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We thank J. Watson and M. Murao for careful reading of the manuscript. This work was
partially supported by the Research for the Future program run by the Japan Society for
Promotion of Science (T.K.), the Special Coordination Funds (``Molecular Sensors for
Aero-Thermodynamic Research''; H.O.) and Scienti®c Research (H.O.) of the Ministry of
Education, Culture, Sports, Science and Technology.
Correspondence and requests for materials should be addressed to T.K.
letters to nature
29 NOVEMBER 2001
Systematic distortions in world
®sheries catch trends
Reg Watson* & Daniel Pauly*
* Fisheries Centre, 2204 Main Mall, University of British Columbia, Vancouver,
British Columbia V6T 1Z4, Canada
Over 75% of the world marine ®sheries catch (over 80 million
tonnes per year) is sold on international markets, in contrast to
other food commodities (such as rice)
. At present, only one
institution, the Food and Agriculture Organization of the United
Nations (FAO) maintains global ®sheries statistics. As an inter-
governmental organization, however, FAO must generally rely on
the statistics provided by member countries, even if it is doubtful
that these correspond to reality. Here we show that misreporting
by countries with large ®sheries, combined with the large and
widely ¯uctuating catch of species such as the Peruvian anchoveta,
can cause globally spurious trends. Such trends in¯uence unwise
investment decisions by ®rms in the ®shing sector and by
banks, and prevent the effective management of international
World ®sheries catches have greatly increased since 1950, when
the FAO of the United Nations began reporting global ®gures
reported catch increases were greatest in the 1960s, when the
traditional ®shing grounds of the North Atlantic and North Paci®c
became fully exploited, and new ®sheries opened at lower latitudes
and in the Southern Hemisphere. Global catches increased more
slowly after the 1972 collapse of the Peruvian anchoveta ®shery
®rst ®shery collapse that had repercussions on global supply and
prices of ®shmeal (Fig. 1a). Even taking into account the variability
of the anchoveta, global catches were therefore widely expected to
plateau in the 1990s at values of around 80 million tonnes, especially
as this ®gure, combined with estimated discards of 16±40 million
, matched the global potential estimates published since the
1960s (ref. 6). Yet the global catches reported by the FAO generally
increased through the 1990s, driven largely by catch reports from
These reports appear suspicious for the following three reasons:
(1) The major ®sh populations along the Chinese coast for which
assessments were available had been classi®ed as overexploited
decades ago, and ®shing effort has since continued to climb
Estimates of catch per unit of effort based on of®cial catch and effort
statistics were constant in the Yellow, East China and South China
seas from 1980 to 1995 (ref. 9), that is, during a period of
continually increasing ®shing effort and reported catches, and in
contrast to declining abundance estimates based on survey data
Re-expressing the of®cially reported catches from Chinese waters on
1970 1975 1980 1985 1990 1995
Global catch (× 10
Corrected, no anchoveta
1970 1975 1980 1985 1990 1995
Chinese catch (× 10
Figure 1 Time series of global and Chinese marine ®sheries catches (1950 to present).
a, Global reported catch, with and without the highly variable Peruvian anchoveta.
Uncorrected ®gures are from FAO (ref. 3); corrected values were obtained by replacing
FAO ®gures by estimates from b. The response to the 1982±83 El Nin
Oscillation (ENSO) is not visible as anchoveta biomass levels, and hence catches were still
very low from the effect of the previous ENSO in 1972 (ref. 4). b, Reported Chinese
catches (from China's exclusive economic zone (EEZ) and distant water ®sheries)
increased exponentially from the mid-1980s to 1998, when the `zero-growth policy' was
introduced. The corrected values for the Chinese EEZ were estimated from the general
linear model described in the Methods section.
© 2001 Macmillan Magazines Ltd
letters to nature
29 NOVEMBER 2001
a per-area basis leads to catches far higher than would be expected
by comparison with similar areas (in terms of latitude, depth,
primary production) in other parts of the world.
We investigated the third reason in some detail by generating
world ®sheries catch maps on the basis of FAO ®sheries catch
statistics for every year since 1950 (see Fig. 2a for a 1998 example).
A statistical model was used to describe relationships between
oceanographic and other factors, and the mapped catch. Most
high-catch areas of the world were correctly predicted by the
model. These areas typically had very high primary productivity
rates driven by coastal upwellings, like those off Peru, supporting a
large reduction ®shery for the planktivorous anchoveta Engraulis
. The exception was the waters along the Chinese coast.
Here, the high catches could not be explained by the model using
oceanographic or other factors. Yet the catch statistics provided to
FAO by China have continued to increase from the mid-1980s
until 1998 when, under domestic and international criticism, the
government proclaimed a `zero-growth policy' explicitly stating
that reported catches would remain frozen at their 1998 value
Mapping the difference between expected (that is, modelled)
catches and those mapped from reported statistics showed large
areas along the Chinese coast that had differences greater than
5 tonnes km
. Overall, the statistical model for 1999 pre-
dicted a catch of 5.5 million tonnes, against an of®cial report of
10.1 million tonnes (see Fig. 1b for earlier years). Although
China was not the only FAO member country reporting relatively
high catches, their large absolute value strongly affects the global
For a number of obvious reasons, ®shers usually tend to under-
report their catches, and consequently, most countries can be
presumed to under-report their catches to FAO. Thus we wondered
why China should differ from most other countries in this way. We
believe that explanation lies in China's socialist economy, in which
the state entities that monitor the economy are also given the task of
increasing its output
. Until recently, Chinese of®cials, at all levels,
have tended to be promoted on the basis of production increases
from their areas or production units
. This practice, which origi-
nated with the founding of the People's Republic of China in 1949,
became more widespread since the onset of agricultural reforms
Figure 2 Maps used to correct Chinese marine ®sheries catch in Fig. 1b. a, Map of
global catches reported by FAO for 1998, generated by the rule-based algorithm
described in the Methods section. We note the anomalously high values along the
Chinese coast, comparable in intensity (not area covered) to the extremely productive
Peruvian upwelling system. b, Map of differences in southeast and northeast Asia
between the catches reported in a and those predicted by the model described in the
© 2001 Macmillan Magazines Ltd
letters to nature
29 NOVEMBER 2001
that freed the agricultural sector from state directives in the late
1970s (refs 10, 11).
The Chinese central government appears to be well aware of this
problem, and its 1998 `zero-growth policy' was partly intended to
prevent over-reporting. Thus, the of®cial ®sheries catches for 1999±
2000 are precisely the same as in 1998 (Fig. 1b), and will be for the
next few years. Such measures, although well motivated, do not
inspire con®dence in of®cial statistics, past or present.
The substitution of the more realistic estimated series of Chinese
catches into the FAO ®sheries statistics led to global catch estimates
which, although ¯uctuating, have tended to decline by 0.36 million
since 1988 (rather than increase by 0.33 million
, as suggested by the uncorrected data). The global
downward trend becomes clearer when the catches of a single
species, the Peruvian anchoveta, which is known to be affected by
o/Southern Oscillation events, is subtracted (see Fig. 1a). In
this case, a signi®cant (P , 0:01), and so far undocumented down-
ward trend of 0.66 million tonnes year
becomes apparent for all
other species and ®sheries. This is consistent with other accounts of
worldwide declines of ®sheries
Ironically, it is likely that, at the lowest levels (individual ®shers),
catches are under-reported in China as elsewhere in the world. The
production targets caused these reports to be exaggerated. At some
times these two distortions may perhaps have cancelled each other
out, and an accurate report of catches may have been submitted to
FAO. Since the early 1990s, however, the exaggerations have appar-
ently far exceeded any initial under-reporting.
The greatest impact of in¯ated global catch statistics is the
complacency that it engenders. There seems little need for public
concern, or intervention by international agencies, if the world's
®sheries are keeping pace with people's needs. If, however, as the
adjusted ®gures demonstrate, the catches of world ®sheries are in
general decline, then there is a clear need to act. The oceans should
continue to provide for a substantial portion of the world's protein
needs. The present trends of over®shing, wide-scale disruption of
coastal habitats and the rapid expansion of non-sustainable
, however, threaten the world's food
Data processing involved a disaggregation of global ®sheries catch statistics ®rstly into
detailed taxonomic groups, and then into ®ne-scale spatial cells (a half-degree of latitude
by a half-degree of longitude), using a variety of databases and systematic rules
spatially disaggregated catches provided the basis for a general linear model of ®sheries
catches (see below). The model predicted the likely catches in the spatial cells in the
Chinese exclusive economic zone (EEZ), thus providing an estimate of Chinese catches
(including Hong Kong and Macau, but excluding Taiwan).
Fisheries catch statistics were provided by the FAO (FishStat
and `Atlas of Tuna and
Bill®sh Catches', http://www.fao.org/®/atlas/tunabill/english/home/htm). The spatial cells
were described by depth (US National Geophysical Data Center), primary productivity
(Joint Research Centre of the European Commission Space Applications InstituteÐ
Marine Environment Unit,http://www.gmes.jrc.it/download/kyoto_prot/glob.marine.pdf),
, the presence of ice (US National Snow and Ice Data Center,
http://www.nsidc.org), surface temperature (NOAA's Marine Atlas, http://www.nodc.noaa.
gov/OC5/data_woa.html), and an upwelling index calculated for each cell by multiplying
negative deviations in surface temperature (from the average for that latitude and ocean) by
the primary productivity in that cell. Fishing access rights were determined using maps of
the exclusive economic zones (EEZ) of coastal states
and a database of ®shing access
The ®sheries statistics of several nations commonly include a large fraction of catches in
`miscellaneous' categories. Chinese catches so reported were disaggregated on the basis of
the breakdown provided by its two nearest maritime neighbours with detailed marine
®sheries statistics (Taiwan and South Korea)
. Assigning catches to lower taxa allowed the
use of biological information in the spatial disaggregation process.
A database of the global distribution of commercial ®sheries species was developed using
information from a variety of sources including the FAO, FishBase
and experts on various
resource species or groups. Some distributions were speci®c; others provided depth or
latitudinal limits, or simple presence/absence data. The spatial disaggregation process
determined the intersection set of spatial cells within the broad statistical area for which
the statistics were provided to FAO, the global distribution of the reported species, and the
cells to which the reporting nation had access through ®shing agreements
. The reported
catch tonnage was then proportioned within this set of cells.
A general linear model was developed in the software package S-Plus
. The model relates
log ®sheries catch (in tonnes km
) for each cell (the dependent variable) to depth,
primary productivity, ice cover, surface temperature, latitude, distance from shore,
upwelling index (the continuous predictor variables), 33 oceanic biogeochemical
provinces and one global coastal `biome'
including most of the area covered by the
world's EEZs, including China's (the categorical predictor variables). Fishing effort was
not used in the prediction and catches were assumed to be generally close to their
maximum biologically sustainable limits. The additive and variance stabilizing transfor-
mation (AVAS) routine of S-Plus
was used to identify transformations ensuring linearity
between the dependent and explanatory variables, and the model was then used to predict
the catch from each spatial cell. Those from Chinese waters were combined, then
compared with the catches obtained from the rule-based spatial disaggregation described
The estimates of recent trends of global catch were estimated by linear regression of catch
versus year, for the period from 1988 (highest catches, anchoveta excluded) to 1999 (last
year with FAO data), for uncorrected global marine catches, global marine catches
adjusted for Chinese over-reporting, and adjusted catches minus the catch of Peruvian
Received 30 July; accepted 28 September 2001.
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We thank V. Christensen for the upwelling index, and A. Gelchu for the species
distribution shape ®les used here. We also thank our colleagues in the `Sea Around Us'
Project. This work was supported by the Pew Charitable Trusts through the `Sea Around
Us' Project, Fisheries Centre, University of British Columbia. D.P. also acknowledges
support from the National Science and Engineering Council of Canada.
Correspondence and requests for materials should be addressed to R.W.
© 2001 Macmillan Magazines Ltd
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