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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. 

Who conducts biological research, where they do it and how results are disseminated vary among geographies and identities. Identifying and documenting these forms of bias by research communities is a critical step towards addressing them. We documented perceived and observed biases in movement ecology, a rapidly expanding sub-discipline of biology, which is strongly underpinned by fieldwork and technology use. We surveyed attendees before an international conference to assess a baseline within-discipline perceived bias (uninformed perceived bias). We analysed geographic patterns inMovement Ecologyarticles, finding discrepancies between the country of the authors’ affiliation and study site location, related to national economics. We analysed race-gender identities of USA biology researchers (the closest to our sub-discipline with data available), finding that they differed from national demographics. Finally, we discussed the quantitatively observed bias at the conference, to assess within-discipline perceived bias informed with observational data (informed perceived bias). Although the survey indicated most conference participants as bias-aware, conversations only covered a subset of biases. We discuss potential causes of bias (parachute-science, fieldwork accessibility), solutions and the need to evaluate mitigatory action effectiveness. Undertaking data-driven analysis of bias within sub-disciplines can help identify specific barriers and move towards the inclusion of a greater diversity of participants in the scientific process.