Code and analysis scripts for the paper:

Bacquelé Q., Barnagaud J.-Y., Violle C., Theunissen F., Mathevon N. (2026). The global biogeography of passerine songs.

Although bird songs are classic models for understanding the evolution of vocal communication, their extraordinary global diversity has long made the development of a unifying framework challenging. By analyzing the acoustic architecture of songs from over 3,000 passerine species worldwide, we show that this acoustic space can be structured around eight elemental motifs. The differential use of these motifs is driven by a combination of species' biological traits (social organization, morphology, mating system) and the physics of sound propagation. In tropical rainforests, environmental filtering for transmission efficiency favors structurally simple motifs like flat whistles. Conversely, in temperate regions, where high population densities facilitate close-range communication and short breeding seasons intensify sexual selection, the balance shifts toward complex, information-rich motifs like ultra-fast trills, despite their susceptibility to acoustic degradation. Ultimately, the global geography of birdsong reflects a spatially varying equilibrium between physical environmental constraints and the biological drive for complex communication.

Explore the global vocal repertoire of passerines: acoustic-biogeography.vercel.app

repo_bacquele_etal2026/
├── data/                           # Acoustic feature data and phylogenetic trees
│   ├── AllBirdsEricson1.tre        # Source phylogenetic trees (1000 trees)
│   ├── consensus_sumtrees.tre      # Majority-rule consensus tree
│   ├── traits_data.csv             # Raw acoustic traits per vocalization
│   ├── traits_data_pc_gmm_8components_proba.csv  # GMM cluster probabilities
│   ├── species_traits_data.csv     # Species-level acoustic traits
│   ├── model_traits_data.csv       # Traits data for modeling
│   ├── model_traits_morpho_social_data.csv  # Morphological and social traits
│   ├── grid_species_lists.csv      # Species occurrence per grid cell
│   ├── grid_1.0deg_species_lists.csv  # Species lists at 1° resolution
│   ├── grid_1.0deg_coordID.gpkg    # Grid cell geometries
│   ├── geographic_model_data_with_biomes.csv  # Geographic data with biome info
│   ├── combined_tei_and_environmental_data.csv  # TEI and environmental variables
│   ├── ses_fdis_random_assembly_results_full.csv  # SES-FDis null model results
│   ├── spatial_mpd.csv             # Mean Pairwise Distance results
│   ├── richness_1deg.csv           # Passerine richness per 1° grid cell
│   ├── matching_final_corrected.csv  # Taxonomy matching table
│   ├── unique_families.txt         # List of passerine families
│   └── selected_data_8x8x20_top/   # Audio subset used for propagation analyses
│
└── scripts/                        # Analysis scripts
    ├── data_parser/                # Data extraction utilities
    ├── mps/                        # Modulation Power Spectrum extraction
    ├── hypervolume/                # Acoustic space construction and motif clustering
    ├── phylogeny/                  # Phylogenetic analyses
    ├── geo models/                 # Spatial regression models
    ├── maps/                       # Global mapping visualizations
    ├── propagation/                # Propagation and classification analyses
    └── species level model/        # Species-level trait analyses

The study identifies eight fundamental acoustic motifs that structure passerine songs:

1. Flat Whistles - Pure tones with no frequency modulation; highest transmission efficiency
2. Slow Modulated Whistles - Whistles with slow frequency sweeps
3. Fast Modulated Whistles - Whistles with rapid frequency modulation; associated with smaller body size and polygyny
4. Slow Trills - Rhythmic repetitions at low rates
5. Fast Trills - Rhythmic repetitions at moderate rates
6. Ultrafast Trills - Extremely rapid rhythmic patterns
7. Harmonic Stacks - Complex harmonic structures; lowest transmission efficiency
8. Chaotic Notes - Atonal, broadband sounds

Acoustic data were acquired from xeno-canto. The processed acoustic features and metadata are archived at Zenodo (DOI: 110.5281/zenodo.19916290).

The data/ folder contains:

• Phylogenetic trees: Source trees (AllBirdsEricson1.tre) and consensus tree (consensus_sumtrees.tre)
• Acoustic traits: Raw traits per vocalization (traits_data.csv), GMM cluster probabilities (traits_data_pc_gmm_8components_proba.csv), and species-level summaries
• Geographic data: Species occurrence grids, environmental variables, and biome classifications
• Richness and propagation inputs: Global richness grid (richness_1deg.csv) and audio subset for propagation analyses (selected_data_8x8x20_top/)
• Model outputs: SES-FDis results, spatial MPD calculations

• Python 3.11+
• numpy, scipy, scikit-learn
• librosa (audio processing)
• soundsig (MPS computation)
• umap-learn (dimensionality reduction)
• matplotlib, seaborn (visualization)

• R 4.4.3+
• ape, phytools (phylogenetics)
• brms (Bayesian modeling)
• R-INLA (spatial regression)
• mFD (functional diversity)
• sf, terra (spatial data)

@article{bacquele2026biogeography,
  title={The global biogeography of passerine songs},
  author={Bacquel{\'e}, Quentin and Barnagaud, Jean-Yves and Violle, Cyrille and Theunissen, Fr{\'e}d{\'e}ric and Mathevon, Nicolas},
  year={2026}
}

Quentin Bacquelé^1,2,*, Jean-Yves Barnagaud^2,†, Cyrille Violle², Frédéric Theunissen³, Nicolas Mathevon^1,4,5,†

Affiliations
¹ ENES Bioacoustics Research Lab, CRNL, CNRS, Inserm, University of Saint-Etienne; Saint-Etienne, France.
² CEFE, Univ Montpellier, CNRS, EPHE-PSL University, IRD; 1919 route de Mende, 34293 Montpellier, France.
³ Department of Neuroscience, University of California, Berkeley; Berkeley, CA 94720, USA.
⁴ École Pratique des Hautes Études - PSL, CHArt Lab, University Paris-Sciences-Lettres; Paris, France.
⁵ Institut Universitaire de France; Paris, France.

^* Correspondence: qbacquele@gmail.com
^† These authors contributed equally to this work.

• University of Saint-Etienne
• Ecole Pratique des Hautes Etudes
• University Paris-Sciences-Lettres (PhD stipend to QB)
• Labex CeLyA (NM)
• Institut Universitaire de France (NM)
• ACOUCENE group (CESAB, French Foundation for Research on Biodiversity)

Creative Commons Attribution 4.0 International.

We are deeply grateful to the xeno-canto citizen science database and all its contributors.