Tracktor: Image‐based automated tracking of animal movement and behaviour

Topic: Arthropod Biodiversity: Ecological and Functional Aspects

Invertebrate animals with a segmented body, exoskeleton, and articulated appendages represent the largest phylum in the animal kingdom, Arthropoda, and account for over 80% of all known living species [...].

Trackoscope: A low-cost, open, autonomous tracking microscope for long-term observations of microscale organisms

Cells and microorganisms are motile, yet the stationary nature of conventional microscopes impedes comprehensive, long-term behavioral and biomechanical analysis. The limitations are twofold: a narrow focus permits high-resolution imaging but sacrifices the broader context of organism behavior, while a wider focus compromises microscopic detail. This trade-off is especially problematic when investigating rapidly motile ciliates, which often have to be confined to small volumes between coverslips affecting their natural behavior. To address this challenge, we introduce Trackoscope, a 2-axis autonomous tracking microscope designed to follow swimming organisms ranging from 10μm to 2mm across a 325cm2 area (equivalent to an A5 sheet) for extended durations-ranging from hours to days-at high resolution. Utilizing Trackoscope, we captured a diverse array of behaviors, from the air-water swimming locomotion of Amoeba to bacterial hunting dynamics in Actinosphaerium, walking gait in Tardigrada, and binary fission in motile Blepharisma. Trackoscope is a cost-effective solution well-suited for diverse settings, from high school labs to resource-constrained research environments. Its capability to capture diverse behaviors in larger, more realistic ecosystems extends our understanding of the physics of living systems. The low-cost, open architecture democratizes scientific discovery, offering a dynamic window into the lives of previously inaccessible small aquatic organisms.

Metabolic plasticity drives mismatches in physiological traits between prey and predator

Metabolic rate, the rate of energy use, underpins key ecological traits of organisms, from development and locomotion to interaction rates between individuals. In a warming world, the temperature-dependence of metabolic rate is anticipated to shift predator-prey dynamics. Yet, there is little real-world evidence on the effects of warming on trophic interactions. We measured the respiration rates of aquatic larvae of three insect species from populations experiencing a natural temperature gradient in a large-scale mesocosm experiment. Using a mechanistic model we predicted the effects of warming on these taxa's predator-prey interaction rates. We found that species-specific differences in metabolic plasticity lead to mismatches in the temperature-dependence of their relative velocities, resulting in altered predator-prey interaction rates. This study underscores the role of metabolic plasticity at the species level in modifying trophic interactions and proposes a mechanistic modelling approach that allows an efficient, high-throughput estimation of climate change threats across species pairs.

Thermal constraints on exercise and metabolic performance do not explain the use of dormancy as an overwintering strategy in the cunner (Tautogolabrus adspersus)

Winter cold slows ectotherm physiology, potentially constraining activities and ecological opportunities at poleward latitudes. Yet, many fishes are winter-active, facilitated by thermal compensation that improves cold performance. Conversely, winter-dormant fishes (e.g. cunner, Tautogolabrus adspersus) become inactive and non-feeding overwinter. Why are certain fishes winter-dormant? We hypothesized that winter dormancy is an adaptive behavioural response arising in poleward species that tolerate severe, uncompensated constraints of cold on their physiological performance. We predicted that below their dormancy threshold of 7--8°C, exercise and metabolic performance of cunner are greatly decreased, even after acclimation (i.e. shows above-normal, uncompensated thermal sensitivity, Q10>1-3). We measured multiple key performance metrics (e.g. C-start maximum velocity, chase swimming speed, aerobic scope) in cunner after acute exposure to 26-2°C (3°C intervals using 14°C-acclimated fish) or acclimation (5-8 weeks) to 14-2°C (3°C intervals bracketing the dormancy threshold). Performance declined with cooling, and the acute Q10 of all six performance rate metrics was significantly greater below the dormancy threshold temperature (Q10,acute8-2°C=1.5-4.9, mean=3.3) than above (Q10,acute14-8°C=1.1-1.9, mean=1.5), inferring a cold constraint. However, 2°C acclimation (temporally more relevant to seasonal cooling) improved performance, abolishing the acute constraint (Q10,acclimated8-2°C=1.4-3.0, mean=2.0; also cf. Q10,acclimated14-8°C=1.2-2.9, mean=1.7). Thus, dormant cunner show partial cold-compensation of exercise and metabolic performance, similar to winter-active species. However, responsiveness to C-start stimuli was greatly cold-constrained even following acclimation, suggesting dormancy involves sensory limitation. Thermal constraints on metabolic and exercise physiology are not significant drivers of winter dormancy in cunner. In fact, compensatory plasticity at frigid temperatures is retained even in a dormant fish.

Toward Real-Time Animal Tracking with Integrated Stimulus Control for Automated Conditioning in Aquatic Eco-Neurotoxicology

Aquatic eco-neurotoxicology is an emerging field that requires new analytical systems to study the effects of pollutants on animal behaviors. This is especially true if we are to gain insights into one of the least studied aspects: the potential perturbations that neurotoxicants can have on cognitive behaviors. The paucity of experimental data is partly caused by a lack of low-cost technologies for the analysis of higher-level neurological functions (e.g., associative learning) in small aquatic organisms. Here, we present a proof-of-concept prototype that utilizes a new real-time animal tracking software for on-the-fly video analysis and closed-loop, external hardware communications to deliver stimuli based on specific behaviors in aquatic organisms, spanning three animal phyla: chordates (fish, frog), platyhelminthes (flatworm), and arthropods (crustacean). The system's open-source software features an intuitive graphical user interface and advanced adaptive threshold-based image segmentation for precise animal detection. We demonstrate the precision of animal tracking across multiple aquatic species with varying modes of locomotion. The presented technology interfaces easily with low-cost and open-source hardware such as the Arduino microcontroller family for closed-loop stimuli control. The new system has potential future applications in eco-neurotoxicology, where it could enable new opportunities for cognitive research in diverse small aquatic model organisms.

The effects of ocean acidification on fishes - history and future outlook

The effects of increased levels of carbon dioxide (CO2 ) on the Earth's temperature have been known since the end of the 19th century. It was long believed that the oceans' buffering capacity would counteract any effects of dissolved CO2 in marine environments, but during recent decades, many studies have reported detrimental effects of ocean acidification on aquatic organisms. The most prominent effects can be found within the field of behavioural ecology, e.g., complete reversal of predator avoidance behaviour in CO2 -exposed coral reef fish. Some of the studies have been very influential, receiving hundreds of citations over recent years. The results have also been conveyed to policymakers and publicized in prominent media outlets for the general public. Those extreme effects of ocean acidification on fish behaviour have, however, spurred controversy, given that more than a century of research suggests that there are few or no negative effects of elevated CO2 on fish physiology. This is due to sophisticated acid-base regulatory mechanisms that should enable their resilience to near-future increases in CO2 . In addition, an extreme "decline effect" has recently been shown in the literature regarding ocean acidification and fish behaviour, and independent research groups have been unable to replicate some of the most profound effects. Here, the author presents a brief historical overview on the effects of elevated CO2 and ocean acidification on fishes. This historical recap is warranted because earlier work, prior to a recent (c. 10 year) explosion in interest, is often overlooked in today's ocean acidification studies, despite its value to the field. Based on the historical data and the current knowledge status, the author suggests future strategies with the aim to improve research rigour and clarify the understanding of the effects of ocean acidification on fishes.

Assessing learning in mosquito larvae using video-tracking

Mosquito larvae display a stereotyped escape response when they rest attached to the water surface. It consists in detaching from the surface and diving, to return to the surface after a brief time. It has been shown that this response can be evoked several times, by repeatedly presenting a moving shadow. Diving triggered by a potential danger revealed as a simple bioassay for investigating behavioural responses in mosquito larvae, in particular their ability to learn. In the present work, we describe an automated system, based on video-tracking individuals, and extracting quantitative data of their movements. We validated our system, by reinvestigating the habituation response of larvae of Aedes aegypti reared in the laboratory, and providing original data on field-collected larvae of genera Culex and Anopheles. Habituation could be demonstrated to occur in all the species, even though it was not possible to induce dishabituation in Culex and Anopheles mosquitoes. In addition to non-associative learning, we characterised motor activity in the studied species, thanks to the possibility offered by the tracking system to extract multiple variables. The here-described system and algorithms can be easily adapted to multiple experimental situations and variables of interest.

Acute Stress Modulates Social Approach and Social Maintenance in Adult Zebrafish

Stress alters social functioning in a complex manner. An important variable determining the final effects of stress is stressor intensity. However, the precise relationship between stressor intensity and social behavior is not well understood. Here, we investigate the effects of varying acute stressor intensity exposure on social behavior using adult zebrafish. We first establish a novel test using adult zebrafish that allows distinguishing fish's drive to approach a social cue and its ability to engage and maintain social interaction within the same behavioral paradigm. Next, we combined this test with a new method to deliver an acute stress stimulus of varying intensities. Our results show that both social approach and social maintenance are reduced in adult zebrafish on acute stress exposure in an intensity-dependent manner. Interestingly, lower stress intensity reduces social maintenance without affecting the social approach, while a higher stress level is required to alter social approach. These results provide evidence for a direct correlation between acute stressor intensity and social functioning and suggest that distinct steps in social behavior are modulated differentially by the acute stress level.

Improving behavioral test data collection and analysis in animal models with an image processing program

Behavioral studies are commonly used as a standard procedure to evaluate anxiety and depression in animal models. Recently, different methods have been developed to improve data collection and analysis of the behavioral tests. Currently available methods, including manual analysis and commercially available products, are either time-consuming or costly. The objective of this study was to improve the collection and analysis of behavioral test data in animal models by developing an image processing program. Eleven behavioral parameters were evaluated by three different methods, including (i) manual detection, (ii) commercially available TopScan software (CleverSys Inc, USA), and (iii) In-housed-developed Advanced Move Tracker (AMT) software. Results obtained from different methods were compared to validate the accuracy and efficiency of AMT. Results showed that AMT software provides highly accurate and reliable data analysis compared to other methods. Less than 5% tolerance was reported between results obtained from AMT compared to TopScan. In addition, the analysis processing time was remarkably reduced (68.3%) by using AMT compared to manual detection. Overall, the findings confirmed that AMT is an efficient program for automated data analysis, significantly enhancing research outcomes through accurate analysis of behavioral test data in animal models.

Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings

Aerial imagery and video recordings of animals are used for many areas of research such as animal behaviour, behavioural neuroscience and field biology. Many automated methods are being developed to extract data from such high-resolution videos. Most of the available tools are developed for videos taken under idealised laboratory conditions. Therefore, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. Methods that are useful for field conditions are often difficult to implement and thus remain inaccessible to empirical researchers. To address this gap, we present an open-source package called Multi-Object Tracking in Heterogeneous environments (MOTHe), a Python-based application that uses a basic convolutional neural network for object detection. MOTHe offers a graphical interface to automate the various steps related to animal tracking such as training data generation, animal detection in complex backgrounds and visually tracking animals in the videos. Users can also generate training data and train a new model which can be used for object detection tasks for a completely new dataset. MOTHe doesn't require any sophisticated infrastructure and can be run on basic desktop computing units. We demonstrate MOTHe on six video clips in varying background conditions. These videos are from two species in their natural habitat-wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track individuals in all these videos. MOTHe is available as an open-source GitHub repository with a detailed user guide and demonstrations at: https://github.com/tee-lab/MOTHe-GUI.

Supervised machine learning aided behavior classification in pigeons

Manual behavioral observations have been applied in both environment and laboratory experiments in order to analyze and quantify animal movement and behavior. Although these observations contributed tremendously to ecological and neuroscientific disciplines, there have been challenges and disadvantages following in their footsteps. They are not only time-consuming, labor-intensive, and error-prone but they can also be subjective, which induces further difficulties in reproducing the results. Therefore, there is an ongoing endeavor towards automated behavioral analysis, which has also paved the way for open-source software approaches. Even though these approaches theoretically can be applied to different animal groups, the current applications are mostly focused on mammals, especially rodents. However, extending those applications to other vertebrates, such as birds, is advisable not only for extending species-specific knowledge but also for contributing to the larger evolutionary picture and the role of behavior within. Here we present an open-source software package as a possible initiation of bird behavior classification. It can analyze pose-estimation data generated by established deep-learning-based pose-estimation tools such as DeepLabCut for building supervised machine learning predictive classifiers for pigeon behaviors, which can be broadened to support other bird species as well. We show that by training different machine learning and deep learning architectures using multivariate time series data as input, an F1 score of 0.874 can be achieved for a set of seven distinct behaviors. In addition, an algorithm for further tuning the bias of the predictions towards either precision or recall is introduced, which allows tailoring the classifier to specific needs.

BatCount: A software program to count moving animals

One of the biggest challenges with species conservation is collecting accurate and efficient information on population sizes, especially from species that are difficult to count. Bats worldwide are declining due to disease, habitat destruction, and climate change, and many species lack reliable population information to guide management decisions. Current approaches for estimating population sizes of bats in densely occupied colonies are time-intensive, may negatively impact the population due to disturbance, and/or have low accuracy. Research-based video tracking options are rarely used by conservation or management agencies for animal counting due to the perceived training and elevated operating costs. In this paper, we present BatCount, a free software program created in direct consultation with end-users designed to automatically count bats emerging from cave roosts (historical populations 20,000-250,000) with a streamlined and user-friendly interface. We report on the software package and provide performance metrics for different recording habitat conditions. Our analysis demonstrates that BatCount is an efficient and reliable option for counting bats in flight, with performance hundreds of times faster than manual counting, and has important implications for range- and species-wide population monitoring. Furthermore, this software can be extended to count any organisms moving across a camera including birds, mammals, fish or insects.

Machine learning based assessment of small-bodied fish tracking to evaluate spoiler baffle fish passage design

Fish passage research is important to mitigate the adverse effects of fragmented river habitats caused by waterway structures. The scale at which this research is undertaken varies from small-scale laboratory prototype studies to in-situ observations at various fish passage structures and bottlenecks. Using DeepLabCut, we introduce and evaluate a machine learning based workflow to track small-bodied fish in order to facilitate improved fish passage management. We specifically studied the behaviour and kinematics of Galaxias maculatus, a widespread diadromous Southern Hemisphere fish species. Upstream fish passage was studied in the presence of three different patches of spoiler baffles at an average water velocity of 0.4 m/s. In semi-supervised mode, the fish locations were extracted, and fish behaviour, such as swimming pathways and resting locations, was analysed based on extracted positions and recorded kinematic parameters. Individual fish behaviour and kinematic parameters were then used to assess the suitability of the three different spoiler baffle designs for enhancing fish passage. Using this technique, we were able to demonstrate where different spoiler baffle configurations resulted in significant differences in fish passage success and behaviour. For example, medium-spaced smaller baffles provided more accessible and uniform resting locations, which were required for efficient upstream passage. Results are discussed in relation to fish passage management at small instream structures.

Exploiting animal personality to reduce chronic stress in captive fish populations

Chronic stress is a major source of welfare problems in many captive populations, including fishes. While we have long known that chronic stress effects arise from maladaptive expression of acute stress response pathways, predicting where and when problems will arise is difficult. Here we highlight how insights from animal personality research could be useful in this regard. Since behavior is the first line of organismal defense when challenged by a stressor, assays of shy-bold type personality variation can provide information about individual stress response that is expected to predict susceptibility to chronic stress. Moreover, recent demonstrations that among-individual differences in stress-related physiology and behaviors are underpinned by genetic factors means that selection on behavioral biomarkers could offer a route to genetic improvement of welfare outcomes in captive fish stocks. Here we review the evidence in support of this proposition, identify remaining empirical gaps in our understanding, and set out appropriate criteria to guide development of biomarkers. The article is largely prospective: fundamental research into fish personality shows how behavioral biomarkers could be used to achieve welfare gains in captive fish populations. However, translating potential to actual gains will require an interdisciplinary approach that integrates the expertise and viewpoints of researchers working across animal behavior, genetics, and welfare science.

Experimental identification of individual insect visual tracking delays in free flight and their effects on visual swarm patterns

Insects are model systems for swarming robotic agents, yet engineered descriptions do not fully explain the mechanisms by which they provide onboard sensing and feedback to support such motions; in particular, the exact value and population distribution of visuomotor processing delays are not yet quantified, nor the effect of such delays on a visually-interconnected swarm. This study measures untethered insects performing a solo in-flight visual tracking task and applies system identification techniques to build an experimentally-consistent model of the visual tracking behaviors, and then integrates the measured experimental delay and its variation into a visually interconnected swarm model to develop theoretical and simulated solutions and stability limits. The experimental techniques include the development of a moving visual stimulus and real-time multi camera based tracking system called VISIONS (Visual Input System Identification from Outputs of Naturalistic Swarms) providing the capability to recognize and simultaneously track both a visual stimulus (input) and an insect at a frame rate of 60-120 Hz. A frequency domain analysis of honeybee tracking trajectories is conducted via fast Fourier and Chirp Z transforms, identifying a coherent linear region and its model structure. The model output is compared in time and frequency domain simulations. The experimentally measured delays are then related to probability density functions, and both the measured delays and their distribution are incorporated as inter-agent interaction delays in a second order swarming dynamics model. Linear stability and bifurcation analysis on the long range asymptotic behavior is used to identify delay distributions leading to a family of solutions with stable and unstable swarm center of mass (barycenter) locations. Numerical simulations are used to verify these results with both continuous and measured distributions. The results of this experiment quantify a model structure and temporal lag (transport delay) in the closed loop dynamics, and show that this delay varies across 50 individuals from 5-110ms, with an average delay of 22ms and a standard deviation of 40ms. When analyzed within the swarm model, the measured delays support a diversity of solutions and indicate an unstable barycenter.

Microengineered devices enable long-term imaging of the ventral nerve cord in behaving adult Drosophila

The dynamics and connectivity of neural circuits continuously change on timescales ranging from milliseconds to an animal's lifetime. Therefore, to understand biological networks, minimally invasive methods are required to repeatedly record them in behaving animals. Here we describe a suite of devices that enable long-term optical recordings of the adult Drosophila melanogaster ventral nerve cord (VNC). These consist of transparent, numbered windows to replace thoracic exoskeleton, compliant implants to displace internal organs, a precision arm to assist implantation, and a hinged stage to repeatedly tether flies. To validate and illustrate our toolkit we (i) show minimal impact on animal behavior and survival, (ii) follow the degradation of chordotonal organ mechanosensory nerve terminals over weeks after leg amputation, and (iii) uncover waves of neural activity caffeine ingestion. Thus, our long-term imaging toolkit opens up the investigation of premotor and motor circuit adaptations in response to injury, drug ingestion, aging, learning, and disease.

Frontiers in quantifying wildlife behavioural responses to chemical pollution

Animal behaviour is remarkably sensitive to disruption by chemical pollution, with widespread implications for ecological and evolutionary processes in contaminated wildlife populations. However, conventional approaches applied to study the impacts of chemical pollutants on wildlife behaviour seldom address the complexity of natural environments in which contamination occurs. The aim of this review is to guide the rapidly developing field of behavioural ecotoxicology towards increased environmental realism, ecological complexity, and mechanistic understanding. We identify research areas in ecology that to date have been largely overlooked within behavioural ecotoxicology but which promise to yield valuable insights, including within- and among-individual variation, social networks and collective behaviour, and multi-stressor interactions. Further, we feature methodological and technological innovations that enable the collection of data on pollutant-induced behavioural changes at an unprecedented resolution and scale in the laboratory and the field. In an era of rapid environmental change, there is an urgent need to advance our understanding of the real-world impacts of chemical pollution on wildlife behaviour. This review therefore provides a roadmap of the major outstanding questions in behavioural ecotoxicology and highlights the need for increased cross-talk with other disciplines in order to find the answers.

Detection of Key Points in Mice at Different Scales via Convolutional Neural Network

In this work, we propose a symmetry approach and design a convolutional neural network for mouse pose estimation under scale variation. The backbone adopts the UNet structure, uses the residual network to extract features, and adds the ASPP module into the appropriate residual units to expand the perceptual field, and uses the deep and shallow feature fusion to fuse and process the features at multiple scales to capture the various spatial relationships related to body parts to improve the recognition accuracy of the model. Finally, a set of prediction results based on heat map and coordinate offset is generated. We used our own built mouse dataset and obtained state-of-the-art results on the dataset.

Voltage-gated sodium channel scn8a is required for innervation and regeneration of amputated adult zebrafish fins

Teleost fishes and urodele amphibians can regenerate amputated appendages, whereas this ability is restricted to digit tips in adult mammals. One key component of appendage regeneration is reinnervation of the wound area. However, how innervation is regulated in injured appendages of adult vertebrates has seen limited research attention. From a forward genetics screen for temperature-sensitive defects in zebrafish fin regeneration, we identified a mutation that disrupted regeneration while also inducing paralysis at the restrictive temperature. Genetic mapping and complementation tests identify a mutation in the major neuronal voltage-gated sodium channel (VGSC) gene scn8ab. Conditional disruption of scn8ab impairs early regenerative events, including blastema formation, but does not affect morphogenesis of established regenerates. Whereas scn8ab mutations reduced neural activity as expected, they also disrupted axon regrowth and patterning in fin regenerates, resulting in hypoinnervation. Our findings indicate that the activity of VGSCs plays a proregenerative role by promoting innervation of appendage stumps.

Collection and Processing of Behavioural Data of the Olive Fruit Fly, Bactrocera oleae, When Exposed to Olive Twigs Treated with Different Commercial Products

The need for the development of sustainable control methods of herbivorous insects implies that new molecules are proposed on the market. Among the different effects the new products may have on the target species, the alteration of insect oviposition behaviour might be considered. At the scope, parallel simple behavioural assays can be conducted in arena. Freely available software can be used to track observed events, but they often need intensive customization to the specific experimental design. Hence, integrating such software with, e.g., R environment, can provide a much more effective protocol development for data collection and analysis. Here we present a dataset and protocol for processing data of the oviposition behaviour of the olive fruit fly, Bactrocera oleae, when exposed to olive twigs treated with different commercial products. Treatments were rock powder, propolis, a mixture of rock powder and propolis, copper oxychloride, copper sulphate, and water as the experimental control. JWatcher was used to simultaneously collect data from 12 arena assays and ad-hoc developed R code was used to process raw data for data analyses. The procedure described here is novel and represents a valuable and transferable protocol to analyse observational events in B. oleae, as well as other biological systems.

Kinematic analysis of social interactions deconstructs the evolved loss of schooling behavior in cavefish

Fish display a remarkable diversity of social behaviors, both within and between species. While social behaviors are likely critical for survival, surprisingly little is known about how they evolve in response to changing environmental pressures. With its highly social surface form and multiple populations of a largely asocial, blind, cave-dwelling form, the Mexican tetra, Astyanax mexicanus, provides a powerful model to study the evolution of social behavior. Here we use motion tracking and analysis of swimming kinematics to quantify social swimming in four Astyanax mexicanus populations. In the light, surface fish school, maintaining both close proximity and alignment with each other. In the dark, surface fish no longer form coherent schools, however, they still show evidence of an attempt to align and maintain proximity when they find themselves near another fish. In contrast, cavefish from three independently-evolved populations (Pachón, Molino, Tinaja) show little preference for proximity or alignment, instead exhibiting behaviors that suggest active avoidance of each other. Two of the three cave populations we studied also slow down when more fish are present in the tank, a behavior which is not observed in surface fish in light or the dark, suggesting divergent responses to conspecifics. Using data-driven computer simulations, we show that the observed reduction in swimming speed is sufficient to alter the way fish explore their environment: it can increase time spent exploring away from the walls. Thus, the absence of schooling in cavefish is not merely a consequence of their inability to see, but may rather be a genuine behavioral adaptation that impacts the way they explore their environment.

Paths towards greater consensus building in experimental biology

In a recent editorial, the Editors-in-Chief of Journal of Experimental Biology argued that consensus building, data sharing, and better integration across disciplines are needed to address the urgent scientific challenges posed by climate change. We agree and expand on the importance of cross-disciplinary integration and transparency to improve consensus building and advance climate change research in experimental biology. We investigated reproducible research practices in experimental biology through a review of open data and analysis code associated with empirical studies on three debated paradigms and for unrelated studies published in leading journals in comparative physiology and behavioural ecology over the last 10 years. Nineteen per cent of studies on the three paradigms had open data, and 3.2% had open code. Similarly, 12.1% of studies in the journals we examined had open data, and 3.1% had open code. Previous research indicates that only 50% of shared datasets are complete and re-usable, suggesting that fewer than 10% of studies in experimental biology have usable open data. Encouragingly, our results indicate that reproducible research practices are increasing over time, with data sharing rates in some journals reaching 75% in recent years. Rigorous empirical research in experimental biology is key to understanding the mechanisms by which climate change affects organisms, and ultimately promotes evidence-based conservation policy and practice. We argue that a greater adoption of open science practices, with a particular focus on FAIR (Findable, Accessible, Interoperable, Re-usable) data and code, represents a much-needed paradigm shift towards improved transparency, cross-disciplinary integration, and consensus building to maximize the contributions of experimental biologists in addressing the impacts of environmental change on living organisms.

Siamese Network-Based All-Purpose-Tracker, a Model-Free Deep Learning Tool for Animal Behavioral Tracking

Accurate tracking is the basis of behavioral analysis, an important research method in neuroscience and many other fields. However, the currently available tracking methods have limitations. Traditional computer vision methods have problems in complex environments, and deep learning methods are hard to be applied universally due to the requirement of laborious annotations. To address the trade-off between accuracy and universality, we developed an easy-to-use tracking tool, Siamese Network-based All-Purpose Tracker (SNAP-Tracker), a model-free tracking software built on the Siamese network. The pretrained Siamese network offers SNAP-Tracker a remarkable feature extraction ability to keep tracking accuracy, and the model-free design makes it usable directly before laborious annotations and network refinement. SNAP-Tracker provides a “tracking with detection” mode to track longer videos with an additional detection module. We demonstrate the stability of SNAP-Tracker through different experimental conditions and different tracking tasks. In short, SNAP-Tracker provides a general solution to behavioral tracking without compromising accuracy. For the user’s convenience, we have integrated the tool into a tidy graphic user interface and opened the source code for downloading and using (https://github.com/slh0302/SNAP).

Argos: A toolkit for tracking multiple animals in complex visual environments

Automatically tracking the positions of multiple animals is often necessary for studying behaviours. This task involves multiple object tracking, a challenging problem in computer vision. Recent advances in machine learning applied to video analysis have been helpful for animal tracking. However, existing tools work well only in homogeneous environments with uniform illumination, features rarely found in natural settings. Moreover, available algorithms cannot effectively process discontinuities in animal motion such as sudden jumps, thus requiring laborious manual review.Here we present Argos, a software toolkit for tracking multiple animals in inhomogeneous environments. Argos includes tools for compressing videos based on animal movement, for generating training sets for a convolutional neural network (CNN) to detect animals, for tracking multiple animals in a video and for facilitating review and correction of the tracks manually, with simple graphical user interfaces.We demonstrate that Argos can help reduce the amount of video data to be stored and analysed, speed up analysis and allow analysing difficult and ambiguous conditions in a scene.Thus, Argos supports multiple approaches to animal tracking suited for varying recording conditions and available computational resources. Together, these tools allow the recording and tracking of movements of multiple markerless animals in inhomogeneous environments over many hours.

A robust bitmap-based real-time position tracking algorithm for rats in radial arm maze tests

This paper aims to develop a position tracking algorithm by which a rat in a radial arm maze can be accurately located in real time. An infrared (IR) night-vision camera was hung above the maze to capture IR images of the rat. The IR images were binarized and then duplicated for subsequent intersection and opening operations. Due to simple operations and a high robustness against the noise spots formed by the droppings of the rat, it took just minutes to process more than 9000 frames, and an accuracy above 99% was reached as well. The maze was intruded by an experimenter to further test the robustness, and the accuracy slightly fell to 98%. For comparison purposes, the same experiments were carried out using a pre-trained YOLO v2 model. The YOLO counterpart gave an accuracy beyond 97% in the absence and in the presence of the intruder. In other words, this work slightly outperformed the YOLO counterpart in terms of the accuracy in both cases, which indicates the robustness of this work. However, it took the YOLO counterpart an hour or so to locate a rat contained in the frames, which highlights the contribution of this work.

The spontaneous location recognition task for assessing spatial pattern separation and memory across a delay in rats and mice

Keeping similar memories distinct from one another is a critical cognitive process without which we would have difficulty functioning in everyday life. Memories are thought to be kept distinct through the computational mechanism of pattern separation, which reduces overlap between similar input patterns to amplify differences among stored representations. At the behavioral level, impaired pattern separation has been shown to contribute to memory deficits seen in neuropsychiatric and neurodegenerative diseases, including Alzheimer's disease, and in normal aging. This protocol describes the use of the spontaneous location recognition (SLR) task in mice and rats to behaviorally assess spatial pattern separation ability. This two-phase spontaneous memory task assesses the extent to which animals can discriminate and remember object locations presented during the encoding phase. Using three configurations of the task, the similarity of the to-be-remembered locations can be parametrically manipulated by altering the spatial positions of objects-dissimilar, similar or extra similar-to vary the load on pattern separation. Unlike other pattern separation tasks, SLR varies the load on pattern separation during encoding, when pattern separation is thought to occur. Furthermore, SLR can be used in standard rodent behavioral facilities with basic expertise in rodent handling. The entire protocol takes ~20 d from habituation to testing of the animals on all three task configurations. By incorporating breaks between testing, and varying the objects used as landmarks, animals can be tested repeatedly, increasing experimental power by allowing for within-subjects manipulations.

LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals

Markerless three-dimensional (3D) pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D poses by multi-view triangulation of deep network-based two-dimensional (2D) pose estimates. However, triangulation requires multiple synchronized cameras and elaborate calibration protocols that hinder its widespread adoption in laboratory studies. Here we describe LiftPose3D, a deep network-based method that overcomes these barriers by reconstructing 3D poses from a single 2D camera view. We illustrate LiftPose3D's versatility by applying it to multiple experimental systems using flies, mice, rats and macaques, and in circumstances where 3D triangulation is impractical or impossible. Our framework achieves accurate lifting for stereotypical and nonstereotypical behaviors from different camera angles. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays and tedious calibration procedures and despite occluded body parts in freely behaving animals.

A review of 28 free animal-tracking software applications: current features and limitations

Well-quantified laboratory studies can provide a fundamental understanding of animal behavior in ecology, ethology and ecotoxicology research. These types of studies require observation and tracking of each animal in well-controlled and defined arenas, often for long timescales. Thus, these experiments produce long time series and a vast amount of data that require the use of software applications to automate the analysis and reduce manual annotation. In this review, we examine 28 free software applications for animal tracking to guide researchers in selecting the software that might best suit a particular experiment. We also review the algorithms in the tracking pipeline of the applications, explain how specific techniques can fit different experiments, and finally, expose each approach's weaknesses and strengths. Our in-depth review includes last update, type of platform, user-friendliness, off- or online video acquisition, calibration method, background subtraction and segmentation method, species, multiple arenas, multiple animals, identity preservation, manual identity correction, data analysis and extra features. We found, for example, that out of 28 programs, only 3 include a calibration algorithm to reduce image distortion and perspective problems that affect accuracy and can result in substantial errors when analyzing trajectories and extracting mobility or explored distance. In addition, only 4 programs can directly export in-depth tracking and analysis metrics, only 5 are suited for tracking multiple unmarked animals for more than a few seconds and only 11 have been updated in the period 2019-2021.

Estimating the effect of tracking tag weight on insect movement using video analysis: A case study with a flightless orthopteran

In this study, we describe an inexpensive and rapid method of using video analysis and identity tracking to measure the effects of tag weight on insect movement. In a laboratory experiment, we assessed the tag weight and associated context-dependent effects on movement, choosing temperature as a factor known to affect insect movement and behavior. We recorded the movements of groups of flightless adult crickets Gryllus locorojo (Orthoptera:Gryllidae) as affected by no tag (control); by light, medium, or heavy tags (198.7, 549.2, and 758.6 mg, respectively); and by low, intermediate, or high temperatures (19.5, 24.0, and 28.3°C, respectively). Each individual in each group was weighed before recording and was recorded for 3 consecutive days. The mean (± SD) tag mass expressed as a percentage of body mass before the first recording was 26.8 ± 3.7% with light tags, 72 ± 11.2% with medium tags, and 101.9 ± 13.5% with heavy tags. We found that the influence of tag weight strongly depended on temperature, and that the negative effects on movement generally increased with tag weight. At the low temperature, nearly all movement properties were negatively influenced. At the intermediate and high temperatures, the light and medium tags did not affect any of the movement properties. The continuous 3-day tag load reduced the average movement speed only for crickets with heavy tags. Based on our results, we recommend that researchers consider or investigate the possible effects of tags before conducting any experiment with tags in order to avoid obtaining biased results.

Male size and reproductive performance in three species of livebearing fishes (Gambusia spp.): A systematic review and meta-analysis

The genus Gambusia represents approximately 45 species of polyandrous livebearing fishes with reversed sexual size dimorphism (i.e. males smaller than females) and with copulation predominantly via male coercion. Male body size has been suggested as an important sexually selected trait, but despite abundant research, evidence for sexual selection on male body size in this genus is mixed. Studies have found that large males have an advantage in both male-male competition and female choice, but that small males perform sneaky copulations better and at higher frequency and thus may sire more offspring in this coercive mating system. Here, we synthesized this inconsistent body of evidence using pre-registered methods and hypotheses. We performed a systematic review and meta-analysis of summary and primary (raw) data combining both published (n = 19 studies, k = 106 effect sizes) and unpublished effect sizes (n = 17, k = 242) to test whether there is overall selection on male body size across studies in Gambusia. We also tested several specific hypotheses to understand the sources of heterogeneity across effects. Meta-analysis revealed an overall positive correlation between male size and reproductive performance (r = 0.23, 95% confidence interval: 0.10-0.35, n = 36, k = 348, 4,514 males, three Gambusia species). Despite high heterogeneity, the large-male advantage appeared robust across all measures studied (i.e. female choice, mating success, paternity, sperm quantity and quality), and was considerably larger for female choice (r = 0.43, 95% confidence interval: 0.28-0.59, n = 14, k = 43). Meta-regressions found several important factors explaining heterogeneity across effects, including type of sperm characteristic, male-to-female ratio, female reproductive status and environmental conditions. We found evidence of publication bias; however, its influence on our estimates was attenuated by including a substantial amount of unpublished effects, highlighting the importance of open primary data for more accurate meta-analytic estimates. In addition to positive selection on male size, our study suggests that we need to rethink the role and form of sexual selection in Gambusia and, more broadly, to consider the ecological factors that affect reproductive behaviour in livebearing fishes.

Ethoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis

Manual monitoring of animal behavior is time-consuming and prone to bias. An alternative to such limitations is using computational resources in behavioral assessments, such as tracking systems, to facilitate accurate and long-term evaluations. There is a demand for robust software that addresses analysis in heterogeneous environments (such as in field conditions) and evaluates multiple individuals in groups while maintaining their identities. The Ethoflow software was developed using computer vision and artificial intelligence (AI) tools to monitor various behavioral parameters automatically. An object detection algorithm based on instance segmentation was implemented, allowing behavior monitoring in the field under heterogeneous environments. Moreover, a convolutional neural network was implemented to assess complex behaviors expanding behavior analyses' possibilities. The heuristics used to generate training data for the AI models automatically are described, and the models trained with these datasets exhibited high accuracy in detecting individuals in heterogeneous environments and assessing complex behavior. Ethoflow was employed for kinematic assessments and to detect trophallaxis in social bees. The software was developed in desktop applications and had a graphical user interface. In the Ethoflow algorithm, the processing with AI is separate from the other modules, facilitating measurements on an ordinary computer and complex behavior assessing on machines with graphics processing units. Ethoflow is a useful support tool for applications in biology and related fields.

A distinct parabrachial-to-lateral hypothalamus circuit for motivational suppression of feeding by nociception

The motivation to eat is not only shaped by nutrition but also competed by external stimuli including pain. How the mouse hypothalamus, the feeding regulation center, integrates nociceptive inputs to modulate feeding is unclear. Within the key nociception relay center parabrachial nucleus (PBN), we demonstrated that neurons projecting to the lateral hypothalamus (^LHPBN) are nociceptive yet distinct from danger-encoding central amygdala-projecting (^CeAPBN) neurons. Activation of ^LHPBN strongly suppressed feeding by limiting eating frequency and also reduced motivation to work for food reward. Refined approach-avoidance paradigm revealed that suppression of ^LHPBN, but not ^CeAPBN, sustained motivation to obtain food. The effect of ^LHPBN neurons on feeding was reversed by suppressing downstream LH^VGluT2 neurons. Thus, distinct from a circuit for fear and escape responses, ^LHPBN neurons channel nociceptive signals to LH^VGluT2 neurons to suppress motivational drive for feeding. Our study provides a new perspective in understanding feeding regulation by external competing stimuli.

TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields

Automated visual tracking of animals is rapidly becoming an indispensable tool for the study of behavior. It offers a quantitative methodology by which organisms’ sensing and decision-making can be studied in a wide range of ecological contexts. Despite this, existing solutions tend to be challenging to deploy in practice, especially when considering long and/or high-resolution video-streams. Here, we present TRex, a fast and easy-to-use solution for tracking a large number of individuals simultaneously using background-subtraction with real-time (60 Hz) tracking performance for up to approximately 256 individuals and estimates 2D visual-fields, outlines, and head/rear of bilateral animals, both in open and closed-loop contexts. Additionally, TRex offers highly accurate, deep-learning-based visual identification of up to approximately 100 unmarked individuals, where it is between 2.5 and 46.7 times faster, and requires 2–10 times less memory, than comparable software (with relative performance increasing for more organisms/longer videos) and provides interactive data-exploration within an intuitive, platform-independent graphical user-interface.

Social modulation of individual preferences in cockroaches

In social species, decision-making is both influenced by, and in turn influences, the social context. This reciprocal feedback introduces coupling across scales, from the neural basis of sensing, to individual and collective decision-making. Here, we adopt an integrative approach investigating decision-making in dynamical social contexts. When choosing shelters, isolated cockroaches prefer vanillin-scented (food-associated) shelters over unscented ones, yet in groups, this preference is inverted. We demonstrate that this inversion can be replicated by replacing the full social context with social odors: presented alone food and social odors are attractive, yet when presented as a mixture they are avoided. Via antennal lobe calcium imaging, we show that neural activity in vanillin-responsive regions reduces as social odor concentration increases. Thus, we suggest that the mixture is evaluated as a distinct olfactory object with opposite valence, providing a mechanism that would naturally result in individuals avoiding what they perceive as recently exploited resources.

Quantifying behavior to understand the brain

Over the past years, numerous methods have emerged to automate the quantification of animal behavior at a resolution not previously imaginable. This has opened up a new field of computational ethology and will, in the near future, make it possible to quantify in near completeness what an animal is doing as it navigates its environment. The importance of improving the techniques with which we characterize behavior is reflected in the emerging recognition that understanding behavior is an essential (or even prerequisite) step to pursuing neuroscience questions. The use of these methods, however, is not limited to studying behavior in the wild or in strictly ethological settings. Modern tools for behavioral quantification can be applied to the full gamut of approaches that have historically been used to link brain to behavior, from psychophysics to cognitive tasks, augmenting those measurements with rich descriptions of how animals navigate those tasks. Here we review recent technical advances in quantifying behavior, particularly in methods for tracking animal motion and characterizing the structure of those dynamics. We discuss open challenges that remain for behavioral quantification and highlight promising future directions, with a strong emphasis on emerging approaches in deep learning, the core technology that has enabled the markedly rapid pace of progress of this field. We then discuss how quantitative descriptions of behavior can be leveraged to connect brain activity with animal movements, with the ultimate goal of resolving the relationship between neural circuits, cognitive processes and behavior.

anTraX, a software package for high-throughput video tracking of color-tagged insects

Recent years have seen a surge in methods to track and analyze animal behavior. Nevertheless, tracking individuals in closely interacting, group-living organisms remains a challenge. Here, we present anTraX, an algorithm and software package for high-throughput video tracking of color-tagged insects. anTraX combines neural network classification of animals with a novel approach for representing tracking data as a graph, enabling individual tracking even in cases where it is difficult to segment animals from one another, or where tags are obscured. The use of color tags, a well-established and robust method for marking individual insects in groups, relaxes requirements for image size and quality, and makes the software broadly applicable. anTraX is readily integrated into existing tools and methods for automated image analysis of behavior to further augment its output. anTraX can handle large-scale experiments with minimal human involvement, allowing researchers to simultaneously monitor many social groups over long time periods.

Metabolite Profiling of Javanese Ginger Zingiber purpureum and Identification of Antiseizure Metabolites via a Low-Cost Open-Source Zebrafish Bioassay-Guided Isolation

The rhizomes of Zingiber purpureum, "Bangle", were investigated for its antiseizure properties using a streamlined and cost-effective zebrafish screening strategy and a mouse epilepsy assay. Its hexane extract demonstrated strong antiseizure activity in zebrafish epilepsy assay and was, therefore, selected for bioactivity-guided fractionation. Twelve compounds (1-12) were isolated, and two bioactive phenylbutenoids, trans- (11) and cis-banglene (12), reduced up to 70% of pentylenetetrazole (PTZ)-induced seizures. These compounds showed moderate activity against PTZ-induced seizures in a mouse epilepsy assay. To understand the specificity of Z. purpureum active compounds, its chemical profile was compared to that of Z. officinale. Their composition was assessed by differential metabolite profiling visualized by a molecular network, which revealed only vanillin derivatives and terpenoids as common metabolites and gave a comprehensive view of Z. purpureum composition. This study demonstrates the efficacy of a streamlined zebrafish epilepsy assay, which is therefore suitable for routine screening in phytochemistry laboratories.

MARGO (Massively Automated Real-time GUI for Object-tracking), a platform for high-throughput ethology

Fast object tracking in real time allows convenient tracking of very large numbers of animals and closed-loop experiments that control stimuli for many animals in parallel. We developed MARGO, a MATLAB-based, real-time animal tracking suite for custom behavioral experiments. We demonstrated that MARGO can rapidly and accurately track large numbers of animals in parallel over very long timescales, typically when spatially separated such as in multiwell plates. We incorporated control of peripheral hardware, and implemented a flexible software architecture for defining new experimental routines. These features enable closed-loop delivery of stimuli to many individuals simultaneously. We highlight MARGO's ability to coordinate tracking and hardware control with two custom behavioral assays (measuring phototaxis and optomotor response) and one optogenetic operant conditioning assay. There are currently several open source animal trackers. MARGO's strengths are 1) fast and accurate tracking, 2) high throughput, 3) an accessible interface and data output and 4) real-time closed-loop hardware control for for sensory and optogenetic stimuli, all of which are optimized for large-scale experiments.