Wildlife migration is a spectacular phenomenon [1]. Studies using telemetry - tracking devices attached on free-living animals - have shown that large topographic barriers and obstacles, such as oceans and deserts, elicit extreme feats of migration [2]. Overcoming the challenges of these obstacles might require experience and skill that young individuals lack [2-5]. Further, younger, inexperienced animals might determine their migration routes using navigation strategies different from those of older animals [6-9], but our knowledge of how orientation mechanisms and experience drive migration strategy is limited. We have studied how experienced (adults) and inexperienced (first-time migrating fledglings) streaked shearwaters (Calonectris leucomelas) approach the challenge of migration using animal-borne tracking devices. The study birds migrate from a colony on the north of a large topographic barrier, Honshu Island, Japan. Shearwaters use a wind- and wave-based flight pattern-dynamic soaring-to extract energy for highly efficient travel over oceans [10]. We therefore expected that shearwaters migrating southward from the colony would make substantial detours to avoid any landmasses. We found that migrating adults followed one of two paths that detour around landmasses that hinder direct southerly migration. In contrast, inexperienced fledglings followed a straight course in a south-oriented direction that forced them to complete a trans-mountain journey, suggesting that the birds rely on an innate compass. Thus, we suggest that fledglings would eventually override the simple compass navigation, which appears to be the primary driver for their extreme migration, before being able to interact appropriately with the marine environment.
A comparative analysis of animal behavior (e.g., male vs. female groups) has been widely used to elucidate behavior specific to one group since pre-Darwinian times. However, big data generated by new sensing technologies, e.g., GPS, makes it difficult for them to contrast group differences manually. This study introduces DeepHL, a deep learning-assisted platform for the comparative analysis of animal movement data, i.e., trajectories. This software uses a deep neural network based on an attention mechanism to automatically detect segments in trajectories that are characteristic of one group. It then highlights these segments in visualized trajectories, enabling biologists to focus on these segments, and helps them reveal the underlying meaning of the highlighted segments to facilitate formulating new hypotheses. We tested the platform on a variety of trajectories of worms, insects, mice, bears, and seabirds across a scale from millimeters to hundreds of kilometers, revealing new movement features of these animals.
Unravelling the secrets of wild animals is one of the biggest challenges in ecology, with bio-logging (i.e., the use of animal-borne loggers or bio-loggers) playing a pivotal role in tackling this challenge. Bio-logging allows us to observe many aspects of animals’ lives, including their behaviours, physiology, social interactions, and external environment. However, bio-loggers have short runtimes when collecting data from resource-intensive (high-cost) sensors. This study proposes using AI on board video-loggers in order to use low-cost sensors (e.g., accelerometers) to automatically detect and record complex target behaviours that are of interest, reserving their devices’ limited resources for just those moments. We demonstrate our method on bio-loggers attached to seabirds including gulls and shearwaters, where it captured target videos with 15 times the precision of a baseline periodic-sampling method. Our work will provide motivation for more widespread adoption of AI in bio-loggers, helping us to shed light onto until now hidden aspects of animals’ lives.
Focal animal sampling and continuous recording of behavior in situ are essential in the study of ecology. However, observation gaps and missing records are unavoidable because the focal individual can move out of sight and recording devices do not always work properly. Using an inverse reinforcement learning (IRL) framework, we have developed a novel gap-filling method to predict the most likely route that an animal would have traveled; within this framework, an algorithm learns a reward function from animal trajectories to find the environmental features preferred by the animal. We applied this approach to GPS trajectories obtained from streaked shearwaters (Calonectris leucomelas) and provide evidence of the advantages of the IRL approach over previously used interpolation methods. These advantages are as follows: (1) No assumptions about the parametric distribution governing movements are needed, (2) no assumptions regarding landscape preferences and restrictions are needed, and (3) large spatiotemporal gaps can be filled. This work demonstrates how IRL can enhance the ability to fill gaps in animal trajectories and construct reward-space maps in heterogeneous environments. The proposed methodology can assist movement research, which seeks to understand phenomena that are ecologically and evolutionarily significant, such as habitat selection and migration.
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