Hello MarMam, Hope this message finds you well. On behalf of my co-authors, I am pleased to share our recent publication in PeerJ “A workflow of open-source tools for drone-based photogrammetry of marine megafauna”. Bierlich KC, Hewitt J, Bird CN, Johnston DW, Dale J, Pirotta E, Schick RS, Stewart JD, New L, Chimienti E, Goldbogen JA, Friedlaender AS, Cantor M, Torres LG. 2025. A workflow of open-source tools for drone-based photogrammetry of marine megafauna. PeerJ 13:e19768 https://doi.org/10.7717/peerj.19768 Abstract       Drones have revolutionized researchers’ ability to obtain morphological data on megafauna, particularly cetaceans. The last decade has seen a surge in studies using drones to distinguish morphological differences among populations, calculate energetic reserves and body condition, and identify decreasing body sizes over generations. However, standardized workflows are needed to guide data collection, post-processing, and incorporation of measurement uncertainty, thereby ensuring that measurements are comparable within and across studies. Workflows containing free, open-source tools and methods that are accommodating to various research budgets and types of drones (consumer vs. professional) are more inclusive and equitable, which will foster increased knowledge in ecology and wildlife science. Here we present a workflow for collecting, processing, and analyzing morphological measurements of megafauna using drone-based photogrammetry. Our workflow connects several published open-source hardware and software tools (including automated tools) to maximize processing efficiency, data quality, and measurement accuracy. We also introduce Xcertainty, a novel R package for quantifying and incorporating photogrammetric uncertainty associated with different drones based on Bayesian statistical models. Stepping through this workflow, we discuss pre-flight setup and in-flight data collection, imagery post-processing (image selection, measuring, linking metadata with measurements, and incorporating uncertainty), and methods for including measurement uncertainty into analyses. We coalesce examples from these previously published tools and provide three detailed vignettes with code to demonstrate the ease and flexibility of using Xcertainty to estimate growth curves and body lengths, widths, and several body condition metrics with uncertainty. We also include three examples using published datasets to demonstrate how to include measurement uncertainty into analyses and provide code for researchers to adapt to their own datasets. Our workflow focuses on measuring the morphology of cetaceans but is adaptable to other taxa. Our goal is for this open-source workflow to be accessible and accommodating to research projects across a range of budgets and to facilitate collaborations and longitudinal data comparisons. This workflow serves as a guide that is easily adoptable and adaptable by researchers to fit various data and analysis needs, and emergent technology and tools. You can learn more about the latest updates to the workflow and hardware and software tools at the Marine Mammal Institute’s Center of Drone Excellence (CODEX) website: https://mmi.oregonstate.edu/centers-excellence/codex. Feel free to reach out if you have any questions. Cheers, KC KC (Kevin) Bierlich, PhD, MEM Assistant Professor Senior Research Center of Drone Excellence (CODEX<https://mmi.oregonstate.edu/centers-excellence/codex>) Marine Mammal Institute, Dept. of Fisheries, Wildlife, & Conservation Sciences, Oregon State University Pronouns: he, him, his kevin.bierlich@oregonstate.edu<mailto:kevin.bierlich@oregonstate.edu>