*Analysis of Avian Point-Count Data in the Presence of Detection Error (APCD01)*
Unlock robust bird‑survey insights using R by modelling detection uncertainty and strengthening inference. Build proficiency with N‑mixture models, time‑removal approaches, and distance sampling techniques through real‑data hands‑on training in R over a focused three‑day live course. Perfect for ecologists looking to elevate point‑count analysis by quantifying detectability. *Course Details & Format* - *Next Session*: November 18–20, 2025 (Tuesday–Thursday) - *Duration*: 3 days × ~4 hours/day = ~12 hours total - *Schedule*: Live online sessions aligned to UK time (GMT/BST); recordings provided for flexible, global access - *Format*: Interactive remote classroom featuring lectures, live demonstrations, and guided R exercises *Who It’s For* Academics, postgraduate students, applied researchers, and data analysts working with avian point‑count data who seek to go beyond naive counts and incorporate detection error into their ecological inference. *What You’ll Learn* - Fit and interpret *N‑mixture models* to estimate abundance while accounting for detection probability - Apply *time‑removal (time‑depletion)* methods to understand detectability over survey intervals - Use *distance sampling* approaches for spatially explicit detection estimation - Build and compare models that handle heterogeneity in detection (observer, behaviour, habitat) - Visualise results and effectively communicate findings via Python-coded R outputs *Fees & Registration* - *Early bird (first 10 places)*: £270 - *Standard fee*: £300 Participants should have basic familiarity with R and statistics; know how to install R packages and have a baseline understanding of statistical modelling. *Why Choose APCD01?* - *Targeted depth*: A three-day immersive module focused exclusively on detection-error methods in point-count analysis - *Hands-on, applied learning*: Real datasets, live coding walkthroughs, and model interpretation - *Seasoned instruction*: Interactive sessions with time for Q&A and tailored problem-solving - *On-demand flexibility*: Access recordings to revisit concepts and refine models in your own time - *Practical outcomes*: By course end, you’ll be equipped to build, assess, and visualise point-count models that explicitly handle detection uncertainty For questions or more info, email *[email protected]* -- Oliver Hooker PhD. PR stats [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
