Novel machine learning methodologies to unpack the complexity of longitudinal movement patterns of facultative amphidromous fish
Rodrigo Ramirez-Alvarez est doctorant à l’Université de Concepcion.
Longitudinal movement and migration play fundamental role in habitat colonization and population establishment of many riverine fish species. Amphidromy is the most frequent fish migratory strategy.
However, to date movement patterns of amphidromous fish species at fine-scales that would allow characterising the direction of movement and factors associated with the establishment of specific life-history strategies in rivers are largely unknown.
We evaluate longitudinal movement variability patterns of facultative amphidromous model fish Galaxias maculatus in order to unfold its life-history variation and associated recruitment habitats.
To do this, we analyzed multi-elemental composition of their ear-bones (otoliths) using machine learning techniques. Assessment of early life-histories identified variation of recruitment habitats, including amphidromous and resident populations with larval stages in headwater lakes, floodplain habitats and estuaries.
As such, resident recruitment of G. maculatus in freshwater and estuarine habitats was more frequent and was strongly linked to habitat heterogeneity. We postulate that life-history variation of facultative amphidromous fish populations is strongly linked to natural hydrologic connectivity and availability of specific recruitment habitats.
We assess migratory patterns using novel machine learning methodologies that could have broad applications in biological research.
Rodrigo Ramírez‑Álvarez, Sergio Contreras, Aurélien Vivancos, Malcolm Reid, Ruby López‑Rodríguez & Konrad Górski.
Laboratoire METIS, Sorbonne Université, Campus Pierre et Marie Curie
4, place Jussieu 75005 Paris
Salle de réunion n°431, tour 56-66, 4e étage.
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