Stichting Wageningen Research
Centre for Fisheries Research (CVO)

European Eel (Anguilla anguilla) stock size,
anthropogenic mortality and silver eel escapement in
the Netherlands 2006-2020.

T. van der Hammen, F. Soudijn, J. Volwater, J.C. van Rijssel, A.B. Griffioen, C. Chen and H.V. Winter
CVO report: 21.023

Commissioned by:
Ministerie van Landbouw, Natuur en Voedselkwaliteit
Postbus 20401
2500 EK, Den Haag
dhr. L. Gorissen

Project number:

4311218541

BAS code:

WOT-05-001-007

Publication date:

28-10-2021

Stichting Wageningen Research
Centre for Fisheries Research (CVO)
P.O. Box 68
1970 AB IJmuiden
Phone. +31 (0)317-487418
Visitor address:
Haringkade 1
1976 CP IJmuiden

This research is conducted as part of the statutory task programme “fisheries research” and subsidised
by the Dutch Ministry of Agriculture, Nature and Food Quality.
DOI: : https://doi.org/10.18174/556153

© 2021 CVO
De Stichting Wageningen Research-

This report was prepared at the request of the client above and

Centre for Fisheries Research is

is his property. All rights reserved. No part of this report may

registered in the Chamber of commerce

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in Gelderland nr. 09098104,

without the written consent of the client.

VAT nr. NL 8089.32.184.B01

CVO rapport ENG V11

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Report CVO 21.023

Table of Contents

Table of Contents ....................................................................................................... 3
Samenvatting ............................................................................................................ 6
Summary ................................................................................................................. 9
1

Introduction ................................................................................................... 11
1.1

EU regulation and the Dutch eel management plan .............................................11

1.2

Description of stock indicators. .........................................................................12

1.3

Structure of the report and flow diagram ...........................................................13
1.3.1

1.4

2

3

Available data and biological keys ..................................................................... 16
2.1

Short description of main data sets ...................................................................16

2.2

Biological Data................................................................................................17

2.3

Sex ratio at length ..........................................................................................18

2.4

Maturation at length ........................................................................................18

2.5

Weight at length .............................................................................................19

2.6

Growth ..........................................................................................................20

2.7

Natural Mortality .............................................................................................20

2.8

Landings per stage and period ..........................................................................20

A static spatial model for yellow and silver eel ..................................................... 22
3.1

Introduction to the model ................................................................................22

3.2

Three scenarios for the static spatial model ........................................................22

3.3

3.4

3.5

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Structure of the report .........................................................................14

Assessment updates........................................................................................15

3.2.1

Catch efficiency ..................................................................................22

3.2.2

Habitat preference ..............................................................................23

3.2.3

Three scenarios ..................................................................................23

Regionally managed water bodies .....................................................................24
3.3.1

GIS data ............................................................................................24

3.3.2

Data availability ..................................................................................24

3.3.3

Non-WFD waters (ditches) ...................................................................26

3.3.4

Standing stock estimation ....................................................................27

3.3.5

Standing stock per WFD water type .......................................................27

3.3.6

Biomass per period and scenario ...........................................................29

3.3.7

Discussion ..........................................................................................29

Nationally managed water bodies......................................................................30
3.4.1

Data availability ..................................................................................30

3.4.2

GIS data ............................................................................................32

3.4.3

Biomass estimate ................................................................................32

3.4.4

Discussion ..........................................................................................34

Discussion regionally and nationally managed waters ..........................................35

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4

A demographic model for yellow eel ................................................................... 36
4.1

Introduction to the demographic model .............................................................36

4.2

Model update .................................................................................................36

4.3

Demographic model ........................................................................................37

4.4

Annual recruitment parameters ........................................................................38

4.5

Age specific model parameters .........................................................................39

4.6

Model fitting ...................................................................................................39

4.7

Periods in fishing effort ....................................................................................41

4.8

Model fit and estimated fishing mortality............................................................41

4.9

Discussion of the demographic model ................................................................43

4.10

Eel biomass estimation in large lakes ................................................................44
4.10.1 Standing stock lakes IJsselmeer and Markermeer ....................................45
4.10.2 Standing stock Lakes Randmeren and Grevelingen ..................................46

5

6

Overview national stock biomass ....................................................................... 48
5.1

Overview .......................................................................................................48

5.2

National stock biomass ....................................................................................48

5.3

Discussion......................................................................................................49

Mortality during silver eel migration due to barriers ............................................. 50
6.1

Silver eel barrier mortality ...............................................................................50

6.2

Barrier types ..................................................................................................50

6.3

Assisted migration ..........................................................................................52

6.4

Model for estimating barrier mortality ................................................................52

6.5

6.6

7

8

9

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6.4.1

1st hierarchy: from polder to boezem or to the sea. .................................53

6.4.2

2nd hierarchy: from boezem to national waters or the sea ........................54

6.4.3

3rd hierarchy: from national waters to sea, including HPS’s .......................55

Summary .......................................................................................................56
6.5.1

Model scheme 2018-2020 ....................................................................56

6.5.2

Mortality estimates per period and hierarchy ..........................................56

6.5.3

Total mortality rates ............................................................................57

Discussion......................................................................................................57

Stock indicators .............................................................................................. 58
7.1

Stock indicators ..............................................................................................58

7.2

Yellow eel anthropogenic mortality rate (𝑭) ........................................................58

7.3

Silver eel anthropogenic mortality .....................................................................59

7.4

%SPR, ΣA, Bcurrent and Bbest ...............................................................................60

Evaluation of the EMP ...................................................................................... 62
8.1

Precautionary approach and limit reference points ..............................................62

8.2

Status of the eel stock in the Netherlands ..........................................................63

8.3

Discussion of the status of the eel stock in the Netherlands ..................................64
8.3.1

Anthropogenic mortality (vertical axis in Figure 8-1)................................64

8.3.2

Biomass escaping silver eel (horizontal axis) ..........................................64

Conclusions and recommendations .................................................................... 65

Report CVO 21.023

9.1

Biomass estimates ..........................................................................................65
9.1.1

10

Pristine biomass estimate (B0) ..............................................................66

9.2

Biological keys ................................................................................................66

9.3

Static spatial model.........................................................................................66
9.3.1

Regionally managed waters ..................................................................66

9.3.2

Nationally managed waters ..................................................................67

9.4

Demographic model ........................................................................................67

9.5

Barrier mortality .............................................................................................68

9.6

Unquantified sources of anthropogenic mortality:................................................68

9.7

Recommendations...........................................................................................69
9.7.1

Spatial Model ......................................................................................69

9.7.2

Demographic Model .............................................................................69

9.7.3

Silver eel migration model....................................................................69

9.7.4

Immigration of glass eel along barriers ..................................................69

9.7.5

Restocking .........................................................................................69

9.8

International “level playing field” stock indicators ...............................................70

9.9

Future of the eel advice (ICES WKFEA) ..............................................................70

References ..................................................................................................... 71

Appendix A0 Retained catches and effort per region ...................................................... 75
Appendix A1: Water Framework Directive (WFD) water types ......................................... 77
Appendix A2: Eel biomass per water type per period ..................................................... 78
Appendix A3: Eel biomass per water board .................................................................. 79
Appendix B1: Details of the demographic model ........................................................... 80
Appendix B2: Recruitment sex ratio for lakes IJsselmeer and Markermeer ........................ 83
Appendix C1: Overview eel mortality pump stations with a propeller pump ....................... 85
Appendix C2: Barrier assessment list Boezem and National waters .................................. 89
Appendix C3: Validation of the assessment method for the North Sea Canal ..................... 91
Appendix C4: Overview of the parameters used in the barrier mortality estimation. ........... 92
Appendix C5: Diadromous fish monitoring programme .................................................. 93
References Appendices ............................................................................................. 95
Justification ............................................................................................................. 97

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Samenvatting
Sinds de jaren 1980 zijn de glasaalintrek en de aalpopulatie zeer sterk teruggelopen. ICES (the
International Council for Exploration of the Sea, www.ices.dk), die op verzoek van de Europese
Commissie (EC) advies uitbrengt over de status en het beheer van visbestanden, heeft daarom aan het
eind van de jaren 1990 advies gegeven voor het opstellen van een internationaal herstelplan. Dit heeft
ertoe geleid dat de Europese Unie in 2007 de “verordening van de Raad tot vaststelling van maatregelen
voor het herstel van het bestand van Europese aal (EC 1100/2007)” heeft ingevoerd. Deze verordening
(de ‘Aalverordening’) verplicht de lidstaten om een nationaal aalbeheerplan op te stellen en te
implementeren. Het doel van deze aalbeheerplannen is daarbij als volgt omschreven:
“Doel van de beheerplannen voor aal is het verminderen van de antropogene sterfte, zodat er
een grote kans bestaat dat ten minste 40% van de biomassa van schieraal kan ontsnappen
naar zee, gerelateerd aan de beste raming betreffende de ontsnapping die plaats zou hebben
gevonden indien de mens geen invloed had uitgeoefend op het bestand. De beheerplannen
voor aal worden opgesteld met het oog op het bereiken van die doelstelling op lange termijn
(Artikel 2.4 van de Aalverordening).”
De maatregelen in het Nederlandse aalbeheerplan zijn vanaf juli 2009 geïmplementeerd (Tabel
1).
Tabel 1 Overzicht van de maatregelen in het Nederlandse aalbeheerplan.
Maatregel aalbeheerplan
 Terugzetten van aal op zee en op binnenwater door sportvissers
 Verbod op recreatieve visserij, gebruikmakend van professionele vistuigen.
 Gesloten aal visseizoen 1 september tot 1 december
 Decentraal aalbeheer in de provincie Friesland (op basis van quotum).
 Stoppen met uitgave van peurvergunningen op Staatswateren.
 Onderzoek naar het kweken van aal in gevangenschap.
 Oplossen van migratieknelpunten bij sluizen, gemalen en andere kunstwerken.
 Aangepast turbinebeheer bij de drie grote waterkrachtcentrales, verminderen sterfte met minstens 35%
 Visserijvrije zones in gebieden die belangrijk zijn voor aal migratie.
 Sluiten van de visserij in de belangrijkste grote rivieren, met als aanleiding dioxineverontreiniging (april 2011).
 Uitzet van glas- en pootaal.
De Aalverordening verplicht lidstaten ook om over de effectiviteit van de aalbeheerplannen te
rapporteren aan de EC. Deze verplichting gold voor de eerste drie rapportages elke drie jaar (tot en met
2018), en daarna elke zes jaar. Echter, aangezien de huidige stand van de aalpopulatie nog steeds
zorgwekkend is, hebben de lidstaten afgesproken om voorlopig drie jaarlijks te blijven rapporteren aan
de EC.
De onderhavige rapportage heeft een aantal updates ondergaan in vergelijking met de vorige rapportage
(van de Wolfshaar et al., 2018). Ten eerste is de driejaarlijkse periode verschoven met één jaar. De
meest recente periode van rapporteren is daardoor 2018-2020 (en niet 2017-2019), zodat de meest
recente data is meegenomen in deze rapportage. Daarnaast zijn de verschillende onderliggende
modellen verbeterd. Als gevolg van de verschuiving zijn de schattingen uit voor de eerdere driejaarlijkse
periodes opnieuw berekend waarbij gebruik is gemaakt van de verbeterde modellen.
In deze rapportage wordt het aalbeheerplan geëvalueerd in het licht van het bovenstaande beheerdoel
uit de Aalverordening (Artikel 2.4). De methodiek die bij deze evaluatie is gehanteerd, komt voort uit
ICES SGIPEE (Study Group on International Post-Evaluation on Eels, 2010a, 2011). De evaluatie is
uitgevoerd door middel van modellen, vangstgegevens, veldwaarnemingen en statistische analyses,
welke worden beschreven in de rapportage. Het geheel van deze inspanning resulteert in schattingen van
een aantal, door de EC gevraagde, bestandsindicatoren voor vijf driejaarlijkse periodes (2006-2008,
2009-2011, 2012-2014, 2015-2017 en 2018-2020). De belangrijkste bestandsindicatoren zijn B0, Bcurrent
en LAM. B0 is de biomassaschatting van uittrekkende schieraal in een pristine situatie. Voor Nederland is

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deze vastgesteld op 10.400 ton op basis van wetenschappelijke onderzoeken, de beoordeling van de
resultaten van die twee onderzoeken door een onafhankelijke commissie van deskundigen en de
beoordeling van dit geheel door ICES (ICES 2010b).
De 10.400 ton hebben betrekking op de binnenwateren. Voor alle wateren (dus ook de kustwateren en
de visserijzone) gaat het om 13.000 ton (B0); 40% hiervan is 5200 ton. Voor de kustwateren en de
visserijzone is er geen monitoringsprogramma, daarom worden alleen de “binnenwateren” meegenomen.
Bcurrent is de schatting van de daadwerkelijke schieraalbiomassa die uittrekt naar zee. De doelstelling voor
de lange termijn (artikel 2.4) is dat de verhouding tussen Bcurrent en B0 hoger is dan 0.40 (40%). LAM
geeft de hoogte van de totale antropogene sterfte aan. Deze bestaat voornamelijk uit barrièresterfte en
visserijsterfte.
Effecten van het Nederlandse aalbeheerplan op de Nederlandse aalpopulatie
De schattingen van de bestandsindicatoren laten zien dat de maatregelen uit het Nederlandse
aalbeheerplan hebben geleid tot een toename van de uittrekkende schieraalbiomassa (Bcurrent) en een
teruggang in antropogene sterfte (LAM) tussen 2006-2008 en 2015-2017 (Tabel 2). In het bijzonder de
eerste periode na de implementatie van de maatregelen uit het Nederlandse aalbeheerplan (2009-2011)
resulteerde in een afname in antropogene sterfte. De daaropvolgende en de tweede periode na invoering
(2012-2014) leidde tot een toename van de uittrekkende schieraalbiomassa (Tabel 2). De reductie in
antropogene sterfte was voornamelijk het gevolg van een afname van de aanlandingen (visserijdruk) in
de commerciële en recreatieve visserij. Echter in de meest recente periode (2018-2020) is de
antropogene sterfte weer toegenomen. Dit wordt hoofdzakelijk veroorzaakt door een toename van de
commerciële visserij (inspanning en aanlandingen) in het IJsselmeer/Markermeer in de in deze periode
(Table 2-1 & Appendix A0). In deze laatste periode (2018-2020) is er ook een afname in de biomassa
uittrekkende schieraal (Bcurrent) in vergelijking met de periodes ervoor. Deze afname is het gevolg van de
hogere antropogene sterfte (LAM), en de lagere biomassa schatting van de aanwezige rode aal en
schieraal. Dit laatste is een resultaat van de lagere vangstsuccessen in de gebruikte monitoringen.
Tabel 2 Schattingen van de belangrijkste bestandsindicatoren. B0 biomassa schatting voor uittrekkende
schieraal in een pristine situatie (tonnen); Bcurrent de schatting van de daadwerkelijke schieraalbiomassa
uittrek (tonnen); 100* Bcurrent /B0 huidige schieraaluittrek als percentage van de pristine uittrek; LAM:
Lifetime Antropogene Sterfte; Mbarrier Schieraal barrière sterfte.
Stock Indicator

B0*

Bcurrent

100* Bcurrent /B0

LAM

Mbarrier

2006-2008

10,400

634

6.1%

83%

17%

2009-2011

10,400

837

8.1%

53%

16%

2012-2014

10,400

1,311

12.6%

42%

14%

2015-2017
2018-2020

10,400
10,400

1,463
974

14.1%
9.4%

40%
55%

11%
13%

* Zonder kustwateren (2,600 t)

Door aanpassingen aan de infrastructuur bij migratieknelpunten, alsmede de verhouding biomassa
tussen verschillende gebieden in Nederland, is het percentage barrière sterfte van schieraal licht
afgenomen (van 17% in 2006-2008 naar 13% in 2018-2020). Van 2015-2017 tot 2018-2020 is de
barrière sterfte licht toegenomen van 11% naar 13%. Deze toename is niet veroorzaakt door nieuwe
barrières, maar door een verschil in de ruimtelijke verdeling van aal.
De status van het aalbestand in Nederland blijft in 2018-2020 verontrustend met hoge sterfte en lage
biomassa. De huidige biomassa van uittrekkende schieraal (9.4%) ligt ver onder de doelstelling van
minimaal 40% van de pristine biomassa en de huidige sterfte door menselijk handelen ligt boven de
geadviseerde sterfte bij een dergelijke lage biomassa aan uittrekkende schieraal.
Een verbetering in de aalpopulatie in Nederland en in de uittrek van schieraal wordt niet op de korte
termijn verwacht omdat aal een langlevende soort is. Het duurt naar schatting 1-3 jaar voordat glasaal
aankomt voor de Nederlandse kust en de binnenwateren op zwemt. Vervolgens duurt het 3-20 jaar

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voordat deze aal “schieraal” wordt, en terugtrekt naar zee. Daarnaast is de aalpopulatie een
panmixtische populatie met een natuurlijke verspreiding van Noorwegen tot noord Afrika. Herstel in
biomassa is daardoor de gezamenlijke verantwoordelijkheid van alle landen.
Uit de analyses is wederom gebleken dat er grote aannames gemaakt moeten worden om tot een
biomassaschatting te komen, welke van invloed kunnen zijn op de resultaten. De omvang van de
opwerking (aalbiomassa in álle Nederlandse wateren) en de beschikbare (historische) gegevens lenen
zich niet tot zeer nauwkeurige berekeningen. De schattingen van de bestandsindicatoren moeten daarom
voorzichtig worden geïnterpreteerd vanwege de aanzienlijke mate van onzekerheid rond deze
schattingen.

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Summary
Since the 1980s, the arrival of glass eel at the coast and the European eel stock have declined sharply.
ICES (the International Council for Exploration of the Sea, www.ices.dk), which provides advice on the
status and management of fish stocks at the request of the European Commission (EC), has therefore
recommended the implementation of a recovery plan since the 1990’s. As a result, in 2007 the EU
introduced the ‘Council Regulation establishing measures for the recovery of the stock of European eel
(EC 1100/2007)’. This regulation (the 'Eel Regulation') requires Member States to develop and
implement a national eel management plan. The purpose of these eel management plans is described as
follows (Article 2.4):
”The objective of each Eel Management Plan shall be to 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. The Eel Management Plan shall be
prepared with the purpose of achieving this objective in the long term.”
The Dutch Eel Management Plan was implemented in July 2009 (Table 0-1).
Table 0-1 Overview of the measures in the Dutch Eel Management Plan.
Measure

Implementation of a program for the improvement of fish migration including eel, which is expected to resolve the issues
at 1800 of the most important migration barriers.

Reduction of eel mortality at hydroelectric stations with at least 35%.



The establishment of zones where fishing is not allowed in areas that are important for eel migration.
Closed area to eel fisheries due to high levels of dioxins and PCB’s (April 2011)
Release of eel caught (a) at sea and (b) at inland waters by anglers.



Ban on recreational fishing using professional gear in coastal areas.



Annual closed season from 1 September to 1 December.



Decentralized eel management in the province of Friesland (a quota system).



Stop the issue of licenses for eel snigglers (Dutch: ‘peur’) by the minister of LNV in state-owned waters.



Restocking of glass eel and pre-grown eel from aquaculture



Research into the artificial propagation of eel





The Eel Regulation also obliges the reporting to the European Commission (EC) on the effectiveness of
the eel management plans. This obligation was intended to apply every three years for the first three
reports (up to and including 2018), and every six years thereafter. However, as the eel population is still
in a worrying state, it was agreed to continue reporting on the status of the eel stock to the EC every
three years.
This report has undergone several updates compared to the previous report. First, the three-year
reporting period was shifted by 1 year. The most recent reporting period is therefore 2018-2020 (and not
2017-2019), as a result of which the most recent data could be included. In addition, the various models
have been improved. The estimates from earlier periods have also been recalculated, taking into account
the improvements.
In this report, the eel management plan is evaluated in the light of the management objective from the
Eel Regulation. The methodology used in this evaluation is derived from ICES SGIPEE (Study Group on
International Post-Evaluation on Eels, 2010a, 2011). The evaluation is carried out using models, catch
data, field observations and statistical analyses, which are described in the report. This effort has
resulted in estimates of a number of stock indicators for five three-year periods (2006-2008, 2009-2011,
2012-2014, 2015-2017 and 2018-2020). The main indicators are B0, Bcurrent and LAM. B0 is the biomass
estimate of escaping silver eel in a pristine situation. The value of B0 was determined in 2010 (ICES
2010b) and has not changed since. Bcurrent is the estimate of the actual silver eel biomass that migrates
to the sea. The long-term objective is that the ratio between Bcurrent and B0 will reach and will remain

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above 0.40 (40%). LAM indicates the level of anthropogenic mortality. This mainly consists of barrier
mortality and fishing mortality.
The results show that the measures from the Dutch eel management plan have led to an increase in
biomass escaping silver eel (Bcurrent) and a decrease in anthropogenic mortality (LAM) between 20062008 and 2015-2017 (Table 0-2). In particular, the first period after the introduction of the Dutch eel
management plan (2009-2011) led to a decrease in anthropogenic mortality and the second period after
implementation (2012-2014) led to an increase in biomass of escaping silver eel (Table 0-2). The
reduction in anthropogenic mortality was mainly due to decreases in catches from both commercial and
recreational fisheries. However, anthropogenic mortality has increased again in the last period (20182020). This is caused by an increase in the commercial fisheries (landings and effort), mainly in the lakes
IJsselmeer and Markermeer (Table 2-1 & Appendix A0). In this same period there is also a decrease in
the biomass of escaping silver eel (Bcurrent). This is caused by the higher anthropogenic mortality, but also
due to the lower biomass estimate of the current standing stock of present yellow and silver eel. This is a
direct result of the lower catch success in several monitoring programs.
Due to adjustments to the infrastructure at migration bottlenecks, as well as the biomass ratio between
different areas in the Netherlands, the barrier mortality (Mbarrier) has decreased slightly between 20062008 and 2018-2020 (from 17% in 2006-2008 en 13% in 2018-2020, Table 0-2). From 2015-2017 to
2018-2020 the barrier mortality increased slightly from 11% to 13%. This increase was not caused by
new barriers, but by a difference in the distribution of eel.
Table 0-2 Estimates of the most important stock indicators. B0 biomass estimate of escaping silver eel
under pristine conditions (tonnes); Bcurrent estimate of the current silver eel escapement to the sea (tonnes);
100* Bcurrent /B0 current silver eel escapement as a percentage of the pristine escapement; LAM total
(lifetime) anthropogenic mortality rate; Mbarrier: barrier mortality rate.
Stock Indicator

B0*

Bcurrent

100* Bcurrent /B0

LAM

Mbarrier

2006-2008

10,400

634

6.1%

83%

17%

2009-2011

10,400

837

8.1%

53%

16%

2012-2014

10,400

1,311

12.6%

42%

14%

2015-2017
2018-2020

10,400
10,400

1,463
974

14.1%
9.4%

40%
55%

11%
13%

*

Excluding coastal waters (2,600 tonnes)

The status of eels in the Netherlands remains worrying in 2018-2020 with high mortality and low
biomass. The current biomass of silver eel escapement as a percentage of B0 (9.4%) is far below the
target of 40%. In addition, the current mortality due to human activity is remains high and has even
increased in the latest period.
An improvement in the eel population in the Netherlands and in the migration of silver eel is not
expected in the short term because eel is a long-lived species. It takes an estimated 1-3 years before
glass eels arrive at the Dutch coast and enter the inland waters. It then takes 3-20 years for these eels
to become silver eels and return to the sea. In addition, the eel population is a panmictic population with
a natural distribution from Norway to North Africa. Biomass recovery of the total eel stock is therefore
the joint responsibility of all countries within the natural range of the eel population.
The analyses once again show that large assumptions are made in order to arrive at a biomass estimate,
which may influence the results. The size of the area for reporting the eel biomass (all Dutch waters) and
the (historical) available data do not lend themselves to very accurate calculations. The estimates of the
stock indicators used to evaluate the status of the stock need to be interpreted with care due to the
significant level of uncertainty surrounding these estimates.

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1

1.1

Introduction

EU regulation and the Dutch eel management plan

The decline in the European eel (Anguilla anguilla) stock since the 1980’s caused the International
Council for the Exploration of the Sea (ICES) to recommend the development of a recovery plan for the
European eel stock. In response to this advice, the EU Regulation for the Recovery of the Eel Stock (EC
1100/2007) was adopted in 2007. It required each Member State (MS) within the natural distribution
area to set up Eel Management Plan’s (EMP’s) by the end of 2008 with the following aim:
”The objective of each Eel Management Plan shall be to 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. The Eel Management Plan shall be
prepared with the purpose of achieving this objective in the long term.”
Each EMP covers an Eel Management Unit (EMU). An EMU covers a specific eel habitat (for example a
river basin) and within MS’s there can be a set of different EMU’s. However, the Netherlands is located in
the joint delta of four major rivers and the rivers are intertwined and confluent. Therefore there are no
sharp boundaries between river basins in the Netherlands and is was decided that the Netherlands is a
single EMU and therefore also a single EMP was drawn up for the Netherlands. The Dutch EMP was
approved by the European Commission (EC) in October 2009. After the approval, several measures as
described in the EMP to reduce eel mortality were implemented (Table 1-1). An adjustment to the EMP
was made in 2018, with approval of the European Commission. In 2012, 2015 and 2018, progress
reports were sent to the EC showing that the status of eel in Dutch waters remained in a situation
regarded as “undesirable”, and below the target of 40% of the estimated pristine situation. However, the
progress reports also show that implementation of the EMP has resulted in an initial increase in biomass
and a decrease in anthropogenic mortality (Bierman et al., 2012; van de Wolfshaar et al., 2015 & 2018).

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Table 1-1 Overview of the implemented measures described in the Dutch Eel Management Plan (Ministry
of Agriculture, Nature and Food quality (2018).
Measure
Planned
Realized
implementation

implementation

Implementation of a program for the improvement of fish migration
including eel, which is expected to resolve the issues at 1800 of the most
important migration barriers.
Reduction of eel mortality at hydroelectric stations by at least 35%.

2015-2027

2015-2027a

2009

November 2011b



The establishment of zones where fishing is not allowed in areas that are
important for eel migration.
 Closed area to eel fisheries due to high levels of dioxins and PCB’s

2010

1 April 2011c

Unforseen

1 April 2011c



Release of eel caught (a) at sea and (b) at inland waters by anglers.

2009

1 October 2009



Ban on recreational fishing using professional gear in coastal areas.

2011

1 January 2011



Annual closed season from 1 September to 1 December.

2009

1 October 2009d



-

2018d

2009

1 May 2009



Decentralized eel management in the province of Friesland (a quota
system).
Stop the issue of licenses for eel snigglers (Dutch: ‘peur’) by the minister of
LNV in state-owned waters.
Restocking of glass eel and pre-grown eel from aquaculture

2009

Early 2010



Research into the artificial propagation of eel:
2010
2014
2015
2017

2010-2015
2014-2017
2015 - ongoing
2017 - ongoing







PRO-EEL (EU-project)
EEL- HATCH
EELRIC (Dutch innovation centrum)
Glasaal Volendam (duurzame palingkweek/innovatief broedhuis)
a

In agreement with the EC, changes have been made to the original schedule of solving migration barriers.

b

Due to technical difficulties, the maximum achievable reduction in mortality through adjusted turbine management is 24%.

c

There was an (unforeseen) closure of eel fishery in contaminated (PCBs, dioxins) areas (all large rivers). The majority of the contaminated

areas that were closed for commercial fisheries on 1/4/2011 include the main rivers. These rivers are the most important migration routes for
diadromous species.
d

In 2011 the province of Friesland started a pilot on a quota system. This system was adopted in the eel management plan in 2018. This

allows those fishermen fishing in the province of Friesland to fish during the closed season based on a TAC (quota of 36.6 tonnes for all
fishermen).

1.2

Description of stock indicators.

In order to assess the status of the stock, the EC requires each MS to estimate a set of stock indicators
(Table 1-2), which are used to evaluate the status of the eel stock in relation to a pristine situation.
Estimates of escaping silver eel biomasses and mortality rates of all eel are requested by the EC (Table
1-2). An explanation of each stock indicator is briefly described below.
B0 is the pristine silver eel biomass escapement to sea to spawn. It is an estimated value of the biomass
that would exist if no anthropogenic mortalities for eel had ever taken place. The B0 value for the
Netherlands is set at 13,000 tonnes, of which 10,400 tonnes in inland waters (ICES 2010b). The target
of the EU Regulation is set at 40% of this measure, i.e. 4,160 tonnes in inland waters in the Netherlands.
An exact value of B0 is extremely difficult to assess. Therefore the estimate has a wide uncertainty range
and has been subject to discussion (see Paragraph 9.1.1).
Bcurrent is an estimate of current silver eel biomass escapement to the sea to spawn. It gives an
indication on how close a MS is to achieve the long-term objective (40% of B0) of the EU Regulation for
the Recovery of the Eel Stock (EC 1100/2007). However, Bcurrent does not depend only on anthropogenic
mortality in a single EMU. The current biomass also highly depends on the number of recruits (glass eels)
arriving at the Dutch coast. This inflow of recruits is, currently at a very low level (ICES 2020). However,
because the European eel is one panmictic population, the inflow of recruits in the Netherlands depends
on the silver eel escapement of all countries within the natural distribution area of European eel.
Bbest is an estimate of the best possible silver eel escapement under recent recruitment conditions. It is
an estimate of the current escaping silver eel biomass if there would be no anthropogenic influences.

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LAM and ΣA, ΣF and ΣH are anthropogenic mortality rates for all eel (Table 1-2). LAM is the Lifetime
Anthropogenic mortality percentage. ΣA is the total anthropogenic mortality rate, ΣF is the fishing
mortality rate and ΣH is the anthropogenic mortality rate other than fishing mortality. For this
evaluation, ΣH includes only the barrier mortality rate. Mortality rates due to, for example, pollution or
parasites, are not taken into account, because they are extremely difficult to assess. The mortality rates
give an indication whether management measures have resulted in a reduction in anthropogenic
mortalities in a single EMU.
Table 1-2 Overview of the main stock indicators to be reported to the EC. The MS’s are also obliged to
report on the amount of glass eel (eel below 12 cm) harvested for restocking. These are not reported
here because this is not relevant for the Netherlands as no glass eel is harvested.
Indicator
Description
B0

Silver eel escapement (biomass) in the absence of any anthropogenic impact and at historic recruitment
levels.

Bcurrent

Silver eel biomass estimate that currently escapes to the sea to spawn.

Bbest

Silver eel biomass estimate without anthropogenic influences on the current stock, i.e. the best biomass
possible under current recruitment levels.

ΣF

Fishing mortality rate (commercial and recreational).

ΣH

Anthropogenic mortality rate other than fishing mortality (e.g., barrier mortality).

ΣA

The sum of anthropogenic mortalities, i.e., ΣA = ΣF + ΣH.

1.3

Structure of the report and flow diagram

To estimate the stock indicators described above (Table 1-2) the following calculations were carried out:
1)
2)
3)
4)
5)

The biomass of the yellow and silver eel standing stock.
The yellow eel fishing mortality.
The mortality of migrating silver eel.
The total biomass of escaping silver eel.
The stock indicators.

Each step is briefly described below. In the following chapters, the methods are described in more detail.
First, the biomass of the yellow eel and silver eel standing stock is estimated with two different models.
For most water bodies, but not the large lakes IJsselmeer, Markermeer, Randmeren and Grevelingen, a
model where survey density from different surveys is scaled up to the total water surfaces is used. This
model is called the ‘Static Spatial Model’. For lakes IJsselmeer and Markermeer, a population dynamics
model is parameterized to estimate fishing mortality (F) in these lakes. This model is called the
‘Demographic Model’. The estimated fishing mortality is used in combination with the amount of yearly
landings to estimate the standing stock biomass. For the lakes Randmeren and Grevelingen, the eel
density as estimated in the lakes IJsselmeer and Markermeer is used as basis. Survey data in the
Randmeren was available from 2012, which were used to correct the Randmeren values for differences in
survey density.
Static spatial model: Stock estimates were made based on data from electric dipping nets, by
scaling data on density (eel biomass per length class per area) to total wetted areas of water
bodies. The amount of silver eel was estimated using a maturation key. This method is used for all
inland waters, except the large lakes IJsselmeer, Markermeer, Randmeren and Grevelingen. The
static spatial model is explained in detail in Chapter 3.
Demographic model: For the large lakes the method of the static spatial model is considered
unreliable, because the surveys are conducted at the shore and raised to the level of the whole
surface of the waterbody. The lakes have a disproportionally large surface area, as compared to
the shores and therefore, strong assumptions would have to be made to use this data as is done

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in the static spatial model. Instead, for the lakes IJsselmeer and Markermeer, the fishing mortality
rates were estimated by fitting a ‘Demographic Model’ to the electric trawl survey time series with
the recruitment index at Den Oever as basis for the level of eel recruitment. The estimated fishing
mortality rates were used in combination with the landings, to obtain estimates of the total eel
standing stock in the lakes. The estimated eel density in the lakes IJsselmeer and Markermeer was
also used to estimate the density for the Randmeren and Grevelingen. The number of silver eel
was estimated using a maturation key. The demographic model is explained in detail in Chapter 4.
Next, a barrier model was used to estimate the silver eel mortality during migration from inland
water bodies to the sea, due to barriers such as pumping stations and HSP’s. The model assumes
that, depending on the starting position, silver eels experience a different mortality risk depending
on the numbers and types of barriers they encounter during migration to the sea. The estimation
of the barrier mortality is described in detail in Chapter 6.
By combining the silver eel biomasses resulting from the static spatial model and the demographic
model and the mortality of the migrating silver eel, the total biomass of escaping silver eel is
estimated. In the final step, the estimated starting biomass, escaping biomass, the landings and
the demographic model are combined to calculate the stock indicators (Table 1-2). The estimation
of the stock indicators is described in detail in Chapter 7.
1.3.1

Structure of the report

As explained above, the stock assessment method consists of several steps. Below the content of each
Chapter is summarized:
Chapter 2: In this chapter the biological keys are presented (maturity-at-length, weightat-length, and sex-ratio-at-length) that are used in the demographic model
and the static spatial model.
Chapter 3: In this chapter the static spatial model is described, which is used for the
estimation of yellow eel and silver eel biomass in the regionally and nationally
managed water bodies other than the large lakes (lakes IJsselmeer,
Markermeer, Randmeren and Grevelingen). For larger, mostly nationally
managed water bodies such as the main rivers and for the majority of smaller,
mostly regionally managed water bodies, data from surveys using electric
dipping nets were available.
Chapter 4: In this chapter the demographic model is described. The model is used for
estimating the silver and yellow eel biomass in the large lakes, IJsselmeer,
Markermeer, Randmeren and Grevelingen.
Chapter 5: In this chapter the total standing stock biomass is estimated by summing the
results from the demographic model for Lake IJsselmeer, Markermeer,
Randmeren and Grevelingen (Chapter 4) and the results from the standing
stock biomass in the spatial spatial model from the other nationally managed
waters and regionally managed waters (Chapter 3).
Chapter 6: In this chapter the migration model for the estimation of silver eel mortality
due to barriers is described.
Chapter 7: In this chapter the results from chapters 2-6 are used for the estimation of the
final key stock indicators (Chapter 7).
Chapter 8: In this chapter the stock indicators are discussed using the modified
precautionary diagram as developed by ICES.
Chapter 9: The report concludes with a general discussion and recommendations for
improvements to the stock assessment methodology.

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The flow diagram below gives a broad overview of the key steps in the stock assessment methodology,
with reference to the chapters.

Figure 1-1 Flow diagram representing the key steps in the stock assessment methodology, and the
structure of this report.
1.4

Assessment updates

Since the latest report, several improvements were made, which are described below:
1) In order to include the most recent (2020) data, the three-year period was shifted by one year
compared to previous reports (Van de Wolfshaar et al., 2015 & 2018), such that the latest period
is 2018-2020 (instead of 2017-2019). This results in the following periods to report on: 20062008, 2009-2011, 2012-2014, 2015-2017 and 2018-2020.
2) Improvements on the estimation of the biological keys (Chapter 2).
a. Sex ratio at length and maturation at length were estimated with a Generalized additive
model (GAM).
b. Growth was fitted with a von Bertalanffy growth function.
c. Weight at length is now estimated for males and females separately.
3) The demographic model was updated and improved (see Chapter 4 for more detail).
4) For the Water Framework Directive (WFD) waters a moving average was estimated, with average
values for each six-year period (Chapter 3).
5) The Randmeren and Grevelingen have been assessed using eel density instead of fishing
mortality from the lakes IJsselmeer and Markermeer, in combination with survey data for the
Randmeren (Chapter 4).

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2

Available data and biological keys

2.1

Short description of main data sets

The main data sets used in the stock assessment are described below. More detailed information is
described in van Keeken et al. (2020a):
1)

Retained catches. Retained catches are defined as the landings from commercial fishers.
Since 2010 all freshwater landings are provided by the Ministry of Agriculture Nature and
Food Quality (LNV) and are stored in a database (‘Visstat’). For the landings in 2006-2008
in the Netherlands, an estimate as given in the EMP (Ministry of Agriculture, Nature and
Food quality, 2018) is used. For the period 2009-2011, the average of 2010 and 2011 is
used. For lakes IJsselmeer and Markermeer, PO (product board) data is available for the
periods before 2010.

2)

Market sampling. Representative samples are taken from retained catches from
commercial fisheries each year and the lengths of the individual eels are measured (van
Keeken et al. 2018). Furthermore, several eels per length class were selected from each
sample for dissection and measurements of maturity, weight and sex (see van Keeken et al.
2020a for methods). These measurements are used to calculate maturity-at-length, weightat-length, and sex-ratio-at-length. From a subsample of these eels, age readings of otoliths
are conducted, in order to estimate sex-specific growth curves. Data from the market
sampling between 2006 and 2020 are used in this assessment. The biological keys (Chapter
2) are used in the demographic model (Chapter 3), in the static spatial model (Chapter 4)
and to calculate the reference points (Chapter 7).

3)

Surveys in regionally managed water bodies. Eel sampling within the Water
Framework Directive (WFD, 2000/60/EC) waters was executed following an EU certified
protocol. In the assessments presented here only data from electrofishing with electric
dipping nets were used. Sampled water bodies are representative for water types defined
within the Netherlands based on WFD regulation. Data collection is managed and stored by
regional water boards. Electric dipping net data for recent years were obtained from ATKB
(consultancy for water, soil, and ecology) and several water boards. A total of ~8800
samples by electric dipping nets were available between 2006 and 2019, covering most of
the combination of water boards and water body types.

4)

Surveys in nationally managed water bodies. Within the survey program “Fish
Monitoring National Waters,” fish species in the main Dutch rivers are monitored yearly
(van Keeken et al., 2020a). In the program, the main rivers and water bodies connected to
the main rivers are sampled in autumn or in some cases early spring. Depending on the
region, sampling started in 1997 or later.

5)

Non - Water Framework Directive waters. Ditches are underrepresented in the set of
WFD water bodies. Therefore, a survey with an electric dipping net is carried out by
Wageningen Marine Research (WMR) every year and is added separately to the spatial
model. A total of ~350 samples by electric dipping nets were available between 2013 and
2020.

6)

FYMA electric trawl survey in lakes IJsselmeer and Markermeer. Since 1989, WMR
has been conducting an annual (yellow) eel survey in lake IJsselmeer (25 sites) and lake
Markermeer (15 sites) with an electrified trawl. The survey takes place in the autumn
(October-November). The data is used to tune the demographic model (Chapter 4, van
Keeken et al., 2020a).

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7)

Glass eel survey liftnet Den Oever Since 1938, recruitment monitoring has been running
at Den Oever. The monitoring is conducted with a liftnet (1x1 m) during March-May. Glass
eel data are presented as the average number of glass eels per haul in the months of April
and May and is used as input for the demographic model (Chapter 4).

8)

Recreational landings Since 2010 a biennial survey has been conducted in the
Netherlands to estimate the total eel catches in the recreational fisheries (Van der Hammen,
2017, Van der Hammen in prep.).

9)

Transponder research Meuse The anthropogenic mortality of migratory silver
eels in the Dutch rivers is determined by means of tracking silver eels equipped
with a transponder. Within this transponder research, 150 silver eels are provided
with a NEDAP transponder once every three years and released in the upper
reaches of the Dutch part of the river Meuse. The data is used to estimate silver eel
escapement in relation to anthropogenic mortality of silver eel by hydroelectric
power stations. Unfortunately, data from the last field season (2019) could not be
used due to database crashes at NEDAP and damaged detection stations.

10) Diadromous fish monitoring programme A survey programme started in 2012
to monitor the abundance of migrating silver eel on five exit points
(Kornwerderzand sluices, Den Oever sluices, North Sea Canal, New Waterway
channel, Haringvliet-West inlet) and two entry points for migratory fish (River
Rhine and River Meuse) during spring and autumn. The programme is a
collaboration between WMR, Rijkswaterstaat and commercial fishermen. The
months September, October and November were selected for illustrating trends in
silver eel abundance at each location. Because the indices are short (9 years), did
not run before the implementation of the EMP and there are missing years, the
monitoring is not used in the evaluations, but the trends are reported in Appendix
C5.

2.2

Biological Data

Biological keys, such as maturity-at-length, weight-at-length, age-at-length, and sex-ratio-at-length are
estimated with the available data from biological market samples. The biological keys are used in the
assessment in the static spatial model and in the demographic model to convert lengths to ages or to
yellow and silver eel biomass (Chapter 3, 4 & 7). The biological keys were based on all sampled eel,
which is assumed to result in estimates representative for a national eel population. The biological keys
that are presented in this chapter differ from previous years (van de Wolfshaar et al, 2018), because 1)
more biological data became available since the previous assessment and 2) some keys were calculated
with a different method.
The data used to calculate the biological keys are measurements from eels that were taken from
commercial catches (i.e., ‘market samples’) throughout the Netherlands (van Keeken et al. 2018). In
addition, for the estimation of the age-at-length key, otolith readings from the DAK project (‘Duurzaam
Aalbeheer door Kennis') are added to the otolith readings from the market samples. In total > 12,000
individual eels collected from the commercial catches between 2006-2020 were used to assess the
biological keys. From 693 individual eels sampled between 2009-2019 (573 from market sampling, 120
from the DAK project) the otoliths were analyzed to assess the interannual growth increments. From
these increments, the ‘years after arrival’ at the coast can be calculated. This differs from age, because
the glass eel has already reached an age of 2-3 years before arriving at the coast. In eel research, ‘age’
usually refers to the age after arrival at the coast. In this report, ‘years after arrival’ and ‘age’ both refer
to the age after arrival at the coast.

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2.3

Sex ratio at length

Males and females have different growth rates and male eel mature and migrate to the sea at smaller
lengths and at younger ages compared to females. Consequently, sex ratio is expected to vary with
length. In the previous assessments a linear relationship was assumed. Because the real relationship is
unknown and unlikely to be linear, in this assessment the length-sex ratio relationship was estimated by
fitting a GAM (Figure 2-1). A GAM does not have a fixed shape and can therefore be used to fit a nonlinear relationship. Sex ratio as a function of length was assessed for lengths of 28 cm and larger,
because determination of the sex is usually not possible at small lengths and there was insufficient data
available at smaller sizes because of the minimal landing size of 28 cm.

Figure 2-1 Percentage of females (dots) and GAM fit based on all samples (≥28cm) from the market
sampling program 2006-2020 per 10mm length class. N (males) = 2,409, N (females) = 10,172.

2.4

Maturation at length

Males become silver eel at smaller sizes than females. Because most eel start their migration to the sea
directly after silvering, most of the silver eel that are seen in the catches represent the eel that became
silver eel only recently. It is also difficult to assess if the catches are representative for the proportion of
silver eel in the stock, because of the timing of the main fisheries. In general, larger numbers of silver
eel are caught during the silver eel migration period. Because of the three months closure of the fishery
during the silver eel migration (September-November) since 20091, the sampling of the commercial
catches could result in an underestimate of the proportion of silver eel in the stock at the start of the
migration season for the years after 2009. However, market sampling during the migration season could
result in an overestimate of the proportion of silver eel as they have higher catchability (in the passive
gears) due to increased mobility. In addition, at downstream locations, the silver eel in the catch may
originate from upstream locations, which could cause an overestimate of the proportion silver eel
downstream and an underestimate upstream. These factors cause uncertainty of the maturity key, which
is not taken into account for this report. Because the shape of the relationship between silvering and
length is unknown, it was fitted with a GAM for both males and females (Figure 2-2). The analyses show
that males start to silver at smaller lengths (~ 33 cm) compared to females (~ 50 cm). The GAM
1 A pilot with decentralized, local eel management was conducted in the province of Friesland starting in 2011 and was fully
implemented in the EMP in 2018, allowing fishermen in Friesland to fish during the closed season with a quota based on catches
in 2010.

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analysis (Figure 2-2) should be interpreted as the probability of becoming a silver eel at a certain length
once that length has been achieved. For example, the ~60% for females at 100cm length does not mean
that ~60% of the original number of females have already become a silver eel. Instead, an eel of 100cm
length that has not yet matured has a ~60% chance of becoming a silver eel in the present year. In the
previous reports (Bierman 2012, van de Wolfshaar et al., 2015 & 2018) the maturity key was fitted with
logistic regression. The logistic regression fit should be interpreted as the proportion of eel that has
become a silver eel at a certain length. However, because eel migrate from the system after becoming
mature, this interpretation does not fit the data very well. The fit of the maturity key can have a large
impact on the estimation of the proportion of eel becoming mature, especially in waters were eel are
relatively large.

Figure 2-2 Observations (circles, average per 10mm class) and predicted GAM fit (lines) of the
percentage of silver eel per length class (10mm). Data source: market sampling program (2006-2020).
a) males (N = 2,648), b) females (N= 10,311).

2.5

Weight at length

A length-weight (LW) relationship is used to estimate eel biomass given numbers-at-length. The length weight relationship is calculated for females and males separately, using individual length and weight
measurements from market samples (Figure 2-3) and the standard LW relationship (weight = exp (-a+
b*log(Length)). In previous reports (Bierman et al. 2012 and van de Wolfshaar et al. 2015 & 2018) no
distinction was made between males and females. For consistency with the other keys, the distinction
was made for this report. However, for lengths <50 cm (which is more or less the maximum length of
males), differences between males and females are very small and are not expected to make much of a
difference.

Figure 2-3 Length-weight relationship for eel based on market sampling data (2006-2020). N (males) =
2,649), N (females) = 10,310. Estimated relationship for males: weight = exp (-13.53 +
3.057*log(L)), for females: weight = exp (-14.71+ 3.247*log(L)). With weight in grams and length (L)
in millimeters.

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2.6

Growth

As in previous assessments, growth is also analyzed for males and females separately. Growth
increments were based on otolith readings from eels collected between 2009-2019 (Figure 2-4).
Individual growth curves were constructed using the relative distances between annual growth rings and
scaling these to the total length of the eel (van Keeken et al., 2011). For the determination of growth
curves and ages, the protocols set by the ICES workshop in Age Reading of European and American Eel
(ICES WKAREA, 2009) were used. For age 0, the mean length of glass eels arriving in Den Oever was
used (7.1cm). The sex of glass eels is not determined yet and therefore no distinction could be made
between glasseel that would become males or females. The sex specific growth curve was constructed
using a von Bertalanffy Growth fit (VBLG, Figure 2-4). The estimated growth curves are used in the
demographic model as the annual transition rates between length classes. The VBLG fit differed from the
previous used growth curve, where a cumulative growth fit was used (Bierman et al. 2012 and van de
Wolfshaar et al. 2015 & 2018). The VBLG was chosen because it reflected the data better compared to
the cumulative growth.

Figure 2-4 Eel growth. Grey lines indicate growth trajectories of individuals based on increments, dark
grey dots are final length and age estimates (age after arrival at the Dutch coast) at the time the eel was
caught. Blue lines: estimated growth using a von Bertalanffy fit. a) Males (N=288), b) females (N=187).

2.7

Natural Mortality

Natural mortality is a difficult parameter to assess. It depends on many factors, such as predation, water
temperature and food availability. The natural mortality used in the demographic model (Chapter 3) is
set to µ= 0.138 (per year) for all ages and lengths. This estimate is based on Dekker (2000), who made
a best guess based on literature and is also used in other eel models (van der Meer, 2009). However, the
above mentioned factors cause the value of natural mortality to be highly uncertain.

2.8

Landings per stage and period

Reporting of landings only became obligatory after the EMP came into place (end of 2009). Therefore, for
the first period (2006-2008) a reconstructed estimate made by the ministry (Ministry of Agriculture,
Nature and Food quality (LNV), 2009, 2018; Table 2-1) was used, 805 tonnes (525 tonnes yellow eel and
280 tonnes silver eel). For the second period (2009-2011) the average amount of reported commercial
catches for 2010 and 2011 were used (410 tonnes, Ministry of LNV), because the data for 2009 were
incomplete. For the other periods (2012-2014, 2015-2017, 2018-2020), an average estimate of the 3
years within the respective periods was calculated from the reported landings (Table 2-1). The catch was
split into yellow and silver eel based on the length frequency distribution, the sex ratio, maturation and
the length-weight relationship (see paragraphs above). This resulted in an estimate of 56% yellow eel in
biomass of the total amount of retained catches in the Netherlands. Recreationally retained freshwater
catches were available biennially starting in 2010 (Van der Hammen, 2017, & van der Hammen in prep).
Therefore, for the first period (2006-2008), the estimate that was also used in the EMP was used (200
tonnes, Ministry of Agriculture, Nature and Food quality, 2009). For the other periods, the estimates from

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the biennial survey were used. For 2012-2014, two estimates were available, from which the average
was taken. It is assumed that the recreational catches consist only of yellow eel2. The sum of retained
commercial and recreational catches decreased from 2006-2008 to 2015-2017, but increased from 20152017 to 2018-2020 (Table 2-1). This increase is mainly caused by an increase in landings in lakes
IJsselmeer and Markermeer, while most other areas did not show such an increase (Appendix A0).
Table 2-1 Overview of average yearly fresh water commercial and recreational retained catches
(landings) in tonnes for each period.
Commercial
Recreational
Total
Period

Total

Yellow

Silver

Yellow

eel

eel

eel

Commercial + Recreational

2006-2008

805

525

280

200

1005

2009-2011

410

234

175

75

485

2012-2014

327

187

140

36

363

2015-2017

334

191

143

10

344

2018-2020

469

268

201

10

479

2 Recreational fisheries consist of > 95% of angling. As silver eel do not feed, likely that anglers catch mostly yellow eel.

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3

A static spatial model for yellow and silver eel

3.1

Introduction to the model

Only the main rivers (Rhine, Waal, Meuse and IJssel) and the large lakes (IJsselmeer, Markermeer,
Grevelingen and Randmeren) are managed at a national level (Figure 3-1). All other water bodies are
managed regionally by the water boards. A consequence of this management system is that the
monitoring of nationally and regionally managed water bodies differs significantly.
The regionally managed water bodies make up around 65% of the total freshwater surface area in the
Netherlands (PBL, 2010). These waters are surveyed in a standardized manner since the implementation
of the European Water Framework Directive (WFD) in 2000 (2000/60/EC). The nationally managed rivers
have been monitored in a standardized manner since 1997. Both the regionally managed water bodies
and the nationally managed rivers are monitored with an electric dipping net in the riverbanks.
For the (nationally managed) large lakes (IJsselmeer, Markermeer, Grevelingen and Randmeren) good
quality survey data were either not available (Grevelingen) or considered unsuitable for the methods as
used for the regionally managed waters or nationally managed rivers. Therefore, stock estimates for the
large lakes were based on a different estimation method (a demographic model, see Chapter 4).
With the exception of the large lakes, the standing stock of both regionally and nationally managed
waters was estimated by a swept area estimate. This is a simple method were eel density is multiplied
with the water surface area. To calculate eel density, three estimations are needed: (1) the survey
density (or catch success) of yellow and silver eel in a survey, (2) the catch efficiency of the survey gear
and (3) the habitat distribution of eel at the survey locations (% eel in the shore versus % eel in the
open water). The survey density (catch success) is estimated based on the catches (number/ha) in the
survey per length class. This was subsequently translated into silver eel and yellow eel based on a
maturity-at-length key, a weight-at-length key and a sex ratio key (Chapter 2). Subsequently, the
standing stock was estimated for three scenarios, with different assumptions on the catch efficiency of
the survey gear and the spatial distribution of eel in the water body. In this chapter, these scenarios will
first be described. Then, the estimations of survey density for the regionally managed waters and for the
nationally managed waters will be presented and subsequently biomass estimates for these three
scenarios will be presented. These estimations are used as input for the Dutch eel stock biomass
estimation (Chapter 5).

3.2

Three scenarios for the static spatial model

Standing stock estimates for three scenarios that differ in catch efficiency of the electric dipping net and
habitat preferences were calculated to account for the major uncertainty.
3.2.1

Catch efficiency

The catch efficiency of survey gear is difficult to assess. Also, the catch efficiency of the electric dipping
net depends on the type of water body, the substrate, the time of day, the settings of the gear and the
experience of the staff operating the gear (Beaumont et al., 2002). Estimates of catch efficiencies of eel
by electrofishing gear are scarce in the scientific literature. Naismith & Knights (1990) assumed a catch
efficiency for eel using electrofishing gear of 27% in a river, whereas Baldwin & Aprahamian (2012)
estimated efficiencies of approximately 60% in small rivers. Aprahamian (1986) showed size-selective
effects of electrofishing, with mean probabilities of capture from 36% for the smallest eels to 59% for the
largest. Carrs et al. (1999) reported estimated capture probabilities of 71.5% and 75.1% for lakes and
streams, respectively. Belpaire et al. (2018) in an evaluation of the Belgian eel management plan
assumed catch efficiencies of 66%.

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3.2.2

Habitat preference

Monitoring with an electric dipping net in rivers is usually done near the shore. However, the distribution
of eel is not equal between the shore and the open water. This habitat preference is important to
consider when scaling biomass at the borders of a water body to the biomass for an entire water body.
Eel may prefer the littoral (‘inshore’) over the open water (‘offshore’, e.g. Jellyman & Chisnall, 1999;,
Schulze et al., 2004). Therefore, a correction was used to account for differences in eel density between
the littoral zone and the open water.
The distribution of eels in lakes and rivers is generally thought to depend on the physical and biological
characteristics of each water body. Literature on how eel is distributed over a water body is scarce and
focuses on the relation between eel density and the distance to the shore, mainly in lakes. Contradicting
results were found for lakes; Chisnall & West (1996) found that eel densities offshore in New Zealand
lakes were on average 40% of those inshore; Schulze et al. (2004) found a decrease in number with
water depth for a reservoir, but did not take the distance to shore into account; Jellyman & Chisnall
(1999) and Yokouchi et al. (2009) found a positive relationship between catch per unit effort (CPUE) and
proximity to the shore. Several others, more-recent studies have found contradicting results for the
depth and distance to shore occupation of eels in lakes and estuarine environments (Walker et al.,
2014;, Barry et al., 2016;, Bašić et al., 2019). Matsushige et al. (2020) found four different rivers
habitat preferences of Anguilla japonica that suggested diversification of habitat with growth and that
differences in the preferred substrate type depended on body size at the channel scale within these river
systems. Despite the contradicting results, the estimated eel densities in habitats that resemble lakes
and rivers in the Netherlands tend to be higher near shore compared offshore. Therefore, this is also
assumed to be the most likely scenario for the Dutch national waters.
In the EMP’s of some of the countries neighboring the Netherlands, different assumptions were made. In
Belgium, the density of eels is also assumed to be highest near the shores. To estimate the offshore
density, they multiply the inshore density with the outcome of a cumulative Gaussian distribution of the
difference between half of the river width and half of the transect width (Stevens et al., 2013; Belpaire et
al., 2018). In France, no difference is made between inshore and offshore areas in rivers given the lack
of evidence otherwise (Briand et al., 2018).
3.2.3

Three scenarios

Estimates of eel standing stock were computed using three different scenarios that differ in catch
efficiency of the electric dipping net and in the habitat preference (Table 3-1).
In scenario 1 a high catch efficiency (66%) and low proportion of eel in the offshore area compared to
the inshore area (33%) is used (Table 3-1). As a consequence, this scenario will lead to the lowest
estimated standing stock of eel. In scenario 2 the catch efficiency of the survey gear is assumed to be
20% (following an EU certified protocol, STOWA Handboek Visstandbemonstering 2003) and the
proportion of eel in the offshore area compared to the inshore area is assumed to be 50% (Table 3-1).
This scenario leads to an intermediate estimate of the eel standing stock. In scenario 3, the same
estimate for catch efficiency is used as in scenario 2 (20%), but the proportion of eel in the offshore area
compared to the inshore area is higher (66%). Scenario 3 will therefore lead to the highest estimate of
the standing stock. Scenario 2 is the best guess estimate. All final calculations will be made with scenario
2, unless stated otherwise (Table 3-1).

Table 3-1 The three main scenarios used in the approach to stock assessment in which survey data are
scaled to wetted areas. A best guess of 20% for catch efficiencies was used with an upper limit of 66%.
Densities in areas of water bodies outside 1.5 meters of the shore/bank (“offshore area”) were assumed
to be either 33%, 50% or 66% of densities within 1.5 meters of the shore/bank (“inshore area”).
Density “offshore” compared to “inshore”
Catch efficiency

33%

66%

Scenario 1

20%

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50%

66%

Scenario 2

Scenario 3

Report CVO 21.023

3.3

Regionally managed water bodies

3.3.1

GIS data

The eel biomass in the regionally managed water bodies was assessed in the same way as presented in
previous reports (Bierman et al, 2012; van de Wolfshaar et al, 2015 & 2018). It is based on the WFD fish
monitoring program and detailed GIS maps. The management of WFD waters is executed by 21 so-called
water boards (Figure 3-1). In the Netherlands, all WFD surface waters are assigned to a waterbody type,
ranging from small ditches to large lakes (Table 3-3). Detailed information per waterbody is obtained
from a publicly available GIS map with polygons and line elements of all WFD surface waters in the
Netherlands (Figure 3-1). The spatial information of the WFD waterbodies in these GIS maps makes it
possible to calculate the area of each type of surface water (van Puijenbroek & Clement, 2010).

Figure 3-1 Left: the 21 water boards that are in charge of the regional management of WFD waters in
the Netherlands (left). Right: The WFD waterbodies (dark blue) and the nationally managed waters (light
blue).
3.3.2

Data availability

WFD waters
Eel monitoring within the regionally managed WFD waters was executed with an electrofishing gear,
following an EU certified protocol (STOWA Handboek Visstandbemonstering 2003). Sampled waterbodies
are expected to be representative for water types as defined in WFD regulation. Water boards are
obliged to sample their WFD waters within a time frame of six years, resulting in a different sampling
scheme for each waterboard. Most water boards sample a part of their area every year, while others
sample a large area within one year, but do not sample every year. Data availability on a yearly basis is
thus not necessarily expected. In this report, the following approach was used to select the periods. To
reduce the variation due to unbalanced sampling, a moving average of six-year periods was chosen to
assess the biomass of eel for the different three-year periods. For the three-year periods in this report, a
six-year period starting two years before and one year after the corresponding three-year period was
chosen, so that in total six years of data were used. Because data from before 2006 was not available,
for the first period (2006-2008) data from 2006-2011 was used. Similarly, 2020 and 2021 data were not
yet available from any water board due to the timing of the data request (spring-summer 2020). This
early timing is necessary due to the time needed by some of the water boards to deliver the data and to
process the data into the right format. Therefore, for the last three-year period (2018-2020), a six-year
period starting in 2014 (2014-2019) was used. Because a six-year moving average was used, only the
waterbodies that had at least one fishing event in each six-year cycle since 2009 were selected in the

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analysis, in order to keep the data balanced between the three-year periods. However, the years before
2009 were not used to select the waterbodies included in the analysis, because in these years sampling
intensity was much lower and this would result in too much data loss over all years. This results in that
the first period (2005-2008) is based on fewer waterbodies than the other periods, whereas the other
periods have the same waterbodies included in the analysis. In addition, some WDF data could not be
used in the analysis for different reasons. For example, many of the regional waters of water board
“Scheldestromen” are brackish, and therefore there was hardly any electrofishing data in this
waterboard. Data for water board “Hollandse Delta” is missing for the period 2014–2020 and could
therefore also not be used for the other years due to the selection criteria (Table 3-2).
Table 3-2 Data per water board per year which were used for the analysis of the standing stock of eel in
WFD water bodies (white numbers); white boxes represent missing/not available/incomplete data; blue
boxes with a zero represent that data was available, but could not be used due to the selection criteria
(see text).
Water board

2006

Aa en Maas

2007

2008

2

2009

2010

2011

66

3

0

54

65

23

51

51

16

34

35

25

27

66

44

8

41

35

55

2013

2014

2015
27

2016

2017

2018

2019

1

48

32

27

74

30

29

71

3

40

34

56

63

6

39

65

35

102

19

24

8

59

70

35

29

Brabantse Delta

13

7

De Dommel

27

12

Drents Overijsselse Delta

19

50

5

5

5

10

16

21

56

140

184

31

71

19

5

19

22

20

5

38

14

11

20

35

54

26

32

29

7

18

27

11

9

71

4

5

73

52

14

25

41

13

59

50

14

52

55

17

Hollandse Delta

0

Hoogh. Amstel, Gooi en Vecht

176

Hoogh. De Stichtse Rijnlanden
Hoogh. Hollands
Noorderkwartier

17

52

2012

0

9

Hoogh. van Delfland

4

Hoogh. van Rijnland

4

Hoogh. van Schieland en de
Krimpenerwaard

10

16

11

Hunze en Aa's

33

23

Limburg

5

24

Noorderzijlvest
Rijn en IJssel

65

Rivierenland

18

29

46

77

46

43

75

18

32

38

47

29

55

20

8

17

18

33

35

8

41

31

17

48

22

60

23

34

31

23

39

50

25

50

2

26

34

23

32

25

19

19

27

26

35

152

116

99

165

239

Scheldestromen
Vechtstromen

77

Vallei en Veluwe
Wetterskip Fryslãn

13
11

17

68

276

197

567

0

0

32

17

54

29

45

84

1

5

60

16

33

48

38

59

49

52

73

59

77

57

28

55
0

348

0
74
49

Zuiderzeeland
Number of fishing events

43

456

63
6

543

686

572

6
601

548

448

653

770

Sampling locations were included if they were located within WFD waterbodies (polygons) or 50 meters
from a line element as defined in the available GIS map. A margin of 50 meters from a line element was
assumed to be reasonable since waterbodies having a width of about 100 meters were defined as line
elements in the GIS map. To link the electrofishing sampling locations to the GIS map, the geographic
coordinates of the electrofishing events were used. Firstly, coordinates which fell into a polygon were
assigned to that polygon. Secondly, for the fishing events which could not be assigned to a polygon, each
was assigned to the nearest line segment if this was within the margin of 50 meters from the sampling
location. Thirdly, for all remaining fishing events without a match, based on the above-mentioned
statements, the waterbody identification code was used to find a match. However, this last attempt to
link a fishing event with a waterbody resulted in only a few matches since different identification
codes/names are in use for a single water body, and might change over time (e.g. after a fusion between

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Report CVO 21.023

777

waterboards). The remaining fishing events after this last step were excluded from the analyses as a
result of lacking information. Finally, only the selected fishing events, in which the sampled area was
known, were used for the analysis. In total, the selection method resulted in 7,442 electrofishing events
in 533 water bodies being included in the eel assessment for regionally managed WFD waters (Table 3-2;
Figure 3-2).
The variability in sampled area is large between the different water types (Table 3-3, see Appendix A1
for a description of each water type). The two water types with the largest surface area (M14 and M27,
both shallow, relatively large lakes, with 28% and 30% of the total surface area, respectively) have a
relatively low sampling intensity. The highest sampling intensity (M3 with 17% of the sampling effort and
R5 with 22%) has been applied to water types with a relatively small surface area (4% and 2%,
respectively). Nevertheless, most of the small ditches (M1a and M2) in the Netherlands are not even
assigned as a WFD waterbody and were thus not included in the WFD sampling program. Those non-WFD
ditches cover a large area of > 59,000 hectares in total (Table 3-3, ‘Non-WFD ditches’) and can
subsequently contribute significantly to the standing stock of eel. In order to include these ditches,
information of an additional fish sampling (“Polderbemonstering”) within these non-WFD water bodies
was incorporated to estimate the total biomass of eel in ditches (van Keeken, 2014a & 2014b).

Figure 3-2 Geographical location of fishing events in WFD waters included in the analysis for two different
periods (six year cycles), 2008-2013 and 2014-2019. The density (kg/ha) is indicated by the different
colors, ranging from 0 (yellow) to >50 kg/ha of eel (red).
3.3.3

Non-WFD waters (ditches)

Eel monitoring of non-WFD ditches was also executed with an electronic dipping net, following the same
protocol as the WFD sampling program. Each year, from 2013 onwards, several ditches within a selection
of water boards were sampled in a way which would be representative for each waterboard. Most small
ditches can be found in the lower parts of the Netherlands (“Polders”). Therefore only 14 out of the 21
waterboards were included within this additional sampling program. In total, 350 electrofishing events
were executed, whereby an area of 12.4 hectares was sampled in non-WFD ditches (Figure 3-3) and
included in the eel assessment for regionally managed waters. Except for the first two years (2013-2014)
of the program, in which the sampling was conducted in (early) summer, the ditches were sampled in
September.

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Figure 3-3 Geographical location of fishing events in non-WFD waters included in the analysis for all
years within 2013-2020. The density (kg/ha) is indicated by the different colors, ranging from 0 (yellow)
to >50 kg/ha of eel (red). Water boards included in this additional program are: Brabantse Delta;
Hollandse Delta; Hoogh. Amstel, Gooi en Vecht; Hoogh. De Stichtse Rijnlanden; Hoogh. Hollands
Noorderkwartier; Hoogh. Van Delfland; Hoogh. Van Rijnland; Hoogh. Van Schieland en de
Krimpenerwaard; Hunze en Aa’s; Noorderzijlvest; Rivierenland; Scheldestromen; Vallei en Veluwe;
Wetterskip Fryslãn.
3.3.4

Standing stock estimation

For each fishing event, the number of eel per length were converted to weight by making use of a
length-weight relationship (Figure 2-3), from which the survey density of eel (in kg/ha) was calculated.
Densities were corrected for the assumed catch efficiency of the electric dipping net (see Paragraph
3.2.3). Water surface area was divided into two areas: littoral zone (inshore) and open water (offshore).
The width of the littoral zone was set equal to the reach of the dipping net (1.5 meters) and its surface
area is the width times the bank length. The open water surface area is the total surface area minus the
surface area of the littoral zone. Eel density outside the littoral zone is assumed to be a fraction of that in
the littoral zone (50% for scenario 2). Subsequently, density is converted to absolute biomass (kg) for
the riverbank and open water surface areas separately. Biomass of silver eel and of all eel (≥ 30 cm) is
estimated according to scenario 2 (Table 3-8).
For upscaling to the total biomass in regional waters, the surface area of each water type was used to
estimate the total biomass (in tonnes) of eel (≥30 cm), yellow- and silver eel combined) and silver eel
(≥30 cm) for each water type. Based on 1) the female:male ratio (Figure 2-1) and 2) the maturity at
length for both males and females (Figure 2-2), the density and biomass of silver eel was estimated. For
water types that were not sampled in a six year-period, the density averaged over all water types was
used to estimate biomass of eel and silver eel for these waters without data. For the additional sampling
in ditches, the same methodology was used to estimate production and total biomass of eel and silver eel
within these waters.

3.3.5

Standing stock per WFD water type

The density and biomass of eel and silver eel per water type was estimated for all defined six-year
periods, so that each estimate covers a full sampling cycle of six years. The result of the latest six-year
period (2014-2019) is presented in Table 3-3. Following scenario 2 (Table 3-1), a total biomass of 1,791

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tonnes of eel (≥ 30 cm) was estimated, of which 399 tonnes silver eel, in regionally managed WFD water
bodies. The highest survey densities were estimated for the R14 (22.1 kg/ha) and R18 (19.5 kg/ha)
water types. However, the surface area of these waters is very small and the contribution to the total
biomass is limited (Table 3-3). Contribution of M14, M27 and M20 waters to the total biomass of eel and
silver eel was estimated to be the highest, mainly because these water types have a large surface area
(Table 3-3). The estimated biomass of eel in WFD waters (1,791 tonnes) combined with an estimated
biomass of 981 tonnes (survey density 3.8 kg/ha) for eel in non-WFD waters resulted in a total
estimated biomass of 2,773 tonnes of eel in regionally managed waters for the period 2014–2019 (Table
3-3). The total biomass estimate of silver eel in regionally managed waters for the period 2014–2019
was 584 tonnes (Table 3-3). Similar density and biomass estimates were done for the other periods
(Appendix A3).
Table 3-3 Estimation of the eel survey density and biomass per WFD water type for the period 2014 –
2019 following scenario 2. Production and biomass estimates were done for yellow and silver eel
combined (≥ 30 cm) and for silver eel (≥ 30 cm) only. For a full description of the WFD-water types, see
Appendix A1 and for the biomass of eel (≥ 30 cm) per period see Appendix A2. Note that survey density
is not the density in the lake, but the ‘catch success’ in the survey. This value is corrected for selectivity
of the gear and the ratio between the inshore and offshore area to calculate the total biomass.
All eel (≥ 30 cm)

0.3
0.2

1.3
7.6
4.8
7.0

13.3
13.7
25.0
0.3

0.4
2.4
0.8
1.7

3.8
4.3
3.9
0.1

11.77
11.06
32.66
31.58

9.8
0.1
1.9
17.6

91.6
0.2
9.2
936.3

2.3
0.0
0.3
4.5

21.0
0.1
1.6
240.3

4,444
90
22,738
8,182

3.25
0*
22.89
6.43

11.1
7.0
6.5
1.3

125.5
1.7
372.7
27.0

2.3
1.7
1.0
0.3

26.4
0.4
56.6
6.1

Slow flowing, upper stream on sand
Slow flowing, lower stream on sand
Slow flowing small river on sand/clay
Slow flowing side stream on sand/clay

73
1,221
3,414
2,272

14.42
81.11
34.37
3.16

0.4
1.7
12.7
7.9

0.1
7.5
114.1
45.3

0.1
0.3
2.4
1.6

0.0
1.6
21.2
9.4

R8
R12
R13
R14

Fresh tidal waters on sand/clay
Slow flowing lower stream on peat
Fast flowing upper stream on sand
Fast flowing lower stream on sand

20
65
4
16

1.05
3.70
0*
0.93

5.6
3.5
7.0
22.1

0.3
0.8
0.2
1.3

2.4
0.9
1.7
5.5

0.1
0.2
0.0
0.3

R15
R17
R18
Total

Fast flowing small river (siliceous)
Fast flowing upper stream (calcium)
Fast flowing lower stream (calcium)

37
7
52

0*
0*
1.44

7.0
7.0
19.5

0.8
0.3
3.7

1.7
1.7
4.6

0.2
0.1
0.9

Description of WFD water types

156
10

Survey
Density
(kg/ha)
0.4
3.9

Silver eel (≥ 30 cm)
Survey
Density
(kg/ha)
0.1
1.9

WFD
water

Swept
area
(ha)
11.09
1.48

Total
area (ha)

M1a
M2

Buffered ditches

M3
M6a
M6b
M7a

Buffered canals
Large shallow canals (shipping)
Large shallow canals
Large deep canals (shipping)

3,324
603
1,780
13

67.51
14.32
7.7
0*

M7b
M8
M10
M14

Large deep canals
Buffered peatland ditches
Peatland canals
Shallow, large, buffered lakes

3,435
1,148
1,362
20,902

M20
M23
M27
M30

Deep, large, buffered lakes
Shallow, large, calcium rich lakes
Shallow, large, peatland lakes
Weakly brackish waters (0.3 – 3 g Cl/l)

R4
R5
R6
R7

Weakly buffered ditches

75,368

361.9

Non-WFD ditches

59,441

12.4

TOTAL

134,809

374.3

Biomass
(tonnes)

1,791.4
3.8

981.2
2,773

Biomass
(tonnes)
0.0
0.1

398.7
0.7

185.3
584

* For those water types were no survey was conducted, the average survey density of all water types was assumed.

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3.3.6

Biomass per period and scenario

Three different scenarios (Table 3-1) were used to estimate the eel biomass, based on different values of
catch efficiency and different ratios between eel densities at the inshore and offshore areas. Eel biomass
estimates vary between scenarios, with scenario 1 providing the lowest and scenario 3 the highest
estimate of eel biomass (Table 3-4). In addition to the different scenarios, estimates were made for the
different periods and for all data combined. The difference in the biomass estimates between the periods
is large. Biomass estimates for the first and the last period were the lowest, while the highest estimates
were seen for the periods 2009–2011 and 2012-2014. Although the estimates are made for a six-year
period and only from waters that are sampled at least once in each such period, with an exception of the
first period, the differences can still reflect (to some degree) an unbalanced sampling. If the same
waterbody is sampled at least once in each period, it can still be sampled on a different location within
the waterbody or by a different person, which can cause some variation. In addition, although six years
of data (2006-2011) was used for the estimate of the first period (2006-2008), it still had lower sampling
effort compared to the sampling effort in the later periods. This was not the case in the latest periods,
where a decline is observed. The last two periods had nearly an equal sampling effort and the same
waterbodies were included in the analysis. Therefore, the difference in estimated biomass of eel in these
last two periods (2015-2017 and 2018-2020) compared to the period before (2012-2014), reflects a
lower estimate in the standing stock of eel in regionally managed waters.
Table 3-4 Estimates of standing stock of eel in tonnes in the regionally managed waters (WFD water
bodies) and non-WFD water bodies; all eel (yellow and silver ≥ 30 cm) and silver eel (≥ 30 cm) biomass
estimates for three periods (and all years combined for the Non-WFD waters) for the three scenarios.
Non-WFD
waters
Scenario
Eel ≥ 30 cm

Silver eel ≥ 30 cm

1

WFD water bodies

All years

2006 – 2008*

2009 – 2011*

2012 – 2014*

2015 – 2017*

2018 – 2020*

283

406

664

776

464

369

2

981

1,947

3,236

3,793

2,265

1,791

3

1,024

2,519

4,219

4,953

2,955

2,331

1

54

69

109

200

148

82

2

185

328

529

984

727

399

3

193

424

689

1,289

952

519

* these are the three-year periods. Each estimate is based on the nearest six-years of data. Period “2006-2008”: data from 2006-2011; Period “2009-2011”:
data from 2007:2012; Period “2012-2014”: data from 2010:2015; Period “2015-2017”: data from 2013-2018;Period “2018-2020”: data from 2014:2019.

3.3.7

Discussion

There are some limitations in the data availability concerning the regionally managed waters. The first
issue is that not all water boards sample at least once every three years. Water boards are obliged to
sample their WFD waters within a time frame of six years, resulting in a different sampling scheme for
each water board. In addition, due to the timing of the data request, the data of 2020 was not yet
available from any water board. A second issue concerning the WFD sampling program is that the
sampling intensity was not well-balanced. Water types with the highest surface areas have relatively low
sampling effort, while the highest sampling effort was performed in water types with relatively (very) low
surface areas. A six-year moving average was chosen here, such that a trend can be estimated and
changes in eel abundance over time are accounted for. By calculating a six-year moving average, a trend
is estimated that takes a great part of the unbalanced sampling into account. For the last two periods,
nearly all water types were sampled with sufficient sampling effort. However, especially in the first
period, there was less sampling effort, which will have influenced the result. Another issue is that not
every fishing event could be linked to a water body and these events had to be excluded from the
analysis. This mismatch might be due to measurement errors with GPS equipment, errors during data
entry or the selection method of sampling locations used in this analysis. This resulted 3,704 of the
fishing events out of 12,518 having to be removed. As in previous reports, three scenarios were used for
catch efficiency and spatial distribution of eel within a habitat, which pose issues that remain problematic
and cause a high level of uncertainty in the absolute biomass estimate (Table 3-4). Variation in the

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biomass estimates in some waters, may also be a result of stocking activities. This is not a problem for
the biomass estimation in this evaluation, because stocked eel are included in the biomass estimates.
However stocking may cause large variation in eel biomass in the waterbodies were eel is stocked over
the years. Finally, the usage of a new set of biological keys influenced the estimates of yellow and silver
eel in the regionally managed waters (see Chapter 2).
In the non-WFD waters (ditches), the sampling scheme was standardized for sampling method, but not
for sampling location (Paragraph 3.3.3). Each year, two different waterboards were selected and within a
given waterboard and only a very small subsample of all ditches was monitored. As a result, only one
single estimate of the non-WFD waters could be conducted over the whole time period. Any variation in
time can therefore not be detected, because the variation between locations is assumed to be higher
than between years within the same location.

3.4

Nationally managed water bodies

3.4.1

Data availability

Within the survey program “Fish Monitoring National Waters”, fish species in the main Dutch rivers are
monitored yearly (van Keeken et al., 2020a). In the program, the main rivers and the water bodies
connected to them are sampled in autumn or early spring (Table 3-6). Six of the twelve regions have
been sampled consistently and yearly since 1997 (Table 3-6). Within a region, sampling is usually
consistently undertaken in the same month(s) of each year, but different regions are sampled in different
months. There are also regions which started to sample later than the first period considered here
(2006-2008), which have some missing years or which do not sample yearly. Consequently, in some
waters, data is not available for every year within the three-year periods that are considered in this
report. For example, Volkerak-Zoommeer has not been sampled in 2018 and 2020 and the latest
estimate in this water is based solely on samples from 2019 (Table 3-5).
Table 3-5 Number of hauls per year per region per habitat (main or connected water).
Region

Habitat

Benedenloop

Main

Gelderse IJssel

Connected

Benedenrivieren Main

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
5

5

5

4

5

5

5

4

5

5

5

5

5

5

5

2

2

2

2

1

2

2

2

2

2

2

2

2

2

2

31

31

31

30

31

24

21

21

21

21

21

21

21

21

21

Connected

1

1

1

1

1

1

1

1

1

1

1

1

1

1

1

Gelderse poort

Main

22

26

25

27

28

29

28

14

14

14

14

15

14

14

10

Connected

15

16

17

22

21

20

22

10

11

11

11

11

11

10

8

Getijdenlek

Main

7

7

7

7

7

7

8

7

7

7

7

7

7

7

7

Connected

2

3

3

3

3

3

3

3

3

3

3

3

3

3

3

Getijdenmaas

Main

5

5

5

5

10

11

11

8

8

10

8

8

8

8

9

Connected

7

7

7

7

9

9

9

9

9

9

8

9

9

9

9

Grensmaas

Main

11

11

11

11

11

11

11

10

11

11

11

11

11

10

11

Connected

1

0

1

1

1

1

1

1

1

1

1

1

1

1

1

Main

0

0

9

0

9

0

0

9

0

0

9

0

0

9

0

VolkerakZoommeer
Zandmaas

Main

0

0

4

4

4

5

6

0

0

4

0

3

4

4

0

Connected

0

0

7

7

6

7

6

0

0

7

0

2

8

8

0

Due to Covid-19 restrictions, some (parts of) regions were not sampled in 2020. These include some
stations in the upper reach of the River Gelderse IJssel, all stations in the River Rijn (both part of the
Gelderse Poort region) and all stations in the Zandmaas (North and South). See Figure 3-3 for the
classification of regions and Table 3-6 for an overview of survey details per region.
In the large lakes (lakes IJsselmeer and Markermeer, Randmeren and Grevelingen) the relationship
between the eel density inshore compared to offshore is even more uncertain because of the large area

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offshore. Therefore, density estimates in lakes IJsselmeer and Markermeer from the demographic model
are used as basis for these lakes instead (Chapter 4).

Figure 3-4 Classification of the main rivers. Regions are represented by different colors.
Table 3-6 Survey information per river region and type of water (main waterway or connected water
body), for the years 2018, 2019 and 2020. Sampled years = the years in which a region has been
sampled, where all = 2018+2019+2020. Survey density in riverbank = density for all eel ≥30 cm (yellow
and silver). Survey density is based on data collected using an electric dipping net at the riverbanks. For
this table, no correction for catch efficiency of the gear is made.
Region
Habitat
Sampled
Sample
Survey density
years

period

(≥30 cm) in
riverbank (kg/ha)

Benedenloop Gelderse IJssel

main

all

Spring

1.38

all

Autumn

24.71

all

Spring

1.46

all

Autumn

11.86

all

Autumn

3.66

all

Spring

connected
Benedenrivieren

main

0.00

connected
Gelderse Poort*

main

0.00

connected
Getijdenlek

main

0.00

connected
Getijdenmaas

main

6.05

connected
Grensmaas

main

1.25

connected
Volkerak-Zoommeer
Zandmaas*

main

2019

Autumn

main

2018, 2019

Spring

connected

2.39
0.00
35.05
1.80
11.22

*Due to Covid-19 restrictions, some (parts of) regions were not sampled in 2020: some stations in the upper reach of the river
Gelderse IJssel, all stations in the river Rijn (both part of the Gelderse Poort region) and all stations in the Zandmaas (North
and South).

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3.4.2

GIS data

Three types of geographical information were collected; surface area, bank length and groin length. The
surface area (ha) and bank length (km) of the rivers and lakes were calculated (Table 3-7) using GIS
data (the ‘Ecotopenkaart’ of Rijkswaterstaat3). For the rivers, additional information on bank length was
collected (Table 3-7). In some parts of the rivers, the bank length is significantly larger than the river
length because of groins (Dutch: ’kribben’) which are placed perpendicular to the riverbank. These groins
are approximately 90 meters long and placed 200 meters apart (www.rws.nl). In the parts of the rivers
with groins, bank length is thus approximately 1.9 times the river length. By visually scanning satellite
images of Google Earth, a rough estimate of the percentage of riverbanks with groins was made: 60% of
the Gelderse Poort is estimated to have groins, and 50% of the Getijdenmaas. The other regions are
assumed to have no groins. The estimates used are the same as in the previous assessments (Bierman
et al. 2012; van de Wolfshaar et al., 2015 & 2018).

Table 3-7 Surface area, river length and bank length per river region. Groins = the percentage of a region
that has groins. Bank length is river length with groins length (1.9 times the river length) included.
Region
Habitat
Surface area
River length
Groins
Bank length
(ha)
(km)
(km)
Benedenloop Gelderse IJssel

main
connected

675

118

271

42

42

Benedenrivieren

main

18,377

703

703

connected

1,670

498

498

main

5,201

557

connected

1,468

191

Gelderse Poort
Getijdenlek
Getijdenmaas
Grensmaas

118

60%

858
191

main

500

52

52

connected

78

19

19

main

1,265

155

connected

753

82

82

main

426

135

135

50%

224

connected

436

49

49

Volkerak-Zoommeer

main

4,814

171

171

Zandmaas

main

2,043

305

305

connected

1,413

160

160

3.4.3

Biomass estimate

Densities were corrected for the assumed catch efficiency of the electric dipping net (20% for scenario
2). Water surface area was divided into two areas: littoral zone (inshore) and open water (offshore). The
width of the littoral zone was set equal to the reach of the dipping net (1.5 meters) and its surface area
is the width times the bank length. The open water surface area is the total surface area minus the
surface area of the littoral zone. Eel density outside the littoral zone is assumed to be a fraction of that in
the littoral zone (50% for scenario 2). Subsequently, density is converted to absolute biomass (kg) for
the riverbank and open water surface areas separately. For the Grensmaas, no correction for habitat
preference is made and density in the open water is assumed to be equal to that in the littoral zone,
because sampling with the dipping net takes place in the open water in this (shallow water) region and is
thus representative for the open water density. Biomass of silver eel and of all eel (≥ 30 cm) is
estimated according to scenario 2 (Table 3-8).

3https://maps.rijkswaterstaat.nl/dataregister/srv/dut/catalog.search#/metadata/8a2sa797-915t-mn3s-pwnr-va1luhr81fos

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Table 3-8 Biomass (tonnes) of eel ≥ 30 cm, yellow eel and silver eel per river region, estimated
according to scenario 2, for 2018-2020.
Biomass (tonnes)
Region

All eel (≥ 30 cm)

Yellow eel (≥ 30 cm)

Silver eel (≥ 30 cm)

Benedenloop Gelderse IJssel
Benedenrivieren

2.4

1.6

0.8

1,141.9

896.8

245.1

Gelderse Poort

19.5

14.6

4.9

Getijdenlek

16.3

13.8

2.5

Getijdenmaas

14.3

10.8

3.5

Grensmaas

5.1

3.3

1.8

Volkerak-Zoom

424.1

315.7

108.4

Zandmaas

49.7

24.6

25.1

For scenario 2, estimated biomass of eel in the period 2018-2020 is also compared to the earlier periods
(Figure 3-5;Table 3-9), showing that there does not seem to be a general biomass trend in all of the
river regions. Most of the biomass can be found in the Benedenrivieren. After the steep increase of
estimated biomass in 2015-2017 in most river regions, biomass estimates have decreased again to levels
similar to those of 2012-2014 or lower. Exceptions are the Volkerak-Zoommeer, Getijdenlek and the
Grensmaas regions. For the Volkerak-Zoommeer region, the biomass estimate from 2015-2017 was
considerably lower than in 2012-2014 but has increased again in 2018-2020 to levels similar to those of
2012-2014. For the Grensmaas River region, there seems to be a steep decline since the period 20092011. For the Getijdenlek region, biomass estimates have increased from 2006-2008 to 2012-2014 and
have remained relatively stable since then (Figure 3-5).

Table 3-9 Biomass of eel ≥ 30 cm (yellow and silver) in tonnes per river region, for five 3-year periods,
following scenario 2.
Region

2006-2008

2009-2011

2012-2014

2015-2017

2018-2020

13.8

1.4

4.2

19.0

2.4

Benedenrivieren

311.0

412.8

908.0

2,147.0

1,141.9

Gelderse Poort

8.8

29.7

26.0

93.4

19.5

Getijdenlek

2.8

6.4

15.5

17.2

16.3

Getijdenmaas

15.0

8.1

42.1

90.7

14.3

Grensmaas

100.5

100.5

19.5

27.7

5.1

Volkerak-Zoommeer

131.3

874.1

381.3

101.8

424.1

Zandmaas

61.3

105.9

49.5

345.6

49.7

Total

645

1,539

1,446

2,842

1,673

Benedenloop
Gelderse IJssel

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Figure 3-5 Biomass of eel ≥ 30 cm (yellow and silver) in tonnes per river region, for five 3-year periods,
following scenario 2.
When combining all the river regions, the estimated biomass for the period of 2015-2017 is the highest,
followed by the most recent biomass estimate from 2018-2020. The biomass estimate of this latter
period ranges from 337 tonnes for scenario 1 to 2,200 tonnes for scenario 3. The best guess scenario 2
biomass estimate for all the river regions combined is 1,673 tonnes (Table 3-10).
Table 3-10 Biomass of all eel and of silver eel (≥30 cm) in tonnes per river region, for five 3-year periods,
following each scenario.

National water bodies
Scenario

2006 – 2008

2009 – 2011

2012 – 2014

2015 – 2017

2018 – 2020

Eel ≥ 30 cm

1

141

320

293

577

337

Eel ≥ 30 cm

2

645

1,539

1,446

2,842

1,673

Eel ≥ 30 cm

3

815

1,993

1,896

3,725

2,200

Silver eel ≥ 30 cm

1

29

43

54

133

79

Silver eel ≥ 30 cm

2

134

200

266

654

392

Silver eel ≥ 30 cm

3

168

255

348

857

515

3.4.4

Discussion

There are some shortcomings and uncertainties in the methodology used for the nationally managed
waters. Various regions are not sampled every year or regions are sampled in different months, which
adds uncertainty to the estimates. However, because each region is sampled within each three-year
reporting period, uncertainty within each three-year is diminished. However, despite the good
comparability of these periods, abiotic factors and sampling deviations could still have large effects on
the catch efficiency and the biomass estimates.
Another factor of uncertainty that can influence the biomass estimates is the lack of detailed information
on the number and distribution of groins in the rivers. Here, we used a very coarse method to estimate
that number per region. But probably the highest level of uncertainty due to insufficient knowledge of
two crucial factors: the catch efficiency of the survey gear and habitat preference of eel. These factors
cause a large variation in the biomass estimate.

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3.5

Discussion regionally and nationally managed waters

Concerning both the nationally and regionally managed waters, there are some uncertainties. As
mentioned before, the most important uncertainty is because the selectivity of the electric dipping net
and the habitat preference are highly uncertain, the biomass estimates of the WFD and non-WFD waters
are also highly uncertain.
A central assumption underlying the stock estimation is that the eels caught in a certain area represent
the inhabitants of that area and that the eels do not move away from this habitat until they are silver
eel. For the main passageway of silver eel to the sea (i.e., mostly through nationally managed rivers and
lakes), this assumption entails much uncertainty. On the one hand, eels surveyed during the migration
season in autumn (i.e., in many of the rivers; see Table 3-3) may partly consist of migrating silver eels.
These eels were perhaps already surveyed in the habitats where they grew up (since areas are surveyed
in different time periods), or these eels may have migrated from other countries after maturation to
silver eel. This would lead to an overestimation of the silver eel stock in the Netherlands. Possibly, this
could explain the high density estimates of the Benedenrivieren, which is the area closest to the coast
where silver eel might concentrate just before and during the migration season.
In contrast, monitoring during or directly after the migration period may lead to an underestimation of
the silver eel stock, because some of the silver eel might have migrated away or might be in the parts of
the water body not surveyed with the dipping net (e.g. the open water). Thus, because the main surveys
in the nationally managed waters take place during the migration period, there is additional uncertainty.
The same reasoning goes for the regionally managed waters surveyed during or following the migration
period. However, with consistent survey periods, this is not expected to affect the trends in biomass
estimates.

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4

A demographic model for yellow eel

4.1

Introduction to the demographic model

A different method as used in Chapter 3 is used for the nationally managed larger lakes (lakes
IJsselmeer, Markermeer, Grevelingen and Randmeren). The sampling with the electric dipping net occurs
along the shore, and the assumed inshore:offshore density ratio used for the smaller water bodies is not
suitable for the lakes because the lakes have a disproportionate amount of surface area compared to the
shores. Moreover, the catchability of the FYMA survey, used to sample the offshore waters of the lakes,
is unknown. Instead of an extrapolation of the survey data to the surface density, another method was
therefore applied to estimate the eel biomass in the larger lakes IJsselmeer, Markermeer, Randmeren
and Grevelingen. First, a demographic model was developed to estimate fishing mortalities in the lakes
IJsselmeer and Markermeer, by fitting the model to relative changes in abundances observed in the
survey. Subsequently, the estimated fishing mortalities were used to calculate the biomasses, based on
the eel landings in the lakes (see Paragraph 4.10).
In addition, the demographic model was also used in the calculation of one of the stock indicators, the %
Spawner-Per-Recruit as a percentage of the best possible spawner-to-recruit ratio (%SPR, Chapter 7).
The %SPR, is needed to calculate the total anthropogenic mortality rate (ΣA), which can be compared
with the 40% escapement target of the Eel regulation. In that case, the demographic model was not
parameterized for lakes IJsselmeer and Markermeer (Chapter 7).
For lakes IJsselmeer and Markermeer, a demographic population model was used to estimate fishing
mortality rates (𝐹) in four periods since 1989. The model estimates 𝐹 values and a glass eel to recruits
conversion factor (𝐾) based on a fit of the abundance per age class between the model output and FYMA
survey data (see overview Paragraph 2.1). The model was fitted to the relative abundances of the FYMA
survey. Also, recruitment in the model is based on a relative measure, the glass eel abundance index.
Subsequently, the estimated 𝐹 values and the registered landings were used to estimate the standing
stock biomass in the lakes. The results were used as input to estimate the total Dutch eel stock biomass
(Chapter 5).
The demographic model tracks annual eel cohorts through time, for eel from 1989 until 2020. The
demographic model has been improved compared to the model that was used in previous assessments
(Bierman et al. 2012, van de Wolfshaar et al., 2015 &, 2018). The changes that were made compared to
the last assessment (van de Wolfshaar et al., 2018) are described below. In the demographic model,
each year individual eels grow, mature and die based on length and sex specific biological keys (Chapter
2). Eels that reach the silver stage migrate away from the lakes and are excluded from the model. The
cohorts are followed through time, resulting in an annual age-frequency distribution. Annual recruitment
is independent from the local freshwater population and is based on the glass eel index.
The estimates of 𝐹 depend heavily on the field data and on the biological parameters used in the model.
For example, maturation is considered a loss of eel in the system, because silver eels are assumed to
migrate to sea directly. Early maturation leads to a decrease of the fishing mortality of the stock.
Likewise changes in sex-ratio and in growth rate affect the migration of silver eel from the modelled
population, and hence the fishing mortality estimate. Uncertainty in the biological parameters increases
the uncertainty in the estimates of 𝐹 (see also Bierman et al., 2012 and van de Wolfshaar et al., 2015 &
2018).

4.2

Model update

Several improvements were made to the demographic model. All changes in the model are described in
Appendix B1. The main changes to the demographic model compared to the model used in the previous
eel assessment (van de Wolfshaar et al., 2018) are:

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-

-

-

4.3

The model was fitted to survey data of lakes IJsselmeer and Markermeer together, due to scarcity
of data in lake Markermeer in some years.
Different periods for which a single F estimate was calculated were changed such that the periods
better represented the changes in eel fisheries management (Paragraph 4.7)
The length-class based fit between model and data that was previously used, has been changed to
an age-class based fit to allow for a better comparison between model and data. Since male and
female eel display different growth patterns, it is not straightforward to choose sizes of length
classes that result in an even distribution of the age classes over the size classes.
The moment of comparison of the model with the survey data was moved from April to October, to
better fit the ages of the individuals in the survey data. Previously, the model output at the start
of the year (April) was compared with survey data that was collected from September- November.
The derivation of some of the biological keys changed (Chapter 2).
Small changes were made in the selectivity of the commercial fisheries to better match the
minimum landing size legislation for eel (Paragraph 4.5).

Demographic model

The demographic model assumes a closed system for the freshwater phase, similar to models previously
described for eel (see Oeberst and Fladung, 2012; Ciccotti et al., 2012). The glass eels that enter the
lakes are assumed to stay there until they mature to silver eel and begin their migration to the sea. For
lakes IJsselmeer and Markermeer, this is a pragmatic simplification, because these are not entirely
closed.
The eel population in lakes IJsselmeer and Markermeer was modeled using a discrete time, Leslie matrix
population model (Caswell, 2001). The model tracks the eels from when they enter the lakes until they
become mature and start their migration to the ocean. We use a “reproductive subsidy” model (Hughes
& Tanner, 2000) for a population that depends on external recruitment. Population projections with
annual, externally driven recruitment follow:
𝐱(𝑡 + 1) = 𝐀 ∙ 𝐱(𝑡) + 𝐫(𝑡).
The vector with the number of individuals in each age class 𝐱, changes through time 𝑡, depending on the
annual projection matrix 𝐀 and the time dependent recruitment vector 𝐫(𝑡).
The model distinguishes between males and females, as eels display sexual disparity in growth and
maturation. The different cohorts, or age classes in the model, are represented by 𝑖 and the sex classes
by 𝑔. The transition probabilities between age classes are defined as 𝑃 . We use two separate matrix
models for the two sexes, for females 𝐀 :

𝑃
0
⋮

0
0
𝑃
⋮

0 …
0 …
0 …
⋮ ⋱

0
𝑃
0
⋮

0
0
𝑃
⋮

0
0
0
⋮

0

𝐀 =

And for males 𝐀 :

𝐀

=

…
…
…
⋱

The transition probability 𝑃 depends on the survival probability 𝑒

( )

and the probability of

maturing 𝑀 :
𝑃 =𝑒

( )

1−𝑀

.

The survival probability depends on the natural mortality 𝜇, fisheries mortality 𝐹(𝑡) and fisheries
selectivity 𝑧 .

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The annual recruitment 𝐫 (𝑡) is independent from the local yellow eel abundance. The recruitment per sex
class depends on the sex ratio of the recruits 𝜌(𝑡) (female ratio in recruits). For female recruitment 𝐫 (𝑡):

⎛𝐾𝜌(𝑡) 𝐼(𝑡)⎞
0
⎟,

𝐫 (𝑡) = ⎜

⎝

0
⋮

⎠

Recruitment further depends on the annual glass eel index 𝐼(𝑡) and the glass eel to recruits conversion
factor 𝐾. Male recruitment 𝐫 (𝑡) follows:

⎛𝐾(1 − 𝜌(𝑡)) 𝐼(𝑡)⎞
0
⎟.

𝐫 (𝑡) = ⎜

⎝

0
⋮

⎠

Numbers through time thus follow 𝐱𝒇 (𝑡 + 1) = 𝐀𝒇 ∙ 𝐱𝒇 (𝑡) + 𝐫𝒇 (𝑡) for females and 𝐱𝒎 (𝑡 + 1) = 𝐀𝒎 ∙ 𝐱𝒎 (𝑡) + 𝐫𝒎 (𝑡)
for males.
The model follows eel in the lakes from 0.5 to 21.5 years after arrival in the lakes (Table B1, Appendix
B1). The reason for starting the model 0.5 years after arrival in the lakes is that the FYMA survey takes
place in September-November while the glass eel arrive at the Dutch coast in spring and the glass eel
survey takes place from March-May. The census moment of the model, or the time at which model and
data are compared with each other, is therefore set to October to match the FYMA survey, half a year
after the glass eels enter the Lakes. The age classes of the model thus run from 0.5-1.5 years in age.

4.4

Annual recruitment parameters

Recruitment in the model is based on the glass eel inflow, which is monitored at Den Oever (Figure 4-1).
The glass eel index 𝐼(𝑡) is based on numbers per haul and needs to be converted to numbers of yellow
eel/trawled surface as used by the FYMA, which is done by multiplying with the glass eel to yellow eel
conversion factor (𝐾). In 2014, 2017 and 2019, stocking with glass eels (all three years) and elvers
(2014) have taken place in lake Markermeer. Here, it is assumed that the amount of stocking is
neglectable compared to the number of glass eels entering the lakes.
The female ratio of the recruits (𝜌(𝑡)) is based on market sampling data between 1978 and 2019. Most
eel gender is determined after being in fresh water for two years (Beullens et al., 1997) and sex
differentiation has been related to factors such as eel density at the time of forming the sexual organs
(e.g., Roncarati et al., 1997; Davey & Jellyman, 2005; Bark et al., 2007). The female ratio varies
annually, based on the sex of individuals at 2 years after arrival (Appendix B2). For the missing years,
the average value over the years with data was used.

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Figure 4-1 Glass eel average numbers per haul presented as an annual index (black circles) from 19792020, monitored at Den Oever, the Netherlands.

4.5

Age specific model parameters

All parameter values can be found in Table B1 in Appendix B1. Parameterization is based on the
biological keys (Chapter 2). Per age and sex class, the length at the mid-age of the age class is used to
derive all the length-dependent and age-specific parameters. This means, for example, that for the first
age class in the model, that runs from 0.5 – 1.5 years after arrival, the mid-age used for
parameterization was 1 year after arrival. The length at the mid-age of the age class was determined
from the von Bertalanffy growth curves (Figure 2-4). Age- and sex-specific probabilities of maturing (𝑀 )
were derived from length-based estimates of the proportion of mature eel in the market samples (Figure
2-2). Fishing selectivity (𝑧 ) is assumed zero for age classes with a length smaller than the minimum
landing size, which is 28 cm. Rings in fyke-nets to allow escapement of undersized eel have been
mandatory since the 1980s and few undersized eel are caught with this commercial gear. Moreover, eel
is a robust species that can easily survive for some time out of the water or while captured in a net.
Catch and release mortality of eels below the minimum landing size caught in fykes is assumed to be
negligible. About one fifth of the eel catches in the lakes are caught by longlines (Dutch: ‘hoekwant’).
Catch and survival of undersized eel from the longlines is unknown and are therefore not taken into
account in this study. In the model, eels in age classes (see below) that include individuals of 28 cm or
more, therefore suffer from fishing mortality (Appendix B, Table B1). Fishing selectivity for age classes
that are partly fished is equal to the proportion of the time an individual is 28 cm or larger in that age
class, according to the standard growth curve (Chapter 2, Figure 2-4). Natural mortality is assumed to
be independent from age or length and constant through time, 𝜇 = 0.138 (Dekker, 2000: see Paragraph
2.7). Parameter values for 𝐹(𝑡) and 𝐾 were estimated based on a log-likelihood estimation procedure by
comparison between sampling data and model output.
4.6

Model fitting

To allow for a comparison of the (age-structured) model with the (length-based) FYMA survey data, the
FYMA data were converted from length to age (Figure 4-2). The FYMA survey data were converted from

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CPUE per length class (1-cm increments) to CPUE per age class based on the von Bertalanffy growth
curves (Chapter 2). The von Bertalanffy growth curves were corrected for the time deviation between the
glass eel survey (March - May) and the market sampling (May - September). The CPUE per age class was
calculated based on the age classes defined for the demographic model (Table B1, Appendix B). Since
the growth curves are sex-specific, the proportion of males to females per length class was set first
(Chapter 2). For the length classes below 28 cm, the model assumes a sex-ratio that is equal to the sex
ratio of the 28 cm length class because there was not a sufficient number of sexed individuals smaller
than 28 cm to determine a length-dependent sex ratio. Parameter values were estimated for a model fit
on a combination of lakes IJsselmeer and Markermeer data. The weighted mean between lake IJsselmeer
and lake Markermeer was calculated based on the surface area between the lakes, which is 62:38.

Figure 4-2 Mean CPUE per year and per age (years since arrival) in the FYMA electric beam trawl survey
for lakes IJsselmeer and Markermeer, together, between 1989-2020, after application of the length-age
key. Note that the scale of the y-axis differs per graph.
There is no information available on the selectivity of the survey gear and therefore the assumption was
made that this selectivity is equal for all lengths. However, the model was fit to data starting from eel at
the age of two years since arrival at the coast, because it seems as if the number of individuals per age
class increases from year 0 to year 1 and in some years from year 1 to 2 after arrival (Figure 4-2). This

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is indicative of a lower catchability of the smallest individuals in the FYMA survey data. As long as an
equal selectivity is assumed, the absolute selectivity of the FYMA survey is not expected to affect the 𝐹
estimates since all estimates are based on relative changes in abundances.
From year 7 after arrival, only a few individuals per age class are observed in the FYMA survey. This
introduces large uncertainties in the estimated CPUE. Therefore, the age class 7+ was used as the last
age class in the model.
Parameter values for 𝐹(𝑡) and 𝐾 were estimated with a minimum log-likelihood Metropolis-Hastings
algorithm following van de Wolfshaar et al. (2015 & 2018) and Bierman et al. (2012). The algorithm
computes population projections for different values of 𝐹(𝑡) and 𝐾 and estimates which population age
distribution to be the best match for the data. Through stochastic iterations (n = 50,000), the algorithm
finds the combination of parameters for which the fit is best. The likelihood was calculated based on a
Poisson distribution and the prior likelihood is based on an even distribution. Jump sizes of 1% of the
first values were used for the estimated parameters 𝐾 and 𝐹(𝑡). The results below are based on initial
values for 𝐾 of 0.1 and for 𝐹 of 1.0, but robustness of the results was tested through the use of different
initial values. For every outcome, the acceptance rate of the stochastic iterations was checked and a
visual check of the convergence and the correlation between the estimated parameter values was
performed. An acceptance rate of maximally 30% was maintained. In case the acceptance rate, the
convergence or correlations were not satisfactory, the number of iterations was increased. In addition,
visual checks were performed on the residual plots of model and data.
4.7

Periods in fishing effort

𝐹 is estimated for five different periods (1968-1988, 1989-1999, 2000-2008, 2009-2014 and 20152020). The selection of the time periods was based on various motivations. The value of 𝐹 may change
for the consecutive periods because of possible changes in fishing effort through time. These are referred
to as possible changes in fishing effort because the number of permits is known, but the realized effort is
not known before 2010, when the registration of used effort became obligatory. Consequently, the
‘potential’ fishing effort is known, but the used effort is unknown. Moreover, it is also unknown if a
reduction in potential effort (number of permits) has led to a reduction in realized effort. The decision on
the breaks in the periods was therefore somewhat arbitrarily determined, based on what we consider
major changes in potential fishing effort due to changes in management. The first period, starting in
2000, is based on a reduction in permits for eel boxes (Bierman et al., 2012). The second break,
between 2008 and 2009, is because in 2009 the EMP came into place, causing fishing to be prohibited
during the main silver eel migration period (September-November). The break between 2014 and 2015
was chosen because, since 2015, the fishing (effort) that was allowed on some types of commercial fish
was limited (Tien et al., 2015), which may have led to an increase in effort for eel fishing. Not all of the
changes in potential fishing effort were included, however. For example, the break due to the 2006 buyout was excluded, because it would create a very short period (2006-2008) for the fishing effort. In
addition, the buy-out did not seem to have a large impact on the trend in landings, which was steadily
decreasing between 2000 and 2009. In the report the 𝐹 values from the period 2000-2008 onwards are
reported, as the first period for the overall eel assessment (2006-2008) falls within this period.

4.8

Model fit and estimated fishing mortality

The model predictions and the actual data on the FYMA catches (number per trawled km2 per age class)
are presented in Figure 4-3. As expected, the eel abundance decreases with age in the data as well as
the model (in the model this is predefined). Quite an obvious decrease in abundance is visible through
the years in the age classes of eel from 2-5 years after arrival. In the age class of year 7+ after arrival,
no such decline is observed. While the model predictions follow the general downward trend in the true
data for the eel age classes between 2-6 years after arrival, there is a large deviation between the model
and the data for the 7+ years age class. The residual plot (Figure 4-4) shows again the strong
underestimation of the model on the abundance in the age classes 7+ years after arrival. In addition,
there seems to be a small overestimation on the abundance in the age classes of 2 and 3 years after
arrival in the most recent years. In the earlier years of the time series, this was an underestimation.

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The estimated fishing mortalities for lakes IJsselmeer and Markermeer are given in Table 4-1, for the
four periods (see previous paragraph). The fishing mortality (𝐹) for lakes IJsselmeer and Markermeer
decreases over the first two periods but increases again in the last period. The estimated 𝐾 value is 0.06,
which is the conversion factor between the relative glass eel index at the Afsluitdijk in Den Oever to the
CPUE for yellow eel 0.5 years after arrival in the lakes in the FYMA. It includes the success rate of
entering the lakes and survival probability from 0.0 to 0.5 years after arrival in the lakes.

Figure 4-3 CPUE per age class (grey dots) and model predictions (minimum, maximum and mean
outcomes of the last 20% of the iterations of the parameters estimated by the model – blue solid
lines), both in number per trawled surface, area (km2), for the model fit of the data, together for
lakes IJsselmeer and Markermeer.

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Figure 4-4 Residuals plot of the difference between the observed and predicted eel abundance per age
class for the model fit of the data, together for lakes IJsselmeer and Markermeer, given the mean F
estimates presented in Table 3.1. Both negative (red) and positive (black) deviations are plotted. The size
of the circles indicates the value of the residual (with larger being a higher value).

Table 4-1 Model-estimated mean fishing mortality values (F) for model fits on lakes IJsselmeer and
Markermeer data together. F is estimated for different periods, and the 90% confidence interval of the
estimated parameter values in brackets indicates the variance in the values estimated by the model
(after 50,000 iterations).
Fishing mortality
lakes IJsselmeer and Markermeer
F (2000 - 2008)

1.10 (1.08 – 1.12)

F (2009 - 2014)

0.76 (0.73 - 0.79)

F (2015 - 2020)

1.04 (1.01 – 1.07)

4.9

Discussion of the demographic model

The increase in eel numbers 7+ years after arrival in lake IJsselmeer in recent years is not captured by
the model. Generally, the model underestimates the numbers for this age class. Potentially, the
underestimation for the older ages stems from the large individual-level variability in eel growth (Panfili
et al., 1994). The assumed growth curves do not allow for variability in age with size or for changes in
growth over time. Part of the individuals that are estimated at 7+ years after arrival may thus actually
be relatively fast growing individuals and be younger than that. In addition, growth patterns of eel could
have changed over the years due to the large decrease in the density of eels (Figure 4-3). Also the
environmental conditions in lakes IJsselmeer and Markermeer have changed substantially over the years
(Soudijn & van de Wolfshaar, 2021), which may cause variation in growth.
The growth curves that we currently use in the model are constant through time. Perhaps the best
solution would be to use an annual age-length key, but the numbers of eel that are aged each year are
not sufficient to support such data analysis. It is unlikely that the increasing eel numbers 7+ years after
arrival in lakes IJsselmeer and Markermeer are migrating silver eel, as silver eel are hardly ever caught
in the FYMA survey.
In the previous assessment, the demographic model was fitted to the data of lake IJsselmeer and
Markermeer separately. However, in recent years the numbers of eel in the lake Markermeer survey

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have decreased to such low numbers that the length frequency distribution was not good enough to fit
the model. As a result, the fit of the demographic model to data of Lake Markermeer alone is very poor
and the estimates cannot be used. Therefore, the choice was made to fit on the data of Lakes IJsselmeer
and Markermeer combined.
Compared to the previous stock assessment (van de Wolfshaar et al., 2018), the estimate of F has
changed substantially from 2000 onwards. The main causes are: 1) a longer times series is used (until
2020 instead of 2016), which also affects the fit in previous years of the time series, 2) different periods
were chosen for the F values, 3) a different growth curve was used, and, 4) the model was improved
from a length-based to an age-based fit. In addition to the changes mentioned above, the model was
now fitted on the data of lakes IJsselmeer and Markermeer together.
The demographic model has been strongly improved compared to the previous assessment. There are
still several possible improvements possible for the model:
-

Add the retained catches data to the model fit to allow an estimate of absolute biomass in the
lakes by the model.

-

Consider the possibility of the use of different length-sex ratio relationships for different time
periods in the model. Currently the model is based on a constant length-sex ratio through time
while it is likely to vary with changes of F and the sex ratio of the recruits.

-

Consider the possibility of the implementation of a varying selectivity of the FYMA survey with
length.

-

Consider the possibility of using different age-length curves for different time periods in the
model, or a variable age-length key. It is not totally clear so far whether there are sufficient
otolith readings available for such an exercise and how much variability in growth occurs through
time.

-

Consider the possibility of using different maturity-length curves for different time periods in the
model. It is not totally clear how much variability in maturity occurs through time or to what
extend this process is affected by environmental variables.

-

Consider the possibility of using 0- or 1-year old individuals in the FYMA as a measure for the
number of ‘recruits’ in the model instead of the glass eel index. There have been considerable
changes in water management regimes over the years that have likely affected the flow of glass
eel from the coast to the lakes between years. It is however impossible to determine how these
changes in water management may have affected the ability of glass eel to reach the lakes.

4.10 Eel biomass estimation in large lakes
In the major large lakes (IJsselmeer, Markermeer, Randmeren and Grevelingen, Figure 4-5) eel biomass
was estimated in a different way compared to other (smaller) water bodies (see Chapter 3 and Bierman
et al., 2012). The major reason for choosing a different method, is that the relationship between the
density inshore compared to offshore is highly uncertain (Chapter 3) and results in large overestimations
of the standing stock in water bodies with a large proportion of offshore water.
The standing stock for the lakes IJsselmeer and Markermeer was estimated using fishing mortality in
these lakes as estimated by the demographic model (Table 4-1) and the commercial landings. For the
biomass in the Randmeren and Grevelingen, no parameterized demographic model is available and the
estimated density in the lakes IJsselmeer and Markermeer (standing stock/ha) was used as basis and –
where available – corrected by the difference in CPUE in the shore as estimated with a dipping net. In
the Randmeren, there is electric dipping net survey data available from 2012 onwards. In the saline lake
Grevelingen, survey data with an electric dipping net is lacking completely. For the Randmeren, we
therefore assumed that the eel density is the same as in the lakes IJsselmeer and Markermeer, corrected
for the difference in CPUE from the electric dipping net surveys in the lakes. For Grevelingen, we do not
have any reliable CPUE estimate available for eel. Therefore we use the uncorrected density from the
lakes IJsselmeer and Markermeer. This is a strong assumption, and unlikely to be entirely true, but
because good quality data is lacking a strong assumption needed to be made. Note that the method used
here is different from previous reports. In van de Wolfshaar et al. (2018), it was assumed that the

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fishing mortality in lakes IJsselmeer and Markermeer also applied to the other lakes. The assumption of
equal fishing mortality, is considered to be an even stronger assumption, because the fishing mortality is
not just depending on the density in the lakes, but also largely depending on management rules such as
the relative amount of fishery, which differs a lot between the Randmeren, Grevelingen and lakes
IJsselmeer and Markermeer.
The estimated biomasses from the lakes IJsselmeer and Markermeer, Randmeren and Grevelingen are
integrated into an estimate of the total Dutch standing stock (Chapter 5).

Figure 4-5 The four large Dutch lakes

4.10.1 Standing stock lakes IJsselmeer and Markermeer
Estimates of the standing stock were calculated by combining the landings in lakes IJsselmeer and
Markermeer (Appendix A0 and Paragraph 2.8). The percentage yellow eel in the total landings was
estimated using the (representative) length data in the market sampling (Paragraph 2.1), sampled in
lakes IJsselmeer, Markermeer, Randmeren and Grevelingen. In total, 74% of the total retained catches
in biomass was estimated to be yellow eel. This percentage was used to convert the reported total
retained catches into yellow eel and silver eel retained catches. Estimates of the standing stock of yellow
eel and silver eel were subsequently calculated by combining the landings and the estimated fishing
mortality as following: biomass = landings/(1-exp(-F)), (Table 4-2). This resulted in an estimated
standing stock of 477 tonnes (355 tonnes yellow eel and 122 tonnes silver eel, Table 4-2) in 2018-2020,
an increase of 179 tonnes since the previous period (2015-2017). This method assumes that the fishing
mortality of silver eel is the same as the mortality of yellow eel. As there are many differences in
behavior when silver eel starts to migrate, this assumption is uncertain, because the increased mobility
of silver eel might also increase the catchability, which would lead to a higher fishing mortality. However,
the absence of fishing in the main migration period of silver eel (closed period in September-November)
since 2009, may have caused a lower fishing mortality for silver eel compared to yellow eel. Therefore, it
is uncertain whether fishing mortality on silver eel is similar to yellow eel, or if it is an over- or
underestimation.

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Table 4-2 Estimated mean landings (tonnes), fishing mortality, and yearly standing stock (tonnes) in
the lakes IJsselmeer and Markermeer per 3-year period.
IJsselmeer and Markermeer
Yellow eel (≥30cm)

Silver eel

Period

Landings

Fishing mortality

Standing stock (tonnes)

2006-2008

202

1.10

305

2009-2011

113

0.76

215

2012-2014

117

0.76

222

2015-2017

143

1.04

222

2018-2020

228

1.04

355

2006-2008

71

1.10

104

2009-2011

40

0.76

74

2012-2014

41

0.76

76

2015-2017

50

1.04

76

2018-2020

80

1.04

122

4.10.2 Standing stock Lakes Randmeren and Grevelingen
For the Randmeren, it is assumed that the eel density as estimated in the lakes IJsselmeer and
Markermeer can be used as basis for the density in the Randmeren, corrected for the difference in CPUE
in the electric dipping nets surveys at the different locations (Table 4-3). Because the electric dipping net
surveys in the Randmeren took place from 2012 onwards, for the first two periods (2006-2008 and
2009-2011) the CPUE from the period 2012-2014 is used to estimate the standing stock. This
methodology results in an estimate of 6.3 tonnes of silver eel in the Randmeren for the latest period
(Table 4-4).
For Grevelingen, the assumption is made that the eel density is the same as in the lakes IJsselmeer and
Markermeer. This results in a value of 9.2 tonnes of silver eel in the latest period (Table 4-4).

Table 4-3 Mean CPUE per reporting period (tonnes/sampled ha) of the electric dipping net in the
inshore surveys of the lakes IJsselmeer and Markermeer and the Randmeren per period.
CPUE
Lake

2006-2008

2009-2011

2012-2014

2015-2017

2018-2020

Markermeer

5.17

4.26

4.98

6.21

6.55

Ketel & Vossemeer
Randmeren-Oost

8.85*

8.85*

8.85

6.88

7.47

0.59*

0.59*

0.59

2.54

3.12

Randmeren-Zuid

0.47*

0.47*

0.47

3.11

2.19

Zwarte Meer

0.27*

0.27*

0.27

0.11

1.52

IJsselmeer/

* as no data was available for the Randmeren from before 2012-2014, for the first two periods (2006-2008 and 2009-2011, the
CPUE from 2012-2014 was assumed)

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Table 4-4 Density (tonnes/ha), surface area (ha), CPUE correction and yellow eel and silver eel
standing stock (tonnes) per lake.
2006-2008

2009-2011

2012-2014

2015-2017

2018-2020

Density yellow eel

0.0017

0.0012

0.0012

0.0012

0.0019

Density silver eel

0.00057

0.00040

0.00041

0.00041

0.00066

Ketel &

Surface area

4,067

4,067

4,067

4,067

4,067

Vossemeer

CPUE correction

1.71

2.08

1.78

1.11

1.14

Yellow eel biomass

11.5

9.9

8.7

5.4

8.9

Silver eel biomass

4.0

3.4

3.0

1.9

3.1

Surface area

6,318

6,318

6,318

6,318

6,318

CPUE correction

0.11

0.14

0.12

0.41

0.48
5.8

IJsselmeer/Marke
rmeer
IJsselmeer/Marke
rmeer

Randmeren-Oost

Randmeren-Zuid

Zwarte Meer

Total Randmeren

Grevelingen

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Yellow eel biomass

1.2

1.0

0.9

3.1

Silver eel biomass

0.4

0.4

0.3

1.1

2.0

Surface area

4142

4142

4142

4142

4142

CPUE correction

0.09

0.11

0.09

0.50

0.33

Yellow eel biomass

0.6

0.5

0.5

2.5

2.7

Silver eel biomass

0.2

0.2

0.2

0.9

0.9

Surface area

1,811

1,811

1,811

1,811

1,811

CPUE correction

0.05

0.06

0.05

0.02

0.23

Yellow eel biomass

0.2

0.1

0.1

0.0

0.8

Silver eel biomass

0.1

0.0

0.0

0.0

0.3

Yellow eel biomass

27.1

19.1

10.2

11.1

18.2

Silver eel biomass

9.3

6.5

3.5

3.8

6.3

13,902

13,902

13,902

13,902

13,902

Yellow eel biomass

Surface area

23

16.3

16.8

16.8

26.8

Silver eel biomass

7.9

5.6

5.8

5.8

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5

5.1

Overview national stock biomass

Overview

In this chapter the total eel stock biomass in the Netherlands is estimated for each period, based upon
the biomass estimates of the different water bodies as described in Chapter 3 and Chapter 4. In Chapter
7, these biomasses are used to calculate the key stock indicators as requested by the EC.
Stock estimates are provided for the periods 2006-2008, 2009-2011, 2012-2014, 2015-2017 and 20182020. In some cases, extrapolation between periods is necessary because insufficient data was available
for every region in all periods (Table 5-1). Extrapolation is needed for the Volkerak-Zoommeer,
Zandmaas, the Randmeren, ditches and the other regionally managed waters (Table 5-2). How
extrapolation was done for the specific water body with missing data, is explained in Chapter 3 and
Chapter 4.

Table 5-1 Eel biomass estimate availability (‘data’) per period and per water body.’-’ indicates that there
was not sufficient data available. * The non-WFD waters (ditches) data are not used per time interval but
are grouped in the analysis.

IJsselmeer/ Markermeer
Large lakes

Grevelingen

2006-2008

2009-2011

2012-2014

2015-2017

2018-2020

data

data

data

data

data

-

-

-

-

-

Randmeren

-

-

data

data

data

Regionally managed

Ditches*

-

-

data

data

data

waters

WFD

data

data

data

data

data

-

data

-

data

data

Nationally managed
waters

5.2

Volkerak-Zoommeer
Zandmaas

-

data

data

data

data

Others

data

data

data

data

data

National stock biomass

In Chapter 3 and Chapter 4, the biomass estimates for all eel (≥30 cm), yellow eel and silver eel, are
estimated for all water bodies in the Netherlands. An overview of these estimates per period is given in
Table 5-2. Subsequently, the total biomass is given by summing up the estimates of all water bodies per
period to get a total estimate of the eel standing stock in the Netherlands (Table 5-2). This total biomass
estimate shows that from the first period (2006-2008) to the second period (2009-2011) there was an
initial large increase in biomass with around 2,000 tonnes and from the second to the third period (20122014) there was a small increase (~ 500 tonnes). After the third period there was a very small decrease
of ~ 100 tonnes to the fourth period (2015-2017) and again a decrease of ~450 tonnes to for the most
recent period (2018-2020, Table 5-2). The current estimate is a standing stock of almost 5,000 (4,961)
tonnes of eel (≥30 cm) in the Netherlands. The yellow and silver eel biomass estimates for each scenario
(see Paragraph 3.2) and three-year period show the same trend as the total biomass, with initial
increasing biomasses, but a decline in the latest two periods for yellow eel. Silver eel biomass increased
until 2015-2017 but declined in the most recent period (Table 5-3).

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Table 5-2 Biomass estimates (all eel ≥ 30 cm, in tonnes) in scenario 2 (see Paragraph 3.2) for each period.
2006-2008

2009-2011

2012-2014

2015-2017

2018-2020

Regionally

Ditches

981

981

981

981

981

managed waters

WFD Waters

1,947

3,236

3,793

2,265

1,791

Nationally

IJsselmeer/Markermeer

409

289

298

299

476

managed waters

Grevelingen

31

22

23

23

36

Randmeren

36

26

14

15

24

Others (main rivers)

645

1,539

1,446

2,842

1,673

Total

4,049

6,093

6,555

6,425

4,981

Table 5-3 Total standing stock biomass (tonnes) estimates for yellow eel and silver eel (≥ 30 cm) for each
period and each scenario. Scenarios 1-3 differ in catch efficiency and habitat preference for all water bodies
except the large lakes (Chapter 3). The silver eel biomass estimates are used in Chapter 7
2006-2008

2009-2011

2012-2014

2015-2017

2018-2020

Yellow eel
Scenario 1

1,032

1,312

1,293

1,240

1,174

Scenario 2

3,280

5,093

5,034

4,773

3,869

Scenario 3

3,926

6,350

6,292

5,953

4,728

Scenario 1

274

291

394

421

352

Scenario 2

769

999

1,521

1,652

1,113

Scenario 3

907

1,222

1,915

2,088

1,365

Silver eel

5.3

Discussion

The values presented here show an initial increase in eel biomass, but a decrease in recent years (Table
5-3). This pattern is, however, not equal for the different water bodies. The biomass estimates in the
WFD-waters had a large influence on the trend in the total biomass. The estimate of the WFD-waters
shows very high biomass estimates in the second and third period, and lower values in the last two
periods (Table 5-2). Other waters do not follow the same trend. For example, the large lakes follow a
opposite trend compared to the WFD-waters, with high biomass in the first and last period. However,
with a lower biomass estimate, the large lakes are less influential on the total biomass estimate of the
stock. Also the other nationally managed waters (excluding the large lakes), follow a different trend.
They have a low biomass estimate in the first period and much higher values in the later periods.
Especially in the period 2015-2017 the value of the national waters is highly influential on the total
biomass estimate, with a much higher value (mainly due to the ‘Benedenrivieren’, see Table 3-9)
compared to all other periods (Table 5-2).

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6

Mortality during silver eel migration due to barriers

6.1

Silver eel barrier mortality

Silver eel suffer barrier mortality during downstream migration when passing through barriers. This
barrier mortality is one of the sources of mortalities which are used in the overall assessment as
presented in Chapter 7 (see the flow diagram of the stock assessment, Figure 1-1). This chapter
describes the methodology and data on which estimates of barrier mortality during silver eel migration
are based.
To update the data since the last assessment (Van de Wolfshaar et al., 2018) an inventory was held
among water boards to renew the information on barrier specifics concerning migration. In addition, an
update of the list of barriers that were replaced by a different type was made for barriers in the WFD
waters.
This chapter also deals with assisted migration in which silver eel is caught upstream from a barrier and
‘lifted’ across it, so called ‘trap and transfer’ mitigation (Paragraph 6.3).

6.2

Barrier types

There are different types of barriers (Figure 6-1):
1.

Pumping stations: pumping stations (Dutch: ‘gemaal’) are mainly used for the drainage of
low-lying land and pump water from a polder into another water. Most pumping stations are
situated in the areas in the Netherlands that lay below sea level and refrain the land from
flooding. In the Netherlands there are thousands of pumping stations (Figure 6-1b).

2.

Ship locks. Locks are built in places where the level of the water within a waterbody changes. A
ship lock allows ships and vessels to travel up or down a water body to a higher or lower water
level. The lock controls the depth in the lock, allowing for different levels at each side of the lock
(Figure 6-1a).

3.

Discharge sluices. Discharge sluices (Dutch: ‘spuisluis’) are built to control water levels, and
discharge excess water by periodically opening them when the water levels of the receiving
water body are lower than the ‘upstream’ water body (Figure 6-1c).

4.

Weirs. Weirs are built to control water levels in both running waters, i.e. streams and rivers,
and smaller polders. They can be lowered or lifted when the upstream water levels are too high
(Figure 6-1d).

5.

Hydroelectric power station (HPS). A hydroelectric power station uses flowing water to set a
turbine in motion. These stations are located on rivers and usually are large barriers. In the
Netherlands there are three HPS’s on two main national rivers. Two in the river Meuse and one
in the river Rhine (Figure 6-1d)

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Figure 6-1 An illustration of different types of barriers. Barriers range from simple, e.g. single ship lock
(a), to combinations, e.g. pump station and ship lock (b), to very complex sites consisting of a combination
of pumping stations, ship locks, sluices or other alternative routes for migration, for example this site at
IJmuiden in the North Sea Canal to sea (c). the more complex a site is, the more routes silver eel can
follow to pass the obstruction. Mortality rates per route can be different, e.g. through a HPS, and therefore
the distribution of eel passing via the different routes per site determines the overall mortality rate for the
entire site, for example this site at Linne in the Meuse (d).

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6.3

Assisted migration

Recent assisted migration (also called trap and transfer) initiatives, in which silver eel is caught above a
barrier and ‘lifted’ across it, are taken into account when calculating the overall migration mortality for
silver eel. Since 2011, several projects have started at migration barriers (mainly pumping stations) to
assist the migration of silver eel. Because not all silver eel passing the selected barriers for assisted
migration suffer from mortality or injuries, an assessment was done to estimate the absolute amount of
saved eel. In 2013, a selection of the main barriers was made (Winter et al., 2013a) to improve the
selection and efficiency of assisted migration initiatives. Applying location-specific mortality rates, the
overall amount of ‘saved’ eels was based on the mortality rate of the given site. This value is subtracted
from the migration mortality biomass estimate.

6.4

Model for estimating barrier mortality

Assessing the mortality of silver eels during their migration from inland water bodies to the sea is difficult
due to the large numbers of barriers. There is a huge amount of pumping stations, many ship locks and
three larger HPS’s in the Netherlands (Kroes et al., 2018; Belletti et al., 2020). To construct a model on
silver eel mortality caused by these barriers, knowledge on the following processes is necessary:
1)
2)
3)

Silver eel migration routes, when migrating from inland water bodies to the sea
The barriers that the silver eels encounter along these routes
Mortality rates during passage of barriers

For this assessment, a silver eel migration model was built, based on a hierarchy of water bodies,
providing a reasonable description of silver eel migration in the Netherlands (Figure 6-2). In this model,
silver eels are split into three ‘hierarchy levels’; each hierarchy level representing a water body type
where they start the migration route to the sea. The three hierarchy levels are:
1)

1st hierarchy (‘polder’ water bodies): water bodies which are below sea level and serviced by a
large number of small pumping stations. In the model, it is assumed that silver eel migrate
through a single pumping station in order to leave a polder (i.e. no multiple pumping stations in
sequence). For most polders, pumping stations discharge water into a ‘boezem’ water body (2nd
hierarchy, see below), which will face additional barriers. Pumping stations of polders close to
the coast can pump water directly into the sea, in which case the silver eels that survive the
passage of these sites do not face another barrier and are able to escape to the sea directly. In
the model, polder waters are represented by the wetted area of non-WFD waters (ditches, see
Paragraph 3.2);

2)

2nd hierarchy (‘boezem’ water bodies): water bodies such as canals, small inland lakes and
smaller streams and rivers. In the model, boezem waters are represented by all regionally
managed WFD water bodies (Paragraph 3.2). Boezem waters are either connected directly to the
sea or to large nationally managed water bodies (3rd hierarchy, see below) via larger pumping
stations, ship locks, weirs and/or discharge sluices. In case they are connected to national water
bodies, they face additional barriers.

3)

3rd hierarchy (‘national’ water bodies): large nationally managed water bodies such as sections of
the main rivers Rhine and Meuse (including downstream parts, Chapter 3), the freshwater lakes
IJsselmeer and Markermeer, Randmeren and Grevelingen (Paragraph 4.9). In the River Meuse
and the Rhine river branch Nederrijn/Lek, there are large HPS’s. National water bodies are
connected to sea mainly by discharge sluices (e.g. IJsselmeer, Lauwersmeer, Haringvliet),
and/or by large pumping stations (e.g. IJmuiden), always in combination with ship locks at each
of these locations or have an open connection (e.g. Nieuwe Waterweg).

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The framework of the model for migration routes is illustrated in Figure 6-2. The hierarchies as described
above are connected with each other and with the sea as presented by the arrows. For each arrow the
proportion of eel choosing that route and the proportion that will not survive a passage is estimated.
Barrier mortality occurs when silver eels pass from one hierarchy to another, or from one hierarchy to
the sea. The model thus assumes that barriers within the 1rst and 2nd hierarchy are never in sequence:
eel cannot experience the barriers that belong to the same hierarchy more than once. There are only a
few polder waters with two or even more boezem layers, in which polder waters are pumped into an
‘inner boezem’ and subsequently pumped into an ‘outer boezem’. Because the area of polder water that
has multiple pumping stations before reaching a boezem water is small, it is therefore of little influence
on the outcome of the model. For the 3rd hierarchy, on the different routes from the 2nd hierarchy to sea
via the national water bodies, different subsequent barriers can be passed along each of the potential
routes.
For the parameterization of the model, the migration routes as described in Winter et al. (2013a &
2013b) are used. For each possible route and three-year period, an average proportion of silver eel going
that route is calculated. Second, the proportion of eel that does not survive passing a barrier is
calculated for each three-year period (Paragraph 1.4). The proportion of eel that has not arrived at the
sea, but has migrated to the next hierarchy will subsequently encounter another barrier, where again a
proportion of eel will survive etc. In the next paragraph the estimated migration routes and mortality
estimates per hierarchy are described.

Figure 6-2 A conceptual model for estimating mortality during silver eel migration due to barriers; for
‘polder’ (1st hierarchy), ‘boezem’ (2nd hierarchy) and national waters (3rd hierarchy , see text). WKCs are
HPS’s in river sections of the national waters.
6.4.1

1st hierarchy: from polder to boezem or to the sea.

Migration routes
Most polders (1st hierarchy) have pumping stations that discharge water into the boezem (2nd hierarchy)
rather than to the sea. Only some coastal polders have pumping stations that discharge water directly to
the sea. In the model it is assumed that 80% of the eel in polder waters migrate to boezem waters
where additional mortality due to sequential barrier passage might occur. The remainder (20%) is
migrating directly from the polder to the sea, such as all polders in Zeeland and part of the polders in
Zuid-Holland, Friesland and Groningen.
Mortality
Silver eel migrating from the polder (1st hierarchy) to the boezem (2nd hierarchy) or directly to the sea
will encounter at least one pumping station. A fraction of these migrating silver eel suffer direct and
indirect mortality when passing this pumping station. The direct mortality is caused by the pumping
station damaging the eel. Indirect mortality can occur at pumping stations because eel that aggregate in
front of a barrier have a higher chance of being predated by piscivorous fish or birds. Also the risk of

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being captured by fishermen is higher around pumping stations when migrating silver eel aggregate
while searching for an opportunity to pass (e.g. Winter et al., 2020). A recent study demonstrated that
for migrating silver eel, pumping stations delayed migration but did not function as a permanent barrier
for most eel (van Keeken et al., 2020b). Here, only the mortality (including additional mortality of
injuries) when the eel pass through a pumping station is taken into account, because the indirect
mortalities have not been quantified and are expected to be small compared to the direct mortality.
Pumping stations can roughly be divided into three groups (Kunst et al., 2008):
1)

Pumps (72%):
a. 54% Propeller pumps,
b. 14% Centrifugal pumps and
c. ~5% Propeller-centrifugal pumps

2)

Archimedes’ screws (27%): A pump type that pumps water by turning a screw-shaped

surface inside a pipe.
3)

Water wheels (0.2%).

An overview of studies on the impact of different pumping station was made and is listed in Appendix C1.
This resulted in that especially the most common Propeller pump (Table 6-1) cause the highest mortality
when silver eel pass through. Other pumps, water wheels and Archimedes’ screws show lower mortality
rates compared to propeller pumps (Table 6-1, Appendix C1).
After passage of a pumping station, eel also suffer from internal injuries which results in delayed
mortality, where a fraction of 0.5 of the damaged eels were assessed to suffer delayed mortality
(Kruitwagen & Klinge, 2008). Therefore, mortality was calculated as direct mortality (%) plus a fraction
of 0.5 of the % damaged eels for each of the pump types. The average silver eel mortality during
passage of pumping stations was estimated as the weighted average of the mortalities for each type of
pumping station and estimated to be 34.7% (Table 6-1). The estimate of mortality at pumping stations is
the same for every three-year period.
Table 6-1 Calculation of the average pumping station mortality used to estimate silver eel mortality
during migration (see also Appendix C1).
Pump type

Proportion

Average mortality* (%)
(Appendix C1)

Weighted Mortality
(%)

Water wheel

0.002

0.0

0.0

Archimedes’ screw

0.27

12.0

3.2

Centrifugal pump

0.14

12.0

1.8

Propeller-centrifugal pump

0.05

9.0

0.4

Propeller pump

0.54

56.0

29.3

Pump Mortality**

34.7

* Mortality is % dead + half of the % damaged.

6.4.2

2nd hierarchy: from boezem to national waters or the sea

Migration routes
At larger boezem waters a combination of different man-made structures is usually present (see Figure
6-1 for examples). An up to date overview was made of the most important barriers for silver eel
migration from boezem to the national waters in the larger waters of the Netherlands (Appendix C2). The
most important barriers for silver eel migration are selected: 1) based on the size of the area that
discharges via the potential barrier and 2) based on the biomass distribution of silver eel as estimated in
Chapter 3 and Chapter 4 (Winter et al., 2013a & 2013b, and updates; Appendix C2). For each of these

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most important barriers, it is known whether a passage leads directly to the sea or to a national water.
Combining this information with information on the amount of silver eel per starting location/water body
(Chapter 3 and Chapter 4), allows for distribution estimates based on a so called ‘bottom up’ approach.
In this approach the estimates are not based on a pooled average (as used in the 1st hierarchy) but on
the real migration routes (see Appendix C2 for the results of this assessment). For each water board, the
biomass of starting eel in the boezem waters (2nd hierarchy) is divided over the different outlets with
potential barriers (fluxes along the routes to sea or national waters) according to Winter et al. (2013a,
2013b). Then for each of these fluxes of silver eel biomass per outlets with potential barriers the biomass
flux was corrected for barrier mortality for each specific site. In case the flux went into a national water
body, then the biomass flux corrected for mortality was added to the starting biomass of silver eel in the
receiving national water body. The total amount of silver eel biomass for a national water body was then
corrected for the mortality that these eels were expected to suffer on their route to the sea. This
approach results in estimates of biomass losses relative to the starting silver eel biomass for each of the
main barriers in the 2nd and 3rd hierarchy (Appendix C2).
Mortality
Similar to the migration routes, the mortality estimates for silver eel migrating from boezem to national
waters are based on an inventory of the most important migration barriers for silver eel (Winter et al.,
2013a & 2013b, and updates; Appendix C2). Given the mortalities of barriers weighted by the amount of
silver eel per barrier relative to the total amount of silver eel, the overall estimated mortality for a
passage from a boezem to national waters is 15.2% and for passage to the sea the estimated mortality
is 5.0%. These estimates are assumed to be the same for every three-year period.
6.4.3

3rd hierarchy: from national waters to sea, including HPS’s

The 3rd hierarchy consist of national waters. Within the national waters there are three hydroelectric
power stations (HPS), and apart from those there are mainly discharge sluices.
Hydroelectric Power Stations (HPS)
The overall mortality of silver eel migrating through a river site with a HPS depends on the proportion of
the silver eel that go through the HPS station and the mortality they suffer when passing this, relative to
the proportion of silver eel that pass through safer routes (weir, ship lock, fishway, see figure 6-1d). For
the HPS station Linne and Alphen mortality rate was initially 24% for the eels that passed through the
HPS station, and when corrected for the proportion that actually migrates through the station, resulting
in 15% at HPS Alphen and 17% at HPS Linne for the total flux of silver eel at these sites for the periods
2006-2008 and 2009-2011. Data on proportion of eels divided over the different routes at a site was
derived from telemetry studies (Winter et al. 2006, Jansen et al. 2007). In mid-November 2011, an
altered turbine management (Buijse 2009) was implemented that resulted is a reduction of mortality rate
for the hydropower stations from 24% to 19%. When corrected for the proportion that migrated through
the hydropower stations from more recent telemetry studies (Griffioen et al. 2020) this resulted in 13%
for HPS Alphen and 14% for HPS Linne for the periods 2012-2014, 2015-2017, 2018-2020. For HPS
Amerongen a hydropower mortality of 9.5% was determined (Kemper 2014). This value was taken as a
best guestimate for all periods.
Migration routes and mortality 3rd hierarchy other than HPS’s
Similar to the boezem waters (2nd hierarchy), the mortality estimates for silver eel migrating from
boezem to national waters are based on an inventory of the most important migration barriers for silver
eel (Winter et al., 2013a & 2013b, and updates; Appendix C2). Apart from the HPS’s, most of the
national waters are connected to the sea by discharge sluice systems which cause no mortality. This
leads to an estimate of the overall mortality rate from national water bodies (apart from mortality at
HPS’s) to sea of 2.0%. This estimate is higher than in previous assessments (0.5% van de Wolfshaar et
al., 2018), mainly because of two new insights. First, recent intensive telemetry studies (Winter et al.,
2019; Winter et al., 2020) on silver eel in the North Sea Canal area, including in lake Markermeer,
showed that a substantial part (~ 40%) of the silver eel migrate from this lake via the sluice-complex
Oranjesluizen to the North Sea Canal and via the pumping station sluice complex at IJmuiden to sea.

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Second, the overall mortality rate at the pumping station sluice complex at IJmuiden with recent
extensive telemetry studies (2017-2018) was demonstrated to be higher (15-28% per year, Winter et
al., 2019; Winter et al., 2020) compared to earlier estimates (2-3%, Winter, 2011). The main reason
causing this increased mortality was that a larger proportion of silver eel select a more hazardous route
via the pumping area than previously estimated. The two new insights combined, i.e. 40% of
Markermeer silver eels go to IJmuiden and the mortality rate at IJmuiden is much higher than earlier
assessed, causes that the overall mortality rate from all national water bodies to sea (of which only a
small proportion migrates to sea via IJmuiden, and the majority via other routes, e.g. via lake IJsselmeer
to the Wadden Sea, via Nieuwe Waterweg and Haringvliet to the North Sea) to be 2.0%, whereas in the
previous evaluation in 2018 this was underestimated at 0.5%. In this report these new insights are
applied to the calculations of migration mortality of all three-year periods.

6.5

Summary

6.5.1

Model scheme 2018-2020

Based on the migration routes and mortality estimates reported above, the model scheme was filled with
the estimated mortalities (Figure 6-3).

Figure 6-3 Migration scheme, listing the used to estimate overall migration mortality of silver eel. ‘WKC’ =
HPS. P: the percentage within each hierarchy (polder, boezem or national) of silver eel migrating to sea or
to the next level. Mortality estimates for the most recent period (2018-2020).
6.5.2

Mortality estimates per period and hierarchy

The final mortality percentages are listed in Table 6-2. For the 1st (polder waters) and the 2nd (boezem
waters) only one estimate is used for all periods. Changes in barrier mortality over time thus only occur
in the HPS’s (Linne and Alphen) and in the national waters (Table 6-2)
Table 6-2 Silver eel migration barrier mortality rates per period.
2006 – 2008

2009 – 2011

2012 – 2014

2015 – 2017

2018 – 2020

34.7%

34.7%

34.7%

34.7%

34.7%

From polder to sea

34.7%

34.7%

34.7%

34.7%

34.7%

Boezem to national waters

15.2%

15.2%

15.2%

15.2%

15.2%

Boezem to passage to the sea

5.0%

5.0%

5.0%

5.0%

5.0%

HPS Linne

17%

17%

13%

13%

13%

HPS Alphen

15%

15%

14%

14%

14%

HPS Amerongen

9.5%

9.5%

9.5%

9.5%

9.5%

Mortality national waters to sea

2.3%

2.3%

2.1%

1.5%

2.0%

From polder to boezem

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6.5.3

Total mortality rates

For the estimation of the key stock indicators in Chapter 7, a single estimate of barrier mortality for
migrating silver eel is needed. The migration routes and mortality rates as described above (Table 6-2)
are combined with the starting biomass estimates per three-year period and location (Chapter 3 and
Chapter 4) for each three-year period. This results in an estimate of the total proportion of barrier
mortality of migrating silver eel (Mbarrier). The proportion of barrier mortality showed a decrease from the
first period (0.17, 2006-2008, Table 6-3) to 0.13 in the latest period (2018-2020, Table 6-3). These
estimates are used in Chapter 7 for the estimation of the key stock indicators.
Table 6-3 Total silver eel barrier mortality for all hierarchies combined.
Period
Barrier mortality (Mbarrier)
2006-2008

0.17

2009-2011

0.16

2012-2014

0.14

2015-2017

0.11

2018-2020

0.13

6.6

Discussion

Given the large number of small polders (1st hierarchy) and the lack of site-specific data for most of
these sites, mortality rates for those waters are based on overall silver eel production estimates
combined with an overall calculated average mortality rate. For the boezem (2nd hierarchy) and national
waters (3rd hierarchy), a so called ‘bottom up’ approach was followed, using site specific mortality
estimates (Winter et al., 2013a &, 2013b, and updates). This approach yielded more accurate estimates
than the more general approach based on averages as used for the 1st hierarchy. However, the quality of
the underlying data that was used in the updated silver eel barrier assessments is highly variable and
often still incomplete. Some sites are very well studied, e.g. the sites with HPS’s in the River Meuse
(Winter et al., 2006 & 2007; Jansen et al., 2007; Griffioen et al., 2020), the discharge sluices complexes
in Haringvliet (Winter & Bierman, 2010) and at the sluices-pumping station complex at IJmuiden (Winter
2011; Winter et al., 2019 & Winter et al., 2020), but for other sites, e.g. ship locks and most of the
pumping stations, data on silver eel mortality at a specific site are usually lacking. However, for some of
these sites conditions have changed since these studies were carried out and updated estimations are
needed to determine current mortality losses due to pumps or HPS’s. Research that determined mortality
for eels that passed through HPS’s in the River Meuse was carried out in 2002 when average silver eel
length in the river Meuse was 65cm (Bruijs et al. 2003). However, the average length of silver eel in the
upper parts of the river Meuse increased over the years and now averages 83cm (period 2012-2020,
WMR data), which will result in higher mortality when passing the HPS’s. In future evaluations the effect
of increased average length on mortality should be determined. In addition, the relative proportion of
silver eels that pass through the turbines over other safer pathways (weir, sluices, lateral canals), will be
affected by changes in discharge patterns (e.g. increasing incidence of dry periods in summer and
autumn) and recent management measures (e.g. periodically closing turbines during nights in autumn
and additional fish friendly turbine operation adaptations). In future evaluations, additional studies and
data need to be incorporated to determine overall HPS’s mortality under the recently changed conditions.
Last, the barrier-mortality approach as used here for the 2nd and 3rd hierarchy waters can be further
developed to enable a full site-specific and data driven approach including the 1rst hierarchy. Several
maps and lists of barriers are available (e.g. Kroes et al., 2018; Buijse et al., 2009 and in the Amber
barrier atlas4, the National Fish Migration Route Map RWS/Nationale Visroutekaart RWS).

4 https://amber.international/barrier-atlas/

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7

Stock indicators

7.1

Stock indicators

Under the eel regulation (EC 1100/2007) MS’s are obliged to report on a list of stock indicators (Table
7-1, this is a similar table as Table 1-2, but listed here again for readability purposes). The stock
indicators are based on the biomasses and silver eel barrier mortalities as estimated in previous chapters
(Chapter 3,4,5 & 6) and additional information on retained catches. In this chapter, these key stock
indicators are estimated. Only B0 (pristine silver eel biomass, Table 7-1) is a constant value that was
calculated in 2010 (ICES 2010b). The B0 value for inland waters in the Netherlands is set at 10,400
tonnes (ICES 2010b). The other stock indicators vary per period.
In this assessment, mortalities during the yellow eel and silver eel stages are split into two groups. The
reason is that yellow eel mortalities apply over a sequence of years as the yellow eel stage usually takes
between 3-20 years. Silver eel mortality is assumed to apply during a single year in the life cycle of an
eel. The yellow and silver eel mortalities are combined in a single mortality rate, the ‘Lifetime
Anthropogenic Mortality’ (ΣA, Table 7-1). ΣA is the mortality that eel experience throughout their lifetime
and it is based on the ratio between the current silver eel biomass escapement (Bcurrent, Table 7-1) and
the best possible escapement under current recruitment levels (Bbest, Table 7-1).

Table 7-1 Overview of the main stock indicators to be reported to the EC. The MS’s are also obliged to
report on the amount of glass eel (eel <12 cm) that are harvested for restocking. These are not reported
here because this is not relevant for the Netherlands; no glass eel are harvested.
Indicator
Description
B0

Pristine silver eel biomass. An estimate of escapement in the absence of any anthropogenic impact and
at historic recruitment levels.

Bcurrent

Silver eel biomass estimate of the current silver eel escapement to the sea.

Bbest

The best silver eel biomass possible under current recruitment levels, i.e. silver eel biomass estimate
without anthropogenic influences on yellow eel and silver eel stock, i.e.

ΣF

Fishing mortality rate (yellow and silver eel, commercial and recreational).

ΣH

Anthropogenic mortality rate from other sources than fishing mortality. This is mainly barrier mortality
during downstream migration.

ΣA

7.2

Total anthropogenic mortality rate, i.e. the sum of ΣF and ΣH.

Yellow eel anthropogenic mortality rate (𝑭)

One of the stock indicators that needs to be reported to the EC is the total anthropogenic mortality rate
ΣA (Table 7-2). ΣA is defined here as the yellow eel fishing mortality over all ages, the silver eel fishing
mortality, and the silver eel barrier mortality (see Paragraph 7.4). The yellow eel fishing mortality rate 𝐹
is used as input to estimate the stock indicators (Paragraph 7.4)
In this section the yellow eel anthropogenic mortality rate 𝐹 is estimated. It is defined as the yellow eel
fishing mortality from both commercial and recreational catches. Yellow eel barrier mortality is not
estimated, because it is expected to be very low and because it will not influence the estimation of the
yellow eel standing stock, because it is already accounted for implicitly in the estimate of the yellow eel
standing stock. However, it might cause for a small underestimation of the anthropogenic mortality.
The yellow eel fishing mortality rate (𝐹) is estimated as a function of the proportions of retained catches
and the estimated biomasses of the standing stock, following the equation:

𝐹 = -log(1 - CatchR/(Biomass+ CatchR))

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where CatchR is the retained catch of yellow eel by commercial and recreational fisheries. Biomass is the
biomass of yellow eel (≥ 30 cm, tonnes) as estimated in Chapters 3 and 4. This calculation of the fishing
mortality is based on the assumption that all mortality during a year occurs at once. All fishing mortality
of yellow eel is assumed to take place before the surveys are conducted. The main fisheries on eel is
from May to August, because of the fisheries closure from September to 1 December (part of the Dutch
EMP5). Most of the electric dipping net surveys in regionally managed waters, ditches, and also the FYMA
survey in lakes IJsselmeer and Markermeer (Chapter 4) take place in the autumn after the period with
the main fisheries.
Based on the equation above, 𝐹 is calculated for each period and scenario (Table 7-2). The biomasses
were estimated in Chapter 3 and Chapter 4 and are presented in Table 5-3 and the yellow eel landings in
Table 2-1.

Table 7-2 Mean yearly yellow eel biomasses, mean yearly retained catches (landings) and mean yearly
fishing mortality rates (𝐹 ) for yellow eel for each period (scenario 2). The biomasses are derived from
Table 5-3 and the total yellow eel removals (landings) from Table 2-1.
Yellow eel
period

Standing stock Biomass

Retained catches

(tonnes)

(commercial and

Fishing mortality rate (𝐅)

recreational, tonnes)
2006-2008

3,280

725

0.20

2009-2011

5,093

309

0.06

2012-2014

5,034

223

0.04

2015-2017

4,773

201

0.04

2018-2020

3,869

278

0.07

7.3

Silver eel anthropogenic mortality

The silver eel anthropogenic mortality proportion α represents the fishing and barrier mortality during
migration from freshwater to the sea (Chapter 6). The mortality is calculated as the proportion of losses
due to anthropogenic mortality relative to the silver eel biomass at the start of migration:

𝛼 = 1 − (𝐵

− 𝐶𝑎𝑡𝑐ℎ𝑅 ) ∗ (1 − 𝑀

)/𝐵

Where Bstart (Table 7-3) represents the silver eel biomass before silver eel mortalities (migration and
fisheries) have occurred; CatchR, (Table 2-1) represents the retained silver eel catch; and Mbarrier
(Chapter 6) represents the proportion barrier mortality. The parameter 𝛼 is calculated for each
assessment period (Table 7-3).
As for yellow eel mortality, the fishing mortality of silver eel is assumed to take place before the surveys
are conducted (see Paragraph 7.1). Therefore, the silver eel biomass estimate before anthropogenic
mortality Bstart is assumed equal to the sum of the estimated standing stock biomass in autumn and the
silver eel landings.

5 With the exception of the water board Wetterskip Fryslan, where fishing in September - November is allowed due to a quota
system.

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Table 7-3 Silver eel biomass standing stock (as estimated in Chapter 3 and Chapter 4), total silver eel
retained catches (CatchR), the biomass prior to anthropogenic mortalities (Bstart), the barrier mortality
proportion (Mbarrier) and the total anthropogenic mortality proportion during migration from freshwater to
the sea (𝛼 ) for scenario 2.
Silver eel
Period

Standing Stock

CatchR

biomass (Ch3,4

(commercial)

Bstart

Barrier mortality

and5)

Anthropogenic

proportion

mortality

(Mbarrier)

proportion (𝜶)
0.37

2006-2008

769

280

1,049

0.17

2009-2011

999

175

1,174

0.16

0.29

2012-2014

1,521

140

1,661

0.14

0.21

2015-2017

1,652

143

1,795

0.11

0.18

2018-2020

1,113

201

1,314

0.13

0.26

7.4

%SPR, ΣA, Bcurrent and Bbest

To calculate the Lifetime Anthropogenic Mortality rate, ΣA, yellow eel and silver eel mortality estimates
were split into a fishing and a barrier component. Barrier mortality is only estimated for downstream
migrating silver eel and is thus assumed not to affect yellow eel. The estimated yellow eel and silver eel
mortalities (see previous paragraphs) are used to estimate the total ‘Lifetime Anthropogenic Mortalities’
(ΣA). To estimate ΣA, first the %SPR (Spawner per Recruit), Bcurrent (current silver eel escapement) and
Bbest (best possible current silver eel escapement) are estimated. The basis for the methods used is
formulated by ICES (WGSGIPEE, 2010a & 2011).
To estimate %SPR, the parameter 𝛼 (percentage of silver eels that die during migration, Paragraph 7.3),
and β, the proportion silver eel production out of the best possible silver eel production are used.
Parameter 𝛼 is calculated in Paragraph 7.2, and parameter β is calculated using the demographic model
(Chapter 4). However, the survey data for lakes IJsselmeer and Markermeer are not used in the model.
Instead, based on the matrices 𝐀 and 𝐀

(Paragraph 4.2), the age and sex specific maturation

probability (𝑀𝑔𝑖 ), the lengths at mid-age of the age classes and the length-weight relationships (Figure
2-4, Chapter 2), a ratio between the maturing biomasses for 𝐹 = 0 and for fishing mortalities equal to
the values as estimated in paragraph 7.2 (𝐹;Table 7-2) was calculated. This ratio of the maturing
biomasses is expressed as the proportion silver eel production out of the best possible production (if no
mortality had taken place). This proportion is represented by the parameter β. Subsequently, the %SPR
is estimated as:

%𝑆𝑃𝑅 = 100 ∗ 𝛽 ∗ (1 − 𝛼)
The estimate of the current escapement of silver eel Bcurrent is equal to the surviving part of the starting
value of silver eel (Bstart) after removal of all silver eel anthropogenic mortalities and is calculated as:

𝐵

= (𝐵

− 𝐶𝑎𝑡𝑐ℎ𝑅 ) ∗ (1 − 𝑀

)

Bcurrent and %𝑆𝑃𝑅 are used for the estimate of Bbest (the best possible escapement of silver eel, if all
anthropogenic mortalities for yellow and silver eel are zero). Bbest is calculated as:

𝐵

= 𝐵

/%𝑆𝑃𝑅

Subsequently, the Lifetime Anthropogenic Mortality rate is calculated as:
𝛴𝐴 = −ln (𝐵𝑐𝑢𝑟𝑟𝑒𝑛𝑡 /𝐵𝑏𝑒𝑠𝑡 )
The indicators %SPR, Bcurrent, Bbest and 𝛴𝐴 were calculated for five different periods (Table 7-4). The
results show that since the first period (2006-2008) the yellow and the silver eel stock biomass have

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increased until the latest period. The starting value of silver eel (Bstart, Table 7-4) increased from 1,049
tonnes in 2006-2008 to 1,795 tonnes in 2015-2017). In the latest period (2018-2020), however, it
decreased with almost 500 tonnes to an estimate of 1,314 tonnes. The anthropogenic mortality rate ΣA
showed a decreasing trend, with a huge decrease between the first and second period, and a slower
decrease until 2015-2017. However, in the most recent period, the anthropogenic mortality increased
again, to a value of ΣA = 0.79 (corresponding to 55% mortality). This is due to a lower estimate of the
standing stock compared to previous years, as well as an increase in the landings (344 tonnes in 20152017 vs. 479 tonnes in 2018-2020, Table 7-4). The results will be discussed in more detail in Chapter 8.

Table 7-4 Overview of all stock indicators per period. Yellow eel and silver eel stock estimates refer to eel
(≥30 cm). Values are for scenario 2 (best guess estimate, see Chapter 3). Values for Bcurrent (tonnes) and
ΣA for the other scenarios are listed in Table 7-5.
2006-2008

2009-2011

2012-2014

2015-2017

2018-2020

Yellow eel Yellow eel stock (tonnes)
Retained catch (tonnes)
𝐹
β

3,280

5,093

5,034

4,773

3,869

725
0.20
0.27

309
0.06
0.66

223
0.04
0.73

201
0.04
0.74

278
0.07
0.61

Silver eel

Silver eel stock (tonnes)
Retained catch (tonnes)
Mortality Barriers (prop)
𝜶

769
240
0.17
0.37

999
175
0.16
0.29

1,521
140
0.14
0.21

1,652
143
0.11
0.18

1,113
201
0.13
0.26

Bstart (tonnes)
Bcurrent (tonnes)
Bbest (tonnes )

1,049
634
3,759

1,174
837
1,791

1,661
1,311
2,270

1,795
1,463
2,420

1,314
974
2,153

%SPR (spawner per recruit)

16.9%

46.8%

57.8%

60.5%

45.2%

%LAM (anthropogenic mortality %)

83.1%

53.2%

42.2%

39.5%

54.8%

ΣA (anthropogenic mortality rate)

1.78

0.76

0.55

0.50

0.79

ΣH (barrier mortality rate)

0.22

0.19

0.20

0.18

0.18

ΣF (fisheries mortality rate

1.56

0.57

0.35

0.33

0.61

Lifetime

Table 7-5 Bcurrent (tonnes) and ΣA for all 3 scenario’s. The scenario’s represent the uncertainty of main
assumptions in the static spatial model. Scenario 2 is the best guess scenario (Chapter 3).
Scenario

Bcurrent

ΣA

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2006-2008

2009-2011

2012-2014

2015-2017

2018-2020

1
2 (best guess)
3

236

249

344

376

317

634

837

1,311

1,463

974

756

1,034

1,662

1,861

1,204

1

3.72

2.01

1.51

1.42

1.95

2 (best guess)

1.78

0.76

0.55

0.50

0.79

3

1.55

0.64

0.46

0.42

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8

8.1

Evaluation of the EMP

Precautionary approach and limit reference points

In this chapter the impact of the EMP is evaluated using the methods as developed by ICES (2014). To
be able to evaluate the status of the eel stock, ICES (2014) developed a precautionary approach (PA)
framework. The PA framework uses limit reference points (Blim and Flim, Table 8-1) reflecting stock states
that should be avoided. Within the eel framework, the limit reference points are set such that they take
the uncertainty of the limit reference points into account, hence the precautionary reference points (Bpa
and Fpa, Table 8-1) are set at the same value as the limit reference points (Blim and Flim).
For the eel stock, no reference points reflecting the total eel stock have been established. The eel stock
is divided over many water bodies in many countries, also outside the EU. This makes an assessment of
the total eel stock and the calculation of reference points extremely difficult. Therefore, a precautionary
diagram is developed for the eel case, such that it can be used by each MS separately. Because the
reference points for the ICES approach (Blim, Flim, Table 8-1) had not been established for eel, alternative
biomass and mortality reference points were developed (ICES, 2014; Table 8-1).
Blim: A universal provisional biomass reference point (Blim) is a level of exploitation which provides 30%
of the pristine (no anthropogenic mortality ever) spawning stock biomass (B0). In 2002, ICES advised to
set the biomass reference point (e.g. Blim) above the universal value, at a value of 50% of the virgin
spawning-stock biomass, to account for uncertainty, such that Bpa = Blim. The EU (Council Regulation
1100/2007), however, decided to set Blim at 40% of B0, in-between the universal level (30%) and the
level advised by ICES (50%).
Alim: Eel experience relative high levels of anthropogenic mortality in addition to fishing mortality
compared to other commercially exploited stocks. Therefore, the mortality reference point (Alim) includes
all anthropogenic mortality (A) and not only the fishing mortality (F). The EU Eel Regulation (Council
Regulation 1100/2007) has set the limit for the escapement of silver eel (Blim) at 40% of the pristine
escapement (B0). Alim is derived from Blim as follows: ΣA=-ln(40%)=0.92 (ICES 2018). Thus, an eel stock
with a biomass of escaping silver eel of 40% of B0 is estimated to correspond to a lifetime anthropogenic
mortality limit of Alim = 0.92. At low biomass, however, the anthropogenic mortality advised is reduced,
to reinforce the tendency for the stock to rebuild (ICES, 2018).
The status of a local eel stock (within an eel management unit) is in an undesirable state if it is below
either Blim or Alim.
Table 8-1 Reference points and stock indicators needed for the precautionary approach.
Reference point

Definition

Value

Blim

Biomass limit below which a stock is considered to have reduced reproductive
capacity.

40% * B0

Alim

Mortality rate limit above which a stock decline is expected.

0.92

B0

Silver eel biomass without any anthropogenic influences (pristine biomass).

10,400 t

Bcurrent

Silver eel biomass that currently (assessment year) escapes to the sea to spawn.

Table 8-2

ΣA

Life time anthropogenic mortality; the fishing mortality and the mortality outside
of fisheries (HPS’s, pumping stations etc.).

Table 8-2

Stock indicator

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8.2

Status of the eel stock in the Netherlands

To assess the stock status, first the current silver eel escapement biomass (Bcurrent) in relation to the
estimated pristine situation (B0) is calculated (Paragraph 7.3, Table 8-2) and subsequently plotted
against the current lifetime anthropogenic mortality rate (ΣA, Table 7-4, Figure 8-1).
The evaluation demonstrates that the status of the eel in the Dutch waters is still in the ‘red’ area of the
precautionary diagram (Figure 8-1) and thus remains in a situation regarded as undesirable, with high
mortality and low biomass. The current biomass of escaping silver eel is 9.4% of the pristine situation
which is below the target of 40%. The value of the current lifetime anthropogenic mortality (ΣA = 0.79)
lies below Alim (Alim = 0.92). However, the recommended mortality at the current estimate of the
percentage of escaping silver eel is below the current anthropogenic mortality (Figure 8-1, ‘red area’).

Table 8-2 Stock indicators used to evaluate the impact of the EMP on the biomass of escaping silver eel
and anthropogenic mortality. Biomasses are in tonnes (t).
Period
B0*
Bcurrent
100* Bcurrent /B0
ΣA
2006-2008

10,400 t

634 t

6.1%

1.78

2009-2011

10,400 t

837 t

8.1%

0.76

2012-2014

10,400 t

1,311 t

12.6%

0.55

2015-2017
2018-2020

10,400 t
10,400 t

1,463 t
974 t

14.1%
9.4%

0.50
0.79

*

Excluding coastal waters (2,600 t)

Figure 8-1 ICES modified precautionary diagram presenting the status of the eel stock in the Netherlands
in 2006-2008, 2009-2011, 2012-2014, 2015-2017 and 2018-2020 with respect to management targets.
The horizontal axis represents the status of the stock in relation to pristine conditions. The vertical axis
represents the impact made by anthropogenic mortality. ΣA = Lifetime anthropogenic mortality, presented
as a rate. Note that the x-axis is on a logarithmic scale.

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8.3

Discussion of the status of the eel stock in the Netherlands

The status of the eel stock in Dutch waters remains in a situation regarded as undesirable with low
biomass. In the precautionary diagram (Figure 8-1), the horizontal axis demonstrates the current
biomass in relation to the best possible biomass, while the vertical axis illustrates the level of
anthropogenic mortality on the stock. Below the interpretation of the axes is discussed in more detail.
8.3.1

Anthropogenic mortality (vertical axis in Figure 8-1)

A reduction in lifetime anthropogenic mortality (ΣA, Figure 8-1) can be achieved by reducing fishing
mortality and barrier mortality. A reduction in anthropogenic mortality is therefore the direct result of the
measures taken by a MS. In the Netherlands, the implementation of the EMP has resulted in a reduction
in ΣA between the first period (2006-2008) and the second-last period (2015-2017) from 1.78 to 0.50,
corresponding to an increase in the percentage spawner per recruit (%SPR) from 17% to 60% (Table
7-4). This reduction in ΣA was mainly the result of a decrease in fishing mortality, both commercial and
recreational: retained catches (landings) of both commercial and recreational fisheries strongly
decreased between 2006-2008 and 2015-2017. The greatest reduction in mortality was achieved in the
second period (2009-2011), showing the result of the implementation of the eel management plan
(2009), as a result of which the fishing mortality has reduced to a large extend (from ΣF =1.56 in 20062008 to ΣF = 0.57 in 2009-2011, Table 7-4). However, in the most recent period (2018-2020), the
mortality rate has increased from 0.50 to 0.79 (Table 7-4, Figure 8-1). This is caused by an increase in
the commercial fisheries (landings and effort), mainly in the lakes IJsselmeer and Markermeer (Table 2-1
& Appendix A0).
Barrier mortality (Mbarrier) showed a decrease from 17% to 13% (Table 6-3) from 2006-2008 to 20182020. The reduction is caused mainly due to measures at HPS’s (new management scheme), replacing
some pumping stations with ‘fish-friendlier’ pumping types and differences in eel distribution. From
2015-2017 to 2018-2020, the barrier mortality increased from 11% to 13% (Table 6-3). This increase is
not caused by new barriers (no HPS’s or pumping stations were placed during this period), but because
of a difference in the distribution of eel.
Lifetime anthropogenic mortalities were estimated using the retained catches and barrier mortalities in
relation to the standing stock. The current ΣA is calculated by taking the sum of the mortalities of all
ages. This is not the same as the ΣA that new recruits (glass eels) are expected to experience
throughout their inland life span. The ΣA in a new cohort recruits may differ from the current ΣA because
of different mortality rates compared to the current rates. This could be a result of effects of the
measures taken to reduce mortality, such as closed areas (main rivers and some large canals) and
reductions in fishing mortalities. The estimated ΣA consist of fisheries mortality over all life stages and
barrier mortality of silver eel. The silver eel biomass is a result of the surviving yellow eel after yellow eel
mortality occurred. Therefore, silver eel mortality contributes usually less to ΣA compared to yellow eel
mortality.

8.3.2

Biomass escaping silver eel (horizontal axis)

Between the periods 2006-2008 and 2015-2017, there was an increase in the biomass estimate of
escaping silver eel (Bcurrent) in every period, with the largest increase between 2009-2011 and 20122014. However, there was a decrease in the estimate of escaping silver eel biomass between the period
2015-1017 and 2018-2020 (horizontal axis; Figure 8-1). Large differences between periods in biomass
were not expected as an increase in glass eel recruitment will, at the earliest, result in an increase of
silver eel after 3-20 years. However, glass eel recruitment has not significantly increased after the
implementation of the EMP in 2009 (ICES 2020). The level of glass eel recruitment, depends only for a
small part on the status of the Dutch part of the eel stock. If one EMU alone, such as the Netherlands,
would reduce all anthropogenic mortality to zero, a recovery of the European eel stock is still not
necessarily expected. In order to maximize the chance of recovery, maximum protection of European eel
will have to be accomplished throughout its natural range, within and outside Europe. The responsibility
for improvement of eel stock lies with all countries in the natural range of the eel distribution.

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9

9.1

Conclusions and recommendations

Biomass estimates

The EC requested the MS’s to evaluate the status of the European eel stock. In this report, the data and
methods which were used to estimate the stock indicators for the Dutch part of the eel stock (Bbest,
Bcurrent, B0 and ΣA) were described. However, the estimates of the stock indicators used to evaluate the
status of the stock (Bcurrent, Bbest , B0, and ΣA, Table 7-4) need to be interpreted with care due to the
significant level of uncertainty surrounding these estimates. In this final chapter, the used methodologies
and results are discussed. Furthermore, recommendations are provided for further improvements of the
models.
The main results of this assessment are that in the most recent period (2018-2020), the current silver
eel escapement Bcurrent (974 tonnes), is still very much below the target of 40% of the estimated pristine
situation (B0). The anthropogenic mortality (ΣA = 0.79) is below Alim (Alim = 0.92). However, at the
current estimate of Bcurrent, the anthropogenic mortality was above the mortality as the target set in the
EMP (Figure 8-1). Therefore, the status of eel in Dutch waters remained in a situation regarded as
“undesirable” for both the silver eel escapement and anthropogenic mortality (red region in Figure 8-1).
After implementation of the EMP in 2009 the estimate of Bcurrent increased in every reporting period until
2015-2017: from 634 tonnes in 2006–2008 to 1,463 tonnes in 2015-2017. However, in the most recent
period (2018-2020) the estimate of Bcurrent declined again to 974 tonnes. The decline is mainly a result of
a lower estimate of the starting silver eel stock (Bstart), which results from a lower estimate of the yellow
eel stock: from 4,773 tonnes in 2015-2017 to 3,869 tonnes in 2018-2020 (Table 7-4). This decline in
yellow eel biomass estimate is a direct result from the national surveys other than the large lakes and
from the regional (WFD) surveys (Chapter 3). The biomass estimated in the national surveys other than
the large lakes declined from 2842 tonnes in 2015-2017 to 1,673 tonnes in 2018-2020 (Table 5-2). The
main decline was observed in the ‘Benedenrivieren’ (Figure 3-4) which declined from 2,147 tonnes to
1,142 tonnes between 2015-2017 and 2018-2020 (Table 3-9). The ‘Benedenrivieren’ is one of the larger
water bodies and (like all large rivers) the eel fishery is closed. Because the ‘Benedenrivieren’ comprises
such a large area, the influence of the survey outcome in this area is large on the total biomass. The
total biomass estimate in the regional waters (WFD waters) also declined from 2,265 tonnes in 20152017 to 1,791 tonnes in 2018-2020 (Table 5-2). Within the regional waters, Wetterskip Fryslân is highly
influential because it represents the highest biomass estimate (Appendix A3). In this region, there is also
a lot of restocking of glass eel, which could cause fluctuation in the biomass estimate between periods,
because also waters with glass eel restocking are monitored. Since eel fishing is based on a yearly set
quota (36.6 tonnes for all fishermen), the lower biomass cannot be related to increased catches. The
biomass in lakes IJsselmeer and Markermeer showed an opposite trend between 2015-2017 to 20182020, with an increase in the standing stock biomass from 299 tonnes to 476 tonnes (Table 5-2).
However, also the estimated fishing mortality is very high in lakes IJsselmeer and Markermeer (F =
1.10) and the landings have also increased (from 193 to 308 tonnes, yellow and silver eel combined,
Table 4-2).
For all components of the standing stock biomass estimates, the accuracy is low. For the static spatial
model (Chapter 3), main sources causing low accuracy are the selectivity of the electric dipping net and
the habitat preference (Paragraph 3.2). However, apart from the selectivity and the habitat preference
there is probably also a high level of sampling error. Even though the water bodies have been sampled at
least once in every three-year period, and the number of hauls is substantial, the amount of sampling
per water body is still small. Variation between years can arise due to the condition during sampling
(water level, weather, exact location, time in the year, sampler), which cause additional sampling error.
Also, high variation may be caused by sampling in water bodies were restocking occurred in recent
years. How much of the changes in eel standing stock biomass is caused by sampling error is impossible
to say. Inaccuracy in the demographic model is mainly caused by low accuracy of many components of
the input data. The biological keys and natural mortality (Chapter 2) are uniform in time and the same

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keys are assumed as for all water bodies. Also, the relative selectivity by the survey gear per length is
unknown. These cause uncertainty in the estimated fishery mortality values, and as a consequence in the
biomass estimate. For the large lakes Grevelingen and Randmeren, strong assumptions had to be made,
also causing a large amount of uncertainty.
9.1.1

Pristine biomass estimate (B0)

Bcurrent and ΣA are not the only stock indicators that affect whether the state of the eel stock in the
Netherlands is in a desired state and reaches the goals set out by the Eel Regulation (Chapter 1). The xaxis of the precautionary diagram represents the ratio between Bcurrent and B0 and thus depends highly on
the estimate of B0. The B0 value for inland waters in the Netherlands is set at 10,400 tonnes. However,
the uncertainty of the value is large and has been subject to discussion. Initially the pristine silver eel
biomass (B0) in the Netherlands, was set at 10,000-15,000 tonnes (Klein Breteler, 2008). In a first
review (Eijsackers et al., 2009) it was concluded that the range was wider and that B0 was between
6,500-20,250 tonnes. However, ICES (review of the national eel management plans, ICES 2010b) did
not accept all arguments of Eijsackers et al. (2009) and set B0 at 13,000 tonnes. A second review
(Rabbinge et al., 2013) concluded that the method to calculate B0 was fundamentally of good quality
with respect to adhering to the guidelines set by the Eel Regulation. However, the estimation of the value
of B0 is generally acknowledged to be extremely difficult. Due to limitations in data from earlier periods,
the variation in numbers per water body, historical restocking levels and uncertainties about density
dependent natural mortality, it is effectively impossible to estimate a reliable estimate of B0 for the
Netherlands.

9.2

Biological keys

The maturity-at-length and the sex ratio-at-length were analysed with a GAM instead of a binomial GLM
(Chapter 2). GAM’s are non-linear and therefore do not have a forced shape. In contrast, a binomial GLM
has a fixed shape between 0% and 100%. Because of this, the previous use of the binomial GLM had
strong assumptions, such as a 100% male sex ratio at small lengths in the sex ratio-at-length key and
100% chance of maturation at large lengths in the maturation-at-length key. These assumptions did not
fit the data distribution and biology very well. Because GAM’s do not have fixed shapes of the fitting
curve, GAM curves fit the data much better compared to the binomial GLM. On the other hand, in a GAM
no underlying relationship is assumed, therefore the final shape has to be chosen by vision and is
consequently partly a result of expert judgement.
The new maturity-at-length had substantial impact on the final results, because a smaller proportion of
eel was assumed to grow into silver eel within a year compared to the binomial GLM. The growth-atlength curve (Paragraph 2.6) also changed. As growth in eel is different in that eel can grow to very large
lengths, a von Bertalanffy growth fit was not necessarily expected. However, it fitted the data quite well
and was therefore used for this assessment.
Natural mortality depends on many factors, such as predation, water temperature, pollution and food
conditions, which makes it a difficult parameter to assess. Natural mortality is also unlikely to be the
same for all stages and is also not constant through time. The natural mortality used in the demographic
model (Chapter 4) is based on Dekker (2000), who made a best guess based on literature. The abovementioned factors cause the used value of natural mortality (M = 0.138) to be highly uncertain.

9.3

Static spatial model

9.3.1

Regionally managed waters

In the biomass assessment for the regional managed water bodies WFD fish survey data was used
(Chapter 3). A problem with this data is that not all water bodies are sampled in the same manner. Some
water boards sample more frequently than others. Also, even though the sampling intensity has
increased, the sampling does not cover all water bodies. The choice of the waterbody, but also the

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location and timing within the water body are important for accurate comparison of the density between
water bodies and years. As the sampling has a time frame of six years, a six-year moving average was
calculated. For each three-year period, the six years closest to the three-year period were chosen. This
allows one to detect changes over time. This differs from previous reports (Bierman et al., 2012; van de
Wolfshaar et al., 2015 & 2018), were a single estimate for all years was used. As a consequence,
irregular or inconsequent sampling has a higher influence on the final biomass estimate.
9.3.2

Nationally managed waters

The most important causes of uncertainties in the biomass estimates of the nationally managed waters
are:



In the assessment of the nationally managed waters, the biomass estimate of one river section
(Benedenrivieren) dominated the overall biomass estimate of the nationally managed waters.
The Benedenrivieren have a relative large surface area (Figure 3-4), and as a consequence a
high biomass estimate, but also the survey density is high (Table 3-6). In this area, the influence
of silver eel migrating from other areas or countries may be high, because it is the area closest
to the coast where silver eel might concentrate before starting migration. However, because the
water body is also relatively wide, the assumption of the habitat selection has a large impact
compared to other smaller waterbodies.



Different river regions are surveyed in different months. As a result, water temperature, eel
behaviour and silver eel migration activity may differ because of the sampling period, causing
additional noise in the estimations.



In the current assessment, the eel stock in the large lakes (IJsselmeer, Markermeer, Randmeren
and Grevelingen) was determined using the demographic model and the landings. This method is
a bit cumbersome. If research was done on a better understanding of eel distribution in the lakes
using all available surveys, upscaling eel densities from the littoral zone to lakes as a whole
could be carried out to validate the results from the demographic model.

9.4

Demographic model

The main decreasing stock trends since 1989 in lakes IJsselmeer and Markermeer could be explained
reasonably well by the demographic model (Chapter 4), but only to a certain extent. For example, the
increase in eel numbers 7+ years after arrival in Lake IJsselmeer in recent years is not captured by the
model. Also, small changes in the data are not captured. For this report, several updates were made to
the model. The most important updates of the demographic model were:



The model is fitted to survey data of lakes Ijsselmeer and Markermeer together, due to scarcity
of data in lake Markermeer in some years.



Different periods for which a single F estimate was calculated were changed such that the
periods still represented the changes in eel fisheries management, but also such that the number
of years for each period does not fluctuate too much.



The length-class based fit between model and data that was previously used, has been changed
to an age-class based fit to allow for a better comparison between the model and the survey
data.



The moment of comparison of the model with the survey data was moved from April to October,
to better fit the ages of the individuals in the survey data.



The updated biological keys were used as input and a different initial sex ratio was assumed.

Several sensitivity analyses showed that the estimated F value by the demographic model is sensitive to
differences in the biological keys. Although the only parameter that can change over time is the fishing

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mortality, the model is sensitive to the assumed maturity-at length, growth rate and initial sex-ratio. As
eel mature to silver eel they migrate to the ocean. This means that they leave the lakes, which means
that they are also ‘removed’ from the model. If this happens at smaller lengths, eel have left the system
at an earlier age, which has consequences for the estimated F.
The demographic model also assumes that the selectivity of the survey gear (FYMA, Chapter 2) is equal
for all length classes at ages from 2 years and older. However, the selectivity of the survey gear is not
known. Changes in the assumption of the survey gear at length will influence the outcome. Similarly, a
single estimate of F is calculated for all eel above the minimum landing size.
Last, the estimate of natural mortality is highly uncertain. It is assumed constant for all ages and sizes,
which is unlikely to be true as smaller eel are more prone to, for example, predation. Different
assumptions of M will result in different estimates of F, and thus in a different biomass estimate.

9.5

Barrier mortality

Water boards did invest substantially in improving migratory opportunities at migration barriers, but
most solutions targeted to facilitate upstream migration. Potentially, this has improved glass eel
immigration into inland waters and as a consequence indirectly enhanced the potential silver eel biomass
starting to migrate in the different waters. Mitigation of mortality in a downstream direction is more
difficult since it requires replacing pumping stations or HPS’s or deflecting silver eel to alternative routes
with no mortality, for which effective measures are still largely lacking (Kroes et al., 2013).
Much investment is still being carried out by the water boards to improve upstream migration along
barriers into and within inland Dutch waters (measures for to the WFD). This may have led to an
increased rate of immigration of glass eel into inland waters and. However, little is known on the
immigration of glass eel and distribution of glass eel over inland water, and no quantification of the
overall outcome of these migration mitigation measures at barriers can be made at present.
As was demonstrated for a validation with extensive telemetry studies and mark-recapture experiments
for the North Sea Canal catchment (see Appendix C3), the estimate in the model as described in Chapter
6 is reasonable.
9.6

Unquantified sources of anthropogenic mortality:

The main sources of mortality of European eel in the Netherlands are certainly the fishing mortality and
the mortality caused by barriers. However, there are other sources of mortality that have not been
quantified and may be substantial. The main sources are:






Poaching (unreported landings or illegal removals).
Yellow eel mortality in HPS’s and pumping stations.
Impact of (human-induced) viruses, parasites and pollution.
Bycatch mortality of undersized eel. Most landings originate from fykes. Only a small amount of
undersized by-catch is expected in this fisheries. However, also (~ 20%) of the catches are caught
with a longline (Dutch: ‘hoekwant’). Undersized bycatch and its survival of this gear is unknown.




Catch and release mortality in recreational fisheries.
Mortality by ship propeller strike. Sometimes, substantial numbers of damaged silver eels are
found at the shores of the river Waal where heavy shipping traffic occurs. Also, in our telemetry
studies, we still have a substantial part of silver eel disappearance during downstream migration in
rivers and canals that cannot all be attributed to other mortality causes. Ship traffic impact is a
potential candidate factor in these cases. So far, these observations are only anecdotal. There are,
however, no research or dedicated studies available on the impact of ship traffic on silver eel. This
can be tackled in meta-analyses of many telemetry studies combined, which is currently
attempted by a cooperation of researchers with the European Tracking Network and EU-Cost
action.

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9.7

Recommendations

In this chapter an overview of (previous) recommendations for further adjustments to improve the
quality of the assessment for the next evaluation is given.
9.7.1

Spatial Model

One of the most important sources of uncertainty in the spatial model are the catch efficiency and the
habitat preference. The recommendation to study these effects has been made since the first evaluation
report (Bierman et al., 2012), but no progress was made. The reason is that these assumptions are
extremely difficult to assess. However, it is still needed to at least get some more knowledge of both
uncertainties. In addition, especially for wider water bodies, assumptions of the distribution of eel over
the water body may lead to unrealistic values.
9.7.2

Demographic Model

The assessment outcome is sensitive to the biological keys. To interpret present-day data or historical
stock trends, a good index of recruitment, trends in sex-ratios, sex specific growth rates, natural
mortalities and migration rates are required. Because eel recruitment has fallen sharply, it is probably
unrealistic to assume that vital parameters have remained constant over time. At present the biological
parameters are assumed to be constant over the entire time period. As more biological data is sampled,
biological keys that are time or location specific should be analysed further. In addition, the natural
mortality estimate is only a crude estimate and assumed to be constant over all lengths, ages and
periods. A more realistic estimate of natural mortality should be investigated. Also, the calculation on the
large lakes is based assumptions and research should be was done on a better understanding of eel
distribution in the lakes using all available surveys.
9.7.3

Silver eel migration model

For the silver eel migration model as used for boezem and national waters (2nd and 3rd hierarchy), the
division of silver eels that end up at a certain barrier site over the different migration routes is needed.
For some sites, good data on route selection is available, e.g. at the HPS’s in the Meuse (Winter et al.,
2006 &, 2007; Jansen et al., 2007) and the large ship lock/sluice/pumping station complex at IJmuiden
(Winter, 2011). However, on most sites, divisions of silver eel are mainly based on assumptions and
extrapolations from research on other sites.
In addition, for some sites conditions have changed and updated estimations are needed to determine
current mortality losses due to pumps or HPS’s. Mortality depends on the eel length and discharge
patterns. In future evaluations, additional studies and data need to be incorporated to determine overall
HPS’s mortality under the recently changed conditions.
9.7.4

Immigration of glass eel along barriers

To quantify and determine the effects of improved migratory opportunities for glass eel and how this
results in increasing local yellow eel production and silver eel escapement, dedicated studies on
population estimates of glass eel at barriers can be carried out, as has been done for the North Sea
Canal catchment, with an estimated 10 million glass eels entering at IJmuiden in 2018 (Winter et al,
2020). When these approaches are carried out alongside the main immigration routes for glass eel into
Dutch waters, such a quantification can be made.
9.7.5

Restocking

In the Netherlands restocking of glass eel and ongrown eel (eels that are grown in culture facilities for
some time before being restocked, also called “pre-grown”, ICES 2016) exists for decades. After the
decline of glass eel availability, this commercial restocking lessened due to the increase of the price of
the glass eel. After restocking became one of the management measures in the Dutch eel management
plan (EMP), restocking was financed by public money, causing the amount of restocked glass eels and
ongrown eel (elvers) to increase (from 818 kg in 2006-2008 to 3024 kg in 2018-2020, ICES 2020b). The

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restocking is commissioned by the ministry of Agriculture, Nature and Food quality (LNV) and is executed
by the DUPAN foundation (www.DUPAN.nl), a foundation representing Eel processers, fish farmers and
eel fishermen. Ongrown eels are usually bought from an aquaculture company in the Netherlands. The
latest ICES advice (2020a) states:
‘ICES notes that the restocking of eels, which is considered a management action in the EU
regulation and in many eel management plans, is reliant on a glass eel fishery catch.
Evidence shows that translocated and stocked eel can contribute to yellow and silver eel
production in recipient waters, but information on the contribution to actual spawning is
missing because of a general lack of knowledge of eel spawning. Internationally coordinated
research is required to determine any net benefit of restocking on the overall population,
including carrying capacity estimates of glass eel source estuaries, detailed mortality
estimates at each step of the restocking process, and performance estimates of stocked vs.
non-stocked eels. Estimation of the prospective net benefit should be carried out prior to
any restocking activity, such as increasing silver eel escapement by restocking to attain
stock recovery. Restocking should take place only where survival in silver eel escapement is
high, and it should not be used as an alternative to reducing anthropogenic mortality.
Where eel are translocated and stocked, measures should be implemented to evaluate their
fate and their contribution to silver eel escapement. Such measures should include
regionally-coordinated mass marking of eels to distinguish stocked eels from natural
recruits in future scientific surveys.’
Because of the stocking practices in the Netherlands, stocked eel are indirectly included in this
assessment, because it is not possible to distinguish between eel originating from natural migration and
stocked eels. It is therefore recommended to follow the ICES advice and conduct the marking of all
stocked eels before release.

9.8

International “level playing field” stock indicators

As many other European countries (France, UK, Ireland) are using similar spatial models to estimate
yellow eel standing stock and silver eel production, close international cooperation and collaboration will
enhance the quality and uniformity of these models in the future. In addition, fundamental differences
exist among the Netherlands, Belgium, Germany and the UK with respect to converting fisheries landings
to silver eel production, selection of the reference period and correcting for glass eel stocking when
calculating B0. Germany, Belgium and the UK probably underestimated B0 (ICES, 2010). Standardization
of assessment methods is of utmost importance to ensure the recovery of the European eel stock and its
sustainable exploitation.
9.9

Future of the eel advice (ICES WKFEA)

In this report, the estimated key stock indicators have been evaluated in relation to management
targets/limits as formulated in the EC Eel Regulation, using the modified ICES precautionary diagram
(Chapter 7 and Chapter 8). However, the Advisory Committee (ACOM) of ICES is reluctant to advise on
the status of the eel stock using these targets, because they have not been scientifically tested to ensure
that they are precautionary and will lead to a recovery of the eel stock. ACOM therefore only analyses
the level of recruitment compared to levels before the recruitment had dropped (ICES, 2020a). For this
reason, the ICES workshop WKFEA “Future of the Eel Advice” was initiated (February 2020). The
objective of WKFEA was to discuss the current advice framework, consider options for future
assessments and draft a roadmap towards recommendations for an adapted or completely new advice
framework on fishing opportunities and, potentially, other anthropogenic pressures on European eel. This
has led to a roadmap describing the (ICES) workshops and (EU) projects aimed at developing a
population model that would include the entire stock, which would lead to new management targets in a
benchmark now proposed in 2026-2027 (ICES, 2021).

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•

Winter HV, Griffioen AB, van de Wolfshaar KE. (2013b). Knelpunten inventarisatie voor de uittrek van
schieraal t.b.v. ‘Paling Over De Dijk’. IMARES-Rapport C134/13.
Winter HV, van Keeken OA, Brockötter J, Griffioen AB (2019). Migratiepatronen en –knelpunten
tijdens uittrek van schieraal uit Noorzeekanaal en ommelanden, inclusief Markermeer: Onderzoek met
akoestische telemetrie en PIT-tags 2017-2018: eindrapport. Wageningen Marine Research Report
C053/19.
Winter, H.V., Griffioen, A.B., de Bruijn, P. (2020). Evaluatie trekvisonderzoeken Noordzeekanaal en
Ommelanden: Onderzoek in het kader van samenwerkingsverband Ecologische Verbindingszone
Noordzeekanaal en Ommelanden Fase 1. Wageningen Marine Research Report C015/20
Yokouchi, K., Aoyama, J., Miller, M.J., McCarthy, T.K. & Tsukamoto, K. (2009). Depth distribution and
biological characteristics of the European eel Anguilla anguilla in Lough Ennell, Ireland. Journal of Fish
Biology 74, 857-871.

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Appendix A0 Retained catches and effort per region

Figure A1. Eel retained catches (tonnes) per region and year (source: RVO).

Figure A2. Eel fyke effort (number of fyke weeks, different types of fykes combined) per region and year
(source: RVO). ). Effort is self reported by the fishermen.

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Figure A3. Eel effort per gear type in lakes IJsselmeer and Markermeer per year (source: RVO). Effort is
self reported by the fishermen. Some corrections have taken place from previous graphs.

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Appendix A1: Water Framework Directive (WFD) water types
Table A1. Water body types defined within the WFD in the Netherlands that were taken into account in
this study of standing stocks in regionally managed waters.
Water type code

Description

M1a

Buffered ditches

M2

Weakly buffered ditches

M3

Buffered canals (regional)

M6a/b

Large shallow canals with/without shipping

M7a/b

Large deep canals with/without shipping

M8

Buffered peatland ditches ("laagveensloten")

M10

Peatland canals ("laagveen kanalen")

M14

Shallow, relatively large, buffered lakes

M20

Deep, relatively large, buffered lakes

M23

Shallow, large, calcium-rich lakes

M27

Shallow, relatively large, peatland lakes ("laagveenplassen")

M30

Weakly brackish waters (0.3 - 3 g Cl/l)

R4

Permanent, slow flowing, upper part stream on sandy riverbed

R5

Permanent, slow flowing, middle- or lower part stream on sandy riverbed

R6

Slow flowing small river on sandy/clay riverbed

R7

Slow flowing river/side stream on sandy/clay riverbed

R8

Fresh tidal waters on sandy/clay riverbed

R12

Slow flowing, middle- or lower part stream on peat riverbed

R13

Fast flowing, upper part stream on sandy riverbed

R14

Fast flowing, middle- or lower part stream on sandy riverbed

R15

Fast flowing small river on siliceous riverbed

R17

Fast flowing, upper part stream on calcium rich riverbed

R18

Fast flowing, middle- or lower part stream on calcium rich riverbed

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Appendix A2: Eel biomass per water type per period
Table A2. Density and biomass of eel (≥30 cm) based on sampling data of WFD water bodies assessed
per water type per period following scenario 2. Note that non-WFD water bodies are not included.

2006 - 2008

2009 - 2014

2015 - 2020

Total area
(ha)

Density
(kg/ha)

Biomass
(tonnes)

Density
(kg/ha)

Biomass
(tonnes)

Density
(kg/ha)

Biomass
(tonnes)

M1a

156

0.0

0.0

1.8

1.2

0.4

0.3

M2

10

3.7

0.2

5.5

0.2

4.3

0.2

M3

3,324

2.1

21.8

3.7

38.4

1.2

12.7

M6a

603

1.4

2.4

5.8

10.3

6.7

12.0

M6b

1,780

3.4

17.9

6.9

35.9

4.8

24.9

M7a

13

3.7

0.1

9.7

0.4

6.9

0.3

M7b

3,435

5.2

48.2

7.2

67.1

8.2

76.2

M8

1,148

0.7

2.4

0.8

2.5

0.1

0.2

M10

1,362

0.1

0.3

8

39.2

1.8

8.9

M14

20,902

10.0

531.1

33.4

1,776.9

17.0

907.2

M20

4,444

7.3

82.2

10.0

112.5

9.7

109.5

M23

90

3.7

0.9

0.0

0.0

6.9

1.7

M27

22,738

4.4

252.6

19.1

1,091.6

6.1

348.6

M30

8,182

3.7

76

5

104.0

1.2

24.6

R4

73

4.8

1.7

2.2

0.8

0.5

0.2

R5

1,221

2.4

10.5

3.4

15.2

1.6

7.3

R6

3,414

8.6

77.5

12.7

114.2

12.7

114.9

R7

2,272

3.7

21.2

37.9

218.4

7.8

45.1

R8

20

3.7

0.2

4.7

0.3

5.3

0.3

R12

65

2.9

0.7

17.4

4.1

2.7

0.6

R13

4

3.7

0.1

9.7

0.2

6.9

0.2

R14

16

3.7

0.2

10.5

0.6

26.9

1.5

R15

37

3.7

0.4

9.7

1.1

6.9

0.8

R17

7

3.7

0.1

9.7

0.4

6.9

0.3

R18

52

2.0

0.4

8.5

1.6

19.4

3.7

Total

75,368

WFD- water

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1,149.1

3,637.1

1,702.2

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Appendix A3: Eel biomass per water board
Density and biomass estimates were done in the same way as was done per water type (paragraph
4.2.3), for each water board in the periods 2006–2008, 2009–2014 and 2015–2020. Both density
(kg/ha) and biomass (tonnes) were estimated. Biomass estimations from the most recent period (2015–
2020) were for most water boards lower than those of the period 2009–2014. Only a few of the water
boards had a higher estimated biomass and the estimations for the remaining were more or less similar.
Table A3. Density and biomass of eel (≥30 cm) based on sampling data of WFD water bodies assessed
per water board per period following scenario 2. Note that non-WFD water bodies are not included.

2006 - 2008
Water board

Total area
(ha)

Density
(kg/ha)

2009 - 2014

Biomass
(tonnes)

Density
(kg/ha)

2015 - 2020

Biomass
(tonnes)

Density
(kg/ha)

Biomass
(tonnes)

Aa en Maas

470

4.8

7.2

1.4

2.2

0.6

0.9

Brabantse Delta

2,358

15.7

96.2

3.8

23.2

3.7

22.6

De Dommel

391

1.0

1.3

1.7

2.1

1.4

1.8

Drents Overijsselse Delta

13,810

4.0

141.0

9.1

319.1

4.3

149.5

Hollandse Delta

925

4.8

11.7

10.1

24.6

6.4

15.4

Hoogh. Amstel, Gooi en Vecht

8,623

9.8

214.5

6.2

134.6

8.4

182.7

Hoogh. De Stichtse Rijnlanden

225

8.4

65.9

4.7

3.3

2.5

1.8

Hoogh. Hollands Noorderkwartier

4,714

3.2

43.4

8.1

110.5

4.2

57.3

Hoogh. van Delfland

298

4.8

4.5

9.7

8.9

4.8

4.4

Hoogh. van Rijnland

4,679

4.8

58.4

16.4

199.8

7.5

91.6

Hoogh. van Schieland en de Kr.

1,084

3.7

11.6

9.6

30.4

13.2

41.7

Hunze en Aa's

2,250

4.1

25.3

8

49.8

4.9

30.5

Noorderzijlvest

3,017

4.8

37.7

10.6

83.5

3.2

25.4

Rijn en IJssel

518

1.8

3.0

0.4

0.8

0.3

0.5

Rivierenland

799

6.0

15.0

4.1

10.2

6.7

16.7

Scheldestromen

10

4.8

0.1

10.1

0.3

6.4

0.2

Vechtstromen

2,816

7.1

53.6

9.9

74.5

9.7

73.5

Waterschap Limburg

289

8.3

8.5

9.4

9.7

15.5

16.0

Waterschap Vallei & Veluwe

414

0.8

1.1

2.5

3.3

2.5

3.2

Wetterskip Fryslân

16,193

7.8

323.5

37.3

1553.2

12.3

514.2

Zuiderzeeland

8,153

4.8

101.3

45.2

953.4

21.3

449.2

Total

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1,224.8

3,597.4

1,699.1

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Appendix B1: Details of the demographic model
Model update
Several improvements were made in the demographic model. The changes to this compared to the
model used in the previous eel assessment (van de Wolfshaar et al., 2018) are:
-

-

-

-

-

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The biological keys and FYMA survey data were updated with the newest information up to
2020.
The length-class based fit between model and data that was previously used has been
changed to an age-class based fit to allow for a better comparison between the model and
survey data. In previous versions of the eel assessment, the abundances per age class
predicted by the model were transformed to abundances per length class such that the
model predictions could be compared to the abundances per length class observed in the
FYMA survey data. In the current assessment, the abundances per length of the FYMA
survey data are converted to abundances per age class using sex-specific von Bertalanffy
growth curves and a general length – sex ratio relationship.
Some biological keys have been changed (see Chapter 2):
- Eel growth is now based on a von Bertalanffy growth curve per sex.
- Maturation at length, sex ratio at length and the sex ratio of the recruits are now based
on GAM analyses.
The age-specific model parameters (maturation, fisheries selectivity), were previously based
on the initial length of the age class and are now based on the length at the mid-age of the
age class. We consider this mid-length more representative for the characteristics of the
individuals in the age class then the length at the start of the age class.
The moment of comparison of the model with the survey data was moved from April to
September to better fit the timing of the collection of the survey data.
The model is fit to survey data of lakes Ijsselmeer and Markermeer together, due to scarcity
of data in Lake Markermeer in some of the years.
Different periods for which a single F estimate was calculated were changed such that the
periods better represented the changes in eel fisheries management (see Chapter 4)
The selectivity of the survey was previously assumed to differ slightly between length
classes. We now assume an equal selectivity of the survey for all age classes, because there
is no information on the selectivity of the survey available.
Small changes were made to the selectivity of the commercial fisheries (see Chapter 4).

Report CVO 21.023

Model parameters
Table B1 Life history parameters; the length at the start of the age class corresponds to the length in
September of each year.
Years in
IJsselmeer/
Markermeer

Length per age class
At start age
class (mm)
Female

Male

Maturation probability

Fisheries selectivity

At mid age
class (mm)
Female

Male

Female (𝑀 )

Male (𝑀 )

Female (𝑧 )

Male (𝑧 )

0.5

86

86

127

128

0

0

0

0

1.5

165

164

202

196

0

0

0

0

2.5

237

222

270

246

0

0

0.34

0

3.5

301

266

330

283

0.001

0

1

0.59

4.5

358

298

384

310

0.003

0.010

1

1

5.5

409

321

433

331

0.010

0.053

1

1

6.5

455

339

476

346

0.028

0.177

1

1

7.5

496

352

514

357

0.048

0.270

1

1

8.5

532

361

549

365

0.086

0.369

1

1

9.5

565

369

580

371

0.124

0.369

1

1

10.5

594

374

607

376

0.167

0.461

1

1

11.5

620

378

632

379

0.198

0.461

1

1

12.5

643

381

654

382

0.229

0.461

1

1

13.5

664

383

674

384

0.260

0.461

1

1

14.5

683

385

692

385

0.290

0.538

1

1

15.5

700

386

707

386

0.320

0.538

1

1

16.5

715

387

721

387

0.335

0.538

1

1

17.5

728

387

734

388

0.350

0.538

1

1

18.5

740

388

745

388

0.378

0.538

1

1

19.5

751

388

755

388

0.392

0.538

1

1

20.5

760

388

764

388

0.392

0.538

1

1

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Table B2 Annual glass eel index based on lift net survey (survey density/haul) and female ratio (see
Paragraph 4.4) of recruits based on the segmented regression model (Appendix B2)
Year

Glass eel index

Female ratio 𝜌(𝑡)

Year

Glass eel

Female ratio 𝜌(𝑡)

1968

32.9

0.67

1995

11.1

0.69

1969

27.1

0.67

1996

12.5

0.65

1970

48.1

0.67

1997

12.6

0.61

1971

36.1

0.67

1998

2.46

0.56

1972

55.0

0.67

1999

3.7

0.52

1973

18.8

0.67

2000

2.8

0.47

1974

63.0

0.67

2001

0.6

0.43

1975

84.3

0.67

2002

1.2

0.38

1976

51.4

0.18

2003

1.3

0.34

1977

75.0

0.67

2004

2.1

0.30

1978

73.6

0.95

2005

1.6

0.29

1979

87.7

0.98

2006

0.6

0.33

1980

59.0

0.97

2007

1.2

0.37

1981

50.4

0.97

2008

0.5

0.42

1982

29.4

0.96

2009

0.9

0.46

1983

14.7

0.95

2010

2.2

0.51

1984

31.6

0.94

2011

1.1

0.56

1985

11.2

0.93

2012

1.0

0.60

1986

11.4

0.92

2013

4.9

0.64

1987

6.2

0.91

2014

4.6

0.69

1988

7.0

0.89

2015

0.2

0.73

1989

4.8

0.87

2016

1.0

0.76

1990

4.9

0.85

2017

2.3

0.79

1991

1.8

0.82

2018

1.3

0.67

1992

5.2

0.79

2019

1.2

0.67

1993

3.5

0.76

2020

1.0

0.67

1994

5.4

0.73

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Appendix B2: Recruitment sex ratio for lakes IJsselmeer and Markermeer
Whether an eel becomes a female or a male depends on external factors, such as food availability or
(intraspecific) competition. This means that the sex ratio can change over time if, for example, the food
levels or the eel density change.
To parametrize the demographic model for the lakes IJsselmeer and Markermeer, an estimate of the
initial sex ratio of the recruitment is required. For this reason, a statistical analysis was conducted to
determine the initial sex ratio of eel in these lakes. The analysis is done with market sampling data
(Chapter 2), as this was the only available data in the Netherlands where sex is determined. As there is
also asymmetric growth between males and females, especially for the larger eel, (fishing) mortality is
expected to influence the sex ratio of the larger individuals. Therefore, in order to parameterize the
demographic model, the sex ratio needs to be determined for eel as young as possible. However, the sex
is difficult to assess for small individuals (or the eel has not determined its own sex yet) and almost no
eels from sizes below 28 cm are caught in the market sampling. Therefore, eels at an estimated age of 2
years are selected for the analysis.
Year class estimate
Sex ratio needed to be estimated by year class. To determine the year class, the age samples across
years from the market samples (see Chapter 2) were used to construct a fixed sex-specific age-length
key using only the age readings from eel in lake IJsselmeer and Markermeer, assuming that for each
sex, the growth rate does not change across the years.
The first step is to estimate the age for the fish for which we have length measurements. In the analysis,
we first applied von Bertalanffy growth model (VBGF) model to the fishes with both age and length
measurements. The model was applied to females and males separately. Only the end measurements
were used. The von Bertalanffy Growth Function (VBGF) estimates the mean length at a given age. We
then applied the inverse transformation of the estimated von Bertalanffy length-age function to get the
estimated age, assuming the length measurement is the mean length. Von Bertalanffy growth, from
Beverton and Holt (1957):

𝐿 = 𝐿 [1 − 𝑒
where 𝐿 is the expected or mean length at age t, 𝐿
the exponential rate at which 𝐿 approaches 𝐿

(

)

]

is the asymptotic mean length, 𝐾 is a measure of

(Schnute and Fournier, 1980) and 𝑡 is the theoretical

age at which 𝐿 would be zero. The estimated parameters were (females: Linf = 85.3, K = 0.13, t0 = 1.00, males: Linf = 45.7, K = 0.16, t0 = -4.02). Inverse VBGF:

𝑡=

−1
𝐿
ln(1 − ) + 𝑡
𝐾
𝐿

The age of the individual fish with length measurements was estimated, after applying the inverse VBGF
function. As the length samples were assumed to be representative of the population in the lakes, this
would give an approximated age distribution which is representative of the population. A young age (i.e.
age 2) was selected for further analysis. The age was estimated using the inverse VBGF and translated to
year class. To get an integer number, the age was rounded to the nearest integer.
Proportion of female at recruitment
The proportion of females was estimated for each yearclass at age 2 (from 1976-2017).
Four statistical models were applied:
1) GLM using year as a continuous predictor. Both GLM’s were estimated through maximum likelihood
estimation.
2)

Segmented regression with one breakpoint, using year as a continuous predictor. The model
coefficients were estimated through maximum likelihood estimation with an interactive procedure of
estimating the best breakpoint.

3)

Segmented regression similar to model 2, but with two breakpoints.

83 van 97

Report CVO 21.023

4)

GAM using thin plate regression spline (i.e. smooth function) with year as the predictors. The
dimension of basis k is set as 6 (number of basis function=k-1=5). We limit a relatively low k to
avoid over-fitting. Smoothing parameters were estimated through generalized Cross Validation
(GCV) criterion.

In all 4 models, the response variable is modeled as a Bernoulli distribution, and a logit link function links
the linear predicter to the mean of the Bernoulli distribution. Model 3, the segmented regression with two
breakpoints, had the lowest AIC (Akaike Information Criterion) and was selected for use in the
demographic model (Chapter 3). The results show that a large number of males from yearclass 19761977 is causing a very low female proportion for these 2 cohorts, and a substantial increase of the
proportion of females for the years after. These two first years are a bit strange, and further data
analysis is needed to determine if they are outliers due to, for example, poor data collection or if they
actually represent the true value. This is also the case for the yearclasses 1978-1980 where a 100%
female sex ratio was observed at the estimated age 2. Yearclass 1983, 1985, 2000 and 2001 are
missing, due to the missing gender samples in 1985, 1987 and 2002-2003. There are a lot of
approximations and pre-processings during the calculation: a time-invariant age length key (ALK) is
assumed and it is estimated from very few age samples. However, it is the result of the best available
information at present. Additional age readings and data analyses are needed to confirm the result
presented here.

Figure B1. estimated proportion of female by yearclass (1976-2017) at age 2. Black: segmented
regression with 2 breakpoints (selected option); Red: one value across all years; blue: segmented linear
regression with one breakpoint; green: spline. Raw annual estimates are plotted as grey bars.

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Appendix C1: Overview eel mortality pump stations with a propeller pump
Table C1: Overview of eel mortality when passing through pumping stations with a propeller pump (axial water flow). * Underestimation as seemingly
undamaged eels did reveal internal damage after dissection which could result in delayed mortality.

axiaalpump

Pump description

Capacity
(m3/min)

Height
(m)

Rotation
(rpm)

Name

n

dead
(%)

damaged
(%)

Reference

Gesloten schroefpomp

60

0.8

355

Kortenhoef

Vriese et al., 2010

81

1

333

FFI

11
8
25

32

Gesloten schroefpomp FFI

0

Vriese, 2009

50

J.L. Hoogland

77

5

5

Kruitwagen & Klinge, 2010a

80

A.F. Stroink

10

0

30

Kroes et al., 2006

Thabor

21

38

500

Stenensluisvaart

?

100

Offerhaus

10

0
8

30

41*

41*

Kralingseplas

?
25
1
19

Krimpenerwaard

19

100

Kruitwagen & Klinge, 2010b

De Waker

69

1.4

VisserijServiceNederland,2010
VisserijServiceNederland,2010

Gesloten schroefpomp

1,500

Gesloten schroefpomp

2,500

0.6

Open schroefpomp

24

0.98

Open schroefpomp

60

2.7

Open schroefpomp

76

Open schroefpomp

200

0.6

165

Den Deel

Bulbpomp Nijhuis

3,000

variable

64

IJmuiden

900

Germonpré et al., 1994
Vriese, 2010
Riemersma & Wintersmans,
2005
Kruitwagen & Klinge, 2008a

Schroefpomp

30

1.35

Schroefpomp

400

1,34-4,64

Schroefpomp

184

1.05

185

Schroefpomp

2,400

Zaangemaal

65

0

Schroefpomp

180

1.07

180

Meldijk

30

33

Kroon & van Wijk, 2012

propeller

60

2.7

500

Woumen (BE)

?

100

Germonpré et al., 1994

propeller

100

480

Avrijevaart/Burgraven (BE)

39

98

INBO

BVOP

255

5.4

360

Lijnden

2

Gesl. Schroefp. (compact)

90

2.7

364

HZ Polder

6

Vriese et al., 2010

Gesl. Schroefp. (compact)

105

2.2

291

Berkel

5

Vriese et al., 2010

Gesl. Schroefp. (compact)

135

0,5-1

307

Antlia

6

Vriese et al., 2010

Gesloten schroefpomp

26

3.08

Makkemermar

2

Vriese et al., 2010

Gesloten schroefpomp

42

2,4 - 3,1

Aalkeet buitenpolder

1

Kruitwagen & Klinge 2010c

Open schroefpomp

40

1.67

Nijverheid

2

Vriese et al., 2010

Open schroefpomp

120

0.1

Tilburg

9

Vriese et al., 2010

Kralingseplas

3

Waning et al., 2012

Offerhaus

2

Kroes & de Boer, 2013

Balgdijk

5

580

Gesloten schroefpomp FFI
Open schroefpomp

90

schroefpomp

120

340

340

Pooled studies with n <10

85 van 97

100

Vriese et al., 2010

Kruitwagen & Klinge, 2010b

Kroon & van Wijk, 2012
32.6

Report CVO 21.023

Table C2 : Overview of eel mortality when passing through pumping stations with a propeller-centrifugal pump (axial-radial water flow).
Pump description
Schroefcentrifugaalpomp

Capacity
(m3/min)
170

semi-axiaal pump

Hidrostal

Height
(m)

Rotation
(rpm)

1.52

Name
Tonnekreek

10

890-1,200

dead
(%)

n
34

0

2,300

0

damaged
(%)

Reference
Vriese et al., 2010

3

Patrick & McKinley 1987

Schroefcentrifugaalpomp

350

2.8

115

Schilthuis

27

22

Vriese et al., 2010

BEVERON

505

2,4

143

Schoute (natuurlijke doortrek)

36

0

Kruitwagen & Klinge, 2008b

BEVERON

525

5.4

200

Lijnden

6

Hidrostal

21

3.6

577

Ypenburg

8

Vriese et al., 2010

Hidrostal

42.5

3.5

552

Wogmeer

8

Vriese et al., 2010

Schroefcentrifugaalpomp

300

4.4

Leemans

4

Kroon & van Wijk, 2013

Abraham Kroes (Ringvaart
gemaal)

8

Kruitwagen & Klinge, 2010b

De Zllk

2

Vriese et al., 2010

Willem-Alexander

1

Vriese et al., 2010

B.B. Polder

2

Vriese et al., 2010

Meerweg

9

Klinge, 2008

Schroefcentrifugaalpomp

250

2-5,5

165

VOPO met schroefomdraaiing

25

0.15

1,000

Schroefcentrifugaalpomp

85

Schroefcentrifugaalpomp

24

1.15

Schroefcentrifugaalpomp

22

1.15

416

735

Pooled studies with n <10

86 van 97

39.6

Report CVO 21.023

Table C3 : Overview of eel mortality when passing through pumping stations with a centrifugal pump (radial water flow).

radial pump

Pump description

Capacity
(m3/min)

Height
(m)

Rotation
(rpm)

Name

dead
(%)

n

damaged
(%)

Reference

Centrifugaalpomp

38

3.5

368

Duifpolder

12

0

Centrifugaalpomp

60

5

49

Elektriek-Zuid

?

1.4

Centrifugaalpomp

400

0.9

205

Boreel

49

49

Vriese et al., 2010

Centrifugaalpomp

1,080

1.7

59

Katwijk

56

0

Kruitwagen & Klinge, 2007

Centrifugaalpomp

325

3.5

168

Grootslag

438

0

Kroon & van Wijk, 2013

Centrifugaalpomp

160

0.3

Centrifugaalpomp

690

1.7

Centrifugaalpomp

690

Centrifugaalpomp

28

Germonpré et al., 1994

JC de Leeuw

5

Kroon & van Wijk, 2013

70

Gouda (natuurlijk)

2

Kruitwagen & Klinge, 2008c

1.7

70

Gouda (gedwongen)

4

Kruitwagen & Klinge, 2008c

0,55-1,05

320

Hoekpolder

1

Kruitwagen & Klinge, 2010c

Pooled studies with n <10

87 van 97

Vriese et al., 2010
1.4

16.7

Report CVO 21.023

Table C4 : Overview of eel mortality when passing through pumping stations with an Archimedes’ screw.
Pump description

Capacity
(m3/mi
n)

Rotatio
n
(rpm)

Height
(m)

Turbinevijzels

Vijzel Bielefeld

Buisvijzel FFI

0.6

1

57

Vijzel

30

2.9

39

FFI (gedwongen
blootstelling)
Sint-Karelsmolen

Vijzel

35

3.6

37

De Seine, Vlaanderen

Spaans Babcock

500

2.2

17

De Wit vijzel

Archimedes’ screw

Name

dea
d
(%)

n

damage
d (%)

Reference

?

0

Spah, 2001

23

0

Vriese, 2009

?

4

10

Germonpré et al., 1994

?

0

37

Denayer & Belpaire, 1992

Overwaard

43

2

Vriese et al., 2010

Halfweg (natuurlijke
doortrek)

24

0

Kruitwagen & Klinge, 2008c

660

0.3

22

40

2.7

39.1

Ennemaborgh

101

8

Vis et al., 2013

23

2.7

23.8

Ennemaborgh

112

3

Vis et al., 2013

Vijzel

335

0.35

Kolhoorn

16

0

Kroon & van Wijk, 2013

Vijzel

350

1.14

Kadoelen

59

8

VisserijServiceNederland,
2010

160

0

Buisvijzel (Landustrie Sneek
BV)
Buisvijzel (Landustrie Sneek
BV)

Vijzel

23-31

0.6

Kibel, 2008

Vijzel

100

25

Isabella

48

13.5

INBO

Vijzel

200

21

Isabella

131

14.5

INBO

Vijzel

90

0.64

Overtoom

7

Vijzel

43

1.25

Bergermeer

3

Vijzel

660

0.3

Halfweg (natuurlijke
doortrek)

5

Kruitwagen & Klinge, 2008c

Buisvijzel FFI

32

Hoekpolder

2

Wanink et al., 2012

Schalsum

2

Koopmans, 2013

Sudhoeke

9

Vriese et al., 2010

Pooled studies with n
<10

28

Vijzel
Vijzel

88 van 97

23

0.73

22

Report CVO 21.023

VisserijServiceNederland,
2010
VisserijServiceNederland,
2010

3.6

Appendix C2: Barrier assessment list Boezem and National waters

Figure appendix C2: All main 2nd and 3rd hierarchy barriers, where green indicates no mortality and red
indicates a mortality estimate; the HPS’s in the rivers: 1: WKC Amerongen, 2: WKC Alphen and 3: WKC
Linne and 4: the barrier-complexes at IJmuiden are numbered. Orange barriers are not taken into
account because 5: the WKC Hagestein has not been in operation since 2005, and no data is availalbe on
6: the canal from Belgium to Terneuzen.

89 van 97

Report CVO 21.023

Table C5. Overview of the most important barriers, their characteristics and their estimated mortalities
(based on Winter et al. 2013a, 2013b and updated for 2018-2020).
Waterboard

from to Potential silver eel
Mortality (%) Potential silver eel losses (ton) Losses per site (%)*
starting biomass (ton) best guess
min
max
min
max
noorderzijlvest
Spijksterpompen
Gema
B
Z
0.85
0.30
0.25
0.25
30
30
noorderzijlvest
Noordpolderzijl
Gema
B
Z
0.56
0.30
0.17
0.17
30
30
noorderzijlvest
Waterwolf Electra
Gema+Keer
B
R
7.05
0.30
0.59
1.95
8
28
noorderzijlvest
De Drie Delfzijlen
Gema+Spui
B
Z
2.11
0.30
0.18
0.58
8
28
noorderzijlvest
Lauwerssluizen
Spui+Sche
B
Z
83.86
0.00
0.00
0.00
0
0
wetterskip
Roptazijl
Gema
B
Z
5.50
0.50
2.75
2.75
50
50
wetterskip
Zwarte Haan
Gema
B
Z
5.50
0.50
2.75
2.75
50
50
wetterskip
Lemmer (Wouda)
Gema + Sche
B
Z
1.83
0.25
0.25
0.44
14
24
wetterskip
Stavoren
Gema + Sche
B
R
36.64
0.06
1.21
2.09
3
6
wetterskip
Ezumazijl
Gema + Sche
B
R
5.50
0.50
2.25
2.69
41
49
wetterskip
Harlingen
Spui+Sche
B
Z
54.96
0.00
0.00
0.00
0
0
wetterskip
Dokkumer Nieuwe Zijlen Spui+Sche
B
R
73.29
0.00
0.00
0.00
0
0
hunze en aa
Duurswolde
Gema+Spui
B
Z
1.61
0.50
0.22
0.74
14
46
hunze en aa
Termunterzijl
Gema+Spui+Sche B
Z
1.45
0.30
0.40
0.43
28
30
hunze en aa
Nieuw Statenzijl
Spui+Sche
B
Z
6.90
0.00
0.00
0.00
0
0
hunze en aa
Delfzijl
Spui+Sche
B
Z
6.10
0.00
0.00
0.00
0
0
reest en wieden
Stroink
Gema
B
R
15.43
0.11
1.70
1.70
11
11
reest en wieden
Zenemuden
Gema+Keer+Sche B
R
5.14
0.50
1.35
1.41
26
28
velt en vecht
Haandrik
WKC+Stuw+Vist B
R
23.58
0.17
4.01
4.01
17
17
amstel gooi en vecht De Ruiter
Gema + Sche
B
R
5.60
0.25
1.15
1.37
21
25
amstel gooi en vecht Mijndense Sluis
Gema + Sche
B
R
4.55
0.10
0.37
0.45
8
10
amstel gooi en vecht Spiegelpolder
Gema + Sche
B
R
2.10
0.25
0.43
0.51
21
25
HHNK
Kadoelen
Gema
B
R
0.93
0.08
0.07
0.07
8
8
HHNK
De Waker
Gema
B
R
0.31
0.02
0.01
0.01
2
2
HHNK
Leemans
Gema
B
Z
2.49
0.10
0.25
0.25
10
10
HHNK
Lely
Gema
B
Z
0.93
0.25
0.23
0.23
25
25
HHNK
Vier Koggen
Gema
B
R
2.18
0.10
0.22
0.22
10
10
HHNK
Grootslag
Gema
B
R
1.56
0.02
0.03
0.03
2
2
HHNK
Zaangemaal
Gema + Sche
B
R
3.74
0.01
0.01
0.03
0
1
HHNK
Overtoom
Gema + Sche
B
R
0.03
0.04
0.00
0.00
1
4
HHNK
Helsdeur
Gema+Spui+Sche B
Z
9.97
0.30
0.84
2.75
8
28
HHNK
Schermersluis
Sche
B
R
0.31
0.00
0.06
0.06
20
20
HHNK
Oostoever
Spui
B
Z
3.74
0.00
0.00
0.00
0
0
rijnland
Katwijk
Gema
B
Z
18.74
0.01
0.19
0.19
1
1
rijnland
Halfweg
Gema
B
R
6.72
0.04
0.27
0.27
4
4
rijnland
Gouda
Gema
B
R
3.54
0.10
0.35
0.35
10
10
rijnland
Leeghwater
Gema
B
R
5.66
0.30
1.70
1.70
30
30
rijnland
Spaarndam
Gema + Sche
B
R
6.36
0.01
0.02
0.06
0
1
Delfland
Schoute
Gema
B
Z
1.16
0.30
0.35
0.35
30
30
Delfland
Zaayer
Gema
B
R
0.09
0.02
0.00
0.00
2
2
Delfland
Westland
Gema
B
R
0.39
0.10
0.04
0.04
10
10
Delfland
Schiegemaal
Gema
B
R
0.39
0.10
0.04
0.04
10
10
Delfland
v.d. Burg
Gema
B
Z
1.16
0.30
0.35
0.35
30
30
Delfland
Parksluizen
Gema + Sche
B
R
0.96
0.25
0.07
0.22
7
23
HHSK
Schilthuis
Gema
B
R
5.07
0.30
1.52
1.52
30
30
HHSK
Verdoold
Gema
B
R
3.67
0.11
0.40
0.40
11
11
HHSK
Johan Veurink
Gema
B
R
1.75
0.50
0.87
0.87
50
50
HHSK
Krimperwaard
Gema
B
Z
1.40
0.30
0.42
0.42
30
30
HHSK
Abraham Kroes
Gema + Sche
B
R
5.42
0.30
0.46
1.50
8
28
rivierenland
J.U. Smit
Gema
B
R
1.58
0.04
0.06
0.06
4
4
rivierenland
Altena
Gema
B
R
1.10
0.50
0.55
0.55
50
50
rivierenland
Hollands-Duits
Gema
B
R
1.10
0.25
0.28
0.28
25
25
zuiderzeeland
Vissering
Gema + Sche
B
R
53.86
0.25
7.41
12.79
14
24
zuiderzeeland
Buma
Gema + Sche
B
R
41.19
0.25
5.66
9.78
14
24
zuiderzeeland
Smeenge
Gema + Sche
B
R
28.51
0.50
7.84
13.54
28
48
zuiderzeeland
Wortman
Gema + Sche
B
R
47.52
0.25
6.53
11.29
14
24
zuiderzeeland
De Blocq van Kuffeler
Gema + Sche
B
R
79.21
0.25
10.89
18.81
14
24
zuiderzeeland
Lovink
Gema + Sche
B
R
28.51
0.25
3.92
6.77
14
24
zuiderzeeland
Colijn
Gema + Sche
B
R
38.02
0.12
2.46
4.25
6
11
R
Sluizen-complex IJmuiden Gema+Spui+Sche R
Z
121.90
0.15
18.28
31.69
15
26
R
Krammersluizen
Sche
B
Z
7.30
0.00
3.65
3.65
50
50
R
Bergse Diep Sluis
Sche
R
Z
0.59
0.00
0.29
0.29
50
50
R
Terneuzen
Sche
R
Z
0.00
0.00
0.00
0
0
R
Volkeraksluizen
Sche
R
Z
7.30
0.00
3.65
3.65
50
50
R
Bathse spuisluis
Spui
R
Z
95.67
0.00
0.00
0.00
0
0
R
Krabbersgat-sluizen
Spui+Sche
R
Z
0.00
0.00
0.00
0
0
R
Houtrib-sluizen
Spui+Sche
R
Z
0.00
0.00
0.00
0
0
R
Haringvliet-sluizen
Spui+Sche
R
Z
265.68
0.00
0.00
0.00
0
0
R
Aflsuitdijk KornwerderzandSpui+Sche
R
Z
228.80
0.00
0.00
0.00
0
0
R
Afsluitdijk Den Oever
Spui+Sche
R
Z
228.80
0.00
0.00
0.00
0
0
R
Oranjesluizen
Spui+Sche+Vist
R
Z
180.99
0.00
0.00
0.00
0
0
R
Nieuwe Waterweg
R
Z
260.80
0.00
0.00
0.00
0
0
* taking alternative routes and blockage into account (cf. Winter et al. 2013)
**Gemaal=Pumping Station; Sche=Ship Lock; Spui=Discharge Sluice; Vist=Fishway; Stuw=Wier; Keer=Protection Sluice
B=Boezem waters; R=National waters; Z=Sea

90 van 97

Site (potential barrier)

Barrier type

Report number CVO 21.023

Appendix C3: Validation of the assessment method for the North Sea Canal
In 2016-2018, extensive acoustic telemetry studies and mark-recapture studies were carried out for the
North Sea Canal region, including lake Markermeer (Winter et al., 2019; Winter et al., 2020). Within
these studies, migration routes and losses of eels along the routes could be determined from the acoustic
telemetry results, and with mark-recapture studies at IJmuiden with PIT-tags. The accurate population
estimates of the number of silver eels that arrive at IJmuiden could be determined. These results can be
compared to the outcome of the hierarchical approach used in this evaluation to assess the biomass of
silver eel that escapes to sea via IJmuiden.
In the evaluation approach to assess silver eel escapement at IJmuiden, this is assessed to be 122
tonnes per year for 2018-2020, using the new insights showing that 40 % of the silver eel from lake
Markermeer also migrate via IJmuiden. The starting of biomass silver eel in this evaluation method for
the North Sea Canal (IJmuiden) catchment was assessed at 212 tonnes, i.e. an average loss rate of
43%.
The mark recapture-studies for silver eel migration at IJmuiden yielded 101.347 ± 10.990 silver eels in
2016 and 89.233 ± 9.791 in 2017 (Winter et al., 2019; Winter et al., 2020). Earlier assessments yielded
70,000-100,000 silver eels arriving at IJmuiden in 2007-2008 (Winter 2011), which is in line with the
later, more-precise estimates of the number of silver eels at IJmuiden. With an average weight of 850 g
per silver eel for the region (Winter, 2011), this yields 86.1 ± 9.3 tonnes in 2016 and 75.8 ± 8.3 tonnes
in 2016. The telemetry data suggests that 45-50% of the starting silver eel do not reach the sea, even
though the representativeness of the various tagged groups in the hinterland of the North Canal
catchment for all starting silver eel within this catchment is not known. This implies that 152-191 tonnes
of silver eel would have started as derived from the combination of mark-recapture experiments and
telemetry studies.
Given the number of assumptions that are present in the evaluation approach, the outcome of the
starting biomass of 212 tonnes vs. 152-191 tonnes in the telemetry studies, the escapement of 122
tonnes, vs. 76-86 tonnes in the telemetry studies and the overall loss rate of 43% vs. 45-50% in the
telemetry studies, these are in relatively close range and add confidence in the following assessment
method in this evaluation for the other regions. These results suggest that the escapement and starting
biomass for this region is slightly overestimated in the evaluation, when compared to the results of the
telemetry experiments.

Report number CVO 21.023

91 of 97

Appendix C4: Overview of the parameters used in the barrier mortality
estimation.
Table C6. Overview of the parameter estimations used in the estimation of the barrier mortality.
Name

Value

Period

Description

fracAm

0.219

all

proportion through Amerongen

CboezemCzee

0.370

all

prop. from boezem to sea

CboezemCrijks

0.630

all

prop. from boezem to national waters

CpolderCzee

0.200

all

prop. from polder to sea

CpolderCboezem

0.800

all

prop. from polder to boezem

Spolder

0.347

all

mortality polder to boezem or sea

Sboezemzee

0.050

all

mortality from boezem to sea

Sboezemrijks

0.152

all

mortality from boezem to national waters

Srijks

0.023

2006-2008

mortality from national waters to sea

Srijks

0.023

2009-2011

mortality from national waters to sea

Srijks

0.021

2012-2014

mortality from national waters to sea

Srijks

0.015

2015-2017

mortality from national waters to sea

Srijks

0.020

2018-2020

mortality from national waters to sea

SWKClinne

0.170

2006-2008

mortality HPS Linne

SWKClinne

0.170

2009-2011

mortality HPS Linne

SWKClinne

0.130

2012-2014

mortality HPS Linne

SWKClinne

0.130

2015-2017

mortality HPS Linne

SWKClinne

0.130

2018-2020

mortality HPS Linne

SWKCalph

0.150

2006-2008

mortality HPS Alphen

SWKCalph

0.150

2009-2011

mortality HPS Alphen

SWKCalph

0.140

2012-2014

mortality HPS Alphen

SWKCalph

0.140

2015-2017

mortality HPS Alphen

SWKCalph

0.140

2018-2020

mortality HPS Alphen

SWKCamer

0.095

all

PODD

0.000

2006-2008

assisted migration

PODD

0.134

2009-2011

assisted migration

PODD

1.362

2012-2014

assisted migration

PODD

1.900

2015-2017

assisted migration

PODD

2.290

2018-2020

assisted migration

92 van 97

mortality HPS Amerongen

Report number CVO 21.023

Appendix C5: Diadromous fish monitoring programme
A survey programme started in 2012 to monitor the abundance of migrating silver eel on five exit points
(Kornwerderzand sluices, Den Oever sluices, North Sea Canal, New Waterway channel, Haringvliet-West
inlet) and two entry points for migratory fish (River Rhine and River Meuse) during spring and autumn
(Figure C3). The programme is a collaboration between WMR, Rijkswaterstaat and commercial
fishermen. The months September, October and November were selected for illustrating trends in silver
eel abundance at each location. In 2015 and 2018 four extra locations were monitored but not shown in
Figure C4. Both eel biomass and numbers fluctuate strongly on a yearly basis at all locations (Figure C4).
As the trends index is relatively short (9 years), there is no information before the implementation of the
EMP and there are missing years (Figure C4), the data is not used in this report.

Figure C3. Fyke Locations in the diadromous fish monitoring programme.

Report number CVO 21.023

93 of 97

Figure C4. CPUE of silver eel caught during the diadromous fish monitoring per catch location. Data is
missing or not used because of inconsistency of sampling locations/period for the Haringvliet-West inlet
in 2018, for the Den Oever sluices in 2012, 2014 and 2015, for the Kornwerderzand sluices in 2012,
2013 and 2015, the River Meuse in 2017 and 2018, the North Sea Channel in 2015 and for the River
Rhine in 2012, 2016 and 2018.

94 van 97

Report number CVO 21.023

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
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

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
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
















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het vlaamse gewest en preliminair onderzoek naar schade van diverse pomptypes op vissen na
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Report number CVO 21.023

Justification

CVO Report: 21.023
Project number: 4311218541
The quality of this report has been peer reviewed by a colleague scientist, director WMR and the head of
CVO.

Approved by:

dr. N. Tien
Researcher Wageningen Marine Research

Signature:
Date:

28/10/2021

Approved by:

dr.ir. T.P. Bult
Director Wageningen Marine Research

Signature:
Date:

28/10/2021

Approved by:

Ing. S.W. Verver
Head Centre for Fisheries Research

Signature:
Date:

Report number CVO 21.023

28/10/2021

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