DOI: 10.5061/dryad.63xsj3v72
title: Cognitive scores of bees exposed to various environmental stressors

description: Environmental stressors have sublethal consequences on animals, often
 affecting the mean of phenotypic traits in populations. However, effects
 on inter-individual variability are poorly understood. Since phenotypic
 variability is the basis for adaptation, any change due to stressors may
 have important implications for population resilience.  Here we
 explored this possibility in bees by analysing raw datasets from 23
 studies (5,618 bees) in which individuals were first exposed to stressors
 and then tested for cognitive tasks. While all types of stressors
 decreased the mean cognitive performance of bees, they increased cognitive
 variability. Focusing on 14 pesticide studies, we found that the mode of
 exposure to stressors and the dose were
 critical. Mean cognitive performance was more affected
 by a chronic exposure than by an acute exposure. Yet, cognitive
 variability increased with increasing doses following both exposure
 durations. Policy implications: Current guidelines for the authorization
 of plant protection products on the European market prioritize acute over
 chronic toxicity assessments on non-target organisms. By overlooking the
 consequences of a chronic exposure, regulatory authorities may register
 new products or doses that are harmful to bee populations. Our findings
 call for more research on stress-induced phenotypic variation and its
 incorporation to policy guidelines to help identify levels and modes of
 exposure animals can cope with.
description: <em><strong>Search
 and selection of datasets</strong></em>
 The search for datasets in
 scientific publications falling within the scope of our research question
 was performed in July 2020 using the PubMed database. The words used for
 the search were (“Stressor” OR “Pesticide” OR “Parasite”) AND (“Cognition”
 OR “Learning”) AND (“Bees”). This search was not restricted to any section
 of the manuscripts and automatically extended to similar terms intended
 under the MeSH hierarchy of the database. A total of 240 studies were
 found, of which 18 met our inclusion criteria regarding the cognitive task
 and the type of stressor (see below). The search terms under which each
 study was found are available in the Supplementary Table 1. Five datasets
 belonging to the authors of this study were also included as they filled
 the inclusion criteria. These studies measured the impact of stressors on
 the cognitive performance of bees. The list of the 23 selected studies is
 available in Table 1.
 <strong>Cognitive
 tasks:</strong> We
 focused on cognitive data from bees exposed to stressors during their
 adult life. The effect of
 stressors on larvae could not be analysed due to the lack of data
 available (two studies). In all the selected studies, cognitive
 performance was assessed using associative learning paradigms testing the
 ability of bees to associate an olfactory or/and a visual stimulus with an
 appetitive or aversive reinforcement (Giurfa, 2007). Olfactory learning was tested in 18 out of the
 23 studies. These studies used learning protocols based on the appetitive
 conditioning of the proboscis extension response (PER; 16 studies) or the
 aversive conditioning of the sting extension response (SER; 2 studies).
 Either response was conditioned by presenting bees a conditioned stimulus
 (an odour) reinforced with an unconditioned stimulus (sucrose solution or
 electric shock), for 3-15 trials in appetitive assays and 5-6 trials in
 aversive assays. Trainings included absolute learning (the odour is
 reinforced) and differential learning (an odour is reinforced; the other
 is not). Visual learning was tested in 5 out of the 23
 studies. These studies used appetitive conditioning protocols in a Y-maze
 or on artificial flowers (i.e. feeders), or aversive conditioning
 protocols with electric shocks. One of these studies applied a multimodal
 appetitive conditioning combining both odour and colour cues to be learnt
 by bees in an array of artificial flowers (Muth et al. 2019). Here again
 bees were tested for differential learning.
 <strong>Stressors:</strong> Stressor types covered different pesticides,
 parasites, predator odours, alarm pheromones, and heavy metal pollutants.
 Experiments performed with pesticides whose median lethal dose (LD50; i.e.
 dose that kills 50% of the population) could not be identified in the
 literature were excluded from our final selection. 
 <strong>Exposure
 duration:</strong> In all the studies, stressors were applied
 before the cognitive tests, except in one study in which it was used as
 the conditioning stimulus to be learned (i.e. alarm and predator
 pheromones (Wang et al. 2016)). We categorised the duration of exposure
 using the common dichotomy between acute and chronic exposures. An acute
 exposure was characterized by a single administration of the pesticide to
 each individual bee. When bees were exposed to the pesticide more than
 once, either as a substance present in their environment or as a food
 directly offered to each individual, the exposure type was considered
 chronic. <strong>Bees:</strong> The bee species studied in the selected
 publications were the honey bees <em>Apis
 cerana</em> and <em>Apis mellifera</em>, and the
 bumblebees <em>Bombus impatiens</em> and <em>Bombus
 terrestris</em>. These species were not selected purposefully, but
 rather emerged as the species most represented in our dataset from the
 refinement obtained with other inclusion criteria. We considered bee genus
 (<em>Apis</em> or <em>Bombus</em>) for the
 analyses.  
 <strong>Table 1: Summary
 of the 23 studies used. </strong> 
 <strong>Stressor</strong>
 
 <strong>Bee
 genus</strong> 
 <strong>Exposure
 type</strong> 
 <strong>Reference</strong>
 
 Pesticide 
 
 <em>Apis</em>
 Acute (Ludicke and Nieh, 2020)
 
 Pesticide 
 
 <em>Apis</em>
 Acute (Hesselbach and Scheiner,
 2018) Pesticide 
 
 <em>Apis </em>
 Acute (Urlacher <em>et al</em>.,
 2016) Pesticide 
 
 <em>Apis</em>
 Acute  (Tan <em>et al</em>.,
 2015) Pesticide 
 
 <em>Apis</em>
 Chronic 
 (Mustard <em>et al</em>.,
 2020) Pesticide 
 
 <em>Apis</em>
 Chronic 
 (Tan <em>et al</em>.,
 2017) Pesticide 
 
 <em>Apis,
 Bombus</em> Acute (Siviter <em>et al</em>.,
 2019) Pesticide <em>Bombus</em>
 Acute
 
 (Muth <em>et al</em>., 2019) 
 Pesticide <em>Bombus</em>
 Acute,
 chronic (Stanley, Smith and Raine,
 2015) Pesticide 
 
 <em>Bombus</em>
 Chronic 
 (Smith <em>et al</em>.,
 2020) Pesticide 
 
 <em>Bombus</em>
 Chronic 
 (Lämsä <em>et al</em>.,
 2018) Pesticide 
 
 <em>Bombus</em>
 Chronic 
 (Phelps <em>et al.</em>,
 2018) Pesticide, coexposure
 
 <em>Apis</em>
 Chronic 
 (Colin, Plath, <em>et al</em>.,
 2020) Parasite 
 
 <em>Bombus</em>
 Acute (Gomez-Moracho <em>et
 al</em>., 2021) 
 Parasite 
 
 <em>Bombus</em>
 Acute (Martin, Fountain and Brown,
 2018) Pollution 
 
 <em>Apis</em>
 Acute (Monchanin <em>et al</em>.
 unpublished) Pollution 
 
 <em>Apis</em>
 Acute (Monchanin, Drujont, <em>et
 al</em>., 2021) Pollution 
 
 <em>Apis</em>
 Chronic 
 (Monchanin,
 Blanc-brude,<em> et al.</em>, 2021) 
 Other <em>Apis</em>
 Acute (Wang <em>et al</em>.,
 2016) Other <em>Apis</em>
 Acute (Shepherd <em>et al</em>.,
 2018) Other <em>Apis</em>
 Chronic 
 (Shepherd <em>et al</em>.,
 2019) Coexposure 
 
 <em>Apis,
 Bombus</em> Acute/Chronic 
 (Piiroinen and Goulson,
 2016) Coexposure 
 
 <em>Bombus</em>
 Acute/Chronic 
 (Piiroinen <em>et al</em>.,
 2016) 
 <em><strong>Dataset organisation and normalisation of
 variables </strong></em>
 All but three raw datasets
 were available online with the published material. Those three datasets
 were kindly provided by their authors, i.e. Dara Stanley and Ken Tan. The raw data were downloaded and saved as .csv
 files. A new dataset was created, which combined
 information on the
 species, the cognitive task studied, the type of stressor, the type of
 exposure (acute/chronic), and, in the case of pesticide studies, the dose
 (µg/bee) or concentration (ppb). The dose (acute exposure) and
 concentration (chronic exposure) were normalized as the percentage of the
 LD50. When learning performance was measured as a binary response (e.g.
 success vs. failure) across multiple trials, the raw data were used to
 calculate a learning score for each individual corresponding to the number
 of successful trials. This was required because the variance in binary
 variables can be mathematically predicted from the mean and sample size
 and does not reflect biological variance (Supplementary Fig. 1). Each
 study provided individual cognitive scores for at least one experimental
 treatment and control group. There was a total of 73 experimental treatments across the 23
 studies. To compare the mean cognitive performance and
 the cognitive variability across studies, we used a standardized method
 for the meta-analysis of variation (Nakagawa et al., 2015; Senior, Viechtbauer and
 Nakagawa, 2020). This
 method controls for the mean – variance linear relationship that may exist
 in a dataset by using unbiased effect size statistics of the mean and
 variability, i.e. the natural logarithm of the ratio between the means
 (lnRR) and the natural logarithm of the ratio between the coefficients of
 variation (lnCVR) of treated and control groups, respectively. Changes in
 lnCVR are not an indirect consequence of changes in lnRR, as would have
 been the case had we analysed the variance and the mean, but they rather
 reflect changes in variability <em>per
 se</em>. The two pre-requisites for this method are (i)
 to use log scale data and (ii) to observe a mean-variance linear
 relationship. Studies for which negative cognitive scores were present
 were transformed to log-scale data by adding the minimum score to all
 individuals. The mean and standard deviation of the cognitive scores, as
 well as sample sizes, were calculated for each experimental treatment and
 control group. A linear relationship and positive correlation were found
 between the log sample mean and standard deviation in our dataset
 (Supplementary Fig. 2). All pre-requisites being met, we then calculated
 the lnRR and lnCVR for each experimental treatment and control group (i.e.
 73 effect sizes) as well as their sampling (error) variance using equations
 corrected for the sample size described in (Senior, Viechtbauer and Nakagawa,
 2020). Individual bees in
 control and treated groups in all study designs were considered
 independent.
 <em><strong>Data
 analyses</strong></em>
 All analyses were conducted in
 R Studio v.1.2.5033 (RStudio Team 2015). The package metafor (Viechtbauer, 2010)was used to compute multilevel meta-analytic
 models (MLMA), multilevel meta-regression models (MLMR) and to generate
 forest plots. The study and experiment identifier were always set as
 nested random effects. For MLMR and depending on the question, the type of
 stressor, the genus, the type of task, the exposure duration and/or the
 percentage of LD50 were defined as fixed effects. The restricted maximum
 likelihood approach (REML) was used to estimate the parameters of the
 meta- analysis models. Forest plots were used to show the effect size
 estimates lnRR and lnCVR and their 95% confidence interval (CI). In this
 approach, positive effect sizes reflect higher means (lnRR) or
 coefficients of variation (lnCVR) in the treated group compared to
 controls. Negative effect sizes reflect lower means (lnRR) or coefficients
 of variation (lnCVR) in the treated group compared to controls. Effects
 are significant when the 95%CI do not span across the zero
 line.
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