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2" ANNEX xx
INDICATOR DESCRIPTIONS
Based on contribution from UNEP-WCMC (Annex III), Sherman and others (Annex IV), and other sources.
Others to be included as they become available from experts
For review at second LME meeting, June 23 25, 2010
Productivity Module
Primary productivity
1. IndicatorIndicator NamePrimary productivityCategory StateDefinition of indicator / DescriptorPrimary production (PP) is the formation of organic compounds through fixation of inorganic carbon by living organisms. While seagrasses, macroalgae and coral reefs contribute significantly to PP in coastal zones, especially in the tropics, the bulk of marine PP is carried out by microscopic planktonic algae (phytoplankton), which can be seen from space, thanks to their photosynthetic pigments (mainly chlorophyll). The data allow the classification of LMEs into 3 categories: Class I, high productivity (>300 gCm-2 year-1), Class II, moderate productivity (150-300 gCm-2year-1), and Class III, low (<150 gCm-2 year-1) productivity.Units of measurements gCm-2 year-12. Relevance Rationale for Inclusion
Primary productivity can be related to the carrying capacity of an ecosystem for supporting fish resources (Pauly and Christensen 1995). It has been reported that the maximum global level of primary productivity for supporting the average annual world catch of fisheries has been reached and that further large-scale increases in biomass yields from marine ecosystems are likely to be at trophic levels below fish in the marine food web (Beddington 1995). Measurements of ecosystem productivity can be useful indicators of the growing problem of coastal eutrophication. In several LMEs, excessive nutrient loadings of coastal waters have been related to harmful algal blooms (HABs) implicated in mass mortalities of living resources, emergence of pathogens (e.g., cholera, vibrios, red tides, and paralytic shellfish toxins), and explosive growth of non-indigenous species (Epstein 1993; Epstein 2000).
Significance for inter-linkages with other transboundary water systemsRivers are a central link in the chain of nutrient transfer from watersheds to coastal systems, and resulting eutrophication and water quality degradation in LMEs. Land-based pollution of coastal waters in LMEs can have sources in multiple countries often located upstream at a considerable distance from the coastal zone. The release of nutrients into rivers can cross national borders and create environmental, social and economic impacts along the way - until reaching the coastal zone, which may be in a different country. Linkage with other indicatorsEcosystem parameters measured and used as indicators of changing conditions in the productivity module are zooplankton biodiversity and species composition; zooplankton biomass; sea surface temperature; water-column temperature, density and salinity structure; photosynthetically active radiation; transparency; chlorophyll a; nitrate; and primary production.
HABs 3. MethodologyDescription of measurement methods and calculation of the indicatorSee Annex IV for details. Primary productivity estimates are derived from satellite borne data. These estimates originate from SeaWiFS (satellite-derived chlorophyll estimates from the Sea-viewing Wide Field-of-view Sensor), Coastal Zone Color Scanner (CZCS), a large archive of in situ near-surface chlorophyll data, and satellite sea surface temperature (SST) measurements to quantify spatial and seasonal variability of near-surface chlorophyll and SST. Productivity is calculated for the 100 layers? in the euphotic zone and summed to compute the integral daily productivity (gC m-2 d-1). Daily estimates of global primary productivity were calculated using the Ocean Productivity from Absorption and Light model, a derivative of the model first formulated in Marra et al. (2003). See Annex IV for a detailed description.
The Sea Around Us project presents PP estimates (spatial resolution of 9 km) based on a model described by Platt and Sathyendranath (1988). The model estimates depth integrated PP based on chlorophyll pigment concentration as derived from SeaWiFS ( HYPERLINK "http://seawifs.gsfc.nasa.gov/SEAWIFS.html" http://seawifs.gsfc.nasa.gov/SEAWIFS.html) data, and photosynthetically active radiation calculated as in Bouvet et al. (2002). The model for estimating primary productivity relies on monthly estimates of chlorophyll and sunlight for any spatial cell of the oceans.
ScaleLME; transboundary hotspots Limitations and uncertainty4. Assessment of DataData sources, availability and quality Properly calibrated satellite data can provide information on ecosystem conditions including physical state (i.e., surface temperature, nutrient characteristics, primary productivity, and phytoplankton species composition ADDIN EN.CITE ADDIN EN.CITE.DATA (Aiken, Pollard et al. 1999; Berman and Sherman 2001; Melrose, Oviatt et al. 2006).
Mean annual values for chlorophyll a (mg/m3) and primary productivity (gC/m/yr) are available for all 64 LMEs (Published in Sherman and Hempel 2008 UNEP LME Report).
Variations among data sources and alternative methodsThe Sea Around Us Project (through the Inland and Marine Waters Unit, Institute for Environment & Sustainability, EU Joint Research Center, Ispra, Italy)5. PartnersPartners/agencies involved in the development of the indicatorNOAAs Northeast Fisheries Science Center, Narragansett Laboratory; University of Rhode Island; Sea Around Us Project; others?6. References
Harmful Algal Blooms
1. IndicatorIndicator NameHarmful Algal Blooms (Incidence)Category StateDefinition of indicator / DescriptorIncidence of HABsUnits of measurements 2. Relevance Rationale for Inclusion
The most direct response of coastal ecosystems to increased nutrient loading is an increase in biomass (e.g., chlorophyll a) of primary producers or primary production rates (Nixon 1995). Eutrophication the overenrichment of waters by nutrients (N and P) threatens and degrades many coastal ecosystems around the world. The two most acute symptoms of eutrophication are hypoxia (oxygen depletion) or dead zones and harmful algal blooms (HABs). Of the 415 areas around the world identified as experiencing some form of eutrophication, 169 are hypoxic and only 13 systems are classified as systems in recovery. (Selman et al. 2008). Many LMEs are currently hotspots of nitrogen loading in both developed and developing countries (Seitzinger and Lee 2008). Eutrophication issues in LMEs are transboundary in nature, as nutrients may originate from watersheds that span multiple countries (Seitzinger et al. 2008) or have transboundary impacts.
Hypoxia, considered to be the most severe symptom of eutrophication, has escalated dramatically over the past 50 years (WRI). Proliferations of microalgae in marine or brackish waters can cause massive fish kills, contaminate seafood with toxins, and alter ecosystems. When HABs contaminate or destroy coastal resources, the livelihoods of local residents are threatened and the sustenance of human populations is compromised.The need to address nutrient over-enrichment as a priority threat to coastal waters and
LMEs has been recognized at national and global levels. The GPA, which was adopted by 108 Governments and the European Commission in 1995, recognized the need for global, regional and national action to address nutrients impacting the coastal and marine environment.Significance for inter-linkages with other water systemsLinked to rivers - the major input of nutrients to coastal areas is from landbased sources (e.g. through rivers) Linkage with other indicatorsNutrients, SST3. MethodologyDescription of measurement methods and calculation of the indicatorDocumentation; satellite observationsScaleLME; transboundary hotspotLimitations Data do not exist or are not publicly available for many areas that
may be experiencing eutrophication (Selman et al. 2008).4. Assessment of DataData sources, availability and quality (Existing datasets)UNESCO-IOC HABs programme and database; regional initiatives, e.g. NOAA Harmful Algal Blooms Observing System (HABSOS) for Gulf of Mexico; LME projects (Benguela Current); WRI?Variations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicatorUNESCO-IOC, WRIReferences
Seitzinger, S. and Lee, R. (2008). Land-based nutrient Loading to LMEs: A Global Watershed Perspective on Magnitudes and Sources. pp.81-97 in The UNEP Large Marine Ecosystem Report: A Perspective on Changing Conditions in LMEs of the Worlds Regional Seas;
Seitzinger, S., Sherman, K., and Lee, R. (2008). Filling gaps in LME nitrogen loadings forecast for 64 LMEs. GEF/LME global project - Promoting Ecosystem-based Approaches to Fisheries Conservation and Large Marine Ecosystems. IOC Technical Series No. 79. UNESCO, 2008;
Selman, M., Greenhalgh, S., Diaz, R., and Zachary, S. (2008). Eutrophication and hypoxia in
coastal areas: a global assessment of the state of knowledge. Washington, DC: World
Resources Institute.
Sea surface temperature
1. IndicatorIndicator NameSea surface temperature (SST); SST anomaliesCategory StateDefinition of indicator / DescriptorAverage temperature of sea surface water (depth??) Units of measurements Degree Centigrade 2. Relevance Rationale for Inclusion
The Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) stated with high confidence that changes in marine biological systems are associated with rising water temperatures affecting shifts in pelagic algae and other plankton and fish abundance in high
latitudes. Consistent global-scale warming of LMEs, with the notable exceptions of twothe California Current and Humboldt Current and change in biomass yields have been reported by Sherman et al. 2009. Significance for inter-linkages with other water systemsLinkage with other indicators3. MethodologyDescription of measurement methods and calculation of the indicatorSee Annex IV. SSTs have been calculated from the U.K. meteorological Office Hadley Centre SST climatology data (Belkin 2009). To calculate and visualize annual SSTs for each LME, the annual SST for each 1 x 1 cell was calculated and the area-averaged annual 1 x 1 SSTs within each LME. Annual anomalies of annual LME-averaged SSTs were calculated by computing the long-term LME-averaged SST for each LME by a simple long-term averaging of the annual area-weighted LME-averaged SSTs. Then, annual SST anomalies were calculated by subtracting the long-term mean SST from the annual SST. Both SST and SST anomalies were visualized using adjustable temperature scales for each LME in order to bring out details of temporal variability that otherwise would be hardly noticeable if a unified temperature scale were used. The resulting plots of SST and SST anomalies are for 63 LMEs. Ice cover precludes a meaningful assessment of the LME-averaged SST for the Arctic Ocean.ScaleLMELimitations 4. Assessment of DataData sources, availability and quality (Existing datasets)The U.K. Meteorological Office Hadley Center SST climatology data was selected for its superior resolution (1 degree latitude by 1 degree longitude globally), for the historic reach of the data, and for its high quality. A highly detailed, research-level description of this data set has been published by Rayner et al. (2003). Completed SST gradients, anomalies and front locations for all 64 LMEs are published in the UNEP LME Report 2008.
Variations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicatorUniversity of Rhode Island, Others?6. References
Sherman, K., I. M. Belkin, K. D. Friedland, J. OReilly and K. Hyde (2009). Accelerated Warming and Emergent Trends in Fisheries Biomass Yields of the Worlds Large Marine Ecosystems. Ambio Vol. 38, No. 4, June 2009
Fish and Fisheries Module
Reported Landings
1. IndicatorIndicator NameReported landings (by species; functional groups; commercial species; total reported landings by LMEs; gears)Category State/StressDefinition of indicator / DescriptorUsually refers only to that part of the catch that is both landed and reported (Ref). Fishing fleets catch fish, but do not retain all they catch. Some are discarded before the vessels return to port. Landings do not include the fish and invertebrates discarded at sea. Moreover, some of the landed catch may remain unrecorded (especially when it is caught illegally). Thus the precise term is reported landings.
Units of measurements Weight (tonnes; kg)2. Relevance Rationale for Inclusion
LMEs account for the overwhelming part of the world catch, i.e., between 76% (1990) and 91% (1968) of global catch (ref). However, the average contribution of LME catches appears to have slightly declined over time, from around 89-90% in the early decades to around 78-81% for recent time periods. Overexploitation/ depletion of fish stocks are among the priority transboundary issues identified in transboundary diagnostic analyses of LMEs and by the Global International Waters Assessment. The transboundary significance of fisheries could be attributed to the transboundary distribution of certain fish stocks (e.g. migratory species), location of spawning and nursery grounds, and exploitation by multiple countries. One of the foci of GEF LME projects is to recover depleted fisheries in Africa, Asia, Latin America, and Eastern Europe.Significance for inter-linkages with other water systemsLinkage with other indicators3. MethodologyDescription of measurement methods and calculation of the indicatorSee Annex IV and Sea Around Us Project ( HYPERLINK "http://www.seaaroundus.org" www.seaaroundus.org) for details. Sea Around Us Project of the University of British Columbia Fisheries Center produces time series (from 1950) of fisheries catches by LME. The method used by the Sea Around Us Project to map catches onto degree lat.-long. spatial cells has been described by Watson et al. (2003, 2004, 2005) in some detail (see Annex IV and http://www.seaaroundus.org/doc/saup_manual.htm#13). The method relies on splitting the world oceans into more than 180,000 spatial cells of degree lat.-long., and mapping onto these cells, by species and higher taxa, all catches that are extracted from such cells. The catches in these spatial cells can then be regrouped into higher spatial aggregates, for example, the EEZs of maritime countries or the LMEs that have been so far defined in the worlds oceans (Watson et al. 2004).
Time series (1950 2005) of reported landings by LMEs published in the UNEP LME Report 2008.
ScaleLMELimitations This and other fisheries indicators require accurate and complete catch data. Such catch data, however, are not available for all LMEs. The methods used for re-expressing FAOs global reported landings dataset on a spatial basis, here through LMEs, cannot compensate for these limitations. Rather, it makes them visible, and emphasizes the need for catch reconstruction at the national level (sensu Zeller et al. 2006, 2007), from which LME catch time series can then be derived.4. Assessment of DataData sources, availability and quality Data available from 1950 from Sea Around Us Project. Data come from a variety of sources that are harmonized by the Sea Around US project to create a single dataset representing global catches since 1950. Data are available by LMEs (annually from 1950 and being updated to 2010). Landings data are sourced from FISTAT, the database of the United Nations Food and Agriculture Organization (FAO) and a number of national and regional sources; FishBase (HYPERLINK "http://www.fishbase.org"www.fishbase.org) for fish, and from various sources, all consolidated in SeaLifeBase (HYPERLINK "http://www.sealifebase.org"www.sealifebase.org) for invertebrates (see Annex IV). In addition to catch by species, the Sea Around Us Project presents, for all but the six Arctic LMEs located fully in FAO Area 18, catches by Commercial groups, Functional groups, Country fishing, and Gear, based on Watson et al. (2006a, 2006b).
FAO Area 18 is an area with extremely low fish catches. However, landings are not as low as reflected by the FAO data from that area, and the negligible (often zero) catches officially reported from this area are mainly the result of Russia, the USA and Canada not reporting adequately on the small-scale fisheries in their section of the Arctic (ref). To complete the coverage of the worlds LMEs, and to produce a baseline against which future fisheries development in the Arctic can be assessed, the Sea Around Us Project undertook a reconstruction of catch time series for FAO Area 18.
Variations among data sources and alternative methodsNA5. PartnersPartners/agencies involved in the development of the indicatorSea Around Us Project, University of British Columbia6. References
Fishing Effort (under development by UBC Sea Around Us project)
1. IndicatorIndicator NameFishing EffortCategory Stress/pressureDefinition of indicator / DescriptorUnits of measurements 2. Relevance Rationale for Inclusion
Significance for inter-linkages with other water systemsLinkage with other indicators3. MethodologyDescription of measurement methods and calculation of the indicatorScaleLimitations 4. Assessment of DataData sources, availability and quality (Existing datasets)Variations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicator6. References
Value of reported landings
1. IndicatorIndicator NameValue of reported landings by major commercial groupsCategory Socioeconomic indicatorDefinition of indicator / DescriptorEx-vessel value of reported landings by LME, based on real 2000 prices, i.e., deflated prices (Sumaila et al. 2007).
Units of measurements US$2. Relevance Rationale for Inclusion
Fishing is an economic operation and the ex-vessel value of the landings has to cover all fixed and variable costs of fishing and still generate a profit, except when fisheries are subsidized (Sumaila & Pauly 2006). LMEs account for most of the value of marine fisheries catches in the world with values ranging from 71-90% of global landings value. Changes in the value of the landings could be due to a number of factors, including changes in the condition of the fish stocks being exploited. Significance for inter-linkages with other water systemsNALinkage with other indicatorsReported landings3. MethodologyDescription of measurement methods and calculation of the indicatorSee Annex IV and Sea Around Us Project ( HYPERLINK "http://www.seaaroundus.org" www.seaaroundus.org) for details. The year-, species- and time-specific prices are adjusted for inflation to year 2000 real prices in US$, using consumer price index (CPI) data from the World Bank, and multiplied by the spatially allocated landings for the corresponding years and species (groups). This yielded time series of the value of fisheries landings in year 2000 inflation adjusted prices, which can be compared in time and space (Sumaila et al. 2007). Ex-vessel prices and landed values are presented in US$ to allow a uniform basis for comparison. However, the starting point for the data is always local ex-vessel prices in local currency, which are converted into US$ equivalents.
Time series (1950 2005) of value of reported landings by LMEs published in UNEP LME Report 2008.
ScaleLMELimitations 4. Assessment of DataData sources, availability and quality (Existing datasets)Data available from 1950 from Sea Around Us Project. To be able to evaluate the ex-vessel value of fisheries worldwide, a database of ex-vessel fish price data was constructed, based on 1) observed prices in different countries at different times for different species; and 2) inferred prices, based on observed prices and an averaging algorithm that took taxonomic affinity, adjacency of countries and time into account (Sumaila et al. 2007).
There are two parallel parts to the database, namely, nominal and real ex-vessel prices and landed values. The real numbers were determined by using local consumer price indices (CPI) to convert local nominal ex-vessel prices into real (year 2000) ex-vessel prices. These are then converted into year 2000 US$ equivalents.Variations among data sources and alternative methodsNA5. PartnersPartners/agencies involved in the development of the indicatorSea Around Us Project6. References
Marine Trophic Index
1. IndicatorIndicator NameMarine Trophic Index (MTI)Category StateDefinition of indicator / DescriptorThe MTI is the Convention on Biological Diversitys (CBD) name for the mean trophic level (TL) of fisheries landings.Units of measurements 2. Relevance Rationale for Inclusion
When a fishery begins in a given area, it usually targets the largest among the accessible fish, which are also intrinsically most vulnerable to fishing (Cheung et al. 2007). Once these are depleted, the fisheries then turn to less desirable, smaller fish. Since 1950, fisheries worldwide are increasingly relying on the smaller, short-lived fish and on the invertebrates from the lower parts of both marine and freshwater food webs, a phenomenon now widely known as fishing down marine food webs (Pauly et al. 1998). Declining TL trends indicate declining abundances among the larger fishes at the top of marine food webs relative to low-TL fishes, and thus impacts on their biodiversity, both in terms of within-species abundance, and, in the longer term, the number of species. The trend in mean trophic level for all LMEs combined indicates a decline in the MTI from a peak in the 1950s to a low in the mid 1980s. This is attributed to fishing down marine food webs (Pauly et al. 1998, Pauly and Watson 2005), attenuated by an offshore expansion of the fisheries (Morato et al. 2006). In the mid 1980s, the continued offshore expansion, combined with declining inshore catches led to a trend reversal in the MTI, i.e., to the fishing down effect being completely occulted.
In February 2004, the Conference of the Parties to the CBD identified a number of indicators to monitor progress toward reaching the target to achieve by 2010 a significant reduction in the current rate of biodiversity loss (CBD 2004). The Marine Trophic Index (MTI) is one of the eight indicators that the Conference of the Parties of the CBD identified for immediate testing of their ability to measure progress towards the 2010 biodiversity target (CBD 2004, Pauly & Watson 2005).
Significance for inter-linkages with other water systemsLinkage with other indicatorsFishing in Balance Index (FIB)3. MethodologyDescription of measurement methods and calculation of the indicatorSee Annex IV and Sea Around Us Project ( HYPERLINK "http://www.seaaroundus.org" www.seaaroundus.org) for details. The original demonstration of this effect, now widely known as fishing down marine food webs, relied upon the global database of fish landing assembled and maintained by FAO. T h i s d a t a b a s e i n c l u d e s t h e a n n u a l f i s h e r i e s c a t c h e s ( s i n c e 1 9 5 0 ) o f m e m b e r c o u n t r i e s , b y s p e c i e s o r g r o u p s o f s p e c i e s ( u s u a l l y g e n e r a o r f a m i l i e s ) . U s i n g t h e s e d a t a a n d c o r r e s p o n d i n g T L e s t i m a t e s , m e a n T L w e r e c o m p u t e d , f o r e a c h y e a r k f r o m :
T L k = " i ( T L i Y i k ) / " i Y i k
w h e r e Y i r e f e r s t o t h e l a n d i n g s o f s p e c i e s ( g r o u p ) i , a s i n c l u d e d i n f i s h e r i e s s t a t i s t i c s .
T h e a s s u m p t i o n i s m a d e t h a t t h e r e l a t i v e a b u n d a n c e o f t a x a i n t h e l a n d i n g d a t a u s e d i n t h e a n a l y s i s i s c o r r e l a t e d w i t h t h e r e l a t i v e a b u n dance of the same taxa in the ecosystem.
It has been proposed that mean TL, if used to document fisheries impact on marine ecosystems, should generally be computed after excluding low-TL species from the analysis (Ref). Thus, the MTI, which is based on mean TL, should be in fact based on time series that exclude low-TL organisms, and hence bottom-up effects. This would lead to an indicator that may be labeled cutMTI, with the superscript referring to the lowest (cutoff) TL value used in the computation, e.g. 3.25MTI. The value of 3.25 is suggested as standard cutoff TL to eliminate, besides herbivores and detritivores, the planktivores whose high biomass tend to vary widely in response to environmental factors for example Peruvian anchoveta - and thus mask TL changes induced by fishing. It is strongly recommend that any omitted species be explicitly mentioned when time series of MTI values are presented (ref).
Time series (1950 2005) of MTI by LMEs published in UNEP LME Report 2008.
ScaleLMELimitations Diagnosing fishing down the food web from the mean TL of landings is problematic, as landings reflect abundances only crudely. Also, a fishery that has overexploited its resource base, e.g., on the inner shelf, will tend to move to the outer shelf and beyond (Morato et al. 2006). There, it accesses hitherto unexploited stocks of demersal or pelagic fish, and the MTI calculated for the whole shelf, which may have declined at first, increases again, especially if the new landings are high. Thus, at the scale of an LME, a trend reversal of the MTI may occur when the fisheries expand geographically. This is the reason why the diagnosis as to whether fishing down occurs or not, performed for many of the LMEs in this volume, generally depends on the species composition of the landings, which may indicate whether a geographic expansion of the fishery has taken place. To facilitate this evaluation, a time series of the Fishing-in-Balance (FiB) index is also presented for each LME (see following Indicator).
4. Assessment of DataData sources, availability and quality (Existing datasets)Data available from 1950 from Sea Around Us Project, being updated to 2010.
The basic assumption of the MTI is that it reflects processes involving shifts in the relative and/or absolute abundance of species in ecosystems. For this assumption to be met, even if partly, catches, therefore, must cover the taxonomic range that is actually extracted from the ecosystems, not only a few species that are reported only because they happen to be of high export value.The Sea Around Us Project works with partners in numerous countries to re-construct catch statistics covering the whole range of their fisheries, but this process is slow, and the data may not be the best available for computing time series of the MTI for a given country.
Another problem is the miscellaneous fishes which certain countries choose to report much of their catch. Clearly, misc. fishes have no distinct trophic level, and the MTI cannot be computed for them. Hence, in countries in which misc. fishes predominate, the MTI is based on a few species only, representing a small fraction of the catch. The computation of the MTI and derived indicators now use only explicitly reported taxa. This will cause only slight difference with MTI values computed earlier, while increasing reproducibility.
Variations among data sources and alternative methodsNA5. PartnersPartners/agencies involved in the development of the indicatorSea Around Us Project. 6. References
Fishing in Balance Index
1. IndicatorIndicator NameFishing in Balance Index (FIB)
Category Definition of indicator / DescriptorUnits of measurements 2. Relevance Rationale for Inclusion
Complementing the MTI is the FIB index. Pauly et al. (2000) defined the FiB index such that its value remains the same when a downward trend in mean trophic level is compensated for by an increase in the volume of catch, as should happen given the pyramidal nature of ecosystems and the transfer efficiency of about 10% between trophic levels. The FIB has the property of increasing if catches increase faster than would be predicted by TL declines, and to decrease if increasing catches fail to compensate for a decrease in TL.The FIB index will decline when both the MTI and landings decline, as now happens in many LMEs. On the other hand, the FIB index will increase if increases in landings more than compensate for a declining MTI. In such cases (and obviously also in the case when landings increases and the MTI is stable or increases), the FiB index increases indicate that a geographic expansion of the fishery has taken place, i.e., that another part of an ecosystem is being exploited (Bhathal & Pauly in press). Marine ecosystems operate as pyramids wherein the primary production generated at TL one is moved up toward the higher TL, with a huge fraction of that production being wasted in the process for the maintenance, reproduction and other activities of the animals in the systems (Pauly and Christensen 1995). Thus, deliberately fishing down should enable more of an ecosystems biological production to be captured by fishing. However, to avoid waste here as well, any decline in the mean TL of the fisheries catches should, in this case, be matched by an ecologically appropriate increase in these catches, the appropriateness of that increase being determined by the transfer efficiency between TL. The absolute value of the FiB index can be applied to assess the change of the FiB index from any baseline. It is standardized to have a value of zero in 1950.
Significance for inter-linkages with other water systemsLinkage with other indicatorsMTI3. MethodologyDescription of measurement methods and calculation of the indicatorSee Annex IV and Sea Around Us Project ( HYPERLINK "http://www.seaaroundus.org" www.seaaroundus.org) for details. The FiB index is:
FiB k = log[Y k (1/TE) TL k] log[Y 0 (1/TE) TL0]
where Y is the catch in year k, TL the mean trophic level in the catch, TE the mean transfer efficiency (specific to an ecosystem, often set at 0.1; see below), i refers to species (groups) in the catch, and 0 refers to any year used as a baseline to normalize the index. Any year may serve as baseline; the year 1950 is here offered as default, but can be replaced by any other year deemed appropriate. Similarly, the default TE value of 0.1, the mean of a number of marine ecosystems (Pauly and Christensen 1995), can be replaced by any realistic value between 0.01 and 0.30.Documentation is important when presenting results.
Time series (1950 2005) of FIB by LMEs published in UNEP LME Report 2008.
ScaleLMELimitations 4. Assessment of DataData sources, availability and quality (Existing datasets)Data available from 1950 from Sea Around Us ProjectVariations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicatorSea Around Us Project6. References
Primary Production Required (PPR) by fisheries landings
1. IndicatorIndicator NamePrimary Production Required (PPR) by fisheries Category Definition of indicator / DescriptorPrimary Production Required (PPR) to produce a given tonnage of fishUnits of measurements 2. Relevance Rationale for Inclusion
Application of the ecological footprint concept to LMEs, the footprint of fisheries is an indicator of sustainability, as shown through the Primary Production Required by fisheries within LMEs. PPR, when related to observed primary production, provides another index for assessing the impact of the countries fishing in LMEs. The fraction of primary production required has increased steadily over the years, in line with increasing reported landings, and is approaching 20%. It has been shown that phytoplanktonic primary production, estimated from an ocean-colour satellite (SeaWiFS), is related to global fisheries catches at the scale of LMEs (Chassot et al. 2010). Global fisheries catches since 1950 have been increasingly constrained by the amount of primary production. The primary production appropriated by current global fisheries is 17112% higher than that appropriated by sustainable fisheries. Global primary production appears to be declining, in some part due to climate variability and change, with consequences for the near future fisheries catches (Chassot et al. 2010).
Significance for inter-linkages with other water systemsLinkage with other indicators3. MethodologyDescription of measurement methods and calculation of the indicatorSee Annex IV and Sea Around Us Project ( HYPERLINK "http://www.seaaroundus.org" www.seaaroundus.org) for details; also Pauly et al. (2008). Calculation of the primary production required to produce a given tonnage of fish is based on the average trophic level of the fish in question, an assumption about trophic efficiency (here 10%) and the equation PPR = landings10(TL-1) (Christensen and Pauly 1995). PPR is calculated separately for each species (or group of species) for the fleets of all countries operating in the LME in question, expressed in terms of the primary production in that LME. The combined footprint of different countries fishing in a given LME area can thus be assessed. To facilitate comparisons between LMEs, the maximum fraction (of PPR, in terms of primary production in each LME) is computed as the mean of values for the five years with the highest PPR value.
Extremely high values of PPR (above a fraction of 0.5) point at serious problems, including:
The assumptions and data used for implementing the method itself (i.e., the use of one years worth of SeaWifs global remote sensing data as a proxy for primary production for all years from 1950 to 2004, everywhere);
Over-reported landings;
Extensive range extension in periods of peak abundance, e.g., in Japanese sardine (Watanabe et al. 1996), or migration of targeted species, especially feeding migrations, extending beyond the limits of an LME;
High reported landings from exploitation of accumulated biomass, rather than exploitation of annual surplus production.
ScaleLMELimitations 4. Assessment of DataData sources, availability and quality (Existing datasets)Data available from 1950 from Sea Around Us ProjectVariations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicatorSea Around Us ProjectReferences
Chassot, E., S. Bonhommeau, N. K. Dulvy, F. Mlin, R. Watson, D. Gascuel and O. Le Pape (2010). Global marine primary production constrains fisheries catches. Ecology Letters (2010) 13: 495505
Stock-Catch-Status Plots
1. IndicatorIndicator NameStock-catch status Category Definition of indicator / DescriptorA newly proposed type of paired Stock-Catch-Status Plots (Percentage of stocks of a given status and Percentage of catches extracted from stocks of a given status), wherein the status of stocks, i.e., species with a time series of landings in an LME, is assessed, based on Froese & Kesner-Reyes (2002), using the following criteria (all referring to the maximum catch in the series): Developing (catches < 50 %); Fully exploited (catches >= 50%); Overexploited (catches between 50% and 10%); Collapsed (catches < 10%). Units of measurements 2. Relevance Rationale for Inclusion
The number of collapsed stocks is increasing alarmingly throughout the world, as can be seen in the LME-specific Stock-Catch-Status Plots included in the UNEP LME Report 2008 (Pauly et al. 2008). Overall, 70% of global stocks within LMEs are deemed overexploited or collapsed, and only 30% fully exploited (Pauly et al. 2008). However, the latter stocks still provide 50% of the globally reported landings biomass, with the remainder produced by overexploited and collapsed. This confirms the common observation that fisheries tend to affect biodiversity even more strongly than they affect biomass.
Significance for inter-linkages with other water systemsLinkage with other indicators3. MethodologyDescription of measurement methods and calculation of the indicatorFroese & Kesner-Reyes (2002) defined for any time series, five phases relative to the maximum reported landing in that time series, representing a stock. They are:
Undeveloped: Year of landing is before the year of maximum landing, and landing is less than 10% of the overall maximum;
Developing: Year of landing is before the year of maximum landing, and landing is between 10 and 50 % of the overall maximum;
Fully exploited: Landing is greater than 50% of maximum years landing;
Overexploited: Year of landing is after year of maximum landing, and landing is between 10 and 50% of the overall maximum; and
Collapsed: Year of landing is after the year of maximum landing, and landing is below 10% of the overall maximum.
Here, a stock is defined as a time series of one species, genus or family for which the first and last reported landings are at least 10 years apart, for which there are at least 5 years of consecutive catches and for which the catch in a given LME is at least 1,000 tonnes (Pauly et al. 2008).
ScaleLMELimitations 4. Assessment of DataData sources, availability and quality (Existing datasets)Variations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicator6. References
Fish Biomass
1. IndicatorIndicator NameFish Biomass Category StateDefinition of indicator / DescriptorFish biomass in the worlds LMEs (first estimate)Units of measurements Tonnes; tonnes/km22. Relevance Rationale for Inclusion
LMEs face serious threats throughout the world. One important threat is that they are overfished due to excessive effort capacity. (To be expanded)Significance for inter-linkages with other water systemsLinkage with other indicators3. MethodologyDescription of measurement methods and calculation of the indicatorSee Annex IV (Christensen et al. 2008) for detailed description. A new methodology for database-driven ecosystem model generation applied to the worlds 66 currently defined LMEs. The method relies on a large number of spatial and temporal databases, including FishBase, SeaLifeBase, as well as several other databases developed notably as part of the Sea Around Us project. The models are formulated using the freely available Ecopath with Ecosim modeling approach and software. ScaleLMELimitations 4. Assessment of DataData sources, availability and quality (Existing datasets)The biggest hurdles at present to further model development and validation are insufficient time series trend information, and data on spatial fishing effort.Variations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicatorReferences
Christensen, V. et al. (2008). Models of the worlds large marine ecosystems. GEF/LME global project Promoting Ecosystem-based Approaches to Fisheries Conservation and Large Marine Ecosystems, Intergovernmental Oceanographic Commissions Technical Series No. 80. UNESCO.
Christensen, V. et al. (2009). Database-driven models of the world's Large Marine Ecosystems. Ecological Modelling 220 (17), 19841996.
Pollution and Ecosystem Health
Pollution
Nutrients (Nitrogen, Phosphorus)
1. IndicatorIndicator NameNutrient input to LMECategory StressDefinition of indicator / DescriptorUnits of measurements Tonnes/year2. Relevance Rationale for Inclusion
Human activities related to food and energy production have greatly increased the amount of nutrient pollution entering the coastal environment from land-based sources (Howarth et al. 1996; Seitzinger and Kroeze 1998; Galloway et al. 2004; Green et al. 2004). A high degree of nitrogen and phosphorus enrichment, causing eutrophication of coastal waters, can result in blooms of phytoplankton, macroalgae, and nuisance/toxic algae. When phytoplankton blooms die and sink, decomposition of the biomass consumes and may deplete dissolved oxygen in the bottom water resulting in hypoxic or dead zones. Many LMEs are currently hotspots of nitrogen loading in both developed and developing countries (Seitzinger and Lee 2008). Eutrophication has contributed to important alterations in the structure and dynamics of certain LMEs (e.g. Black Sea LME). Over the next 50 years, human population, agricultural production, and energy production are predicted to increase especially rapidly in many developing regions of the world (Hassan et al. 2005). Focus is on N because it is often the most limiting nutrient in coastal waters and thus important in controlling coastal eutrophication. Dissolved Inorganic Nitrogen (DIN) is often the most abundant and bioavilable form of nitrogen, and therefore contributes significantly to coastal eutrophication.
Land-based pollution of coastal waters in LMEs can have sources in multiple countries often located upstream at a considerable distance from the coastal zone. The release of nutrients into rivers can cross national borders and create environmental, social and economic impacts along the way - until reaching the coastal zone, which may be in a different country. Thus an LME transboundary approach is essential for identifying watershed nutrient sources and coastal nutrient loading to support policy development and implementation in LMEs that will reduce current and future coastal eutrophication. The need to address nutrient over-enrichment as a priority threat to coastal waters and LMEs has been recognized at national and global levels.
The Global Plan of Action for the Protection of the Marine Environment from Land-based Activities (GPA), which was adopted by 108 Governments and the European Commission in 1995, recognized the need for global, regional and national action to address nutrients impacting the coastal and marine environment. Continued widespread
government support to address nutrients has been noted in both the Montreal and Beijing Declarations. In 2002, the World Summit on Sustainable Development convened in Johannesburg identified substantial reductions in land-based sources of pollution by 2006
as one of their 4 marine targets.
Significance for inter-linkages with other water systemsStrong linkage with rivers - Rivers are a central link in the chain of nutrient transfer from watersheds to coastal systems (Seitzinger and Lee 2008). Related Rivers indicators: water quality index, fertilizer consumption, average dischargeLinkage with other indicatorsPrimary productivity, HABs, (relevant socioeconomic activities that give rise to elevated nutrient inputs; response indicators such as adoption of ICZM, IWCAM, GPA); water quality index, fertilizer consumption, average discharge3. MethodologyDescription of measurement methods and calculation of the indicatorSee Annex IV for detailed description. A spatially explicit model - Global Nutrient Export from Watersheds model (NEWS) is used to relate human activities and natural processes in watersheds to nutrient inputs to LMEs, with a focus on nitrogen (Seitzinger and Lee 2008). The model is a multi-element, multi-form, spatially explicit global model of nutrient (N, P, and C) export from watersheds by rivers. The model output is the annual export at the mouth of the river (essentially zero salinity). The NEWS model is calibrated and validated with measured export near the river mouth from rivers representing a broad range of basins sizes, climates, and land-uses.Over 5000 watersheds are included in the model with the river network and water discharge defined by STN-30 (Fekete et al. 2000; Vrsmarty et al. 2000a and b). The input databases are at the scale of 0.5o latitude by 0.5o longitude. The model can be used to predict magnitudes and sources of multiple bio-active elements (C, N, and P) and forms (dissolved/particulate,organic/inorganic).
The NEWS model has provided the first spatially distributed global view of N, P and C export by world rivers to coastal systems.
Total DIN load to each LME was aggregated from all watersheds with coastlines along that LME for point sources and only those watersheds with discharge to that LME for diffuse sources. This work was part of the GEF Medium-Sized Project: Promoting Ecosystem-based Approaches to Fisheries Conservation and LMEs (Component 3: Seitzinger and Lee 2007). Using the NEWS model, a range of alternative scenarios for the years 2030 and 2050 based on the Millennium Ecosystem Assessment (www.millenniumassessment.org) are currently being analyzed to provide insights into how changes in technological, social, economic, policy and ecological considerations could alter future nutrient export to coastal systems around the world (Seitzinger et al. in prep.). Based on a business-as-usual scenario, inorganic N export to coastal systems is predicted to increase 3-fold by the year 2050 (relative to 1990) from Africa and South America (Kroeze and Seitzinger, 1998; Seitzinger et al., 2002b). Substantial increases are predicted for Europe (primarily eastern Europe) and North America. Alarmingly large absolute increases are predicted for eastern and southern Asia; almost half of the total global increased N export is predicted for those regions alone.
ScaleRiver basin; LMELimitations 4. Assessment of DataData sources, availability and quality (Existing datasets)Seitzinger and Lee (2008) have produced estimates of DIN inputs to all LMEs (except Antarctic) from land-based sources predicted by the NEWS DIN model Variations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicatorIGBP6. References
Freshwater Discharge
1. IndicatorIndicator NameFreshwater discharge (average)Category State/stressDefinition of indicator / DescriptorAverage volume of water discharged from rivers Units of measurements Volume/year2. Relevance Rationale for Inclusion
Freshwater input (and associated silt and nutrients) plays an important role in the health and productivity of coastal habitats (mangroves, estuaries, deltas, etc). Human activity (e.g. land use changes, impoundment of natural water courses) and climate variability are altering the natural input of freshwater (along with nutrients and silt) to coastal areas, transforming coastal habitats such as deltas, estuaries, mangroves, seagrass beds and coral reefs. This phenomenon is already evident in a number of LMEs (e.g.). Alteration of these habitats has serious consequences for provisioning of ecosystem services and the human communities and socioeconomic activities that depend on them. Significance for inter-linkages with other water systemsKey interlinkage with Rivers Linkage with other indicators3. MethodologyDescription of measurement methods and calculation of the indicatorCould be determined for LMEs using the Global NEWS model (see above)ScaleLME, River basin, Critical transboundary habitats (e.g. deltas, estuaries)Limitations 4. Assessment of DataData sources, availability and quality (Existing datasets)IGBP; FAO Aquastat (available by country); Global Runoff Data Centre (Germany)?Variations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicatorIGBP; Global Runoff Data Centre6. References
Ocean Acidification (From Carol Turley, Open Ocean Group)
1. IndicatorIndicator NameOcean Acidification (OA)Category Definition of indicator / DescriptorOcean acidification is the term commonly used for the change in carbonate chemistry, including carbonate ion saturation, pH and the depth of the saturation horizon for the carbonate minerals, aragonite and calcite. Ocean acidification is caused by ocean uptake of anthropogenic C02 and ocean acidity has increased by 30% over the last 200 yrs. During this time, the ocean has absorbed nearly 500 billion tons of carbon dioxide from the atmosphere, or about one-third of anthropogenic carbon emissions.and will continue changing rapid; as atmospheric CO2 increases.
Units of measurements 2. Relevance Rationale for Inclusion
Over the past 200 years, the oceans have absorbed approximately a quarter of the CO2 produced from human activities. This has affected ocean chemistry and has caused the oceans (which are on average slightly alkaline) to become more acidic. Ocean acidification impacts on marine life will depend on the rate and magnitude of changes in ocean chemistry and biological responses. Impacts are already being observed in the polar and tropical regions. Coral calcification rates have declined in recent decades, although attributing causes for these impacts among multiple drivers (acidification, warming, pollution, etc.) is a challenge. Fundamental ecological ocean processes will be affected as many marine organisms depend directly or indirectly
on calcium carbonate saturated waters and are adapted to current levels of seawater pH for physiological and metabolic processes such as calcification, growth and reproduction. The pH changes expected will exceed the seasonal and regional variations currently experienced naturally. Coastal upwelling of deep CO2 and nutrient rich water makes many of the LMEs especially vulnerable to early OA impacts (e.g. Western Coast of North America).
Significance for inter-linkages with other water systemsLinkage with other indicators3. MethodologyDescription of measurement methods and calculation of the indicatorNumerous experiments have shown that corals are extremely sensitive to OA and therefore represent excellent sentinel indicator species and ecosystems for assessing acidification effects the ocean equivalent of the canary in the cage. They are sites of special socio-economic and biodiversity importance. Thus monitoring these sites for both OA chemical changes and the growth or calcification of reefs is proposed at the range of current and future sites.ScaleThe scale for indicator application is of key importance at open ocean, regional (LME) and local level (e.g. estuaries, upwelling, coastal zones, shelves). It is a key issue for TWAP.Limitations 4. Assessment of DataData sources, availability and quality (Existing datasets)There are insufficient current data sources (time series off Hawaii, Bermuda and Canaries, outputs from research programmes (e.g. WOCE and JGOFS programmes in the 1980;1990s, OCB, CarboOceans, Ships of Opportunity)). There is an undoubted need for an Ocean Acidification Observation Network to continually provide data products, indices, publications, and data synthesis activities and products. Data synthesis activities should include standardizing and merging of basin and global scale data sets, synthesis with data from other platforms and model outputs, and integrated synthesis reports. The synthesis process should include the development of indices for acidification and its consequences, science and technical workshops; product development workshops; peer review publication and international synthesis meetings should occur to ensure state and stress indices are founded in sound science. An international program on ocean acidification would provide the necessary framework for producing coordinated global basinwide synthesis and indices reports on a regular basis to resource managers and policymakers.
Variations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicatorCoordinated global effort is needed, and a framework of observation needs implementing urgently. This has been proposed by Feely et al. (2009) in an Ocean Observation White Paper. These activities will require a coordinated international research effort that is closely linked with other international carbon research programs, such as the CLIVAR/CO2 Repeat Hydrography Program. Many of the data synthesis activities, data archiving and international data management activities could be shared between the carbon and ocean acidification programs. Presently, many countries are engaged in ocean acidification research and monitoring activities. For example, the European ocean acidification community has developed a major multi-nation program known as the European Program on Ocean Acidification (EPOCA). The total cost of the present observational efforts for ocean acidification is estimated at about $10 Million US dollars per year. We estimate that the cost of an expanded international observational program as described below to be approximately $50 Million US dollars per year.
6. References
Multiple Marine Ecological Disturbance
1. IndicatorIndicator NameMultiple Marine Ecological Disturbance (MMED) EventsCategory Definition of indicator / DescriptorUnits of measurements 2. Relevance Rationale for Inclusion
Past anomalies can be used as a diagnostic tool for evaluating multiple marine ecological disturbance (MMED) events.Significance for inter-linkages with other water systemsLinkage with other indicators3. MethodologyDescription of measurement methods and calculation of the indicatorSee Annex IV. Information technology approaches will include data-mining, data integration, data-modeling and geographic information system outputs. The methodology has been used in the HEED (Health Ecological and Economic Dimensions) study, which reconstructed historic marine disturbance events in the Northwestern Atlantic, the Gulf of Mexico LME and the Caribbean Sea LME ADDIN EN.CITE Sherman200113091309130917Benjamin H. ShermanPaul R. EpsteinPast anomalies as a diagnostic tool for evaluating multiple marine ecological disturbance eventsHuman and Ecological Risk AssessmentHuman and Ecological Risk Assessment1493-1517752001(Sherman and Epstein 2001). The study retrospectively derived co-occurring MMEDs, including indices of morbidity, mortality and disease events affecting, for example, humans and marine invertebrates. Correlations between space and time occurrences, event coincidences, and climate and oceanographic forcing were used to better define MMED types. Systematic derivation of these types is part of diagnostic approach that can assist or guide marine ecological risk assessment.ScaleLimitations 4. Assessment of DataData sources, availability and quality (Existing datasets)Variations among data sources and alternative methods5. PartnersPartners/agencies involved in the development of the indicatorHarvard University and the Rensselaer Polytechnic Institute (RPI)
6. References
Other pollution indicators in preparation by GESAMP
Marine Habitats
(Based on contribution by UNEP-WCMC see Annex III)
Extent of warm water coral habitat (km2)
Indicator Indicator NameExtent of warm water coral habitat Category (Pressure-State-Impact-ResponseStateDefinition of indicator / DescriptorExtent (area) of warm water coral habitat providing measurement of current stateUnits of measurements (spatial and temporal)1km2 Raster dataset
Indication of presence no time series componentRelevance Rationale for Inclusion
(why shortlisted)Tropical coral 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, Annex III). Coral reefs are of transboundary significance, including because they provide habitat for species of interest that have transboundary distribution, harbour globally significant biodiversity, and because of potential interconnectivity (transborder) among them.
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 systemsInfluences from transboundary rivers could impact coral reefs (e.g. through inputs of freshwater and pollutants). Warm water coral reefs are closely linked and often functionally interdependent on other tropical, coastal marine ecosystems including seagrass beds and mangrove forest. Linkage with other indicatorsExtent of mangrove habitat; Extent of seagrass habitat; Number of Ramsar sites with estuarine waters, tidal/mud flats, lagoons, and kelp 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.
Extent of mangrove habitat (km2)
1. Indicator Indicator NameExtent of mangrove habitatCategory (Pressure-State-Impact-ResponseStateDefinition of indicator / DescriptorExtent (area) of mangrove habitat providing measurement of current state.Units of measurements (spatial and temporal)km2
Shapefile dataset where data is attached to a standard 1:1000000 coastline (MundoCart) where possible.
Indication of presence 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 related products, as well as adjacent seagrass and coral 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. Many mangrove forests are shared by two or more countries or affected by transboundary influences (e.g. alteration of freshwater discharge in transboundary river basins) and harbour species of transboundary significance.
Mangroves are included in the following international framework(s): CBD; MEA; WSSD; RAMSAR; IMO (see Table 1, Annex III).
This indicator is derived from the most comprehensive global data set on mangrove habitat extent currently available.Significance for inter-linkages with other water systemsInfluences from transboundary rivers could impact mangroves (e.g. through changes in inputs of freshwater and pollutants). Mangrove 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 indicatorsExtent of warm water coral habitat; Extent of seagrass habitat; Number of Ramsar sites with estuarine waters, tidal/mud flats, lagoons, and kelp 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 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 errors.
Data were matched to the MundoCart digital coastline database, except in cases where matching would cause serious loss of detail and degrade the data.
The data were cleaned and built using the smallest tolerances and manually labeled and checked for label errors. Data which came in digital form were treated in the same manner as 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.
Extent of seagrass habitat (km2)
1. Indicator Indicator NameExtent of seagrass habitatCategory (Pressure-State-Impact-ResponseStateDefinition of indicator / DescriptorExtent (area) of seagrass habitat providing measurement of current state.Units of measurements (spatial and temporal)km2
Source scale denominator: 1,000,000
Indication of presence 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 (some may be transboundary), 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 habitats.Linkage with other indicatorsExtent of warm water coral habitat; Extent of mangrove habitat; Number of Ramsar sites with estuarine waters, tidal/mud flats, lagoons, and kelp 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.
Number of Ramsar sites with estuarine waters, tidal/mud flats, lagoons, and kelp beds, recorded
1. Indicator Indicator NameNumber of Ramsar sites with estuarine waters, tidal/mud flats, lagoons, and kelp 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, Annex III).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 indicatorsExtent of warm water coral habitat; Extent of seagrass habitat; Extent 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/
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 / DescriptorNumber 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.
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 / DescriptorNumber of observations of cold seep and hydrothermal vent habitat per LMEUnits of measurements (spatial and temporal)Point dataset available as KML.
Indication of presence 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
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 presence 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
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 presence 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.
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
Other Indicators and data sets identified by the UNEP-WCMC review process
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 availableMethodolgyApproach:
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
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 projectMethodolgyApproach:
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
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D Aiken19993383383385Aiken, J.R. PollardR. WilliamsG. GriffithsI. BellanSherman, KennethTang, QishengMeasurements of the upper ocean structure using towed profiling systemsLarge marine ecosystems of the Pacific Rim: Assessment, sustainability, and management346-3621999Malden, MABlackwell Science, IncBerman200134634634617Berman, Mark S.Sherman, KennethA towed body sampler for monitoring marine ecosystemsSea Technology48-524292001Melrose200610461046104617Melrose, D. ChristopherOviatt, Candace A.O'Reilly, John E.Berman, Mark S.<style face="normal" font="default" size="100%">Comparisons of fast repetition rate fluorescence estimated primary production and </style><style face="superscript" font="default" size="100%">14</style><style face="normal" font="default" size="100%">C uptake by phytoplankton</style>Marine Ecology Progress SeriesMarine Ecology Progress Series37-463112006April 13, 2006l
D Aiken19993383383385Aiken, J.R. PollardR. WilliamsG. GriffithsI. BellanSherman, KennethTang, QishengMeasurements of the upper ocean structure using towed profiling systemsLarge marine ecosystems of the Pacific Rim: Assessment, sustainability, and management346-3621999Malden, MABlackwell Science, IncBerman200134634634617Berman, Mark S.Sherman, KennethA towed body sampler for monitoring marine ecosystemsSea Technology48-524292001Melrose200610461046104617Melrose, D. ChristopherOviatt, Candace A.O'Reilly, John E.Berman, Mark S.<style face="normal" font="default" size="100%">Comparisons of fast repetition rate fluorescence estimated primary production and </style><style face="superscript" font="default" size="100%">14</style><style face="normal" font="default" size="100%">C uptake by phytoplankton</style>Marine Ecology Progress SeriesMarine Ecology Progress Series37-463112006April 13, 2006 $$If !v h5 " 5d#v " #vd:V l O0 " 5 " 5d/ / / / / a $$If !v h5 " 5d#v " #vd:V l O0 " 5 " 5d/ / / / / a $$If !v h5 "#v ":V l 0 " , 5 "/ a $$If !v h5 " 5d#v " #vd:V l 0 " 5 " 5d/ a $$If !v h5 "#v ":V l 0 " , 5 "/ a $$If !v h5 "#v ":V l 0 " 5 "/ a $$If !v h5 "#v ":V l 0 " , 5 "/ a $$If !v h5 " 5d#v " #vd:V l 0 " 5 " 5d/ / / / / a $$If !v h5 " 5d#v " #vd:V l 0 " 5 " 5d/ / / / / a $$If !v h5 " 5d#v " #vd:V l O0 " 5 " 5d/ / / / / a $$If !v h5 " 5d#v " #vd:V l 0 " 5 " 5d/ / / / / a $$If !v h5 "#v ":V l 0 " , 5 "/ a $$If !v h5 " 5d#v " #vd:V l 0 " 5 " 5d/ / / / / a $$If !v 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h5 j 5d#v j #vd:V l 0 " 5 j 5d/ / / / / al $$If l !v h5 j 5d#v j #vd:V l i0 " 5 j 5d/ / / / / al $$If l !v h5 j 5d#v j #vd:V l 0 " 5 j 5d/ / / / / al $$If l !v h5 "#v ":V l 0 " , 5 "/ al $$If l !v h5 j 5d#v j #vd:V l O0 " 5 j 5d/ / / / / al $$If l !v h5 j 5d#v j #vd:V l 0 " 5 j 5d/ / / / / al $$If l !v h5 "#v ":V l 0 " , 5 "/ al $$If l !v h5 j 5d#v j #vd:V l 0 " 5 j 5d/ al $$If l !v h5 "#v ":V l 0 " , 5 "/ al $$If l !v h5 "#v ":V l 0 " 5 "/ al $$If l !v h5 "#v ":V l 0 " , 5 "/ al $$If l !v h5 j 5d#v j #vd:V l 0 " 5 j 5d/ / / / / al $$If l !v h5 j 5d#v j #vd:V l 0 " 5 j 5d/ / / / / al $$If l !v h5 j 5d#v j #vd:V l O0 " 5 j 5d/ / / / / al $$If l !v h5 j 5d#v j #vd:V l 0 " 5 j 5d/ / / / / al $$If l !v h5 "#v ":V l 0 " , 5 "/ al $$If l !v h5 j 5d#v j #vd:V l 0 " 5 j 5d/ / / / / al $$If l !v h5 j 5d#v j #vd:V l 0 " 5 j 5d/ al $$If l !v h5 j 5d#v j #vd:V l 0 " 5 j 5d/ al $$If l !v h5 "#v ":V l 0 " , 5 "/ al D y K w w w . s e a a r o u n d u s . o r g y K P h t t p : / / w w w . s e a a r o u 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