The fact the wealthy dominate any aspect of life should be a surprise to anyone in today’s world but is always good to see some numbers about it. Among the plethora of papers coming from the sudden availability and IAS data here is one that tackles how some nations (mostly without a lot of fish) dominate the catches worldwide by fishing in poorer countries with fish.
Is not the people that work in the ports and boats the ones making the money unfortunatelly
The paper just published by a group of researchers (mostly from UC Santa Barbara) is one of those that verify facts the one suspects, but at least in my case, I was staggered by the impact of some nations... and you should read the paper here.
While we know that in the ABNJ (aka high seas) fishing effort is dominated (97%) by vessels flagged to higher-income nations, with less than 3% of effort attributed to vessels flagged to lower-income nations. I was really confronted by the fact that 84% of the industrial fishing effort in lower-income EEZs was conducted by foreign countries, with the majority of this industrial fishing effort (78%) from vessels flagged to high- and upper middle–income nations.
Most AIS-detectable industrial fishing effort that was observed within all EEZs was detected in the Pacific Ocean and the Atlantic Ocean (60 and 35% of total fishing effort observed in all EEZs respectively). Patterns were consistent across the 2 years studied with a nearly identical pattern as recorded in 2015.
The discussion (part of which I quote below) is eye opening;
The new view afforded from this open AIS-based analysis of global fishing activity reveals stark levels of unevenness with respect to wealth class for industrial fishing effort. Globally, 97% of all industrial fishing effort detectable using AIS (on the high seas and within EEZs) comes from vessels flagged to higher-income nations—or 23 million total hours of industrial fishing effort in 2016. This same pattern of dominance by higher-income nations repeats itself on the high seas, within the EEZs of higher-income nations, and within the EEZs of lower-income nations.
On the high seas, 97% of all such fishing effort detectable by AIS is conducted by vessels flagged to higher-income nations. Dominance of this high seas industrial fishing effort at the level of flag nation was highly uneven.
The vast majority (86%) of this effort can be attributed to only five higher-income countries/entities, in rank order (China, Taiwan, Japan, South Korea, and Spain. When China and Taiwan are analyzed together, they account for approximately 52% of the industrial fishing effort we detected on the high seas, which, by reference, is an amount approximately 12 and 27 times greater than the high seas fishing effort detected for the United States and Russia (two other large nations), respectively.
The only two lower-income nations that ranked among the top 20 nations with the highest amount of AIS detectable industrial fishing effort on the high seas were Vanuatu and Ukraine (both lower middle–income nations). Vanuatu is a nation with an open vessel registry (colloquially known as a “flag of convenience”) that has been reported to include many vessels owned and controlled by higher-income foreign nations (mostly TW and CN in my experience) (26). The majority of the Ukraine fleet is owned by the Ukrainian government.
Density distribution of global industrial fishing effort, derived using AIS data. (A) Vessels flagged to higher-income countries and (B) vessels flagged to lower-income countries.
Very similar dominance patterns were reported in our analysis of the world’s EEZs, where the majority of AIS-detectable industrial fishing effort within national waters was executed by vessels flagged to higher-income nations. We emphasize, however, that a strongly divergent pattern emerges from our analyses of fishing effort density within the EEZs of higher- and lower-income nations. The vast majority of AIS-detected fishing effort within the EEZs of higher-income countries came from their own fishing fleets. Nearly the inverse was true for lower-income nations, where foreign fishing vessels (mostly flagged to high- and upper middle–income countries) dominated the industrial fishing effort in their EEZs.
Most of the industrial fishing effort in lower-income EEZs was conducted by foreign countries, with the majority of this effort from vessels flagged to high- and upper middle–income nations.
Globally, the three countries showing the greatest fishing activity in other nations’ EEZs were (from high to low) China, Taiwan, and South Korea. China and Taiwan together accounted for 44% of this global foreign fishing. We detected fishing effort from China alone in the marine waters of approximately 40% of all non-landlocked nations (n = 60 distinct EEZs). China, Taiwan, and South Korea (from high to low) also carried out the highest amounts of foreign fishing effort recorded globally in lower-income EEZs, or approximately 63% of all such effort detected
There are certainly exceptions to the bulk pattern of higher-income dominance of fishing effort in lower-income EEZs. In some lower-income nations, such as India, there was virtually no detectable higher-income fishing within their EEZs. These patterns may be explained in part by national legislation prohibiting or limiting foreign fishing within such EEZs, but could also result from joint fishing regimes occurring within these EEZs.
The patterns of industrial fishing effort within EEZs derived using these AIS-based techniques reinforce and extend conclusions drawn elsewhere using other methodologies and data sources. For example, analyses of fisheries production and trade data reveal a persistent trend whereby wealthy nations fish in the waters of less wealthy nations, but not vice versa (28, 29).
The relatively recent emergence of the capacity to track industrial fishing effort using AIS prevents examination of the history of this buildup. Elsewhere, however, it has been suggested that the ascendancy in dominance of more wealthy nations fishing within the waters of less wealthy nations (for example, Europe in Northwest Africa) has occurred within the last several decades (28).
Our analysis also does not differentiate between gear types used by industrial fishing vessels. Self-reporting of gear type in AIS data suggests that our pooled analysis of global industrial fishing is dominated numerically (that is, proportion of unique vessels) by trawlers, purse seiners, and longline vessels. Certainly different gear types fish in different ways, which may complicate our estimations of fishing effort made using fishing hours; for example, the extreme time efficiency of purse seiners setting rapidly upon fish aggregating devices is not comparable to more time-intensive fishing methods, such as longline fishing. To investigate the sensitivity of our conclusions to this choice of fishing hours as our currency of measure for fishing effort, we reanalyzed our data measuring fishing effort in the time currency of fishing days. Effort analyses made using fishing days did not change the direction or pattern of our major conclusions for the high seas or within national waters.
I really like their acknoledgement on the limitations of using AIS
We highlight here three major shortcomings of using AIS. First, international and national regulations for the use of AIS and enforcement of these regulations are insufficient in many parts of the high seas and in many EEZs. Many countries adhere to IMO requirements on AIS usage; however, the specifics by which these regulations are codified into national law vary widely, with examples of strict and lax regulation found among both higher- and lower-income nations (9). Second, industrial fishing vessels in lower-income nations may be less likely to carry and use AIS for reasons unrelated to AIS policy. We note that we detected fewer vessels using AIS than are represented on FAO vessel registries and that there is less AIS visibility for vessels registered to lower-income nations. There are a variety of explanations for these discrepancies. For example, some vessels listed by the FAO may have been inactive during our study or regional officials may have overreported fleet sizes to emphasize local growth. By using VMS data derived from Indonesia, we were able to conservatively estimate upper bound corrections for AIS underreporting in lower-income nations. This correction, however, only increases the global contribution of lower-income fishing on the high seas by approximately 6% and within the EEZs of lower-income nations by 29%. A third potential weakness of AIS stems from reliance on a vessel’s reported maritime identification digits (MID) to identify flag state. These MID are typically self-reported and may be entered incorrectly. This also relates to the larger, well-known problem of flag states not always corresponding to the state of vessel control or owner residence [rates estimated at 22.4% based on one analysis (26)], as many vessels operate with flags of convenience to take advantage of lower operational costs, less regulation, and reduced tax liability (26, 34).
Consequently, many vessels that we class in this analysis as flagged to lower middle– or low-income nations may actually have economic ties that are more closely aligned with higher-income nations. A related important nuance not treated in our analysis is that we do not track the actual firms or companies that own or fund the vessels observed through AIS, despite the influence that these firms have over vessel behaviour.
Collectively, some of these uncertainties and potential biases inherent to AIS data may act to overestimate fishing effort from higher-income nations (for example, reduced visibility of smaller vessels from lower-income nations), and some may act to underestimate higher-income nation fishing effort (for example, a large number of vessels originating from higher-income nations flagged to lower-income nations known as flags of convenience).
Our general conclusion that vessels flagged to higher-income nations dominate industrial fishing on the high seas and within EEZs largely persisted when we aggregated effort by day instead of fishing hour , retested our conclusions using a smaller size threshold (that is, >12 m) for defining industrial fishing vessels (fig. S6), and added a VMS-informed correction for undetected fishing effort in lower-income nations. Nevertheless, responsible interpretation of the new patterns we report using AIS requires direct consideration of all the aforementioned potential weaknesses and uncertainties.
And a polite foray into the reality of Fisheries Colonialism!
On one side, many researchers and managers have expressed unease concerning the potential vulnerabilities that may be created by concentrating dominance over fisheries in the hands of a few wealthy nations. These groups sometimes refer to this skew in control over marine resources as “ocean grabbing” or “marine colonialism” and connect the potential risks involved to those often associated with the practices of land or resource grabbing that occurs when wealthy foreign nations or foreign companies take control of terrestrial or agricultural resources or infrastructure in less-wealthy nations (36)
Significant concern has also been raised about how corruption in some lower-income nations may facilitate misuse of fisheries access payments that prevent such cash from constructively aiding health, development, and growth goals of these nations (17, 19–21). Policy options for meeting rising demand for fish in the Pacific region include actions such as diverting some of the tuna currently exported (and captured mostly by foreign fishing vessels) onto domestic markets of lower-income states (39). Another possible opportunity for intervention for stakeholders concerned about foreign dominance of industrial fishing in their national waters derives from the open nature of the data we report and the transparency it fosters.
Access to these publicly accessible data feeds creates opportunities for all citizens in lower-income nations to put meaningful questions to their local leaders regarding sanctioned and unsanctioned foreign industrial fishing in their home waters.
Others have argued that allowing higher-income nations to dominate fisheries presents a desirable and efficient pathway for developing nations to turn their natural capital (for example, fish resources) into financial capital (for example, access fees, license fees, taxes, foreign exchange earnings). Building up a domestic industrial fishing fleet, maintaining it, and servicing it require port infrastructure, a trained workforce, processing and handling capacity, and considerable financial capital—all of which can be challenging to mobilize or lacking in many fish-rich lower-income countries.
Kiribati provides an example of a country where arguments have been made for the efficiency of translating fish into cash. Kiribati is a lower middle–income nation for which we determined that 99% of the industrial fishing effort within its EEZs was delivered by foreign flagged vessels, with the majority of this effort (91%) coming from higher-income nations. Kiribati reported generating 121.8 million USD in 2016 by selling access to fishing rights in its EEZs, with similarly substantial revenues collected in surrounding years (39, 40). Generally, it is not entirely clear that allowing industrial fisheries from wealthier countries to dominate offshore fisheries within less-wealthy nations’ EEZs always has negative food security impacts.
The efficiencies of industrialized fisheries allow them to put large quantities of lower-cost fish onto the global market, and this results in a net import of lower-priced processed fish from wealthier nations to poorer nations that, in terms of overall per-capita supply, may help counterbalance the net movement of higher-priced fish from poorer to richer countries (35, 41).
The capacity to view and analyze large portions of publicly accessible data that reveal how the world divides up a major global resource, like marine fish, is unique. Analogous sources of detailed insight are not, unfortunately, available for other environmentally, socially, and economically important large transnational resource harvest domains, such as logging or mining.
The results presented in this analysis represent data-driven hypotheses surrounding distributions of industrial fishing effort that can be thoughtfully considered during the ongoing high seas biodiversity treaty proceedings at the United Nations and by regional fishing management organizations. This information can help these leaders more effectively pursue shared goals for maximizing equity, food security, and sustainability on the high seas in the near future. These patterns also help to clearly identify which states may stand to win or lose from alterations to the current order of high seas biodiversity management and highlight how the hegemonic powers in high seas fishing can constructively assume more responsibility in leading toward this improved future.
Observations of the apparent dominance of wealthy foreign nations in the EEZs of less-wealthy nations can similarly empower and inspire both citizens and leaders in these regions to have more constructive discussion about best pathways toward securing sustainable and equitable futures for their domestic fisheries. These data also provide an improved understanding of the scope for potential competition between foreign industrial fleets flagged to wealthy nations and domestic small-scale fisheries—competition that is known to create numerous challenges for affected small-scale fisheries and the stakeholder communities linked to these fisheries (6, 7, 42).
The extent and lopsided nature of the dominance of higher-income flag states in industrial fishing can and should also inform ongoing conversations about how fisheries subsidies reform can potentially curb socioecological abuses associated with distant water fishing (25). Addressing all of these issues is a time-sensitive matter. Significant stresses are likely to be placed very soon upon the food future and political stability of many of the marine regions where we highlight greatest levels of imbalance in regimes of industrial fishing (3–5).