Morphological traits and machine learning for genetic lineage prediction of two reef-building corals.

ABSTRACT: Integrating multiple lines of evidence that support molecular taxonomy analysis has proven to be a robust method for species delimitation in scleractinian corals. However, morphology often conflicts with genetic approaches due to high phenotypic plasticity and convergence. Understanding morphological variation among species is crucial to studying coral distribution, life history, ecology, and evolution. Here, we present an application of Random Forest models for coral species identification based on morphological annotation of the corallum and corallites. We show that the integration of molecular and morphological trait analysis can be improved using machine learning. Morphological traits were documented for Porites and Pocillopora coral species that were collected and genotyped through ge

SUBMITTER: Mitushasi G 

PROVIDER: S-EPMC12176140 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature