Effect of a delay following handling and exposure to a new testing situation on the behaviour of woodlice

Authors
Affiliations

Brice Beffara

Nantes Université, Univ Angers, Laboratoire de psychologie des Pays de la Loire, LPPL, UR 4638, F- 44000 Nantes, France

Jean Leurent

Nantes Université, Univ Angers, Laboratoire de psychologie des Pays de la Loire, LPPL, UR 4638, F- 44000 Nantes, France

Université de Poitiers, UFR Sciences fondamentales et appliquées, Laboratoire Ecologie et Biologie des interactions UMR CNRS 7267, Poitiers, France

Freddie-Jeanne Richard

Laboratoire Ecologie et Biologie des interactions UMR CNRS 7267, Poitiers, France

UR 406 Abeilles Et Environnement, Domaine Saint-Paul, 84914, Avignon, France

This article has not yet undergone peer review and remains subject to potential changes based on reviewers’ feedback. Readers are advised to consider this when citing the article or interpreting its results.

Created: June 27, 2025

Last edited: January 30, 2026

Version: 5


Abstract

Behavioural experiments, whether conducted in the laboratory or in the wild, often involve altering the context for the organisms. Such constraints may include environmental changes or manipulation by experimenters. These disturbances can induce stress, potentially affecting the physiology and behaviour of the animals and thus compromising the initial conditions of the experiment. When stress is directly related to the study subject, it can act as a confounding variable. This study aimed to investigate the impact of introducing an acclimation period, i.e., a delay between handling and the commencement of a task, on animal behaviour, specifically in woodlice. By incorporating this delay, we hypothesised that the animals would return to a baseline psychophysiological state, thereby reducing the stress effects of pre-experimental manipulations. We set up a simple task in which woodlice had to cross a corridor and we manipulated the delay between handling and the task itself. Contrary to our main hypothesis, our findings indicate that acclimation did not significantly affect the speed of woodlice. However, exploratory analyses revealed that a longer pre-experimental delay reduced the likelihood of U-turns in the corridor. Additionally, in trials without U-turns, woodlice spent more time in the experimental apparatus when the acclimation period was longer. These preliminary results only partially support previous research linking stress and exploration in woodlice. Further studies are necessary to confirm these effects and better characterise the relationship between acclimation and subsequent behaviour in scientific experiments.

Keywords

Acclimation Stress Behaviour Woodlice Methods

Introduction

When behaviour is studied experimentally, individuals are often placed in a specific set-up that allows the variables of interest to be manipulated, controlled, or measured. On the one hand, this involves a change in environment, as subjects are moved from their initial environment (e.g. home tank) to the experimental apparatus. On the other hand, it typically requires interaction with the experimenter, who handles the subjects in order to transfer them.

These two experimental constraints—environmental change and handling—have been identified as potential sources of methodological bias within the STRANGE framework (Webster & Rutz, 2020). Specifically, they relate to the “Acclimation and Habituation” factor, which can influence behavioural and physiological responses. Based on definitions proposed by Nanninga et al. (2017), we consider acclimation to be the process by which an individual becomes familiar with a new stimulus or environment upon first exposure, whereas habituation refers to a learned decrease in response to repeated exposure.

It is now widely accepted that handling and environmental change can induce stress in a broad range of animal species (Balcombe et al., 2004; Webster & Rutz, 2020). However, the nature and intensity of these effects may vary depending on the type and purpose of handling or environmental transition. For instance, manipulation may occur when animals are collected from the wild and brought to the laboratory, during laboratory rearing, or when subjects are transferred from their rearing environment to the test apparatus.

Several studies have examined the impact of handling on animal stress, mostly in vertebrates and particularly in mammals. For example, Gessner et al. (2022) reported that habituating mice to handling improved performance in a novel object recognition task. Gouveia & Hurst (2019) showed that reducing the invasiveness of handling procedures lowered stress in mice. Shishis et al. (2023) found that different methods of transferring zebrafish from their home tank to an experimental device affected multiple behavioural variables, with more invasive handling associated with increased immobility. Philippe et al. (2023) similarly identified stress-related physiological responses (e.g. cortisol elevation) in zebrafish after handling, even though they did not observe significant differences across handling types; however, they noted both habituation and acclimation effects over time. Comparable results were reported by Wilson et al. (2021) in green shore crabs, where less invasive techniques were associated with lower stress indicators (oxygen consumption, haemolymph glucose, and L-lactate). Taken together, these findings underscore the importance of standardising handling procedures, as well as reporting them in detail to improve replicability and interpretation of experimental outcomes.

The transfer from one environment to another thus appears to be a consistent stressor, even when attempts are made to minimise its invasiveness. A commonly recommended mitigation strategy is to impose an acclimation period between handling and the onset of the experimental task. Acclimation is believed to promote a return to homeostasis following the stress induced by manipulation and/or environmental change (Arbon et al., 2023). According to our literature review, fewer studies have focused specifically on acclimation duration compared to the growing literature on handling effects. Most research on this topic appears to have been conducted in the context of fish behaviour (Burns, 2008; Makaras et al., 2021; O’Neill et al., 2018), showing that pre-test acclimation duration can influence both stress levels and behavioural responses, but that these effects are highly species- and context-dependent.

These authors argue that acclimation is essential to allow subjects to recover from transfer-related stress, which could otherwise interfere with spontaneous behaviour. In this sense, the lack of acclimation may affect both the psychometric properties of behavioural measures and the validity (particularly internal validity) of experimental protocols, thereby undermining the robustness and reproducibility of findings. Although this issue could theoretically affect a wide range of behaviours, it is likely to be especially problematic in studies investigating stress-related mechanisms.

In the present study, we focus on acclimation in terrestrial isopods, where experimental research on stress has both theoretical and applied relevance. These organisms are known bioindicators of environmental quality. Laboratory studies have shown that environmental perturbations—such as vibrations or chemical stressors—negatively affect their physiology and behaviour (Ďurajková et al., 2022; Roques et al., 2020). Their ecological role as decomposers has prompted investigations into the impact of climate-related variables (e.g. heat or moisture stress) on their biological functioning (Delhoumi et al., 2023; Depeux et al., 2023; Wu et al., 2021). Meanwhile, several studies have explored how different stressors influence behavioural responses in various woodlouse species (Anselme, 2018, 2019; Anselme et al., 2023), helping to bridge the gap in our understanding of invertebrate behaviour, which remains underrepresented in comparison to vertebrate models (Schnell & Clayton, 2021).

Here, we adapted the protocol developed by Broly & Deneubourg (2015) in their third experimental condition, with four key modifications to explore acclimation effects at the individual level. First, we manipulated the duration of acclimation, i.e. the waiting time imposed on the woodlouse before the task (referred to as “retention time” by Broly & Deneubourg, 2015) as a continuous variable. Second, we included three species of woodlice—Porcellio dilatatus dilatatus (Brandt, 1833), Oniscus asellus (Linnaeus, 1758), and Porcellio scaber (Latreille, 1804), thereby increasing interspecies variability. Third, this also enabled a larger overall sample size. Fourth, we used the time to traverse a straight corridor as our main dependent variable, whereas Broly & Deneubourg (2015) focused primarily on departure time (which we analysed as a secondary outcome). Consequently, in our design, acclimation duration served as the independent variable, while straight-line traversal time was the principal dependent variable.

Preregistered Hypotheses

We formulated a set of concurrent hypotheses concerning the effect of pre-task waiting time (i.e., acclimation duration) on woodlice behaviour. Pre-registration can be consulted online at osf.io/6cbnp. The null hypothesis (H0) posits that waiting time before the task has no impact on behavioural performance. In this case, regardless of how long a woodlouse waits before the beginning of the task, the speed at which it travels the straight line would remain unchanged.

The first alternative hypothesis (H1a) suggests that a longer waiting time before the task leads to a slower traversal of the straight line. This prediction is consistent with the findings of Broly & Deneubourg (2015), who observed that individuals subjected to longer initial retention times were slower after release. The underlying rationale is that handling and environmental change initially provoke stress, which induces a heightened state of arousal, often associated with increased locomotor activity. As acclimation proceeds, this stress is reduced, and the individual enters a calmer physiological state, resulting in slower movement. This interpretation aligns with Anselme (2019) dual-process model, which proposes that exposure to a novel and non-rewarding experimental environment triggers a moderate stress response that promotes horizontal escape behaviour (i.e., running). Reduced stress through acclimation would therefore attenuate this escape drive, decreasing speed.

Conversely, the second alternative hypothesis (H1b) predicts that a longer waiting time before the task may result in a faster traversal of the straight line. Although this hypothesis is less supported by existing literature, it accounts for the possibility that handling and environmental change induce a high-intensity stress response, sufficient to temporarily suppress behavioural activity or even result in immobility. In this view, a prolonged pre-task delay might allow individuals to recover from this intense stress, thereby restoring or even enhancing locomotor performance. This reasoning is supported by observations in other species showing behavioural inhibition following strong stressors (Anselme, 2019; Shishis et al., 2023).

Finally, the third hypothesis (H2) proposes a non-linear effect of waiting time. Specifically, it predicts that as acclimation duration increases, woodlice will initially move more slowly, but this trend may reverse if the waiting period becomes excessively long. Prolonged isolation without sensory stimulation or opportunities to explore (conditions typical of extended acclimation) could itself become a source of stress, particularly due to social separation or environmental deprivation (O’Neill et al., 2018). As a result, stress levels may first decrease with acclimation, but begin to rise again after a certain threshold, leading to a U-shaped relationship between acclimation duration and behavioural performance.

Methods

Animals

Three species of woodlice were involved: Porcellio dilatatus dilatatus (Brandt, 1833), Oniscus asellus (Linné, 1758) and Porcellio scaber (Latreille, 1804). These species were specifically chosen because of their reaction to disturbance. These three species do not conglobate. When disturbed, their fleeing or freezing behaviour can be observed without latency. They were derived from natural populations collected in France and Scotland (Saint-Martin de Connée, Mayenne, 2012 and Rom, Deux-Sèvres, 1988, for P. dilatatus dilatatus, Cosne-sur-Loire, Nièvre, 2007 and Edimbourg, south-east Scotland 2001 for O. asellus, Mazé, Maine-et-Loire 2012 and La-Molle-St-Héray, Deux-Sèvres 2009 for P. scaber). The animals were maintained in standard laboratory conditions (in boxes of moistened compost, at 20°C, and with the natural photoperiod of Poitiers, France 46.58°N, 0.34°E) and fed ad libitum fresh organic carrots and dried leaves of linden (Tilia sp.). None of the tested individuals were moulting during the course of the experiment. Gravid females were excluded from the experimental pool. French and European reglementations do not require formal ethical evaluation concerning research on arthropods. We followed the guidelines for ethical conduct in the care and use of nonhuman animals in research (American Psychological Association, 2022). The protocol was designed to avoid any unnecessary distress.

As our individuals come from populations bred in the laboratory for several generations, their dimensions are on average smaller than those of wild individuals. For each species, we measured the length and width of 20 randomly selected males and 20 randomly selected females, and calculated the mean dimensions for each species and their standard deviations (see Table 1). The individuals were collected from the rearing box at least 72 h before the experiment, separated by sex and placed in groups of 10 for P. dilatatus dilatatus and P. scaber and a maximum of 15 individuals for O. asellus, in plastic boxes measuring 16.9 cm × 14.2 cm × 5.9 cm (Figure 1). The bottom of each pre-experimental box was covered with moist potting soil and rehydrated dried leaves of linden. Water was added regularly.

638 trials were run after official data sharing (i.e. after the first pre-tests and pilots). Data was collected during the experiment by the experimenter and double-checked a posteriori by independent video coders. After data verification, the total number of woodlice for valid trials was 473 (see Figure 3). 39 woodlice performed the task twice. This did not significantly affect the main results of the experiment (see this document for more details). The minimum interval between the two sessions for the animals concerned was 15 days. Unpublished results from previous experiments conducted by the last author indicated that memory of the experiment and the apparatus appeared to be preserved for a maximum of one week in woodlice. The minimum interval between the two sessions for our current experiment was therefore at least twice the estimated limit of possible memory retention.

In order to approximate a correct estimate of the optimal waiting time before the task, we aimed to collect as much data as possible in April and May 2023. The goal was to test at least 480 woodlice in total, belonging to the three species. This minimum sample size was planned on the basis of a stability approach. According to the simulations, considering a pessimistic scenario where the correlation between waiting time and travel time exists but is weak (ρ = .1), we needed 470 individuals for the correlation to stabilise. In other words, we needed 470 individuals (critical point of stability) to ensure that the correlation does not drop below 0 and does not rise above 0.2, with 95% confidence (Schönbrodt & Perugini, 2013). Based on our pret-tests, anticipating potentially 10 invalid trials due to possible technical problems or unusual behaviour, we therefore planned a minimum sample size of 480 woodlice. This preregistered sample size is therefore respected.

Apparatus

We presented individuals with a task consisting in crossing a corridor linking a rectangle starting arena to a smaller rectangle finishing arena (Figure 1). The corridor and the arenas were built with LEGOⒸ blocks inspiered by McCreery et al. (2016) and Carlesso et al. (2023), allowing both modularity and easy reproducibility. Two apparatus were built. For the first one (experiments on P. dilatatus), the blocks were assembled to form a wall 2.9 cm high and 1.6 cm wide. This wall surrounded two arenas, a starting arena (5.5 cm × 4.9 cm), a finishing arena (3.9 cm × 2.4 cm), and a corridor (21.5 cm × 2.3 cm) connecting them. A second apparatus, in which the width of the corridor was reduced to 1.5 cm, was built in parallel. This second apparatus was used for P. scaber and O. asellus, which are smaller than P. dilatatus dilatatus (see Table 1). In the centre of the start area was a transparent plastic cylinder (diameter 2.4 cm, height 2.8 cm). This cylinder was used as a waiting area within the apparatus where the animals were placed at the start of the task. The apparatus was placed on a sheet of thick white paper which was changed regularly to avoid any potential chemical residual bias.

Panel 1 - Material and procedures.
(a) Overview of the set-up
(b) Example of an pre-experimental box
(c) Corridors used in the experiments
(d) Focus on the robot
(e) Schematic representation of the corridor
Figure 1: The different parts of equipment used for the experiment.
Female Oniscus asellus. Trajectory = "Turn"
Female Porcellio dilatatus dilatatus. Trajectory = "Turn"
Female Porcellio scaber. Trajectory = "Straight"
Male Porcellio dilatatus dilatatus. Trajectory = "Straight"
Figure 2: Four examples of valid trials with differents species and retention times
Figure 3: Flowchart indicating the different steps leading to the selection of the final set of valid trials
Table 1: Size of animals
Species Pdd Oa Ps
Sex M F M F M F
Dimensions L l L l L l L l L l L l
Mean 14.7 7.95 14.9 8.2 10.95 5.05 12.45 5.92 11.2 4.5 10.9 5.05
SD 1.95 1.23 2.53 1.11 1.00 0.67 1.88 0.98 1.64 0.69 1.25 0.61
Table 2: Temperature and illuminance of the experimental room for each species

As perturbation and contact with the experiment were central in our research questions, we wanted to control all the potential sources of disturbance outside handling. Hence, we automated the removal of the inner retention cylinder in order to avoid any action of the experimenter on the apparatus after the acclimation phase. To do this, we built and programmed a robot whose function was to lift the plastic cylinder delimiting the waiting area (see Figure 1). This robot was assembled from components of the LEGOⓇ kits “SPIKE Prime” and “Mindstorms Inventor”. The robot consists of a central hub connected by Bluetooth to a computer and wired to two motors, which are in turn connected to an arm to which the plastic cylinder of the waiting area is attached. During the experiment, the computer sent instructions to the hub that activated the two motors, causing the cylinder to be lifted. This model of robot has the advantage of being highly modular. What is more, it can be controlled in Python, giving considerable flexibility in robot control and easy reproducibility of the functions implemented.

Procedure

The entire procedure for each trial was managed and assisted by the PsychoPyⓇ software (Peirce et al., 2019). For the waiting time in the retention cylinder, we programmed the random generation of the waiting time and a series of commands giving instructions to the robot. The waiting time generated varied between 0 and 600 seconds following the range used by Broly & Deneubourg (2015) but with an interval of 1 second between the different possible waiting times. Once the waiting time has elapsed the software instructs the robot to move the arm, which lifts the cylinder delimiting the waiting area, giving access to the corridor.

All procedures were carried out in laboratory conditions during daylight hours. The experiment started on 17 April 2023 and ended on 31 May 2023. The experiments took place in the morning between 9:00 and 12:00 for O. asellus and in the afternoon between 13:00 and 17:30 for P. dilatatus and P. scaber species. Descriptive statistics for room temperature and illuminance are presented in Table 2. For each trial, a woodlouse was picked up and placed with tweezers in the retention cylinder for a random duration before the transparent plastic cylinder was removed by the robot. In order to ensure that all the woodlice were in the same initial conditions at the start of the waiting time countdown, the countdown was started only when the woodlouse was on its legs.

The sequence of events was recorded continuously using an IPEVO V4K camera and associated Visualizer software, and stopped shortly after the woodlouse reached the arrival area. The time elapsed between the moment the plastic cylinder was lifted and the moment it entered the corridor, as well as the time taken by the woodlouse to traverse the corridor, were timed in realtime by the experimenter by pressing a key on the keyboard, recorded with PsychoPy. The woodlouse was considered to have entered a new zone when its two antennae crossed the dividing line between two zones.

At the beginning of each trial, we recorded the value of the brightness in the room, of the temperature in the room and the running order (relative to the pre-experimental box). At the end of the task, the animal was placed in a new box (17.8 cm × 11.2 cm × 4.9 cm) to avoid testing the same individual. If, 10 minutes after the cylinder was lifted, the woodlouse had not left the start zone or reached the finish zone, the task was stopped and the trial considered as invalid. Data was sent online manually by the experimenter after each trial.

Data was checked by independent video coders (psychology undergraduate students during their internship in the laboratory), who watched videos a posteriori through the AnimaTA software (Chiara & Kim, 2023). Each video coder measured the retention time, the time taken to leave the starting area, the time to cross the corridor, the type of trajectory (“straight” or presence of at least one U-turn inside the corridor), estimated the validity of the trial, and the consistency of their measures with the measures taken by the experimenter. In case of bad congruency between the measures of the experimenter and the measure of the video coder, a third individual (the first author of the current paper) watched the video in order to determine the exact values for the problematic variables. Statistical analyses were performed when agreement between the two measurements was achieved for all trials.

Data analysis

We used both Bayesian and frequentist models for more robust estimations of effects. One type of model was designed to compare woodlice speed at different steps (Time to leave the starting square, or time to cross the corridor, or both) as a dependent variable and another category of model has been used for the presence of (at least one) U-turn in the corridor.

Models

Time

Bayesian models

\[\begin{align*} T_i &\sim \text{LogNormal}(\mu_i, \sigma_e) + \text{shift} \\[1.1em] \mu_i &= \alpha + \beta \cdot WT_i \\[1.1em] \alpha &\sim \text{Normal}(0, 10) \\[1.1em] \beta &\sim \text{Normal}(0, 10) \\[1.1em] \sigma_e &\sim \text{HalfCauchy}(0, 10) \\[1.1em] \end{align*}\]

Frequentist models

\[\begin{align*} \text{Log}(T_i) &\sim \text{Normal}(\mu_i, \sigma^2_e) \\[1.1em] \mu_i &= \alpha + \beta \cdot WT_i \end{align*}\]

Where \(T_i\) is the time (Time to leave the starting square, or time to cross the corridor, or both) as the dependent variable and \(WT_i\) the waiting time as the independent variable.

U-turn

Bayesian Models

\[\begin{align*} R_i &\sim \text{Bernoulli}(p_i) \\[1.1em] \text{logit}(p_i) &= \alpha + \beta \cdot WT_i \\[1.1em] \alpha &\sim \text{Normal}(0, 10) \\[1.1em] \beta &\sim \text{Normal}(0, 10) \\[1.1em] \\ \end{align*}\]

Frequentist Models

\[\begin{align*} R_i &\sim \text{Bernoulli}(p_i) \\[1.1em] \text{logit}(p_i) &= \alpha + \beta \cdot WT_i \\[1.1em] \end{align*}\]

Where \(R_i\) is the presence of U-turn as the dependent variable (1 = at least 1 U-turn in the corridor, 0 = no U-turn), and \(WT_i\) the waiting time as the independent variable.

ROPE

We defined arbitrary regions of practical equivalence (ROPEs, see Kruschke, 2018) in order to frame the interpretation of the effects computed from the Bayesian models. These ROPEs aim to set a minimal effect under which we consider that an effect is not practically significant. We did not have robust expectations concerning what effects could be considered as negligeable on the scale of unstandardised variables. For each parameter of interest, we set the boundaries of the ROPE by moving the width of the 89% HDI (more stable than 95%) around 0. We plot the ROPE aside the distribution of each parameter of interest in order to guide the interpretation for the significance of each effect.

Note that the ROPE can not help testing the null hypothesis since our study was not designed for that. A larger sample size would have been necessary for this specific goal.

U-shape test

We pre-registered an alternative hypothesis (called ‘H2’ in the registration) stating that the effect of waiting time could be reversed when the waiting time is too long. In order to test this hypothesis, we relied on the u-shape test developed by Simonsohn (2018). This test consists of an interrupted regression, i.e. two slopes, one before and one after a breakpoint set on the independent variable. The breakpoint is determined by the ‘Robin Hood’ algorithm (Simonsohn, 2018). The test is considered significant if i) the two slopes have opposite signs (reversed effect), and ii) each slope taken individually is itself statistically significant.

Statistical significance

As preregistered, we report the results of frequentists and Bayesian analyses. In order to avoid redundancies, we report p-values and r2 from frequentists analyses, and parameter HDI and model predictions from Bayesian analyses. If frequentists and Bayesian analyses lead to different conclusions, we report it explicitly. As pre-registered, we consider p-values between 0.01 and 0.05 (the conventional alpha level in ecology and psychology) with caution for pre-registered analyses, unless the effect appears to be very strong. By default, we consider a result to be robust if the p-value is less than an alpha of 1%. However, even below this 1% threshold, we approach results with caution if the analyses were not pre-registered. We wanted to lower the alpha threshold in order to make recommendations based on results that are as reliable as possible. Imposing a 10-minute pre-experimental waiting period represents a significant long-term cost. We consider that an alpha threshold of 5% is not strict enough to support a recommendation of this type (see, for example, Maier & Lakens, 2022 on the adaptation and justification of the alpha threshold). ROPEs are also used to interpret the significance of the results.

Results

Panel 2 - Plots associated with main analyses.
Figure 4: Probability that an individual makes at least one U-turn as a function of waiting time (based on all valid trials). Moving lines represent credible resgressions lines according to the Bayesian models.
Figure 5: Time taken to leave the starting area as a function of waiting time (based on all valid trials). Moving lines represent credible resgressions lines according to the Bayesian models.
Figure 6: Time spent in the apparatus as a function of waiting time for trials without U-turn. Moving lines represent credible resgressions lines according to the Bayesian models.
Figure 7: Posterior distributions of parameters extracted from Bayesian models on the four main dependent variables. Grey zones represent the ROPE. Estimates around the median value of the parameter are 66%, 89% and 95% HDIs.
Figure 8: Posterior distributions of parameters extracted from Bayesian models on the three main dependent variables for trials without U-turn. Grey zones represent the ROPE. Estimates around the median value of the parameter are 66%, 89% and 95% HDIs.
Figure 9: Distributions of the time taken to cross the corridor for trials without U-Turn. Ranges around the median values are 50% (IQR) and 95% central ranges.

Preregistered analyses

The following analyses have been performed on all valid trials (n = 473).

Confirmatory analyses

Contrary to our hypothesis, there is no significant effect of waiting time on the time to cross the corridor (β = -4.30 × 10-4, 95% HDI [-1.00 × 10-3, 1.20 × 10-4], 53.63% in ROPE, p = .072, r2ajd = .005). For one supplementary minute of retention, this value of parameter corresponds to an estimated decrease of 2.57% of the time spent to cross the corridor. The model predicted a median duration of ≈ 43 s to cross the corridor without acclimation (retention = 0 s). With 600 s (max) of retention, the predicted median duration was ≈ 34s.

We cannot conclude to a u-shape effect according to the u-shape test. Detailed results of this analysis, presented graphically, can be consulted here.

Exploratory analyses

Time to leave the starting area
1This result would traditionally be considered significant at the conventional alpha threshold of 5%, but is not significant at the pre-registered alpha threshold of 1%.

There is no clear significant effect of waiting time on the time spent to leave the starting area (β = 7.70 × 10-4, 95% HDI [9.90 × 10-5, 1.50 × 10-3], 24.99% in ROPE, p = .026, r2ajd = .008)1. This parameter value means that an additional minute of waiting is associated with an estimated increase of 4.75% in the time spent in starting area. The model predicted a median duration of ≈ 62 s to leave the starting area without acclimation (retention = 0 s). With 600 s (max) of retention, the predicted median duration was ≈ 98 s.

We cannot conclude to a u-shape effect according to the u-shape test. Detailed results of this analysis, presented graphically, can be consulted here.

Secondary analyses

All the results reported below concern exploratory analyses conducted on the basis of previous observations described in the previous section. The following statistical tests and estimations were not pre-registered. All the following results should be interpreted with caution and would need to be put to the test in a replication as pre-registred confirmatory analyses.

Probability of U-turn in the corridor

There is an effect of waiting time on the probability of U-turn (β = -2.70 × 10-3, 95% HDI [-4.20 × 10-3, -1.20 × 10-3], 0% in ROPE, p <.001, r2Tjur’s = .031). In other words, individuals are less likely to make a U-turn when they wait longer: more precisely, each minute of waiting leads to an estimated decrease of 14.87% in the odds of making a U-turn. The model predicted a probability of (at least one) U-turn during the task ≈ 0.29 without acclimation (retention = 0 s). With 600 s of retention, the predicted probability was ≈ 0.08.

We cannot conclude to a u-shape effect according to the u-shape test. Detailed results of this analysis, presented graphically, can be consulted here.

Analyses on trials without U-turn

The following analyses have been carried out on valid trials for which individuals crossed the corridor following a “straight” trajectory (i.e. no U-turn, n = 395)2.

2One outlier was excluded from the analyses (see the supplementary material for more information).
Time to cross the corridor

Contrary to our hypothesis, there is no significant effect of waiting time on the time to cross the line (β = 2.50 × 10-4, 95% HDI [1.20 × 10-5, 5.00 × 10-4], 33.9% in ROPE, p = .047, r2ajd = .007)1. This parameter value means that an additional minute of waiting is associated with an estimated increase of 1.53% in the time taken to cross the corridor. The model predicted a median duration of ≈ 17 s to cross the corridor without acclimation (retention = 0 s). With 600 s (max) of retention, the predicted median duration was ≈ 19 s.

Descriptive distribution of crossing times are presented in Figure 9. We cannot conclude to a u-shape effect according to the u-shape test. Detailed results of this analysis, presented graphically, can be consulted here.

Time to leave the starting area

There is no clear significant effect of waiting time on the time spent to leave the square (β = 8.00 × 10-4, 95% HDI [4.10 × 10-5, 1.60 × 10-3], 32.24% in ROPE, p = .040, r2ajd = .008)1. This parameter value means that an additional minute of waiting is associated with an estimated increase of 4.93% in the time taken to leave the starting area. The model predicted a median duration of ≈ 54 s to leave the starting area without acclimation (retention = 0 s). With 600 s (max) of retention, the predicted median duration was ≈ 87 s.

We cannot conclude to a u-shape effect according to the u-shape test. Detailed results of this analysis, presented graphically, can be consulted here.

Full time

There might be an effect of waiting time on the time spent in the apparatus (time to leave the starting area + time to cross the line, (β = 7.40 × 10-4, 95% HDI [1.60 × 10-4, 1.30 × 10-3], 15.49% in ROPE, p = .007, r2ajd = .018). This parameter value means that an additional minute of waiting is associated with an estimated increase of 4.55% in the time spent in the apparatus. The model predicted a median duration of 63 s spent in the appratus without acclimation (retention = 0 s). With 600 s (max) of retention, the predicted median duration was ≈ 94 s.

We cannot conclude to a u-shape effect according to the u-shape test. Detailed results of this analysis, presented graphically, can be consulted here.

Secondary parameters (pre-registered)

Differences between species

There were significant behavioural differences between species (Panel 3). Porcelio dilatatus dilatatus took more time to leave the starting area and presented a higher proportion of U-turn than Porcelio scaber and Oniscus asellus (Statistics are reported directly on the plots below). Considering only straight trials, P. dilatatus dilatatus spent more time inside the apparatus (i.e. time to leave the starting square + time to cross the corridor) than individuals of other species.

Panel 3 - Plots associated with analyses of differences between species.

Differences between males and females

There were no significant main effect of sex on the main dependent variables.

Discussion

This experiment aimed to test the effect of acclimation on the behaviour of woodlice. We manipulated the duration of retention prior to the task to impose different periods of pre-experimental acclimation. Our main hypothesis was that acclimation duration would modify behaviour within the apparatus, particularly the time taken to cross the corridor. However, our hypothesis was not supported, as no significant association was observed between retention duration and crossing time.

We also pre-registered a hypothesis regarding the effect of acclimation on the latency to leave the starting area (i.e., before crossing the corridor). While results suggest that longer acclimation may increase the time spent in the starting area, this effect remains uncertain and might be too small to hold practical significance.

We had anticipated that nearly all woodlice would cross the corridor in a “straigh”t trajectory (i.e., without U-turn). Based on this, we formulated hypotheses and planned analyses focused only on the latency to leave the starting area and the time to cross the corridor. However, contrary to our pre-tests, a non-negligible proportion of individuals did not cross the corridor in a straight trajectory. Instead, several individuals made at least one U-turn, which we defined as any directional deviation of 90° or more, regardless of whether it resulted in a return toward the starting point. This specific behaviour affected our data, particularly the time taken to cross the corridor, prompting exploratory (non-pre-registered) analyses to account for these deviations.

The most robust finding from these exploratory analyses was that longer acclimation times reduced the probability of U-turn within the corridor. Focusing on trials where woodlice crossed without turning, we found that increased retention time was associated with longer durations spent within the apparatus after release. This likely reflects a combination of increased time in the starting area and slower trajectory within the corridor.

In summary, our exploratory findings suggest that acclimation increases the likelihood of a straight crossing but also increases the overall time spent in the apparatus during straight trials. This pattern corroborates previous findings showing that shorter retention times are associated with faster departures (Broly & Deneubourg, 2015). The authors suggested that woodlice exhibit heightened arousal following handling, resulting in faster movement, whereas a calmer state following acclimation results in delayed departure. Stress levels are thought to decrease during retention, thereby reducing fleeing behaviour.

These findings are also consistent with the dual-process model of exploration in woodlice (Anselme, 2019), which posits that: (i) exposure to a novel environment without reward induces moderate stress, and (ii) this stress triggers horizontal exploration (walking or running) aimed at escape. In our experiment, horizontal exploration can be characterised by trajectory (with or U-turn) and speed (when the trajectory is straight). The observed pattern suggests that handling-induced perturbation increases running speed and the probability of U-turn within the corridor.

A substantial body of work (see Ďurajková et al., 2022 for a review) links trajectory modification to stress. However, these results most often concern turn alternation rather than complete directional change (Moriyama et al., 2016). Several mechanisms may underlie the effect of stress on turn alternation, but one explanation is that turn alternation facilitates escape from aversive conditions. Nevertheless, in their study, Ďurajková et al. (2022) found that mechanical perturbation increased running speed in P. scaber (for temperatures 18°C ≤ T ≤ 24°C, similar to our own experiment) but did not increase—and sometimes even decreased—turn alternation, random ramble, or directional change. The experimental setup used by Ďurajková et al. (2022) was very different from our own apparatus, making direct comparisons difficult. However, we can observe that the behavioral effects of stress depend on its intensity, its source, and the woodlouse species.

Our exploratory findings indicate that implementing a pre-experimental acclimation period can alter woodlice behaviour. If replicated, these results would support the inclusion of acclimation phases to reduce pre-experimental stress. Many behavioural experiments with invertebrates measure variables sensitive to stress; thus, if animals are already stressed at the experiment’s outset, this could compromise the validity of conclusions. Acclimation is now widely advocated to restore baseline psychophysiological states (Arbon et al., 2023). Research shows that acclimation can improve the psychometric properties of behavioural measures (Burns, 2008; O’Neill et al., 2018), and the necessity of recovery from handling is increasingly recognised as crucial for interpreting results (Webster & Rutz, 2020).

To improve experimental protocols, we must better understand the characteristics of acclimation. One key question concerns the optimal duration. In our study, 10 minutes appeared to be possibly effective, but longer durations may yield larger effects. Optimal acclimation time likely depends on species, the type and degree of pre-experimental stress, and housing conditions (or environmental conditions in the wild). Future research is needed to more precisely estimate these parameters.

Another important aspect involves the conditions under which acclimation occurs. Our design could be viewed as a form of deprivation (e.g., hydric deprivation, see Anselme et al., 2023). Time spent in small, possibly suboptimal enclosures—differing in light, humidity, and social context from normal conditions—might elevate stress rather than reduce it (Hardege et al., 2024). Therefore, it is crucial to find a balance between comfort and ecological validity during acclimation.

We offer several suggestions for improving future studies. One is to adopt sequential analysis approaches and determine sample sizes dynamically (Beffara Bret et al., 2021). This would allow data collection to be based not only on statistical power but also on the behaviour observed (e.g., number of straight crossings), and thus guarantee a final sample size that is independent of unexpected behaviours. Since different terrestrial isopod species respond differently to environmental conditions (e.g., Ďurajková et al., 2022), testing acclimation effects across species will be essential for developing generalizable guidelines. In our study, the release of animals was automated using a robotic device, standardising the procedure and avoiding additional disturbance by the experimenter. However, robotic movement should be further optimised—a vertical release might be preferable to the current curved trajectory. A further step will be to compare the effects of acclimation between wild and lab-reared arthropods, as the latter are generally more accustomed to laboratory conditions.

In conclusion, we present a preliminary attempt to characterise acclimation needs in terrestrial isopods. While our primary pre-registered hypothesis was not supported, exploratory analyses suggest that longer acclimation periods influence behaviour and may reduce stress. Although definitive guidelines cannot yet be established, we encourage researchers to systematically consider and report acclimation duration and conditions in detail, as this could substantially impact psychophysiological outcomes.

Supplementary materials

Supplementary materials, reproducible code and figures are available :

Authorship contribution

Conceptualization: B.B., F-J. R., Data curation: B.B., Formal analysis: B.B., Funding acquisition, B.B., F-J. R., Investigation: J.L., Methodology: B.B., J.L., F-J. R., Project administration: B.B., F-J. R. Resources: B.B., F-J. R., Software: B.B., Supervision: B.B., F-J. R., Validation: B.B., F-J. R., Visualization: B.B., Writing – original draft: B.B., Writing – review & editing: B.B., J.L., F-J. R..

Funding

The research leading to this publication has received funding (PULSAR) from Région Pays de la Loire with the support of Nantes University.

Conflict of Interest Disclosure

Conflict of interest: None declared.

Acknowledgements

We would like to extend our warmest thanks to the undergraduate interns in the laboratory who helped with coding the videos: Maelys Morantin, Nathan Mariot, Adil Mouhib, Inès Régereau-Jou, Charlotte de Weert, Maina Le Bert, Juliette Lemen, Lara Siquiera, Flavie Briere, Noa Bié, Killian Boumard and Axelle Salingue.

Many thanks to Juan Sandoval for his help with the choice of the robot.

We also thank Alexandra Lafitte for her help with identification of isopods.

A huge thank to Solomon Kurz (https://bookdown.org/content/3686/), Jonas Kristoffer Lindeløv (https://lindeloev.github.io/shiny-rt/) and Matthew Kay (https://mjskay.github.io/tidybayes/articles/tidy-brms.html). Their excellent blog posts provided us with extremely valuable assistance in analysing and visualising data.

Analyses, visualisation and writing were performed using ‘DT’ v. 0.33 (Xie et al., 2024), ‘dplyr’ v. 1.1.4 (Wickham et al., 2023), ‘tidyr’ v. 1.3.1 (Wickham et al., 2024), ‘pakret’ v. 0.3.0 (Gallou, 2025), ‘ggplot2’ v. 3.5.2 (Wickham, 2016), ‘patchwork’ v. 1.3.1 (Pedersen, 2025), ‘tidyverse’ v. 2.0.0 (Wickham et al., 2019), ‘here’ v. 1.0.1 (Müller, 2020), ‘brms’ v. 2.22.0 (Bürkner, 2017), ‘readxl’ v. 1.4.5 (Wickham & Bryan, 2025), ‘tibble’ v. 3.3.0 (Müller & Wickham, 2025), ‘gt’ v. 1.0.0 (Iannone et al., 2025), ‘ggthemes’ v. 5.1.0 (Arnold, 2024), ‘RColorBrewer’ v. 1.1.3 (Neuwirth, 2022), ‘ggdist’ v. 3.3.3 (Kay, 2025), ‘extrafont’ v. 0.19 (Chang, 2023), ‘hrbrthemes’ v. 0.8.7 (Rudis, 2024), ‘ggsci’ v. 3.2.0 (Xiao, 2024), ‘tidybayes’ v. 3.0.7 (Kay, 2024), ‘scales’ v. 1.4.0 (Wickham et al., 2025), ‘gganimate’ v. 1.0.11 (Pedersen & Robinson, 2025), ‘performance’ v. 0.14.0 (Lüdecke, Ben-Shachar, et al., 2021), ‘see’ v. 0.11.0 (Lüdecke, Patil, et al., 2021), ‘lme4’ v. 1.1.37 (Bates et al., 2015), ‘ggstatsplot’ v. 0.13.1 (Patil, 2021).

Large language models were used at certain stages in the finalisation of this work in order to optimise the code and improve the writing style. The original ideas and developments, on the other hand, systematically stem from the work of the research team.

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