may lead to inconsistencies in component data sets due to variation in methodologies, along with geographic biases in availability and quality of data. The formulation of time series data is challenging due to the range of methodologies used (and different time frames of data collection in different locations). Does not provide an indicator of habitat quality, e.g. live coral cover/coral disease prevalence.Assessment of DataData sources, availability and quality (Existing datasets)Global data layer has been compiled by UNEP-WCMC from multiple data sources. The data set is publicly available. Considered the best available data source for global warm water coral extent.Variations among data sources and alternative methodsBased on multiple data sources primarily representing national reporting entities and non-governmental organizations. Submitted data therefore represents a range of methodologies and data sources which are harmonized as far as possible in the global layer.5. PartnersPartners/agencies involved in the development of the indicator UNEP-World Conservation Monitoring Centre (compiled through contributions from a wide range of organizations). The 2003 data set described above is pending a new release in 2010 based upon the Millennium Coral Reefs Project. Partners for this updated layer are NOAA, NASA, UNEP-WCMC, University of South Florida and also the World Resources Institute. 6. ReferencesCitationUNEP-WCMC (2003) Coral reef 1km data in Raster format (V7.0, 2003), compiled for Spalding, M.D., Ravilious C., and Green E.P. (2001). World Atlas of Coral Reefs. Prepared at the UNEP World Conservation Monitoring Centre. University of California Press, Berkeley, USA.  2. Global eExtent of mangrove habitat (km2) 1. Indicator Indicator NameGlobal eExtent of mangrove habitatCategory (Pressure-State-Impact-ResponseStateDefinition of indicator / DescriptorGlobal eExtent (area) of mangrove habitat providing measurement of current state.Units of measurements (spatial and temporal)Shapefile dataset where data is attached to a standard 1:1000000 coastline (MundoCart) where possible. Indication of presence no time series componentIndication of present state no time series component2. Relevance Rationale for Inclusion (why shortlisted)Mangrove forests have been identified as extremely valuable in terms of their ecosystem services and benefits, supporting coastal populations directly through a wide array of timber and non-timber forest related products, as well as supporting adjacent seagrass and coral reef habitats via supporting and regulating services. Mangroves are currently under serious threat from deforestation and land-use change, as well as from anthropogenic climate change. Mangroves are included in the following international framework(s): CBD; MEA; WSSD; RAMSAR; IMO (see Table 1) This indicator is derived from the most comprehensive global data set on mangrove habitat extent currently available.Significance for inter-linkages with other water systemsMangrove forests are closely linked and interdependent on the functioning of other tropical, coastal marine ecosystems including seagrass beds and warm water coral reefs.Linkage with other indicatorsGlobal eExtent of warm water coral habitat; Global eExtent of seagrass habitat; Number of Ramsar sites with Eestuarine waters, Ttidal/mud flats, Llagoons, and Kkelp beds recorded.Suitability for inclusion in TWAPAdvantages: Considered the most comprehensive dataset currently available Global coverage Available for non-commercial use Spatial data available Disadvantages: In need of updating (FAO leading an update to be available in 2010) No time series Not ground-truthed Reporting biases3. MethodologyDescription of measurement methods and calculation of the indicatorMangrove data were mainly hand digitized at UNEP-WCMC from a wide range of sources at a wide range of scales. After digitization the data were manually edited to eliminate node-dangle errors and duplicate or sliver polygons. Data were matched to the MundoCart digital coastline database, except in cases where matching would cause serious loss of details and degrade the data. The data were cleaned and built using the smallest tolerances and was manually labeled and checked for label errors. Data which came in digital form were treated in the same manner as hand digitized maps for quality control purposes.ScaleGlobal (tropical regions)Validation processesAttribute accuracy has been verified by ensuring that all polygons have a vegetation attribute of either mangrove or non mangrove. All data must have an ISO3 country code. Visual comparisons with original source material through hard copy print outs was undertaken after the digitization/incorporation of every source, before incorporation into the global mangrove layer. Limitations and uncertaintyThis dataset has been mapped for the world to the best of current knowledge and data availability at the time of publication. Further revisions will be required as knowledge develops an improved data becomes available. Based on multiple data sources in various GIS formats and mapping software which can be challenging to harmonise accurately. Largely dependent on reporting from countries and organizations which may lead to errors and location bias. The formulation of time series data is challenging due to the range of methodologies used and reporting biases.4. Assessment of DataData sources, availability and quality (Existing datasets)Global data layer has been compiled by UNEP-WCMC from multiple data sources, in collaboration with the International Society for Mangrove Ecosystems. The data set is publicly available. Considered the best available data source for global mangrove extent.Variations among data sources and alternative methodsBased on multiple data sources primarily representing national reporting entities and non-governmental organizations. Submitted data therefore represents a range of methodologies and data sources which are harmonized as far as possible in the global layer.5. PartnersPartners/agencies involved in the development of the indicatorUNEP-World Conservation Monitoring Centre (compiled through contributions from a wide range of organizations); International Society for Mangrove Ecosystems (ISME). 6. ReferencesCitationUNEP-WCMC (1997) Mangrove GIS data in shapefile format (V3.0, 1997). Published in Spalding, M.D., Blasco, F. and Field, C.D. (Eds). 1997. "World Mangrove Atlas". The International Society for Mangrove Ecosystems, Okinawa, Japan. 178pp. 3. Extent of seagrass habitat (km2) 1. Indicator Indicator NameGlobal eExtent of seagrass habitatCategory (Pressure-State-Impact-ResponseStateDefinition of indicator / DescriptorGlobal eExtent (area) of seagrass habitat providing measurement of current state.Units of measurements (spatial and temporal)Source scale denominator: 1,000,000 Indication of presencet state no time series component2. Relevance Rationale for Inclusion (why shortlisted)Seagrass beds have been identified as extremely valuable in terms of their ecosystem services and benefits, supporting coastal populations directly through providing habitat for commercial fish species, as well as supporting adjacent mangrove and coral reef habitats via supporting and regulating services. Seagrasses are currently under serious threat from land conversion, as well as from anthropogenic climate change. Seagrasses are included in the following international framework(s): CBD; MEA; WSSD; RAMSAR; IMO (see Table 1) This indicator is derived from the most comprehensive global data set on seagrass habitat extent currently available.Significance for inter-linkages with other water systemsSeagrass beds are closely linked and interdependent on the functioning of other tropical, coastal marine ecosystems including mangrove forests and warm water coral reefshabitats.Linkage with other indicatorsGlobal eExtent of warm water coral habitat; Global eExtent of mangrove habitat; Number of Ramsar sites with Eestuarine waters, Ttidal/mud flats, Llagoons, and Kkelp beds recorded.Suitability for inclusion in TWAPAdvantages: Considered the most comprehensive dataset currently available Global coverage Available for non-commercial use Spatial data available Disadvantages: In need of updating (fundraising activities currently underway) No time series Not ground-truthed Reporting biases3. MethodologyDescription of measurement methods and calculation of the indicatorFirst major source of data were derived from scientific papers which were then transferred to GIS (ArcView 3.1) where it was managed as a Shapefile with associated attributes. This enabled the thorough checking of coordinates with other geographic information. The dataset was moved to an ArcSDE geodatabase. Other spatial seagrass datasets were collected from collaborators in a wide range of formats, ranging from paper maps, sketch maps, digital map images and vector and raster GIS formats. Data conversion was required and was efficiently handled by the ESRI software tools.ScaleGlobal (tropical regions)Validation processesThousands of records from hundreds of sources were reviewed for inclusion, based on published and peer reviewed scientific literature and outreach to expert knowledge. Validation was also undertaken through a global seagrass workshop comprising experts from 23 countries. All points in the dataset are fully documented with their own metadata, including individual reference.Limitations and uncertainty This dataset has been mapped for the world to the best of current knowledge and data availability at the time of publication. Further revisions will be required as knowledge develops an improved data becomes available. Based on multiple data sources in various GIS formats and mapping software which can be challenging to harmonise accurately. Largely dependent on scientific literature and experts which may lead to location bias. The formulation of time series data is challenging due to data recording biases.4. Assessment of DataData sources, availability and quality (Existing datasets)Global data layer has been compiled by UNEP-WCMC from multiple data sources from scientific papers, peer-reviewed literature, and a range of contributors, in collaboration with Dr Frederick T. Short, The data set is publicly available. Considered the best available data source for global seagrass extent.Variations among data sources and alternative methodsBased on multiple data sources primarily representing the peer-reviewed literature and contributions from experts and organizations. Submitted data therefore represents a range of methodologies and data sources which are harmonized as far as possible in the global layer.5. PartnersPartners/agencies involved in the development of the indicator UNEP-World Conservation Monitoring Centre; Dr Frederick T. Short (University of New Hampshire).6. ReferencesCitationUNEP-WCMC (2005) Global distribution of seagrasses (V2.0, 2005). Published in Green, EP. & Short, FT. (2003) World Atlas of Seagrasses. Prepared by UNEP World Conservation Monitoring Centre. University of California Press, Berkeley, USA. 4. Number of Ramsar sites with estuarine waters, tidal/mud flats, lagoons, and kelp beds, recorded 1. Indicator Indicator NameNumber of Ramsar sites with Eestuarine waters, Ttidal/mud flats, Llagoons, and Kkelp beds recordedCategory (Pressure-State-Impact-ResponseState; ResponseDefinition of indicator / DescriptorNumber of Ramsar sites with a range of coastal habitats recorded as the dominant or co-dominant habitat type within the site, particularly estuarine waters, tidal/mud flats, lagoons and kelp beds. Rate of Ramsar site establishment by LME (i.e. sites per year) representing a response indicator Units of measurements (spatial and temporal)Point dataset Data can be queried by year of designation2. Relevance Rationale for Inclusion (why shortlisted)Provides an indicator of habitats within LMEs for which there are no other globally comprehensive datasets available. Number of Ramsar sites containing these critical coastal habitats per LME can be used as a proxy for the importance of specific LMEs for these habitats. Estuaries, tidal/mud-flats, lagoons, and kelp beds are included in the following international framework(s): MEA; WSSD (tidal/mudflats) (see Table 1).Significance for inter-linkages with other water systemsAt the interface between land and ocean, coastal habitats are closely linked to facilitate the flow of ecosystem services from land to the ocean.Linkage with other indicatorsGlobal eExtent of warm water coral habitat; Global eExtent of seagrass habitat; Global eExtent of mangrove forest habitat.Suitability for inclusion in TWAPAdvantages: Provides an indicator of habitats within LMEs for which there are no other globally comprehensive datasets available. Number of Ramsar sites containing these critical coastal habitats per LME can be used as a proxy for the importance of specific LMEs for these habitats. Time series component rate of protection of habitats within LMEs. Global coverage in terms of Ramsar sites. Available for non-commercial use. Spatial data available. Disadvantages: Does not provide information on habitat extent outside of the designated Ramsar sites can only be indicative of habitat presence within an LME. Boundary information may not be available.3. MethodologyDescription of measurement methods and calculation of the indicatorPoint dataset of location of Ramsar sites. Data can be queried for presence of dominant and co-dominant habitat types. Rate of Ramsar site establishment per LME (Ramsar sites year-1).ScaleGlobal Validation processesBased on the official dataset compiled through the Ramsar Secretariat.Limitations and uncertainty At the global scale, any wetland type distribution point information can only be approximate. Does not provide information on habitat extent outside of the designated Ramsar sites can only be indicative of habitat presence within an LME. Boundary information may not be available.4. Assessment of DataData sources, availability and quality (Existing datasets)Data is derived from the official reporting process of the Ramsar Secretariat and so can be considered validated and good quality. The data set is publicly available.Variations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicator Ramsar Secretariat; Wetlands International; Center for International Earth Science Information Network (CIESIN).6. ReferencesCitationRamsar Sites Information Service (2007). Wetlands International, accessed from: HYPERLINK "http://www.wetlands.org/rsis/"http://www.wetlands.org/rsis/ 5. Number of observations of cold water coral habitat 1. Indicator Indicator NameNumber of observations of cold water coral habitatCategory (Pressure-State-Impact-ResponseStateDefinition of indicator / Descriptor Number of cold water coral habitat locations present within an LMEUnits of measurements (spatial and temporal)Point dataset 2 occurrences recorded for the year 1915, remaining records date 1994-20062. Relevance Rationale for Inclusion (why shortlisted)Cold water corals represents biodiversity hotspots on the seabed and seamounts, creating complex, three-dimensional habitats that support a disproportionate amount of marine life in relation to the surrounding water column, including essential habitat for commercial fish species. Cold water corals are included in the following international framework(s): CBD (see Table 1). Cold water coral habitats are under significant, practically irreversible threat from destructive fishing practices and ocean acidification.Significance for inter-linkages with other water systemsCold water coral reefs are often associated with sediments found on seamounts.Linkage with other indicatorsNumber of seamount observations; Number of large seamount areas.Suitability for inclusion in TWAPAdvantages: Global coverage Spatial data available Also includes biodiversity data (e.g. species observed) Disadvantages: Reporting bias In need of updating (fundraising activities currently underway) Not ground-truthed3. MethodologyDescription of measurement methods and calculation of the indicatorData were compiled and imported into point GIS Featureclass of 6553 records by UNEP-WCMC sourced from A. Freiwald, Alex Rogers, Jason Hall-Spencer and other contributors. Points on the map indicate observed reefs of varying size and stages of development, but not actual area covered.ScaleGlobal Validation processesNo record of a formal validation process, however full source information is provided within the attributes for each record within the database.Limitations and uncertaintyLikely that there is geographical bias in reporting, particularly in the North Atlantic where the high density of reefs most probably reflects the intensity of research in the region. Further discoveries are expected worldwide, particularly in the deeper waters of the subtropical and tropical regions.4. Assessment of DataData sources, availability and quality (Existing datasets)Global data layer has been compiled from multiple data sources from scientific papers, peer-reviewed literature, and a range of contributors. The data set is publicly available. Considered the best available data source for global cold water coral habitat occurrence.Variations among data sources and alternative methodsBased on multiple data sources primarily representing the peer-reviewed literature and contributions from experts and organizations. Submitted data therefore represents a range of methodologies and data sources which are harmonized as far as possible in the global layer.5. PartnersPartners/agencies involved in the development of the indicatorUNEP-WCMC; A. Freiwald, Alex Rogers, Jason Hall-Spencer and other contributors6. ReferencesCitationCold-water corals extracted from version 2.0 of the global point dataset compiled by the UNEP-World Conservation Monitoring Centre, 2005. Sourced from A. Freiwald, A. Rogers and J. Hall-Spencer, and other contributors. 6. Number of observations of cold seep and hydrothermal vent habitat 1. Indicator Indicator NameNumber of observations of cold seep and hydrothermal vent habitatCategory (Pressure-State-Impact-ResponseStateDefinition of indicator / Descriptor Number of observations of cold seep and hydrothermal vent habitat per LMEUnits of measurements (spatial and temporal)Point dataset available as KML. Indication of present statepresence no time series component2. Relevance Rationale for Inclusion (why shortlisted)Cold seep and hydrothermal vent habitats support unique communities of organisms, often endemic to a single vent/seep. These organisms may be of interest to future bioprospecting activities. Vents and seeps have also been demonstrated to play a key role in the nutrient and chemical cycling of the oceans. Cold seep and hydrothermal vents are included in the following international framework(s): CBD (see Table 1) These habitats are under particular threat from the emerging activity of deep-sea mining for the mineral resources deposited through the hydrothermal processes.Significance for inter-linkages with other water systemsVents and seeps are found in areas of volcanic activity and have thus been associated with seamount habitats.Linkage with other indicatorsNumber of seamount observations; Number of large seamount areas.Suitability for inclusion in TWAPAdvantages: Spatial data available Global coverage Publicly available for non-commercial use Contains additional biodiversity information Disadvantages: Reporting bias Not ground-truthed No time series3. MethodologyDescription of measurement methods and calculation of the indicatorCurrently, ChEssBase includes data on1649 speciesfrom177 chemosynthetic sitesaround the globe. These data contain information (when available) on the taxonomy, diagnosis, trophic level, reproduction, endemicity, and habitat types and distribution. There are now1859 papersin the reference database. ChEssBase is in active development and new data are being entered periodically.ScaleGlobal Validation processesNo record of a formal validation process, however full source information for each data record is provided.Limitations and uncertainty Sampling bias - only presence of cold seeps/hydrothermal vents can be known and absence of data does not indicate absence of cold seep.4. Assessment of DataData sources, availability and quality (Existing datasets)Global data layer has been compiled from multiple data sources from scientific papers, peer-reviewed literature, and a range of contributors. The data set is publicly available. The data is being continually updated and so represents one of the most up-to-date information sources available for hydrothermal vent and cold seep locations.Variations among data sources and alternative methodsBased on multiple data sources primarily representing the peer-reviewed literature and contributions from experts and organizations. Submitted data therefore represents a range of methodologies and data sources which are harmonized as far as possible in the global layer.5. PartnersPartners/agencies involved in the development of the indicatorCensus of Marine Life (CoML); CHEss project members: National Oceanography Centre, Southampton, UK; Woods Hole Oceanographic Institute (WHOI), USA; Institut de Cincies del Mar, Spain.6. ReferencesCitationChEssBase: Ramirez-Llodra, E., Blanco, M. and Arcas, A., 2004. ChEssBase: an online information system on biodiversity and biogeography of deep-sea chemosynthetic ecosystems. Version 1. World Wide Web electronic publications,HYPERLINK "http://www.noc.soton.ac.uk/chess/database/db_home.php"www.noc.soton.ac.uk/chess/db_home.php 7. Number of seamount observations 1. Indicator Indicator NameNumber of seamount observationsCategory (Pressure-State-Impact-ResponseStateDefinition of indicator / DescriptorNumber of seamount observations per LMEUnits of measurements (spatial and temporal)Point dataset Indication of presencet state no time series component2. Relevance Rationale for Inclusion (why shortlisted)Seamounts represent key areas for biodiversity in the open ocean, supporting a range of vulnerable habitats, such as cold-water corals, and providing habitat for a large number of species including commercial fish. Seamounts are included in the following international framework(s): CBD (see Table 1) Seamounts are seriously threatened and disproportionately targeted by destructive fishing practices, such as bottom trawling, compared to other areas of the continental slope.Significance for inter-linkages with other water systemsSeamounts provide secondary habitat for cold water corals and sponge beds, in addition to also being associated with hydrothermal vents and cold seeps.Linkage with other indicatorsNumber of observations of cold water coral habitat; Number of observations of cold seep and hydrothermal vent habitat; Number of large seamount areas.Suitability for inclusion in TWAPAdvantages: Spatial data available Global coverage Publicly available for non-commercial use Contains additional biodiversity information Disadvantages: Reporting bias Not ground-truthed No time series3. MethodologyDescription of measurement methods and calculation of the indicatorSeamountsOnline holds data on species that have been recorded from seamounts compiled them from the published literature and from the electronic data holdings of researchers and institutions. Taxonomically, all metazoan species are considered and, spatially, all seamounts globally are included (if biological data are available). It does not follow a strict geological definition of a seamount, so data on features smaller than 1000m high are included. Both hydrothermally-active and non-active seamounts are included, though the coverage is better for non-venting seamounts.ScaleGlobal Validation processesNo record of a formal validation process, however full source information for each data record is provided.Limitations and uncertaintyGeographic sampling bias and undersampling. Complete sampling information is provided to assist with the identification of true species absence or artifacts from undersampling in a given area.4. Assessment of DataData sources, availability and quality (Existing datasets)Global data layer has been compiled from multiple data sources from scientific papers, peer-reviewed literature, and the electronic data holdings of researchers and institutions. The data set is publicly available. The data is being continually updated and so represents one of the most up-to-date information sources available for hydrothermal vent and cold seep locations.Variations among data sources and alternative methodsBased on multiple data sources primarily representing the peer-reviewed literature and contributions from experts and organizations. Submitted data therefore represents a range of methodologies and data sources which are harmonized as far as possible in the global layer.5. PartnersPartners/agencies involved in the development of the indicatorCensus of Marine Life Censeam/OBIS; National Institute of Water and Atmospheric Research (NIWA); University of California, San Diego.6. ReferencesCitation Stocks, K. (2009). SeamountsOnline: an online information system for seamount biology. Version 2009-1. World Wide Web electronic publication. http://seamounts.sdsc.edu 8. Number of large seamount areas 1. Indicator Indicator NameNumber of large seamount areasCategory (Pressure-State-Impact-ResponseStateDefinition of indicator / DescriptorNumber of large seamount areas per LMEUnits of measurements (spatial and temporal)Point dataset Indication of presencet state no time series component2. Relevance Rationale for Inclusion (why shortlisted)Seamounts represent key areas for biodiversity in the open ocean, supporting a range of vulnerable habitats, such as cold-water corals, and providing habitat for a large number of species including commercial fish. Seamounts are included in the following international framework(s): CBD (see Table 1) Seamounts are seriously threatened and disproportionately targeted by destructive fishing practices, such as bottom trawling, compared to other areas of the continental slope.Significance for inter-linkages with other water systemsSeamounts provide secondary habitat for cold water corals and sponge beds, in addition to also being associated with hydrothermal vents and cold seeps.Linkage with other indicatorsNumber of observations of cold water coral habitat; Number of observations of cold seep and hydrothermal vent habitat; Number of large seamount areas.Suitability for inclusion in TWAPAdvantages: Spatial dataset Global coverage Publicly available Ground-truthing undertaken as part of the validation process Disadvantages: Model under-reporting of seamounts expected3. MethodologyDescription of measurement methods and calculation of the indicatorThis model depicts over 14,000 large seamounts identified from a mid-resolution ETOPO2 elevation map, using methods outlined in Kitchingman & Lai (2004). There are more small seamounts, but their distribution should be roughly similar to that shown.ScaleGlobal Validation processesGround truthing was performed on a dataset of known seamounts set at a 30-minute resolution and produced from a combination of data from the US Department of Defence Gazetteer of Undersea Features (1989)3 and SeamountsOnline. It was found that approximately 60% of the known seamounts were within 30 minutes of predicted seamounts.Limitations and uncertaintyThe set of location data generated should be considered a subset of a much larger global set of seamount locations, as 50,000 or more seamounts could probably be identified, using bathymetric maps of higher resolution that are presently classified, combined with a broader definition of seamounts, which would take into account the true extent of their variety in shape and groupings.4. Assessment of DataData sources, availability and quality (Existing datasets)Global model derived from a mid-resolution ETOPO2 elevation map. Using a mid-resolution base map is likely to lead to the under-reporting of seamounts. The data set is publicly available.Variations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicatorSea Around Us Project, University of British Columbia6. ReferencesCitation A. Kitchingman & S. Lai. 2004. Inferences on Potential Seamount Locations from Mid-Resolution Bathymetric Data. In T. Morato & D. Pauly, FCRR Seamounts: Biodiversity and Fisheries, Fisheries Centre Research Reports. University of British Columbia. 12:7 - 12. 9. Percentage habitat covered by Protected Area Indicator Indicator NamePercentage habitat covered by Protected Area (PA)Category (Pressure-State-Impact-ResponseResponseDefinition of indicator / DescriptorExtent (area) of critical habitat falling within the boundaries of a Protected Area.Units of measurements (spatial and temporal)1km2 protected area coverage PA establishment over timeRelevance Rationale for Inclusion (why shortlisted)Terrestrial and Marine Protected Areas have been identified by the international community, as a key indicator of success towards reaching the 2010 Biodiversity Target implemented by the CBD. This indicator is derived from the World Database on Protected Areas (WDPA) which is considered the most comprehensive global spatial dataset on marine and terrestrial protected areas available.Significance for inter-linkages with other water systemsPercentage area of coverage of one critical habitat may directly or indirectly affect the ecosystem service provision of an adjacent habitat.Linkage with other indicatorsAll habitat state indicators included in Part 4 of this chapter.Suitability for inclusion in TWAPAdvantages: Considered the authoritative data set on protected areas Global coverage Available for non-commercial use Spatial data available Continually updated Time series available through date of establishment Disadvantages: Not globally ground-truthed Reporting biases Inconsistencies in data collection/submission by national governmentsMethodologyDescription of measurement methods and calculation of the indicatorData is submitted by national governments or approved NGOs in a variety of formats; Data is then processed into a standard GIS format and published online; Boundary data is specifically requested; if not available, information on PA area is used to create buffers around point data.ScaleGlobal Validation processesAlthough the World Database on Protected Areas (WDPA) is seen at present to be the best and most accurate global dataset of PAs in existence, there are remain issues related to inconsistencies in reporting between countries and there are limited resources available for systematic ground-truthing to take place. Limitations and uncertaintyBased on multiple data sources in various GIS formats and mapping software which can be challenging to harmonize together accurately. Largely dependent on voluntary reporting from countries and organizations; this may lead to inconsistencies in component data sets due to variation in methodologies, along with geographic biases in availability and quality of data. Does not provide an indicator of PA management effectiveness.Assessment of DataData sources, availability and quality (Existing datasets)Global data layer has been compiled by UNEP-WCMC from multiple data sources. The data set is publicly available. Considered the best available data source for global Protected Area coverage.Variations among data sources and alternative methodsBased on multiple data sources primarily representing national reporting entities and non-governmental organizations. Submitted data therefore represents a range of methodologies and data sources which are harmonized as far as possible in the global layer.5. PartnersPartners/agencies involved in the development of the indicatorUNEP-World Conservation Monitoring Centre (compiled through submissions from national governments and authoritative NGOs). IUCN - WCPA6. ReferencesCitationIUCN and UNEP-WCMC (2010),The World Database on Protected Areas (WDPA): Annual Release[On-line]. Cambridge, UK: UNEP-WCMC. Available at: HYPERLINK "http://www.wdpa.org" www.wdpa.org PART 5: SUMMARY This report, as part of the Transboundary Waters Assessment Project (TWAP), recommends eight nine indicators to assist the immediate methodological development for assessment of marine habitat extent within Large Marine Ecosystems (LMEs). The habitats put forward for inclusion in the TWAP-LME project have been identified as critical habitat types by a number of international conventions and thus any work undertaken toward the development of indicators, and their subsequent use in assessments, will also benefit the longer-term goals defined by these overarching frameworks. The indicators recommended by this chapter therefore represent the most robust options currently available given the fundamental caveats in the underlying data. They are intended to be viewed as a starting point and an impetus for international cooperation towards the development of validated, global data sets which are urgently needed to underpin global and regional assessment processes such as the TWAP-LME. As reflected in Part 3.1 and the review of the data sets listed in Annex 1 and Part 4, The data underpinning the development of indicators for marine habitats has many inherent challenges. Comprehensive global and spatial global data sets are lacking and there has been a significant bias historically toward tropical regions; resolution of data varies due to disparate reporting methods, with global data sets often being too coarse to provide reliable results and data from local and national level initiatives often being too fine-scale to contribute to a global or regional assessment; data primarily represents geographic coverage of habitats and rarely includes information on habitat quality and levels of degradation; and finally, comparable time series information required for assessing change over time is almost impossible due to evolution in assessment methodology practices and the time lag between data collection and reporting. Based upon these data gaps and caveats, we provide below recommendations for future indicator development and associated data needs, with the view to supporting the longer term objectives of the TWAP/LME assessment and international biodiversity conventions. 5.1 Recommendations for future indicator development and associated data needs Data collection and management Greater uniformity in data collection methods, data processing, and data sharing has substantial potential for advancing knowledge, and standard protocols for sample collection and processing should be developed and adopted (Clark et al. 2010). Future research should target the current gaps in geographic and bathymetric coverage and focus on those under-researched areas such as temperate and deep-sea habitats. The value of historical data should be promoted. Development of internationally agreed frameworks and guidelines for future data collection is essential for undertaking robust, large-scale assessments and should be positioned as a high priority activity on the international assessment agenda. To ensure the most reliable data possible is being made available, data sets should continue be subject to standard expert- and peer-review processes, and the precautionary approach should, as always, be adopted in any decision-making process. Integrated marine habitat mapping An integrated approach to habitat mapping which combines historical data, existing data collection and mapping activities, with habitat modeling and remote sensing information should be promoted. Ground-truthing should be carried out where possible. Robust methods of integrating information from multiple, small-scale sources is needed to provide standardized reporting at the regional and global scales. Emphasis should be placed on reviewing historical data and developing methodologies for its integration with present-day data collection and management techniques in order to ensure that indicator development for baseline establishment, and ultimately analysis of trends, is building on as many previous efforts as possible. A more holistic, ecosystem based approach to assessment and management which recognizes that ecosystem service provision is highly dependent of the flow of these services from one habitat to another should be adopted. Collaboration and partnerships Future data collection and habitat mapping efforts should aim to benefit both small- and large-scale decision-making processes via a multi-scale approach. The ability to aggregate and disaggregate data over a range of hierarchical scales would provide a comprehensive information base for all members of the marine community, encouraging participation and collaboration across different sectors in its development. This integrated approach will require cooperation between many different organizations and sectors, often fostering unprecedented partnerships. Mechanisms to facilitate these working relationships, such as through a global data partnership, should be encouraged. Robust methods of integrating this existing information to provide standardized reporting at the regional and global scales are needed, and the development of internationally agreed frameworks and guidelines for future data collection is essential. Predictive modeling techniques may be used here. Future data collection and habitat mapping efforts should aim to benefit both small- and large-scale decision-making processes via a multi-scale approach. The ability to aggregate and disaggregate data over a range of hierarchical scales would provide a comprehensive information base for all members of the marine community, encouraging participation and collaboration across different sectors in its development. Building on the above example, the continued development of agreed global standards and related reporting processes will contribute significantly to overcoming these challenges, and should be positioned as a high priority activity on the international assessment agenda. This single-system paradigm is now changing and the necessity to take a more holistic, ecosystem based approach to assessment and management which recognizes that ecosystem service provision is highly dependent of the flow of these services from one habitat to another, is increasingly acknowledged (Silvestri & Kershaw, 2010). In spite of this evolution in attitude, such as that being undertaken by the Transboundary Waters Assessment Project, it will be some time until this is filtered down to available marine habitat data. Considerable effort should be placed on reviewing historical data and developing methodologies for its integration with present-day data collection and management techniques in order to ensure that indicator development for baseline establishment, and ultimately analysis of trends, is building on as many previous efforts as possible. To ensure the most reliable data possible is being made available, these multiple-source data sets should continue be subject to standard expert- and peer-review processes, and the precautionary approach should, as always, be adopted in any decision-making process. Identifying that what we do have is useful (compilation of historical data) Greater uniformity in data collection methods, data processing, and data sharing has substantial potential for advancing knowledge Standard protocols for sample collection and processing should be developed and adopted (Clark et al. 2010). 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Status of coral reefs of the world: 2008, Global Coral Reef Monitoring Network and Reef and Rainforest Research Centre, Townsville, Australia, pp. 296. ANNEX I: Potential data sources for the development of marine habitat extent indicators It will be helpful if you say what kind of data each can provide (in a table form) Summary of draft potential data sources UNEP-WCMC University of North Carolina Australian Institute of Marine Science (AIMS) International Society for Mangrove Ecosystems (ISME) International Tropical Timber Organization (ITTO) Dr. Frederick T. Short (University of New Hampshire, USA) James Cook University University of Colorado Ramsar Secretariat Wetlands International Center for International Earth Science Information Network (CIESIN) University of British Columbias Sea Around Us Project (SAUP) The Nature Conservancy (TNC) Global Ocean Biodiversity Initiative (GOBI) Marine Conservation Biology Institute (MCBI) Department of Biology, Dalhousie University, Halifax, CA Institute of Zoology, Zoological Society of London, UK Department of Oceanography, Florida State University, USA San Diego Supercomputer Center, UC San Diego, USA National Institute of Water and Atmospheric Research (NIWA), NZ Marine Institute, University of Plymouth, UK Faculty of Science, Health and Education, University of the Sunshine Coast, AUS. Census of Marine Life (CoML) and its Ocean Biogeographic information System (OBIS) National Oceanography Centre, Southampton, UK Woods Hole Oceanographic Institute (WHOI), USA; Institut de Cincies del Mar, Spain. National Institute of Water and Atmospheric Research (NIWA); University of California, San Diego. Yale University Columbia University Habitats not included due to lack of available information Beaches and dunes Sponge reef Data sets identified by the review process which did not meet the indicator template selection parameters Suggest that these tables be merged with the indicators templates so all info is in one place. Yes I agree with this comment from Sherry - there are some good potential sources of information here that could be used as indicators. However I suspect that you have included some more information on other indicators that you think are not entirely suitable for use as indicators now. So I would suggest that you separate the datasets. So all the information associated with the 8 indicators you are recommending should go in the main body of text. Then here you should only include information on those indicators that you feel are not quite right to be used as indicators (for whatever reason). So there is the A list and the B list. Org(s)University of North Carolina (Authors affiliation); Australian Institute of Marine Science (AIMS)IndicatorPercentage live coral cover for Indo-Pacific and Caribbean [state]GeneralCitation: Indo-Pacific: Bruno, JF. & Selig, ER. (2007) Regional decline of coral cover in the Indo-Pacific: Timing, Extent, and Sub regional Comparisons. PLoS ONE 2(8):e711. Caribbean: Shutte et al. (in review) Scale: Regional (Indo-Pacific and Caribbean) Availability: Not publically availableMethodol-gyApproach: Analysis of coral cover database with 6001 quantitative surveys that measured the % of bottom covered by living scleractinian corals on subtidal coral reefs (1-15m depth, mean survey depth = 6.2m) within 10 subregions of the Indo-Pacific coral reefs performed between 1968 and 2004. Included data from several sources inc. the published results of academic, governmental, and NGO scientists and, for one source (Reef Check) volunteers trained and supervised by professional scientists, and also survey results from the AIMS long term monitoring program (LTMP). Spatial Resolution: 10 subregions within the Indo-Pacific Temporal Resolution: Data included from 1968 to 2004 aggregated into three time groups: 1969-1980, 1984-1996, 1997-2004 (most reefs surveyed only once). Validation: As the effects of a variety of disturbances on coral cover are depth-dependent, spatial and temporal variability of the depth of reef surveys could affect the analysis, extensive analyses of the potential confounding effects of depth on the subregional comparisons and rate estimations were undertaken. Limitations/Uncertainty: There is a shift in the dominant data sources over time as well as standardization of survey techniques. Location bias in quantity and quality of data recording within subregions. Data quality/completeness e.g. variance estimates, sample size, repeated reef sampling etc. generally low.Suitability for inclusion in TWAPAdvantages: Previously used as an indicator of the 2010 BIP Examines quality of habitat (i.e. live coral) in addition to habitat extent Extensive data source (6001 surveys) Time series component Disadvantages: Regional only applicable to a subset of LMEs how many out of 64? No spatial data (e.g. maps) only a descriptive, aggregated measure Data quality/completeness generally low Not publicly availableWebsite HYPERLINK "http://www.twentyten.net/assortedhabitats" http://www.twentyten.net/assortedhabitatsOrg(s)International Tropical Timber Organization (ITTO), building previous assessments by FAO and UNEP.IndicatorMangrove habitat extent (km2) for 1980, 1990, 2000, 2005 [state; pressure]GeneralCitation: FAO (2007) The worlds mangroves 1980-2005. FAO Forestry Paper 153, Food and Agriculture Organization of the United Nations, Rome, 2007. Scale: Global Availability: Need to contactMethodol-gyApproach: Based on information collected during a preliminary assessment (FAO, 2003), a national profile was compiled for each country that has mangroves. These included both quantitative data of mangrove area over time and qualitative information on mangrove species composition and distribution, an indication of their uses and threats to survival. This documentation, together with a country-specific questionnaire, was distributed to 110 mangrove experts worldwide and to 107 officially nominated correspondents to FRA 2005 for feedback. The information was also circulated to members of the International Society for Mangrove Ecosystems (ISME), specific discussion lists and uploaded to an interactive Web page. Spatial Resolution: National, regional and global scale data aggregates are available. Temporal Resolution: Trend in area over four time periods: 1980, 1990, 2000, and 2005. Validation: Cross-checking of data was done where possible and the information analyzed with the assistance of specialists. An initial screening disregarded rough estimates and selection of one estimate for the trend analyses for those years for which more than one was available. Drafts of the study were sent to all official national correspondents for the FRA process for comments and validation. Limitations/Uncertainty: Varying methodologies over time and general lack of recent, reliable information for a few countries mean that the results of this study can only be considered indicative. Suitability for inclusion in TWAPAdvantages: Previously used as an indicator of the 2010 BIP Global coverage with national and regional data aggregates available Time series component provides information on trends in habitat extent Assesses qualitative pressures to mangroves as well as habitat extent Disadvantages: Not publicly available Reporting bias No spatial data (e.g. maps) Not ground-truthedWebsite HYPERLINK "http://www.twentyten.net/assortedhabitats" http://www.twentyten.net/assortedhabitatsOrg(s)Author affiliation: James Cook University IndicatorLoss estimates of seagrass habitat extent (km2) from 2006 [state; pressure]GeneralCitation: Waycott, M. et al. (2009) PNAS, 106(30):12377-12381 Scale: Global Availability: Not publicly availableMethodol-gyApproach: Synthesized quantitative data from 215 sites with a total of 1,128 global observations covering the time period 1879-2006. Spatial Resolution: Seagrass area recorded in km2 Temporal Resolution: Declines in seagrass extent from 1879-2006 Validation: Bootstrap analysis supported the robustness of the results. Subsampling recovered similar overall rates of change independent of subsample size. Limitations/Uncertainty: Extrapolation to the global scale must be qualified by limited seagrass mapping efforts in turbid water systems and in some geographic regions that have received less attention from the scientific community. Temporal changes in reporting and standardized reporting procedures.Suitability for inclusion in TWAPAdvantages: Previously used as an indicator of the 2010 BIP Global coverage Time series component provides information on trends in habitat extent Assesses qualitative pressures to seagrass as well as habitat extent Disadvantages: Not publicly available Reporting bias Low sample size to extrapolate to global scale/trends over 127 years Not ground-truthed No spatial data (e.g. maps)Website HYPERLINK "http://www.twentyten.net/assortedhabitats" http://www.twentyten.net/assortedhabitatsOrg(s)University of ColoradoIndicatorExtent (km2) of delta habitat derived from the World Deltas Database [state]GeneralCitation: World Deltas Database. Comparative Institute for Research in Environmental Sciences (CIRES), University of Colorado, USA. Scale: Global Availability: No longer active - boundary information available on request.Methodol-gyNo information located.Suitability for inclusion in TWAPAdvantages: Global coverage Spatial data available Disadvantages: In need of updating Not publicly availableWebsite-Org(s)University of British Columbias Sea Around Us ProjectIndicatorExtent of estuarine habitat (km2); Mean freshwater input (m3 s-1 day-1) [state]GeneralCitation: Estuaries of the World. Sea Around Us Project, University of British Columbia. Scale: Global Availability: Available to colleagues who are interested in a collaborative projectMethodol-gyApproach: The database is the first to be designed at the global scale and contains over 1200 estuaries (inc. some lagoon systems and fjords) in over 120 countries and territories. These water bodies (over 97% have Shapefiles) were selected so that all the estuaries of the Worlds major rivers were included, as well as the small estuaries of countries without major rivers. Overall the database accounts for over 80% of the worlds freshwater discharge, and contains information about the name, location, area (km2) and mean freshwater input (in m3 s-1 day-1), calculated over a specified number of years. Spatial Resolution: All estuaries are linked to the 16,000+ coastal degree latitude/longitude cells used for all spatial features by the SAUP (so estuarine cells are actually coastal cells overlapping with one or several estuaries). Estuarine area in given in km2. Temporal Resolution: Indication of present state no time series component Validation: No information located Limitations/Uncertainty: No information locatedSuitability for inclusion in TWAPAdvantages: Global coverage with 80% completeness in over 120 countries/territories. Spatial data available for 97% entries. Disadvantages: Not publicly available requires collaboration with SAUP Information on validation process and limitations/uncertainties not publicly available.Website-Org(s)UNEP-WCMC; TNCIndicatorExtent of saltmarsh habitat (km2) [state]GeneralCitation: Global Saltmarsh Database (2005). UNEP-World Conservation Monitoring Centre, Cambridge, UK. Scale: Global Availability: Available non-commercial useMethodol-gyApproach: Data were compiled from a range of sources of published and grey literature, databases, conference proceedings, direct communication with experts etc. The primary database was built in Microsoft Access with the spatial database components also compiled into a map using ArcGIS 9.0. There are five main tables: Location, species, protected areas, international protected areas, and estuaries. Spatial Resolution: Point and Polygon dataset at global scale. Where available, area values are provided in hectares. Temporal Resolution: Indication of state in 2005 no time series component Validation: This dataset has not been peer-reviewed. Limitations/Uncertainty: Geographic reporting bias due to a combination of lack of data, a failure to locate available data, and possibly a lower density of saltmarshes particularly for tropical and/or non-tidal coasts. Where a specific GIS location was unavailable, a general location was entered and recorded in the comments field. Certain sites may be duplicated as a result of the data being compiled for a number of sources.Suitability for inclusion in TWAPAdvantages: Global coverage Available for non-commercial use Spatial data available Also includes biodiversity data and protected areas Disadvantages: Not peer-reviewed Reporting bias Inconsistencies in data inclusion In need of updating (fundraising activities currently underway)Website-Org(s)Global Ocean Biodiversity Initiative (GOBI); Marine Conservation Biology Institute (MCBI)IndicatorExtent of habitat suitable for reef forming corals (km2) [state]GeneralCitation: Guinotte, J., Davies, A. & Ardron, J. (2009). Global habitat suitability for reef-forming coral reefs. Annex 2: Illustrations from defining ecologically or biologically significant areas in the open oceans and deep seas: Analysis, tools, resources and illustrations. Technical Background Document for the CBD Expert Workshop, Ottawa, 2009. Scale: Global Availability: Not publicly availableMethodol-gyApproach (Model): Uses known locations of the six reef-forming cold water coral species, amassed from research and cruise data bases (2732 records), which were incorporated into a maximum entropy model that estimates the distribution of a given species taking into consideration the known occurrences of that species in relation to a series of 26 environmental variables likely to influence its distribution. Spatial Resolution: 1km2 grid Temporal Resolution: Indication of present habitat suitability no time series component Validation: Presence data were split into 75% training and 25% test data for model validation purposes. The outputs are statistically significant, but external validation of some of these areas by field surveys is warranted. Limitations/Uncertainty: Maps indicate potential habitat suitability but do not indicate species presence; therefore predicted localities should be ground-truthed through directed surveys.Suitability for inclusion in TWAPAdvantages: Spatial data available Global coverage High resolution applicable to decision-making Provides comprehensive global indicator unaffected by reporting or location bias Disadvantages: Model can only indicate suitable habitat, not species presence External validation (e.g. ground-truthing) needed Cannot detect influences of biological/complex environmental interactionsWebsite-Org(s)Author affiliations: Department of Biology, Dalhousie University, Halifax, CA; Institute of Zoology, Zoological Society of London, UK; Department of Oceanography, Florida State University, USA; San Diego Supercomputer Center, UC San Diego, USA; National Institute of Water and Atmospheric Research (NIWA), NZ; Marine Institute, University of Plymouth, UK; Faculty of Science, Health and Education, University of the Sunshine Coast, AUS.Indicator Extent of habitat suitable for stony corals on seamounts (km2) [state]GeneralCitation: Tittensor, DP. et al. (2009). Predicting global habitat suitability for stony corals on seamounts. Journal of Biogeography, 36, 1111-1128. Scale: Global Availability: Not publicly availableMethodol-gyApproach (Model): Two habitat-suitability modeling approaches, parameterized by a recently compiled database of stony coral samples from seamounts (SeamountsOnline), were developed for presence-only data and used to predict global habitat suitability for seamount scleractinians: maximum entropy modeling (Maxent) and environmental niche factor analysis (ENFA). Habitat suitability maps were generated and then a cross-validation process with a threshold-independent metric was undertaken to evaluate model performance. Maxent was found to consistently out-perform ENFA on every cross-validation partition. Spatial Resolution: All data mapped to an Equal-Area Scaleable Earth (EASE) grid to remove bias integral to a one-degree grid. Temporal Resolution: Indication of present habitat suitability no time series component Validation: Models were run on a one-degree grid to assess the effects of unequal grid-cell size on the robustness of results. A cross-validation procedure was used to evaluate the performance of the models, by creating 10 random partitions of the occurrence localities, splitting the data in each partition between calibration (70%) and evaluation (30%) data sets. The same 10 partitions were used for both models. A threshold-independent measure, AUC (area under the curve), was calculated for the evaluation data. Limitations/Uncertainty: Relationships between environmental variables and habitat suitability are complex and the assignment of relative importance to each variable is complicated and requires background knowledge of the ecosystem and coral biology. Numerous seamounts are undoubtedly not detected through this approach. Habitat suitability on seamount summits is likely to differ to that on seamount flanks. Modeling provides no information about the role of biological interactions in determining the distribution of stony corals on seamountsSuitability for inclusion in TWAPAdvantages: Spatial data available Global coverage Rigorous comparison of two habitat suitability models EASE used to remove spatial bias arising from grid cells Provides comprehensive global indicator unaffected by reporting or location bias Disadvantages: Model can only indicate suitable habitat, not species presence External validation (e.g. ground-truthing) needed Restricted to seamounts Cannot detect influences of depth, or biological/complex environmental interactions Not publicly availableWebsite-Org(s)UNEP-WCMC; NOAA NMFSIndicatorIncidence of coral disease (number of observations) [pressure]GeneralCitation: UNEP-WCMC/NOAA (2010). Global Coral Disease Database (GCDD). UNEP-World Conservation Monitoring Centre, Cambridge, UK. Accessible at: http://www.coraldisease.org Scale: Global Availability: Available for non commercial use. Methodol-gyApproach: The first version of the GCDD was launched in 2000 and was populated with some 2000 points of data from 155 references, mostly peer reviewed scientific literature. This initial data set was heavily biased to the Western Atlantic Ocean. The current data set now represents the third phase of the collaboration between UNEP-WCMC and NOAA, and the database now holds more than 7,000 data points from around the world. A key aim of the GCDD is to develop a standardized reporting system for coral disease to provide a globally comparable data set. Spatial Resolution: Point dataset of transect results Temporal Resolution: Data includes field for year recorded Historical data dating back to the 1970s Validation: No formal validation process has been undertaken although many entries are sourced from peer-reviewed literature Future data entries will adhere to a standardised reporting format Limitations/Uncertainty: Reporting bias No standardised methodology used for recording disease incidence for the historical data holdings Often there not a clear indication of area surveyed Inconsistencies in naming of coral species and diseaseSuitability for inclusion in TWAPAdvantages: Spatial data available Global coverage Publicly available for non-commercial use Time series Indicator of habitat quality rather than only extent Disadvantages: Reporting bias Inconsistencies in disease data recorded Not ground-truthed External validation (e.g. ground-truthing) neededWebsitehttp://www.coraldisease.orgOrg(s)Yale University; Columbia UniversityIndicatorTrawling Intensity (Environmental Performance Indicator) [impact]GeneralCitation: Esty, Daniel C., M.A. Levy, C.H. Kim, A. de Sherbinin, T. Srebotnjak, and V. Mara. 2008. 2008 Environmental Performance Index. New Haven: Yale Center for Environmental Law and Policy. Scale: Global Availability: Need to contact. Methodol-gyApproach: The 2008EPITrawling Intensity indicator consists of the percentage of the shelf area in each countrysEEZthat is fished using trawling. There are no direct data available for the area trawled on a country-by-country basis. However, fish landings data are acceptable as a proxy for each countrys fishing fleet. Thus trawling ships can be counted and incorporated into this trawling metric. The target level selected for this indicator is 0% area trawled, reflecting the opinion that any use of this fishing method is ecologically undesirable. Spatial Resolution: National level Temporal Resolution: Present state for 2008 Validation: Data standardized to facilitate cross-country comparisons. Cluster analysis was undertaken to identify groupings of relevant peer countries - so taking into account the stage of development of each country. Sensitivity analysis undertaken to test robustness. Limitations/Uncertainty: Persistent data gaps, lack of time series data, or incomparability of data across countries means that several important policy challenges cannot be addressed adequately at present. Geographic coverage had to balance with availability and validity of data.Suitability for inclusion in TWAPAdvantages: Global coverage Publicly available for non-commercial use Indicator of habitat impacts Validated through sensitivity analysis Disadvantages: No spatial data available No time series Some countries/sectors still data poorWebsitehttp://epi.yale.edu:2008/Home  near-shore marine areas     Marine Habitat Extent Working Group Draft Chapter version 32.0  PAGE \* MERGEFORMAT 1   &/5z{|ɷq[8EHhkh|}hf0J1CJ0OJQJ\aJ0k*CJaJ*Hhkhf0J1CJ0OJQJ\aJ0#hahXx0J1CJ0OJQJ\aJ0#hahXx0J1CJ OJQJ\aJ Bhah0^h 0J1CJ OJQJ\aJ cHdhdhdh#hah0^0J1CJ OJQJ\aJ AHhkhfhf0J1CJ OJQJ\aJ k*0J *Hhkhf0J1CJ OJQJ\aJ   5{^YYgd-oPC$Eƀkgdfokd&FQC$Eƀkgdfokd&$a$Kh{|fQC$Eƀkgdfokd&$a$GC$Eƀkgd-o|ذ؈^ZOZO6!(Hhh.hGe0JmHnHu1jHhh.hGe0JUmHnHujh&\h&\Uhy%Shfhfh>0J1CJ0OJQJ\aJ0cHdhdhdhkk*0J NHhkh|}h0^0J1CJ0OJQJ\aJ0k*B*CJaJphNHhkh|}h|}0J1B*CJOJQJ\aJphk*CJaJNHhkh|}hf0J1CJOJQJ\aJk*B*CJaJph 1 ] 4 6 ttttttt DgdGeod&C 8# gdGeod&B 8# gdGeod&>dgdGeod&PC$Eƀkgdfokd&F    + , - . / 0 1 2 3 O P ۿەfWF4"HhhGe_HmHnHu!HhkhCmHnHujhGeUmHnHu0j}HhhGeUmHnHu*jHhhGeUmHnHu!HhhGemHnHu1jHhh.hGe0JUmHnHu7jHhh.hGe0JUmHnHu(Hhh.hGe0JmHnHuHhhGemHnHuP Q R v w x ʵufUCʵ3HhhGemHnHu"HhhGe_HmHnHu!HhkhCmHnHujhGeUmHnHu0jwHhhGeUmHnHu*jHhhGeUmHnHu!HhhGemHnHu(Hhh.hGe0JmHnHu1jHhh.hGe0JUmHnHu7jHhh.hGe0JUmHnHu   ʳsdSC.C.(Hhh.hGe0JmHnHuHhhGemHnHu!HhkhCmHnHujhGeUmHnHu0jqHhhGeUmHnHu*jHhhGeUmHnHu!HhhGemHnHu,Hhh.hGe0J_H mHnHu1jHhh.hGe0JUmHnHu7jHhh.hGe0JUmHnHu   ; < = W X Y Z [ \ ] ^ _ { | ʰpaP@+@+(Hhh.hGe0JmHnHuHhhGemHnHu!HhkhCmHnHujhGeUmHnHu0jkHhhGeUmHnHu*jHhhGeUmHnHu!HhhGemHnHu2Hhh.hGe0J6]_H mHnHu1jHhh.hGe0JUmHnHu7jHhh.hGe0JUmHnHu| } ~ ʰpaP@+@+(Hhh.hGe0JmHnHuHhhGemHnHu!HhkhCmHnHujhGeUmHnHu0jeHhhGeUmHnHu*jHhhGeUmHnHu!HhhGemHnHu2Hhh.hGe0J6]_H mHnHu1jHhh.hGe0JUmHnHu7jHhh.hGe0JUmHnHu    . / 0 1 2 3 4 5 6 R S ʰpaP@+@+(Hhh.hGe0JmHnHuHhhGemHnHu!HhkhCmHnHujhGeUmHnHu0j_HhhGeUmHnHu*jHhhGeUmHnHu!HhhGemHnHu2Hhh.hGe0J6]_H mHnHu1jHhh.hGe0JUmHnHu7jHhh.hGe0JUmHnHuS T U h i j ʰpaP@+@+(Hhh.hGe0JmHnHuHhhGemHnHu!HhkhCmHnHujhGeUmHnHu0jYHhhGeUmHnHu*jHhhGeUmHnHu!HhhGemHnHu2Hhh.hGe0J6]_H mHnHu1jHhh.hGe0JUmHnHu7jHhh.hGe0JUmHnHu ʰpaP@+@+(Hhh.hGe0JmHnHuHhhGemHnHu!HhkhCmHnHujhGeUmHnHu0jSHhhGeUmHnHu*jHhhGeUmHnHu!HhhGemHnHu2Hhh.hGe0J6]_H mHnHu1jHhh.hGe0JUmHnHu7jHhh.hGe0JUmHnHu     0 1 2 3 4 5 6 7 8 T U ʳsdSC.C.(Hhh.hGe0JmHnHuHhhGemHnHu!HhkhCmHnHujhGeUmHnHu0jMHhhGeUmHnHu*jHhhGeUmHnHu!HhhGemHnHu,Hhh.hGe0J_H mHnHu1jHhh.hGe0JUmHnHu7jHhh.hGe0JUmHnHuU V W g h i ʰpaP/@HhhGehCcHdhdhdhkmHnHu!HhkhCmHnHujhGeUmHnHu0jG HhhGeUmHnHu*jHhhGeUmHnHu!HhhGemHnHu2Hhh.hGe0J6]_H mHnHu1jHhh.hGe0JUmHnHu7jHhh.hGe0JUmHnHu ¦׌{e{L=,e!HhkhCmHnHujhGeUmHnHu0jA HhhGeUmHnHu*jHhhGeUmHnHu!HhhGemHnHu2Hhh.hGe0J6]_H mHnHu7j Hhh.hGe0JUmHnHu(Hhh.hGe0JmHnHu1jHhh.hGe0JUmHnHuHhhGemHnHu , - . 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