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

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

Ioan Daniel Hunyadi1, Cristina Cismaș1

  • 1Department of Mathematics and Informatics, Faculty of Science, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania.

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|July 29, 2025
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Summary

This study uses data mining to find linked conservation actions for fish in Romania

Keywords:
association rule miningcomplex ecosystem patternsecological information systemsentropy-based data analysisfish species distribution modelingknowledge discovery in databasesprotected area management

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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.