From Detections to Decisions: Improving Motus Data Quality and Analytical Tools for Species of Greatest Conservation Need

The rapid expansion of the Motus Wildlife Tracking System (Motus; Taylor et al. 2017) as a hemispheric migratory tracking platform has created new challenges and opportunities for improving data processing, interpretation, and conservation relevance, especially for Species of Greatest Conservation Need (SGCN). This project addresses those challenges and opportunities by demonstrating how enhanced data filtering and stakeholder-guided tool development can dramatically increase the conservation value of Motus tracking data. Focusing on 48 SGCN with at least 100 historical Motus tag deployments (Appendix 1), this Phase 1 effort will pursue two objectives: (1) improve data quality by finalizing and implementing an updated tag detection algorithm (“tagfinder”) to reduce false detections, resolve tag collisions and aliasing, and streamline analysis-readiness across a diverse and representative set of Motus-tagged SGCN, and (2) launch a stakeholder-driven process to identify and co-develop analytical tools most relevant to state wildlife agencies and land managers. Tool development will be guided by a newly formed steering committee composed of state agency biologists and other conservation partners, and will focus on real-world decision support needs such as landscape and migratory connectivity, population health, habitat prioritization, threat mitigation, and climate adaptation, grounded in full annual cycle information needs for SGCN. This project will result in cleaned Motus detection datasets for 48 SGCN, a scalable analytical tool development framework, and a set of prototype tools developed under the guidance of state agencies. It will also lay the groundwork for standardized best practices in processing and applying Motus data to conservation planning across species, agencies, and landscapes.