Advancing provenance assignment using machine learning and time series analysis of chemical chronologies in archival tissues

Accurate provenance assignment is critical for understanding ecological connectivity and guiding conservation and management. Natural chemical chronologies stored in metabolically inert, incrementally growing tissues (e.g., otoliths) provide a powerful tool for this purpose. However, traditional approaches face biological challenges, collapse chronological data into single values, and require subjective interpretation, limiting accuracy and scalability. We present a novel framework that integrates machine learning, time series analysis, and ensemble modeling to improve provenance assignment from archival tissue chemistry. Using otolith ⁸⁷Sr/⁸⁶Sr profiles from 17 distinct natal sources ( n = 255) of California Central Valley Chinook salmon, we developed automated feature extraction, explicit time series classification (including dynamic time warping [DTW] with k -nearest neighbors [KNN]), and ensembles combining multiple classifiers. Furthermore, to address gaps in under-sampled life histories within natal sources, we added simulated chemical profiles to the reference baseline and tested whether they improved model performance. Time series approaches (mean accuracy: 0.56–0.62) consistently outperformed traditional methods (0.48), particularly for sources influenced by maternal signatures or early dispersal. Feature extraction approaches performed best when profiles followed predictable life-stage patterns, while explicit time series classification (DTW + KNN) excelled for distinct profile “shapes”. Ensembles leveraged complementary strengths and outperformed any single method. Our results highlight the advantages of treating archival chemical data as time series and applying machine learning and ensemble strategies to enhance the accuracy and scalability of provenance assignment. This framework is broadly applicable across taxa, tissue types, and chemical markers, offering a roadmap for advancing ecological inference and conservation.