Data-Driven Ecosystem Modeling for Sustainable Fish Species Management in Protected Areas Using the FP-Growth...

Area of Science:

• Ecological modeling
• Conservation science
• Data mining

Background:

• Conservation planning requires actionable ecological insights.
• Protected areas, like Romania's Natura 2000 network, face complex management challenges.
• Identifying synergistic conservation actions is crucial for species protection.

Purpose of the Study:

• To develop a data-driven framework for conservation planning using association rule mining (ARM).
• To uncover frequent co-occurrence patterns among conservation actions for 13 fish species.
• To provide evidence-based guidance for integrated conservation strategies.

Main Methods:

• Applied the FP-Growth algorithm, a type of ARM, to ecological and management datasets.
• Encoded expert-evaluated habitat indicators into binary transactional data.
• Identified high-confidence association rules revealing interdependent management measures.

Main Results:

• Discovered 44 high-confidence association rules specific to the studied fish species and protected areas.
• Revealed interdependent management actions essential for maintaining favorable species conservation statuses.
• Demonstrated the utility of ARM in uncovering complex ecological relationships.

Conclusions:

• ARM offers a scalable and replicable method for adaptive management in protected area networks.
• The extracted rules enhance decision-making efficiency for stakeholders and policymakers.
• Findings align with ecosystem-based management and metapopulation dynamics, strengthening ecological interpretability.