Content uploaded by Gilles Hosch
Author content
All content in this area was uploaded by Gilles Hosch on Jan 27, 2020
Content may be subject to copyright.
:@=9,7:1$.0,9,9/:,>?,7.:9:84.>:@=9,7:1$.0,9,9/:,>?,7.:9:84.>
*:7@80 >>@0 =?4.70
@90
9D%:=?49,'?:=8*0>>07.?4A4?D,9/?30&4>6:1)),@23?9D%:=?49,'?:=8*0>>07.?4A4?D,9/?30&4>6:1)),@23?
4>3%,>>492?3=:@23?30+:=7/F>":>?8;:=?,9?4>3492%:=?>4>3%,>>492?3=:@23?30+:=7/F>":>?8;:=?,9?4>3492%:=?>
4770>:>.3
%:>04/:9<@,?4.&0>:@=.0>",9,20809?!?/!D8492?:9,8;>34=0)94?0/ 492/:8
=,/70D':@70
$.0,9"49/4/.:?)94?0/ 492/:8
",C'.3:G07/
$.0,9"49/4/.:?)94?0/ 492/:8
(=0A:=(3:8,>
(:9>@7?492!484?0/,8-=4/20)94?0/ 492/:8
3,=70> 472:@=
$.0,9"49/4/.:?)94?0/ 492/:8
'0090C?;,201:=,//4?4:9,7,@?3:=>
:77:B?34>,9/,//4?4:9,7B:=6>,?3??;>.-0844>0/@5:.0
%,=?:1?30<@,.@7?@=0,9/4>30=40>:88:9>0:2=,;34.91:=8,?4:9'.409.0>:88:9>
9?0=9,?4:9,7&07,?4:9>:88:9>#,?@=,7&0>:@=.0>",9,20809?,9/%:74.D:88:9>,9/?30%:74.D
0>4299,7D>4>,9/A,7@,?4:9:88:9>
&0.:8809/0/4?,?4:9&0.:8809/0/4?,?4:9
:>.34770>':@70=,/70D'.3:G07/",C(3:8,>(=0A:= 472:@=3,=70>,9/@9?492?:9(48
9D%:=?49,'?:=8*0>>07.?4A4?D,9/?30&4>6:1)),@23?4>3%,>>492?3=:@23?30+:=7/F>":>?
8;:=?,9?4>3492%:=?>
:@=9,7:1$.0,9,9/:,>?,7.:9:84.>
*:7>>=?4.70
$3??;>/:4:=2
(34>&0>0,=.3=?4.704>-=:@23??:D:@1:=1=00,9/:;09,..0>>-D424?,7:88:9>09?0=1:=?307@0.:9:8D
?3,>-009,..0;?0/1:=49.7@>4:949:@=9,7:1$.0,9,9/:,>?,7.:9:84.>-D,9,@?3:=4E0/0/4?:=:1424?,7
:88:9>09?0=1:=?307@0.:9:8D:=8:=0491:=8,?4:9;70,>0.:9?,.?..:72,9844>0/@
9D%:=?49,'?:=8*0>>07.?4A4?D,9/?30&4>6:1)),@23?4>3%,>>4929D%:=?49,'?:=8*0>>07.?4A4?D,9/?30&4>6:1)),@23?4>3%,>>492
?3=:@23?30+:=7/F>":>?8;:=?,9?4>3492%:=?>?3=:@23?30+:=7/F>":>?8;:=?,9?4>3492%:=?>
.69:B70/2809?>.69:B70/2809?>
(34>=0;:=?3,>-009;=0;,=0/B4?3?30G9,9.4,7>@;;:=?(30%0B3,=4?,-70(=@>?>%0B(30A40B>
0C;=0>>0/49?34>>?@/D,=0;@=07D?3:>0:1?30,@?3:=>,9//:9:?90.0>>,=47D=0H0.??30A40B>:1%0B
9:=49,9DB,D,9?4.4;,?0>?304=1@?@=0;:74.D49?34>,=0,.69:B70/20809?>+0B:@7/7460?:0C;=0>>
,;;=0.4,?4:9?:,B9:=2:>?,9E4,9/%0?0=:=9:1%0B1:=?304=.:9?49@0/092,20809?=0A40B,9/
2@4/,9.049?30;=0;,=,?4:9:1?34>;,;0=
@?3:=>@?3:=>
4770>:>.3=,/70D':@70",C'.3:G07/(=0A:=(3:8,>3,=70> 472:@=,9/(48@9?492?:9
(34>=0>0,=.3,=?4.704>,A,47,-7049:@=9,7:1$.0,9,9/:,>?,7.:9:84.>3??;>.-0844>0/@5:.0A:74>>
1 INTRODUCTION
1.1 Background and purpose of this study
Fishing ports, the fishing vessels calling to them, and the transactions taking place
in them have become the focus of increasing scrutiny coupled with work to develop
the Agreement on Port State Measures to Prevent, Deter and Eliminate Illegal,
Unreported and Unregulated Fishing, also known as the Port State Measures
Agreement (PSMA), and its entry into force in June 2016. Since 2016, fishing ports
have come to embody the latest statutory frontline in combatting illegal,
unregulated and unreported (IUU) fishing. The centerpiece of port State action
revolves around the principle that foreign vessels involved in fishing operations,
visiting designated fishing ports, will be denied authorization to land their catch if
that catch has been obtained by flouting national or international fisheries
regulations – including, but not limited to those issued by regional fisheries
management organizations (RFMOs).
While other fisheries-related national, regional and global data sets can be quite
consolidated, complete and advanced – e.g. on the size of Exclusive Economic
Zones (EEZs), the authorization regimes applying to them, RFMO membership of
given States, etc. – knowledge and information about fishing ports, and the rules
applying therein remains highly fragmented and, in many cases, limited. A
comprehensive and up-to-date list of fishing ports, or designated fishing ports, does
not exist. At a global level we do not know how many fishing ports there are of
different sizes, and which classes of vessels they cater to.
Other important gaps in current port State related datasets and knowledge are
the degree of exposure of port States to the risk of IUU fishing and of IUU products
flowing through their ports, and related performance in combatting these
phenomena. Given the very recent nature of the PSMA, this is not surprising.
This paper explores these issues in order to gain a better understanding of port
State-related dynamics (numbers of ports, amount of traffic, etc.), port State
exposure to IUU risks, and perceived performance in combatting IUU fishing.
The purpose of this study is twofold. Firstly, to assess the potential for using
(remotely collected) Automatic Identification System (AIS) data to identify and
characterize fishing port activities, thus enabling a possible long-term, cost-
1
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
effective monitoring tool. Secondly, to establish how risk assessment
methodologies can be applied to estimate IUU risks associated with port States and
fishing ports, based upon a suite of internal and external indicators that are used to
build a Port State IUU risk index.
2
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
2 METHODOLOGY
2.1 Overall approach
The study builds upon an earlier assessment conducted by Poseidon Aquatic
Resource Management Limited for Pew entitled ‘Fish Landings at the World’s
Commercial Fishing Ports (Huntington et al, 2015) which ranked the world’s top
100 ports by volume of commercial fish landed by industrial scale fishing vessels.
This new research differs in intent and approach from the previous study.
Firstly, it is based on an entirely different methodology using global ship-based AIS
data to pinpoint likely shore-side activity by fishing vessels - the latter covering
both fish catching vessels and fish carrier vessels. Secondly it uses AIS-derived
information on flag State, vessel type and vessel size to categorize activities by flag
type (e.g. foreign and domestic), hold size, visit rates and temporal and spatial
distribution characteristics. Thirdly it develops an innovative risk assessment
methodology to determine the quality of port State response (expressed as internal
risk and determined by governance indicators) and port State exposure to IUU risk
(expressed as external risk and determined by the profile of fishing vessels visiting
a State’s ports). For each port State, the two risk components are combined to yield
an overall Port State IUU Risk Index.
‘Risk’ is defined as the probability of IUU-related events to occur in ports of
given port States and is qualitative in nature. Scores rating risk serve to rank States
across this study, and do not embody a concise measure of probability. A high score
merely signifies a “comparatively high risk”, while a low score signifies a
“comparatively low risk”.
The study is global in scope. Over seven million vessel stopping events from
2017 have been analyzed to identify and characterize fishing vessel activities in
over 3,000 ports and anchorages worldwide. This information was then used and
complemented by a suite of fact-based indicators to characterize port State
performance at the level of the individual State. The combination of both sets of
data was the basis for the development of a global level port State IUU risk index,
and related ranking.
It should be emphasized that this study is the first time such an approach has
been used to assess IUU risk at port State level. The authors recognize that this
process is based on machine learning algorithms which are at an early stage of
development and implementation, and that improvements to methodology,
3
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
efficiency, and elimination of errors associated with large volumes of AIS-derived
data are likely to be beneficial in the future. An important part of our findings
relates to the identification of current shortcomings in data and necessary further
work.
2.2 Detailed Methodology
2.2.1 Global analysis
2.2.1.1 Fishing vessel tracking and analysis
AIS is a maritime collision avoidance system transmitted on marine Very High
Frequency (VHF) radio. AIS transmissions provide information on the position,
speed, course and identity as recorded by the transmitting vessel. The system is
regulated by the International Maritime Organization (IMO) International
Convention for the Safety of Life at Sea (SOLAS), and while mandatory on all
passenger vessels and merchant vessels over 300 gross tons, fishing vessels are
generally exempted from carriage requirements. Some flag States have required
AIS on larger fishing vessels, but this is not the standard globally. Consequently,
AIS does not provide ‘the complete’ picture of all vessel activity. However, its
prevalence on larger fishing vessels makes it useful for this study, which looks
especially at fishing vessels that may travel between countries and trigger the
requirements of the PSMA. AIS is transmitted on VHF radio communication
systems. These transmissions are line of sight, meaning the earth’s curvature limits
its horizontal reception. However, its vertical transmission is readily captured by
commercial satellite arrays, extending the range of AIS to a near global footprint.
This project utilized global AIS data captured by both exactEarth’s exactView
satellite constellation, and terrestrial antenna sourced data collected by exactEarth’s
terrestrial AIS partner FleetMon – for the calendar year 2017. All methods of
capturing AIS data are limited by the fact that unless a station receives and records
the transmission, there is no record. This combination of a global satellite
constellation and terrestrial network was determined to be the most cost-effective
combination with the widest reach of recorded position and identity reports,
although it is not possible to record every AIS message broadcast in the world with
current technology, despite multiple service providers operating in different regions
across the world.
The starting point for the analysis was to identify all vessel stopping events
within 12nm from shore around the world, which would capture all ports and
4
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
anchorages commonly used by fishing vessels and fish carriers. Due to Global
Positioning System (GPS) variation and a vessel’s movement when alongside a
quay or at anchor, a vessel never remains perfectly stationary. To account for this
slight movement, an algorithm was developed that reviewed each vessel track for
the 12-month study period and identified groups of consecutive transmissions
where the distance travelled was less than 500m, at a speed of under 0.5 knots. Any
group with a total time period under one hour was also discounted. Each one of
these groups was labelled as a Vessel Stop Event and given a unique ID.
The analysis then developed an algorithm that converted the Vessel Stop Events
into Port Visit Events. This was critical to avoid duplicate counting of multiple
Vessel Stop Events by a single vessel within a given port as multiple port visits.
When a fishing vessel arrives at a port, it may move between anchorages,
transshipment events, the quayside or a dry dock. In this case, all individual internal
port movements were grouped into one single Port Visit Event by using an
algorithm to group Vessel Stop Events likely to be associated with a single port
visit. The grouping algorithm created a new Port Visit Event if all the following
criteria were satisfied:
1. The maximum distance moved since last Stop Event was more than 12
nautical miles;
2. The time since the last Stop Event was more than 6 hours;
3. The subsequent Stop Event was not brought about by an AIS
irregularity.
An additional step was to create a new Port Visit Event when a vessel travelled
more than 25 nautical miles between Stop Events occurring at the same port.
Applying the grouping algorithm to the Vessel Stop Event data resulted in a total
of 775,454 Port Visit Events.
2.2.1.2 Port Identification
A database of potential port locations was compiled based on algorithmically-
identified worldwide concentrations of Vessel Stop Events. Locations of
concentrations of stops were compared to the known port names and locations from
the World Port Index, a dataset produced by the U.S. National Geospatial
Intelligence Agency that includes the names and single point locations of major
5
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
global ports
1
. In total, 2,961 of the ports algorithmically identified from AIS data
were linked to the World Port Index records, and a further 106 ports were created
and named manually. Regions and countries with high concentrations of ports that
were not included in the World Port Index and with many fishing vessel visits were
especially prevalent in eastern Russia, China, Japan, Antarctic, Iran, and South
Korea.
Once all the 3,067 identified ports had been named, each port was given a radius
to represent the port’s area of jurisdiction. Radii were informed by the size
documented in the World Port Index, or else a fixed standard radius of 5.5
kilometers was allotted (3.5 kilometers for European ports, due to their proximity
to one and another). In general, port areas were coarsely defined, potentially
encapsulating many different localized ports or landing places into a larger scale
regional port area. An example of this is Hong Kong which – with a radius of 26
kilometers – encompasses many local ports which for the scope of this study were
grouped under Hong Kong as a single port. Within each port’s radius, a concave
polygon was drawn around the vessel visits to determine the extent of vessel activity
possibly associated with the port. Each polygon was reviewed and where they were
inappropriate i.e. missed some vessel visits associated with the port, the radii were
manually adjusted to capture all vessel activity that would likely be considered
under the jurisdiction of the relevant port.
Some clusters of likely vessel port visits remained outside of the list of ports
because the number of Vessel Stop Events was very small, or if there was no known
port in the close vicinity likely to have jurisdiction over the observed activity as
determined by a manual review of satellite imagery. Some of these clusters may
represent coastal anchorages to help vessels avoid inclement weather or allow crew
rest between fishing activities. Clusters were classified as unknown ports if they
were within 400 meters of land and unknown anchorages if they were further
offshore. These unknown ports and anchorages are relevant for understanding the
implementation of the PSMA as they represent a risk if vessels are stopping in port
State waters at otherwise unknown ports. Concentrations of unknown ports were
found in Europe where AIS is mandated for vessels of 12m and up, and these
vessels can easily cross borders within the EU to smaller unidentified ports.
1
https://msi.nga.mil/NGAPortal/MSI.portal?_nfpb=true&_pageLabel=msi_portal_page_62&pubC
ode=0015
6
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
Examples of unknown anchorages can also be found in places like eastern Russia,
the Norwegian archipelago of Svalbard and Antarctica where fish carriers operate
and transship in remote bays and anchorages.
2.2.1.3 Vessel identification and hold size estimates
59,906 fishing vessels and fish carriers that broadcast on AIS in 2017 for more than
one day were identified. Fishing vessels were identified by either being on a list of
fishing vessels such as RFMO authorization lists, or the vessel self-reporting as a
fishing vessel on AIS. In total, 59,226 fishing vessels were included in the study.
All the fish carrier vessels identified were either on an RFMO carrier list, identified
as a fish carrier within a propriety identity database maintained by OceanMind, or
listed as a fish carrier by IHS Markit
2
. From this list, any fish carriers that were
identified as servicing fish farms were removed. This resulted in a total of 680 fish
carrier vessels (also known as refrigerated fish carriers or “reefers”) being included
in the dataset of this study. The flag State of these fishing vessels and fish carriers
was identified using the pre-fix of each unique Maritime Mobility Service Identity
(MMSI) broadcast with every AIS transmission. The three-digit pre-fixes of these
MMSIs are linked to a list of countries published by the International
Telecommunications Union (ITU)
3
. Because MMSIs are manually entered into the
transmitter, this results in a significant amount of human error on setup. Therefore
many AIS transmissions have faulty or unknown identity information and MMSIs
with 9% of unique MMSIs associated with fishing and fish carrier vessels having
an unknown flag State. Unknown MMSI prefixes are frequently associated with
fishing buoys or Fish Aggregating Devices (FADs). An effort was made to remove
probable fishing buoy and FAD data from the data set, but some unknown MMSIs
that were retained may not represent fishing vessels.
Unknown MMSIs represented 7.5% of Port Visit Events globally, and over nine
in ten of these Port Visit Events occurred in China, likely representing domestic
Chinese vessels. Because domestic-flagged Port Visit Events did not inform the risk
analysis in this study, and these vessels are likely Chinese-flagged, the probability
that these unknown MMSIs influenced any of the substantial findings and outcomes
of the study is extremely low.
2
https://maritime.ihs.com/
3
https://www.itu.int/en/ITU-R/terrestrial/fmd/Pages/mid.aspx
7
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
The study uses Port Visit Events by catching and fish carrier vessels to
understand the fishing-related vessel traffic for each port over the course of an entire
year. The initial analysis to identify Port Visit Events was expanded to consider the
capacity of the vessels visiting the ports. The estimated refrigerated vessel hold size
was used as an indicator to determine the capacity of fleets visiting ports.
A complete dataset of vessel hold capacity was not available and only a small
number of RFMOs (ICCAT, WCPFC, IATTC and SPRFMO) publish the hold size
of their authorized vessels. Known hold size data from 5,286 vessels was used to
build independent power regression models, for each vessel type, to estimate vessel
hold size based on a vessel’s length. Power regression models were created for each
of the following vessel types: fish carriers, longliners, purse seiners, trawlers,
and others (obtained regression model formulae are shown in Appendix E; see
supplementary material). When vessel length was not known, then hold size was
taken as the average hold size of vessels with similar identity information, i.e. vessel
type and vessel flag.
The following hierarchical rules were used to determine vessel hold size
based on the information available for the vessel:
1. If vessel type and length were known: power regression analysis estimating
hold size based on length;
2. If vessel type and flag were known: average hold size from the known data
with the same vessel type and flag combination;
3. If vessel flag was known: average hold size from the known data for the
same flag.
The ranking of ports based on the hold size associated with unique visits must
also be considered in the context of the limitations of the data set. The ranking of
ports based on aggregate hold size is of great interest because it represents the
aggregate potential for the loading, unloading, or transshipment of fish by either
fishing vessels or fish carriers, but should not be interpreted as an estimate of the
volume of landings or transshipment in port. Some ports are primarily used as
berthing/home ports, others embody a significant transit point that triggers port visit
events (Panama), and others are merely used for anchoring visits while awaiting
instructions to proceed to another location.
8
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
2.2.1.4 Port State IUU Risk Index and trend analysis
This study develops a port State IUU risk index. The index generates a score for
IUU risk affecting port States globally and enables the ranking of port States by
risk. The index is composed of two main risk components; internal risks and
external risks. Internal risk provides a measure of the performance of the port State
to address potential IUU risk, while the external risk component provides a measure
of the exposure of the port State to potential IUU fishing operations and related
transactions in its ports. The former relies primarily on published open-source data
and information, such as the ratification of major international agreements and
performance in RFMOs, while the latter is more grounded in AIS-based data
sources such as vessel characteristics and movement data.
The straight arithmetic average of the scores of both risk categories yields the
overall IUU risk index for any given port State. Given the inconclusive correlation
between internal and external risk scores at the level of the port State, it appeared
appropriate to assign the same weighting to both components, and to treat them as
cumulative, rather than progressive.
9
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
Table 1. Indicators forming the Port State IUU Risk Index
Component
AIS-
based
Weighting
Indicator name
General
yes
n/a4
1. Operates commercial ports in which fishing vessels do business
Internal
yes
3
2. Number of commercial fishing ports
no
2
3. Party to the 2009 Agreement on Port State Measures
no
2
4. Contracting Party (CP) or Cooperating Non-Contracting Party
(CNCP) of an RFMO with a binding PSM resolution & transparent
compliance monitoring
no
3
5. Compliance record with binding RFMO port State conservation
and management measures (CMMs)
no
2
6. Transparency International Corruption Perceptions index of the
port State
no
1
7. Identification status of the port State - by the EU
no
1
8. Identification status of the port State - by the USA
no
2
9. Identification status of the port State - within any RFMO
External
yes
2
10. Port visits by foreign fishing vessels
yes
3
11. Flag of Convenience (FOC) State fishing vessels entering ports
(plus unknown MMSI)
yes
3
12. Average flag State Governance Index of fishing vessels
entering ports
yes
3
13. IUU listed fishing vessels entering ports
yes
2
14. EU carded flag State fishing vessels entering ports
yes
2
15. US carded flag State fishing vessels entering ports
yes
2
16. Average internal port State risk of fishing vessels entering
ports5
The internal and external risk components are both made up of a number of
indicators. Individual indicators may be conceived of as “risk factors” that either
mitigate or aggravate risk of exposure to IUU and/or facilitation of IUU, depending
on their relative or nominative presence or absence. Eight indicators make up the
internal risk component of the Index, and seven make up the external risk
component (see Table 1). Indicators are individually weighted as low, medium or
high, determining their relative weight within each of the two risk components. A
4
This indicator is not weighted. It is used to merely decide whether a country is included in the
overall data set of countries assessed, or conversely, whether it is to be excluded.
5
As calculated from indicators 1 to 9 in the same table.
10
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
high weighting was assigned to indicators where a direct link to IUU fishing is
given. An intermediate weighting was given to indicators where a more indirect,
but strong and generally recognized correlation with IUU fishing exists. A low
weighting was assigned to indicators where a direct link and/or a strong correlation
is not given, but where port State related IUU fishing transactions would be
expected to arise as a concomitant phenomenon.
Indicator scores are all divided into five tiers, ranging from 1 to 5 as full
integers. 1 stands for “yes” and “very good”, while 5 stands for “no” and “very
poor”. Care was taken to ensure indicators are symmetrically arranged, when not
all five tiers are used (e.g. in yes/no type indicators). In this study, all indicators use
2, 3 or 5 tiers to assign scores. Overall, this implies that low Index scores provide
for “low IUU risk”, and that high scores stand for “high IUU risk”. Table 1 also
shows which indicators are based on AIS data. Overall, 9 out of 16 indicators are
AIS-based, while seven are drawn from other fact-based sources.
One hundred and fifty-three independent coastal States were first selected as
the object of this study. Only States in which AIS-fitted fishing vessels were
detected to have entered ports were retained for scoring. This led to the elimination
of 13 coastal States from the initial group of 153 States,
6
leaving 140 port States as
the object of the more detailed analysis. Some of the coastal States that were
eliminated, e.g. Barbados and Cambodia, are clearly port States, providing an early
reflection of limitations of using AIS-determined data.
Data for all indicators are sourced from the most recently available full datasets
– mostly 2017 – with possible minor variations between indicators.
A detailed description of individual indicators is provided in Appendix A,
including notes on individual indicator methodology, where needed. Country scores
for all indicators are provided in Appendix B.
6
The 13 States eliminated from the analysis are: BRB, BLZ, BIH, KHM, DMA, ERI, HTI, HND,
JOR, MCO, NIC, NIU, LCA (Note: consult the final table in Appendix D in supplementary material
for a list of country names against alpha-3 country codes)
11
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
2.2.1.5 Risk analysis
Risk analysis is based on the computation of an internal risk score, an external risk
score, and the combination of both, yielding an overall port State IUU risk index
for every single port State covered by the study.
Since the study focuses on an assessment of IUU risks in light of the PSMA
framework, and the PSMA regulates control of foreign vessel movements in and
out of domestic ports, a focus on foreign fishing vessel movements is implied.
Foreign vessel visits are an exclusive component of external risk, and the
assessment of internal risk is not affected by the existence or absence of foreign
vessel movements. However, external risk, and the external port State risk
indicators can only be raised for ports into which foreign vessels have been found
to enter. Out of the 140 coastal States which have been identified to operate fishing
ports based on AIS data, a further three port States were identified as not having
had any visits by foreign vessels in 2017; these are Bahrain, Comoros, and Saint
Vincent and the Grenadines. In the global risk analysis, in which the external risk
component plays a structural part, these three countries have been eliminated from
that dataset. Also, 137 States obtain an overall port State IUU risk score based on
the arithmetic average of both internal and external scores, while the overall IUU
risk index score for the three countries with no detected foreign vessel visits is the
same as their internal score. In the latter case, using an external score of 1 to
compute an overall score based on an average between an actual internal and an
artificial external score would have falsified the overall ranking by deflating those
scores, rendering a risk score largely unhinged to the actual performance and
exposure of those port States to IUU risks.
The internal, external and overall risk scores and index are compared to a range
of factors, including indices external to this study (such as national income level
and quality of governance), in order to establish how such specific factors correlate
– or do not correlate – with port State IUU risk.
These comparisons have been graphed out, and statistical analysis was
performed. To compare the means between two samples (e.g. the risk scores of port
States having signed the PSMA against those that have not), a one-tailed two-
sample t-test with equal variance was used, having established in all cases that
variance in both samples was comparable. To test the significance of the correlation
(i.e. causal effect relationship) between two variables (e.g. influence of internal port
risk on external port risk), a simple linear regression analysis using the least squares
12
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
method to fit a line through the set of observations was performed, having
established in all cases that residuals were randomly distributed around the average,
and verifying in all cases that the relationship was linear indeed – validating the
appropriateness of simple linear regression analysis.
The significance level used in these tests, for the observed difference between
sample means and/or the observed slope, is 0.05.
2.2.2 Data sources and robustness
Port State risk analysis was informed by an important number of indicators for
which the vast majority of information and data used in the analysis was obtained
from existing information sources outside of this study. The indicator sources used
in the study fall into two categories, as follows:
1. AIS data
2. Published public-domain data sources hosted by international bodies
2.2.2.1 AIS and vessel identity data
AIS data are key to both the global and the deep dive analyses. Overall, larger
vessels are inherently more likely to carry AIS transmitters and more powerful radio
broadcasting equipment, being more likely to be detected by AIS receivers on
satellites or terrestrial antennas. This creates a generic bias in the study, favoring
the counting of port visits by larger vessels, which in turn are also more likely to
operate in offshore and international fisheries. Given the focus of this study on
identifying port visits by foreign-flagged vessels, this bias increases the confidence
of the findings related to foreign visits, while under-estimating domestic port
arrivals by smaller, local vessels.
Some countries and regions, for example USA and Europe, flag more fishing
vessels operating on AIS because of regulations making AIS compulsory for given
vessel sizes. In contrast, fewer vessels operate on AIS in the Indian Ocean,
especially in proximity to Somalia, owing to the threat of piracy, or close to Yemen,
due to detection risks relating to the conflict zone. In polar regions AIS coverage is
superior as the majority of satellites are polar orbiting, increasing the visibility of
vessels in these regions to AIS receiving satellites, hence increasing the frequency
of observation of AIS transmissions in higher latitudes.
There are several regions generating generally poor AIS data owing to the
limited number of terrestrial receivers and high traffic density. High traffic affects
13
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
the collection of AIS from satellites due to the limited ability of the satellite to
record and process transmissions during a single pass. Some of the regions affected
by this issue include the Strait of Malacca and the English Channel. The
combination of vessel traffic and interference from other radio transmissions is also
suspected to interfere with the observation of transmissions in the South China Sea
and in some waters adjacent to Russia.
The poor quality of some transmitted AIS data led to some data being excluded
from the analysis. Poor data quality generally related to invalid positions, vessels
transmitting on MMSIs shared with other vessels, and vessels transmitting
insufficient identity information to distinguish them as catching vessels or fish
carriers. AIS data quality issues are more common across the Asian region and
exacerbated by the limited number of terrestrial receivers in this area.
Some invalid positions recorded among other valid positions on a vessel’s track
can contribute to a small percentage of instances where port visits may have been
incorrectly assigned. Many of these instances were manually corrected and the
algorithms refined to capture different permutations of vessel movement, but future
endeavors of this nature should expect to invest significant time in the review and
refinement of global analysis methods such as those used here to ensure that such
invalid positions do not lead to inaccurate grouping of Vessel Stop Events or the
mis-association of Port Visit Events with an incorrect port name.
The variable satellite coverage, AIS usage and AIS data quality imply that this
analysis does not capture every fishing vessel in the world, even those fitted with
functioning AIS transponders.
Finally, the use of AIS-derived data to identify the number of ports in States,
may itself pose potential problems, for two main reasons:
1. AIS-derived data will not capture ports utilized by smaller vessels
and/or domestic vessels which do not transmit on AIS;
2. In cases such as Thailand, individual ports (such as those of the Bangkok
metropolitan area along the Chao Phraya river) are identified as a single
port in this study using the AIS-derived data, while being counted (and
factually embodying) separate, individual ports in reality.
Overall, it is expected that the impact of data quality issues will affect the global
analysis less the deep dives, as effects at the global level will have the tendency to
14
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
cancel out over larger areas, while it may have a more pronounced and immediate
impact on the deep dive analysis results at the level of individual ports.
2.2.2.2 Published public-domain data
Open source public-domain data were used in the analysis, enabling the study to
not look into countries individually, but to merely collect such information,
assigning it to countries, and then assigning scores to it.
Such publicly hosted data are generally centralized – i.e. found in a single place
– and generally cover all countries in the study, or alternatively, the countries to
which given data sets apply (e.g. the parties of an RFMO, and their compliance
standing; indicator 5 of the analysis). Such data (and their sources) are used in the
following indicators:
• Ind. 3: countries having adhered to the PSMA agreement (held by FAO
7
)
• Ind. 4: countries participating in an RFMO that has binding PSM rules and
transparent compliance monitoring (RFMO websites)
• Ind. 5: countries presenting compliance issues with RFMO rules on PSM
(RFMO compliance reports)
• Ind. 6 & 12: value of the Corruption Perceptions Index of flag and port
States (produced by Transparency International
8
)
• Ind. 7 & 14: countries carded by the EU under the EU IUU Regulation
(Decisions published by the EU)
• Ind. 8 & 15: countries carded by the USA under the MSRA (biennial
reports published by NOAA)
• Ind. 9: countries identified by RFMOs, with sanctions levelled against them
(RFMO compliance reports)
• Ind. 11: countries listed as flag of convenience State (ITF Seafarers
9
)
7
http://www.fao.org/fileadmin/user_upload/legal/docs/037s-e.pdf
8
https://www.transparency.org
9
https://www.itfseafarers.org/index.cfm
15
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
• Ind. 13: individual vessels identified on consolidated IUU vessel list (Trygg
Mat Tracking
10
)
Transparency international’s CPI lacked scores for some countries. In the
analysis where the CPI is used, those countries are eliminated from the sample. This
leads to a smaller yet fully representative sample, does not affect the validity of the
analysis, and is documented in the results.
Generally, datasets for 2017 were used to coincide with vessel movement
analysis. Only where historic datasets could not be used (e.g. the IUU vessel list),
the current dataset of 2018 was used. Such potential misalignment of data between
years is viewed to have had no palpable impact on the global level analysis results.
The period applying to the dataset is invariably referenced in the detailed indicator
descriptions (in Appendix A, see supplementary material).
The good quality of these data overall is unquestionable and is determined by
the processes applied by the individual organizations producing and hosting them.
However, the discrepancy between style and content of RFMO compliance reports
introduced the need for a certain amount of discretion in deciding whether
individual States ought to be considered as being in default with given PSM rules
or not. In some cases the EU is mentioned as being in default, rather than a specific
EU member State. In such cases, all EU members with vessels active in that RFMO
were negatively scored in their capacity as a port State – the approach constituting
a conservative bias ensuring countries do not appear with better scores than they
should have in reality.
10
http://tryggmat.no/combined-iuu-vessel-list
16
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
3 RESULTS
3.1 Fishing Ports
3.1.1 Port numbers
This study identified 3,067 ports in the world utilized by fishing vessels and fish
carrier vessels transmitting on AIS. The definition of ports was driven by the
location of fishing vessel stops on AIS. The World Port Index (WPI) dataset formed
the initial basis for naming the AIS-derived ports. This was complemented by 106
additional ports that were designated and researched to capture clusters of vessel
stops on AIS that were not associated with a previously known port from the WPI.
Pre-existing port information was of an inconsistent quality globally, with a
significant number of additional ports identified in China, eastern Russia around
the Sea of Okhotsk and Kuril Islands, and in western Russia and Norway relative
to the rest of the world.
3.1.2 Global ranking of ports
The top 100 ports as classified by total number of vessel visits, total foreign vessel
visits, domestic hold size, foreign fishing vessel hold size (harvester) and foreign
carrier vessel hold size (reefer) are presented in Appendix C, with the top 15 ports
based on number of vessel visits shown in the table below.
Table 2. Top 15 ports based on total number of vessel visits
Rank
Port
Country
Visits
1
Zhoushan
CHN
59,830
2
Wenzhou
CHN
20,874
3
Lanshan
CHN
11,579
4
Rizhao
CHN
9,501
5
Dongshan
CHN
9,406
6
Quanzhou
CHN
8,826
7
Xiamen
CHN
7,649
8
Qingdao
CHN
6,842
9
Shanghai
CHN
6,834
10
Shantou
CHN
6,032
11
Busan
KOR
5,585
12
Longyan
CHN
5,514
13
Zhuhai
CHN
5,408
14
Dalian
CHN
4,654
15
Shanwei
CHN
4,475
17
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
Fourteen of the top 15 ports in the world based on the total number of port visits
are Chinese (Table 2). This is a consequence of the Chinese government’s policy
of heavily subsidizing commercial fleets, resulting in China having a
disproportionately large domestic fishing fleet, the bulk of which is operating out
of Chinese ports. This is likely also an underestimate because of the generally poor
quality of both AIS data and AIS coverage around China. China also dominates the
top 15 ports based on domestic hold size (see Table 4) with the domestic hold
capacity estimated to enter Zhoushan port being an order of magnitude greater than
the majority of ports in the same table. The dominance of China in terms of total
port visits is not reflected in Table 3, Table 5 and Table 6 which examine foreign
flagged vessel metrics, demonstrating that the activities at Chinese ports are
dominated by domestic vessel movements.
For the purpose of this paper, all non-Taiwanese flagged vessel visits to Taiwan
were considered foreign (including Chinese-flagged vessel visits) as were all
Taiwanese-flagged visits to China. The legal status of the PSMA in Taiwan is
complicated by the issue that Taiwan is not a member of the United Nations, under
whose authority the PSMA is promulgated. This kind of unique relationship
between different political jurisdictions was common in the analysis and required
binary determinations which affect the interpretation and counting of “foreign”-
flagged vessel visits. Kaohsiung is the main Taiwanese fishing port and is in the
top 15 ports based on domestic, foreign fishing and foreign carrier vessels hold
sizes. This demonstrates the prevalence of both large Taiwanese long line and purse
seine vessels as well as Kaohsiung being used as an offload port frequented by the
Korean and Chinese fleet on route to the Western and Central Pacific.
Busan (Republic of Korea) is the only port to feature in the top 15 ports across
all five metrics assessed (Table 2 to Table 6). Busan is frequented by both domestic
and foreign vessels. The diversity of foreign flagged vessel visiting Busan is
limited, with Russian, Chinese and Panamanian flagged vessels representing 91%
of the foreign visits.
Mid-ocean ports Majuro, Suva, Port Louis, Port Victoria and Pohnpei are
frequented by foreign fishing vessels in terms of visit numbers as well as hold size
of both fishing and carrier vessels (Tables 3, 5 and 6). These ports are much
frequented for transshipment and/or unloading of tuna catches, notably because
purse seine vessels in the Western Pacific Ocean and the Indian Ocean are not
permitted by the relevant RFMOs (the Western and Central Pacific Fisheries
18
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
Commission and the Indian Ocean Tuna Commission respectively) to transship at
sea.
Table 3. Top 15 ports based on number of foreign vessel visits
Rank
Port
Country
Visits
1
Busan
KOR
1,528
2
Majuro
MHL
1,168
3
Kirkenes
NOR
1,148
4
Nouadhibou
MRT
1,078
5
Suva
FJI
983
6
Port Louis
MUS
957
7
Vila Real De Santo Antonio
PRT
683
8
Manta
ECU
634
9
Dakar
SEN
614
10
Las Palmas
ESP
601
11
Castletown-Bearhaven
IRL
594
12
Hanstholm
DNK
549
13
Abidjan
CIV
502
14
Kaohsiung
TWN
492
15
Pohnpei
FSM
457
A number of European ports appear in Table 3. While EU-flagged vessel visits
in fellow EU member ports may be treated as ‘domestic’ vessel movements rather
than foreign movements for the purpose of EU controls, this study considers these
as foreign visits, and the table captures all visits by vessels not flagged to the port
State. European ports located closer to major fishing grounds are convenient
landing sites for the EU fleet. We see this for Las Palmas in the Atlantic Ocean,
Kirkenes in the Barents Sea, Hanstholm in the North Sea and Castleton-Bearhaven
in the North Atlantic. The outlier in Table 3 is Vila Real De Santo Antonio, a small
Portuguese port located on the Spanish-Portuguese border, dominated by Spanish
fishing vessel visits.
Globally very few domestic carrier vessel port visits occur in domestic ports.
This is primarily a result of fish carriers operating globally and receiving and
landing fish in prominent transshipment and landing ports, irrespective of flag. Due
to this, domestic carrier vessel and domestic fishing vessel data were aggregated in
Table 4 overleaf; however, the dominant contributor was domestic fishing vessels.
19
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
Dakhla (Morocco) and Coronel (Chile) were the only ports outside of Asia to
feature in the top 15 ports when ranked by domestic hold size.
Table 4. Top 15 ports based on domestic hold size
The top 15 ports based on foreign fishing vessel hold size are a combination of
offload ports where fishing vessels transfer fish to fish carriers, and terminal ports
where fish is offloaded for processing (Table 5 below). Las Palmas is the most
important European port in terms of foreign fishing and fish carrier vessel offloads.
The West African mainland ports of Tema, Abidjan, Walvis Bay and Nouadhibou
are important ports in terms of both foreign fishing vessel and fish carrier vessel
hold size. Dakar features in the top 15 foreign fishing vessel ports and Tema is
ranked in the top 15 foreign fish carrier vessel ports.
Rank
Port
Country
Total m3
1
Zhoushan
CHN
12,549,704
2
Vladivostok
RUS
4,460,936
3
Wenzhou
CHN
2,863,021
4
Shanghai
CHN
2,498,576
5
Busan
KOR
2,096,918
6
Lanshan
CHN
1,404,034
7
Dalian
CHN
1,370,861
8
Rizhao
CHN
1,249,217
9
Quanzhou
CHN
1,247,898
10
Dongshan
CHN
1,206,586
11
Coronel
CHL
1,010,734
12
Petropavlovsk Kamchatskiy
RUS
974,505
13
Kaohsiung
TWN
956,518
14
Dakhla
MAR
951,304
15
Yantai
CHN
916,467
20
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
Table 5. Top 15 ports based on foreign fishing vessel hold size
Rank
Port
Country
Total m3
1
Majuro
MHL
943,000
2
Manta
ECU
761,748
3
Dakar
SEN
561,418
4
Busan
KOR
545,080
5
Nouadhibou
MRT
468,553
6
Kirkenes
NOR
381,074
7
Walvis Bay
NAM
375,292
8
Abidjan
CIV
335,405
9
Pohnpei Harbour
FSM
331,692
10
Port Louis
MUS
319,985
11
Cape Town
ZAF
232,970
12
Callao
PER
219,884
13
Las Palmas
ESP
217,222
14
Port Victoria
SYC
211,991
15
Montevideo
URY
199,120
Manta (a major tuna port), Callao (where small pelagics are mainly landed and
Montevideo were the only South American ports to feature in the top 15 ports for
foreign fishing vessel hold size (Table 5 above). Montevideo has been documented
as a base of operations for domestic and foreign toothfish vessels operating in the
CCAMLR area (Cajal, J. & García Fernández, J., 2002), with the port operating as
a landing, transshipment, processing and re-exportation hub. This is likely to be the
case for other major fisheries in the South-West Atlantic also.
Cristobal yields large volumes of foreign fish carrier traffic, a likely
consequence of vessels waiting to transit through the Panama Canal (Table 6
below). The top 15 ports based on foreign fish carrier hold size are mostly terminal
ports where fish carriers unload catches for processing, or where fish is
transshipped from fishing vessels to carriers before transiting to such processing
locations.
21
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
Table 6. Top 15 ports based on foreign carrier vessel hold size
Rank
Port
Country
Total m3
1
Busan
KOR
4,152,292
2
Las Palmas
ESP
2,397,544
3
Dalian
CHN
1,943,959
4
Zhoushan
CHN
1,391,968
5
Kaohsiung
TWN
1,299,084
6
Abidjan
CIV
1,002,135
7
Majuro
MHL
912,474
8
Rabaul
PNG
908,397
9
Bangkok
THA
826,104
10
Pohnpei
FSM
816,970
11
Tema
GHA
808,808
12
Qingdao
CHN
754,417
13
Cristobal
PAN
687,137
14
Nouadhibou
MRT
686,089
15
Walvis Bay
NAM
624,869
Bangkok, which does not feature in the top 15 ports in any other metric, is
frequented by fish carriers and receives a large proportion of global tuna, hence
why it shows in the table above
11
.
Taking all of the above tables together, most of the major regions are
represented in the top 15 ports for foreign vessel visits, foreign fishing vessel hold
size, and foreign fish carrier vessel hold size. There are five prominent East Asian
ports (Busan, Kaohsiung, Dalian, Zhoushan and Qingdao), one South East Asian
(Bangkok), four Pacific (Majuro, Suva, Pohnpei, Rabaul), two eastern South
American ports (Manta, Callao), two western South American ports (Montevideo
and Cristobal), five West African ports (Nouadhibou, Dakar, Abidjan, Walvis Bay,
Tema and Cape Town), six European ports (Las Palmas, Castle-Bearhaven, Vila
Real De Santo Antonio, Hanstholm, Kirkenes) and two East African ports (Port
Louis, Port Victoria). The major areas missing are both the coastlines of North
America and Middle East and Australasia. The lack of prominent American and
11
Thailand absorbs in the order of 20-25% of the global commercial tuna harvest, mostly destined
to processing and re-exportation as value-added products.
22
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
Australasian ports is a likely consequence of a relatively uniform distribution of
activity of a largely domestic fishing fleet.
3.1.3 Port State IUU risk analysis
The port State IUU risk index allows scoring and ranking of port States according
to internal, external and overall risk. Furthermore, countries can be grouped and
ranked by ocean basin, FAO region, Governance Index, or World Bank income
group.
Figure 1 overleaf is composed
of three graphs, showing the
distribution of internal (A), external
(B) and overall port State risk (C)
across the range of tiers used for the
indicator, and all 153 coastal States
originally part of the study.
The global average internal risk
score is 2.30 and ranges from a
minimum of 1.21 for Grenada, to a
maximum of 3.38 for Papua New
Guinea and Russia. The global
average external risk score is 2.48
with individual country scores
between 1.76 for Antigua and
Barbuda, and a maximum of 3.41
for Russia and Venezuela. The
global average for the overall risk
score is 2.40, with a minimum of
1.55 for Grenada, and a maximum
of 3.39 for Russia – with both
countries representing the best
performer on one hand, and the
worst performer on the other, across
the port State IUU risk index.
It can be seen in Figure 1 that internal risks are distributed more evenly across
the spectrum of scores between 1 and 3.5, while external scores are more
Figure 1: Distribution of port State IUU risk
scores (n=153)
23
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
concentrated in the range between 2 and 3 – making up 78% of all scores, versus
53% of all scores in the internal score distribution. This entails an overall
distribution of risks which is more heavily concentrated in the band between 2 and
3.
There are thirteen coastal States not operating ports (8%). These have not been
assigned scores and have been excluded from further analysis.
Table 7 overleaf presents the top three and bottom three performing countries
by internal, external and overall risks, grouped into their respective world regions.
The full table of country ranks is appended in Appendix D (in supplementary
material).
Table 7 reveals that countries generally appear as top performers in either
internal or external risk categories, but rarely in both. Exceptions are Sweden,
Grenada and the Cook Islands, which appear as top performers in both categories
for their respective regions, and consequentially also as top performers in the
overall score. It is noted that countries have much more control over their internal
risk score, primarily based on their performance in applying port State measures,
while they have less control over their external risk score, providing a measure of
exposure to IUU risk – which can only be partially mitigated through domestic
policies.
Table 7. Top & bottom performers across the Port State IUU Risk Index (by region)
Region
Internal risk score
External risk score
Overall risk score
Top 3 (starting with the strongest)
Africa
Sao Tomé & Principe
Senegal
Mauritania
Gabon
Kenya
Tanzania
Gabon
Senegal
Sao Tomé & Principe
Asia
Sri Lanka
Korea
Thailand
Timor Leste
Brunei
Korea (PRK)
Sri Lanka
Pakistan
Myanmar
Europe
Slovenia
Belgium
Sweden
Romania
Sweden
Germany
Romania
Sweden
Belgium
Latin
America &
the
Caribbean
Grenada
Uruguay
St Vincent & the
Grenadines
Antigua and Barbuda
Saint Kitts and Nevis
Grenada
Grenada
St Vincent & the
Grenadines
Uruguay
Near East
Oman
Egypt
Lebanon
Kuwait
Lebanon
Djibouti
Oman
Lebanon
Djibouti
North
America
USA
Canada
Canada
24
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
Region
Internal risk score
External risk score
Overall risk score
Southwest
Pacific
New Zealand
Australia
Cook Islands
Vanuatu
Cook Islands
Tonga
Cook Islands
Vanuatu
New Zealand
Bottom 3 (starting with the weakest)
Africa
Congo (DRC)
Congo, Rep. of
Benin
Sudan
Algeria
Ghana
Congo (DRC)
Benin
Congo, Rep. of
Asia
Vietnam
Korea (PRK)
Timor Leste
China
Korea
Taiwan
China
Vietnam
Japan
Europe
Russia
Great Britain
Italy
Russia
Norway
Ukraine
Russia
Ukraine
Italy
Latin
America &
the
Caribbean
Dominican Republic
Mexico
Argentina
Venezuela
Guatemala
Cuba
Jamaica
Venezuela
Dominican Republic
Near East
Bahrain
Kuwait
Iraq
Saudi Arabia
Libya
Egypt
Bahrain
Saudi Arabia
Iraq
North
America
Canada
USA
USA
Southwest
Pacific
Papua New Guinea
Solomon Islands
Fed. States of
Micronesia
Australia
Western Samoa
Kiribati
Papua New Guinea
Solomon Islands
Tuvalu / Kiribati
(same rank)
Table 8 overleaf provides the average risk score by category (internal, external
and overall) for every world region, allowing for the ranking of world regions
according to their average score. The regional ranks in the table provide guidance
as to which world regions lead or lag in the three components of the Port State IUU
Risk Index.
For internal risks, the spread in scores is quite large, reflecting the spread shown
in Figure 1 above. Europe is the region with the lowest average score, very closely
followed by North America. This entails, inter alia, that port States in these two
regions have adopted advanced policies in the domain of PSM and are participating
and performing well in RFMOs. It has to be noted that internal indicators 7 and 8
on carding status have a latent tendency to bias the analysis in favor of the Europe
region, as many of its countries are EU members, and since EU members cannot be
carded by the EU Commission. The same holds true for the US carding system, and
the USA. The Southwest Pacific and the Near East rank last, with the Near East
figuring as the bottom performer by a very wide margin. The results suggest that
25
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
the Near East is the world region where PSM is afforded the lowest priority in
public policy making.
Table 8. Ranking of world regions across the different risk categories
Rank
Internal risk score
External risk score
Overall risk score
1
Europe (2.06)
Southwest Pacific (2.31)
North America (2.24)
2
North America (2.06)
North America (2.41)
Europe (2.27)
3
Africa (2.22)
Latin America &
Caribbean (2.42)
Latin America &
Caribbean (2.35)
4
Latin America &
Caribbean (2.26)
Near East (2.47)
Africa (2.40)
5
Asia (2.48)
Europe (2.48)
Southwest Pacific (2.41)
6
Southwest Pacific (2.51)
Africa (2.54)
Asia (2.54)
7
Near East (2.68)
Asia (2.59)
Near East (2.65)
For external risks, the overall spread in scores is much more limited. This
indicates that while exposure to IUU risks differs between countries and regions,
the variance is comparatively smaller – and the risks comparatively higher – than
the variance and risks relating to internal risks and the policy and governance
frameworks. The Southwest Pacific and North America are the regions where
external risks are lowest, while they are highest in Africa and in Asia.
In terms of overall risk, North America is the region with the lowest overall
risk, followed by Europe. Though Europe and North America achieve an almost
identical internal risk score, Europe’s higher external risk score is not entirely
surprising; it is a highly diverse continent made up of many sovereign port States
performing differently, it represents the biggest consumer seafood market globally,
and has a more important exposure to external risks as a consequence
12
. Asia and
the Near East are the worst performing regions overall. Across all three categories
it arises that both these bottom performing regions suffer in terms of important and
combined internal and external risk exposure, with internal risks being relatively
more important to the Near East, and external risks – typically embodied by weak
flag State performance of vessels visiting ports – to the Asia region. The latter is
12
The USA and the EU represent 42.7% of the global seafood import market in 2016. However, EU
seafood imports outrank US imports by USD6.7 billion (or 32.6%). (FAO 2017)
26
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
not surprising, as Asia has globally important seafood markets (both for processing
and consumption), while the Near East does so to a substantially lesser degree.
Having assessed regional
scores across the spectrum of
the port State risk index and
having gained an impression of
the interplay between internal
and external risk factors, and
how they define the overall
outcome for each individual
country, and regions as a whole,
it is of use to consider issues of
interdependence and
correlation. Figure 2 renders the
outcome of such analysis, when
risk scores are plotted against
each other, with the internal risk score along the x axis as the independent variable,
and the external risk score along the y axis as the dependent variable. In this dataset,
all countries not operating ports (13), and those operating ports but not receiving
foreign vessels (3), have been eliminated.
There is high scatter in the data, leading to a low goodness-of-fit for the
regression line. However, as would be expected, the fitted line indicates a mild
positive trend, indicative of the fact that when a country improves its internal
processes relating to PSM and to the mitigating IUU risks, the exposure to external
risks has a tendency to decline. In practical terms, this implies that fishing vessels
in poor standing would tend to avoid ports in States with good PSM performance.
The fact that the rate of change is limited is partially expected, as the scores for
external risk are much more limited in their overall measured variance, than the
variance of internal risk scores (see Figure 1 also). Regression analysis finds the
correlation and resulting slope (trend) to be insignificant at the 0.05 level (p=0.27),
yielding a >27% probability that the observed correlation is due to chance.
In light of the importance of the PSMA, and its entry into force in 2016, another
key element to assess is the potential influence of PSMA adherence on the
performance of parties in the domain of PSM. Adherence to the PSMA implies that
countries seek guidance from the terms of the agreement to upgrade their domestic
Figure 2: Distribution of internal versus external
risk scores (n=140)
27
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
PSM frameworks, resulting in improvements in their internal risk scores. Figure 3
shows the results of this analysis. The dataset used for this analysis is the same as
for the dataset represented in Figure 2, with the difference that it is split into 2
groups, regrouping parties to the PSMA on one hand, and non-parties to the PSMA
on the other. Indicator 3, establishing the status of the country with regards to
PSMA adherence has been eliminated from the internal score of both groups in
Figure 3, as it naturally works to separate both groups. This analysis thus compares
all internal against all external risk factors – except the adherence to the PSMA
itself, whose influence is neutralized.
28
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
The cluster of parties to
the PSMA yield an average
internal and external risk
score of 2.12 and 2.45
respectively. The countries
not having adhered to the
agreement yield both a
higher internal and
external average risk score,
of 2.28 and 2.52
respectively. This means
that PSMA parties do form
a group within which both
internal and external risks
are lower. Again, the wider
spread between internal
risk scores (0.16) and the
more limited spread between external scores (0.07) is observed. The difference in
average internal risk between PSMA parties on one hand, and non-parties on the
other, is statistically significant at the 0.05 level (p=0.017). The same is true for the
difference in average external risk (p=0.045). The result establishes that adherence
to the PSMA either leads to improvements in the application of PSM in general, or
that it is an associated phenomenon of such improvements. The analysis verifies
that PSMA adherence may be used as a general proxy for lower IUU risk exposure
and better PSM performance. However, given the scatter in the data, such proxy
cannot be applied to individual countries with any degree of confidence. The overall
difference of both internal and external scores between both groups is small. In
order to gauge the global impact of the PSMA, it would be of interest to understand
how this difference evolves over time by running the same analysis on a recurrent
basis, with a specific focus on internal risks.
The relationship between the incidence of IUU fishing and the perceived levels
of government corruption – as a proxy for the quality of governance – has been
established in the past (Agnew et al. 2009). It is of interest to assess how the overall
port State risk index evolves as a function of corruption, using the CPI produced by
Transparency International. In addition to the countries not operating ports and not
having received any foreign vessel visits, sixteen more countries have no allocated
Figure 3: Distribution of internal versus external risk
scores for two groups of countries (n=140)
29
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
CPI scores, limiting the dataset used for this analysis to 124 port States. Given that
CPI is an indicator and
component of internal
risk, it has been
neutralized as an
internal risk component
for this particular
analysis.
The results,
rendered in Figure 4,
confirm earlier findings
on the relationship
between IUU fishing
and corruption, in that
higher CPI scores
(signifying better
performance / lower corruption), induce a downward trend in internal, external and
overall risk. The drop in external risk with improving port State CPI scores is more
than twice as important as the drop in internal risk. External risk diminishes from
2.66 to 2.35 (a total of 0.31 points), when the CPI scores rises from 10 (very high
perceived corruption) to 90 (very low perceived corruption), while internal risk falls
from 2.19 to 2.05 (a total of 0.14 points) over the same range of CPI scores.
The significance of the correlation of external port State IUU risk to CPI scores
is 2.7 times higher than the one relating to PSMA adherence, underscoring the
importance of the corrosive effect of corruption on deterrence and law enforcement
outcomes. Scatter, while still important, is also diminished, leading to higher R2
values on the fitted regression line for external risk, indicative of a better fit, which
in turn is indicative of the structuring effect of good governance. Regression
analysis finds the linear correlation and resulting slopes to be significant for
external risk (pexternal=0.017), while correlation of internal and overall risk are both
insignificant (pinternal=0.459; poverall=0.069).
Given the strong relationship between port State CPI and external risk
established above, it is opportune to examine the relationship between the CPI
scores of port States and the average CPI score of the flag States of all foreign
fishing vessels visiting their ports.
Figure 4: Port State IUU Risk Index versus TI Corruption
Perceptions Index scores (n=124)
R² = 0.05
R² = 0.03
R² = 0.00
1.00
1.50
2.00
2.50
3.00
3.50
020 40 60 80 100
risk scores
Transparency International Perceived Corruption Index
external risk
overall risk
internal risk
Linear (external risk)
Linear (overall risk)
Linear (internal risk)
30
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
The same selection
of 124 port States used
for the analysis in
Figure 4 is used here.
The results are
represented in Figure
5. The spread in the
average CPI score of
fishing vessel flag
States visiting ports (y
axis) is less than the
spread of the port State
CPI scores (x axis).
This owes to the fact
that the scores along
the x axis are
individual port State
scores, while the scores plotted against the y axis are average scores of all flag
States having visited individual port States, naturally reducing the spread in values.
The regression analysis results in a positive trend. Regression analysis finds the
linear correlation and resulting slope to be highly significant (p=0.00000002). As
the governance index of the port State goes up, the average governance index of the
flag States conferring flags to vessels visiting ports goes up too. While the predicted
average flag State governance score of fishing vessels visiting a port State with a
CPI score of 10 is 40, the same score is predicted to be just over 60 when visiting a
port State with a CPI score of 90 – embodying a >50% mean flag State CPI score
increment across the full range of port State CPI scores.
The other remarkable outcome of this analysis is the fact that average flag State
CPI scores clearly split into two distinct groups for visited port States with a CPI
score above 50, one group falling above the regression line (green oval), and the
other falling below the regression line (orange oval). The upper group in the green
Figure 5: Average flag State CPI score versus CPI scores
of visited port States (n=124)
31
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
oval (22 countries)
13
trends strongly upwards against higher port State CPI scores,
while the lower group (18 countries)
14
trends flat.
The upper group is dominated by countries from North America (100% of the
North America region countries contained in this group), and Europe, providing 16
out of the total 22 countries. 50% of all existing Europe region port States are in
this group, and Europe makes up 73% of all countries in this upper group. With the
exception of Iceland, all of the European countries are EU Member States. The
lower group in the orange oval contains countries more evenly spread across world
regions, with Europe providing another 33% of all countries, and Asia 28%. With
regards to the six Europe region countries, only two are EU Member States, while
the five Asian countries represent 26% of all the countries in the Asia region
15
,
dominating this particular metric.
These results – partially reflecting findings conveyed in Table 8 – underscore
the dominance of the North America and Europe regions as consistent performers
in PSM matters; with Europe being more diverse in outcomes, owing to its larger
number of countries, its wider spread of national income levels, its more diverse
fisheries make-up, and its higher exposure to direct seafood imports via foreign
13
BEL, BHS, CAN, CPV, DEU, DNK, ESP, EST, FRA, GBR, IRL, ISL, LTU, LVA, NLD, NZL,
POL, PRT, SVN, SWE, SYC, USA
14
ARE, AUS, BRN, CHL, CRI, CYP, FIN, GEO, ISR, JPN, KOR, MLT, NAM, NOR, QAT, SGP,
TWN, URY
15
Only Cambodia has been eliminated from the Asia region in the dataset underlying this analysis.
32
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
fishing vessel and reefer
landings.
16
With regards to the
split in trends noted above for
a port State CPI score of 51 or
higher, the results also imply
that the use of any of the above
indicators and metrics to
predict the performance of
individual port States (or ports
therein) would be ill-advised.
Finally, it is of interest to
assess the effect of national
income levels
17
on the
distribution of port State IUU
risk index scores, bearing in
mind that Monitoring, Control
and Surveillance (MCS) and the combatting of IUU fishing invariably implies
important budgetary commitments. In running this analysis, the potential influence
and dynamics relating to world regions and/or ocean basins was assessed. Figures
6 & 7 show the results. Figure 6 plots overall average risk scores by region versus
income, while Figure 7 plots overall average risk scores by ocean basin versus
income. One country (the Cook Islands) had to be removed from this dataset, as no
income level has been assigned to it by the World Bank.
The average global trend (dashed line) is the same for both datasets, owing to
the fact that it shows the global average score per income group, which is not
affected by either region or ocean basin influences. The global trend of the average
overall port State IUU risk score by income group is declining across the four tiers
in income levels. The difference between low income and lower middle-income
groups is very small. The average score of low-income countries is 2.484, followed
by 2.478 (lower middle income), 2.42 (upper middle income), and 2.326 (high
income). This implies that income level overall has a measurable and important
16
Note that 819 foreign vessel movements in and out of US ports were detected, while the single
EU member State of Denmark scored 2,121 foreign vessel port visits.
17
National income levels are obtained from the 2018 World Bank list of economies.
Figure 6: Overall average Port State IUU Risk
score by region versus income (n=139)
33
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
impact on PSM
performance, with the
biggest rate of
improved performance
occurring between
countries of the upper
middle income and
high-income groups.
Figure 6 also shows
scores for seven world
regions, by income
group. In some regions,
not all income groups
are represented. Both
Europe and the South
West Pacific are lacking low income countries, while North America harbors two
high income countries only.
With regards to regional trends, there are two fundamentally different types of
world regions. In one set of regions, overall average scores improve consistently
with higher income, while this is not the case in the other group. The regions where
progression in income does not give rise to a marked trend in improved risk scores
are Asia and the Near East, the lowest performing world regions overall (see Table
8). Not only are these lines flat or, in the case of the Near East, rising – the latter
signifying a worsening performance with rising income, moving opposite to the
global trend line – but the overall average scores for these two world regions are
also higher than all others across the entire range – with the exception of two out of
a total of 14 available points of comparison. Overall, a relative consistency in trends
for any single world region across the four income groups is verified. With the
exception of Asia and the Near East, scores consistently fall from lower to higher
income groups, suggesting that income underpins and drives the performance of
individual countries which are part of the same world region.
Figure 7 shows scores for seven ocean basins by income groups of the countries
bordering them. The Arctic and Antarctic basins were not considered in the global
analysis, owing to the very limited number of countries bordering those oceans.
This graph differs markedly from Figure 6 with regards to trend consistency. In
Figure 7: Overall average Port State IUU Risk score by
ocean basin versus income (n=139)
34
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
fact, no ocean basin displays trend consistency in the way it is observed for four out
of six world regions – with the exception of the Eastern Pacific Ocean. All other
scores are invariably switching trend direction at least once, mostly twice. When
looking at the structuring influence and net effect of country groupings when
organized along world regions or ocean basin lines, ocean basin groupings seem to
have a much more limited directional influence on average overall port risk scores
– if any.
3.1.4 Discussion
3.1.4.1 Global ranking of ports
The external indicators of global risk and the global ranking of fishing ports
produced by this study are based primarily on a data source, AIS, that has
limitations which must be considered when reviewing results. The limitations have
been addressed partly through the methodology, but all of the findings must be
viewed through an understanding of this data source, as this is the first time it has
been used for a global port analysis of the type proposed here. The results provide
great value in understanding the relative risks between ports and countries, even if
data limitations may impact the absolute values of reported port visits and
especially the estimated hold capacity of these visits. These data and algorithmic
limitations have different impacts in different countries and ports due to the unique
physical circumstances of these locations and how those must be translated to a
computational approach. There are opportunities for different approaches to be
used, but the results represent an important first step in understanding the global
risks related to ports, and this crucial opportunity for interventions to stop illegally
harvested fish products from entering global supply chains.
The method for grouping different stops into single events was also impacted
by the combined effects of poor detection of AIS transmissions in some regions and
substantial gaps in transmission (intentional or not) that in a minority of instances
led to the inappropriate naming of a port visit event. However, while these instances
resulted in an inappropriate association of an event with a specific port, in rare cases
only did the grouping result in the mis-identification of the country of a port visit.
This means that the global risk indicators produced from this analysis were
unaffected by the issue, although it had some small impact on the absolute value of
port visits, which in turn could affect the rankings.
35
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
Some stop events could not be grouped with a visit to a known port as it was
not possible to implement an algorithm that accounted for every configuration of
port in relation to land and vessel movements. This grouping methodology did
capture and properly group the majority of port visit events from a typical slow
down or delay on approach to a major port by a fishing vessel and all the subsequent
internal movements the vessel makes. However, the unique circumstance of some
ports likely lead to some overcounting of Port Visit Events by failing to group all
Vessel Stop Events into a single Port Visit Event, although significant effort was
made to account for the different circumstances of ports around the world.
Significant effort also went into properly assigning the names of the ports
identified through this AIS analysis. This effort revealed significant gaps in current
global databases for the name and location of ports. Significant research and effort
was made to add and properly name possible ports that captured all of the major
concentrations of port visit events identified from AIS, even when they were not
located near a known port. However, there were many visit events that could not be
assigned to a known port identity and were categorized as visits to unknown ports
or unknown anchorages depending on the distance from land.
At a global level, over 36% of fishing vessels port visits produced by this
analysis were characterized as to “unknown” ports and anchorages. Three quarters
of these visits to unknown locations were in China with a small proportion in
Norway and the remainder distributed across many port States. Interestingly, when
accounting for only visits by known “foreign” vessels to port States, only 8.5% of
foreign-flagged visits were to unknown ports. Approximately one-fifth of these
foreign-flagged visits occurred in China with the remainder distributed across many
port States. 21% of visits were reefer vessels, the majority of these at unknown
anchorages.
36
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
The majority of reefer visits to unknown ports were in China, the Philippines,
the Maldives and Spain. This indicates that the analysis was able to identify the
location and associated name of the ports visited by a significant majority of
foreign-flagged vessels using AIS, a key goal of this study.
The limitations of the analysis to identify port visits and likely errors range are
amplified when these visits were linked to estimates for hold size, due to the poor
globally available records of actual hold size. Several RFMOs publish this
information but it is concentrated on certain size classes and types vessels, primarily
larger ones engaged in international fisheries. These sources were useful for the
purpose of this study as they support more accurate assessment of hold capacity of
port visits by vessels travelling to foreign countries which are generally larger. But
it is much weaker for the smaller vessels that primarily operate within their flag
State’s waters, which is less of a concern given the focus of this paper on foreign-
flagged vessels falling under the PSMA. This issue about source data with smaller
vessels also led to more questionable results when the hold size was estimated based
on variables like vessel type, length, and flag. Flag States without significant
numbers of hold records on international registries were the most likely to have
weaker estimates, while those flag States with many fishing vessels and carriers in
international service likely yield more accurate results.
When comparing the results of this study with those from an earlier study
(Huntington, et al, 2015) that ranked the world’s fishing ports by landings, several
ports are identified in both studies but fundamental differences in the approach and
the data lead to many major landing ports in the previous study failing to make an
appearance here. This is because there is a difference between landing, which is the
first point at which fish is discharged under the responsibility of a national
authority, and an arrival by a vessel that has the potential to carry a certain amount
of fish based on its hold capacity. Not every vessel arrival was linked to the actual
discharge or transfer of a full hold of fish, but could have been associated with
partial unloading, loading of fish, or unrelated activity such as refueling or
resupplying (which is still relevant to the PSMA). This study did not attempt to
ascertain what percentage of visits was linked with those activities at the global
scale.
While earlier discussion has highlighted some potential data and
methodological limitations of this analysis, the findings in general appear to be
consistent with understandings of the global fishing industry in terms of the relative
37
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
scale of vessel visits between ports, even if the absolute values are indicative only,
owing to the fact that not all fishing vessels carry AIS as well as the various
limitations of AIS outlined above.
3.1.4.2 Port State IUU risk analysis
The Port State IUU risk analysis in this study provides a ranking of world (FAO)
regions by overall port State IUU risk index score (Table 8). Regions rank from
high risk to low risk as follows:
Near East > Asia > Southwest Pacific > Africa > Latin America > Europe >
North America
The sequence of regions, ranked for overall port risk in the IUU Fishing Index
(Macfadyen et al. 2019),
18
also a global level analysis, is as follows:
Asia > Middle East > South America > Africa > Caribbean & Central America
> Oceania > Europe > North America
While the regions used in both studies are not exactly the same (Latin America
in the latter study is split into 2 sub-regions), the overall findings resonate between
the two studies.
19
Near East and Asia, carrying highest overall port risks in this
study, are matched in inverse order by Asia and the Middle East in the IUU Fishing
Index. Similarly, Europe and North America are ranked in the same order as the
two regions with the lowest risk. And in both studies, Africa is sitting in the middle
of the range, leaving only Oceania (equivalent to the Southwest Pacific) with a
lower risk in the IUU Fishing Index ranking, than in this study.
In the IUU Fishing Index, China, Russia and Cambodia are the countries with
the highest IUU port-related risks. In this study – in which Cambodia has been
eliminated for lack of AIS-fitted vessel port entries – Russia and China also rank
amongst the three top-risk port States. This underlines that there is an important
18
www.iuufishingindex.net/ranking
19
Note that 5 out of the 16 port State indicators establishing overall risk in this study and serving to
rank countries, mirror indicators used in the IUU Fishing Index. Conversely, these 5 indicators
embody 71% of the indicators used to compute overall port risk in the IUU Fishing Index study,
implying that an important influence for the alignment of regional ranks between studies owes to
indicator alignment.
38
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
degree of coherence in the findings between studies of the same nature, providing
a good degree of confidence in the general validity of the approach and findings.
Analyses of scored port State risks against other port State-related factors,
represented in Figures 2 to 7, confirms a number of expected relationships, the
majority of which being statistically significant. When internal port State risk rises,
external risk rises as well, indicating that better PSM performance leads to reduced
risks carried ashore by visiting foreign vessels – broadly speaking (Figure 2). This
relationship and resulting positive trend are weak (statistically insignificant at
p=0.27), indicating that many other factors determining external risks are also at
work. However, the related trend in figures 4 and 5 are highly significant, indicating
that the effect of improved port State governance on lowering external risk is real.
Being a party to the PSMA (Figure 3) yields a lower and statistically significant
average risk score across all dimensions measured – albeit modest – indicative of
the fact that the adoption of this international regulatory framework has a positive
and structuring influence on port State performance in the domain of PSM.
The relationship of external port State risks against the CPI of the same port
State (Figure 4) is revealing, as the correlation is much stronger than the one of
internal versus external risks, and it is clearly established through this study that the
quality of governance – in its broad sense, and as measured through the CPI – is a
major determining factor of port State performance in the domain of PSM, and its
exposure to foreign vessel IUU risks. The related analysis (see Figure 5) using the
CPI, produces the clearest trend, and strongest correlation. Fishing vessels from
flag States with a low CPI have a tendency to visit ports with a low CPI and a
generally higher port State IUU risk score, and vice-versa. This cements earlier
findings of the same nature. The underlying data and the analysis confirm that the
corrosive effect of corruption – or weak governance in general – directly favors the
existence of high port-associated IUU risks.
Finally, it is established that country income is an important factor determining
port State performance, with higher income countries generally performing better,
and lower income countries performing worse. This is partially explained by the
fact that IUU mitigation measures at the port level require important human and
financial resources that are less available in lower income countries. Two factors
susceptible in modulating this response were analyzed, namely the region and the
ocean basin in which a port State is located (Figures 6 and 7). It was found that
39
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
regions – as an assemblage of countries - produce consistent trends in their response
to income changes, with most regions yielding improving risk scores with
increasing income. However, two regions (Asia and the Near East) were
conspicuously inert to this effect, producing risk that was trending flat or even rising
with increasing income levels.
On the other hand, when countries are regrouped by ocean basin, no
consistent trends were detected – leading to the understanding that regions have a
structuring effect on their countries, while ocean basins do not. This is a finding
that is of true importance for RFMOs, in order to understand and to incorporate
these fundamentals in PSM work targeting their members across the fisheries and
the ocean basins they regulate. The structuring effect that CMM 16/11
20
of the
Indian Ocean Tuna Commission (IOTC) – the first of its kind, and one of the most
advanced in terms of implementation modalities – has on the group of countries
bordering the Indian ocean basin, is largely impalpable in the data (Figure 7),
considering that the two trend lines for the West and East Indian Ocean basins are
separated by a notable difference in average total scores (2.50 and 2.43
respectively), and trend in opposite directions.
20
IOTC Resolution 16/11 On port State measures to prevent, deter and eliminate illegal, unreported
and unregulated fishing
40
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
4 DISCUSSION
4.1 Conclusions
This study firmly cements the value and utility of AIS (and its resulting public-
source data) in the domain of fisheries monitoring, control and surveillance. This
has been the preserve of VMS for decades, a satellite-based communication system
of which the resulting data are generally richer, better quality, and largely publicly
unavailable. AIS technology has reached a degree of maturity and adoption which
allows stakeholders to take it to the next level, although it is important to keep in
mind the limitations of the technology; in this context by aiming it specifically at
IUU-related risk analysis to inform monitoring, law enforcement and capacity
development endeavors. This type of analysis could be made more robust by
incorporating VMS data, as well as new forms of vessel tracking such as GSM-
based reporting tools for small, inshore vessels – noting that the majority of the
world’s fishing vessels are mainly small-scale and do not carry transponders.
It is possible to determine the locations and identities of global ports important
to the industrial fishing industry using AIS data if it is properly layered with other
sources and a comprehensive methodology for identifying port visits is used. A
careful methodology is critical to this type of analysis to account for some of the
inconsistencies of satellite-derived AIS data and the particular and diverse
geographies of different ports. However, there will always be some abnormal
results in this type of global analysis unless all data are manually reviewed, as it is
not possible to develop an algorithm that accounts for the unique circumstances of
every port in the world. Without synthesis with other sources (especially identity
and hold capacity), AIS data is unlikely to produce these results for fishing vessels
and fish carrier vessels.
Most of the publicly available global port information, especially the location
and names of ports, is incomplete, and currently insufficient as a starting point for
this type of analysis. There were major gaps in the knowledge of known world port
locations used by major fishing fleets that the study had to fill. By using AIS-
derived port locations, it is possible to identify “visits” by fishing vessels and carrier
vessels to specific ports. Given the focus of the study on informing implementation
of the PSMA, it is notable that the analysis was able to identify and associate over
91% of port visits by foreign-flagged vessels with ports and anchorages that were
defined through this study. When only foreign-flagged vessel visits are considered,
41
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
the names and relative rankings of the identified ports are familiar to those
knowledgeable with the global fishing industry.
There are differences in the mandatory use of AIS by fishing vessels as well
as the ability of satellites and terrestrial antenna networks to record transmissions
that affect any global analysis. The discrepancies within AIS positional and identity
information, both intentional and unintentional, add another layer of difficulty and
reduce the potential data available for analysis.
The risk analysis – rooted in both AIS and AIS-independent data – show that
AIS data can be combined with data from other sources to build useful indicators.
In this study, many indicators with an AIS component also had an AIS-independent
component, turning them into powerful hybrid indicators; the average governance
index of foreign vessels’ flag States visiting ports is one such example. Other
indicators were either fully AIS, or fully non-AIS based, but worked in unison to
produce relevant IUU risk scores in their respective internal and external
components.
The port State IUU risk analysis allowed for the identification of major regions
and major fishing nations where high port State IUU risks prevail, and where –
specifically with regard to regions – positive trends of improving risk mitigation
with improving national incomes would seem to apply as the general rule, but with
the notable exception of Asia and the Near East. The methodology used is capable
of analyzing and identifying national, regional and global trends – through the use
of weighted indicators and resulting risk scores – that allow a deeper understanding,
not only of how IUU risk is distributed, but also how it would seem to evolve along
gradients such as national income or the quality of governance.
In the same vein, the study established that the quality of governance – using
Transparency International’s Corruption Perceptions Index – of a port State is the
strongest structuring factor that determines the magnitude of its external risks to
IUU exposure – within the set of factors analyzed. For countries with high levels of
endemic corruption/weak governance, this implies that focusing on the
improvement of PSM, in the absence of concomitant improvements in governance
in general, is unlikely to generate substantial results.
While the study finds important differences between regions in terms of IUU
risk mitigation and risk exposure, it also shows that every region harbors weak and
strong performers. The study finds that for a port State being part of a given income
42
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
group, a given region, having a particular CPI score, or receiving visits from
particular types of fleets, is never sufficient to confidently predict its performance
in the domain of PSM – owing to the wide scatter in data.
The ‘deep-dive’ analysis of fourteen individual ports, published separately as a
supplement to this paper, led to the conclusion that a lot of progress remains to be
achieved in the domain of translating key PSMA provisions into national practice
– starting with the designation of ports and the publicly available information
accompanying these port State measures. In general terms, the study found that
national PSMA- or PSM-related information has been very hard to locate in all
cases and that publicizing of PSM information, by individual States and by FAO,
as provided for in the PSM Agreement, is severely lacking. This lack of public
information also limits the depth of analysis that may be achieved by studies such
as this one when looking into the performance of individual ports.
That analysis also found that individual ports do not necessarily reflect the
performance of their countries, nor their region – except by chance – implying that
substantial variation in the performance between individual ports of the same
country is to be expected as a rule, rather than an exception.
4.2 Recommendations
The following recommendations are derived from results and conclusions, and
ordered by specific domain first, and by target audience next.
For AIS-related work in this domain
1. National authorities should consider requirements that make AIS as reliable
as VMS for determining compliance. These may include requiring tamper-
proofing to prevent the manipulation of position and identity. This may
enable greater use of AIS and other tracking technologies for fisheries
control that is more cost effective than traditional VMS.
2. Countries not having done so should publish national registries, update
identity information associated with their vessels’ IMO numbers, and
provide vessel data for inclusion in FAO’s Global Record of Fishing
Vessels, Refrigerated Transport Vessels and Supply Vessels, in order to
enable a greater understanding of the legal standing of vessels operating in
given areas. This should include the MMSI for all authorized vessels
required to have AIS.
43
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
3. Given potential current and/or future resolutions regulating effort, RFMOs
and States should collect and publish vessel hold capacity data. While
creating transparency and improving capacity knowledge at RFMO and
State levels, this would also strengthen the type of analysis presented in this
study.
4. The number of terrestrial AIS receiver networks should be expanded, to
ensure greater port coverage of AIS data in high traffic areas. This will
increase processing requirements.
5. Flag States should mandate the use of AIS on fishing vessels and carriers
leaving their waters.
44
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
For port and flag States
1. Flag and port States should sanction the intentional or unintentional
transmission of false identity and/or positional AIS data. This is important
for safety of life at sea as well as for compliance monitoring efforts and
studies such as this one.
2. Port States should publish vessel movement data on port authority websites
(based on physical vessel monitoring routines). Such data should be kept in
a format that can be readily used (e.g. as a downloadable spreadsheet), with
the port of Las Palmas presenting the best practice case identified in this
study.
3. Port States not having done so to date should plan for the formal designation
of their ports and ensure robust prior notification and authorization regimes
are put in place.
4. Port States having ratified the PSMA should ensure that their PSM-related
information is submitted to FAO for public hosting of the relevant
information – including on designated ports.
5. Port States should develop an easy-to-locate national PSMA-themed web
portal providing third party access to a comprehensive set of resources
regarding port State rules, designated ports, rules of port entry, forms, and
contacts.
6. Port States should consider the use of AIS, among other tools, to actively
monitor sections of known ports frequented by fishing vessels and fish
carrier vessels that may not be part of current compliance plans.
7. Port States should consider the use of AIS, among other tools, to identify
stopping events outside of known ports that may indicate attempts to evade
inspection.
45
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
For FAO
1. FAO should endeavor to greatly improve the collection of comprehensive
data on PSMA implementation by its Members, for public hosting. Such
data should go beyond the strict requirements of the PSMA, for States that
wish to submit and/or publicize such information. Ideally, such data would
include the following:
a. Name and location of designated port.
b. Links to port authority websites.
c. Link(s) to rule set(s) governing prior notification and authorization
for port entry, including risk assessment inspection requirements
and potential penalties.
d. Link(s) to legislation establishing designated ports.
e. Contacts (central fisheries administration and port-specific
authorities).
46
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097
REFERENCES
Agnew, D.J., Pearce, J., Pramod, G., Peatman, T., Watson, R., Beddington, J.R 2009.
Estimating the Worldwide Extent of Illegal Fishing. PloS ONE 4(2): e4570.
https://doi.org/10.1371/journal.pone.0004570
Cajal, J. and García Fernández, J. 2002b. Report on Uruguay. In: Fishery activities and trade
of Patagonian toothfish Dissostichus eleginoides in South America. Quito, TRAFFIC South
America.
FAO. 2017. Globefish Highlights - a quarterly update on world seafood markets. April 2017
issue, with annual 2016 statistics. 2/2017. Rome. www.fao.org/3/ai7332e.pdf
Hosch, G. 2016. Design Options for the Development of Tuna Catch Documentation
Schemes. FAO Fisheries & Aquaculture Technical Paper no. 596. Rome,
FAO.http://www.fao.org/documents/card/en/c/a01d2002-42e4-49eb-acfb-4de035eb8be2/
Huntington, T., Nimmo, F. and Macfadyen, G. 2015. Fish Landings at the World’s Commercial
Fishing Ports. Journal of Ocean and Coastal Economics: Vol. 2, Article 4.
https://cbe.miis.edu/cgi/viewcontent.cgi?article=1031&context=joce
International Telecommunications Union. 2019. Table of Maritime Identification Digits.
https://www.itu.int/en/ITU-R/terrestrial/fmd/Pages/mid.aspx
IOTC. 2016. Resolution 16/11. On port State measures to prevent, deter and eliminate illegal,
unreported and unregulated fishing. Victoria, Seychelles.
Macfadyen, G., Hosch, G., Kaysser, N., and Tagziria, L. 2019. The IUU Fishing Index.
Poseidon Aquatic Resource Management Limited and The Global Initiative Against Transnational
Organized Crime. www.iuufishingindex.net
World Port Index. A dataset produced by the U.S. National Geospatial Intelligence Agency
that includes the names and single point locations of major global ports.
https://msi.nga.mil/NGAPortal/MSI.portal?_nfpb=true&_pageLabel=msi_portal_page_62&pubCo
de=0015
47
Hosch et al.: Vessel Activity and Risk of IUU-Caught Fish in Fishing Ports
Published by Digital Commons @ Center for the Blue Economy, 2019
48
Journal of Ocean and Coastal Economics, Vol. 6, Iss. 1 [2019], Art. 1
https://cbe.miis.edu/joce/vol6/iss1/1
DOI: 10.15351/2373-8456.1097