Exploring coral reef communities in Puerto Rico using Bayesian networks.

Area of Science:

• Marine Biology
• Ecology
• Environmental Science

Background:

• Coral reef health assessments traditionally focus on scleractinian (stony) corals.
• Other key reef assemblages like fish, gorgonians, and sponges are often studied in isolation.
• A comprehensive, community-level perspective is needed for robust ecological evaluation.

Purpose of the Study:

• To develop and apply a community-based analytical framework for Puerto Rican coral reefs.
• To explore inter-assemblage relationships and community structure patterns.
• To enhance ecological risk assessment and ecosystem service evaluation.

Main Methods:

• Utilized probabilistic clustering and Bayesian networks to analyze community structure.
• Employed an expectation-maximization algorithm to create taxon-specific clusters.
• Identified cross-taxa relationships and developed meta-factor clusters.

Main Results:

• Found stronger associations within than between taxa, with three significant cross-taxa relationships identified.
• Developed clusters characterizing density, size, and diversity for each assemblage.
• Demonstrated relationships between stony coral clusters and fish variables.

Conclusions:

• A community-level approach provides a more holistic understanding of coral reef ecosystems.
• Taxon-specific and meta-clusters offer valuable tools for regional and site-specific ecological monitoring.
• This framework supports improved risk assessment, management, and ecosystem service delivery in coral reefs.