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METHODOLOGY FOR ASSESSING CHANGING CONDITIONS OF THE WORLDS LARGE MARINE ECOSYSTEMS
K. Sherman, D. Pauly, V. Christensen, R. Sumaila, J. OReilly, K. Hyde, I. Belkin, S. Seitzinger, J. Sutinen, S. Olsen, T. Hennessey, L. Juda, P. Hoagland, D. Jin, M. Aquarone, S. Adams
Introduction
Coastal areas around the margins of the Atlantic, Pacific, and Indian Ocean are encompassed by 64 Large Marine Ecosystems (LMEs) (figure 1). Annually, these waters contribute $12.6 billion to the world economy ADDIN EN.CITE Costanza199742842842817Costanza, R.R. d'ArgeR. de GrootsS. FarberM. GrassoB. HannonK. LimburgS. NaeemR.V. O'NeillJ. ParueloR.G. RaskinP. SuttonM. van den BeltThe value of the world's ecosystem services and natural capitalNatureNature253-2603871997(Costanza, d'Arge et al. 1997). Human expansion around the globe has led to suboptimal utilization of LME goods and services resulting in perturbations to primary productivity, depletion of fish and fisheries, habitat degradation, increasing coastal pollution, introductions of non-indigenous species, nutrient over-enrichment, loss of biodiversity, and increases in climate warming and acidification.
In an effort to bring the extent of global degradation of marine resources to the attention of governments, ocean institutions, and experts in the Regional Seas Programme responsible for the conservation and management of ocean resources, UNEP published the first global assessment of changing conditions in LMEs ADDIN EN.CITE Sherman200810371037103727Sherman, K.Hempel, Gotthilf, eds.The UNEP Large Marine Ecosystem Report: A perspective on changing conditions in LMEs of the world's Regional Seas. UNEP Regional Seas Report and Studies No. 182
8722008Nairobi, KenyaUNEPReport(Sherman and Hempel 2008). This effort was followed by the preparation of an Assessment of Assessments (AoA) of ocean goods and services by a Group of Experts (GoE) supported by the IOC UNESCO and UNEP.
The published report of the AoA is considered by the GoE as the start-up phase towards a regular process for global reporting and assessment of the state of the marine environment ADDIN EN.CITE UNEP20091369136913696UNEP IOC UNESCOAn Assessment of Assessments, Findings of the Group of Experts. Start-up Phase of a Regular Process for Global Reporting and Assessment of the State of the Marine Environment including Socio-economic AspectsRegional and Supra-regional Summaries and Technical Annexes32009United Nations Environment Programme and the Intergovernmental Oceanographic Commission of UNESCO(UNEP and UNESCO 2009). In this regard, it is important to recognize that the 2008 UNEP Report on LMEs was based on the analyses of the same indicators listed for LMEs in Annex 5 of the AoA. Our objective for the Full Size Proposal (FSP) to the GEF is to extend the time series of indicators to 2010 and, where it is feasible, include additional indicators of changing LME conditions.
Several suites of indicators are used to measure the changing states of LMEs in relation to a driver pressure state impact response (DPSIR) system in support of adaptive management actions. The DPSIR process, contributes to the bottom-up, country-driven preparation of the GEF supported transboundary diagnostic analysis (TDA) and strategic action plan required for all GEF supported LME projects.
Figure 1. Map of the 64 Large Marine Ecosystems of the world and their linked watersheds ADDIN EN.CITE Sherman200410701070107027Sherman, K.Celone, P.Adams, S.NOAA Fisheries Service's Large Marine Ecosystems Program: Status Report, NOAA Technical Memorandum NMFS-NE-1832004NOAA, NEFSC(Sherman, Celone et al. 2004)
Figure 2. The Driver-Pressure-State-Impact-Response (DPSIR) model of indicators in relation to climate warming of the North Sea LME. Courtesy of Michael Fogarty, NMFS.
For example, the effort to better understand climate variability, to improve the long-term sustainability of marine goods and services, and to move in the direction of ecosystem-based management, applies to all 64 LMEs and linked watersheds. In the case of the North Sea LME, climate warming of 1C can result in strengthened thermocline and reduced nutrient mixing, leading to a reduction in primary productivity and a decline in fisheries biomass yield (figure 2).
The methodology used for input to the DPSIR is based on the application of the five-module LME approach (figure 3). Taken together during the TDA and SAP preparation process, the modules provide metrics to determine changing states of LMEs and support actions for the recovery, sustainability, and management of LME marine resources and their habitats. The level of assessment based on indicator metrics is limited to measurements that are spatially and temporally repeatable across all LMEs based on a practical and feasible methodology to track changes and the results of interventions that allow for comparisons among LMEs. The description of the monitoring and assessment approach that follows can be considered a level I mandated monitoring and assessment process where ecological data are collected as a stipulated requirement of a government directive (SAP) to identify and follow trends indicating (i) improvement, (ii) degradation, or (iii) no major change ADDIN EN.CITE Lindenmayer201013671367136717David B. LindenmayerGene E. LikensThe science and application of ecological monitoringBiological ConservationBiological Conservation1317-13281432010(Lindenmayer and Likens 2010). The 5-module LME assessment approach includes suites of indicators for (1) primary production, (2) fish and fisheries, (3) pollution and ecosystem health, (4) socioeconomics, and (5) governance.
Figure 3. LME modules as suites of ecosystem indicators ADDIN EN.CITE Sherman20051173117311735Sherman, K.Eugene LevnerIgor LinkovJean-Marie ProthA modular strategy for recovery and management of biomass yields in large marine ecosystemsStrategic Management of Marine Ecosystems65-8050Nato Science Series: IV: Earth and Environmental Sciences2005the NetherlandsSpringer1568-1238(Sherman 2005)
Productivity Module Indicators
Primary productivity can be related to the carrying capacity of an ecosystem for supporting fish resources ADDIN EN.CITE Pauly199540140140117Pauly, D.V. ChristensenPrimary production required to sustain global fisheriesNatureNature255-2573741995(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 ADDIN EN.CITE Beddington199534234234217Beddington, J.R.The primary requirementsNatureNature213-2143741995(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 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 ADDIN EN.CITE Epstein199337037037017Epstein, P.R.Algal blooms and public healthWorld Resource Review190-206521993Epstein200042942942917Epstein, P.R.Algal blooms and public healthWorld Resource Review, 1993: The state of the world fisheries and aquaculture. FAO Rome 2000190-206. 142p522000(Epstein 1993; Epstein 2000).
The ecosystem 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. Plankton can be measured over decadal time scales by deploying Continuous Plankton Recorder systems monthly across ecosystems from commercial vessels of opportunity. Advanced plankton recorders can be fitted with sensors for temperature, salinity, chlorophyll, nitrate, nitrite, light, and primary productivity, providing the means for in situ monitoring and for calibrating satellite-derived oceanographic data (Figure 4).
Figure 4. Continuous Plankton Recorder. Image courtesy of Sir Alister Hardy Foundation for Ocean Science (SAHFOS).
Figure 6. The Mariner Shuttle Mk II - . Instrumentation includes a Seabird 19plusV2 CTD, a Chelsea Technologies Group Autonomous Plankton Sampler to assess zooplankton species diversity, a Chelsea Technologies Fast Repetition Rate Fluorometer with attached PAR sensor to study photosynthesis and primary productivity, a Turner Designs SCUFA fluorometer to measure both chlorophyll and turbidity, a Satlantic/MBARI ISUS nitrate sensor and a Seabird 43 dissolved oxygen sensor. The system is towed at up to 8 kts and includes intelligent flight control software that can automatically adjust the flight pattern to match the bottom depth ADDIN EN.CITE Melrose200713201320132017D. Christopher MelroseCandace A. OviattMark S. BermanHypoxic events in Narragansett Bay, Rhode Island, during the summer of 2001Estuaries and CoastsEstuaries and Coasts47-533012007(Melrose, Oviatt et al. 2007)
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).
Primary productivity data
The LME productivity descriptions to be applied in the FSP are from J. OReilly, K. Hyde, and T. Ducas. They include primary productivity estimates derived from satellite borne data of NOAAs Northeast Fisheries Science Center, Narragansett Laboratory. 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 in the LMEs of the world. The chlorophyll and primary productivity values for the worlds LMEs were initially published by UNEP in 2008 ADDIN EN.CITE Sherman200810371037103727Sherman, K.Hempel, Gotthilf, eds.The UNEP Large Marine Ecosystem Report: A perspective on changing conditions in LMEs of the world's Regional Seas. UNEP Regional Seas Report and Studies No. 182
8722008Nairobi, KenyaUNEPReport(Sherman and Hempel 2008). Daily binned global SeaWiFS chlorophyll a (CHL, mg m-3), normalized water leaving radiances, and photosynthetically available radiation (PAR, Einsteins m-2 d-1) scenes at 9 km resolution for the period January 1998 through December 2006) are obtained from NASAs Ocean Biology Processing Group. Daily global SST (oC) measurements at 4 km resolution are derived from nighttime scenes composited from the AVHRR sensor on NOAAs polar-orbiting satellites and from NASAs MODIS TERRA and MODIS AQUA sensors. Daily estimates of global primary productivity (PP, gC m-2 d-1) are calculated using the Ocean Productivity from Absorption and Light (OPAL) model (Marra, personal communication), a derivative of the model first formulated in Marra et al. ADDIN EN.CITE Marra200311541154115427Marra, J.C. HoC. TreesAn alternative algorithm for the calculation of primary productivity from remote sensing data. Technical Report # LDEO-2003-12003LDEO(2003). The OPAL model generates profiles of chlorophyll estimated from the SeaWiFS chlorophyll using the algorithm from Wozniak et al. ADDIN EN.CITE Wozniak200311561156115617Wozniak, B.J. DeraD. FicekR. MajchrowskiModeling light and photosynthesis in the marine environmentOceanologiaOceanologia171-2454522003(2003) and uses the absorption properties in the water column to vertically resolve estimates of light attenuation in approximately 100 strata within the euphotic zone. Absorption by pure water is assumed to be a constant value over PAR wavelengths; chlorophyll-specific phytoplankton absorption is parameterized empirically ADDIN EN.CITE Bricaud199811571157115717Bricaud, A.A. MorelM. BabinK. AllaliH. ClaustreVariations of light absorption by suspended particles with chlorophyll a concentration in oceanic (case 1) waters: Analysis and implications for bio-optical modelsJournal of Geophysical Research Journal of Geophysical Research31033-31044103C31998(Bricaud, Morel et al. 1998); absorption by photosynthetic pigments is distinguished from total absorption; and absorption by colored dissolved organic matter (CDOM) is calculated according to Kahru and Mitchell ADDIN EN.CITE Kahru200111531153115317Kahru, M.B.G. MitchellSeasonal and nonseasonal variability of satellite-derived chlorophyll and colored dissolved organic matter concentration in the California Current Journal of Geophysical Research Journal of Geophysical Research2517-2529106C22001(2001). The chlorophyll-specific phytoplankton absorption is used to calculate productivity, while absorption by photosynthetic pigments, water, and CDOM are used to vertically resolve light attenuation. SST, which is used as a proxy for seasonal changes in the phytoplankton community, is related to the chlorophyll-specific absorption coefficient. The quantu m e f f i c i e n c y i s o b t a i n e d f r o m a h y p e r b o l i c t a n g e n t a n d a c o n s t a n t m a x . P r o d u c t i v i t y i s c a l c u l a t e d f o r t h e 1 0 0 l a y e r s i n t h e e u p h o t i c z o n e a n d s u m m e d t o c o m p u t e t h e i n t e g r a l d a i l y p r o d u c t i v i t y ( g C m - 2 d - 1 ) .
M o n t h l y a n d a n n u a l m e a n s o f P P w e r e e x t r a c t e d f or each LME and a simple linear regression of the annual PP was used to determine the rate of change over time. The significance (alpha = 0.01 and 0.05) of the regression coefficient was calculated using a t-test according to Sokal and Rohfl ADDIN EN.CITE Sokal19951094109410946Sokal, R.R.F.J. RohlfBiometry: The Principals and Practical Statistics in Biological Research1995New YorkW.H. Freeman and Company(1995) (Table 1). The data allowed for classifying the 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.
Sea Surface Temperature
An SST time series for the worlds LMEs has previously been reported by UNEP ADDIN EN.CITE Sherman200810371037103727Sherman, K.Hempel, Gotthilf, eds.The UNEP Large Marine Ecosystem Report: A perspective on changing conditions in LMEs of the world's Regional Seas. UNEP Regional Seas Report and Studies No. 182
8722008Nairobi, KenyaUNEPReport(Sherman and Hempel 2008). The SSTs have been calculated by Igor Belkin (University of Rhode Island) from the U.K. meteorological Office Hadley Centre SST climatology data ADDIN EN.CITE Belkin200912891289128917Belkin, I.Rapid warming of Large Marine EcosystemsProgress in OceanographyProgress in Oceanography207-213811-42009(Belkin 2009). 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. ADDIN EN.CITE Rayner200310921092109217Rayner, N.A.D.E. ParkerE.B. HortonC.K. FollandL.V. AlexanderD.P. RowellE.C. KentA. KaplanGlobal analyses of SST, sea ice and night marine air temperature since the late nineteenth centuryJ. Geophys. Res.108doi:10.1029/2002JD002670,20032003(2003). The Hadley data set consists of monthly SSTs calculated for each 1 x 1 rectangular cell (spherical trapezoid, to be exact) between 90N-90S, 180W-180E. 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. Since the square area of each trapezoidal cell is proportional to the cosine of the middle latitude of the given cell, all SSTs were weighted by the cosine of the cells middle latitude. After integration over the LME area, the resulting sum of weighted SSTs was normalized by the sum of the weights, that is, by the sum of the cosines. 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. John OReilly (NOAA) kindly provided a data set of the LME coordinates for these processes.
Ocean front maps
The fronts assessment maps for the FSP will be provided by Igor Belkin of the University of Rhode Island. He provided earlier descriptions and maps of LME oceanographic fronts for each of the 64 LMEs ADDIN EN.CITE ADDIN EN.CITE.DATA (Sherman and Hempel 2008; Belkin, Cornillon et al. 2009; Belkin and O'Reilly 2009). An oceanographic front is a relatively narrow zone of enhanced horizontal gradients of physical, chemical and biological properties (e.g. temperature, salinity, nutrients). Fronts occur on a variety of scales, from several hundred meters up to many thousand kilometers. Some of them are short-lived, but most are quasi stationary and seasonally persistent: they emerge and disappear at the same locations during the same season, year after year. The temperature and salinity ranges across the strongest fronts can be as high as 10-15 degrees C and 2 to 3 parts per thousand (ppt) salinity, although somewhat smaller numbers, such as 5 degrees C and 1 ppt, are far more common. The width of fronts varies widely: from less than 100 m to 200 km. Vertically, many fronts extend several hundred meters in depth. Major fronts can extend as deep as 2,000 m. Fronts are crucial in various processes that evolve in the ocean and at the ocean interfaces with the atmosphere, sea ice and sea bottom. Fronts are important for climate change monitoring and prediction, the fishing industry, pollution control, waste disposal and hazards mitigation, marine transportation, marine mining, including the oil and gas industry, submarine navigation and integrated coastal management.
Fronts are associated with current jets, so that any frontal pattern represents a circulation pattern;
The along-frontal current jets are accountable for the bulk of water/heat/salt transport;
Fronts separate different water masses and spawn rings responsible for the bulk of cross-frontal and meridional transport of water, heat and salt;
Fronts usually coincide with major biogeographical boundaries associated with zones of enhanced bio-productivity, including fisheries grounds;
The surface heat fluxes, wind stress and other meteorological parameters may differ drastically between the warm and cold sides of a front. Fronts strongly interact with the marine atmospheric boundary layer and separate regions with different response to atmospheric forcing, so they are crucial for weather forecasting and climate monitoring;
Some high-latitude fronts are directly related to sea ice conditions, so the front locations are determined by the maximum extent of the sea ice cover;
Fronts profoundly influence acoustic environment so that solving any sound propagation problem requires knowledge of the fronts locations and characteristics;
Ocean sedimentation regimes are largely determined by the circulation (hence frontal) pattern, therefore the interpretation of paleo-oceanographic and paleoclimatic information recorded in marine sediments requires a priori knowledge of the modern frontal situation;
Because fronts are associated with convergent currents, oceanic and riverine pollutants can be concentrated thousands of times on fronts, thus endangering the fish, sea birds and marine mammals that inhabit the frontal zones.
The descriptions and maps provide both textual and visual summaries of dominant frontal patterns and principal individual fronts. The frontal schematics are annual long-term means, based largely on a 12-year data set of frontal maps assembled at the University of Rhode Island. They are the result of a comprehensive global analysis, based on Pathfinder Sea Surface Temperatures. The maps show the most robust and well-defined fronts, regardless of the seasons during which they develop and peak.
Productivity Module:
Cross-cutting theme: Vulnerability to climate change the effects of warming on fisheries yields
The International Institute for Applied Systems Analysis (IIASA) is an international non-governmental research organization located in Austria that conducts interdisciplinary scientific studies on environmental, economic, technological and social issues in the context of human dimensions of global change. IIASAs mission is "to provide insights and guidance to policymakers worldwide by finding solutions to global and universal problems through applied systems analysis in order to improve human and social wellbeing and to protect the environment." IIASA has a long experience in modeling complex systems linking science and society. The work is based on original state-of-the-art methodology and analytical approaches.
II Fish and Fisheries
The FSP will include methodologies used by the Sea Around Us Project at the University of British Columbia to assess fish and fisheries of LMEs around the world. The following paper first appeared in The UNEP Large Marine Ecosystem Report: A Perspective on Changing Conditions in LMEs of the Worlds Regional Seas (pp.23-40) ADDIN EN.CITE Pauly20081120112011205D. PaulyJ. AlderS. BoothW.W.L. CheungC. CloseU.R. SumailaW. SwartzA. TavakolieR. WatsonL. WoodD.ZellerK. ShermanG. HempelFisheries in large marine ecosystems: Descriptions and diagnosesThe UNEP Large Marine Ecosystems Report: A Perspective on Changing Conditions in LMEs of the World's Regional Seas2008Nairobi, KenyaUNEP(Pauly, Alder et al. 2008). The original figure numbering and reference list are retained here.
Fisheries in Large Marine Ecosystems: Descriptions and Diagnoses
Abstract
We present a rationale for the description and diagnosis of fisheries at the level of Large Marine Ecosystems (LMEs), which is relatively new, and encompasses a series of concepts and indicators different from those typically used to describe fisheries at the stock level. We then document how catch data, which are usually available on a smaller scale, are mapped by the Sea Around Us Project (see HYPERLINK "http://www.seaaroundus.org"www.seaaroundus.org) on a worldwide grid of half-degree lat.-long. cells. The time series of catches thus obtained for over 180,000 half-degree cells can be regrouped on any larger scale, here that of LMEs. This yields catch time series by species (groups) and LME, which began in 1950 when the FAO started collecting global fisheries statistics, and ends in 2004 with the last update of these datasets. The catch data by species, multiplied by ex-vessel price data and then summed, yield the value of the fishery for each LME, here presented as time series by higher (i.e., commercial) groups. Also, these catch data can be used to evaluate the primary production required (PPR) to sustain fisheries catches. PPR, when related to observed primary production, provides another index for assessing the impact of the countries fishing in LMEs. The mean trophic level of species caught by fisheries (or Marine Trophic Index) is also used, in conjunction with a related indicator, the Fishing-in-Balance Index (FiB), to assess changes in the species composition of the fisheries in LMEs. Also, newly conceived Stock-Catch Status Plots are presented which document graphically, for each LME, both the increase in the number of stocks that moved from the fully exploited to the overexploited and collapsed stages, and the relative biomass of fish extracted from stocks in these various stages. Finally, original time series of estimated catch data are presented for the six LMEs of the coast of North Siberia, Arctic Alaska and Arctic Canada (all entirely contained within FAO Statistical Area 18), for which even crude catch estimates were previously unavailable. Altogether these descriptors of fisheries and ecosystem states over the last 50+ years allow a diagnosis of the fisheries of each LME, and inferences on global trends, as LMEs are the source of 80% of the global marine catch.
Introduction
Fisheries have been seen traditionally as local affairs, largely defined by the range of the vessel exploiting a given resource (Pauly & Pitcher 2000). The need for countries to manage all fisheries within their Exclusive Economic Zones (EEZ), a consequence of the United Nations Convention on the Law of the Sea (UNCLOS), led to attempts to derive indicators for marine fisheries and ecosystems at the national level (see e.g., Prescott-Allen 2001). Also, it was realized that, given the large scale migrations of some exploited stocks, and of distant-water fleets (Bonfil et al. 1998), an even better integration of fisheries could be achieved at the level of Large Marine Ecosystems (LMEs)( Sherman et al. 2003, Sherman & Hempel 2008.).
However, no national or international jurisdiction reports, at the LME level, for catches and other quantities from which fisheries sustainability indicators could be derived were available. Indeed, if the fisheries of LMEs are to be assessed, and if comparisons of the fisheries in, and of their impact on LMEs, are to be performed, then the fisheries within LMEs must be documented for this explicit purpose, mainly by assembling data sets from national and other sources.
The Sea Around Us Project was created in 1999 with the explicit purpose of assessing the impact of fisheries on marine ecosystems and of developing policies which can mitigate this impact (Pauly 2007). Thus, we set ourselves, from the very beginning, the task of assembling data on all the fisheries that impacted on a place, i.e., any area of the sea, since whatever ones definition of an ecosystem is, it must include reference to a place. Indeed, the concept of place has a profound implication on our ability to implement ecosystem based management of fisheries (Pauly 1997; Sumaila 2005).
When dealing with the fisheries of places such as LMEs, the physical and other features that are relevant to the fisheries must also be expressed at the LME scale. The Sea Around Us website provides such statistics, which are used in the LME-specific accounts in this volume. These are:
1) The percentage of global coral reef area in a given LME (rather than the area itself, which is highly variable between authors), based on a global map produced by the World Conservation Monitoring Centre (HYPERLINK "http://www.unep-wcmc.org"www.unep-wcmc.org);
2) The percentage of seamounts in a given LME (rather than their number, for the same reason), based on a global map of Kitchingman & Lai (2004);
3) The percentage of the area of a given LME that is part of a Marine Protected Area (MPA), based on an MPA database documented in Wood et al. (in press).
Other fisheries-relevant information, not used here, but available through the Biodiversity option on our website (HYPERLINK "http://www.seaaroundus.org"www.seaaroundus.org), are fish species by LME (from HYPERLINK "http://www.fishbase.org"www.fishbase.org), and of marine mammals and other marine organisms, to be consolidated in SeaLifeBase (HYPERLINK "http://www.sealifebase.org"www.sealifebase.org). Additionally, the Ecosystem option allows access to maps of primary production (see Sherman & Hempel 2008 for details), major estuaries (Alder 2003), ecosystem models, and other features of LMEs.
However, the major exhibit of the website, and the major product of the Sea Around Us Project are time series of fisheries catches by LME. They were obtained using a method developed by Watson et al. (2004), which 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, as is relevant here, the LMEs that have been so far defined in the worlds oceans (Watson et al 2004).
As these aggregates of spatial cells can then be combined with other data, for example, the price of the fish caught therein, or their trophic level, one can straightforwardly derive other time series, e.g., of indicators of the value, or the state of fisheries in any area. In the following, we present how the primary (i.e., catch) time series were obtained, along with a set of four derived time series included in this volume for all (except some of the Arctic) LMEs. As these time series are presented through graphs (the tabular data are available from HYPERLINK "http://www.seaaroundus.org"www.seaaroundus.org); each section below refers to the graph that presents one of these time series. A final section is devoted to the newly derived catch graphs for the six Arctic LMEs that are entirely within FAO Statistical Area 18, and which are the sole fisheries-related exhibit presented for these LMEs.
Graph 1 - Reported landings by species, per 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. Here, we summarize it in 5 steps:
Assemble the catch data to be mapped. Data were sourced from FISTAT, the database of the United Nations Food and Agriculture Organization (FAO; HYPERLINK "http://www.fao.org"www.fao.org), the STATLANT database and selected reports of the International Council for the Exploration of the Seas (ICES, HYPERLINK "http://www.ices.int/fish/statlant.htm"www.ices.int/fish/statlant.htm), the Northwest Atlantic Fisheries Organization (NAFO; HYPERLINK "../AppData/Local/Temp/www.nafo.ca/"www.nafo.ca/), FAO Regional bodies (Southeast Atlantic, Mediterranean and Black Sea (GFCM), Eastern Central Atlantic (CECAF) and RECOFI), Cuban fisheries catch data (Baisre et al. 2003); Estonian fisheries catch data (Ojaveer 1999), nationally disaggregated catch data for the components of the former USSR (Zeller & Rizzo (2007) and the former Yugoslavia (Rizzo & Zeller 2007),
Guam, and the Commonwealth of the Northern Mariana Islands (Zeller et al. 2007), American Samoa (Zeller et al. 2006), and, for the Antarctic, from the Convention on the Conservation of Antarctic Marine Living Resources (CCAMLR; HYPERLINK "http://www.ccamlr.org"www.ccamlr.org). These data consist of marine finfish, brackishwater and diadromous finfish, and marine invertebrates. They exclude marine mammals, and reptiles (i.e., sea turtles), algae, and invertebrates harvested for purposes other than food (e.g., corals harvested as construction material). All freshwater organisms are excluded, as are fish and invertebrates produced in mariculture operations. The latter is not always clear-cut, due to the similarity between sea ranching and capture fisheries, and it may be the cause for some of the large recent catch increases in some LMEs, notably those along the Chinese coast. Another cause of catch mis-estimation (although one with reversed sign) is discarded by-catch (Zeller & Pauly 2005), and Illegal, Unregulated and Unreported (IUU) fishing, which is generally not accounted for by our sources of catch data. For this reason, we nearly always refer to reported landings. Thus, when encountering catches, readers should be aware that these are not really catches, i.e., landings + discards + IUU, etc. Finally, in the case of some countries for which the FAO database does not provide catches, or reports without correcting the unrealistic figures submitted by member countries, the Sea Around Us Project has attempted to reconstruct or correct the catch, using concepts initially presented by Pauly (1998). However, for the analyses presented in this volume, this concerned only the following areas: China (Watson & Pauly 2001); Cuba, Estonia and US flag territories in the Pacific (see references above), and the seven Arctic LMEs in FAO area 18, for which we provide preliminary catch time series, to replace the landings of zero that FAO often reports for this area (see below). For all other countries, we stress again, the catches reported here originated from FAO and other official sources.
Create, for each taxon (species, genera, families and orders) for which at least one country reports landings, a distribution range map, constrained by an external polygon, based on the known depth and latitudinal range, and within which account is taken of the habitat preference of this taxon (Watson et al. 2004, Close et al. 2006). These range maps rely heavily on data extracted from 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. The range maps were all revised for the catch allocation used here (see Close at al. 2006; HYPERLINK "http://www.seaaroundus.org"www.seaaroundus.org). Also, a new procedure, which we call demersal creep, was implemented which accounts (only in demersal taxa, generally caught by trawling) for the fact that when exploitation is light (and catches low), only the near-shore part of the distribution is fished, with the fraction of the distribution range covered increasing ratchet-like when catches increase, up to the entire distribution being covered when the catch reaches its maximum (and remaining there when catches subsequently decrease).
Combine the landings reported by various countries and species (or higher taxa) with the corresponding distribution range maps, and allocate these landings to spatial cells, subject to fishing access status (does country A fish in the EEZ of country B?), and other constraints (Watson et al. 2004). The procedure used here considers where countries have been fishing, which can be in their own waters (or EEZ, since the early 1980s), in the waters (EEZ) of countries to which they have legal access (as documented by access agreements), or to which they have traditional or illegal access (as documented by other sources). The allocation procedure thus uses a large database of access agreements, which grew from a smaller database called FARISIS (FAO 1999), which was kindly made available by FAO to the Sea Around Us Project. Published or online reports of countries observed fishing in the waters of other countries, even without any known access agreements, were also considered and incorporated in the access agreement database.
When under these rules the landings of a given taxon reported by a given country cannot be allocated to its own waters (because that taxon does not occur there), or to the waters of other countries (because no access agreement is known, nor is it known to fish there traditionally or illegally), the case is investigated until resolution is found.
Once the landings by species is allocated to degree lat.-long. spatial cells and the landings reported by different countries are thus reassigned to the ecosystem(s) from which they originated, a procedure is implemented which attempts to reduce the fraction of the reported landing assigned to the miscellaneous fish category. These miscellaneous fish, particularly abundant in reports from tropical developing countries and from China, make it extremely difficult to understand what happens to the underlying stocks. The procedure used for this, which relies on a set of simple heuristics, does not affect total catch levels. Rather, it only reassigns fish from the miscellaneous fish category to some of the identified taxa already reported by either the country itself or its neighbors. As presently implemented, these rules disaggregate > 50 % of the reported miscellaneous fish landings of the world (R. Watson, Sea Around Us Project, August 2007, unpublished data).
These steps, though they typically do not modify the reported landings by FAO statistical areas, produce a radically different view of landings at the level of the EEZ of individual countries. Thus, in the Mauritanian EEZ, for example, which is a component of the Canary Current LME, the landings derived from this procedure are much higher than suggested by looking at the FAO data for Mauritania because we put back into the Mauritanian EEZ fish that was landed in other countries, but which was caught in Mauritanian waters (Watson et al. 2005). Another feature of Mauritanian landings (and of landings elsewhere in the world) is that since 2007, they do not include the ex-USSR, even for the period from 1950-1991, when its fleets were active through the oceans. This is so because we have retroactively re-assigned ex-USSR catches to its components maritime republics (Estonia, Georgia, Latvia, Lithuania, Russian Federation and Ukraine), based on their relative reported landings in the first years of the post-dissolution period, and rules about who tended to fish where (Zeller & Rizzo). Thus, we show Russian, Estonian, etc. catches from 1950 onward. However, their sum, it must be stressed, still adds up to the FAO catch for the ex-USSR. An analogous procedure was used for the relevant components of the former Yugoslavia, i.e., Croatia, Slovenia and Montenegro (Rizzo & Zeller).
Our procedure was recently tested independently by Gascuel (2007), who found that our approach approximates well the values that would have been generated by Mauritania, were it to also report the landings by all the distant water fleets operating in its EEZ.
Figure 1 shows the landings, by species for all LMEs in the world. Since this graph is normalized to show the 11 most abundant species (with the remainder pooled into mixed group), and not many species are globally important, this graph exhibits more mixed group landings (as 12th category) than typically occur in any specific LME. Also, it will be noted that LMEs account for the overwhelming part of the world catch, i.e., between 76% (1990) and 91% (1968) of global catch. 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. Indeed, the only major group not caught primarily in LMEs is represented by large pelagic fishes, primarily tunas.
Figure 1. Landings by species in all LMEs (colored time series), and in the world ocean (black line). As this graph individually identifies only the 11 species with the highest global catch (with the remainder pooled into mixed group), this graph exhibits more mixed group landings (as 12th category) than reported from any specific LME. The only major group not caught primarily in LMEs is large pelagic fishes, primarily tunas. Our website (HYPERLINK "http://www.seaaroundus.org"www.seaaroundus.org) also presents catches by Commercial groups (as used in Figure 2), Functional Groups, as used in Ecopath models (see HYPERLINK "http://www.ecopath.org/"www.ecopath.org), Country fishing, and Gear, based on Watson et al. (2006).
In addition to the catch by species, the website of the Sea Around Us Project presents, for all but the six Arctic LMEs located fully in FAO Area 18 (see section Catch graphs for Arctic LMEs in FAO Statistical Area 18 below), catches by Commercial groups (as used in Graph 2, see below), Functional groups, as used in Ecopath models (see HYPERLINK "http://www.ecopath.org"www.ecopath.org), Country fishing (not to be mistaken for the PPR by, or footprint of countries, see Graph 3 below), and Gear, based on Watson et al. (2006a, 2006b).
Graph 2 - Value of reported landings by major commercial groups, per LME
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). 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 which took taxonomic affinity, adjacency of countries and time into account (Sumaila et al. 2007). As observed prices were available for the most important commercial species, the inferred prices have little influence on the total value of landings from any LME fishery.
The year-, species- and time-specific prices in the database were then 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), and which, in the aggregate, match, for example, estimates of the ex-vessel values of fisheries catches produced by the OECD.
Here we present graphs of reported landing value by Commercial groups, to facilitate comparison between LMEs which may not share species. This may also facilitate their interpretation by readers who do not know biological details on the various species caught in different LMEs, but know market categories. Again, we stress that all values presented here are based on real 2000 prices, i.e., deflated nominal prices (Sumaila et al 2007). Figure 2 shows the value by major commercial groups of reported landings in all LMEs of the world. As might be seen, LMEs account for most of the value of marine fisheries catches in the world with values ranging from 71-90% of global landings value. However, this is a slightly smaller fraction than for catch biomass, as many of the offshore fishing grounds for extremely valuable tunas are not included in LMEs.
Figure 2. Ex-vessel value of reported landings in all LMEs of the world, by Commercial groups (colored time series), with the value of the global marine catch also added (black line). All values presented are based on real 2000 prices, i.e., deflated prices (Sumaila et al. 2007).
Graph 3 Primary production required to sustain fisheries within LMEs
Footprint analysis consists essentially of expressing all human activities in terms of the land area required for generating products that are consumed by us, or for absorbing the waste generated in the course of supplying these products. Numerous conversion tables exist which allow footprint analysis, for example for producing crops, or absorbing carbon emissions, and these are being used to account for the human impact on ecosystems in standardized fashion (Wackernagel & Rees 1996). Also important to the footprint concept is that, generally, they are expressed in relative terms, i.e., in terms of the surface area of a country. Thus, a country which has a footprint exceeding its surface area relies on resources from other countries. The footprint concept, and conversion tables which are used to implement it, are, however, tied to land areas. There has been to date no published application of this concept to LMEs.
Here, we present extensions of the footprint concept to LMEs. However, the productivity of a given area of ocean is determined by the local primary production, which can vary tremendously over small distances, depending on local mixing processes (Longhurst 2007). Thus we shall not consider the surface area of LMEs, but their average primary production as reference for footprint analysis; hence the concept of Primary Production Required (PPR) (Christensen & Pauly 1993) used here.
The Primary Production Required (PPR) by fisheries landings is a function of the trophic level of the fishes that are caught. Thus, far more primary production is required to produce one tonne of a high-level trophic fish, for example tuna, than a tonne of a low level- trophic fish, for example sardine. This is because the transfer efficiency between trophic levels in the ocean is relatively low, estimated at 10 % on the average (Pauly & Christensen 1995). Thus, to calculate the primary production that was required to produce a given tonnage of fish, we need 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).
The landings data used to estimate footprints are those presented above. 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 also shown. It is computed as the mean of values for the five years with the highest PPR value.
The primary production data used here refer to the average from October 1997 to September 1998 and will not be representative of observed primary production in specific years, nor of the average primary production from 1950 to 2004 in each LME. While this may cause some errors, there is no reason to believe it should cause any systematic bias, and we consider it warranted to use the PPR measure for comparisons between LMEs. Thus the low level of relative PPR in Australian LMEs compared with the high values in the north Atlantic is likely not an artifact, nor will the relative contribution of various countries fleets to the overall footprint within a given LME be an artifact.
On the other hand, 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.
Which of these problems is likely to apply is indicated in the LME-specific chapters. By way of generalization, however, we may mention here that (2) tends to occur in East Asian LMEs (Watson & Pauly 2001), (3) in the Kuroshio LME, and some of the smaller LMEs of the North Atlantic, and (4) with regard to Atlantic cod in the Northwest Atlantic in the earlier periods. The problem in (1), on the other hand, occurs throughout the world. However, it is not likely to be the cause of the geographic pattern just mentioned.
Figure 3 shows the fraction of primary production required to sustain the landings reported by countries fishing within 57 LMEs of the world, as fractions of their combined primary production. (The Arctic LMEs exclusive to FAO Area 18 are not included here, due to their small catches and variable ice-free zones). The fraction of primary production required has increased steadily over the years, in line with increasing reported landings, and is approaching 20%. In recent years, the countries with the largest footprint in all LMEs combined were China, USA and Indonesia, with China outpacing all others (even with correction of over-reporting of landings, Watson & Pauly 2001).
Figure 3. Primary Production Required (PPR; Pauly & Christensen 1995) to sustain fisheries in the 57 most important LMEs of the world, an expression of their footprint (Wackernagel & Rees 1996). PPR is calculated separately for each species (or group of species) caught by the fleets of all countries operating in a LME (or here: in 57 of 64 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.
Graph 4 The Marine Trophic Index and the FiB index, by LME
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. This pattern has been repeated innumerable times in the history of humankind (Jackson et al. 2001) and also since the 1950s, when landing statistics began to be collected systematically and globally by FAO.
With a trophic level assigned to each of the species in the FAO landings data set, Pauly et al (1998) were able to identify a worldwide decline in the trophic level of fish landings. This phenomenon, now widely known as fishing down marine food webs, has been since shown to be ubiquitous when investigated on a smaller scale, e.g., in countries such as Greece (Stergiou & Koulouris 2000) or subdivisions of large countries, e.g. India (Bhathal 2005). This ubiquity of fishing down is one of the reasons why the Convention on Biological Diversity (CBD) adopted the mean trophic level of fisheries catch, which it renamed Marine Trophic Index (MTI) as one of eight biodiversity indicators for immediate testing (CBD 2004, Pauly & Watson 2005).
Diagnosing fishing down the food web from the mean trophic level of landings is problematic, however. 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. 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 alluded to above.
The FIB index will decline, obviously, when both the MTI and landings decline, as now happens, unfortunately, in many LMEs. On the other hand, the FIB index will increase if landings increases 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). Note that the absolute value of the FiB index can be applied to assess the change of the FiB index from any baseline we like. It is here standardized to have a value of zero in 1950.
Figure 4 presents the trophic level and FIB index for all LMEs combined, but with Peruvian anchoveta (Engraulis ringens) and large pelagic fishes (large tunas and billfishes) excluded. The very localized fishery for Peruvian anchoveta, a low trophic level species, is the largest single-species fishery in the world, and it exhibits extreme fluctuations in landings (see Figure 1, top, and Chapter XVII-56 Humboldt Current LME), which mask the comparatively more subtle patterns in trophic level changes by the rest of the worlds fisheries. The reason for excluding large tunas and billfishes is that much of their catch is taken in pelagic waters outside of the currently defined LMEs. Thus, the inclusion of these landings from only part of their stock-exploitation ranges would artificially inflate trophic level patterns, especially in recent decades, where the tuna fisheries expanded tremendously (Pauly and Palomares 2005). The trend in mean trophic level for all LMEs combined (Figure 4, top) 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 (Figure 4, bottom, and see 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. Analyses at smaller scales (i.e., as documented in the LME-specific chapters, or in smaller-scale studies, see above) confirm this.
Figure 4. Two indicators based on the trophic levels (TL) of exploited fish, used to characterize the fisheries in the LMEs of the world. Top: trend of mean TL, indicating fishing down marine food webs, recently masked by offshore expansion of the fisheries (Pauly et al. 1998, Pauly & Watson 2005). Bottom: corresponding trend of the Fishing-in-Balance (FiB) index, which is defined such that its increase in the face of stagnating or increasing MTI suggests a geographic expansion of the fisheries (see text and Bhatal & Pauly, in press).
Graph 5 Stock-Catch Status Plots, by LME
These graphs have their origin in the work of Granger & Garcia (1996), who fitted time series of landings of the most important species in the FAO database with high-order polynomials, and evaluated from their slopes whether the fisheries were in their developing, fully utilized or senescent phases. Froese & Kesner-Reyes (2002) simplified these graphs by defining 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.
Figure 5. A newly proposed type of paired Stock-Catch-Status Plots (here presented for all LMEs in the world), 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%). Top: Percentage of stocks of a given status, by year, showing a rapid increase of the number of overexploited and collapsed stocks. Bottom: Percentage of catches extracted from stocks of a given status, by year, showing a slower increase of the percentage of catches that originate from overexploited and collapsed stocks. Note that (n), the number of stocks, i.e., individual landings time series, only include taxonomic entities at species, genus or family level, i.e., higher and pooled groups have been excluded.
The fisheries in a given area can then be diagnosed by plotting time series of the fraction of stocks in any of these categories (Froese & Kesner-Reyes, 2002). Such graphs were used in a paper by Froese & Pauly (2004) documenting the state of the North Sea LME. This method of diagnosis suggests that the number of collapsed stocks (as defined in Figure 5) is increasing alarmingly throughout the world, as can be seen in the LME-specific Stock-Catch Status Plots included in this book. 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 1000 tonnes.
Here, we propose a variant of what may be called stock number by status plots: a catch by status plot, defined such that it documents, for a series of years, the fraction of the reported landings biomass that is derived from stocks in various phases of development (as opposed to the number of such stocks). As might be seen in Figure 5, such a plot of relative catch by status (lower panel) is quite different from the stock number by status plots (upper panel). We call the combination of these two plots Stock-Catch-Status Plots (Figure 5).
Figure 5 illustrates the dual nature of the newly derived Stock-Catch Status Plots, for all LMEs in the world combined. It illustrates that, overall, 70 % of global stocks within LMEs are deemed overexploited or collapsed, and only 30% fully exploited (Figure 5, top). However, the latter stocks still provide 50% of the globally reported landings biomass, with the remainder produced by overexploited and collapsed (Figure 5, bottom). This confirms the common observation that fisheries tend to affect biodiversity even more strongly than they affect biomass.
Catch graphs for Arctic LMEs in FAO Statistical Area 18
The Arctic, generally defined as the area within the 10o Celsius summer isotherm, has about four million human inhabitants. FAO Statistical Area 18, ranging from Novaya Zemlya in the west to the Hudson Bay in the east, is comprised of the Siberian coast (Russia), the Arctic coast of Alaska (USA), the Arctic coast of Canada, and parts of the northern coast of Greenland, or about two-third of what is generally defined as the Arctic. FAO Area 18 is also an area with extremely low fish catches. However, landings are not as low as the FAO data from that area would have it, 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. This obviously affects the seven LMEs presently defined for this area, i.e., from west to east, the Kara, Laptev, East Siberian, Chukchi and Beaufort Seas, Hudson Bay, and the large Arctic LME (soon to be differentiated into [Canadian] Arctic Archipelago, Baffin Bay/Davis Strait as new Arctic LMEs). Six of these seven LMEs are located entirely within FAO Area 18, while the Arctic LME has substantial coverage also in FAO Areas 21 (NW Atlantic) and 27 (NE Atlantic).
Thus, to complete our 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. We present here key results from this work on northern Siberia (Pauly & Swartz 2007) and Arctic Canada (Booth & Watts 2007), and from an ongoing study on Arctic Alaska (S. Booth and D. Zeller, Sea Around Us Project, unpublished data). These results are summarized, for the Arctic LMEs, in Table 1 and Figure 6, and are presented individually in their respective chapters.
Figure 6. Estimated catches from the six LMEs fully comprised within FAO Statistical Area 18, and based on Pauly & Swartz (2007), Booth & Watts (2007) and Booth & Zeller (unpubl. data). These conservative estimates of (small-scale fisheries) catches are considerably higher than reported by FAO for the same area, an extreme case of the tendency, by FAO member countries, of underreporting the catches of their small-scale fisheries (see also Zeller et al. 2006, 2007).
Because the catches in Figure 6 are usually not destined for commercial markets, and relatively small, we abstain here from presenting their ex-vessel value, and indeed, from deriving any catch-based indicators (MTI, PPR, etc). Consequently, the LME-specific accounts do not include graphs illustrating catch-based indicators, either.
Table 1: Estimated average annual catches (1950-2004) and major taxa for LMEs within FAO Statistical Area 18, arranged from west to east. LMEAverage catch
(tonnesyear-1)Major taxaKara Sea7,239Coregonus sardinella, C. lavaretus, C. nasusLaptev Sea3,667Coregonus sardinella, C. autumnalis, C. muksunEast Siberian Sea2,717Coregonus nasus, C. sardinella, C. autumnalisChuckchi Sea1,727Oncorhynchus keta, Coregonus spp., Stenodus leucichthysBeaufort Sea127Coregonus spp., Stenodus leucichthys, Clupea pallasii(Canadian) Arctic Sea11,066Salvelinus alpinus, Gadidae, Salmo salarHudson Bay484Salvelinus alpinus, Gadidae, Salmo salar1 These data apply only to that part of the currently defined Arctic LME that comprises the (Canadian) Arctic Archipelago, and are not included in Figure 6. These data exclude the much higher catches from the Arctic LME areas within FAO areas 21 (Baffin Bay, Davis Strait) and 27 (NE Atlantic waters).
Discussion
Traditionally, the local and sectoral focus of fisheries science, monitoring and management has precluded the development and use of indicators at large spatial scales. With the advent of ecosystem-based concerns and concepts such as the Large Marine Ecosystems (Sherman et al. 2003), it has become evident that such indicators will be needed for better integration of fisheries in ecosystem-based management approaches.
However, existing national and international institutions, due to their historic sectoral, local and national focus, are not in a position to report fisheries information, i.e., catches, their values and associated indicators at an ecosystem level, such as LMEs. In contrast, the Sea Around Us Project was specifically established to assess the impacts of fisheries at an ecosystem level. We therefore developed tools and concepts to present available fisheries data via degree lat.-long. spatial cells, allowing consideration of various spatial scales, such as LMEs. It is this place-based, rather than sector-based approach which allows us to document fisheries impacts at the scale of LMEs. We have also derived a standard set of indicators and graphical representations, presented here on a global scale (i.e., for all currently defined LMEs combined). They are presented in LME-specific format in the various chapters of this book, and as well, through our website (www.seaaroundus.org).
The five types of graphs presented here allow comprehensive overviews of the general status of fisheries and ecosystem of each LME, as they account for the characteristics of fisheries, biology and ecology of the exploited species and ecosystem. Catch and catch values indicate status and trends of the fisheries, e.g., through changes in species composition and catches. These relate strongly to the status of stocks in the LME, as indicated by the Stock-Catch Status Plots developed here. Changes in fisheries and stock status have direct impacts on the ecosystem which can be indicated by the MTI and FiB. These also determine the footprint of fisheries an indicator of sustainability, as shown here through the Primary Production Required by fisheries within LMEs.
All of these indicators require accurate and complete catch data. Such catch data, however, are not available for all LMEs. The methods we use 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. This was here specifically illustrated by reconstructed catch time series from Northern Siberia (Russian Federation), Arctic Alaska (USA), and parts of Arctic Canada, with the help of which the fisheries of six arctic LMEs could be characterized for the first time. In the next years, the Sea Around Us Project, working in close collaboration with national scientists, will radically expand its coverage of countries with reconstructed catches, to overcome the data problems highlighted in the LME-specific chapters. Also, we will expand our list of indicators, and include several that do not rely on catch trends, but on biomass (or catch/effort) trends, which are far more informative.
The LME framework, populated with relevant and current catch and related fisheries data, is set to provide the information needed to develop policies for ecosystem-based fisheries management. It provides a neutral platform for jurisdictions (national and sub-national) to come together to discuss resource management issues as a single ecological unit and look at the consequences of policies, irrespective of boundaries. This information will also provide guidance on information gaps (e.g., spatial effort data) and areas for research (e.g., large scale fisheries-independent biomass estimation), so that ecosystem based management of fisheries and marine areas can be strengthened in many of the worlds coastal regions.
The LME system can also enhance the global assessments of marine areas and resources. Until now, large-scale assessments have primarily focused on ocean basins (Pauly et al. 2005) or FAO Statistical Areas (Pauly et al. 1998, Alder et al. 2007). However, these are large areas, and the important differences needed for developing policy can be lost in such a large scale management unit. Assessments based on LMEs can give much better resolution. LME units also lend themselves to ecosystem modeling software such as Ecopath with Ecosim (EwE), which can be used to simulate developments scenarios (Christensen & Walters 2004). Recent experience with EwE and FAO Statistical Areas as modelling units has highlighted that these areas are too large for meaningful treatment (Alder et al. 2007). For example, FAO area 21 includes the Barents Sea and the North Sea, which are strongly divergent ecosystems in terms of structure and fisheries (Alder et al. 2007). LMEs do not have this problem. Also, they can be interfaced with other spatial entities, e.g., ecoregions (Spalding et al. 2007), i.e., with smaller scale systems defined in terms of their biodiversity.
Thus, the present volume presents globally, and for the first time, comprehensive fisheries data and indicators assembled at a large spatial ecosystem scale, namely for all 64 currently defined Large Marine Ecosystems.
Acknowledgement
The work presented here is a product of the Sea Around Us Project, initiated and funded by the Pew Charitable Trusts, Philadelphia, USA. We also thank the Lenfest Foundation for support to study the Alaska marine fisheries in the Chukchi and Beaufort Sea LMEs.
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II Fish and Fisheries Global Carrying Capacity continued
The following paper was first printed in Ecological Modelling 220 (2009) 19841996, and is used here with permission of the authors.
Database-driven models of the Worlds Large Marine Ecosystems
Villy Christensen1*, Carl J. Walters1, Robert Ahrens1, Jacqueline Alder2, Joe Buszowski1, Line Bang Christensen1, William W.L. Cheung1,3, John Dunne4, Rainer Froese5, Vasiliki Karpouzi1, Kristin Kaschner6, Kelly Kearney7, Sherman Lai1, Vicki Lam1, Maria L.D. Palomares1,8, Aja Peters-Mason9, Chiara Piroddi1, Jorge L. Sarmiento7, Jeroen Steenbeek1, Rashid Sumaila1, Reg Watson1, Dirk Zeller1, and Daniel Pauly1
1Fisheries Centre, University of British Columbia, 2202 Main Mall, Vancouver BC, Canada V6T 2K9
2United Nations Environmental Program, Nairobi, Kenya
3University of East Anglia, Norwich, England, UK
4NOAA Geophysical Fluid Dynamics Laboratory, Princeton NJ, USA
5Leibniz-Institut fr Meereswissenschaften IfM-GEOMAR, Kiel, Germany
6Albert Ludwigs University, Institute of Biology I (Zoology), Freiburg, Germany
7Princeton University, Atmospheric and Ocean Sciences Program, Princeton NJ, USA
8SeaLifeBase Project, WorldFish Center, Los Baos, Philippines
9Marine Conservation Biology Institute, Bellevue WA, USA
*Corresponding author. Tel.: +1 604 822 5751; fax: +1 604 822 8934. E-mail address: HYPERLINK "mailto:v.christensen@fisheries.ubc.ca"v.christensen@fisheries.ubc.ca (V. Christensen)
Keywords:
Ecopath with Ecosim; ecosystem model; global modeling; LME.
Abstract
We present a new methodology for database-driven ecosystem model generation and apply the methodology to the worlds 66 currently defined Large Marine Ecosystems. 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. We tune the models by fitting to available time series data, but recognize that the models represent only a first-generation of database-driven ecosystem models. We use the models to obtain a first estimate of fish biomass in the worlds LMEs. The biggest hurdles at present to further model development and validation are insufficient time series trend information, and data on spatial fishing effort.
Introduction
There is a global trend toward ecosystem-based management of marine resources. This is in line with international agreements, most recently as expressed through the Johannesburg and Reykjavik Declarations, and supported by the UN Food and Agricultural Organization through the Code of Conduct for Responsible Fisheries ADDIN EN.CITE FAO200325752575257510FAOThe ecosystem approach to fisheries1122003FAO Technical Guidelines for Responsible Fisheries. No. 4, Suppl. 2. Rome, FAO Fisheries Department(FAO 2003). Ecosystem modeling has an important role to play in implementation of ecosystem-based fisheries management through its capabilities to examine ecological, economical and social tradeoff in an integrated manner. Though there has been progress, we are still far from seeing ecosystem models used for management in more than a few of the worlds Large Marine Ecosystems (LMEs). LMEs refer to 66 marine ecosystems with unique sets of ecological, oceanographic and biogeochemical characteristics identified by Sherman over the last two decades ADDIN EN.CITE Sherman200531603160316017K. ShermanM. SissenwineV. ChristensenA. DudaG. HempelC. IbeS. LevinD. Lluch-BeldaG. MatishovJ. McGladeM. OTooleS. SeitzingerR. SerraH.-R. SkjoldalQ. TangJ. ThulinV. VandeweerdK. ZwanenburgA global movement toward an ecosystem approach to management of marine resources Marine Ecology Progress SeriesMarine Ecology Progress Series275-2793002005(Sherman, Sissenwine et al. 2005). We attribute the limited application of ecosystem modeling in the LME context to a combination of factors, of which lack of experience may be more important than lack of data. Ecosystem modeling indeed calls for integration and analysis of data from the entire ecosystem, and this can be a daunting task for anyone. Ecosystem models are data hungry, and few models have been fed sufficiently. This is not, generally, because data are not available, as many believe. Rather, it is a question of realizing what is needed, what is available, and how to best use the data for analysis. Particularly, there are increasing numbers of global databases that greatly help researchers obtain the basic biological and physical parameters to develop ecosystem models. The many training courses that we have conducted around the world have served to build capacity for ecosystem modeling. We have realized, however, that training alone does not suffice; there is considerable work involved in the steps described above, and we here report on a procedure for database-driven ecosystem model generation, expected to further enhance the level of ecosystem modeling, as well as to make it more accessible. In this paper, we describe how we link into a large number of spatial and temporal databases describing the worlds oceans, their resources, and how the resources are exploited. We extract data from these databases, and use these data to modify a generic ecosystem model in order to obtain ecosystem models for each of the 66 LMEs in the Worlds oceans. Here we use these models to estimate fish biomass in the worlds LMEs, and anticipate that the models will see much further use and enhancement.
Model methodology
The Ecopath with Ecosim (EwE) modeling approach
EwE is an ecosystem modeling approach and software that is being used for ecosystem-based fisheries management throughout the world ADDIN EN.CITE Christensen20053154see 3154315417Christensen, V.Walters, C.J.Using ecosystem modeling for fisheries management: Where are we?ICES C.M. M:192005(see Christensen and Walters 2005). The approach started out in the early 1980s when Jeff Polovina of the NOAA Pacific Islands Fisheries Science Center in Honolulu was tasked with developing an ecosystem model to integrate information from a major, multi-disciplinary study of productivity in the French Frigate Shoals ecosystem in the Northwestern Hawaiian Islands ADDIN EN.CITE ADDIN EN.CITE.DATA (Polovina 1984; Polovina 1993). Polovina examined the ecosystem models then in use for fisheries research ADDIN EN.CITE Andersen19772751notably 2751275117Andersen, K.PE. UrsinA multispecies extension to the Beverton and Holt theory of fishing, with accounts of phosphorus circulation and primary productionMeddelelser fra Danmarks Fiskeri og HavundersgelserMeddr. Danm. Fisk.-og Havunders.319-43571977Laevastu19804240 and 4240424017Laevastu, T.Favorite, F.Fluctuations in Pacific herring stocks in the eastern Bering Sea as revealed by an ecosystem model (DYNUMES 3)Rapports et Proces-Verbaux des Reunions (Denmark)445-4591771980(notably Andersen and Ursin 1977; and Laevastu and Favorite 1980), and developed a simple mass-balance model, with the main purpose of evaluating consistency in estimates of production (and by deduction, state variables) for ecosystem components at all trophic levels, as well as to estimate how much demand there was for production (and, again, by deducing state variables) for groups where no estimates of biomass were available. Polovina called his model Ecopath, and this quantified food web model has since been further developed to become the most-widely applied approach for ecosystem modeling, with hundred of published models ADDIN EN.CITE Morissette200740864086408632Morissette, LyneDaniel PaulyComplexity, cost and quality of ecosystem models and their impact on resilience: a comparative analysis, with emphasis on marine mammals and the Gulf of St. LawrenceZoology260Ph.D.2007VancouverUniversity of British ColumbiaPh.D.(Morissette 2007). We have described the modeling approach in many publications over the years, and refer to such for computational details, ADDIN EN.CITE ADDIN EN.CITE.DATA (e.g., Christensen and Pauly 1992; Walters, Christensen et al. 1997; 1999; 2000; Christensen and Walters 2004; Christensen, Walters et al. 2005).
Of special importance here is that we recently have re-developed the approach in an object-oriented programming environment ADDIN EN.CITE Christensen200742544254425417Christensen, V.Lai, S.Ecopath with Ecosim 6: the sequelThe Sea Around Us Project Newsletter1-4 (September-October)432007(Christensen and Lai 2007), a prerequisite for the automated model setup. We rely on being able to call the various components of the EwE modules, read, add, and change parameters, run the various models, make new scenarios etc, all from code, in order to be successful with an undertaking of this scale.
Data sources
We base the database-driven model-generation approach for the worlds LMEs on a number of spatial, global databases, the majority of which were and are being developed by the Sea Around Us project at the Fisheries Centre of the University of British Columbia (www.seaaroundus.org). The project is designed to document how we exploit the oceans living resources, the consequences of the exploitation, and what can be done to improve ocean conditions ADDIN EN.CITE Pauly200742514251425117Pauly, D.The Sea Around Us Project: documenting and communicating global fisheries impacts on marine ecosystemsAMBIO: A Journal of the Human Environment290-2953642007(Pauly 2007). As part of this the Sea Around Us project has developed spatial databases for catches, effort, and prices, and other information related to productivity and fisheries (see below). Here, we build on these databases in combination with the EwE ecosystem modeling approach and software, which is developed as part of the project to construct ecosystem models of each of the worlds 66 LMEs.
Given that most of the databases we use for the ecosystem model construction have been developed and described elsewhere, we give here only a very brief introduction to the individual data sources, and we concentrate our description on the aspects that have direct relevance for the model construction. We present an overview of the data sources in Table 1.
Functional groups and basic parameters
Ecopath, and also the time-dynamic Ecosim model ADDIN EN.CITE ADDIN EN.CITE.DATA (Walters, Christensen et al. 1997; Walters, Pauly et al. 2000) and the time- and spatial-dynamic Ecospace model ADDIN EN.CITE Walters199913131317Walters, C.Pauly, D.Christensen, V.Walters, C.
Univ British Columbia, Fisheries Ctr, Vancouver, BC V6T 1Z4, Canada
Univ British Columbia, Fisheries Ctr, Vancouver, BC V6T 1Z4, Canada
Int Ctr Living Aquat Resources Management, Makati City 0718, PhilippinesEcospace: Prediction of mesoscale spatial patterns in trophic relationships of exploited ecosystems, with emphasis on the impacts of marine protected areasEcosystemsEcosystems539-55426fisheries managementtrophic modelscascadespolicy evaluationspatial dynamicsdispersalhabitat preferencefish populationmodelpredationdynamics1999NOV-DECISI:000084534100007<Go to ISI>://000084534100007(Walters, Pauly et al. 1999), all rely on describing quantified food webs of life in the ocean. For practical reasons (notably due to uncertainty about diets for individual species but also to make the model parameterization more manageable) we aggregate species in functional groups, which may consist of ecologically similar species, of individual species, or of life-stages of individual species or groups of species.
To develop the database-driven models we have cooperated with FishBase ADDIN EN.CITE Froese200944314431443128Froese, R.D. PaulyFishBase. World Wide Web electronic publication2009www.fishbase.org, version (01/2009)(Froese and Pauly 2009) to define a functional taxonomy for fishes based on their asymptotic length, their feeding habits, and their vertical distribution characteristics. While the information is available from FishBase for splitting the fishes into piscivores, benthivores, and herbivores, we simplify the model parameterization by omitting this classification in the definition of the functional groups. We do, however, consider the feeding habits implicitly when deriving diet compositions for the individual LMEs.
We separate between small species with asymptotic length <30 cm, medium with length 30-89 cm, and large with asymptotic length of 90 cm or more. We further separate between pelagics, demersals, bathypelagics, bathydemersals, benthopelagics, reef fishes, sharks, rays, and flatfishes. We separate invertebrates into cephalopods, other molluscs, krill, shrimps, lobsters and crabs, jellyfishes, zooplankton, megabenthos (>10 mm), macrobenthos (1-10 mm), meiobenthos (0.1-1 mm), and corals, soft corals, sponges, etc. Marine mammals are split into baleen whales, toothed whales, dolphin and porpoises, and pinnipeds (seals and sea lions), and aggregate all seabirds in one functional group. Primary producers are included as phytoplankton and benthic plants.
An overview of the functional groups is presented in Table 2, which also shows the basic (default) input parameters for all groups, as well as indicating the parameters that are supplied as part of the database-driven model-generation. The combined excretion and egestion rate was set to 0.2 (dimensionless) for all groups, apart from zooplankton where 0.4 was used based on experience from many other models ADDIN EN.CITE Christensen200426462646264617Christensen, VillyWalters, Carl J.Ecopath with Ecosim: methods, capabilities and limitationsEcological ModellingEcological Modelling109-1391722-420042004/3/1http://www.sciencedirect.com/science/article/B6VBS-4B0WM82-1/2/ae491fbe0d161bc9b9135ea079d8d728http://authors.elsevier.com/sd/article/S030438000300365X(Christensen and Walters 2004).
The ecotrophic efficiencies (EE), in Table 2 for the exploited species are used only for initial parameterization. Once the model-generation procedure is past the initial step, the default EE input would be used to calculate a start biomass. The calculated biomass will subsequently be changed to ensure that the functional group does not crash (i.e., is reduced by 99%) over time when observed catches are removed by subtraction from biomass at each time step, and in order to fit the biomass better using a random optimization search procedure (Christensen and Walters, in prep.) We explain the fitting in more details in the sections database-driven model generation and time series weighting for SS, below.
We used an assumed diet composition for each functional group as a starting point ADDIN EN.CITE Christensen200843144314431427Villy Christensen Carl J. Walters Robert Ahrens Jackie Alder Joe Buszowski Line Bang Christensen William Wai Lung Cheung John Dunne Rainer Froese Vasiliki Karpouzi Kristin Kastner Kelly Kearney Sherman Lai Vicki Lam Maria L.D. Palomares Aja Peters-Mason Chiara Piroddi Jorge L. Sarmiento Jeroen Steenbeek Rashid Sumaila Reg Watson Dirk Zeller Daniel PaulyModels of the worlds large marine ecosystems. GEF/LME global project Promoting Ecosystem-based Approaches to Fisheries Conservation and Large Marine Ecosystemsiv+432008Intergovernmental Oceanographic Commissions Technical Series No. 80. UNESCO(Christensen, Walters et al. 2008). For each LME, however, we modify the diets through an automated procedure based on diet data extracted from global databases of marine animals, notably, for fish from FishBase and for invertebrates from SeaLifeBase ADDIN EN.CITE Palomares200944324432443228Palomares, M.L.D.D. PaulySeaLifeBase. World Wide Web electronic publication.2009www.sealifebase.org, version (02/2009)(Palomares and Pauly 2009), for marine mammals from Pauly et al. ADDIN EN.CITE Pauly199817171717Pauly, D.Trites, A. W.Capuli, E.Christensen, V.Pauly, D.
Univ British Columbia, Fisheries Ctr, Vancouver, BC V6T 1Z4, Canada
Univ British Columbia, Fisheries Ctr, Vancouver, BC V6T 1Z4, Canada
Int Ctr Living Aquat Resources Management, Manila, PhilippinesDiet composition and trophic levels of marine mammalsIces Journal of Marine ScienceIces J Mar Sci467-481553marine mammalsdietstrophic levelsseals halichoerus-grypusaustralian fur-sealfeeding-habitsstomach contentsphoca-vitulinaringed sealsst-lawrenceharbor sealbering seaharp seals1998JUNISI:000074867800013<Go to ISI>://000074867800013(1998) and Kaschner ADDIN EN.CITE Kaschner200442154215421532Kristin KaschnerDaniel PaulyModelling and Mapping Resource Overlap between Marine Mammals and Fisheries on a Global ScaleDepartment of Zoology225Ph.D.2004VancouverUniversity of British ColumbiaPh.D. thesis(2004), and for marine birds from Karpouzi ADDIN EN.CITE Karpouzi200542224222422232Karpouzi, V. S.Daniel PaulyModelling and mapping trophic overlap between fisheries and the worlds seabirdsFaculty of Graduate Studies (Zoology)1412005VancouverUniversity of British ColumbiaM.Sc. thesis(2005). We refer to these sources for details.
Production rates for exploited groups
To obtain a weighted production/biomass ratio (which for biomass-dynamic groups corresponds to total mortality rate, Z, when there is no biomass accumulation) for each of the exploited functional groups, we develop a simple population dynamics model with monthly time steps for each species ( EMBED Equation.DSMT4 ) represented in the catches. For this, we estimate bodyweight, EMBED Equation.DSMT4 at age (t, months) based on the von Bertalanffy growth equation,
EMBED Equation.DSMT4
where EMBED Equation.DSMT4 is the von Bertalanffy metabolic parameter (year-1), and EMBED Equation.DSMT4 is the asymptotic weight (g). The natural mortality rate at age ( EMBED Equation.DSMT4 , year-1) is then estimated from the weight at age, based on Lorenzen ADDIN EN.CITE Lorenzen199643344334433417Lorenzen, K.The relationship between body weight and natural mortality in juvenile and adult fish: a comparison of natural ecosystems and aquacultureJournal of Fish Biology627-6424941996(1996) as,
EMBED Equation.DSMT4
where EMBED Equation.DSMT4 is 3.08 at latitudes <30, 3.13 at latitudes between 30 and 60, and 1.69 at higher latitudes. The values for EMBED Equation.DSMT4 are -0.21, -0.309 and -0.292 for the same latitudes, respectively. For each LME we estimate the mean latitude of all cells of a degree latitude by degree longitude grid, and use this for the calculations.
We next assume that the fishing mortality at age ( EMBED Equation.DSMT4 , year-1) in 1950 can be estimated from a logistic function,
EMBED Equation.DSMT4
where EMBED Equation.DSMT4 is the catch for the species in 1950, EMBED Equation.DSMT4 is the maximum annual catch during 1950-2004 for the species, EMBED Equation.DSMT4 is the weight at recruitment to the fishery, here assumed to be EMBED Equation.DSMT4 With this, we can now estimate the number at age ( EMBED Equation.DSMT4 ) as,
EMBED Equation.DSMT4
by setting EMBED Equation.DSMT4 as we only need relative numbers and biomass. The biomass of the age class is estimated as
EMBED Equation.DSMT4
For the species ( EMBED Equation.DSMT4 ), we sum up, to get EMBED Equation.DSMT4 and, EMBED Equation.DSMT4 Next, we want to integrate over species within a functional group. For this, we assume that the contribution of the individual species ( EMBED Equation.DSMT4 ) can be based on their contribution to catches. We acknowledge that this is a very rough assumption, assuming the same catchability and targeting for all species within a group, but see this as the only possible first assumption. It will be possible to modify this assumption later; this is only a first step. We thus estimate the functional group production/biomass ratio, EMBED Equation.DSMT4 P D B , y e a r - 1 ) f r o m ,
E M B E D E q u a t i o n . D S M T 4
w h i c h i s s i m p l y a w e i g h t e d a v e r a g e o f E M B E D E q u a t i o n . D S M T 4 , w i t h e a c h ( E M B E D E q u a t i o n . D S M T 4 ) w e i g h e d b y E M B E D E q u a t i o n . D S M T 4 .
M a x i m u m f i s h i n g m o r t a l i t y r a t e s
W e e s t i m a t e a n o v e r a l l n atural mortality rate ( EMBED Equation.DSMT4 , year-1) for each exploited fish species based on Pauly ADDIN EN.CITE Pauly198012621262126217Pauly, DOn the interrelationships between natural mortality, growth parameters, and mean environmental temperature in 175 fish stocks.J. Cons. int. Explor. MerJ. Cons. int. Explor. Mer175-192.391980(1980),
EMBED Equation.DSMT4
where EMBED Equation.DSMT4 is the ambient temperature (C), EMBED Equation.DSMT4 is the von Bertalanffy curvature parameter (year-1), and EMBED Equation.DSMT4 is the asymptotic weight (g). We weigh the exploited species by their overall catch over time to obtain a weighted natural mortality rate for each exploited functional group.
For each functional group, we then set the maximum allowable fishing mortality, Flim, to four times the natural mortality rate obtained from the Pauly equation. We use Flim as a reference point in Ecosim, so that if the estimated fishing mortality (obtained using a conditioned on catch model forcing procedure where F = (observed catch)/(model biomass)) exceeds Flim we limit the fishing mortality to this reference value. This ensures a smooth decline in population size (but not immediate collapse) even if EMBED Equation.DSMT4 has been underestimated during early steps of the time series fitting procedure. That fitting procedure then seeks to move the population out of the crash zone.
Primary productivity
Ecosim models are sensitive to changes in ecosystem productivity, and we have generally found a need to include both fisheries impact and temporal change in system productivity to reproduce historic abundance trends in ecosystems ADDIN EN.CITE Christensen200531543154315417Christensen, V.Walters, C.J.Using ecosystem modeling for fisheries management: Where are we?ICES C.M. M:192005(Christensen and Walters 2005). It is therefore extremely important to include changes in system productivity in the models throughout the simulation period. While global, spatial estimates are available from satellites for the recent decade, we do, however, need to use models to obtain estimates going back in time to the start of our simulation, i.e. to 1950, just like we need models to go forward to evaluate impact of climate changes. Fortunately such models are being developed in response to the need to evaluate the impact of climate change, and we here include four different models, though we have only used one set of data to date for the actual simulations conducted.
We used two different modeling approaches to simulate primary production. The first approach uses an empirical model to estimate chlorophyll based on physical properties. This technique, described in detail in Sarmiento et al. ADDIN EN.CITE Sarmiento200435753575357517Sarmiento, J. L.Slater, R.Barber, R.Bopp, L.Doney, S. C.Hirst, A. C.Kleypas, J.Matear, R.Mikolajewicz, U.Monfray, P.Soldatov, V.Spall, S. A.Stouffer, R.Response of ocean ecosystems to climate warmingGlobal Biogeochemical Cyclesdoi:1029/2003GB0021341832004Jul 14ISI:000222797400001<Go to ISI>://000222797400001 http://www.gfdl.noaa.gov/reference/bibliography/2004/jls0402.pdf (2004), fits observed SeaWiFS (HYPERLINK "http://oceancolor.gsfc.nasa.gov/SeaWiFS/"http://oceancolor.gsfc.nasa.gov/SeaWiFS/) chlorophyll data to a function of sea surface temperature, sea surface salinity, maximum winter mixed layer depth, and growing season length for different biogeochemical provinces ADDIN EN.CITE Longhurst19983302330233026Longhurst, A.R.Ecological Geography of the Sea1998San DiegoAcademic Press(Longhurst 1998), and then uses the empirical fits to predict chlorophyll under varying physical conditions. The resulting chlorophyll values were converted to primary production values based on three different algorithms: Carr ADDIN EN.CITE Carr200242434243424317Carr, M.-E.Estimation of potential productivity in eastern boundary currents using remote sensingDeep Sea Res., Part II59-80492002(2002), Marra et al. ADDIN EN.CITE Marra200342454245424517Marra, J.Ho, C.Trees, C.An alternative algorithm for the calculation of primary productivity from remote sensing dataLamont Doherty Earth Observatory Technical Report (LDEO-2003-1)2003(2003), and Behrenfeld and Falkowski ADDIN EN.CITE Behrenfeld199773773773717Behrenfeld, M.J.P.G. FalkowskiPhotosynthetic rates derived from satellite-based chlorophyll concentrationLimnology and OceanographyLimnol. Oceanogr.1-20421997(1997). All three algorithms estimate primary production as a function of surface chlorophyll, light, and temperature. The second modeling approach used was a lower trophic level biogeochemical model run within a coupled atmosphere ocean general circulation model ADDIN EN.CITE Dunnein prep.40774077407717Dunne, J. P.R. A. ArmstrongA. GnanadesikanS. M. Griffies J. L. SarmientoR. D. SlaterElemental coupling and air-sea CO2 flux variability in a global ocean biogeochemistry / general circulation modelin prep.(Dunne, Armstrong et al. in prep.).
Our intention for including four different primary production series (as a starter) is to be able to evaluate different scenarios for how future fish production may be impacted by climate change. We see this as an important use of the database-driven ecosystem models.
The primary production estimates were available on a 1 latitude by 1 longitude basis, with coastal cells excluded. We estimated primary production by LME by averaging the monthly primary production estimates over all cells with estimates within a given LME. We further estimated the average annual primary production by LME by averaging the monthly estimates within each year. In the averaging we did not consider that the cells had variable sizes; since coastal cells were excluded, all cells within an LME will have similar size.
The primary production estimates were obtained as mg Chlm-3day-1; we assumed this pertained to a water column of 50 meters, and that the average chlorophyll content in phytoplankton was 2.6% by weight of organic carbon ADDIN EN.CITE Riemann198942264226422617Riemann, BoSimonsen, PeterStensgaard, LarsThe carbon and chlorophyll content of phytoplankton from various nutrient regimesJournal of Plankton Research1037-10451151989January 1, 1989http://plankt.oxfordjournals.org/cgi/content/abstract/11/5/1037 http://plankt.oxfordjournals.org/cgi/reprint/11/5/1037.pdf 10.1093/plankt/11.5.1037use 2.6% as average percentage of chlorophyll of organic carbon(Riemann, Simonsen et al. 1989). We next converted the estimate of g carbon m-2 to g wet weight m-2 based on a conversion factor of 1:9 ADDIN EN.CITE Pauly199594949417Pauly, D.Christensen, V.Fish. Cent., Univ. British Columbia, 2204 Main Mall, Vancouver, BC V6T 1Z4, CanadaPrimary production required to sustain global fisheriesNature255-257 [Erratum in Nature, 376: 279]3746519primary productionfisheriestrophic levelsalgaeaquaticecosystemsecosystem analysisfish catch statisticsfisherymanagementfishery resourcestrophic relationshipsenergy flowecosystem managementWorld OceansMarineK 03009 AlgaeD 04700 ManagementQ1 01604 Stock assessment andmanagementO 5020 Fisheries and Fishery Biology19953688797(Pauly and Christensen 1995). We note that the conversion factors used will have negligible impact on the simulations performed here; what is important is not the overall level of system productivity, but how productivity changes over time. We consider it safe to assume that the conversion factors are not time varying, and that they, therefore, have little impact on the overall results.
From the sources above we estimated total primary production as well as standing stock of phytoplankton (from the SeaWiFS chlorophyll estimates) for use as biomass measures, and from the ratio of the two we obtained production/biomass ratios to use for the individual LMEs. In Ecosim simulations, we forced the biomass over time to match the selected series, and also fixed the production/biomass ratio over time, so that modeled total primary production would follow the selected series closely.
Primary production estimates were missing for some of the inland seas, and for those we followed a prioritized list where we used the Carr estimates if available. If not, we used the Marra et al. estimates, the Behrenfeld and Falkowski estimates, or, finally, the Dunne et al. estimates. In all cases, we used the annual primary production estimates to drive the ecosystem models, as we are not evaluating seasonal match-mismatch, and the monthly estimates will likely add more noise than signal.
An example of the primary production estimates is shown in Fig.1 for the Humboldt Current LME. It is noteworthy that at the scale of the LME, which stretches from northern Peru to the south tip of Chile, there is relatively little inter-annual variability, even though this area is strongly influenced by periodic El Nio/La Nia Southern Oscillations events. There were, e.g., El Nio events in 1976-1977, 1982-1983, 1986-1987, 1991-1994, and 1997-1998. We actually see stronger temporal variation in other LMEs, e.g., the Gulf of Mexico.
Zooplankton
The biomass estimates are based on a map of zooplankton abundance in the upper 100 m of the worlds oceans, published by FAO ADDIN EN.CITE FAO19724225422542256FAOAtlas of the living resources of the seas1972FAO Fisheries Department, Food and Agriculture Organization of the United Nations, Rome, Italy.FAO19811219121912196FAOAtlas of the living resources of the seas1981FAO Fish. Ser. 15. FAO Fisheries Department, Food and Agriculture Organization of the United Nations, Rome, Italy(1972; FAO 1981), and based on the work of V.G. Borogov et al. ADDIN EN.CITE Bogorov196842244224422417Bogorov, V.G.Vinogradov, M.D.Varonina, N.M.Kanaeva, I.P.Suetova, I.A.Distribution of zooplankton biomass within the surficial layer of the world oceanDokl. Akad. Nauk. USSR1205-12071821968(1968). The original map was digitized by the Sea Around Us project, and the original estimates in mgm-3 (wet weight) were re-expressed in tkm-2. We apply the estimates of zooplankton biomass to the upper 100 meter of the water column, and assume that abundances at greater depths are negligible.
Benthos
Biomass estimates for two size-categories of benthos, macro-benthos and meio-benthos are from a spatial GIS-layer developed at the Conservation Biology Marine Institute, Bellevue WA, USA in cooperation with the Sea Around Us project ADDIN EN.CITE Peters-Masonunpublished data42234223422317Aja Peters-MasonJohn GuinotteGilbert RoweReg WatsonDaniel PaulyEstimation of global benthic biomassunpublished data(Peters-Mason, Guinotte et al. unpublished data). Peters-Mason et al. evaluated 28 publications with geo-referenced estimates of meio-fauna (0.1 1 mm, N = 184 samples, notably foraminiferans, nematodes, and harpacticoid copepods) and macro-fauna (1 10 mm, N = 140 samples, notably polychaetes, crustaceans, and mollusks). Samples of larger benthos (mega-fauna, notably cnidarians, crustaceans and echinoderms) were too sparse in the literature to allow derivation of global estimates. We extract estimates of benthos abundance from this source with a half-degree by half-degree resolution globally, and sum the abundance by LME. No information about temporal trends in benthos abundance was available at the scale of interest, and we therefore let the abundance and productivity patterns be estimated from the time-dynamic simulations.
Mesopelagics
A combined spatial biomass of small and large mesopelagic fishes was obtained from the information provided by Gjsaeter and Kawaguchi ADDIN EN.CITE Gjsaeter198025932593259327Gjsaeter, J.Kawaguchi, K.A review of the world resources of mesopelagic fish.151DP/AB:FAODOC2:971980FAO Fish. Tech. Pap. 193(1980) based on extensive trawl-surveys in the world oceans. The maps were digitized and validated by Lam and Pauly ADDIN EN.CITE Lam200533953395339527Lam, V.D. PaulyMapping the global biomass of mesopelagic fishes30:1-42005The Sea Around Us Project Newsletter, July/August(2005). The derived GIS-layer is incorporated in the Sea Around Us database, and we extract estimates by half-degree and sum up to the LME-level for all LMEs.
Marine mammals
We used published reconstructions of marine mammal population estimates and trends for all extant species of marine mammals with an exploitation history ADDIN EN.CITE Christensen200531403140314017Christensen, Line BangMartell, S.J.D. A stochastic framework for reconstructing historical marine mammal abundance from catch records and sparse abundance information: application to the Antarctic blue whale and North Atlantic and Arctic fin whale.ICESC.M.2005/R:322005Christensen20064238423842386Christensen, Line BangReconstructing historical abundances of exploited marine mammals at the global scale1612006Fisheries Centre Research Reports 14(9), University of British Columbia(Christensen and Martell 2005; Christensen 2006). This work included creation of a global database of marine mammal whaling, sealing and bycatch/discards estimates, and we combined this database with a spatial database of marine mammal distribution and relative abundance, covering all marine species ADDIN EN.CITE Kaschner200442154215421532Kristin KaschnerDaniel PaulyModelling and Mapping Resource Overlap between Marine Mammals and Fisheries on a Global ScaleDepartment of Zoology225Ph.D.2004VancouverUniversity of British ColumbiaPh.D. thesisKaschner200644024402440217Kaschner, K.Watson, R.Trites, A. W.Pauly, D.Mapping world-wide distributions of marine mammal species using a relative environmental suitability (RES) modelMarine Ecology Progress SeriesMarine Ecology Progress Series285-3103162006(Kaschner 2004; Kaschner, Watson et al. 2006).
Combining the estimates of abundance by species by year, and the relative species distributions we obtain estimates of the spatial abundance of marine mammal species by year. For each spatial cell we sum up the abundance to the LME-level, and thus obtain species-weighted marine mammal abundance by LME. We are currently not allocating the catch database of marine mammal kills to spatial cells, because the whaling database as implemented does not have the required spatial information, and we are thus unable to estimate mortality by LME by year. Instead, we force marine mammal abundance directly in the Ecosim model runs, i.e. we provide that abundance as a known biomass time series from which time series Ecosim predictions of marine mammal food consumption and impact on prey are generated. We assume that the trend has been the same throughout the species distribution area.
We obtain estimates of annual consumption for marine mammal species based on estimated consumption/biomass ratios and species abundance estimates, and for each LME summed up by species to obtain the total consumption by the marine mammal biomass. The consumption/biomass (Q/B, year-1) estimates are based on an assumption of baleens feeding eight months a year. Based on Reilly et al. ADDIN EN.CITE Reilly200442164216421617Reilly, S.Hedley, S.Borberg, J.Hewitt, R.Thiele, D.Watkins, J.Naganobu, M.Biomass and energy transfer to baleen whales in the South Atlantic sector of the Southern OceanDeep Sea Research Part II: Topical Studies in Oceanography1397-14095112-132004http://www.sciencedirect.com/science/article/B6VGC-4DD934J-2/2/c4b7efc3555bb42a1711c8815e498561(2004), we have for baleen whales,
EMBED Equation.DSMT4
where EMBED Equation.DSMT4 indicates average individual weight (kg). For toothed whales and dolphins we use an empirical equation developed by Hunt et al. ADDIN EN.CITE Hunt200042484248424827Hunt, G.L.J.Kato, H.McKinnel, S.M.Predation by marine birds and mammals in the subarctic Pacific Ocean165 2000PICES Scientific Report 14. North Pacific Marine Science Organisation, Sidney, BC, Canada. (2000), as modified by Piroddi ADDIN EN.CITE Piroddi200842464246424632Chiara PiroddiVilly ChristensenAn ecosystem-based approach to study two dolphin populations around the Island of Kalamos, Ionian Sea, GreeceZoology122M.Sc.2008February 2008VancouverUniversity of British ColumbiaM.Sc. thesis(2008),
EMBED Equation.DSMT4
For otariids (eared seals),
EMBED Equation.DSMT4
And for other pinniped species,
EMBED Equation.DSMT4
where the last three equations are described in more detail by Piroddi ADDIN EN.CITE Piroddi200842464246424632Chiara PiroddiVilly ChristensenAn ecosystem-based approach to study two dolphin populations around the Island of Kalamos, Ionian Sea, GreeceZoology122M.Sc.2008February 2008VancouverUniversity of British ColumbiaM.Sc. thesis(2008).
Marine birds
We used a global database of seabird distribution, abundance, and utilization, ADDIN EN.CITE ADDIN EN.CITE.DATA (Karpouzi 2005; Karpouzi, Watson et al. 2007), to obtain spatial estimates of marine bird abundance by species, as well as estimates of food consumption by marine birds. Details about the approximation approach is presented by Christensen et al ADDIN EN.CITE Christensen200843144314431427Villy Christensen Carl J. Walters Robert Ahrens Jackie Alder Joe Buszowski Line Bang Christensen William Wai Lung Cheung John Dunne Rainer Froese Vasiliki Karpouzi Kristin Kastner Kelly Kearney Sherman Lai Vicki Lam Maria L.D. Palomares Aja Peters-Mason Chiara Piroddi Jorge L. Sarmiento Jeroen Steenbeek Rashid Sumaila Reg Watson Dirk Zeller Daniel PaulyModels of the worlds large marine ecosystems. GEF/LME global project Promoting Ecosystem-based Approaches to Fisheries Conservation and Large Marine Ecosystemsiv+432008Intergovernmental Oceanographic Commissions Technical Series No. 80. UNESCO(2008).
The daily food intake (DFI) for marine birds was estimated based on the bioenergetic model of the ICES Working Group on Seabird Ecology ADDIN EN.CITE ICES200042494249424927ICESReport of the Working Group on Seabird Ecology722000 ICES CM 2000/C: 04http://www.ices.dk/reports/occ/2000/ (ICES 2000), expressing DFI for each bird species as
EMBED Equation.DSMT4
where ER is the energy requirement, DCj is the fraction that each prey species j contributes to the birds diet, EDj is the energy density of prey j, and AE is the mean assimilation efficiency for the bird (assumed to be 0.75). See Karpouzi ADDIN EN.CITE Karpouzi200542224222422232Karpouzi, V. S.Daniel PaulyModelling and mapping trophic overlap between fisheries and the worlds seabirdsFaculty of Graduate Studies (Zoology)1412005VancouverUniversity of British ColumbiaM.Sc. thesis(2005) for details of the calculations.
Based on diet information collated by Karpouzi ADDIN EN.CITE Karpouzi200542224222422232Karpouzi, V. S.Daniel PaulyModelling and mapping trophic overlap between fisheries and the worlds seabirdsFaculty of Graduate Studies (Zoology)1412005VancouverUniversity of British ColumbiaM.Sc. thesis(2005), we derive estimates for 24 prey types of how to allocate the bird diet composition (prey composition) to the functional groups used in the present study, ADDIN EN.CITE Christensen20084314see for details4314431427Villy Christensen Carl J. Walters Robert Ahrens Jackie Alder Joe Buszowski Line Bang Christensen William Wai Lung Cheung John Dunne Rainer Froese Vasiliki Karpouzi Kristin Kastner Kelly Kearney Sherman Lai Vicki Lam Maria L.D. Palomares Aja Peters-Mason Chiara Piroddi Jorge L. Sarmiento Jeroen Steenbeek Rashid Sumaila Reg Watson Dirk Zeller Daniel PaulyModels of the worlds large marine ecosystems. GEF/LME global project Promoting Ecosystem-based Approaches to Fisheries Conservation and Large Marine Ecosystemsiv+432008Intergovernmental Oceanographic Commissions Technical Series No. 80. UNESCO(see Christensen, Walters et al. 2008 for details). For each LME, we used the relative bird species abundance by year to calculate annual consumption and biomass as well as an initial diet for 1950, i.e. bird abundance was treated as a forcing variable like marine mammal abundance.
Abundance trends for marine populations
We have developed a database with more than 2600 trends for marine populations with focus on fish species. The trends are from a variety of sources and represent survey estimates, estimates from assessments, as well as fisheries-dependent estimates such as commercial CPUE series. The vast majority of trend series are from temperate areas, but we have taken care to increase the spatial coverage, and we, e.g., have a fair representation from the western and southern Africa. The trend database is important for fitting the time-dynamic LME models, notably with regards to assessment of compensatory responses to fishing (density-dependence). This aspect is very important for evaluating carrying capacity of LMEs to support future fisheries.
We extract trends for the LMEs by functional group by first selecting all trend series for which the taxon is allocated to the given functional group in the Sea Around Us taxon database, and which are from the same FAO statistical area (www.fao.org) as the given LME. All trend series are geo-referenced, and we weigh the series by a squared inverse distance weighting to the LME (border nearest the trend location, to obtain a weighted trend series by functional group by LME.
While the trend series derived in this manner are only to be considered a first attempt at providing comprehensive time series information, they do provide a starting point that goes beyond what we most often have seen for ecosystem models. We emphasize though, that it is very important to thoroughly search and evaluate all sources of information for a given LME as part of the modeling process.
Fisheries
Catches
The Sea Around Us project studies the impact of fisheries on the world's marine ecosystems. To this end, the project uses a Geographic Information System to map global fisheries catches from 1950 to the present, with explicit consideration of coral reefs, seamounts, estuaries and other critical habitats of fish, marine invertebrates, marine mammals and other components of marine biodiversity ADDIN EN.CITE Watson200443274327432717Watson, R.Kitchingman, A.Gelchu, A.Pauly, D.Mapping global fisheries: sharpening our focusFish And Fisheries168-17752fisheries landingsfisheries statisticsglobal fisheriesmappingWORLD FISHERIESECOSPACE2004Jun1467-2960ISI:000221948700006<Go to ISI>://000221948700006(Watson, Kitchingman et al. 2004). Summary data are freely available from the project website, and are meant to support studies of global fisheries trends and the development of sustainable, ecosystem-based fisheries policies. For the present study, we link directly to the underlying spatial catch dataset, enabling analysis with (rule-based) spatial resolution, albeit here summed up to the LME-level. The catches are available online at HYPERLINK "http://www.seaaroundus.org"www.seaaroundus.org.
Fishing effort
Ecosims ability to explain historical abundance trend patterns is typically best in cases where historical fishing impacts can be estimated from changes in historical fishing efforts, rather than by subtracting historical catches from model biomasses over time, (which often causes dynamic instability in the model equations). At present, the effort measures we have access to are quite tentative and lacking in spatial resolution ADDIN EN.CITE Alder200742214221422127Jackie AlderSylvie GunetteJordan BeblowWilliam CheungVilly ChristensenEcosystem-based global fishing policy scenarios912007VancouverFisheries Centre Research Report 15(7), University of British ColumbiaGelchu200634663466346632Ahmed GelchuDaniel PaulyGrowth and distribution of port-based global fishing effort within countries EEZsFisheries Centrein prep.2006VancouverUniversity of British ColumbiaPh.D.(Gelchu 2006; Alder, Gunette et al. 2007). We are currently expanding on the effort estimation procedures ADDIN EN.CITE ADDIN EN.CITE.DATA (Watson, Revenga et al. 2006; Watson, Revenga et al. 2006), and expect to have more detailed, spatial effort measures available by mid-2009 (Watson et al., in prep.) For the present study, we have been unable to use effort estimates to drive the modeling as the available estimates have too little detail with regard to fleet definitions to be able to determine the diversity of fleets needed to capture changes in target species over time. We therefore do not use effort as a model driver here; instead we use only the catch estimates by target groups and years to drive the models over time.
Prices and cost of fishing
A global ex-vessel price database has been developed as part of the Sea Around Us project ADDIN EN.CITE Sumaila20074250`, available online at www.seaaroundus.org4250425017Sumaila, R. A.D. MarsdenR. WatsonD. PaulyA global ex-vessel price database: construction and applicationsJournal of Bioeconomics39-5192007(Sumaila, Marsden et al. 2007, available online at www.seaaroundus.org). The database includes all catch categories (typically at the species-level), and gives nominal and real (standardized to 2000 $US) prices by country for 1950 onwards. We calculate average price by functional groups from this database, expressed as real prices for 2000, based on the species catch composition in the individual LMEs. We have access to regional prices by the functional groupings (see HYPERLINK "http://www.seaaroundus.org"www.seaaroundus.org) used for the model, and will consider using these in subsequent iterations of this modeling complex.
Work on populating cost estimates for the various fisheries is presently underway in connection with the further development of the ex-vessel price database. We recognize that the cost of fishing is very different in various parts of the world, while the prices of export-quality fish commodities are of a more global character. This has implications for what price/cost structure to use for the individual, spatial regions in the forward-looking simulations. This will need further consideration in the next round of simulations. For the time being, we use a global price average in the models, not country-specific prices from the countries fishing in the individual LMEs. All catches are allocated to countries fishing, and as we have country-specific ex-vessel prices, we will use these in coming iterations of the ecosystem models.
Database-driven model generation
We have developed an approach that relies on a number of databases, spatial and temporal, to construct ecosystem models using an automated procedure. We call this approach database-driven ecosystem model generation, and have described aspects of many of the databases we build on above.
Based on the database-parameterized Ecopath models for each of the LMEs, we have developed a modeling process to represent time-dynamics and to tune the models to the time series data (Fig. 2). For each LME, we identify the spatial cells within it, and search a series of databases (as described above) for information about these cells. This information is passed to the static Ecopath model, and the time-dynamic Ecosim model. The Ecopath model is then balanced, Ecosim is run with time series, and the tuning may impact both Ecopath and Ecosim parameters. We consider this tuning necessary for evaluating carrying capacity, as well as for any other study that seeks to evaluate the potential impact of changes in fishing pressure or environmental productivity.
For each LME model, we extract time series information from a range of sources as explained above, and illustrated in Fig. 3.
In summary form, the method for extracting the data, parameterizing the model, and fitting it to time series data follows a stepwise approach, most easily explained in pseudo-code form:
Read information assigning all latitude by longitude spatial cells to LMEs, and read size of all cells.
Read how all exploited species are assigned to taxonomic categories.
Extract real ex-vessel prices by taxonomic unit (typically species), and by year, 1950-2004.
Do the following steps for each of the 66 LMEs:
Open a generic Ecopath model; copy and rename it to indicate the current LME number, e.g., LME1.
Make a list of all cells included in the current LME.
Read Ecopath parameters for these cells; set EE to be estimated for groups with data, and add remarks to the model. This is initially for:
Mesopelagic biomass.
Macro- and meio-benthos biomass.
Zooplankton biomass.
Read catches for each cell by taxonomic unit (typically species) and by year.
Assign catches to functional groups.
Sum up catches over all cells by functional groups.
Calculate total ex-vessel price over all cells by functional groups.
Calculate average ex-vessel price.
Add the catches for the first year as landings estimates in the Ecopath model.
Store time series of catches for use in Ecosim.
We currently do not use the time series of prices.
Read effort estimates.
Our current effort estimates show too little detail. We therefore omit this step at present.
Read marine mammal information.
Estimate consumption rates.
Estimate marine mammal abundance by year from distributions and population trends.
Estimate marine mammal diet and consumption/biomass ratio for the first year from total consumption by prey species over all cells.
Read marine birds information.
Sum up biomass by year.
Sum up consumption for each prey species and estimate diet and consumption/biomass ratio.
Read fish diets from FishBase and diet for other species from SeaLifeBase.
Allocate species information to functional groups.
Calculated average diet for functional groups with information.
Add a tentative biomass (as a prior estimate of absolute biomass) to the time series data set used for Ecosim fitting for each of the exploited groups, based on the assumption that the fishing mortality in the year with maximum catch corresponds to the natural mortality.
Check if there are any groups that lack biomass estimates, and have neither catch nor predators.
For such groups, Ecopath cannot estimate biomasses, and the biomass is initially set to 0.001 tkm-2.
Run Ecopath; load Ecosim scenario, and read time series information obtained above.
Read primary production and chlorophyll estimates.
We currently have included four approaches for estimating primary production, and for each of these we include monthly and annual estimates as forcing functions.
One of the series is used to force the production/biomass ratio for phytoplankton, (order of selection described earlier).
Chlorophyll estimates are (after conversion) used to force the phytoplankton biomass.
Check model for mass balance.
If any of the ecotrophic efficiencies, (EE, indicating the proportion of production that is used in the system mainly for catches and predation), exceeds unity then:
Predation on pinnipeds can be overestimated; if so reduce the contribution of pinnipeds to their predators diets.
For groups where we calculate EE based on other basic input, change this to an assumed EE of 0.95 and calculate P/B instead.
Repeat this procedure until the model is balanced.
Run Ecosim and store the initial model log residuals (SS) between observed and estimated series (Table 3).
Check if there are any groups that are crashing, (i.e. end biomass < start biomass / 100), or whose catch is lower than in the Ecosim data time series (F has exceeded Flim).
If there are such problem groups, then gradually increase the biomass of the groups in question, while ensuring that no other group in the system becomes unbalanced because of increased predation pressure.
Iterate a gradual biomass increase until every group is capable of having produced observed catches without collapsing completely.
Run Ecosim and store the SS for Table 3 again.
Fit the model to the time series data using a random optimization procedure (Matyas search, Christensen and Walters, in prep.) now incorporated in EwE6.
Set initial wide bounds for the biomass, P/B (and hence Q/B as Q/B here is estimated from P/B), and vulnerabilities.
Sample each parameter based on a narrow coefficient of variation.
When a better fit (lower SS) is obtained, resample the parameters from a normal distribution with a band around the last best fit parameters.
Iterate until there have been at least 10,000 iterations, and continue until there has not been a better fit in the last 1000 iterations.
Open the spatial- and time-dynamic Ecospace model.
Create a base map for the LME with habitat definitions based on depth strata.
Extract spatial primary production estimates and store these.
The Ecospace models are not described in this contribution, but are included with the data files.
Save the model
Move to the next LME.
Time series weighting for SS
The random optimization search procedure for parameter estimates that better fit historical abundance trend data relies upon improving a sum of squares fitting criterion, SS. For fitting relative abundance data, the SS term for each abundance trend series is a sum over time of squared deviations between observed trend index value and predicted index value, where the predicted index value is a scaling or catchability coefficient (evaluated at its conditional maximum likelihood value) times modeled biomass. When several time series contribute sums of values over time to the overall SS, the weight W of individual time series are estimated from the inverse spatial distance from the LME, raised to the third power. If the distance is more than 40 half-degree cells or if the time series is from another FAO area, it is not used. Further, we halved the weight if the method used for estimating the relative abundance time series is fishery-dependent, while we doubled the weight if the time series is from an assessment. The weights are scaled so that the average trend time series weight for each LME-model is 1.
For catches, we used a high weighting factor (10) for all catch series. Given that we force the catches in Ecosim to match the time series catches, (thus, by default, the observed catch = simulated catch), this factor should not contribute to the SS calculation, unless Ecosim for some reason cannot match the forced catch. This can either be because the population has crashed, or because the estimated fishing mortalities exceed a set maximum. If the simulation cannot match the catch, then the high weighting factor will penalize the model parameter values leading to the poor match, by assigning those values a high SS value. Prior biomasses for each of the exploited groups were obtained based on the assumption that fishing mortality equaled natural mortality in the year with maximum catch; these estimates were assigned a weight of 1. Each such biomass contributes EMBED Equation.DSMT4 to the fitting SS, where EMBED Equation.DSMT4 is model predicted biomass for whatever year had maximum catch, and EMBED Equation.DSMT4 is the catch-based prior estimate, EMBED Equation.DSMT4 .
Results
Model parameters
A notable finding from this first round of database-driven ecosystem model generation is that the initial approach (where we use generic parameters for many of the basic input parameters for the Ecopath model) will need to be substantially improved. We find from trial runs of the EwE policy optimization procedure, for instance, that it tends to overestimate potential yield from high-latitude systems. This is connected to our use of a generic production/biomass (P/B) factor for many functional groups. We have partly remedied this by using P/B-estimates based on the Lorenzen-model ADDIN EN.CITE Lorenzen199643344334433417Lorenzen, K.The relationship between body weight and natural mortality in juvenile and adult fish: a comparison of natural ecosystems and aquacultureJournal of Fish Biology627-6424941996(1996), but find that further work is required. In the next iteration of the procedure, we intend to test the empirical equation of Gascuel et al. ADDIN EN.CITE Gascuel200842534253425317Gascuel, D.L. MorissetteM.L.D. PalomaresV. ChristensenTrophic flow kinetics in marine ecosystems: toward a theoretical approach to ecosystem functioningEcological modeling33-472172008(2008) for estimation of P/B as a function of trophic level and mean water temperature. It is also clear that we need more detailed estimates of fleet effort to improve the drivers for the time-dynamic simulations.
Time series fitting
The present study represents a first attempt to automate the model time series fitting procedure. Over the last years, we have worked with numerous ecosystem models and fitted these models to time series data ADDIN EN.CITE Christensen20053154see 3154315417Christensen, V.Walters, C.J.Using ecosystem modeling for fisheries management: Where are we?ICES C.M. M:192005(see Christensen and Walters 2005), but this has always been done with careful inspection of the models, and with a qualified eye evaluating the tactics of the fitting by focusing attention on poor model fits. When doing this, we look for model time series that diverge greatly from data; then ask why that divergence has occurred, and modify the Ecosim parameters and time series inputs accordingly. In the manual fitting, emphasis is on careful examination of how individual groups react in the model.
We present an example of some of the diagnostic plots showing how the time-series biomass trend data affect simulated outputs from Ecosim (Fig. 4). In this example there are numerous time series for biomass (indicated by the different-colored circles on the biomass plot) indicating a downward trend over time. This trend is picked up well by Ecosim (the line on the plot), and we see from the second plot (mortality) that the downward trend in the early 1970s may be associated with predation increase, rather than catches, which only increased some years later.
The development of the automatic fitting procedure has now reached a state where the model fits are beginning to be comparable to many manually conducted model fits, and we know that we can improve the procedure further through inclusion of additional rules. We have taken great care to make the fitting procedure rule-based to ensure reproducibility, to enable us to develop finer scale ecosystem models, and to be able to continuously update the models as more data become available. A manual element in the fitting procedure would make this impossible.
In Table 3 we review the sum of squared log residuals (SS) fitting criterion for the individual LME models before the automated time series fitting, after the fitting, and the ratio between the two. For 60% of the models the automated procedure has reduced the SS with 99% or more, while the average reduction is 98.6%. The low SS values after fitting indicates that we have been able to fit several or most relative abundance time series quite well.
Even if the reduction is quite impressive for many models, we note that this is usually because the models with high initial SS will have a number of groups that crashed. Once a crash happens, the SS will shoot up (since the SS calculation heavily penalizes inability to explain historical catch data due to collapse in simulated population size to levels too low to have produced the catch). Avoiding such crashes will therefore have a disproportionally large impact on the SS compared to what subsequent fitting may provide. The reduction is mostly obtained by increasing the start biomass for the impacted group, but we also provide other diagnostics and remedies as described in the methodology section. Notably, as part of the random optimization-fitting procedure we vary both the initial biomasses and vulnerabilities (Christensen and Walters, in prep.) The procedure may thus find that a lower initial biomass can be used for a group, if the group is assumed to be closer to its carrying capacity (i.e. to be taking a higher proportion of the prey potentially available to it).
For manual model fitting, Ecosim provides important diagnostics in the form of a plot showing all the time series fits in a model. We here give an example of such a plot comparing population trend time series with Ecosim predictions for the New Zealand Shelf model (LME 46) in (Fig. 5). The fits are perfect for the marine mammals in Fig. 5 (first four plots) since for these groups we force Ecosim to use biomasses from the estimated time series; the same is the case for the phytoplankton (bottom row). For the other groups the fits are of variable quality, and it is clear that the fitted parameter values generally are not very capable of reproducing variation in the population trend series. However, tight fits should not be expected due to variance in the observed data and because the trends are for individual species, while the Ecosim simulations are for functional groups including numerous species.
In this initial iteration of the database-driven ecosystem models, we have used catches to drive the Ecosim simulations. For groups where we have no trend series, this may cause the groups biomass to be too stable over time; the initial biomass may be overestimated as this reduces the risk of the group crashing due to high catches. If, for such groups, the catches decrease over time, this may well result in the groups biomasses being estimated to increase due to perceived lower fishing pressure. It may well be, in reality, that the fishing pressure stays high, and that the catches decline because of lower biomass. We cannot avoid such cases given our quite limited number of population trend series, and this serves to (1) strengthen the case for using fishing effort to drive the simulations, and (2) illustrate why we do not currently want to use the models for predictions about how the ecosystems may react to future changes in fishing pressure. To do so calls for improved detailed estimates of spatial fishing effort.
Biomass of fishes in the worlds LMEs
We use the 66 LME models to obtain a first estimate of the total biomass in 1950 of fishes in the worlds LMEs, see Fig. 6. The term fishes is here defined as being represented by functional groups 1-24 in Table 2. The biomass is estimated so as to be sufficient to support the catches obtained in the LMEs from 1950 to 2004, while accounting for predator demand through the food web as well.
We estimate the total biomass of fish in the LME areas to 1.1 billion tonnes. There are to our knowledge only two other estimates of fish biomass, one, estimating the total fish biomass to approximately 1 billion tonnes based on size spectra ADDIN EN.CITE Jennings200844334433443317Jennings, SimonMlin, FrdricBlanchard, Julia LForster, Rodney MDulvy, Nicholas KWilson, Rod WGlobal-scale predictions of community and ecosystem properties from simple ecological theoryProceedings of the Royal Society B: Biological SciencesProceedings of the Royal Society B: Biological Sciences1375-138327516412008June 22, 2008http://rspb.royalsocietypublishing.org/content/275/1641/1375.abstract10.1098/rspb.2008.0192(Jennings, Mlin et al. 2008), the other, which is based on the approach presented there, estimates the global fish biomass to approximately 2 billion tonnes ADDIN EN.CITE Wilson200943304330433017R.W. WilsonF.J. MilleroJ.R. TaylorP.J. WalshV. ChristensenS. JenningsM. GrosellContribution of fish to the marine inorganic carbon cycleScienceScience359-3622009(Wilson, Millero et al. 2009).
The biomass estimate for global LMEs can be compared to a total annual catch of approximately 60 million tonnes per year since the mid-1980s, the vast majority of which was obtained from within the LMEs. While this may seem to indicate a low exploitation pressure (catch/biomass ratio) we note that the biomass is dominated by fish groups of little or no commercial interest (Table 4). No less than 58% of the estimated total biomass is thus represented by small demersals (group 3) and small bathypelagics (group 7), both with asymptotic lengths of less than 30 cm, and both with no or only minimal potential commercial interest given their sparse densities ADDIN EN.CITE Pauly199838383817Pauly, D.Christensen, V.Dalsgaard, J.Froese, R.Torres, F., Jr.Fish. Cent., 2204 Main Mall, Univ. British Columbia, Vancouver, BC V6T 1Z4, Canada; E-mail: pauly@fisheries.comFishing down marine food websScienceScience (Wash.)Science860-8632795352marine ecosystemsfood webssustainable developmentresourceutilizationOverfishingLanding statisticsFishery managementFishery resourcesoverfishingMarineD 04700 ManagementQ1 01604 Stock assessment and managementO5020 Fisheries and Fishery Biology1998Feb4245165(Pauly, Christensen et al. 1998).
We here abstain from presenting estimates of temporal trend in biomass, primarily for lack of reliable, detailed estimates of fishing effort over time and space. Development of such is a priority for further development of the approach reported on here.
Discussion
We are presenting a new approach to modeling, and this raises a pertinent question: what is it good for? We regard it a major advantage that by making the model construction database-driven we enrich the models with information that likely would not otherwise have been used for the model construction. We are also making it much easier to get started with the modeling process by presenting a draft model for improvement.
We consider the models of appropriate quality for use to address large-scale issues, such as for instance how marine ecosystems biodiversity and productivity may be impacted by policy questions, e.g., in connection with UNEPs Global Environmental Outlook series. For more local use, i.e., for use of the individual LME-models, we see the models providing a well-defined starting point, but one, which should be enriched through local data from the LME. Notably, we do not supply effort time series, and such are very important to drive the models over time. Also, the species-resolution is very poor in the models as the functional groups are defined in a very generic manner. This poses a problem for using the models for management purposes as well as to address more specific biodiversity questions. For such use it is important to further enrich the models, and this is indeed a case where modelers should consider whether it isnt better to actually develop the ecosystem models from scratch.
It is a potential danger that by automating the model construction process, the potential users may not have a full understanding of the data limitations and of what is required to use the models as part of the actual management process. We would be very hesitant to use any model for management without a thorough understanding of the models behavior. We thus caution strongly against the direct use of the database-driven models for management purposes.
Large Marine Ecosystems face serious threats throughout the world. One important threat is that they are overfished due to excessive effort capacity. To evaluate what has happened, what is happening, and what may happen under alternative future scenarios, it is important to have ecosystem modeling as part of the toolbox for ecosystem-based management. Ecosystems models integrate a diversity of information, including ecological, economical and social considerations, and provide our best hope for expanding our understanding of how to sustainably manage the oceans resources for our and future generations benefit.
We have taken a step for making ecosystem modeling more accessible by developing capabilities for database-driven ecosystem model generation. We encourage the scientific community to cooperate with us on developing model capabilities within the projects and to enable cooperation that will further enrich the models, and lead to their successful application.
Overall we see a need for developing better databases related to spatial effort estimation, and we encourage analysis of the economical and social aspects of the fish production chain, from sea to consumer. Given information from throughout the fishing sector, ecosystem models combined with economical value chain modeling can be used to evaluate how food security, economic and social parameters may be impacted by fisheries management decisions.
Acknowledgements
This activity was funded by the Global Environment Facility through the United Nations Environment Programme, and was part of the UNESCO/IOC activity Promoting Ecosystem-based Approaches to Fisheries Conservation in LMEs (Global activity), Component 2.a (UNESCO Contract Number 4500039066). A more detailed version of this contribution was published as IOC Tech. Rep. 80. The activity was made possible through the activities and support of the Sea Around Us project, initiated and funded by the Pew Charitable Trusts, Philadelphia. We also thank the Lenfest Oceans Program for funding the redevelopment of the Ecopath with Ecosim software, a necessary requirement for the present activity. We further thank Dr Kenneth Sherman, NOAA, for defining the project scope through many interesting discussions, seeing it through the GEF funding process, as well as for his sustained support of the research and capacity building that this activity has resulted in. Finally, we thank two anonymous reviewers for useful edits and suggestions.
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III Pollution and Ecosystem Health
The following methodology was first published in The UNEP Large Marine Ecosystem Report: A Perspective on Changing Conditions in LMEs of the Worlds Regional Seas (pp.81-97) ADDIN EN.CITE Seitzinger20081327132713275S. SeitzingerR. LeeK. ShermanG. HempelLand-based sources of nutrients to Large Marine EcosystemsThe UNEP Large Marine Ecosystem Report: A Perspective on Changing Conditions in LMEs of the World's Regional Seas
81-98UNEP Regional Seas Report and Studies No. 1822008NairobiUNEP(Seitzinger and Lee 2008). The original figure numbering and reference list are retained here.
Land-based Nutrient Loading to LMEs: A Global Watershed Perspective on Magnitudes and Sources
Seitzinger and Lee
Abstract
Land-based nutrient (nitrogen and phosphorus) inputs to coastal systems around the world have markedly increased due primarily to the production of food and energy to support the growing population of over 6 billion people. The resulting nutrient enrichment has contributed to coastal eutrophication, degradation of water quality and coastal habitats, and increases in hypoxic waters, among other effects. There is a critical need to understand the quantitative links between anthropogenic activities in watersheds, nutrient inputs to coastal systems, and coastal ecosystem effects. As a first step in the process to gain a global perspective on the problem, a spatially explicit global watershed model (NEWS) was used to relate human activities and natural processes in watersheds to nutrient inputs to LMEs, with a focus on nitrogen.
Many LMEs are currently hotspots of nitrogen loading in both developed and developing countries. A clear understanding of the relative contribution of different nutrient sources within an LME is needed to support development of effective policies. In 73% of LMEs, anthropogenic sources account for over half of the dissolved inorganic nitrogen (DIN) exported by rivers to the coast. In most of these, agricultural activities (fertilizer use and wastes from livestock) are the dominant source of DIN loading, although atmospheric deposition and, in a few LMEs, sewage can also be important.
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. Regions of particular note are in southern and eastern Asia, western Africa, and Latin America. Unless substantial technological innovations and management changes are implemented, this will lead to further increases in nutrient inputs to LME coastal waters with associated water quality and ecosystem degradation. An approach is needed such as that being developed in GEF-sponsored LMEs programs where all stakeholders including scientists, policy makers and private sector leaders work together to develop a better understanding of the issues and to identify and implement workable solutions.
Introduction of the Problem
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). Small amounts of nutrient enrichment can have beneficial impacts to some coastal waters and marine ecosystems by increasing primary production which can have potentially positive impacts on higher trophic levels. However, a high degree of nitrogen and phosphorus enrichment, causing eutrophication of coastal and even inland waters, tends towards detrimental effects including degradation of fisheries habitats. The negative effects of eutrophication begin with nutrient uptake by primary producers that 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. There are many other effects of nutrient over-enrichment including increased water turbidity, loss of habitat (e.g., seagrasses), decreases in coastal biodiversity and distribution of species, increase in frequency and severity of harmful and nuisance algal blooms, and coral reef degradation, among others (National Research Council 2000; Diaz et al. 2001; Rabalais 2002).
Nutrient over-enrichment and associated coastal ecosystem effects are occurring in many areas throughout the world and a number of recent assessments have begun to document their regional and global distribution. The European Outlook reported that in 2000, more than 55% of ecosystems were endangered by eutrophication. This includes the notable hypoxic/anoxic zones in the Baltic Sea, Black Sea and Adriatic Sea, among many others. In the USA, a recent assessment of over 140 coastal systems by the National Oceanic and Atmospheric Administration found that in 2004 50% of the assessed estuaries had a high chlorophyll a (phytoplankton) rating and 65% of the assessed estuaries were moderately to highly eutrophic (Bricker et al. 2007). In a recent literature review by the World Resources Institute (Selman et al. 2008), 375 eutrophic and hypoxic coastal systems were identified around the world, including many areas in developing countries.
The need to address nutrient over-enrichment as a priority threat to coastal waters and Large Marine Ecosystems (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. Over 60 countries have developed national policies or national action plans to address coastal nutrient-enrichment within the context of sustainable development of coastal areas and their associated watersheds.
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). Unless substantial technological innovations and management changes are implemented, this will lead to further increases in nutrient (nitrogen and phosphorus) inputs to the coastal zone with associated water quality and ecosystem degradation. In order to optimize use of land for food and energy production while at the same time minimizing degradation of coastal habitats, there is a critical need to understand the quantitative links between land-based activities in watersheds, nutrient inputs to coastal systems, and coastal ecosystem effects.
In this chapter we primarily address the links between land-based activities in watersheds and nutrient inputs to coastal systems around the world. Here we use a global watershed model (NEWS) to examine the patterns of nutrient loading and source attribution at global and regional scales and then apply the model at the scale of large marine ecosystems (LMEs) (Sherman & Duda 1999). Within all LMEs, 80% of the worlds marine capture fisheries occur (Sherman 2008) which emphasizes the importance of cross political-boundary management of these international marine ecosystem units, as in the Global International Waters Assessment (GIWA; UNEP 2006). Various aspects including ecosystem productivity, fish and fisheries, pollution and ecosystem health, socioeconomic conditions, and governance, have been examined for many individual LMEs, but limited assessments across all LMEs have been made with a primarily fisheries emphasis (e.g., Sea Around Us Project 2007). In individual LMEs, few estimates of nutrient loading have been made, and only in the Baltic Sea LME has source apportionment been investigated (HELCOM 2004, 2002). At the end of the chapter we return to coastal ecosystem effects.
A Watershed Perspective
Rivers are a central link in the chain of nutrient transfer from watersheds to coastal.systems. Nutrient inputs to watersheds include natural (biological N2-fixation, weathering of rock releasing phosphate) as well as many anthropogenic sources. At the global scale, anthropogenic nitrogen inputs to watersheds are now greater than natural inputs (Galloway et al. 2004). Anthropogenic nutrient inputs are primarily related to food and energy production to support the over 6 billion people on Earth with major sources including fertilizer, livestock production, sewage, and atmospheric nitrate deposition resulting from NOx emissions from fossil fuel combustion.
Figure 1. Watershed schematic of nitrogen inputs and transport to coastal systems. Symbols for diagram courtesy of the Integration and Application Network (ian.umces.edu/symbols), University of Maryland Center for Environmental Science.
Uneven spatial distribution of human population, agriculture, and energy production leads to spatial differences in the anthropogenic alterations of nutrient inputs to coastal ecosystems (Howarth et al. 1996; Seitzinger and Kroeze 1998; Green et al. 2004; Seitzinger et al. 2005). While many site-specific studies have documented river transport of nutrients (nitrogen (N), phosphorus (P), carbon (C) and silica (Si)) to coastal systems, there are many more rivers for which there are no measurements; sustained monitoring of temporal changes in exports is rarer still. A mechanism is needed to develop a comprehensive and quantitative global view of nutrient sources, controlling factors and nutrient loading to coastal systems around the world under current conditions, as well as to be able to look at past conditions and plausible future scenarios.
A Global Watershed Nutrient Export Model (NEWS)
In order to provide regional and global perspectives on changing nutrient transport to coastal systems throughout the world, an international workgroup (Global NEWS Nutrient Export from WaterSheds; HYPERLINK "http://www.marine.rutgers"http://www.marine.rutgers. edu/globalnews) has developed a spatially explicit global watershed model that relates human activities and natural processes in watersheds to nutrient inputs to coastal systems throughout the world (Beusen et al. 2005; Dumont et al. 2005; Harrison et al. 2005a and b; Seitzinger et al. 2005). Global NEWS is an interdisciplinary workgroup of UNESCOs Intergovernmental Oceanographic Commission (IOC) focused on understanding the relationship between human activity and coastal nutrient enrichment.
In addition to current predictions, the NEWS model is also being used to hindcast and forecast changes in nutrient, carbon and water inputs to coastal systems under a range of scenarios. In this chapter we briefly describe the NEWS model and then present results for mid-1990s conditions at both global scales and as specifically applied to LME regions.
NEWS Model Basics. The NEWS model is a multi-element, multi-form, spatially explicit global model of nutrient (N, P, and C) export from watersheds by rivers (Table 1). The model output is the annual export at the mouth of the river (essentially zero salinity). The NEWS model was 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.
Table 1. Nutrient forms modeled in Global NEWS. DIC and DSi sub-models (in italics) are currently in development.
DissolvedParticulateInorganicOrganicNDINDONPN
P
C
Si
DIP
DIC
DSi
DOP
DOC
PP
POC
Whereas previous efforts have generally been limited to a single element or form, the Global NEWS model is unique in that it can be used to predict magnitudes and sources of multiple bio-active elements (C, N, and P) and forms (dissolved/particulate, organic/inorganic). It is important to know coastal nutrient loading of multiple elements because different elements and elemental ratios can have different ecosystem effects. The various forms of the nutrients (dissolved inorganic and organic and particulate forms) also have different bioreactivities. For example, the dissolved inorganic nitrogen (DIN) pool is generally considered to be bio-available, while only a portion of river transported dissolved organic nitrogen (DON) is readily available for uptake by micro-organisms, including bacteria and some phytoplankton (Bronk, 2002; Seitzinger et al., 2002a). However, DON can be an important N source and it is implicated in the formation of some coastal harmful algal blooms (Paerl, 1988; Berg et al., 1997 and 2003; Granli et al., 1999; Glibert et al., 2005a and b). Particulate and dissolved species can also have very different impacts on receiving ecosystems.
The NEWS model predicts riverine nutrient export (by form) as a function of point and non-point nutrient sources in the watershed, hydrological and physical factors, and removal within the river system (Figure 2) (Beusen et al. 2005; Dumont et al. 2005; Harrison et al. 2005a and b; Seitzinger et al. 2005). A further feature of the model is that it can be used to estimate the relative contribution of each watershed source to export at the river mouth. The NEWS model builds on an earlier model of dissolved inorganic N (DIN) export (Seitzinger and Kroeze 1998).
Figure 2. Schematic of some of the major inputs and controlling factors in the Global NEWS watershed river export model.
There is considerable detail in the input databases and model parameterizations that reflect food and energy production and climate (Figure 2). For example, crop type is important in determining fertilizer use, the amount of manure produced is a function of animal type (e.g., cows, camels, chickens, goats, etc.), nutrient loading from sewage depends not only on the number of people in a watershed but also on their connectivity to a sewage system and level of sewage treatment, atmospheric nitrate deposition is related to fossil fuel combustion. A number of hydrological and physical factors are important in transferring nutrients from soils to the river, with water runoff being important for all elements and forms. Once in the river, N and P can be removed by biological and physical processes during river transport within the river channels, in reservoirs, and through water removal for irrigation (consumptive water use).
NEWS Model Output: The NEWS model has provided the first spatially distributed global view of N, P and C export by world rivers to coastal systems. At the global scale rivers currently deliver about 65 Tg N and 11 Tg P per year according to NEWS model predictions (Tg = tera gram = 1012 g) (Figure 3). For nitrogen, DIN and particulate N (PN) each account for approximately 40% of the total N input, with DON comprising about 20%. This contrasts with P, where particulate P (PP) accounts for almost 90% of total P inputs. However, while DIP and dissolved organic P (DOP) each contribute only about 10% of total P, both of these forms are very bioreactive and thus may have a disproportionate impact relative to PP on coastal systems.
Figure 3. Global N and P river export to coastal systems by nutrient form based on the NEWS model (Dumont et al. 2005; Harrison et al. 2005a).
There is large spatial variation around the world in river nutrient export, including different patterns for the different nutrient forms (DIN, DON and PN) (Figure 4). Using N yield (kg N per km2 watershed per year that is exported to the river mouth), DIN yield shows considerable variation at regional and continental scales, as well as among adjacent watersheds. As might be expected based on past measurements of river nutrient export, the NEWS model predicts relatively high watershed yields in the eastern USA, the Mississippi basin, and much of western Europe. Of particular note, however, are also the high DIN yields from developing regions including much of southern and eastern Asia, Central America and small coastal watersheds in western Africa.
The large spatial variation in N yield reflects the variable magnitudes of the different nutrient sources and controlling factors among watersheds. This underscores the importance of the need for a clear understanding of the nutrient sources and controls within LMEs at many scales in order to develop effective policies and implementation strategies to control coastal nutrient loading.
Figure 4. NEWS-model-predicted A) DIN, B) DON, and C) PN yield (kg N km-2 yr-1) to coastal systems from basins globally. Model output replotted from Harrison et al., 2005b, Dumont et al 2005, and Beusen et al. 2005.
N and P differ markedly in the relative contribution of different nutrient sources to river nutrient export (Seitzinger et al. 2005). At the global scale, natural sources account for about 40% of DIN and DIP river export (biological N2-fixation and rock weathering, respectively) (Figure 5). Anthropogenic sources for DIN export are dominated by agriculture (fertilizer and manure) in contrast to DIP where sewage accounts for ~60% of river export. This difference in major sources, illustrates the need for different strategies to reduce nitrogen or phosphorus loading to coastal systems.
Of course there is considerable variation in the relative contribution of nutrient sources at continental, regional and watersheds scales, and this must be known and taken into consideration when developing nutrient reduction strategies. At the continental scale, for example, in South America livestock production (manure) is by far the largest anthropogenic N source contributing to river DIN loading to coastal systems (Figure 6). This contrasts with Asia where fertilizer use is about twice as great as livestock production in contributing to river DIN loading.
Figure 5. Contribution of different sources to DIN and DIP river export globally.
Figure 6. Contribution of N sources in watersheds to model predicted DIN river export to the coastal zone of each continent. (Figure from Dumont et al. 2005)
NEWS Model Application to 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. 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.
Few estimates of nutrient loading have been made in individual LMEs, and only in the Baltic Sea LME has source apportionment been investigated (HELCOM 2004, 2002). As a first step in bridging the gap between land-based activities and LME waters, we examined the relative magnitudes and distribution of DIN loading from watersheds to LMEs globally. We focused on N because it is often the most limiting nutrient in coastal waters and thus important in controlling coastal eutrophication. DIN is often the most abundant and bioavilable form of nitrogen, and therefore contributes significantly to coastal eutrophication.
Watershed DIN export to rivers predicted by the NEWS model described above was compiled for each of the 64 LMEs (2002 delineation; Duda & Sherman 2002) except for the Antarctic (LME 61) where database information was limited. 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).
Figure 7. DIN inputs to LMEs from land-based sources predicted by the NEWS DIN model. Watersheds discharging to LMEs are grey; watersheds with zero coastal discharge are white. Units: Tons N/y. See Table 2 for LME identification. (Figure from Lee and Seitzinger submitted).
DIN export from watersheds to LMEs varies globally across a large range of magnitudes (Figure 7). The smallest loads are exported to many polar and Australian LMEs, while the largest loads are exported to northern tropical and subtropical LMEs. Of particular note are the large loads exported to the Gulf of Mexico, South China Sea, East China Sea, and North Sea LMEs in which high anthropogenic activity occurs in their watersheds. The Caribbean Sea, Mediterranean Sea and Indonesian Sea LMEs, among others, also receive substantial DIN loads.
Table 2. LMEs identified by name and number (see Fig. 7 and 8)
LME#LME nameLME#LME name1East Bering Sea33Red Sea2Gulf of Alaska34Bay of Bengal3California Current35Gulf of Thailand4Gulf of California36South China Sea5Gulf of Mexico37Sulu-Celebes Sea6Southeast U.S. Continental Shelf38Indonesian Sea7Northeast U.S. Continental Shelf39North Australian Shelf8Scotian Shelf40Northeast Australian Shelf-Great Barrier Reef9Newfoundland-Labrador Shelf41East-Central Australian Shelf10Insular Pacific-Hawaiian42Southeast Australian Shelf11Pacific Central-American Coastal43Southwest Australian Shelf12Caribbean Sea44West-Central Australian Shelf13Humboldt Current45Northwest Australian Shelf14Patagonian Shelf46New Zealand Shelf15South Brazil Shelf47East China Sea16East Brazil Shelf48Yellow Sea17North Brazil Shelf49Kuroshio Current18West Greenland Shelf50Sea of Japan19East Greenland Shelf51Oyashio Current20Barents Sea52Okhotsk Sea21Norwegian Sea53West Bering Sea22North Sea54Chukchi Sea23Baltic Sea55Beaufort Sea24Celtic-Biscay Shelf56East Siberian Sea25Iberian Coastal57Laptev Sea26Mediterranean Sea58Kara Sea27Canary Current59Iceland Shelf28Guinea Current60Faroe Plateau29Benguela Current61Antarctic (not included in this analysis)30Agulhas Current62Black Sea31Somali Coastal Current63Hudson Bay32Arabian Sea64Arctic Ocean
The NEWS model also predicts substantial DIN export from the North Brazil Shelf LME which has relatively low anthropogenic activity in its watersheds. Further investigation is underway to evaluate the NEWS model for these large and relatively pristine tropical river basins. The high DIN load may reflect a number of factors including the large role that high water runoff from tropical rivers plays in the export of DIN, high biological N2-fixation, low denitrification, and model uncertainty.
Identification of Land-based Nutrient Sources to LMEs. DIN loading to each LME was attributed to diffuse and point sources including natural biological N2-fixation, agricultural biological N2-fixation, fertilizer, manure, atmospheric deposition and sewage. Dominant sources of DIN to LMEs were also identified which may be useful for the management of land-based nutrient loading to LMEs.
Figure 8. Histogram of anthropogenic contribution to total DIN load to LMEs. LME numbers are shown in each bar. See Table 2 for LME identification.
Land-based sources of DIN include natural sources (biological N2-fixation in natural landscapes) and anthropogenic activities. In watersheds draining to LMEs, anthropogenic activities contribute to over half of the total DIN load in 73% of LMEs (Figure 8). These anthropogenic DIN dominant LMEs are distributed across most continents, except sub-Saharan Africa and most polar regions. Some of the highest proportions (> 90%) of anthropogenic DIN loads are to European LMEs, such as the North Sea and Mediterranean LMEs, and East Asian LMEs, such as the Yellow Sea and East China Sea LMEs.
Agriculture is a major source of the anthropogenic DIN export to LMEs (Lee and Seitzinger submitted). In 91% of the LMEs with agriculture occurring in their related watersheds, over half their anthropogenic export is due to agricultural sources such as agricultural biological fixation, manure, and fertilizer. Attribution of agricultural DIN export to these three sources reveals the predominance of fertilizer and manure over agricultural biological fixation. For example, LMEs with the largest agricultural loads have less than 20% of the total DIN load due to biological fixation and over 50% due to either fertilizer (e.g., in many northern temperate and Southeast Asian LMEs such as the Bay of Bengal, East China Sea and South China Sea LMEs), to manure (e.g., in most Central and South American LMEs such as the Caribbean and North Brazil Shelf LMEs) or to a combination of both (e.g., in the North Sea and Celtic-Biscay Shelf LMEs) due to local agricultural practices. There is no agricultural export to most polar LMEs.
Atmospheric deposition is important in regions where there are few other land-based inputs (e.g., in polar regions such as the West and East Greenland Shelf LMEs), where fossil fuel combustion from development is extreme (e.g., in the North- and Southeast U.S. Continental Shelf LMEs), or where extensive landscape burning occurs (e.g., in the Guinea Current LME which is fed by savannah fires in Western Central African watersheds; Barbosa et al. 1999). Sewage is an important source of DIN to only a few LMEs (as a primary source to the Kuroshio Current, Red Sea, West-Central Australian Shelf, and Faroe Plateau LMEs), while agricultural fixation plays an even lesser role as a primary source to only the Southwest Australian Shelf LME and a secondary source to the Benguela Current, North Australian Shelf, and West-Central Australian Shelf LMEs.
Figure 9. DIN export predicted by the NEWS DIN model from watersheds within the Yellow Sea, Humboldt Current and Gulf of Mexico LMEs. Units: Tons N/yr.
The variability in watershed DIN export and source attribution within individual LMEs exhibits comparably large differences as with across LMEs. Examples from different world regions including Asia, South America and the US-Latin America are presented below. Among the Yellow Sea, Humboldt Current and Gulf of Mexico LMEs, the DIN load from individual watersheds ranges over several orders of magnitude across both small and large watersheds (Figure 9). For example, similarly sized watersheds in both the Yellow Sea and Humboldt Current LMEs exhibit both the largest and smallest magnitudes of watershed DIN export. In contrast, the Mississippi watershed is the largest watershed contributing to the Gulf of Mexico LME and also exports the largest load of DIN to the Gulf of Mexico.
Figure 10. Source attribution of DIN export predicted by the NEWS DIN model to the Yellow Sea, Humboldt Current and Gulf of Mexico LMEs. Units: Tons N/yr.
The relative importance of different watershed sources of DIN to LME loading also varies, e.g., among the Yellow Sea, Humboldt Current and Gulf of Mexico LMEs (Figure 10). Agricultural sources dominate the DIN export in all of these LMEs, but fertilizer contributes the most to export to the Yellow Sea and Gulf of Mexico LMEs while manure is relatively more important than fertilizer to the Humboldt Current LME. In the Yellow Sea LME, sewage is also a significant source (19%) to DIN export, while less so to the Humboldt Current and Gulf of Mexico LMEs. Nitrogen fixation occurring in natural landscapes is a significant source (28%) to the DIN export to only the Humboldt Current LME. Atmospheric deposition is a lesser source of DIN export to all three example LMEs, but contributes, relatively, the largest percentage (11%) to the Gulf of Mexico LME. The identification of dominant sources of DIN and their relative contribution at the individual LME level is essential for developing effective nutrient management strategies on an ecosystem level.
Implications of Future Conditions in LME Watersheds
At the global scale, river nitrogen export to coastal systems is estimated to have approximately doubled between 1860 and 1990, due to anthropogenic activities on land (Galloway et al., 2004). Over the next 50 years the human population is predicted to increase markedly in certain world regions, notably Southern and Eastern Asia, South America, and Africa (United Nations, 1996). Growing food to feed the expanding world population will require increased use of nitrogen and phosphorus fertilizers (Alcamo et al., 1994; Bouwman et al., 1995; Bouwman, 1997). Increased industrialization, with the associated combustion of fossil fuels and NOx production, is predicted to increase atmospheric deposition of N (Dentener et al., 2006; IPCC, 2001). Thus, unless substantial technological innovations and management changes are implemented, increasing food production and industrialization will undoubtedly lead to increased export of N to coastal ecosystems (Galloway et al. 2004), with resultant water quality degradation.
Based on a business-as-usual (BAU) 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 (Figure 11) (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.
Figure 11. Predicted DIN export to coastal systems in 1990 and 2050 under a business-as-usual (BAU) scenario. Modified from Kroeze and Seitzinger (1998).
The above scenario for 2050 was based on projections made from early 1990 trajectories and using a relatively simple DIN model (Seitzinger and Kroeze 1998). The NEWS model has more parameters and more detail behind the inputs (e.g., fertilizer use by crop type, level of sewage treatment, etc.) (Figure 2) thus facilitating more advanced scenario development and analyses. For example, it is now possible to explore the effects of a range of development strategies, effects of climate change, production of biofuels, increase in dams for hydropower, and consumptive water use (irrigation) on coastal nutrient loading. Using the NEWS model, we are currently analyzing a range of alternative scenarios for the years 2030 and 2050 based on the Millennium Ecosystem Assessment (www.millenniumassessment.org) 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.).
Coastal Ecosystem Effects
As noted at the beginning of this chapter, nutrient over-enrichment can lead to a wide range of coastal ecosystem effects. 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). How might land-based DIN loading be affecting primary production in LMEs? As a preliminary examination, we compared land-based DIN loads predicted by the NEWS model to LME primary production (modeled SeaWiFS data; Sea Around Us Project 2007) (Figure 12). This analysis suggests that land-based DIN export supports a significant portion of primary production at the level of an entire LME. In areas with upwelling, nutrient-rich bottom waters support high rates of photosynthetic production. This is reflected in the generally higher primary productivity than predicted by the regression solely with land-based DIN inputs in LMEs characterized by upwelling (the Guinea Current, Arabian Sea, Pacific Central-American, Humboldt Current, California Current, Gulf of Alaska, Benguela Current, Canary Current, Northwest Australian, and Southwest Australian LMEs).
Figure 12. Phytoplankton production vs. DIN load to the 63 LMEs. Orange points are LMEs in upwelling regions. Phytoplankton production rates are from the Sea Around Us Project; DIN loads are from the NEWS model (Dumont et al. 2005). Figure from Lee and Seitzinger submitted.
The above analysis compares land-based N loading to average primary production for waters in the entire LME. In the near shore areas of LMEs, land-based N loading likely supports a much higher proportion of primary production than suggested by the overall relationship in Figure 12 and should be investigated. The additional effects of high nutrient loading to estuaries and near shore waters in LMEs on hypoxia, biodiversity, toxic and nuisance algal blooms, habitat quality, and fisheries yields also warrants further analysis.
Future Needs
We are beginning to make significant advances in understanding the relationship between human activities in watersheds and coastal nutrient loading at a range of scales (e.g., watershed, LME, and global) as illustrated by the application of the NEWS model. However, this is only a start. For example, to date the LME, regional, and global analyses have relied on input databases at the scale of 0.5 o latitude x 0.5o longitude. The use of higher spatial resolution input databases based on local knowledge from specific LME regions could significantly improve the model predictions. Similarly, additional data for model validation is need. Development of scenarios based on local projections of population, agricultural production, biofuels, dam construction, and climate change, among others could provide information of use to policy makers.
Development of nutrient reduction policies and effective mitigation strategies also requires widely applicable, quantitative relationships between nutrient loading and coastal ecosystem effects. While there is considerable information on nutrient sources and coastal impacts, this information is often much dispersed and has not yet been compiled into a consistent database so that nutrient sources in specific LMEs can be linked to impacts in their associated coastal system. This is a critical next step in order for a toolbox to be developed so that effective policy measures can be formulated and measures taken, and for the outcomes of those policies and measures to be evaluated.
Many technical and political options are available to reduce fertilizer use, decrease nutrient runoff from livestock waste, decrease NOx emissions from fossil fuel burning, and enhance sewage treatment. The fact that many of these tools have not yet been implemented on a significant scale suggests that additional technological options and new policy approaches are needed. In addition, policy approaches to address non-point source pollution are often nonexistent or very limited. To ensure that the science used to develop these technologies and polices is sound and complete, existing data on nutrient sources, mobilisation, distribution, and effects need to be assessed. An approach is needed such as that being developed in GEF-sponsored LME programs and as promoted by the International Nitrogen Initiative (INI: INitrogen.org) where all stakeholders including scientists, policy makers and private sector leaders work together to develop a better understanding of the issues and to identify and implement workable solutions.
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Seitzinger, S.P., Harrison, J.A., Dumont, E., Beusen, A.H.W., and Bouwman, A.F. (2005). Sources and delivery of carbon, nitrogen and phosphorous to the coastal zone: An overview of global nutrient export from watersheds (NEWS) models and their application. Global Biogeochemical Cycles 19(4):GB4S01.
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Seitzinger, S.P., Kroeze, C., Bouwman, A.F., Caraco, N., Dentener, F., and Styles, R.V. (2002b). Global patterns of dissolved inorganic and particulate nitrogen inputs to coastal systems: Recent conditions and future projections. Estuaries 25(4b):640-655.
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Vrsmarty, C.J., Fekete, B.M., Meybeck, M., and Lammers, R. (2000a). Geomorphometric attributes of the global system of rivers at 30-minute spatial resolution. Journal of Hydrology 237:17-39.
Vrsmarty, C.J., Fekete, B.M., Meybeck, M., and Lammers, R. (2000b). A simulated topological network representing the global system of rivers at 30-minute spatial resolution (STN-30). Global Biogeochemical Cycles 14:599-621.
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III. Pollution & Ecosystem Health continued
Cross-cutting Issues see LME Table, Section II, Component 1:
Habitat changes over time: coral reefs, seagrass beds and mangroves
Section on UNEP-WCMC removed by IOC - pending review by UNEP-WCMC
Pollution and Ecosystem Health module continued
Cross-cutting themes: Vulnerability to Climate Change and Biological Productivity:
Multiple Marine Ecological Disturbances (MMEDs) and Public Health
[III, component 3 in LME Table]
In the search for the best ecological and economic indicators of ecosystem change, the approach taken by Sherman and Epstein (2001) provides a unifying solution for joining data from disparate fields to allow convergence and integration of data from different scales. Three published articles on this topic are: (i) P. Epstein, 1996. Emergent stressors and public health implications in Large Marine Ecosystems. An overview; (ii) B. Sherman and P. Epstein, 2001. Past Anomalies as a Diagnostic Tool for Evaluating Multiple Marine Ecological Disturbance Events; and (iii) B. Sherman, 2001. Assessment of multiple marine ecological disturbances: applying the North American prototype to the Baltic Sea Ecosystem.
Past anomalies can be used as a diagnostic tool for evaluating multiple marine ecological disturbance (MMED) events. 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. Partners to provide the assessments are: Harvard University and the Rensselaer Polytechnic Institute (RPI).
Pollution and Ecosystem Health continued
Cross Cutting Theme : Metals Mercury
Fish is a critical source of protein for the poverty-stricken populations of the community of developing countries in Asia, Africa, and Latin America. Access to healthy fish protein is a necessity for providing food security to over half the worlds human population. However, the incidence of emerging diseases caused by contaminated fisheries products is a growing problem that needs to be better controlled particularly in relation to the consumption of medium and large size fishes contaminated with mercury.
Mercury exists in many forms, and can enter ponds, lakes, and rivers through pollution. Mercury as a pollutant can get into the fish that live there. Mercury is commonly found in fresh water fish and in several types of saltwater fish (e.g. Swordfish, Shark, Bluefish, Tilefish, King Mackerel, and Striped Bass). These are important species for human consumption and in international trade. Bluefish and striped bass are also impacted by another contaminant, polychlorinated biphenyls (PCBs).
High levels of mercury in the body can cause harm to an adult's kidneys and brain, can affect pregnancies and impact the development of young children. The US Food and Drug Administration has issued warnings about mercury levels in swordfish since 1986. An article discussing this issue was published by the FDA (The Occurrence of Mercury in the Fishery Resources of the Gulf of Mexico, 2000).
Reference to GESAMP removed - pending inputs by GESAMP experts
IV. Socioeconomics Module Indicators
The socioeconomics module emphasizes the practical application of scientific findings to managing LMEs and the explicit integration of social and economic indicators and analyses with all other scientific assessments to assure that prospective management measures are efficient. Economists and policy analysts work closely with ecologists and other scientists to identify and evaluate management options that are both scientifically credible and economically practical with regard to the use of ecosystem goods and services. In order to respond adaptively to enhanced scientific information, socioeconomic considerations should be closely integrated with science. This component of the LME approach to marine resources management has recently been described as the human dimensions of LMEs ADDIN EN.CITE Hennessey20053363363366Hennessey, Timothy, M.Sutinen,Jon, G., edssherman, KennethSustaining large marine ecosystems: The human dimension368132005AmsterdamElsevier Science0-444-51026-5(Hennessey and Sutinen 2005). A framework has been developed by the Department of Environmental and Natural Resource Economics at the University of Rhode Island for monitoring and assessment of the human dimension of LMEs and for incorporating socioeconomic considerations into an adaptive management approach for LMEs ADDIN EN.CITE Sutinen200041641641617Sutinen, J., edA framework for monitoring and assessing socioeconomics and governance of large marine ecosystemsNOAA Technical Memorandum NMFS-NE-15832p2000(Sutinen 2000).
Linking economic and ecological models in LMEs
An initial step toward the development of a global overview of the socioeconomic aspect of LMEs was made by Hoagland and Jin of the Marine Policy Center at the Woods Hole Oceanographic Institution ADDIN EN.CITE Hoagland200610431043104327Hoagland, PorterJin, DiAccounting for economic activities in Large Marine Ecosystems and Regional Seas, UNEP Regional Seas Reports and Studies 1812006Nairobi, KenyaUNEP Regional Seas Programme(Hoagland and Jin 2006) These researchers used indices of socioeconomic activity based on data from several marine economic sectors, including fish landings, aquaculture production, ship building, cargo traffic, merchant fleet size, oil production, oil rig counts, and tourism. The data were examined for the years between 2002 and 2004, to compare marine industry activity with indices for socioeconomic, fishing and aquaculture, tourism and shipping and oil activities. A summary ranking of LMEs by area adjusted marine industry activity (MIA) is shown in Figure 1.
Figure 1. Ranking of LMEs by Area-adjusted Marine Industry Activity ADDIN EN.CITE Hoagland200610431043104327Hoagland, PorterJin, DiAccounting for economic activities in Large Marine Ecosystems and Regional Seas, UNEP Regional Seas Reports and Studies 1812006Nairobi, KenyaUNEP Regional Seas Programme(Hoagland and Jin 2006).
From a comparison of ranked socioeconomic and marine industry activity, indices for LMEs (Table 1), countries bordering the Yellow Sea, East China Sea, East Bering Sea and Insular Pacific-Hawaiian are the most economically active LMEs (>40.0 MIA) in contrast to the low marine industry activity level of the Agulhas Current, Guinea Current and Somali Current LMEs (<1.0 MIA). High levels of marine industry activity often are associated with significant levels of environmental degradation.
Table 1. Socioeconomic and Marine Industry Activity Indexes for LMEs, ranked in order of Marine Activity Index ADDIN EN.CITE Hoagland200610431043104327Hoagland, PorterJin, DiAccounting for economic activities in Large Marine Ecosystems and Regional Seas, UNEP Regional Seas Reports and Studies 1812006Nairobi, KenyaUNEP Regional Seas Programme(Hoagland and Jin 2006)
At the LME scale, Hoagland and Jin ADDIN EN.CITE Hoagland200610431043104327Hoagland, PorterJin, DiAccounting for economic activities in Large Marine Ecosystems and Regional Seas, UNEP Regional Seas Reports and Studies 1812006Nairobi, KenyaUNEP Regional Seas Programme(2006) examined economic sector activity for the Yellow Sea LME. The four sectors examined included (1) socioeconomic activity, (2) shipping and offshore oil, (3) fisheries and aquaculture, and (4) tourism. The calculated index values for the three industry sectors were greater than the world average (Figure 2).
Figure 2. YSLME activity index values for three major marine sectors and the HDI (socioeconomic) in comparison to the LME world average.
From the analyses it is clear that the countries bordering the YSLMEChina and Koreaare significantly challenged to recover depleted fisheries, restore degraded habitat, control nutrient over enrichment, halt the loss of biodiversity, and abate coastal pollution ADDIN EN.CITE Sherman200810371037103727Sherman, K.Hempel, Gotthilf, eds.The UNEP Large Marine Ecosystem Report: A perspective on changing conditions in LMEs of the world's Regional Seas. UNEP Regional Seas Report and Studies No. 182
8722008Nairobi, KenyaUNEPReport(Sherman and Hempel 2008). Both countries are moving forward within the framework of a GEF-supported joint strategic action plan (SAP) to recover and sustain the critically important environmental goods and services of the YSLME. Both countries are planning continuing joint efforts to assess and manage the shared resources of the YSLME (HYPERLINK "http://www.YSLME.org"www.YSLME.org) through the development of a joint YSLME Commission ADDIN EN.CITE UNDP/GEF200812921292129227UNDP/GEFUNDP/GEFThe Yellow Sea: Governance Analysis Reports. UNDP/GEF Yellow Sea Project3344: Governance AnalysisUNDP/GEF Project: Reducing Environmental Stress in the Yellow Sea Large Marine Ecosystem2008Ansan, Republic of Korea(UNDP/GEF 2008).
The economic value of ecosystem goods and services and natural capital
Evaluating the services of ecological systems and the natural capital that produce them is critical to the functioning of the Earths life support system ADDIN EN.CITE Costanza199785385385317Costanza, Robertd'Arge, Ralphde Groot, RudolfFarber, StephenGrasso, MonicaHannon, BruceLimburg, KarinNaeem, ShahidO'Neill, Robert V.Paruelo, JoseRaskin, Robert G.Sutton, Paulvan den Belt, MarjanThe value of the world's ecosystem services and natural capitalNatureNature253-26038715 May 19971997(Costanza, d'Arge et al. 1997). The economic value of an LME is equivalent to the net present value of goods and services that flow from uses and non-uses of its resources and environment. Costanza et al. have calculated that the coastal waters encompassing LMEs annually contribute US $12.6 trillion to the global economy. Although this estimate does not reflect the benefits or costs of marginal changes in marine ecosystem goods and services, it highlights the critical importance of LMEs to the economies of the world. The LME goods and services contribute to human socioeconomic wellbeing, both directly and indirectly.
The global Costanza et al. estimate was based on published studies and original calculations, based on 17 services, including climate regulation, hydrographic flows, water supply, erosion control, sediment retention, nutrient cycling, waste treatment, trophodynamics and biological control, habitats for resident and transient populations, food production, raw materials, genetic resources, opportunities for recreational activities, and the aesthetic, educational, cultural and scientific values of marine ecosystems.
Porter Hoagland and Di Jin of the Marine Policy Center at Woods Hole Oceanographic Institution (WHOI) are prepared to estimate the value of the goods and services of all LMEs. They will update and complete estimates of activity levels of the relevant marine sectors (e.g. fisheries, aquaculture, tourism, shipping, oil and other goods and services) of countries bordering the worlds LMEs. They have developed an index approach to assess the extent of the human uses of large marine ecosystems including socio-economic development. They have also published on the scale of economic rents (revenues minus costs) and direct output impacts (gross revenues) that could be a source of sustainable financing for conserving and managing large marine ecosystems ADDIN EN.CITE Hoagland20053813813815Hoagland, P.D. JinE. ThunbergS. SteinbackHennessey, T.Sutinen, J., edsEconomic activity associated with the Northeast Shelf large marine ecosystem: Application of an input-output approachSustaining large marine ecosystems: The human dimension159-181. 371p132005AmsterdamElsevier(Hoagland, Jin et al. 2005). Their work will continue to target government policy- and decision-makers with the aim of highlighting the potential value of goods and services provided by the large marine ecosystems.
Fisheries Economics and Fishing Effort in LMEs
Overfishing of LMEs is a persistent ecological and economic problem that requires an approach that will restore and sustain the ecosystems supporting fisheries. The University of British Columbia (UBC) Fisheries Center and Sea Around Us Project have published a catch, price, and small scale fisheries database. The UBC Fisheries Center is known for its work on HYPERLINK "http://en.wikipedia.org/wiki/Bioeconomics" \o "Bioeconomics"bioeconomics and bioeconomic models, and analysis of global issues such as fisheries subsidies, illegal, unreported and unregulated (IUU) fishing and the economics of HYPERLINK "http://en.wikipedia.org/wiki/High_seas" \o "High seas"high and deep seas HYPERLINK "http://en.wikipedia.org/wiki/Fisheries" \o "Fisheries"fisheries.
R. Sumaila and V. Christensen of UBC will develop cost of fishing and fishing effort databases by LME, a database of economic multipliers, as well as ECOST, ecological and socioeconomic models that are being developed as part of the Ecopath with Ecosim ecological modeling framework. In this project component, LME carrying capacity in relation to MSY, and cost of fishing in relation to fishing effort will be evaluated for each of the 64 LMEs. Articles already published include ADDIN EN.CITE Sumaila200613741374137417Sumaila, U.R.J. AlderH. KeithGlobal scope and economics of illegal fishingMarine PolicyMarine Policy696-7033062006Perry200713731373137317Perry, R.I.U.R. SumailaMarine ecosystem variability and human community responses: The example of Ghana, West AfricaMarine PolicyMarine Policy125-1343122007(Sumaila, Alder et al. 2006; Perry and Sumaila 2007)
V. Governance Module Indicators
In order to respond adaptively to enhanced scientific information, governance considerations should be closely integrated with science and with the socioeconomic value of LME goods and services. As discussed by Sutinen et al. ADDIN EN.CITE Sutinen20051293129312935Sutinen, J.G.P., ClayC.L. DyerS.F. EdwardsJ. GatesT.A. GrigalunasT.M. HennesseyL. JudaA. W. KittsP.N. LoganJ.J. Poggie, Jr.B.P. RountreeS.R. SteinbackE.M. ThunbergH.F. UptonJ.B. WaldenT. HennesseyJ. SutinenA framework for monitoring and assessing socioeconomics and governance of large marine ecosystemsSustaining large marine ecosystems: The human dimension2005Elsevier(2005) governance arrangements have traditionally developed along sectoral lines and this has resulted in intersectoral problems as decisions are made without regard to any additive or integrative processes from a complete ecosystems-wide perspective. Ecosystem based governance actions need to consider multiple legal jurisdictions and governance levels (e.g. municipal, state, regional, national, international) as well as the interests of multiple user sectors (e.g. fisheries, mining, oil and gas production, waste disposal, transportation, recreation) and stakeholders (e.g. fishermen, corporations, real estate interests). As problems across sectors increase in complexity, legislative efforts are undertaken for advancing ecosystem based management ADDIN EN.CITE ADDIN EN.CITE.DATA (Hennessey 1994; Juda 1996; Juda 1999; Juda and Hennessey 2001; Hennessey and Sutinen 2005).
Mapping LME Governance Profiles
Institutional, sociocultural and economic factors are of substantial significance in the use and management of the natural environment. In seeking to move toward governance arrangements that are more appropriate for ecosystem-based management, it is necessary to understand how existing institutional, economic and cultural systems operate, their implications for the natural environment and its resources, and how needed change may emerge, given societal structures and norms. Ecosystem-based governance actions need to consider multiple legal jurisdictions and governance levels (e.g. municipal, state, regional, national, international) as well as the interests of multiple user sectors (e.g. fisheries, mining, oil and gas production, waste disposal, transportation, recreation) and stakeholders (e.g. fishermen, corporations, real estate interests).
The LME Governance Module engages multiple scales of national, regional, and local jurisdictional frameworks needed to select and support ecosystem-based management practices leading to the sustainable use of resources. There are inherent difficulties in a changing world to measure governance, whether effective, moderately effective or ineffective, using data measurements and perspectives coming from the public sector, the private sector and LME stakeholders. Governance profiles of LMEs are being explored to determine their utility in promoting long-term sustainability of ecosystem resources ADDIN EN.CITE Juda200138738738717Juda, L.T. HennesseyGovernance profiles and the management of the uses of large marine ecosystemsOcean Development and International LawOcean Development AND International Law41-67322001(Juda and Hennessey 2001). In each of the LMEs, governance jurisdiction can be scaled to ensure conformance with existing legislated mandates and authorities ADDIN EN.CITE Olsen200610421042104227Olsen, Stephen B.Sutinen, Jon G.Juda, LawrenceHennessey, Timothy M.Grigalunas, Thomas A.A Handbook on Governance and Socioeconomics of Large Marine Ecosystems942006Kingston, RICoastal Resources Center, University of Rhode Island(Olsen, Sutinen et al. 2006).
An example of multiple governance-related jurisdictions is shown in Figure 3 for the Northeast US Continental Shelf LME. The 260,000 km2 spatial extent of the ecosystem encompasses multiple levels of marine management (governance) jurisdiction. The fisheries are managed in the New England area of the ecosystem by the New England Fishery Management council, and the fisheries in the Mid-Atlantic area of the ecosystem, by the Mid-Atlantic Fishery Management Council. The estuaries and near-coastal areas within 3 miles of the coast are under jurisdiction of the coastal states from Maine to North Carolina where the USEPA provides grants to the states for monitoring changing ecological conditions using the 5 LME pollution and ecosystem health indicators. Other governance/management units are the National Estuarine Research Reserve System (NERRS), several marine fisheries protected areas and management sites, and a national marine sanctuary (Figure 3). Each of the governance jurisdictions within the LME represents an input component to an additive and integrated ecosystem assessment (IEA). The process for preparing IEAs requires scaling up from urban coastal municipality levels for applying the five module indicator suites through NEERS to the marine sanctuary, the Fishery Management Council jurisdictions, and the full scale of the LME.
Figure 3. Multiple jurisdictions of the U.S. Northeast Continental Shelf LME.
Matrices have been employed in an effort to understand interactions between ocean uses. Governance efforts are important for two major reasons: (i) incompatible human uses of the LME and its goods and services that result in mutual interference; and (ii) human uses of the LME environment that interfere with natural processes and limit the potential for future use of the LME environment. The first two matrices ADDIN EN.CITE Juda200138738738717Juda, L.T. HennesseyGovernance profiles and the management of the uses of large marine ecosystemsOcean Development and International LawOcean Development AND International Law41-67322001(Juda and Hennessey 2001) directly address these matters. The premise of this methodology is that sustainability of LMEs is a matter of concern to decision-makers. It is then necessary for those decision-makers to consider the nature and character of the effects of human use on natural systems.
Matrix 3 relates a finding of depleted oxygen, for example, to the practical, down the line potential consequences of fewer opportunities for commercial fishermen since such findings sometimes serve to motivate public concern and lead to action. This matrix reflects the impact and feedback implications of ecosystem effects listed in Matrix 2 on outcomes of interest to stakeholders and the wider public.
LME Governance profiles ADDIN EN.CITE Juda200138738738717Juda, L.T. HennesseyGovernance profiles and the management of the uses of large marine ecosystemsOcean Development and International LawOcean Development AND International Law41-67322001(Juda and Hennessey 2001) can allow for comparisons and lessons learned in assessing whether an LME governance mechanism is progressing in its task. Governance indicators serve, directly or indirectly, to signal transition toward ecosystem based management. Process indicators refer to changes in the way decisions are made, institutions work, and values are prioritized (a task already begun in the TDA and SAP process of GEF-funded LME projects). Result indicators focus on whether the process changes have, in fact, led to better results in terms of actual management.
Traditionally, governance arrangements have developed along sectoral lines on an ad hoc, piecemeal basis. The mapping of governance systems deserves attention. The interplay of different elements of government and governance can play a positive role by widening perspectives. It is also important to consider the level at which a problem should be addressed. Matrix 4 is an example of governance mechanisms in relation to LME uses. The example is taken from the Gulf of Maine, a sub-area of the Northeast U.S. Continental Shelf. It can, however, be modified for the mapping of governance profiles for all 64 LMEs.
Governance mechanisms in ecosystem-based management
A framework has been developed by the Department of Environmental and Natural Resource Economics at the University of Rhode Island for monitoring and assessment of the human dimension of LMEs and for incorporating socioeconomic considerations into an adaptive management approach for LMEs ADDIN EN.CITE Sutinen200041641641617Sutinen, J., edA framework for monitoring and assessing socioeconomics and governance of large marine ecosystemsNOAA Technical Memorandum NMFS-NE-15832p2000(Sutinen 2000). As discussed by Sutinen et al. ADDIN EN.CITE Sutinen20051293129312935Sutinen, J.G.P., ClayC.L. DyerS.F. EdwardsJ. GatesT.A. GrigalunasT.M. HennesseyL. JudaA. W. KittsP.N. LoganJ.J. Poggie, Jr.B.P. RountreeS.R. SteinbackE.M. ThunbergH.F. UptonJ.B. WaldenT. HennesseyJ. SutinenA framework for monitoring and assessing socioeconomics and governance of large marine ecosystemsSustaining large marine ecosystems: The human dimension2005Elsevier(2005), governance arrangements have traditionally developed along sectoral lines and this has resulted in intersectoral problems as decisions are made without regard to any additive or integrative processes from a complete LME-wide perspective.
Olsen, Sutinen et al. ADDIN EN.CITE Olsen200610421042104227Olsen, Stephen B.Sutinen, Jon G.Juda, LawrenceHennessey, Timothy M.Grigalunas, Thomas A.A Handbook on Governance and Socioeconomics of Large Marine Ecosystems942006Kingston, RICoastal Resources Center, University of Rhode Island(2006) have developed tangible indicators of progress in ecosystem-based LME governance through four orders of LME outcomes. This set of governance indicators can be used to follow the LME management system as it progresses from the baseline conditions documented by the GEF TDA and SAP process to more sustainable conditions and patterns of use. The first order (i) assembles the enabling conditions for ecosystem-based management. These conditions are created by a successful TDA/SAP process. The second order (ii) shows evidence of a successful implementation of an ecosystem based management program. The third order outcomes (iii) mark the achievement of the programs goals, including targets achieved for the reduction of coastal pollution, the restoration of damaged habitats, the recovery of depleted fisheries, and improved local community incomes and social conditions (socioeconomic benefits). The fourth order outcomes (iv) include achieving a dynamic equilibrium among both social and environmental indicators (Figure 4).
Olsen and Sutinen propose to assess the management process in relation to management outcomes for all 64 LMEs, with special focus on the 17 LMEs where GEF is funding LME projects. Some of these projects have already established the premises of an ecosystem-based governance commission. For the Benguela Current LME, the multisectoral approach to assessment and management has already been adopted in the establishment of the three-country Benguela Current Commission. South Africa, Namibia, and Angola signed an agreement in August 2006 to formally establish the Benguela Current Commission (BCC) that provides for joint management of the goods and services of the Benguela Current LME. Establishment of the BCC is a culmination of 10 years of effort by scientists, stakeholders, resource managers, and multisectoral ministerial representatives (e.g. fisheries, mining, energy, tourism, environment) from the three countries who began to share their knowledge and understanding of the Benguela Current LME in 1995 during the first operational phase of the Global Environment Facility (GEF) supported BCLME assessment and management project (HYPERLINK "http://www.bclme.org/news"www.bclme.org/news). The three Southwest African countries will collectively manage transboundary environmental and resource issues including recovering and sustaining fish stocks, mitigating effects of offshore mining and oil and gas production, mariculture, shipping, and transport, energy production, tourism, and mining, and improving the condition of degraded habitats. The BCC in partnership with the GEF, UNDP, and other agencies including US-NOAA, Norwegian, German, and Icelandic marine specialists is advancing the understanding of the physical and biological drivers of change through research and assessment actions that will support management actions for protecting and sustaining the highly valued goods and services of the BCLME. Now in its fifth year, the Benguela Current Large Marine Ecosystem Program has allocated US$10 million in support of 75 scientific and economic research projects in the region. Considerable effort is presently underway by the BCLME Project in bringing the information from each of the five assessment modules together into an integrated ecosystem-based assessment (IEA), describing the overall ecological condition and prospects for recovery and sustainability of the goods and services of the ecosystem.
Effort is presently underway to extend the LME Commission approach to the 16 countries bordering the Guinea Current presently participating in a GEF-supported Guinea Current LME Project (igcc.gclme.org). The participating countries have agreed to establish an Interim Guinea Current Commission. In addition, initial steps for establishing a joint LME Commission for the Yellow Sea is under consideration by the GEF-supported Yellow Sea LME project (HYPERLINK "http://www.yslme.org/doc/rstp4/reg%20gov.pdf"www.yslme.org/doc/rstp4/reg%20gov.pdf). The LME indicators have also been brought to the Baltic Sea LME through the efforts of a GEF-supported Baltic Sea project headed by Dr. Jan Thulin on behalf of the International Council for the Exploration of the Sea, and the Helsinki Commission (HELCOM).
Figure 4. The Four Orders of Outcomes in Ecosystem-Based Management. Source: Olsen, 2003.
Modelling across 64 LMEs
IIASAs modeling capacity will quantify the effects of human activities in the 64 lmes in relation to the sustainable natural production of LME goods and services. Through empirical analyses and modeling, IIASA can provide a framework, analyses and integrated ecosystem assessments (IEAs) for each of the 64 LMEs based on quantitative considerations linking indicators of changing status in LME productivity, fish and fisheries, pollution and ecosystem health, socioeconomics, and governance. For the productivity module, IIASA can model the effects of warming on fisheries biomass yields and primary productivity; and provide forecasts of thermocline change. The modeling framework can integrate the 3 science-based modules of productivity, fish and fisheries, and pollution and ecosystem health with sustained socioeconomic benefits to be derived from healthy large marine ecosystems under governance regimes focused on the transboundary use of LME goods and services.
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\ & $0d ^`0a$gdC Ƴ dz ʳ t C _ ` b ѵ j ݶ A j ? A Ǻ b ݻ L c d e ȼ ռ ּ ؼ c P ( ڶڶڶ "hC hC 5^J aJ mH nH u "hC hC >*^J aJ mH nH u hC hC ^J aJ mH nH u&hN hC 5^J aJ mH nH sH u #hN hC ^J aJ mH nH sH u&hN hC >*^J aJ mH nH sH u9
ө 練zk hZp 5CJ OJ QJ ^J aJ hM 5CJ OJ QJ ^J aJ hi CJ OJ QJ ^J aJ #j h; CJ OJ QJ U^J aJ hC ^J aJ mH nH u%hC hC 56^J aJ mH nH uU"hC hC >*^J aJ mH nH u "hC hC 5^J aJ mH nH u hC hC ^J aJ mH nH u & Watson, R., C. Revenga, et al. (2006). "Fishing gear associated with global marine catches II. Trends in trawling and dredging." Fisheries Research 79(1-2): 103-111.
Wilson, R. W., F. J. Millero, et al. (2009). "Contribution of fish to the marine inorganic carbon cycle." Science 323: 359-362.
Wozniak, B., J. Dera, et al. (2003). "Modeling light and photosynthesis in the marine environment." Oceanologia 45(2): 171-245.
Summary of Partners and Estimated Costs
Table removed by IOC. Partnerships to be discussed at second LME meeting in Arendal
Fisheries Centre Research Reports can be downloaded from the Fisheries Centre Website (www.fisheries.ubc.ca/publications/reports/fcrr.php).
DRAFT for LME TWAP contribution DRAFT Monday, May 17, 2010
PAGE \* MERGEFORMAT 88
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