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Biotechniques
Biotechniques
BioTechniques
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38425192
10.2144/btn-2024-0001
nihpa2021438
Article
A free and user-friendly software protocol for the quantification of microfauna swimming behavior
http://orcid.org/0000-0002-3561-8586
McMaken Colleen M 1
http://orcid.org/0000-0002-8781-9523
Gribble Kristin E *1
1 Josephine Bay Paul Center for Comparative Molecular Biology & Evolution, Marine Biological Laboratory, Woods Hole, MA, USA
Author contributions

Each author listed participated sufficiently in the work to take responsibility for the content, and all those who qualify are listed. CM McMaken and KE Gribble conceptualized and designed the study; CM McMaken developed the methods, collected and analyzed the data and led the writing of the manuscript. Both authors contributed critically to the manuscript drafts and approved the final version for publication.

* Author for correspondence: kgribble@mbl.edu
17 9 2024
5 2024
29 2 2024
22 9 2024
76 5 174182
https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 Unported License. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Characterizing swimming behavior can provide a holistic assessment of the health, physiology and ecology of microfaunal species when done in conjunction with measuring other biological parameters. However, tracking and quantifying microfauna swimming behavior using existing automated tools is often difficult due to the animals’ small size or transparency, or because of the high cost, expertise, or labor needed for the analysis. To address these issues, we created a cost-effective, user-friendly protocol for behavior analysis that employs the free software packages HitFilm and ToxTrac along with the R package ‘trajr’ and used the method to quantify the behavior of rotifers. This protocol can be used for other microfaunal species for which investigators may face similar issues in obtaining measurements of swimming behavior.

TWEETABLE ABSTRACT

Tracking microfauna behavior can be challenging due to their small size or transparency, or the cost and labor required. We created a user-friendly, cost-effective protocol to help quantify swimming behavior metrics for microfaunal studies.

METHOD SUMMARY

The protocol created allows users to add a semi-automated motion tracker to individuals of interest using HitFilm and to acquire tracking coordinates for each specimen without coding by using ToxTrac. Our approach allows quantification of swimming speed and acceleration, swimming efficiency, directional behavior, trajectory descriptors (distance and length) and movement time using the ‘trajr’ package in R. All code used for R, instructional information and tutorial videos for this protocol are provided to aid in the usability of the method.

Executive summary

Background

Characterizing and quantifying an organism’s behavior can provide insights to its health, social interactions, psychology, ecology, life history and more, making behavioral studies relevant across a range of disciplines (including ecology, evolutionary biology, developmental biology, biomechanics and biomedicine).

A plethora of software options for tracking animals are available, ranging from fully manual to completely automated, but many are labor-intensive, expensive, require a high degree of computational expertise, or are optimized for particular species.

Experimental

We created a protocol that uses free software to apply motion trackers to individuals (HitFilm), acquire tracking coordinates in each video frame for the targeted individuals (ToxTrac) and analyze the coordinates for various behavioral metrics (‘trajr’ package in R), to quantify and understand microfaunal behavior.

Swimming behavior metrics that can be obtained from the protocol include speed and acceleration, swimming efficiency, directional behavior, trajectory descriptors and movement time.

Results & discussion

The protocol developed was proven to be efficient and effective when analyzing the behavior of rotifers supplemented either with the mitochondrial enhancer, elamipretide, or the mitochondrial inhibitor, rotenone.

Conclusion

This protocol for analysis of microfaunal behavior can be applied to other microfaunal species and used for projects with limited funding, applied to videos taken with any imaging system and implemented without specialized coding expertise.

behavior
elamipretide
microfauna
rotenone
rotifer
swimming
tracking
video assay
zooplankton
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pmcCharacterizing and quantifying an organism’s behavior can provide insights to its health, social interactions, psychology, ecology, life history and more. Behavioral studies are thus relevant across a range of disciplines, including ecology, ecotoxicology, evolutionary biology, developmental biology, biomechanics and biomedicine. Behavior can serve as an indicator of, or proxy for, health status and can be used to detect changes in function induced by disease, injury, age, genetic modification, or exposure to various environmental conditions or chemical agents. Animal behavior studies typically involve observing and tracking individuals’ movements through coordinate space over time. These studies can be done at a range of scales and with a variety of methods, such as using GPS collars to track large-scale migration patterns or recording videos in a confined area (e.g., in a cage or under a microscope) to study changes in speed or kinematics caused by treatment or environment. Although there are a plethora of options for tracking animals, ranging from fully manual to completely automated, many are labor-intensive, expensive, or require a high degree of computational expertise. In addition, the tools relevant for some species may not work adequately for others.

Given the insights that behavioral studies can provide, there is growing interest in developing methods to quantify behavior efficiently and accurately in a range of taxa. Microfauna comprise a large and diverse group of microscopic animals and protists that are categorized either by the mesh size used to retain them (typically <100 μm) or by taxon (e.g., ciliates, copepods, rotifers, sarcodines and larval stages of diverse vertebrate and invertebrate species) [1,2]. These small organisms live in every habitat on Earth and play important roles in food web dynamics and ecosystem functioning. Microfauna are thus often used as bioindicators for monitoring ecosystem health [3,4].

Increasingly, microfaunal species are also being used as laboratory experimental systems for studies relevant to human health and basic biology. Species such as Daphnia magna, Caenorhabditis elegans, Brachionus manjavacas and Hypsibius exemplaris have become tractable and powerful model organisms for behavioral, aging, molecular biology and genetic research due to their short lifespans, rapid reproductive cycles, small size, easy culturability, and available genomic and genetic tools [5–9].

Quantifying behavior can provide insights across a range of biological scales and questions. Measuring swimming behavior in aquatic microfauna can serve as an indicator of the health of individuals and populations and can shed light on important ecological processes (e.g., conspecific recognition, mating behavior, foraging activity and predator avoidance). Swimming behavior also can serve as a visual and quantitative representation of an organism’s physiology, including nervous and muscular system function. In addition, analyzing the swimming behavior of individual microfauna can allow researchers to estimate and predict demographic composition and survivorship of species and populations [10]. Integrating swimming behavior studies of microfauna with CRISPR/Cas9 gene editing, established for Daphnia [11] and recently developed for B. manjavacas [12], may also be useful for characterizing phenotypes of knockout or knockin mutants.

Rotifers (phylum Rotifera) are microscopic, multicellular invertebrates found globally in both fresh- and salt-water environments, including highly ephemeral habitats [13]. The use of rotifers for experimental research has been driven by their small size, transparency, ease of laboratory culturing, direct development (no larval stages or metamorphosis), short generation times and (for some species) environmentally cued reproduction strategies. Among the 2000+ species in phylum Rotifera, sizes vary from 40 to 2000 μm, with most species between 200 and 500 μm in length. As with other aquatic microfauna, the lack of coloration and small size of rotifers (Figure 1) makes them ideal for some imaging studies but can pose challenges for automated analyses of swimming behavior that rely on background subtraction or binarization to identify individuals.

Previous aquaculture and toxicology research has integrated behavior analysis with studies of survival, growth and reproduction of rotifers [10,14–20]. Studies have shown that rotifer swimming behavior is affected by environmental conditions such as pH, temperature, levels of un-ionized ammonia [21,22], food availability [21,23,24], conspecific density [24], light [25], oxygen availability [26], presence of mating stimulus [27] and female biomass [22]. However, these studies often do not provide methods detailed enough to allow replication, or they use labor-intensive manual techniques or costly software. Recent studies using cost-effective and automated tracking analysis for rotifer swimming behavior have employed open-source software [28,29], but require substantial coding experience and do not resolve issues with accurate tagging of egg-bearing females or with loss of individual tracking due to collisions between animals. Free, open-source, automated software that addresses these issues in rotifers could be applied to other microfaunal species that have similar challenges in tracking experiments.

Despite the growing number of tracking applications and algorithms available, users still face issues when determining the best analysis option. The recent review by Panadeiro et al. provides a thorough overview of several popular and powerful behavior analysis software packages [30]. Some applications limit the number of individuals that can be tracked simultaneously, have high computational requirements, or require programing or high-level, domain-specific knowledge. Others offer a wide range of features and high performance but at high cost. Some software is designed to track only specific species or is optimized for tracking multiple points on larger or limbed animals. Many applications require image background subtraction or binarization but lack identity preservation. Methods that rely on high contrast between the background and target may not be effective for some small or transparent microfauna species. Additional software or R packages capable of tracking particles in a video but not necessarily designed for animal behavior (e.g., TRACKDEM [31] and TrackMate [32]) are available but can also face similar limitations.

The protocol developed in this study is intended to provide a cost-efficient, user-friendly method for analyzing behavior of microfauna, such as the rotifer B. manjavacas. To determine the effectiveness of this method, we compared the swimming behavior of B. manjavacas supplemented with either the mitochondrial enhancer, elamipretide, or the mitochondrial inhibitor, rotenone. We expected these drugs to alter swimming behavior via changes in mitochondrial efficiency and homeostasis. Elamipretide (also known as SS-31, MTP-131, or D-Arg-Dmt-Lys-Phe-NH2) is a cell-permeable, mitochondrial-targeted peptide known to improve mitochondrial function by eliminating reactive oxygen species, improving membrane stability, membrane potential and cellular respiration, and increasing ATP production [33,34]. Rotenone is a naturally occurring complex ketone known to be a highly selective inhibitor of complex I in the mitochondrial electron transport chain, causing a decline in ATP production and promoting the formation of reactive oxygen species [35,36]. Both elamipretide and rotenone affect lifespan, reproduction, heat stress resistance and phototaxis in B. manjavacas [GRIBBLE KE, UNPUBLISHED DATA].

The protocol developed here can be used to measure swimming speed and travelling distance, and to quantify behavioral metrics such as swimming efficiency and directional behavior of individuals. This user-friendly method can easily be applied to studies of a variety of microfaunal species.

Materials & methods

Culture & experimental conditions

Brachionus manjavacas (L5 strain) was cultured in filter-sterilized 15 ppt Instant Ocean (IO) artificial seawater at 21°C on a 12:12 h light:dark cycle. Rotifers were fed with the chlorophyte algae Tetraselmis sueccica, which was maintained in bubbled f/2 medium [37,38] under the same temperature and light conditions.

On the day of experimentation, ten mature female rotifers were placed into 5 ml of T. suecica diluted to 6 × 105 cells/ml with IO in each well of a CytoOne® 6-well plate (USA Scientific, FL, USA). One well was used for each treatment: 25 μM elamipretide (MTP-131; Cayman Chemical, MI, USA), 0.1 μM rotenone (Sigma-Aldrich, MO, USA) and the 15 ppt IO control. Our preliminary experiments showed these drug concentrations to have positive and negative effects, respectively, on rotifer lifespan. Rotifers were exposed to these treatments for approximately 2 h before swimming behavior was recorded.

Video acquisition

After the 2-h incubation period, rotifers were transferred to new wells containing only filtered 15 ppt IO to stop drug exposure and to remove algae. Five rotifers in 80 μl of filtered 15 ppt IO were transferred to a microscope slide with a taped perimeter of 18 × 9.5 mm (Figure 2A). The taped perimeter allowed for swimming behavior to be observed within a confined area and supported a glass coverslip. The shallow well between the slide and the coverslip permitted free 2D movement of the rotifers, while preventing 3D movement out of the focal plane. After the coverslip was applied, rotifers were given 1 min to acclimate prior to recording a 30-s video. Videos were recorded at a frame rate of 30 frames per second using an iPhone 11 mounted on a LabCam™ Ultra Microscope Adapter (iDu Optics®, NY, USA) on a ZEISS Stemi 508 microscope (Carl Zeiss Microscopy, NY, USA) set to 10× total magnification (Figure 2B). iPhone auto-focus was disabled to ensure all videos were recorded using the same settings. This process was repeated twice so that a total of ten rotifers were recorded for each treatment. An additional 1-s video of a micrometer slide was recorded using the same microscope and video settings to allow calibration of the tracking measurements.

Tracking analysis

Prior to analysis, motion trackers were applied to each recorded rotifer using the free video editing software, HitFilm [39] (see detailed methods in Supplementary Data 1 & 2). Motion trackers are overlays created on a video or image sequence that track the location of a specified object in every frame or image, allowing users to then apply the recorded movement data to a target object, such as an image, mask, text, or animation. Adding a motion tracker to each rotifer allows tracking software to easily locate and follow individual animals through the entire video. Without motion trackers, other objects of similar size, shape and/or optical density (e.g., eggs attached to or dropped by tracked females) can be mistaken as separate items to be tracked (Figure 2D). In addition, motion trackers help to maintain individual identities when rotifers collide, cross paths, or contact the slide perimeter.

After adding motion trackers, we analyzed rotifer swimming speed and acquired tracking coordinates and visuals for each video using the free tracking software, ToxTrac (version 2.98) [40,41] (see detailed methods in Supplementary Data 1 & 3). ToxTrac provided data files for instantaneous speed, instantaneous acceleration and tracking coordinates for each frame, and produced overall trajectory visuals (e.g., path trajectory; Figure 3) for each individual. Measurements were calibrated in the instantaneous speed (‘Instant_Speed 1.txt’), instantaneous acceleration (‘Instant_Accel_1.txt’) and tracking coordinates (‘Tracking_RealSpace_1.txt’) files in R (version 4.2.3) [42] using the pixel-to-mm conversion factor obtained from a still image of a micrometer slide video in ImageJ (see detailed methods in Supplementary Information 4).

After calibration, mean instantaneous speed and acceleration were computed in R. The ‘trajr’ package in R [43] was used to analyze swimming behavior, including swimming efficiency, directional behavior, trajectory descriptors (distance and length) and movement time. Swimming efficiency was assessed using three indices: straightness, sinuosity and maximum expected displacement (Emax) of a trajectory. Straightness is the ratio of the distance between the first and final points in the trajectory to the length of the path traveled; a value of 1 indicates a perfectly straight path [44]. Sinuosity measures the amount of turning within a given trajectory length; a value of 0 indicates a perfectly straight path [45,46]. Emax of a trajectory can be used to determine whether the path was straighter (values approaching infinity) or more sinuous (values closer to zero) [47].

In addition to quantifying swimming efficiency, we also analyzed directional behavior by measuring directional change (DC) and turning angles between each video frame. DC is the angular change (in degrees) between each frame over time and can be used to determine the linearity and regularity of the path [48]. The mean of the DC can be used as an index for linearity of the path; a value of 0 indicates a completely linear path. The standard deviation of the directional change (SDDC) can be used as an index for regularity of the path; a value at 0 indicates a regular path [48]. These metrics differ from the efficiency indices because they incorporate the speed of change, indicating how frequently and how fast an animal changes direction. The turning angles, or step angles between successive frames, can be used to describe directional behavior, including the maximum or mean angular change (in radians) or directional tendency (clockwise or counter-clockwise) of an individual.

Additional behavioral metrics of interest included trajectory distances, trajectory lengths and percentage of movement time. Trajectory distance (i.e., displacement) is the straight-line distance between the first and final coordinates of each trajectory. Trajectory length provides the total distance traveled during the observation period. To calculate the percentage of time each rotifer spent moving, stationary time was calculated using the ‘TrajSpeedIntervals()’ function to determine the amount of time (in seconds) each rotifer was motionless (moving slower than 0.01 mm/s). A nonmoving reference speed of 0.01 mm/s, rather than 0 mm/s, was selected to take into account any noise from the video processing, which was detected when analyzing the ‘speed’ of a dead rotifer. The stationary times were then used to calculate the percentage of time rotifers spent moving during the videos (‘time moving’).

Statistical analysis

All tracking data were compiled into a single file for statistical analysis in R (Supplementary Data 4). Prior to significance testing, each behavior metric was analyzed for outliers using the Rosner’s test. If outliers were detected, the samples were removed from analysis for the specified behavior metric. Normality of the data was assessed using a Shapiro–Wilk test, then homogeneity of variance was checked using a Bartlett’s test. If the data met the assumptions of normality and homogeneity of variances (p > 0.05 for each test), a parametric one-way ANOVA test was conducted to detect significant differences between the control and each of the drug treatments. If these assumptions were not met, a non-parametric Kruskal–Wallis test was conducted. Where significant differences were found between the experimental treatments, a Tukey test (for parametric data) or Kruskal–Wallis multiple comparisons test (for nonparametric data) was used to correct for multiple comparisons and test for significant differences. Results were plotted using ‘ggplot2’ [49] and ‘ggpubr’ [50] packages in R.

Results & discussion

Here, we have developed a simple, user-friendly and economical start-to-finish protocol for the assessment of swimming behavior in microfauna using video acquisition with an inexpensive camera and microscope set-up, behavior quantification using the free software HitFilm and ToxTrac, and further behavioral and statistical analyses and plotting with R. An experiment in which we treated B. manjavacas with a mitochondrial enhancer (elamipretide) or inhibitor (rotenone) demonstrated this protocol to be effective in detecting drug-induced differences in swimming behavior.

In our proof-of-principle experiment of this method, we found that treatment with elamipretide or rotenone significantly affected some, but not all, facets of rotifer swimming behavior. Rotifers supplemented with elamipretide had significantly higher instantaneous speed, faster acceleration, more linear and straighter paths (values for sinuosity and mean DC closer to 0), longer trajectory lengths and higher movement time than did the control rotifers (Figure 4). Rotifers supplemented with rotenone had significantly lower instantaneous speed and shorter trajectory lengths than did the control rotifers (Figure 4). These patterns can be verified by viewing the full trajectory visual output provided by ToxTrac (Figure 3). No significant differences were detected in the other behavior metrics, possibly due to the small sample size (n = 10) used for each treatment.

When using this protocol, it is essential to understand which metrics best characterize a particular microfaunal species’ swimming behavior under a given set of conditions. For example, straightness is a reliable measure of efficiency for a directed walk, but not for random trajectories or for fast swimming organisms that can reach the boundaries of the video arena multiple times over the course of the observation period, as in our experiment [45]. This metric would be ideal for experiments that measure phototaxis, chemotaxis, or thermotaxis. Sinuosity, however, can be a reliable estimate of the tortuosity for random search paths. In addition, trajectory distance may not be a reliable estimator of behavior for fast-moving specimens confined within a chamber, as they are forced to stop or turn when encountering the perimeter of the visualized area. This can result in the specimen returning to the starting location instead of continuing its trajectory as might occur in an open arena.

ToxTrac was selected as the tracking software for this protocol because it was one of four free applications reviewed by Panadeiro et al. that provided innovative algorithms, useful features and user-friendly interfaces [30]. Of the four, ToxTrac had the most flexible tracking tools for trajectory generation, required substantially less processing time, provided advanced toolkits that allow nonprogrammers to analyze parameters (e.g., movement, time spent in selected areas, time spent moving) at an individual or population level, and had extra features to facilitate user experience or to add versatility [30]. No coding was required to obtain track information (x, y coordinates) and all code we used for postprocessing is provided in our Supplementary data to facilitate use of this protocol. ToxTrac is currently only supported by the Windows operating system, but a wrapper or virtual machine software can potentially be used to run ToxTrac on other platforms.

Using ToxTrac software with the addition of motion trackers applied via HitFilm enabled us to detect changes in rotifer swimming behavior in a short experiment with relatively small sample sizes (n = 10). HitFilm was one of the only free software packages that allowed for the easy application of motion trackers to the videos. HitFilm also retained the resolution of the original source video and had a semi-automated method for adding the trackers, so the user does not have to manually add the tracker to each frame of the video. In our case, it was critical to add motion trackers prior to analysis to ensure maintenance of individual identities. Without the motion trackers, the large, pigmented eggs carried by reproductive female rotifers were sometimes mistaken as separate or new specimens (Figure 2D). In addition, underfed rotifers lacking coloration in their digestive system can be lost due to their relative transparency. Collisions or overlap of rotifers can cause the software to identify multiple individuals as a single specimen, leading to instances of missing or incorrect trajectory data. In instances where these issues are not present, ToxTrac can be used without the addition of motion trackers to individuals.

One limitation of not relying solely on a binarization method to identify individuals is the lack of ability to collect phenotypic trait data (e.g., body size, shape) in an automated way. Body size and shape often correlate with animal movement, thus recording these phenotypic parameters can be important in some experiments. Given the low magnification used to obtain a large field of view and the issues of transparency and egg carrying in our current rotifer experiment, estimates of body size and shape from binarized or thresholded data would be imprecise; another method would need to be used to collect body phenotype measurements.

By incorporating the ‘trajr’ package in R with the tracking coordinates from ToxTrac, we were able to expand our understanding of the impact of mitochondrial drug treatments not only on rotifer speed and acceleration, but also on swimming efficiency, directional behavior, trajectory descriptions and movement time. These metrics can be changed or added as needed for a given experiment. For example, in this study we analyzed the mean of the turning angles, but additional analyses could be conducted using the turning angles output, such as comparing the maximum turning angle in each track, calculating the portion of turning angles greater or less than a specific angle, or computing the ratio of clockwise to counterclockwise turns within tracks.

Conclusion

Combining inexpensive video-acquisition methods with the currently free and open-sourced software HitFilm, ToxTrac, ImageJ and R for analysis provided an effective protocol for capturing and quantifying the swimming behavior of the microfaunal species, B. manjavacas. This protocol can be adapted for other organisms that face similar issues with obtaining swimming behavioral measurements due to their small size or transparency. In addition, it can be used for projects with limited funding, applied to videos taken with any imaging system or camera, and implemented without specialized coding expertise.

Future perspective

Microfaunal species are increasingly being used as laboratory experimental systems for studies relevant to human health and basic biology. Monitoring and measuring behavior in microfauna in tandem with biological or genetic studies is important for understanding the impact of any treatment or genetic modification. Acquiring behavioral information can be a laborious, expensive, or time-consuming part of an experiment, however. Therefore, a time-saving, cost-effective and user-friendly method is essential for scientists to implement this extra analysis. With the continuous expansion and improvement of software for analyzing behavior, we anticipate that additional research groups will implement swimming behavior analyses together with other biological studies to gain a more holistic understanding of the health, behavior and ecology of microfauna.

Supplementary Material

1 Supplement_protocol

4 Supplement_Code

2 Supplement_HitFilm_Tutorial

3 Supplement_ToxTrac_Tutorial

Financial disclosure

Financial and material support was received for this research by the National Institute of Aging (NIA grants R01AG076592 and R21AG067034 to KE Gribble). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Figure 1. Brachionus manjavacas L5 strain (A) neonate, (B) mature female and (C) egg-bearing female under the light microscope.

B. manjavacas is generally bilaterally symmetrical with a head, trunk and foot. One of the most distinctive features of rotifers is the apical ciliated corona, used for locomotion and feeding. A tail-like ‘foot’ is located posterior to the trunk and possesses separated toes, which can aid in attachment to surfaces but is not used for swimming. The lorica, or shell-like protective outer covering, is transparent. Any coloration (green, brown or reddish) is typically caused by pigments from ingested food and is thus transient.

Figure 2. Video collection set-up.

(A) Ideal taped perimeter measurements and orientation for the microscope slide used in the video collection when using (B) an iPhone 11 with a LabCam™ Ultra Microscope Adapter. (C) Resulting image of the rotifers at 10× magnification using the described set-up. (D) Example of issues with binarized tracking methods without motion tracking: corona, digestive system and egg appear as separate objects due to rotifers’ transparent bodies.

Figure 3. Trajectory projections from ToxTrac of rotifers swimming after treatment with mitochondrial inhibitor or enhancer drugs.

Example trajectories from rotifers (n = 5) supplemented with (A) rotenone, (B) 15 ppt Instant Ocean and (C) elamipretide. The graphical outputs (Trajectory.jpeg) provide a colored representation of the rotifer trajectories superimposed onto the arena picture in real scale (10× total magnification). Each color represents a separate individual rotifer’s swimming track over the course of the 30-s video.

Figure 4. Swimming behavior of Brachionus manjavacas treated with mitochondrial inhibitors or enhancers.

Both rotenone and elamipretide supplementation caused significant differences from the control in (A) instantaneous speed and (J) total length traveled. Only elamipretide supplementation caused significant differences in (B) acceleration, (D) sinuosity, (F) mean of directional changes and (K) percentage of time spent moving relative to the control. Neither drug caused a significant change in (C) straightness, (E) maximum expected displacement (Emax), (G) standard deviation of directional change, (H) average turning angle, or (I) distance from start and final point of the trajectory. Outliers were removed from (E) Emax and (K) time moving.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, stock ownership or options and expert testimony.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

No institutional review board approval was required for the use of the marine invertebrate Brachionus manjavacas (rotifers) in this study.

Supplementary data

Data files from ToxTrac and R, along with the R script used for this analysis, can be found at https://github.com/colmcmaken/Microfauna-Swimming-Behavior. A step-wise protocol can be found in the Supplementary data. The ToxTrac tutorial video can be found at https://drive.google.com/file/d/1nEHCW5XpdBXC-dv0QWBA8vZ3PeTxPIla/view?usp=sharing and the HitFilm tutorial video can be found at https://drive.google.com/file/d/1Y-bCoPxCzCHsbyS64t4Q-dWRUGYIfbCt/view?usp=sharing. To view the supplementary data that accompany this paper please visit the journal website at: www.future-science.com/doi/suppl/10.2144/btn-2024-0001
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