标题： 关联规则挖掘在考虑非生物和生物因素的情况下评估意大利森林物种分布的应用 显示英文标题

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

摘要： 生物多样性监测是一项紧迫的全球优先事项，由于其对生态系统功能的影响，评估森林群落组成起着关键作用。 森林物种的空间分布对于理解生物多样性动态、领土规划、促进自然保护以及在全球变化中增强生态系统韧性至关重要。 关联规则挖掘通常应用于其他科学领域，现在被创新地应用于生态领域，以探索共现植物物种之间的关系并提取隐藏的可解释模式，同时也考虑了非生物和生物条件。 通过数据预处理，多种异构数据源被整合成一个独特的数据集，包括意大利6,784个样方内151种植物的地理参考信息以及多个生物气候指数、土壤相关因素和地球观测变量。 用于关联规则挖掘的频繁模式增长算法提供了有趣且鼓舞人心的发现，表明植物物种和环境条件之间存在生态规则。 事实上，温度季节性在650-700之间和降水季节性在45-50之间与冷杉（confidence = 90.9%，lift = 7.13）高度相关。 检测到的冷杉模式突显了其生态特异性，表明它与寒冷、高度季节性的环境以及特定的植物群落有很强的关联。 一些物种似乎充当着社区“中心”，经常与其他物种共同出现，表明与特定环境或生物条件的联系。 这些发现为未来的研究提供了宝贵的资源，特别是在具有相似环境条件的地区以及当存在先前的生态知识时，也强调了公开可访问的高质量生态数据的重要性。 显示英文摘要

摘要： 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.