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

## TL;DR

This paper uses data mining to find patterns in conservation actions that help protect fish species in Romania's protected areas.

## Contribution

The novel application of FP-Growth algorithm to discover conservation rules from ecological data in Natura 2000 areas.

## Key findings

- 44 high-confidence association rules were identified linking conservation measures and fish species resilience.
- The approach shows ARM's effectiveness in revealing actionable insights for biodiversity management.
- The method is scalable and interpretable, offering a replicable framework for adaptive conservation planning.

## Abstract

This study introduces a data-driven framework for enhancing the sustainable management of fish species in Romania’s Natura 2000 protected areas through ecosystem modeling and association rule mining (ARM). Drawing on seven years of ecological monitoring data for 13 fish species of ecological and socio-economic importance, we apply the FP-Growth algorithm to extract high-confidence co-occurrence patterns among 19 codified conservation measures. By encoding expert habitat assessments into binary transactions, the analysis revealed 44 robust association rules, highlighting interdependent management actions that collectively improve species resilience and habitat conditions. These results provide actionable insights for integrated, evidence-based conservation planning. The approach demonstrates the interpretability, scalability, and practical relevance of ARM in biodiversity management, offering a replicable method for supporting adaptive ecological decision making across complex protected area networks.