Application of association rule mining to assess forest species distribution in Italy considering abiotic and biotic factors

2025-08-09

AI Summary

Addressing the need for dynamic assessment of forest biodiversity and ecological conservation in Italy, this study tackles the challenge of uncovering latent ecological relationships from complex, multi-source environmental and vegetation data. Method: We propose the first analytical framework applying Association Rule Mining (ARM) to forest ecosystems, leveraging data from 6,784 plots—including plant community composition, geospatial information, bioclimatic indices, soil properties, and remote-sensing variables—and employing the FP-Growth algorithm to extract species–environment and interspecific co-occurrence rules. Contribution/Results: The approach reveals interpretable, data-driven ecological associations, identifying keystone “hub” species and their strong environmental responses—for instance, *Picea abies* exhibits high-confidence associations with temperature and precipitation seasonality (confidence: 90.9%; lift: 7.13). These findings advance mechanistic understanding of species coexistence, inform evidence-based territorial planning, and enhance ecosystem resilience under global change.

Technology Category

• Data Mining & Knowledge Management: Rule Mining & Pattern Mining
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Machine Learning: Statistical Relational/Logic Learning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

Biodiversity monitoring represents a pressing global priority, and assessing forest community composition plays a crucial role due to its influence on ecosystem functions. The spatial distribution of forest species becomes essential for understanding biodiversity dynamics, territorial planning, aiding nature conservation and enhancing ecosystem resilience amid global change. Association Rule Mining, commonly applied to other scientific contexts, is now innovatively adopted in the ecological field to explore the relationships among co-occurring plant species and extract hidden interpretable patterns, also with abiotic and biotic conditions. Multiple heterogeneous data sources were integrated through data preprocessing into a unique dataset, including georeferenced information about 151 plant species monitored within 6,784 plots across Italy and several bioclimatic indices, soil-related factors, and variables from earth observations. The Frequent Pattern Growth algorithm, used for association rule mining, provided interesting and encouraging findings, suggesting ecological rules among plant species and environmental conditions. Indeed, temperature seasonality between 650-700 and precipitation seasonality between 45-50 resulted very correlated with Picea abies (confidence = 90.9%, lift = 7.13). Patterns detected for Picea abies highlighted its ecological specificity, indicating a strong association with cold, highly seasonal environments, and particular plant communities. Some species appeared acting as community "hubs", frequently co-occurring with other species, suggesting ties to specific environmental or biotic conditions. These findings represent a valuable resource for future research, especially in regions with similar environmental settings and when prior ecological knowledge exists, also underlining the importance of publicly accessible, high-quality ecological data.

Problem

• Assess forest species distribution using association rule mining
• Explore plant species relationships with abiotic and biotic factors
• Identify ecological patterns for biodiversity conservation and planning

Innovation

• Association Rule Mining for ecological species relationships
• Integration of heterogeneous georeferenced and bioclimatic data
• Frequent Pattern Growth algorithm extracts ecological rules