Mining Complex Ecological Patterns in Protected Areas: An FP-Growth Approach to Conservation Rule Discovery

Area of Science:

• Ecology and Conservation Biology
• Data Science and Machine Learning
• Fisheries Management

Background:

• Sustainable management of fish species is crucial for Romania's Natura 2000 protected areas.
• Ecological monitoring data over seven years provides a basis for understanding fish population dynamics and habitat needs.

Purpose of the Study:

• To develop a data-driven framework for enhancing sustainable fish management.
• To identify interdependent conservation measures for improving fish species resilience and habitat conditions.

Main Methods:

• Applied association rule mining (ARM), specifically the FP-Growth algorithm, to analyze ecological data.
• Utilized seven years of monitoring data for 13 fish species and 19 codified conservation measures.
• Encoded expert habitat assessments into binary transactions to identify co-occurrence patterns.

Main Results:

• Extracted 44 robust association rules revealing interdependent management actions.
• Identified high-confidence co-occurrence patterns among conservation measures.
• Demonstrated the effectiveness of ARM in uncovering actionable insights for conservation planning.

Conclusions:

• The framework provides a scalable and interpretable method for evidence-based conservation planning.
• Association rule mining offers practical relevance for adaptive ecological decision-making in protected areas.
• Results support integrated management strategies for biodiversity conservation.