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ABSTRACT Understanding animal movement is at the core of ecology, evolution and conservation science. Big data approaches for animal tracking have facilitated impactful synthesis research on spatial biology and behavior in ecologically important and human-impacted regions. Similarly, databases of animal traits (e.g. body size, limb length, locomotion method, lifespan) have been used for a wide range of comparative questions, with emerging data being shared at the level of individuals and populations. Here, we argue that the proliferation of both types of publicly available data creates exciting opportunities to unlock new avenues of research, such as spatial planning and ecological forecasting. We assessed the feasibility of combining animal tracking and trait databases to develop and test hypotheses across geographic, temporal and biological allometric scales. We identified multiple research questions addressing performance and distribution constraints that could be answered by integrating trait and tracking data. For example, how do physiological (e.g. metabolic rates) and biomechanical traits (e.g. limb length, locomotion form) influence migration distances? We illustrate the potential of our framework with three case studies that effectively integrate trait and tracking data for comparative research. An important challenge ahead is the lack of taxonomic and spatial overlap in trait and tracking databases. We identify critical next steps for future integration of tracking and trait databases, with the most impactful being open and interlinked individual-level data. Coordinated efforts to combine trait and tracking databases will accelerate global ecological and evolutionary insights and inform conservation and management decisions in our changing world. 

Long‐term studies are critical for ecological understanding, but they are underutilized as inclusive opportunities for training ecologists. We use our perspective from the Año Nuevo elephant seal programme along with surveys from community members to propose that long‐term studies could be better leveraged to promote inclusive education and professional development in ecology. Drawing on our experiences as mentors and mentees, we demonstrate how long‐term studies can use their resources, including rich data, robust logistics and extensive professional networks, to improve recruitment and retention of diverse groups of trainees. However, practices such as unpaid labour and unclear expectations limit the utility of these resources for diversifying ecology. We discuss how we have structured our long‐term study to create more inclusive and equitable training opportunities. Acknowledging these transformations required substantial resources, we highlight funding sources and organizational partnerships that can promote investment in long‐term studies for broadening participation. 

What new questions could ecophysiologists answer if physio-logging research was fully reproducible? We argue that technical debt (computational hurdles resulting from prioritizing short-term goals over long-term sustainability) stemming from insufficient cyberinfrastructure (field-wide tools, standards, and norms for analyzing and sharing data) trapped physio-logging in a scientific silo. This debt stifles comparative biological analyses and impedes interdisciplinary research. Although physio-loggers (e.g., heart rate monitors and accelerometers) opened new avenues of research, the explosion of complex datasets exceeded ecophysiology’s informatics capacity. Like many other scientific fields facing a deluge of complex data, ecophysiologists now struggle to share their data and tools. Adapting to this new era requires a change in mindset, from “data as a noun” (e.g., traits, counts) to “data as a sentence”, where measurements (nouns) are associate with transformations (verbs), parameters (adverbs), and metadata (adjectives). Computational reproducibility provides a framework for capturing the entire sentence. Though usually framed in terms of scientific integrity, reproducibility offers immediate benefits by promoting collaboration between individuals, groups, and entire fields. Rather than a tax on our productivity that benefits some nebulous greater good, reproducibility can accelerate the pace of discovery by removing obstacles and inviting a greater diversity of perspectives to advance science and society. In this article, we 1) describe the computational challenges facing physio-logging scientists and connect them to the concepts of technical debt and cyberinfrastructure , 2) demonstrate how other scientific fields overcame similar challenges by embracing computational reproducibility, and 3) present a framework to promote computational reproducibility in physio-logging, and bio-logging more generally. 

Some marine birds and mammals can perform dives of extraordinary duration and depth. Such dive performance is dependent on many factors, including total body oxygen (O2) stores. For diving penguins, the respiratory system (air sacs and lungs) constitutes 30-50% of the total body O2 store. To better understand the role and mechanism of parabronchial ventilation and O2 utilization in penguins both on the surface and during the dive, we examined air sac partial pressures of O2 (PO2) in emperor penguins (Aptenodytes forsteri) equipped with backpack PO2 recorders. Cervical air sac PO2s at rest were lower than in other birds, while the cervical air sac to posterior thoracic air sac PO2 difference was larger. Pre-dive cervical air sac PO2s were often greater than those at rest, but had a wide range and were not significantly different from those at rest. The maximum respiratory O2 store and total body O2 stores calculated with representative anterior and posterior air sac PO2 data did not differ from prior estimates. The mean calculated anterior air sac O2 depletion rate for dives up to 11 min was approximately one-tenth that of the posterior air sacs. Low cervical air sac PO2s at rest may be secondary to a low ratio of parabronchial ventilation to parabronchial blood O2 extraction. During dives, overlap of simultaneously recorded cervical and posterior thoracic air sac PO2 profiles supported the concept of maintenance of parabronchial ventilation during a dive by air movement through the lungs. 

Abstract Bio-logging devices equipped with inertial measurement units—particularly accelerometers, magnetometers, and pressure sensors—have revolutionized our ability to study animals as necessary electronics have gotten smaller and more affordable over the last two decades. These animal-attached tags allow for fine scale determination of behavior in the absence of direct observation, particularly useful in the marine realm, where direct observation is often impossible, and recent devices can integrate more power hungry and sensitive instruments, such as hydrophones, cameras, and physiological sensors. To convert the raw voltages recorded by bio-logging sensors into biologically meaningful metrics of orientation (e.g., pitch, roll and heading), motion (e.g., speed, specific acceleration) and position (e.g., depth and spatial coordinates), we developed a series of MATLAB tools and online instructional tutorials. Our tools are adaptable for a variety of devices, though we focus specifically on the integration of video, audio, 3-axis accelerometers, 3-axis magnetometers, 3-axis gyroscopes, pressure, temperature, light and GPS data that are the standard outputs from Customized Animal Tracking Solutions (CATS) video tags. Our tools were developed and tested on cetacean data but are designed to be modular and adaptable for a variety of marine and terrestrial species. In this text, we describe how to use these tools, the theories and ideas behind their development, and ideas and additional tools for applying the outputs of the process to biological research. We additionally explore and address common errors that can occur during processing and discuss future applications. All code is provided open source and is designed to be useful to both novice and experienced programmers.