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

Environmental management·2026
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Data-Driven Ecosystem Modeling for Sustainable Fish Species Management in Protected Areas Using the FP-Growth

Cistina Cismaș1, Daniel Hunyadi2

  • 1"Lucian Blaga" University of Sibiu, Faculty of Sciences, Sibiu, Romania. cristina.cismas@ulbsibiu.ro.

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|November 19, 2025
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Summary

Association rule mining (ARM) identified key conservation actions for 13 fish species in Romania. This data-driven approach reveals interdependent management strategies for effective protected area planning.

Keywords:
Association rulesBiodiversity conservationData miningEcosystem modelingProtected areasSustainable management

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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.