Methodological choices influence ecological inference in passive acoustic monitoring of a Neotropical nightjar

Methodological choices influence ecological inference in passive acoustic monitoring of a Neotropical nightjar DOI [blinded for peer review] Overview This repository contains the data, code, and reproducible analytical workflow for the manuscript: Authors (2026). Methodological choices influence ecological inference in passive acoustic monitoring of a Neotropical nightjar. Journal of Avian Biology, Special Issue: From Chirps to Insights. [blinded for peer review] The study evaluates how sequential analytical decisions in passive acoustic monitoring (PAM) workflows — automated detector choice, confidence thresholds, and statistical modeling framework — jointly influence ecological inference about the environmental drivers of vocal activity in Nyctidromus albicollis (Common Pauraque) in the Pantanal wetlands, Brazil. Study design Species: Nyctidromus albicollis (Caprimulgidae) Study area: Pantanal wetlands and adjacent Cerrado, Mato Grosso do Sul, Brazil Monitoring sites: 7 sites along a ~180 km environmental gradient Recording period: November 2023 – February 2025 Total recordings: 97,906 one-minute audio files Classifiers: BirdNET pre-built global model (v2.4.0) and locally trained custom classifier Confidence thresholds: MaxF1 and MaxPrecision Modeling frameworks: Bernoulli GLMM (lme4) and Bayesian single-season occupancy model (spOccupancy) Repository structure All files are deposited as individual uploads on Zenodo (no folder structure). Helper functions (to_mcmc_list, tidy_spocc, etc.) are defined within the Quarto documents and do not require a separate script. Quarto documents (reproducible workflow) File Description 01_detector_evaluation.qmd Classifier performance evaluation: precision, recall, F1, ROC/AUC 01_detector_evaluation.html Rendered output 02_threshold_processing.qmd Threshold application and data aggregation (produces analysis-ready datasets) 02_threshold_processing.html Rendered output 03_GLMM_modelling.qmd GLMM analysis 03_GLMM_modelling.html Rendered output 04_occupancy_modelling-model_comparasion.qmd Bayesian occupancy analysis (spOccupancy PGOcc) 04_occupancy_modelling-model_comparasion.html Rendered output Input data — classifier evaluation File Description CM-BirdNet.txt Confusion matrix data for BirdNET pre Built classifier CM-Custom.txt Confusion matrix data for Custom classifier (test dataset) Input data — threshold processing File Description df_night_clean.txt Full dataset (97,906 recordings) with confidence scores from both classifiers and environmental covariates Processed data — recording level (occupancy model input) Each file contains one row per site-night combination, with columns for detection status (pres_thresh), environmental covariates (Temp, RH, moon_illumination), acoustic indices (BIO, ACI), site and night identifiers. These files are produced by 02_threshold_processing.qmd. File Description Div_MaxF1.txt Custom classifier, MaxF1 threshold (recording-level) Div_MaxPrecision.txt Custom classifier, MaxPrecision threshold (recording-level) PreB_MaxF1.txt BirdNET pre-built, MaxF1 threshold (recording-level) PB_MaxPrecision.txt BirdNET pre-built, MaxPrecision threshold (recording-level) Processed data — nightly aggregated (GLMM input) Each file contains one row per site-night combination, with binary nightly detection and environmental covariates. File Description Div_maxf1_agg.csv Custom classifier, MaxF1 threshold (nightly aggregated) preB_maxf1_agg.csv BirdNET pre-built, MaxF1 threshold (nightly aggregated) Note on file naming: The threshold processing script (02_threshold_processing.qmd) generates output files with labels PreB_MaxF1, PreB_MaxPrecision, Div_MaxF1, and Div_MaxPrecision. Some downstream scripts reference files with slightly different naming conventions (e.g., PB_MaxPrecision.txt instead of PreB_MaxPrecision.txt; preB_maxf1_agg.csv instead of PreB_MaxF1_agg.txt). The deposited files use the names expected by the analysis scripts. Classifier model File Description Compau_DiverSons.tflite Custom-trained BirdNET classifier for N. albicollis Pre-fitted models File Description nyctidromus_occ_models.rds All 4 fitted PGOcc models in a single named list model_Custom — MaxF1.rds Individual PGOcc model (Custom, MaxF1) model_Custom — MaxPrecision.rds Individual PGOcc model (Custom, MaxPrecision) model_BirdNET — MaxF1.rds Individual PGOcc model (BirdNET, MaxF1) model_BirdNET — MaxPrecision.rds Individual PGOcc model (BirdNET, MaxPrecision) The individual model .rds files are copies saved separately to enable memory-efficient posterior predictive checks (see Methods). The file nyctidromus_occ_models.rds contains the same four models in a named list and is loaded at the start of the occupancy analysis document. Reproducibility Requirements R >= 4.5.2 Quarto >= 1.6 Required R packages (with versions used): Package Version Purpose lme4 2.0.1 GLMM fitting spOccupancy 0.9.0 Bayesian occupancy models DHARMa 0.4.7 Residual diagnostics coda 0.19-4 MCMC diagnostics bayesplot 1.11.1 MCMC visualization glmm.hp 1.0.0 Hierarchical partitioning ggeffects 2.3.2 Marginal effects tidyverse 2.0.0 Data wrangling and visualization purrr 1.0.2 Functional programming patchwork 1.3.0 Plot composition ggdist 3.3.2 Posterior distribution plots kableExtra 1.4.0 Table formatting soundecology 1.3.3 Acoustic indices (BIO, ACI) lunar 0.2-1 Moon illumination easystats 0.7.5 Statistical reporting Running the analysis The analytical workflow is split across two Quarto documents: 03_GLMM_modelling.qmd — GLMM-based regression analysis (runs in ~5 minutes) 04_occupancy_modelling-model_comparasion.qmd — Bayesian occupancy modeling (runs in ~6–8 hours) To reproduce: # Clone the repository # 1. Render the GLMM analysis quarto render 03_GLMM_modelling.qmd # 2. Render the occupancy analysis (WARNING: \~6-8 hours) quarto render 04_occupancy_modelling-model_comparasion.qmd Note on computational requirements: The Bayesian occupancy models (4 models × 4 chains × 20,000 iterations) require substantial computation time (~6–8 hours on a MacBook with Apple Silicon) and memory (~8–16 GB RAM). Pre-fitted model objects are provided (.rds files) for users who wish to reproduce downstream analyses (diagnostics, figures, tables) without re-running the MCMC sampler. To use pre-fitted models, set eval: false on the fit-models chunk. MCMC settings Parameter Value Iterations per chain 20,000 Burn-in 4,000 Thinning 10 Chains 4 Posterior samples retained 6,400 (1,600 per chain) PPC draws 250 Convergence criteria Rhat < 1.05, ESS > 400 Data description Recording-level data (Div_MaxF1.txt, Div_MaxPrecision.txt, PreB_MaxF1.txt, PB_MaxPrecision.txt) Each row represents one one-minute recording. These files are the input for the occupancy models. Column Description night_site_id Unique identifier for each site-night combination (occupancy “site”) Site Monitoring site identifier (ARA, BAI, BEP, MIM, REF, UEM, XAR) night Date of the recording night (YYYY-MM-DD) DataHora_Formatada Date-time of the recording pres_thresh Binary detection (1 = species detected above threshold, 0 = not detected) Temp Temperature (°C) at recording time RH Relative humidity (%) at recording time moon_illumination Fraction of lunar disc illuminated (0–1) BIO Bioacoustic Index (0–4,000 Hz) ACI Acoustic Complexity Index (full frequency range) Nightly aggregated data (Div_maxf1_agg.csv, preB_maxf1_agg.csv) Each row represents one site-night combination. These files are the input for the GLMM analysis. Column Description site Monitoring site identifier date Date of the recording night n_minutes Number of one-minute recordings in the night presence Binary nightly detection (1 = species detected at least once) temp_mean Mean nightly temperature (°C) rh_mean Mean nightly relative humidity (%) moon_illumination Fraction of lunar disc illuminated (0–1) lag_presence Detection status on the previous night (0 or 1) Fitted occupancy models The file nyctidromus_occ_models.rds contains a named list with four fitted PGOcc model objects. Each model object includes: beta.samples: Posterior samples for occupancy (β) coefficients alpha.samples: Posterior samples for detection (α) coefficients z.samples: Posterior samples for latent occupancy states sigma.sq.p.samples: Posterior samples for detection random effect variance y: Binary detection matrix (J sites × K replicates) used for fitting X: Design matrix for occupancy covariates X.p: Design matrix for detection covariates Individual model files (model_Custom — MaxF1.rds, etc.) contain the same objects saved separately to enable memory-efficient posterior predictive checks. Classifier details Classifier Model file Threshold (MaxF1) Threshold (MaxPrecision) AUC BirdNET pre-built BirdNET_GLOBAL_6K_V2.4_Model_FP32.tflite 0.10 0.95 0.869 Custom Compau_DiverSons.tflite 0.40 0.95 0.971 BirdNET-Analyzer version: 2.4.0. Sensitivity setting: 1.0. Frequency range: 1–4,000 Hz. Audio data Raw audio files (97,906 WAV files, ~380 GB) are not included in this repository due to their size. Audio data are available upon reasonable request to the corresponding author [blinded for peer review]. Metadata for all recordings (site, date, time, duration, associated environmental data) are included in the processed data files. License Data and code are released under CC BY 4.0. Please cite the associated publication when using these materials. Citation Authors (2026). Methodological choices influence ecological inference in passive acoustic monitoring of a Neotropical nightjar. Journal of Avian Biology, Special Issue: From Chirps to Insights. https://doi.org/10.5281/zenodo.20402320 Contact [blinded for peer review] Acknowledgements [blinded for peer review] AI disclosure AI tools (Claude, Anthropic; ChatGPT, OpenAI) were used during manuscript preparation as described in the AI Use Statement of the published article. No AI tool was used to generate or modify primary data. All code was reviewed, tested, and validated by the authors on the study dataset.