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Moreover, the transition from one state e. These were dramatic findings, yet they generated little press and even less action. For the first time, the authors used these trends to project a date by which all stocks would "collapse": The expert press release that accompanied the article see Baron focused on this newsy aspect of what was a broad study, triggering an enormous amount of press coverage on all continents.

The headlines were uniformly alarmist: "Fisheries collapse by " The Economist , "Seafood may be gone by " National Geographic , and "The end of fish, in one chart" The Washington Post , among many more. A strong pushback emerged, including wide and understandable criticism of the precise date, , which mingled Mayan and Orwellian undertones.

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Stock assessment experts mocked the projection, which was mistaken for a prediction, with many arguing that scientists shouldn't extrapolate beyond the data. Yet good science always implies some inference beyond one's data; otherwise, it would consist only of descriptions. Moreover, most critiques overlooked the fact that "collapsed" stocks can continue to be fished.

Indeed, this is what already occurs in vast areas of the ocean. This is — now. Still, the criticism was so strong that several co-authors of the study opted not to defend it publicly.

Consequently, fisheries scientists such as me, who are concerned with the state of global fisheries, had to either duck or defend the spirit of the projection, even if we did not agree with all its particularities. To its credit, the projection was based on catch time series from virtually the entire world. The overwhelming majority showed that peak catches occurred several decades ago, with current catches increasingly derived from "overexploited" and "collapsed" stocks figures 1.

Although there is no way to predict where anything will be in or even 10 years from now, it would certainly be better if we could reverse current trends. So far we have not done so, even though some stocks are rebuilding figures 1. Before this defense could be mounted, the detractors began focusing on another criticism of the projection, claiming that catch data do not contain any information about stock status.

In interviews, keynote lectures, and other outlets, they argued that full-fledged stock assessments are essential to understanding fisheries; without them, we are essentially left in the dark. This is a case of allowing the perfect to become the enemy of the good. Even without perfect data, we can infer when fisheries are in serious trouble and make efforts to conserve them. One can and should infer, at least tentatively, the status of fisheries from the catch data — if this is all we have see figure 1.

It is a mistake to assume that we must remain in Muggle-like ignorance unless we have access to the magic of stock assessments. Accepting this doctrine would put us at the mercy of stock assessment models that can be fatally flawed. For example, the models used to study the Canadian northern cod fishery in the s Walters and Maguire were considered the best in the world.

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In fact, experts thought the models were so good that it was not necessary to consider the catch data from the coastal trap fisheries, which could not, like the trawlers, follow the cod to where they retreated as their numbers declined. Thus, the stock assessment experts were as surprised as the general public when the fishery had to be closed. The trawlers had decimated the stock under their noses, which they could have seen if they had analyzed the coastal trap data. Note that it is not even faulty stock assessments that are at issue here; it is the notion that one type of approach is so good that it makes all other approaches superfluous.

More importantly, this doctrine would discourage efforts to improve the quality of fisheries statistics worldwide, which is bemoaned by FAO in successive issues of SOFIA. It would also thwart attempts to manage, to the extent possible, the fisheries of developing countries. If leading fisheries scientists claim that catch data are useless, why would resource-starved governments invest in reforming and improving their statistical systems? This flawed thinking would affect not only developing countries but also the community of stock assessment experts themselves.

Without the collection of catch data, experts could end up either with beautiful stock assessment models applied to lousy data, as in the northern cod example above, or needing more of the costly fishery-independent data that can be used to correct for misreported commercial catch data Beare et al. We gain nothing from the notion that only a select group has the key to understanding fisheries, especially if that key cannot open any doors outside a small number of developed countries.

Such claims undermine the credibility of the many fisheries scientists throughout the world who attempt to extract actionable insights from sparse data and to advise their governments on how to manage their fisheries even if they cannot afford formal stock assessments. Fortunately, there is a solution: We all agree that many stocks need to be rebuilt and that doing so would lead to sustainable increases of catches and economic benefits Sumaila et al. In fact, the more depleted the stocks currently are, the more is to be gained by rebuilding them.

One notable exception is China, which overreports its catches because officials are rewarded for high yields. This new perspective suggests that fisheries play a far more important role in the rural economy of developing countries than previously assumed and that rebuilding depleted fish populations on a grand scale would have greater benefits than so far imagined other implications are presented in chapter Consequently, more attention should be given to the reliable collection of catch data throughout the world.

In particular, we need to devise cost-effective systems to acquire accurate fisheries catch data, along with ancillary data on fishing effort, and its economic equivalent, catch value and fishing cost. These data were then used in the vertical distribution equation to adjust the total particle count of plastic for each station. To estimate the increased mass due to vertical distribution, we attributed the same percentage increase in particle count to particle weight. We use conservative estimates of fragmentation rates to show that the model results of particle count in each size class differ substantially from our expected particle counts.

To estimate fragmentation rates, we assumed that all particles, including the largest ones had a thickness of 0. This assumption is conservative, because it is well known that many larger items have a wall thickness substantially larger than this. We assumed smaller particle sizes for the largest size classes, while for the smallest size class 0. Thus, our fragmentation estimates are highly conservative because for the macroplastics that generate plastic fragments we consider lower initial mass than commonly found at sea, while for the microplastics in our fragmentation exercise we consider larger particles than typically found at sea.

Fragmentation of one macroplastic item mm diameter into typical mesoplastic fragments 50 mm diameter would result in 16 particles, fragmentation of one 50 mm diameter mesoplastic item into typical large microplastics 2 mm diameter results in particles, and fragmentation of one large microplastic item 2 mm diameter into small microplastics with a diameter of 0. We then used these ratios in a stepwise approach to estimate particle counts in each size class based on the model results of particle count in the next-higher size category.

For example, in the North Pacific the modeled data show 0. These fragmentation ratios between size categories are utilized to estimate the expected particle count for large and small microplastic particles.

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This stepwise approach is simplistic, because it assumes that the system is close to equilibrium. We recognize that rates of new plastic entering the ocean are unknown, as well as outputs of plastic due to beaching, sinking and mechanisms of degradation, and use these fragmentation estimates as first crude intent to reveal the dynamics of floating plastics in the oceans. During these sampling procedures, no permits were required as we only collected plankton samples, and those samples were collected in international waters. Based on our model results, we estimate that at least 5. There was a good correspondence between the model prediction and measured data for particle count and weight Figs.

S1 and S2 , Table S4. Our estimates suggest that the two Northern Hemisphere ocean regions contain In the Southern Hemisphere the Indian Ocean appears to have a greater particle count and weight than the South Atlantic and South Pacific oceans combined.

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  7. The vast majority of these plastics were small fragments. Although net tow durations varied, the majority of all tows This pattern is consistent with our model prediction that ocean margins are areas of plastic migration, while subtropical gyres are areas of accumulation. The visual surveys revealed that foamed polystyrene items were the most frequently observed macroplastics out of items , while derelict fishing buoys accounted for most These observations are conservative, recognizing that items with marginal buoyancy, dark color and small size are more difficult to see, especially during challenging environmental conditions depending on sea state, weather and sun angle.

    The data from the four size classes small microplastics, large microplastics, meso- and macroplastics were run separately through the model, producing four maps each for count and weight density Figs. Combining the two microplastic size classes, they account for Most small microplastics were fragments resulting from the breakdown of larger plastic items; therefore we expected the smallest microplastics to be more abundant than larger microplastics.

    We observed the opposite in all regions globally except in the S. Pacific where large and small microplastic counts were nearly equal. The majority of global weight is from the largest size class. The expected numbers of microplastics large and small were an order of magnitude larger than the data-calibrated model counts of microplastics in the world's oceans Fig. The expected numbers were derived from conservative estimates of fragmentation from macroplastic to smaller size classes.

    In contrast to the apparent dearth of microplastics mesoplastics were observed more frequently than expected by the fragmentation ration. This discrepancy could be due to lags in the fragmentation of buoyant mesoplastic and macroplastic, or because mesoplastic items, such as water bottles and single-use packaging, enter the ocean in disproportionate numbers when compared to macroplastic.

    However, the magnitude of the discrepancy between all size classes suggests that there is differential loss of small microplastics from surface waters.

    This item appears in the following Collection(s)

    We found a similar pattern of material loss from the sea surface when comparing the weight of the four size classes. The data showed the weight of plastic pollution globally was estimated to comprise Our data suggest that a minimum of , tons of larger plastic items are afloat in the world's oceans compared to 35, tons of microplastics. This is the first study that compares all sizes of floating plastic in the world's oceans from the largest items to small microplastics.

    Plastics of all sizes were found in all ocean regions, converging in accumulation zones in the subtropical gyres, including southern hemisphere gyres where coastal population density is much lower than in the northern hemisphere. While this shows that plastic pollution has spread throughout all the world's oceans, the comparison of size classes and weight relationships suggests that during fragmentation plastics are lost from the sea surface. Simple comparisons across size classes allowed us to suggest possible pathways for oceanic plastics, and below we discuss these pathways and mechanisms involved.

    Plastic pollution is moved throughout the world's oceans by the prevailing winds and surface currents. This had been shown for the northern hemisphere where long-term surface transport years leads to the accumulation of plastic litter in the center of the ocean basins [6] , [7]. Our results confirm similar patterns for all southern hemisphere oceans. Surprisingly, the total amounts of plastics determined for the southern hemisphere oceans are within the same range as for the northern hemisphere oceans Table 1 , which is unexpected given that inputs are substantially higher in the northern than in the southern hemisphere [28].

    This could mean that plastic pollution is moved more easily between oceanic gyres and between hemispheres than previously assumed [28] , leading to redistribution of plastic items through transport via oceanic currents. Furthermore, there might also be important sources of plastic pollution in the southern hemisphere that had not been accounted for, such as currents from the Bay of Bengal that cross the equator south of Indonesia.

    Alternatively, a large proportion of plastics might be lost from the sea surface, more so than considered by previous models, and these losses might be disproportionally higher in the northern hemisphere, leading to similar magnitudes in remaining plastic litter at the sea surface. Indeed, stranding of floating plastics on local seashores seems to be more important in the northern than in the southern hemisphere [28] , [33].

    Other losses sinking, degradation may also be responsible for the fact that northern hemisphere oceans contain relative plastic loads that are lower than expected based on global input scenarios. Herein we applied a correction for vertical distribution to all samples related to wind-driven turbulence [27]. Other hydrodynamic processes including downwelling at convergence zones may also influence the vertical distribution of slightly buoyant particles such as microplastics. We suggest that future sampling campaigns use the spatial distribution of sea surface features to better design their sampling efforts and come up with improved global plastic mass inventories.

    Other estimates of global and regional weight of microplastic pollution are within the same order of magnitude as our estimates. A study using an year data set in the North Pacific [9] estimates a weight of 21, metric tons of floating microplastic, and ours for the same region is 12, metric tons.

    A recent study on the global distribution of microplastic [25] suggests that the total floating microplastic load ranges between 7, and 35, metric tons, and ours is 35, metric tons. This study [25] also found a fold discrepancy between expected microplastic weight and abundance and their observations, indicating a tremendous loss of microplastics. The similarities between our results and those of this study [25] gives us further confidence in our estimates and support our hypothesis that the ultimate fate of buoyant microplastics is not at the ocean surface. The observations that there is much less microplastic at the sea surface than might be expected suggests that removal processes are at play.

    These include UV degradation, biodegradation, ingestion by organisms, decreased buoyancy due to fouling organisms, entrainment in settling detritus, and beaching [4]. Fragmentation rates of already brittle microplastics may be very high, rapidly breaking small microplastics further down into ever smaller particles, making them unavailable for our nets 0.

    Many recent studies also demonstrate that many more organisms ingest small plastic particles than previously thought, either directly or indirectly, i. Numerous species ingest microplastics, and thereby make it available to higher-level predators or may otherwise contribute to the differential removal of small particles from the sea surface, e. Furthermore, there is increasing evidence that some microbes can biodegrade microplastic particles [38] — [40]. Thus, bacterial degradation and ingestion of smaller plastic particles by organisms may facilitate their export from the sea surface.

    In this manner, incorporation of smaller plastics into marine food chains could not only generate impacts on the health of the involved organisms [17] — [20] , but also contribute to the removal of small microplastics from the sea surface [37].

    Ocean Plastics Pollution

    Plastics Europe, a trade organization representing plastic producers and manufactures, reported that million tons of plastic were produced worldwide in [41]. Our estimate of the global weight of plastic pollution on the sea surface, from all size classes combined, is only 0.

    However, we stress that our estimates are highly conservative, and may be considered minimum estimates. Our estimates of macroplastic are based on a limited inventory of ocean observations, and would be vastly improved with standardization of methods and more observations. They also do not account for the potentially massive amount of plastic present on shorelines, on the seabed, suspended in the water column, and within organisms.

    In fact, the larger weight of macroplastic relative to meso- and microplastic, and the global estimate of floating plastic weight relative to the weight of plastic produced annually, indicates that the sea surface is likely not the ultimate sink for plastic pollution. Though significant proportions of meso- and macroplastics may be stranding on coastlines where some of it could be recovered , removal of microplastics, colonized by biota or mixed with organic debris, becomes economically and ecologically prohibitive, if not completely impractical to recover.

    This leaves sequestration in sediment the likely resting place for plastic pollution after a myriad of biological impacts along the way, thus reinforcing the need for pre-consumer and post-consumer waste stream solutions to reverse this growing environmental problem. By generating extensive new data, especially from the Southern Hemisphere, and modeling the plastic load in the world's oceans in separate size classes, we show that there is tremendous loss of microplastics from the sea surface.

    Comparison of mean and modeled densities.

    Plastics in Our Oceans

    Regression analysis of measured and modeled data. Linear regression of modeled vs. The data-calibrated model results of particle count for the global oceans see Table 1 in each size class differ substantially from conservative estimates of particle counts based on assumed fragmentation of the number if particles in the next-larger size category. We used simple estimates of particle sizes with 0. Field locations where weight density was measured. Expeditions contributing field data. Locations marked with an asterisk indicate unpublished data and circles show the type of data collected at each expedition.

    Percent distribution of items from visual survey transects. Mean weights for macroplastic items Extended Data Table 4 were used to determine percent weight distribution. Using beached macroplastic items to determine mean weight. Mean weight of macroplastic items collected from coastal surveys in Chile eastern S. Pacific , western South Africa eastern S. Atlantic , east coast United States western N.

    Atlantic , and the Hawaiian Islands, was applied to observed macroplastic items drifting in the ocean and then put through the model to calculate global weight densities. Comparison of measured to modeled means. There is generally a good correspondence between the measured and modeled means for each region. Error margins from the linear regression. Financial support from the Will J. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

    National Center for Biotechnology Information , U. PLoS One. Published online Dec Lebreton , 2 Henry S. Carson , 3 , 4 Martin Thiel , 5 , 6 , 7 Charles J. Moore , 8 Jose C. Borerro , 9 Francois Galgani , 10 Peter G. Ryan , 11 and Julia Reisser Laurent C. Henry S. Charles J. Jose C. Peter G. Hans G. Dam, Editor. Author information Article notes Copyright and License information Disclaimer. Lebreton is affiliated with Dumpark Creative Industries Ltd.

    Received May 6; Accepted Oct 2. Copyright notice. This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. This article has been cited by other articles in PMC. Figure S2: Regression analysis of measured and modeled data. Figure S4: Field locations where weight density was measured. Table S1: Expeditions contributing field data. Table S2: Percent distribution of items from visual survey transects.

    Table S3: Using beached macroplastic items to determine mean weight.