Demographic identification of Greater Caribbean manatees via acoustic feature learning

TL;DR

This study shows that machine learning can identify the sex of manatees from their vocalizations, but age classification is less reliable due to limited data and overlapping acoustic features.

Contribution

The study introduces a novel application of machine learning to classify manatee vocalizations for demographic inference, achieving reliable sex classification and exploring acoustic body size regression.

Findings

Sex classification achieved 85–87% accuracy using acoustic features like MFCCs and spectral skewness.

Age classification had lower reliability, especially for juveniles, with significant overlap in acoustic features between age groups.

Acoustic body size regression provided continuous demographic profiling with moderate accuracy (R2 = 0.33).

Abstract

Demographic inference from vocalizations is essential for monitoring endangered Greater Caribbean manatees (Trichechus manatus manatus) in tropical environments where direct observation is limited. While passive acoustic monitoring has proven effective for manatee detection and individual identification, the ability to classify sex and age from vocalizations remains unexplored, limiting ecological insights into population structure and reproductive dynamics. We investigated whether machine learning can accurately classify sex and age from manatee acoustic signals using 1,285 vocalizations from 20 wild individuals captured in the Changuinola River, Panama. Acoustic features including spectral envelope descriptors (MFCCs), harmonic content (chroma), and temporal-frequency parameters were extracted and analyzed using two feature sets: SET1 (30 spectral-cepstral features) and SET2 (38 features augmented with explicit pitch and temporal descriptors). Four classification algorithms (Random Forest, XGBoost, SVM, LDA) were trained under Leave-One-Group-Out cross-validation with SMOTE oversampling to address class imbalance. Sex classification achieved 85%–87% accuracy (75%–78% macro-F1) with balanced performance across both classes (female: 86%, male: 79%), validating operational feasibility for passive monitoring applications. However, subject-level bootstrap analysis revealed substantial individual heterogeneity (female: 95% CI: 68.7%–96.4%, male: 75.1%–83.6%), indicating that approximately 10%–15% of individuals exhibit systematic misclassification due to atypical acoustic signatures. Spectral envelope characteristics (MFCCs, spectral skewness) rather than fundamental frequency were most discriminative, suggesting sex-related variation manifests in vocal tract resonance patterns. Age classification achieved 73%–85% global accuracy but exhibited severe juvenile under-detection (14%–26% recall), with bootstrap confidence intervals spanning 9.3%–86.3% for juveniles vs. 60.7%–84.7% for adults. Dimensionality reduction (PCA, t-SNE) revealed substantial overlap between juvenile and adult acoustic feature distributions, with clearer age structure visible primarily within female clusters, contributing to systematic misclassification of male juveniles. Threshold optimization improved juvenile recall to 63% but increased false positives to 37%, presenting trade-offs for conservation surveillance. Acoustic body size regression demonstrated promising continuous estimation (MAE = 0.208 m, R2 = 0.33), offering an alternative to categorical age classification by enabling coarse demographic profiling when integrated with sex inference. These findings establish the operational viability of acoustic sex classification for manatee conservation while highlighting fundamental challenges in categorical age inference due to continuous ontogenetic variation and limited juvenile samples. However, acoustic body size regression offers a promising complementary approach, enabling continuous demographic profiling across size classes rather than discrete age categories. Integration with established individual identification frameworks would enable comprehensive acoustic mark-recapture, simultaneously estimating abundance, sex ratios, size distributions, and demographic structure from long-term hydrophone deployments without requiring visual confirmation of body dimensions.

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