EIFAC Occasional Paper No. 43

ICES Advisory Committee on Fisheries Management
ICES CM 2008/ACOME:15
Ref. ACOM

Report of the 2008 session
of the Joint EIFAC/ICES
Working Group on Eels
Leuven, Belgium, 3–9 September 2008

ISSN 0258-6096

European Inland Fisheries
Advisory Commission

Report of the 2008 Session of the
Joint EIFAC/ICES Working Group on Eels

Leuven, Belgium, 3–9 September 2008

European Inland Fisheries Advisory Commission
Food and Agriculture Organization of the United Nations
Rome
International Council for the Exploration of the Sea
Copenhagen

FOOD AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS
Rome, 2009
INTERNATIONAL COUNCIL FOR THE EXPLORATION OF THE SEA
Copenhagen, 2009

The designations employed and the presentation of material in this information product do
not imply the expression of any opinion whatsoever on the part of the Food and Agriculture
Organization of the United Nations (FAO) and the International Council for the Exploration
of the Sea (ICES) concerning the legal or development status of any country, territory, city or
area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The
mention of specific companies or products of manufacturers, whether or not these have been
patented, does not imply that these have been endorsed or recommended by FAO and ICES
in preference to others of a similar nature that are not mentioned.
The views expressed in this publication are those of the author(s) and do not necessarily
reflect the views of FAO and ICES.
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© FAO and ICES, 2009

EIFAC/ICES WGEEL Report 2008

iii

Preparation of this document
This publication is the report of the 2008 session of the Joint European Inland
Fisheries Advisory Commission (EIFAC) and International Council for the Explora‐
tion of the Sea (ICES) Working Group on Eels which was held in Leuven, Belgium
from 3 to 9 September 2008.
The Working Group would like to acknowledge ICES for undertaking the editing and
formatting of this publication and FAO for the printing and distribution of hard cop‐
ies.

Contact addresses:
European Inland Fisheries Advisory Commission
Food and Agriculture Organization of the United Nations
Viale delle Terme di Caracalla
00153 Rome, Italy
Telephone (+39) 06 5705 4376
Telefax (+39) 06 5705 3360
www.fao.org publications-sales@fao.org
International Council for the Exploration of the Sea
H.C. Andersens Boulevard 44–46
DK-1553 Copenhagen V, Denmark
Telephone (+45) 33 38 67 00
Telefax (+45) 33 93 42 15
www.ices.dk info@ices.dk

iv

EIFAC/ICES WGEEL Report 2008

Abstract
Available information on recruitment, stock and fisheries continues to support and
reinforce the advice that the European eel stock has declined in most of the distribu‐
tion area and is outside safe biological limits. Recruitment of glass eel to the continen‐
tal stock continues to decline with no obvious sign of recovery. Current levels of
anthropogenic mortality are not sustainable and there is an urgent need that these
should be reduced to as close to zero as possible, as soon as possible. All glass eel re‐
cruitment series demonstrate a clear decline since about 1980 with no sign of recov‐
ery. The Baltic indices of young yellow eel recruitment demonstrate a clear decline
since about 1950. The decline in recruitment appeared stronger in the more northern
and southern parts of the distribution.
In the 1970s, recruitment of glass eel was still at historically high levels indicating that
Spawning Stock Biomass was not limiting the production of recruits at that time.
Quantifying the 1970s spawner escapement therefore is the simplest derivation of a
restoration threshold. The reference threshold should be set at 100% of the 1970s sil‐
ver eel escapement where data are available, or in the absence of data, at a percentage
(40%) of the notional pristine state which would have existed if no anthropogenic
mortalities had impacted on the stock.
It is of utmost importance that existing recruitment monitoring is continued and im‐
proved, easing the dependence on commercial fisheries, and extended where inade‐
quate. A radical improvement in the assessment of the current state of the stock,
including quantification of the impact of anthropogenic mortalities, is urgently
needed. Although comprehensive datasets exist in some river basins, this assessment
will not be achievable in most river basins from currently limited data. Data disconti‐
nuities are likely to occur simultaneously and unlike in the past, statistical modelling
will not be able to correct for this.
The first post‐evaluation of the EU Regulation is required by mid‐2012. Timely devel‐
opment of stock‐wide assessment procedures is required, geared to the data becom‐
ing available, while indicating the progress towards recovery of the stock. The
absence of any internationally driven requirement to maintain a recruitment dataser‐
ies needs to be corrected, with reference to the recommendations of the EU contract
98/076: Establishment of a recruit monitoring system for glass eel. The current legisla‐
tive instruments including the Eel Regulation, DCR, CITES and WFD do not, either
individually on in combination, contain sufficient provisions to ensure adequate data
supply for such assessments.
It is suggested that managers define interim targets for the management measures in
order to integrate local action efficiently to the aim of long‐term recovery of the
European eel stock. For this purpose sub‐targets defining the magnitude of manage‐
ment measures will be linked with eel sub‐targets reflecting the expected short‐term
response of the local eel population. Eel sub‐targets should therefore allow a fairly
rapid evaluation of the management measures taken but sensitivity and time re‐
sponse of some of the proposed eel sub‐targets would need further investigation be‐
fore their application would be operational. Eel sub‐targets should finally be
integrated into the evaluation of the status of the whole eel stock. However it has to
be recognized that adequate methods, or modelling approaches, for achieving this are
still lacking.
There are few quantitative estimates of pristine (pre‐1980) and current silver eel pro‐
duction (Regulation EU 1100/2007) to allow comparisons to be made between systems

EIFAC/ICES WGEEL Report 2008

v

and there is few data on the importance of estuarine and coastal populations to over‐
all production. Modelling will be needed to transfer estimates from data rich to data
poor systems. Some approaches have been outlined by this Working Group which
compliment those presented in previous working groups and in EU SLIME (Dekker
et al., 2006).
Implementation of EMPs requires the development of methods to obtain silver eel
escapement data. They can include either direct (e.g. mark‐recapture) or indirect
measures (yellow eel proxies to determine habitat‐based silver eel production). Use of
direct methods, though preferable in many respects, will be severely restricted by
uneven distribution of silver eel fisheries within and between regions, limited fishery
monitoring resources and extreme fluctuations in river flows during migratory runs
affecting the efficiency of capture methods.
A variety of indirect methods, mostly dependant on yellow eel proxies and model‐
ling, are available for areas where direct measurements of silver eel escapement are
not possible and should be extensively used to estimate regional and national silver
eel escapement. Validation of indirect methods should be undertaken on an ongoing
basis for a network of river systems where reliable direct estimation of silver eel es‐
capement biomass is possible. Direct assessment of silver eel may, however, not in‐
form on the impacting factors that require management, where yellow eel monitoring
and assessment would be more informative.
Estimation of effective spawner biomass requires quantification of the adverse effects
of contaminants, parasites, diseases, low fat levels, non‐lethal turbine damage, along
the lines previously proposed for Anquillicola crassus, as well as other mortality rates
throughout the river basin. Present knowledge does not fully permit quantitative as‐
sessment of the effects of these factors on the overall stock. The European Eel Quality
Database (EEQD) has been updated with data on contaminants, parasites and fat lev‐
els in eel, allowing the compilation of an overview of the contaminant load in eel over
its distribution area. The data are highly variable within river basin districts, accord‐
ing to local anthropogenic pollution, linked with land use. Persistently elevated con‐
tamination levels, above human consumption standards, are seen in many European
countries. Fat content of the yellow eels (i.e. in Belgium and the Netherlands) has de‐
creased over the last number of years, which raises concern regarding the migratory
and reproductory success of silver eels. A. crassus is spreading further into new areas
and new data indicate the presence of the nematode in Canada for the first time.
At present, it is estimated that around 7.5 to 15% of the glass eel catch is used for
stocking, either directly or as on‐grown eels. Estimates suggest an insufficient supply
of glass eel from the total fishery for stocking to full capacity at the European level.
Nevertheless, the Regulation 1100/2007 requires that 35%, rising to 60%, of glass eel
catches are made available for stocking to enhance the stock. If these percentages
were applied to recent annual catches of glass eel, the potential lifetime effect of this
increased level of stocking, in the absence of anthropogenic mortalities, could be in
the same order of magnitude as current fisheries or eel culture. However, there is a
continuing and urgent requirement for robust evidence of the extent to which stock‐
ing and transfers on local, national and international scales can increase silver eel es‐
capement and spawner biomass.
The risks remain of disease and parasite transfer via stocked material, both from
stocking glass eel and on‐grown eels. For example, eels in aquaculture infected with
pathogens (viruses, etc.) should not be used for stocking purposes. At least half the
countries surveyed (17) do not have formal stocking protocols. These should include
procedures to prevent the introduction and spreading of parasites and diseases, and

vi

EIFAC/ICES WGEEL Report 2008

eel should be included in the European fish disease prevention policies to help mini‐
mize the risks.
Sufficiently long time‐series of glass eel recruitment, covering several periods of the
natural climatic oscillation over the North Atlantic, reflect the same periodicity.
However, the causal link between climate and recruitment strength, is unknown, as
well as where and when ocean environmental factors operate on the eel. As long as
the causal factors of oceanic influence are unknown, it is not safe to assume that the
decline is explained by climate alone, especially while anthropogenic influences are
known to be large and better understood. The fact that oceanic climate may contrib‐
ute to recruitment variation is not grounds for abstaining from all possible measures
to increase silver eel escapement to boost spawning‐stock biomass. The recent, pro‐
longed strong decline in eel recruitment is out of phase with the dominating climate
cycle, the North Atlantic Oscillation.

FAO European Inland Fisheries Advisory Commission; International Council for the
Exploration of the Sea.
Report of the 2008 session of the Joint EIFAC/ICES Working Group on Eels. Leuven,
Belgium, 3–7 September 2008. EIFAC Occasional Paper. No. 43. ICES CM
2009/ACOM:15. Rome, FAO/Copenhagen, ICES. 2009. 192p. (Includes a CD‐ROM).

EIFAC/ICES WGEEL Report 2008

vii

Executive summary
This report summarizes the presentations, discussions and recommendations of the
2008 session of the Joint EIFAC/ICES Working Group on Eels which took place in
Research Institute for Nature and Forest, Leuven, Leuven (Belgium) from 3 to 9 Sep‐
tember 2008.
In this section, the main outcomes from the report are summarized, a forward focus is
proposed in the light of the EU Regulation for the Recovery of the Eel Stock and the
main recommendations are presented.
It is clear from this report that recruitment is still low, the stock is in decline and ur‐
gent protection measures are required. Significant pressures have been placed on the
scientific and technical system to support the delivery of Eel Management Plans by
December 2008 with parallel processes and undetermined actions resulting in some
uncertainties to be coped with by the Working Group in 2008.

viii

EIFAC/ICES WGEEL Report 2008

Summary of this report
Reviewing the available information on recruitment, stock and fisheries continues to
support and reinforce the advice that the global European Eel stock has declined in
most of the distribution area and is outside safe biological limits. Recruitment of glass
eel to the continental stock continues to decline with no obvious sign of recovery.
Current levels of anthropogenic mortality are not sustainable and there is an urgent
need that these should be reduced to as close to zero as possible, as soon as possible.
All glass eel recruitment series demonstrate a clear decline since about 1980 with no
sign of recovery. The Baltic indices of young yellow eel recruitment demonstrate a
clear decline since about 1950. The decline in recruitment appeared stronger in the
more northern and southern parts of the distribution. It is recommended to use re‐
cruitment indices per area (Baltic, North Sea, British Isles, Atlantic Coast, eastern and
western Mediterranean), and to collect and analyse additional data to confirm the
spatial pattern, and to establish the reliability and bias in the different sampling
methods.
In the 1970s, recruitment of glass eel was still at historically high levels. This indicates
that SSB was not limiting the production of recruits at that time. Quantification of the
1970s spawner escapement therefore is the simplest derivation of a restoration
threshold. Note that in this case, the full escapement of the silver eels in the 1970s
(given the anthropogenic mortality of that time) corresponds to the escapement level
advised by ICES (2002). That is: one should either set the reference threshold at 100%
of the 1970s silver eel escapement where data are available, or in the absence of data,
at a percentage (40%) of the notional pristine state which would have existed if no
anthropogenic mortalities had impacted on the stock.
It is of utmost importance that existing recruitment monitoring is continued and im‐
proved, easing the dependence on commercial fisheries, and extended where inade‐
quate. A radical improvement in the assessment of the current state of the stock,
including quantification of the impact of anthropogenic mortalities, is urgently
needed. Although comprehensive datasets exist in some river basins, this assessment
will not be achievable in most river basins from currently limited data. Data disconti‐
nuities are likely to occur simultaneously and unlike in the past, statistical modelling
will not be able to correct for this. Therefore, discontinuities will have to be taken for
granted.
The first post‐evaluation of the EU Regulation is required by mid‐2012. Timely devel‐
opment of stock‐wide assessment procedures is required, geared to the data becom‐
ing available, while indicating the progress toward recovery of the stock. The absence
of any internationally driven requirement to maintain a recruitment dataseries needs
to be corrected, with reference to the recommendations of the EU contract 98/076: Es‐
tablishment of a recruit monitoring system for glass eel. The current legislative in‐
struments including the Eel Regulation, DCR, CITES and WFD do not, either
individually on in combination, contain sufficient provisions to ensure adequate data
supply for such assessments.
It is suggested that managers define interim targets for the management measures in
order to integrate local action efficiently to the aim of long‐term recovery of the
European eel stock. For this purpose sub‐targets defining the magnitude of manage‐
ment measures will be linked with eel sub‐targets reflecting the expected short‐term
response of the local eel population. Eel sub‐targets should therefore allow a fairly
rapid evaluation of the management measures taken but sensitivity and time re‐
sponse of some of the proposed eel sub‐targets would need further investigation be‐

EIFAC/ICES WGEEL Report 2008

ix

fore their application would be operational. Eel sub‐targets should finally be inte‐
grated into the evaluation of the status of the whole eel stock. However it has to be
recognized that adequate methods, or modelling approaches, for doing this exercise
are still lacking.
There are few quantitative estimates of pristine (pre‐1980) and current silver eel pro‐
duction (Regulation EU 1100/2007) to allow comparisons to be made between systems
and there is few data on the importance of estuarine and coastal populations to over‐
all production. Modelling will be needed to transfer estimates from data rich to data
poor systems. Some approaches have been outlined by this Working Group which
compliment those presented in previous working groups and in EU SLIME (Dekker
et al., 2006).
Implementation of EMPs requires the development of methods to obtain silver eel
escapement data. They can include either direct (e.g. mark‐recapture) or indirect
measures (yellow eel proxies to determine habitat‐based silver eel production). Use of
direct methods, though preferable in many respects, will be severely restricted by
uneven distribution of silver eel fisheries within and between regions, limited fishery
monitoring resources and extreme fluctuations in river flows during migratory runs
affecting the efficiency of capture methods.
A variety of indirect methods, mostly dependant on yellow eel proxies and model‐
ling, are available for areas where direct measurements of silver eel escapement are
not possible and should be extensively used to estimate regional and national silver
eel escapement. Selection of models should take account of SLIME conclusions (Dek‐
ker et al., 2006) and advice given elsewhere in this report. Validation of indirect
methods should be undertaken on an ongoing basis for a network of river systems
where reliable direct estimation of silver eel escapement biomass is possible. Direct
assessment of silver eel may, however, not inform on the impacting factors that re‐
quire management, where yellow eel monitoring and assessment would be more in‐
formative.
Estimation of effective spawner biomass requires quantification of the adverse effects
of contaminants, parasites, diseases, low fat levels, non‐lethal turbine damage, along
the lines previously proposed for Anquillicola crassus, as well as other mortality rates
throughout the river basin. Present knowledge does not fully permit quantitative as‐
sessment of the effects of these factors on the overall stock.
The European Eel Quality Database (EEQD) has been updated with data on contami‐
nants, parasites and fat levels in eel, allowing the compilation of a comprehensive
overview of the contaminant load in eel over its distribution area. Results demon‐
strate highly variable data within river basin districts, according to local anthropo‐
genic pollution, linked with land use. Persistently elevated contamination levels,
above human consumption standards, are seen in many European countries. The
most important reported impact is seen on the fat content of the yellow eels (i.e. in
Belgium and the Netherlands) which has decreased over the last number years and
which raises concern regarding the migratory and reproductory success of silver eels.
There is growing evidence that A. crassus is spreading further into new areas and new
data indicate the presence of the nematode in Canada (not included in the EEQD yet)
for the first time.
At present, it is estimated that around 7.5 to 15% of the glass eel catch is used for
stocking, either directly or as on‐grown eels. Estimates suggest an insufficient supply
of glass eel from the total fishery for stocking to full capacity at the European level.
Nevertheless, the Regulation 1100/2007 requires that 35%, rising to 60%, of glass eel
catches are made available for stocking to enhance the stock. If these percentages

x

EIFAC/ICES WGEEL Report 2008

were applied to recent annual catches of glass eel, the potential lifetime effect of this
increased level of stocking, in the absence of anthropogenic mortalities, could be in
the same order of magnitude as current fisheries or eel culture. However, there is a
continuing and urgent requirement for robust evidence of the extent to which stock‐
ing and transfers on local, national and international scales can increase silver eel es‐
capement and spawner biomass.
The risks remain of disease and parasite transfer via stocked material, both from
stocking glass eel and on‐grown eels. For example, eels in aquaculture infected with
pathogens (viruses, etc.) should not be used for stocking purposes. At least half the
countries surveyed (17) do not have formal stocking protocols. These should include
procedures to prevent the introduction and spreading of parasites and diseases, and
the eel should be included in the European fish disease prevention policies to help
minimize the risks.
Sufficiently long time‐series of glass eel recruitment, covering several periods of the
natural climatic oscillation over the North Atlantic, reflect the same periodicity.
However, the causal link between climate and recruitment strength, is unknown, as
well as where and when ocean environmental factors operate on the eel. As long as
the causal factors of oceanic influence are unknown, it is not safe to assume that the
decline is explained by climate alone, especially while we know that the anthropo‐
genic influences during the continental life stage of the eel are large and better under‐
stood. The fact that oceanic climate may contribute to recruitment variation is not
grounds for abstaining from all possible measures to increase silver eel escapement to
boost spawning‐stock biomass. More research is needed to compare the relative im‐
pact of climatic effects and continental factors on reproductive success. The recent,
prolonged strong decline in eel recruitment is out of phase with the dominating cli‐
mate cycle, the North Atlantic Oscillation.

EIFAC/ICES WGEEL Report 2008

xi

Forward focus
This report constitutes a further step in an ongoing process of documenting eel stock
status and fisheries and developing a methodology for giving scientific advice on
management to affect a recovery of the European eel. A European plan for recovery
of the stock was adopted in 2007 by the EU Council of Ministers. This plan obliges the
Member States to develop Eel Management Plans by the 31st December 2008. This
will require further scientific advice, on the national and international level. The im‐
plementation of these plans, foreseen in 2009, will improve and extend the informa‐
tion on stock and fisheries. Improved reliability and better spatial coverage, however,
will also generate a breakpoint in several currently available time‐series; correction
procedures need to be considered. In 2012, Member States will report on protective
measures implemented in their territories, and their effects on the stock, for which
methodology is currently limited. International post‐evaluation requires that data,
gathered within this framework of national/regional management plans, become
available to the Working Group, although gaps have been identified where these data
may fall short of that required. Establishment of an international database and the
development of international post‐evaluation procedures for measuring the impact
on the stock will be required.
The Eel Regulation and eel management plans, CITES and the DCR for Eel will likely
radically change management of eel and the Working Group is therefore entering
into a dynamic period in which it is difficult to be categorical on itʹs future focus. The
future focus of the Working Group might concentrate on:
•

the assessment of the trends in recruitment and stock, for international
stock assessment, in light of the implementation of the Eel Management
Plans;

•

the development of methods to post‐evaluate effects of management plans
at the stock‐wide level;

•

the development of methods for the assessment of the status of local eel
populations, the impact of fisheries and other anthropogenic impacts, and
of implemented management measures;

•

the establishment of international databases on eel stock, fisheries and
other anthropogenic impacts, as well as habitat and eel quality related
data, and the review and development of recommendations on inclusion of
data quality issues, including the impact of the implementation of the eel
recovery plan on time‐series data, on stock assessment methods;

•

reviewing and developing approaches to quantifying the effects of eel
quality on stock dynamics and integrating these in stock assessment meth‐
ods;

•

responding to specific requests in support of the eel stock recovery Regula‐
tion, as necessary; and

•

reporting on improvements to the scientific basis for advice on the man‐
agement of European and American eel.

xii

EIFAC/ICES WGEEL Report 2008

Main recommendations
1 ) Since recruitment remains at an all time low since records began, the stock
continues to decline and stock recovery will be a long‐term process for bio‐
logical reasons, all exploitation and other negative anthropogenic factors
impacting on the stock and affecting the production/escapement of silver
eels should be reduced to as low as possible, until long‐term stock recovery
is achieved.
2 ) Assessment of the current and future status of the European spawning
stock, in light of implementation of EMPs, including an assessment of the
impact of anthropogenic mortalities and management actions, is urgently
needed. This process should include:
2.1 )

The aggregation of river basin specific data and assessments, into
stock‐wide assessments;

2.2 )

The further development of models to assess compliance with the
recovery target and evaluate management actions;

2.3 )

The development of coherent local stock assessment procedures;

2.4 )

The development of proxies for mortality rates;

2.5 )

The international assessment of recruitment and stock trends to as‐
sess the response of the stock to management actions.

3 ) Eel Management Plans and their accompanying data should be made
available to the joint EIFAC/ICES Working Group on Eel at the earliest op‐
portunity to facilitate the assessments of the stock.

EIFAC/ICES WGEEL Report 2008

A toast to Leuven, by WGEEL
There are many ways to measure eel
Length, weight, number found in creel
But if the numbers were your only policy
Don’t forget to test the quality.
We tried to do this, here in Belgium
Without drinking to delirium
Writing decision trees on table mats
While beer flowed fast from the taps.
Our SPR curves were made from chips
And designed us surveys for big ships
To re‐search the uncertain ocean
For leptocephali in motion.
Now ‐Instead of moving down the text
We back‐track from what should come next
So go back to line nineteen‐twenty
For targets set when eels were plenty.
But all this thought is much too hard
For the inebriated bard
So let us re‐check the strength of drink
Before our research vessels sink.
Yes, the best beer’s rather strong
Best drunk from glasses short, not long
Test them all‐find what you like
But don’t ride home on a condemned bike.
Or you’ll fall on Leuven’s cobbled lanes
Tear your stockings, or rip your genes
So after an evening of perfect libation
Take a taxi home‐in assisted migration.

xiii

xiv

EIFAC/ICES WGEEL Report 2008

EIFAC/ICES WGEEL Report 2008

xv

Glossary
Eels are quite unlike other fish. Consequently, eel fisheries and eel biology come with
a specialised jargon. This section provides a quick introduction for outside readers. It
is by no means intended to be exhaustive.

Glass eel
Leptocephalus
Elver

Eggs

Ocean

Continent

2 year

5-50 year

Yellow eel
Spawning
Silver eel

The life cycle of the European eel. The names of the major life stages are indicated. Spawning and
eggs have never been observed in the wild.

Glass eel

Young, unpigmented eel, recruiting from the sea into continental wa‐
ters

Elver

Young eel, in its 1st year following recruitment from the ocean. The
elver stage is sometimes considered to exclude the glass eel stage, but
not by everyone. Thus, it is a confusing term.

Bootlace,
fingerling

Intermediate sized eels, approx. 10–25 cm in length. These terms are
most often used in relation to stocking. The exact size of the eels may
vary considerably. Thus, it is a confusing term.

Yellow eel

Life stage resident in continental waters. Often defined as a sedentary
phase, but migration within and between rivers, and to and from
coastal waters occurs. This phase encompasses the elver and bootlace
stages.

(Brown
eel)
Silver eel

Migratory phase following the yellow eel phase. Eel characterized by
darkened back, silvery belly with a clearly contrasting black lateral line,
enlarged eyes. Downstream migration towards the sea, and subse‐
quently westwards. This phase mainly occurs in the second half of cal‐
endar years, though some are observed throughout winter and
following spring.

xvi

EIFAC/ICES WGEEL Report 2008

Eel River
Basin

“Member States shall identify and define the individual river basins
lying within their national territory that constitute natural habitats for
the European eel (eel river basins) which may include maritime waters.
If appropriate justification is provided, a Member State may designate
the whole of its national territory or an existing regional administrative
unit as one eel river basin. In defining eel river basins, Member States
shall have the maximum possible regard for the administrative ar‐
rangements referred to in Article 3 of Directive 2000/60/EC [i.e. River
Basin Districts of the Water Framework Directive].”

River Basin
District

The area of land and sea, made up of one or more neighbouring river
basins together with their associated surface and groundwaters, transi‐
tional and coastal waters, which is identified under Article 3(1) of the
Water Framework Directive as the main unit for management of river
basins. Term used in relation to the EU Water Framework Directive.

Stocking

Stocking is the practice of adding fish [eels] to a waterbody from an‐
other source, to supplement existing populations or to create a popula‐
tion where none exists.

EIFAC/ICES WGEEL Report 2008

xvii

Contents
Preparation of this document ............................................................................................. iii
Abstract .................................................................................................................................. iv
Executive summary ............................................................................................................. vii
Summary of this report .....................................................................................................viii
Forward focus ........................................................................................................................ xi
Main recommendations ..................................................................................................... xii
Glossary .................................................................................................................................xv
Contents .............................................................................................................................. xvii
1

Introduction .................................................................................................................... 1
1.1

2

The 2008 WGEEL .................................................................................................. 1

Trends in recruitment, stocking, yield and aquaculture ........................................ 3
2.1

Data ......................................................................................................................... 3
2.1.1
2.1.2
2.1.3
2.1.4
2.1.5

2.2

Recruitment .............................................................................................. 3
Data on landings ...................................................................................... 4
Recreational and non‐commercial fisheries ......................................... 6
Trends in stocking.................................................................................... 9
Aquaculture ............................................................................................ 11

Analysis of trends in recruitment ..................................................................... 13
2.2.1 Area effect on glass eel and young of the year recruitment ............. 15
2.2.2 Sampling type effect on glass eel and young of the year
recruitment ............................................................................................. 18
2.2.3 Area effect on young yellow eel older than 1 year ............................ 21
2.2.4 Discussion ............................................................................................... 23

2.3

Conclusions and recommendations for Chapter 2: Trends in
recruitment, stocking, yield and aquaculture ................................................. 24
2.3.1 Conclusions............................................................................................. 24
2.3.2 Recommendation ................................................................................... 24

3

International stock assessment and data needs ..................................................... 26
3.1

Introduction on stock assessment and data needs ......................................... 26

3.2

International stock assessment.......................................................................... 26
3.2.1
3.2.2
3.2.3
3.2.4

3.3

International management and stock assessment ............................. 26
Only recruitment and escapement trends? ........................................ 28
Issues of time‐scale ................................................................................ 28
If recruitment continues to decline ...................................................... 28

Data requirement ................................................................................................ 29
3.3.1 River Basin vs. international uses of data ........................................... 29
3.3.2 Use of yellow eel data ........................................................................... 30

xviii

EIFAC/ICES WGEEL Report 2008

3.3.3 The EU Eel Regulation .......................................................................... 30
3.3.4 Checklist of actions required under the Eel Regulation and
associated guidelines ............................................................................. 30
3.3.5 Data Collection Regulation ................................................................... 31
3.3.6 Recruitment dataseries are not secured .............................................. 32
3.3.7 Water Framework Directive ................................................................. 32
3.3.8 Data availability for international analyses........................................ 32
3.4

Stock assessment vs. research needs ................................................................ 34

3.5

Stock assessment ................................................................................................. 35
3.5.1 Mortality based management targets.................................................. 35
3.5.2 Density dependence and stock assessment ........................................ 35
3.5.3 Assessment tools .................................................................................... 36

3.6

4

Conclusions and recommendations for Chapter 3: International
stock assessment and data needs ...................................................................... 37

Assessing stocks and management actions ............................................................. 38
4.1

Background theory on population dynamics ................................................. 38
4.1.1 Introduction ............................................................................................ 38
4.1.2 Eel stock and stock decline ................................................................... 40

4.2

Targets .................................................................................................................. 41

4.3

Estimation of spawner escapement .................................................................. 42
4.3.1 Estimation of silver eel escapement pre‐ and post‐1980 ................... 43
4.3.2 Modelling approaches ........................................................................... 46

4.4

Future methods for silver eel escapement (yellow eel proxies) ................... 57
4.4.1 At the catchment level ........................................................................... 58
4.4.2 At the regional level .............................................................................. 60

4.5

Methods for evaluation of management measures ........................................ 60
4.5.1 Management measures and methods for evaluation ........................ 61
4.5.2 Eel sub‐target .......................................................................................... 64

4.6

Conclusions and recommendations for Chapter 4: Assessing stocks
and management actions ................................................................................... 70
4.6.1 Conclusions............................................................................................. 70
4.6.2 Recommendations ................................................................................. 71

5

Stocking and aquaculture .......................................................................................... 72
5.1

Introduction ......................................................................................................... 72

5.2

Methods to assess the relative contribution of stocking to the
regeneration of the European stock, and for EMPs........................................ 72
5.2.1 Source of glass eel .................................................................................. 72
5.2.2 Yield potential ........................................................................................ 73

5.3

Review of stocking activity across Europe ...................................................... 73

5.4

Decision framework ........................................................................................... 76
5.4.1 Management policies............................................................................. 76
5.4.2 Ecological considerations ...................................................................... 77
5.4.3 Fisheries considerations and considerations for other users ........... 81

EIFAC/ICES WGEEL Report 2008

xix

5.4.4 Implementation constraints .................................................................. 82
5.5

Artificial reproduction of eel ............................................................................. 84
5.5.1
5.5.2
5.5.3
5.5.4
5.5.5

5.6

Conclusions for Chapter 5: Stocking and aquaculture .................................. 86
5.6.1
5.6.2
5.6.3
5.6.4
5.6.5

5.7

Introduction ............................................................................................ 84
Silver eels ................................................................................................ 85
Embryo and larval development ......................................................... 85
Artificial reproduction techniques ...................................................... 85
The Japanese Experience ....................................................................... 86
Potential benefit of stocking to regenerate the stock......................... 86
Identifying local surplus ....................................................................... 86
Post‐evaluation of the net benefit of stocking .................................... 86
Risks of stocking..................................................................................... 86
Aquaculture/on‐growing to support stocking for
enhancement........................................................................................... 87

Recommendations .............................................................................................. 87
5.7.1 Methods to support the basis of stocking for enhancement
purposes .................................................................................................. 87
5.7.2 Risks associated with stocking ............................................................. 87

6

Eel quality ..................................................................................................................... 89
6.1

Introduction ......................................................................................................... 89

6.2

Contaminants ...................................................................................................... 90
6.2.1
6.2.2
6.2.3
6.2.4

Introduction ............................................................................................ 90
The eel and the Water Framework Directive ..................................... 90
Eel pollution monitoring networks‐status and trends ...................... 93
Contamination in eel and its role in the decline of the stock ........... 95

6.3

Parasites/pathogens ............................................................................................ 99

6.4

Quality assessment of spawners using genomic tools................................... 99

6.5

The European Eel Quality Database ................................................................ 99
6.5.1 Introduction ............................................................................................ 99
6.5.2 Analysis of the EEQD .......................................................................... 100
6.5.3 Future development of the database ................................................. 102

6.6

Conclusions and recommendations for Chapter 6: Eel quality .................. 102
6.6.1 Conclusions........................................................................................... 102
6.6.2 Recommendations ............................................................................... 103

7

Oceans, climate and recruitment............................................................................. 104
7.1

Introduction ....................................................................................................... 104

7.2

Review of ocean change/controlling mechanisms ....................................... 104

7.3

Review of recruitment patterns in eels .......................................................... 105

7.4

Review of hypotheses of causal linkages between oceanic factors
and recruitment patterns ................................................................................. 107

7.5

Ocean factors as reason (or contributory factor) for recruitment
decline (1980s onwards) ................................................................................... 110

xx

EIFAC/ICES WGEEL Report 2008

7.6

Conclusions and recommendations for Chapter 7: Oceans, climate
and recruitment................................................................................................. 112
7.6.1 Conclusions........................................................................................... 112
7.6.2 Recommendations ............................................................................... 113

8

Research needs ........................................................................................................... 114
8.1

Introduction ....................................................................................................... 114

8.2

Priority research needs..................................................................................... 114
8.2.1 International stock assessment and trend monitoring.................... 115
8.2.2 Local stock assessment and post‐evaluation of management
actions .................................................................................................... 115
8.2.3 Process based research on biological parameters required
for estimating escapement .................................................................. 115

9

8.3

Other research needs ........................................................................................ 116

8.4

Proposals for study groups ............................................................................. 116

References ................................................................................................................... 117

Annex 1 – List of participants........................................................................................... 131
Annex 2 – Agenda............................................................................................................... 136
Annex 3 – Recruitment, landings and stocking dataseries ......................................... 138
Annex 4 – The use of genetics in the management of European eel ......................... 158
Annex 5 – Country overview of contaminant and parasite/pathogens in eel ......... 176
Annex 6 – Draft WGEEL terms of reference 2009 ......................................................... 187
Annex 7 – Technical minutes Eel Review Group 2008 ................................................ 188
Annex 8 – Country Reports: Eel stock and fisheries reported by country‐
2008 ............................................................................................................................... 192

EIFAC/ICES WGEEL Report 2008

1

Introduction

1.1

The 2008 WGEEL

1

At the 95th Statutory Meeting of ICES (2007) and the 25th meeting of EIFAC (2008) it
was decided that:
2007/2/ACOM15 The Joint EIFAC/ICES Working Group on Eels [WGEEL] (Chair:
Russell Poole, Ireland), will meet in Leuven (INBO/KUL), Belgium, 3–9 September
2008, to:
(i)

assess the trends in recruitment, stock and fisheries indicative of the status
of the European stock, and of the impact of exploitation and other anthro‐
pogenic factors; analyse the impact of the implementation of the eel re‐
covery plan on time‐series data (i.e. data discontinuities). This might also
include the establishment of an international database for data on eel
stock and fisheries, as well as habitat and eel quality (update EEQD) re‐
lated data; review and make recommendations on data quality issues;

(ii)

develop methodologies for the assessment of the status of the eel stock,
the impact of fisheries and other anthropogenic impacts and of imple‐
mented management measures; this might include, for example, support
for EMPs on the determination of ʺpristineʺ spawner production levels
and relative contribution of stocking;

(iii)

review hypotheses and information on the possible relationships between
the European (and American?) eel stock(s), recruitment patterns and cli‐
matic and oceanic factors;

(iv)

respond to specific requests in support of the development and imple‐
mentation of the stock recovery Regulation as necessary;

(v)

report on progress in work on improvements in the scientific basis for ad‐
vice on management of European eel fisheries.

WGEEL will report by 16 September 2008 for the attention of ACOM and DFC.
41 people attended the meeting, from seventeen countries (see Annex 1).
The current Terms of Reference and Report constitute a further step in an ongoing
process of documenting the status of the European eel stock and fisheries and compil‐
ing management advice. As such, the current Report does not present a comprehen‐
sive overview, but should be read in conjunction with previous reports (ICES, 2000;
2002; 2003; 2004, 2005a, 2006, 2007).
In addition to documenting the status of the stock and fisheries and compiling man‐
agement advice, in previous years the Working Group also provided scientific advice
in support of the establishment of a recovery plan for the stock of European Eel by
the EU. In 2007, the EU published the Regulation establishing measures for the recov‐
ery of the eel stock (EC 1100/2007). This introduced new challenges for the Working
Group, requiring development of new methodologies for local and regional stock
assessments and evaluation of the status of the stock at the international level. Im‐
plementation of the Eel Management Plans will likely introduce discontinuities to
data trends and may require a shift from fisheries‐based to scientific survey‐based
assessments.
The structure of this report does not strictly follow the order of the Terms of Refer‐
ence for the meeting, since different aspects of subjects were covered under different

2

EIFAC/ICES WGEEL Report 2008

headings, and a rearrangement of the Sections by subject was considered preferable.
The meeting was organized using the Agenda in Annex 2. Five subgroups, under the
headings of ʺData and International Stock Assessmentʺ, ʺMethods and Methodolo‐
giesʺ, ʺStockingʺ, ʺEel Qualityʺ and ʺOceans and Climateʺ addressed the Terms of
Reference.
Chapter 2 presents trends in recruitment, stock, fisheries and aquaculture (ToR a).
Chapter 3 introduces the concept of post‐evaluation and stock assessment at the in‐
ternational level, discusses data sources and gaps and presents a decision structure
for stock assessment. (ToR a, b and e).
Chapter 4 discusses methods for the estimation of pristine and current escapement,
(ToR a and e).
Chapter 5 reviews the data for stocking and aquaculture and updates previous ad‐
vice on best practice for stocking (ToR a and b).
Chapter 6 updates the European Eel Quality Database (EEQD) and discusses the im‐
portance of the inclusion of spawner quality parameters in stock management advice
(ToR a).
Chapter 7 reviews the hypotheses and information on possible relationships between
recruitment, and climatic and ocean factors (ToR c.).
Terms of Reference a. (revision of catch statistics) is the follow‐up of the analysis
made in the report of the 2004 meeting of the Working Group (ICES 2005, specifically
Annex 2). Following that meeting, a Workshop was held under the umbrella of the
European Data Collection Regulation (DCR), in September 2005, Sånga Säby (Stock‐
holm, Sweden). The Workshop report presented catch statistics in greater detail than
had been handled by this Working Group before. Additionally, a further improve‐
ment of the catch statistics is foreseen, when the DCR is actually implemented for the
eel fisheries across Europe.
It is envisaged that additional data and improved data will become available under
the Eel and Data Collection Regulations.

EIFAC/ICES WGEEL Report 2008

3

2

Trends in recruitment, stocking, yield and aquaculture

2.1

Data
This Section collects the time‐series datasets for the analysis of the status of the Euro‐
pean eel population through the trends in recruitment, commercial landings, non‐
commercial and recreational catches stocking and aquaculture production of eel.
2.1.1

Recruitment

Information on recruitment is provided by a number of datasets, relative to various
stages (glass eel and elver, yellow eel) recruiting to continental habitats (Dekker,
2002). Data of recruiting glass eels and elvers (young of the year) and yellow eels
from 28 rivers in 11 countries are updated to the last season available (2007 and in
some cases 2008) and provide the information necessary to examine the trends in re‐
cruitment. These data were derived from fishery‐dependent sources (i.e. catch re‐
cords) and fishery‐independent surveys across much of the geographic range of
European eel, and cover varying time intervals. Some of them date back as far as 1920
(glass eel, Loire France) and even the beginning of 20th century (yellow eel, Göta Älv
Sweden). All of them, however, date back as far as 1970. The recruitment time‐series
data in European rivers are presented in Annex 3 (Tables 1 and 2).
Declining trends were evident over the last two decades for all time‐series. After the
high levels of the late 1970s, there was a rapid decrease that still continues to the pre‐
sent time. The trend is similar in recruitment dataseries for glass eels in estuarine ar‐
eas (Figure 2.1) and in time‐series for yellow eel colonization, monitored in northern
countries where transition to yellow eel stage occurs before entering fresh waters
(Figure 2.2).
Latest data for 2007 and 2008 demonstrates that recruitment continues to be at a very
low level in most catchments. Although some series demonstrated a slight increase,
most series remained at similar or lower levels to the previous season for both eel
developmental stages.

10000

% of 1979‐1994 value

1000

100

10

1

IYFS (new data)

Ringhals

V iskan

Bann

Erne

S hannon

Vidaa

Ems

Lauwersoog

DenOever

Ijmuiden

Katwijk

S tellendam

Ijzer

Vilaine

Loire

Gironde

Adour

Nalon

Minho

Tiber

IYFS (old data)

All countries

0,1
1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

Figure 2.1: Time‐series of monitoring glass eel recruitment in European rivers. Each series has
been scaled to its 1979–1994 average. Note the logarithmic scale on the y‐axis.

4

EIFAC/ICES WGEEL Report 2008

10000

% of 1979-1994 value

1000

100

10

1

0,1
1900

Imsa

Dalälven

Motala Ström

Mörrumsån

Kävlingeån

Rönne Å

Lagan

Göta Älv

Tange

Harte

All countries

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

Figure 2.2: Time‐series of monitoring yellow eel recruitment in European rivers. Each series has
been scaled to the 1979–1994 average. Note the logarithmic scale on the y‐axis.

2.1.2

Data on landings

Data on yellow/silver eel landings obtained from country reports 2008 are presented
in Annex 3 (Table 3) and in Figure 2.3. Data on official eel landings from FAO sources
are presented in Annex 3 (Table 4) and in Figure 2.4. Those two datasets do not in‐
clude aquaculture production. To compare the two datasets the mean values for cor‐
responding periods were compared (Figure 2.5).
20000

C ountry R eport L anding s

18000
16000
14000

(t)

12000
10000
8000
6000
4000
2000
0

Netherlands

Norway

S weden

L atvia

L ithuania

S pain

G ermany

UK

Italy

P oland

P ortugal

F inland

F ranc e

E s tonia

Denmark

Ireland

B elgium

Figure 2.3: Landings of European eel in Europe (tonnes). Source: Country Reports 2008.

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

1959

1957

1955

1953

1951

1949

1947

1945

Ye a r

EIFAC/ICES WGEEL Report 2008

5

20000

F A O L anding s

18000
16000
14000

(t)

12000
10000
8000
6000
4000
2000
0
2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

1959

1957

1955

1953

1951

1949

1947

1945

Year
Netherlands

Norway

S weden

L atvia

L ithuania

S pain

G ermany

UK

Italy

P oland

P ortugal

F inland

F ranc e

E s tonia

Denmark

Ireland

B elgium

Figure 2.4: European eel landings in Europe (tons). Source: FAO.

2000.0
1800.0

IT

1600.0
SE NL

1400.0
FAO

1200.0

FR
DE

1000.0

PL

800.0

UK

DK

600.0
ES

400.0

NO

200.0
IE
EE

FI
LT
LV
0.0 PT
0.0

500.0

1000.0

1500.0

2000.0

CR
Figure 2.5: Differences in data on European eel landings in Europe obtained from FAO and simi‐
lar data presented in country reports.
2.1.2.1 Data discontinuities

Both the data officially reported to FAO and the best estimates presented in the
Country Reports suffered from reporting discontinuities in the past. Implementation
of the EU Eel Regulation will require Member States to implement a full catch regis‐
tration system. This will lead to considerable improvement of the coverage of the
fishery, i.e. underreporting will probably reduce markedly. Dekker, 2003 analysed
the trend in historical catch records, correcting for historical discontinuities on the
basis of a series of increasingly complex statistical models. Since the discontinuity
caused by the implementation of the EU Eel Regulation will affect all dataseries in the

6

EIFAC/ICES WGEEL Report 2008

same year, statistical analyses will not be able to cope with this. Consequently, the
discontinuity will have to be taken for granted.
However, future assessment of the status and trends in the stock, the anthropogenic
impacts and the effect of recovery and restoration measures will heavily depend
upon new data, which will be collected from the implementation of the Regulation
onwards (see also Chapter 3). It seems not that likely, that before/after‐comparisons
will be achievable. Consequently, the discontinuity in landings data might be of rela‐
tive minor importance. Direct stock estimates, such as scientific stock surveys, will
not suffer from discontinuities, and these might therefore be used to mend the gap. It
is therefore of utmost importance, that existing monitoring series will be continued,
and additional series be implemented long before the first post‐evaluation in 2012.
2.1.3

Recreational and non-commercial fisheries

Non‐commercial (i.e. non‐commercial usage of fishing gear except angling, which is
classed as recreational fishing) catch data of glass eel were made available by France
and Spain (Basque Country). For the Gironde Basin in France, non‐commercial
catches 1978–1982 exceeded commercial landings of glass eel (given in Table 2.1), but
thereafter the dominance changed to commercial landings. Non‐commercial fishery
catches of glass eel have decreased over the time‐series available.
Table 2.1: Non‐commercial glass eel catches (t) for 1978–2007. FR Total applies to total catch of
non‐commercial fisheries in France.
GLASS EEL

Year

FR Adour

FR Gironde

FR Loire

FR Total

1978

107.8

647

1979

116.2

697

1980

217.1

1303

1981

150.6

904

1982

36.5

219

1983

26.9

161

1984

26.0

156

1985

11.8

71

1986

14.4

87

1987

28.6

172

1988

6.7

40

1989

17.3

110

1990

9.0

54

1991

14.5

87

1992

12.8

77

1993

21.7

130

1994

18

12.4

74

1995

10

18.9

113

1996

12

4.2

25

1997

6

6.4

39

1998

7

1.0

6

1999

2

2.7

1

6

0.3

1

2

2000

ES Basque
country

EIFAC/ICES WGEEL Report 2008

7

GLASS EEL

2001

0.1

1

2002

6.2

37

2003

0.1

0.9

2004

0.1

1.2

2005

0.5

2

1.3

2006

0.7

2007

0.1

There is a lack of data on eel catches by non‐commercial fisheries. Where estimates
are available for some countries or regions it appears that commercial catches are
generally dominating non‐commercial catches but latter may comprise up to one
third of total yields (Figure 2.6). Therefore, recreational yields and other non‐
commercial catches are a very important source of mortality in fresh‐water eel stocks
and reliable estimates are urgently needed.
Estimates of yellow eel catches of anglers were available only for four countries/rivers
(Table 2.2). National angling catches of yellow eels of between 86 and 3300t have been
reported and can comprise a relatively important part of the total yield.
Table 2.2: Yellow eel landings (t) of anglers from River Elbe, Germany (DE), Netherlands (NL),
France (FR) and Poland (PL).
YELLOW EEL ( ANGLING )

Year

DE Elbe
1970

NL

FR

PL
3300

1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985

114.5

1986

116.9

1987

117.5

1988

118.4

1989

112.2

1990

104.6

1991

92.1

1992

83.7

1993

88.0

8

EIFAC/ICES WGEEL Report 2008

YELLOW EEL ( ANGLING )

1994

86.5

1995

87.8

1996

89.9

1997

91.1

1998

106.0

1999

108.3

2000

103.8

2001

111.2

2002

112.2

2003

113.6

2004

107.5

2005

105.1

2006

104.1

2007

111.2

508.655
200

100

Data for non‐commercial catches on yellow eel are given in Table 2.3. In contrast to
Norway, where catches have been remaining in the same order of magnitude since
1989, they collapsed in the Gironde Basin.
Table 2.3: Yellow eel landings (t) of non‐commercial fisheries other than angling from Norway
(NO) Denmark (DK), Netherlands (NL) and France, Gironde Basin (FR).
YELLOW EEL ( NON - COMMERCIAL )

Year

NO

DK

NL

FR Gironde

1978

204.1

1979

229.5

1980

155.7

1981

148.8

1982

133.1

1983

76.2

1984

164.1

1985

170.3

1986

160.5

1987

134.3

1988

97.7

1989

124.9

40.2

1990

133.9

28.3

1991

130.6

15.8

1992

143.0

27.7

1993

116.3

21.4

1994

180.5

21.1

1995

297.6

18.4

1996

178.2

7.7

1997

242.3

9.7

1998

171.9

7.3

1999

187.4

1.5

2000

108.6

1.4

EIFAC/ICES WGEEL Report 2008

9

YELLOW EEL ( NON - COMMERCIAL )

2001

127.9

0.6

2002

138.5

1.1

2003

107.2

0.5

2004

97.3

2005

106.0

138.1

1.3
0.6

2006
2007

1.3
25.0

1.3

Figure 2.6: Non‐commercial catches (Sum of angling and other fishing gear) against commercial
catches as an average in 2000–2007. Note that there are inconsistencies in the data quality for
commercial vs. non‐commercial catches.

2.1.4

Trends in stocking

Data on stocking were obtained from a number of countries, separated for glass eels
and for young yellow eels. The size of ʹyoung yellow eelʹ varies between countries.
Most data available were on a weight base. Weights were converted to numbers, us‐
ing estimates of average individual weights of the eels at the size stocked. These were
3.5 g for Denmark, 10 g for Poland, 33 g for the Netherlands, 20 g for (eastern) Ger‐
many, 30–60 g for Elbe RBD (up to 2005, after which actual counts are available), and
90 g for Sweden. An overall number of 3000 glass eels per kg was applied to data
from Belgium and Northern Ireland. An overview of data available up to 2008 is
compiled in Annex 3 (Tables 5 and 6). Stocking in other EU countries, for which there
are no time‐series data, and hence are not included in Tables 5 and 6, are also sum‐
marized in Annex 3.
In the 2007 report of the WGEEL a sharp drop in glass eel stocking series around 1969
was mainly explained with the fact that Polish stocking figures ceased to be recorded.
However, now the old Polish data have been included, but the graph still demon‐
strates a remarkable drop in glass eel stocking at that time. Obviously, there must
have been other causes for the observed decrease.

10

EIFAC/ICES WGEEL Report 2008

Stocking with glass eel has decreased strongly since the early 1990s and appears now
to be on a very low level with a still decreasing trend (Figure 2.7). However, this has
partly been compensated for by an increasing number of young yellow eels stocked
since the late 1980s. During the 1990s stocking of young eel demonstrated an increase
but dropped again in the late 1990s (Figure 2.8). During the last years, a slight in‐
crease could be observed again. If several countries use stocking as a management
option in their EMP’s, an increasing tendency in stocking numbers may be expected,
if sufficient glass eels are available on the market.
Figures 2.9 and 2.10 give a country by country breakdown of glass eel and young yel‐
low eel numbers stocked respectively. Poland, Germany and the Netherlands stocked
the largest numbers of glass eel and Germany, Denmark and the Netherlands stocked
the largest numbers of young yellow eel.
180
160
Number (milion)

140
120
100
80
60
40
20
2007

2004

2001

1998

1995

1992

1989

1986

1983

1980

1977

1974

1971

1968

1965

1962

1959

1956

1953

1950

0

total

Figure 2.7: Stocking of glass eel and young yellow eel in Europe (East Germany and Elbe RBD,
Lithuania, Netherlands, Denmark, Poland, Sweden, Northern Ireland, Belgium, Finland, Estonia

2007

2004

2001

1998

1995

1992

1989

1986

1983

1980

1977

1974

1971

1968

1965

1962

1959

1956

1953

20
18
16
14
12
10
8
6
4
2
0
1950

Number (milions)

and Latvia), in millions re‐stocked.

Total (yellow eel)

Figure 2.8: Stocking of young yellow eel in Europe (East Germany and Elbe RBD, Lithuania,
Netherlands, Denmark, Poland, Sweden, Belgium, Finland, Estonia and Latvia), in millions
stocked.

EIFAC/ICES WGEEL Report 2008

11

166.3
80.4

817.5
Germ any
Netherlands
Poland
N.Ireland

1777.5

741.5

Other countries

Figure 2.9: Total numbers of stocked glass eels in Europe (former East Germany and Elbe RBD,
Netherlands, N. Ireland, Poland and other countries) cumulated for all reported years, in millions
stocked.

15.2

11.18

Germany
93

Netherlands
Sweden

61.7

Denmark
Poland
Other countries
11.8
35.5

Figure 2.10: Total numbers of stocked young yellow eels in Europe (former East Germany and
Elbe RBD, Netherlands, Sweden, Denmark, Poland and other countries) cumulated for all years
reported, in millions stocked.

2.1.5

Aquaculture

Aquaculture production data for European eel limited to European countries from
1996 to 2007 are compiled by integrating different sources, FAO (Table 2.4), FEAP
(Table 2.5), and Country Reports to WGEEL 2008 (Table 2.6). Some discrepancies still
exist between databases and the national reports annexed to this report. These differ‐
ences are, in some cases, caused by different purposes of using aquaculture produc‐
tion. For example, the total aquaculture production of eel in Germany in 2007 was 740
tons, where 300 tons was used for stocking and 440 tons for human consumption. The
peak of production in Europe was reached in 2000 (11 000 tons), although most re‐
cently it seems to be fluctuating around 8000–9000 t. Fifty‐nine eel farms were esti‐
mated to exist in 2006, twenty‐nine of which were in the Netherlands, nine in
Denmark and the rest scattered in other countries.

12

EIFAC/ICES WGEEL Report 2008

Table 2.4. Aquaculture production of European eel in Europe. from 1996 to 2006, in tonnes. Source:
FAO.
1996

1997

1998

1999

2000

2001

Belgium

125

125

125

100

100

100

Czech

4

3

3

1

1

1

1

1

1400

1689

2468

2717

2674

2100

1166

Denmark
Estonia
France

160

160

42

42

Germany
Greece

584

545

681

518

2005

2006

<0.5

1

1

2012

1883

1673

1739

5

15

7

40

40

42

42
150

150

150

322

329

567

602

639

433

544

557

372

385

73

36

11

11

6

20

25

1

3100

3150

3200

2700

2500

1699

1550

1220

1132

807

2800

2443

2634

3228

3700

4000

3868

4200

4500

4000

4200

5

4

6

2

4

7

4

5

2

1

1

Italy

3000

Malta

<0.5

Netherlands
Portugal

2003 2004

150

Hungary
Ireland

2002

Romania

1

Serbia

2

2

3

7

5

7

4

6

9

9

Spain

249

335

347

383

411

339

424

339

424

427

403

Sweden

161

189

204

222

273

200

167

170

158

222

191

Total

8491

8595

9684

10445

10663

10158

7957

9003

9094

8212

8334

Table 2.5. Aquaculture production of European eel in Europe from 1996 to 2007, in tonnes. Source:
Aquamedia (FEAP).
1996

1997

1998

1999

2000

2001

2002

2003 2004

2005

2006

2007

Belgium

150

150

150

40

Denmark

1200

1700

2468

2700

2675

2100

2300

2050

1500

1700

1900

2100

5

5

13

24

17

23

30

150

150

350

350

350

350

400

500

300

550

500

500

500

500

450

450

19

13

104

48

3100

3100

2900

2400

1400

1400

1200

1200

1000

1000

2

2

1

5

17

20

9

8

14

40

3800

4000

4000

4000

4200

4500

4400

3800

4200

Estonia
France

160

Germany

140

150

150

Greece

350

312

500

Hungary
Italy

3000

3100

Lithuania
Netherlands 1800

1800

3250

Norway

200

200

200

Portugal

200

200

200

200

200

200

Spain

210

266

270

300

425

330

355

325

350

400

400

450

Sweden

184

215

250

250

250

230

230

230

230

230

230

230

200

200

200

200
25

50

8192

9000

Turkey

50

Croatia
Total

7594

8293

10740

11109 11111 10074

8863

9075

8663

8805

EIFAC/ICES WGEEL Report 2008

13

Table 2.6. Aquaculture production of European eel in Europe from 1996 to 2007, in tonnes: Coun‐
try reports (CR 2007 and 2008).

Denmark

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

1568

1913

2483

2718

2674

2000

1880

2050

1500

1700

1900

2100

5

7

15

18

26

19

27

52

Estonia

2.2

Germany

204

221

260

400

422

347

381

372

328

329

567

740

Netherlands

2800

2450

3250

3500

3800

4000

4000

4200

4500

4500

4200

4000

Portugal

21

13

3

4

7

4

2

1

Sweden

161

189

204

222

273

200

167

170

158

222

191

175

1

Total

4754

4773

6210

6843

7178

6561

6447

6810

6514

6771

6885

7068

Analysis of trends in recruitment
The trends in recruitment data available were analysed in relation to life stage, type
of monitoring and geographical area. The objective of this analysis is to derive a reli‐
able index of recruitment, both for the assessment of the stock‐to‐recruit phase, as for
the management and assessment of the recruit‐to‐stock phase. The available dataser‐
ies were qualified regarding:
•

life stage (unpigmented glass eel; pigmented young‐of‐the‐year; immigrat‐
ing yellow eel older than 1 year);

•

sampling type (trapping all incoming recruits in a river, trapping the re‐
cruits only partially, commercial total landing figures, commercial cpue,
scientific survey estimates);

•

geographical area (Baltic Sea including Kattegat and Skagerrak, North Sea,
Channel, British Isle, Atlantic Ocean, Mediterranean Sea). No datasets are
available at the moment for the Channel area.

Considering the small number of datasets, the dataseries for glass eel and for young‐
of‐the‐year were merged, and analysed together. Given the spatial distribution of dif‐
ferent sampling techniques in Europe (commercial fisheries in the South, trapping
mostly in the north), the effect of sampling type and of area can not be analysed con‐
currently; for young yellow eel older than 1 year only trapping dataseries exist. Con‐
sequently three analyses were feasible:
•

area effect on glass eel and young‐of‐the‐year (combined);

•

sampling type effect on glass eel and young‐of‐the‐year (combined);

•

area effect on young yellow eel older than 1 year.

The analyses used generalized linear models (GLMs) with a site effect as a scaling
parameter, a log link (site effect and other effects are assumed to be multiplicative)
and a gamma error (variance is varying with the square of the mean, i.e. a constant
coefficient of variation). The resulting time‐trends are scaled to the 1970–1979 geo‐
metric mean. Figure 11 and Table 2.7 gives the main characteristics of the 40 datasets
used.

14

EIFAC/ICES WGEEL Report 2008

Figure 2.11: Map of the recruitment monitoring sites across Europe. Life stage and sampling
method are indicated by the symbols.
Table 2.7: Data sets used for recruitment analysis. YOY = Young‐of‐the‐year.
LIFE STAGE

AREA

MONITORING TYPE

C OUNTRY

RIVER

LOCATION

L ATITUDE

LONGITUDE

51.08

2.45

glass eel North sea

scientific est.

Belgium

Ijzer

Nieuwpoort

glass eel North sea

comm. landing Denmark

Vidaa

Højer sluice

55.58

8.4

glass eel North sea

comm. landing Germany

Ems

Herbrum

53.02

7.2

glass eel North sea

scientific est.

Netherlands

Lauwersoog

53.25

6.12

glass eel North sea

scientific est.

Netherlands Rhine

Ĳmuiden

52.27

4.36

glass eel North sea

scientific est.

Netherlands Oude RIjn

Katwijk

52.12

4.24

glass eel North sea

scientific est.

Netherlands Haringvliet Stellendam

51.50

4.02

glass eel North sea

scientific est.

Netherlands Rhine

DenOever

52.56

5.03

glass eel North sea

scientific est.

Sweden

IYFS

58

10

glass eel North sea

scientific est.

Sweden

IYFS2

58

10

glass eel North sea

scientific est.

Sweden

Kattegat‐

Ringhals

57.15

12.07

glass eel British Isle

comm. landing UK

Severn

EA

51.36

‐2.42

glass eel British Isle

comm. landing UK

Severn

HMRC

51.36

‐2.42

Sèvres

Estuary

46.18

‐1.08

glass eel Atlantic Ocean comm. cpue

France

EIFAC/ICES WGEEL Report 2008

LIFE STAGE

AREA

15

MONITORING TYPE

C OUNTRY

RIVER

LOCATION

L ATITUDE

LONGITUDE

glass eel Atlantic Ocean comm. landing France

Adour

Estuary

43.32

‐1.32

glass eel Atlantic Ocean comm. cpue

France

Adour

Estuary

43.32

‐1.32

glass eel Atlantic Ocean comm. cpue

France

Gironde

Estuary

45.02

‐0.36

glass eel Atlantic Ocean comm. landing France

Gironde

Estuary

45.02

‐0.36

glass eel Atlantic Ocean comm. landing France

Loire

Estuary

47.18

‐2.00

glass eel Atlantic Ocean trapping all

Vilaine

Arzal

47.3

‐2.24

glass eel Atlantic Ocean comm. landing Portugal

France

Minho

portugese

41.52

‐8.51

glass eel Atlantic Ocean comm. landing Spain

Minho

spanish part

41.52

‐8.51

glass eel Atlantic Ocean comm. landing Spain

Nalon

Estuary

43.31

‐6.04

glass eel Mediterannean comm. landing Italy

Tiber

Fiumara

41.44

12.14

Albufera de

39.20

0.23
12.07

glass eel Mediterannean comm. landing Spain
YOY

Baltic Sea

trapping

Sweden

Viskan

Sluices

57.12

YOY

British Isle

trapping all

Ireland

Shannon

Ardnacrusha

52.42

‐8.36

YOY

British Isle

trapping all

Ireland

Erne

Ballyshannon

54.3

‐8.15

YOY

British Isle

trapping

Northern

Bann

Coleraine

55.12

‐6.42

older

Baltic Sea

trapping all

Sweden

Dalälven

60.34

17.26

older

Baltic Sea

trapping all

Sweden

Mörrumsån

56.20

14.40

older

Baltic Sea

trapping all

Sweden

Lagan

56.31

13.03

older

Baltic Sea

trapping all

Sweden

Motala

58.35

16.11

older

Baltic Sea

trapping all

Sweden

Göta Älv

58.16

12.16

older

Baltic Sea

trapping all

Sweden

Kävlingeån

55.43

12.59

older

Baltic Sea

trapping all

Sweden

Rönne Å

56.16

12.50

older

North sea

trapping

Belgium

Meuse

Lixhe dam

50.45

5.40

older

North sea

trapping all

Denmark

Guden Å

Tange

older

North sea

trapping all

Denmark

Harte

older

North sea

trapping all

Norway

Imsa

2.2.1

Sandnes

56.21

9.36

55.21

9.25

58.54

5.59

Area effect on glass eel and young of the year recruitment

The model explains 72% of deviance (Table 2.8) and all effects were highly significant
(p<0.001). Table 2.9 and Figure 2.12 give results from this model, i.e. a recruitment
index per year by area. Every area demonstrates a declining trend since the end of
1970s or the beginning of 1980s. Before, no particular trend is detected. In recent
years, recruitment is continuously declining in all areas. The mean recruitment for the
past 5 years (2004–2008) is 10%, 9%, 3%, 3% and 1% of the 1970s reference level, for
the British Isles, Atlantic Ocean, Baltic Sea, Mediterranean Sea and North Sea respec‐
tively. Apparently, the decline is stronger in northernmost and southernmost area of
the species distribution than in the central part. A unique and uniform recruitment
index all over the distribution area would require weighing the specific contributions
by area, which is not achievable at the moment. More importantly, however, such an
index would incorrectly represent the actual trend in each area.
Table 2.8: Analysis of deviance of the area effect on glass eel and young of the year GLM.
MODEL

RESIDUAL DF

RESIDUAL DEVIANCE

NULL

1051

1763.27

Site effect

1023

1545.73

Year x area effect

776

501.83

16

EIFAC/ICES WGEEL Report 2008

Table 2.9: Recruitment index per area. Each series have been scaled to 1970–1979 average = 100%.
YEAR

B ALTIC S EA

N ORTH S EA

B RITISH I SLES

A TLANTIC O CEAN

M EDITERRANEAN S EA

1950

32.7

25.2

1951

34.6

48.6

1952

129.9

48.2

1953

112.2

30.8

1954

181.8

41.1

1955

172.8

61.4

1956

133.0

57.5

1957

71.9

51.7

1958

124.5

61.1

1959

170.2

63.2

1960

209.2

1961

130.2

1962

228.0

1963

308.2

123.3

1964

129.4

44.1

63.5

92.8

1965

98.7

68.7

158.0

139.1

1966

94.2

110.2

59.7

115.9

1967

107.8

30.8

93.6

92.8

1968

132.2

66.9

156.3

92.8

1969

92.2

19.4

70.6

115.9

1970

112.4

63.9

117.2

23.2

63.6

60.4

23.2

121.4

87.5

394.2

76.5

60.7

255.1

142.4

127.4

371.0

214.2

255.1

1971

3.9

79.8

1972

28.5

118.7

70.9

62.8

23.2

1973

57.3

57.5

90.0

77.2

46.4

1974

4.2

154.1

140.9

82.2

23.2

1975

32.1

69.9

59.4

81.3

220.4

1976

162.3

114.8

48.7

131.4

149.8

1977

275.4

105.1

106.4

138.8

161.7

1978

172.6

85.8

131.0

112.2

98.7

1979

163.7

101.8

225.2

136.5

230.3

1980

23.5

80.4

165.6

104.7

224.8

1981

104.1

58.7

144.0

116.1

70.0

1982

94.0

30.0

179.1

73.1

62.3

1983

63.6

31.1

37.0

80.4

82.5

1984

7.7

12.5

63.5

68.5

59.2

1985

41.8

11.5

55.3

42.3

38.9

1986

25.6

12.6

60.4

50.4

35.7

1987

24.1

15.9

90.0

43.5

150.8

1988

19.1

9.2

74.0

46.1

173.1

1989

9.8

4.4

49.4

39.6

90.7

1990

11.4

17.1

69.0

27.2

72.8

1991

3.5

2.9

14.8

23.2

20.6

1992

18.4

5.8

31.8

31.5

11.7

EIFAC/ICES WGEEL Report 2008

17

YEAR

B ALTIC S EA

N ORTH S EA

B RITISH I SLES

A TLANTIC O CEAN

M EDITERRANEAN S EA

1993

16.3

6.2

40.4

31.3

10.4

1994

28.0

7.9

73.8

33.2

9.2

1995

7.7

8.7

59.5

40.9

7.3

1996

2.7

7.9

57.1

24.8

5.6

1997

4.1

6.6

80.8

27.6

2.7

1998

4.9

3.7

38.5

18.7

8.9

1999

3.9

8.0

32.8

24.5

4.6

2000

12.2

5.3

20.1

25.7

8.8

2001

1.1

1.0

14.5

8.7

5.9

2002

8.5

2.7

13.1

15.6

4.4

2003

9.6

1.9

26.7

8.2

3.0

2004

1.6

0.9

13.7

8.8

2.8

2005

6.9

1.1

18.9

11.2

0.8

2006

1.5

0.5

9.4

7.8

3.8

2007

2.9

2.3

8.4

7.2

3.8

2008

1.7

0.8

1.0

8.2

mean 2004–2008

2.9

1.1

10.3

8.6

2.8

18

EIFAC/ICES WGEEL Report 2008

400%
Atlantic Ocean
Baltic
350%

British Isles
Mediternnean Sea
North Sea

300%

% 1970-1979

250%

200%

150%

100%

50%

0%
1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

1985

1990

1995

2000

2005

2010

year

1000%

% 1970-1979

100%

10%

Atlantic Ocean
Baltic

1%

British Isles
Mediternnean Sea
North Sea

0%
1950

1955

1960

1965

1970

1975

1980
year

Figure 2.12: Recruitment (glass eel and young of the year) index per area in regular (upper panel)
and in logarithmic scale (lower panel). Each series have been scaled to 1970–1979 average.

2.2.2

Sampling type effect on glass eel and young of the year recruitment

This model explains 66% of deviance (Table 2.10) and all effects are highly significant
(p<0.001). Table 2.11 and Figure 2.13 give results from this model. Recruitment indi‐
ces per sampling type demonstrate the same trend as recruitment index per area: de‐
creasing trend since the end of 1970s or the beginning of 1980s. Depending on
sampling type the present level is between 1% and 11% (2004–2008 average) of 1970–
1979 level. Commercial cpue and trapping all, only represented by datasets in the
central part of the eel distribution, have the highest present level (11% and 10%).
Commercial catch and trapping partial, represented in the central and extreme part of
the eel distribution, have intermediate present level (5%), while scientific sampling,
only taking place in North Sea, has the lowest present level (1%). The analysis did not
suppose any particular distribution pattern of the recruitment; we can thus build an

EIFAC/ICES WGEEL Report 2008

19

index of recruitment of all Europe. The European index is calculated as the geometric
mean of each of the monitoring indices, i.e. the least‐squares mean (Table 2.11 and
Figure 2.12). This combined index demonstrates that the present recruitment is only
5% of the 1970–1979 level.
Table 2.10: Analysis of deviance of the area effect on glass eel and young of the year GLM.
MODEL

RESIDUAL DF

RESIDUAL DEVIANCE

NULL

1051

1763.27

Site effect

1023

1545.73

Year x monitoring type effect

764

593.15

Table 2.11: Recruitment index per monitoring type and geomean. Each series have been scaled to
1970–1979 average.
COMMERCIAL CPUE

YEAR

COMMERCIAL CATCH

SCIENTIFIC ESTIMATE

TRAPPING ALL

TRAPPING PARTIAL

GEOMEAN

1950

39.5

12.0

21.8

1951

45.7

24.3

33.3

1952

62.8

156.1

99.0

1953

88.4

26.6

48.5

1954

139.0

39.5

74.1

1955

139.9

54.7

87.5

1956

124.8

14.3

42.2

1957

71.5

31.9

47.8

1958

86.6

105.0

95.3

1959

138.1

57.6

89.2

1960

246.6

43.5

1961

130.2

45.7

1962

186.6

181.2

1963

198.3

346.7

1964

135.3

1965

114.2

201.8

101.3

41.3

48.7

85.9

1966

76.9

73.8

87.6

64.2

79.2

75.9

1967

87.7

90.3

131.6

13.8

24.3

51.1

1968

147.3

145.7

118.3

68.8

32.3

89.2

1969

79.4

88.2

92.0

27.5

5.4

39.5

1970

81.4

113.0

138.9

27.5

51.1

71.0

1971

79.1

67.4

69.3

43.3

29.7

54.4

1972

94.6

70.6

89.5

55.0

41.2

67.0

1973

67.6

87.2

63.9

112.9

61.7

76.5

1974

95.9

92.1

161.5

95.8

40.9

89.0

1975

111.3

65.5

64.9

41.0

55.2

64.0

1976

130.6

149.2

95.0

85.9

147.6

118.6

1977

121.9

112.6

118.9

65.2

260.0

122.6

1978

100.7

119.2

91.3

105.9

169.3

114.5

1979

116.8

123.2

107.0

367.2

143.4

152.0

1980

101.8

107.2

77.7

241.0

34.6

93.3

1981

85.2

105.1

62.0

152.9

151.4

105.1

56.4

94.9

87.1

75.1

28.9

63.2

60.6

176.5

113.3

86.3

142.3

251.9

19.7

116.2

131.8

39.8

9.6

40.2

38.0

20

EIFAC/ICES WGEEL Report 2008

YEAR

COMMERCIAL CATCH

COMMERCIAL CPUE

SCIENTIFIC ESTIMATE

TRAPPING ALL

TRAPPING PARTIAL

GEOMEAN

1982

63.9

64.1

24.7

235.8

146.1

81.0

1983

65.8

54.1

26.4

67.2

80.9

55.2

1984

51.3

60.6

10.5

53.5

15.1

30.5

1985

31.1

34.7

12.3

69.5

58.0

35.2

1986

37.5

31.3

11.5

75.8

50.7

34.9

1987

51.7

45.1

13.9

118.8

47.0

44.8

1988

51.4

45.0

8.5

74.2

40.6

35.8

1989

32.4

51.1

5.7

46.4

18.5

24.1

1990

27.4

21.0

20.9

72.2

25.5

29.4

1991

16.3

20.2

3.4

18.5

4.4

9.8

1992

18.0

36.7

7.6

36.8

24.4

21.4

1993

18.4

38.0

8.5

43.3

20.9

22.2

1994

22.6

28.6

11.3

91.6

27.6

28.4

1995

25.2

38.6

10.5

66.5

10.4

23.4

1996

19.1

23.3

8.6

39.3

19.7

19.7

1997

17.1

32.5

7.4

109.6

18.2

24.1

1998

15.0

15.8

4.9

31.3

9.5

12.8

1999

14.6

30.2

10.0

24.7

9.1

15.8

2000

12.8

46.0

7.8

22.4

7.2

15.0

2001

5.9

7.8

1.3

22.2

2.5

5.0

2002

8.3

20.5

3.4

16.3

13.7

10.5

2003

6.2

7.9

2.3

29.7

19.4

9.2

2004

6.8

9.1

1.0

10.4

3.5

4.7

2005

7.2

14.3

1.6

17.9

12.1

8.2

2006

5.5

11.7

0.7

6.6

3.6

4.1

2007

4.8

9.9

2.7

8.6

3.4

5.2

2008

0.6

11.7

0.9

4.4

0.3

1.5

mean 2004–2008

5.0

11.4

1.4

9.6

4.6

4.7

EIFAC/ICES WGEEL Report 2008

21

400%

350%

300%

%1970-1979

250%

commercial catch
commercial CPUE
scientific estimate
trapping all
trapping partial
geomean

200%

150%

100%

50%

0%
1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

1985

1990

1995

2000

2005

2010

year

1000%

%1970-1979

100%

10%

commercial catch
commercial CPUE
scientific estimate
trapping all
trapping partial
geomean

1%

0%
1950

1955

1960

1965

1970

1975

1980
year

Figure 2.13: Recruitment (glass eel and young of the year) index per monitoring and geomean of
these series in regular (upper panel) and in logarithmic scale (lower panel). Each series have been
scaled to 1970–1979 average.

2.2.3

Area effect on young yellow eel older than 1 year

Data of two areas only (Baltic Sea including Kattegat, Skagerrak and North Sea) are
available to fit this model. It explains 59% of deviance (Table 2.12) and all effect are
highly significant (p<0.001). Table 2.13 and Figure 2.14 give results from this model,
i.e. a young yellow eel older than 1 year recruitment index per area. The Baltic Sea
(including Kattegat and Skagerrak) index demonstrates a continuous decline since
the beginning of the period (1950). The North Sea index demonstrates the same trend,
at least since the mid 1970s. The current level (2004–2008) is only 25% and 6% of the
1970s level for Baltic Sea (including Kattegat and Skagerrak) and North Sea respec‐
tively and the Baltic Sea (including Kattegat and Skagerrak) is at 8% of the 1950s
level. None of theses series demonstrates any sign of recovery.

22

EIFAC/ICES WGEEL Report 2008

Table 2.12: Analysis of deviance of the area effect on young yellow eel older than 1 year GLM.
MODEL

RESIDUAL DF

RESIDUAL DEVIANCE

NULL

448

886.01

Site effect

438

725.79

Year x area effect

342

363.41

Table 2.13: Young yellow eel older than 1 year index per area. Each series have been scaled to
1970–1979 average.

YEAR

B ALTIC S EA ( INCLUDING
K ATTEGAT AND
S KAGERRAK )

1950

269

1980

122

1951

360

1981

38

70

1952

356

1982

60

116

1953

572

1983

62

51

1954

290

1984

42

38

1955

431

1985

68

78

1956

207

1986

32

65

1957

226

1987

72

25

1958

232

1988

82

72

1959

492

1989

38

47

1960

245

1990

30

78

1961

249

1991

62

29

1962

244

1992

27

16

1963

214

1993

17

21

1964

82

1994

94

15

1965

152

1995

14

10

1966

214

1996

17

4

1967

117

213

1997

25

19

1968

245

85

1998

22

7

1969

166

74

1999

27

18

1970

68

100

2000

28

9

1971

92

25

1972

146

1973

197

N ORTH
S EA

B ALTIC S EA ( INCLUDING
K ATTEGAT AND
S KAGERRAK )

N ORTH
S EA

134

2001

24

11

2002

66

11

50

2003

31

13

1974

77

90

2004

40

7

1975

155

175

2005

11

5

1976

49

139

2006

21

4

1977

79

152

2007

36

8

1978

73

101

2008

1979

64

68
27

6.2

mean 2004–2008

EIFAC/ICES WGEEL Report 2008

23

600%

500%

% 1970-1979

400%
Baltic
North Sea
300%

200%

100%

0%
1950

1960

1970

1980

1990

2000

2010

year

1000%

Baltic
North Sea

% 1970-1979

100%

10%

1%

0%
1950

1960

1970

1980

1990

2000

2010

year

Figure 2.14: young yellow eel older than 1 year index per area in regular (upper panel) and in
logarithmic scale (lower panel). Each series have been scaled to 1970–1979 average.

2.2.4

Discussion

Area effect and sampling type effect on glass eel and young of the year recruitment
models are fitted on the same data. The area effect model explained more deviance
while using fewer degrees of freedom than sampling type effect. On a statistical basis,
the geographical pattern seems to fit the data better than the sampling effect, but the
difference is not very clear. The geographical pattern can also be explained by the
difference found in sampling type. When comparing datasets in different areas with
the same sampling type (trapping partial in Baltic Sea including Kattegat and Skager‐
rak and in British Isles or commercial catches in the North Sea, British Isles, Atlantic
Ocean and Mediterranean Sea), the geographical pattern is confirmed. Although
sampling biases may exist, geographical pattern (stronger decrease in extreme part of
the species distribution area) is the more likely interpretation.

24

EIFAC/ICES WGEEL Report 2008

The implementation of the EU Eel Regulation might result in discontinuities in the
data on recruitment. First, commercial fisheries might be reduced, affecting the series
based on commercial landings and commercial cpues. Second, the Regulation obliges
Member States to implement a full registration programme for landings and fishing
efforts, probably resulting in more complete coverage of the fishery. The recruitment
series based on trapping (all or partial) and the scientific estimates will not be af‐
fected. For the (international) analysis of trends, the dataseries suffering from discon‐
tinuities will have to be split into “before” and “after”, reducing the continuity of the
overall analysis. Since this (unwanted but unavoidable) breakpoint will occur in just
some sampling methods, it is all the more important to settle the area/sampling prob‐
lem, i.e. to collect additional unpublished archive dataseries, strengthening the dis‐
criminating power of the above analyses.
The Baltic Sea (including Kattegat and Skagerrak) index of young yellow eel older
than one year and to a lesser extent the North Sea index for this stage demonstrates a
quite different pattern with a decrease starting earlier (at least since 1950 for the Bal‐
tic). Unfortunately, the Baltic Sea index for glass eel begins in 1971 only. This index
does not differ from other area indices. Two hypotheses can explain these observa‐
tions;
•

the Baltic Sea including Kattegat and Skagerrak glass eel and young of the
year index does not start early enough to strongly distinguish from other
areas;

•

young yellow eel older than 1 year in the Baltic Sea including Kattegat and
Skagerrak area started to decline whereas glass eel and young of the year
recruitment was constant. The reason for the yellow eel decline is unclear.

The first hypothesis better fits the data, although further information (young yellow
eel data in the rest of Europe, or glass eel/young‐of‐the‐year data in the Baltic Sea in‐
cluding Kattegat and Skagerrak area) will be needed to confirm this.
2.3

Conclusions and recommendations for Chapter 2: Trends in recruitment,
stocking, yield and aquaculture
2.3.1

Conclusions

All glass eel and young of the year recruitment series demonstrate a clear decline
since about 1980 with no sign of recovery. Recruitment is currently at only 5% of the
1970–1979 level. The Baltic Sea, including Kattegat and Skagerrak indices of young
yellow eel recruitment, demonstrates a clear decline since about 1950. The decline in
recruitment appeared stronger in the more northern and southern parts of the distri‐
bution. It is recommended to use recruitment indices per area (Baltic, North Sea, Brit‐
ish Isles, Atlantic Coast, eastern and western Mediterranean), and to collect and
analyse additional data to confirm the spatial pattern, and to establish the reliability
and bias in the different sampling methods.
There needs to be an improvement in the data collected and data reported, particu‐
larly on landings and on stocking. Hopefully, the traceability requirements under the
EU Regulation and CITES will improve this situation.
2.3.2

Recommendation

The analysis of aquaculture is complicated by the existence of three different datasets.
We recommend that the collection of such data are centrally coordinated to provide a
single dataset.

EIFAC/ICES WGEEL Report 2008

25

The situation is even more complicated for stocking, since in some countries no cen‐
tral databases exist. Therefore, information on stocking is incomplete. This situation
should be improved in order to obtain a more comprehensive picture of the stocking
activities in Europe.
It is recommended to use glass eel indices per area (i.e. Baltic, North Sea, British Isles,
Atlantic Coast, Mediterranean), and to collect and analyse additional data to confirm
the spatial pattern, and to establish the reliability and bias in the different sampling
methods.

26

EIFAC/ICES WGEEL Report 2008

3

International stock assessment and data needs

3.1

Introduction on stock assessment and data needs
The European Union has decided on a protection and restoration plan (Eel Regula‐
tion) in 2007, aiming at the protection of 40% of the silver eels, relative to a situation
without human influence. At the heart of the Regulation is the obligation for all
Member States to develop a (national or river basin specific) management plan for the
eel stock and fisheries, aiming at the agreed 40% target. Each management plan
should contain an assessment of the current status of the local stock, a description of
future monitoring and registration of catch and fisheries for future assessments of the
stock and anthropogenic impacts.
The WGEEL considers its tasks (ICES and EIFAC ToRs) to assess and evaluate the
overall status of the stock, and the impact of protection measures taken. There is an
apparent overlap with the obligation in the Eel Regulation, to report on the status of
the stock in individual Eel Management Units, and with the evaluation by the Com‐
mission. However, the assessment of the working group will focus on the total popu‐
lation, independent of the split over jurisdictions and management units. Only where
the biological processes are inherently spatially diversified, will the assessment of the
working group go into disaggregate analyses.
This chapter will elaborate the concepts of an international assessment of a regionally
managed stock (Section 3.2), and derive criteria for a minimally required dataset on
eel (Section 3.3).

3.2

International stock assessment
3.2.1

International management and stock assessment

The EU Regulation on eel sets a common target for the escapement of silver eels, at
40% of the natural escapement in the absence of anthropogenic impacts. In accor‐
dance with the precautionary advice provided by ICES (2002), it is assumed that a
stock recruitment relationship exists. Member States are obliged to implement protec‐
tive measures to achieve the escapement target, and should provide a time schedule
for the attainment of this target. This time schedule is certainly much more deter‐
mined by the slow biological restoration of the stock (decades; Åström and Dekker,
2007), than by the time required to implement the protection measures completely
(years?). The Regulation sets no limit on the time frame for restoration. Implicitly, this
rules out the hypothesis that the stock–recruitment relationship is determined by de‐
pensatory processes, as tentatively found in historical data (Dekker, 2004; ICES 2007).
As an alternative to the depensation hypothesis, it has been hypothesized, that the
decline of the stock might have been caused by climate factors (Chapter 7 of this re‐
port), pollution or parasitism (chapter 6 of this report), and others acting in the oce‐
anic phase.
Noting that the EU Eel Regulation has set targets for the quantity (biomass) of silver
eels escaping from the continent, and obliges Member States to take protective meas‐
ures primarily focusing on the quantities escaping, but has not set targets and does
not oblige to take actions with respect to other processes (related to silver eel quality,
or climate change) in relation to eel management (if possible), the international as‐
sessment of the status of the stock will presently focus on the dynamics of stock in
numbers and quantities, and on the effect of protection and restoration measures
taken. This does not, in principle, rule out potential effects of other factors, including
silver eel quality and/or climate factors. However, since the mechanisms involved

EIFAC/ICES WGEEL Report 2008

27

have not been cleared up, and the quantitative impact on the stock is unclear, there is
no way forward to include these aspects in international stock assessment at this
moment in time. Further research will be needed, to elucidate the processes, to quan‐
tify the impacts, to find mitigation measures, to advise management targets, and to
assess the net effects of measures taken on the eel stock. Until that has been done,
prime focus in the stock assessment will necessarily rest with “classical” fish stock
assessment, which for the eel case, will be complex enough.
Under the EU Eel Regulation, an international assessment will be required of the
population‐wide status of the stock, and an assessment of the impact of the manage‐
ment measures taken. The Regulation focuses on stock dynamics in terms of quanti‐
ties and biomass and thus the assessment leaves aside scientific debates on the impact
of spawner quality and/or climate factors. A decision tree diagram for this assessment
is presented in Figure 3.1. The indicated steps are elaborated in the text below;
START
1. Trend in recruitment?

increase

1.a increase continues over a long period (decades)?

increase

Recruitment does not restore, despite increasing silver eel escapement.
This is NOT consistent with the precautionary assumption of a
(standard) stock-recruitment relationship. Several hypotheses arise:
- incorrect assessment of trends in recruits or silver eels
- stock-recruitment relationship is non-standard (e.g. depensatory)
- unaccounted anthropogenic impacts (e.g. pollution, parasites, ...)
- external pertubating processes (e.g. climate, ...)
Further research will be required in order
- to correct monitoring deficiencies,
- to resolve the cause of the apparent mismatch between trends
- to develop mitigation measures, if possible
- to set targets for cause, mitigation measure and effect on the eel stock
- to post-evaluate net effects of these measures on the eel stock.

increase

Locally successful management, encourage other areas to do the same,
and consider stronger measures to speed up the recovery process!

decrease, or
minor increase

2. Global trend in silver eel

decrease

3. Local trend in silver eel escapement ?
Assess by River Basin District!

decrease

4. Is density dependence dominating
the current local silver eel escapement?
Assess by River Basin District!

Success !!

yes

Density dependence generally occurs where stock density is close to the potential
maximum (carrying capacity). If local silver eel escapement declines, while available
habitat does not, this indicates that that maximum is NOT reached anymore.
Consider the following:
- incorrect assessment of trends
- density dependence was actually not dominating the stock dynamics
- unaccounted (non)-anthropogenic impacts, changing the carrying capacity.
Research will be required, to find cause and remedy. Meanwhile, a further
precautionary reduction in anthropogenic impacts will be required.

yes

Await slow recovery, according to your local time schedule,
or take stronger measures to speed up the recovery process!

no

5. Is relative survival (spawner per recruit)
consistent with your local time schedule?
Assess by River Basin District!

yes

no
"Standard" fish stock assessment tools, including cohort-models, length-based assessments, etc.
Tool-box development and standardisation of post-evaluation techniques is recommended.
Development of standard targets for survival and time schedule is recommended.
Escapement and survival below expectation, much stronger protective measures required!!

Figure 3.1: Decision tree for international assessment of the impact of protective measures taken
under the EU Eel Regulation. International issues are depicted in a single‐lined box, whereas
River Basin Specific issues are in a double‐lined box.

28

EIFAC/ICES WGEEL Report 2008

3.2.2

Only recruitment and escapement trends?

Taking a superficial view on first examination of the task of international stock as‐
sessment, it might appear that the time‐series data on spawner emigration and glass
eel recruitment are the only data items of information essential to international as‐
sessment of eel stock and recruitment. This view, however, ignores the reality of the
current and probable future situation. Only if recruitment were to recover rapidly
following measures to increase spawning stock, resulting in confidence that recovery
is underway, would these two data items suffice. Such a rapid recovery is an unlikely
scenario, given that our ability to increase spawning stock escapement significantly
will be limited for at least an eel generation, as a consequence of the past 15 to 25
years of low recruitment yet to feed through to spawner emigration (Chapter 2 of this
report). It is quite probable, therefore, that recruitment will continue to decline for
some time, and it will be almost unavoidable that silver eel escapement will also de‐
cline considerably further for at least some years. The effectiveness of protection and
restoration measures taken under the EU Eel Regulation will therefore have to be
judged on a relative scale: the relative improvement of survival from recruit to silver
eel. This necessitates the analysis of the full continental phase of the life cycle.
3.2.3

Issues of time-scale

The principal objective of WGEEL at its future meetings will be assessment, renewed
annually, of the state of the stock and recruitment at an international level. The de‐
sired objective of current management is clearly that measures taken to protect and
enhance spawner escapement result in increased recruitment. The time‐scale for full
evaluation of such success is long, and for assured confidence that recovery is un‐
derway, any recovery will have to be successfully tracked through the generations,
that is: over decades.
3.2.4

If recruitment continues to decline

Should recruitment not respond positively to increased spawner biomass, and con‐
tinue to fall whereas spawning‐stock biomass is rising, then there are other factors
operating than those included in the assumptions (that eel will follow classical stock–
recruitment relationships).
Where such conclusion is reached, at any point in the assessment and study of eel, it
would be evident that unknown factor(s) are acting on the stock–recruitment rela‐
tionship. This brings in possibilities such as a problem in oceanic processes affecting
migration, eel “quality” factors affecting spawning ability, genetic issues, or a new
and unforeseen problem resulting in depensation in the S‐R process. These scenarios
would all force an urgent search through research programmes on possible addi‐
tional causes of decline, which is of course an option at any stage where new evi‐
dence of detrimental factors arises. These “new” problems, however, have always to
be researched through a process involving data gathering, correlation, quantification
of cause and effect, development, proposal and adoption of mitigation measures, and
post evaluation before they can be fully built in to the assessment of S‐R or R‐SSB
processes. It is therefore necessary that the “new” lines of potential impact research
are continually progressed through research programmes alongside the annual stock
assessment process, so that when and if numerical estimates of their impact are avail‐
able, these can be taken into account.
If recruitment does not respond to spawner enhancement measures, and spawning
stock continues to decline, then the assessment process is required to investigate bio‐
logical and mortality processes at a spatially disaggregated level. In principle, the
analysis could proceed, at a biologically meaningful disaggregated level. In practice,

EIFAC/ICES WGEEL Report 2008

29

however, the RBD level will be much more easily achievable. At this level, manage‐
ment measures have been taken, and data on stock, fisheries and other anthropogenic
impacts have been gathered. Indeed, the probable situation over the coming two dec‐
ades is at best continued low spawner emigration protected to some degree by meas‐
ures to be taken under management plans required the eel regulation, with glass eel
recruitment at best displaying a slow recovery but perhaps continuing its decline.
Management plans may fail to generate any increase in spawners, some through no
fault of the plans but simply as a consequence of the history of low recruitment, and
some through inadequacy in the plan. In this scenario, it will become necessary to
carry out a spawner‐per‐recruit analyses at the international level (that is probably
the simple sum of river basin specific analyses) to distinguishing between these two
possible causes of unpredicted low spawner production. This analysis will require
access to data to examine processes operating at least at the eel management unit, and
preferably the river basin level.
3.3

Data requirement
An internationally coordinated international stock and recruitment assessment for
European eel has a minimum data requirement, which is not yet met. The data
needed for future international stock–recruitment assessment are a minimum of:
1 ) Escapement estimates from all Eel River Basins, in absolute terms (biomass
and numbers, by sex), combined with
2 ) Recruitment indices indicative of recruitment strength over the whole dis‐
tribution area.
3.3.1

River Basin vs. international uses of data

The sum of the escapement estimates over the distribution area provides a proxy es‐
timator of the spawning stock size, whereas recruitment indices quantify the off‐
spring. The combination of spawning stock size and recruitment index facilitates
assessments of stock status and analysis of the stock–recruitment relationship and
potential effects of climatic factors on the oceanic life phase.
The analysis of the stock dynamics in the continental phase, i.e. a spawner‐per‐recruit
analysis, requires data from the continental phase, which resides within national wa‐
ters, within Eel Management Units (EMUs). Since the biological characteristics, as
well as the anthropogenic impacts on the stock vary from region to region, a single
unified assessment of the status of the stock will not be feasible, other than on an
EMU by EMU basis.
Neither the Eel Regulation, nor the Water Framework Directive programmes oblige
Member States to make the basic data available, though they do contain an obligation
to report on the results to the Commission. The Data Collection Regulation, in con‐
trast, does require Member States to make data available upon request, but no central
database exists. A future WGEEL might have to specially request these data from the
Commission. As indicated above, partial spatial coverage may allow for an analysis
of trends in recruitment, but neither the assessment of the trend in silver eel escape‐
ment, nor the assessment of the relative survival over the continental life stages, will
be feasible. A formal requirement, and a practical procedure to present and store the
data, will have to be developed. Development of protocols, exchange procedures and
databases will not be feasible within the framework of the international assessment
working group.

30

EIFAC/ICES WGEEL Report 2008

3.3.2

Use of yellow eel data

Therefore, data on the growing yellow eel phase are not directly applicable to the first
level of an international scale stock assessment. They are, however, essential to indi‐
vidual member states, or regions, for example as inputs to modelling silver eel es‐
capement, or for providing interim data check points during the long growth term
between recruitment and silver eel production. If the national or regional stock as‐
sessments are to be checked at an international level, the data on which these are
based must be available in an accessible form.
3.3.3

The EU Eel Regulation

The EU eel recovery regulation requires specific national actions including the gather‐
ing of some eel data, and the supply of these data to the EU Commission upon re‐
quest. It does not specify or require that this information is directly available to the
WGEEL or to any organization except the EU commission. Furthermore, the data to
be gathered as part of the management plan and subsequent reporting to EC under
the stock recovery regulation is to be supplied to the EC at relatively long intervals of
at least three years. The reporting cycle starts with the detail of management plans by
the end of 2008, with subsequent reporting every three years, reducing in frequency
to every six years after 2021. The Commission itself will make its first report to the EU
Parliament in 2013.
Notwithstanding the fact that there is no built‐in obligation to report these data to the
WGEEL for stock assessment, the intervals in the reporting cycle under the EU Regu‐
lation are far too long to enable any rapid progress by WGEEL. For an assessment
working group to make significant progress toward bringing eel in line with other
international species stock assessments, annually updated data are required. The
cross‐compliance requirement between the recovery regulation and the CFP fishery
data collection regulation obliges countries to make some data available annually.
However, the DCR does not (yet) cover all data sources required for an assessment of
the status of the stock, either at EMU or wider scales.
3.3.4 Checklist of actions required under the Eel Regulation and associated
guidelines

Where data may be useful to international stock assessment this is displayed in bold
text.
•

Establishment of management plans by country or other eel management
unit by end 2008, including:

•

A list of management units and authorities responsible.

•

An inventory or individual basins in each management unit.

•

Justification for the use of a national scale plan if this option is selected.

•

Maps revealing eel management units in relation to WFD river basin dis‐
tricts.

•

Annual catch, if fished, in Kg for each RBD of glass, yellow and silver
eel. (this is not included in the regulation itself but is included in the
Commission implementation guidelines).

•

Quantitative and qualitative description of eel fishery units.

•

A list of fishers, licences, vessels licensed to local and EU waters, plus auc‐
tioneers and licensed dealers.

•

Quantitative and qualitative descriptions of eel fishery effort reflecting
local situation and any reductions imposed.

EIFAC/ICES WGEEL Report 2008

•

A quantitative description of recreational eel fishing, i.e. numbers of fishers
and their catches of eels.

•

Statement of which optional method(s) is used to define silver eel es‐
capement of target 40%.

•

A description of the silver eel 40% escapement target mode of measure‐
ment system used including its precision and accuracy.

•

A description of habitat condition, including non fishery mortalities e.g.
caused by pollution, migration obstacles (quantify this mortality if possi‐
ble).

•

An indication of the proportion of each life stage affected by contami‐
nants, pathogens, parasites and degree of contamination.

•

Qualitative and quantitative descriptions of past restocking and any in‐
tended as part of the management plan, with stocked areas.

•

A quantitative estimate of how stocking, if to be used, will contribute to
achieving the 40% escapement.

•

The proportion of captured less than 12 cm eel to be used for restocking.

•

Actual or estimated escapement relative to the 40% target, at time of plan
submission (2008) with description of estimation methods used.

•

Price monitoring for glass eel markets.

•

Description of the sampling system for catches and effort concerning all
life stages of eel, with regard to Regulation (EC) No 1639/2001 (DCR).

•

Measures to identify origin and traceability of live imports and exports.

•

Determination that eel imported and exported from territory are captured
within national (EMP) and international (CITES) rules.

31

In summary, this checklist identifies several items of potential use to future working
groups, assuming that the WGs have access to all data, preferably in the year pro‐
duced, rather than having to wait for the reporting cycle. By far the most relevant
data for international use will be the silver eel potential and actual escapement esti‐
mates.
There are, however, very significant deficiencies in this data source as an aid to inter‐
national stock assessment. Perhaps the most obvious gap is the failure of the regula‐
tion to secure a fishery‐independent glass eel recruitment dataseries. The reliance on
catch monitoring focuses the relevant part of the regulation on commercial glass eel
fisheries, which may change markedly, resulting in loss of individual dataseries. As
outlined above, the reporting cycle of three years is at intervals too long for any rapid
progress to be made on international scale stock assessment. Many of the data high‐
lighted, while of supporting interest, are not core requirements. The absence of a re‐
quirement for eel quality data are noted.
3.3.5

Data Collection Regulation

The cross‐compliance link between the Eel regulation and the DCR process is a useful
provision for stock assessment purposes. The DCR driven data provision is, however,
dependent on continuation of commercial and recreational eel fisheries. There is no
requirement for any fishery‐independent eel sampling in the DCR or for any sam‐
pling to continue where and when fisheries close. Continuation of commercial eel
fishing is far from guaranteed given the continuing downward trends in catches, the
possibility of approaching economic extinction, and the probability of widespread

32

EIFAC/ICES WGEEL Report 2008

cuts in eel fishing activity as a consequence of MS or RBD scale failure to meet the
“40%” silver escapement targets required in the eel regulation.
Even while the DCR does apply and forces data collection, the minimum prescribed
sampling is unlikely to provide sufficient data to compile a meaningful international
scale eel stock assessment, as it does not contain yellow eel surveys. Although there
will be some silver eel data where fisheries still exist, most DCR data will be on yel‐
low eel fisheries, and as such will be of indirect value to international stock assess‐
ment. Silver eel fisheries are also likely to be the first target for closure when
escapement targets are failed.
According to the DCR minimum stipulation for data precision level, the “fallback”
option is to measure 100 eels for every 20 t landed. A dedicated workshop on national
data collection of European eel (Dekker et al., 2005) concluded that “… one sample
per 20 t catch … was found to be inadequate …” and recommended that “15 samples
per life stage, per management unit would be more appropriate”. This workshop also
stated that “The number of individuals per sample for length analysis was examined
and there has been no analysis to date determining the precise levels required. Com‐
mon practice would indicate that 100 individuals per sample may not be adequate for
length and this should be increased to 200 per sample. SGRN (STECF) have strongly
endorsed this recommendation in its December 2007 meeting. However, for some
RBDs with small fisheries, the DCR sampling requirement exceeds the typical annual
catch of yellow or silver eel, but as the EU Eel Regulation constitutes an international
recovery plan, normal exemption rules do not apply.
The DCR on its own will not provide a framework to estimate the size of the spawn‐
ing stock as the programme does not provide estimates of eel abundance in small
fisheries or in those waters not fished.
3.3.6

Recruitment dataseries are not secured

EU concerted action 98/076 (Dekker, 2002) brought together the Europe‐wide dataser‐
ies of recruitment sampling which now form the basis of the recruitment data re‐
ported annually to this WG. It was concluded at the time that better coverage was
needed and proposals were made to establish new sites. Only two of these new sites
(research sites in Greece) have been started, and some of the formerly active sites are
now effectively stopped as a consequence of their dependence on fisheries now not
commercially viable or a lack of glass eel produced for restocking.
3.3.7

Water Framework Directive

The WGEEL has noted on many occasions that the requirement for MS to monitor
eels as part of inland fish populations under Water Framework Directive provisions
may also aid stock assessment. Such monitoring is likely to gather some data on yel‐
low eel and as such will be a data input to silver eel output models. However, given
the broader aims of the Water Framework Directive, there is a high risk that the
monitoring related to the WFD will be inadequate for the assessment of the eel stock.
Inadequate spatial coverage, low selectivity for eel, underreporting actual eel catches
and non‐reporting for eel, have been observed.
3.3.8

Data availability for international analyses

Table 3.1 summarizes the assistance that currently active initiatives, including the eel
regulation, DCR provisions, and WFD monitoring, may bring to international stock
assessment. It is concluded that these, while welcome, will not provide any rapidly
available source of data for a full international eel stock assessment. This objective

EIFAC/ICES WGEEL Report 2008

33

can only be achieved by the establishment of nationally maintained database, made
available for international compilation, of the key stock descriptors. These descriptors
are emigrating silver eel numbers, biomass and sex ratio, and recruitment in terms of
glass eel or young of the year numbers and biomass. The component and compiled
data must be annually updated to enable examination of any stock–recruitment rela‐
tionship. Only when such data exist will it be possible to bring eel population and
stock‐ recruitment assessments to the level given to most other major internationally
exploited fish species.
The list of data elements and supply in Table 3.1 includes the EMU or RBD level data
as a requirement, over and above the simple need for aggregated total spawner emi‐
gration and glass eel recruitment indices. In almost all cases, these data do not cur‐
rently exist and new dataseries need to be commenced, with international
coordination ensuring a compatible approach end allowing future analyses of the
disaggregated individual area components of the aggregated spawner production per
recruit relationship.
Table 3.1: Summary of potential data provision as required by EU and other international legisla‐
tive instruments, and WG data requirements for post‐evaluation of the Regulation.

EC E EL
RECOVERY
REGULATION

D ATA E LEMENT 1100\2007

EMUs and
Y
River Basins
List
commercial
Fishermen

Y

Catch by
recreational
fishers

Y

List of
primary
sellers

Y

Traceability
in trade

Y

Fishing
Capacity

G UIDANCE
DOCUMENT
FOR
PREPARATION
OF EMP S

CITES

DCR WFD

Y

Y

REQUIREMENT
( IF INTER NATIONAL
TRADE EXISTS )

(Y)

A DEQUATE
COVERAGE ?
OK
N OT OK .

R EQUIRED
FOR STOCK –
RECRUITMENT
ANALYSIS

R EQUIRED

( OCEAN )

( CONTINENT )

FOR
SURVIVAL
ANALYSIS

Listavailable
2009

+

Tri‐annual
insufficient

+

(Y)

Y

(Y)

Y
Y

Y
Y

(Y)

Silver eel
escapement

Y

Y

(Y)

Tri‐annual
insufficient

+

+

Potential SE
escapement

Y

Y

(Y)

One off

+

+

Fishing
effort by
métier

Y

Y

Y

(Y)

From DCR

+

Landings,
glass eel

Y

Y

Y

(Y)

From DCR

+

Landings,
yellow eel

Y

Y

(Y)

From DCR

+

Landings,
silver eel

Y

Y

(Y)

From DCR

+

in 2008/9

N OT IN
ASSESSMENT
( FURTHER
RESEARCH
REQUIRED ).

34

EIFAC/ICES WGEEL Report 2008

EC E EL
RECOVERY
REGULATION

D ATA E LEMENT 1100\2007

G UIDANCE
DOCUMENT
FOR
PREPARATION
OF EMP S

CITES
REQUIREMENT

DCR WFD

Catch
composition
‐Length

(+)

Biological
sampling for
length, age,
sex, maturity

+

( IF INTER NATIONAL
TRADE EXISTS )

(Y)

Recruitment
surveys

A DEQUATE
COVERAGE ?
OK
N OT OK .

Y

R EQUIRED
FOR
SURVIVAL
ANALYSIS

RECRUITMENT
ANALYSIS

( OCEAN )

( CONTINENT )

From DCR

+

DCR, but
only where
fisheries
exist

+

Incomplete,
and no
obligation!

Yellow eel
surveys

R EQUIRED
FOR STOCK –

+

WFD, low
coverage and
detail

+

Silver eel
”surveys”

Y

Hydropower
mortality –
No Stations

Y

Hydropower
mortality

Y – If info
available

(Y)

Not for all
sites

+

Predation
Losses

Y – If info
available

(Y)

only partial
coverage

+

Eel Quality
data1

Y

Only local
data

+

1

Tri‐annual
insufficient

+

Y

(Y)

EMP ,WFD

+

+
+

Hydromorph
data

e.g. fat content, contaminants, parasites and diseases.

Y =Required as a primary function; (Y)=Required as cross‐compliance; + =Adequately covered; (+)
=Partially covered but inadequate; entries in bold indicate data deficiencies, while entries in italics meet
requirements. Eel quality includes pollution, parasites, pathogens and fat levels.

3.4

Stock assessment vs. research needs
The EU Regulation on eel aims at the restoration of the spawning stock and recruit‐
ment. Implicitly, it assumes that a stock–recruitment‐relation (of the standard type)
exists for the total stock. In Figure 3.1, a decision tree diagram is presented, in which
the international assessment of the state of the stock and of the impact of protective
measures under the EU Regulation are evaluated, on the basis of trends observed at
the global and local level.
The EU Eel Regulation has set targets for the quantity (biomass) of silver eels escap‐
ing from the continent, and obliges Member States to take protective measures pri‐
marily focusing on the quantities escaping. No targets have been set with respect to
other processes (e.g. related to silver eel quality, or climate change) in relation to eel
management (if possible). The international assessment of the status of the stock
therefore focuses on the dynamics of stock in numbers and quantities, and on the ef‐
fect of protection and restoration measures taken.
However, the evaluation process depicted in Figure 3.1 (left hand column), provides
diagnostics at several points in the evaluation process, judging the adequacy of the
focus on quantities escaping only. When these diagnostics indicate a deviation from
expectation, further research will be required to clear up the processes, to quantify

N OT IN
ASSESSMENT
( FURTHER
RESEARCH
REQUIRED ).

EIFAC/ICES WGEEL Report 2008

35

their impacts, to find mitigation measures, etc. The right hand column of the decision
diagram of Figure 3.1 presents a bare skeleton for the decision processes for these
cases.
The final two columns in Table 3.1 reflect the current state of development of data
and quantitative knowledge of how they affect processes, separating those data items
essential now for database building to feed SPR analyses from those where there may
be an impact on eel biology but where current and further research programmes need
to be completed to quantify impacts and to allow these to be incorporated into
mathematically based analyses of stock and recruitment processes.
3.5

Stock assessment
3.5.1

Mortality based management targets

If and when recruitment continues to decline and silver eel escapement is not im‐
proved (which situation is quite likely to occur in the coming years) a critical assess‐
ment of the stock status will be required for each River Basin District, indicating
whether or not the targets of the EU Eel Regulation have been met. The target of the
Eel Regulation has been set in terms of silver eel escapement biomass (40% in relation
to a notional pristine production). There are many areas where that target can not be
reached in the foreseeable future, as a consequence of the low recruitment in recent
years, even if all anthropogenic mortality would have been removed immediately.
Additionally, a phased implementation of protective measures might slow down the
recovery in the earlier years following implementation of the Regulation.
However, the Regulation also requires Member States to specify a time schedule for
the attainment of the target. The Regulation does not specify what time schedules will
be accepted. Restoration times are more likely to be in the order of decades or centu‐
ries, than in terms of years (Åström and Dekker, 2007); if total anthropogenic mortal‐
ity remains above a critical threshold (fishery mortality F plus other anthropogenic
mortality H is 0.08 in that analysis), no long‐term recovery is expected. Preliminary
re‐assessment of the time till recovery for specific parts of the distribution area (nota‐
bly the southern areas with higher growth rates), presented during the working
group meeting, confirms a decadal or centennial recovery period, and a threshold
mortality level for long‐term recovery, though the results differ in absolute values.
Since the Eel Regulation biomass target is not achievable in the near future in many
areas, the mortality threshold for recovery is expected to represent the effective target
to which the stocks, the anthropogenic impacts and the protection measures will have
to be judged. The implicit character of this mortality threshold (being derived from
the time schedule, as an unacceptable “keep steady” limit) pleads for the derivation
of an explicit mortality target, corresponding to the time schedule requirement and/or
the biomass target of the EU Eel Regulation. A general, area‐independent target is
recommendable, e.g. %SPR. Whether this index of life time mortality actually suffices
for eel, needs to be investigated.
3.5.2

Density dependence and stock assessment

The long continued and widespread decline of the European eel stock has led to
adoption of a protection and recovery plan, based on classical concepts in fisheries
biology for precautionary reasons. This concerns, first and foremost, the assumption
of a classical stock–recruitment relationship in the oceanic phase. In its continental
phase, however, the eel is scattered over a multitude of small water bodies (Dekker,
2000), in almost all EU Member States and surrounding areas, often under local man‐
agement. Biological characteristics of the continental waters vary, both at short dis‐

36

EIFAC/ICES WGEEL Report 2008

tance (e.g. from the coast, via rivers and lakes, to headwaters, marshes and ditches)
and between geographical areas (from productive lagoons in the Mediterranean, via
densely populated rivers in the Bay of Biscay, to extensive cold waters in the Baltic,
producing low densities of large old females). Because of the wide variety of ecosys‐
tems in the continental phase, no single uniform approach to protection and assess‐
ment will suffice. Some local stocks will be adequately represented by classical
population models such as life table models, but others will not. Perhaps the most
conspicuous deviation is found in places where density‐dependence dominates the
local stock dynamics. Where this occurs, an increase in recruitment, as strived for by
the recovery plan, will not result in a (proportional) increase in the stock and in the
silver eel escapement. Where and when this occurs, the classical models of (density
independent) stock dynamics will not be applicable. Loss of production potential can
occur in these waters as a consequence of habitat loss, or loss of accessibility (migra‐
tion barriers). Otherwise, the presence of density‐dependence indicates that the stock
is at or, close to, its maximum density (carrying capacity), and restrictions of anthro‐
pogenic impacts will probably not increase silver eel escapement very much. Conse‐
quently, management actions should primarily focus on mitigation of habitat loss.
However, we do not know in how many rivers density‐dependence is evident, and
the continued decrease in recruitment will decrease their number over the years.
Where and when density‐dependence is insignificant, classical concept in fish stock
dynamics, such as life time survival, spawner per recruit, and maximum sustainable
yield can be applied. Derivation of (standardized) criteria for density‐dependence,
and adaptation of (standard) fish stock assessment models to the peculiarities of the
eel for density independent cases is required.
3.5.3

Assessment tools

The EU Eel Regulation obliges Member States to assess the current state of their
stocks, and to assess the expected impact of proposed management actions. The in‐
ternational stock assessment, as discussed here, will post‐evaluate the status of the
stock, and the net effect of management measures taken. That is: the focus is on the
actual state of the stock, rather than on expected impacts. The field of fish stock as‐
sessment is particularly well developed for marine fish stocks, including techniques
such as cohort analysis, length frequency based assessments, survey based assess‐
ments, etc. Existing experience in post‐evaluation assessment techniques for eel fish‐
eries is extremely limited (see Dekker et al., 2006 for an overview). Taking advantage
of the experiences in marine fish stock assessments, the construction of adequate
post‐evaluation techniques for eel stocks is an achievable challenge. In contrast with
“standard” marine fish stock assessment techniques, anthropogenic impacts other
than fisheries (e.g. predation, hydropower, eel quality), the spatial distribution of lo‐
cal stocks within river systems, migration and migration barriers should also be taken
into account. It is recommended to develop these tools internationally, making opti‐
mal use of available expertise and funding, and involving data and experts from
various geographical areas.
The adoption and implementation of the EU Eel Regulation will set an unprece‐
dented breakpoint in eel stock management, and will it is to be hoped lead to a major
breakpoint in stock trends. Consequently, the application of the above mentioned
post‐evaluation assessment techniques will have to cope with unprecedented data‐
sets. It is therefore suggested to explore the use of constructed reality, that is: to apply
the tools being developed on data derived from (other) simulation models.

EIFAC/ICES WGEEL Report 2008

3.6

37

Conclusions and recommendations for Chapter 3: International stock
assessment and data needs
The absence of any internationally driven requirement to maintain a recruitment
dataseries needs to be corrected, with reference to the recommendations of the EU
contract 98/076: Establishment of a recruit monitoring system for glass eel.
Internationally coordinated eel recruitment monitoring should be included in the re‐
quirements for the DCR.
The WGEEL notes that for future meetings it will need:
•

The means to compile data on spawner emigration and glass eel recruit‐
ment,

•

The means to assess RBD level spawner output per recruit relationships
with the full access to EMU level data that entails.

The WGEEL further notes that:
•

Current legislative instruments including the Eel Regulation, DCR, CITES
and WFD do not, either individually on in combination, contain sufficient
provisions to ensure adequate data supply for such assessments.

•

There is an urgent need to develop assessment and post‐evaluation tools
adapted to the eel case.

38

EIFAC/ICES WGEEL Report 2008

4

Assessing stocks and management actions

4.1

Background theory on population dynamics
4.1.1

Introduction

The reproductive process is one of the main mechanisms that controls and maintains
fish populations. In fisheries science, the phase from adult spawning stock to new‐
born recruits contributing to the stock is known as Stock‐Recruitment (S/R) relation‐
ship. It is the evolutionary mechanism by which fish stocks “buffer” the effect of
varying food and spatial resources. The S/R relationship is most often explored by
examining the empirical relationship between the spawning stock size (or its proxy)
and the subsequent recruitment output which results from a complex chain of events
through spawning, ova deposition and larval and juvenile growth and survival. In
fish stocks, the S/R relation is often the main resilient mechanism buffering the ex‐
ploitation mortality.
The mechanisms that determine the S/R relationship can be categorized as density‐
dependent and density independent. Density independent mechanisms imply that
the individual chance of survival for a youngster is independent of its parent’s stock
size and the number of eggs produced, giving rise to a linear relationship between
the spawning stock size and the number of recruits produced across the range of
spawning stock sizes. This model must have limits since no population can increase
indefinitely given that resources are finite, and fully density independent relations
are not observed in practice. At high spawning stock size, compensatory mechanisms
ultimately limit population size by maintaining some ceiling on the level of recruit‐
ment, i.e. density‐dependence becomes dominating. Several mathematical models
have been used to describe the shape of S/R models (i.e. Beverton‐Holt, Ricker) but
these all take a similar general shape at low stock sizes and largely only differ in the
upper ranges of stock size, which is of little concern for depleted stocks.
Figure 4.1 describes a theoretical S/R relationship of the Beverton‐Holt type. The solid
line describes the relation between the number of spawners and the subsequent
number of offspring (recruits). This has an almost density independent phase (nearly
linear) at low stock density (spawning stocks of 0 to 10, recruitment of 0 to 40) and an
upper density‐dependent phase, when the curve levels off (see above).
It is relatively simple to understand this relationship for local stocks such as salmon
or sea trout where the spawning effort and juvenile production takes place in indi‐
vidual catchments and where density‐dependent factors such as space for spawning
and food availability are clearly finite resources. It is much more difficult to envisage
how this might operate for eel which has an oceanic spawning and larval phase,
given the lack of knowledge of the spawning and early life history of the eel in the
Sargasso.

EIFAC/ICES WGEEL Report 2008

39

F current

pristine

Flim

100
limit

Recruitment

80
F=0

60
40
20

Rcurrent
0

Blim
0

20

40

BF=0
60

80

100

Spawning Stock Biomass

Figure 4.1: Hypothetical Stock‐Recruitment relationship, showing a Beverton and Holt‐type rela‐
tionship, the solid line indicates what recruitment is produced at what spawning stock size; the
broken lines indicate what spawning stock can be derived from a given recruitment, at no fishery
(F=0, dashes line), at maximal, just sustainable fishery (Flim, dotted curve) and current non sus‐
tainable fishery (and other anthropogenic sources of mortality) (Fcurrent, dot‐dashed curve). Both
Recruits and Spawning Stock Biomass are given in arbitrary units. The EU Regulation sets the
minimum target at 40% of the pristine spawning‐stock biomass, which will keep recruitment
close to its maximum, but on the brink of impaired recruitment. The intersections between the
two types of curves determine equilibrium biomasses (densities).

So, population dynamics and resulting equilibrium levels can be analysed through
the use of curves for SSB‐>R (from Spawning Stock Biomass to Recruitment) and R‐
>SSB (from Recruitment to Spawning Stock Biomass) (see Figure 4.1) where:
•

Recruitment in this context is assumed to be the biomass (or number) of
glass eels that successfully arrive to continental waters after having sur‐
vived juvenile density‐dependent mortality in the oceanic phase. An alter‐
native could be to define recruits as a somewhat later stage like: glass eels
settling (or elvers) in continental waters, and thus include the possible lo‐
cal density‐dependence in the early processes when glass eels arrive at the
continent.

•

Spawning stock biomass is the magnitude of the effective spawners, i.e. the
ones that are successfully reaching the Sargasso Sea and actually spawn‐
ing.

Equilibrium points correspond to intersections points between the two types of
curves.
The SSB‐>R curve depends upon oceanic factors such as spawner, success, currents,
food availability, etc, whereas the R‐>SSB curve depends upon mortality cumulated
during continental lifespan. Particularly, mortality rates F+M (anthropogenic and
natural) cumulated in the lifespan (from glass eel to spawner) determines the slope of
the R‐>SSB relationship and consequently the equilibrium level. Higher levels of mor‐
tality rates determine equilibrium points corresponding to lower values of both R and
SSB.

40

EIFAC/ICES WGEEL Report 2008

Note that spawner quality might be explicitly included in the SSB‐>R relationship as
an additional mortality considered that “bad spawners” die before spawning, but
after leaving the continent, or simply produce less offspring.
4.1.2

Eel stock and stock decline

In recent years 1, the development of the precautionary approach in fisheries man‐
agement and the exploitation of stocks have received much attention along with the
development of fisheries management tools and the provision of scientific advice.
The precautionary approach dictates a risk‐averse strategy, in which no fish stock is
exploited at a rate higher than one that generates maximum yield, and no spawning
stock is reduced to low levels, at which recruitment impairment occurs. In the ab‐
sence of pertinent knowledge to the contrary, a S/R relationship should be assumed
to exist, even for eel. The existing trends in eel landings and recruitment indices sup‐
port this view, although the exact form of the S/R relationship has not been possible
to determine so far.
Recruitment of European eel has been in decline since the early 1980s, and is below
5% of the historical level, since 2000. Total landings have revealed a gradual decline
since the 1960s, down to approx. 25% of the former level (Dekker, 2003). The causes of
the decline in recruitment are not well known, but might well be related to a low
spawning stock. Given the continuously declining trend, data suggest that the pre‐
sent equilibrium point corresponds to extinction or very close to extinction. The ecol‐
ogy of eels makes it difficult to demonstrate a stock–recruitment relationship.
However, the precautionary approach requires that such a relationship should be
assumed to exist. Therefore, ICES (1999) advised to restrict fisheries and other an‐
thropogenic impacts to the lowest level possible, in order to ensure that the spawning
stock returns to then remains above the critical level Blim, above which recruitment is
not impaired by the size of the spawning stock. Classical fishery management set the
critical spawning‐stock biomass level (Blim) at 30% of that in absence of fishery. Due to
the fundamentally different biology of the eel (semelparous with high longevity,
panmictic and scattered over the whole continent), the WGEEL suggested a higher
Blim of 50% for eels, and EU Regulation opted for a 40% objective.
As an alternative strategy to setting SSB target at an uncertain (30, 40 or 50%?) per‐
centage of the notional pristine SSB (which is not easily estimated), with an unknown
corresponding level of recruitment, another approach might be the following: In the
1970s, recruitment of glass eel was still at historically high levels. This indicates that
SSB was not limiting the production of recruits at that time. Quantification of the pre‐
1980 spawner escapement therefore is the simplest derivation of a reference level.
Note that in this case, the full escapement (100%) of the silver eels in the 1970s (given
the anthropogenic mortality of that time) then is assumed to correspond to the es‐
capement level advised by ICES (2002). That is, one could either set this interim refer‐
ence threshold at 100% pre‐1980 silver eel escapement where the data are available, or
in the absence of data, at a percentage of the notional pristine state.

1

United Nations Convention on Law of the Sea (1982).

UN Conference on Environment and Development (Rio de Janeiro, 1992).
FAO, Code of Conduct for Responsible Fisheries (1995).

EIFAC/ICES WGEEL Report 2008

4.2

41

Targets
In general it can be expected that achieving the target defined by the European Coun‐
cil (council regulation No 1100/2007) through management actions will take a very
long time. Åström and Dekker, 2007 estimated the time to full recovery of recruit‐
ment (the ultimate goal of the management of the eel) to be at least 80 years, if all eel
fisheries were closed and none of the other mortality issues addressed. The long‐term
target defined by the EC then becomes hard to apply in practical management terms.
So, for practical reasons short‐term, management unit based, interim targets (here
called interim targets) need to be defined in connection with management measures
to be taken (Figure 4.2).
These interim targets need then to be translated into sub‐targets for action on the lo‐
cal scale, which can range in geographical scale from a point source such as a hydro‐
power plant or fishery to the catchment or the scale of the Eel management Unit. This
is required as the efficiency of management action has to be evaluated in a short term
compatible with the time‐scale of the responsible managers and this is shorter than
the expected time span for the recovery of the eel stock. Therefore short‐term, sub‐
targets are needed to optimize regional management according to the long‐term ob‐
jective of full stock recovery (Figure 4.2). The sub‐targets will be split into manage‐
ment sub‐targets directly linked to the set‐up of management and into eel sub‐targets
aiming at increasing the production of eel on a local or regional scale. Management
sub‐targets may be defined as the number or magnitude of actions taken, i.e. number
of dams with passes installed, reduction of fishing mortality, number of habitats and
amount of eel stocked. In contrast an eel sub‐target could, as an example, be related
to the abundance or density for 0+ eel in predefined sections of a catchment.

European level
(several decades)

EU target:
40% of the pristine escapement

MS, RBD, EMU level
(decade or less)

Interim target

…

Interim target
% of current escapement

…

Interim target
Local level
(several years)

Mngt sub-target

Eel sub-target … Mngt sub-target
N of equipped dams

Eel sub-target

Mngt sub-target

Eel sub-target

Density of yellow eel

Management set-up
Post Evaluation
Figure 4.2: Schematic representation of different targets, interim targets and eel and management
sub‐targets.

42

EIFAC/ICES WGEEL Report 2008

Both types of target (eel life stage sub‐target; management sub‐target) should be pos‐
sible to post‐evaluate, i.e. it should be possible to empirically measure the outcome of
the management effort relatively soon after it has been applied, and member states
are required to collect relevant data to achieve this. For each type of management
measures different time‐scales for the response in the relevant eel life stage can be
expected.
A link between the outcome of the post‐evaluation and future management restric‐
tions should be established. In principle, one could use a qualitative link; i.e. when‐
ever the spawner production is below the sub‐target, the managers increase their
restrictions. However, a quantitative link is preferable, if not a prerequisite as the EC
target is defined in quantitative terms; i.e. the post‐evaluation method should indi‐
cate what level of restrictions is required to achieve the sub target.
The level of the interim and sub targets should be defined so that the long‐term target
defined by the council regulation No 1100/2007 (ʺ… reduce anthropogenic mortalities
so as to permit with high probability the escapement to the sea of at least 40% of the
silver eel biomass relative to the best estimate of escapement that would have existed
if no anthropogenic influences had impacted the stock. ʺ), (or other relevant, stricter,
targets), has a high probability of being reached, in reasonable time. The expected
differences in time until different measures result in increased spawner escapement
have to be considered in this context.
If reaching the long‐term target is not possible, based on the current eel stock within
an Eel Management Unit (EMU), and the time schedule for the attainment of the tar‐
get level of escapement cannot be calculated (although such a time schedule is re‐
quired by the council regulation No 1100/2007), managers might consider using a
stepwise approach with increasingly more ambitious interim targets in sequence over
time. This could mean starting out with interim targets and short‐term measures
based on currently achievable improvements in the eel stock, given the current low
recruitment (e.g. a high % of current possible escapement) then moving to an interim
target related to escapement of pre‐1980 (e.g. 40% of possible (without anthropogenic
impact) escapement of the pre‐1980), then increasing the required percentage of pre‐
1980 escapement (e.g. 100% of escapement pre‐1980) to finally be able to aim directly
for 40% of pristine escapement.
It will need to be remembered that when calculating expected spawner escapement
from each RBD/EMU, in response to management measures, it must be emphasized
to consider information on the recent recruitment decline, which in most cases will
impose a decreasing local stock of eels in the near future, and consequently a declin‐
ing spawner escapement, which need to be counteracted by the level of the manage‐
ment measures. This also raise the risk of getting a situation where an escapement
target might be reached one year, just to drop below the next year being in the risk of
a continued decline despite the management measures taken.
4.3

Estimation of spawner escapement
The Regulation suggests three options for determining the target level of escapement
(Article 2.5):
(a)

using data collected in the most appropriate period prior to 1980 to estimate silver
eel escapement, provided these are available in sufficient quantity and quality;
(b) a habitat-based assessment of potential eel production, in the absence of anthropogenic mortality factors;
(c) extrapolating with reference to the ecology and hydrography of similar river systems.

EIFAC/ICES WGEEL Report 2008

4.3.1

43

Estimation of silver eel escapement pre- and post-1980

The definition of silver eel needs to be standardized for escapement estimates. The
difference between silvering and silver eels has to be clear and the adoption of the
same criteria all over Europe is therefore required. These distinctions have been made
clear by some authors (e.g. Acou et al., 2005; Durif et al., 2005) and following them we
propose three criteria, which include eye diameter, state of lateral line (presence of
black corpuscles) and body colour contrast. It is essential that a standardized method
of silver eel identification is adopted for escapement studies.
Estimations of silver eel escapement are available from a number of studies; these are
summaries in Tables 4.1a and b for assessments pre and post 1980, respectively. The
geographical distribution of the studies data are shown in Figure 4.3. Silver eel es‐
capement is defined as the total number or weight of silver eel that left the catchment,
expressed per unit wetted area available to eel for comparison between catchments.
Potential silver eel escapement is defined as the number, or weight, of silver eel that
would leave the catchment, without anthropogenic mortality, and for Tables 4.1a and
b, this has been calculated as the sum of silver eel escapement and the catches of sil‐
ver and yellow eel and any mortality from other causes (hydropower, illegal fishing,
etc).
Pre‐1980 data are available from 25 locations (Table 4.1a). For river systems where
lakes are a small proportion of the available habitat for eel production estimates
ranged from 1.9–49 kg/ha (n=4). For catchments where there is a sizeable lake com‐
ponent to the overall wetted area (>50%) the estimates ranged from 0.3–17.4 kg/ha
(n=4). For lakes, with the exception of the Ijsselmeer where production was estimated
at 40 kg/ha only minimum estimates based on silver eel yields from fisheries suggest
a range of 0.1–11.7 kg/ha (n=14). For marsh type habitat there is a minimum estimate
of 43.7 kg/ha and for lagoons one estimate of 20 kg/ha.
Post‐1980, the number of assessments of production has increased, but remains
dominated by lake studies from Sweden; 66% of the 50 studies (Table 4.1b). Estimates
of potential silver eel escapement for rivers varied from 2.7–16.4 kg/ha (n=3), for lake
dominated catchments from 0.2–6.4 kg/ha (n=4) and for lakes from a minimum (based
on silver eel yield) of 0.04–4.4 kg/ha (n=35). Of the 35 lake studies, two the Shannon
and Ijsselmeer provide an estimate of potential silver eel production of 2.7 and 4.4
kg/ha, respectively. There are three lagoon studies with estimates ranging from 6.2–
30 kg/ha.

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EIFAC/ICES WGEEL Report 2008

Table 4.1a: Estimated silver eel yield and production (in kg/ha wetted area), pre‐1980.

EIFAC/ICES WGEEL Report 2008

Table 4.1b: Estimated silver eel yield and production (in kg/ha wetted area), post‐1980.

45

46

EIFAC/ICES WGEEL Report 2008

Pre - 1980

Post - 1980

Figure 4.3: The location of the catchments for which historical (pre‐1980 black) and recent silver
eel production estimates (post‐1980 red) are presented in the Tables above.

4.3.2

Modelling approaches

A number of modelling approaches have been made to estimate escapement or a ref‐
erence condition to assess compliance with the EU escapement target.
The models are:
•

Reference Condition Model (RCM)

•

Eel Length Structure Analysis (ELSA)

•

Scenario‐based Model for Eel Populations (SMEP)

•

Global Anguille (GLOBANG)

•

Length‐based Virtual Population Analysis (LVPA)

•

Swedish Analytical Models (SWAM)

•

Demographic Model of the Camargue (DEMCAM)

•

Glass eel model to assess compliance (GEMAC)

These models and their potential to support the EMPs have been described by EI‐
FAC/ICES WGEEL and Dekker et al., 2006. In addition during the meeting a number
of other approaches have been presented to the WGEEL as non peer‐reviewed
worked examples.

EIFAC/ICES WGEEL Report 2008

47

4.3.2.1 Elbe population dynamic model (Oeberst et al., submitted)

An age based model has been developed by Oeberst et al., submitted to examine the
population dynamics of eel in the River Elbe (Germany) and estimate the number of
eel emigrating from the catchment. The model inputs are quantity of immigrating
young eel, number (and weight) of eel stocked, natural mortality and mortality
caused by commercial and recreational fishing, cormorants, and hydropower plants.
The structure of the model allows the sensitivity of the parameters to the overall es‐
timate to be examined. The model may be used to develop management strategies
and to assess the effectiveness of different management options to meet the EU target.
It has the advantage of simple adjustment by being modular constructed and can be
further developed using MS‐EXCEL.
The model assumes that eel remain in fresh water for a maximum of 20 years. The
available data for describing the different factors which influence the stock dynamics
have different quality. Total catch (kg) per year is estimated for the commercial fish‐
ers and angler. The mean weight of the catch (g) and age–length based samples are
only available from some areas of the Elbe and short periods. Length based estimates
exist for the transformation of yellow eel to silver eel and for the eel which are taken
by cormorants based on stomach samples. To combine the different data types a pro‐
cedure is necessary for transferring length based data into age based data.
For the model it was assumed that eel age >8 were fully recruited to the fishery, this is
based on a minimum size limit of 45 cm for commercial and recreational fishers. Re‐
cruitment is composed of natural immigration of glass / yellow eel based on monitor‐
ing estimates and stocked eel from published reports. Natural mortality is assumed
to be constant at 13% (M=0.14) per year (Dekker, 2000). For recreational anglers the
total weight of eel caught was the product of the total number of anglers and the
mean weight of the catch. The amount of eel consumed by the cormorant population
was estimated based on the number of cormorants, their residency time, the daily
food intake and the average proportion of eel in their diet (Brämick and Fladung,
2006). The total catch of the commercial and recreational fishers and the consumption
of eel by cormorants were converted from a weight based estimate to a number per
age using weight to length conversions and a von Bertalanffy growth model. A length
based logit‐function was used to estimate the proportion of silver eel in the catch of
eel by fishers and in the eel consumed by cormorants.
In addition, some general assumptions were used for estimating the catch in number
by age group and year because appropriate data are lacking: The age frequency of the
catches by fishers and anglers is similar to the age frequency of the stock combined
with the requirement that eel younger than eight years are not landed; silver eel are
not landed by fishers or the landings can be neglected.
Even though the model is adjusted to the conditions in the river system and to the
availability of data, it also includes several assumptions and uncertainties. Therefore,
the results of the model will have to be validated by monitoring the stock, especially
by silver eel monitoring.
4.3.2.2 Irish model to estimate silver eel escapement (Ó’Néill and Poole, in prep.)

Catch based estimates of historic/pristine escapement

The calculation of pristine productivity for exploited catchments requires estimates of
silver eel escapement along with historic silver and brown eel catches (Figure 4.4).
Historical catch records for silver eel fisheries were available for the five catchments
of the Corrib, Moy, Garavogue and Erne. The efficiencies of the fisheries had been

48

EIFAC/ICES WGEEL Report 2008

previously estimated for the Shannon, Corrib and Erne silver eel fisheries. Where
fishery efficiency was not measured an approximately average value of 33% was used
to calculate escapement. In addition to the catch at the recording station and escape‐
ment past the recording station the brown eel and silver eel catches made upstream
were included to estimate pristine productivity. In the absence of historical data for
these latter parameters (brown and silver eel catches upstream of the recording sta‐
tion) it was assumed that the yields were equal to those currently observed (2001–
2007).
Brown eel yield was assumed to be equivalent to the same weight of potential silver
eel. This assumption was based on the logic that in a system subject only to natural
mortality, migration would only be delayed such that fecundity (related to weight)
would be maximized. Consequently, it is unlikely that there would be a net loss of
weight in subsequent years from a cohort. Finally, the productivity estimates were
corrected by the level of unreported and illegal fishing. Unreported yield was derived
as the ratio of unreported licences to licences issued within the relevant River Basin
District between the years 2001–2007. The proportion of the fishery yield taken ille‐
gally was assumed to be equal to that estimated for the Shannon by the DEMCAM
(SLIME) model (Dekker et. al., 2006). For those catchments with hydropower, an es‐
timate of the impact was derived by imposing a 28.5% mortality per turbine passage
(WGEEL, 2002). Therefore, the probability of surviving passage through ‘n’ number
of hydropower installations is (0.715)n.
The estimated pristine spawner escapement ranged from 0.9–5.4 kg/ha with a mean
of 3.9 kg/ha (Table 4.2).
Index catchment
current escapement

Index catchment
potential escapement

Hydropower impact
Total fishing impact

National authorisation statistics

Proportion unreported

Proportion illegal

Reported fishery impact

Silver catch

EIFAC 2002
report – mean of
estimated hydropower mortalities

Estimate of illegal catch
for the Shannon lakes
based on DEMCAM
model

Input variables
Brown catch
Calculated variables

National catch statistics

Target

Figure 4.4: Description of how potential production (escapement) was derived from the current
escapement of catchments where estimates of silver eel escapement, fishery yield and the impact
of hydropower are available.

EIFAC/ICES WGEEL Report 2008

49

Table 4.2: Estimated pristine spawner productivity from five Irish catchments based on either
direct measurement and/or catch data.
M OY

G ARAVOGUE

E RNE

C ORRIB

B URRISHOOLE

1942–
1952

1962–1975

1955–
1982

1976–
1982

1971–1980

Silver eel catch at
recording station
(t)

3.4

0.9

9.2

19.4

0.0

Escapement past
recording station
(t)

6.8

4.4

51.3

38.8

427.5

Brown eel yield
upstream (t)

Reported

4.0

1.7

13.4

9.0

0.0

Brown eel yield
upstream (t)

Unreported

3.0

1.2

23.4

6.5

0.0

Silver eel yield
upstream (t)

Reported

18.6

0.0

Silver eel yield
upstream

Unreported

Hydropower
impact (t)
Wetted area (ha)
Productivity
(kg/ha)

0.0
29.1

1.2

9.2

13.4

0.0

0.0

0.0

25.4*

0.0

0.0

8418.0

1783.0

25.9

28869.0

475.0

5.3

5.4

4.5

3.4

0.9

*occurs following recording station.

Potential production based on habitats of similar characteristics

The method involved determining the relationship between productivity and the
geological characteristics of the catchment.
Growth rate of eel were available for 17 catchments (Moriarty, 1988, Central Fisheries
Board). The wetted area within each catchment was quantified using a geographical
information system and classified according to the proportion of the catchment area
comprising non‐calcareous geology. For 17 catchments growth rate was found to be
closely negatively related to the proportion of the catchments comprising non‐
calcareous geology (Figure 4.5) (r2=0.67; p<0.0001).

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Growth rate compared with geological classification

Growth Rate (mm/yr)

40
35
30
25
20
15
10
5
0
0

10

20

30

40

50

60

70

80

90

100

Proportion of catchment non-calcareous

Figure 4.5: The relationship between growth rate and the proportion of the catchment comprising
non‐calcareous geology.

The four catch‐based production estimates along with the direct estimate for the
Burrishoole (Table 4.2) were plotted against the proportion of non‐calcareous geology
within the catchment (Figure 4.6). These historic estimates suggest that in exclusively
non‐calcareous catchments silver eel productivity was approximately 0.9kg/ha
whereas in predominantly calcareous catchments silver eel productivity averaged
about 4.5kg/ha.
An obvious weakness in the relationship presented in Figure 4.6 is the distribution of
the data, with very few data for intermediate or non‐calcareous catchments. To in‐
crease the robustness of the model the 5 available productivity estimates were used to
convert the growth‐rate estimates for 17 catchments into pristine production esti‐
mates.
Potential silver eel productivity was regarded as a product of recruitment, natural
survival and average silver eel weight. Natural mortality was imposed at a constant
rate of 14% per annum. This rate was chosen because the average age of Irish silver
eels is approximately 18 years and the cumulative natural mortality over the conti‐
nental life stages is approximately 2.5 (Dekker, 2004). The residence time was the time
required for glass eels (70 mm) to grow to the Irish average silver eel length of 480
mm (sexes combined).

EIFAC/ICES WGEEL Report 2008

51

Historic productivity estimates

Silver eel productivity
(kg/ha)

6
5
4
3
2
1
0
0

20

40

60

80

Percentage of catchment non-calcareous
Corrib

Garavogue

Moy

Burrishoole

Erne

Figure 4.6: Catch based productivity estimates plotted against the percentage of catchment with
siliceous (non‐calcareous) geology.

For each of the 17 catchments the proportion of fish surviving (S) was thus estimated
as follows:
S = (1‐0.14)((480‐70)/G)
Where G = growth rate (mm/yr)
For those five catchments data on silver eel production was also available (Table 4.2)
and these where used as index catchments to estimate potential spawner escapement
as follows:
Spawner productionx = (Survivalx/Survivali) * Spawner productivityi
Where i= “index” river; x = river where no estimate of spawner production is avail‐
able.
This calculation was repeated using the survival and spawner productivity for each
of the five “index” catchments and the mean computed. The relationship between the
estimated productivity and geology for the 17 catchments is shown in Figure 4.7 to‐
gether with the estimate for those five catchments where productivity had been
measured either from catches of by direct measurement.

100

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EIFAC/ICES WGEEL Report 2008

Pristine productivity

Silver eel productivity (kg/ha)

7
6
5
4
3
2
1
0
0.00

20.00

40.00

60.00

80.00

100.00

Proportion of catchment comprising non-calcareous geology

Average productivity based on growth rates (calibrated with direct count and historic
fishery data)
Productivity based on direct count (n = 1) or historic fishery data (n = 5).

Figure 4.7: Relationship between silver eel productivity (kg/ha) and percentage of catchment with
siliceous (non‐calcareous) geology. The pink points are based on catch based or direct estimates
of productivity. The blue points are based on the relative productivity of the catch based esti‐
mates but these are not included in the regression.

These data now allow for calculation of pristine productivity (kg/ha) based on either:
1 ) The relationship between silver eel productivity (based on four historic
catch records and one historic total count) and the proportion of non‐
calcareous geology in the catchment using the regression equation:
Productivity (kg/ha) = ‐0.041*(percentage of catchment non‐calcareous) + 5.18
2 ) The relationship between silver eel productivity (based on 17 growth rates
calibrated with four historic catch records and one historic total count) and
the proportion of non‐calcareous geology in the catchment using the re‐
gression equation:
Productivity (kg/ha) = ‐0.030*(percentage of catchment non‐calcareous) + 4.97
For Ireland pristine spawner production is estimated at 641 928 kg (4.17 kg/ha) using
the regression based on historical catch or total count data and 651 092 kg (4.23 kg/ha)
using the regression based on growth rates calibrated with historical catch or total
count data.
As reliable data becomes available this approach will be taken to extrapolate from
data rich to data poor situations where applicable. This approach is well established
for salmon management in Ireland. The regression approach, as described, allows the
transfer of data from index catchments with production estimates to catchments
where little or no data exists on the basis of geological proxy for production.

EIFAC/ICES WGEEL Report 2008

53

4.3.2.3 French methodology to estimate silver eel production (Hoffman, unpublished)

The evaluation of the biomass of silver eel produced in continental waters at the
French scale is based on modelling the yellow eel abundance. 20 000 electrofishing
operations were used to fit the model. They corresponded to 9000 stations and cover
the period 1980–2005. The model describes presence absence and abundance of total
densities and densities per class size of 15 cm.
Four categories of variables were used: environment (distance to the sea, tempera‐
tures, altitude, geographical area), temporal (month, year), variables linked with an‐
thropogenic pressure (habitat quality, obstacles, glass eel and yellow eel fisheries)
and variables associated with electrofishing (fishing method).
The work is based on a GIS database of the French river network, which has been
analysed to extract some environmental parameters (distance to the sea, cumulated
river length upstream, river width, Strahler rank). Environmental parameters are ex‐
tracted and densities are predicted in all points of the network. Setting anthropogenic
parameters to zero, it is possible to predict the actual pristine productions. Temporal
variables allow the prediction of past densities. The combination of both provides a
figure of past pristine productions. Densities are converted into numbers by multiply‐
ing by the water surfaces.
The aim is to compare the estimated ʺpristineʺ 1989 densities with those determined
during the 1960s and 1970s and if the latter are higher adjust the pristine 1989 esti‐
mate by a factor. This density would then be compared with current estimates (Fig‐
ure 4.8).
The yellow standing stock will then be compared with actual estimates of silver eel
production.

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EIFAC/ICES WGEEL Report 2008

Figure 4.8: Model prediction in Loire Bretagne of the spatial variation in yellow eel densities
(nb/100 m²). Surfaces are not yet calculated so the median of eel densities is shown on each graph.
Pristine correspond to predictions without dam and with no glass eel fishery.

The predicted temporal trend in yellow eel densities estimated at the mouth of the
river in the absence of dams for the period 1982–2005 is shown in Figure 4.9. After
1989 there is a steady decline in density. It should be noted that prior to 1989 the
method of data collection differed, and the difference may reflect the lower density
estimates.

EIFAC/ICES WGEEL Report 2008

55

Figure 4.9: Model prediction of the temporal trend in yellow eel densities (nb/100 m²) in Loire
Bretagne. The year effect was classified as category in both presence‐absence and abundance
when present models, the densities are those predicted at the mouth of the river, in the absence of
anthropogenic impact.

For each obstacle the severity of the obstruction was estimated on a scale of 0–5 and
for obstacles in series the impact was estimated to be cumulative. Obstacles have the
effect of reducing the density of eel upstream (Figure 4.10). There is a rapid decline in
density with an increase in the number and severity of the obstruction falling to ap‐
proximately a third at a cumulative obstruction score of 50.
The model also predicted that eel density declines with distance from the sea (Figure
4.11).

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EIFAC/ICES WGEEL Report 2008

Figure 4.10: Model prediction in Loire Bretagne of the cumulated effect of obstacles. The effect of
obstacles is expressed as a scoring (from 0 to 5 impassable). Densities (nb/100 m²) are predicted at
the sea, in 1995, in the Loire, without anthropogenic impact.

Figure 4.11: Model prediction in Loire Bretagne of the effect of the distance to the sea. Densities
(nb/100 m²) are predicted in 1995, in the Loire, without anthropogenic impact.

EIFAC/ICES WGEEL Report 2008

57

4.3.2.4 Silver eel production in Danish streams (Pedersen, pers.comm.)

Silver eel production in Koge Lellinge stream was estimated to be 105 kg/ha river
(wetted area) (Rasmussen and Therkildsen, 1979). The estimate was based on the
density of resident yellow eels, observed growth (derived from age reading) and
mortality with data collected during the period 1965–1968. The estimate is therefore
based on glass eel recruitment during the period the late 1950s and early to mid‐
1960s. The population consisted mostly of males with mean weight of 100 gramme.
The experiment was undertaken in the lowest part of the stream and downstream of
a weir, the estimate therefore can not be taken as representative of silver eel escape‐
ment for the catchment as a whole but only the lower part of the river.
Silver eel production in River Brede was estimated to be 49 kg/ha river (wetted area)
(Nielsen, 1982). The silver eel were caught in autumn 1981 using fykenets with the
escapement estimated using mark‐recapture and is thus based on the recruitment of
glass eel during the period 1965–1975. The population of silver eel was 82% males
and 18% females. Average weight was 120 grammes.
Silver eel production in the River Bjornsholm was estimated in 1988 to be in the range
9–39 kg/ha river (wetted area) (Bisgaard and Pedersen, 1990). Densities of resident
yellow eel, observed growth rate (derived from age reading) and mortality produced
an estimate of 39 kg/ha river (wetted area). This compares with an estimate of 9
kg/ha river (wetted area) from mark‐recapture on silver eel carried out in August and
September and therefore should be considered a minimum estimate of escapement.
Sex ratios of silver eel were 40% males and 60% females with an average weight of
280 grammes.
In Denmark, it is proposed that 50 kg/ha (wetted area) represents “pristine” escape‐
ment for the fresh‐water environment. This translates into the EU escapement target
of 20 kg/ha (wetted area).
4.3.2.5 Quebec approach (Verreault and Lambert, pers. comm.)

A Canada‐France‐Québec research project was set up to evaluate impacts of barriers
opening in terms of escapement and net productivity gain in the fresh‐water habitats
of the St. Lawrence watershed. This GIS decision tool will be based on eel habitat sur‐
face and eel distribution in a watershed. More precisely, the model is based on the
exponential decrease of potential yellow eel abundance with distance from marine
waters. Then the potential yellow eel density for every river stretch is modulated by
the cumulative mortality and passiblity of downstream barriers then converted in
absolute abundance of silver eel escaping the system. The model final output will be
an estimate of potential production of oocytes by using a size‐fecundity relationship.
4.4

Future methods for silver eel escapement (yellow eel proxies)
It is essential to adopt standardized methods to estimate escapement, potential bio‐
mass (e.g. biomass available in the river system) and also effective biomass (that will
escape and that has reasonable probability to reproduce) derived from silver eel qual‐
ity and mortality within the river catchment. Possible methodologies are outlined
below and in the INDICANG methodological guide (Adam et al., in press) not yet
seen by the WGEEL.
Silver eels biomass production is a primary management target to be urgently
achieved for starting the restoration of the European eel (Anguilla anguilla) popula‐
tion. An assessment of the proportion of individuals actually escaping from catch‐
ments‐and able to reproduce‐compared to a theoretical pristine production under no
human intervention, is of critical importance for preserving this resource, and the EU

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EIFAC/ICES WGEEL Report 2008

obliges Member States to implement measures. However, there is currently very little
information on silver eel escapement and even less information on silver eel quality
(e.g. defined by parasite burdens, metallic and organic contamination of tissues, fat
content). In order to estimate effective breeding biomass in data‐poor catchments,
research is required to develop and implement methods and protocols describing
reliable proxies. Such research has recently started during the EU programme INDI‐
CANG that proposes to clarify some of the basic concepts needed to implement as‐
sessment tools for a characterization of the production of spawning biomass in a
catchment. These concepts rely mostly on the influence of the catchment context
(conditions for the eel growth) on the silver eel population characteristics (biomass
and numbers, sex ratio, size and age structure, condition indices) before migration.
The effective breeding biomass (escapement of high quality future spawners) is then
estimated by attributing anthropogenic mortality rates (fisheries, hydroelectric tur‐
bines, dams and reservoirs). Risk analysis is also needed to define the proportion of
eels that are healthy enough (low parasite burdens, high condition and fat content as
well as low chemical contamination levels) for successful migration and to contribute
to the gene pool. As a first step, this effective breeding potential should be estimated
at the catchment area level from the sources to the sea. Then, regional approaches
need to be developed and implemented to model relations between catchment char‐
acteristics and silver eel population characteristics (Acou et al., in press).
Here we review different approaches which have been implemented or proposed to
estimate silver eel escapement. The methods will soon be available and translated in
four languages (French, English, Castillano, Portuguese) in Chapters 8 and 9 of a
“Guide book for European eel monitoring” produced from INDICANG project (web‐
site references). Parts of the results are also presented in Robinet et al., 2008. In addi‐
tion to being able to quantify the status of the stock information is also needed on
processes, particularly growth and mortality, as such there is a requirement to ensure
standardization of the method(s) used to estimate age.
4.4.1

At the catchment level

4.4.1.1 Estimating silver eel biomass escapement

Direct estimates intercepting silver eel runs

a. Commercial silver eel fisheries can, depending on their location and scale, provide
good opportunities for direct estimation of the numbers and biomass of silver eels
escaping from hydrosystems, by analyses of annual variation in either yield or cpue
provided that it is possible to determine the efficiency (proportion of run captured) of
the eel capture systems involved. Examples of such investigations, of population dy‐
namics and seasonal patterns of seaward migrating eels, include those undertaken on
the River Loire, River Shannon and Corrib, River Bann (Lough Neagh outlet), the
River Imsa, the Baltic basin and the St Lawrence. Difficulties can occur when the fish‐
ing season does not cover the full migration period or when there is significant eel
production downstream of the fishery area. Use of mark/ recapture methods for esti‐
mation of fishery capture efficiency allows for estimation of the numbers and biomass
of migrating eels at the fishing sites. This can involve use of a variety of tags and
marks (see Concerted Action for Tagging of Fish: www.hafro.is/catag). Experimental
fisheries could be established in data poor areas and used to improve fishery moni‐
toring methodologies. (Vollestad and Jonsson, 1988; Caron et al., 2000; Feunteun et al.,
2000; Feunteun et al., in press; Allen et al., 2006; WGEEL Baltic sea; and McCarthy et
al., 2008).

EIFAC/ICES WGEEL Report 2008

59

b. Wolf traps, or related systems, or use of winged nets deployed for research pur‐
poses can provide precise estimates of migrating eel population dynamics and under
some circumstances all silver eels can be counted and weighed. However, this is usu‐
ally only possible in smaller river systems where discharge patterns allow for silver
eel trapping throughout the migration season. Examples of this type of silver eel es‐
capement estimation include the studies undertaken on the Norwegian River Imsa
(Vollestad and Jonsson, 1988), the French Rivers Frémur (Feunteun et al., 2000) and
Oir (Acou et al., in press), the Burrishoole (Poole et al., 1990; 1994) and the outlet of
Lough Ennel in the River Shannon, Ireland (McCarthy, unpublished data).
c. Counters and various acoustic technologies can allow for the estimation of silver
eel escapement in locations where eel capture is not possible. For example, hy‐
droacoustic methods, such as were used by McCarthy et al., 2008 to investigate varia‐
tions in numbers of silver eels migrating downstream in the headrace canal of the
Ardnacrusha hydropower plant in the River Shannon, and resistivity counters and
Didson acoustic cameras trialled for counting emigrating silver eel in the UK (M.A.
Aprahamian, pers. comm.). Such eel counts, and linked data on size frequencies of
the migrating eels, are only possible in locations where other fish species (with target
strengths in the same range as the silver eels) are not also migrating downstream at
the same time as eels. Work is in progress in Ireland, UK, Poland and other European
countries that should lead to improved sampling protocols and to more widespread
use of this method for estimation of eel escapement rates.
Indirect estimates using yellow eel proxies

In many water basins, lack of data concerning silver eel estimates, requires the use of
alternative approaches to meet the demands of Council Regulation 1100/2007 for es‐
timating silver eel escapement. The use of proxy indicators from sedentary eels and
habitat population models seem to be the most promising approaches (Feunteun et
al., 2000; Aprahamian et al., 2007; Lobon‐Cervia and Iglesias, 2008; Feunteun et al., in
press,). These procedures should nevertheless be standardized so that methodologies
used can provide representative estimates of silver eel production, e.g. sampling at
the beginning of the migratory season (late summer in southern latitudes and middle
summer in northern latitudes).
Mark‐recapture or other more locally adequate methods could be used to estimate
density of yellow and silver eels. Several habitat types representative of each catch‐
ment should be evaluated in order to be able to extrapolate for the whole catchment
and include it in habitat population models.
Eel mortality rates need to be determined throughout the river basin including the
estuary as well as fresh‐water habitat (see also Chapter 3).
In some countries, lack of data on both yellow and silver eels requires a different ap‐
proach in which, habitat data collected within the WFD should be used in conjunc‐
tion with eel population data from similar regional areas. However, EMPs based on
this provisional approach should also include details of sampling programmes to
provide a basis for future determination of spawner escapement.
Estimating effective silver eel biomass escapement

Effective silver eel biomass {proportion of the potential silver eel biomass * mortality
(Fishery, Hydropower, Natural) * quality} estimation is essential if the actual contri‐
bution made by particular rivers, river basin districts or larger scale European regions
is to be evaluated now and during post evaluation of EMPs. This integration of data
on population dynamics and eel quality has not been subject to the detailed level of

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EIFAC/ICES WGEEL Report 2008

discussion to which other elements of the EU eel recovery plan have been subjected.
However, a more standardized approach to this topic is required if results of ongoing
studies on contaminants, parasites and diseases of silver eels are to be integrated at a
European level.
4.4.1.2 Quality

Monitoring quality of silver eels should aim to establish the proportion of migrating
eels that have sufficient quality to reach the spawning grounds, breed and produce
adequate numbers of viable larvae. In analyses of silver eel populations the extent of
quality monitoring will be more limited for eels released following capture and
measurement. For released eels, the life‐history stage determination, and the usual
length and weight measurements, must be recorded for representative subsamples.
Observations of significant decreases in fat levels in yellow eels over 15 years in Bel‐
gium and the Netherlands raises serious concerns about their reproductive potential
(Belpaire et al., in press), and warrant the inclusion of eel quality estimates within the
quantitative targets for escapement.
Considerably more parameters should be requested on a subsample of silver eels.
These can involve data on contamination levels of metals and organic (for methods
refer to quality section), fat content and condition factor, otolith age reading, A. cras‐
sus and EVEX and other viral diseases. Information on life‐history traits and popula‐
tion characteristics should also be provided for sampled silver eel populations and
this can involve sex ratio estimated from size frequencies (with calibration using sac‐
rificed eels). There is a need to establish a size‐age relationship and also an index re‐
lating eel quality to breeding success.
4.4.2

At the regional level

It is anticipated that the EMPs are developed under the River Basin District (RBD)
level. The success of EMPs depends on a good coordination and consistency between
measures taken under Regulation 1100/2007 and European Directives having impact
in the river basin. Therefore, to make EMPs more effective, it is desirable that catch‐
ment based models are also developed at a regional level (involving each RBD), aim‐
ing at predicting silver eel escapement.
4.5

Methods for evaluation of management measures
A close link between both management and eel sub‐target will be established in the
following sections with regard to selected management measures (Table 4.3). The re‐
lationship between management and eel sub‐targets will allow for a direct feedback
to management if measures are not achieved and/or the locally targeted eel popula‐
tion responded, or failed to respond, in the predicted manner (e.g. an increase in yel‐
low eel density). The methods to evaluate management measures and the response of
the targeted eel life stages should be applied locally and therefore give a feedback to
the authorities in the eel management units. By this feedback loop local managers
will be able to adapt their management approach without regard to the delayed re‐
sponse of the whole eel stock (e.g. changes in recruitment). However the proposed
management‐ and eel‐targets are not intended to be an exhaustive list of all possible
management measures. It should be taken as a first step in filling the gap between
local management and the long‐term recovery.
It is also recognized that methods for evaluating the outcome of management meas‐
ures on the population level (eel sub‐targets) are not always fully available and need
further research. The same holds true for the definition of different quantitative levels

EIFAC/ICES WGEEL Report 2008

61

of eel sub‐target. The levels have to be related to the actual status of the eel popula‐
tion with respect to the global objective of full recovery (e.g. 40% of spawner escape‐
ment without anthropogenic impact). The level of the management action finally
depends on how far a certain management unit is away from the objective (refer to
Figure 4.2).
4.5.1

Management measures and methods for evaluation

4.5.1.1 Commercial fishery

EMP´s will involve fisheries regulation measures throughout the distributional area
and across all continental life stages. A range of different measures can be identified
and applied to the different life stages. Evaluating the effects may require different
approaches and time frames.
Management sub‐target 1.1: Effort restrictions. For all life stages, the regulation and
limitation of the access to the fishery is a common measure that can be applied.
4.5.1.2 Glass eel fishery

Quotas and partial or total closure of fishing activities are the most plausible methods
in managing a glass eel fishery.
Management sub‐target 1.1; Achievement of Quota. A defined proportion of the re‐
cruits to a management unit is excluded from the local fishery. Evaluation should be
based on knowledge of variation in abundance/catchability over time in the season
and monitoring of landed quantities.
Management sub‐target 1.2; Total or part time closure. A given degree of closure re‐
sults in a predetermined reduction of fishery mortality. This target must be based on
the knowledge of glass eel abundance over time in the fishing area and may be moni‐
tored by field control of fishing activities.
4.5.1.3 Yellow eel fishery

Quota, total or part time closure, size limits and closed areas are measures applicable
in regulating most fisheries, including fishing for yellow eel. Technical regulations of
the fishery for yellow eel may have different effects depending on where they are
imposed in a catchment. If they are imposed downstream, in an estuary or near the
area of primary recruitment, they may have an effect on density‐dependent migration
and thus proliferate upstream in the river basin. On the other hand, if they are intro‐
duced upstream in a system, where the subpopulation has a higher degree of resi‐
dence, the expected effect will primarily concern demography and mortality. The
statements above suggest different designs of monitoring and short‐term evaluation.
Management sub‐target 1.3; Quota. A defined proportion of the yellow eel stock in a
management unit is excluded from the local fishery. Evaluation should be based on
knowledge of the local production in the area and effects of a regulation can be moni‐
tored in landed quantities.
Management sub‐target 1.4; Total or part time closure. A given degree of closure re‐
sults in a predetermined reduction of fishing mortality. Evaluation of this target
needs stock assessment models, which are often dependent of an existing fishery. A
total closure thus is easier to evaluate.
Management sub‐target 1.5; Size limits. Imposing size limits is targeting reduction of
fishing mortality. Evaluation of this target needs stock assessment models, which are
often dependent of an existing fishery.

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EIFAC/ICES WGEEL Report 2008

Management sub‐target 1.6; Protected areas. A management target for protected ar‐
eas could be evaluated as the proportion of the available habitat or productive poten‐
tial that is taken out of fishery. In this case, as in most other cases, the proper
management target is a certain reduction of fishing mortality in the management
unit.
4.5.1.4 Silver eel fishery

The most plausible tools to manage a silver eel fishery are total or part time closure
and size limits. Protected areas may also be considered.
Management sub‐target 1.7; Total or partial closure should fulfil the target to reduce
mortality by a predetermined value. Evaluation must be based on a good estimate of
the number of eel that would be caught if fishing was open and the total catch in the
open season. This target may be monitored using landings and historical information
on distribution of catches over the entire season.
Management sub‐target 1.8; Legal size limits may include exclusion of the smallest as
well as the biggest individuals from the landed part of the catch. This target should
be set bearing in mind the total effect on effective SSB. Egg production may not be/is
not linearly related to body weight (Verreault, 2002). Compliance with the target
must rely on sampling of the size distribution in the total catch, discards included.
Management sub‐target 1.9.; Protected areas. The effect of protected areas should tar‐
get a certain proportion of the silver eel production in a management unit and should
be restricted/closed for all types of fishing activity, i.e. F=0 for x% of the potential
production.
4.5.1.5 Recreational fishing

In parts of Europe recreational fishery generates a major part of the fishing mortality.
This kind of fishing is to a great extent focusing on the yellow eel stage but capturing
silver eel may also occur (Staas, 2006). The measures available for managing this sec‐
tor of fishery are primarily the same as those for the commercial fishery. Thus the
biological targets are similar to those presented above under yellow eel fishery. All
management actions described in the same section could be applied to recreational
fishery. The presence of poaching though, may introduce the need for official control.
Management target 1.10.; Control of effort. This target should be the control of effort
taken in a management unit or in predefined parts of a management unit.
4.5.1.6 Actions to make rivers passable and enhance habitat quality
Upstream migration

Management sub‐target 2.1; number of dams where eel ladders will be installed, es‐
pecially in and near the zone of active colonization:
Management sub‐target 2.2; surface area of river channels and lakes in a catchment or
a percentage of lost habitats that could become recolonized by eels.
Evaluation of management sub‐target 2.1 and 2.2 could be achieved annually by list‐
ing of the recently equipped dams combined with GIS techniques of upstream sur‐
face measurement.
The conversion of the management targets into an eel sub‐target assume that all habi‐
tats within a river system are equally productive per unit surface area and eels were
totally excluded upstream of man‐made obstacles (Verreault et al., 2004; ICES 2007).

EIFAC/ICES WGEEL Report 2008

63

Management sub‐target 2.3; number of fish passing over the obstacles. To determine
these numbers an eel ladder should be equipped with a fish counting device (trap,
video camera etc.). However, passage of fish may fluctuate a lot with several peaks
during the migration period. For example, in the River Couesnon, 75% of the fish
trapped occurred within four weeks whereas 17 weeks contributed to less than 10%
of the total catch (Legault, 1994). The target in absolute numbers could be difficult to
achieve in the short term especially when a decreasing recruitment trend is observed.
It is probably better to define a relative target i.e. percentage of fish that succeed to
migrate upstream. Briand et al., 2005a undertook a survey of arrival near the obstacle
by using a mark and recapture technique but this kind of application is difficult to
execute and repeat for a long period.
Downstream migration

Current practice of stocking and the (recent) construction of fish passes have led to
the establishment or maintenance of yellow eel population in habitats situated up‐
stream of hydropower plants. In many cases these areas will be included into the
natural eel habitat. But when reaching the silver eel stage a fairly large proportion of
these eel are lost as a consequence of turbines passages and or impingement. Possible
mitigation measures consists of installing bypass systems, switching off hydropower
turbines temporarily and capturing downstream migrating silver eels (and inciden‐
tally yellow eels) before entering hydropower turbines.
Management sub‐target 2.4; number of obstacles where appropriate bypass systems
will be installed, or where hydroelectric power turbines should be switched off tem‐
porarily, or where trap‐and‐transport measures will be carried out.
Evaluation of this management target could be achieved annually by listing the re‐
cently equipped dams.
4.5.1.7 Reduction of environmental contamination

Management sub‐target 2.5; reduction of pollutant discharge until total prohibition of
use for the most dangerous contaminants.
The direct evaluation of such target is not simple because it is difficult to estimate the
quantity of pollutants being input to the river, especially when sources of pollutant
are diffuse.
4.5.1.8 Increase of habitat quality

Management sub‐target 2.6; Wetted surface area of river where eel habitat quality is
improved.
As with contaminants the direct evaluation of such a target is not simple because it is
difficult to estimate the habitat quality and the relationship with the quantity of eel.
4.5.1.9 Stocking of glass eel or pre-grown (farmed) yellow eels

If stocking is to be used as a management measure according to the EU‐Regulation, it
has to be assumed that stocking is performed at the actual state‐of‐the‐art (decision
tree and stocking protocols are available; see Chapter 5) with respect to carrying ca‐
pacity and sufficient quality of the chosen habitats (see relevant data collected under
the WFD). The health status of material used for stocking with special regard to para‐
sites, viruses and other pathogens has to be checked. Additionally silver eels pro‐
duced from such stockings should be able to escape from the habitats without major
losses as a consequence of pumping stations or hydropower turbines.

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EIFAC/ICES WGEEL Report 2008

Management sub‐target 3.1 Defined proportion of habitat with low recruitment in the
management unit for supplementation of eels and number of eels stocked per surface
unit (ha) according to available eel surplus for stocking.
Management sub‐target 3.2 Defined proportion of natural eel habitats without re‐
cruitment and number of eels stocked per surface unit (ha) according to available eel
surplus for stocking.
Stocking activities will in future rely on the assumption that a surplus of glass eel
from at least some European estuaries is still available. In view of high prices for glass
eel the locally available budget may limit stockings more than biological or logistical
aspects.
4.5.1.10 Measures related to aquaculture for stocking

A great proportion of glass eel captured in Europe is currently used for eel produc‐
tion in aquaculture. This proportion is assumed to diminish in the next years as ac‐
cording to the EU‐Regulation up to 60% of all eel below 12 cm should be reserved for
stocking. On the other hand stocking, as a conservation measure, can include eels up
to 20 cm in length. This is in accordance with current stocking practice using pre‐
grown eels from aquaculture for release in natural eel habitats. As prices of glass eel
tended to be high in recent years and glass eel are assumed to face a high natural
mortality in the first years this practice will probably continue in coming years. As a
consequence of rearing conditions there is a concern about the quality of such eel re‐
leased after a time in conventional eel aquaculture with regard to health status and
genetic diversity (see Chapter 5.4.2.3).
Management sub‐target 4.1 Ensure the production of sufficient numbers of eels (for
stocking) with a good health status with respect to parasites (Ang. crassus), viruses
(HVA, Eve, EVEX) and other pathogens.
Management sub‐target 4.2 Ensure the production of eels from aquaculture with a
minimum genetic selection and avoid stocking of slow growing individuals sorted
out from aquaculture.
4.5.2

Eel sub-target

4.5.2.1 Glass eel sub-target

Eel sub‐target 1.1; Density target for wild (and stocked) 0+ in predefined sections of a
catchment.
This can be monitored in ladders and/or by electro‐fishing and to be evaluated
against historical data. A short‐term response is expected (few months).
Time frame for revision management action: 1 year.
Indicators; n/ha, absence/presence, front of colonization.
4.5.2.2 Yellow eel sub-target

Eel sub‐target: Profile of eel occurrence according to longitudinal position in the
catchment. More precisely, this target can be expressed in distance from the sea
where the probability of eel presence is 50%.
No information on time‐scale of response, probably few years depending on latitude.

EIFAC/ICES WGEEL Report 2008

65

Example of application

This methodology is based on an analysis electro‐fishing data with logistic regres‐
sions. Lasne and Laffaille, 2007 estimated that study of temporal trends of “eels’ lo‐
gistic profiles along the longitudinal gradient” allow the assessment of the
improvement of colonization after mitigation of local impacts.
Eel sub‐target: Density of yellow eel in the upstream reaches.
A short‐term target could be set as a specific increase in density on a certain level in a
river system after a certain period of time. Compliance can be monitored by mark‐
recapture, counting in ladders, by electrofishing, with fykenets or other kinds of fish‐
ery‐independent methods (ICES 2007) and can be evaluated referring to historical
data or expected densities from models.
No information on time‐scale of response, probably few years depending on latitude.
Indicators

Numbers passing, n/ha, cpue.
Example of application

An illustrative example is given by the reopening of the Vilaine watershed (Briand et
al., 2005b). The construction of the eel ladder resulted in high densities (>1 eels/m‐2) in
the downstream and middle stream areas after two or three years after. These
changes remain clear and the examination of five years of data has changed little of
the conclusions expressed after only two years. Number of glass eels climbing the fish
ladder led to the colonization of the entire basin and a possible saturation in the
downstream and middle stream areas. But decrease of glass eel arrival and density‐
dependant mortality could complicate the interpretation of the results, by inducing a
decrease in density in some parts of the catchments (Briand et al., 2005b). A similar
approach was performed on the Fremur River and stressed again the importance of
maintaining longitudinal connectivity in rivers.
Eel sub‐target; Degree of habitat saturation of yellow eel.
Response in distribution/habitat saturation level in the entire catchment can vary in
time frames according to latitude, altitude, climate, etc. A reasonable estimate is that
a sub‐target like this could be set to 3–5 years in the central area of distribution. The
target fulfilment can be evaluated against historical data or densities from models.
Indicators

Ratio between saturated and unsaturated surface, ration between actual density and
carrying capacity.
Eel sub‐target: Sex distribution.
An increase in density induced by a reduced fishing mortality may result in a den‐
sity‐dependent change in sex ratios. Evaluation of the appropriate target level will be
difficult, but may be based on historical data.
Example of application

The Baltic eel stock declined sharply in the 1960s and the 1970s following a preceding
decline in recruitment of young yellow eel into the Baltic Sea. The hypothesis was
raised that the reduced recruitment was due density‐dependent processes in the ar‐
eas of primary recruitment, i.e. the Kattegatt and the Danish straits (Svärdson, 1976).

66

EIFAC/ICES WGEEL Report 2008

Following this male eel almost completely disappeared in SW Sweden. An effect of
density on sex ratio was also observed in Lough Neagh in Northern Ireland (Rosell et
al., 2005).
The time frame for a response in density‐dependent sex differentiation is uncertain,
as there is a period of time between recruitment and sexual differentiation.
Indicator

Proportion of sexes per size group.
Eel sub‐target: Short term response in mortality rate.
Local estimate of global mortality (fishing mortality, other source of anthropogenic
mortality) rate can inform on pressure on the stock.
Example of application

The LVPA assessment models quantify the population state and the impact of fishing,
for the data years Dekker, 1996 #865}. A minimum of assumptions and a maximum of
data ensure a close tracking of the true population state in recent years; in particular,
estimates of both the population number and the fishing mortality by length class are
updated annually. The Beverton and Holt methodology easily allows for simulation
of alternative fishing regimes, and derivation of reference points. Application on the
yellow eel fisheries in Lake Ijsselmeer demonstrated that this fishery overexploit the
local stock of eel. Current fisheries reduce male spawner escapement to one in seven
parts and reduce female spawner escapement to one in seven hundred parts of the
unexploited situation (Dekker, 2000).
ELSA is a modelling approach based on eel length taken into account relative change
in recruitment, sex ratio, growth, natural and fishing mortality and rate of silvering. It
is useful to estimate total mortality rate from a simple length structure above 30 cm
(Lambert et al., 2006). The information about eel stock status provided by an applica‐
tion on the Gironde estuary present analysis urges to implement management actions
in fresh‐water part of the estuary.
Time frame for revision management action

Two to five years (should be revised).
4.5.2.3 Silver eel sub-target

Eel target: level of mortality rate for each obstacle, maximum delay for migration. For
global river management, cumulative mortality and delay can be targeted.
An approximate estimate of turbine mortality can be obtained using empirical for‐
mula from literature (Larinier and Travade, 1999). More accurate estimations of eel
mortality rates can be obtained by telemetry procedure although they are difficult to
obtain as a consequence of the uncertain behaviour of eels during their downstream
migration (ICES 2007).
Evaluation of such target should take into account the variability induced by envi‐
ronmental fluctuations and therefore a multi‐annual survey is advised.
Time frame for revision management action

Two or three years.
Eel target: Number of silver eels escaping

EIFAC/ICES WGEEL Report 2008

67

This can be monitored by catch statistics, direct counting methods, or mark–recapture
experiments (ICES 2007).
The potential production of silver eels can be deduced by converting the re‐
established yellow eel population or production (data from electro‐fishing) into silver
eel using simple population models. Where downstream dams are present, escape‐
ment estimates should be adjusted to account for cumulative mortality from dam
passage.
Time frame for revision management action

One to five years.
4.5.2.4 All life stage sub-targets

Eel sub‐target 4.1: Level of contaminant load in eel. Measurements in fish are possible
for many contaminants, especially for lipophilic ones since eels are particularly sensi‐
tive to bioaccumulation of such contaminants. Eel measures give better responses (%
of detection) than measurements in water or in sediment (Belpaire and Goemans,
2007a) and an adaptation of the Flemish survey (Belpaire and Goemans, 2007a) to the
relevant scale of the studied source of pollution should be advised.
The time response of these management actions depends mainly on the persistence of
the contaminant in the field. For example, in Flanders lindane load decreased rapidly
after its ban in 2002, whereas DDT continues to slowly decrease 30 years after prohi‐
bition (Maes et al., 2008).
Eel sub‐target 4.2; Level of quality index. An index of (yellow or silver) eel quality is
important for evaluating the net effect of silver eel escapement on reproduction.
Fat content could also be good proxy indicator of the contamination level and a sub‐
sequent decrease in yellow eel fat content has been tentatively linked to the capability
of silver eels to perform the migration to the Sargasso (Belpaire et al., 2008). Health
status of eels used for stocking especially with regard to A. crassus and viruses such
as HVA, EVE and EVEX can complete this index.

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EIFAC/ICES WGEEL Report 2008

Table 4.3: Linking management sub‐target and eel sub‐target. Relation in a short term between management sub‐targets (level or magnitude of action in local management) and eel sub‐
targets (response of the eel population to management) (GE: glass eel YE: yellow eel; SE silver eel).

Eel sub-target
GE
1.1;
Den
sity

YE
2.1
Occurrence

2.2 :
Density

9 9
1.2; GE time
9 9
closure
1.3; YE Quota
9 9
1.4; YE time
9
closure
1.5; YE Size limits
9
1.6; YE Protected
9 9
areas

SE

2.3.;
Habitat
saturation

2.4. Sex
Distribution.

2.5
Mortality
rate

9
9
9
9

9
9
9
9

9
9
9

3.1;
Obstacle
mortality

3.2
Escapee
number

Fishery

Management sub-target

1.1 ; GE Quota

9
9
9

1.7; SE time
closure
1.8; SE size limits
1.9. SE protected
areas
1.10. Control effort

9

4.1
Contaminant
load

4.2;
Quality
index

EIFAC/ICES WGEEL Report 2008

69

Table 4.3: continued.

Eel sub-target
GE

Aquaculture

Stocking

Pollution

Downstream
mig.

Upstream migration

1.1;
Den
sity

2.1; Equipped dam
number

2.2; Opened surface
area
2.3; Fish passing
number
2.4 No. of obstacles

YE

SE

2.3.;
Habitat
saturation

2.4. Sex
Distribution.

9 9

9

9

9 9
9 9

9
9

9
9

2.1
Occurrence

2.2 :
Density

2.5
Mortality
rate

2.5; Pollutant
reduction
2.6; Improved
habitat surface
3.1 Surface of
habitat with low
recr. and No. of
stocked
3.2 Surface of
habitat without recr.
and No. of stocked
4.1 No. of produced
eels with a good
health status
4.2 No. of produced
eels with minimum
genetic selection

3.1;
Obstacle
mortality

3.2
Escapee
number

9

9

4.1
Contaminant
load

4.2;
Quality
index

9 9
9

9

9 9

9

9

9 9

9

9
9

9

9

9

70

4.6

EIFAC/ICES WGEEL Report 2008

Conclusions and recommendations for Chapter 4: Assessing stocks and
management actions
4.6.1

Conclusions

It is suggested that managers define interim targets for the management measures in
order to integrate local action efficiently to the aim of long‐term recovery of the
European eel stock. For this purpose management sub‐targets defining the magni‐
tude of actions (e.g. number of dams removed) will be linked with eel sub‐targets
reflecting the expected short‐term response of the local eel population. Eel sub‐targets
should therefore allow a fairly rapid evaluation of the management measures taken
but sensitivity and time response of some of the proposed eel sub‐targets would need
further investigation before their application would be operational.
Eel sub‐targets should finally be integrated into the evaluation of the status of the
whole eel stock. However it has to be recognized that adequate methods or modelling
approaches for doing this exercise are still lacking.
Implementation of EMPs requires the development of methodologies to obtain those
data. They can include either direct (e.g. mark‐recapture) or indirect measures (yellow
eel proxies to determine silver eel production and eel habitat modelling production).
It is important to ensure standardization and quality control of the method(s) used to
estimate age.
Use of direct methods, though preferable in many respects, will be severely restricted
by: uneven distribution of silver eel fisheries within and between regions; limited
fishery monitoring resources; and in extreme fluctuations in large river flows. How‐
ever, where possible, use of direct methods should be prioritized.
A variety of indirect methods, mostly dependant on yellow eel proxies and model‐
ling, are available for areas where direct measurements of silver eel escapement are
not possible and should be extensively used to estimate regional and national silver
eel escapement. Selection of models should take account of SLIME conclusions and
advice given elsewhere in this report (Dekker et al., 2006). Validation of indirect
methodologies should be undertaken on an ongoing basis for a network of river sys‐
tems where reliable direct estimation of silver eel escapement biomass is possible.
Estimation of effective spawner biomass should be undertaken in all EMPs (i.e. at lo‐
cal, regional and national levels) and this will require quantification of adverse effects
of contaminants, low fat levels, non‐lethal turbine damage, viral diseases, along the
lines previously proposed for A. crassus as well as other anthropogenic mortality
rates along the river catchment. Local management decisions should then be made
by reference to effective silver eel escapement rather than total spawner biomass es‐
timates.
There are very few quantitative estimates of pristine (pre‐1980) and current silver eel
production to allow comparisons to be made between systems and there is very few
data on the importance of estuarine and coastal populations to overall production.
Modelling will be needed to transfer estimates from data rich to data poor systems.
Some approaches have been outlined by this Working Group which complements
those from presented in previous working Group reports and in Dekker et al., 2006.

EIFAC/ICES WGEEL Report 2008

4.6.2

Recommendations

•

well defined sub targets for short‐term, local management efforts should
be used, and that data should be collected so that they can be post‐
evaluated both regarding the fulfilment of the management efforts and the
anticipated effects on eel;

•

population model(s) should be used to assess the status of stock, compli‐
ance with (sub) target(s), to evaluate management actions and to evaluate
the influence of biotic and abiotic factors on the stock at a range of geo‐
graphical scales;

•

adaptive feedback links are established between post‐evaluation results
and resulting changes in management efforts;

•

care should be taken so that locally established (short‐term) sub targets en‐
sure long‐term recovery, eventually leading to the restoration of the
spawning stock so that the eel reach full recruitment capacity.;

•

since short time evaluation of management actions urges for a list of moni‐
toring activities, fishery dependent as well as fishery‐independent, meth‐
ods for monitoring in connection to the sub targets presented by the
WGEEL in this report and in the report of 2007 should be implemented
ASAP within the DCR and elsewhere and that where possible these activi‐
ties should be coordinated nationally with related monitoring activities, i.e.
regarding biodiversity within the WFD;

•

the concept of effective spawner biomass escapement should be adopted
for all EMPs and comprehensive protocols for integration of standardized
eel quality data should be developed for application of this concept;

•

standardized terminology, and identification criteria be adopted, for use in
all European eel programmes;

71

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EIFAC/ICES WGEEL Report 2008

5

Stocking and aquaculture

5.1

Introduction
Stocking and transfers of juvenile eel have been discussed at length by the Working
Group (most recently ICES, 2006 and 2007). These discussions have covered the prin‐
ciples and extent of stocking, stock transfer practices and their contributions to fisher‐
ies. Their effect on escapement has been discussed mainly in conceptual and
theoretical frameworks as a consequence of a lack of hard data. The WG 2007 recom‐
mended that “guidelines, or best practice manuals, should be established for meth‐
odologies for ……. stocking of eel”.
ToR b) develop methodologies for the assessment of the status of the eel stock, the
impact of fisheries and other anthropogenic impacts and of implemented manage‐
ment measures; this might include, for example, support for EMPs on the determina‐
tion of ʺpristineʺ spawner production levels and relative contribution of stocking.
Extract from 2006 WGEEL report‐the changing scientific advice regarding stocking.
ʺScientific advice on re‐stocking has changed over the years, from clearly in favour
(Moriarty and Dekker, 1997), to against on precautionary grounds (ICES, 2000). In
our previous report (ICES, 2005b), the risks involved were discussed, balancing po‐
tential genetic effects against the risk that the current stock might suffer from depen‐
satory effects in the reproductive phase, for which re‐stocking might be one solution.
Clearly, arguments both pro and contra re‐stocking remain valid, and no final and
scientific advice can be derived. However, the previous advice was based on the po‐
tential for depensation occurring in the reproductive phase. All arguments pro and
con being as they are a more practical and nearby argument has come to the fore in
this report: that seed stock areas might progressively become depleted as a conse‐
quence of a continued decline in glass eel immigration. Options for potentially suc‐
cessful restoration of the stock by glass eel restocking are fading. Re‐stocking of glass
eel, either in southern areas rapidly contributing to silver eel production, or in north‐
ern areas with a long postponed and long lasting contribution to silver eel produc‐
tion, therefore needs urgent consideration.ʺ
The Working Group revisited this topic in 2008 in order to provide updates on stock‐
ing figures and practical information to support stocking best practice and will pro‐
vide support to EMP’s and the EU Commission.

5.2

Methods to assess the relative contribution of stocking to the regeneration
of the European stock, and for EMPs
5.2.1

Source of glass eel

Advice from ICES to the EU commission (ICES, 2005a) was that the recent glass eel
catch (ca. 100 tonnes) is less than that required (150 to 1000 tonnes) to supply the total
potential productive habitat (about 40 000 km2), and ACFM further concluded that
full‐scale restocking alone is unlikely to achieve the EU objectives in the medium
term (ICES, 2006).
Therefore, the advice remains that there are likely to be insufficient glass eel available
from the fishery to meet the demands for stocking at the European level. However,
the Regulation EU: 1100/2007, requires that fisheries make at least 35% of eel <12 cm
available for stocking in 2009, rising to 60% by 2013. The implementation of EMPs in
2009 may effect the reduction in some glass eel fishing effort, either as part of local

EIFAC/ICES WGEEL Report 2008

73

Management Plans or as a consequence of the 50% cut required where plans are not
submitted and approved. This outcome will not be known until the EMPs are pub‐
lished. Here, we consider the potential effect (benefit) of this stocking material.
5.2.2

Yield potential

The yield potential can be calculated from Yield/Recruit (Y/R) estimates. Most of the
data on Y/R available are obtained from stockings in lakes and an Italian lagoon. The
data for lakes range from 5–72 g.stocked eel‐1, but most are in the range 20–50
g.stocked eel‐1. The yield‐per‐recruit in the Italian lagoon appears to be more than
twice as high. If the total catch of glass eels in Europe is in the region of 100 tonne
(ICES, 2005) of glass eels, with 3000 glass eels per kilo, and 35% (minimum requested
by the Eel Regulation) provided in 2009 for stocking, this would have a production
potential for approximately 2000–5000 tons of silver eel after one eel generation time.
When 60% of the catch becomes available in 2013, it will have a lifetime potential for
3500–8500 tons of silver eels given no anthropogenic mortality. ICES 2006 produced
comparable results (10 000 tons of silver eels when stocking 100 tons of glass eels)
when using population dynamic calculations and data from Moriarty and Dekker,
1997. The above estimates are maximum estimates, based on the assumption that the
catch of glass eel will be in the region of 100 tons. There is of course the possibility
that there may be no surplus of glass eels in the near future (ICES, 2007).
Glass eel are caught using moving and stationary fishing gears. There is evidence that
handling mortality of some of these gears, e.g. trawls may be up to 40%. Reduction of
these mortalities would lead to the more efficient use of the limited and declining
resource of glass eels.
5.3

Review of stocking activity across Europe
Before the WG meeting, a simple questionnaire was sent to the WG members in order
to obtain additional information. The responses to this questionnaire are briefly de‐
scribed in the following section. Information from 17 countries is included. For this
purpose, UK and Northern Ireland were considered as two countries, since there is a
considerable transfer of glass eels from the “UK” to Northern Ireland.
A. Does your country buy eels for stocking?

Yes:

11 (DE, PL, N.Irl, SE, NL, BE, FI, EE, LT, LV, DK)

No:

6 (FR, ES, PT, UK, IE, NO)

A clear geographical pattern can be seen. Countries at the Atlantic coast do not buy
eels whereas countries further east of the Atlantic, and in particular around the Baltic
Sea, usually purchase eels for stocking.
It has to be noted that this is a dynamic picture, which may change from year to year
depending on several factors (availability and price of glass eels, situation of the fish‐
ery in the respective country, political and administrative decisions).
B. If so which life stage, glass or yellow eels? (only countries with “Yes” under question 1)

Glass eels:

6 (DK, LT, EE, FI, SE, N.Irl)

Yellow eels (elvers, pre‐grown eels):

1 (LV)

Both:

4 (BE, NL, PL, DE)

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EIFAC/ICES WGEEL Report 2008

Clear changes in the stocking strategies have occurred in the past and will probably
re‐occur in the future depending on several factors, in particular the availability and
price of glass eels vs. pre‐grown eels from farms. New scientific results may also in‐
fluence the decision for one of the stocking types (e. g. survival and growth rates of
glass eels vs. pre‐grown eels, gender selection based on farm densities and risk of in‐
fection with diseases from the farms). There are risks and benefits for each type,
which are considered in another section of this report, and which should be consid‐
ered in the stocking strategy.
C. How much stock was purchased in 2008?

The data for 2008 were not complete and did not allow a useful analysis. Therefore,
the data for 2007 were considered here.
Total glass eels 2007:

5.7 Million individuals

Total yellow eels 2007: 5.6 Million individuals
There are uncertainties in these numbers and the data are not complete for all coun‐
tries (but all 11 countries which answered “yes” under question A, are included).
Therefore, these numbers must be considered as minimum values. The calculation is
difficult, since some countries buy glass eels and rear them in farms for a while before
stocking. In some of these cases, the original numbers of imported glass eels are not
available (just the numbers of young yellow eels stocked).
A rough estimate was made about the total amount of glass eels finally used for
stocking. For that purpose, yellow eel numbers were translated into glass eel num‐
bers (glass eel equivalents) by correction factors usually used in Denmark (1 farmed
eel equals 1385 glass eels; M. I. Pedersen, pers. comm..) and Germany (1 farmed eel
equals 3 glass eels; e. g. Knösche et al., 2004).
Based on these factors, the total numbers of glass eel (equivalents) used for stocking
ranged from 13.5 Millions to 22.5 Millions. If a mean weight of 0.3 g for glass eels is
assumed, these numbers translate into biomasses of 4.5 t to 7.5 t. Even though these
are rough estimates, they may indicate the order of magnitude of glass eels used for
stocking of natural waters in Europe. If this is compared to the total glass eel catch in
Europe, which was between 50–60 tons in 2007, a proportion of 7.5–15% of the total
glass eel catch was used for stocking. This is in the same order of magnitude as pre‐
vious estimates. These figures may be influenced by incomplete recordings of stock‐
ing as well as of glass eel catches.
D. From where or whom?

It does not appear possible to provide very clear analyses about the trade paths of
glass eels since the situation is very dynamic or poorly reported (Figure 5.1). Glass
eels are mainly purchased from France or from the UK. However, even glass eels
bought from the UK, may previously have been imported from France. When pre‐
grown eels from farms are used for stocking, they are either imported as glass eels
and reared in farms within their own country (e. g. DK, NL, partly DE, LT) or directly
imported as young yellow eels (mainly from NL, DE, but possibly also DK and in
smaller amounts from other countries). The information is probably incomplete.

EIFAC/ICES WGEEL Report 2008

75

7
15
11
China

23

85

47

Europe (Aquaculture +
restocking)

23

27
8

15

Figure 5.1: Mean trade volumes of glass eel (T) 1996–2006 in Europe analysed from EUROSTAT
database.

The analysis of the questionnaire demonstrated that at present it is not possible to
trace the origin and trade paths of glass eels and young yellow eels. However, as a
consequence of the obligations in 2009 from CITES and from the EU Council Regula‐
tion (1100/2007‐the “Eel Regulation”), Member States have to develop systems for the
traceability of traded eels. Consequently, the availability of information on num‐
bers/biomasses of eel traded and their trade paths are expected to improve in the fu‐
ture.
E. Does your country have a protocol in place by which it stocks its waters?

Yes:

6 (DE, ES, SE, UK, EE, DK)

No:

7 (PT, PL, LT, LV, IE, NO)

Will be developed:

1 (BE, NL)

No info/unclear status:

3 (FR, FI, N.Irl)

The information shown here contains some uncertainties. The type of protocol may
be very different between countries. It may contain just rules on were to place the eels
and at what density whereas in other cases a screening for diseases or parasites may
be included. Other countries are at the stage of developing protocols at present. The
situation may even differ within one country if regional authorities or government
are responsible for fisheries issues as is the case in Germany. A considerable number
of countries do not have protocols in place suggesting room for further improvement
in this area.
F. Does your country intend using stocking as a tool in its eel management plans?

Yes:

12

No:

1 (NO)

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EIFAC/ICES WGEEL Report 2008

Still in discussion:

2 (IE, FR)

Unclear/no information:

2 (PT, FI)

The majority of countries intend to use stocking as an option in the Eel Management
Plans. This raises the question if, given the possibility of a further decline in glass eel
catches and the obligation to achieve the management targets also in the donor
catchments, sufficient numbers of glass eel will be available to reach the stocking tar‐
gets. However, the decision whether the export of glass eel from those catchments
(mainly in France and the UK) to other countries for stocking will be permitted, in‐
cludes an economic and political dimension, which is difficult to assess.
5.4

Decision framework
The WG has presented and made use of various decision frameworks in our earlier
reports (ICES 2006 and 2007; Williams and Aprahamian, 2004; Symonds, 2006; Mont‐
real report (Williams and Threader, 2007)).
5.4.1

Management policies

5.4.1.1 Objectives

“Whenever stocking of fish is to be considered, the aims and specific objectives of the
exercise must be clearly defined and adhered to” (Cowx, 1999).
Only more recently has stocking been done to mainly enhance local stocks in order to
improve or provide the basis for a profitable fishery. In some circumstances stocking
was done to mitigate or compensate for depleted stocks, as a result of upstream dams
related to hydropower. Such stocks may be depleted as a consequence of dams as
migration obstacles for young ascending eels and as turbine‐induced mortalities in
silver eels.
Concurrently with the awareness of the serious decline in the European eel stock and
in connection with the preparation of eel management plans, stocking has become
one measure to improve the stock. This time stocking is done with the main purpose
of increasing the production of silver eels leaving the managed unit and contributing
to the spawning biomass, i.e. not to support a fishery.
COM has proposed stocking in waters with free access to the sea as one measure
among others to enhance local stocks with the ultimate goal aim to increase the bio‐
mass of spawners to produce a sufficient number of recruiting glass eels (COM
1100/2007).
Stocking as part of management plans may also occur in new water bodies, or areas
where eel are absent, in order to produce additional potential spawners where access
to the sea is open.
Another objective might be to restore local stocks in order to improve or preserve
biodiversity (Verreault, pers. comm.) and this also might be beneficial if there is an
olfactory cue to upstream migration. Alternative strategies to stocking have to be
considered and analysed. Improving the possibilities for eels to migrate upstream
might be a sufficient measure where dams are obstructing upstream migration, given
that the emigration route is secured. Improved environmental conditions in eel grow‐
ing waters, thus increasing survival and growth, may also be an alternative or addi‐
tion to stocking.
As there is a general lack of stocking material (glass eels) there is no room for a mis‐
use of this restricted resource. Therefore stocking should only be done as part of a

EIFAC/ICES WGEEL Report 2008

77

management plan ensuring a significant escape of silver eels. The potential availabil‐
ity of central funding through the European Fisheries Fund (EFF) to support stocking
for enhancement purposes, may ensure parity with other competitors for seed stock
(e.g. aquaculture, fisheries enhancements).
5.4.2

Ecological considerations

5.4.2.1 What size of eel should be stocked?

There are three main options; stocking of glass eel, young yellow eels and ongrown
eel from aquaculture. Apart from that there is the option of moving eels “over the
dam” in cases of migration obstructions (assisted migration). The latter option is not
dealt with here.
The risks concerned with diseases, parasites, biased sex‐ratios and genetic selection
may best be avoided by stocking with eels that are as young as possible from a natu‐
ral state. Stocking with yellow eel caught in the wild poses the additional risk of their
being contaminated. If ongrown eels from aquaculture are considered for stocking,
there are risks of disease spread, reduced genetic fitness (Section 5.4.2.3) and skewed
sex ratios. When purposely infected with herpes virus in aquaculture, as seems to be
widely practised, when these eels are stocked the spread of disease is a certainty, not
a risk. However, stocking of healthy ongrown eels will result in comparable growth
rates and mortalities compared to the stocking of glass eels (ICES, 2007).
Another risk associated with using ongrown eels, is the stocking of Anguilla rostrata.
Stocking of A. rostrata seems to have occurred in the past (German Country Report,
2008; Ubl and Frankowski, 2008), A. rostrata is grown in European aquaculture and
discrimination between A. rostrata and A. Anguilla, when grown up, is not possible in
practice.
5.4.2.2 Contaminants

One of the potential ecological and environmental risks which stocking programmes
should consider is contamination as a potential risk to produce (in stocked systems)
reproducers not able to reach spawning grounds at the Sargasso Sea and/or produce
enough gametes of high quality.
Consideration should be given to pollution with PCBs, flame retardants, pesticides
and heavy metals. Priority should be given to those sites where such contaminants
are absent or at permissible levels (information available through the European Eel
Quality Database Chapter 6).
Detrimental effects of pollution on fitness and fecundity have been suggested earlier
on (Larsson et al., 1990), but recently, there are indications that poor quality of the
spawners, namely the silver eels migrating to the oceanic spawning grounds, might
be a key factor in the decline, e.g. decrease of body fat content. Palstra et al., 2006 ar‐
gued that gonadal levels of dioxin‐like contaminants, including PCBs, in eels from
most European locations impair embryonic development. Pollution might also im‐
pact reproductive success through effects on genotype: a significant negative correla‐
tion between heavy metal pollution and eel genetic variability was reported by Maes
et al., 2005. Insufficient condition and energy resources (Svedäng and Wickström,
1997), high bioaccumulation of persistent organic pollutants (especially polychlori‐
nated biphenyls‐PCBs) (Larsson et al., 1990; Robinet and Feunteun, 2002; Palstra et al.,
2006) and pathological agents (Palstra et al., 2007) have been reported as potential
restrictive factors, disabling long distance migration and successful reproduction
with prime quality gametes.

78

EIFAC/ICES WGEEL Report 2008

Where spawner quality is poor and lipid content low, silver eels may not contribute
to the overall spawning and recruitment of the European stock. Accumulation of en‐
ergy through lipid storage may be affected by different environmental factors such as
disease agents, changes in food availability, other global changes in the environment,
changes in (density‐dependent) sex ratios even life‐history characteristics, i.e. restock‐
ing itself and pollution pressure as a consequence of disruption of the endocrine
processes.
5.4.2.3 Genetics, diseases and health issues
Genetics

The importance of maintaining genetic diversity can be divided into a short‐term im‐
pact (in the order of few generations), by avoiding inbreeding and fitness decrease
(population survival) and a long‐term impact (over decades or even centuries), and
by conferring the possibility to adapt to changing conditions (species survival). Ge‐
netic data may help to assess species integrity within the North Atlantic, evaluate the
genetic stock structure of the European eel, clarify the spatio‐temporal stability of the
genetic structure, define the influences of oceanic conditions on genetic variability,
monitor and guide the stocking policy in Europe, and evaluate the effect of popula‐
tion decline and habitat degradation on genetic variability and the overall fitness of
eels (see also Annex 4 for a more detailed review).
Genetic consequences of stocking practices

Below are listed some important points to consider in regards to genetics when plan‐
ning stocking measures and provide some advice for sustainable stocking.
1 ) Deciding on mass stocking practices to supplement populations can lead to
the rapid introduction of non‐native genetic material from non‐indigenous
eel species. Monitoring the correct species identity (tracing) is therefore
crucial to preserve genetic integrity of the European eel. Examples of this
phenomenon have already been observed, mainly in Germany (Trautner et
al., 2006), where A. rostrata were found, prompting for up to date molecu‐
lar identification methods for species discrimination (Maes et al., 2006a).
The European eel has been listed under CITES, potentially leading to in‐
creased importations of other eel species. Such exotic eel introductions
have been a major problem in Asia, where European eels were introduced
to supplement Japanese eel stocks (Okamura et al., 2002; 2004).
2 ) Aquaculture grown glass eels (grown from glass eels to 10 cms) are often
used for stocking purposes. Although at first sight no significant problem
is expected from the genetic diversity point of view (glass eels are natural
recruits), potential consequences could be other than expected. Indeed,
keeping glass eels too long in such facilities will adapt them to aquaculture
conditions (such as artificial food and temperature regimes), and will
lower their competitiveness in the natural environment. Currently juvenile
eels are deliberately exposed to water contaminated with the highly viru‐
lent Herpes virus anguillarum (HVA) in order to induce a limited infection
which, although causing some mortality, will autovaccinate the fish prior
to them meeting the infection at the most vulnerable fast growing stage.
This process causes a significant drop in food intake and growth rate but is
considered the lesser evil by the industry at present in the absence of an
approved commercial vaccine. As such, ongrown eels used for stocking
which have been reared under such practices pose an epidemiological

EIFAC/ICES WGEEL Report 2008

79

threat given that they can infect natural populations. Additionally, such
practices create a high selective pressure on glass eels, reducing total ge‐
netic diversity and directionally selecting at the functional level for specific
disease resistance genes (such as MHC). This has been revealed to have a
very detrimental effect in salmonids when such individuals are released in
the wild, as a consequence of a lower fitness for natural pathogens. Timing
of stocking should be carefully considered in order to optimize survival.
Stocking material should not be composed of the slow growers of aquacul‐
ture, which have been revealed to exhibit a lower functional genetic diver‐
sity and could demonstrate lower survival rates and skewed sex ratios.
3 ) At the population level, stocking practices can have major consequences on
intraspecific biodiversity, as a consequence of the mixing of genetically dif‐
ferentiated populations. No stable geographical differentiation has been
detected to date (Wirth and Bernatchez, 2001; Dannewitz et al., 2005; Maes
et al., 2006). However, given long‐term stocking practices since the 1950s, it
is possible that these might contribute to a homogenization of populations
as a consequence of massive translocations. Indeed, the presence of only a
small level of geographical genetic differentiation at neutral genetic mark‐
ers may lead to seriously underestimating of quantitative and adaptive dif‐
ferentiation between populations. From recent studies on marine fish
populations we know that adaptive differences might be present but not
detectable with the current molecular markers. Indeed, apart from analys‐
ing neutral genetic variation to assess the demographic independence and
stability of fisheries stocks, knowledge of geographic and temporal scales
of adaptive genetic variation is crucial to species conservation (Conover et
al., 2006; Maes and Volckaert, 2007). If distinct populations exist, the intro‐
duction of genetically different glass eels can potentially break up any ex‐
isting adaptation in local stocks and have major fitness consequences on
life‐history traits, such as migration duration and timing, temperature re‐
sistance and size at maturation sizes. The homogenization of these traits
can lead to a decrease in diversity and the loss of important traits for sur‐
vival. However, given these concerns and the absence of data the following
advice for different levels of natural recruitment is therefore precautionary.
Regions with no recruitment: stock with glass eels in high quality habitats originating
in if possible the same main hydrographical region (Northern Europe, West Atlantic,
Southern Europe, Mediterranean).
Regions with low recruitment: Preserve natural recruits, while preferably stocking
glass eels from estuaries or neighbouring river basins in high quality upstream habi‐
tats.
Regions with high recruitment: care should be taken not to overfish glass eels for
stocking purposes, as this will weaken the donor region and deplete the rivers from
escapees.
If neither neutral nor adaptive differences can be detected in the European eel, stock‐
ing practices may have a beneficial effect. However, the question remains, whether
stocked individuals will find their way to the Sargasso Sea and ultimately contribute
to the spawning stock. The most important issue is then to preserve the total genetic
diversity to allow adaptation to a changing environment. Keeping the highest level of
biodiversity in phenotypic (quantitative) and genetic traits is crucial to the survival of
the entire species.

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Pathogens and parasites

The occurrence of diseases and parasites in eels has been recorded for some time. Up
to now, consequences on the ability of eels to carry out their long‐distance migration
and reproduction were unknown, although these have been suggested as potential
causes for the decline in eel populations. Available information on the introduction
and spread of A. crassus in Europe illustrates how through live‐transport of eels,
within and between countries, and through stocking programmes the parasite has
been rapidly dispersed to all major spawner producing areas.
In the proceedings of a recent workshop held in Montreal (Canada) in 2007, the risk
of disease transfer when stocking eel was specifically addressed (Williams and
Threader, 2007) because eel transfers increase the risk of pathogen introduction. In
her review, Symonds, 2007 described several parasites, viruses, bacteria and fungi
that have been found in eel communities in North America. In Europe, many studies
on eel parasites and diseases indicate that stocking and transfers have been responsi‐
ble for rapid spreading of their fellow travellers (Szekely, 1994; Van Ginneken et al.,
2004; EELREP 2005). The rapid spread of A. crassus throughout Europe indicates that
eel transfer or stocking done without screening is a practice that can be detrimental
for the population and aquatic community.
In Canada eel stocking and transfers must be done under ʺThe National Code on In‐
troductions and Transfers of Aquatic Organismsʺ to avoid risks to aquatic animal
health from the potential introduction and spread of pathogens and parasites that
might accompany eels being moved. Screenings are routinely done for elvers before
their stocking in fresh‐waters locations. Screenings for viruses (IHNV, ISAV, IPNV
and EVH) and A. crassus in individuals prior to stocking were negative since the ini‐
tiation of the stocking programmes, four years ago.
In spite of warnings concerning viruses and diseases issued from WGEEL in 2006,
there is still no common protocol for parasite and disease screening prior to stocking.
Each country applies its own regulation and screening procedure for stocking. For
example, Sweden practises quarantine for imported glass eel prior their stocking in
brackish and fresh‐water areas whereas no specific procedures are in place for other
countries. Table 5.1 shows what is done for each European country prior to glass eel
and/or elver stocking to prevent the introduction of parasites, viruses and pathogens.
It appears that few countries have put in place procedures to prevent the introduction
and spreading of parasites and diseases when stocking young eels. This could be very
detrimental for the future of eel populations since stocking will presumably be part of
many national Management Plans. A robust protocol for screening stocked stocks
should be put in place as soon as possible.
Table 5.1: Current procedures for stocking glass eel/young eel to European countries.
S CREENING FOR PARASITES , VIRUSES
C OUNTRY

S TOCKING

Q UARANTINE

AND PATHOGENS

Belgium

Yes

No

N0

Denmark

Yes

Yes

Yes

Estonia

Yes

Yes

No

Finland

Yes

Yes/No

Yes

Poland

Yes

No

France

Yes

No

No

Germany

Yes

Yes/No

No

EIFAC/ICES WGEEL Report 2008

81

S CREENING FOR PARASITES , VIRUSES
C OUNTRY

S TOCKING

Q UARANTINE

AND PATHOGENS

Ireland

Yes1

No

No

Italy

No

‐

‐

Latvia

Yes

No

No

Lithuania

Yes

No

No

Netherlands

Yes

No

No

Norway

No

‐

‐

Portugal

No

‐

‐

Spain

Yes

Yes

‐

Sweden

Yes

Yes

Yes

UK

Yes

Yes*/No

No

1

Stocking restricted within the same water catchment.

* For England and Wales only.

5.4.3

Fisheries considerations and considerations for other users

Generation times of eels decrease with temperature and increase with latitude and
may be 2–3 times lower in the most Southern parts of the distribution range as com‐
pared to the Northern parts of Scandinavia. Growth of eels varies between 14–62
mm.year‐1 within its distribution range (ICES, 2006) and this means that for male sil‐
ver eels of 37 cm it will take then 5–21 years to reach that size. For female eels of 67
cm it will take twice as long, while for longer females it will take even longer. If the
glass eels were stocked in 2009, the effects on silver eel escapement could be expected
from 2014 (at the earliest) to approximately 2050, depending partly on stocking loca‐
tion and partly on sexual differentiation and eel growth. Therefore this is a measure
that might be valuable over a longer time‐scale. If the stocked eels are not hampered
by anthropogenic factors, they could contribute significantly to silver eel escapement
after 10 years or more. Eels stocked in suitable habitats may well grow faster than if
left in situ and, therefore, mature earlier (Aprahamian, 1988).
5.4.3.1 Effects on recipient eel populations

The surface area of available habitats in Europe is estimated at 5–10*106 ha (ICES,
2005). A possible stocking of 60 tonne (at most) when well spread over the available
habitat, will have no significant negative effect on the growth of the existing eel
populations. However, if high stocking rates are applied locally, this will be different
because of density‐dependent growth rates (reviewed by ICES, 2007).
Effects on existing populations may occur when stocked eels are diseased. Change in
sex ratios (as demonstrated on Lough Neagh under differing recruitment and stock‐
ing patterns (Rosell et al., 2005)), in favour of males, potentially affecting the yearly
production of the non‐stocked eels. Effects on the whole stock may occur if the ge‐
netic fitness of the stocked eels is further reduced. The latter might occur when stock‐
ing eels from aquaculture without additional care for reducing possible genetic
effects.
5.4.3.2 Effects on the remainder of the exploited fishery

The effects on the fishery depend largely on the total quantity of eels to be stocked. If
the aforementioned 35–60 tons would be stocked, it has a yield potential in the same
order of magnitude as the eel aquaculture production in Europe or the current eel
landings in Europe. This potential would be fully realized after one generation time.
If not fished at all, this would increase the production of silver eels (ICES, 2006). The

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quantity of 35–60 tons of glass eels is more or less equal to, or more than the historical
maximum of stocking rates (40 tons).
5.4.3.3 Effects in mixed-stock fisheries

There are no additional effects expected in mixed‐stock fisheries.
5.4.4

Implementation constraints

5.4.4.1 Introduction

Cowx, 1999 recognized a number of potential constraints associated with any stock‐
ing programme, and posed these as a series of checks for managers, regarding the
availability of:
•

sufficient quantity and quality of fish;

•

suitable methods of the transportation and expertise;

•

sufficient funds; and,

•

have the access rights been defined.

The issues of funding for stocking programmes, and access to donor and recipient
waters are political rather than scientific issues, and so we will not consider them
here. The first two bullet points have been considered previously by ICES (2006, 2007)
and others (Williams and Aprahamian, unpublished; Symonds, 2007; Montreal, 2008).
Here, we summarize the outcomes and update supporting materials where they have
become available since the 2007 report was compiled.
5.4.4.2 Are sufficient quantities of eel available for stocking, at the local level?

At the local or catchment level, there may be a surplus stock of glass eel, arising as a
result of density‐dependent mortality being higher in the absence of fishing (ICES,
2006). The prime assumption for a local surplus of eel is that removing the eel has no
impact on the donor population (on silver eel output). That is, reductions in density‐
dependent mortality (or other limiting effects such as growth rates and gender de‐
termination) result in enhanced production of silver eel in the stocked population
exceeding the putative loss (from fishing elvers) in production from the donor popu‐
lation.
Lobón‐Cerviá and Iglesias, 2008 studied long‐term variations in the density of eels in
the Rio Esva (northwestern Spain) at an estuary site and at nine sites distributed
among three tributaries (1986–2006). Mortality rates calculated for age cohorts re‐
vealed a consistent positive trend, with 53.3% of the variation in cohort mortality rate
explained by variation in glass eel abundance. Note, however, that this population is
characterized by fast growing and early maturing eels, almost all of which become
male.
Although the Regulation (1100/2007) does not specifically require that eel for stocking
are sourced only from catchments where such a surplus exists, it is prudent to focus
collection on such catchments. However, previous ICES reports and other reviews
have provided little guidance on how managers could assess whether a surplus ex‐
ists, and thereafter, quantification of this surplus.
The direct means for this assessment is to quantify the size of the donor population,
typically glass eel, and compare this with estimates of the amount of settled elver re‐
quired to produce the target silver eel output. The EU InterReg programme, Indicang,
considered methods for the absolute quantification of glass eel in estuaries, recom‐
mending flux quantification (filtration) or mark recapture exercises, but noting that

EIFAC/ICES WGEEL Report 2008

83

these methods are difficult in large and stratified estuaries (Feunteun, pers. comm.).
Alternatively, an indirect assessment can be made based on studying the associated
yellow eel population under conditions of varying glass eel exploitation to establish
lack of impact of said fishery. For example, a glass eel fishery in the Severn estuary,
England, does not yet seem to have had any measurable negative impact on up‐
stream stocks of eel (See UK country report 2006).
ICES, 2006 discussed the concept of the carrying capacity of eel in relation to deciding
whether to stock eel in a water body, but it should be considered also in deciding
whether a potential donor water body can sustain the loss of eels to be stocked else‐
where.
There are two considerations; immediate effects of loss to the estuary, and subsequent
effects to the yellow eel population and silver eel output from the river basin.
A method of calculating carrying capacity of a river or lake for eel has not been iden‐
tified; in part as a consequence of the difficulty in assessing density and/or biomass of
eel accurately in a given body of water (Williams and Aprahamian, 2004). Whether a
site is at carrying capacity is linked to ease of access for colonization and the produc‐
tivity of the water.
In tributaries of the lower Severn, Aprahamian, 2000 found eel density ranged from
0.12‐1.14 m‐2 and biomass from 2.56–25.24 gm‐2. The absence of any relationship be‐
tween growth and either density or biomass, suggests that the sites were limited by
their productivity and may indicate that they were close to or at their carrying capac‐
ity, defined as the maximum density or biomass that the habitat can sustain under
average conditions.
In the southern part of their range the carrying capacity is likely to be higher as a con‐
sequence of higher temperatures and productivity resulting in a shorter generation
time, even if extremely variable among sites. No recent evaluations are available, but
given the potential for spawner production of those environments, the enhancement
of evaluation studies on this aspect is recommended. Greater importance should be
given to biomass when trying to assess whether a site is or is not at carrying capacity.
This is because there is a smaller variation in biomass when compared to density both
within and among river systems (Aprahamian, 1986) and it is more related to carry‐
ing capacity (Knights et al., 2001).
The analysis of eel fishery ‘outputs’ from L. Neagh in relation to glass eel stocked
(ICES 2007) suggests a density‐dependent relationship with a negative exponential
between input stock and eventual output. That is, outputs are maximal for inputs in
the range of 150 to 200 glass eel per hectare.
Similarly, Knösche et al., 2004 give a formula, how to estimate the recapture rate in
the fishery after stocking for a range of common stocking densities for German wa‐
ters (50–00 glass eel equivalents per hectare).
Recapture rate (%) = 611 * stocking density ‐0.81
Thus, at a stocking density of 50 glass eels/ha, this would result in a recapture rate of
26%, whereas at 500 glass eels/ha it would decrease to 4%.
However, the general lack of information on carrying capacity in eel populations
noted by ICES 2006 continues to this day.
A method of calculating carrying capacity of a river or lake for eel has not been iden‐
tified, in part as a consequence of the difficulty in assessing density and/or biomass of

84

EIFAC/ICES WGEEL Report 2008

eel accurately in a given body of water (Williams and Aprahamian, 2004). Whether a
site is at carrying capacity is linked to ease of access for colonization and the produc‐
tivity of the water and recruitment. However, the general lack of information on car‐
rying capacity in eel populations noted by ICES 2006 continues to this day. The most
likely sources of eel for stocking (and seeding ongrowing facilities) are glass eel fish‐
eries in estuaries, and traps where upstream migrating eel are concentrated, such as
eel passes on weirs and dams. In considering the effects of removing glass eel from
estuaries and lower reaches of rivers, the carrying capacity of the estuary may be im‐
portant. There is, however, no information currently on this, and it is an area that
should be addressed. A study group to address this area has been proposed to the
Diadromous Fish Committee 2008.
5.4.4.3 Potential indirect impacts on donor stock

ICES, 2006 noted that under the current situation of critically low stock levels, re‐
moval of glass eel from any site to stock another should only be done with a full as‐
sessment of the effect on recruitment into the growing areas dependent on that donor
site. In addition to the direct effects on the size of the local population, there are two
other potential risks with removing glass eel from the donor site, a reduction of dis‐
persal of juvenile eel to upstream habitats, and possible alterations to sex ratio of sil‐
ver eel.
Upstream migration may be driven by intraspecific competition and higher densities
downstream. For example, the construction of an estuarine dam on the Vilaine pre‐
vented recruitment of eels for 25 years, but the installation of an eel pass resulted in a
density‐dependent migration behaviour; 1+ groups being forced into the periphery of
the high‐density area (about 0.8 eels m‐2), which extended further upstream in succes‐
sive years (Feunteun, 2002). Note, however that this “wave” type migration, is in con‐
trast to that reported by Ibbotson et al., 2002 for eels colonizing the River Severn
where upstream migration was mainly through diffusion. Removal of stock from
downstream areas may reduce the propensity for colonization of upstream areas.
Although the physiological mechanisms for gender differentiation in eel (reviewed
by Davey and Jellyman, 2005) are still unclear, evidence supports the concept that it is
density driven. There is a risk that removing glass eel from estuaries will affect sub‐
sequent gender differentiation and sex ratio of yellow eel (and hence silver eel).
Transporting undifferentiated eels from high to relatively low density habitats may
well influence ultimate sex ratio of the silver eel output, and by association, the
weight of output and distribution across time.
5.4.4.4 Issues of ownership

In considering where to stock, managers must evaluate the subsequent potential ex‐
ploitation and other mortalities of the eel, e.g. fisheries, turbines, etc. There may be a
number of users who potentially benefit from the stocking, and therefore, they
should all contribute to funding of the stocking.
5.5

Artificial reproduction of eel
5.5.1

Introduction

Summary of the main findings relevant to WGEEL from the European Aquaculture So‐
ciety Thematic Group Workshop on European Eel Reproduction (October 24th, 2007, Istan‐
bul).
Given the complex nature of the eel life cycle and that maturation occurs during the
oceanic phase there is very little information on natural maturation and reproduction.

EIFAC/ICES WGEEL Report 2008

85

Consequently much of this work is derived from laboratory studies which examine
the environmental effects, endocrine control and artificial reproductive techniques on
the production of larval European eel. Details of abstracts on this work can be found
at http://www.easonline.org.
The onset of sexual differentiation in eels:
Studies from Israel into the hormonal development in young farmed eels <25 cms
found a difference in the hormones released from the pituitary gland depending
upon the density of eels held in tanks. Those with fewer eels in them, developed into
female eels associated with the hormone release of the female hormone estradiol,
while those in higher densities became male associated with the release of the male
hormone 12 Keto‐testosterone.
5.5.2

Silver eels

Several studies presented evidence that silver eels leaving continental Europe should
be considered as being in a pre‐pubertal state given that swimming appears to be a
strong natural trigger for the development of advanced maturation. During the
swimming phase lipid stores in the eel are utilized for the production of energy to
fuel their migration and to produce gametes through a variety of hormonally induced
metabolic pathways. Research into the thermodynamic influence of hydrostatic pres‐
sure on swimming ability found that the metabolism of the eel’s fat stores was much
more efficient at depth thus optimizing their energy expenditure during migration.
Once they have arrived at the spawning grounds several studies into the olfactory
capabilities of silver eels and their reactions to specific eel odours suggested that ol‐
faction maybe crucial to synchronizing final maturation in both sexes.
5.5.3

Embryo and larval development

The natural development of embryos appears to be influenced by hydrostatic effects
(that had not been used previously during artificial attempts at fertilization) which
induce a slower egg cleavage rate and thus embryo development period. It’s likely
that this may be caused by the pressure influence on thermodynamics and or me‐
chanical stress on egg membranes and water transfer through them at these depths.
Despite many previous attempts to artificially breed European eel the hatching of
larvae has only been achieved on a few occasions with a maximal larval life of 3.5
days. The main obstruction has been the intricate hormonal control mechanisms that
inhibit gonadal maturation at the onset of puberty. Repeated hormonal treatments to
produce gametes have been successfully applied to produce viable eggs and larvae of
the Japanese eel. Similar methods have been applied to the European eel, but defi‐
ciencies in genitor quality causing fertilization failure had hampered the ability to
produce larvae in the past. Investigations into the failure found that an essential fatty
acid was missing from the feed given to the broodstock which when included pro‐
duced fertile eggs. Mass hatchings from these eggs have been achieved and the larvae
were fully developed and ready to feed 12 days post‐hatching. However further de‐
velopment of the larvae past this stage failed as a suitable feed has yet to be
found/developed.
5.5.4

Artificial reproduction techniques

The hormonal induction of maturation is a fundamental requirement for artificial
reproduction but this presents difficulties in terms of synchronizing the development
of males and females. To aid this cryopreservation techniques have now been devel‐
oped for eel sperm which yielded viable eel sperm several months after deep freeze

86

EIFAC/ICES WGEEL Report 2008

storage. Prior to storage the hormonal induction of the males yielded sperm after four
weeks the quality and quantity of which increased up to eight weeks after induction.
Developments in the maturation of females have found that low initial temperatures
increase the sensitivity of the female and that temperatures <17°C during gonadal
maturation produced better results.
5.5.5

The Japanese Experience

Japanese glass eel were successfully produced in captivity in 2005 (Kagawa et al.,
2005), and since then this work has progressed to produce hybrid larvae of European
(male) and Japanese eel (female). The success of this work has relied heavily upon the
production of a suitable feed for the larval stage, details of which are currently con‐
tained in a Japanese Government registered patent.
5.6

Conclusions for Chapter 5: Stocking and aquaculture
5.6.1

Potential benefit of stocking to regenerate the stock

At present, it is estimated that around 7.5 to 15% of the glass eel catch is used for
stocking, either directly or as on‐grown eels. Estimates suggest an insufficient supply
of glass eel from the total fishery for stocking to full capacity at the European level.
Nevertheless, the Regulation 1100/2007 requires that 35%, rising to 60%, of glass eel
catches are made available for stocking to enhance the stock. If these percentages
were applied to recent annual catches of glass eel, the potential lifetime effect of this
increased level of stocking, in the absence of anthropogenic mortalities, could be in
the same order of magnitude as current fisheries or eel culture. However, there is a
continuing and urgent requirement for robust evidence of the extent to which stock‐
ing and transfers on local, national and international scales can increase silver eel es‐
capement and spawner biomass.
The general lack of information on carrying capacity in eel populations noted by ICES
2006 is still an issue hampering management of eel.
5.6.2

Identifying local surplus

It is anticipated that assessments conducted for EMPs will decide whether or not
there is a local supply of eel sufficient to meet demands for stocking (either within
catchment, RBD, nation or elsewhere in Europe). However, there is a limited under‐
standing on methods by which to make assessments of a local surplus on a quantita‐
tive, biological basis.
5.6.3

Post-evaluation of the net benefit of stocking

The assessment post‐evaluation of the contribution of stocking to silver eel produc‐
tion is still hindered by the limited quantitative information available on sur‐
vival/mortality rates (stage specific and glass eel to silver eel), both for stocked eel
and wild/natural eel for comparative purposes, for habitats representing the variety
available across Europe, and especially for stocking in rivers.
5.6.4

Risks of stocking

It appears that few countries operate procedures to prevent the introduction and
spreading of parasites and diseases when stocking young eels and this could be det‐
rimental for the future of eel populations since stocking will presumably be part of
many national Management Plans. The risks remain of disease and parasite transfer
via stocked material, potentially both from the ‘wild’ and on‐grown in aquaculture.
For example, the practice of aquaculture in terms of viral inoculations needs to be

EIFAC/ICES WGEEL Report 2008

87

addressed. A robust protocol for screening stocked stocks should be put in place as
soon as possible.
New techniques are currently used for genetic analyses of the eel stock and results
are expected in a few years. These results may prompt a re‐assessment of the poten‐
tial risks associated with stocking.
There is a clear need for assurance that donor populations are not impaired by the
removal of glass eel. Notwithstanding the potential risks to the donor population, it is
anticipated that assessments conducted for EMPs will determine whether or not there
is a local supply of eel sufficient to meet demands for stocking (either within catch‐
ment, RBD, nation or elsewhere in Europe).
5.6.5

Aquaculture/on-growing to support stocking for enhancement

Spawner quality in terms of levels and composition of lipids and contaminants ap‐
pears to be a key issue for the success of both natural and artificial reproduction.
Given the future requirements for stocking glass eel or deciding to stock on‐grown
eel, the implications of the findings on hormonal release and subsequent gender de‐
velopment depending upon stocking densities must be considered.
Spawner quality in terms of lipid levels and contaminants appears to be a key issue
for the success of both natural and artificial reproduction.
5.7

Recommendations
5.7.1

Methods to support the basis of stocking for enhancement purposes

The WG recommends that developing methods to make assessments of local surplus
of stocking material on a quantitative, biological basis is a priority for research in the
near future. Data to post‐evaluate the relative contribution of stocking to silver eel
production can only be supplied by experimental studies, and although acknowledg‐
ing that some studies are ongoing, we recommend concerted action to address this
area, especially with regard to stocking in rivers, and the relative performance and
yield‐per‐recruit of stocked cultured eels compared with glass eels.
A study group to address eels in saline habitats has been proposed to the Diadro‐
mous Fish Committee.
5.7.2

Risks associated with stocking

The eel should be included in the European fish disease prevention policy in order to
minimize the risks of transfer of diseases associated with stocking.
A robust protocol for screening stocked stocks should be put in place as soon as pos‐
sible.
Purposely infected eels in aquaculture with pathogens (viruses, etc.) should not be
used for stocking purposes.
The culture of A. rostrata in European aquaculture will make it impossible to dis‐
criminate between stockings of A. anguilla and A. rostrata and should be avoided; the
same applies to possible growing of other eel species in the future. The improved sys‐
tems to trace glass eel trade, for CITES and the Regulation (EU 1100/2007), should
facilitate this, and the WG strongly support these developments also to address the
risks highlighted here. Besides the Eel Regulation and CITES, the following EU
Council Regulation (EC) N° 708/2007 concerning the use of alien and locally absent

88

EIFAC/ICES WGEEL Report 2008

species in aquaculture is also likely to allow better control of farmed alien species like
A. rostrata.
Despite limited evidence and a complicated variety of possible impacts of environ‐
mental factors, such as contaminants, on silver eel quality, conservative advice re‐
mains that stocking for stock enhancement purposes should not be conducted in
waters heavily polluted with substances that might pose risks for spawner quality.

EIFAC/ICES WGEEL Report 2008

6

Eel quality

6.1

Introduction

89

In recent years (e.g. ICES, 2006) the Working Group has described the risks of dete‐
riorated biological quality of eels. In 2005 the EU‐EELREP programme (Estimation of
the reproduction capacity of European eel) concluded that contamination with PCBs
impaired fertility while infections with pathogens and parasites were devastating for
swimming eels.
The recommendations of the WG EEL 2006 highlighted the need to monitor and to
collect information on (1) pollution and disease to be able to designate areas produc‐
ing high quality spawners (e.g. with low contaminant and parasite burdens in order
to maximize protection for these areas; and (2) the chemical status of eel under the
implementation of the WFD.
An increasing level of evidence on the detrimental impact of contamination and dis‐
eases on the eel has been made available.
ICES 2007 reported on the advances made in the collection of data on contaminants,
parasites and fat levels in eel, and reported that many Member States started the
monitoring of eel quality. In 2007, the WGEEL initiated the set‐up and development
of a European Eel Quality Database (EEQD), allowing the compilation of a compre‐
hensive overview on the contaminant load in eel over its distribution area. Results
from the EEQD demonstrated that considerable variation in contaminant load exists
within river basin districts, according to local anthropogenic pollution, linked with
land use. There is evidence that, on a pan‐European scale, large differences in eel
quality occurs between catchments. Furthermore, ‘black spots’ with low quality eels
were detected. Lipid content, which is believed to be an important index of fitness,
was highly variable between sites. New evidence (Geeraerts, et al., 2007) was pre‐
sented on the negative impact of certain contaminants on the fitness of eel.
The recommendations of the Working Group 2007 (ICES 2007) proposed that:
1 ) MS should further develop and maintain the European Eel Quality Data‐
base.
2 ) MS should initiate harmonized monitoring strategies to develop a Euro‐
pean Eel Quality Monitoring Network, to collect the relevant data to be fed
into the EEQD. National eel management plans, should take account of
these data for evaluation of the quality of spawners.
3 ) Under the implementation of the WFD eel specific extensions should be in‐
cluded, using the eel as an indicator of river connectivity and ecological
and chemical status, and making cost‐effective use of collected data, also
for the benefit of the EU Eel Regulation and recovery of the eel stock.
During the WGEEL 2008 session, new scientific evidence of eel quality as an impor‐
tant factor in the decline of the species has been presented and discussed. The
WGEEL 2008 also updated the EEQD. In the light of the introduction of the EU Regu‐
lation in 2007, the WGEEL proposed recommendations and discussed urgent research
needs/demonstrated gaps in eel quality knowledge.

90

6.2

EIFAC/ICES WGEEL Report 2008

Contaminants
6.2.1

Introduction

Due to specific ecological and physiological traits, eels are particularly sensitive to
bioaccumulation of lipophilic contaminants. From recent scientific evidence (Belpaire,
2008) there is reason for serious concern as the level of measured concentrations of
some contaminants has been demonstrated to have adverse effects on the reproduc‐
tion success of the silver eel.
Current gonadal levels of dioxin‐like contaminants, including PCBs, in eels from
most European locations impair normal embryonic development and that PCBs and
other contaminants may have contributed to the decline of eel recruitment observed
since 1980 (van den Thillart et al., 2005; Palstra et al., 2006), a conclusion consistent
with the fact that the emission of PCBs in the environment (van Leeuwen and Her‐
mens, 1995) preceded the decline of European eel.
An extensive dataset of contaminants has been analysed by statistical modelling, to
demonstrate relationships between fitness (lipid content and eel condition) and vari‐
ous environmental variables and PCBs (especially the higher chlorinated ones) and
DDTs were revealed to have a negative impact on the lipid content of the eel. (Geer‐
aerts et al., 2007).
Extensive information has already been provided in the WGEEL 2006, and 2007 re‐
ports (ICES 2006; 2007). Recently, Belpaire, 2008 compiled an overview of research on
contaminants in Flanders (Belgium). The status and trends of eel quality factors and
the potential role of contamination in the collapse of the stock are presented and dis‐
cussed here.
6.2.2

The eel and the Water Framework Directive

The EU Water Framework Directive requires monitoring of a selection of priority
substances in the aquatic phase, including lipophilic substances. However, there are
strong arguments for measuring the latter in biota (Belpaire and Goemans, 2007a, b).
Yellow eel is a good candidate because it is widespread, sedentary and accumulates
many lipophilic substances in its muscle tissue. Several authors have described the
indicative value of measured concentrations, yet few studies have investigated the
extent to which the spectrum of contaminants present characterizes the local envi‐
ronmental pollution pressure. To evaluate the value of the pollution profile of an eel
as a fingerprint of the chemical status of the local environment, two datasets were
selected from the Flemish Eel Pollutant Network database. One set from a small
catchment area to investigate site‐specific profiles, and one from seven large Flemish
rivers to investigate river‐specific profiles. The pollution profiles of persistent organic
pollutants in individual eels along a river (even at distances <5 km) proved to be sig‐
nificantly different (Figure 6.1). Analysis of pooled contaminant data from multiple
sites and sampling years within rivers allows characterization of river‐specific chemi‐
cal pressures. The results highlight the usefulness of eel as a bio‐indicator for moni‐
toring pollution with lipophilic chemicals like polychlorinated biphenyls and
organochlorine pesticides in rivers (Belpaire et al., 2008). It was concluded that, as
such, eel may be used effectively within the monitoring programme for a selection of
priority substances referred to in the Water Framework Directive (Table 6.1). Some
countries reported planning reporting eel quality data within the WFD chemical
status report.

EIFAC/ICES WGEEL Report 2008

91

Dimension 1

GN4
GN5

20
GN2

15

GN3
KN2

KN1

10

5
KN3

0

KN4

-10

-5

0

5

10

Dimension 2
Figure 6.1: Canonical discriminant analysis of eels collected at eight sites in the Grote Nete and
Kleine Nete on the basis of their PCB and OCP concentrations (N= 61). Distance between loca‐
tions varied between 4 and 20 km.
Table 6.1: WFD substances mentioned under CEC (2007), and available data from measurements
of Flemish eels. All data are expressed in ng g–1 wet weight. DL, detection limit (from Belpaire and
Goemans, 2007).
NO.
N OTE

R ANGE
M IN – M AX (M EAN )

%<DL

OF
SITES

Y EARS

Benzene

a

1.2–18.9 (5.7)

0

20

1996–1998

j

Brominated diphenylethers

a

6.9–5 284.4
(369.1)c

0

18

2001

l

Cadmium and its compounds

a

D.L.‐151.4 (11.7)d

19

357

1994–2005

k

S UBSTANCE

S OURCE

1,2‐Dichloroethane

a

D.L.‐4.9 (1,2)

55

20

1996–1998

j

Hexachlorobenzene

a

D.L.‐61.6 (5.7)

<1

357

1994–2005

k

Hexachlorobutadiene

a

D.L.‐12.2 (1.8)

50

20

1996–1998

j

Alfa‐Hexachlorocyclohexane

a

D.L.‐13.7 (0.8)e

13

357

1994–2005

k

(gamma‐isomer, Lindane)

a

0.1–2 076.4 (46.9)

0

357

1994–2005

k

Lead and its compounds

a

D.L.‐1 744.2 (56.6)f

3

357

1994–2005

k

Mercury and its compounds

a

10–535.4 (113.5)g

0

355

1994–2005

k

Naphthalene

a

1.5–63 (5.8)

20

20

1996–1998

j

Nickel and its compounds

a

D.L.‐2 944.7
(186.2)h

16

297

1994–2005

k

(1,2,4‐Trichlorobenzene)

a

D.L.‐30.9 (6.0)

15

20

1996–1998

j

Trichloromethane
(chloroform)

a

D.L.‐96.0 (13.4)

25

20

1996–1998

j

DDT total

b

6.6–1 102.7 (90.2)i

0

357

1994–2005

k

p,p’‐DDT

b

D.L.‐62.6 (2.9)

38

357

1994–2005

k

Aldrin

b

D.L.‐11.4 (1.3)

33

96

1994–2005

k

92

EIFAC/ICES WGEEL Report 2008

NO.
N OTE

R ANGE
M IN – M AX (M EAN )

%<DL

OF
SITES

Y EARS

S OURCE

Dieldrin

b

D.L.‐237.6 (19.1)

15

357

1994–2005

k

Endrin

b

D.L.‐29.1 (1.1)

80

346

1994–2005

k

Tetrachloroethylene

b

D.L.‐88.9 (13.4)

50

20

1996–1998

j

Trichloroethylene

b

D.L.‐30.3 (2.0)

95

20

1996–1998

j

S UBSTANCE

a

Priority substances.

b

Other pollutants, which fall under the scope of Directive 86/280/EEC and which are included in List I

of the Annex to Directive 76/464/EEC, are not in the priority substances list. Environmental quality
standards for these substances are included in the Commission’s proposal to maintain the regulation of
the substances at Community level.
c

The data present the Sum of 10 BDEs.

d

Cd.

e

alpha‐hexachlorocyclohexane.

f

Pb.

g

Hg.

h

Ni.

i

Sum of p,p’‐DDD, p,p’‐DDT, and p,p’‐DDE.

j

Data from Roose et al. (2003).

k

INBO Eel Pollutant Monitoring Database.

l

Data from de Boer et al. (2002) and Belpaire et al., 2003.

EIFAC/ICES WGEEL Report 2008

6.2.3

93

Eel pollution monitoring networks-status and trends

Most of the countries submitted data on contaminants to the EEQD (see Annex 5 for
Country reviews). In many sampling sites, concentration of contaminants fell and this
probably reflects decreasing contaminant exposure. However, the monitoring does
not evaluate the presence of new contaminants not to mention the increasing number
of non‐native species. Nevertheless there are widespread industrialized regions
where contaminant loads still exceed reference levels.
Some countries are operating Eel Pollution Monitoring Networks on a national scale.
The networks allow the follow‐up of contamination in eels and allow detailed analy‐
ses of the status and trends for a specific contaminant, or a group of contaminants.
They also allow detailed analysis of status and trends of contamination on a certain
spatial scale (site, river, catchment, town, province, region). In some countries (e.g.
Belgium) these trends can be viewed in reports via predefined queries on a national
database available on the Internet, and maps are available for contamination in eel for
PCBs, pesticides and heavy metals (e.g. Goemans et al., 2008). As an example the dis‐
tribution of PCB 156 in eel from Flanders (Belgium) is represented in Figure 6.2. This
allows the indication of good and bad quality eel areas.
Eels from different river basins differ in contamination. Belpaire et al., 2008 presented
PCB and OCP contamination profiles for some basins in Belgium. Eels from the river
Yser are characterized by high OCPs, especially dieldrin and lindane (γ‐HCH), and
low PCB levels. In the River Maas, PCB concentrations are high, and are dominated
by the higher chlorinated (and higher toxic) PCBs.

Figure 6.2: Distribution of PCB 156 in yellow eel in Flanders (2002–2005); means on muscle wet
weight basis, classified following the deviation from the reference value (Goemans et al., 2008).

High concentrations of some substances in eel tissue confirmed the previously known
high pollution load of some specific areas, but in many cases however, eel analyses
revealed unknown environmental problems. In a few cases analysis of eels from a
specific location has demonstrated unsuspected high pollution levels of several con‐
taminants. But several contaminants (e.g. BTEX (benzene, toluene, ethylbenzene and

94

EIFAC/ICES WGEEL Report 2008

the xylenes) compounds, PCBs and some very persistent OCPs like DDTs) are wide‐
spread in certain countries. (Roose et al., 2003)
Results of measurements of dioxins on eight locations in Belgium (Flanders) indicate
some reason for concern. Dioxin concentrations in eel vary considerably between
sampling sites, suggesting that eel may be good indicators of local pollution levels.
The European Commission has set maximum levels of 4 pg TEQ g‐1 fresh weight for
the sum of dioxins (WHO‐PCDD/F TEQ) and 12 pg TEQ g‐1 fresh weight for the total‐
TEQ i.e. the sum of dioxins and dioxin‐like PCBs (WHO‐PCDD/F‐PCB TEQ) in mus‐
cle meat of eel and products thereof (Directive 2002/69/EC). Half of the sampling sites
in Belgium demonstrate DL‐PCB levels exceeding the European consumption level
(with a factor three on average). The levels of PCDD/FS and DL‐PCBS measured in
some sites gave rise to serious concern about the reproduction potential of the eels
from these sites. Human consumption of eels, especially in these highly contaminated
sites, seems unwise (Geeraerts et al., in press).
Trend analysis in a Belgian study (Maes et al., 2008) over the period 1994–2005 indi‐
cated that there were significant decreases in the average wet weight concentration of
all PCB congeners, nearly all pesticides and four metals. The observed decline of
PCBs in eel tissue was in agreement with other studies reporting on time‐series of
contaminants in fish. PCBs were banned from the EU in 1985 and since then, several
time‐series have indicated decreasing levels of contamination. Also concentrations of
most pesticides decreased significantly over time. This was especially evident for α‐
HCH and lindane, demonstrating that the ban of lindane in 2002 has positive effects
on the accumulation in biota. Similar reductions were modelled for HCB, dieldrin
and endrin; however these compounds were banned many years ago. Unexpectedly,
concentrations of p,p’‐DDT increased while at the same time, p,p’‐DDD and p,p’‐DDE
demonstrated significant decreases.
The ratio of DDE over DDT was >1 in all eels analysed, normally suggesting that
DDT had not been recently reapplied. At some locations in Flanders, however, the
ratio of DDE over DDT rapidly decreased by an order of magnitude of three over a
few years. Such a steep decrease, even if the ratio was higher than one, probably indi‐
cates recent application of DDT and demonstrates that not all stock was depleted.
This urged regional policy‐makers to make a serious attempt in order to collect the
remaining stock of banned pesticides.
Some heavy metal concentrations decreased in the eel, in particular lead, arsenic,
nickel and chromium were notably reduced. The concentration of lead in eel muscle
tissue was consistently decreasing between 1994 and 2005, which possibly is related
to the gradual changeover from leaded to unleaded fuels and a reduction of indus‐
trial emissions. For arsenic, nickel and chromium, the trend may be biased as data
were available only since 2000. Cadmium and mercury, however, did not demon‐
strate decreasing trends and remain common environmental pollutants in the indus‐
trialized region of Flanders.
Following the very high levels of BFRs encountered in eels from Oudenaarde, new
measurements were carried out in 2006 (Roosens et al., 2008). A descending trend in
the contamination with BFRs was observed from 2000 to 2006 on this site. For PBDEs,
levels have decreased by a factor 35 (26 500 to 780 ng g‐1 LW), whereas for hexabro‐
mocyclododecane (HBCD), the decrease was less conspicuous, (35 000 to 10 000 ng g‐1
LW). Based on these results we can conclude that in 2006, fish seem to be less exposed
to PBDEs than 6 years earlier. This is probably as a consequence of the restriction re‐
garding the use of the penta‐BDE technical mixture (since 2004), a better environ‐
mental management and a raising awareness concerning PBDEs. However, since

EIFAC/ICES WGEEL Report 2008

95

there are no restrictions regarding its usage, HBCD can still be detected in large quan‐
tities, especially in aquatic environmental samples taken next to industrialized areas,
where it is used in specific applications. The textile industry is likely the cause of ele‐
vated BFR levels in fish on this part of the river Scheldt, but further studies should be
set up to determine the exact origin and how far this contaminated area extends over
the whole river.
It was concluded that Eel Pollution Monitoring Networks, such as the ones operated
in Belgium and The Netherlands, allow getting a comprehensive overview of con‐
taminants indicating environmental pressure, and they are able to document the
temporal evolution of some of these pressures. These national monitoring networks
should be upscaled at the European level. The intensity of pollution, at least at some
sites, may well indicate potential negative effect on the health of these contaminated
eels. These data underline the large variation in quality status of the eel over its dis‐
tribution range. It is believed that this variation in quality is indicative with a varia‐
tion in reproduction potential (Belpaire et al., in press).
6.2.4

Contamination in eel and its role in the decline of the stock

We summarize the main findings of work in this field (see also Belpaire, 2008) in the
following section and draw some conclusions related to the potential role of contami‐
nation in the collapse of the stock.
As a consequence of the increased international concern about the decline of the
stocks, also research actions have paid increasing attention to analyse contaminants
in the eel and to investigate the effects of these substances in the eel. As a result a
large and growing quantity of information became available, and as suggested ICES
2007 a review on the effects of contaminants is underway. Many studies have exam‐
ined the impact of a wide variety of xenobiotics on various aspects of fish biochemis‐
try, physiology and population structure. In some cases of acute pollution, direct
effects are clearly visible as fish may be moribund or dying. But contaminant expo‐
sure can lead to a decrease in growth or a lowered or deficient immunological sys‐
tem, causing an increased sensitivity to infectious diseases and parasites. But in most
cases, these effects have been induced by effects on molecular and subcellular level.
The last 20 years, an increasing number of reports deal with studying causality be‐
tween pressure of xenobiotics and response at the subcellular level. In the eel, the im‐
pacts of contaminants on metabolic functions and on behaviour of the eel are widely
divergent and act through various mechanisms. Figure 6.3 shows a simplified con‐
ceptual model of the effects of pollution exposure on the population structure of the
European eel (after Geeraerts et al., in prep, adapted from Lawrence and Elliott, 2003).
A significant negative correlation between heavy metal pollution load and condition
was observed, suggesting an impact of pollution on the health of subadult eels (Maes
et al., 2005. In general, a reduced genetic variability was observed in strongly polluted
eels, as well as a negative correlation between levels of bioaccumulation and allozy‐
matic multi‐locus heterozygosity (Maes et al., 2005).
Van Campenhout et al., 2008 studied the effect of metal exposure on the accumulation
and cytosolic speciation of metals in livers of European eel by measuring metal‐
lothioneins (MT) induction. This research was carried out in four sampling sites in
Flanders revealing different degrees of heavy metal contamination (Cd, Cu, Ni, Pb
and Zn). It was concluded that the metals, rather than other stress factors, are the ma‐
jor factor determining MT induction. The effects of perfluorooctane sulfonic acids
(PFOS) in Flemish eels were studied by Hoff et al., 2005, indicating that PFOS induces
liver damage.

96

EIFAC/ICES WGEEL Report 2008

In France, migrating silver eels A. anguilla were collected in a river system where al‐
gal blooms occurred yearly. Fifty per cent of eel livers were contaminated by micro‐
cystin‐LR (the most common and toxigenic compounds associated with
cyanobacterial blooms). Contaminated silver eels had lower fish condition compared
to non‐contaminated eels. Consequences of this impact for the breeding potential of
these migrating eels are discussed, in particular the importance of lipids and energy
reserve allocation. The consequences of contamination by microcystins on the breed‐
ing potential of silver eels should be further investigated (Acou et al., 2008).

Figure 6.3: A simplified conceptual model of the effects of pollution exposure on the population
structure of the European eel, A. anguilla. Adapted from Lawrence and Elliott, 2003. Numbers refer
to references: (1) Vollestad, 1992; (2) Tuurula and Soivio, 1982; Svobodova et al., 1994; Azzalis et al.,
1995; Stohs and Bagghi, 1995; Sancho et al., 1997; Ibuki and Goto, 2002; Pacheco and Santos, 2002; (3)
Nigro et al., 2002; Jha, 2004; Maes et al., 2005; Nogueira et al., 2006; (4) McKinney and Waller, 1994; Ver‐
sonnen et al., 2004; (5) Jobling et al., 2002; (6) Jimenez and Burtis, 1989; Sancho et al., 1998; Fernandez‐
Vega et al., 1999; Robinet and Feunteun, 2002; Hu et al., 2003; Pierron et al., 2007; (7) Roche et al., 2002;
(8) Sures and Knopf, 2004; Sures, 2006; (9) Sancho et al., 1997; (10) Gony, 1987; (11) Ceron et al., 2003; van
den Thillart et al., 2005; (12) Van Ginneken et al., 2005; (13) Johnson et al., 1998; Palstra et al., 2007; (14)
Sures, 2006; (15) Van Ginneken et al., 2005; (16) Corsi et al., 2003; (17) Van Campenhout et al., 2008; (18)
Ahmad et al., 2006; Maria et al., 2006; (19) Jha, 2004; Maes et al., 2005; (20) Belpaire et al., 2003 (after Gee‐
raerts et al., 2008, in prep).

Geeraerts et al., 2007 analysed an extensive dataset of contaminants by statistical
modelling and concluded that PCBs, especially the higher chlorinated ones, and
DDTs, have a negative impact on lipid content of the eel. It was further demonstrated
that fat stores and condition decreased significantly during the last 15 years in eels in
Flanders and in The Netherlands (Belpaire et al., 2008), jeopardizing a normal migra‐
tion and successful reproduction. In Belgium and The Netherlands over the past 15
years, lipid contents dropped by about one‐third (from ca. 20% to 13%) (Figure 6.4).
Also the condition (Le Cren’s relative condition factor) of the eels decreased. Lipid
reserves are essential to cover energetic requirements for silver eel migration and re‐
production. On the basis of the somatic energy reserves, reproductive potential of
eels from various latitudes over Europe was estimated, assuming fat levels in yellow
eel are indicative of those in silver eels. Only large individuals, females as well as
males, with high lipid content seem to be able to contribute to the spawning stock
(Belpaire et al., 2008). Belpaire et al., 2008 argue that the decrease in fat content in yel‐

EIFAC/ICES WGEEL Report 2008

97

low eels may be a key element in the stock decline and raises serious concerns about
the chances of the stock to recover (Figure 6.4).
It is therefore important to gain insight of the quality, lipid reserves and condition of
the eels leaving continental waters and to include quality aspects in eel stock man‐
agement. Both muscle lipid content and condition factor seem to be important inte‐
grative indicators in an overall estimate of the quality of the eels escaping to their
spawning grounds.
Contaminant pressure is a plausible concern for the recovery of eel stocks and here
we summarize arguments and hypotheses to underpin this:
1 ) Contamination has been demonstrated as the cause of population collapse
of many other biota from the 1970s on (e.g. the collapse of several birds of
prey in the 1960s as a consequence of DDT).
2 ) Many chemicals have been developed and put on the market, simultane‐
ous with the intensification of agricultural and industrial activities during
the 1970s. The timing of this increase in the production and release of
chemicals may fit with the timing of the decrease in recruitment from 1980
on.
3 ) Eels bioaccumulate many chemicals to a high extent.
4 ) The more or less comparable decreases in recruitment in the Northern‐
hemisphere Anguilla species, like A. rostrata and A. japonica, during the last
30 years, might suggest that some new contaminants quickly spreading
over the industrialized world, might have contributed to the decline.
5 ) Many reports have been dealing with direct adverse effects of contamina‐
tion on individual, population and community level in fish. In eel, many
detrimental effects of contaminants on the individual level have been
demonstrated, including impact on cellular, tissue and organ level. Also
genetic diversity seems to be lowered by pollution pressure.
6 ) Considering the high levels of contamination in eels from many areas, en‐
docrine disruption in mature silver eels might be expected, jeopardizing
normal reproduction. Dioxin‐like contaminants have been reported to
hamper normal larval development.
7 ) Lipid levels in eels have decreased considerably over the past 15 years.
This decrease in lipid levels is mainly induced by contamination. Low lipid
levels may have contributed to a reduction in migration and reproduction
success.

Joint EIFAC/ICES WGEEL REPORT 2008

n = 71

a

a

n = 112

n = 147

n = 66

a

b

bc
14

0.96

n = 99

18

Muscle lipid content (% w.w.)

20

1.06
0.98

1.00
1.02
1.04
Condition factor

Muscle lipid content (% w.w.)
16
18
14

c

1994-1998

1999-2003

12

0.94

12

n = 65

16

n = 83

1.08

n = 370

20

n = 59

22

98

2004-2006

1977-1981

40

1982-1986

1987-1991

1992-1996

1997-2001

2002-2004

25,0

1000,00

IJsselmeer, Medemblik
Meuse, Eijsden
Roer, Vlodrop

Percentage of 1979-1994 value

Fat content (% w.w.)

y = -0,0121x2 + 47,956x - 47371
R 2 = 0,867

Twentekanaal, Hengelo

30

25

20

15

10

20,0

100,00
15,0

10,0
10,00

Muscle lipid content (% w.w.)

35

Glass eel recruitment
5,0

5

Muscle lipid content

0
1975

1980

1985

1990

1995
Year

2000

2005

2010

0,0

1,00
1950

1960

1970

1980

1990

2000

2010

Figure 6.4: Temporal trend in fat contents (% of wet muscle weight) of yellow eels in Belgium (upper left panel) and The
Netherlands (upper right panel) (means, bars indicating standard errors). The number of sites is indicated. Means of periods
with the same letter are not significantly different from each other. For the Belgian eels also condition factor is presented.
Lower left panel: Temporal trends in fat contents in yellow eels from four water bodies of different typology. Time trend of
the fat content in muscle tissue (pooled samples) from yellow eels in a lake (IJsselmeer), a large river (Meuse), a small river
(Roer) and a canal (Twentekanaal) in The Netherlands. Lower right panel: Time‐series of glass eel recruitment in Europe
(ICES 2007) and of muscle lipid contents in yellow eels from The Netherlands. Data of the time‐series of glass eel recruit‐
ment are geometric means of monitoring data of recruiting biomasses in 21 European rivers, each series being scaled to its
1979–1994 average. Data of muscle lipid contents are means of pooled yellow eel samples from The Netherlands between
1977 and 2004 (Belpaire et al., 2008).
Note: in the lower right panel: recruitment is on a log scale and muscle lipid is on a normal scale.

EIFAC/ICES WGEEL Report 2008

6.3

99

Parasites/pathogens
A. crassus can be considered widespread throughout Europe and there is a growing
evidence that A. crassus is spreading further into new areas. New data in 2008 indi‐
cated the presence of the nematode in Canada (not included in the EEQD yet) for the
first time. Further process research is required before the impact of contaminants and
parasites can be included in the quantitative stock assessments.

6.4

Quality assessment of spawners using genomic tools
Eel decline might depend not only on the quantity of adult eels leaving the continent,
but also upon their quality. Good quality spawners are those that succeed in crossing
the Atlantic Ocean and reproduce. Parasites, such as the exotic swimbladder nema‐
tode A. crassus can impair eel viability by both increasing continental mortality and
affecting the swimming ability of adult eels. Organic and inorganic pollutants may
significantly reduce the quality and reproductive capacity of vertebrates. This is espe‐
cially the case in fish, where pollutants may accumulate in the water and sediment
and in the benthic biota (food). Additionally, infections and pollution have been re‐
vealed to impair strongly the survival and reproductive capacity of eels in experi‐
mental trials, resulting in an even stronger response to pollution and vice‐versa
(Palstra et al., 2006; 2007). A thorough analysis of pollutants and pathogen stress lev‐
els and a better understanding of the biological response (besides measures of condi‐
tion index) are missing. Pujolar et al., 2005 and Maes et al., 2005 assessed whether the
genetic background of European eels could be linked to two fitness traits, early
growth and pollutant bioaccumulation. Summarizing both studies here, there was
strong evidence of a relation between genetic diversity and fitness measures (also
called Heterozygosity‐Fitness‐Correlations or HFCs). It might be explained either by
an effect of direct overdominance at functional markers. Recently, it became possible
to reliably quantify the gene and protein expression levels during exposure to pollut‐
ants and parasites, allowing the early detection of decreased fitness and survival.
Such knowledge would provide the chance for early warning systems, facilitating
management actions before major mortality events in natural populations and pro‐
vide a long‐term assessment of success rates of conservation measures. Using suffi‐
cient background information on the identity and concentration of pollutant, this
approach may yield better insights into the factors influencing the recently observed
decrease in fat content, a potentially crucial measure for eelʹs ability to reach the Sar‐
gasso Sea. The ongoing analyses of northern (Belgium) and Southern (Italy) eel popu‐
lations for their gene expression level and health status will allow adding a quality
status tag on silver eels, while identifying good quality habitat for preservation.

6.5

The European Eel Quality Database
6.5.1

Introduction

In 2006 the EEL WG recommended that further sampling and ongoing monitoring
into eel quality was urgently required. Member countries should set up a national
programme on RBD scale to evaluate the quality of emigrating spawners. This should
include at least body burden of PCBs, BFRs, infestation levels with A. crassus, and
EVEX. It should be included in the national management plans while special empha‐
sis should be given to standardization and harmonization of results (units and meth‐
ods). To this effect the European Eel Quality Database was created in Belgium in 2007
and circulated among members of the EELWG requesting data on fat composition,
contaminant analysis and infection parameters of A. crassus. During the intersession
period and during the Working Group meeting 2008 eel quality data has been pro‐
vided and included in the EEQD.

100

EIFAC/ICES WGEEL Report 2008

The database is coordinated by the Research Institute for Nature and Forest (Bel‐
gium) and includes data on eel quality elements, such as condition, contaminant con‐
centrations and epidemiological parameters, in addition to the relevant descriptors of
date and place of sampling and sample characteristics (eel life stage, number and
morphometrics). The database was initially restricted to a limited number of quality
elements (lipid content, ca. 30 chemicals and A. crassus infection parameters). During
WGEEL 2007 some countries reported on some more elements, and the list of ICES7
(CB28, CB52, CB101, CB118, CB138, CB153 and CB180) congeners was extended with
non‐ortho and mono‐ortho congeners, as they exhibit the highest dioxin‐like toxicity
and contribute most to the TEQ (toxic equivalency). Also one pesticide, several metals
and some bacterial disease agents were added.
During the WGEEL, 2008 evidence has been presented that condition factors are im‐
portant elements for estimating eel quality (Acou et al., 2008; Belpaire et al., 2008). It
was recommended that condition should be included in the EEQD, this requires
however a standardized methodology (Froese, 2006).
6.5.2

Analysis of the EEQD

During the Working Group session, new data were compiled and the EEQD now
contains information from 14 countries reviewed in Table 6.2. Data from Norway,
France and Estonia also are available and will be included in 2008. Data source is het‐
erogeneous, data deriving most from national or local level surveys, but also from
eco‐toxicological studies. Belgium has presented the most exhaustive information, as
a consequence of the availability of data from the Flemish eel pollution network, in
place since 1994 (Belpaire and Goemans, 2007). Norway also provided a long time
monitoring series in the Grenland fjords (S. Norway) following the discovery of
PCDF/PCDDs in edible organisms after a 99% reduction in the load of waste compo‐
nents from the Hydro Porsgrunn magnesium factory (Knutzen et al., 2001). However,
the longest dataseries for bioaccumulation of contaminants is from the Netherlands,
because in this country a monitoring network for PCBs, OCPS and mercury in eel is
in place since the 1979s, linked to the safety for consumption norms. Germany and
UK have provided data on concentration of pollutants and contaminants relative to
some river basins, carried out within local monitoring programmes. Some countries
(Italy, Portugal, Spain) did report data drawn from eco‐toxicological studies carried
out within specific researches. Some countries (e.g. France and the Netherlands) have
published reports demonstrating that considerable information is available. At the
present moment this information is not accessible for inclusion in the EEQD. On the
whole, eel quality data were provided for approximately 600 different sites over
Europe; at the present however, the database is overbalanced, most of the sites being
situated in Belgium. Most information is available for heavy metals (771 records),
PCBs (695 records) and organochlorine pesticides (OCPs) (656 records) while 566 ob‐
servations on lipid content were also included. Apart from some observations on bac‐
terial diseases available for three sites in Spain and one site in UK, disease agents
included in the database are restricted to the swimbladder nematode A. crassus, with
epidemiological data from 335 sites across Europe.
Given the importance of lipid levels as an energy resource utilized during the eels’
migration and for the production of gametes, disturbing data are seen in Europe.
Four out of twelve countries have a fat percentage above 20% (Figure 6.5, the minimal
lipid storage needed for a successful reproduction (Boëtius and Boëtius, 1980; Van
den Thillart et al., 2004; 2005).
Research on the fat content in yellow eels has been done on two (independent) large
datasets of lipid contents in yellow eels from Belgium and the Netherlands. A 7.7%

EIFAC/ICES WGEEL Report 2008

101

decrease in lipid content on wet weight basis over a 13 year period has been revealed
in Belgium. Whereas in the Netherlands before 1990 the mean fat content was gener‐
ally superior to 20%, a clear and significant decrease occurred after 1990. Notwith‐
standing the differences in both network concepts, and large variation in lipid
contents of eels from various water bodies, similar trends were obvious in Belgium
and the Netherlands: a drop in lipid contents over the past 15 years by about one‐
third (from ca. 20% to 13%) (Belpaire et al., in press).
Table 6.2: Overview of the number of records of eel quality data compiled during the WGEEL
2008 and incorporated in the European Eel Quality Database.

F AT

PCB

P ESTICIDES

H EAVY
METALS

A. CRASSUS

BFR

D IOXIN

Belgium

409

408

373

373

140

24

8

Denmark

7

6

6

3

4

C OUNTRY

PFOS

12

Estonia
Finland
France

12

3

Germany

14

12

23

Ireland

13

9

7

Italy

24

24

20

23

26

2

6
7

7

7

10

Latvia
Lithuania
8

8
7

52

52
51

Sweden

25

10

1

179

The Netherlands

37

99

99

76

UK

1

39

39

39

16

TOTAL

566

695

656

771

335

N= 2

N= 1

N= 19

N= 5

N= 11

N= 16

N= 409

N= 30

THE
NETHERLANDS

73

BELGIUM

60

8

SPAIN

18

21

GERMANY

Spain

7
12

DENMARK

1

SWEDEN

7

1

NORTH
IRELAND

7

Portugal

POLAND

Poland

7

7

35

31

N= 1

N= 15

N= 12

N= 1

PORTUGAL

8

IRELAND

Norway

3

ITALY

2

UNITED
KINGDOM

Northern Ireland

12

30.00

Mean fat percentage of eel

25.00
20.00
15.00
10.00
5.00
0.00

Figure 6.5: Variations in mean muscle lipid content (%) in yellow end silver eels in Europe. N
indicates the number of sites on which the mean values are calculated.

102

EIFAC/ICES WGEEL Report 2008

6.5.3

Future development of the database

The development of a European Eel Quality Database in the WGEEL 2007 has been
updated during the 2008 session and now forms the basis for compiling a compre‐
hensive pan‐European overview of eel quality data.
There is a wide range of information widely scattered over Europe by location and
collecting agency. The collection and reporting of eel quality data are recommended
within the international framework for the restoration of the species (Data Collection
Regulation) as proposed by the Working Group on Eel (ICES, 2006) and the Scientific,
Technical and Economic Committee for Fisheries of the EC (STECF, 2006). The collec‐
tion of such data are now also included in the guidelines for the preparation of Eel
Management Plans.
Some information is missing and the database has to be expanded and further up‐
dated in the future. For instance some countries (e.g. France and the Netherlands)
have published reports that demonstrate considerable information is available but
data were not presented for inclusion in the EEQD. It is also presumed that many
unpublished results are available in some countries and should be utilized by inclu‐
sion in the database. Some were provided during the Working Group meeting, but
could not be included in the database at the time.
Considering that eel quality could be a major element in the decline of the species, the
database may become a useful tool for the (inter)national eel conservation measures.
The database allows the identification and designation of good quality sites where
special measures for maximum protection of stocks and emigrating spawners of good
quality can be proposed (e.g. restriction of fisheries, priority places for restocking,
priority for habitat restoration measures, etc). From preliminary analyses it was clear
that many contaminants and lipid reserves varied a lot over the distribution area of
the eel (ICES, 2007) and the presence of ʹblack spotsʹ was identified. EEQD data on
disease agents such as A. crassus demonstrated a widespread distribution over
Europe. From an environmental point of view it is clear that the database will give
information about specific environmental chemical pressures and will indicate pollu‐
tion areas for specific contaminants. The database will allow an overview and in‐
depth analysis of eel quality on a Europe wide scale and follow‐up of emerging prob‐
lems of a chemical or epidemiological nature and could also be used as an early
warning system for the spread of new eel diseases or contaminants. Yellow eels have
been proposed as a sentinel organism for evaluating the chemical quality of priority
hazardous substances in biota in accordance with the WFD. EEQD can integrate these
data and make them available for eel stock management. The database will pinpoint
sites where the quality of eels is below that deemed suitable for human consumption,
so adequate fisheries management measures, like closing fisheries or preventing con‐
sumption of eels, can be taken in these areas.
6.6

Conclusions and recommendations for Chapter 6: Eel quality
6.6.1

Conclusions

Estimation of effective spawner biomass requires quantification of the adverse effects
of contaminants, parasites, diseases, low fat levels, non‐lethal turbine damage, along
the lines previously proposed for A. crassus, as well as other mortality rates through‐
out the river basin. Present knowledge does not fully permit quantitative assessment
of the effects of these factors on the overall stock.
The European Eel Quality Database (EEQD) has been updated with data on contami‐
nants, parasites and fat levels in eel, allowing the compilation of a comprehensive

EIFAC/ICES WGEEL Report 2008

103

overview of the contaminant load in eel over its distribution area. Results demon‐
strate highly variable data within river basin districts, according to local anthropo‐
genic pollution, linked with land use. Persistently elevated contamination levels,
above human consumption standards, are seen in many European countries. The
most important reported impact is seen on the fat content of the yellow eels (i.e. in
Belgium and the Netherlands) which has decreased over the last number years and
which raises concern regarding the migratory and reproductory success of silver eels.
There is growing evidence that A. crassus is spreading further into new areas and new
data indicate the presence of the nematode in Canada (not included in the EEQD yet)
for the first time.
Clear ecotoxicological effects of contaminants have been demonstrated. The most im‐
portant impact is seen on the fat content of the eels which is decreasing over the last
number of years and which may jeopardize migration and reproduction success.
The value of monitoring contaminants in eel for environmental issues has been dem‐
onstrated. But the eel as a bio‐indicator is not recorded in the Water Framework Di‐
rective and the number of contaminants recorded is insufficient for safeguarding
sufficient eel health.
6.6.2

Recommendations

The Working Group recommends the continuation on a local scale of the long‐term
monitoring of quality (contaminants, parasites and disease) in eel with an emphasis
on standardizing the methodological approach, analysis of new compounds, an ap‐
propriate communication system and robust data management. The European Eel
Quality Database should be developed and maintained. Member States should initi‐
ate harmonized monitoring strategies aimed toward the development of a European
Eel Quality Monitoring Network, to collect the relevant data to be fed into the EEQD.
The Working Group recommends investigations into eel quality of the eels leaving
continental waters so as to include quality aspects in eel stock management and
evaluation of effective spawning escapement.
Carry out a Europe wide study to comprehend relationships between contamination
and eel stock decline. An important focus should be to study the effects of contami‐
nants on lipid metabolism and condition.
The Working Group repeats its recommendation that contaminant monitoring in eel
should be included as a tool for measuring the chemical status of our water bodies as
defined in the Water Framework Directive.

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EIFAC/ICES WGEEL Report 2008

7

Oceans, climate and recruitment

7.1

Introduction
Term of Reference c. tasked the Working Group to, “review hypotheses and informa‐
tion on the possible relationships between the European (and American) eel stock(s),
recruitment patterns and climatic and oceanic factors”.
European A. anguilla and north American A. rostrata eel spawn in the Sargasso Sea.
This part of the life cycle has not been witnessed or quantified and therefore the full
stock–recruitment relationship circle cannot be closed at present. Oceanic factors, bio‐
logical and physical, may influence the recruitment of eel through impacting on both
the migrating silver eels and on the subsequent return of juvenile recruits. Overlaid
on this, recruitment of European eel has decreased by approximately 95% since 1982
(Dekker, 2003) and is below 5% since 2000 (ICES, 2007).
In addressing this ToR, the WG in its pre‐meeting undertook a literature review and
invited submissions to this review. The WG would like to acknowledge inputs from
Beaulaton, Bonhommeau, Cairns, Dekker, Friedland, Kettle, Knights, and Miller.

7.2

Review of ocean change/controlling mechanisms
Long‐term climate variation in the North Atlantic has been revealed to correlate with
observed trends in aquatic and terrestrial ecosystems throughout Europe (Ottersen et
al., 2001). SST (sea surface temperature) differences may be the main drivers of the
North Atlantic Oscillation (NAO) and associated continental climate change. Cycles
of change could result from slow transfers of warmer/colder water by the major
thermohaline and wind‐driven gyre currents (Hurrell, 1995). Changes in the NAO
winter index (NAOI) since the 1820s appear to follow cycles with periods varying in
the range 7 to 13 years. In addition to the NAO there are other natural longer period
climate cycles i.e. the approximately 60 year Atlantic Multidecadal Oscillation (AMO,
Sutton and Hodson, 2005) (Figure 7.1). Superposed on the natural climate oscillation
is the steady anthropogenic increase of global temperature.
The widely used NAO index quantifies alterations in atmospheric pressure between
the subtropical Atlantic (Azores) and the Arctic (Iceland). An increased Azores High
indicates more and stronger winter storms crossing the Atlantic in a more northerly
track, and shifts the Gulf Stream to a more northerly position. A number of alterna‐
tive indices have been defined, varying in the months included, the analysis proce‐
dure and the exact locations measured (Dekker, 2004a). The NAO winter index is
always used, because it provides the most pronounced signal. The North Atlantic SST
demonstrates a long‐time downward trend expressing the combined effects of NAO
and AMO from the early 1940s until the early 1970s followed by a gradual increase
until the mid 2000s, amplified by the anthropogenic warming. The most recent data
indicate the beginning of a cooling period. The unusual warming of the North Atlan‐
tic is also indicated by the relationships to the Sargasso Sea Surface Temperature (SS‐
SST) (Figure 7.2). Other parameters have also been analysed by various authors and
their putative effects are described in Table 7.1.

EIFAC/ICES WGEEL Report 2008

105

Figure 7.1: The effect of the natural climate oscillations over the North Atlantic area on the mean
yearly sea temperature of the Kola section in the Barents Sea illustrating the interaction between
decadal and multidecadal time‐scales. From Svein Sundby presented at Fisheries Management and
Climate Change in the Northeast Atlantic Ocean and the Baltic Sea, Bergen 17–18 April 2008.

7.3

Review of recruitment patterns in eels
Leptocephali larvae of European eel are transported along the Gulf Stream and
North‐Atlantic Drift for a journey taking somewhere between an estimated 8–9
months (Lecomte‐Finiger, 1992) and 2–3 years (Tesch, 2003; Kettle and Haines, 2006;
Bonhommeau et al., 2008) to arrive back to the eastern Atlantic coast where they
metamorphose to glass eels, ascend rivers and grow as yellow eels until reaching par‐
tial maturity (Tesch, 2003). American eel leptocephali must also reach the Florida
Current or Gulf Stream, although they later have to leave that current system to re‐
cruit to the coast of North America. Leptocephali grow larger and have a longer lar‐
val duration than most fish species, taking up to a year or longer before they recruit
to fresh‐water habitats as glass eels or elvers. This long larval duration is thought to
make leptocephali particularly susceptible to changes in ocean currents and food
availability (Friedland et al., 2007).
A fundamental question in resolving the role of ocean circulation in life cycle of the
European eel is the duration of the larval migration. Schmidt, 1923 made a careful
analysis of the age cohort size structure for leptocephali captures across the Atlantic
Ocean and concluded that the passive transatlantic migration lasts two years with the
metamorphosed glass eels entering fresh and brackish waters in spring at the end of
their third year. Direct Lagrangian simulations (Harden Jones, 1968) indicated that
the migration should take 2.5–3 years. A more recent Lagrangian study (Kettle and
Haines, 2006) suggested that the duration of the larval eel migration was probably
about two years. On the other hand, glass eel otolith ring counts have indicated an
oceanic migration time of less than a year (Lecomte‐Finiger, 1992), but there is debate
about whether the growth rings are deposited daily. Knights, 2003 and Friedland et
al., 2007 suggest that there may only be a one year time delay oceanic perturbations
represented by the NAO and the DenOever glass eel index, implying a one year mi‐
gration period. The most recent study by Bonhommeau et al., 2008 has indicated that
there is a 2–3 year time‐lag between perturbations of ocean temperature and primary

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productivity in the Sargasso Sea and glass eel recruitment indices in Atlantic France,
and there is a convergence of opinion that the duration of the larval migration may be
approximately two years.
Table 7.1. Oceanic parameters that have been analysed by various authors and their putative ef‐
fects on eels.
O CEANIC FACTOR

M ECHANISM OF INFLUENCE

North Atlantic oscillation
NAO

NAO quantifies the alteration in
atmospheric temperatures between the
Azores and Iceland.

A UTHOR

Dekker, 2004

It indicates a more northerly position of
the Gulf Stream. Impacts larval
migration
The marine production increases with
sea surface temperature in the cooler
waters from the North Atlantic but
decrease in warmer waters. This effect is
as a consequence of a reduced vertical
mixing and lower marine production
thus impacting larval feeding

Bonhommeau et al., 2008

Sargasso Sea Winds

Surface current, caused by the combined
effect of wind and Coriolis forces, have
diminished, reducing the westward
transport towards the Florida current
into the Gulf Stream–could affect
transport of leptocephali

Friedland et al., 2007

Mean Temperature of the
northern hemisphere

Would reflect climate change and its
impact on primary production in the
ocean and larval feeding.

Knights and
Bonhommeau,
unpublished

Gulf Stream Index (GSI)

Latitude of the Gulf Stream, from
monthly charts of the north wall

Bonhommeau, 2008

Transport index (TI)

Strength of the Gulf Stream and North
Atlantic current system (baroclinic gyre
circulation in the North Atlantic)

Bonhommeau, 2008

Sargasso Sea Sea Surface
Temperatures
(SS‐SST), average 0‐100 m
deep

(NHT)

Calculated from potential energy
anomalies (PEA) between Bermuda and
Labrador basin – could affect transport
of leptocephali
PP (Bermuda biological
station, North of spawning
area)

Primary production. Considered as a
good proxy for leptocephali food.

Bonhommeau, 2008

Sea surface temperatures
anomalies (SSTA)

Food availability for leptocephali would
be expected to be reduced during warm
high SSTA periods as a consequence of
reduced spring mixing, nutrient
recirculation and productivity

Knights, 2003

Surface expression of the
22.5°C isotherm

The 22.5 °C isotherm is a useful indicator
of the northern limit of spawning by
both species of eels in the Atlantic.

Friedland et al., 2007

Therefore, changes in the latitude or
intensity of these fronts may affect both
the spawning location and the
subsequent transport of the leptocephali
to continental habitats.

EIFAC/ICES WGEEL Report 2008

107

A very short time‐lag compared to the drift estimates seems unlikely considering the
swimming ability of the leptocephalus larvae (Bonhommeau, 2008). To gain one year
in the transatlantic migration the larvae has to sustain a continuous, directed swim‐
ming velocity of 15 to 20 cm/sec, or 3–5 body‐lengths/sec. A typical anguilliform
swimming speed is of the order 0.5 body‐lengths/sec (Ellerby et al., 2001).
Meta‐analyses of many local dataseries have revealed common trends in the popula‐
tion. The breakpoint in the recruitment series in south and middle Europe from 1980
points to a shared process causing the decline thereafter. Recruitment series of young
yellow eel in northern Europe deviates from this, with an earlier decline starting dur‐
ing the 1950s. This could be interpreted as a different climatic effect on the north‐ and
south‐ going branches of the North Atlantic drift, which splits into the North East
Atlantic and the Canary currents to the southwest of Ireland.
7.4

Review of hypotheses of causal linkages between oceanic factors and
recruitment patterns
The mechanism or mechanisms behind the observed correlation between glass eel
recruitment and climate oscillations are unknown. The migratory phase of adults and
larvae, as well as the egg and larvae production might have been influenced by cli‐
mate variation. Currently it is difficult to separate out the impact of ocean and climate
on spawner migrations and on subsequent migrations of larvae and recruiting glass
eels.
It has long been recognized that there may be a direct link between larval migration
success and the density, or thermohaline circulation, of the ocean. The NAO might
impact the larval migration by changing the ocean currents or by influencing ocean
productivity and food availability for the migrating larvae (Knights, 2003). The long‐
term variations in glass eel recruitment indices may be modulated by characteristic
time‐scale of the NAO index, which varies in periodicity between 7 and 13 years. This
had important implications in explaining the long‐term decline in glass eel recruit‐
ment across Europe since the late 1970s as it has been recognized that the NAO index
had been in a prolonged positive phase over this period (ICES, 2001; Friedland, 2007).
Focussing on the long term DenOever glass eel index, Friedland et al., 2007 estab‐
lished the existence of significant correlations with environmental parameters in the
North Atlantic during the spawning period between February and May: the surface
expression of the 22.5°C isotherm, the eastward windspeeds, and the NAO. Explana‐
tions for the observed relationships focused on the possible influence of wind‐
induced geostrophic transport in advecting larvae into the Gulf Stream and on the
impact of interannual variability of the mixed layer depth on nutrient supply and
ocean productivity in providing food to the developing larvae.
A close negative relationship has been found over the last four decades between long‐
term fluctuations in recruitment and in sea temperature (Table 7.2). By contrast,
variations in integrative indices measuring ocean circulation, i.e. latitude and
strength of the Gulf Stream, did not seem to explain variations in glass eel recruit‐
ment (Bonhommeau et al., 2008).
The impact of food availability in the Sargasso Sea on the success of the larval eel mi‐
gration was suggested and rejected by Desaunay and Guerault, 1997. Using informa‐
tion about the length of the oceanic migration from otoliths the conclusion was that
the number and physical condition of glass eels arriving on the coast of France was
linked to chlorophyll concentration and food availability in the Sargasso Sea at the
time of spawning. The largest glass eels near the spring arrival peak in coastal France

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were assumed to have started in the Sargasso Sea during the spring chlorophyll
bloom of the previous year.
Bonhommeau et al., 2008 used a short time‐series to demonstrate a correlation be‐
tween recruitment and primary production in the Sargasso Sea demonstrating a
strong bottom–up control of leptocephali survival and growth. On a longer time‐
scale, SST is used as a proxy for primary production and related to recruitment indi‐
ces. Sea warming in the eel spawning area since the early 1980s may have modified
marine production and eventually affected the survival rate of European eels at early
life stages (see Figure 7.2). Direct measurements of primary productivity in the north‐
ern Sargasso Sea were also found to be correlated with a three‐year lag to the Loire
River recruitment time‐series in France, but not those at the other locations (Bon‐
hommeau et al., 2008). Changes in ocean productivity may also be associated with
changes in the length and condition of glass eels recruiting to Europe (Desaunay and
Guerault, 1997; Dekker 1998; 2004b).
Kettle et al., 2008 have demonstrated that the NAO repeat cycle is present both in the
glass eel catches and the FAO eel landing statistics. This means that there may be a
resonant amplification between silver eel escapement and glass‐eel recruitment. All
stages of the life cycle appear to respond to interannual climate variability associated
with the NAO, but it is not clear if the larval migration success is impacted directly by
meteorological conditions over the Sargasso Sea or if it is modulated by the number
of silver eels that are triggered to spawn by NAO‐associated rainfall patterns in
Europe.
Table 7.2: Correlations between various glass eel recruitment series and oceanic parameters.

R ECRUITMENT SERIES

O CEANIC PARAMETER

C ORRELATION

T IME L AG
( YEARS )

A UTHOR

Transport related parameter
Series from Loire,
LʹEms & Den Oever,
1950–2001

NAO (winter index)

‐0.13

0 (max 1
and 6
years)

Dekker, 2004

DenOever 1938–2005

NAO (winter index)

‐0.35

0 (max 0
and 8)

Friedland et al., 2007

10 series

NAO (winter index)

GAM model
significant effect but
no linear trend

26 series

NAO (winter index)

Anticorrelated‐
significant

1 to 4

Kettle et al., 2008

Drakkar model,
particles that
succeeding in reaching
the 20 W

NAO (winter index)

0.5

0

Bonhommeau, 2008

GSI

0.73

PEA

0.57

Mercator model,
particles succeeding

NAO (winter index)

0.78

0

Bonhommeau, 2008

GSI

0.80

in reaching the 20 W

TI

0.47

Drakkar model,
minimum migration
duration

NAO (winter index)

‐0.57

0

Bonhommeau, 2008

GSI

‐0.75

TI

‐0.48

21 series

NAO

1935–2007
7 series

TI

Beaulaton, 2008

‐0.28

2

‐0.31

3

‐0.35

7

Knights and
Bonhommeau,
unpublished

NS

3

Bonhommeau et al., 2008

EIFAC/ICES WGEEL Report 2008

R ECRUITMENT SERIES

109

O CEANIC PARAMETER

C ORRELATION

T IME L AG
( YEARS )

A UTHOR

7 series

GSI

NS

3

Bonhommeau et al., 2008

DenOever 1947–2004

Latitude of the
surface expression of
the 22.5°C isotherm
in the Sargasso Sea

‐0.15 to ‐0.39
according to month
and longitude

1

Friedland et al., 2007

DenOever 1949–2003

Winds

‐0.09 to ‐0.48

1 year

Friedland et al., 2007

Production related parameters
1955–2007

SS‐SST

NS

1–6
years

Knights and
Bonhommeau,
unpublished

1935–2007

NHT

NS

2–3
years

Knights and
Bonhommeau,
unpublished

Loire series from trader
1994–2004

PP

0.74

2.5 years

Bonhommeau et al., 2008

Ems DenOever, Loire
Nalon

SS‐SST

‐0.88

2.5‐year

Bonhommeau, 2008

DenOever (3 year
average) 1952–1995

SST anomaly at 100–
250 m

‐0.47

0 year

Knight, 2003

‐0.30

1 year

DenOever (1960–1996)

Size of glass eels

0.7

0 year

1960–2005

(Dekker, 1998)

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Figure 7.2: Time series of DenOever recruitment index (5‐yr moving average; solid line with cir‐
cles) and temperature (°C; 5‐yr moving average; solid line with squares) in the Sargasso Sea from
1960 to 2003. Bold lines indicate regime shift detection (Rodionov and Overland, 2005) and verti‐
cal dashed line indicates the regime shift in temperature in 1979. (Reproduced from Bonhommeau
et al., 2008).

7.5

Ocean factors as reason (or contributory factor) for recruitment decline
(1980s onwards)
The historic record shows strong evidence that the abundance and size of glass eels
recruiting to the continent have the same periodicity as natural climate oscillations
(Figure 7.3). Evidently NAO, and other climate cycles, are primarily meteorological
indices that, at most, can be proxies to those ecological and hydrographic changes in
the North Atlantic that could be the primary causes for variations of eel recruitment.
Several parameters are possible candidates for the cause of the decline e.g. sea surface
temperature anomalies and changes in productivity linked to temperature.
A shift in sea temperature in 1979 marked the beginning of changes in the Sargasso
Sea environment and was followed by the large shift in eel recruitment detected in
1982 in most of the European rivers that have been analysed (Bonhommeau et al.,

EIFAC/ICES WGEEL Report 2008

111

2008). Correlation analysis of the glass eel catches revealed that almost all the moni‐
toring indices across Europe vary in phase, providing support that they are modu‐
lated by a large‐scale meteorological disturbance (i.e. as previously suggested by
Knights 2003; Friedland et al., 2007). However, measuring ascending young eels
(young of the year, and older), the drop in recruitment in northern European rivers
was observed considerably earlier. This leaves the possibility open that conditions
closer to the European shelf may be important or that the decline in southern Europe
started earlier also (see l’Adour and Gironde series, Chapter 2).
Temperature may be one of the main governing factors influencing eel larvae sur‐
vival by decreasing food availability in the Sargasso Sea (Bonhommeau et al., 2008).
The size of glass eels is positively correlated with abundance and with the NAO‐
cycle. This also points to a role of ocean primary production on the feeding of glass
eel and possible starvation of leptocephali. (Dekker, in prep, Figure 7.3).
-6

NAO
Glass eel
length

-3

0
7

NAO winter index (reversed) .

Glass eel length (cm)

8

3

6
1950

1960

1970

1980

1990

2000

6
2010

Figure 7.3: Trends in glass‐eel length entering Lake IJsselmeer (short dash), and the NAO winter
index (long dash). (Data from Dekker, 1998 and Hurrell, 1995). (From Dekker, in prep).

Changes in ocean currents, particularly in the Sargasso area, may have also affected
glass eel recruitment. This assertion is supported by the correlation between recruit‐
ment series and NAO. It is also supported by results from modelling demonstrating
the positive effect of transport indices on both success and time of migration. How‐
ever, when looking at indices related to the strength of the Gulf Stream (TI and GSI),
no significant correlation was found (Bonhommeau, 2008).
The steep decline in recruitment between 1980 and 1983 and the continued low and
still declining recruitment since then cannot be easily explained by oceanic factors
alone. The demonstration of a possible stock recruitment relationship (Dekker, 2003;
2004b; updated by WGEEL 2007) demonstrates strong evidence of a depensatory
mechanism in the relationship. In this S/R relationship, landings have been used as a
proxy for continental stock and it is assumed that continental stock varies in parallel
with SSB. It is possible that this relationship between stock and SSB is not constant
and that SSB has declined faster than the stock, possibly as a consequence of a break‐
down in the migratory phase, the spawning process and/or the quality of the spawn‐
ers, leading to a smaller number of recruits per spawner than observed prior to the
1980s. Isolation or fragmentation of spawning effort as a consequence of low SSB may

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EIFAC/ICES WGEEL Report 2008

have exacerbated this. The steep stock related decline in recruitment could be over‐
laid on the oceanic influences and might have drowned out the ocean signals in latter
years.
A different view to this is proposed by Knights and Bonhommeau, unpublished.
They found that combined and geographical‐area stock trends are more meaningful
than landings data for use in formulating stock–recruitment hypotheses and model‐
ling and in developing management targets. Their results predict that glass eel re‐
cruitment would to be able to recover in less than 10 years from very low levels if
ocean‐climate conditions become more favourable. This conflicts with the life cycle
modelling study of Astrom and Dekker, 2007 which concluded that stock recoveries
could take >80 years. Also, Dekker et al., 2003 and Dekker, 2004b assumed the general
decline in combined landings was a proxy for stocks and hence spawning stock and
that depensation could have led to the falls in recruitment 20 years later. The study
by Knights and Bonhommeau, unpubl. however, suggests that fluctuations in envi‐
ronmental factors, both oceanic and near‐continent, are the main determinants of re‐
cruitment over shorter periods and that classical stock–recruitment models cannot be
applied to the European eel. It also debated the assumption that large female eels
produced in the Baltic make a major contribution to overall production of the Euro‐
pean eel (e.g. Tesch, 2003), as North Atlantic/North Sea glass eel recruitment was rela‐
tively very high around 1980, despite the major declines in Baltic stocks beginning in
the 1950–1960s. In conclusion, Knights and Bonhommeau, unpubl. suggest that com‐
bined European landings data cannot be used as a simple direct proxy for stocks, cer‐
tainly in different regions in NW Europe. The lack of any clear recovery in
recruitment during the low NAO periods in the late 1990s led Dekker, 2004a to ques‐
tion the role of the NAO in affecting glass eel recruitment. However, the continual
warming of the N Atlantic signalled by the rising SS‐SST and NHT has probably
overridden the effects of the NAO (Knights and Bonhommeau, unpubl.).
7.6

Conclusions and recommendations for Chapter 7: Oceans, climate and
recruitment
7.6.1

Conclusions

•

Sufficiently long time‐series of glass eel recruitment, covering several peri‐
ods of the natural climatic oscillation over the North Atlantic, reflect the
same periodicity.

•

The causal link between climate and recruitment strength, is unknown.

•

It is unknown where and when during the oceanic life of the eel larvae the
climate effect operates. It may be in the Sargasso Sea or closer to the Euro‐
pean coastal area.

•

The recent, prolonged strong decline in eel recruitment is out of phase with
the dominating climate cycle, the North Atlantic Oscillation, although con‐
tinual warming has probably overridden the effects of the NAO.

As long as the causal factors of oceanic influence are unknown, it is not safe to as‐
sume that the decline is explained by climate alone, especially while we know that
the anthropogenic influences during the continental life stage of the eel are large and
better understood. The fact that oceanic climate may contribute to recruitment varia‐
tion is not grounds for abstaining from all possible measures to increase silver eel
escapement to boost spawning‐stock biomass. At some level the stock/recruitment
relation will always be important‐there is no recruitment without eggs. Ocean envi‐
ronmental factors can never justify a lack of conservation measures.

EIFAC/ICES WGEEL Report 2008

113

The options and expectations for management outcomes can be summarized in Table
7.3 that can be used in a risk analysis:
Table 7.3: Options and expectations for management outcomes.
H YPOTHESIS ABOUT CAUSE OF DECLINE

Pure stock/recruitment
A CTION TAKEN

Ocean environment
Improving

Deteriorating

Reduce
anthropogenic
mortality

Recovery if measures
are sufficient

Recovery faster than
expected

No recovery or slower
than expected

No action

No recovery

Possible recovery

Faster continued
decline

7.6.2

Recommendations

To address the difficulties comparing ocean environmental cycles with biological cy‐
cles of eel it is necessary to improve our knowledge of the oceanic phases of the eel
life cycle. This will allow us to better understand which oceanic factors are behind the
climate effects. This in turn will allow for a more sophisticated analysis than mere
correlations and the weighting of the role of climate effects on reproductive success,
compared to continental factors. Some key questions are:
•

The question of the interaction between leptocephali mortality and disper‐
sion.

•

The role of leptocephali in the ecosystem, including feeding and predation.

WGEEL proposes that an ICES Study Group is established to coordinate and plan
research on the oceanic effects on leptocephali and metamorphosis to glass eel.

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EIFAC/ICES WGEEL Report 2008

8

Research needs

8.1

Introduction
The Working Group on Eel identified a considerable need for new research on eel
population dynamics and its influencing factors. Due to the current implementation
of the EU eel recovery plan (EU Regulation 1100/2007), the primary focus of discus‐
sions on research requirements at WGEEL 2008 was on supporting the assessment of
the stock and its recovery as sought by the implementation of this regulation. WGEEL
2008 did not, however, lose sight of the continuing lack of knowledge of the funda‐
mental biology (i.e. carrying capacity and density‐dependence) and of the European
eel’s ocean phase (including spawner quality and migrations). It is recognized that
methods for evaluation of the outcome of management measures are not yet fully
available either at the population (international target), or local (sub target) level.

8.2

Priority research needs
WGEEL believes that the best approach is a series of integrated and internationally
coordinated projects and is set out in Figure 8.1. A programme of research is needed
to address gaps in knowledge, gather data to evaluate the status of the stock, and fur‐
ther develop stock assessment methods to determine compliance with targets and the
effectiveness of management actions at the international and local level.

Eel Research Needs
Model for international
stock assessment
this will need
to be based on
SLIME models

Data transfer
from data rich to
data poor EMUs
INDICANG 2
EELIAD

EMU estimates
these need to be interpreted in
quantative manner over Europe
• Stock assessment (incl. coastal)
• Quality
• Habitat quality
• Mortality
– natural, anthropogenic

Methodologies

Density
dependence

• Growth
• Migration
• Ocean effects
Figure 8.1: Flow diagram showing linkages between research needs.

The priorities for integrated research are as follows:
•

International Stock Assessment and trend monitoring

•

Local stock assessment and post‐evaluation of management actions

•

Process based research on biological parameters required for estimating
escapement.

EIFAC/ICES WGEEL Report 2008

8.2.1

115

International stock assessment and trend monitoring

Improved annual trends on recruitment, stock and yield
Emigrating silver eel biomass, numbers and sex ratio
The aggregation of river basin specific data and assessments, into stock‐wide assess‐
ments in support of stock to recruitment (S‐R) and recruitment to spawner stock bio‐
mass (R‐SSB) assessment and modelling (e.g.VPA).
The further development of models to assess compliance with the recovery target and
evaluate management actions
The international assessment of recruitment and stock trends to assess the response of
the stock to management actions under the Regulation, noting the WGEEL recom‐
mendation on accessibility to national eel management plans and supporting eel data.
8.2.2

Local stock assessment and post-evaluation of management actions

The development of local stock assessment procedures and estimates of silver eel es‐
capement
The further development of models and methodologies to assess compliance at the
local scale with the recovery target and evaluate management actions
The development and testing of methods to characterize and quantify eel stocks in
deeper areas of rivers, lakes, estuaries and coastal waters
The testing of relationships between habitat characteristics, eel quality and eel pro‐
duction as indicators of the relative production potential for different habitats
To develop methods for quantitative assessment of the availability of local surplus for
stocking, and for the contribution of stocking to escapement
Implementation of EMPs requires the development of methodologies to obtain esti‐
mates of escapement. These can be direct (e.g. mark‐recapture or acoustic counting) or
indirect methods (e.g. yellow eel proxies to determine silver eel production and eel
habitat modelling production). Validation of indirect methodologies is required.
8.2.3 Process based research on biological parameters required for estimating
escapement

Quantify the possible density‐dependence effects in various processes including mor‐
tality, growth, movement, maturation and sex differentiation.
Quantify the impacts of pathogens, parasites, diseases, and low chemical quality on
effective silver eel escapement and spawning success. This should include the rela‐
tionship between eel quality and body fat content.
Quantify any impact of aquaculture, transport and stocking of eel in terms of reduced
spawner production
Research is required on the relative importance of the habitat types used by eels and
what demographic characteristics they exhibit in these habitats, such between fresh
(rivers and lakes) and saline (brackish/salt) waters.
Recent research has suggested that processes in the oceanic phase (including spawner
quality) may be important in determining recruitment levels. Improved knowledge of
the oceanic phases of the eel is needed to further the initial search for correlations be‐
tween eel recruitment and oceanic processes.

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8.3

EIFAC/ICES WGEEL Report 2008

Other research needs
WGEEL 2008 focused heavily on the requirements of the EU Regulation and the need
for international and national stock assessment. Additional research will be required
in order to fill many gaps in the biology and management of eel.
Research on optimum collection and transport methods for glass eel to reduce mor‐
tality for stocking
Timing and frequency of stocking
Related eel health issues
Post‐evaluation methods for the net benefit of stocking for conservation
Investigations examining the competitiveness, survival and reproductive capacity of
stocked glass eels, compared with their naturally recruited counterparts by marking
the stocked individuals and comparing their recapture at sexual maturity
Quantify the relation between fat content and eel quality, the effects of specific con‐
taminants and parasites on fat metabolism and a possible relationship between eel fat
content and environmental variables such as changing temperature, changing trophic
status, and food availability
Predator prey relationships (e.g. cormorants).

8.4

Proposals for study groups
WGEEL proposes that an ICES Study Group is established to coordinate and plan
research on the oceanic effects on leptocephali and metamorphosis to glass eel.
WGEEL notes and approves the proposal for an eel age calibration workshop.
WGEEL notes and approves the proposal to the DFC (2008) for a study group on an‐
guillid eels in saline (brackish/salt) waters.

EIFAC/ICES WGEEL Report 2008

9

117

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Vollestad, L. A. and Jonsson, B. 1988. A 13‐year study of the population dynamics and growth
of the European eel Anguilla anguilla in a Norwegian river: evidence of density‐dependent
mortality, and development of a model for predicting yield. Journal of Animal Ecology, 57:
983–997.
Wirth, T., and Bernatchez, L. 2001. Genetic evidence against panmixia in the European eel. Na‐
ture, 409: 1037–1040.
Williams, B. and Threader, R. 2007. A review of the proceedings and outcomes of the work‐
shop on the American eel (Anguilla rostrata) stocking in Canadian waters. Montréal, Can‐
ada, March 26 and 27, 2007. 37p.
Williams, B. and Aprahamian, M.W. 2004. Management Guidelines for the Stocking of Eel and
Elver (Anguilla anguilla L.). Environment Agency, Bristol.
Zoeller, R.T. 2001. Polychlorinated biphenyls as disruptors of thyroid hormone action. In:
Robertson, L.H. and Hansen, L.W., eds. PCBs: recent advances in environmental toxicol‐
ogy and health effects. Kentucky: University Press, pp.265–272.

EIFAC/ICES WGEEL Report 2008

131

Annex 1 – List of participants

N AME

A DDRESS

P HONE /F AX

E MAIL

Andersson, Jan

Swedish Board of
Fisheries Institute of
Coastal Research
Simpevarp, Ävrö 16
SE‐572 95 Figeholm
Sweden

Phone +46
491 76 28 41
Fax +46 491
76 28 45

jan.andersson@fiskeriverket.se

Aprahamian,
Miran

Environment Agency
Richard Fairclough
House Knutsford Road

Phone +44
1925542713

miran.aprahamian@environment‐
agency.gov.uk

WA4 1HG Warrington

Fax +44
1925415961

UK
Åström,
Mårten

Institute of Freshwater
Research National
Board of Fisheries
Stångholmsvägen 2

Phone 8
6990610

marten.astrom@fiskeriverket.se

Fax 8 6990650

SE‐178 93
Drottningholm
Sweden
Beaulaton,
Laurent

ONEMA
16, avenue Louison
Bobet
FR‐94132 Fontenay‐
Sous‐Bois Cedex

Phone +33 1
45 14 36 34

Laurent.beaulaton@onema.fr

Mobile 06 81
47 52 71

France
Belpaire,
Claude

Instituut voor Natuur‐
en Bosonderzoek

Phone +32
26580411

Duboislaan 14

Fax +32
26579682

1560 Hoeilaart

Bevacqua,
Daniele

Belgium

Mobile +32
475678992

Universita degli Studi
di Parma Dipartimento
di Scienze Ambientali

Phone +39
3333024144

Via Usberti 10A

Claude.Belpaire@inbo.be

daniele.bevacqua@poste.it

Fax +39
521905402

143100 Parma
Italy
Birzaks, Janis

Latvian Fish Resources
Agency

Phone +371 7
612 536

8 Daugavgrivas Str.

Fax +371 7
616 946

LV‐1048 Riga

janis.birzaks@lzra.gov.lv

Latvia
Brämick, Uwe

Institute of Inland
Fisheries

Phone +49
33201 4060

Potsdam‐Sacrow

Fax +49
3320140640

Im Königswald 2
14469 Potsdam Zufahr
über 14476
Potsdam/OT
Germany

uwe.braemick@ifb‐potsdam.de

132

EIFAC/ICES WGEEL Report 2008

N AME

Breteler, Jan
Klein

A DDRESS

Vivion BV
Handelstraat 18

P HONE /F AX

E MAIL

Phone +31
302940318

kb@vivion.nl

Phone +33
99908844

cedric.briand@lavilaine.com

3533 GK Utrecht
Netherlands
Briand, Cedric

Institution
d’Amenagement de la
Viliane
France

de Casamajor,
Marie‐Noelle

IFREMER

Marie.Noelle.De.Casamajor@ifremer.fr

Alleé du Parc Montowy
64 600 Anglet
France

Dekker,
Willem

Wageningen IMARES
PO Box 68
Netherlands

Fax +31 255
564 644

Institute of Inland
Fisheries, Potsdam

Phone +49
3320140628

Im Königswald 2

Fax +49
3320140640

NL‐1970 AB IJmuiden

Doering‐Arjes,
Peer

Phone +31
255 564 646

14469 Potsdam

Willem.Dekker@wur.nl

peer.doering‐arjes@ifb‐potsdam.de

Germany
Domingos,
Isabel

Instituto de
Oceanografia, FCUL

Phone +351
217500970

Campo Grande,

Fax +351
21700009

1749‐016 Lisbon

idomingos@fc.ul.pt

Portugal
van Eijk, Wim

Dutch Fish Product
Board

w.eijk@pvis.nl

PO Box 72
NL‐2280 AB Rijswijk
Netherlands
Evans, Derek

Agri‐food and
Biosciences Institute

Phone +44
2890255551

derek.evans@afbini.gov.uk

Phone + 33
223185381

Feunteun@mnhn.fr

UK
Feunteun, Eric

CRESCO MNHN
28 Rue du pont blanc
France

Mobile +33
607375923

Instituut voor Natuur‐
en Bosonderzoek

Phone +32
26580425

Duboislaan 14

Fax + 32
26579682

35800 Dinard
Geeraerts,
Caroline

1560 Hoeilaart

Gomes da
Silva, Serge

Belgium

Mobile +32
473977727

Groupe dʹIntérêt pour
les Poissons, la Pêche et
lʹAquaculture

Tel + 32
81626429

Av. Maréchal Juin 23
B‐5030 Gembloux
Belgium

Fax + 32
81615727
Mobile + 32
495637419

Caroline.Geeraerts@inbo.be

sgomsilva@gmail.com

EIFAC/ICES WGEEL Report 2008

133

N AME

Ingendahl,
Detlev

A DDRESS

Landesamt für Natur,
Umwelt und
Verbraucherschutz
Nordrhein‐Westfalen

P HONE /F AX

Phone +49
272377940

E MAIL

detlev.ingendahl@bezreg‐
arnsberg.nrw.de

Fax +49
272377577

Wanderfischprogramm
Heinsberger Str. 53
D‐57399 Kirchhundem‐
Albaum
Germany
Jarvalt, Ain

Estonian University of
Life Sciences

Phone +372
7454544

Kreutzwaldi 64

Fax +372
7454546

EE‐51014 Tartu

ain.jarvalt@emu.ee

Estonia
Korta, Maria

AZTI‐Tecnalia AZTI
Pasaia

Phone +34
943004800

Herrera Kaia

Fax +34
943004801

Portualde z/g

mkorta@pas.azti.es

20110 Pasaia
(Gipuzkoa)
Spain
Knutsen, Jan
Atle

Institute of Marine
Research

Phone +47
37059028

Flødevigen

Fax +47
37059001

N‐4817 His

Jan.Atle.Knutsen@imr.no

Norway
Lambert,
Patrick

CEMAGREF
50 Av. de Verdun
France

Fax +33
557890801

Institute of Ecology of
Vilnius University

Phone +370
52729284

Akademijos str. 2

Fax +370
52729352

33612 Gestas
Lozys, Linas

Phone +33
557890809

LT‐08412 Vilnius‐12

patrick.lambert@bordeaux.cemagref.fr

lozys@ekoi.lt

Lithuania
Maes, Gregory

Katholieke Universiteit
Leuven Lab. of Aquatic
Ecology Res. Group on
Fish Genetics Ch. de

Phone +32
16324296

gregory.maes@bio.kuleuven.be

Fax +32
16324575

Bériotstraat 32
B‐3000 Leuven
Belgium
McCarthy,
Kieran

National University of
Ireland

Phone +353
91492333

University Road

Fax +353
91495426

Co. Galway

Nermer,
Tomasz

Ireland

Mobile +353
864018223

Sea Fisheries Institute
in Gdynia

Phone +48‐
587356211

ul. Kollataja 1

Fax +48
587356110

PL‐81‐332 Gdynia
Poland

tk.mccarthy@nuigalway.ie

nermer@mir.gdynia.pl

134

EIFAC/ICES WGEEL Report 2008

N AME

O’Neill,
Lughaidh

A DDRESS

P HONE /F AX

The Central Fisheries
Board

Phone +353
98‐42300

Newport, Co. Mayo

Fax +353
9842340

Ireland
O’Toole, Ciar

The Marine Institute
Furnace
Newport, Co. Mayo
Ireland

Pedersen,
Michael
Ingemann

Phone +353
9842300

E MAIL

lughaidh.oneill@marine.ie

ciar.otoole@marine.ie

Fax +353
9842340
Phone +45
89213128

mip@aqua.dtu.dk

Sea Fisheries Institute
in Gdynia

Phone +48 58
7356232

wpelczar@mir.gdynia.pl

ul. Kollataja 1

Fax +48 58
7356110

The National Institute
of Aquatic Resources
Department of Inland
Fisheries
Vejlsøvej 39
DK‐8600 Silkeborg
Denmark

Pelczarski,
Wojciech

PL‐81‐332 Gdynia
Poland
Poole, Russell
Chair

Rosell, Robert

Marine Institute
Furnace Newport

Phone + 353
98 42300

Co. Mayo
Ireland

Fax +353
9842340

Agri‐food and
Biosciences Institute

Phone +44
2890255506

Newforge Lane

Fax +44
2890255004

BT9 5PX Belfast

russell.poole@marine.ie

robert.rosell@afbini.gov.uk

UK
Verrault, Guy

Ministére des
Ressources Naturelles
et Faune

Phone +1 418
862 8213 ext.
306

186 rue Fraser

Fax +1 418
862 8176

Rivière‐du‐Loup

guy.verreault@mrnf.gouv.qc.ca

Qc Québec G5R 1C8
Canada
Volckaert, Filip

Katholieke Universiteit
Leuven Lab. of Aquatic
Ecology Res. Group on
Fish Genetics Ch. de
Bériotstraat 32

Phone +32
16324296

filip.volckaert@bio.kuleuven.be

Fax +32
16324575

B‐3000 Leuven
Belgium
Walker, Alan
M.

Centre for
Environment, Fisheries
& Aquaculture Science
Lowestoft Laboratory
Pakefield Road
NR33 0HT Lowestoft
Suffolk
UK

Phone +44 (0)
1502 524351
Fax +44 (0)
1502 526351

alan.walker@cefas.co.uk

EIFAC/ICES WGEEL Report 2008

135

N AME

Westerberg.
Håkon

A DDRESS

P HONE /F AX

Swedish Board of
Fisheries

Phone +46
317430333

PO Box 421

Fax +46
317430444

SE‐40126 Göteburg

E MAIL

hakan.westerberg@fiskeriverket.se

Sweden
Wickström,
Hikan

Swedish Board of
Fisheries, Institute of
Freshwater Research
Stångholmsvägen 2

Phone +46
866990670

hakan.wickstrom@fiskeriverket.se

Fax +46
86990650

SE‐178 93
Drottningholm
Sweden
Wysujack,
Klaus

Johann Heinrich von
Thünen‐Institute

Phone:
+4102‐51128

klaus.wysujack@vti.bund.de

Ahrensburg
Wulfsdorfer Weg 204
D‐22926 Hamburg
Germany
Zane, Lorenzo

University of Padovo
Italy

lorenz@bio.unipd.it

136

EIFAC/ICES WGEEL Report 2008

Annex 2 – Agenda
Agenda for Joint EIFAC/ICES WGEEL 2008, Leuven
Wednesday 3rd September

9.00

Get organized

9.30–10.00

Welcome RP
Welcome Dr Jurgen Tack, INBO
Local Welcome and Information: Filip Volckaert/Greg Maes

10.00–10.30

Intro to Working Group, ToR, etc. RP

10.30

Coffee

10.45–11.15

EEQD and eel Quality, introduced by Belpaire

11.15–11.45

Aquaculture and Restocking, introduced by Wickstrom and Evans

11.45–12.15

Methodologies‐concepts, time frames, introduced by Astrom

12.15–13.30

Lunch

13.30–14.00
mian

Methodologies‐biomass, escapement and targets, intro by Apraha‐

14.00–14.30

Data Group, introduced by Dekker/Beaulaton

14.30–15.30

Ocean and Climate, introduced by O’Toole and Westerberg

15.30

Coffee

16.00–16.30

Genetics and the EU Regulation, introduced by Maes

16.30–17.00

Genetics, introduced by Zane

17.00–17.15

Update from Norway on marine data on eel, introduced by Knutsen

17.15–17.30

Update from N. America/Canada, introduced by Verrault

until 18.00

Breakout to get organized, subgroups, rapporteurs, approaches, etc.

Thursday-Sub Groups breakout

16.00–18.00

Plenary

Friday-Sub Groups breakout

16.00–18.00

Plenary

Saturday morning-Sub Groups breakout

9.00–10.00

Plenary (optional depending on progress on Friday pm)

14.00–15:00

Present conclusions and recommendations draft 1.

15.30–18.00

Producing draft report [DEADLINE 18:00]

EIFAC/ICES WGEEL Report 2008

Sunday-Sub Group leaders and Chair to do initial draft of technical advice

Print hard copies of report
Monday

9.00–13:00

Circulate draft advice and hard copy report for comment

14.00–18:00

Discuss and agree Report, and Recommendations

Tuesday

9.00–13:00

Discuss Report, and Recommendations and agree technical advice

Conclude at 14.00 The afternoon is available to tie up loose ends.

137

138

EIFAC/ICES WGEEL Report 2008

Annex 3 – Recruitment, landings and stocking dataseries
Table 1 Part 1 Recruitment dataseries of glass eel: Sweden, Northern Ireland (N.Irl) and Ireland.
C OUNTRY

SE

SE

SE

N.I RL

IE

IE

Bann

Erne

Shannon

(old data)

IYFS/IBTS
(new
Ringhals Viskan
data)

Index

Index

Kg

t

t

IYFS/IBTS
Year
Unit

SE

Kg

Kg

1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936

7333

1937

9000

1938

8000

1939

6333

1940

9000

1941

10 000

1942

7000

1943

6000

1944

5333

1945

5667

1946

7000

1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959

0.24

1960

7409

1.23

1961

4939

0.63

1962

6740

2.47

EIFAC/ICES WGEEL Report 2008

C OUNTRY

139

SE

SE

SE

SE

N.I RL

IE

1963

9077

0.43

1964

3137

0.21

1965

3801

0.90

1966

6183

1.40

1967

1899

0.30

1968

2525

1.50

1969

422

0.60

1970

3992

0.60

12,00

4157

0.50

1972

88,00

2905

1973

177,00

2524

1974

13,00

5859

1971

IE

0.80

1975

45.00

99,00

4637

0.40

1976

655.00

501,00

2920

0.40

1977

405.00

850,00

6443

0.10

1.00

1978

126.00

532,60

5034

0.30

1.30

1979

122.00

505,20

2089

0.50

6.70

1980

6.00

72,50

2486

1.40

4.50

1981

134.00

849.00

513,10

3023

2.90

2.10

1982

90.00

710.72

472,00

3854

4.50

3.10

1983

355.00

553.48

308,40

242

0.70

0.60

1984

26.00

175.39

20,70

1534

1.10

0.50

1985

54.00

304.64

211,50

557

0.50

1.09

1986

72.00

45.09

150,90

1848

0.90

0.95

1987

24.00

51.78

140,90

1683

2.40

1.61

1988

19.00

168.60

91,90

2647

3.00

0.15

1989

34.00

183.95

32,70

1568

1.80

0.03

1990

186.03

42,10

2293

2.40

0.47

1991

0.001

138.14

0,40

677

0.50

0.09

1992

0.003

282.97

70,30

978

1.40

0.03

1993

0.007

373.94

43,40

1525

1.80

0.02

1994

0.012

636.41

76,10

1249

4.50

0.29

1995

0.009

276.66

5,50

1403

2.40

0.40

1996

0.001

43.80

10,00

2668

1.00

0.33

1997

0.001

116.89

7,60

2533

1.09

2.12

1998

0.002

164.40

5,00

1283

0.74

0.28

1999

0.003

147.19

1,80

1345

1.06

0.02

2000

0.011

399.67

14,10

563

0.91

0.04

2001

0.001

31.89

1,80

315

0.70

0.00

2002

0.003

170.95

26,20

1092

0.11

0.18

2003

0.002

92.00

45,10

1210

0.69

0.38

2004

0.000

30.65

5,00

342

0.29

0.06

2005

0.002

110.44

25,80

852

0.84

0.04

2006

0.001

41.95

2,70

456

0.12

0.04

2007

0.000

102.40

2,10

445

0.19

0.05

2008

0.000

34.00

3,40

25

0.03

0.00

140

EIFAC/ICES WGEEL Report 2008

Table 1 Part 2 Recruitment dataseries of glass eels: UK, Denmark, Germany and Netherlands.
*HMRC = nett export data from Her Majesty’s Revenue and Customs (see UK Country report)
C OUNTRY

UK

DK

DE

NL

NL

NL

NL

NL

Year

Severn
(HMRC)*

Vidaa Ems

Lauwersoog DenOever Ĳmuiden Katwijk

Stellendam

Unit

t

Kg

Index

Index

Kg

Index

1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938

20.75

1939

46.68

1940

17.46

1941

14.90

1942

23.61

1943

15.77

1944

45.88

1945
1946

600

7.56

1947

1438

7.37

1948

1640

6.41

1949

1182

6.34

1950

875

8.23

1951

719

16.60

1952

1516

106.71

1953

3275

18.17

1954

5369

27.03

1955

4795

37.37

1956

4194

9.76

1957

1829

21.82

1958

2263

71.79

1959

4654

39.37

1960

6215

29.74

1961

2995

51.34

1962

4430

120.66

1963

5746

172.22

1964

5054

53.57

Index

Index

EIFAC/ICES WGEEL Report 2008

C OUNTRY

UK

141

DK

DE

NL

NL

NL

1965

1363

110.71

1966

1840

26.64

1967

1071

40.88

1968

2760

27.91

1969

1687

23.96

47.30
31.50

1970

NL

NL

683

54.59

1971

787.00

1684

24.12

1972

780.00

3894

43.24

1973

641.00

289

31.05

32.80

13

1974

464.00

4129

35.93

119.30

23

1975

888.00

1031

46.60

66.80

14

1976

828.00

4205

14.40

38.21

73.10

11

1977

91.00

2172

28.40

80.27

159.20

130.25

42

1978

335.00

2024

83.90

54.29

131.70

30.23

42

15
4

1979

40.10

220.00

2774

66.20

75.47

176.00

3.23

27

1980

32.80

220.00

3195

80.30

37.82

101.50

171.60

45

1981

226.00

962

55.10

32.09

113.90

31.65

47

1982

30.40

490.00

674

17.40

20.24

20.80

4.13

11

1983

6.20

662.00

92

15.10

13.58

15.60

2.10

14

1984

29.00

123.00

352

7.10

18.07

11.40

23.62

4

1985

18.60

13.00

260

25.20

18.28

1.00

6.67

9

1986

15.50

123.00

89

1.30

19.25

4.70

1987

17.70

341.00

8

52.00

7.46

7.70

14.00

10

1988

23.10

141.00

67

0.50

5.72

3.50

1989

13.50

9.00

13

12.10

3.95

1.60

1990

16.00

5.00

1991

7.80

1992

17.70

1993

20.90

1994

22.30

1995

99

5.00

4.71

4.70

52

6.30

1.44

2.00

6

7.30

3.79

20

20.80

3.80

52

22.50

40

11.60

6
8
3.67

4
11

5.10

2

2.50

8.20

10

1.60

13.50

5

5.98

3.60

15.10

3

8.37

13.10

27.10

3

4.00

25.40

0

1996

23.90

20

34.40

9.49

1997

16.20

5

20.90

15.24

1.30

10.90

3

1998

20.10

4

9.90

2.73

1.20

38.80

1

1999

18.00

3

15.10

4.23

1.60

101.30

1

2000

7.60

4

6.60

2.06

1.50

8.80

6

2001

5.40

1

1.70

0.68

0.40

8.10

1

2002

5.10

3.40

1.36

0.05

9.80

4

2003

10.00

1.20

1.84

0.00

11.80

0

2004

14.40

1.70

1.87

0.11

4.50

0.3

2005

8.80

0.90

1.02

0.00

4.40

0.2

2006

8.20

1.39

0.43

0.07

1.33

0

2007

1.13

1.35

0.09

24.77

0

2008

2.54

0.36

0.06

4.31

0

142

EIFAC/ICES WGEEL Report 2008

Table 1 Part 3 Recruitment dataseries of glass eels: Belgium and France.
C OUNTRY

BE

FR

FR

FR

FR

FR

FR

FR

Year

Ijzer

Vilaine

Loire

Sèvres
Gironde
Gironde Adour
Niortaise (cpue) (cpue)

Adour
(cpue)

Unit

Kg

Kg

Kg

cpue

cpue

cpue

1923

t

t
46.0

1924

65.0

1925

70.0

1926

90.0

18.7

1927

65.0

34.1

1928

102.0

22.4

1929
1930

22.5
1.0

28.2

1931

26.9

1932

31.1

1933

13.5

1934

90.0

13.4

1935

150.0

19.7

1936

30.0

1937

7.0

1938

15.0

1939

17.0

1940

27.0

1941

21.0

1942
1943
1944

10.0

1945

66.0

1946

43.0

1947

178.0

1948

197.0

1949

193.0

1950

86.0

1951

166.0

1952

121.0

1953

91.0

1954

86.0

1955

181.0

1956

187.0

1957

168.0

1958

230.0

1959

174.0

1960

411.0

1961

334.0

1962

185.0

30.00

217.8

1963

116.0

72.00

363.0

32.2

EIFAC/ICES WGEEL Report 2008

C OUNTRY

BE

143

FR

FR

FR

FR

FR

FR

FR

1964

3.70

142.0

1965

115.00

134.0

17.00

352.5

1966

385.00

253.0

13.00

27.6

1967

575.00

258.0

8.00

162.8

1968

553.50

712.0

15.00

284.2

1969

445.00

225.0

14.00

36.6

1970

795.00

453.0

15.00

203.8

1971

399.00

44

330.0

12.00

47.1

1972

556.50

38

311.0

11.00

69.0

1973

354.00

78

292.0

8.50

20.0

1974

946.00

107

557.0

9.00

54.6

1975

274.00

44

497.0

8.50

44.1

1976

496.00

106

770.0

17.00

120.9

1977

472.00

52

677.0

15.00

121.6

1978

370.00

106

526.0

18.00

64.7

1979

530.00

209

642.0

17.50

19.7

73.2

1980

252.00

95

526.0

12.00

25.9

124.7

1981

90.00

57

303.0

9.00

20.0

84.9

1982

129.00

98

274.0

8.50

15.0

61.0

1983

25.00

69

260.0

6.00

13.6

66.7

1984

6.00

36

183.0

19.2

45.0

1985

15.00

41

154.0

9.6

27.0

1986

27.50

52.6

123.0

10.6

35.3

8.00

1.5

1987

36.50

41.2

145.0

14.0

44.6

9.50

3.3

1988

48.20

46.6

177.0

10.9

27.9

12.00

3.7

1989

9.10

36.7

87.0

7.2

45.9

9.00

4.1

1990

218.20

35.9

96.0

5.6

29.3

3.20

1.2

1991

13.00

15.35

36.0

7.7

38.4

1.50

0.7

1992

18.90

29.57

39.0

3.7

22.5

8.00

2.9

1993

11.80

31

91.0

8.2

42.4

5.50

2.4

1994

17.50

24

103.0

8.7

45.5

3.00

1.4

1995

1.50

29.7

133.0

8.2

43.5

7.50

2.6

1996

4.50

23.286

81.0

4.8

27.9

4.10

1.53

1997

9.80

22.85

71.0

6.5

49.3

4.60

1.6

1998

2.25

18.9

66.0

4.3

18.4

1.50

1.07

16

87.0

7.5

43.1

4.30

1.82

1999

2.40

2000

17.85

14.45

80.0

6.6

28.5

10.00

4.43

2001

0.70

8.46

33.0

1.9

8.2

2.00

0.49

2002

1.40

15.9

42.0

4.9

35.1

1.80

0.89

2003

0.54

9.37

53.0

2.7

9.6

0.60

0.31

2004

0.38

7.49

27.0

2.5

14.4

1.80

0.6

2005

0.79

7.36

17.0

17.2

3.20

1.13

2006

0.07

6.6

15.0

9.3

1.70

0.72

2007

2.21

7.7

21.0

8.0

1.40

0.66

2008

0.96

5.1

0.76

144

EIFAC/ICES WGEEL Report 2008

Table 1 Part 4 Recruitment dataseries of glass eel: Spain, Portugal and Italy.
C OUNTRY

ES

Year

Nalon

Unit

Kg

ES

Albufera
Kg

ES

ES/PT

IT

Minho

Minho

Tiber

Kg

Kg

t

A LL C OUNTRIES

Geo mean

1923
1924

44.74
58.58

1925
1926

69.37
77.02

1927
1928

89.04
64.77

1929
1930

55.96
39.00

1931
1932

13.00
33.24

1933
1934

106.35
154.02

1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950

35 000
48 000
45 000
30 000
40 000

171.46
186.74
237.53
277.85
224.47
240.02
237.68
193.96
165.03
175.47
161.63
158.41
181.24
186.83
201.97
217.48

1951
1952

14 529

212.26
226.69

1953
1954

8318
13 576

271.49
277.86

1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966

16 649
14 351
12 911
13 071
17 975
13 060
17 177
11 507
16 139
20 364
11 974
12 977

10 000
17 000
11 000
16 000
11 000
4000
6000
5000

261.82
294.95
291.36
298.88
315.73
375.14
400.34
359.92
346.25
342.57
302.23
295.73

1967

20 556

4000

324.68

EIFAC/ICES WGEEL Report 2008

145

Table 1 Part 4 cont. Recruitment dataseries of glass eel: Spain, Portugal and Italy.
C OUNTRY

ES

ES

ES

ES/PT

IT

Year

Nalon

Albufera

Minho

Minho

Tiber

Unit

Kg

Kg

Kg

Kg

t

A LL C OUNTRIES

Geo mean

1968

15 628

4000

321.77

1969

18 753

5000

291.24

1970

17 032

1000

284.07

1971

11 219

1000

253.16

1972

11 056

1000

256.52

1973

24 481

2000

1974

32 611

1000

1600

1650

1975

55 514

6000

5600

10 600

11.00

308.72

1976

37 661

5000

12 500

20 000

6.70

333.15

1977

59 918

21 600

36 600

5.90

359.93

1978

37 468

17 300

24 300

3.60

380.91

1979

42 110

15 400

28 400

8.40

371.26

1980

34 645

13 000

16 000

8.20

331.89

1981

26 295

1309

18 000

50 000

4.00

268.50

1982

21 837

640

9700

16 400

4.00

207.08

1983

22 541

2387

14 000

30 000

4.00

152.15

1984

12 839

2980

15 300

30 100

1.80

114.54

1985

13 544

402

6000

13 000

2.50

99.85

1986

23 536

2845

6539

16 039

0.20

92.32

1987

15 211

4255

5600

8200

7.40

79.45

1988

13 574

2513

7359

10 359

10.50

77.32

1989

9216

1321

3962

8462

5.50

63.56

1990

7117

1079

5743

8243

4.40

53.62

1991

10 259

831

2835

7335

0.80

48.83

1992

9673

299

4893

8493

0.60

52.91

1993

9900

302

2068

4968

0.50

50.79

1994

12 500

199

4701

10 001

0.50

54.08

1995

5900

271

6523

15 223

0.30

53.06

1996

3656

366

4283

8683

0.10

47.36

1997

3273

2878

7378

0.10

39.70

1998

3815

616

3812

7412

0.13

35.35

1999

1330

323

3812

6812

0.06

26.73

2000

1285

678

1519

2719

0.07

22.88

2001

1569

466

1427

2527

0.04

20.60

2002

1231

357

1755

3198

0.02

16.54

2003

506

233

1562

2376

0.02

14.20

2004

914

209

1331

2505

0.03

12.67

2005

836

320

3056

0.03

11.26

2006

615

1140

2045

0.00

7.91

2007

871

2008

165

250.27

750

285.49

7.41
5.78

146

EIFAC/ICES WGEEL Report 2008

Table 2 Part 1 Recruitment dataseries of yellow eel: Norway and Sweden.
C OUNTRY

NO

SE

SE

SE

SE

SE

SE

SE

Site

Imsa

Dalälven

Motala
Ström

Mörrumsån

Kävlingeån

Rönne
Å

Lagan

Göta
Älv

Unit

Numbers

Kg

Kg

Kg

Kg

Kg

Kg

Kg

1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953

530
5100
340
858
552
8700
2000
275

5728
6529
20
2828

45
5

49
445
0
0
147

210
324
241.5

14
283
773
406
280
273
120
43
305
2713
1543.5
2698

30
6
6
39
94
1
9.1
70

331
358
581
212
5
268
316
408
304
236
54
25
1
107
36
684
321
454
1248
1090
1143
767
441
495
604
420
281.8
379.1
802.4

1465
800
1555
455
1732
4551
5463
3893
4796
47
756
5753
2103
7238
6333
6338
1336
2537
8711
3879
4775
1894
2846
427
1848
2342
2636
2452
675
1702
1711
2947
1744
3662
5071

EIFAC/ICES WGEEL Report 2008

C OUNTRY

1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008

NO

42 945
48 615
28 518
12 181
2457
34 776
15 477
45 750
14 500
6640
3412
5145
3434
17 500
10 000
32 500
6250
4450
8625
525
1950
1000
5500
1750
3750
1625
1875
1375
3775
375
1550
350
100

147

SE

SE

508.5
550
215
161.5
336.7
612.6
289
303
289
445.4
158
276.4
157.5
331.8
265.5
333.7
149.8
242
87.6
159.7
49.5
148.7
44
176.4
35.1
34.3
71.2
6.8
0.5
112.1
33.9
69.7
28.4
73.5
69

1030
1871
429
826
172
1837
799
706
870
581
181.6
500
1423
283
184
135
2
1
51
46
58.5
224
24
353
266
112
7
31
22
12
48
15.2
26
201
169.5
35.2
21
2
108
89
650
32
14
8.1
5.5
85
270.1
177.5
338.8
19
42
24.8
25.9
30

9.6
6.6
71.9
7.6
17.5
7.5
14.7
15.5
12.4
8.2
58.6
126.1
26.4
30.9
35.1
18.4
30.5

SE

29
665.5
534.8
241.2
177.8
292.3
196.3
353.6
334.8
276.8
80.4
141.1
139.9
375
65.4
93.3
147.2
89.6
168.4
61.4
36.5
72.8
129
204.6
189.9
138.1
220.3
54.5
241
30
72.5
151
14
45.7
283
72.4
51.9
148
12.9
84.2
1
19.3
37.4
11
1.5
2.5
2.5
112.6

SE

SE

SE

SE

2.7
42.6
14.1
46.8
73.2
80
93
143.7
113
32.5
34.7
87.1
48.5
6.6
398
85.7
29.8
53.3
249
282.3
120.7
206.7
17.1
32.1
10.8
56.1
165.7
49.2
40
37.6
0.5

511.3
506.9
501.6
336.1
497.2
910.5
552.4
314.8
261.9
298.1
27.5
28
216.5
24.4
74.4
117.1
24.7
45.3
106.2
107.1
33.6
78.4
20.2
26.4
75.8
165.9
226
78
90.8
87.8
68
234.1
2.5
69.8
191.7
44
21.6
161.3
42.2
8.7
30.7
11.6
2.8
31.7
62.6
49.5
13
26.8
102
31.7
29
20.5
38.1
77
25

1031
2732
1622
1915
1675
1745
1605
269
873
1469
622
746
1232
493
849
1595
1046
842
810
1179
631
1230
798
256
873
190
906
40
882
113
325
77
143
168
475
598
149
264
404
64
377

8.6
84.8
4.9
32
12.5
25.8
4
2.9
13.5
19.4
15.3
22.2
5
34.5
19.3
9.7
248.3
3.4
94.4
76

10.4
24
4.2
1.8
27
9.1
2
0.1
0.1
4.45

277
180

693
266
125
105
0.04
>0
>0

148

EIFAC/ICES WGEEL Report 2008

Table 2 Part 2 Recruitment dataseries of yellow eel: Ireland, Denmark and Belgium.
C OUNTRY

Site
Unit

IE

DK

DK

BE

A LL C OUNTRIES

Meuse

Shannon
(Parteen)

Tange

Kg

Kg

Harte

GeO mean
(Lixhe dam )

Kg

Kg

1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912

431.01
417.75
375.37
656.92
544.76
522.12
565.16
747.03
328.77
556.39
2711.05
402.41
534.63

1913
1914

534.63
318.05

1915
1916

129.79
169.26

1917
1918

135.94
172.77

1919
1920

227.85
229.01

1921
1922

476.60
597.87

1923
1924

758.60
739.67

1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944

1118.82
1229.23
1042.09
958.06
1007.62
984.53
1034.89
1039.92
791.69
624.67
325.48
279.10
224.80
300.35
392.97
490.04
558.65
748.44
793.63
743.91

EIFAC/ICES WGEEL Report 2008

149

Table 2 Part 2 cont. Recruitment dataseries of yellow eel: Ireland, Denmark and Belgium.
C OUNTRY

IE

DK

DK

Shannon (Parteen) Tange

Harte

BE

A LL C OUNTRIES

Meuse
Site

GeO mean
(Lixhe dam )

Unit

Kg

Kg

Kg

Kg

1945

801.02

1946

606.09

1947

449.17

1948

399.33

1949

366.84

1950

454.81

1951

637.36

1952

679.34

1953

743.05

1954

783.63

1955

769.58

1956

656.79

1957

810.48

1958

692.09

1959

721.90

1960

730.62

1961

710.24

1962

492.69

1963

460.66

1964

443.88

1965

354.52

1966

333.47

1967

500

369.91

1968

200

285.38

1969

175

205.28

1970

235

213.55

1971

59

201.87

1972

170.90

1973

117

220.02

1974

212

229.87

1975

325

217.71

1976

91

196.31

1977

386

189.19

1978

334

164.10

1979

291

152.73

1980

93

522

133.34

1981

187

279

122.33

1982

257

239

108.26

1983

146

164

100.89

1984

84

172

100.00

150

EIFAC/ICES WGEEL Report 2008

C OUNTRY

IE

DK

DK

BE

A LL C OUNTRIES

1985

984

315

446

1986

1555

676

260

111.98

1987

984

145

105

120.01

1988

1265

252

253

115.23

1989

581

354

145

112.06

1990

970

367

101

97.76

1991

372

434

44

76.79

1992

464

53

40

1993

602

93

26

1994

125

312

35

1995

799

83

23

1996

95

56

6

1997

906

390

9

1998

255

29

18

3061

42.81

1999

701

346

15

4664

43.72

2000

389

87.9

18.9

3365

48.39

2001

3

239

11.4

2915

52.90

2002

677

278.2

17

1790

45.00

2003

873

260.2

9.6

1842

40.37

2004

320

246.1

8.7

423

33.64

2005

612

87.7

7.4

758

24.01

2006

467

122.5

6.8

559

14.48

2007

757

62

7

6619

11.84

2008

1236

103.08

5613

74.26
62.15
51.00

4240

50.31
46.37

2706

44.80

10.06

Table 3 Landings of European eel in Europe (tons). Data obtained from Country Reports 2008.
BE

DK

EE

FI

FR

DE

IE

IT

LV

LT

1945

NL

NO

PL

PT

ES

SE

2668

102

1664

3492

167

1512

1946

1

1947

10

8

4502

268

1910

1948

10

14

4799

293

1862

1949

11

21

3873

214

1899

14

29

4152

282

90

2188

1951

13

32

3661

312

102

1929

1952

14

39

3978

178

80

1598

1953

30

80

3157

371

98

2378

1954

24

147

2085

327

103

2106

1955

47

163

1651

451

106

2651

1956

26

131

1817

293

80

1533

1957

25

168

2509

430

115

2225

1958

27

149

2674

437

100

1751

1950

`

1959

84

30

155

3413

409

98

2789

1960

51

44

165

2999

430

95

1646

1961

48

50

139

2452

449

91

2066

1962

67

46

155

1443

356

95

1908

UK

EIFAC/ICES WGEEL Report 2008

BE

DK

EE

151

FI

FR

DE

IE

IT

LV

LT

NL

NO

PL

PT

ES

SE

UK

1963

55

64

260

1618

503

92

2071

1964

56

43

225

2068

440

76

2288

1965

56

41

125

2268

523

79

1802

566

1966

68

43

238

2339

510

80

1969

617

1967

92

46

153

2524

491

66

1617

570

1968

103

34

165

2209

569

57

1808

586

1969

302

2469

43

134

2389

522

0

1675

607

1970

238

2300

29

118

1111

422

43

1309

754

1971

255

2113

29

124

853

415

44

1391

844

1972

239

1997

25

126

857

422

1973

257

589

27

120

823

409

705

1974

224

2122

20

86

840

368

747

1975

226

2886

19

114

1000

407

44

1204

634

33

1212

725

0

25

1034

767

869

5

17

1399

764

1976

28

205

2596

24

88

1172

386

804

8

14

935

627

1977

63

214

2390

16

68

783

352

911

15

0

989

692

1978

77

163

2172

18

70

719

347

929

7

0

1076

825

1979

77

158

2354

21

57

530

374

1025

13

0

956

1206

1980

79

140

2198

9

45

664

387

1233

3

11

1112

1110

1981

39

131

2270

10

27

722

369

970

32

19

887

1139

1982

38

166

2025

12

28

842

385

939

7

16

1161

1189

1983

38

155

2013

9

23

937

324

896

18

14

1173

1136

1984

28

114

2050

12

27

691

310

846

19

11

1073

1257

1985

28

477

2135

18

29

679

352

1048

10

14

1140

1035

1986

28

2462

405

2134

19

32

721

272

947

13

12

943

926

1987

19

2720

359

2265

25

20

538

282

914

6

15

897

1006

1988

2816

364

2027

15

23

425

513

943

6

10

1162

1110

1989

2266

379

1243

13

21

526

313

813

8

0

952

1172

1990

2170

374

1088

13

19

472

336

768

5

4

942

1014

1991

1925

335

1097

14

16

573

323

670

7

0

1084

1058

1992

1585

322

1084

17

12

548

372

638

7

5

1180

915

1993

59

1736

250

782

19

10

293

340

568

9

5

1210

857

1994

47

1694

246

771

19

12

330

472

635

7

4

1553

1077

1832

242

1047

38

9

354

454

638

10

4

1205

1312

FR

DE

IT

LV

LT

NL

NO

PL

PT

ES

SE

UK

55

1562

220

953

24

9

300

353

632

6

6

1134

1246

1995

45
BE

DK

1996

EE

FI

IE

1997

797

59

1537

263

727

25

11

285

467

533

5

23

1382

1190

1998

597

44

1345

28

668

30

17

323

331

551

5

43

645

943

1999

717

65

1253

38

634

26

18

332

447

592

4

45

734

963

2000

628

67

1200

36

539

17

11

363

281

438

2

90

561

702

2001

707

65

1103

141

98

438

15

12

371

304

434

1

106

543

742

2002

609

50

130

123

105

19

13

353

311

371

2

80

633

650

2003

649

49

125

111

105

11

12

279

240

359

2

70

565

574

2004

546

39

117

136

382

11

16

245

237

330

2

71

551

634

2005

534

36

108

101

75

11

22

230

249

251

4

74

628

545

2006

595

33

87

133

56

8

293

217

2

39

670

408

152

EIFAC/ICES WGEEL Report 2008

2007

BE

DK

EE

43

537

31

FI

FR

DE

IE

317

114

IT

LV

LT

10

NL

NO

PL

PT

130

194

193

2

ES

SE

UK

568

427

Table 4 Landings of European eel in Europe (tons). Source: FAO.
BE

DK

EE

FI

FR

DE

IE

IT

LV

LT

NL

NO

PL

PT

ES

SE

UK

1950

4500

500

400

100

895

4200

300

700

100

2200

100

1951

4400

500

400

100

849

3700

300

700

100

1900

100

1952

3900

700

400

100

873

4000

200

900

200

1600

100

1953

4300

600

500

100

846

3100

400

900

200

2400

400

1954

3800

500

300

100

830

2100

300

800

200

2100

500

1955

4800

500

500

100

814

1700

500

1000

700

2600

700

1956

3700

500

400

100

1796

1800

300

900

800

1500

600

1957

3600

500

400

100

1776

2500

400

800

501

2200

600

1958

3300

600

400

100

1754

2800

400

1200

500

1800

600

1959

4000

900

500

100

2614

3400

400

700

600

2800

700

1960

4700

1300

400

100

2276

3000

400

1000

400

1600

800

1961

3900

1300

500

100

2134

2500

500

900

400

2100

800

1962

3900

1300

400

100

2589

1600

400

1000

801

1900

700

1963

4000

1400

2100

100

2939

1900

500

1000

1300

1900

700

1964

3300

1400

1900

100

2884

2500

400

1100

1800

2368

600

1965

3200

1700

1500

200

2524

2600

500

900

1400

1868

800

1966

3700

1300

1700

100

2357

2800

500

1000

1400

2070

1000

1967

3500

2000

1900

100

2286

3100

500

1100

1500

1667

600

1968

4300

2700

1800

100

2306

2700

600

1100

1400

1872

600

1969

3700

1900

1600

100

2418

2800

500

1100

1500

1773

600

1970

3400

3091

1600

200

3292

1500

400

1000

1100

1270

800

1971

3200

4521

1300

200

3408

1200

400

900

1100

1469

800

1972

3300

2600

1300

200

2893

1100

400

900

1500

1274

700

1973

3554

3937

1282

91

2910

1105

409

825

47

700

1213

800

1974

2870

2493

1285

67

2697

1029

368

891

42

1300

1030

817

1975

3293

1590

1398

79

2973

1213

407

917

44

570

1492

833

1976

2926

28

2959

1322

150

2677

1353

386

674

38

675

1023

694

1977

2381

63

1538

1317

108

2462

961

352

996

52

666

1084

742

1978

2379

77

2455

1162

76

2237

891

347

941

44

655

1162

877

1979

1860

77

3144

1164

110

2422

729

374

1007

25

460

1038

879

1980

2254

64

1921

1051

75

2264

877

387

910

32

344

1205

1053

1981

2229

31

1425

1033

94

2340

898

369

752

33

250

976

858

1982

2538

30

1469

1027

144

2087

1153

385

895

14

269

1250

1032

1983

2120

30

1856

1029

117

2076

1288

324

1103

11

188

1302

1113

1984

1855

24

2306

911

88

2361

723

310

1698

20

170

1161

957

1985

1601
BE

DK

EE

23

2228

866

87

1907

FI

FR

DE

IE

IT

LV

LT

688

352

1337

16

215

1211

781

NL

NO

PL

PT

ES

SE

UK

42

226

922

997

297

703

939

1986

1643

25

2687

887

87

1928

685

272

1134

1987

1273

1

1978

731

230

2076

359

282

962

EIFAC/ICES WGEEL Report 2008

153

BE

DK

EE

FI

FR

DE

IE

IT

LV

LT

NL

NO

PL

PT

ES

SE

UK

1988

<0.5

1784

11

1

2109

746

215

2165

3

94

433

513

1087

224

965

715

1989

30

1696

32

1

1672

678

400

1301

8

81

332

313

1109

119

952

1075

1990

30

1674

74

1674

978

256

1199

120

209

336

913

28

104

941

1039

1991

125

1464

3

1450

1010

245

1106

16

160

323

1097

44

85

1085

822

1992

125

1448

9

1164

1026

234

1662

19

12

89

372

1095

52

97

1180

782

1993

125

1081

59

864

1027

260

1307

18

10

419

340

1116

77

1144

752

1994

125

1200

54

607

585

300

986

39

12

358

472

1090

80

1298

873

1995

125

904

38

320

584

400

886

28

10

433

454

627

68

1100

808

1996

125

735

54

22

403

696

400

883

26

12

336

353

639

68

1042

895

1997

125

796

56

22

1782

746

400

1010

29

11

316

497

489

72

1073

807

1998

125

600

44

22

449

717

400

682

27

17

344

363

454

23

645

741

1999

100

711

60

289

746

250

645

17

18

372

475

474

30

39

736

697

2000

100

620

67

399

686

250

549

15

11

351

281

429

29

70

561

796

2001

100

658

67

415

638

110

446

19

12

374

304

425

37

62

580

595

2002

569

55

402

636

104

402

11

13

373

311

361

36

93

634

571

2003

620

64

412

251

81

458

11

13

366

240

321

13

40

565

588

2004

534

47

321

243

119

387

12

16

331

237

270

11

57

568

504

2005

531

69

186

285

87

115

17

22

317

249

220

9

55

668

493

Stocking

•

Lithuania: the first stocking was in 1928–1939, when 3.2 million elvers were
released in the lakes. Since the 1960s, about 50 million elvers or young yel‐
low eels have been stocked.

•

Estonia: stocking on a national level.

•

France: no stocking on a national level.

•

Italy: historic stocking in considerable amounts in lagoons and lakes, but
no national recording.

•

Germany: No national database for eel stocking, but data available for
some river basins. Situation will improve next year, when all data become
available in the EMP’s. Stocking data for the Elbe RBD‐system 1950–1980
are restricted to about 30% of the total basin area.

•

Lithuania: stocking of glass eel on a national level.

•

Spain: no stocking on a national level.

•

Poland: stocking in the Vistula and Szczecin Lagoons on a national level.

•

Portugal: no stocking on a national level.

•

Ireland: no stocking on a national level. Upstream transport of glass eel
(elver) and young yellow (bootlace) eel on the Shannon and Erne‐see
Country Report.

Table 5 Stocking of glass eel. Numbers of glass eels (in millions) stocked in (eastern) Germany
(DE)*, Lithuania (LT), the Netherlands (NL), Sweden (SE), Poland (PL), Northern Ireland (N.Irl),
Belgium (BE), Estonia (EE), Finland (FI) and Latvia (LV).
* Values for Germany are for East Germany until 1990 and for East Germany and data from some west‐
ern German states in the River Elbe RBD since 1991.

154

EIFAC/ICES WGEEL Report 2008

DE

NL

SE

PL

N.I RL .

BE

EE

FI

LT

1927

LV

0.3

1928

0.1

1929

0.2

1930

0.0
0.0
0.0

1931

0.2

0.4

1932

0.2

0.0

1933

0.2

0.3

1934

0.3

0.0

1935

0.6

0.2

1936

0.3

0.0

1937

0.3

0.3

1938

0.4

0.0

1939

0.1

0.2

1940

0.0

1941

0.0

1942

0.0

1943

0.0

1944

0.0

1945

0.0

1946

7.3

0.0

1947

7.6

0.0

1948

1.9

0.0

1949

10.5

0.0

1950

0.0

5.1

0.0

1951

0.0

10.2

0.0

1952

0.0

16.9

17.6

0.0

1953

2.2

21.9

25.5

0.0

1954

0.0

10.5

26.6

0.0

1955

10.2

16.5

30.8

0.0

1956

4.8

23.1

21.0

1957

1.1

19.0

24.7

0.0

1958

5.7

16.9

35.0

0.0

1959

10.7

20.1

52.5

0.0

1960

13.7

21.1

64.4

0.6

1961

7.6

21.0

65.1

0.0

1962

14.1

19.8

61.6

0.9

2.0

1.9

1963

20.4

23.2

41.7

0.0

1.0

1.5

1964

11.7

20.0

39.2

0.2

2.4

0.9

1965

27.8

22.5

39.8

0.7

2.1

0.4

1966

21.9

8.9

69.0

0.0

1.1

0.7

0.0

1967

22.8

6.9

74.2

0.0

3.9

0.5

1.0

1968

25.2

17.0

16.6

1.4

2.8

3.0

3.7

1969

19.2

2.7

2.0

0.0

0.0

0.0

1970

27.5

19.0

23.5

1.0

2.8

1.8

1971

24.3

17.0

17.4

0.0

1.6

0.0

0.2

0.3

2.3

0.0

3.2
0.0

EIFAC/ICES WGEEL Report 2008

155

DE

NL

SE

PL

1972

31.5

16.1

21.5

1973

19.1

13.6

1974

23.7

24.4

1975

18.6

1976

N.I RL .

BE

EE

FI

LT

LV

0.1

0.3

1.6

61.9

0.0

1.4

0.0

71

1.8

1.8

0.0

14.4

70

0.0

2.2

0.0

31.5

18.0

68

2.6

1.0

0.6

1977

38.4

25.8

77

2.1

1978

39.0

27.7

73

2.7

1979

39.0

30.6

74.3

1980

39.7

24.8

1981

26.1

1982

30.6

1983
1984

1.4

0.5

2.7

0.0

0.0

0.75

0.0

52.9

1.3

1.8

0.0

22.3

60.5

2.7

3.0

1.8

17.2

64

3.0

4.6

0.0

25.2

14.1

25.1

2.5

3.7

1.5

31.5

16.6

49.2

4

1.8

0.0

0.0

1985

6.0

11.8

36.3

11

2.4

1.6

1.5

1986

23.8

10.5

54.4

17.8

2.5

2.6

0.0

1987

26.3

7.9

56.8

13.7

2.5

0.3

1988

26.6

8.4

15.9

6.3

0.0

2.2

1989

14.3

6.8

5.9

0.0

0.0

0.0

0.0

1990

16.7

6.1

0.7

8.6

0.0

0.0

0.1

0.0

1991

3.2

1.9

0.3

1.7

0.0

2.0

0.1

0.0

1992

6.5

3.5

0.3

13.8

2.4

2.5

0.1

0.0

1993

8.6

3.8

0.6

10.6

0.0

0.8

0.0

0.1

1994

9.5

6.2

1.7

12.2

2.3

0.5

1.9

0.1

0.1

0.0

1995

6.6

4.8

1.5

23.7

2.1

0.5

0.0

0.2

1.0

0.6

1996

0.8

1.8

2.4

2.8

0.1

0.5

1.4

0.1

0.4

0.0

1997

1.0

2.3

2.5

5.1

0.2

0.4

0.9

0.1

1998

0.4

2.5

2.1

2.5

0.1

0.0

0.5

0.1

0.1

0.0

1999

0.6

2.9

2.3

4.0

3.6

0.8

2.3

0.06

0.3

2000

0.3

2.8

1.4

3.1

0.5

0.0

1.1

0.06

0.0

2001

0.3

0.9

0.8

0.7

0.0

0.2

0.05

0.0

2002

0.3

1.6

1.7

0.0

3.0

0.0

0.06

0.23

2003

0.1

1.6

0.8

0.5

3.9

0.3

0.0

2004

0.2

0.3

1.3

2.3

1.2

0.0

0.06

0.0

2005

0.6

0.1

1.0

0.0

2.4

0.0

0.06

0.12

2006

0.0

0.6

1.1

0.0

1.0

0.3

0.05

0.006

2007

0.0

0.2

1.0

0.0

3.6

0.0

0.1

0.018

2008

0.0

0.0

0.0

1.3

0.3

0.1

0.0

3.7

0.0

0.0

0.4

0.0

Table 6 Stocking of young yellow (bootlace) eel. Numbers of young yellow eels (in millions)
stocked in (eastern) Germany (DE)*, Lithuania (LT), ´The Netherlands (NL), Sweden (SE), Den‐
mark (DK), Belgium (BE), Estonia (EE), Finland (FI) and Latvia (LV).
* Values for Germany are for East Germany until 1990 and for East Germany and data from some west‐
ern German states in the River Elbe RBD since 1991.

156

EIFAC/ICES WGEEL Report 2008

DE

NL

SE

DK

BE

EE

FI

1946

LT

LV

PL

0.0

1947

1.6

0.0

1948

2.0

0.0

1949

1.4

0.0

1950

0.9

1.6

0.0

1951

0.9

1.3

0.0

1952

0.6

1.2

0.0

1953

1.5

0.8

0.0

1954

1.1

0.7

0.0

1955

1.2

0.9

0.0

1956

1.3

0.7

0.0

1957

1.3

0.8

0.0

1958

1.9

0.8

0.0

1959

1.9

0.7

0.0

1960

0.8

0.4

0.0

1961

1.8

0.6

0.1

1.0

1962

0.8

0.4

0.1

0.7

1963

0.7

0.1

0.0

0.4

1964

0.8

0.3

0.1

0.4

1965

1.0

0.5

0.1

0.3

1966

1.3

1.1

0.1

0.0

1967

0.9

1.2

0.0

0.8

1968

1.4

1.0

0.0

0.0

1969

1.4

0.0

0.0

0.0

1970

0.7

0.2

0.0

0.4

1971

0.6

0.3

0.0

1972

1.9

0.4

0.0

1973

2.7

0.5

0.0

1974

2.4

0.5

0.0

1975

2.9

0.5

0.0

0.0

1976

2.4

0.5

0.0

0.3

1977

2.7

0.6

0.0

0.0

1978

3.3

0.8

0.0

0.0

1979

1.5

0.8

0.1

0.0

1980

1.0

1.0

0.0

1981

2.7

0.7

0.0

1982

2.3

0.7

0.3

0.1

1983

2.3

0.7

0.4

2.3

1984

1.7

0.7

0.0

0.3

1985

1.1

0.8

0.0

0.5

1986

0.4

0.7

0.0

0.2

1987

0.3

0.4

1.6

1988

0.2

0.3

0.8

1989

0.2

0.1

1990

0.4

0.0

0.8

0.2

0.1

0.0
0.2

0.8

0.1

0.4

0.0

0.7

3.5

0.0

1.0

EIFAC/ICES WGEEL Report 2008

157

DE

NL

SE

DK

1991

0.5

0.0

0.9

3.1

1992

0.4

0.0

1.1

3.9

1993

0.7

0.2

1.0

4.0

0.2

1994

0.8

0.0

1.0

7.4

0.1

1995

0.8

0.0

0.9

8.4

0.1

1996

1.1

0.2

1.1

4.6

0.1

0.0

0.5

1997

2.2

0.4

1.1

2.5

0.1

0.0

1.1

1998

1.7

0.6

0.9

3.0

0.1

0.1

0.0

0.6

1999

2.4

1.2

1.0

4.1

0.04

0.1

0.0

0.5

2000

3.3

1.0

0.7

3.8

0.003

0.0

0.8

2001

2.4

0.1

0.4

1.7

0.004

0.4

0.0

0.6

2002

2.4

0.1

0.3

2.4

0.008

0.4

0.2

0.6

2003

2.6

0.1

0.3

2.2

0.005

0.5

2004

2.2

0.1

0.2

0.8

0.009

0.4

2005

2.1

0.1

0.3

0.008

0.4

0.7

2006

5.5

0.0

1.6

0.4

1.1

2007

4.7

0.0

0.8

0.3

0.9

0.8

0.2

1.0

2008

0.2

BE

EE

FI

LT

LV

PL

0.0

0.1

0.0

0.1

0.0
0.1
0.2

0.0

0.1

0.0

0.5
0.1

0.5

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EIFAC/ICES WGEEL Report 2008

Annex 4 – The use of genetics in the management of European eel
A working paper presented to the WGEEL by: Gregory Maes, Lorenzo Zane and Filip
Volckaert.
Note: This working paper was used by the WGEEL to inform its discussions within
the various subgroups and reviewed text is included in the relevant chapters. The
whole document is annexed here for reference, but may not reflect the views of the
Working Group.
Introduction
The life history of the catadromous European eel (Anguilla anguilla L.) depends on
oceanic conditions; maturation, migration, spawning, larval transport and recruit‐
ment dynamics are completed in the open ocean (Knights, 2003; Tesch, 2003; Van
Ginneken and Maes, 2005; Kettle and Haines, 2006). Despite the biological impor‐
tance of the marine phase (Knights, 2003) to date most research has focused on the
fresh‐water phase of the life history. European eels have several life‐history character‐
istics that make them particularly vulnerable to overexploitation: they are long‐lived,
are large, mature late, produce all their offspring at once, are subject to heavy mortal‐
ity, and migrate long distances, right across the Atlantic. There is significant interna‐
tional trade demand for the species, both for live glass eels (from Europe to Asia) and
the highly valued meat of adults. Given that poaching and the illegal trade are of ma‐
jor concern, as indicated by several reports, a better regulation of international trade
is necessary. In addition, the decline may be exacerbated by other anthropogenic fac‐
tors such as fresh‐water and coastal habitat loss, pollution, parasitism, climate
change, change in ocean currents, and blocking of inland migration routes (Dekker,
2003; Knights, 2003). A synergy between all these factors seems the most likely cause
of the declines (Wirth and Bernatchez, 2003). All these factors have contributed to
some extent that the European eel is beyond safe biological limits (Dekker, 2003), and
recruitment is at a historical minimum (1% of the 1960 recruitment level). Many ques‐
tions on the basic biology eel remain unanswered. For example, genetic data may
help assess species integrity within the North Atlantic, evaluate the number of ge‐
netic stocks of the European eel, clarify the spatio‐temporal stability of genetic
structure, estimate the population sizes, define the influences of oceanic conditions
on genetic variability, and evaluate the effect of population decline on genetic
variability, the origin of biological material (tracing) and the overall fitness of eels.
The European Commission recently produced a community action plan for the re‐
covery of the European eel stock , which aims to strengthen the return rate of adult
eels to the Sargasso Sea and includes the development of eel management plans
(EMP) (CEC, 2007). Further, the European eel has been added recently to Appendix II
of CITES, implying drastic restrictions on trading. A number of restorative eel man‐
agement responses are envisaged including; 1) assessing and reducing the impact of
the fishery, 2) monitoring recruitment, 3) preserving migration routes (removing mi‐
gration barriers), 3) the translocation of glass eel within the natural range of the spe‐
cies using glass eels from sources where there is still a demonstrable surplus and the
assessment of the impact of the restocking practice (preserving potential local popula‐
tions, disturbing homing behaviour, competition between local and introduced or‐
ganisms), 4) the stocking of eels sourced from aquaculture production (justified on
the basis that these are developed entirely on the basis of wild recruits), 5) assessing
anthropogenic influences (pollution, parasites), and estimating the spawning popula‐
tion size (CEC, 2005; ICES, 2006).

EIFAC/ICES WGEEL Report 2008

159

When considering the use of genetics to complement these measures, immediately
the need arises to assess the spatio‐temporal population genetic structure at spawn‐
ing grounds (in the Sargasso Sea), to analyse the census population size (Nc) and to
determine the relationship between historical and current effective population
sizes (Ne), to analyse genetic markers located in functional regions to unveil possi‐
ble adaptive variation under natural and anthropogenic conditions, and to gain un‐
derstanding of molecular mechanisms involved in important traits for aquaculture
and artificial reproduction. Knowledge of population structuring will provide in‐
sights on the appropriateness of trans‐locating eels between river basins and between
regions such as between the Mediterranean and the Atlantic or even the North Sea
and the Baltic. To transfer eels between genetically different populations maybe
counter productive to the long‐term health of the resource. To protect the species, it is
important to maintain intraspecific genetic diversity, to develop sound restocking
programmes for broodstock (wild spawning stock) enhancement (avoiding the risk
to introduce genetic depauperate individuals), and to help realize profitable artifi‐
cial breeding. The present text synthesizes the most recent genetic knowledge of the
European eel and provides an overview of possible better use of genetics in future
management decisions on this declining species.
Genetic structure of the European eel populations
The European eel has been studied for more than 100 years, and hypotheses concern‐
ing its population structure have been tested using novel techniques each time they
appeared. The most recent genetic information has answered several evolutionary
challenges along the life cycle of the European eel (Figure 1). Many factors of its
catadromous life strategy increase the chance of panmixia, such as the variable age‐
at‐maturity, the highly mixed spawning cohorts, the protracted spawning migration,
the sex‐biased latitudinal distribution, and the unpredictability of oceanic conditions.
Historically, early population genetic studies, based on differences in transferrins and
liver esterases, resulted in claims that European eel populations differed between
continental European locations (Drilhon et al., 1966, 1967; Pantelouris et al., 1970),
suggesting a southeastern Mediterranean reproductive area. Later allozymatic stud‐
ies failed to detect obvious spatial genetic differentiation (de Ligny and Pantelouris,
1973; Comparini et al., 1977; Comparini and Rodinò, 1980; Yahyaoui et al., 1983). Mi‐
tochondrial DNA initially provided only limited insight into the geographical parti‐
tioning of genetic variability in the European eel, suggesting a single common gene
pool (Lintas et al., 1998). This commonly accepted view of a panmictic genetic popula‐
tion structure, based on oceanographic (Sinclair, 1988; Tesch, 2003) and genetic fea‐
tures, was, however, recently challenged by three independent studies (Daemen et al.,
2001; Wirth and Bernatchez, 2001; Maes and Volckaert, 2002). Wirth and Bernatchez,
2001 and Maes and Volckaert, 2002 detected a relationship between genetic and geo‐
graphic distance (the so‐called Isolation‐By‐Distance, IBD), suggesting a subtle spatio‐
temporal separation of spawning populations, with some degree of gene flow. Hy‐
drodynamics, causing differential distribution of eel larvae, have also been suggested
to explain partly the observed clinal genetic variation (Kettle and Haines, 2006).
However, the unstable genetic architecture of European eel populations over time
may be linked to oceanic factors (Dannewitz et al., 2005). Neutral genetic markers are
generally able to discriminate between populations with a gene flow of less than 1%.
Hence, a lack of structure does not mean that there is no structure, but prompt for the
use of more discriminatory markers to detect potential structuring.
Most recently, Maes and Volckaert, 2007 wrote a comprehensive review on the popu‐
lation genetics of the European eel, which should be consulted for a more detailed
synthesis of the most recent research. In this review, the suggestion that the eel be

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managed as a catadromous species (including the crucial marine phase) is a signifi‐
cant insight on how the eel should be viewed in terms of its likely population organi‐
zation, at least from the genetic perspective. The eel in fact, because of its assumed
reproductive biology i.e. a prolonged spawning period, variance in age‐at‐maturity,
high variability in parental contribution and reproductive success, might be expected
to exhibit a high level of genetic variability, high exchange between populations
(gene flow) resulting in low genetic differentiation (low genetic signal/noise ratio)
and a high genetic population size, all of which are characteristics observed in other
typically marine pelagic species with high migration potential such as cod, Gadus
morhua (Nielsen et al., 2006) and herring, Clupea harengus (Bekkevold et al., 2005).
Also, as has been observed by Rousset, 1997, widely distributed species are rarely
fully panmictic (mating randomly), but are commonly divided into subgroups in a
pattern that can be described by one of the classical population models, such as the
island model, stepping‐stone model or Isolation‐by‐Distance (IBD) model. In popula‐
tions composed of a mixture of individuals reproducing at different times within a
reproductive season, temporal differentiation can supplement possible geographical
partitioning. Under these conditions, gene flow is expected to be limited between
early and late reproducers, possibly creating a pattern of Isolation‐by‐Time (IBT)
(Hendry and Day, 2005; Maes et al., 2006). Additionally, temporal heterogeneity in
the genetic composition of recruits is likely to result from a large variance in parental
reproductive success driven by the unpredictability of the marine environment
(Waples, 1998, Pujolar et al., 2006). Under the hypothesis of “sweepstakes reproduc‐
tive success” (Hedgecock, 1994), chance events determine which adults are successful
in each spawning event, attributing the variation in reproductive success of adults to
spatio‐temporal variation in oceanographic conditions, occurring within and among
seasons. Many marine species split their reproductive effort among several events
during a protractive spawning season, to maximize their reproductive success
(Hutchings and Myers, 1993; Maes et al., 2006).
Ocean currents and diffusive processes, resulting in a differential distribution of eel
larvae, have recently been suggested to explain this observed genetic structure (Kettle
and Haines, 2006). Maes et al., 2006 detected a significant correlation between genetic
distance and temporal distance among recruitment waves indicative of Isolation by
Time. Yet, despite these glimpses of putative structuring, Dannewitz et al., 2005 still
concluded from their detailed investigations that European eels from the coasts of
Europe and Africa most probably belong to a single spatially homogeneous popula‐
tion. However the existence of discrete and stable spawning aggregations is not com‐
pletely unrealistic. In explaining the high incidence of American and European eel
(Anguilla rostrata and Anguilla anguilla) hybrids in Icelandic rivers, Albert et al., 2006
suggest that intermediate larval development times for the hybrids are plausible with
the effect that ocean currents will deliver the hybrids to rivers positioned in the mid‐
dle of the natural range. Larval development times would have to be adaptive (trans‐
porting American eels into American rivers and European eels into European and
African rivers) and therefore has to have some heritable basis. That American and
European eels are described as two distinct species in itself suggests that possibility
of structuring and maintenance of structuring over time, as it has been suggested that
the spawning grounds of both species overlap in space and time (McCleave, 1987). It
is also plausible that larvae and glass eel imprint during ocean transport and that this
allows homing of adult eel to natal spawning areas (Maes, 2005).
Identifying and sampling discrete reproductive aggregations in the spawning areas
will most effectively resolve the genetic structure of the European eel. This is a chal‐
lenge because European eels spawn in an area that is not well defined and very re‐
mote. Since Schmidt, 1923 identified concentrations of eel leptocephali in the Sargasso

EIFAC/ICES WGEEL Report 2008

161

Sea in the 1920s there has been little progress in locating eel spawning areas. How‐
ever it is likely that recent advances in physical oceanography (Kettle and Haines,
2006) offer a reasonable opportunity of overcoming this deficit in the near future. In
addition, tagging and tracking of fish has progressed such that monitoring from feed‐
ing
to
spawning
ground
is
feasible.
An
international
project
(http://www.Galathea3.dk, Spring 2007) lead by Danish scientists has recovered ge‐
olocational pop up tags in the Sargasso Sea from adult eels previously tagged leaving
European rivers. Adult eels were tracked swimming to the spawning grounds for the
first time.
There is now sufficient evidence available to suggest that small but significant levels
of genetic structuring exist in European eel and that this diversity should be pro‐
tected:
•

Geographical clinal variation at enzymatic and neutral genetic markers be‐
tween recruiting glass eels and adults.

•

Large (yearly) and small (seasonal) scale temporal genetic differences be‐
tween spawning cohorts and recruiting glass eels.

•

Homing behaviour between North‐Atlantic eel species and even hybrid
individuals endemic to Icelandic waters. This points to the possibility of in‐
traspecific homing behaviour based on adaptive traits, instead of neutral
variation (see further).

•

Correlation between genetic variability and fitness traits in natural popula‐
tions, prompting for maintenance of genetic diversity for long‐term sur‐
vival of the entire species.

Within a precautionary principle framework, eel fisheries management should be
aware of the genetic structure suggested by recent studies and that management
strategies designed for recovering stocks should incorporate this possibility. Besides
the existence of these small‐scale level of genetic differentiation, many new initiatives
are ongoing to determine the long‐term genetic (effective) population size of eel, the
presence of functional/adaptive genetic diversity which is more relevant to changing
life‐history traits, the assessment of oceanic influences on larval survival and the
monitoring of individual responses to pollutants and parasites at the gene expression
level (see further).
Genetic research perspectives and management of the European eel
Earlier conclusions drawn from molecular studies are not only important for infer‐
ring the panmictic status of the eel, but also to preserve the genetic resources in
European eels and to define additional research priorities. For each priority, one can
define a specific management objective and the time frame during which changes or
reversal may be achieved (Table 1). It is obvious, for instance, that genetic diversity
may be lost rapidly (i.e. genetic erosion), and that it recovers very slowly within
populations (ICES, 2005). To assist with a sound management of European eel, future
genetic research may therefore focus on the conservation issues listed above. We pro‐
pose four major lines of research: assessment of the spawning population structure
and effective population size, inclusion of adaptive genetic variation in manage‐
ment plans, monitoring stress responses of eels under heavy anthropogenic pressure
(pollution, physical barriers and parasites) and improving artificial reproduction
through aquaculture genomics.

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Spawning population structure and size

The genetic structure of natural marine populations is best understood by identify‐
ing, sampling and analysing discrete reproductive aggregations (Waples, 1998). Our
knowledge of the spawning biology and migration routes of North Atlantic eels re‐
mains poor. Identifying the precise location of the spawning grounds, nurseries and
retention zones, along with a greater knowledge of the ecosystem where spawning
takes place would help management decisions considerably. To date no observations
have been made of adult eels in the Sargasso Sea, and their eggs have yet to be identi‐
fied there (Tesch, 2003). In the Pacific Ocean, based on the distribution of newly
hatched larvae, the spawning grounds of the Japanese eel have been reconfirmed by
genetic identification techniques (Tsukamoto, 2006). The continental populations con‐
stitute mixed feeding aggregations, complicating interpretation of patterns of genetic
structure (Dannewitz et al., 2005; Maes et al., 2006b; Pujolar et al., 2006). Sampling pu‐
tative populations on the continental shelf remains challenging, because of the con‐
founding effect of overlapping generations in adults and the site‐dependent age
structure. The most effective solution is to sample spawning eels and newly hatched
larvae across the Sargasso Sea, and to analyse them with a representative set of ge‐
netic markers. This would allow a reassessment of the spatial and temporal segrega‐
tion found so far and a rough calculation of the size of the spawning stock (Ne),
which still poses problems in marine fish. The development of precise, performing
genetic markers (such as SNPs) for application on highly degraded or old DNA,
would also provide new opportunities to compare present genetic patterns with the
patterns found some 100 years ago, based on the available larval samples of Schmidt,
1923. Importantly, as a consequence of the long restocking practices since the 1950s,
one can expect to see a homogenization of populations as a consequence of such
large‐scale translocations. To fully assess the effect of such translocations on the spe‐
cies level, it would be of interest to study the population structure before such major
translocations. This can be done by studying historical material from different Euro‐
pean sources from the mid‐century and comparing this pattern with the present one
at neutral and adaptive genetic markers (see later). Potential translocations of exotic
species in Europe (such as American eel or other less exploited eel species) for re‐
stocking is also an important issue, requiring up to date molecular identification
methods (Maes et al., 2006a). This problem is already of great importance in Asia
(Okamura et al., 2002; 2004). This would enable reliable tracing of the location and
species of origin of glass eels to be stocked.
Additionally, analysis of successive recruitment waves of European eels at sites with
year‐round recruitment would permit better understanding of the fine‐scale genetic
composition of glass eels and possibly pinpoint discrete spawning groups. A sharp
break or clinal pattern in relatedness and genetic differentiation may point to repro‐
ductively isolated aggregations (Maes et al., 2006b). In turn, stochastic variance in ge‐
netic composition might point to genetic patchiness, most likely under the influence
of annual and seasonal oceanic and climatological fluctuations (such as the North‐
Atlantic Oscillation; Knights, 2003; Friedland et al., 2007). These are thought to influ‐
ence the reproductive success of adults and the survival rate of larvae (Dekker, 2004;
Pujolar et al., 2006).
Accurately estimating the effective (genetic) population size (Ne) is another aim to
develop appropriate conservation strategies for eels. Ne predicts the rate of loss of
neutral genetic variation, the fixation rate of deleterious and favourable genetic vari‐
ants, and the rate of increase of inbreeding experienced by a population (Frankham et
al., 2002). Importantly, the Ne of a population is often several orders of magnitude
smaller than the census size (Nc) of the population, owing to unequal sex ratios, vari‐
ance in reproductive success and assortative mating. In marine fish (including eels)

EIFAC/ICES WGEEL Report 2008

163

Ne/Nc ratios may be expected to be more extreme than in other vertebrates because of
the high female fecundity that allows large census numbers to be obtained from
minimal numbers of breeding animals. Indirect methods for estimating Ne based on
molecular marker data have been developed to facilitate the inference of population
size, a very difficult task in marine fish with their lack of confined geographic
boundaries. When considering census population data of European eels, which indi‐
cate that the species is in serious decline over most of its range, it is essential to main‐
tain the spawning stock(s) at sufficiently large levels to ensure that effective
population sizes (Ne) as well as absolute population sizes (Nc) are optimized above
safe limits. European eels are long‐lived animals with reproductive ages roughly
ranging from 6 to 60 years (Tesch, 2003). To assess fully the temporal fluctuation in
population size (Ne), a long‐term analysis over several generations would be ideal.
An analysis of time‐series of historical material may increase the confidence in ge‐
netic estimates of population sizes. This should be done over a period as long as pos‐
sible to avoid the shifting‐baselines trap and the influence of overlapping generations
(Jorde and Ryman, 1995; Pauly, 2007). Realistically, the past 100 years should suffice,
because anthropogenic impact seems to have been greatest during that period (e.g.
endocrine disruption of spawning, overfishing, river management). Such an analysis
is now feasible thanks to the development of appropriate genetic techniques for an‐
cient DNA (Nielsen et al., 1997). For example, reliable estimates of population size
have been calculated for several fish species in a pre‐ and post‐industrial fishery
(Nielsen et al., 1997; Turner et al., 2002; Hauser et al., 2002). This knowledge is of great
importance in managing genetic variation, which is known to correlate with fitness
components in eel (Maes et al., 2005; Pujolar et al., 2005), and to define sound man‐
agement strategies.
Finally, the accurate interpretation and extrapolation of genetic results in eels re‐
quires an assessment of demographic scenarios through the development of new
population dynamics models. Such models have been the basis of fisheries research
for a long time, but here we ask for a joint assessment of demographic, hydrodynamic
and genetic parameters. Simulating a range of scenarios of reproductive success, mi‐
gration, survival, dispersal, age structure, maturation, fisheries pressure, and anthro‐
pogenic stress, preferably in an ecosystem perspective, looks a promising field.
Subsequent validation with empirical genetic and population dynamic data may con‐
firm the key factors.
Adaptive genetic variation for fisheries management

Heavy fishing and other anthropogenic influences, such as pollution and barriers of
migration, will not only impact the census size and the effective population size of
eels. Large declines in mature adults and recruiting individuals may trigger pheno‐
typic and adaptive genetic changes over generations of harvesting (Law, 2000). Such
phenotypic changes may include shifts in age‐ and size‐at‐maturity, less reproductive
success, greater mortality, changes in growth patterns of juveniles and adults, lower
fecundity and fertility, and changes in the sex ratio. If changes are heritable, this may
lead to almost irreversible genetic changes in life‐history traits (Law, 2000). Recent
recommendations from the EU (ICES, 2005) urge the assessment of fisheries and cli‐
matologically induced changes in declining marine stocks. A suitable strategy would
be a joint analysis of phenotypic and genetic data from contemporary populations,
compared with a reference situation (preferably before the population decrease).
There is clearly the need for reliable investigations of possible adaptive responses in
exploited marine organisms using archival material (Nielsen et al., 1997; Myers and
Worm, 2003). Although some evidence exists for phenotypic changes in the European
eel stock throughout the past 50 years (increasing adult size and decreasing glass eel

164

EIFAC/ICES WGEEL Report 2008

size since the 1960s), the evolutionary interpretation of overfishing is complicated by
there being too few age‐specific data, such as on age‐at‐maturation and growth rate
(Dekker, 2004). The long‐term genetic consequences of heavy fishing at the adaptive
molecular level, such as a decrease or shift in genetic variability at important func‐
tional genes related to maturity and growth, have not been assessed yet.
Further, the presence of only a small level of geographical genetic differentiation at
neutral microsatellites may lead to seriously underestimating quantitative and adap‐
tive differentiation between populations that might be present but not detectable
with these molecular markers. Indeed, apart from analysing neutral genetic variation
to assess the demographic independence and stability of fisheries stocks, knowledge
of geographic and temporal scales of adaptive genetic variation is crucial to species
conservation (Conover et al., 2006). Local adaptation is one of the most significant
components of intraspecific biodiversity, and the relevance of local adaptation to
fisheries management can be divided into two main issues, each differing in temporal
scale (ICES, 2006). First, local adaptations and population structure affect short‐term
demographics through effects on local recruitment patterns. Second, local adapta‐
tions and genetic heterogeneity affect long‐term population dynamics, with respect to
the connectivity among stocks/populations and their resilience and response to envi‐
ronmental change and harvesting. Local adaptation and the maintenance of biodiver‐
sity on the long term for sustainable fisheries management has yet to be implemented
into management strategies (ICES, 2006). Unfortunately, the understanding of these
phenomena is particularly difficult in marine organisms. The spatial and temporal
scale of adaptive divergence has been assumed to be very large. However, evidence
of geographically structured local adaptation in physiological, morphological and
functional genetic traits has become apparent (Giger et al., 2006; Nielsen et al., 2006).
The proportion of quantitative trait variation at the among‐population level (QST) has
repeatedly been demonstrated to be much higher than for neutral markers (FST)
(Cousyn et al., 2001; Conover et al., 2006). As both metrics of genetic variation are
poorly correlated, knowledge of neutral variation does not provide much information
about adaptive variation (McKay and Latta, 2002; see Conover et al., 2006, for a re‐
view). Given the important link between population genetics and dynamics, and the
strong potential for selection in species with large population sizes, the application of
both selected and neutral markers is obviously needed to resolve the stock structure
of marine fish effectively.
Genetic stress responses to pollution and parasitic load

Organic and inorganic pollutants can significantly reduce the quality and reproduc‐
tive capacity of vertebrates. This is especially the case in fish, where pollutants can
accumulate in the aquatic and sedimentary environment and in the benthic biota
(food). A benthic feeder can at the same time be seen as a good candidate to monitor
environmental quality of aquatic habitats, but at the same time suffers most from the
ability to bioaccumulate strongly all kinds of lipophilic substances, leading to the
possible destabilization or even extinction of the species. Additionally, parasitic infec‐
tion and pollution have been revealed to impair strongly the survival and reproduc‐
tive capacity of eels in experimental, resulting in an even stronger response to
pollution and vice‐versa (Palstra et al., 2006; 2007). However, although recent results
have displayed a strong correlation between pollutants and decrease body fat concen‐
tration (crucial to spawning migration and egg production), the influence of stressors
need a more in depth analysis at the population or stock level, to allow a reproduc‐
tive success assessment and sound management options (Belpaire, 2008). A thorough
analysis of pollutants and parasite stress level and better understanding of the organ‐

EIFAC/ICES WGEEL Report 2008

165

ismal response is crucially needed. This will enable parallel analysis of responses (or
not) and find out the synergetic fitness influences of pollution and parasite load.
Indeed, genetic diversity is the product of thousands of years of evolution, yet irre‐
versible losses may occur rapidly (Kenchington et al., 2003). It is essential to long‐term
survival, to adapt to climate change and anthropogenic pressure leading to the loss of
populations, with the likely subsequent loss of adaptive variation. For fisheries man‐
agement, the extent of genetic variability within populations is crucial in assessing
the quality of stocks, the potential productivity or growth of a population, and the
sustainability of fisheries. Pujolar et al., 2005 and Maes et al., 2005 assessed whether
the genetic background of European eels could be linked to two fitness traits, early
growth and pollutant bioaccumulation. Summarizing both studies here, there was
strong evidence of Heterozygosity‐Fitness‐Correlations (HFC), likely explained either
by an effect of direct overdominance at functional markers. The positive consequence
of the catadromous life history of eels is that locally polluted rivers will only have a
low impact on the entire population, because of the lack of spatial genetic structure at
a local level. Nevertheless, selection during each generation will erode local genetic
variability differentially, slowly reducing overall genetic variability. Differential se‐
lective pressures might induce variation between spawning cohorts in time and
space, possibly increasing the temporal differentiation pattern described by Maes et
al., 2006b and Pujolar et al., 2006.
Recently, it became possible to reliably quantify the gene and protein expression lev‐
els during exposure to pollutants and parasites, allowing the early detection of de‐
creased fitness and survival. Such knowledge would provide the chance for early
management actions before major mortality events in natural populations and pro‐
vide a long‐term assessment of success rates of conservation measures. Using suffi‐
cient background information on the identity and concentration of pollutant, this
approach can yield better insights into the factors influence the recently observed de‐
crease in fat content, a crucial measure for eels’ fitness to reach the Sargasso Sea.
Artificial reproduction and aquaculture genomics

Current fishing pressure on European eels could be decreased considerably if artifi‐
cial reproduction were possible (but see Palstra et al., 2005 and references therein).
Despite numerous attempts over the past 30 years, it remains impossible to produce
economically profitable quantities of eels in aquaculture. Until now, naturally recruit‐
ing glass eels are caught and grown in tanks for later consumption. Additionally, eel
aquaculture individuals are often used for restocking purposes, with the aim of res‐
cuing depleted rivers and lakes. However, the fitness consequences of this practice
remains to be thoroughly studied, as the fast growers and most fit individuals are
first sold for food consumption and the remaining (most likely less fit) individuals
are sold for restocking. No study has ever monitored life‐long fitness of such indi‐
viduals, an important point considering the link between genetic variability and fit‐
ness in eel and other organisms such as salmonids (Pujolar et al., 2005; McGinnity et
al., 2003).
Recently, methodologies developed to produce eel larvae of A. japonica have been
tested in Europe on A. anguilla resulting in fertilized eggs, embryonic development,
and occasional hatching (Palstra et al., 2005; Kagawa et al., 2005). Success, however,
remains low, calling for further study of the husbandry of eels, and of reproductive
and general eel biology. Original insights on physiology and endocrinology may be
expected from advanced genomic tools. For instance, Miyahara et al., 2000 produced
196 Expressed Sequence Tags (ESTs) from a spleen library of Japanese eels, and Kalu‐
jnaia et al., 2007 was able to identify, through subtractive hybridization and micro‐

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arrays, a large number of genes down‐ and unregulated during osmoregulation in
gill, kidney, and intestinal tissue. As new genetic tools become available in related
anguillids (e.g. Japanese eel; Nomura et al., 2006) and related genome information rich
species, promising insights in functional and comparative genomics are expected in
the near future. EST sequencing and linkage maps may be other feasible genomic ap‐
proaches, representing the first steps toward identifying important genes and Quanti‐
tative Trait Loci (QTL), the basis for Marker Assisted Selection. Although larvae of
Japanese eel have only been bred with great effort, Nomura et al., 2006 have managed
to prepare a low‐density linkage map based on 43 microsatellite markers, and many
more are being developed (K. Nomura, pers. comm.). Given the numerous genetic
markers known to cross‐amplify between Anguilla species (Maes et al., 2006a), once
progeny become available for European eels, reliable paternity screening, gene ex‐
pression and microarray analyses and a linkage map become realistic goals. Quantita‐
tive traits such as growth rate, food conversion, postponed maturity, stress tolerance,
and parasite resistance strongly correlate with the possibilities of artificial rearing.
One long‐term issue where QTL may be of great help is in the management of feed
supply. Currently, wild‐caught fishmeal is an important ingredient of dry feeding
pellets, but it is expected to shift to a proportionally larger vegetarian diet.
Genetic implications and recommendations for the Eel Management Plan
The importance of maintaining genetic diversity can be divided into a short‐term im‐
pact (in the order of few generations), by avoiding inbreeding and fitness decrease
(population survival) and a long‐term impact (over decades or even centuries), by
conferring the possibility to adapt to changing conditions (species survival). Genetic
data may help to assess species integrity within the North Atlantic, evaluate the
genetic stock structure of the European eel, clarify the spatio‐temporal stability of
the genetic structure, define the influences of oceanic conditions on genetic vari‐
ability, monitor and guide the stocking policy in Europe, and evaluate the effect of
population decline and habitat degradation on genetic variability and the overall
fitness of eels. For the current ToRs genetic considerations can be focused on the is‐
sues of restocking policies and eel quality assessment.
Genetic consequences of stocking practices
Stocking of glass eels has been defined as a practice to increase the population abun‐
dance of European eel. Although an immediate effect on populations can been seen in
an early phase, the long‐term success of this practices has not been assessed yet, nei‐
ther the genetic consequences. Stocking should be performed carefully and with
knowledge of potential negative implications on eel populations. Importantly, stock‐
ing should not been seen as the only solution for stock recovery, as the fishing
pressure may dramatically increase at source locations for glass eels and later spawn‐
ing success of stocked individuals is not at all guaranteed. To supplement river popu‐
lations impacted by migration barriers, hydropower, pollution, pathogens, a standard
strategy to catch glass eels from the estuaries (or neighbouring sites) and transport
them upstream to repopulate low‐density habitats or surplus good habitat. Ideally,
high quality habitats should be chosen and rivers with the least anthropogenic im‐
pacts selected. There should be a long‐term plan to improve habitat in disturbed ba‐
sins over the full river basin. In areas with no recruitment, the origin of glass eels
should be the nearest from the target location. In areas with low recruitment, care
must be taken to reduce competition and to stock smaller individuals. Areas with
heterogeneous recruitment should focus on relocating recruits from neighbouring
rivers and not from distant sites.

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167

Below we list some important points to consider when planning restocking measures
and provide some advice for sustainable stocking.
Deciding on mass stocking practices to supplement populations, can lead to the rapid
introduction of non‐native genetic material. Monitoring the correct species identity
(tracing) is therefore crucial to preserve genetic integrity of the European eel. Exam‐
ples of this phenomenon have already been observed, mainly in Germany (Trautner
et al., 2006), prompting for up to date molecular identification methods for species
discrimination (Maes et al., 2006a). The European eel has been listed under CITES,
potentially leading to an increased import of other eel species. Such exotic eel intro‐
ductions have been a major problem in Asia, where European eels were introduced to
supplement Japanese eel stocks (Okamura et al., 2002; 2004).
Aquaculture glass eels (grown from glass eels to 10 cm elvers) are often used for
stocking purposes. Although at first sight no significant problem is expected from
the genetic diversity point of view (glass eels are natural recruits), fitness conse‐
quences could be higher than expected. Indeed, keeping glass eels too long in such
facilities will adapt them to aquaculture conditions (such as artificial food and tem‐
perature regimes), and will lower their competitiveness and fitness in the natural en‐
vironment. Second, a common practice in aquaculture facilities is to deliberately
infect new glass eels with the highly virulent Herpes virus, to decrease later mortal‐
ity during grow‐out. As such, after a large initial mortality, stocked eels are in many
cases infected with Herpes (up to 50%) and can infect natural populations. Addition‐
ally, such practices create already a high selective pressure on glass eels, reducing
total genetic diversity and directionally selecting at the functional level for specific
disease resistance genes (such as MHC). This has been demonstrated to have a very
detrimental effect in salmonids when such individuals are released in the wild, as a
consequence of a lower fitness for natural pathogens. Further, large restocked indi‐
viduals might cannibalise local recruits, which are much younger. Stocking should
be performed at well‐chosen moments, namely at the end of the natural recruitment
season. Additionally, attention should be paid that stocked individuals are not only
composed of the slow growers of aquaculture, which have been demonstrated to ex‐
hibit a lower functional genetic diversity and could demonstrate lower survival rates
under pollution stress (lower fitness). Additionally, using slow growing and small
individuals for stocking can significantly bias the sex‐ratio of stocked fish, inducing
a non‐natural distribution of sexes in stocked systems. We advise to perform experi‐
ments on competitiveness, survival and reproductive capacity of stocked glass eels,
besides the marking of stocked individuals and their recapture at sexual maturity.
At the population level, stocking practices can have major consequences on the in‐
traspecific biodiversity, as a consequence of the mixing of genetically differentiated
populations. Although no stable geographical differentiation could be detected using
past research efforts (Wirth and Bernatchez, 2001; Dannewitz et al., 2005; Maes et al.,
2006), as a consequence of the long restocking practices since the 1950s, one can ex‐
pect to contribute to a homogenization of populations as a consequence of massive
translocations. Indeed, the presence of only a small level of geographical genetic dif‐
ferentiation at neutral genetic markers may lead to seriously underestimating quanti‐
tative and adaptive differentiation between populations. From recent studies on
marine fish populations we know that adaptive differences might be present but not
detectable with the current molecular markers. Indeed, apart from analysing neutral
genetic variation to assess the demographic independence and stability of fisheries
stocks, knowledge of geographic and temporal scales of adaptive genetic variation is
crucial to species conservation (Conover et al., 2006; Maes and Volckaert, 2007). For
eel, no assessment has been made of the functional diversity yet, although work is in

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progress to contrast data on neutral and adaptive markers (Maes, Zane, pers. comm.),
besides novel data on differing life‐history traits (Feunteun, pers. comm.). If distinct
populations exist, the introduction of genetically different glass eels can potentially
break up any existing adaptation in local stocks and have major fitness consequences
on life‐history traits, such as migration duration and timing, temperature resistance
and size at maturation sizes. The homogenization of these traits can lead to a decrease
in diversity and the loss of important traits for survival. However, until results are
available (within 1–2 years) we can only advise on the following stocking strategies,
depending on the natural recruitment level.
Regions with no recruitment and very low escapement: Preserve natural recruits (if
any) and escapees, while stocking glass eels in high quality habitats originating in the
same main hydrographical region (Northern Europe, West Atlantic, Southern
Europe, Mediterranean).
Regions with low recruitment: Preserve natural recruits and escapees, while prefera‐
bly stocking glass eels from estuaries or neighbouring river basins in high quality
upstream habitats.
Regions with high recruitment: care should be taken not to overfish glass eels for
stocking purposes, as this will weaken the source region and deplete the rivers from
escapees.
On the other hand, if neither neutral nor adaptive differences can be detected in the
European eel, stocking practices may have a beneficial effect, as they would expand
the feeding habitat size of eels, and help recover the total population. The question
however remains, whether stocked individuals will find their way to the Sargasso Sea
and ultimately contribute to the spawning stock. The most important issue is then to
preserve the total genetic diversity to allow adaptation to a changing environment.
Keeping the highest level of biodiversity in phenotypic (quantitative) and genetic
traits is crucial to the survival of the entire species.
Lastly, the ongoing investigation of the historical genetic (neutral but especially
adaptive) structure and stability before the start of large‐scale stocking practices
(1950s) and the monitoring of the evolutionary consequences from 50 years of re‐
stocking will enable to fully assess the effect of such translocations on the species
level. This is being done by studying historical material (otoliths) from different
European sources in the mid‐twentieth century and by comparing this pattern with
today’s observations at neutral and adaptive genetic markers.
Quality assessment of spawners using genomic tools
Eel decline might depend not only on the quantity of adult eels leaving the continent
but also, if not mainly, upon their quality. Good quality spawners are those that suc‐
ceed in crossing the Atlantic Ocean and reproduce. Parasites, such as the exotic
swimbladder nematode Anguillicola crassus can impair eel viability by both increasing
continental mortality and affecting the swimming ability of adult eels. Organic and
inorganic pollutants may significantly reduce the quality and reproductive capacity
of vertebrates. This is especially the case in fish, where pollutants may accumulate in
the water and sediment and in the benthic biota (food). Additionally, infections and
pollution have been revealed to impair strongly the survival and reproductive capac‐
ity of eels in experimental trials, resulting in an even stronger response to pollution
and vice‐versa (Palstra et al., 2006; 2007). A thorough analysis of pollutants and
pathogen stress level and a better understanding of the organismal response (besides
measures of condition index) are missing. Pujolar et al., 2005 and Maes et al., 2005 as‐
sessed whether the genetic background of European eels could be linked to two fit‐

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169

ness traits, early growth and pollutant bioaccumulation. Summarizing both studies
here, there was strong evidence of a relation between genetic diversity and fitness
measures (also called Heterozygosity‐Fitness‐Correlations or HFCs). It might be ex‐
plained either by an effect of direct overdominance at functional markers. Recently, it
became possible to reliably quantify the gene and protein expression levels during
exposure to pollutants and parasites, allowing the early detection of decreased fitness
and survival. Such knowledge would provide the chance for early warning systems,
facilitating management actions before major mortality events in natural populations
and provide a long‐term assessment of success rates of conservation measures. Using
sufficient background information on the identity and concentration of pollutant, this
approach may yield better insights into the factors influencing the recently observed
decrease in fat content, a crucial measure for eels’ fitness to reach the Sargasso Sea.
The ongoing analyses of northern (Belgium) and Southern (Italy) eel populations for
their gene expression level and health status will allow adding a quality status tag on
silver eels, while identifying good quality habitat for preservation.
Recommendations
Using the current knowledge of the genetic structure, pollution and pathogens influ‐
ence on eel and the potential risks of using aquaculture eels for restocking, we draft
some conclusions, main recommendations for further research and management op‐
tions, and potential advice to be issued by ICES. Besides developing the control of
artificial reproduction, it is our opinion that an integrated analysis of phenotypic,
demographic and genetic data of contemporary and historical (otoliths) popula‐
tions would significantly increase our knowledge of human vs. natural impacts on
eel stocks the last century (genetic baseline). Additional research focus on the marine
part of its life cycle, including hydrodynamics, ecotoxicology, archived material,
and neutral vs. adaptive genetic variation, are the next steps in developing a global
management strategy. This should be integrated in a broader ecosystem perspective.
The consequences of earlier and future restocking practices needs more attention to
avoid weakening even more the species and disturbing the natural spawning cycle of
this species. In light of emerging information suggesting putative stock structure of
European eel it is recommended from the genetic viewpoint that glass eels, elvers and
other life‐history stages should not be trans‐located between distant river basins for
restocking purposes. However, given the need for rapid action and that stocking is
one of the actions proposed by the EC, the precautionary approach should still apply
in order to avoid imminent collapse of specific river stocks, where possible the trans‐
location should be done within geographically proximate areas e.g. within the Medi‐
terranean basin, the West Atlantic, the North Sea or the Baltic Sea. It is of crucial
importance to assess the success of this practice and to overview actions to be taken
along the complete life cycle of eels.
Finally, a thorough assessment of the success of such management options should be
done in 2012, a time frame where new results on potential adaptive differences be‐
tween eel stocks and loss of functional diversity the last 50 years will also be avail‐
able.

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Van Ginneken V., and Maes G. E. 2005 The European eel (Anguilla anguilla, Linnaeus), its life
cycle, evolution and reproduction: a literature review. Reviews in Fish Biology and Fisher‐
ies, 15: 367–398.
Waples, R. S. 1998. Separating the wheat from the chaff: patterns of genetic differentiation in
high gene flow species. Journal of Heredity, 89: 438–450.
Wirth, T., and Bernatchez, L. 2001. Genetic evidence against panmixia in the European eel. Na‐
ture, 409: 1037–1040.

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Wirth, T., and Bernatchez, L. 2003. Decline of North Atlantic eels: a fatal synergy? Proceedings
of the Royal Society of London Series B‐Biological Sciences, 270: 681–688.
Yahyaoui, A., Bruslé, J., and Pasteur, N. 1983. Etude du polymorphisme biochimique de deux
populations naturelles (Maroc Atlantique et Rousillon) de civelles et anguillettes dʹAnguil‐
la anguilla L. et de deux échantillons dʹélevages. IFREMER Actes de Colloques, 1: 373–390.

Figure 1. European eel: life cycle of the main recent evolutionary questions relevant to the man‐
agement of eels (Maes and Volckaert, 2007).

Joint EIFAC/ICES WGEEL REPORT 2008

175

Table 1. European eel management objectives related to the loss of genetic diversity (Maes and Volckaert, 2007).

C ONSIDERATION

1. Genetic integrity at the species level

E XAMPLE MANAGEMENT OBJECTIVE

1.1. Avoid species translocations for restocking or aquaculture

T IME - SCALE
( GENERATIONS )

1

1.2. Trace species identity of endangered fish (products)
2. Genetic diversity within and among populations

2.1. Maintain population size in river sheds

>100

2.2. Decrease glass eel fishery and export
2.3. Increase silver eel escapement to contribute to spawning stock
3. Population structure and relative abundance

3.1. Avoid large‐scale translocations within Europe and between continents

>100

3.2. Detect possible local adaptation between river basins
3.3. Maintain relative size of populations
4. Effective population size and demographic stability

5. Evolutionary potential

4.1. Maintain large number of individual populations

>10

4.2. Minimize environmental degradation (pollution, habitat fragmentation)

>10

4.3. Assess influence of parasites (e.g. Anguillicola) and pathogens
(e.g. virus infection ‐ EVEX) on reproductive potential

>10

5.1. Minimize fisheries‐induced selection

>10

5.2. Avoid directional adaptation to anthropogenic and environmental changes

>10

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Annex 5 – Country overview of contaminant and parasite/pathogens in
eel
Contaminant analyses: Overview by country
Belgium

Extensive information has already been provided in the WG Eel 2006 and WG 2007
reports. During WGEEL 2008 a considerable amount of new information has been
made available to the Working Group and to the EEQD (see the Belgian country re‐
port and Belpaire, 2008).
Canada

Concentrations of many contaminants in the North American environment were high
in the 1960s and 1970s, then decreased as bans and restrictions took effect. The St.
Lawrence River‐Great Lakes system receives a wide variety of pollutants, some of
which have lethal (Dutil et al., 1987; Castonguay et al., 1994a) or sublethal (Couillard
et al., 1997) effects on eels. Concentrations of most contaminants, including PCBs and
mirex, in eels migrating through the St. Lawrence Estuary fell in the 1980s (Hodson et
al., 1994). This trend presumably reflects decreased contaminant exposure, but does
not takes into account the presence of new contaminant (for example the brominated
compounds) and the increasing number of non native species in the Great Lakes wa‐
tershed that alter fish community composition and foodweb energy flow, leading to
subsequent change to pathways and fate of contaminants.
Recently, a 3‐year research project on the role of chemicals in the decline of the
American eel was initiated to evaluate if eels accumulate sufficient chemical con‐
taminants during their growth and maturation to cause embryo toxicity, and to esti‐
mate when contaminants might have affected eel. Under the leadership of Dr Peter V.
Hodson (Queen’s University), a team of university and government scientists, includ‐
ing colleagues in the US and Europe are collecting fresh and archived samples of eels
from reference and contaminated ecosystems. The eels are analysed for concentra‐
tions of chemicals known to be embryo‐toxic, such as chlorinated and brominated
organic compounds, selenium, and alkyl tin. The toxicity of extracted chemicals will
be assessed with a battery of tests using fish embryos and fish cells in culture.
Denmark

There are few surveys and mostly of older date. Recent data for PFAS and organotin‐
compounds in the aquatic environment extracted from report by Strand et al., 2007
and unpublished data from Århus Amt, 2003. (see Appendix. A in the Danish coun‐
try report).
Estonia

During last 20 years the feeding and the condition factor of eel in L. Võrtsjärv have
been studied. The data will be provided to the EEQD.
France

Some data on PCBs and heavy metals in yellow and glass eel were made available
from the Gironde and Adour basins, and will be included in the EEQD.

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177

Germany

Concentrations of pollutants/contaminants in the musculature of eels from the river
Elbe have been measured by the Elbe River Water Quality Board (ARGE ELBE) in
1999 and 2000 (e.g. ARGE ELBE 2000). Along the entire German length of the Elbe,
contaminant levels were measured in excess of the maximum allowable levels. This
was particularly evident for HCB (hexachlorobenzene) content. Occasionally, maxi‐
mum levels were also exceeded for other contaminants, e.g. DDT. The most recent
publication from the ARGE Elbe (ARGE ELBE 2008) provides data on concentrations
of contaminants for eels from the river Elbe from a location close to the border to the
Czech Republic in 2005 and 2006. Concentrations of mercury have remained rather
constant (around 0.25 mg/kg wet weight), whereas the values for cadmium demon‐
strated a decreasing tendency (<0.008 mg/kg w. w.). Several PCB’s had constant levels
or a slightly decreasing tendency. Clearly decreasing values were observed for HCB
(from 1.8 mg/kg Fat in 2001 to 0.56 mg/kg Fat in 2006). However, HCB‐concentrations
are still on a critical level.
The data are provided in detail for inclusion into the quality database. The reports
from the Elbe River Water Quality Board are available at www.arge‐elbe.de.
Concentrations of PCB’s and dioxins were clearly below the maximum allowable lev‐
els in eels from the Baltic Sea (Bladt, 2007, cited in Karl, 2008). Mean values were 7.4
ng/kg w. w. for dioxin/dl‐PCB.
Ireland

Some samples have been taken in 2005 and 2007 and these have been analysed for
contaminants (PCBs, dioxins, BFRs) and presence of Anguillicola (included in the
EEQD).
Italy

Only incidental samplings within specific research projects have been performed in
the past and examined contaminants loads, eel condition and fat levels. Some recent
data based on available information has been provided to the database. Some analy‐
ses for contaminants in relation to human or veterinary health have been monitored
by official sanitary or veterinary services, but no information is ever made available,
and it’s most likely that only scattered sporadic samplings have taken place.
Latvia

No contaminant analysis is undertaken.
Lithuania

No contaminant analysis in eel is currently undertaken; however analyses are per‐
formed for other species. Lithuania will propose to analyse contaminants and fat lev‐
els in eels in future.
Netherlands

There is a long dataseries for bioaccumulation of contaminants in eels is available
from the Netherlands, where a monitoring network for PCBs, OCPs and mercury in
eel is in place since the 1970s.
This year, no new information about contaminants in the Netherlands was provided.

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Norway

Data on PCBs and pesticides from 1996 and 2000 were provided during the WGEEL
2007 session for inclusion in the database.
An extensive set of data of contaminants in eels from 1970 onward from southern
Norway is available at the NIVA institute. Data will be incorporated in the database
as soon as possible.
Poland

In 2008 research on several factors influencing quality of eel was made in the Sea
Fisheries Institute in Gdynia. Samples of eel were collected during autumn 2007 and
spring 2008 in Vistula Lagoon and Szczecin Lagoon. Number and size of fish col‐
lected are in Table PL.H
In the laboratory chemical examinations were made on:
•

fat contents,

•

dioxins, furans and dl‐PCB’s

•

heavy metals: Cd, Pb, As, Cr, Ni, Hg.

Results of heavy metals and PCDD/F and dl‐PCB’s were compared to maximum al‐
lowable values obligatory in UE and described in Regulation (EC) 18881/2006 and
assessed to classes described by Belpaire and Goemans, 2007. The results were also
compared to maximal values given in FAO Fisheries Circular No 825, 1989.
Resulting data of those all examinations were supplied to ICES WGEEL database.
Fat contents

Values of fat contents ranged from 15,1% to 31,4% with mean 15,1% ±5,46. There was
observed slight tendency to increase fat contents with increase of eel length.
Heavy metals contents

It was found that presence of all heavy metals, of which contents in the food is lim‐
ited in EU countries, was much lower in eel tissue comparing to allowed levels given
in EU regulations.
The maximum contents of those metals in eel ranged from 2% (Cd) to 22,5% (Hg) of
allowed values. In case of Ca, Pb and Cr all samples were classified as Class I, accord‐
ing to As as Class II, and according to Ni and Hg as Class I or II.
PCB’s contents

It was found that according to majority of indicative congeners, all samples were of
class I or class II. According to sum of six indicative PCB’s six of seven samples were
qualified as class I. Comparing results to very restrictive German regulations it was
found that in none of samples allowed limits were not achieved.
Results of eel samples were also compared to samples from herring, sprat, flounder,
cod and salmon. Sum of seven indicative PCB’s expressed as μg/kg of tissue in case of
eel was comparable to those of salmon and higher in case of rest of species.
Chloroorganic pesticides

In case of HCB four of seven samples were classified as class I and three others as
class II. In case of ∑DDT four samples were classified as class I, two as class II and

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179

one as class IV. None of samples exceeded limits of ∑DDT 4 and HCB given in FAO
Fisheries Circular No 825, 1989.
Dioxin-like-PCB’s

In all samples the dominating congener among non‐orto PCB’s was congener penta‐
PCB 126, which demonstrated highest toxicity in that group, and dominating conge‐
ner among mono‐orto PCB’s was congener 118.
Dioxin/furans (PCDD/Fs)

In most of samples concentration of PCDF was twofold higher than PCDD concentra‐
tion, except sample no WTN1, where both concentrations were similar. In none of
samples was found exceeding of limits PCDD/F nor sum of PCDD/F and dl‐PCB’s.
In all samples highest share of total toxicity constituted non‐orto PCB’s and that share
was of 40–50% depending on sample.
Portugal

At national level several eco‐toxicological studies using eels from different catchment
areas have been published, e.g.: Aveiro lagoon (Ahmad et al., 2006; Pacheco and San‐
tos, 2001), Pateira de Fermentelos (Ahmad et al., 2006; Maria et al., 2006; Teles et al.,
2007) and Minho, Lima, Douro rivers (Gravato et al., 2007). Information about trace
metals in several fish species of the Ria de Aveiro, including eels is also provided by
Cid et al. 2001.
Information about trace metals in several fish species of the Ria de Aveiro, included
eels is given by Cid et al., 2001 and PCB’s in Minho River by Santillo et al., 2005. Neto,
2008 analysed and compared Cd, Cu, Pb and Zn concentrations in muscle and liver of
eels and sediment of the Tejo estuary.
Spain

Although there is not any specific survey to analyse the presence of contaminants on
eel, eel is sometimes among the species included in the biomonitoring of water
masses made by the public administrations. Additionally, in some studies that evalu‐
ate the contamination in the biota, the eel is among the studied species. In this way,
information regarding PCBs, pesticides and heavy metals bioaccumulation in eels
from rivers of the Basque Country (Sanchez et al., 1998), from the river Ebro (Santillo
et al., 2006), river Miño (Santillo et a.,. 2006), river Jucar (Bordajandi et al., 2003) and
river Guadalquivir (Usero et al., 2003) is available. Few studies represent a specific
survey to analyse the presence of contaminants in eel, as heavy metals determination
in eels from the Albufera lacuna (Alcaide and Esteve, 2007). These authors concluded
that among the tested HM. bioaccumulation of Cd, Hg, Zn, and Cu in liver tissue is
related to the age/length of individuals [W and B values; p< 0.01] and so recommen‐
dations are remarked on standardization on length and/on age of the eels used in
such studies (Alcaide and Esteve, 2007). On the other hand, Ureña et al., 2007 con‐
cluded for the same location of the latter study that the eels with similar length dem‐
onstrate different pattern of metal distribution among tissue depending on there are
from the wild or farmed.
Sweden

The National Food Administration in Sweden has analysed both yellow and silver
eels sampled in 2000 and 2001 from nine different sites in Sweden with respect to 17
dioxins and furans and 10 dioxin‐like PCB congeners (www.slv.se). Pooled samples

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demonstrated that eels had less than 1 pg TEQ/g fresh weight of sum TCDD/F in
muscle (TEQ = Toxic Equivalents, TCDD = C12H4O2Cl4). To this came about 3.8 pg
PCB‐TEQ/g fresh weight. Silver eels had higher levels than yellow ones. Compared to
the other fish species analysed, eels have a higher ratio of PCB to dioxins. Due to the
high costs for this type of analyses only few eels will be sampled regularly in future.
Recently yellow eels from the Sound (between Sweden and Denmark) outside a heav‐
ily loaded industrial area in Helsingborg were analysed for dioxins and dioxin‐like
PCBs. Pooled samples from 2005 contained 5.7 WHO‐PCDD/F‐TEQ pg/g and 11
WHO‐PCB‐TEQ pg/g, both based on fresh weights. In 2006 another five pooled sam‐
ples from the same area were analysed. The dioxins varied between 0.9 and 4.7 with
an average of 2,2 WHO‐PCDD/F‐TEQ pg/g. The PCBs varied between 3.9 and 12.7
with an average of 6,6 WHO‐PCDD/F‐PCB‐TEQ. At some sites the level of dioxins in
eel muscle exceeded by that the 4 p/g level of dioxins or the 12 pg/g level of summed
up dioxins and dioxin‐like PCBs, set as maximum allowed levels in eel by the Com‐
mission of the European Communities. In 2007 further samples were analysed from
this area. Both yellow and silver eels were analysed in seven pooled samples. The
dioxin levels varied between 0,6 and 2,7 pg/g and the summed up dioxins and dioxin‐
like PCBs between 2.3 and 8.3 pg/g, i.e. all below the maximum allowed levels. How‐
ever, the sample sites were not exactly the same as in 2005 and 2006 (Source: SLV
(The National Food Administration)).
Recent analyses of mercury (Hg) in eels from a number of lakes did demonstrate very
low levels.
UK

Recent surveys investigating concentrations of most metals including mercury, arse‐
nic, cadmium, chromium, copper, lead, nickel and zinc, Poly‐chlorinated biphenyls
(PCBs), Dichloro‐diphenyl‐trichloroethanes (DDTs), Hexa‐chlorocyclo‐hexanes
(HCHs) and Aldrin and Endrin (‘Drins) found they had decreased substantially in
eels from Sussex rivers between 1994–1995 and 2005–2006 (Foster and Block, 2006).
The EU regulation limit of 8 pg/g of dioxin‐like PCBs in eels was significantly ex‐
ceeded for the dioxin‐like PCB‐118 at 100% of sampled sites in 1994–1995 and 2005–
2006. Current levels of dioxin‐like contaminants in eels in Sussex rivers are higher
than those necessary to impair survival of fertilized eel eggs (Palstra et al., 2006).
Whilst Northern Ireland has the largest eel fisheries in the UK no contaminant analy‐
sis of eels is undertaken. However, from 2006 samples of silver and yellow eels from
Lough Neagh are now routinely monitored for lipid content.
England and Wales

Concentrations of most metals including mercury, arsenic, cadmium, chromium,
copper, lead, nickel and zinc, Poly‐chlorinated biphenyls (PCBs), Dichloro‐diphenyl‐
trichloroethanes (DDTs), Hexa‐chlorocyclo‐hexanes (HCHs) and Aldrin and Endrin
(‘Drins) decreased substantially in eels from Sussex rivers between 1994–1995 and
2005–2006 (Foster and Block, 2006). In 2005–2006 more eels were in the low to moder‐
ate risk bands (to people) and fewer eels were in the high risk band for PCBs pro‐
posed by the Oslo and Paris Commissions. The EU regulation limit of 8 pg/g of
dioxin‐like PCBs in eels was significantly exceeded for the dioxin‐like PCB‐118 at
100% of sampled sites in 1994–1995 and 2005–2006. Current levels of dioxin‐like con‐
taminants in eels in Sussex rivers are higher than those necessary to impair survival
of fertilized eel eggs (Palstra et al., 2006).

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181

Northern Ireland

No routine sampling undertaken but available by request.
Scotland

No assessments of contaminants in eels have been undertaken in Scotland.
Parasites/pathogens: overview by country
Belgium

Since WGEEL, 2006 no new information is available on Anguillicola in Belgium. An‐
guillicola infection rates were monitored in 1987, 1997 and 2000 in which year 139 of
140 sites had the infection. The high infection level in Flanders is thought to be the
result of restocking with glass eel and yellow eel, both of which are susceptible to A.
crassus. For distribution maps of the parasite, see Belpaire, 2006 or Audenaert et al.,
2003. Previous studies into endoparasitic helminth communities of eel have been un‐
dertaken (Schabuss et al., 1997).
Canada

To avoid parasite transfers, screenings are routinely done for elvers caught in Nova
Scotia and southern New Brunswick before their stocking in fresh‐waters locations in
the upper St‐Lawrence River and estuary. Screenings for viruses (IHNV, ISAV, IPNV
and EVH) and Anguillicola crassus in individuals prior to stocking were negative dur‐
ing these years. During summer 2006 and 2007, 914 yellow eels were collected from
17 sites in the Maritime provinces, Québec and Ontario and Anguillicola crassus was
found for the first time in the country. This swimbladder parasite is now present in
New Brunswick and Nova Scotia (Antigonish and Cape Breton) (Ken Oliveira, Uni‐
versity of Massachusetts, pers. comm.).
Denmark

Anguillicola crassus was discovered in Danish wild eels in 1986. Since 1988 a monitor‐
ing programme on the abundance of the parasite in the eel population in different
fresh and brackish water bodies has been continued annually.
Estonia

Since 1992 the intensity of Anguillicola infection in the eel population of L. Võrtsjärv
has been studied. The data will be provided for inclusion in the EEQD.
France

No new information from France was made available.
Germany

Detailed information of Anguillicola crassus has been provided in WGEEL, 2007.
Monitoring has been established at the rivers Elbe and Weser and Ems, which are all
important rivers for eel. For this monitoring, commercial fisher collect eel swimblad‐
ders from commercial catches on a weekly basis. As a consequence, no data on length
or weight of the fish are available.
Generally, the prevalence in eels from German waters appears to be between 80%
and 90% (Knösche et al., 2004; Lehmann et al., 2005; Leuner, 2006; 2007; Lehmann et
al., 2007). Lehman et al., 2007 also reported the presence of Trypanosoma granulosum in
more than 90% of all investigated eels from the Rhine system.

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The German country report presents more details with data of monitoring of infec‐
tion of eels from the Rivers Weser, Elbe and Ems with Anguillicola crassus.
Ireland

Anguillicola crassus was first recorded in Irish eels in the Waterford area in 1997. They
were subsequently recorded in the Erne (see below) and this invasion probably oc‐
curred between 1997 and 1998, as they were apparently absent in 1996 (Copely and
McCarthy, 2005). Anguillicola has now also spread to the R. Shannon (McCarthy and
Cullen, 2000). A summary of the known distribution of Anguillicola in Ireland was
compiled in 2003 (McCarthy et al., in press) and the database is currently being up‐
dated, following discovery of the species in small and reputedly unexploited western
Irish catchments. Current information would indicate that Anguillicola is now present
in approximately 50% of the wetted area in Ireland, see map and Figure I.1 in the
Irish country report.
Investigations of parasites assemblages of eels in marine, mixohaline and fresh‐water
habitats in the Shannon and other Irish rivers are being undertaken by the National
University of Ireland, Galway, as part of a research project funded by the Higher
Education Authority (HEA PRTLI‐3).
Annual surveys of yellow and silver eels in the Shannon fisheries, undertaken since
1992, demonstrate that Anguillicola was first detected in 1998 at Killaloe and that since
then it has become well established in the lower catchment and that it has more re‐
cently spread to lakes further up in the river system.
Eight parasitic endohelminth worm species (2 Cestoda, 3 Nematoda and 3 Acantho‐
cephala) were found in the intestines of 1089 brown eel examined from throughout
the Erne system, 1998–2001. Of greatest concern was the discovery of the pathogenic
blood‐sucking nematode Anguillicola crassus in the swimbladder of brown and silver
eel from the Erne.
Initially detected in the R. Barrow in 1997, the parasite has since spread to the lower
reaches of the R. Shannon and was first recorded from brown eel in southern Lower
Lough Erne in 1998 (Evans and Matthews, 1999). By 1999 the parasite was detected as
far upstream as L. Garadice with 90% of brown eel from the Narrows, Lower L. Erne
is infected.
Anguillicola has not been recorded to date in Burrishoole.
Preliminary analysis of information available on the presence of Anguillicola in differ‐
ent catchments would indicate that approximately 50% of the wetted area is now po‐
tentially infected by the parasite (Figure I.1).
Italy

Among the samplings and examinations performed within specific parasitology re‐
search projects, the presence of Anguillicola crassus has occasionally been examined
but no eel specific monitoring is in place. The infection is widespread throughout It‐
aly but temporal variations in infection parameters have been noted.
Latvia

There is no new information from Anguillicola in Lithuania.
Lithuania

There is no new information from Anguillicola in Lithuania.

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183

Netherlands

No new information from Anguillicola in the Netherlands was provided.
Norway

Infection of eels from the river Imsa by Anguillicola crassus was first reported in July
2008. In total seven out of 22 silver eels contained the parasitic nematode Anguillicola
crassus in their swimbladder, therefore a prevalence of 32%.
All eels were female and at the silver migrating stage. Infected eels tended to be big‐
ger in length and weight, but their condition factor was not significantly different
(Mann‐Whitney test, P=0.934). Two eels contained mature worms filled with eggs, in
their swimbladder. Small and medium sized worms were also found.
Poland

During recent fishery surveys in the Vistula Lagoon eels were analysed by SFI for
stomach fullness, and presence of Anguillicola crassus in the swimbladder. In 2006, 190
eels were inspected and infection rate indicated almost 90% were infected.
The most recent data on occurrence of parasite Anguillicola crassus in eel of Polish wa‐
ters was collected in 2007–2008, however, some earlier data are also presented.
Data were collected and calculated according to three categories:
•

Prevalence‐proportion between infested eel and number of eel in sample.

•

Mean intensity of infection‐mean number of parasites per one infected eel.

•

Density‐mean number of parasites per one eel in sample.

The range of prevalence varied from 0,0 in Szczecin Lagoon in 1971 to 100,0 in Lake
Łebsko (2001, 2004).
Intensity of infection varied from 0,0 in Szczecin Lagoon in 1971 to 14,6 in Lake Łeb‐
sko (2007).
The density varied between 0,0 in Szczecin Lagoon (1971) to 9,4 in Lake Jamno (2007).
In 2007–2008 total of 168 samples of eel were collected from 15 places of rivers, lakes
and lagoons in both RBD’s, namely Vistula and Odra. Those samples were examined
on presence of viruses EVEX, AgHV‐1, VHS, IHN, SVC and IPN. All examinations
were made in the Department of Pathology and Immunology of Inland Fisheries In‐
stitute in Olsztyn.
Portugal

Anguillicola crassus is present in several regions but no standard monitoring pro‐
grammes have been established to examine its distribution. Different works dedi‐
cated to eel parasites are available:
•

Nematoda‐Ria de Aveiro (Cruz et al., 1992), Douro River catchment
(Saraiva et al., 2002; Saraiva et al., 2002).

•

Intestinal Helminth communities‐Lima, Cavado, Ave and Douro catch‐
ment areas (Saraiva et al., 2005).

•

Protozoa‐Âncora, Lima, Cávado, Douro and Tejo catchment areas (Car‐
valho‐Varela, 1984; Cruz and Davies, 1998; Cruz and Eiras, 1997).

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•

Parasite fauna in general including Anguillicola‐Minho River catchment
(Antunes, 1999; Aguilar et al., 2005; Hermida et al., 2006), Tejo river estuary
(Neto, 2008), several rivers (Saraiva and Molnar, 1990; Saraiva, 1994, 1995,
1996; Saraiva and Chubb, 1996; Saraiva and Eiras, 1996; Rodrigues and
Saraiva, 1996; Cardoso and Saraiva, 1998).

Spain

Some studies have been carried out regarding the presence of Anguillicola crassus in
rivers from Spain (See Table. ES.j. in the Spanish country report). These studies have
demonstrated that the parasite is widespread in Spain. However, there are still some
rivers in Asturias and Galicia that have not been colonized yet; therefore special
measures should be taken to avoid the infection of these basins. It is difficult to follow
the sequence of A. crassus introduction in Spain since the first data we have is from
2000 and probably the nematode arrived before that data. However, it looks like in
the Mediterranean the presence of the parasite is lower than in the Atlantic (lower
prevalence, intensity and abundance). In the Basque Country, comparing the results
of Gallastegi et al., 2002 in the Butron in year 2000, with those of Díaz et al., 2007 in the
Basque rivers in 2006, we can see that there is an increase in the prevalence of the
parasite, but that the infection intensity has decreased.
Researchers of the University of Valencia have studied the incidence of infectious
diseases in the Albufera´s eel population (Jucar basin, Valencia), through a 3‐years
period (from October 2003 to July 2005. They analysed 122 individuals of different
growth stage (Durif et al., 2005) and health condition and observed that eels suffer
from acute diseases such as those produced by highly virulent bacteria belonging to
Edwardsiella tarda and Vibrio vulnificus species (Alcaide et al., 2006; Esteve et al., 2007;
Esteve and Alcaide, 2007). Edwardsiella tarda disease was present along the study pe‐
riod with a prevalence ranging from 5.6 to 27.8% in the nine surveys performed
(Esteve and Alcaide, 2007). Vibrio vulnificus disease had a sporadic incidence during
the study; it was detected in November 2003 with a very high prevalence of 77.2%
(Esteve et al., 2007). In addition, chronic and mixed infections caused by weakly viru‐
lent bacteria (Aeromonas sp. and Pseudomonas sp.) and fungi (Saprolegnia sp.) were ob‐
served along the study period with a prevalence ranging from 10.5 to 22.2% in the
nine surveys performed (Esteve and Alcaide, 2007). In fact, authors remarked that
pathogenic bacteria may play a leading role in the decline of Albufera´s eel popula‐
tion as the prevalence of each bacterial disease was at the same level than that ob‐
served for the swimbladder parasitic disease (Esteve and Alcaide, 2007).
Interestingly, the correlation between the sanitary status of an eel [Healthy; Acute
bacterial disease; and Chronic disease] and its growth stage [Young Yellow; Sexually
differentiated Yellow; and Mature Silver] was statistically significant: observed num‐
ber of both “young yellow eels which present acute bacterial disease” and “silver eels
which present chronic illness” notably exceed those expected [Pearson Χ2= 10.812; P(4
d.f.)= 0.029] (Esteve and Alcaide, 2007). Thus, authors suggested that youngest eels
could suffer high mortality rates in the natural habitat (Albufera lacuna), and that
low quality of mature adults could reduce their survival along the downstream mi‐
gration to the sea.
Sweden

The swimbladder parasite (Anguillicola) does occur in eels from most sites. All eels
dissected at the Swedish Board of Fisheries are analysed macroscopically for the
prevalence (at both Institutes involved) and intensity (at the Institute of Freshwater
Research only) of Anguillicola in their swimbladders. The prevalence in coastal waters

Joint EIFAC/ICES WGEEL REPORT 2008

185

in 2002–2005 was close to 10% in the marine habitats of RBD 5 and about 60% in the
central parts of RBD 4. The straight between Sweden and Denmark (Öresund, SD 23)
took an intermediate position.
Prevalence of Anguillicola crassus is a mandatory variable in all coastal sampling of eel
in Sweden, including the DCR sampling. The rate of infestation in the pooled data
from 2002–2006 was less than 15% in the most marine areas, 47% in Öresund and
close to 60 in the Baltic sites.
Between 2000 and 2008 the Institute of Freshwater Research analysed 3608 eels from
41 different fresh‐water sites. Infested eels were found in all sites and the prevalence
varied from 37% to 91%. Data have been presented for inclusion in the EEQD.
UK
England and Wales

Anguillicola crassus is now considered ubiquitous throughout the UK (Nigel Hewlett,
Environment Agency National Fisheries Laboratory, pers. comm.). Foster and Block,
2006 reported infestation levels in eels (~300 mm total length) sampled across the Sus‐
sex area in 2005–2006 ranging from 60% to 88% (regional mean 72%). Similar levels of
infestation were reported for eels in Kent rivers in 1996–1998 (Cave, 2000).
In October 2007, 50% and 83% of eels from the River Thames (respectively the estuary
and the fresh‐water part) were infected A. crassus.
On 30 elvers examined from UK glass eels (Gloucester, April 2008) low level granu‐
lomas were present in kidney region of one elver. In 30 elvers examined from River
Severn at Maisemore (April 2008) occasional trichodinids were found on the gills.
A. crassus was found in small numbers in 23% of fish (n=30) from tidal River Thames
(June 2008); also P. laevis found in small numbers in 7% of fish.
A. crassus was found in small numbers in 73% of fish (n=30) from Roman River (July
2008);
Eight eels were examined from Southern Leisure Lake (August 2007)‐A. crassus was
recorded in the swimbladder and kidney, Myxobolus sp. in fins and nematodes likely
to be Daniconema anguillae in the muscle. Significant pathology was recorded in the
gills of the fish examined, indicative of a water quality problem. Bacterial examina‐
tion returned negative results. Virology testing was also negative for the presence of
Infectious Pancreatic Necrosis (IPN) and Eel Rhabdovirus.
Northern Ireland
L. Erne

Anguillicola crassus was first recorded in the swimbladders of eels in Ireland during
an extensive fykenet survey of the Erne system in July 1998. Of 328 yellow eels exam‐
ined in 1998, 24 (7.3%) were infected, with a mean intensity of 4.3 worms per eel. In‐
fected eels were only recorded in southern Lower Lough Erne and northern Upper
Lough Erne. Examination of 432 yellow eels in 1999, demonstrated an increase in
both mean intensity (6.7 worms per eel) and prevalence (9.9%) of A. crassus. The
range of the parasite had also increased, with infected eels recorded from the lower
reaches of the Erne, 30 km downstream of the original area of infection. Monthly
samples of silver eels taken by commercial nets near the outlet of the Erne during Oc‐
tober–December 1998 and 1999 confirmed active migrants contained the parasite.

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Prevalence and mean intensity among silver eels rose from 4.5% and 2.5 worms per
silver eel in 1998 to 15% and 8.6 worms per eel in 1999 (Evans et al., 2001).
L. Neagh

A. crassus was found in Lough Neagh yellow and silver eels for the first time in 2003,
and its spread has been monitored via the analysis of a total of 1100 yellow and 400
silver eels from 2003 to 2006. Samples were stored in 70% alcohol and in the lab;
swimbladders were examined macroscopically for the presence of pre‐adult and
adult A. crassus, but not for larval A. crassus. Recorded prevalence and mean intensity
in yellow eels rose from 24.4% and 2.2 in 2003 to 69% and 3.6, and to 100% and 7.7 in
2004 and 2005, respectively. However, the same infection parameters recorded for
silver eel were significantly different, with almost 60% infected in 2003 rising to al‐
most 90% in 2004. By 2005, 100% of yellow and silver eels were found to be infected
with A. crassus (Evans and Rosell, 2006). In 2007 the prevalence of A. crassus in both
yellow and silver eels had fallen to 70% and 76%, respectively.
Scotland

There is to date only a single reported instance of Anguillicola crassus in Scottish RBD
(Lyndon and Pieters, 2005), for a fish farm near Bridge of Earn, on the Tay system.
However, the absence of targeted effort on the identification of A. crassus in the Scot‐
tish RBD may have led to under‐recording. The parasite is currently being sought in
eel samples collected in the catchments of central Scotland, and there is an uncon‐
firmed report of an infected eel from the Forth (Willie Yeomans, pers. comm.). How‐
ever, the likelihood is that A. crassus is not sufficiently widespread as yet in Scotland,
as a consequence of low levels of stock transfer, to have had possible impacts on eel
populations.

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Annex 6 – Draft WGEEL terms of reference 2009
2008/2/ACOM15
The Joint EIFAC/ICES Working Group on Eels [WGEEL]
(Chair: Russell Poole, Ireland), will meet in ICES, 9–15 September 2009, to:
a ) assess the trends in recruitment and stock, for international stock assess‐
ment, in light of the implementation of the Eel Management Plans;
b ) Evaluate the EU eel management plan;
c ) develop methods to post‐evaluate effects of management plans at the
stock‐wide level;
d ) develop methods for the assessment of the status of local eel populations,
the impact of fisheries and other anthropogenic impacts, and of imple‐
mented management measures;
e ) establish international databases on eel stock, fisheries and other anthro‐
pogenic impacts, as well as habitat and eel quality related data, and the re‐
view and development of recommendations on inclusion of data quality
issues, including the impact of the implementation of the eel recovery plan
on time‐series data, on stock assessment methods;
f ) review and develop approaches to quantifying the effects of eel quality on
stock dynamics and integrating these in stock assessment methods;
g ) respond to specific requests in support of the eel stock recovery Regula‐
tion, as necessary; and
h ) report on improvements to the scientific basis for advice on the manage‐
ment of European and American eel
WGEEL will report by 22 September 2009 for the attention of ACOM and DFC.

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Annex 7 – Technical minutes Eel Review Group 2008
•

RGEEL

•

By correspondence 29–30 October 2008

•

Participants: André Forest (Chair), Russell Poole (WG Chair), Martin Cas‐
tonguay (Canada), David Cairns (Canada), Dietrich Schnack (Germany),
Maris Pliskhs (Latvia), Henrik Svedäng (Sweden).

•

Working Group: WGEEL

General comments
This is a comprehensive, informative and well organized report. It is at the same time
highly educational, as it includes a great amount of basic scientific background in‐
formation for a good understanding of the specific problems related to the assess‐
ment of an eel stock. However, the report is clearly a result of an ongoing process that
started years ago, and therefore does not present a comprehensive overview but
should be read in conjunction with previous reports.
A great deal of emphasis is put on various risks of impaired reproduction and similar
ecosystem based considerations but no data were presented on neither population
dynamics nor the fisheries and a section dedicated to the fishery is not included. At
least some studies aiming to describe fishing mortality and efforts have been per‐
formed over the years; these should be referred to.
The main message is that the eel stock is in a very poor state since many years, and
this is consistent with the previous report. Obviously, securing fish with the highest
fitness should be a top priority given the low recruitment, i.e. a ban on silver eel fish‐
ery is the quickest and safest measure to protect the European eel stock from a final
and total collapse. The possibility that the effective spawning biomass is lowered due
to parasite loads and contaminants, underscores the necessity of reducing the fishing
pressure in both the short and long term perspectives as well as improving the habi‐
tat conditions.
The WG group has put a lot of effort to summarize available information on eel ecol‐
ogy (predation, mortalities), possible anthropogenic impacts, etc. There is listed a
very wide range of possible measures that have to be taken into account, but a
judgement of the potential efficiency or relative value of these measures is missing; so
there is no basis for ranking the measures giving no guidance to the managers.
A certain number of questions were posed by the Review Group in 2007, but the ma‐
jority remains unanswered.
Section 2 Trends in recruitment, stocking, yield and aquaculture
Landings

Existing data should be very or more(?) thoroughly analysed as it is probably the best
indicatives on what is going on regarding the SSB (NB: increased catchability due to
technological creeping).
Recruitment

Some observations concerning recruitment and stock size are overstated whereas
others are neglected or considered to be of less importance without any obvious rea‐
son (for instance, commercial cpue series on glass eels fishery have been given greater
weight than non‐commercial series on yellow eel upstream migration).

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189

Annex 3 includes the data basis for presenting trends in recruitment for different
European rivers (Figures 2.1 and 2.2.), and defines also the different measures that
have been used. It is however not clearly defined, how the “all countries” line has
been obtained. It seems to be the geometric mean of the scaled data from the individ‐
ual systems, but this should be mentioned in the heading of the table and the legend
of the Figure.
The trend analysis on the commercial glass eel indices should have as a starting year
when most if not all indices were running, i.e. about 1970. Otherwise, great weight is
given to a few fishery‐dependent catch records. It is also questionable why fisher‐
dependent data are given greater emphasis than upstream migration of yellow eels.
The Göta älv index is a strong indication on a decline in recruitment already in the
beginning of the 1950s, 30 yrs before such a decline was recognized in the commercial
cpue time series. The Göta älv index and similar evidence from the Baltic region is
now presented as a problem for this region and its data collection as two rather ir‐
relevant hypotheses are put forward. The thing is that the recruitment decline in this
region that began already in the 1950s, and the subsequent fall in the silver eel fishery
in the Baltic Sea about a decade later fit strongly together. Moreover, the indices from
the Mediterranean are similar to the Baltic development. This observation points at a
declining recruitment (due to decline in SSB?) occurred much earlier than the 1980s,
as it is reasonable that a fall is detected in the periphery of a species distribution
rather at the core (i.e. the Celtic arc).
In Chapter 2.2.1 (and several other places where a corresponding summary is given)
it is stated that “the decline is stronger in northernmost and southernmost area of the
species distribution than in the central part”. This cannot be seen from the data pre‐
sented. The Baltic Sea and North Sea river systems are not more northern than the
British Isles systems and the Mediterranean systems are hardly more southern than
the Atlantic systems indicated in Figure 2.11. The decline is stronger in the more east‐
ern areas or least in the more western areas, i.e. at the Atlantic cost.
In Figures 2.1 and 2.2 the scaling is done relative to the average over the period 1979–
1994, whereas in all later figures the reference period is 1970–1979. Is there any reason
to not use the same reference period for the scaling of the trend data?
Figure 2.5 compares eel landings from country reports with data from FAO. It would
be helpful to receive some information on the time periods compared in each case
and to include some comment on the possible reasons of major discrepancies in some
cases.
In Figure 2.6 the legend for non‐commercial catches does not show up.
In Chapter 2.2.2 (and corresponding summaries) it is argued that “we can thus build
an index of recruitment of all Europe … calculated as the geometric mean of each of
the monitoring indices” (based on different sampling methods). This argument is not
convincing. It has been pointed out that the recruitment index is different among ar‐
eas and also that sampling types are largely specific for the individual areas; thus
each method is not representative for all Europe and any mean from all methods may
not be expected to be representative for all Europe as well. Thus, it could be sug‐
gested presenting even in a summary the range of recruitment levels of 1–10% com‐
pared to the period 1970–1979, obtained for the different areas. It can also be stated
that in all areas apart from the Atlantic coast the lever is below 5%.

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Joint EIFAC/ICES WGEEL REPORT 2008

Section 3 International stock assessment and data needs
In Chapter 3.3.3 it is argued that “the intervals in the reporting cycle under the EU
Regulation are far too long to enable any rapid progress by WGEEL”. It may sound
like a contradiction to the statement given before that the restoration process for the
eel stock will take decades. It may be important to state that to get an international
assessment started and supported by adequate data, a yearly availability of data
would be necessary, though on a long run assessment could perhaps be arranged on
a multiannual scale.
Last sentence in second paragraph of Chapter 3.3.8 seems difficult to understand.
Also the message of the last paragraph of Chapter 3.4 is not obvious.
Section 4 Assessing stocks and management actions
Table 4.1 is not readable.
Chapter 4.4: Achieving a reasonable estimate of the total spawning stock biomass ap‐
pears to be a rather difficult and demanding task for the eel stock. It could be asked, if
it has ever been thought of carrying out regular larval surveys in the Sargasso Sea to
receive an index of effective spawning stock size? This would be rather demanding as
well, but compared to the effort required for receiving an estimate of the total effec‐
tive spawning stock size on the basis of silver eel escapement (if at all possible with
sufficient reliability), the effort for a larval survey campaign e.g. every 3 years may
not be too unrealistic. This would provide an index completely independent of all
other methods and could allow at the same time to develop research programmes on
the oceanic phase of the species.
Section 5 Stocking and aquaculture
Stocking

The effectiveness of using stocking of glass eels/ elver/ yellow eels as a way of han‐
dling the eel decline is debatable:
(a)

Compiled data in the report quite effectively demonstrates the low rewards from
already performed stockings, even on a regional scale. In spite of intense stockings in the 1960s in East Germany and Poland in the Baltic Sea region, the yield
in the Danish and Swedish eel fisheries declined in the 1970s,
(b) The most important objection is the still unknown fate of translocated eels in
terms of ability to return to their natal spawning area(s). There is some evidence
that eel for instance removed from Western Europe to the Baltic Sea do not find
their way back at spawning, whereas no data support the opposite.
(c) Unless the fishery on yellow and silver eel is completely stopped, there is an apparent risk of rather boostering the eel fishery, i.e. increasing the fishing pressure
on those individuals that are naturally recruited. Accordingly, it should be stated
crystal clear that stocking is NOT an option but a cul-de-sac unless it can be
proved that the navigational skills of the stocked eels are as good, or at least almost as good, as the ability of the naturally recruited ones. It may be considered,
however, that in cases where eels are so depleted that a river basin is at risk to fail
completely in contributing to the spawning population, stocking might be used as
a last resort, provided that a surplus of glass eels is locally available. In such
cases, procedures to prevent the introduction and spreading of parasites and diseases according to the European fish disease prevention policies have to be applied.
In conclusion, the contribution of translocated eels to SSB is not known; this means
that it might be nil, but it could as well have a positive effect. This chance, thought

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191

uncertain, should be utilized as a last resort in case it does not conflict with other de‐
mands and where an adequate river basin is otherwise depleted from eel.
Section 6 Eel quality
Section 7 Ocean climate and recruitment
Section 8 Research needs
There is listed of very wide range of additional research required in order to fill many
gaps in the biology, stock assessment, post‐evaluation of management actions, etc.
However these proposals are not prioritised and as money will be a limiting factor for
research in the future, a clear ranking of research needs as basis for management ad‐
vice is imperative.

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Annex 8 – Country Reports: Eel stock and fisheries reported by country –
2008
In preparation to the Working Group, participants of each country have prepared a
Country Report, in which the most recent information on eel stock and fishery are
presented. These Country Reports aim at presenting the best information, which does
not necessarily coincide with the official status. This Annex reproduces the Country
Reports in full detail.
Participants from the following countries provided an (updated) report to the 2008
meeting of the Working Group:
•

Norway

•

Sweden

•

Finland

•

Estonia

•

Latvia

•

Lithuania

•

Poland

•

Germany

•

Denmark

•

The Netherlands

•

Belgium

•

Ireland

•

The United Kingdom of Great Britain and Northern Ireland

•

France

•

Spain

•

Portugal

•

Italy

•

Canada

For practical reasons, this report presents the country reports in electronic format
only
(URL).
Available
at
http://www.ices.dk/reports/ACOM/2008/WGEEL/Country_reports_2008.pdf In the
printed version, these can be found on an enclosed CD‐ROM.

ISBN 978-92-5-106156-5

9

ISSN 0258-6096

789251 061565
TC/M/I0532E/1/03.09/120