Dear MARMAM colleagues, My co-authors and I are pleased to share our new publication in *Endangered Species Research*: *Drone-calibrated acoustic footprints infer group size in franciscana dolphins (Pontoporia blainvillei)* The article is available at: https://www.int-res.com/abstracts/esr/v59/esr01470 *Abstract* Conventional visual surveys of the cryptic franciscana dolphin (*Pontoporia blainvillei*) are constrained by turbid near-shore waters and the species’ tendency to occur in small groups. We combined passive acoustic monitoring (PAM) with synchronized drone surveys to calibrate an acoustic footprint detection model and infer group size directly from acoustic data. Click trains were detected in PAMGuard, localized, and clustered using HDBSCAN into click-train clusters (CTCs). Drone imagery provided visual group sizes during synchronized sampling periods. A 2-step spatiotemporal matching procedure paired CTCs with visually observed groups, and an independent manual audit estimated a 6.4% false-positive rate, which was used to correct automated detections. For paired clusters, we fitted generalized linear models with an offset for animal-seconds and alternative distance terms; AIC supported a linear negative binomial model. The calibrated baseline acoustic footprint at the trackline was r₀ = 0.028 click trains animal⁻¹ s⁻¹ (95% CI: 0.014–0.056; CV = 0.34; R² = 0.54), equivalent to 1.68 min⁻¹ (95% CI: 0.84–3.36). We obtained 28 paired CTCs from 8 visual groups and applied the calibration to 267 unpaired CTCs. Predicted group sizes had a median of 1.34 individuals (95% CI: 0.79–2.57; CV = 0.15), consistent with predominantly small units. This framework provides a practical pathway from clicks to counts and represents a key step toward passive acoustic density estimation for this endangered species. Kind regards, João Mura