Final Report Honeybee Surveillance
Program the Netherlands 2014-2018
Koos Biesmeijer
Naturalis Biodiversity Center
12 februari 2018
Jesus Aguirre Guttierez, Maarten van ’t Zelfde, Nieke Knoben - Naturalis Biodiversity Center
Sjef van der Steen, Bram Cornelissen - Bijen@wur,
Arjen de Groot, Ruud van Kats - Wageningen Environmental Research

1

Contents
1. Summary of results 2014-2018

4

1.1 Executive Summary

4

1.2 Nederlandse samenvatting

5

2 Introduction to the surveillance program

6

2.1 Main objective of the surveillance program

6

2.2 The structure of the surveillance program

7

3 Results
3.1 Honeybee Survival Monitor 2017/2018

9
9

3.1.1. Results from Honeybee survival Monitor

9

3.1.2. Estimate of the number of honeybee colonies in the Netherlands

10

3.2 Honeybee Surveillance Study 2017-2018

11

3.2.1. Set-up of the field campaign

11

3.2.2. Selection of samples for analysis

12

3.2.3. Single factor results: pathogens, residues, pollen sources and landscape

12

3.2.3. Integrated analysis results from Honeybee Surveillance Study 2017-2018

17

4. Comparison across four years of study: Results and Discussion

25

4.1. Single factor comparison

25

4.2. Integrated analysis results from Honeybee Surveillance Study 2014-2018

28

5. Conclusions & Remarks

33

6. Appendices

35

Appendix A

36

Appendix B

40

Appendix C

45

Appendix D

47

Appendix E

49

Appendix F

50

Appendix G

51

Appendix H

52

2

Justification of the report and its results
The Honeybee Surveillance Program of the Netherlands is initiated to obtain insight in the
level of winter mortality in honeybee colonies as well as in the different factors underlying
this mortality. The program is commissioned by the Netherlands Ministry of Agriculture,
Nature and Food Quality to Naturalis Biodiversity Center and is a collaboration between the
important research parties in the field. This report summarizes the overall conclusions of
the program which ran from 2014-2018.
The results of the winter mortality questionnaire are robust and representative. A random
sample of approximately 500 beekeepers has been questioned about colony survival in their
operation each year, apart from the first year when the procedure was slightly different. In
2018 it has been a coordinated effort in collaboration with the Netherlands Beekeeping
Association (NBV) and Imkers Nederland. The results of the surveillance study are also
robust and representative, as they are based on a large-scale stratified random sample from
across the Netherlands.
The duration of the program has been four years and this report summarizes findings for all
years. The four years are needed to obtain a longer-term view of both winter mortality and
the underlying causing factors; and to take into account the substantial inter-annual
variation.
Prof. Dr. Koos Biesmeijer – Naturalis Biodiversity Center
Contact: koos.biesmeijer@naturalis.nl

Verantwoording bij het rapport en de resultaten
Het Nederlandse honingbijen surveillance programma heeft als doel inzicht te krijgen in de
wintersterfte van honingbijenvolken in Nederland en in de onderliggende factoren voor de
sterfte. Het wordt uitgevoerd in opdracht van het Ministerie van Landbouw, Natuur en
Voedselkwaliteit door Naturalis Biodiversity Center en is een samenwerking van de
belangrijkste partijen in het onderzoeksveld. Dit rapport vat de resultaten van verschillende
deelprojecten samen.
De resultaten van de Wintersterfte Monitor zijn robuust en representatief. Deze uitvoering
is gebaseerd op een a-selecte steekproef van ongeveer 500 imkers die gevraagd zijn naar de
sterfte in hun bijenvolken. De winter monitor is uitgevoerd in samenwerking met de
Nederlandse Bijenhouders Vereniging (NBV) en ImkersNederland. De resultaten van de
surveillance studie zijn gebaseerd op een gestratificeerde a-selecte steekproef waaraan een
groot aantal imkers heeft meegedaan.
De duur van het programma is vier jaar. Op die manier kan een robuuste analyse gemaakt
worden van de sterftepatronen en hun factoren, waarbij variatie tussen jaren meegenomen
kan worden. Dit rapport omvat de resultaten van alle vier de jaren.
Prof. Dr. Koos Biesmeijer – Naturalis Biodiversity Center
Contact: koos.biesmeijer@naturalis.nl
3

1. Summary of results 2014-2018
1.1 Executive Summary

1 Commissioned by the Dutch ministry of Agriculture, Nature and Food Quality, the Honeybee
Surveillance Program assessed honeybee winter mortality in the Netherlands between 2014-2018
and aimed to unravel the factors explaining winter colony losses. To achieve this, two studies
were combined: the Honeybee Mortality Monitor, a random online beekeepers’ survey, and the
Honeybee Surveillance Study, a random field survey of honeybee hives, samples of which were
analysed in the lab. This final report describes the results of the fourth year, 2017-2018, but the
main focus is on the final results of the four year study.

2 National-level colony survival. The Honeybee Mortality Monitor reveals that winter survival in
2017-2018 was 84.3% (15.7% of colonies died) and 55% of beekeepers suffered no loss at all.
While higher survival is always preferred, this figure is close to the ‘normal’ variation of around 515% winter mortality. Survival has now been above 80% for the last six years and above 85% for
five of the last six years. The number of managed honeybee colonies in the Netherlands is
estimated to be between 71 and 81 thousand.

3 Apiary-level mortality: High winter survival in apiaries is correlated with low Varroa mite levels
and absence of ABPV virus in the apiary. The surrounding landscape shows a complex interaction
with survival. Good beekeeping practice, e.g. control of parasites and diseases, will further
improve colony survival and seems the single best way to do so.

4 Colony-level mortality: A large number of variables each contribute just a little bit to explain
colony winter mortality and are often correlated with each other making it difficult to separate
their roles. A few factors seem to have slightly more importance, but no single factor emerges as
the main driver of colony winter loss. Colonies containing more Varroa mites and placed in highly
fragmented landscape with substantial amounts of maize crops have lower survival probability.
When stored pollen contained more Asteraceae, survival was also slightly lower. Finally, chemical
residues of neonicotinoids and other compounds were detected in 30% of colonies, their
presence is not correlated with winter mortality, except for dimethoate which was rarely
detected, but when detected had a strong negative effect on colony survival.

5 Main messages:
 Honeybee winter loss was lower than 20% in the last 6 years, but still above 10% in most
years, while the majority of beekeepers do not lose colonies in winter.






The factors underlying honeybee colony loss in colonies managed by beekeepers in our
Dutch landscapes, are many, are variable in space and time, and are likely to interact.
Improving beekeeping practice (e.g. treatment of pests and diseases) seems the best way
to improve honeybee survival.
Factors linked to beekeeping practice (e.g. pests and diseases) emerge as the most
consistent factor determining colony winter mortality.
Chemical residues, e.g. insecticides and acaricides, were detected in 30% of samples, but
their presence is not correlated with colony winter mortality, except for dimethoate which
is rarely found, but when it is found, it has a negative effect.
4

1.2 Nederlandse samenvatting

1 Het Nederlandse Ministerie van Landbouw, Natuur en Voedselkwaliteit gaf opdracht tot het
honingbijen surveillance programma, om de wintersterfte onder Nederlandse honingbijen (20142018) en de oorzaken die de wintersterfte kunnen verklaren te bestuderen. Hiervoor gebruiken
we twee methoden. Ten eerste de Wintersterfte Monitor, een online vragenlijst die wordt
gestuurd naar een aselecte steekproef van imkers. Ten tweede de Honingbijen Surveillance
studie. Hierin worden van een steekproef van de Nederlandse bijenhouders in het veld
bijenvolken bemonsterd voor nadere analyse in het laboratorium. Dit rapport geeft de resultaten
weer van het 4e jaar, 2017-2018, maar de focus ligt op de eindrapportage van de 4-jarige studie.

2 Bijensterfte in Nederland. De Wintersterfte monitor laat zien dat de overleving van bijenvolken
in Nederland in 2017-2018 hoog was, namelijk 84.3% (15.7% van de volken ging dood) en dat
55% van de imkers geen sterfte had. Lagere wintersterfte is altijd beter, maar de huidige sterfte
ligt dicht bij de als normaal geziene variatie (rond de 5-15%). Overleving is de laatste zes jaar
altijd hoger geweest dan 80% en in vijf van de zes jaar hoger dan 85%. Op basis van de monitor
kunnen we een schatting maken van het aantal bijenvolken in Nederland; dat ligt tussen de 71 en
81 duizend.

3 Sterfte per bijenstand. De overlevingskans is hoger in bijenstanden met lagere Varroa
infectieniveaus en bij afwezigheid van ABPV virus in de bijenstand. Het omringende landschap
heeft een complexe relatie met het overlevingspercentage in een bijenstand. Verbetering van de
imkerpraktijk, o.a. Varroa- en ziektebestrijding, lijkt de beste manier om overleving te verhogen.

4 Sterfte per bijenvolk. Een groot aantal factoren lijkt elk een heel kleine bijdrage te leveren aan
wintersterfte. Veel van deze factoren zijn gecorreleerd, waardoor hun bijdrage niet goed te
scheiden is. Enkele factoren verklaren een iets groter deel van de wintersterfte, maar niet één
factor kan aangewezen worden als de belangrijkste. Volken met meer Varroamijten en geplaatst
in zeer gefragmenteerde landschappen met veel mais hebben een kleinere overlevingskans. Bij
volken met veel Asteraceae stuifmeel was overleving ook iets lager. Tenslotte werden er in 30%
van de volken residuen van chemische middelen aangetroffen. De aanwezigheid daarvan had
geen invloed op de wintersterfte, behalve wanneer er dimethoaat aangetroffen werd (niet vaak
het geval) dan was de sterfte flink hoger.

5 Belangrijkste boodschappen:
 De wintersterfte onder Nederlandse honingbijen was lager dan 20% in de laatste 6 jaar,
maar meestal boven de 10%; de meerderheid van de imkers had geen wintersterfte.






Wintersterfte onder Nederlandse honingbijen is gecorreleerd aan verschillende factoren
die variëren in ruimte en tijd en vaak interacteren.
Verbetering van de imkerpraktijken (o.a. varroa- en ziektebestrijding) lijkt de beste manier
om overleving te verhogen.
Factoren die gerelateerd zijn aan de imkerijpraktijk, zoals controle van ziekten en plagen,
komen consequent boven als link met wintersterfte.
Residuen van chemische middelen (o.a. insecticides of acaricides) werden in 30% van de
monsters gevonden, maar hun aanwezigheid is niet gecorreleerd met wintersterfte,
behalve voor dimethoaat. Dit wordt zelden gevonden, maar bij vondst heeft het wel een
negatief effect.
5

2 Introduction to the surveillance program
The Netherlands Honeybee Surveillance Program has been initiated as a result of the public debate
hosted by the former Secretary of State for Economic Affairs, Sharon Dijksma, with many societal
partners as participants. The top priority that was identified was to assess the status of bees,
particularly honeybees, and unravel the main factors that contribute to honeybee winter mortality in
the Netherlands.
It was concluded that such a program requires an integrated approach towards honeybee health and
a substantial investment. The Dutch government approached Prof. Dr. Koos Biesmeijer, Naturalis
Biodiversity Center and University of Leiden, to assemble a consortium and program to address this
important issue. The consortium consists, besides Naturalis, of Dr. Sjef van der Steen and Bram
Cornelissen (Bijen@Wur) and Dr. Arjen de Groot (Wageningen Environmental Research), whereas
Theo de Rijk (RIKILT, Wageningen UR) is the subcontractor for chemical residue analysis. The
financial support for the program, € 1.2M total, was provided by the Ministry of Agriculture, Nature
and Food Quality (51%) with Nefyto as co-financer (49%). The program started in 2014 and lasted
four years.

2.1 Main objective of the surveillance program
The main objective of this program is to study patterns in and causes of winter mortality of
honeybees in the Netherlands. To this end, we determined colony winter loss each year for four
years using a randomized survey and we correlated various potential drivers of winter loss to colony
fate. These factors include exposure to agro-chemicals, bee diseases and pests, use of pollen
sources, landscape configuration and beekeeping practices (Figure 1).
In addition to the main aim, the program aims to meet several other objectives:
1- The results should be representative and be informative for ongoing European initiatives, e.g. the
annual CoLoSS colony loss questionnaires that estimate winter mortality in many countries,
initiatives like the Epilobee project that has addressed bee diseases and pests in a single year
across Europe, activities of EFSA. The present research program is more complete (more possible
drivers of loss are assessed) than the above-mentioned initiatives. Through the EU COST Action
Super-B (Sustainable Pollination in Europe, joint research on bees and other pollinators), led by
Koos Biesmeijer at Naturalis, the consortium links to all other honeybee surveillance initiatives in
Europe, e.g. Austria, Germany, UK, Italy, USA.

2- We use standardized protocols, most of which are applied in other projects and all of which have
been validated before (see for details the CoLoSS beebooks on standard methods;
http://www.coloss.org/beebook/) . In some cases small changes are being incorporated, but
these will not be detrimental to the comparability of the results. The results are used in
comparative studies on honeybee colony loss. The Super-B network mentioned above strived to
explore whether more standardization could be achieved across EU countries to increase the
impact of our national programs.

6

3- The knowledge that will be gained from the project should benefit the Dutch honeybees through
the close collaboration of consortium partner Bijen@WUR with the Dutch beekeeping
community. Results have been and will be disseminated through their established
communication channels with the Dutch beekeepers.

Figure 1. Overview of the main risk factors for honeybee colony survival that will be addressed in

the surveillance program.

2.2 The structure of the surveillance program
The program merges two different approaches to the problem of bee mortality and its causes. The
first approach is a beekeeper survey (honeybee survival monitor), the second approach is a field
campaign actually sampling and analysing different factors directly (honeybee surveillance study).
The Honeybee Survival Monitor is an annual survey that questions beekeepers about the survival of
their colonies. The method of monitoring the winter survival in honey bees is based on the
international standard, the CoLoSS survey, and was set up by Naturalis, Bijen@WUR and the NBV to
replace the monitor of the Netherlands Centre for Bee Research (NCB). This change was needed as a
result of NCB’s decision not to join our project from 2016 onward. It was decided to conduct an
integrated survey together with the Netherlands Beekeeping Association (NBV) and Bijen@wur,
because they already conducted a more simple mortality survey in the past few years to be able to
obtain an indication of honeybee mortality early in the season. This year also the new beekeepers
association Imkers Nederland participated. The honeybee survival monitor is a survey based on
CoLoSS protocols (www.coloss.org) to facilitate comparison with other countries. The survey is,
however, more extended than the previous NBV survey, but more compact than the CoLoSS longsurvey (for survey see appendix A). We conducted the survey as follows: To obtain a reliable
estimate of honeybee winter mortality in the Netherlands we aim to obtain survival figures from
about 500 beekeepers, randomly drawn from association membership lists (>8000 beekeepers in
7

total). Since not all members own bee colonies and many beekeepers did not respond to our request
by e-mail, we started with a larger selection of 2000 beekeepers from NBV and 200 from Imkers
Nederland. The differences in numbers per association are linked to their representative total
number of members. In 2018, after approaching 2200 beekeepers, we got responses from 780 on
which this year’s results are based. These beekeepers all filled in the survey online using a Google
form questionnaire.
The Honeybee Surveillance Study is set-up specifically for this program and consists of a random
sampling of colonies in apiaries from around the Netherlands. Samples of bees, honey and pollen are
taken by beekeepers themselves after which the consortium uses the samples to identify diseases,
chemical residues and food sources. Beekeepers were also questioned about their beekeeping
methods. In this way we can assess the influence of the beekeeper (interviews and field survey),
diseases (laboratory analysis of bees), food sources (pollen analysis), chemical products (residue
analysis of honey), and the local landscape in which the bees live (GIS analysis). In the first year of
this study bee health inspectors that were trained by Bijen@wur staff to conduct the field survey
and collect samples. This method did not render the best results. Therefore, in the following years
beekeepers were instructed by a clear manual with pictures how to take their samples themselves
each year in May and August from 3 up to 5 of their hives at the same apiary. Only a subset of the
samples, up to 400 per year, could be analysed (due to limited funds), but all have been stored for
future analyses.

Figure 2. Overview of the main risk factors for honeybee colony survival that will be addressed in the Surveillance
Program

The distribution of tasks among the consortium partners (Figure 2) made Bijen@WUR responsible
for the field sampling, distribution of samples and disease analyses; Wageningen Environmental
Research was responsible for the pollen analysis; Naturalis was responsible for the landscape GIS
analysis and for the integrated analysis of all results. The analysis of chemical residues was
conducted by subcontractor RIKILT. RIKILT is the Dutch National Reference Laboratory for pesticides
in food of animal origin. Naturalis was in charge of the overall program.
8

3 Results
3.1 Honeybee Survival Monitor 2017/2018
3.1.1. Results from Honeybee survival Monitor
This year a large number of 780 beekeepers participated in the survival monitor. Both because we
selected more beekeepers in the first place to ensure sufficient participants and because the newly
merged association Imkers Nederland (former ANI and ABTB) also contributed. The 763 usable
surveys (beekeepers that reported to have more hives after winter than before the winter were
assessed as unreliable and were taken out of analysis) had a survival rate of 84.3%, therefore winter
mortality was 15.7%. In total 5591 hives were prepared for winter and 4696 remained alive until April
2018 (table 1). However a large number of beekeepers 417 (55%) did not have any winter mortality
at all.

Table 1. Winter survival of honey bee colonies in the Netherlands 2005-2018

Winter

Number of
beekeepers

2005-2006
2006-2007
2007-2008
2008-2009
2009-2010
2010-2011
2011-2012
2012-2013
2013-2014
2014-2015
2015-2016
2016-2017
2017-2018

737
1422
808
1193
1326
1541
1673
1589
1594
1549
580
470
763

Number of
colonies
(October)

% winter
1
survival

7.050
13.591
9.616
10.678
11.265
13.726
14.915
13.920
15.280
14.650
5919
3479
5591

73.7
84.1
76.3
78.3
70.9
78.6
79.2
86.3
91.4
86.3
93.5
85.7
84.3

% winter
1
mortality

Method

NBC [CoLoSS]
26.3
NBC [CoLoSS]
15.9
NBC [CoLoSS]
23.7
NBC [CoLoSS]
21.7
NBC [CoLoSS]
29.1
NBC [CoLoSS]
21.4
NBC [CoLoSS]
20.8
NBC [CoLoSS]
13.7
NBC [CoLoSS]
8.6
1
HB-Surv [CoLoSS]
13.7
HB-Surv NBV random sample
6.5
HB-Surv NBV random sample
14.3
15.7 HB-Surv NBV/Imkers NL random
sample

1

based on HB surveillance reports: 14-15 NCB voluntary survey, 15-16 NBV random sample

9

100
90
80
70
60
50
40
30
20
10
0

Figure 3. Winter survival 2005-2018. Added are two lines that indicate survival between 85% (red) and 95% (green) that
are regarded as accepted.

The 15.7% mortality is a little bit higher than in the previous year(s), but as can be seen in figure 3,
the annual winter mortality has not returned to the high percentage of more than 20% that was
customary between 2005 and 2012. A mortality of 15.7% is, however, higher than the beekeeping
community would want and on the high margin of what is thought to be a ‘normal and manageable
winter mortality level’ which ideally should be below 10%. The percentage mortality that we report
here is higher than that reported in an early spring quickscan by the beekeeper organization (9.3%).
The main reason for this are that (1) the quickscan is non-random and failures are less likely
reported than successes, (2) the quickscan had been performed earlier in the year , possibly
excluding colonies that die in the last cold spell of winter.
3.1.2. Estimate of the number of honeybee colonies in the Netherlands
The Netherlands needs to submit an estimate of the number of honeybee hives in the Netherlands
annually to the EU. This figure can be estimated using the winter monitor data, given that they
represent a random sample of all Dutch beekeepers. The largest source of error in the calculation is
the uncertainty about the percentage of Dutch beekeepers that is a member of one of the two main
beekeeping associations, the NBV and Imkers Nederland (formerly two separate associations: ABTB
and the ANI). Therefore, we give estimates for various membership percentages in table 2
(superscripts in text below refer to the lines in the table).
Data on the number of hives going into winter 2017-2018 were received from 763 beekeepers1. In
total these beekeepers had 55912 hives in late autumn 2017. The average number of hives was 7.3
across all beekeepers3 with a few large beekeepers and many with fewer hives. A total of 8292
beekeepers7 is registered with one of the two beekeeping associations4-5. The total number of hives
of these beekeepers is about 60761 (beekeepers * 7.3 hives on average)8.
The question that remains for estimating total bee hives in the Netherlands is the percentage of
registration of all Dutch beekeepers and also the number of double memberships among the
beekeepers. Both are unknown. We calculated the population of Dutch bee hives for degrees of
registration between 70 and 95%11-16. The estimate increases from 60 thousand at complete
registration to 86 thousand at 70% registration.
10

In conclusion: there were at least 60761 managed bee hives in the Netherlands in late Autumn 2017.
This is certainly an underestimate due to incomplete registration. The best estimate may be
between 71,000 and 81,000 bee hives present in the Netherlands in 2017.
Table 2. Procedure to estimate the number of bee hives in the Netherlands in 2016. For explanation see text. Line
numbers indicate the various steps and numbers taken into account and line numbers are referred to in the text as
superscript numbers.

1
2
3
4
5
6
7
8

9

10
11
12
13
14
15

Beekeepers in sample 2017
Total number of hives going into
winter
Average number of hives per
beekeeper
Number of beekeepers on NBV list
(minus new members)
Members of ImkersNederland
Total number of beekeeper members
Members without bees (percentage
last year)
Number of hives in associations (
beekeepers * average hives per
beekeeper)
Estimated percentage of beekeepers
member of one of the two
associations
95%
90%
85%
80%
75%
70%

763
5591
7.3
7092
1200
8292
9.6%
60760

Estimated
total number
of hives
63959
67512
71483
75951
81015
86801

3.2 Honeybee Surveillance Study 2017-2018
3.2.1. Set-up of the field campaign
The field campaign is based on a random selection of beekeepers (more specifically single apiaries
from beekeepers) from across the Netherlands. The participating beekeepers are asked to take their
own samples, based on instructions outlined in an extensive manual with pictures describing exactly
what has to be done. Samples are taken in May and August and the beekeepers are instructed to
keep them cooled and send them to Bijen@WUR by mail. Three to five hives are sampled in a single
apiary of each beekeeper (maximum number of samples: 200 apiaries x 5 hives x 2 samples (May
and August) = 2000 samples). The maximum number is unlikely to be reached for several reasons:
(1) Not all beekeepers have five hives that can be sampled; (2) many beekeepers do not want to
participate even after originally agreeing to join; (3) not all hives sampled have sufficient honey and
pollen stored; (4) other circumstances may prevent us from sampling, e.g. American Foulbrood
outbreaks. Given the large investment needed for the field campaign, we decided to collect a large
number of samples, more than we can analyse, and store all samples for future analysis (e.g.
available for follow-up projects).
11

3.2.2. Selection of samples for analysis
The laboratory analyses are costly, therefore we select a subset of the samples for analysis. In short
the procedure for 2017/2018 was as follows:
1- Hive number 1 and 2 per apiary was selected for analysis in Autumn 2017. Samples were
distributed from Bijen@wur (pathogen and disease analysis based on bee sample) to
Wageningen Environmental Research (food sources analysis based on pollen sample), RIKILT
(chemical residue analysis based on honey sample), Naturalis (location information of apiaries
for landscape analysis).
2- In April 2018, beekeepers were contacted to obtain information on survival of each of their
hives.
3- The third sample for analysis was selected based on this survival/mortality information. We aim
at selecting hives such that we obtain, for every beekeeper, at least a pair of colonies one of
which has survived the winter, the other of which has died during winter. In that case we can
eliminate the influence of the landscape in general and the beekeeping treatments as
explanatory variables. For those apiaries for which this is not possible, i.e. if all colonies survived
or all died, we did not analyse a third one. Third colonies were only analysed for selected
apiaries to arrive at matched pairs of dead-alive colonies for as many apiaries as possible.
4- The samples of the third colony for the selected apiaries are distributed to the partners for
analysis in April 2018. After that all data have been analysed in the respective laboratories, data
has been integrated and analysed by Naturalis.
3.2.3. Single factor results: pathogens, residues, pollen sources and landscape
Here we first summarize the main findings per possible driver of mortality of the single factor
analysis and after that we provide an integrated analysis of all drivers. Comparisons over four years
are discussed in chapter 4. Note that the number of analysed samples can be different for each
factor. This can have various reasons, for example, insufficient honey/beebread/bees to sample or
to analyse.
Parasites and pathogens
Samples of bees collected in autumn (n=296) were analysed for the presence and quantity of the
parasite Varroa destructor and for the presences of 4 pathogens associated with winter mortality:
Nosema apis (microsporidian),
Nosema ceranae (microsporidian),
DWV (deformed wing virus)
ABPV (Acute Bee Paralysis Virus).
The average number of Varroa mites was 3 mites per 100 bees, while 35% (n=102) of the samples
did not contain Varroa.. DWV was found in 76% of the colonies (table 3). This indicates that even in
colonies in which no Varroa mites have been detected at the end of the summer Varroa mites must
have been present previously or still were present at very low numbers as DWV is largely
transmitted by Varroa-mites. Nosema ceranae and ABPV virus were both found a little over 10%,
while Nosema apis was only found in 1% of the colonies.

12

Table 3. Presence of various pests and pathogens in honey bee samples in 2014 (n=91), 2015 (n=331), 2016 (n=314) and
2017 (296).

Pest/Disease
Varroa present (%samples)
Varroa mites / 100 bees
Nosema ceranae
Nosema apis
DWV virus
ABPV virus

2014
73%
7
89%
0%
98%
0%

2015
63%
3
59%
0.6%
93%
1%

2016
68%
5
22%
1%
96%
9%

2017
65%
3
12%
1%
76%
13%

Pollen sources used by colonies
In the 265 samples of beebread that were analysed, 68 different pollen types were found. The pollen
types were counted only when they contributed 5% or more to the sample (see table 4 for 10 most
recorded pollen types and appendix C for the full list). On average 5.4 different types of pollen were
found per sample, ranging from 2 to 11 types. Note that not all pollen types indicate the presence of
a single plant species. Some types in fact represent a genus of plants and some even a whole family.
Still pollen analysis gives a good indication of the important food plants honeybees collect pollen
from and the variety of pollen that the bees have collected.

Table 4. Main pollen sources and their percentages in colonies in late 2017. Pollen types that occur in at least 10% of the
samples, for the complete list see Appendix C. Note that pollen of Trifolium (clover) can sometimes be assigned to a
single species Trifolium pratense, but often not. In the latter case it is included in the Trifolium spp. category. That
category includes other clover species, the hybrid clovers often used in agriculture and flower strips and probable some
of the red clover.

Species

Brassicaceae
Trifolium spp.
Asteraceae
Rosaceae
Castanea
Phacelia
Trifolium pratense
Impatiens
Hedera
Calluna
Caryophyllaceae
Ranunculaceae (Clematis)
Parthenocissus

found
in #
samples
134
105
92
86
58
58
58
51
49
43
34
31
28

% of
total
50.6
39.6
34.7
32.5
21.9
21.9
21.9
19.2
18.5
16.2
12.8
11.7
10.6

min

max

0
0
0
0
0
0
0
0
0
0
0
0
0

100
100
95
85
85
65
85
100
100
95
50
80
35

ave.
When
present
31.8
27.9
19.7
22.0
32.9
18.4
25.5
38.1
41.8
33.6
11.9
17.4
10.2

13

Chemical residues detected in honey
Stored honey samples from August 2017 were analysed for the presence of a long list of chemicals
including neonicotinoids, other pesticides, acaricides and other chemicals reported to be a potential
threat for bees (for complete list see appendix D). For the chemical analysis we have taken into
account the fact that a chemical is present or not (the LOD or Level of Detection; above LOD =
present, below LOD = absent) and the level at which we can tell how much is actually present (the
LOQ or level of quantification; above LOQ = quantity known, below LOQ = may be present (if above
LOD), but level is too low to quantify; see box 1). Note that the LOD and LOQ thresholds are purely
methodological thresholds and do not have any relation to the potential hazard and safety of these
compounds for any organism, including humans. Note that the ‘honey samples’ may not always refer
to honey collected by the bees themselves. The stored reserves in August is the fuel that the colony
is ‘given’ by the beekeeper. This may be honey actually stored by the bees, if beekeepers have not
extracted it from the hive. It may, however, also be sugar or other honey substitute provided by the
beekeeper after honey extraction. For our research, the importance is in the stored food reserves
that the bees use through winter and in the chemical residues that may be present therein.
Of the 110 chemical compounds we
aimed to detect, only fourteen
compound were encountered in the
samples from August 2017 and mostly
at low frequency and concentration
(see table 5 and 6). Honey samples in
318 hives (92%) did not contain any of
the chemical residues we screened for
at a level above the LOQ (Level of
Quantification) and 86% of hives did
not contain any traces above the LOD
of any tested chemical, so no chemicals
were detected in these samples.
Neonicotinoids (imidacloprid,
thiacloprid, acetamiprid, thiamethoxam
or clothianidin) were found in 39 hives
(11.0%) of which 26 (7.5%) above LOQ,
i.e. at a quantifiable dose. Acaricides
(amitraz, coumaphos) were found in 28
hives (8%). The concentration of all the
chemical residues found in the stored
honey were (often very far) below the
LD50 for oral toxicity for an adult
honeybee.

14

Table 5. Chemical residues present above LOQ level in samples of 2014 (90 hives), 2015 (327), 2016 (342), 2017 (294).
Neonicotinoids are indicated with *, Acaricides used by beekeepers with ^ and fungicides with #. Given are percentage
of hives in which each residue has been found above the level of quantification - LOQ (see box 1). ~During the study a
change of analysis of residues was chosen; several residues were added after 2015 and due to high cost and low number
of positive tests Fluvalinate-tau was taken out after 2015.

Chemical residue

2014

2015

2016

2017

Acetamiprid *

2.2%

2.8%

3.5%

3.4%

Amitraz ^ (banned)

8.9%

2.1%

1.8%

0.7%

Boscalid #

Not tested~

Not tested

4.1%

2.7%

Carbendazim #

Not tested

Not tested

0.6%

0.3%

Chlorfenvinphos

Not tested

Not tested

0.6%

0.3%

1.1%
0.0%

2.4%
0.9%

0.0%
0.3%

1.0%
0.7%

0.0%
Not tested

0.9%
Not tested

Not tested
0.9%

Not tested
1.4%

6.7%

2.8%

0.3%

1.0%

0.0%
Not tested

0.3%
Not tested

0.0%
1.5%

0.0%
0.0%

Thiacloprid *

2.2%

9.8%

8.2%

7.5%

Thiamethoxam/Clothianidin *
Neonicotinoids total *
Acaricides total ^

0.0%
7.7%
7.7%

0.9%
15.0%
5.2%

0.6%
11.3%
8.1%

0.0%
11.6%
1.7%

Coumaphos ^ (banned)
Dimethoate
Fluvalinate-tau ^
Fluopyram #
Imidacloprid * (banned for use
in open cultivation)
Permethrin
Tebuconazole #

Landscapes in which bees forage
Landscapes determine in part the health of bee colonies. Not only do landscapes provide pollen and
nectar sources (pollen sources are assessed in this study through pollen analysis, nectar sources are
not assessed as stored winter food is often sugar), colonies that are exposed to mass-flowering crops
and wild plants are also exposed to unhealthy conditions in the landscapes they forage in (e.g. agrochemicals, pollution, drought and water shortage). To assess the importance of landscape factors,
that cannot be related to bee diseases or pollen sources that were assessed separately, we use
landscape factors as a proxy for the other aspects of landscape and habitat that may influence
honeybee health.
Information for land use and habitat factors has been compiled from a range of sources to create upto-date relevant spatially explicit layers for analysis. Data are available on crops and groups of crops
grown on each parcel and for each year (2017 data from BRP: basis registratie percelen). Detailed
land use data are available from CBS land use database for 2010 (latest version). One important
variable that we created was the number of land use classes per area around the colonies (1000 or
3000m, see below). All land use classes are included here, not only the bee-friendly classes, but also
urban areas, crops, (water)ways, cemeteries and other landscapes. A high value in number of land
use classes indicates a highly fragmented and heterogeneous landscape containing a mix of many
land use types. While heterogeneity in the landscape can generally be regarded as positive to
biodiversity, landscapes containing more than 10 different classes within a kilometer are most likely
too fragmented for bees and lack large forage areas.
15

Table 6. Chemical residues encountered in 294 honey samples: presence, concentrations and LD50 for honeybees.
Samples are scored as ‘absent’ (column 2; indicating nothing was found), LOD = ‘detected but <LOQ’ (column 3; very
small quantity detected, but not sufficient to quantify, i.e. below LOQ). Several compounds can be detected as the
compound itself or its metabolites, their values are recalculated generally according to standard residue definitions.
Names of compounds referring to sets of chemicals are indicated with superscript numbers and are: 1 Amitraz
(Amitraz + DMA + DMF + DMPF), 2 dimethoate (dimethoate + omethoate) both with independent definitions in
Regulation (EC) No1107/2009), 3 Imidacloprid (Imidacloprid + Imidacloprid_5-Hydroxy + Imidacloprid_olefin +
Imidacloprid_desnitro + Imidacloprid_desnitro_olefin + Imidacloprid_urea+ 6-Chloronicotinic_acid; note that the
metabolites are not regulated). EcoTox database values are from: http://www.ipmcenters.org/ecotox/. Values in this
database are in ug per bee, (with an individual bee weighing of 100 milligrams). The values in the last column are
multiplied by 10.000 to be comparable to the detected concentrations in the previous columns.

absent
280
281
264
292
291
257
286
293
292

>LOD
14
13
30
2
3
37
8
1
2

>LOQ
10
2
3
2
3
22
8
1
1

average conc.
(μg/kg) if
present
3.8
17.1
3.2
1.9
1.0
6.9
14.3
4.4
1.4

Epoxiconazole

293

1

1

4.2

4.2

Fluopyram

290

4

4

4.6

9.9

Metazachlor

293

1

1

6.7

6.7

Pyridate
Spinosyn

293
293

1
1

0
0

only >LOD
only >LOD

Teflubenzuron

291

3

3

2.8

Compound
Acetamiprid
1
Amitraz
Coumaphos
2
Dimethoate
3
Imidacloprid
Thiacloprid
Boscalid
Carbendazim
Chlorfenvinphos

maximum
concentration
found
19
25
3.6
2.3
1.6
55
92
4.4
1.4

LD50 (μg / kg in
48h tests ) USDA
EcoTox database
810000
10000000
5600
380
1794000
16600000
5000000

1023000

3.5

In addition, we created a separate data layer called ‘Natural habitat’ which aggregates the different
categories of land use referring to natural areas, semi-natural areas and other areas under specific
nature management schemes. Another layer that we constructed, we refer to it as ‘Crop area’,
aggregates all cropping types into one layer. This allows us to summarize the combined impact of
agriculture. Finally, we created a layer we refer to as ‘Bee forage’ which aggregates all land use and
habitat types that we rate as providing decent to good forage for bees at least part of the year. Note
that this is a subjective assessment based on our experience with bees and bee foraging and follows
a similar assessment previously carried out for the UK. We calculated all parameters around the
apiary for a 1000m and a 3000m circle. Most foraging is expected to take place within 1km from the
hive, while good forage opportunities further afield are also readily discovered and exploited.
Landscapes differed substantially in several of the factors that are known to be potentially beneficial
or detrimental to honeybee colony health (table 7).

16

Table 7. Summary of occurrence of important landscape parameters around apiaries (within 1km) in 2014, 2015, 2016
and 2017. Annual variation in the values for different landscape factors is small, indicating that we have sampled very
similar landscapes in each year, a reflection of the random sampling protocol we apply in selecting the locations.

Landscape factor

2015 landscapes
average (range)
9.4 (4-14)

2016 landscapes
average (range)
9.6 (3-17)

20.7 (0-72)

18.0 (0-68)

15.4 (0-70)

15.8 (0-98)

% Natural habitat

10.7 (0-50)

8.6 (0-57)

9.1 (0-68)

9.3 (0-98)

% Crop area

29.1 (0-92)

30.1 (0-91)

29.4 (0-80)

28.6 (0-90)

% Maize cultivation

5.9 (0-29)

5.6 (0-32)

6.7 (0-31)

3.9 (0-24)

Number of land use
classes
% Bee Forage

2014 landscapes
average (range)
9.1 (3-15)

2017 landscapes
average (range)
9.1 (3-15)

3.2.3. Integrated analysis results from Honeybee Surveillance Study 2017-2018
Integrated analysis: We aim to answer two related, but separate questions in the integrated
analysis:
Q1: Is the percentage of survival at apiary level related to specific explanatory variables?
[this may reflect the overall quality of the beekeeper and the landscape pressures (food, diseases]
Q2: Is colony survival related to specific explanatory variables?
[this may reflects the specific conditions of the individual beehive (food, agro-chemicals, diseases
found in the colony)]
Both questions have been addressed by applying generalized linear models (Q1: GLMs; Q2: GLMMs),
the best current approach for this type of problem. This method relates the focal variable (Q1:
percentage of survival of colonies in apiary; Q2: survival/mortality of the single colony) to a range of
potential factors influencing the survival (see Table 8). Given that there are many possible factors for
each of the main categories (‘pests and diseases’, ‘beekeeping aspects’, ‘agro-chemicals’, ‘food
sources’, ‘landscape characteristics’), the method first selects the main candidate causes within each
category. Next, a full model is constructed using of these selected factors and model selection is
performed to find those factors that significantly contribute to the percentage of colonies surviving
within an apiary (Q1) or to the probability for a single colony to survive (Q2).

Q1 for 2017-2018: Is the percentage of survival at apiary level related to specific explanatory
variables?
Here we try to explain the % of winter survival (reverse of mortality) in the 2017-2018 season using
land use, disease and size of the apiary. Factors that were tested in the model are given in table 8. A
total of 122 apiaries could be included in this analysis.
Result: A few factors contributed to the percentage of colony survival in apiaries and some variation
could be explained by the factors taken into account. There is one best model (Best 1 in table 9), the
17

factors appearing in that model contribute to survival, whereas the factors present only in the LU
Full model do not contribute to explain winter survival at apiary level.
Factors negatively correlated to survival at apiary level are (in order of importance): The presence of
ABPV virus and Nosema ceranae, the maximum percentage of Varroa mites found in an apiary, and
the amount of maize grown close to the apiary. The number of land use classes had a positive
influence, with a high number of classes correlated to slightly higher survival (for specific
relationships see figure 4).
Other factors play a negligible role (the amount of food and crops in 3 km range; i.e. low coefficients
in table). They may of course have been responsible for the mortality of individual colonies or low
survival at some apiaries, but are not consistently contributing to mortality across the country’s
apiaries (table 9).
Conclusion: Higher survival in 2017-2018 is linked to lower disease and mite prevalence and thus to
better beekeeping practices. The landscape in which the bees forage also has an impact on survival
with more heterogeneous landscapes (at 1km scale) leading to slightly higher survival.

18

Table 8. Factors used in surveillance analysis for questions Q1 and Q2.

Factor use in
models
% winter survival
in apiary
Winter survival
% Varroa

Presence of DWV
Presence of
ABPV
Presence of
Nosema apis
Presence of
Nosema ceranae
Number of hives
going into winter
Presence of
neonicotinoids
Presence of
individual
chemical
compounds
% maize area
% nature

% cropped area
Number of land
use elements
Number of
pollen sources
% of pollen of
plant X

Description
Proportion of colonies in the apiary that survived the winter. This
is what we try to explain in Q1.
Colony survived the winter (YES) or died in the winter (NO). This
is what we try to explain in Q2.
Number of mites occurring on 80 bees (first sample was 50 bees)
of a single hive. For Q1, the maximum value of a single hive in
the apiary is included.
Presence of deformed wing virus in honeybees (YES/NO)
Presence of ABPV virus in honeybees (YES/NO)
Presence of the microsporidian Nosema apis in honeybees
(YES/NO)
Presence of the microsporidian Nosema ceranae in honeybees
(YES/NO)
Indication from the beekeeper how many hives he had before
the winter. This is an indication of size of the beekeeping
operation
This variable is YES if any neonicotinoids have been detected in
the honey sample of a hive, and NO if none have been detected
Each chemical residue observed at least 5 times in the sample
under analysis was included as a separate variable in step 1 of
model 2. Only the significant ones at step 1 were used in the full
model in step 2. For details see below.
Area of maize cultivation around the apiary (we analysed this at
two levels: 1000m and 3000m radius)
Area of (semi-)natural habitats around the apiary (we analysed
this at two levels: 1000m and 3000m radius). Note that nature as
defined here ranges from flower-rich chalk grassland to
biodiversity poor dense conifer stands.
Area of cropland, all crops summed, around the apiary (we
analysed this at two levels: 1000m and 3000m radius)
Sum of the different types of land use around the apiary (we
analysed this at two levels: 1000m and 3000m radius)
The sum of the number of different pollen types detected in the
pollen sample of a hive.
The percentage of pollen grains of plant X in a hive pollen
sample. We analysed the dominant pollen types separately.

Included
in Q1
YES

Q2
NO

NO

YES

YES

YES

YES
YES

YES
YES

NO

YES

YES

YES

YES

NO

NO

YES

NO

YES

YES

YES

YES

YES

YES

YES

YES

YES

NO

YES

NO

YES

19

Table 9. Factors related to the survival percentage of colonies in an apiary. Values indicate the estimates from the model
with standard error in parentheses. The final model is Best 1, which is better than the full model, i.e. within 2 AIC points.
The full model is the one with all variables included, after which variables are deleted until the best model is found.

Q1 LU Models 2017
(Intercept)
Land Use classes in 1km
Maize area in 1km
Forage in 1km
Land Use classes in 3km
Natural area in 3km
Crop area in 3km
Varroa mites / 100 bees
Nosema ceranae presence
DWV presence
ABPV presence
Number of hives in apiary
AIC
BIC
Log Likelihood
Deviance
Number of apiaries

LU Full
1.90 (0.44)
0.15 (0.04)
-0.03 (0.02)
-0.00 (0.00)
-0.04 (0.04)
-0.00 (0.00)
-0.00 (0.00)
-0.05 (0.01)
-0.68 (0.23)
0.16 (0.20)
-0.56 (0.20)
-0.01 (0.00)
526.72
560.37
-251.36
318.26
122

Best 1
1.65 (0.28)
0.12 (0.04)
-0.03 (0.02)
-0.00 (0.00)

-0.00 (0.00)
-0.05 (0.01)
-0.65 (0.23)
-0.54 (0.20)
523.36
545.79
-253.68
322.90
122

Figure 4. Relationships of factors affecting survival percentage at apiary level in season 2017-2018 (question Q1).

20

Q2: Is colony survival related to specific explanatory variables?
Here we assess whether the winter survival of an individual colony can be explained by any of the
main factors assessed in the surveillance study. In this mixed model apiary was included as a random
factor, whereas we assessed all other variables. Given the large number of variables within each
category (land use, chemicals, diseases, pollen), we perform the analysis in two steps (figure 5). In
step one we constructed models for each category separate to identify the main variables within
each category (details in appendix B). Step two analysed the final model using all the relevant
variables resulting from the step 1 models.

Figure 5. Schematic overview of analysis for question 2. STEP 1 selects the main variables within each of the
four variable categories (boxes on the left) using GLMM models. STEP 2 uses the variables selected in STEP 1
(indicated in the two other boxes) in a final GLMM model [factors in [] were included in the full model, but
were not included in the final ‘best’ model]. Difference between A0 and A1 models is explained in the text.
‘---‘ indicates that none of the variables in that subcategory explained significant amount of variation in
colony survival.

This procedure is the same as was performed each year. Again we have taken into account the fact
that a chemical is present or not (the LOD or Level of Detection; above LOD = present, below LOD =
absent) and the level at which we can tell how much is actually present (the LOQ or level of
quantification; above LOQ = quantity known, below LOQ = may be present (if above LOD), but level is
too low to quantify; see box 1). We have now added an analysis in which all cases above LOD
(compounds B,C,D in Box 1) but cannot take the quantity into account in that case. Results of the
two main models (Q2 A0 model = below LOQ recorded as 0, in Box 1: A=B=0, C and D actual
concentration; Q2 A1 model = above LOD recorded as 1, in Box 1: A=0, B=C=D=1) are given below.
The main reason for adding this complication is that one may argue that even the presence of
chemical at very low levels may have an effect. Also note that the LOD and LOQ thresholds are
purely methodological thresholds and do not have any relation to the potential hazard and safety of
these compounds for any organism.
21

Result: The best model explaining winter survival contains a number of variables with differing
impact and results very similar for the Q2A0 and Q2A1 models. In other words, there were no
differences in outcome whether we include chemical traces (Q2A1) or only those detected at
quantifiable amounts (Q2A0). A few factors play an important role (Tables 10 and 11; figures 6 and
7).
ABPV levels in single hives could partially explain mortality over the winter of 2017-2018 with
presence of ABPV having a negative correlation with survival. The incidence of Varroa mites in single
hives in autumn did not have a negative correlation (not retained in the best model), and the
presence of DWV had a small positive effect on survival.
Of the chemical compounds encountered only Acetamiprid was included in the STEP 2 model and
retained in the best model. It was encountered in a few samples only and had a marginal positive
relation to colony survival, with a lot of variation (see figures 6 and 7).
The amount of natural area had a strong negative effect on colony survival, whereas the amount of
forage area had a small positive effect. Natural areas can consist of bee friendly habitats (e.g. chalk
grasslands), but often consist of habitat that are only marginally beneficial to honeybees (coniferous
forest, reed and march land or meadows managed for meadowbirds all of which can be largely
devoid of pollen/nectar flowers) and are probably worse than cities, villages and public spaces.
Two pollen types were selected for the STEP 2 model (Asteraceae and Castanea pollen) but both are
not included in the best model, indicating that they did not contribute to explaining colony survival
in this study.
Conclusion: Only a small part of the mortality of bee colonies in the winter of 2017-2018 could be
explained by the main factors that were analysed. More natural area and more ABPV virus
correlated to lower survival and more forage area in the wider landscape had a positive effect. Other
factors, including residues of chemical, had no or only marginal effect on colony survival in the
winter of 2017-2018.

22

Table 10. Summary of factors related to the survival (where presence of chemicals is included only when they were
above quantifiable amounts (LOQ).

Q2 A0 Final overall models
Q2A0 Full Model
STEP 2
(Intercept)
Natural area in 1km
Forage area in 3km
Acetamiprid
Boscalid
Asteraceae
Castanea
Varroa mites / 100 bees
DWV present
ABPV present
AIC
BIC
Log Likelihood
Num. obs.
Num. groups: Imker.x
Var: Imker.x (Intercept)

1.88 (0.54)
-0.71(0.34)
0.80 (0.45)
7.99 (24.76)
-1.14 (1.17)
-0.32 (0.20)
-0.24 (0.21)
-0.05 (0.04)
0.72 (0.52)
-1.62 (0.66)
187.39
224.52
-82.69
216
116
0.77

Q2A0 best model
STEP 2
1.74 (0.44)
-0.73 (0.33)
0.85 (0.45)
28.58 (1130298)

0.81 (0.50)
-1.84 (0.63)
184.71
208.34
-85.36
216
116
1.16

Table 11. Summary of factors related to
colony survival in Q2A1 models (where

presence of chemicals is included (LOD), not their quantifiable amounts (LOQ)

Q2 A1 Final overall models
Q2A1
Q2A1
Full model
Best model
STEP 2
STEP 2
(Intercept)
1.68 (0.53)
1.02 (0.59)
Natural area at 1km
-0.63 (0.33)
-0.01 (0.01)
Forage area at 3km
0.72 (0.43)
0.00 (0.00)
Acetamiprid
25.3 (1024)
13.9 (418)
Asteraceae
-0.30 (0.20)
Castanea
-0.22 (0.21)
Varroa mites / 100 bees
-0.24 (0.21)
DWV present
0.70 (0.52)
0.75 (0.52)
ABPV present
-1.72 (0.66)
-1.88 (0.68)
AIC
185.50
183.62
BIC
219.25
207.25
Log Likelihood
-82.75
-84.81
Num. obs.
216
216
Num. groups: Imker.x
116
116
Var: Imker.x (Intercept)
0.75
1.05

23

Figure 6. Relationships of factors affecting survival percentage of individual colonies in season 2017-2018 only
considering those chemicals that have been detected at quantifiable amounts (>LOQ) (question Q2A0).

Figure 7. Relationships of factors affecting survival percentage of individual colonies in season 2017-2018 considering all
chemicals that have been detected (>LOD) (question Q2A0). Note that results are very similar to the Q2A0 models. This
is a result of chemical compounds playing no significant role in colony survival.

24

4. Comparison across four years of study: Results and Discussion
With this report, the fourth and final year of the study has been conducted and we can compile the
results for the whole period of four years. First, we compare findings for individual factors, second
we compare findings of the integrated model.
4.1. Single factor comparison
Parasites and pathogens
Over four years the most important observation is that presence of Nosema ceranae varies from
89% in 2014 to 22% in 2016 and only 12% in 2017. Presence of Varroa is very high and quite
consistent over the years. Nosema apis is consistently low, whereas ABPV varies from 0% in 2014 to
13% in 2017. While beekeepers are well aware of the problem of the Varroa mite and its associated
viruses, our study shows that Varroa mites are indeed present in most colonies at an average density
of 3-7 mites per 100 bees depending on the year (see table 3 above). Even when Varroa mites are
absent (or present at a very low density), DWV is omnipresent. DWV is transmitted by Varroa mites
and its omnipresence indicates that virtually all colonies have or have had Varroa mites or their
associated viruses. The rise in ABPV virus over the years is interesting and may need special
attention.
100%
80%
Varroa present
60%

Nosema ceranae
Nosema apis

40%

DWV virus
ABPV virus

20%
0%
2014

2015

2016

2017

Figure 8. Presence of various pests and diseases in bee samples in 2014 (n=91), 2015 (n=331), 2016 (n=314) and 2017
(296).

Pollen sources used by colonies
A comparison over all four years (table 12) shows that the plant species most often used as pollen
source, sampled as bee bread in late summer, are largely the same across years. Brassicaceae and
Trifolium are found in a large part of the colonies in all years. Some differences can be explained by
differences in sampling period. In 2014 Hedera was found in more than 50% of the colonies, in the
two years after that it was 16.7 resp. 8.5 percent, which can be explained by the earlier sampling
(compared to flowering period) in later years.
25

Table 12. Overview of most important pollen types found in 2014, 2015 and 2016. Figures indicate the percentage of
samples each pollen type was present in. The table is a composition of all species that were found in more than 10% of
the hives in at least one of the sampled years.

Pollen type

English name

% of
% of
% of % of Average
total
total
total total % over
2014
2015
2016 2017 4 years
33.3
49.8 53.3 50.6
46.8
25.6
47.1 42.3 39.6
38.7
52.6
16.7
8.5 18.5
24.1

Brassicaceae

mustards, rapeseed

Trifolium

clovers

Hedera

ivy

Calluna

heather

15.4

21.6

30.1

16.2

20.8

Asteraceae

dandelion family

3.8

20.4

32

Rosaceae
Lotus
Caryophyllaceae
Impatiens
Phacelia
Castanea
Fabaceae
Zea
Heracleum
Fagopyrum
Cornus
Hypericum
Rubus spp.

rose family
birds foot trefoil
ragged robin and relatives
Himalayan balsam and relatives
phacelia
chestnut
legumes (various spp)
maize
hogweed
buckwheat type
dogwood
St. John’s wort and relatives
bramble

10.3
11.5
8.9
10.3
11.5
0
2.6
0
3.8
1.3
0
3.8
0

23.1
26.7
15.3
8.6
12.1
0
2.4
8.6
3.5
14.5
2.4
5.9
11.4

21
0.3
9.1
13.5
8.2
25.4
16.9
11.9
10
0.6
13.8
4.1
1.9

34.7
32.5
1.1
12.8
19.2
21.9
21.9
8.7
1.9
9.1
0
1.1
4.2
3

22.7
21.7
9.9
11.5
12.9
13.4
11.8
7.7
5.6
6.6
4.1
4.3
4.5
4.1

Chemical residues detected in honey
The majority of the honey samples (70%) did not contain any trace of any chemical compound that
we assessed and above the very low levels of detection we assessed them at (see appendix D for
complete list and LODs of 2017 and Appendix E for summary of all chemicals detected). Across all
samples (1051 samples assessed in total), quantifiable neonicotinoid traces were found in 12%, and
in most of these cases a single neonicotinoid compound was detected. Of the neonicotinoids,
thiacloprid was present most often (and had also the highest presence of any chemical compound)
and seems to have increased since the start of the study. Imidacloprid showed a steady decline
(figure 9). Quantifiable amounts of Acaricides (mostly the illegal compounds Amitraz and
Coumaphos) were detected in 6% of the samples. Other compounds were detected regularly, e.g.
the systemic insecticide acetamiprid, and the fungicides boscalid and fluopyram (see table 5).

26

Percentage of samples detected

16
14
Acetamiprid

12

Imidacloprid

10
8

Thiacloprid

6
Thiamethoxam/Clothianidin

4

Neonicotinoids total

2
0
2014

2015

2016

2017

Figure 9. Presence of Neonicotinoid pesticide residues in bee samples. Note that despite the ban on imidacloprid for
several cultivations, the presence of neonicotinoids has not decreased. Imidacloprid has been found rarely in honey
samples for the last two years, but Thiacloprid has increased strongly and acetamiprid considerably over the period of
our study.

Percentage of samples detected

16
14
12
Neonicotinoids total

10
8
6

Acarides total

4
2
0
2014

2015

2016

2017

Figure 10. Presence of Neonicotinoid pesticides and Acaricides detected in honey samples (only samples were
concentration above LOQ was detected). Every year we detected (illegal) acaricides in several samples (e.g. Amitraz,
Coumaphos). It is most likely that these derive from beekeeping practices (probably used as Varroa treatment) and not
from the wider landscape.

Landscapes in which bees forage
Dutch landscapes do not change a lot from year to year. Crop rotations probably make up a large
part of the change. Our sample is large and representative of the Dutch landscape and very similar
across the four years (table 7; see also appendix F for sampling sites). This was the aim of our survey
and the selection approach has thus been successful. The small differences observed between years
are a result of the sampling sites themselves and not a result of changes in the Dutch landscape
itself.
27

4.2. Integrated analysis results from Honeybee Surveillance Study 2014-2018
In this study we aim at explaining honeybee winter survival and mortality by analysing various
factors that have been indicated previously to be responsible for colony loss in at least some cases:
beekeeping practice, pathogens and parasites, pollen sources, chemical residues and the forage
landscape. In each of the years, variation between the number of colonies that dies is large and the
list of factors that we analyse seems to be able to explain just a small part of colony loss. Real world
surveys are notoriously difficult to show similar effects as those found under the controlled
circumstances in the laboratory. Yet it is very important to assess the real world situation, which is
what we aim for.
When we consider survival at apiary level (Q1), we see (table 13) that disease presence has a
negative effect (most notably maximum Varroa levels and ABPV virus presence in the apiary) and
that landscape factors show a complex interaction with survival. More maize grown close to the
apiary is slightly positive, but more crop area close by and a landscape full of maize has a negative
effect on survival. On the other hand, more forage habitat nearby decreases survival slightly, but a
forage rich landscape is positive for survival.
When we consider survival of individual colonies (Q2), we also take into account the pollen sources
stored, the bee diseases and chemicals detected in that specific colony. The main factors we analyse
can explain only a small part of the colony loss. This indicates that there are many potential
influences on honeybee colonies next to the ones we measure. For example, every beekeeper uses
slightly different methods, queen age and quality varies a lot, queens can die or can be replaced all
factors that are very difficult to capture. Finally, interactions between different factors may be of
importance. By building up the dataset across the years we have reached a critical mass of data
(>1000 colonies assessed) to obtain the best, broadest, most detailed picture of the factors
influencing honeybee winter mortality in the Netherlands (see Appendix G for spatial distribution of
sampling sites across the four years).
We assess survival factors in two steps explained above (see 3.2.3, table 8 and figure 5), because the
total number of potential factors is too large to include all together. In step 1 we identify the most
important specific factors from within the different factor groups. In addition, based on the
attention in the media on the influence on pesticides on bee mortality, we report here analyses
using the presence of any chemical trace in the samples (Q2A1 models; see 3.2.3). The following
main factors are included in the integrated model: land use factors: number of land use classes in
3km area around the colony, area of maize both in 1 km and 3 km around the colony; chemicals:
imidacloprid or dimethoate presence; pollen sources: percentage of Asteraceae or Trifolium; disease
and pests: incidence of Varroa mites and presence of Nosema ceranae or ABPV virus. Other factors
did not pass the selection at step 1 (for factor specific models see appendix B).
The analysis shows the following (table 14 and figure 12): there is no difference in survival across the
years. Survival is much lower when the incidence of Varroa is high, when maize presence is high in
the landscape or the landscape is highly fragmented (more land use classes within 3km area), when
Asteraceae pollen is present or when dimethoate is detected (which is the case in very few colonies).
28

The area of maize close to the colonies (<1km) was positively correlated to survival, whereas
presence of imidacloprid was correlated slightly positively.
Table 13. Factors related to the survival percentage of colonies in an apiary. Values indicate the estimates from the
model with standard error in parentheses. The final model is Best 1, which is better than the full model, i.e. within 2 AIC
points. The full model is the one with all variables included, after which variables are deleted till the best model is
found.

Q1 LU-Virus Models 2015-2018
LU Full model
(Intercept)
2.04 (0.24)
Number of land-use
-0.04 (0.07)
classes in 1km
Maize area in 1km
0.22 (0.09)

Best model 1
1.93 (0.16)

0.23 (0.08)

Forage area in 1km

-0.24 (0.14)

-0.12 (0.07)

Nature area in 1km
Crop area in 1km

0.15 (0.14)
-0.12 (0.10)

-0.20 (0.05)

Number of land-use
classes in 3km
Maize area in 3km

-0.05 (0.06)
-0.11 (0.09)

-0.16 (0.08)

Forage area in 3km

0.46 (0.18)

0.30 (0.13)

Nature area in 3km

-0.44 (0.19)

-0.27 (0.12)

Crop area in 3km
Varroa mites /100
bees
Nosema ceranae
present
DWV present

-0.06 (0.10)
-0.17 (0.04)

-0.17 (0.04)

ABPV present
AIC

-0.52 (0.15)
1538.42

-0.49 (0.15)
1530.47

BIC
Log Likelihood

1602.17
-753.21

1570.31
-755.23

Num. obs.
Num. groups: Year

397
4

397
4

Var: Year Intercept)

0.10

0.09

-0.14 (0.14)
-0.03 (0.17)

29

Figure 11. Relationships of factors affecting survival percentage at apiary level for the whole study (Q1). There is no
difference in survival across the years (see figure lower right). Survival is much lower when the maximum level of Varroa
in the apiary is high and when ABPV is present. Several landscape factors are slightly negative for bee survival (all graphs
declining) with only amount of forage in the landscape and amount of maize grown close to the hives being slightly
positive.

30

Table 14. Factors related to the survival percentage of individual colonies. Values indicate the estimates from the model
with standard error in parentheses. The final model is Best 1, which is better than the full model, i.e. within 2 AIC points.
The full model is the one with all variables included, after which variables are deleted till the best model is found. In
bold are the factors showing most impact (see also figure 12).

Q2 A1 Final overall models 1518
Q2A1 full
model
2.05 (0.24)
-0.38 (0.15)

Q2A1 best
model
4.60 (0.84)
-0.16 (0.06)

0.42 (0.20)

0.02 (0.01)

Maize area in 3km

-0.34 (0.20)

-0.00 (0.00)

Imidacloprid
Dimethoaat

8.5 (35.1)
-1.38 (0.74)

254.4 (1.9x10 )
-1.26 (0.71)

Asteraceae

-0.26 (0.11)

-2.42 (0.88)

Trifolium
Varroa mites / per 100 bees

-0.15 (0.11)
-0.32 (0.10)

-0.05 (0.02)

Nosema ceranae present

0.27 (0.26)

ABPV present

-0.41 (0.44)

(Intercept)
Number of land-use classes in
1km
Maize area in 1km

7

AIC
BIC

620.71
680.71

618.60
664.76

Log Likelihood

-297.35

-299.30

Num. obs.
Num. groups: Year:Imker

747
373

747
373

Num. groups: Imker

328

328

Var: Year:Imker (Intercept)

0.91

0.93

Var: Imker (Intercept)

0.00

0.00

31

Figure 12. Relationships of factors affecting survival percentage of individual colonies for the whole study including any
trace of any chemical observed (Q2A1). There is no difference in survival across the years (see figure lower right).
Survival is much lower when the incidence of Varroa is high, when more maize is present in the landscape or the
landscape is highly fragmented (3km area), when Asteraceae pollen is present or when dimethoate is detected. Positive
effect on survival has the area of maize close to the hives (<1km), whereas presence of imidacloprid has a slight effect

32

5. Conclusions & Remarks
Conclusions: honeybee winter survival monitor 2017-2018
1 Winter survival was 84.3% in 2017-2018 (15.7% of colonies died). While higher survival is always
preferred, this figure is almost in line with the normal variation of around 5-15% winter mortality.
Survival has now been above 80% for the last six years and above 85% for five of the last six
years.
2 Most beekeepers do not lose colonies in the winter (unknown% in 2014-15; 64% in 2015-16;
53% in 2016-17 and 55% in 2017-18)
3 Dutch beekeepers managed at least 71-81,000 colonies in 2017.

Conclusions: factors correlated to honeybee winter survival 2014-2018
4 Factors linked to beekeeping practice emerge as the most consistent factor determining colony
winter survival. Colony survival in apiaries is lower when Varroa mite numbers are high and
when ABPV virus is present. Better control of parasites and diseases will further improve colony
survival.
5 A large number of variables each contribute just a little to explain why a honeybee colony
survives or dies. A few factors seem to have slightly more importance, but no single factor comes
out as the main driver of colony winter loss. Colonies containing more Varroa mites and placed in
highly fragmented landscape with substantial amounts of maize crops have lower survival
probability. When stored pollen contained more Asteraceae, survival was also slightly lower.
6 Chemical residues of neonicotinoids and other compounds were detected in 30% of samples,
but their presence is not correlated with winter mortality, except for the rarely detected
chemical dimethoate the presence of which correlated strongly with colony mortality.
7 Most other factors we studied (pollen sources, landscape features) also played small or no role
in colony survival. Many factors are correlated with each other and each year a different subset
is related to winter loss (see Appendix F). This indicates that the factors underlying honeybee
colony loss in the real world, i.e. hives managed by beekeepers in our Dutch landscapes, are
many, are variable in space and time, and are likely to interact to produce the final outcome:
survival or not.
8 Impact of the five main factors that have been analysed can be summarized as follows [Note that
for interpretation of all findings in this study, as in other studies, it is important to stress that the
absence of a significant correlation does not prove the absence of any effect]:
Bee management practice: Honeybee colonies survive best if beekeepers keep Varroa-mite
infestation levels low, which also keeps associated viruses (DWV and ABPV) low. Factors linked to
beekeeping practice come out as the most consistent factor determining colony winter survival.
Training and education of beekeepers is of great importance when honeybee health and survival is
to be improved.
Pests and diseases: Varroa and its associated viruses (e.g. DWV and ABPV) are omnipresent in Dutch
honeybee hives. Nosema ceranae seems to be decreasing each year (see Appendix H).
Chemical residues: In this study we assessed whether the presence of chemical residues in stored
winter food was correlated to colony winter survival. We did not study the effect of such compounds
on the survival of individual bees exposed to these chemicals. Of the more than 100 chemical
33

compounds we screened for, including all neonicotinoids, several were detected in stored winter
food in autumn (see Appendix H for spatial distribution on the most frequently found chemicals).
Three of these substances are used by beekeepers for Varroa-control. Systemic insecticides, e.g.
neonicotinoids, continue to be present in honey samples despite the severe limitations on the use of
some of them in open cultivation. Samples positive for thiacloprid and to a lesser extent acetamiprid
increased, whereas samples positive for imidacloprid decreased over the four years. Other chemical
residues were rarely found and when present were not related to colony mortality, except for
dimethoate, which has a strong link to colony mortality.
Pollen sources: Each year slightly different relationships turn up in the final models, but the four
year study only reveals a slight negative trend in survival when Asteraceae pollen is present in large
amounts. In individual years a similar correlation was found with presence of clover pollen. Both
plant groups are good pollen sources, but a high amount stored in the hive may indicate an
overreliance of these single pollen sources or lack of diversity of pollen sources (although no
correlation with number of pollen sources was observed). Further study is needed to reveal what is
behind this finding.
Landscape conditions: Highly diverse, fragmented, landscapes are linked to lower colony survival.
Most Dutch landscapes where apiaries are positioned are quite diverse (on average more than nine
different land use categories within 1km of the apiary). The number of land use classes is strongly
correlated to the area used for agricultural crops. In the most fragmented landscapes (with
substantial crop area) colony survival was slightly lower. In addition, the relation with specific
landscape variables is rather complex with maize at landscape scale being negative, but slightly
positive when occurring close to the hive.

Remarks and suggestions for further research
1. A randomized survey as an annual monitor of honeybee winter survival can be easily
organized by Dutch beekeeper associations and Bijen@wur and requires limited external
funds. This is possible based on (1) the online forms and procedure developed by our
consortium; (2) the experience from the last three years; and (3) the willingness of beforementioned parties.
2. Beekeepers should refrain from using illegal chemical substances for disease and pest
control. Among the chemicals we detected in winter food stored in bee hives, illegal
acaricides, such as coumaphos and amitraz were regularly encountered.
3. The role of the forage sources and surrounding landscape is complex and warrants
additional study. Analysis of the spring samples taken from selected colonies may give,
together with the already analysed autumn samples, a more complete insight in the
relationship between landscapes and colony survival.
4. The clear patterns in disease prevalence in our sampling (ABPV increasing, Nosema ceranae
decreasing) may need further study. Given that pests and disease are prominent among the
factors linked to colony loss, understanding the relationship better would be useful as would
training of beekeepers in diagnosis of such diseases.
5. Beekeeping in the Netherlands is largely a hobby and beekeeping associations should be
responsible for education and training on bee management, diseases and other threats,
and for (legal) solutions for disease treatment. Government-imposed measures and control
are less likely to render good results.

34

6. Appendices
A

Winter mortality survey based on CoLoSS questionnaire

B

Overview of results from GLMM analyses surveillance study

C

List of food plants found in stored pollen

D

List of chemical residues and their detection limits used for screening honey samples

E

Summary of residues detected in winter food / honey samples

F

Overview of the factors important for honeybee winter survival over 4 years

G

Overview of sampling points used in the four years of surveillance

H

Spatial occurrence of different factors across the Netherlands over 4 years

35

Appendix A
Winter mortality Survey based on CoLoSS questionnaire

36

37

38

39

Appendix B
Overview of results from GLMM analyses surveillance study
Full model for Q1
Q1 LU Models 2017
LU Full

Best 1

(Intercept)

1.90 (0.44)

1.65 (0.28)

Number of land-use
classes in 1km

0.15 (0.04)

0.12 (0.04)

Maize area in 1km

-0.03 (0.02)

-0.03 (0.02)

Forage area in 1km

-0.00 (0.00)

-0.00 (0.00)

Number of land-use
classes in 3km

-0.04 (0.04)

Nature area in 3km

-0.00 (0.00)

Crop area in 3km

-0.00 (0.00)

-0.00 (0.00)

Varroa mites / 100 bees -0.05 (0.01)
Nosema ceranae
present
-0.68 (0.23)

-0.05 (0.01)
-0.65 (0.23)

DWV present

0.16 (0.20)

ABPV present

-0.56 (0.20)

Hives IN

-0.01 (0.00)

AIC

526.72

523.36

BIC

560.37

545.79

Log Likelihood

-251.36

-253.68

Deviance

318.26

322.90

Num. obs.

122

122

-0.54 (0.20)

40

STEP 1 for Q2 A0 models (analysis at colony level, residues considered present when above LOQ)
Q2 A0 LU Models
LU Full

Best 1

(Intercept)
Number of land-use
classes in 1km

2.19 (0.48)

2.27 (0.52)

Maize area in 1km

-0.12 (0.27)

Nature area in 1km
Number of land-use
classes in 3km

-0.65 (0.34)

Forage area in 3km

0.72 (0.42)

0.67 (0.41)

Crop area in 3km
AIC

0.24 (0.31)
199.63

194.46

BIC

226.63

207.96

Log Likelihood

-91.81

-93.23

Num. obs.

216

216

Num. groups: Imker.x

116

116

Var: Imker.x (Intercept) 1.46

1.91

0.15 (0.37)
-0.66 (0.36)

-0.51 (0.36)

Q2 A0 Virus Models
Virus Full

Best 1

(Intercept)

1.74 (0.54)

1.85 (0.54)

Varroa mites / 100 bees

-0.06 (0.04)

-0.06 (0.04)

Nosema ceranae present

0.99 (0.92)

DWV present

1.02 (0.55)

0.99 (0.54)

ABPV present
AIC

-1.49 (0.65)
187.87

-1.49 (0.65)
187.23

BIC

208.12

204.11

Log Likelihood

-87.93

-88.62

Num. obs.

216

216

Num. groups: Imker.x

116

116

Var: Imker.x (Intercept)

1.41

1.42

41

Q2 A0 Pollen
Pollen Full

Best 1

(Intercept)

2.30 (0.54)

2.35 (0.57)

Number of pollen

-0.23 (0.26)

Asteraceae

-0.50 (0.27)

Brassicaceae

-0.27 (0.33)

Calluna

0.14 (0.39)

Castanea

-0.46 (0.26)

Cornus

-0.06 (0.22)

Fabaceae

-0.23 (0.24)

Heracleum

-0.30 (0.23)

Impatiens

-0.32 (0.29)

Rosaceae

-0.24 (0.25)

Trifolium

-0.00 (0.29)

Zea
AIC

-0.06 (0.25)
208.88

194.01

BIC

256.13

207.51

Log Likelihood

-90.44

-93.00

Num. obs.

216

216

Num. groups: Imker.x

116

116

Var: Imker.x (Intercept)

1.85

2.25

-0.40 (0.23)

-0.33 (0.23)

Q2 A0 Chemicals Models
Chemicals Full

Best 1

(Intercept)

2.24 (0.28)

2.28 (0.27)

Acetamiprid present

26.04 (304026.72) 25.15 (261852.11)

Thiacloprid present

1.70 (1.76)

Boscalid present
AIC

-2.48 (1.81)
195.67

-1.14 (1.18)
195.20

BIC

212.55

208.70

Log Likelihood

-92.84

-93.60

Num. obs.

216

216

Num. groups: Imker.x

116

116

Var: Imker.x (Intercept) 2.23

2.18

42

STEP 1 for Q2 A1 models (analysis at colony level, residues considered present when above LOD)

Q2 A1 LU Models
LU Full

Best 1

(Intercept)
Number of land-uses
classes in 1km
Maize area in 1km

2.19 (0.48)

2.27 (0.52)

Nature area in 1km
Number of land-uses
classes in 3km
Forage area in 3km

-0.65 (0.34)

Crop area in 3km

0.24 (0.31)

AIC

199.63

194.46

BIC

226.63

207.96

Log Likelihood

-91.81

-93.23

Num. obs.

216

216

Num. groups: Imker.x

116

116

Var: Imker.x (Intercept) 1.46

1.91

0.15 (0.37)
-0.12 (0.27)
-0.66 (0.36)

-0.51 (0.36)
0.72 (0.42)

0.67 (0.41)

Q2 A1 Virus Models
Virus Full
(Intercept)

Best 1

1.54 (0.52) 1.64 (0.52)

Varroa mites / 100 bees -0.30 (0.21) -0.31 (0.21)
Nosema ceranae
present
0.99 (0.92)
DWV present
1.02 (0.55) 0.99 (0.54)
ABPV present
-1.49 (0.65) -1.49 (0.65)
AIC

187.87

187.23

BIC

208.12

204.11

Log Likelihood

-87.93

-88.62

Num. obs.

216

216

Num. groups: Imker.x

116

116

Var: Imker.x (Intercept)

1.41

1.42

43

Q2 A1 Pollen Models
Pollen Full

Best 1

(Intercept)

2.30 (0.54)

2.35 (0.57)

Number of pollen

-0.23 (0.26)

Asteraceae

-0.50 (0.27)

Brassicaceae

-0.27 (0.33)

Calluna

0.14 (0.39)

Castanea

-0.46 (0.26)

Cornus

-0.06 (0.22)

Fabaceae

-0.23 (0.24)

Heracleum

-0.30 (0.23)

Impatiens

-0.32 (0.29)

Rosaceae

-0.24 (0.25)

Trifolium

-0.00 (0.29)

Zea
AIC

-0.06 (0.25)
208.88

194.01

BIC

256.13

207.51

Log Likelihood

-90.44

-93.00

Num. obs.

216

216

Num. groups: Imker.x

116

116

Var: Imker.x (Intercept)

1.85

2.25

-0.40 (0.23)

-0.33 (0.23)

Q2 A1 Chemicals Models
Chemicals Full

Best 1

(Intercept)

2.14 (0.27)

2.17 (0.26)

Acetamiprid present

26.99 (521821.81)

26.15 (391123.39)

Thiacloprid present

0.73 (0.99)

Boscalid present
AIC

-1.76 (1.48)
195.38

192.86

BIC

212.26

202.99

Log Likelihood

-92.69

-93.43

Num. obs.

216

216

Num. groups: Imker.x

116

116

Var: Imker.x (Intercept)

1.98

1.98

44

Appendix C
List of food plants found in stored pollen
Pollen type sorted by pollen most frequently found in 2017. Total of 68 different pollen types were
found.

Species
Brassicaceae
Trifolium
Asteraceae
Rosaceae
Castanea
Phacelia
Trifolium pratense
Impatiens
Hedera
Calluna
Caryophyllaceae
Ranunculaceae
(Clematis)
Parthenocissus
Heracleum
Taraxacum
Fabaceae
Aesculus
Centaurea jacea
Ligustrum
Persicaria
Amaryllidaceae
Lythrum
Origanum
Hypericum
Centaurea cyanus
Chenopodium
Cirsium
Rubus
Ranunculaceae Papaver
Melilotus
Pisum
Spiraea
Tropaelum majus
Apiaceae

found in #
samples
% of total
min
134
50.6
105
39.6
92
34.7
86
32.5
58
21.9
58
21.9
58
21.9
51
19.2
49
18.5
43
16.2
34
12.8

0
0
0
0
0
0
0
0
0
0
0

max
100
100
95
85
85
65
85
100
100
95
50

ave. When present
31.8
27.9
19.7
22.0
32.9
18.4
25.5
38.1
41.8
33.6
11.9

31
28
24
24
23
21
18
15
14
13
13
13
11
10
9
8
8

11.7
10.6
9.1
9.1
8.7
7.9
6.8
5.7
5.3
4.9
4.9
4.9
4.2
3.8
3.4
3.0
3.0

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

80
35
15
35
85
65
25
20
10
35
20
20
20
15
40
10
50

17.4
10.2
6.0
9.4
30.7
15.7
10.0
8.7
5.7
16.2
7.7
8.8
10.9
8.5
10.6
6.3
20.0

7
6
6
6
6
5

2.6
2.3
2.3
2.3
2.3
1.9

0
0
0
0
0
0

70
45
10
60
15
5

31.4
15.8
8.3
26.7
8.3
5.0
45

Eryngium
Zea
Asparagus
Epilobium
Foeniculum
Robinia
Salvia
Aegopodium
Cornus
Lotus
Medicago
Poacceae
Polygonum
Symphoricarpus
Acer
Campanulaceae
Echium
Filipendula
Quercus
sporen
Borago
Cytisus-type
Datura
Helianthus-type
Ilex
Jasione
Mahonia
Nymphaea
Nymphoides
Oreganum
Reseda (?)
Rhamnus
Solanum
Vitis

5
5
4
4
4
4
4
3
3
3
3
3
3
3
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1

1.9
1.9
1.5
1.5
1.5
1.5
1.5
1.1
1.1
1.1
1.1
1.1
1.1
1.1
0.8
0.8
0.8
0.8
0.8
0.8
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

95
10
65
10
20
80
20
10
5
5
25
15
25
15
30
10
15
10
15
15
5
10
5
10
5
5
15
5
5
5
45
5
50
85

33.0
6.0
36.3
6.3
8.8
30.0
12.5
6.7
5.0
5.0
13.3
13.3
13.3
8.3
17.5
7.5
10.0
10.0
12.5
10.0
5.0
10.0
5.0
10.0
5.0
5.0
15.0
5.0
5.0
5.0
45.0
5.0
50.0
85.0

46

Appendix D
List of chemical residues and their detection limits used for screening honey samples
LOQ = Limit Of Quantification in the analytical methods we apply (see also box 1 in main text). This
value does not have anything to do with the hazard and safety threshold for any organism.

Component

LOQ
(µg/kg)

Component

2017

LOQ
(µg/kg)
2017

10

Fluopyram

1

Abamectin

10

Fluquinconazole

1

Acetamiprid

0.5

Flusilazole

1

Aldicarb sulfon

5

Fluvalinate tau

2

Azamethifos

1

Haloxyfop-methyl

5

Bendiocarb

1

Imidacloprid

0.5

Bifenazate

1

Imidacloprid_5-Hydroxy

5

Bifenthrin

1

Imidacloprid_desnitro

0.5

Bixafen

1

Imidacloprid_desnitro

0.5

Boscalid

1

Imidacloprid_olefin

5

Carbaryl

1

Imidacloprid_urea

0.5

Carbendazim

1

Indoxacarb

2

Chlorfenvinphos

1

Ioxynil

1

Chlorpyriphos

1

Malathion

1

Chlorpyriphos-methyl

5

Metaflumizone

1

Clothianidin

2

Metazachlor

1

Coumaphos

2

Methidathion

5

Cyfluthrin-Beta

1

Methomyl

1

Cypermethrin

5

Novaluron

5

Cyproconazole

1

Omethoate

1

Deltametrin

5

Paraoxon-methyl

1

Diazinon

1

Pendimethalin

5

Dichlorprop

5

Permethrin

5

Dichlorvos

5

Phorate

1

Dimethoate

1

Phorate sulfon

1

Disulfoton-sulfone

1

Phorate sulfoxide

1

Disulfoton-sulfoxide

5

Phorate-O sulfoxide

1

DMA

25

Phosmet

1

DMF

5

Phoxim

1

DMPF

5

Pirimiphos-methyl

1

Edifenphos

1

Prochloraz

1

Emamectin

2

Profenofos

1

Epoxiconazole

1

Propetamphos

1

Esfenvalerate

1

Propiconazole

5

6-Chloronicotinic_acid

47

Ethiofencarb

1

Propoxur

1

Ethiofencarb sulfon

1

Prothioconazole-desthio

1

Ethiofencarb sulfoxide

1

Pyrazophos

1

Etofenprox

1

Pyridaben

1

Famoxadone

5

Pyridate

1

Fenpropidin

5

Rotenone

1

Fenpropimorph

1

Spinosyn A

1

Fensulfothion

1

Spinosyn D

5

Fensulfothion-O sulfon

1

Spiroxamine

1

Fensulfothion-O

1

Tebuconazole

1

Fensulfothion-sulfon

1

Teflubenzuron

1

Fenthion

1

Tepraloxydim

5

Fenthion sulfon

1

Tetrachlorfenvinphos

5

Fenthion sulfoxide

1

Tetraconazole

5

Fenthion-O sulfon

1

Thiabendazole

1

Fenthion-O sulfoxide

1

Thiabendazole

5

Fipronil

0.5

Thiacloprid

1

Fipronil-carboxamide

0.5

Thiamethoxam

2

Fipronil-desulfinyl

0.5

Thiophanate-methyl

1

Fipronil-sulfide

0.5

Triazophos

1

Fipronil-sulfone

0.5

Triflumizole

1

Fluazifop-P-butyl

1

48

Appendix E
List of chemical residues detected in honey samples in each of the four years. Given are the
percentages of samples in which traces were found (all samples >LOD; columns 3-6) and in
which quantifiable amounts were found (only samples >LOQ; columns 7-10). The final two
columns indicate the percentage of traces (all) and quantifiable (>LOQ) samples in the
whole study.
LOQ = Limit Of Quantification in the analytical methods we apply (see also box 1 in main text). This
value does not have anything to do with the hazard and safety threshold for any organism. Marked
components are found in at least one of the samples, others are not found.

Use type

Chemical
compound

% of samples with traces

samples with
concentration > LOQ

% samples
(1052)

2015

2016

2017

2018

2015

2016

2017

2018

all

>LOQ

Acaricide

Amitraz

7.8

7.3

2.6

4.4

5.6

2.1

1.8

0.7

5.0

1.9

Acaricide

Coumaphos

1.1

14.7

5.9

10.2

1.1

2.4

0.0

1.0

9.4

1.1

Acaricide

Fluvalinate tau

0.0

2.1

0.0

0.0

0.0

0.9

0.0

0.0

0.7

0.3

Fungicide

Boscalid

0.0

0.0

5.9

2.7

0.0

0.0

4.1

2.7

2.7

2.1

Fungicide

Carbendazim

0.0

0.0

0.6

0.3

0.0

0.0

0.3

0.3

0.3

0.2

Fungicide

Epoxiconazole

0.0

0.0

0.0

0.3

0.0

0.0

0.0

0.3

0.1

0.1

Fungicide

Fluopyram

0.0

0.0

0.9

1.4

0.0

0.0

0.6

1.4

0.7

0.6

Fungicide

Tebuconazole

0.0

0.0

1.5

0.0

0.0

0.0

0.6

0.0

0.5

0.2

Herbicide

Metazachlor

0.0

0.0

0.0

0.3

0.0

0.0

0.0

0.3

0.1

0.1

Herbicide

Pyridate

0.0

0.0

0.0

0.3

0.0

0.0

0.0

0.1

0.0

Insecticide

Chlorfenvinphos

0.0

0.0

0.6

0.3

0.0

0.0

0.6

0.3

0.3

0.3

Insecticide

Dimethoate

1.1

2.8

0.3

0.7

1.1

0.9

0.3

0.7

1.2

0.7

Insecticide

Permethrin

0.0

0.3

0.0

0.0

0.0

0.3

0.0

0.0

0.1

0.1

Insecticide

Spinosyn A/D

0.0

0.0

0.0

0.3

0.0

0.0

0.0

0.1

0.0

Insecticide

Teflubenzuron

0.0

0.0

0.0

1.0

0.0

0.0

0.0

1.0

0.3

0.3

Neonic

Acetamiprid

2.2

8.3

3.8

4.8

2.2

2.8

3.8

3.4

5.3

3.2

Neonic

Imidacloprid

6.7

2.8

0.3

1.0

6.7

2.8

0.3

1.0

1.8

1.8

Neonic

Thiacloprid
Thiamethoxam
/Clothianidin

2.2

37.6

8.2

12.6

2.2

9.8

4.4

7.5

18.1

6.7

0.0

3.1

0.6

0.0

0.0

0.9

0.0

0.0

1.1

0.3

Neonic

49

Appendix F
Overview of the factors important for honeybee winter survival for each of the four years of the
honeybee surveillance study and the overall model for Questions 1 (Q1 model) and 2 (Q2A1 model).
POS=important positive correlation with survival, pos= positive correlation in some models, [pos]
small positive correlation. NEG=important negative correlation with survival, neg= negative
correlation in some models, [neg] small negative correlation.

Q2 A1 model
Acetamiprid
Boscalid
Dimethoate
Imidacloprid
Tebuconazole
ABPV
DWV
Nosema ceranae
Varroa%
Crop area 1km
Forage 3km
Land Use Classes [1km]
Maize area 1km
Maize area 3km
Nature area 1km
Nature area 3km
Asteraceae
Brassicaceae
Heracleum
Impatiens
Trifolium

2015 2016 2017 2018 Overall
POS
neg
NEG
NEG
POS
NEG
NEG [neg]
POS
POS
[pos]
NEG
NEG
POS
POS
neg NEG NEG
NEG
POS
NEG
NEG
neg
NEG
POS
pos
neg
POS
NEG NEG
[neg]

50

Appendix G
Overview of sampling points used in the honeybee surveillance study in each of the four years of
survey.

51

Appendix H
Spatial occurrence of different factors and variables across the Netherlands and across the four
years of study. For chemical residues only samples with residue concentration above Level of
Quantification for that chemical compound is indicated as presence.

52

53

54

55

56

57

58

59

60

61

62