# Ecological relevance in honeybee pesticide risk assessment: developing context-dependent scenarios to manage uncertainty

Mickaël Henry, Axel A. Decourtye

<figure><img src="image_1.png" /></figure>

## To cite this version:

Mickaël Henry, Axel A. Decourtye. Ecological relevance in honeybee pesticide risk assessment: developing context-dependent scenarios to manage uncertainty. Frontiers in Physiology, 2013, 4, 3 p. 10.3389/fphys.2013.00062. hal-02645051

HAL Id: hal-02645051

https://hal.inrae.fr/hal-02645051

Submitted on 29 May 2020

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

# Ecological relevance in honeybee pesticide risk assessment: developing context-dependent scenarios to manage uncertainty

Mickaël Henry<fnref n="1" />,<fnref n="2" />* and Axel Decourtye<fnref n="2" />,<fnref n="3" />

**Edited by:**

Monique Gauthier, University Paul Sabatier Toulouse 3, France

**Reviewed by:**

Monique Gauthier, University Paul Sabatier Toulouse 3, France

We reported 1 year ago (Henry et al., 2012a) that a sublethal dose of thiamethoxam, a neonicotinoid pesticide used on some common flowering crops, reduces the ability of exposed honeybees to find their way back to the colony. More recently, Guez (2013) have raised concerns about the relevance of our study design. The obvious intention of the author was to discredit our study, both by seeking after inconsistencies in calculations and gathering new arguments against the experimental dose of thiamethoxam. In both case, we would like to carry out major rectifications of his statements. We fully agree that there is still place for improvement in the way one may estimate pesticide exposure levels in honeybees, and Guez (2013) brought interesting thoughts in that direction. However, this should be done with respect to the recent advance on that topic (ANSES, 2012; Cresswell and Thompson, 2012; EFSA, 2012a,b, 2013; Henry et al., 2012b) and with respect to the need for hierarchizing the sources of uncertainty. Herein, we show that the overestimation issue raised by Guez has already been addressed before and with an even greater magnitude of uncertainty. But at the heart of the debate is the way to deal with uncertainty in estimates of honeybee field exposure to pesticides. In that respect, we further underline (1) the need to properly hierarchize uncertainties using conventional ecological scaling approaches and (2) the need to investigate context-dependent exposure scenarios, whereby worse-case situations are explicitly linked with their spatial or temporal occurrence likelihood.

Our main concern about Guez's comment is that the conclusions on the peer review of the risk assessment for bees, published consecutively to our study, were basically ignored (ANSES, 2012; EFSA, 2012a,b, 2013). The updated expertise and field measurements of nectar residues intake returned exposure levels of 0.10–0.33 ng.bee⁻<fnref n="1" />.day⁻<fnref n="1" /> (ANSES, 2012), 0.184–5.888 ng.bee⁻<fnref n="1" />.day⁻<fnref n="1" /> (EFSA, 2012a) and 0.553–2.903 ng.bee⁻<fnref n="1" />.day⁻<fnref n="1" /> (EFSA, 2013) for winter oilseed rape treated with thiamethoxam. Based on these results, one must admit that uncertainties in field exposure levels are so high that they should be the prime focus of risk assessors interested in refining honeybee exposure scenarios. Depending on the considered scenario, our 1.34 ng dose may actually overestimate by a factor of 13.4 or underestimate by a factor of 4.4 the real field exposure level. This actually overwhelms the uncertainty levels raised by Guez due to temporal variations in sugar content (<6-fold overestimations, Figure 1A in Guez, 2013) and due to spatial variations in daylight time available for foraging (Figure 1B in Guez, 2013). Although Guez made valuable efforts to challenge our study design, there is a higher level of uncertainty to deal with first.

## HIERARCHIZING UNCERTAINTIES

This merely illustrates the need to conveniently hierarchize uncertainty levels in pesticide risk assessment. Each parameter used for the field exposure assessment (e.g., sugar concentration, flight time, pesticide residuals in nectar; Rortais et al., 2005) is associated with an intrinsic

uncertainty, denoted by an upper and a lower bound. The successive multiplication of uncertainty bounds considerably increases the overall uncertainty. As the pesticide risk assessment debate slides from pure toxicology to ecotoxicology and behavioral ecology, this problem is being exacerbated by the accumulation of ecological parameters, each piling up an additional uncertainty. Guez (2013) offers a typical counterexample of uncertainty ranking. His comment first expands on why we have reported daily exposure estimates of 0.17–2.3 ng instead of an expected 0.197–2.375 ng. The discussion turns around this deviation equivalent to 2.0–5.6% of our experimental dose (1.34 ng), whereas we learn in the next section that our dose may actually overestimate real exposure by as much as 140–600% (Figure 1A in Guez, 2013). We feel that two orders of magnitude should appeal for a better discernment of which concern to put forward. Notwithstanding, would some readers be interested, the range deviation results from successive rounding at the different stages of the calculation. We have obviously rounded off at coarser levels than Guez, resulting in a different level of resolution (±0.01–0.1 ng, not 0.001 ng). We feel it was reasonable not to go into thinner levels of resolution given the high uncertainty inherent to all the involved parameters. Likewise, we found it unnecessary to further translate weight/volume data into w/w data because this results anyway in a slight deviation toward the conservative direction (underestimation of exposure range). We agree that specialists may not perceive

<footnote n="1">¹ INRA, UR406 Abeilles et Environnement, Site Agroparc, Avignon, France</footnote>

<footnote n="2">² UMT Protection des Abeilles dans l'Environnement, Site Agroparc, Avignon, France</footnote>

<footnote n="3">³ Association de Coordination Technique Agricole, Site Agroparc, Avignon, France</footnote>

<footnote>*Correspondence: mickael.henry@avignon.inra.fr</footnote>
this as a good message, and if it were to
be done over again we would probably
proceed differently. But one should also
keep in mind that experimental imprecis-
sions might significantly outweigh round-
ing imprecisions, and therefore should
be accounted for whenever possible. For
instance, readers will notice from our
methods (Supplementary data in Henry
et al., 2012a) that our experimental dose
(1.34 ng) was actually the result of a 34%
excess dosage in our attempt to target a
1 ng dose. We could ascertain this 34%
deviance because we ordered an expertise
of our solution to ensure it was in the tar-
get range. We think that such an effort to
remove an uncertainty level is rare enough
to be worthy of note.

INVESTIGATING
CONTEXT-DEPENDENT SCENARIOS TO
DEAL WITH UNCERTAINTY

One option to deal with the uncertainty
scaling issue is to move on from worse
*case* scenarios toward *context-dependent*
scenarios. This is at least how we may
reformulate Guez's (2013) comment on
temporal variations of oilseed rape nec-
tar concentration. Risk assessors use the
term *worse case* to describe exposure sce-
narios computed with the parameters
upper bounds. Official conclusions on
the peer review of the risk assessment
may not always explicitly report worse
case calculations (e.g., ANSES, 2012), with
the underlying assumption that the con-
junction of upper bound values in real
field conditions would be exceptional—
not to say unlikely. There is indeed a
knowledge gap here. One should try and
determine in which situations exposure
parameters reach their upper bound, and
whether the worse case combinations are
likely to occur in real world. In other
words, exposure levels in real world are
context-dependent, and one should com-
pute exposure scenarios with explicit ref-
erence to this context dependency. For
instance, Guez (2013) computed tempo-
rally explicit thiamethoxam exposure sce-
narios, with regard to oilseed rape nectar
concentration, and found that our dose
actually respects the worse case scenario
during a single flowering week out of
four. He also investigated daylight time
latitudinal variations with the underlying
idea that a *spatially explicit* choice of the

flight duration upper bound would help
refine the exposure scenario. These are
two nice examples of context-dependent
scenarios with, respectively, a temporally
and a spatially explicit adjustment. That
said, the whole simulation remains to be
updated using the recent advance in thi-
amethoxam residues measurements, and
considering a complete range of latitudes
(there was absolutely no reason why Guez
should focus on the specific geographi-
cal locations of our study area). Spatially
explicit adjustments may also consider
temperature-dependent worse case flight
durations or soil-dependent worse case
nectar concentrations.

HOMING FAILURE

We have emphasized here two new avenues
of research, namely the uncertainty scal-
ing issue and the context-dependency in
pesticide risk assessment. But the great-
est forthcoming challenge will be to lead
this research throughout the future step
forward into the biological levels, i.e., the
model-based risk assessment at the colony
scale (Osborne, 2012). The scale change
from individuals to colony will require
a rigorous management of uncertainty.
This is the place to introduce our last,
but not least, concern about Guez's com-
ment. His criticism about the formula we
used to investigate the consequences of
homing failure from individuals to colony
scale is misleading. What we need is a
mortality *rate*, intended for use in the
demographic models, not a *simple differ-
ence* between control and treated groups.
The simple difference as an estimate of
treatment-induced mortality is biased by
the mortality due to the experiment *per
se*. The difference must be expressed rel-
atively to a reference value. To illustrate
this, one can use voluntarily exaggerated
values: imagine the studied dose causes
half the individuals to fail homing under
normal conditions (mortality due to hom-
ing failure = 0.5, this is what we want
to estimate), but that on the same time,
the experiment is so stressful that an over-
all 90% of control bees would fail hom-
ing. Then, homing success would be 10%
for control groups and half less (5%) for
treated groups. Based on these observa-
tions, the “simple” difference would return
a much underestimated mortality rate due
to homing failure [10–5% = 0.05], while

our “relative” difference would return the
appropriate rate [(10–5%)/10% = 0.5].
Think also about a scenario where 100%
of exposed bees would fail homing.
Then it would be impossible to get the
proper mortality rate using a “simple”
difference, by definition. In our exam-
ple, the simple difference would return
a 0.1 mortality due to homing failure
instead of 1.

There is certainly place for improve-
ment in the way to assess mortality due
to homing failure, but this will be achiev-
able providing the ecological dimension is
properly implemented into classical toxi-
cological approaches. In that respect, we
fully support Guez's initiative to challenge
our study. But unlike his concluding warn-
ing on the absence of empirical support for
Rortais et al.'s (2005) and Khoury et al.'s
(2011) methods, we would not refrain
readers from exploring pesticide effects on
honey bees using innovative tools.

ACKNOWLEDGMENTS

This contribution proceeds from a project
funded by the European Community pro-
gram (797/2004) for French beekeeping
coordinated by the French Ministry of
Agriculture (convention FranceAgriMer
11-45R).

REFERENCES

ANSES. (2012). Avis de l'Agence nationale de sécu-
rité sanitaire de l'alimentation, de l'environnement
et du travail relatif à une demande d'appui scien-
tifique et technique dans la perspective de la pub-
lication de l'article "A common pesticide decreases
foraging success and survival in honey bees". *Avis
de l'Anses Saisine* n° 2012-SA-0092.

Cresswell, J. E., and Thompson, H. M. (2012). Comment on “A common pesticide decreases foraging success and survival in honey bees”. *Science* 337, 1453.

EFSA. (2012a). Statement on the findings in recent studies investigating sub-lethal effects in bees of some neonicotinoids in consideration of the uses currently authorised in Europe. EFSA J. 10:2752. doi: 10.2903/j.efsa.2012.2752

EFSA. (2012b). Scientific Opinion on the science behind the development of a risk assessment of plant protection products on bees (Apis mellifera, Bombus spp. and solitary bees). EFSA J. 10:2668. doi: 10.2903/j.efsa.2012.2668

EFSA. (2013). Conclusion on the peer review of the pesticide risk assessment for bees for the active substance thiamethoxam. EFSA J. 11:3067. doi: 10.2903/j.efsa.2013.3067

Guez, D. (2013). A common pesticide decreases foraging success and survival in honey bees: questioning the ecological relevance. *Front. Physiol.* 4:37. doi: 10.3389/fphys.2013.00037
Henry, M., Béguin, M., Requier, F., Rollin, O., Odoux, J.-F., Aupinel, P., et al. (2012a). A common pesticide decreases foraging success and survival in honey bees. *Science* 336, 348–350.

Henry, M., Béguin, M., Requier, F., Rollin, O., Odoux, J.-F., Aupinel, P., et al. (2012b). Response to Comment on “A common pesticide decreases foraging success and survival in honey bees”. *Science* 337, 1453.

Khoury, D. S., Myerscough, M. R., and Barron, A. B. (2011). A quantitative model of honey bee colony population dynamics. *PLoS ONE* 6:e18491. doi: 10.1371/journal.pone.0018491

Osborne, J. L. (2012). Bumblebees and pesticides. *Nature* 491, 43–45.

Rortais, A., Arnold, G., Halm, M. P., and Touffet-Briens, F. (2005). Modes of honeybees exposure to systemic insecticides: estimated amounts of contaminated pollen and nectar consumed by different categories of bees. *Apidologie* 36, 71–83.

Received: 05 March 2013; accepted: 14 March 2013; published online: 04 April 2013.

Citation: Henry M and Decourtye A (2013) Ecological relevance in honeybee pesticide risk assessment:

developing context-dependent scenarios to manage uncertainty. *Front. Physiol.* 4:62. doi: 10.3389/fphys. 2013.00062.

This article was submitted to *Frontiers in Invertebrate Physiology, a specialty of Frontiers in Physiology*.

Copyright © 2013 Henry and Decourtye. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.