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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.07076 |
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| _version_ | 1866911100537143296 |
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| author | Aloisi, Valeria Noce, Sergio Epicoco, Italo Cipriano, Cristina Cafaro, Massimo Brundu, Giuseppe Arcidiaco, Lorenzo Spano, Donatella Aloisio, Giovanni Mereu, Simone |
| author_facet | Aloisi, Valeria Noce, Sergio Epicoco, Italo Cipriano, Cristina Cafaro, Massimo Brundu, Giuseppe Arcidiaco, Lorenzo Spano, Donatella Aloisio, Giovanni Mereu, Simone |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07076 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Application of association rule mining to assess forest species distribution in Italy considering abiotic and biotic factors Aloisi, Valeria Noce, Sergio Epicoco, Italo Cipriano, Cristina Cafaro, Massimo Brundu, Giuseppe Arcidiaco, Lorenzo Spano, Donatella Aloisio, Giovanni Mereu, Simone Computational Engineering, Finance, and Science J.2 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. |
| title | Application of association rule mining to assess forest species distribution in Italy considering abiotic and biotic factors |
| topic | Computational Engineering, Finance, and Science J.2 |
| url | https://arxiv.org/abs/2508.07076 |