Wall-following - Phylogenetic context of an enhanced behaviour in stygomorphic Sinocyclocheilus (Cypriniformes: Cyprinidae) cavefishes.

With 75 known species, the freshwater fish genus Sinocyclocheilus is the largest cavefish radiation in the world and shows multiple adaptations for cave-dwelling (stygomorphic adaptations), which include a range of traits such as eye degeneration (normal-eyed, micro-eyed and eyeless), depigmentation of skin, and in some species, the presence of "horns". Their behavioural adaptations to subterranean environments, however, are poorly understood. Wall-following (WF) behaviour, where an organism remains in close contact with the boundary demarcating its habitat when in the dark, is a peculiar behaviour observed in a wide range of animals and is enhanced in cave dwellers. Hence, we hypothesise that wall-following is also present in Sinocyclocheilus, possibly enhanced in eyeless species compared to eye bearing (normal-/micro-eyed species). Using 13 species representative of Sinocyclocheilus radiation and eye morphs, we designed a series of assays, based on pre-existing methods for Astyanax mexicanus behavioural experiments, to examine wall-following behaviour under three conditions. Our results indicate that eyeless species exhibit significantly enhanced intensities of WF compared to normal-eyed species, with micro-eyed forms demonstrating intermediate intensities in the WF distance. Using a mtDNA based dated phylogeny (chronogram with four clades A-D), we traced the degree of WF of these forms to outline common patterns. We show that the intensity of WF behaviour is higher in the subterranean clades compared to clades dominated by normal-eyed free-living species. We also found that eyeless species are highly sensitive to vibrations, whereas normal-eyed species are the least sensitive. Since WF behaviour is presented to some degree in all Sinocyclocheilus species, and given that these fishes evolved in the late Miocene, we identify this behaviour as being ancestral with WF enhancement related to cave occupation. Results from this diversification-scale study of cavefish behaviour suggest that enhanced wall-following behaviour may be a convergent trait across all stygomorphic lineages.

Risk-sensitive response of soaring birds to crosswind over dangerous sea highlights age-specific differences in migratory performance.

Challenges imposed by geographical barriers during migration are selective agents for animals. Juvenile soaring landbirds often cross large water bodies along their migratory path, where they lack updraft support and are vulnerable to harsh weather. However, the consequences of inexperience in accomplishing these water crossings remain largely unquantified. To address this knowledge gap, we tracked the movements of juvenile and adult black kites Milvus migrans over the Strait of Gibraltar using high-frequency tracking devices in variable crosswind conditions. We found that juveniles crossed under higher crosswind speeds and at wider sections of the strait compared with adults during easterly winds, which represent a high risk owing to their high speed and steady direction towards the Atlantic Ocean. Juveniles also drifted extensively with easterly winds, contrasting with adults who strongly compensated for lateral displacement through flapping. Age differences were inconspicuous during winds with a west crosswind speed component, as well as for airspeed modulation in all wind conditions. We suggest that the suboptimal sea-crossing behaviour of juvenile black kites may impact their survival rates, either by increasing chances of drowning owing to exhaustion or by depleting critical energy reserves needed to accomplish their first migration.

Learning Rich Feature Representation and State Change Monitoring for Accurate Animal Target Tracking.

Animal tracking is crucial for understanding migration, habitat selection, and behavior patterns. However, challenges in video data acquisition and the unpredictability of animal movements have hindered progress in this field. To address these challenges, we present a novel animal tracking method based on correlation filters. Our approach integrates hand-crafted features, deep features, and temporal context information to learn a rich feature representation of the target animal, enabling effective monitoring and updating of its state. Specifically, we extract hand-crafted histogram of oriented gradient features and deep features from different layers of the animal, creating tailored fusion features that encapsulate both appearance and motion characteristics. By analyzing the response map, we select optimal fusion features based on the oscillation degree. When the target animal's state changes significantly, we adaptively update the target model using temporal context information and robust feature data from the current frame. This updated model is then used for re-tracking, leading to improved results compared to recent mainstream algorithms, as demonstrated in extensive experiments conducted on our self-constructed animal datasets. By addressing specific challenges in animal tracking, our method offers a promising approach for more effective and accurate animal behavior research.

Nature 4.0: A networked sensor system for integrated biodiversity monitoring.

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade-off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real-world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low-cost, and open-source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area-wide ecosystem mapping tasks, thereby providing an exemplary cost-efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services.

DAMM for the detection and tracking of multiple animals within complex social and environmental settings.

Accurate detection and tracking of animals across diverse environments are crucial for behavioral studies in various disciplines, including neuroscience. Recently, machine learning and computer vision techniques have become integral to the neuroscientist's toolkit, enabling high-throughput behavioral studies. Despite advancements in localizing individual animals in simple environments, the task remains challenging in complex conditions due to intra-class visual variability and environmental diversity. These limitations hinder studies in ethologically-relevant conditions, such as when animals are concealed within nests or in obscured environments. Moreover, current tools are laborious and time-consuming to employ, requiring extensive, setup-specific annotation and model training/validation procedures. To address these challenges, we introduce the 'Detect Any Mouse Model' (DAMM), a pretrained object detector for localizing mice in complex environments, capable of robust performance with zero to minimal additional training on new experimental setups. Our approach involves collecting and annotating a diverse dataset that encompasses single and multi-housed mice in various lighting conditions, experimental setups, and occlusion levels. We utilize the Mask R-CNN architecture for instance segmentation and validate DAMM's performance with no additional training data (zero-shot inference) and with few examples for fine-tuning (few-shot inference). DAMM excels in zero-shot inference, detecting mice, and even rats, in entirely unseen scenarios and further improves with minimal additional training. By integrating DAMM with the SORT algorithm, we demonstrate robust tracking, competitively performing with keypoint-estimation-based methods. Finally, to advance and simplify behavioral studies, we made DAMM accessible to the scientific community with a user-friendly Python API, shared model weights, and a Google Colab implementation.

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.

Behavioral Adaptations of Nursing Brangus Cows to Virtual Fencing: Insights from a Training Deployment Phase.

Virtual fencing systems have emerged as a promising technology for managing the distribution of livestock in extensive grazing environments. This study provides comprehensive documentation of the learning process involving two conditional behavioral mechanisms and the documentation of efficient, effective, and safe animal training for virtual fence applications on nursing Brangus cows. Two hypotheses were examined: (1) animals would learn to avoid restricted zones by increasing their use of containment zones within a virtual fence polygon, and (2) animals would progressively receive fewer audio-electric cues over time and increasingly rely on auditory cues for behavioral modification. Data from GPS coordinates, behavioral metrics derived from the collar data, and cueing events were analyzed to evaluate these hypotheses. The results supported hypothesis 1, revealing that virtual fence activation significantly increased the time spent in containment zones and reduced time in restricted zones compared to when the virtual fence was deactivated. Concurrently, behavioral metrics mirrored these findings, with cows adjusting their daily travel distances, exploration area, and cumulative activity counts in response to the allocation of areas with different virtual fence configurations. Hypothesis 2 was also supported by the results, with a decrease in cueing events over time and increased reliance with animals on audio cueing to avert receiving the mild electric pulse. These outcomes underscore the rapid learning capabilities of groups of nursing cows in responding to virtual fence boundaries.

Behavioral changes of food allergic model mice during light and dark period.

Although an animal model of food allergy has been used to investigate its progression mechanism, most researcher could not assess its symptoms for long especially under dark environment. We assessed the behavioral changes of food allergic mice using an image analysis system to track a mouse under both light and dark environments. Mice were sensitized with intraperitoneal ovalbumin (OVA) injections and challenged ten times with oral OVA administration. The OVA challenges induced weight loss and diarrhea. We assessed their behavior and found that the OVA challenges decreased their total moving distance during the dark period. We also revealed that the OVA challenges increased the inactive time of mice during the dark period. Interestingly, these changes were not observed or very small during the light period. We next assessed the location of mice in the home-cage and found that the OVA challenges increased the time when mice stayed at corners and decreased the time at the center during the dark period. These observations suggest mental abnormality of mice. Indeed, the OVA challenges increased the immobility time of mice in the tail suspension test. Thus, food allergic mice exhibited reduced activity and might exhibit psychological symptoms during dark period.

Comparison of automated video tracking systems in the open field test: ANY-Maze versus EthoVision XT.

BACKGROUND: ANY-Maze and EthoVision XT are two commonly used automated animal tracking systems employed to produce reliable and consistent results in behavioural paradigms. Data obtained with both tracking systems have presented differences, particularly when varying laboratory lighting conditions and contrasts of mice coat colour against the arena background in both water maze and tunnel maze. METHOD: In this study, two fluorescent lighting conditions (58 and 295 lux), local to our laboratory, and different coat-coloured mouse lines (C57BL/6 J - black; CD1 - agouti; C3H/HeN - white) were used to compare reproducibility in measures of tracking systems (ANY-Maze versus EthoVision) in the open field test. RESULTS: Differences between systems were reliant on the contrasts between coat and background colours. Surprisingly, black animals presented the greatest differences in read-outs between tracking systems, regardless of lighting conditions. Data from both video observation tools differed mainly in exploration-related parameters (distance travelled), but less in more static proxies (time in thigmotaxis zone). Overall, EthoVision XT returned higher values for most parameters analysed relative to ANY-Maze. More inconsistencies in recording and analysis can be expected from other video recording systems. CONCLUSION: Data analysis software provides an additional source of variation in need of consideration when reproducibility in behavioural neuroscience is required.

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.

AlphaTracker: a multi-animal tracking and behavioral analysis tool.

Computer vision has emerged as a powerful tool to elevate behavioral research. This protocol describes a computer vision machine learning pipeline called AlphaTracker, which has minimal hardware requirements and produces reliable tracking of multiple unmarked animals, as well as behavioral clustering. AlphaTracker pairs a top-down pose-estimation software combined with unsupervised clustering to facilitate behavioral motif discovery that will accelerate behavioral research. All steps of the protocol are provided as open-source software with graphic user interfaces or implementable with command-line prompts. Users with a graphical processing unit (GPU) can model and analyze animal behaviors of interest in less than a day. AlphaTracker greatly facilitates the analysis of the mechanism of individual/social behavior and group dynamics.

ViewShedR: a new open-source tool for cumulative, subtractive and elevated line-of-sight analysis.

Many environmental and ecological studies require line of sight (LOS) and/or viewshed analyses. While tools for performing these analyses from digital elevation models (DEMs) are widespread, they are either too restrictive, inaccessible or pricey and difficult to use. This methodological gap is potentially imperative for scholars using solutions like telemetry tracking systems or spatial ecology landscape mapping. Here we present ViewShedR-a free, open-source and intuitive graphical user-interface application for performing LOS calculations, including cumulative, subtractive (areas covered by towers A + B or by A but not by B, respectively), and elevated-target analyses. ViewShedR is implemented in the widely used R environment, thus facilitating usage and further modification by end-users. We provide two working examples for ViewShedR in the context of permanent animal-tracking systems requiring simultaneous tag-detection by multiple towers (receivers): first, the ATLAS system for terrestrial animals in the Harod Valley, Israel; and second, an acoustic telemetry array for marine animals in the Dry Tortugas, Florida. ViewShedR allowed effective tower deployment and finding partially detected tagged animals in the ATLAS system. Similarly, it allowed us to identify reception shadows cast by islands in the marine array. We hope ViewShedR will facilitate deployment of tower arrays for tracking, communication networks and other ecological applications.

Open-source software for automated rodent behavioral analysis.

Rodent behavioral analysis is a major specialization in experimental psychology and behavioral neuroscience. Rodents display a wide range of species-specific behaviors, not only in their natural habitats but also under behavioral testing in controlled laboratory conditions. Detecting and categorizing these different kinds of behavior in a consistent way is a challenging task. Observing and analyzing rodent behaviors manually limits the reproducibility and replicability of the analyses due to potentially low inter-rater reliability. The advancement and accessibility of object tracking and pose estimation technologies led to several open-source artificial intelligence (AI) tools that utilize various algorithms for rodent behavioral analysis. These software provide high consistency compared to manual methods, and offer more flexibility than commercial systems by allowing custom-purpose modifications for specific research needs. Open-source software reviewed in this paper offer automated or semi-automated methods for detecting and categorizing rodent behaviors by using hand-coded heuristics, machine learning, or neural networks. The underlying algorithms show key differences in their internal dynamics, interfaces, user-friendliness, and the variety of their outputs. This work reviews the algorithms, capability, functionality, features and software properties of open-source behavioral analysis tools, and discusses how this emergent technology facilitates behavioral quantification in rodent research.

PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment.

PyMouseTracks (PMT) is a scalable and customizable computer vision and radio frequency identification (RFID)-based system for multiple rodent tracking and behavior assessment that can be set up within minutes in any user-defined arena at minimal cost. PMT is composed of the online Raspberry Pi (RPi)-based video and RFID acquisition with subsequent offline analysis tools. The system is capable of tracking up to six mice in experiments ranging from minutes to days. PMT maintained a minimum of 88% detections tracked with an overall accuracy >85% when compared with manual validation of videos containing one to four mice in a modified home-cage. As expected, chronic recording in home-cage revealed diurnal activity patterns. In open-field, it was observed that novel noncagemate mouse pairs exhibit more similarity in travel trajectory patterns than cagemate pairs over a 10-min period. Therefore, shared features within travel trajectories between animals may be a measure of sociability that has not been previously reported. Moreover, PMT can interface with open-source packages such as DeepLabCut and Traja for pose estimation and travel trajectory analysis, respectively. In combination with Traja, PMT resolved motor deficits exhibited in stroke animals. Overall, we present an affordable, open-sourced, and customizable/scalable mouse behavior recording and analysis system.

Challenges in small cetacean telemetry: an attempt at developing a remotely deployed attachment device for single-pin dorsal fin satellite transmitters.

Satellite telemetry is critical for collecting fine-scale temporal and spatial data on individual animals that has broad-scale applicability at population and species levels. There have been significant advances in the remote deployment of satellite telemetry devices on large cetacean species. However, the development of comparable remote attachment methodologies for small cetaceans is still limited. Currently, satellite tag attachment for small cetaceans requires manual capture that increases the risk to the target animal, can be logistically challenging, and cost prohibitive. The goal of this project was to develop a novel tool to remotely attach single-pin satellite telemetry devices to the dorsal fin of individual small cetaceans. Three different spring-loaded designs and one pneumatic version of the remote attachment device were built in an iterative process to identify a successful deployment methodology. Ultimately, as a result of logistical challenges associated with a Category 5 hurricane, the COVID-19 pandemic, and engineering complexities related to dorsal fin morphology and small cetacean behavior, the objective of this project was not met. However, lessons learned from these attempts to develop this new sampling tool have applicability for future researchers in the successful completion of a safe and effective methodology for remote attachment of satellite tags to small cetacean dorsal fins.

Crabs ride the tide: incoming tides promote foraging of Giant Mud Crab (Scylla serrata).

BACKGROUND: Effective fisheries management of mobile species relies on robust knowledge of animal behaviour and habitat-use. Indices of behaviour can be useful for interpreting catch-per-unit-effort data which acts as a proxy for relative abundance. Information about habitat-use can inform stocking release strategies or the design of marine protected areas. The Giant Mud Crab (Scylla serrata; Family: Portunidae) is a swimming estuarine crab that supports significant fisheries harvest throughout the Indo-West Pacific, but little is known about the fine-scale movement and behaviour of this species. METHODS: We tagged 18 adult Giant Mud Crab with accelerometer-equipped acoustic tags to track their fine-scale movement using a hyperbolic positioning system, alongside high temporal resolution environmental data (e.g., water temperature), in a temperate south-east Australian estuary. A hidden Markov model was used to classify movement (i.e., step length, turning angle) and acceleration data into discrete behaviours, while also considering the possibility of individual variation in behavioural dynamics. We then investigated the influence of environmental covariates on these behaviours based on previously published observations. RESULTS: We fitted a model with two well-distinguished behavioural states describing periods of inactivity and foraging, and found no evidence of individual variation in behavioural dynamics. Inactive periods were most common (79% of time), and foraging was most likely during low, incoming tides; while inactivity was more likely as the high tide receded. Model selection removed time (hour) of day and water temperature (°C) as covariates, suggesting that they do not influence Giant Mud Crab behavioural dynamics at the temporal scale investigated. CONCLUSIONS: Our study is the first to quantitatively link fine-scale movement and behaviour of Giant Mud Crab to environmental variation. Our results suggest Giant Mud Crab are a predominantly sessile species, and support their status as an opportunistic scavenger. We demonstrate a relationship between the tidal cycle and foraging that is likely to minimize predation risk while maximizing energetic efficiency. These results may explain why tidal covariates influence catch rates in swimming crabs, and provide a foundation for standardisation and interpretation of catch-per-unit-effort data-a commonly used metric in fisheries science.

Linking movement and dive data to prey distribution models: new insights in foraging behaviour and potential pitfalls of movement analyses.

BACKGROUND: Animal movement data are regularly used to infer foraging behaviour and relationships to environmental characteristics, often to help identify critical habitat. To characterize foraging, movement models make a set of assumptions rooted in theory, for example, time spent foraging in an area increases with higher prey density. METHODS: We assessed the validity of these assumptions by associating horizontal movement and diving of satellite-telemetered ringed seals (Pusa hispida)-an opportunistic predator-in Hudson Bay, Canada, to modelled prey data and environmental proxies. RESULTS: Modelled prey biomass data performed better than their environmental proxies (e.g., sea surface temperature) for explaining seal movement; however movement was not related to foraging effort. Counter to theory, seals appeared to forage more in areas with relatively lower prey diversity and biomass, potentially due to reduced foraging efficiency in those areas. CONCLUSIONS: Our study highlights the need to validate movement analyses with prey data to effectively estimate the relationship between prey availability and foraging behaviour.

The rise of animal biotelemetry and genetics research data integration.

The advancement and availability of innovative animal biotelemetry and genomic technologies are improving our understanding of how the movements of individuals influence gene flow within and between populations and ultimately drive evolutionary and ecological processes. There is a growing body of work that is integrating what were once disparate fields of biology, and here, we reviewed the published literature up until January 2023 (139 papers) to better understand the drivers of this research and how it is improving our knowledge of animal biology. The review showed that the predominant drivers for this research were as follows: (1) understanding how individual-based movements affect animal populations, (2) analyzing the relationship between genetic relatedness and social structuring, and (3) studying how the landscape affects the flow of genes, and how this is impacted by environmental change. However, there was a divergence between taxa as to the most prevalent research aim and the methodologies applied. We also found that after 2010 there was an increase in studies that integrated the two data types using innovative statistical techniques instead of analyzing the data independently using traditional statistics from the respective fields. This new approach greatly improved our understanding of the link between the individual, the population, and the environment and is being used to better conserve and manage species. We discuss the challenges and limitations, as well as the potential for growth and diversification of this research approach. The paper provides a guide for researchers who wish to consider applying these disparate disciplines and advance the field.

A multi-species evaluation of digital wildlife monitoring using the Sigfox IoT network.

Bio-telemetry from small tags attached to animals is one of the principal methods for studying the ecology and behaviour of wildlife. The field has constantly evolved over the last 80 years as technological improvement enabled a diversity of sensors to be integrated into the tags (e.g., GPS, accelerometers, etc.). However, retrieving data from tags on free-ranging animals remains a challenge since satellite and GSM networks are relatively expensive and or power hungry. Recently a new class of low-power communication networks have been developed and deployed worldwide to connect the internet of things (IoT). Here, we evaluated one of these, the Sigfox IoT network, for the potential as a real-time multi-sensor data retrieval and tag commanding system for studying fauna across a diversity of species and ecosystems. We tracked 312 individuals across 30 species (from 25 g bats to 3 t elephants) with seven different device concepts, resulting in more than 177,742 successful transmissions. We found a maximum line of sight communication distance of 280 km (on a flying cape vulture [Gyps coprotheres]), which sets a new documented record for animal-borne digital data transmission using terrestrial infrastructure. The average transmission success rate amounted to 68.3% (SD 22.1) on flying species and 54.1% (SD 27.4) on terrestrial species. In addition to GPS data, we also collected and transmitted data products from accelerometers, barometers, and thermometers. Further, we assessed the performance of Sigfox Atlas Native, a low-power method for positional estimates based on radio signal strengths and found a median accuracy of 12.89 km (MAD 5.17) on animals. We found that robust real-time communication (median message delay of 1.49 s), the extremely small size of the tags (starting at 1.28 g without GPS), and the low power demands (as low as 5.8 µAh per transmitted byte) unlock new possibilities for ecological data collection and global animal observation.

Satellite tracking reveals sex-specific migration distance in green turtles (Chelonia mydas).

Satellite tracking is a key tool for studying sea turtles in the wild. Most tracking has been performed on adult females however, leaving knowledge gaps regarding other population segments, such as adult males. By satellite tracking 12 male green turtles (Chelonia mydas) at a breeding site in West Africa, we describe their movements from the breeding to the foraging grounds and compare migrations with those of 13 females tracked in the same season. During the mating period, some males remained near the focal nesting site, while others performed exploratory movements, apparently to visit other nearby rookeries. Males migrated on average shorter distances to foraging grounds (377 km, range 50-1081, n = 9) compared to females (1038 km, range 957-1850, n = 11]). Importantly, male foraging areas overlapped with previously described areas for females, suggesting sex-specific migration distances are not derived from differences in habitat selection. Strong support for differential migration by sex in sea turtles has hitherto been found in just one other species, but indications are that it may be a general feature in this group. These findings have important implications for our understanding of the interplay between reproductive roles and movement ecology of these emblematic animals.