Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades.

ABSTRACT: Stable isotopes are an important tool to uncover animal migration. Geographic natal assignments often require categorizing the spatial domain through a nominal approach, which can introduce bias given the continuous nature of these tracers. Stable isotopes predicted over a spatial gradient (i.e., isoscapes) allow a probabilistic and continuous assignment of origin across space, although applications to marine organisms remain limited. We present a new framework that integrates nominal and continuous assignment approaches by (1) developing a machine-learning multi-model ensemble classifier using Bayesian model averaging (nominal); and (2) integrating nominal predictions with continuous isoscapes to estimate the probability of origin across the spatial domain (continuous). We applied this in

SUBMITTER: Arai K 

PROVIDER: S-EPMC10231828 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature