# Assessing the effect of complex ground types on ground-dwelling arthropod movements with video monitoring: Dealing with concealed movements under a layer of plant residues

Blanche Collard, Philippe Tixier, Dominique Carval, Claire Lavigne, Thomas Delattre

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

## To cite this version:

Blanche Collard, Philippe Tixier, Dominique Carval, Claire Lavigne, Thomas Delattre. Assessing the effect of complex ground types on ground-dwelling arthropod movements with video monitoring: Dealing with concealed movements under a layer of plant residues. Ecology and Evolution, 2022, 12 (7), pp.e9072. 10.1002/ece3.9072. hal-03963405

HAL Id: hal-03963405

https://hal.science/hal-03963405

Submitted on 30 Jan 2023

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>
# Assessing the effect of complex ground types on ground-dwelling arthropod movements with video monitoring: dealing with concealed movements under a layer of plant residues

## Author details

Collard, B<sup>a*, b, c</sup>; Tixier, P<sup>b,c</sup>; Carval, D<sup>b, c</sup>; Lavigne, C<sup>a</sup>; Delattre, T<sup>a</sup>

thomas.delattre@inrae.fr

claire.lavigne@inrae.fr

tixier@cirad.fr

dominique.carval@cirad.fr

blanche.collard.inra@gmail.com

INRAE, UR 1115 INRAE, Unité PSH, Domaine St Paul, 228 Route Aérodrome, F-84914 Avignon 9, France

<footnote><sup>a</sup>INRAE, UR 1115 PSH, 84000 Avignon, France</footnote>

<footnote><sup>b</sup>CIRAD, UPR GECO, F-34398 Montpellier, France</footnote>

<footnote><sup>c</sup>GECO, Univ Montpellier, CIRAD, Montpellier, France</footnote>

<footnote>*Corresponding author details:</footnote>
**Abstract**

1. Understanding the effect of ground types on foraging movements of ground-dwelling arthropods is a key step to managing their spatial distribution as required for successful conservation biological control. Indeed, fine movements at the centimetre scale can strongly influence the foraging ability of pest predators. However, because RFID or harmonic tracking techniques are not yet suitable for small species and video tracking focuses on uniform and light backgrounds, foraging movements have rarely been studied in relation to ground types.

2. We present a method to track a ground-dwelling arthropod (the earwig *Euborellia caraibea*) at night, walking on two contrasted ground types: bare soil and soil partly covered with a stratum of banana plant residues allowing individuals to hide periodically.

3. The tracking of individuals within these ground types was achieved by infrared light, tagging individuals, video treatments and semi-automatic cleaning of trajectories. We tested different procedures to obtain segments with identical durations to quantify speeds and sinuosities. These procedures were characterised by the junction time gap between trajectory fragments, the rediscretisation time of trajectories, and whether or not to use interpolation to fill in missing points in the trajectories.

4. Earwigs exhibited significantly slower and more sinuous movements on soil with banana plant residues than on bare soil. Long time gaps for trajectory junction, extended rediscretisation times and interpolation were complementary means to integrate concealed movements in the trajectories. The highest slowdown in plant residues was detected when the procedure could account for longer periods under the residues.

5. These results suggest that earwigs spent a significant amount of time concealed by the residues. Additionally, the residues strongly decreased the earwigs' movement. Since the technical solutions presented in this study are inexpensive, easy to set up and replicate, they represent valuable contributions to the emerging field of video monitoring.
**Keywords**

Insect, Mesocosm, Monitoring, Movement ecology, Spatial heterogeneity, Video tracking
# 1 Introduction

Ground type quality and spatial configuration are key factors for managing the spatial distribution of organisms for conservation purposes or improving the efficacy of conservation biological control. At the landscape scale, the effect of ground type and spatial distribution on dispersal have been extensively studied (Baguette and Van Dyck, 2007; Haddad et al., 2015), resulting in the development of management practices like movement corridors (Peng et al., 2017; Russell et al., 2018).

In contrast, at the patch scale, foraging movements of ground-dwelling arthropods (Bell, 1991) have primarily been studied in relation with the distribution of trophic resources (Kareiva and Odell, 1987; Valeix et al., 2009) but not the ground type distribution. Yet, there is evidence that ground type could influence the foraging movement of ground-dwelling arthropods, by providing a wider and more complex range of resources, such as favourable microclimatic conditions or protection from predators, or by deterring movement by increasing its risks and costs. A typical example is the reluctance of biological control agents to move inside the agricultural plot despite the presence of trophic resources, a frequently proposed explanation for failures in conservation biological control (Al Hassan et al., 2013; Albrecht et al., 2020). Individual-based models and a meta-analysis suggest that the interaction between movement behaviour and ground type spatial distribution could solve this problem (Albrecht et al., 2020; Collard et al., 2018; Delattre et al., 2019). Therefore, new data on the effect of ground type on patch-scale foraging movement are necessary for improving conservation biological control at this scale.

Analysing movements inside an agricultural plot requires collecting high-resolution data on a small spatial scale, which raises technical obstacles. Mark-Recapture (M-R) studies may provide insights on inter-patch movements but generally lack the resolution needed to document intra-patch movements especially for small ground-dwelling species (e.g., arthropods) that represent a high proportion of species of agronomic interest. M-R techniques using radio frequency identification (RFID) active tags are limited to the relatively large species that can handle the chip weight (1 to 0.2 g; Kissling et al., 2014). Moreover, these techniques provide coarse-grained data (Kissling et al., 2014; Noskov et al., 2021; Vinatier et al., 2010). Harmonic radar seems to hold the best promises for the future, with small
tags (6-20 mg; Kissling et al., 2014) and high-resolution data over a 1 km range. However, this
technique has yet to be proven functional in complex vegetation covers (Daout et al., 2017; O'Neal et
al., 2004). Its complex development process strongly reduces its immediate availability.

An alternative to *in-situ* remote sensing techniques resides in mesocosm studies, in which one or
several ground types (e.g., earthy soil, sand, plants) can be replicated in an enclosed arena, and the
behaviour of the target organism is monitored by video (Reynolds and Riley, 2002). Since mesocosm
studies allow for high-resolution data collection, they are well suited for studying foraging behaviours.
However, they generally focus on diurnal movements on a uniform and light background to preserve a
high contrast with the targets and allow automatic movement tracking. Some more advanced
computing methods such as deep learning have been employed to track individuals in more complex
or *in-situ* environments and simultaneously track several individuals (Bernardes et al., 2021; Imirzian
et al., 2019; Romero-Ferrero et al., 2018). Nevertheless, to our knowledge, the slightly more
heterogeneous and realistic backgrounds were studied in open areas with no vegetation layer, filmed in
a rather tight shot allowing only short movements to be recorded (Bjerge et al., 2021; Kindvall et al.,
2000) and rarely addressed nocturnal movements (but see Imirzian et al., 2019).

Observing the effect of diverse ground types on movements using heterogeneous mesocosms and
video monitoring is challenging, because not all ground types provide good contrast, especially at
night-time, with some concealing parts of the movement paths. Thus, the difficulties of these types of
studies resides in (1) separating the target animal from the background by image analysis and (2)
reconstructing and analysing the scattered paths caused by complex three-dimensional strata (e.g.,
shelters above ground).

In this study, we developed a method to track ground-dwelling arthropods in mesocosms that mimic
real agricultural ground types including one with two strata that conceals them periodically. This
method aimed to investigate how their movement could be affected by the ground type quality, i.e.
whether it can cause a difference in their sinuosity or their speed. Because the upper stratum could
directly affect the movement of these arthropods, even more so if it conceals them, special attention
has been given to consider the concealed movements in the analyses. We applied this method to analyse movements of the earwig *Euborellia caraibea* (Hebard), an endemic polyphagous predator of *Cosmopolites sordidus* (Germar) in the Caribbean islands (Brindle, 1971; Carval et al., 2016; Mollot et al., 2014) in mesocosms mimicking two banana field ground types, bare soil and banana plant residues. Preliminary experiment showed that *E. caraibea* is nocturnal (**Appendix A**) and seems to prefer the banana plant residues (Collard, pers. data), likely due to the humidity and shelter they provide (Burr, 1939). By using infrared light, different temporal resolutions and different hypotheses on the behaviour of concealed earwigs, we were able to address the problem of night-time and partially concealed earwig movements.

**2 Materials and methods**

The movement of 17 and 18 *E. caraibea* (including five males per group) were tracked one at a time
on one of two ground types: bare soil and banana plant residues, respectively. In this study, the
recording of one individual on one ground type represents a replicate.

**2.1 Capture and laboratory maintenance of *Euborellia caraibea***

*E. caraibea* adults were caught in a field at the Petit Morne site in Martinique (14°37′N, 60°58′W).

Species and sex determinations of individuals were performed according to Brindle (1971). Before its movements were tracked, each individual was kept in a 6 cm diameter non-hermetic boxes at 25 °C (12:12, Light:Dark) in the laboratory for 5 to 34 days. Each box contained a shelter constructed of corrugated cardboard wrapped around a vegetable sponge soaked with distilled water to maintain a constant level of humidity and a water resource. A 1 cm³ cube of food (based on Guennelon et al., 1981) was provided once a week.

This device allowed us to keep *E. caraibea* earwigs alive for a relatively long time compared to the
duration of the experiments. Out of the 67 earwigs kept in the lab until the end of the experiment - 35
of which were used for this study - only 12 died.

**2.2 Experimental mesocosms design**
Two 1 x 1 m arenas were built from a square base of 1 m² expanded PVC and 20 cm high Plexiglas edges (Fig. 1a) set up at the CIRAD facilities in Martinique (CAEC ; 14°37′N, 60°58′W). One arena mimicked a ground with banana plant residues (hereafter referred to as "residues"; Fig. 1b and 1d), and the second arena mimicked a ground of bare soil (hereafter referred to as "bare soil"; Fig. 1c and 1e).

Each arena contained 22 x 22 cells of 4.5 cm side length made from seedling plates, filled with potting soil and sprinkled with soil from a CIRAD insecticide-free experimental banana field ("Rivière lézarde" site, 14°39' N, 60°58' W) sifted with a 0.5 cm mesh. In order to prevent the earwigs from escaping, seedling plates were sealed together and connected to the tops of the Plexiglas edges using transparent plastic sheetings coated with talcum powder. For the "residues" arena, 3 x 3 cm units of overlaid dried banana leaves were placed evenly over the bare soil and fixed to a 1 x 1 m wire mesh, with free space between them to allow the detection of the earwig's movement (Fig. 1a and 1d). Only solvent-free hot glue, wire and tape were used in both arenas. Circular white platforms of 6.5 cm diameter were positioned in the centre of each arena to accommodate the earwig in its shelter at the beginning of the experiment.

Each arena was placed under one Trendnet TV-IP310PI infrared camera (3.2 megapixels, 2048 × 1536), and four projectors containing 48 infrared LED each, were placed on the corners of each arena to provide more uniform lighting (Fig. 1a). Red or infrared lights are commonly used to observe the behaviour of earwigs (Diehl and Meunier, 2018) or other nocturnal insects, as they are considered invisible to insects (Dias et al., 2012; Drees et al., 2008; Heise, 1992). Infrared lights were preferred, as they are even less likely to have an impact on nocturnal insect behaviours (Allema et al., 2012). The two arenas, video devices and infrared lights were placed in a climatic chamber maintained at 25 °C and equipped with white light LEDs (Linear power led, 25W/4000 K, 2500 LM, Lamentin) providing a 12:12 (L:D) photoperiod (Fig. 1a). The light spectrum of similar white light LED can be found in Sharakshane (2017). The two cameras filmed the arenas in parallel using a network switch, a Synology DS216 NAS and its Surveillance Station software.
The cameras created an image distortion depending on their distance from the arenas. It was estimated that 1 cm of the object corresponded to an average of 6.82 pixels over the entire arena with a standard error of ±0.01 cm (see details in **Appendix B**).

<figure><img src="image_3.png" /><figcaption>**Fig. 1:** Experimental design for monitoring *E. caraibea* movements in heterogeneous environments: a. arena diagram, b. and d. photos of the "residues" arena, c. and e. photos of the "bare soil" arena. d. and e. images extracted from infrared night-time videos.</figcaption></figure>

## 2.3 Movement monitoring

Individuals were chosen following their order of capture to homogenise the time spent in captivity. Each day of experiment, two earwigs were randomly assigned to the two arenas.

The shelter containing the chosen individual was placed on the white platform in its arena at 5:00 pm on the day of the experiment. The cameras then filmed the arenas from 5:30 pm to 8:00 am (i.e., including the entire night period, which started at 6:00 pm and ended at 6:00 am). This period corresponds to the estimated activity period for *E. caraibea* under natural outdoor lighting conditions (see the preliminary experiment in **Appendix A**). The earwigs left the departure platform at various times between 6:06 p.m. and 9:14 p.m., always after the light had been switched off. Since we tracked 17 individuals on bare soil and 18 individuals on residues, we obtained 35 × 14.5 hours of videos
(resolution 1024 × 768 pixels; 1 frame.s⁻¹) between April 26 and May 29, 2017. Only night-time recordings were used in the rest of this study.

Each tested individual was tagged with a 1 × 1 mm square of reflective material (~ 1 mg, SKU Ref. HEBBR09001, Lecyclo, France) to allow the infrared light reflection enhancing the visibility of the individuals in the arena. The tag was fixed to the earwig's pronotum with a strong adhesive (cyanoacrylate, Super Glue®) at least 24 hours before testing (Fig. 2; see detailed tagging protocol in **Appendix C**). The tagged earwigs showed no visible changes in movements, survival (all tagged earwigs that were recovered after the experiment survived for at least 8 days) and reproduction (some tagged females laid eggs).

<figure><img src="image_4.png" /><figcaption>Fig. 2: Picture of a tagged earwig *E. caraibea* (personal picture)</figcaption></figure>

The day after the experiment, the arenas were transferred outdoors, the residue grid was removed (for the "residues" treatment), and the bare soil was exposed to the sun to remove or, at a last resort, kill individuals from previous experiments and any organisms that might have entered the arena. A total of 28 earwigs were removed: 14 out of 17 on the bare soil and 14 out of 18 on the residue arenas.

## 2.4 Trajectories extraction from videos

We developed a method to extract trajectories from videos to characterise the movements (i.e., speed, sinuosity, and earwig activity) in both ground types (Fig. 3a and 3b). The trajectories are defined as a
time series of locations of one individual (= points), and each point is characterised by its coordinates
and time. Points were grouped into a given trajectory if separated by less than a time gap tg and a
distance gap dg (hereafter referred to as junction criteria) (Tinevez et al., 2017).

<figure><img src="image_5.png" /><figcaption>Fig. 3: The successive steps to extract trajectories and estimate movement parameters. Each step is illustrated with a 30 minute-long data selection from both "residues" and "bare soil" treatments. a. Images representing the maximum intensity obtained for each pixel over a video of 30 min after stationary background and background noise subtraction. b. Locations extracted via the TrackMate plugin and after cleaning with R software. Examples of trajectories obtained with a specific procedure: c. a short junction (criteria 10 s, 50 pixels) with a rediscretisation at 1 s without interpolation, and d. a long junction between trajectories (criteria 5 min, 50 pixels) and a rediscretisation at 5 s with interpolation (for definitions, see section 2.4).</figcaption></figure>
The experimental design of this study posed some specific problems that made the extraction of trajectories from the videos difficult, including (i) significant background noise caused by heterogeneous backgrounds and the infrared light and (ii) the simultaneous presence of several individuals in an arena for some replicates where a previous individual had not been found and removed. This methodological problem was a consequence of the presence of soil in the arenas, making it difficult to recover individuals at the end of the experiment. To account for these issues, we used (i) a technique to subtract background and residual noise from the videos and (ii) semi-automated *cleaning* of the trajectories in R to eliminate conflicting and interacting trajectories.

The videos were treated twice to increase contrast between the moving individuals and the heterogeneous background. The first treatment consisted in subtracting an image of the stationary background from each image of the video. Images summarizing the background were created by calculating the median value (intensity) of each pixel over every 30 minutes of video. This treatment produced images with the tag of moving individuals in light grey - white on a black background. Those images still contained residual noise in the background that may have been caused by microvariations in light intensity, micromovements in the substrate, or by noise in the camera's infrared light sensors. The second treatment consisted in subtracting an image of this background noise, approximated from the sum of pixel intensities over every 30 minutes of video. The sum of intensities reached a high value for the pixels that frequently recorded low intensities (dark grey static background noise) and a low value for the pixels that remained black or exhibited rare periods with high intensities (light grey moving individuals). This treatment thus preserved more contrasting black and white images by decreasing pixel intensities mostly for background noise and only slightly for moving individuals (see Fig. 3a and videos extracts in Appendix D).

After video treatments, the trajectories were extracted using ImageJ2, an image processing software (Schindelin et al., 2012; version 1.51) and its *TrackMate* plugin (Tinevez et al., 2017; version 3.6.0). *TrackMate* plugin use a DoG detector algorithm (Difference of Gaussian) and a Simple LAP tracker algorithm (Linear Assignment Problem) (based on Jaqaman et al., 2008). DoG detector allows the detection of small moving points from their size (3 pixels) and a quality threshold (15) (Tinevez et al.,
2017). The Simple LAP tracker then associates the points within trajectories (i.e. the junction), one time step after the other, according to the junction criteria dg (30 pixels, i.e. ≈ 4.3 cm), and tg (5 s) without signal.

A semi-automated *cleaning* treatment (see diagram in **Appendix E**) was performed with the R software (R Core Team, 2018; version 3.6.3) to eliminate conflicting and interacting trajectories. Conflicting trajectories are trajectories that occur simultaneously and that are either false positive (being derived from residual background noise) or real trajectories from other earwigs moving simultaneously. The treatment involved basic automated processes for removing conflicting points under certain circumstances (e.g., when the number of conflict points was small) and, visual observations and manual editing of trajectories to remove residual conflicting trajectories otherwise.

For example, when trajectories of several earwigs were observed in an arena, the trajectory which was the most consistent with the anterior and posterior trajectories was retained (except in one case where two trajectories, distant from each other by less than 5 cm, were both deleted). Most of conflicting trajectories were removed with automated processes and only few trajectories had to be cleaned manually per individual (mean ± standard deviation : 2.0 ± 5.4 % of trajectories; max of 34 trajectories). This indicated that most of the conflicting trajectories were generated by residual background noise. Once the conflicts were removed and the confusion between individuals and noise was no longer observed, the trajectories were reconstructed with less restrictive junction criteria (dg = 50 pixels and tg = 10 s) (**Fig. 3b** and **Fig. 4**).
<figure><img src="image_6.png" /><figcaption>Fig. 4: Diagram of the junction of two trajectories according to junction criteria dg (distance gap) and tg (time gap)</figcaption></figure>

## 2.5 Estimating movement variables

We developed a method to estimate comparable movement variables, i.e. the time-series of distances and turning angles calculated from segments with identical durations, in both ground types (Fig. 3c and 3d). Two successive points within a trajectory define a segment. A distance is the length of a segment. A turning angle is calculated between the direction of a segment and the direction of the previous segment. Therefore, it takes two successive segments to obtain a turning angle. Due to the disappearance of the tag and missing points within the trajectories in the "residues" arena, segments with an identical duration were rare. The method relies on the design of different procedures to obtain segments of identical duration. These procedures varied in their time resolution and were based on different hypotheses on the behaviour of concealed earwigs. Each procedure resulted in a set of movement variables for each individual.

### Removing near-edge movements

To estimate movement variables that were not biased by the arena's edges, we removed all trajectory points within 35 pixels (≈ 5.1 cm) of the arena borders (Fig. 3). This treatment split some trajectories into several trajectories. This operation was always performed after conducting the junction operation for specific time and distance gaps (see next paragraph).
## Procedures to obtain segments of identical duration

A procedure transformed the extracted trajectories into movement variables by means of three different operations: a junction, an interpolation and a rediscretisation of the trajectories (cf. Fig. 4 and 5). Each procedure has a corresponding parameter value for each operation (e.g. Fig. 3c and 3d). All operations were performed with the R software (R Core Team, 2018; version 3.6.3) and the *adehabitatLT* package (Calenge, 2006; version 0.3.25).

The junction operation consisted of reconstructing trajectories with a less restrictive junction time gap (*tg*) (Fig. 4). The interpolation operation involved placing one point for each missing point within trajectories (Fig. 5), utilising linear interpolation between the previous and following extracted points (see interpolation function proposed by *adehabitatLT*). By filling in the missing trajectory points, interpolation allowed the disappearance periods to be included in the speed calculation. The rediscretisation operation consisted in modifying the fixed duration of the segments required to estimate the movement variables within each trajectory.

<figure><img src="image_7.png" /></figure>
**Fig. 5:** Trajectory procedure scheme for rediscretisation at 1 or 5 s, with or without interpolation. The segments for calculating distances and examples of turning angles are shown for each procedure. Interpolation was performed before rediscretisation. Consequently, point 15 (Fig. 5d) is taken from the interpolated point 15 (Fig. 5b) and not by linear interpolation between points 10 and 20.

In the absence of interpolation, the segments with identical duration were selected from successive direct observations corresponding to the selected rediscretisation time (**Fig. 5a** and **c**). If there were no observations exactly at the next re-discretisation time, the segment and consequently the distance and the *turning angle* were not calculated. In the case of interpolation (**Fig. 5b** and **d**), rediscretisation consisted of placing all missing points with interpolation and selecting the segments corresponding to the chosen rediscretisation times. Therefore, each dataset of movement variables obtained by a specific procedure was based on a specific way of obtaining segments with identical duration.

## Sensitivity of movement variables to operation values

Choices made for the three main operations used to obtain segments of identical duration (junction, interpolation, rediscretisation) may affect the values of movement variables describing the paths (i.e., distances and *turning angles*). Long junction values (*tg*) allow the inclusion into the trajectories of longer times spent under the residues but may lead to a higher occurrence of missing values within the trajectories. Every rediscretisation value reveals the individual movement at a different scale and requires a different interpretation. Short rediscretisation values indicate the detailed individual trajectory and permit the calculation of mobility parameters like maximum velocity. Long rediscretisation values reveal individual net displacement over the corresponding period, allowing the inclusion into the trajectories of longer times spent under the residues in the limits permitted by the dimensions of the arena. Linear interpolation makes it possible to keep all the information in a trajectory but artificially translates time spent under the residues into a straight displacement of equivalent velocity. This can cause underestimation of average speed and sinuosity, for short rediscretisation in particular. Because of the strong impact interpolation can have on sinuosity, the calculation of *turning angles* was only performed on trajectories without interpolation.
To investigate those potential effects, we tested the impact of procedures on movement variables with all combinations of the following values: (i) junctions (tg) of 10 s, 30 s, 1 min and 5 min; (ii) rediscretisations of 1, 5, 10, 20, 30, 40, 50 and 60 s, (iii) with and without interpolation.

## 2.6 Comparison between ground type

### Individual activity

The activity during the night of the experiment was considered only for individuals that were known to have been alone in the arena and which were found at the end with their tag still in place. This represented 9 and 7 individuals for bare soil and residues, respectively. The activity was measured from the entire recorded movements, i.e. all “cleaned” trajectories (Fig. 3).

We calculated the amount of movements per night per individual (= number of seconds an individual was observed in movement, later termed “apparent intensity of activity”), the number of minutes that an individual was observed with movement (at least one second recorded, termed “estimated intensity of activity”) and the time elapsed between the first and last movement of an individual (termed “total activity period”). As residues potentially concealed a significant portion of the movements, the two last measures were performed to limit this information loss bias and better compare activity between the two ground types.

### Statistical analysis of movement variables

We tested the effect of residues on travel speed and sinuosity by comparing the movement variables of trajectories (distances and turning angles) between the two ground types. We considered the autocorrelation of successive segments in our analyses by testing the effect of ground types for different rediscretisation values (i.e., at different time scales) and taking an individual resolution for our statistical analyses (Fieberg et al., 2010). Individuals released the same day were considered as independent because they could not interact and external conditions were controlled by the climatic chamber. Individuals that had too little data to have a correct estimate of their movement variables were removed from analyses (see below).
Distances were compared between ground types using datasets of all procedures with at least 10 individuals with at least 10 distance records per ground type. Statistical analyses were performed using mixed linear models (package *lme4*; Bates et al., 2015; version 1.1-26) with individuals as a random effect and a square root transformation of distances expressed in pixels ($\sqrt{distance}$) to address the overdispersion of the model residuals. Model residuals were inspected for dispersion, uniformity and outliers with the “DHARMa” R package (Hartig, 2020; version 0.3.3.0). The effect of the individual's sex and its interaction with ground type were removed from the model since they had no significant effect on distance (see analysis in **Appendix F**).

Turning angles were compared between ground types for all procedures without interpolation and with a junction of 10 s comprising at least 10 individuals with at least 10 turning angles records per ground type. Only one junction time gap was tested because, in absence of interpolation, the junction time gap does not affect the *turning angles* used for analysis (**Fig. 6b**). Individual-based statistics were used to test the effect of ground type on the concentration of turning angles (1 - the variance of turning angles around the mean). Concentration is a measure of the sinuosity of a trajectory, ranging from 0 to 1, with low values corresponding to sinuous trajectories. Statistics developed on R by Pewsey et al. (2013) were used. These analyses rely on the non-parametric test statistics of Wallraff (1979) and allow the comparison of two circular statistical distributions for the concentration. A randomization test was performed on the test statistics. The statistical distribution under $H_0$ was constructed by calculating 1000 statistics obtained after random permutations of the individuals between the ground types to account for the differences in numbers of calculated angles among individuals. The p-values were calculated by comparing the observed statistics with the statistical distribution under the null hypothesis $H_0$ of similar values in the two ground types.

# 3 Results

## 3.1 Activity

The apparent intensity of activity appeared to be lower on average on the residues than on the bare soil (inter-individual mean ± standard deviation, Residues: 1.5 ± 0.8 hours, Bare soil: 2.5 ± 2.0 hours). We
found no such large difference when comparing the estimated intensity of activity (inter-individual
mean ± standard deviation, Residues: 5.9 ± 2.1 hours, Bare soil: 5.1 ± 2.7 hours) or the total activity
period (inter-individual mean ± standard deviation, Residues: 9.4 ± 2.1 hours, Bare soil: 10.0 ± 1.0
hours). All measures of activity revealed a highly variable activity pattern (intensity and distribution
over time) depending on the earwig's identity and not linked to ground type. The detailed activities of
each individual are presented in **Appendix G**.

**3.2 Movements variables**

Samples generated according to procedures

The trajectories selected for the movement analysis (i.e. excluding near-edge movements) represented
31.9 ± 18.6 % of the total seconds of movement (inter-individual mean ± standard deviation).

We did not get enough distances to allow statistical comparison of distances for 23 (over 64)
procedures (Fig. 6a). The average number of segments sampled (= distances) per earwig varied largely
depending on the procedure (from 5.6 to 3454.5 segments ; **Fig. 6a**). The number of segments was
larger for procedures with short rediscretisation values, interpolation and long time gaps for junction
(tg) (starting at tg = 30 s). The number of segments was also very variable among the earwigs (from 5
to 1268 segments per earwig for the reference procedure). Segment statistics per individual and per
ground type corresponding to data used for statistical analyses for all procedures are given in
**Appendix H**.
<figure><img src="image_8.png" /><figcaption>**Fig 6:** Average number of movement variables sampled per individual according to ground type (colour) and procedure. a. and b. show the number of distances and turning angles, respectively. The grey banner of the four subsections indicates the junction time gaps. The full circles represent the procedures that were used for the statistical comparison. The plus symbols represent the procedures with too few individuals with enough values of movement variables.</figcaption></figure>

Similar effects of procedures on the number of turning angles were found with the difference that interpolation was not used and turning angles were always fewer (Fig. 6b). We did not obtain enough data to allow statistical comparison of turning angles for 24 (over 32) procedures, only procedures with rediscretisation values of 1 and 5 s analysed.

## Speed

Speeds were calculated from distances as the ratio between distances and the rediscretisation value and are presented to simplify the comparison between the different rediscretisation values. During their
movements, earwigs reached a maximum velocity of 2.19 ± 0.84 cm.s⁻¹ (inter-individual mean ± standard deviation calculated from trajectories with rediscretisation at 1 s without interpolation), with a maximal speed of 4.93 cm.s⁻¹ registered for an individual on residues.

All statistical analyses, regardless of the procedure, show a significant effect of ground type on distances (all p-values < 10⁻³). The earwigs' mean speeds were estimated 2.3 to 4.2 times slower on residues than on bare soil (Fig. 7; see detailed statistical results in **Appendix I**). For example, the mean speed was 0.36 ± 0.11 cm.s⁻¹ (inter-individual mean ± standard deviation) on residues vs 0.85 ± 0.27 cm.s⁻¹ on bare soil, for a procedure with few assumptions, i.e., junction of 10 s, without interpolation and rediscretisation of 5 s. Estimated slowdowns on residues varied depending on the procedure (Fig. 7b). Stronger slowdowns were estimated with long time gaps for junction (starting from 1 min), intermediate rediscretisations (10 to 30 s) and interpolation. Junction only affected slowdown for procedures with interpolation. In particular, allowing a junction of 5 min with interpolation strongly increased the estimated slowdown. Rediscretisation had different effects with and without interpolation. Without interpolation, rediscretisations of 5 s and more increased the estimated slowdown. With interpolation, increasing rediscretisation values (starting from 30 s) tended to decrease slowdown. The standard error associated with model estimates also increased for high rediscretisation values (see **Appendix I**).
<figure><img src="image_9.png" /><figcaption>Fig. 7: Effect of ground type on the speed of earwigs according to procedures. a. Mean speeds per earwig (inter-individual mean ± standard errors) according to ground type (yellow or brown lines), junction (horizontal division), rediscretisation (horizontal axis) and interpolation (solid or dotted lines). b. Estimated ratio of speeds on bare soil vs residues. These ratios were calculated with the predicted distances extracted with the “effects” R package (Fox & Weisberg, 2018; version 4.2-0). All procedures represented here show a significant effect of ground type on speed (all p-values <10⁻³).</figcaption></figure>

Turning angle

The sinuosity of a trajectory increases as the concentration of turning angles decreases. Trajectories obtained for short rediscretisation (1 and 5 s) showed significantly higher sinuosities on the residues than the bare soil, caused by smaller concentrations (Fig. 8). For the 5 s rediscretisation, concentrations were almost twice as large on bare soil (inter-individual mean ± standard deviation: 0.40 ± 0.16 on residues and 0.77 ± 0.14 on bare soil, p-value < 10⁻³, statistic = 219.81; Fig. 8a).
<figure><img src="image_10.png" /><figcaption>Fig. 8: Effect of ground type and rediscretisation (1 or 5 s) on the earwig's turning angles distribution (procedures without interpolation and a junction at 10 s). a. inter-individual mean (± standard errors) of turning angles concentration according to ground type (colour) and rediscretisation value. The significance of the ground type effect on the concentration is represented by:"***" p < 0.001. The circular plots b., c., d. and e. indicate inter-individual median, first quartile and third quartile of the number of turning angles per earwig for each 10° according to ground type and rediscretisation value. Turning angles are expressed in radians which means that positive value are left and negative values are right.</figcaption></figure>

4 Discussion

Analysing foraging movement of small organisms at a fine scale, over long periods and on realistic
ground is crucial for understanding their spatial and temporal dynamics and help managing them in
real agricultural environments. Based on generic video and trajectory processing, we developed a
method that made it possible to detect more sinuous and slower movements of the nocturnal earwig *E. caraibea* walking on soil covered by banana leaf residues rather than on bare soil.

**4.1 Tracking earwigs' movements with an experimental mesocosm and video recording.**

Our experimental device made it possible to monitor several hours of movement on both ground types.
A crucial step of the method was to distinguish earwigs from the background. The use of reflective
material on earwigs' pronotum was decisive in increasing the earwigs' contrast. In addition to
background and noise subtraction, semi-automatic cleaning of trajectories was necessary to suppress
the remaining noise and to make robust choices between some simultaneous trajectories. The
reflective material and video treatments have made the manual editing more manageable given the
number of hours, and the movement resolution registered.

The experimental device developed in this study appeared to be more efficient for calculating speeds
and highlighting differences in earwig movements for the fine temporal grains (rediscretisation $\le$
30 s). Indeed, we observed a decreasing trend in the estimated slowdown in the residues arena when
the values of rediscretisation increased (starting at 30 s) with interpolation (Fig. 7b). Above 30 s, the
dimensions of the arena ($< 1 \text{ m}^2$) were probably too small, biasing the sampling of distances towards
shorter distances than they might be in open ground. This limitation should be considered when
extrapolating the study results to larger spatial and temporal scales, as the distances travelled do not
change linearly with sampling time (Morales & Ellner, 2002).

This experimental device opens up the possibility for future analyses on the effects of a wide variety of
ground types on the movement of ground-dwelling arthropods. Additional video treatments or
improved tracker algorithms (e.g., Assali et al., 2020) may be required in more complex environments,
such as plant cover, where background noise management for trajectory extraction and individual
recovery is challenging. In all cases, the spatial distribution of the cover units that could hide
individuals should be set to allow the brief but regular appearance of the target species. Previous
knowledge of the species, their maximum and mean speed can help optimise the cover design.
Similarly to our case, studying cryptic species would probably require improving the contrast between
the target and the cover. Thus, choosing an appropriate tag is critical and depends on the species
targeted, the ground type tested, and the camera device chosen. In the case of diurnal species, there are
more possibilities for distinguishing individuals from the background. Indeed, recording in visible
light results in less noise than infra-red light (Semenishchev et al., 2018) and standard cameras under
visible light return three values per pixel, providing more possibilities to find colours (not just
intensities) that distinguish individuals from the background (Sebastian et al., 2010; Wu et al., 2020
preprint). The standard infra-red camera used in this experiment returns only one value per pixel
(intensity), suggesting that reflective material is probably the best option for nocturnal species.

In recent years, advances in computational methods, such as deep learning, have enabled advances in insect tracking, resulting in significant progress in counting, species recognition and movement tracking (Bjerge et al., 2021; Høye et al., 2021). However, these methods are still time-consuming to develop and require a large amount of data, specific to the particular conditions of a given experiment, to train the algorithm. Although in the future we can hope for an improvement and greater transferability of the algorithms developed to a wider range of experiments, the use of simpler but faster methods, such as those developed in this study, still seem to us to be of great relevance.

## 4.2 Characterising movements with trajectories containing missing points

Most trajectories included times where the location of the earwig was not known (missing points).
Even on bare soil, missing points were rare but existed (Appendix J). Missing values within
trajectories in our experiment could have several explanations: (i) the individual hid under the soil, (ii)
the individual movements were not captured by our device or (iii) the individuals were concealed by
the residues. Since individuals hiding under the soil (situation i) were rarely observed, and situation
(ii) corresponded to a short and random loss of the earwig's tag tracking (e.g., bad inclinations of the
tag toward infrared LED), those missing points should not have significantly influenced the
quantification of movement on bare soil.
We were interested in the period during which movements were concealed by residues (situation iii) because they could be the source of significant differences in movement variables between the two ground types. Because situation (ii) could also happen on residues, the time with missing points on residues did not exactly match the time individuals spent under residues (situation iii). However, in our experiment, the missing points between residues are unlikely to have affected the estimation of movement variables, particularly slowdown. Indeed, if we had lost track of individuals moving between two or more residues, resulting in an artificially long concealed period, these individuals probably travelled distances larger than the distance gap of 7.3 cm, such that the next data point would be the beginning point of a new trajectory. Herein, we used long time gaps for trajectory junction, long rediscretisations of trajectories and interpolation to include concealed movements in the trajectories used to quantify movement variables.

The duration of junction time gaps determined the maximum duration of concealed movements included in the trajectories. Long junction time gaps were necessary to account for long concealed movements (up to 5 min). These long junction time gaps affected the quantification of speed and sinuosity only when (i) they were combined with interpolation (because numerous successive interpolated points were added), and (ii) rediscretisation was longer than the junction time gaps (because concealed periods shorter than this time gap could be included within a segment).

Varying the rediscretisation value without interpolation allowed to include concealed movements while making few assumptions, because only observed movements were taken into account. Short rediscretisations were adapted to analyse speeds and sinuosity on a fine time scale (1 to 5 s, Fig. 7 and Fig. 8) because they provided enough data to perform statistical analyses without interpolation (Fig. 6a). Some slightly longer rediscretisations (10 and 20 s) generated enough data to compare speeds between ground types including longer concealed movements in trajectories (up to 19 s). However, because the inclusion of these longer concealed movements was only possible for long rediscretisation, it was not possible to disentangle the effects of concealed movement from those of time scale on speed and sinuosity. Notably, it has been proposed that each time scale can reflect different behaviours (Nathan et al., 2008; Owen-Smith et al., 2010). For example, individuals showing
(i) a high sinuosity at a time scale but (ii) straight movement at a slightly longer time scale could have
indicated a local avoidance of residues (i) while quickly crossing the arena (ii).

Interpolation was another means to include long concealed movements when calculating speeds (and not turning angles). In contrast to long rediscretisation, interpolation allowed for the analysis of the concealed movement effects regardless of time scale because it could be combined with both short or long rediscretisation values. This approach relies on the ability of the device to successfully track individual movements between residues and proved relevant for the analysis of speeds on larger time scales (5 to 30 s). The comparison of the speeds estimated with interpolation with speeds from another experiment should be done only under the same data handling procedure because linear interpolation may underestimate real speeds over each ground type.

Missing values are a common problem in animal movement tracking. Even the most advanced
techniques such as small remote sensing tags (O'Neal et al., 2004) or deep learning algorithms (Høye
et al., 2021) can lose track of the individual when it is hidden by a complex ground cover such as
vegetation. By reconstructing trajectories with different temporal resolutions and with different
hypotheses on the behaviour of concealed earwigs, our method suggests a way to address this problem
directly.

4.3 Slower and more sinuous movements on the residues

Our study shows that the ground type strongly affected the movement of the earwig *E. caraibea*.
Earwigs moved significantly slower on the residues, with speeds estimated to be two to four times
slower than on bare soil (Fig. 7b). These results are particularly robust because they have been found
for all the procedures carrying enough data, whether with interpolated or observed trajectories, long or
short junction and long or short rediscretisation values.

An increase in the sinuosity of visible movements on residues was also observed for short
rediscretisations times of 1 to 5 s (Fig. 8a), i.e., the fine movements of the earwig were more sinuous
between (or over) the residues than on bare soil. The residues, therefore, seem to have affected the fine
movements of the earwigs, speeds, and sinuosity, even when earwigs were visible and not hidden
under residues.

Furthermore, a larger slowdown on residues was estimated when concealed movements were included
in the speed's calculation (large rediscretisation values, **Fig. 7b** or interpolated trajectories with long
junctions, **Fig. 7b**), confirming that accounting for concealed movements matters. Earwigs seem to
move slower or more sinuously (or both) under the residues than between them. Although procedures
with long junction and interpolation must be interpreted with more care, the highest estimated
slowdown of the earwigs (up to four times; **Fig. 7b**) obtained for the procedure with interpolation and
with the longest junction value (5 min) indicates that earwigs likely stayed hidden for more than 1 min
and that this stay strongly slowed down their exploration of the arena. Since the residues hid the
earwig's movement from the camera, we expected longer concealed periods on residues. However, the
signal loss caused by residues could not account for the strong slowdown by itself (see detailed
analysis in **Appendix J**). Those long periods under residues suggest that residues have not simply
slowed down the movements of earwigs as mere obstacles to movement (i.e., a barrier effect) but have
caused a behaviour change, possibly related to the perception of safety (shelter).

The slowdown and sinuosity of earwigs on residues observed in the present study are consistent with findings on mammals (Beisner and Isbell, 2009; Morales et al., 2004), birds (Da Silveira et al., 2016) and flying insects (Schtickzelle et al., 2007) that show more sinuous and slower movements in their habitats in comparison to unsuitable environments. Such behaviour is also supported by the optimal foraging theory according to which animals slow down in food rich areas to maximise their yield of food (Andersson, 1978; Bell, 1991) or by the effect of random patterns on animal cognition that increase exploratory behaviour (Anselme, 2015). That increased slowdown and sinuosity observed in absence of food resources suggests that ground type quality per se can have a strong effect on the movement of ground-dwelling arthropods and confirms the importance to consider the effect of more realistic ground types on their movement. Such knowledge, combined with modelling, can be of great importance to optimise the spatial distributions of their habitats for pest management practices such as conservation biological control (Collard et al., 2018).
## 4.4 Conclusion

To our knowledge, the method described in this study is the first to track the movement of nocturnal ground-dwelling arthropods in a realistic environment, with a high temporal resolution, an intermediate spatial extent (1 m²), and over an entire activity cycle. It allowed us to estimate the slowdown and increase in sinuosity of earwig movements on a complex ground cover. The technical solutions we set-up aimed to investigate how a terrestrial arthropod was affected by different ground types. Because they are based on generic principles, we think they are of great interest for the recent growing field of video monitoring and movement ecology. The relatively low cost of the cameras and the free software made it an inexpensive solution, easy to set up and replicate.

## Acknowledgements

This work was supported by INRAE (French National Research Institute for Agriculture, Food and Environment), CIRAD (French Agricultural Research Centre for International Development), and the STRADIV project (n° 1504-003) funded by the Agropolis foundation.

We want to thank the CIRAD centre in Martinique for providing us with equipment and several rooms and the many researchers, engineers, and technicians from the Centre who took the time to answer our many questions.

## Conflicts of interest statement

The authors declare no conflicts of interest.
## Authors contribution statements

BC, TD, PT, DC and CL conceived the ideas and designed methodology; BC collected the data; BC and TD analysed the data; BC and TD led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

## Data Accessibility

Cleaned trajectories : CIRAD dataverse, doi: https://doi.org/10.18167/DVN1/NPWMUP
References

Al Hassan, D., Georgelin, E., Delattre, T., Burel, F., Plantegenest, M., Kindlmann, P., Butet, A., 2013.
Does the presence of grassy strips and landscape grain affect the spatial distribution of aphids
and their carabid predators? Agric. For. Entomol. 15, 24–33.
https://doi.org/10.1111/j.1461-9563.2012.00587.x

Albrecht, M., Kleijn, D., Williams, N.M., Tschumi, M., Blaauw, B.R., Bommarco, R., Campbell, A.J., Dainese, M., Drummond, F.A., Entling, M.H., Ganser, D., Arjen de Groot, G., Goulson, D., Grab, H., Hamilton, H., Herzog, F., Isaacs, R., Jacot, K., Jeanneret, P., Jonsson, M., Knop, E., Kremen, C., Landis, D.A., Loeb, G.M., Marini, L., McKerchar, M., Morandin, L., Pfister, S.C., Potts, S.G., Rundlöf, M., Sardiñas, H., Sciligo, A., Thies, C., Tscharntke, T., Venturini, E., Veromann, E., Vollhardt, I.M.G., Wäckers, F., Ward, K., Wilby, A., Woltz, M., Wratten, S., Sutter, L., 2020. The effectiveness of flower strips and hedgerows on pest control, pollination services and crop yield: a quantitative synthesis. Ecol. Lett. 23, 1488–1498.
https://doi.org/10.1111/ele.13576

Allema, A.B., Rossing, W.A.H., van der Werf, W., Heusinkveld, B.G., Bukovinszky, T., Steingröver, E., van Lenteren, J.C., 2012. Effect of light quality on movement of Pterostichus melanarius (Coleoptera: Carabidae). J. Appl. Entomol. 136, 793–800.
https://doi.org/doi:10.1111/j.1439-0418.2012.01728.x

Andersson, M., 1978. Optimal foraging area - size and allocation of search effort. Theor. Popul. Biol. 13, 397-409. https://doi.org/10.1016/0040-5809(78)90054-0

Anselme, P., 2015. Enhanced exploratory activity in woodlice exposed to random visuo-tactile patterns. Learn. Motiv. 50, 48–58. https://doi.org/10.1016/j.lmot.2014.09.002

Assali, C., Bez, N., Tremblay, Y., 2020. Raking the ocean surface: new patterns of coordinated motion in seabirds. J Avian Biol 51, jav.02258. https://doi.org/10.1111/jav.02258

Baguette, M., Van Dyck, H., 2007. Landscape connectivity and animal behavior: functional grain as a
key determinant for dispersal. Landscape Ecol. 22, 1117-1129.
https://doi.org/10.1007/s10980-007-9108-4

Bates, B., Mächler, M., Bolker, B., Walker, S., 2015. Fitting Linear Mixed-Effects Models Using lme4.
2015 67, 48. https://doi.org/10.18637/jss.v067.i01

Beisner, B.A., Isbell, L.A., 2009. Movement ecology in a captive environment: the effects of ground substrate on movement paths of captive rhesus macaques, Macaca mulatta. Animal Behaviour 78, 1269-1277. https://doi.org/10.1016/j.anbehav.2009.09.004

Bell, W.J., 1991. Searching Behaviour - The behavioural ecology of finding ressources, Chapman and hall animal behaviour series.

Bernardes, R.C., Lima, M.A.P., Guedes, R.N.C., da Silva, C.B., Martins, G.F., 2021. Ethoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis. Sensors 21, 3237. https://doi.org/10.3390/s21093237

Bjerge, K., Mann, H.M.R., Høye, T.T., 2021. Real-time insect tracking and monitoring with computer vision and deep learning. Remote Sens Ecol Conserv rse2.245. https://doi.org/10.1002/rse2.245

Brindle, A., 1971. The Dermaptera of the Caribbean. Studies on the fauna of Curaçao and other caribbean islands 131, 75.

Burr, M., 1939. Modern work on earwigs. Science Progress (1933-) 34, 20-30.

Calenge, C., 2006. The package "adehabitat" for the R software: A tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 516-519.
https://doi.org/10.1016/j.ecolmodel.2006.03.017

Carval, D., Resmond, R., Achard, R., Tixier, P., 2016. Cover cropping reduces the abundance of the banana weevil Cosmopolites sordidus but does not reduce its damage to the banana plants. Biol. Control 99, 14-18. http://dx.doi.org/10.1016/j.biocontrol.2016.04.004

Collard, B., Tixier, P., Carval, D., Lavigne, C., Delattre, T., 2018. Spatial organisation of habitats in
agricultural plots affects per-capita predator effect on conservation biological control: An
individual based modelling study. Ecol. Model. 388, 124–135.
https://doi.org/10.1016/j.ecolmodel.2018.09.026

Da Silveira, N.S., Niebuhr, B.B.S., Muylaert, R. de L., Ribeiro, M.C., Pizo, M.A., 2016. Effects of Land Cover on the Movement of Frugivorous Birds in a Heterogeneous Landscape. PLoS ONE 11, e0156688. https://doi.org/10.1371/journal.pone.0156688

Daout, F., Amara, M.-S., Schmitt, F., Delattre, T., 2017. Mesure de la SER de végétaux en propagation
avant – Aspect expérimental, in: Journées Du GDR Ondes 2451 “interférences d’ondes” -
Assemblée Générale. Sophia Antipolis, France. https://hal.inrae.fr/hal-02733581

Delattre, T., Collard, B., Lavigne, C., 2019. Keep your enemies closer: enhancing biological control through individual movement rules to retain natural enemies inside the field. Web Ecol. 19, 15–26. https://doi.org/10.5194/we-19-15-2019

Dias, N., Hassall, M., Waite, T., 2012. The influence of microclimate on foraging and sheltering behaviours of terrestrial isopods: Implications for soil carbon dynamics under climate change. Pedobiologia 55, 137–144. https://doi.org/10.1016/j.pedobi.2011.10.003

Diehl, J.M.C., Meunier, J., 2018. Surrounding pathogens shape maternal egg care but not egg production in the European earwig. Behav. Ecol. 29, 128–136. https://doi.org/10.1093/beheco/arx140

Drees, C., Matern, A., Assmann, T., 2008. Behavioural patterns of nocturnal carabid beetles determined by direct observations under red-light conditions, in: Penev, L., Erwin, T., Assmann, T. (Eds.), Back to the Roots and Back to the Future: Towards a New Synthesis Amongst Taxonomic, Ecological and Biogeographical Approaches in Carabidology, Proceedings, Pensoft Series Faunistica. Pensoft Publishers, Sofia, pp. 409–423.

Fieberg, J., Matthiopoulos, J., Hebblewhite, M., Boyce, M.S., Frair, J.L., 2010. Correlation and studies of habitat selection: problem, red herring or opportunity? Philosophical Transactions of the
Royal Society B: Biological Sciences 365, 2233–2244. https://doi.org/10.1098/rstb.2010.0079

Guennelon, G., Audemard, H., Fremond, J.-C., Abdelmajid el Idrissi Ammari, M., 1981. Progrès réalisés dans l’élevage permanent du Carpocapse (Laspeyresia pomonella L.) sur milieu artificiel. Agronomie 1, 59–64.

Haddad, N.M., Brudvig, L.A., Clobert, J., Davies, K.F., Gonzalez, A., Holt, R.D., Lovejoy, T.E., Sexton, J.O., Austin, M.P., Collins, C.D., Cook, W.M., Damschen, E.I., Ewers, R.M., Foster, B.L., Jenkins, C.N., King, A.J., Laurance, W.F., Levey, D.J., Margules, C.R., Melbourne, B.A., Nicholls, A.O., Orrock, J.L., Song, D.-X., Townshend, J.R., 2015. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1. https://doi.org/10.1126/sciadv.1500052

Hartig, F., 2020. DHARMA: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package. https://CRAN.R-project.org/package=DHARMA

Heise, B.A., 1992. Sensitivity of mayfly nymphs to red light: implications for behavioural ecology 28, 331–336. https://doi.org/10.1111/j.1365-2427.1992.tb00591.x

Høye, T.T., Ärje, J., Bjerge, K., Hansen, O.L.P., Iosifidis, A., Leese, F., Mann, H.M.R., Meissner, K., Melvad, C., Raitoharju, J., 2021. Deep learning and computer vision will transform entomology. Proc. Natl. Acad. Sci. U.S.A. 118, e2002545117. https://doi.org/10.1073/pnas.2002545117

Imirzian, N., Zhang, Y., Kurze, C., Loreto, R.G., Chen, D.Z., Hughes, D.P., 2019. Automated tracking and analysis of ant trajectories shows variation in forager exploration. Sci Rep 9, 13246. https://doi.org/10.1038/s41598-019-49655-3

Jaqaman, K., Loerke, D., Mettlen, M., Kuwata, H., Grinstein, S., Schmid, S.L., Danuser, G., 2008. Robust single-particle tracking in live-cell time-lapse sequences. Nat. Methods 5, 695. https://doi.org/10.1038/nmeth.1237
https://www.nature.com/articles/nmeth.1237#supplementary-information

Kareiva, P., Odell, G., 1987. Swarms of Predators Exhibit “Preytaxis” if Individual Predators Use Area-Restricted Search. The American Naturalist 130, 233–270.
Kindvall, O., Nordlander, G., Nordenhem, H., 2000. Movement behaviour of the pine weevil Hylobius abietis in relation to soil type: an arena experiment. Entomol. Exp. Appl. 95, 53–61.
https://doi.org/10.1046/j.1570-7458.2000.00641.x

Kissling, W.D., Pattemore, D.E., Hagen, M., 2014. Challenges and prospects in the telemetry of insects. Biol. Rev. 89, 511–530. https://doi.org/10.1111/brv.12065

Mollot, G., Duyck, P.F., Lefeuvre, P., Lescourret, F., Martin, J.F., Piry, S., Canard, E., Tixier, P., 2014. Cover cropping alters the diet of arthropods in a banana plantation: a metabarcoding approach. PLoS One 9, e93740. https://doi.org/10.1371/journal.pone.0093740

Morales, J.M., Haydon, D.T., Frair, J., Holsinger, K.E., Fryxell, J.M., 2004. Extracting more out of relocation data: building movement models as mixtures of random walks. Ecology 85, 2436–2445. https://doi.org/10.1890/03-0269

Nathan, R., Getz, W.M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D., Smouse, P.E., 2008. A movement ecology paradigm for unifying organismal movement research. Proc Natl Acad Sci U S A 105, 19052–19059. https://doi.org/10.1073/pnas.0800375105

Noskov, A., Bendix, J., Friess, N., 2021. A Review of Insect Monitoring Approaches with Special Reference to Radar Techniques. Sensors 21, 1474. https://doi.org/10.3390/s21041474

O’Neal, M.E., Landis, D.A., Rothwell, E., Kempel, L., Reinhard, D., 2004. Tracking Insects with Harmonic Radar: a Case Study. Am. Entomol. 50, 212–218. https://doi.org/10.1093/ae/50.4.212

Owen-Smith, N., Fryxell, J.M., Merrill, E.H., 2010. Foraging theory upscaled: the behavioural ecology of herbivore movement. Philos. Trans. R. Soc. B-Biol. Sci. 365, 2267–2278.
https://doi.org/10.1098/rstb.2010.0095

Peng, J., Zhao, H., Liu, Y., 2017. Urban ecological corridors construction: A review. Acta Ecologica Sinica 37, 23–30. https://doi.org/10.1016/j.chnaes.2016.12.002

Pewsey, A., Neuhäuser, M., Ruxton, G.D., 2013. Circular statistics in R. Oxford university press, New
York.

R Core Team, 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Reynolds, D.R., Riley, J.R., 2002. Remote-sensing, telemetric and computer-based technologies for investigating insect movement: a survey of existing and potential techniques. Comput. Electron. Agric. 35, 271–307. https://doi.org/10.1016/S0168-1699(02)00023-6

Romero-Ferrero, F., Bergomi, M.G., Hinz, R., Heras, F.J.H., de Polavieja, G.G., 2018. idtracker.ai: Tracking all individuals in large collectives of unmarked animals.

Russell, K.N., Russell, G.J., Kaplan, K.L., Mian, S., Kornbluth, S., 2018. Increasing the conservation value of powerline corridors for wild bees through vegetation management: an experimental approach. Biodivers Conserv 27, 2541–2565. https://doi.org/10.1007/s10531-018-1552-8

Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J.-Y., White, D.J., Hartenstein, V., Eliceiri, K., Tomancak, P., Cardona, A., 2012. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676. https://doi.org/10.1038/nmeth.2019
https://www.nature.com/articles/nmeth.2019#supplementary-information

Schtickzelle, N., Joiris, A., Van Dyck, H., Baguette, M., 2007. Quantitative analysis of changes in movement behaviour within and outside habitat in a specialist butterfly. BMC Evol. Biol. 7, 4. https://doi.org/10.1186/1471-2148-7-4

Sebastian, P., Vooi Voon, Y., Comley, R., 2010. Colour Space Effect on Tracking in Video Surveillance. ijeei 2, 298–312. https://doi.org/10.15676/ijeei.2010.2.4.5

Semenishchev, E.A., Voronin, V.V., Balabaeva, O.S., 2018. Stitching of IR images into a single content based on data which analysis obtained by an optical camera, in: Huckridge, D.A., Bursing, H., Hickman, D.L. (Eds.), Electro-Optical and Infrared Systems: Technology and Applications Xv, Proceedings of SPIE. Presented at the Conference on Electro-Optical and Infrared Systems -
Technology and Applications XV, Spie-Int Soc Optical Engineering, Berlin, GERMANY.
https://doi.org/10.1117/12.2326839

Sharakshane, A., 2017. Whole high-quality light environment for humans and plants. Life Sciences in Space Research 15, 18–22. https://doi.org/10.1016/j.lssr.2017.07.001

Tinevez, J.Y., Perry, N., Schindelin, J., Hoopes, G.M., Reynolds, G.D., Laplantine, E., Bednarek, S.Y., Shorte, S.L., Eliceiri, K.W., 2017. TrackMate: An open and extensible platform for single-particle tracking. Methods 115, 80–90.

Valeix, M., Loveridge, A.J., Davidson, Z., Madzikanda, H., Fritz, H., Macdonald, D.W., 2009. How key habitat features influence large terrestrial carnivore movements: waterholes and African lions in a semi-arid savanna of north-western Zimbabwe. Landscape Ecol. 25, 337–351. https://doi.org/10.1007/s10980-009-9425-x

Vinatier, F., Chailleux, A., Duyck, P.-F., Salmon, F., Lescourret, F., Tixier, P., 2010. Radiotelemetry unravels movements of a walking insect species in heterogeneous environments. Anim. Behav. 80, 221–229. https://doi.org/10.1016/j.anbehav.2010.04.022

Wallraff, H.G., 1979. Goal-oriented and compass-oriented movements of displaced homing pigeons after confinement in differentially shielded aviaries. Behav. Ecol. Sociobiol. 5, 201–225. https://doi.org/10.1007/bf00293306

Wu, M., Cao, X., Guo, S., 2020. Accurate detection and tracking of ants in indoor and outdoor environments (preprint). Animal Behavior and Cognition. https://doi.org/10.1101/2020.11.30.403816