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

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Publicado en:Entropy vol. 27, no. 7 (2025), p. 725-742
Autor principal: Hunyadi, Ioan Daniel
Otros Autores: Cismaș Cristina
Publicado:
MDPI AG
Materias:
European Union
Romania
Water quality
Ecology
Data mining
Datasets
Decision making
Codification
Biodiversity
Data analysis
Conservation
Algorithms
Strategic planning
Habitats
Ecological monitoring
Fish
Protected areas
Entropy
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1099-4300
DOI:10.3390/e27070725
Fuente:Engineering Database