Demographic Identification of Greater Caribbean Manatees via Acoustic Feature Learning

Fernando Merchan¹Kenji Contreras¹Héctor Poveda¹Rocío M. Estévez²Hector M. Guzman²Javier E. Sanchez-Galan^1*

• ¹Universidad Tecnologica de Panama, Panama, Panama
• ²Smithsonian Tropical Research Institute, Panama City, Panama

Demographic inference from vocalizations is essential for monitoring endangered Greater Caribbean manatees (\textit{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 (female: 86%, male: 79%), validating operational feasibility. However, subject-level bootstrap analysis revealed substantial individual heterogeneity (female 95% CI: 68.7--96.4%, male: 75.1--83.6%), indicating 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 versus 60.7--84.7% for adults. Dimensionality reduction revealed substantial overlap between juvenile and adult acoustic distributions, with clearer age structure primarily within female clusters. Threshold optimization improved juvenile recall to 63% but increased false positives to 37%. Acoustic body size regression demonstrated promising continuous estimation (MAE = 0.208 m, $R^2 = 0.33$). These findings establish operational viability of acoustic sex classification for manatee conservation while highlighting fundamental challenges in categorical age inference due to continuous ontogenetic variation. Integration with 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 visual confirmation.