1.         Seamounts in the Sea Around Us Project's database

Seamounts are (extinct) underwater volcanoes that did not grow tall enough to break to the sea surface, and thus turn into islands.  Once formed, seamounts tend to gradually sink under their own weight, and the depths of the oceans are thus littered with the remains of seamounts, which may be called ‘seamounds’.

Seamounts occur throughout the world ocean, but their number (which may surpass 100,000) is difficult to estimate, even roughly, because it depends on the resolution of the bathymetric map used, as well as the detection threshold employed, i.e. the limit used to distinguish between seamounts and seamounds.

For the purposes of the Sea Around Us Project, the locations of a subset of the seamounts of the world were identified from a bathymetric map distributed by NOAA, using two algorithms (to be presented elsewhere), which rely on the depth differences between adjacent cells of that electronic map.  About 30,000 likely seamounts were located, but a different number would have been found, had we used different thresholds.  Thus, the area-specific estimates of seamount abundance we present here are expressed in relative terms, as a percent of the unknown ‘total’ number of seamounts in the world ocean, under the assumption of proportionality.  Known seamount locations supplied by NOAA and from Seamounts Online were matched against the corresponding seamounts we located, which led to an integrated set of seamounts, and some degree of ‘ground-truthing’.

Another preliminary study we conducted suggested that only a small subset of the thousands of seamounts that exist have tops close enough (less than 100 m) to the sea surface for their enhancing effect on primary production to be detectable from satellite images.  This confirms that the high fish and invertebrate biomasses observed on seamounts (including deep ones) are maintained by the capture of drifting plankton and detritus, rather than by in-situ production.

Information on seamount biodiversity, which is very high, and characterized by a high degree of endemism, may be found in Seamount Online, maintained by Ms Karin Stocks, Scripps Institution for Oceanography, San Diego.

2.         Coral reefs

Trophical coral reefs, along with tropical rainforest, are the most diverse ecosystems on earth.  They contain a multitude of species connected through a myriad of complex feeding and behavioral interactions that are still being unraveled.  The bulk of these interactions involve coral reef fishes, here made accessible on a per-country basis using FishBase.

Coral reefs do not occur in deep waters; most live between the water surface and about 30 m as established by Charles Darwin about 170 years ago.  Yet, the surface area covered by coral reefs in various parts of the world has long been a matter of controversy.  One of the first estimates was by Newell (1971) but it was so uncertain (150,000-1,500,000 km2) as to be nearly useless.  Smith (1978) presented the first credible estimates, which were divided into nine zones ranging from the South Atlantic, with 8,000 km2, to Southeast Asia with 182,000 km2.

Other estimates followed, again ranging from very low (112,000 km2; De Vooys 1979) to very high (1,994,000 km2; Cooper 1994).  Spalding and Grenfell (1997) identified the source of discrepancy between these estimates as issues of definition (‘what is a reef?’) and issues of scale (‘what maps are used to identify reefs?’).  They also provided their own estimate of 255,000 km2, which was near the lower range of previous estimates.

We abstain from presenting country specific coral reef area estimates in absolute terms.  Rather, we have interfaced the coral reef maps of the World Conservation Monitoring Centre with the EEZ definitions used for other products on this website to calculate the fraction of the world’s global coral reef area that occurs in the EEZ, or part thereof, of a given country.  To obtain absolute surface areas, users can thus multiply this percentage by their preferred estimates of the global surface area covered by coral reefs.

We are aware that this procedure will lead only to approximate values as certain countries may boast more of certain types of corals than others, which via one’s definition of coral, might influence what one perceives as coral reef coverage.  The problem here, however, is not the absolute amount of coral reef cover, but the fact that in most countries, terrigenous pollution, overfishing, coral extraction, and global warming, have much reduced live coral cover in most countries. and will increasingly do so in the next decades (see entries in ReefBase, also accessible through this website on a per-country and regional basis).  Thus our percentage indicates for each country the fraction of the problem that each country ought to resolve.

2.1 References

De Vooys, C.G.N. 1979. Primary production in aquatic environments. In: B. Bolin, E.T. Degens, S. Kempe and P. Ketner (eds.) SCOPE, 13: The global carbon cycle. John Wiley and Sons. Chichester, UK.

Newell, N.D. 1971. An outline history of tropical organic reefs. Am. Mus. Novit. 2465: 1-37.

Smith, S.V. 1978. Coral reef area and the contributions of reef to processes and resources of the world’s oceans. Nature 273: 225-226.

Spalding, M.D. and A.M. Grenfell. 1997. New estimates of global and regional coral reef areas. Coral Reefs 16: 225-230.

3.         Primary Production

By Sherman Lai,
Sea Around Us Project, Fisheries Centre, UBC
[Version of Jan. 2004]

Primary production (PP) is the fixation of inorganic carbon by living organisms, leading to the formation of organic compounds.  Most of the world’s PP relies on energy provided by sunlight, i.e., on the process know as photosynthesis, though, in the deep sea, some PP occurs which is based on different chemical processes.  While seagrasses, macroalgae and coral reefs contribute significantly to PP in coastal zones, especially in the tropics, the bulk of marine PP is carried out by microscopically small, planktonic algae (‘phytoplankton’), which can be seen from space, thanks to their photosynthetic pigments (mainly chlorophyll).  The PP estimates presented here are based on a model described by Platt and Sathyendranath (1988), whose parameterization varies between biomes and biogeochemical provinces (Longhurst et al. 1995, Hoepffner et al. 1999).  The model estimates depth integrated PP based on chlorophyll pigment concentration as derived from SeaWiFS (http://seawifs.gsfc.nasa.gov/SEAWIFS.html) data, and photosynthetically active radiation calculated as in Bouvet et al. (2002).

The PP estimates were processed at the Inland and Marine Waters Unit (IMW), Institute for Environment & Sustainability, EU Joint Research Center (JRC), Ispra, Italy, under the responsibility of Nicolas Hoepffner (nicolas.hoepffner@jrc.it) and Frédéric Mélin (fredreric.melin@jrc.it), and made available on a monthly basis from October 1997 with a spatial resolution of 9 km.

These data were used here to derive estimates of PP for the EEZ of the maritime countries of the world, and other areas of the world ocean, following application of an interpolation procedure, described below, to fill in missing data points in the data set.

3.1        Interpolation

The data processed by the IMW unit of the JRC were made available in a format (*.pp) readable by any Hierarchical Data Format viewer.  This was translated into an ASCII formatted table consisting of 2048 rows and 4096 columns, and spanning the globe.

The model for estimating primary productivity relies on monthly estimates of chlorophyll and sunlight for any spatial cell of the oceans.  One of these parameters was missing in a number of cases, e.g., due to clouds during satellite passages.  The interpolation method newly developed to fill the original data aims to avoid some of the deficiencies of standard interpolation methods (i.e., kriging and spline-based methods cannot be used because they would tend underestimate missing costal data, while standard inverse distance method tends to ‘fade away’ over large areas with missing data).

Our new interpolation method first draws a circle around a cell to be interpolated, and 8 radii, at 45 degrees intervals, and a length of 12 cells; I to VIII in Fig. 1.  Up to 8 non-empty cells between two radii that are closest to the central cell are identified (e.g., A1 and A2 between the radii I/II and V/VI respectively).  An estimate of the central value is obtained by taking the mean between each of the available pairs.  The mean of the resulting four (or less) estimates is taken as an estimate of the missing central value.

Figure 1 - Schematic representation of interpolation method used to fill in 9 km x 9 km cell-specific estimates of primary production.  Roman numeral identify the radii, between which the filled cells closest to the central value (e.g. A1 between radii I and II and A2 between V and VI).  This illustrates a case where one pair of filled cells is missing.

Figure 2 gives an example of our procedure filled in.  As expected, PP estimates were missing mainly in the polar regions, and areas with high cloud cover and/or aerosol loads (e.g., the Arabian Sea and off northwest Africa).  Note that the interpolation method cannot generate estimates higher than the highest observed value.  Thus, the high PP values near the poles are not an artifact of the interpolation method.

Figure 2 - SeaWiFS map of global primary production for June 1998, distinguishing areas with original from areas with interpolated data.  Note large areas without data along polar regions.

Some areas with empty cells were larger than the threshold distances (12 cells; see above), which prevented the interpolation procedure from completely filling in the missing values.  In these cases, we then interpolated between months, (here only between May and July, to fill in for missing June values), then re-applied the interpolation procedure.

3.2        Monthly means PP for the EEZ of maritime countries, LME or High Sea areas

Estimation of mean monthly PP for specific areas was done by summing cell-specific values in a given area, and dividing by the number of cells, based on GIS objects representing the Exclusive Economic Zone (EEZ) of maritime countries (by FAO areas), Large Marine Ecosystems, or High Sea areas (that part of FAO statistical areas outside of countries’ EEZ).

This mapping process caused, in some cases, the loss of some fine scale data.  This however, affected only countries/territories with extremely small EEZ, i.e., Bosnia and Herzegovina, Macau (China) and Jordan.  In these cases, and for two other countries with small EEZ and unrealistic estimates of PP (Republic of Congo; Democratic Republic of Congo), possibly due to the turbidity of their coastal waters, the original monthly PP estimates were replaced by values averaged from adjacent cells.  Finally, the monthly means were averaged to provide annual estimates.

3.3        References

Platt T. ,and S. Sathyendranath, 1988. Oceanic primary Production: estimation by remote sensing at local and regional scales. Science, 241: 1613-1620.

Longhurst A., S. Sathyendranath, T, Platt, and C. Caverhill, 1995. An estimate of global primary production in the ocean from satellite radiometer data. Journal of Plankton Research, 17(6): 1245-1271.

Bouvet M., N. Hoepffner, and M.D. Dowell, 2002. Parameterization of a spectral solar irradiance model for the global ocean using multiple satellite sensors. Journal of Geophysical Research, 107(C12), 3215, doi:10.1029/2001JC001126.

Hoepffner N., Z. Finenko, B. Sturm, and D. Larkin. 1999. Depth-integrated primary production in the eastern tropical and sub-tropical North Atlantic basin from ocean colour imagery. International Journal of Remote Sensing, 20: 1435-1456.

4.         Scientific and Common Names

The names provided here for the taxa included in this Sea Around Us catch database originate largely from FAO, but were verified using FishBase for fishes CephBase for cephalopods, and a variety of other sources for invertebrates other than cephalopods.

Common names - which is what most people know about most organisms - are provided only in English; FishBase and CephBase provide common names in other languages for fish and cephalopods, the former being covered, indeed, by nearly 200,000 different names in over 200 languages.  FishBase also provides a rationale for the use of common names, and the way the names it contains were assembled (FishBase manual).

Scientific names differ in various features, depending on whether they pertain to species, genera, families, orders, or broader taxonomic groups.

Species names always consist of two parts, a unique genus name (whose first letter is always capitalized), and a species epithet (whose first letter is never capitalized).  Both components of the names should be written in italics whenever possible, i.e., Gadus morhua.

The name of a genus (plural = genera) must be unique (i.e., there is no other such name in the entire animal kingdom) and its first letter is always capitalized.  A genus can include one or several species, i.e. Chanos sp., or Stolephorus spp.) (Click here for more rules regarding the naming of species and genera)

Families consist of one, or more commonly, several genera.  Family names among animals always end in -idae, e.g. Gadidae (cods).  Sometimes, "common" names are derived from the scientific names of families, e.g. "loliginids" for squids of the Family Loliginidae, but this usually leads to names that are little used, even when the family was based on a generic name itself based on a (Latin) common name 'Loligo'.  We have kept such names, however, if they occurred in the FAO catch database, in order to maintain as much compatibility as possible.

Orders consist of one or more families, and their names, in animals, end in -formes.  Thus, for example the Gadiformes include the families Gadidae (cods), Merluccidae (hakes), and others, all more closely related to each other than, e.g. the herrings, sardines, etc. (the Clupeiformes).

The Sea Around Us database also include broader, but taxonomically ill-defined groups, usually the result of suboptimal systems having been set up by various countries for collecting and reporting fisheries catch data.  The Sea Around Us Project strives to disaggregate such data, i.e., to allocate them to the appropriate families, or lower level, and we anticipate that the number of broad categories in the database, and especially the amount of catch they represent, will gradually decline.

5.         Consideration of habitat association in the distribution of commercial species

William W.-L. Cheung, Adrian Kitchingman and Reg Watson,
Sea Around Us Project, Fisheries Centre, UBC

In the SAUP database, the distribution of taxa plays an important role, notably in the spatial catch allocations.  Habitat preference is an important factor affecting the taxon’s distribution.  Thus our aim is to enhance predictions on the taxon distribution based on their association to different habitats.  We assume that relative abundance of a taxon in a spatial grid is partly determined by the area of habitat(s) with which it associates as well as how far the association effect will extend from the habitat.  The latter is assumed to be a function of the taxon’s body size (maximum length) and its habitat ‘versatility’.  Thus a large-sized taxon that inhabits a wide range of habitats is more likely to extend their range further from their associated habitats (Kramer & Chapman 1999).

We classify the maximum length and versatility of the taxa into three categories.  A taxon can associate to one or more category with different degree of membership (0 to 1); higher membership value means higher possibility of the taxon to be associated with the particularly category.  The membership values are defined by pre-specified membership function for each of the length and versatility categories (Fig. 1).  For example, greasy grouper (Epinephelus tauvina) has maximum length of 107 cm.  Thus based on our defined membership functions (Fig. 1a), greasy grouper has medium to large body size with memberships of 0.8 and 0.2 respectively (maximum membership=1).

Here, versatility refers to the taxon’s ability to inhabit different habitat types and is defined as the ratio between the number of associated habitats to total number of defined habitats (Table 1).  For instance, based on descriptions from FishBase (http://www.fishbase.org), the greasy grouper (Epinephelus tauvina) is associated to coral reef, estuaries and “other habitats”.  Given the total number of defined physical habitat is five (coral reef, estuary, seagrass, seamount, other habitats, while excluding shelf/slope/abyssal and inshore/offshore), versatility of greasy grouper is estimated to be 0.6.  Based on the defined membership functions (Fig. 1b), versatility of this taxon is classified as medium to high with membership of about 0.5.

5.1        Maximum distance of habitat effect

Maximum distance of habitat effect (maximum effective distance) refers to the maximum distance from the nearest perimeter of the habitat within which the “attraction” effect to their associated taxa exists.  We define this maximum effective distance by the maximum length and habitat versatility of the taxa using a set of heuristic rules (Table 2).  For example:

-                  IF maximum length is large (0.2) AND versatility is high (0.5) THEN maximum occurrence distance from the associated habitat is high (0.2)

where the number in parentheses represent the degree of membership to the categories.  In this example, the degree of membership to the conclusion is the minimum memberships of the two predicates.  When the same conclusion is reached from different rules, the final degree of membership to the conclusion is their average.

The maximum effective distance from the associated habitat can be estimated from the ‘centroid value’ of each conclusion categories weighted by their degree of membership.  We define the centroid values for small, medium and large maximum effective distance as 1 km, 50 km and 100 km respectively.  As such, if a taxon has memberships of 0.2 and 0.5 to small and medium maximum effective distance respectively, the estimated maximum effective distance is:

(0.2*1 + 0.5*50 + 0*100)/(0.2 + 0.5 + 0) = 36.1 km (Fig. 2)

5.2        Determining importance of habitat

Based on the qualitative descriptions from published literature, database or personal communications from experts, we determined each taxon’s degree of association to different habitats.  We look for keywords which related to the taxon’s dependence on habitats (Table 1, 3).  Thus greasy grouper which prefers coral reef, and sometimes occur in estuaries and other habitats will scored 0.75 for coral-association and 0.25 for the latter two.

5.3        Estimating relative abundance in a spatial cell

We make several assumptions to simplify the computations.  Firstly, we assume that the habitat always occur in the centre of a cell, and is circular in shape.  Secondly, we assume that density of a taxon (per unit of area) is the same across any habitat types.  We also assume a linear decline in density from the habitat perimeter to the taxon’s maximum effective distance.  As such, the total relative abundance of a taxon in a cell is the sum of abundance on and around its associated habitat:

where B’T is the final abundance, αj is the density away from the habitat from cell j, A is the habitat area of the cell.  The relative abundance resulted from the different habitat types is the sum of relative abundance, weighted by their importance to the taxon.

5.4        Overall conclusion

This component enables consideration of taxon’s habitat preferences in predicting its distribution.  Although our assumptions on the relationship between maximum length, habitat versatility and maximum distance from habitat may render predicted distributions at fine spatial scale uncertain, this routine provides an explicit and consistent way to incorporate habitat factors which enhances the distribution predictions at large spatial scale.  Moreover, we attempt to incorporate qualitative information in estimating habitat influences, which allows improved distribution predictions particularly when other quantitative information (e.g. depth range, occurrence distance from shoreline etc.) is lacking.

Table 1.  Keywords and their interpreted habitats

Categories

Keywords for defining associated habitats

Estuary

Estuaries, mangroves, river mouth

Coral

Coral reef, coral, atoll, reef slope

Seagrass

Seagrass bed

Seamounts

Seamount

Other habitats

muddy/sandy/rocky bottom

Continental shelf

Continental shelf, shelf

Continental slope

Continental slope, upper/lower slope

Abyssal

Away from shelf and slope

Inshore

Shore, inshore, coastal, along shoreline

Offshore

Offshore, oceanic

Table 2.  Heuristic rules that define the maximum effective distance from the associated habitat.  The bolded columns and rules represent the predicates (categories of maximum body size and taxon’s versatility), while the italics represent the resulted categories of maximum effective distance.

 

Maximum body size

Versatility

Small

Medium

Large

Low

Small

Small

Small

Moderate

Moderate

Moderate

Large

High

Moderate

High

High

Table 3.  Heuristic descriptions on taxa’s relative association to habitat and their assigned weighting factor.  Weighting factor for “other habitats” is assumed to be 0.1 when no information on habitat association is available.

Heuristic descriptions

Weighting factor

Presence

0.5

Absence/rare

0

Occasionally, sometimes

0.25

Often, regularly, seasonally

0.5

Usually, abundant in, prefer

0.75

Always, mostly, only occurs

1

a)

b)

Figure 1.  Fuzzy membership functions for the three categories of (a) maximum length and (b) taxon’s versatility.  Habitat versatility is defined as ratio of number of habitat types that a taxon occurs to the total number of defined habitat types.

Figure 2.  Maximum effective distance estimated from habitat versality and maximum length of the taxa.

5.5        Reference

Kramer DL, Chapman MR (1999) Implications of fish home range size and relocation for marine reserve function. Environmental Biology of Fishes 55:65-79

6.         Predicted marine mammal species distributions.

By Kristin Kaschner,
Sea Around Us Project, Fisheries Centre, UBC
[Version of April 2004]

The maps describing the distribution of marine mammals made available here by the Sea Around Us Project require brief explanation as to their nature and construction.

The first important point is that, for brevity’s sake, we refer to these maps as species’ distributions although shown maps effectively represent hypotheses about the relative environmental suitability (RES) of a specific area for a specific species rather than actual densities or probabilities of occurrences.  We generated these predictions using a new, semi-quantitative habitat suitability modeling approach that is outlined below.  When viewing the maps, the second point to keep in mind is that these maps, meant to document the global distribution of species, cannot be assumed to correctly represent their local distribution (at scales of less than 2-300 kilometers).  This problem is amplified by our maps being based on a global grid with cell dimensions of half a degree latitude by half a degree longitude (i.e., 30 miles at the Equator), which gives them a ‘grainy’ appearance at high resolutions.

We constructed distribution maps of 115 species of marine mammals using a rule-based generic modeling approach to relate what is know about a species’ general habitat preferences to the locally averaged environmental conditions in a global grid system.  During model development, the universal applicability of the model was given highest priority, i.e. we selected predictors and types of data that would likely be available for most if not all species of marine mammals.  Since very little is known about many marine mammal species and reliable surveys covering the entire range of species are rarely available, we chose to use mainly qualitative descriptions of species’ habitat preferences.  Consequently, the RES model visualizes the geographic regions that experts describe essentially when they talk about a “coastal, tropical species” (such as e.g. Atlantic humpbacked dolphins) or a species that “prefers offshore, polar waters” (such as e.g. hooded seals).

6.1        Model input parameters

To generate predictions, we started by compiling information about known General areas of a species’ occurrence (i.e., North Atlantic or southern hemisphere).  This information is included on the ‘Parameter Used’ pop-up window as it served as first rough geographical constraining factors in the prediction model.  Often ‘General areas’ were further modified by excluding smaller clearly defined regions of known absences (e.g. Mediterranean or Baltic Seas) and/or major latitudinal and longitudinal range restrictions.  Again, such restrictions were listed in the ‘Parameter Used’ pop-up window.


Next we assigned each species to broad-scale categories of habitat preferences with respect to bottom depth, sea surface temperature (SST) and ice-edge association.  In a few cases, maximum distance to land was included as an additional factor.  We defined habitat preference categories to represent broad predictor ranges, which roughly describe real marine physical/ecological niches inhabited by different marine mammal species.  Species allocation to categories was based on synopses of published qualitative and quantitative habitat preference information obtained by screening more than a thousand sources of primary and secondary literature.

Selected habitat preference categories for each species can be viewed in the ‘Parameter Used’ pop-up window.  Mathematically, categories were described by trapezoid probability distributions (see Figure 1), i.e. the relative environmental suitability was assumed to be uniformly highest throughout what we call the ‘Preferred predictor range’ (MinP to MaxP, see Figure 1).  Beyond this, suitability decreases linearly towards what was considered to be the minimum or maximum thresholds for a species (MinA or MaxA in Figure 1), representing the limits of the ‘Predictor range’.  Probabilities were set to zero outside the absolute minimum or maximum values.

Figure 1 - Assumed trapezoid probability distribution describing habitat preference categories

A summary of all qualitative and quantitative definitions of habitat preference categories used in the model can be viewed by clicking on the quantitative & qualitative habitat preference link in the Remarks section in the ‘Parameter Used’ pop-up window.

6.2        Generating RES predictions

Using the input parameter settings and geographic restrictions summarized in the ‘Parameter Used’ pop-up window, we then generated an index of the relative environmental suitability (RES) of each cell within the Sea Around Us global grid for a given species by relating quantified habitat preferences to locally averaged oceanographic conditions.  For each cell c the relative environmental suitability – ranging between 0.00 and 1.00 – was calculated using:

where RESc represents the product of the generated RES in that cell c for D (depth), T (SST), distance from the ice edge (I) and, in some cases, from land (Dis), respectively.

If available habitat preference information suggested that there were multiple category options for any or all of the three predictor variables, the model was run with a number of possible predictor combinations.  We then selected the hypothesis considered to represent the best model fit through an iterative process and by qualitative comparison of outputs with all available information about the species’ distribution and occurrence patterns within its range.

6.3        Results, model testing & evaluation

To allow comparison of the model results with existing information on species distributions, we have included published maximum range extents when available.  As can be seen, generated RES predictions matched known boundaries of species occurrences in most cases.  Our results suggest that the model-based approach for identifying marine mammal habitat may represent a useful, more objective alternative to sketched distributional outlines.  In addition, unlike the homogenous within-range occurrence of a species that is effectively implied by existing outlines, grid-based RES predictions provide more detailed information about heterogeneous patterns of potentially suitable habitat for species throughout their range.

We have tested model outputs extensively for a number of species using sightings, strandings and catch data (Kaschner et al., manuscript in prep for submission, a, b ) and results indicate that we really can explain known large-scale patterns of species occurrence relatively well using the RES approach.

Nevertheless, the actual occurrence of a species in an area will depend on a multitude of additional factors and our predictions are impacted by a host of biases.  As a consequence, there are many cases in which there are obvious discrepancies between RES model predictions and known local species’ occurrences.  Discrepancies may manifest themselves in the form of predicted false presences, i.e. the model predicts species to occur in areas where it has not been documented.  Alternatively, there may be species’ absences may be predicted in areas that are definitely known to be part of a species distributional range.  We have tried to identify and summarize ‘Problem areas’ of ‘Predicted false absences’ or ‘Predicted false presences’ on the ‘Parameter Used’ popup window and included ‘Potential causes’ for the observed discrepancies.  In addition, we have included a qualitative ‘Level of confidence in predictions’ of the model’s ability to adequately describe the species’ distribution, representing a qualitative evaluation of the model fit based on the uncertainties associated with the data used to select input parameter settings, the degree to which predicted distributions match available qualitative descriptions of species’ occurrence and the outcome of performed quantitative tests of generated predictions.

6.4        Conclusions & request for feedback

Despite the present shortcomings of our RES model, we regard this approach as a useful tool to evaluate current assumptions and knowledge about species’ occurrences, especially for data poor species.  Moreover, we believe that RES modeling may help to focus research efforts on smaller geographic scales and usefully supplement other, statistical, habitat suitability models.  Most importantly, however, the extent to which the RES model fails to capture patterns of species’ occurrence will allow us to ask specific questions about the role other factors may play in influencing marine mammal distributions.

We conclude by inviting comments on the RES modeling approach and the maps presented here.  Only a few of these maps have been reviewed by experts, and most of them would benefit from such review.  We are specifically seeking feedback on selected input parameter settings that may allow us to improve model predictions and would be pleased to share the existing alternative hypotheses for species’ distributions that we have generated with all interested parties.

Please use the ‘Feedback’ button to contact us kaschner@fisheries.ubc.ca

7.         Catches

Strictly speaking, the 'catch' data presented on this website refer to 'reported landings', which are only a component (though usually the most important) of total catches, defined by

Total catches = reported landings + unreported landings + discards + ghost kills

where unreported landings refer to either illegally caught fish, or fish caught and landed by fisheries that are not monitored officially (often recreational, subsistence or small-scale fisheries); discards refer to fish that are caught, but subsequently thrown overboard, and ghost kills refer to fish killed by lost gear (e.g., traps or gill nets).  Note that the 'by-catch', consisting of fish that were caught, although they were not targeted, may be discarded or landed (and then either reported or not).  Thus, although representing a near intractable problem for fisheries management, by-catch issues are, at least conceptually, not a problem in fisheries statistics.

The Sea Around Us Project will strive to gradually replace, for as many maritime countries as possible, the official landings (mainly from FAO) which form the base of the data presently available, by a full accounting of what is now known as IUU (Illegal, Undocumented and Unreported) catches.

The catch data presented in our various tables are always in tonnes (t, or metric tons) rounded to the nearest t.  This implies, among other things, that taxa whose catch in any given country or territory never reach 500 kg in a given year, are not included in the Sea Around Us database.

Also note that Sea Around Us statistics quantify catches from specific areas (e.g. the EEZ of a given country or the part of that EEZ overlapping with a given FAO Area, or one of 64 Large Marine Ecosystems).  Thus, the Sea Around Us statistics do not necessarily correspond to the amounts landed by/in a given country, and reported as such in national or FAO statistics.  See FAQ for details on this important point.

Note, finally, that the single catch amount presented for each taxon in the Summary Table is the cumulative catch of that taxon since 1950.  This is used here to allow ranking of taxa in order of their contribution to the overall catch from a given area, irrespective of short-term fluctuations.

8.         Frequently Asked Questions – Sea Around Us Project Catch Mapping

8.1        What how do we define ‘catch’ and ‘landings’?

Fishing fleets catch fish, but do not retain all they catch.  Some are discarded before the vessels return to port.  Strictly speaking, most of the statistics we are working with are ‘landings’, since they do not include the fish and invertebrates discarded at sea,.  Moreover, some of the landed catch may remain unrecorded (especially when it is caught illegally).  Thus the precise term should be ‘reported landings’.  The term ‘landings’, however, is not in general use and also implies that we are associating the statistics with individual port statistics (which usually record ‘landings’).  Therefore, when we use the term ‘catch’ in this document, we will usually refer only to that part of the catch that is both landed and reported.

8.2        Where did the catch data come from?

Our data comes from a variety of sources that we harmonize to create a single dataset representing global catches since 1950.  We use data on capture landings from the Food and Agriculture Organization (FAO) of the United Nations as the foundation for our global data as it has global coverage since 1950.  Then, step by step, we substitute data from regional organizations such as the International Council for the Exploration of the Sea (ICES) (www.ices.int/fish/statlant.htm), the Northwest Atlantic Fisheries Organization (NAFO) (www.nafo.ca/) and others.  This provides a finer spatial catch breakdown for most of the Atlantic and the Mediterranean.  We also add national datasets, such as that from Canada’s Department of Fisheries and Oceans (DFO) for Atlantic Canada. (More).  We plan to include more national and smaller scale datasets in other areas as well.

8.3        Do you adjust the ‘official’ catch statistics you use?

As our global dataset is prepared from the many sources we have to make a number of adjustments to harmonize them.  Firstly, we must standardize taxonomic and other codes, and exclude some of the items from the statistics.  For example, we do not include the catch of marine mammals, reptiles, plants, or harvested corals.  As our catches are meant to originate from the marine environment, we remove any reports from freshwater areas or from strictly freshwater species.  Next, we correct for species misidentifications.  We use  FishBase as our standard for fishes and CephBase for cephalopods.  Following this, we may have to correct the area from which the catch was reported if it becomes obvious that the species in question does occur in that area. We also make adjustments for documented over-reporting by China in recent years (Watson and Pauly, 2001). (More). 

Each new version of the data (version numbers appear on the web forms) will include improvements.

8.4        How do we work out which catch came from a specific country’s EEZ or from an individual LME?

The catch data we use are supplied from a variety of sources (see Where did the catch data come from?). Unfortunately, most sources identify the origin of catch only to very large areas of the world's oceans.  Figure 1 shows the reporting areas used for the catch data we use.  Levels of catch identified only to these areas would generate a patch work similar to the coloured areas in Figure 1 (where redder colours have greater catches).

Figure 1.  Reporting areas used to identify the origin of catch data in the sources used by the Sea Around Us Project.  Note that many areas are large, especially the FAO statistical area representing the East Central Pacific, which covers 48 million km2 (18.5 million mi2).

In order to identify which catch was taken within a country’s exclusive economic zone (EEZ), or from a specific Large Marine Ecosystem (LME), it is necessary to work out more precisely where the catch was taken, or at least where it is likely to have been taken.

We have three basic clues to help us, namely the reporting area, a species distribution, and a countries fishing access.

8.4.1           Reporting Area

Firstly, we can assume that the catch was actually taken from within the large statistical area it was reported from to the various organizations receiving the catch statistics (notably FAO).  That is, that fleet operators’ reports of catch to national authorities, who in turn maintain catch statistics that are accurate to at least the precision of the large statistical reporting areas used.  As stated, this does not by itself provide much of a constraint, but it helps.

8.4.2           Species Distribution

For each ‘species’ of organisms being reported (whether they are individual species or very large diverse groups), we usually know something about where in the world's oceans they can be found (and in what relative abundance).  In some cases, detailed studies have been made and we know with considerable precision where the areas are located from which the fish or other organism could have been taken. FishBase, a powerful database of biological and other aspects of fish species, has information about the minimum and maximum depths where you can find many of the world's fishes (see www.fishbase.org).  Similarly, there is also information on the latitude range (north-south limits) for many taxa.  The FAO has similar records for many species including commercial non-fish species.  We must assume that aggregated taxa 'inherit' the greatest ranges of depths and latitudes that their members (species, etc) have records for, and hence the need for disaggregation (see 8.4.5 below).

The distribution obtained are then refined by considering the affinities of various species for certain habitats, and/or habitat features, as explained in Section 5 above (“Consideration of habitat association of commercial species”). An example of a global distribution is given in Fig. 2.

Figure 2. The global distribution of the Atlantic cod, Gadus morhua, used in our catch mapping process.

The distribution of higher taxa is also often available in the literature. For groups where this is not the case, we can still apply depth limits to restrict their distribution. For highly aggregated groups, we can combine the distributions of more specific members, for example species or genera).


8.4.3           Country Fishing Access

Fishing fleets do not fish in all coastal waters of the world.  This is not only a logistical impossibility and an economic improbability, it would also be illegal.  With the declaration of territorial seas, and more recently the 200-nautical mile Exclusive Economic Zone claims, it became necessary for fishing nations to negotiate access to the coastal waters of other nations.  There is great incentive to do this as coastal waters produce most of the ocean’s commercial landings.  Furthermore, many countries with rich marine resources have developed some enforcement capability necessitating other countries to negotiate fishing access.  Whether these agreements are bilateral agreements between countries or between companies and countries, or even between international associations such as the European Union and non-European countries, the contents of these agreements may remain private and the information is often considered to have some strategic commercial value.  Nevertheless, many of these negotiated agreements are reported in the press or in government notices.  The FAO created a database of these agreements called FARISIS (FAO 1998).  We have further developed and widely expanded the contents of this database (see ‘Fishing access’ under ‘Governance’ for any country with a distant water fleet) so that it could be used in the spatial allocation process to restrict, where possible, the locations of catches in coastal waters.  This additional information includes all reports we could locate documenting fishing by one nation in another nation’s coastal waters.  It is also necessary to include two other types of records in such a database.  One that records the likelihood that unauthorized or illegal fishing has occurred leading to illegal, unreported and unregulated (IUU) catches, and another that there is likely that some arrangement existed but that this has not yet been properly documented.  It is not simply enough to record that fishing did occur or could occur; it is much more useful to know what types of animals were targeted, for example was it fishing for tuna or for shrimp.  Thus, our database includes records of which countries were fishing (or could have been fishing) for each of a broad range of target species for each spatial cell on the year in question.

8.4.4              Mapping The Overlap

It was possible, therefore, to determine the catch of any species for any country for each of the spatial cells (30 minutes of latitude by 30 minutes of longitude) used in our database.  Each catch record provides the reporting area, the reporting country, and the identity of the taxa being reported.  By examining the overlapping spatial cells using the criteria described above, those cells included in all three sets share the catch reported.  If there are no spatial cells that satisfy the requirements then we must re-examine the data we were provided.  Perhaps the species was misidentified? Maybe the global distribution we have for the species is not extensive enough? Maybe it was not caught in the area that it was reported from? Perhaps the reporting country did access the EEZ of other countries for which we have neither any documented agreements nor known fishing access? Was the vessel taking the catch re-flagged from another country which has greater coastal access to this species? There are many possibilities and we must track down the likely cause and either update our databases for species distribution or fishing access, or correct the catch record (Figure 3).  Such corrections are part of our data preparation process.

Figure 3.  Flowchart showing the spatial disaggregation process used by the Sea Around Us Project.  The rule-based process uses reported taxa (what), reporting country (who) and reported FAO area (where) data fields from FAO landing statistics, in conjunction with databases of the distribution of commercial taxa, access by fishing countries and spatial reference by area to divide large-scale fisheries landings into smaller (30-minute of latitude x 30-minute of longitude) spatial cells (the ‘Yes’ pathway).  Catch records that cannot processed in this way (the ‘No’ pathway) arecorrected, or the rules and/or databases used in process are refined.

8.4.5       Taxonomic disaggregation: ‘reported’ and ‘inferred’ catches

When speaking about the composition of catches, we prefer to use the term 'taxa' rather than 'species', because commercial fishing catch records vary widely in how precisely they identify the animals taken.  Some are very specific and identify the catch to the level of species.  Others report the catch in highly aggregated groups including 'miscellaneous marine fishes'.  We use the term ‘species’ for the lowest level of aggregation in our database, though this at times may represent genera, or even families. 

To make over-aggregated catch records more useful, we use various heuristic rules to draw inference on the taxa which may have been included in the ‘miscellaneous’ catch. These rules include: (1) a taxon assumed to occur in the catch must also occur in the area (as inferred from its distribution); (2) taxa of low values (as inferred from our price database) are more likely to remain unreported than high-value species, even if they contribute a large fraction of the catch, and (3) a taxon reported from the neighbours of a given country is likely to also occur in the catch of that country. This last rule was emphasized in the example given here as Figure 4.

Figure 4.  Highly aggregated groups in catch reports are disaggregated into species, genus or family levels prior to mapping.  Here ‘miscellaneous fishes’ from statistics supplied to FAO by China are divided into species such as the Club mackerel using the catch breakdown provided by Japan, Taiwan and South Korea, three adjacent countries which supply a more detailed catch composition in their statistics.

 

To prevent these rules from generating false reports, all our graphs (of catch and values) combine such inferred taxa back into the catch of ‘other taxa”, while our output tables present catches separately for ‘reported catch’ (composition) and ‘inferred catch’ (composition).

It must be understood that taxonomic disaggregation does not change the tonnage reported from a given EEZ or LME, and thus can be simply ignored if felt to be too approximate.

8.5        Why do you report catches as tonnes per square kilometer?

The ocean area included in each of our spatial cells varies.  Those nearer the poles are much narrower than those at the equator, and within some cells there may be islands and coastline.  The catch (tonnes) provided in catch records was attributed and accumulated as catch rates (tonnes per square km per year) in our maps, so that the results are comparable from cell to cell.  For more details see Watson et al. (2004).

8.6        What are the other groups you report on other than species?

Because there are more than 1,200 species and other groups included in global fisheries catches we have decided to provide data on only the top 250 species (based on total catch since 1950), but we also provide data using two other types of aggregated groups for all catch.

The first is a general grouping of the catch by 12 broad groups that we call ‘higher groups’.  These are anchovies, herring-like fishes, perch-like fishes, tuna and billfishes, cod-like fishes, salmons and smelts, flatfishes, scorpionfishes, sharks and rays, crustaceans, mollusks, and 'other fishes and invertebrates'.

The other grouping is based partly on taxonomy but mostly on habitat preferences, feeding habits, and maximum size, which define what we call ‘functional groups’ as required for ecosystem modelling.  This grouping separates fish by where they live in the water column. Demersal animals that live on the bottom are separated from those that live on the surface (pelagic).  Pelagics are in turn separated to those that are under 30 cm when at maximum length (small herring species), those 30 to 90 cm, and those over 90 cm (such as tunas).  There are 30 functional groups.  This grouping system, besides facilitating ecological studies, is useful for studying the impacts of fishing gear as different functional groups tend to be impacted and targeted by fishers differently.

 

8.7        What does catch from a country’s EEZ mean?

This is the catch of fisheries products (in tonnes) that was taken from the waters delimited by the Exclusive Economic Zone (EEZ) that a given country claims (i.e., including ‘overlapping claims’ which here are called ‘disputed zones’). The approach to distribute catches in space uses rule-based procedures (see Watson et al. 2004) and auxiliary databases of species distribution and fishing access.

Disclaimer: Maritime limits and boundaries depicted on Sea Around Us Project maps are not to be considered as an authority on the delimitation of international maritime boundaries. These maps are drawn on the basis of the best information available to us. Where no maritime boundary has been agreed, theoretical equidistance lines have been constructed. Where a boundary is in dispute, we attempt to show the claims of the respective parties where these are known to us and show areas of overlapping claims. In areas where a maritime boundary has yet to be agreed, it should be emphasized that our maps are not to be taken as the endorsement of one claim over another.

 

Figure 5.  Mapped landings taken by Belgian fleets during 2000 based on the Sea Around Us Project’s procedures and catch data from FAO and ICES.  Rectangular cells show where landings were taken.  More was taken from the deep blue cells.  The red line shows the limits of the Belgian EEZ.

Our reports also include the landings taken from foreign countries in the EEZ of the country in question.  In this way, the landings we report may, in some cases, actually exceed the official landings of the country.

8.8        How can catch from a country’s EEZ differ from the ‘official’ catch reported by that country?

It is quite likely that the official catch reported by a country was actually used in our mapping procedure and was spatially distributed by that procedure. In all cases the weight of catch reported by the country in question is the same before and after this procedure is applied. In some cases, however, only part of the catch reported by the country in question is deemed to come from its own EEZ waters. Some may come from the EEZ of adjacent countries, or from areas under dispute, or even from the waters of distant countries or the high seas. This implies that, when we report EEZ catch for a country, the totals may be less than the official figures – some catch simply comes from elsewhere. This ‘missing’ catch is not really missing but is included either in the catch taken from the EEZ of other countries or in reports for high seas areas (i.e., not in any country’s EEZ area).

Figure 5 shows, for example, where we believe the catch taken by Belgian fleets was taken. Note how much larger the total area that is fished by Belgium is compared with the area of Belgium’s EEZ (marked with a red line). Because of this, only a portion of the official catch reported by Belgium (about 30,000 tonnes annually) are included in our report of the EEZ catch for Belgium (<10,000 tonnes annually) - that is only the part of the catch taken within the red line. Other Belgian catch is reported from the EEZ of the other countries shown, but overall, all catch is accounted for.>

Our reports also include the landings taken from foreign countries in the EEZ of the country in question. In this way, the landings we report may, in some cases, actually exceed the official landings of the country.

Disclaimer: Maritime limits and boundaries depicted on Sea Around Us Project maps are not to be considered as an authority on the delimitation of international maritime boundaries. These maps are drawn on the basis of the best information available to us. Where no maritime boundary has been agreed, theoretical equidistance lines have been constructed. Where a boundary is in dispute, we attempt to show the claims of the respective parties where these are known to us and show areas of overlapping claims. In areas where a maritime boundary has yet to be agreed, it should be emphasized that our maps are not to be taken as the endorsement of one claim over another.

 

8.9        How catch from a country’s EEZ differ from port landings?

While port landings might well have been the basis for a country’s official report to the FAO or other body, and these were data likely used as input to our procedure, it does not mean that all these landings were taken from the EEZ waters of the country (see above). It may well be that some landings were taken elsewhere by the fleets of the country in question. Some port landings may come from foreign fleets that also use national ports. Conversely, it is possible that catches taken within the EEZ waters of a country are transshipped and/or taken away by fishing vessels, and landed at ports outside of that country, a common practice for distant-water fishing fleets.

Disclaimer: Maritime limits and boundaries depicted on Sea Around Us Project maps are not to be considered as an authority on the delimitation of international maritime boundaries. These maps are drawn on the basis of the best information available to us. Where no maritime boundary has been agreed, theoretical equidistance lines have been constructed. Where a boundary is in dispute, we attempt to show the claims of the respective parties where these are known to us and show areas of overlapping claims. In areas where a maritime boundary has yet to be agreed, it should be emphasized that our maps are not to be taken as the endorsement of one claim over another.

8.10   Why do you report fisheries catches this way?

The FAO, regional fisheries organizations such as the International Council for the Exploration of the Sea (ICES) (www.ices.int/fish/statlant.htm), the Northwest Atlantic Fisheries Organization (NAFO) (www.nafo.ca/), and national governments usually report highly aggregated fisheries statistics. It is common to use large reporting areas (see those used by FAO). For some purposes this is sufficient. For others, such as assessing global trends in fisheries, it is usually not. We disaggregate landings into spatial cells measuring ½ degree latitude by ½ degree longitude. Our procedure makes it possible to report landings taken with a range of statistical boundaries, including the EEZ boundaries that countries claim. Many countries are interested in seeing what landings are taken from their waters for a variety of purposes.

Disclaimer: Maritime limits and boundaries depicted on Sea Around Us Project maps are not to be considered as an authority on the delimitation of international maritime boundaries. These maps are drawn on the basis of the best information available to us. Where no maritime boundary has been agreed, theoretical equidistance lines have been constructed. Where a boundary is in dispute, we attempt to show the claims of the respective parties where these are known to us and show areas of overlapping claims. In areas where a maritime boundary has yet to be agreed, it should be emphasized that our maps are not to be taken as the endorsement of one claim over another.

8.11   Why do you report EEZ areas in the Mediterranean and other places where EEZs do not exist?

Not all countries of the world have declared an EEZ, and in many places where they are claimed they are disputed by neighboring countries, resulting in overlapping claims (here called ‘disputed zones’). For reporting purposes we use EEZ areas to delimit marine areas in proximity to countries. We ignore relatively small disputed areas and include these into the EEZ areas of adjacent countries as appears appropriate. Larger disputed areas are reported with each of the countries making claims, i.e., as overlapping claims. For areas like the Mediterranean, we use 'hypothetical' EEZs as a reporting mechanism. Our use of EEZs is for the convenience of reporting and does not represent any opinion on our part as to the appropriate maritime claims of coastal states.

Disclaimer: Maritime limits and boundaries depicted on Sea Around Us Project maps are not to be considered as an authority on the delimitation of international maritime boundaries. These maps are drawn on the basis of the best information available to us. Where no maritime boundary has been agreed, theoretical equidistance lines have been constructed. Where a boundary is in dispute, we attempt to show the claims of the respective parties where these are known to us and show areas of overlapping claims. In areas where a maritime boundary has yet to be agreed, it should be emphasized that our maps are not to be taken as the endorsement of one claim over another.

If you have a question we have not answered then please contact us.
We would like your help to make this FAQ as useful as possible.

8.12 References

Watson, R., Kitchingman, A., Gelchu, A. and Pauly, D. (2004) Mapping global fisheries: sharpening our focus. Fish and Fisheries 5: 168-177.

10.      Global ex-vessel fish price and landed value database

10.1   What?

The database provides the ex-vessel price and landed values of fish caught from the exclusive economic zone (EEZ) of each country, by major groupings of species.

10.2   Why?

Besides being economic indicators in their own right, the ex-vessel price and landed value of fish are essential pieces of information needed to help manage fishery resources to achieve economic and social benefits from these resources without depleting the resource base.  This is because the financial value that is obtainable from catch when it is landed is one of the primary motivators for fishers to go fishing.  There is no single database available publicly where interested members of the public, researchers and managers can easily find landed values of the world's major commercial fish catches.  The United Nations Food and Agricultural Organisation (FAO) compiles product and processed fish prices, but not ex-vessel prices, from which landed values are calculated.

10.3   How?

10.3.1       Data collection and compilation

Ex-vessel price data for the world's commercial species were compiled from published sources.  The aim was to add value by taking the data already available but widely scattered to a higher level: One that will permit more policy-relevant ecological and economic analysis of fisheries.  We concentrated, in the first instance, on data for the major fishing countries in each continent.  In this way, we collected data that covered the major fisheries of the world, while putting in place a database structure that will allow further inclusion of data for more countries over time.

taxon) was the primary determinant of the price.  Following this, in order of importance, were the country fishing and the year when the catches were reported.  At each step in the interpolation process, the level of specificity in the documentation was recorded.  If a more specific price for a catch record occurred in a subsequent step in the process, then the old price, and its record of specificity, was overwritten with the new price.  In this way, all catch records recorded in the global database were matched with the most specific and relevant price recorded in the price database, or weighted averages of these (weighted by their individual specificity) when several prices were available.  A measure of the price specificity/applicability is computed for each taxon for which a landed value is presented.  These measures will be used to guide the priorities in further price data research.

10.3.4       The database

The primary data in the database is nominal ex-vessel prices, in most case obtained by dividing officially reported landed values by landings.  Ex-vessel prices and landed values are presented in US$ to allow a uniform basis for comparison.  However, the starting point for the data is always local ex-vessel prices in local currency, which are converted into US$ equivalents.

There are two parallel parts to the database, namely, nominal and real ex-vessel prices and landed values.  The user can ask for either of these on the web.  The real numbers were determined by using local consumer price indices (CPI) to convert local nominal ex-vessel prices into real (year 2000) ex-vessel prices.  These are then converted into year 2000 US$ equivalents.

Landed values are presented in both graph and table formats, while ex-vessel prices are presented only in table format

11.      Maximum body length estimates in the Sea Around Us database

The size of an organism is, at any phase of its life-cycle, its most important attribute (Peters 1983; Schmidt-Nielsen 1984; Goolish 1991).  Thus, for example, the predation animals experience is largely a function of their body size.  Arrow worms (Sagitta spp.) are innocuous to bull sharks not because the latter have evolved particular adaptation to escape predation by Sagitta spp., but because they are, throughout their life cycle, too large to be bothered by arrow worms.  Conversely, bull sharks are perfectly innocuous to copepods, which must instead avoid arrow worms.

However, tracking the sizes of marine organisms on a global basis is impossible, even if one were to limit oneself to commercially exploited species.  Thus, to enable size to be considered when evaluating the impact of fisheries on marine ecosystems, we have added to the Sea Around Us database an estimate of the mean maximum length (ML, in cm) reached by the oldest representatives of all taxa for which distribution and/or catch data are also included.

These ML estimates were obtained mostly from the ‘ISCCAAP Table’ of FishBase 2000 (Froese and Pauly 2000), and consist of ‘standard lengths’ for all species of bony fishes.  Means were taken for genera, families and higher groupings.  Note that, in many cases, this mean ML will be smaller than the ML of component species.  For sharks, the precaudal length and for rays the width were taken as the measures best expressing ‘size.’ Similarly, for invertebrates, lengths were selected which corresponded best to body length, i.e., excluding antennae or tentacles.  However, for some groups, width was also used to represent body size, notably in crabs and most bivalves.  The ISSCAAP Table cited above provides detailed sources for these entries, which we complemented with maximum length from FAO Species Catalogues and Identification Guides (see www.fao.org) for a number of invertebrate taxa.

We are well aware that using a single estimate of ML for each of the taxa in the Sea Around Us database is problematic, as ML values vary in space and time.  However, the values of ML included in this table are rarely, if ever used singly.  Rather, a large number of estimates are usually combined to calculate trends in mean ML for the total (multispecies) catch of entire regions.  Thus, it can be expected that some of the errors in our ML estimates will cancel out.  Let us know if you still think that this table contains values you think we should change.

11.1   References

Froese and D. Pauly (Editors) 2000. FishBase 2000: Concepts, Design and Data Sources. ICLARM, Los Baños, Philippines, 346 p. [Distributed with 4 CD-ROMs; previous annual editions: 1996-1999; updates in www.fishbase.org]

Goolish, E. 1991. Aerobic and anaerobic scaling in fish. Biological Reviews. 66:33-56.

Peters, R.H. 1983. The ecological implications of body size. Cambridge University Press, Cambridge.

Schmidt-Nielsen, K. 1984. Scaling: why is animal size so important? Cambridge University Press, Cambridge.

12.      Trophic levels estimates in the Sea Around Us database

Trophic levels (TL) express where fish and other organisms tend to operate in their respective food webs.  Unlike, e.g., counts of gill-rakers, TL are not attributes of the fish whose feeding is being described, but of their interactions with other organisms.  Thus, to estimate the TL of fish or invertebrates, we must consider both their diet composition, and the TL of their food item(s).  The TL of a given group of animals (individuals, population, species) is then estimated from:

TL = 1 + mean TL of the food items

where the mean is weighted by the contribution of the different food items.

Following a convention established in the 1960s by the International Biological Program, primary producers and detritus (including associated bacteria) a definitional TL of 1.

Thus, for example, an anchovy whose diet would consist of 50% phytoplankton (TL = 1) and 50% herbivorous zooplankton (TL = 2) would have a TL of 2.5.  The last value is an estimated, fractional TL, differing conceptually and numerically from the integer values that are often assumed for higher TL, and which are too imprecise and inaccurate to be useful in any kind of analyses.

The TL estimates documented in this table are those currently used by the Sea Around Us Project team for their analyses of fisheries impacts on marine ecosystems.

The TL estimates for finfish were obtained from FishBase (www.fishbase.org), as follows:

1.      For species with one or more sets of diet composition data, the TL taken from FishBase was either the lone estimate, or the median of values pertaining to the juveniles/adults, or adult stages.  In a few cases, pertaining to very low (2.0) or very high (4.5) estimates, the TL values were adjusted upward, and downward, respectively, if closely related species had less extreme values;

2.      For species with only food item data, the TL estimate was taken ‘as is’ only if it fell within the TL range of other, closely related species.  If not, the estimated TL were adjusted as in (1);

3.      For fish genera, TL estimates were derived from the mean TL of the component species with estimates, with more weight given to species with estimates of TL based on diet composition data;

4.      For fish families, TL estimates were derived by averaging the mean TL of the component genera, and correspondingly for orders;

5.      For invertebrates, TL estimates were based on the ‘ISCCAAP Table’of FishBase 2000 (Froese and Pauly 2000), itself based largely on estimates from fod web models (Ecopath) models These estimates were then complemented by data from more recent models, documented in www.ecopath.org, and in Sea Around Us reports (see e.g., www.seaaroundus.org/report/impactmodels.htm).

We are well aware that TL change during the ontogeny of organisms, and that they also vary in space and time (Pauly et al. 2001).  However, the values of TL included in this table are rarely, if ever used singly.  Rather, a large number of estimates are usually combined to calculate trends in mean TL for the total (multispecies) catch of entire regions.  Thus, it can be expected that some of the errors in our TL estimates will cancel out.  Let us know if you still think that this table contains values you think we should change.

12.1   References:

Froese and D. Pauly (Editors) 2000. FishBase 2000: Concepts, Design and Data Sources. ICLARM, Los Baños, Philippines, 346 p. [Distributed with 4 CD-ROMs; previous annual editions: 1996-1999; updates in www.fishbase.org]

Pauly, Daniel, Ma. Lourdes Palomares, Rainer Froese, Pascualita Sa-a, Michael Vakily, David Preikshot, and Scott Wallace 2001. Fishing down Canadian aquatic food webs. Canadian Journal of Fisheries and Aquatic Science. 58:51-62.

13.      Description of SAUP Global Fisheries Mapping Data Versions

13.1 Version 4.0 (1950 - 2003) January 2006

As in previous versions except:

  • A modification to the output structure which improves on the handling of overlapping EEZ cells. Catch is now allocated to the relative overlapping entities (countries) of a cell in accordance to the permission of the fisher to access a particular entity and the proportion of the cell claimed by the entity. This greatly reduces the blurry nature of cell data located in bordering EEZs, EEZ/high seas borders and disputed areas.
  • Refinement of some commercial taxa distributions

Data sources

1) FAO (Fishstat)

2) ICES (1973-1997) from electronic database

3) ICES (1903-1974) from publications

4) NAFO Catch and Effort 1960-1997 from electronic database

5) Arctic Char data from Exploitation of Arctic Fishes by R. Crawford, March 1989, Can Man Rept Fish and Aq Sci No 2002, pg 26

6) Southeast Atlantic capture production

7) Mediterranean and Black Sea capture production 1970-2000: "GFCM Nominal Catches", Vol.9

8) Eastern Central Atlantic capture production 1970-2000:   "CECAF Nominal Catches", Vol.7

9) Estonian Data   Henn Ojaveer (corrected from Ojaveer, 1999) herring, spat, cod and flounder

10) CCAMLR.   2004.   Statistical Bulletin, Vol. 16 (Electronic Version).   CCAMLR, Hobart, Australia

11) RECOFI (part of Western Indian Ocean) capture production 1986-2003

 

Data Adjustments

Extraction/Adjustment Rules by Reg Watson [RW], Adrian Kitchingman [AK] and William Cheung [WC]

Remove: Unwanted Groups   

  • No freshwater fish (ISSCAAP=22)
  • No freshwater crustacean(ISSCAAP =41)
  • No freshwater molluscs (ISSCAAP =51)
  • No higher ISSCAAP groups except for Sea-squirts and other tunicates; Horseshoe crabs and other arachnoids; Sea-urchins and other echinoderms
  • No Artemia salina

Corrections: Country

  • French Southern Territories becomes Kerguelen Is
  • French Southern Territories (now Kerguelen Is) catch in FAO51 becomes FAO58
  • All variations on Canada coded to standard Canada
  • East and West Germany coded to Germany
  • All variations on the UK coded to standard UK
  • US Minor Islands in FAO area 31 changed to Navassa Is
  • US Minor Islands in FAO area 77 considered Johnston I, Midway Is, Palmyra Is, Jarvis Is, Baker Howland Is, Wake I.

Corrections: Taxonomy

  • Take Dentex macrophthalus reported in FAO 47 and code it as D. angolensis   [RW]
  • Sarda chiliensis chiliensis in 77 must be S. chiliensis lineolata [RW]
  • Pleurogrammus azonus (Okhostk Atka mackerel) in 67 must be replaced with Pleurogrammus monopterygius (Atka mackerel) [AK]
  • Merluccius gayi gayi   caught by Peru and Columbia is actually Merluccius gayi peruanus [AK]
  • Sprattus sprattus European sprat from Icelandic (ICES-Va) waters are deleted as wrong/redundant [AK]
  • Makaira nigricans Atlantic Blue Marlin caught in the Indo-Pacific is actually Makaira mazara Indo-Pacific Blue Marlin [AK]
  • Penaeus monodon is only produced in Australia through AQUACULTURE - REMOVED - Peter Rothisberg CSIRO (May, 2003)
  • Mercenaria mercenaria (Northern Quahog) only cultured in FAO area 61 - REMOVED from FAO area 61 - FAO FIGIS factsheets AK/WC
  • Penaeus vanname (Whiteleg shrimp) is only wild caught in FAO area 77 on the Nth American Coast - REMOVED from non FAO 77 areas   : FAO FIGIS fact sheets AK/WC
  • Atlantic salmon, Rainbow trout, Chinook salmon and Coho salmon are only Mariculture in south east pacific (FAO 87) - REMOVED from FAO 87 : AK
  • Trachurus trachurus Atlantic horse mackerel caught in ICES Areas 400022, 400020 or 426 is actually Trachurus picturatus Blue jack mackerel [ICES - AK]
  • Scomber scombrus caught in ICES Area 400022 or Area 426 is actually Scomber japonicus Chub mackerel [ICES - AK]
  • Crassostrea angulata is actually Crassostrea gigas [AK]

Corrections: Areas

  • Santer seabream only Indopacific (FAO51) not Atlantic (FAO34 or FAO47) [RW]
  • Take the Japanese splitfin Synagrops japonicus, reported in FAO47 and put it in 51 (nearest location) [RW]
  • Trachurus picturatus Blue jack mackerel from FAO 41 must be from 34 [RW]
  • Sardinella aurita Round sardinella from area 3402000 to 34 [ICES - AK]
  • Trichiurus lepturus Largehead hairtail from area 3402000 to 34 [ICES - AK]
  • Scomber japonicus Chub mackerel from area 3402000 to 34 [ICES - AK]
  • Engraulis encrasicolus European anchovy from area 3402000 to 34 [ICES - AK]
  • Dentex macrophthalmus Large-eye dentex from area 3402000 to 34 [ICES - AK]
  • Trachurus trachurus Atlantic horse mackerel from area 3402000 to 34 [ICES - AK]
  • Pomatomus saltator Bluefish from area 3402000 to 34 [ICES - AK]
  • Polynemidae Threadfins from area 3402000 to 34 [ICES - AK]
  • Pseudotolithus from area 3402000 to 34 [ICES - AK]
  • Stromateus fiatola Blue butterfish from area 3402000 to 34 [ICES - AK]
  • Australian non-Toothfish (Dissostichus spp) catch reported in FAO 81 excluded from Macquarie Is (81.2) [AK]

Adjustments to Landing Totals

Correct apparent over-reporting by China since 1995 (Watson and Pauly, 2001)

   1995: Chinese catch reduced to 0.82

   1996: Chinese catch reduced to 0.63

   1997: Chinese catch reduced to 0.65

   1998: Chinese catch reduced to 0.63

   1999 and later: Chinese catch reduced to 0.68

Future versions

  • Introduce national data sources compiled for Venezuela, Cuba, Faeroe Island, Iceland, Azores, Morocco, Spain, Portugal, France, Germany, many Caribbean countries, Thailand and the U.S.
  • Include improved global landings of sea cucumbers
  • Review and if necessary correct fishing arrangements in the Mediterranean  

13.2 Version 3.0 (1950 – 2002) June 2005

This version incorporates some major changes that involve both the Sea Around Us’ spatial definition of the world and the methodology used to model taxa distributions.   Below are summaries on these changes as well as its baseline components. 

  • New world cell structure

A new world cell structure has been implemented to conform to the LOICZ system.   The world is partitioned into 30x30 arc minute cells with a top left bounding box corner coordinate at 90°N and 180°W.  

  • EEZs

Cells are no longer exclusively a single EEZ or marked as a disputed zone.   Cells now incorporate all EEZs that intersect a cell.   EEZ data extractions will reflect the fuzzy nature of EEZ claims and EEZ borders to other EEZs or high seas.  

  • Taxa habitat

Taxa distributions now have an enhanced methodology that both incorporate hard habitat parameters as well as soft (fuzzy) parameters.   Hard parameters include latitudinal constraints, explicit depth range and published spatial constraints.   Soft parameters have now been incorporated which include a number of new parameters described below.   A detailed description of the fuzzy model can be viewed here.

New Parameters

  • Estuaries : World cells now indicate the estuarine area contained.
  • Seamounts : World cells now indicate presence of large seamounts.
  • Bathymetric Zones : Three broad zones Shelf, Slope and Abyssal have been defined to categorise those taxon that do not have adequate depth ranges recorded.

(Shelf: 0-200m, Slope: 200-4000m, Abyssal: > 4000m)

  • Coastal Distance : Two zones Coastal and Offshore are defined by an incorporated distance to coast and depth thresholds.

(Coast: To 50km or 100m, Offshore: From 100km or 200m)  

13.3 Version 2.2 (1950 – 2001)

  • Disaggregation is now driven by the existing catch data, fishing permissions and commercial taxa distributions.   Multiple queries extract the appropriate lower taxa levels (family, genus, and species) that occur within the accessible fishing area of a fishing country.   The extracted species composition is then used to disaggregate the appropriate higher taxa catch records based on a species probability and abundance index.   This method still solves the disaggregation problems solved in version 2.1 as well as eliminating allocation problems where disaggregated species records occurred in areas where the fishing country was not permitted to fish.
  • Refinement of some taxa commercial distributions including distributions for cephalopods documented in CephBase.

13.4 Version 2.1 (1950 – 2001)

  • Estonian data (1929-1994) was included from Henn Ojaveer (corrected from Ojaveer, 1999) but restricted to herring, spat, cod and flounder. ICES data for the Former USSR in the Baltic reduced where overlapping.
  • Introduction of NAFO data to replace of FAO FishStat data in FAO statistical area 21 was deferred to 1964 (1960 was used in Version 2.0) as this NAFO source was found to be missing the landings of key species in its earliest years.
  • Disaggregation of miscellaneous groups was done differently, now based on likely low valued species abundant in the reporting area based on species distributions and the reports of other countries. This eliminates the problem found in earlier versions where wide ranging species were used disproportionately for the identity of aggregated groups in non-coastal areas.
  • Annual distributions of major tunas and billfish species were used based on the smoothed Atlas of Tuna and Billfish landings
  • Correct missing records of echinoderm and sea cucumber landings

13.5 Version 2.0 (1950 – 2001)

  • FAO data sourced from FishStat 2001, now exclusively capture data (1950-2001)
  • Catch data was sourced additionally from ICES (Statlant (1903-1972) and Electronic sources (1973-2000)), NAFO (1960-2001), Canadian DFO (1986-2003), Southeast Atlantic capture production (1975-2001), Mediterranean and Black Sea capture production (1970-2001) and Eastern Central Atlantic capture production (1970-2001) records
  • Correct over-reporting by China (Watson and Pauly, Nature 2001)
  • Catch records which could not be allocated were checked against known ‘reflagging’ schedules and ‘deflagged’ where appropriate.
  • Fish distributions did not use primary productivity but a parameter was introduced which determined the distribution with reference to distance from land
  • Corrections and additions to fishing arrangements and observed fishing databases
  • Known quotas on fishing arrangements applied

13.6 Version 1.0 (1950-2000)

  • Catch data sourced from FAO FishStat (Production and Capture sources 1950-2000) with additions of Arctic Char data from Exploitation of Arctic Fishes by R. Crawford, 1989 (Can Man Rept Fish and Aq Sci, No 2002, pg 26).
  • Disaggregation of miscellaneous groups performed after spatial disaggregation.
  • Fish distributions used primary productivity as a determining variable.

 

13.7 References

General References

R. Crawford, March 1989, Can Man Rept Fish and Aq Sci No 2002, pg 26

Systematic distortions in world fisheries catch trends. Watson, R. and Pauly. D. 2001. Nature: 414 (29 Nov): 534-536.

Methodology References

Mapping marine fisheries catches and related indices of West Africa: 1950 to 2000. Watson, R. 2005. In Marine fisheries, ecosystems, and societies in West Africa: half a century of change, Dakar, Senegal, June 24-28, 2002. IRD, France.

Mapping global fisheries: sharpening our focus. Watson, R, A Kitchingman, A Gelchu and D Pauly. 2004. Fish and Fisheries  5:168-177.

Spatial allocation of global fisheries landings using rule-based procedures. Watson, R. 2004. InProceedings of Second International Symposium on GIS/Spatial Analysis in Fishery and Aquatic Sciences (3-6 September, 2002, University of Sussex, Brighton, U.K.).

Multiscale decision support for aquatic protected area placement. Watson, R. 2003. In Proceedings from the World Congress on Aquatic Protected Areas, August, 2002, Cairns, Australia. Australian Society for Fish Biology, Australia.

Mapping fisheries landings with emphasis on the North Atlantic. Watson, R., A. Gelchu and D. Pauly. 2001. In D. Zeller, R. Watson, T. Pitcher and D. Pauly ( Eds), Fisheries Impacts on North Atlantic Ecosystems: Catch, Effort and National/Regional Data Sets. Fisheries Centre Research Reports, University of British Columbia, Vancouver. 9(3): 1-11.

Mapping fisheries onto marine ecosystems: a proposal for a consensus approach for regional, oceanic and global integrations. Pauly, D., V. Christensen, F. Rainer, A. Longhurst, T. Platt, S. Sathyendranath, K. Sherman and R. Watson. 2000. In Methods for evaluating the impacts of fisheries on North Atlantic ecosystems. Fisheries Centre Research Reports, University of British Columbia, Vancouver. 8(2): 13-22.

Published results

Hundred-year decline of North Atlantic predatory fishes. Christensen, V., S. Guénette, J. J. Heymans, C.J. Walters, R. Watson and D. Zeller. 2003. Fish and Fisheries 4: 1-24.

The future for fisheries. Pauly, D., J. Alder, E. Bennett, V. Christensen, P. Tyedmers and R. Watson. 2003. Science 302(5649): 1359-1361.

Counting the last fish. Pauly, D. and R. Watson. 2003. In Scientific American. July: 42-47.

What’s left: the emerging shape of the global fisheries crisis. Watson, R., P. Tyedmers, A. Kitchingman and D. Pauly. 2003. In Conservation in Practice. 4(3): 20-21.

Estimating illegal and unreported catches from marine ecosystems: two case studies. Forrest, R., T. Pitcher, R. Watson, H. Valtýsson and S. Guénette. 2002. Fisheries Centre Research Reports, University of British Columbia, Vancouver. 9(5): 83-91.

Modeling and mapping trophic overlap between marine mammals and commercial fisheries in the North Atlantic. Kaschner, K., R. Watson, V. Christensen, A.W. Trites and D. Pauly. 2002. In D. Zeller, R. Watson, T. Pitcher and D. Pauly ( Eds), Fisheries Impacts on North Atlantic Ecosystems: Catch, Effort and National/Regional Data Sets. Fisheries Centre Research Reports, University of British Columbia, Vancouver. 9(3): 35-45.

Towards sustainability in world fisheries. Pauly, D., V. Christensen, S. Guénette, J. Heymans, T.J. Pitcher, U.R. Sumaila, C.J. Walters, R. Watson and D. Zeller. 2002. Nature 418: 689-695.

[When fish lack] Quand le poisson vient à manquer. Pauly, D., R. Watson and V. Christensen. 2002. In La Recherché. July-August.355: 80-83.

Key features of commercial and recreational fisheries statistics from the US Atlantic coast. Ryan, T., R. Watson and D. Pauly. 2002. In D. Zeller, R. Watson, T. Pitcher and D. Pauly, Fisheries Impacts on North Atlantic Ecosystems: Catch, Effort and National/Regional Data Sets. Fisheries Centre Research Reports, University of British Columbia, Vancouver. 9(3): 225-227.

The marine fisheries of China: development and reported catches. Watson, R., L. Pang and D. Pauly. 2001. Fisheries Centre Research Report, University of British Columbia, Vancouver. Vol 9(2) pp. 58

Systematic distortions in world fisheries catch trends. Watson, R. and D. Pauly. 2001. Nature 414: 534-536.

Global overfishing. Watson, R. and D. Pauly. 2001. In S. Earle (Ed.), National Geographic Atlas of the Ocean: the deep frontier. National Geographic, Washington, D.C. pp. 192

For clarification or feedback please contact r.watson@fisheries.ubc.ca

14.   Using the global marine catches GIS map

-            Overview

-            The toolbar

-            Zooming in and out

-            Panning the map display

-            Identifying features

-            Querying data

-            Printing the map

14.1   Overview

The Global Marine Catches interface consists of a map toolbar, layer list, legend, scale bar, display area and overview map.

The toolbar appears to the left of the map display area.  The Global Marine Catches viewer toggles between the Legend and Layer List and Toggle Overview Map.  Toggling the overview map adds or removes it from the top left corner of the map display area.  Use the Toggle between Layer List and Legend button to switch between the layer list (with active and visibility options) and legend (with symbology).

The layer list appears to the right of the map display area.  Click the Active option to make a layer active.  The active layer is the layer within which querying will be performed.

Check a layer checkbox to make it visible, uncheck it to remove it from the map display.  After making changes, you must click the Refresh Map button for your changes to be reflected on the map.

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14.2   The toolbar

Toggle between the Legend and Layer List: Switches back and forth between a legend with symbology and layer list with visibility options.

Toggle Overview Map: Adds or removes the overview map from the map display area.

Zoom In: Zooms in to the area of the map that you click or drag a box around

Zoom Out: Zooms out from the area of the map that you click or drag a box around.

Zoom to Full Extent: Zooms to the full extent of the map.

Zoom to Last Extent: Zooms to the previous extent.

Pan: Pans the display in the direction that you drag the mouse.

Identify: Displays attribute information for the feature that you clicked.

Query: Searches for features based on a query expression.

Print: Prints the map to your default printer.

Help: HTML Page how to use the Global Marine Catches.

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14.3   Zooming in or out

The zoom and pan tools allow you to change the map extent.

Zoom in or out by clicking the centre of the area to zoom into or out from or by clicking and dragging a box around it.

Zoom to the full extent of the map, the extent of the active layer by clicking the appropriate button on the toolbar, or the last extent .

1.      Click Zoom In  or Zoom Out  on the toolbar.

2.         Move the mouse pointer to the desired location on the map, then click and drag a box around it.

3.         Release the mouse button.

The map display zooms in to or out from the selected area.

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14.4   Panning the map display

Pan the map by clicking the map and dragging it.

1.      Click Pan  on the toolbar.

2.         Click the map and while pressing the button, drag the mouse in the direction you want to pan.    
To see more to the left, click and drag the map to the right.        
To see more to the right, click and drag the map to the left.        
To see more at the top, click and drag the map downward.        
To see more at the bottom, click and drag the map upward.

3.         Release the mouse button.  The map display refreshes.

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14.5   Identify features

The Identify tool provides attribute information about the features of the active layer.  To get more information about a feature on the map, click the Identity tool then click the feature.

1.      Click the Active button beside a layer to make it active.

2.         Click the Identity tool  on the toolbar.

3.         Point to a feature and click.

Attribute information for the feature you clicked appears in tabular form in the panel below the map display area.

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14.6   Querying data

The Query tool allows you to find catch map matching a query expression.  Click the Query button, then choose a Fished group and a Period using the tools that appear below the map display area.

1.      Click the Query tool  on the toolbar.

The query panel appears below the map display area.

2.         Select a Fished group and a Period from the dropdown list and click Select button.

3.         A new prompt message with Go and Back buttons will appear on the display panel.  Click Go to display catch map or Back to go back to the default display query.

4.         After you click the Go button the catch map displays in the map frame.

5.         Select a Fished group and a Period from the dropdown list and click Description button.

6.         The query will returned a list of taxa in tabular form based on the Fished group and the Period you have selected.  Providing link keys to further information such as distribution maps and biological information.

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14.7   Printing the map

When you use the Print tool to print your map, the printed version of your map does not look exactly like what you see in the browser window.  The map extent is the same, but the toolbar is not included, and the overview map appears above the legend.

When you click the Print button, a text box appears allowing you to type a title for your map.

1.      Click the Print button  on the toolbar.

Print options appear in the panel below the map display area.

2.         Type a title for the printed map.

3.         Click Create Print Page.

A new browser window opens containing a printable version of your map.

4.         Click File and click Print.

The Print dialog box appears.

5.         Check the print settings and make any necessary changes.

6.         Click OK

The map prints, complete with a title, legend, overview map, scale bar, and North arrow.

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15.   Exclusive Economic Zones (EEZ) and shelf areas

Disclaimer: Maritime limits and boundaries depicted on Sea Around Us Project maps are not to be considered as an authority on the delimitation of international maritime boundaries. These maps are drawn on the basis of the best information available to us. Where no maritime boundary has been agreed, theoretical equidistance lines have been constructed. Where a boundary is in dispute, we attempt to show the claims of the respective parties where these are known to us and show areas of overlapping claims. In areas where a maritime boundary has yet to be agreed, it should be emphasized that our maps are not to be taken as the endorsement of one claim over another.

Historically, the oceans of the world were considered ‘free’ to anyone wanting to use them for travel, trade or resource exploitation. However, claims to territorial seas date back at least to the 1700s when the Dutch first claimed such an area based on the range of land-based cannons, which, at the time, were taken to be at most three nautical miles (nm). Thus, the three-mile territorial seas concept was born.

Countries have ever since tried to exert control over parts of the ocean that border their shores, as maritime boundaries and claims can have significant impacts on many marine activities, including ownership of living and non-living resources and fishing access (Cimino et al. 2000). The United Nations Convention on the Law of the Sea (UNCLOS), initiated in the 1960s, established a framework that permitted countries to define their claims over the ocean areas, and provided agreed upon definitions for territorial seas (now defined as 12 nm), contiguous zones (24 nm, for prevention of infringements of customs, fiscal, immigration and sanitary regulations) as well as 200 nm Exclusive Economic Zones (EEZ), which now cover most shelf areas down to the continental shelf margins at which the slope of the continental shelf merges with the deep ocean seafloor. Most countries declared EEZs right after the adoption of UNCLOS as international law in 1982. Within its EEZ, the country has the sovereign right to explore and exploit, conserve and manage living and non-living resources in the water column and on the seafloor, as defined by Part V of the Law of the Sea.

The Law of the Sea also makes allowances, through the Commission on the Limits of the Continental Shelf, for countries to claim extended jurisdiction over shelf areas beyond 200 nm, if they can demonstrate that their continental shelf extends beyond the established 200 nm EEZ.  National claims for EEZs and extended jurisdiction may overlap, creating areas of disputed ownership and jurisdiction.  Settlements through boundary agreements may take many years to develop and are complex, resulting in numerous disputed areas and claimed boundaries.

15.1   What is the data source for maritime boundaries used here?

The EEZ boundaries we use in our database were adapted from the public domain ‘Maritime Boundaries Geodatabase’ available from the Flanders Marine Institute (VLIZ, Belgium), overlaid onto the ½ degree x ½ degree spatial cells GIS system of our database. Given the ½ x ½ degree nature of our GIS system, area measurements of EEZs based on our data may differ slightly from those of other systems, and should be considered approximations. Note also that we deal with major disputed areas and unsettled boundary disputes by presenting the areas as non-country specific ‘disputed areas’ with reference to those countries involved in the claim.

Also note (1) that some countries (e.g., around the Mediterranean) have not declared EEZ, in which case we defined EEZ boundaries for these countries based on data and the general methods used by the Flanders Marine Institute, as if these coutrnies were to apply the UNCLOS rules to their definitions, (2) that some countries (notably European Union member states) do not use EEZ for fisheries management.

Surface areas are expressed in km2 and were obtained by overlaying a global 2-minute cell ESRI GRID of surface area values with a matching ESRI GRID of EEZs (based on General Dynamics Advanced Information Systems database, see above). For each EEZ the intersecting surface area based on the 2-minute raster was extracted and summed.

The area of each 'EEZ shelf' was prepared in a similar way but was truncated at 200 m depth, i.e., at the shelf edge, based on the United States National Geophysical Data Center’s ETOPOS GLOBAL 2’ ELEVATION data.

15.2   How were the areas calculated?

Surface areas are expressed in km2 and were obtained by overlaying a global 2-minute cell ESRI GRID of surface area values with a matching ESRI GRID of EEZs (based on General Dynamics Advanced Information Systems database, see above).  For each EEZ the intersecting surface area based on the 2-minute raster was extracted and summed.

The area of each 'EEZ shelf' was prepared in a similar way but was truncated at 200 m depth, i.e., at the shelf edge, based on the United States National Geophysical Data Center’s ETOPOS GLOBAL 2’ ELEVATION data.

15.3   References

VLIZ (2005). Maritime Boundaries Geodatabase. Available online at http://www.vliz.be/vmdcdata/marbound . Consulted on 2008-11-26

16.   High Sea at FAO statistical areas

Surface areas are expressed in km2 and were obtained by overlaying a global 2-minute cell ESRI GRID of surface area values with a matching ESRI GRID of EEZs (based on General Dynamics Advanced Information Systems 2002 Global Maritime Boundary database).  We give both the surface covered by an FAO statistical area ('FAO Area') and that port of it that represents the High Seas, i.e., that is not part of any EEZ ('High Seas').

17.   Explanation of Fishing Access Records

There are three broad groups of marine areas reported here.  The first and most important are the exclusive economic zones of coastal countries (some are subdivided in our presentation for convenience, for example Alaska versus mainland USA).  Most countries have declared (and restrict through enforcement) either an exclusive economic zones (EEZ) or a restricted fishing zone which usually extends 200 nautical miles around their shores.  Those boundaries we display are those obtained from a commercial database.  These boundaries can be in dispute and we recognize the largest of these ‘disputed zones’ as the second type of marine area, within which we expect access only by the countries involved in the dispute.  The third area is the high seas, which are the areas of the ocean outside current national claims.

When assigning landings to the EEZ of countries it is very helpful to know whether a country fishing had arrangements to access the waters of another country’s EEZ, and also the period of this arrangement and what (if any) fish groups or species were specified.  Most of our records have to do with such arrangements.  Some arrangements are very general, others specific and may include quotas or limits.  It is difficult to produce a comprehensive list of such arrangements because they are not formally recorded in any central registry and some have commercial confidentiality concerns associated with them.  We have started with those recorded in the FARISIS database assembled by FAO in late 1990 and built upon these.  Any assistance is welcome in expanding and updating the records listed here.

There are other types of records included here as well.  For example, when vessels of a fishing nation have been reported fishing in the waters of another nation (under an agreement or otherwise) we have recorded the details.  For example, all countries have access to the high seas (and therefore do not need any arrangements to allow this) but we have attempted to document which countries actually did fish in the high seas (when and for what).  Fleets from some countries do not leave their own waters, others are strictly coastal – whereas they do fish in the coastal waters of neighbouring countries they do not fish in the high seas, and some countries have distant water fleets which fish the high seas (and likely the waters of other countries as well).  When landings are reported by countries that could only have been taken within the waters of another country (within the statistical area reported on) we record this ‘access’ and also the years involved.  It is worth noting that most of the world’s fish species are limited in their distribution to coastal waters and as such must be taken from the EEZ of some country (i.e. they cannot be taken on the high seas).

18.  Marine Protected Areas of the World

This project falls within the Sea Around Us Project (SAUP), a project managed under the auspices of the University of British Columbia's Fisheries Centre.  It is the result of a formal collaboration between the World Wildlife Fund (WWF), United Nations Environment Programme - World Conservation Monitoring Centre (UNEP-WCMC) and the World Conservation Union - World Commission on Protected Areas (IUCN-WCPA).

The project has two main goals: firstly, to develop a more robust global MPA baseline than currently exists for either terrestrial or marine protected areas; and secondly, to develop alternative scenarios of global MPA networks using spatial modelling techniques.

Towards achieving the first goal, this website publishes the world’s first explicitly marine-focused database of the world’s protected areas that have some intertidal and/or subtidal component.  It is currently based largely on information in the World Database on Protected Areas (WDPA) Version 6.1.  The aims of this database are to:

7.      provide information that has not, to date, been readily available

8.      attract feedback to greater improve the quality and breadth of information available.

The database is freely searchable.  Should you wish to provide edits to the database, you are kindly invited to register, thereby gaining the authorisation to do so.  Registration is necessary only to enable us to fully acknowledge your contributions to the database.  Suggested edits will be reviewed, before being incorporated (or not) into the official version of the database.  It is emphasized that ALL suggested edits will be retained for comparative purposes and explicitly referenced to their provider.

Note: In order to represent very small marine protected areas (MPAs) on a global map, many MPAs have been represented as points. This tends to significantly exaggerate the area that is actually protected.

19.   The Marine Trophic Index

In February 2004, the Conference of the Parties to the Convention on Biological Diversity (CBD) identified a number of indicators to monitor progress toward reaching the target to “achieve by 2010 a significant reduction in the current rate of biodiversity loss” (CBD 2004).  The “Marine Trophic Index” (MTI) is one of the eight indicators that the Conference of the Parties of the CBD identified for “immediate testing” of their ability to measure progress towards the 2010 target.

The MTI is the CBD’s name for the mean trophic level (TL) of fisheries landings, originally used by Pauly et al. (1998) to demonstrate that fisheries, since 1950, are increasingly relying on the smaller, short-lived fish and on the invertebrates from the lower parts of both marine and freshwater food webs.

The original demonstration of this effect, now widely known as “fishing down marine food webs, relied upon the global database of fish landing assembled and maintained by the Food and Agricultural Organization of the United Nations (FAO).  This database includes the annual fisheries catches (since 1950) of member countries, by species or groups of species (usually genera or families).  Using these data and corresponding TL estimates, mean TL were computed, for each year k from:

TL k = ∑ i ( TL i × Y ik)/∑ i Y ik                                                                    …1)

where Y i refers to the landings of species (group) i, as included in fisheries statistics.

The time series of mean TL thus obtained showed, for most FAO areas, a smoothly declining trend.  Based on the assumption that the relative abundance of taxa in the landing data used in this analysis correlated with the relative abundance of the same taxa in the ecosystem, these declining trends were interpreted as representing a decline in the abundance of high-TL fishes relative to low-TL fishes.  From, this, and given that high-TL fishes tend to grow slowly toward large sizes, and are thus very sensitive to fishing effort, it can be straightforwardly inferred that declining TL trends indicate declining abundances among the larger fishes on top of marine food webs, and thus impacts on their biodiversity (both in terms of within-species abundance, and, in the longer term, in term of number of species).

This use of mean TL as a measure of impact of fisheries on marine ecosystem was questioned by Caddy et al. (1998), and detailed responses are provided in Pauly and Palomares (2005) and Pauly and Watson (2005).  Two items originally brought forward by Caddy et al. (1998) are recalled below, as they led to improved definitions of the ‘fishing down’ concept as implemented on this website.

19.1 “Fishing down marine food web does not account for bottom up processes”

Here, Caddy et al. (1998) had in mind processes such as the eutrophication of the Mediterranean, which has indeed led to increases in the biomass and production of small pelagic fishes such as anchovies and sardines (Caddy 1993).  Analyzed naively, such increase of small pelagic fishes would lead, via a decrease of computed mean TL, to an inference of high-TL fishes becoming scarcer, even though the latter may not have declined in absolute terms.  Pauly et al. (1998) noted a related problem due to fluctuations in the abundance of Peruvian anchoveta ( Engraulis ringens), whose enormous catches strongly influence the mean TL of global catches.

Now that the fishing down effect has been established (Pauly and Palomares 2005, Pauly and Watson 2005), in spite of the biasing effect of small pelagic fishes, the time has come to propose that mean TL, if used to document fisheries impact on marine ecosystems, should generally be computed after excluding low-TL species from the analysis.  Thus, the MTI, which is based on mean TL, should be in fact based on time series that exclude low-TL organisms, and hence bottom-up effects.  This would lead to an indicator that may be labeled cutMTI, with the superscript referring to the lowest (‘cutoff’) TL value used in the computation, e.g. 3.25MTI.

The value of 3.25 is here suggested as standard cutoff TL to eliminate, besides herbivores and detritivores, the planktivores whose high biomass tend to vary widely in response to environmental factors – for example Peruvian anchoveta - and thus mask TL changes induced by fishing.

This website allows computation of the MTI for any cuttoff value; moreover, this can be done following exclusion of any number of species (or groups) from the calculation.  We strongly recommend, however, that any omitted species be explicitly mentioned when time series of MTI values are presented, and, to this effect, a table is output and can be printed which indicates whether a species was included or not.

19.2 "Fishing down may be a deliberate policy to catch more fish"

Marine ecosystems operate as pyramids wherein the primary production generated at TL one is moved up toward the higher TL, with a huge fraction of that production being wasted in the process for the maintenance, reproduction and other activities of the animals in the systems (Pauly and Christensen 1995).  Thus, notwithstanding our preference for catching and consuming large predators, deliberately fishing down should enable more of an ecosystem’s biological production to be captured by fishing.  However, to avoid waste here as well, any decline in the mean TL of the fisheries catches should, in this case, be matched by an ecologically appropriate increase in these catches, the appropriateness of that increase being determined by the transfer efficiency (TE) between TL.

Thus, a Fishing-in-Balance ( FiB) index can be defined, which:

  • Will remain constant (remains = 0) if TL changes are matched by ‘ecologically correct’ changes in catch;
  • Will increase (>0) if: either ‘bottom up effect occurs, e.g., increase in primary production in the Mediterranean (which triggered Caddy et al.’s concerns), or if a geographic expansion of the fishery occurs, and the ‘ecosystem’ that is exploited by the fishery has been in fact expanded (see below);
  • Will decrease (<0) if discarding occurs that is not considered in the ‘catches’, or if the fisheries withdraws so much biomass from the ecosystem that its functioning is impaired.

The fishing-in-balance ( FiB) index meeting these criteria is:


FiB k = log[Y k · (1/TE) TL k] – log[Y 0 (1/TE) TL0]                         …2)


where Y is the catch in year k, TL the mean trophic level in the catch, TE the mean transfer efficiency (specific to an ecosystem, often set at 0.1; see below), i refers to species (groups) in the catch, and 0 refers to any year used as a baseline to normalize the index.  Any year may serve as baseline; the year 1950 is here offered as default, but can be replaced by any other year deemed appropriate.  Similarly, the default TE value of 0.1, the mean of a number of marine ecosystems (Pauly and Christensen 1995), can be replaced by any realistic value between 0.01 and 0.30.  Here again, documentation is important when presenting results, and this is facilitated by the TE value used being included in the output file.

19.3 The spatial expansion of fisheries

Recall that the FiB index, as defined above, has the property of increasing if catches increase faster than would be predicted by TL declines, and to decrease if increasing catches fail to compensate for a decrease in TL.  This is due to the fact that, in the absence of geographic expansion or contraction, and with an ecosystem that has maintained its structural integrity, moving down the food web should result in increased catches (and conversely for increasing TL), with the FiB index remaining constant.

Examination of various case histories (e.g., Bhathal 2005; Pauly and Palomares 2005) shows that the FiB index increases where geographic expansion of the fisheries is known to have occurred.  This begs the question whether consideration of this expansion can be made explicit in a form of the FiB index normalized for the areas covered by the fishery in a given year ( A k), relative to the area covered in the baseline year ( A o).  This leads to a modified equation for an area-weighted FiB index, which could be called the Balance-in Fishing ( BiF) index:

BiF k = log[Y k·(1/TE) TL k· A o] – log[Y 0·(1/TE) TL0· A k]     …3)

Thus, we can define we can what might be called a spatial ‘Expansion factor’ ( A k/A o):

A k/ A o  = 10 ( FiBk - BiFk)                                                                            …4)

Since, given accurate catch data, correct estimates of TE, TL i and A k, the value of the BiF index should (by definition) remain zero throughout a time series, we can interpret equation (4) as implying that:

Expansion factor k = 10 FiBk                                                                    ...5)

Thus, given an estimate of the FiB index, one can compute, at least in principle, for any year k, the extent of the geographic expansion of fishing along and away from any coastline, given the assumption that this expansion involved areas with the same productivity as that of the area exploited in the baseline year (i.e., A k can support, on a per-area basis, the same catch as A o).

Spatial expansion is one of the processes which, until recently, have masked the decline of global fisheries, and thus the potential importance of being able to quantify this process, even if crudely.  This is facilitated, on this website, by the expansion factor (i.e., the antilog of the FiB index) being included in the table output.

19.4 The MTI and related measures as ecosystem indicators

Much confusion surrounds the notion of ecosystem indicators.  Some believe that ecosystem indicators are whatever one can measure that impacts ecosystems, for example sea surface temperatures.  To be of any use, indicators, however, must summarize in a single number a variety of complex processes that are otherwise hard to apprehend.  Moreover, besides description, indicators must also allow for communication, and, ideally, for intervention as well.  This is the case for the MTI and FiB index, which describe a major aspect of the complex interactions between fisheries and marine ecosystems and communicate a measure of species replacement induced by fisheries.  Specific MTI values, moreover, may eventually be used as target for management interventions, though the present state of our knowledge does not allow for the identification of critical threshold values.

However, the present usefulness of the indicator is not based on a certain number, e.g., a 3.25MTI value of say 3.72 being important; rather it is the presence of a downward trend that matters.  Sustainability, however defined, must imply some notion of permanence in at least some of the entities or processes being evaluated.  Thus, if there is, in a given ecosystem, a clear trend of the relative abundance of high-TL vis-à-vis low-TL fishes, as indicated by declining MTI values, then this indicates the absence of sustainability and the need for intervention.  A multispecies fishery can safely be assumed to be unsustainable if the mean TL of the species it exploits keeps going down.

Moreover, there are a number of countries in which TL declines are accompanied by stagnating or even declining catches, inducing sharp declines in the FiB index.  Such trends, which also describe what is happening to global fisheries, imply a collapse of the underlying ecosystems, and establishing the TL values at which such collapse occurs in different ecosystem types would be extremely useful.

Selecting cutMTI as an indicator will have a number of implications for the biodiversity, conservation and fisheries research communities.  One such implication is that the quality of the underlying fisheries catch data must be improved.  There are presently two major sources of global catch data: one is the FAO fisheries statistics (see www.fao.org).  The other is the this website, which presents FAO catch data complemented with regional and national catch statistics, all re-expressed on a spatial bass.

Ideally, FAO member countries, most of which also happen to be Parties to the CBD, will have to increase the quality of the data they submit to FAO:  these countries cannot monitor marine biodiversity if they do not monitor, in some taxonomic details, the fisheries catches extracted from their waters.  In the meantime, the Sea Around Us Project and its partners will continue to work at improving the catch statistics of various countries with inaccurate and/or over-aggregated data.

For some countries, notably those where coral reef fisheries are important, the cutoff TL proposed here, of 3.25 is probably too high, as it eliminates the very herbivores whose occurrence in fisheries catches (and thus decline in the ecosystem) induces massive ecological changes, all detrimental to coral reef biodiversity.

The interpretation of the FiB index as the logarithm of an ‘Expansion factor’ is preliminary in that the sensitivity of the new metric to violation of its various underlying assumptions is still to be tested.  Notably, it will be important to account for the potentially different area-specific productivities in A k and A o.  However, it may be appropriate to make this new measure available here and now, given the interest of various countries in implementing some form of ecosystem management.

 

19.5 Caution on the use of the MTI when the underlying catch data are not sufficiently detailed and accurate

Recall that the basic assumption of the MTI is that it reflects processes involving shifts in the relative and/or absolute abundance of species in ecosystems. For this assumption to be met, even if partly, catches, therefore, must cover the taxonomic range that is actually extracted from the ecosystems, not only a few species that are reported only because they happen to be of high export value. Some Central American countries, for example, report to FAO only their lobster catches, and leave the important catches of finfishes by their inshore fisheries completely undocumented; the same happens with many Pacific Island countries, which document only their tuna fisheries, etc.  The Sea Around Us Project works with partners in numerous countries to re-construct catch statistics covering the whole range of their fisheries, but this process is slow, and the data presented here may not be the best available for computing time series of the MTI for a given country.

Another problem is the ‘miscellaneous fishes’ which certain countries choose to report much of their catch. Clearly, ‘misc. fishes’ have no distinct trophic level, and the MTI cannot be computed for them. Hence, in countries in which ‘misc. fishes’ predominate, the MTI is based on a few species only, representing a small fraction of the catch.

To partly mitigate this problem, we used earlier a routine which disaggregates the ‘misc. fishes’, replace them by species, genera or families likely to have been included therein, and used these ‘inferred’ taxa in the MTI computation. After a critical review, we have now (March 2006) decided to exclude all inferred taxa from the computation of the MTI and derived indicators, which now use only explicitly ‘reported’ taxa. This will cause only slight difference with MTI values computed earlier, while increasing reproducibility.

19.6    References

Bhathal, B. 2005. Historical reconstruction of Indian marine fisheries catches, 1950-2000, as a basis for testing the ‘Marine Trophic Index’. Fisheries Center Research Report 13(4), 121 pp.

Caddy, J. 1993. Toward a comparative evaluation of human impacts on fisheries ecosystems  of enclosed and semi-enclosed seas. Rev. Fish. Sci. 1: 57-95.

Caddy, J., Csirke, J., Garcia, S.M. and Grainger, R.J.L. v. . How pervasive is “Fishing down marine food webs”. Science 282: 183 [full text (p. ‘1383a’) on [www.sciencemag.org/cgi/content/full/282/5393/1383].

CBD, 2004. Annex I, Decision VII/30, p. 351 In:  The 2020 Biodiversity Target: a Framework for Implementation. Decisions from the Seventh Meeting of the Conference of the Parties of the Convention on Biological Diversity, Kuala Lumpur, 9-10 and 27 February 2004. Secretariat of the CBD, Montreal.

Pauly, D. and V. Christensen. 1995. Primary production required to sustain global fisheries. Nature 374: 255-257.

Pauly, D., V. Christensen, J.Dalsgaard, R. Froese and F.C. Torres Jr. 1998. Fishing down marine food webs. Science 279: 860-863.

Pauly, D. and M.L. Palomares.2005. Fishing down marine food web: it is far more pervasive than we thought. Bull. Mar. Sci. 76(2): 197-211.

Pauly, D. and R. Watson. Background and interpretation of the ‘Marine Trophic Index’ as a measure of biodiversity. Philosophical Transaction of the Royal Society (B) 360: 415-423.

 

20.       Estuaries of the World


When most people try to visualize 'The Sea', they envisage large marine expanses of surface waters, and perhaps the underlying ecosystems. Until recently, the Sea Around Us Project was way offshore, too. Yet, the sea also includes the coast - where the land meets the sea, and where one finds some of the world's most productive marine areas such as reefs, mangroves and seagrass beds. Coastal areas are of great importance to fisheries, not to mention tourism, aquaculture, transportation and gas and oil. This is particularly important as the Sea Around Us covers low latitude areas, i.e., the Caribbean, West Africa and the tropical Indo-Pacific, where large numbers of fishers depend on coastal resources.

Dealing explicitly with coastal areas opens up a wealth of research opportunities, e.g.: quantifying the relationships between estuaries and shrimps; re-valuating the ecosystem services of various coastal habitats; linking marine protected area habitats with communities and small-scale fishers with coastal habitats; assessing river-basin impacts on coastal systems; etc.
As a contribution to such efforts, the Sea Around Us Project includes a global database of estuaries, all linked to the 16,000+ 'coastal' ½ degree latitude/longitude cells, used for all spatial features by the Sea Around Us Project (see also Alder 2003, Watson et al. 2004).

Specifically, the database, the first to be designed at a global scale, contains over 1200 estuaries (including some lagoon systems and fjords), in over 120 countries and territories. These water bodies (of which over 97 % have shape files) were selected such that the estuaries of all the world major rivers were included, as well as the small estuaries of countries without major rivers. Overall, the database accounts for over 80 % of the world's freshwater discharge, and contains information about the name, location, area (in km2) and mean freshwater input (in m3·s-1 ·day -1), calculated over a specified number of year.

The shape files for the estuaries are not on this website, but would be made available to colleagues interested in a collaborative project (write us if you are). On the other hand, these shape files were used here to identify the 'estuarine cells' among the ½ degree coastal cells in our map of global ocean. The estuarine cells (or more precisely: coastal cells with a given fraction of their area overlapping with one or several estuaries) were subsequently used to refine the distribution of fishes and invertebrates with estuarine affinities.

20.1.       References:


Alder, J. Putting the Coast in the Sea Around Us Project. The Sea Around Us Newsletter. No. 15:1-2.

Watson, R., J. Alder, S. Booth, V. Christensen, K. Kaschner, A. Kitchingman, S. Lai, M.L.D. Palomares, F. Valdez and D. Pauly. 2004. Welcome to www.seaaroundus.org: launching our 'product' on the web. The Sea Around Us Newsletter. No. 22:1-8.

 

21.      Catch and value by jurisdiction


In general, marine fisheries began as coastal fisheries; as such, they were confined to the shelf (i.e., down to 200m) around continents and islands. As demand grew and inshore stocks became depleted, the fisheries ventured further offshore. This was especially the case in those of countries which could build and maintain the larger sea-going vessels require by offshore expansion.

Eventually, distant water fleets (DWF) emerged, operating far from their home countries, either in the high seas, but more commonly on the shelves of other countries. Access to these often far-away fishing grounds was widely accepted as a traditional right. In the 1980s, these arrangements were gradually replaced by the United Nations’ Convention on the Law of the Sea (UNCLOS), which regulated access to the Exclusive Economic Zone (EEZ) of maritime countries.

These developments created, for the fisheries of each country, four broad jurisdictions in which they could operate:

  • The EEZ of the country in question (“Own EEZ”);
  • The EEZ of any overseas territory held by that country (“Territories’ EEZs”);
  • The EEZs of other countries, to which DWFs can (legally) gain access only through explicit access agreements (“Others’ EEZs”); and
  • The High Seas (i.e., outside of any EEZ), where fishing is unregulated, or regulated by Regional Fisheries Management Organization (RFMO), often with only limited powers (“High Seas”).


The catch taken by any country in these four jurisdictions was obtained by spatial disaggregation of FAO and other catch statistics, as described elsewhere.

Please note that catches taken by ‘territories’ in EEZ waters of their ‘administrative’ country are here included under ‘own EEZ’, reflecting the notion that both territories and ‘administrative’ countries’ EEZ are, with regards to UNCLOS, the EEZ of that country. For example, Hong Kong’s catches taken in EEZ waters of mainland China are included under ‘own EEZ’ in China’s listing.

Catches formerly reported by Ethiopia to FAO (prior to independence of Eritrea) have been reassigned to Eritrea.

The value of the catch (in 2000 real USD) was obtained by multiplying the catch (by species) by the deflated ex-vessel price, adjusted by the CPI.  

 

22.      Seamounts in the Sea Around Us Project's database

Seamounts are (extinct) underwater volcanoes that did not grow tall enough to break to the sea surface, and thus turn into islands. Once formed, seamounts tend to gradually sink under their own weight, and the depths of the oceans are thus littered with the remains of seamounts, which may be called ‘seamounds’.

Seamounts occur throughout the world ocean, but their number (which may surpass 100,000) is difficult to estimate, even roughly, because it depends on the resolution of the bathymetric map used, as well as the detection threshold employed, i.e. the limit used to distinguish between seamounts and seamounds.

For the purposes of the Sea Around Us Project, the locations of a subset of the seamounts of the world were identified from a bathymetric map distributed by NOAA, using two algorithms (to be presented elsewhere), which rely on the depth differences between adjacent cells of that electronic map. About 30,000 likely seamounts were located, but a different number would have been found, had we used different thresholds. Thus, the area-specific estimates of seamount abundance we present here are expressed in relative terms, as a percent of the unknown ‘total’ number of seamounts in the world ocean, under the assumption of proportionality. Known seamount locations supplied by NOAA and from Seamounts Online were matched against the corresponding seamounts we located, which led to an integrated set of seamounts, and some degree of ‘ground-truthing’.

Another preliminary study we conducted suggested that only a small subset of the thousands of seamounts that exist have tops close enough (less than 100 m) to the sea surface for their enhancing effect on primary production to be detectable from satellite images. This confirms that the high fish and invertebrate biomasses observed on seamounts (including deep ones) are maintained by the capture of drifting plankton and detritus, rather than by in-situ production.

Information on seamount biodiversity, which is very high, and characterized by a high degree of endemism, may be found in Seamount Online, maintained by Ms Karin Stocks, Scripps Institution for Oceanography, San Diego.

23. Fisheries subsidies

 

By A. Dyck and U.R. Sumaila

 

23.1 Overview

 

The Sea Around Us Project and the Fisheries Economics Research Unit (http://feru.org/) of the Fisheries Centre currently identify 144 geo-political entities (countries or ‘sub-country’ spatial entities) that provide subsidies to the marine capture fishing sector. A list of these entities can be found in Khan et al. (2006) and Sumaila et al. (2008).

 

Data are collected regarding government financial support for the years 1989 to present. Sources of data include government statistical publications, international organizations such as the Organization for Economic Cooperation and Development (OECD), articles published in academic journals, internet resources, and media items. The full list of data sources is currently available in Khan et al. (2006) and Sumaila et al. (2008).

 

Collected data are organized into thirteen subsidy categories (Table 1)

 

Table 1:  Subsidy categories

Category

Description

Beneficial (Good)

 

A1

Fisheries management & services

A2

Research & development

Harmful (Bad)

 

B1

Boat construction & modernization

B2

Development projects & support

B3

Port construction & renovation

B4

Marketing & storage support

B5

Tax exemption

B6

Foreign access agreements

B7

Fuel

Ambiguous (Ugly)

 

C1

Fisher assistance programs

C2

Vessel buyback

C3

Rural development programs

Other

 

 

Marine Protected Areas[1]

 

 

Based on the available information, each data point for a given year, country, and subsidy type is classified as type I, II, or III. Type I data are encountered when specific information is available regarding the subsidy and its magnitude. This information is divided across the program’s explicit duration or an assumed length of 5 years and converted to USD where appropriate. Type II data occur when information regarding the existence of a subsidy is available but its magnitude is not available. Where there is no information indicating that a country provides a given category of subsidy it is classified as Type III data and entered as a zero subsidies.

 

Data presented on the Sea Around Us Project website currently represent year 2000 subsidies.

 

23.2 Estimation

 

Non-Fuel Subsidies

 

A simple method is used to estimate values for Type II data:

 

                                                                                                                   ( 1 )

 

for countries i = [1,144],

subsidy categories j = [A1, A2, B1, B2, B3, B4, B5, B6, C1, C2, C3], and

years t = [1989, present)

 

where the factor β is calculated as:

 

                                                                                                        ( 2 )

 

and LV is the Landed Value of marine captures fisheries as calculates by Sumaila et al. (2007).

 

 

Fuel Subsidies

 

Fuel subsidies are estimated in a similar manner. However, instead of estimating average total subsidy, we compute the average subsidy per litre of fuel and multiply this by fuel usage data presented in Sumaila et al. (2008) to arrive at total fuel subsidy for each of the 144 spatial entities.

 

 

Subsidies for Marine Protected Areas

 

Costs for Marine Protected Areas (MPA) are computed by Cullis-Suzuki & Pauly (2008). Here, MPA costs in excess of 15% of Landed Value are not considered as subsidies since it is unlikely that anything above this amount creates value for fishers. Applying this assumption, subsidies for MPAs are included as calculated by Cullis-Suzuki & Pauly (2008) up to a maximum of 15% of Landed Value.

 

23.3 Disaggregation

 

Subsidies are collected and estimated for 144 geo-political entities (administrative countries), however, some of these countries are responsible for the administration of one or more dependant regions or overseas territories. Since we may be interested in subsidies provided by a given country to one of its dependencies, we estimate these dependencies’ subsidies using Landed Value to separate subsidy estimates among a country’s dependencies. This is done utilizing the assumption that a dependency will receive subsidies from its administrative parent that are proportional to its contribution to total Landed Value.

 

For example, where a geo-political entity (administrative country) consists of a major continental population and two overseas dependencies, the total subsidy is split among the parts using the expression:

 

                                                                                                     ( 3 )

 

where SubsidyI is estimated for the geo-political aggregate (administrative country) as described above. Where we have disaggregated an entity’s subsidy among its dependencies, the values will be reported as estimates (i.e., in parentheses) even for Type I data when values are known.

 

 

23.4 Further Information

 

For more information regarding fisheries subsidies you may contact:

 

Dr. Rashid Sumaila: r.sumaila@fisheries.ubc.ca

Mr. Andrew Dyck: a.dyck@fisheries.ubc.ca

 

23.5 References:

 

Cullis-Suzuki, S. and Pauly, D. (2008) Preliminary estimates of national and global costs of marine protected areas. p. 85-90 In: Alder, J., and Pauly, D. (eds.). A comparative assessment of biodiversity, fisheries and aquaculture in 53 countries' Exclusive Economic Zones. Fisheries Centre Research Reports 16(7). Fisheries Centre, University of British Columbia, Vancouver.

Khan, A.S., Sumaila, U.R., Watson, R., Munro, G. and Pauly, D. (2006). The nature and magnitude of global non-fuel fisheries subsidies. p. 5-37 In: Sumaila, U.R. and Pauly, D. (eds.). Catching more bait: a bottom-up re-estimation of global fisheries subsidies. Fisheries Centre Research Reports 14(6). Fisheries Centre, University of British Columbia, Vancouver.

Sumaila, U.R., Marsden, D., Watson, R. and Pauly, D. (2007). Global ex-vessel fish price database: construction, spatial and temporal applications. Journal of Bioeconomics 9:39-51.

Sumaila, U.R., Teh, L., Watson, R., Tyedmers, P. and Pauly, D. (2008). Fuel prices, subsidies, overcapacity, and resource sustainability. ICES Journal of Marine Science 65:832-840.

 

 

 



[1] Data concerning Marine Protected Areas (MPA) were collected from Cullis-Suzuki & Pauly (2008). This category was not included in the original work by Khan et al. (2006) and is reported separately for this reason.