基于元胞自动机模型的松材线虫病小班尺度预测

doi:10.11707/j.1001-7488.LYKX20240521

摘要/Abstract

摘要：

目的: 为探究影响松材线虫病传播扩散的主要影响因素，结合自然气候、人类活动以及地理空间特征多源数据，围绕松材线虫病“传入-定殖-扩散”的生态入侵过程，构建适用于更小空间尺度数据的传播预测模型，实现对松材线虫病高风险发生地区的精准预测和早期预警。方法: 基于国家林业和草原局公布的江苏省松材线虫病小班本底发生数据，结合松材线虫病的生态特性和地理空间分布规律，选取包含自然气候、人类活动因素以及空间特征等25项影响因子数据，采用主成分分析方法进行数据预处理，通过Spearman相关性分析方法和Apriori数据挖掘算法，探究各影响因子与松材线虫病发生之间的相互作用关系。结合贝叶斯估计方法对影响因子数据进行特征增强，建立灰狼优化算法-元胞自动机模型模拟松材线虫病的传播扩散过程，同时与其他5种主流机器学习模型预测结果进行横向对比验证，通过计算其精确率、召回率和AUC等评价指标对模型性能进行验证。结果: 构建的灰狼优化算法-元胞自动机模型在松材线虫病新发小班预测中表现出优异的性能，模型召回率达到78.5%，显著优于其他5种主流机器学习模型；同时，其AUC值达到89.0%，表明模型在识别新发疫情点位的同时，兼顾较高的整体预测准确性与判别能力。本研究进一步证实地理空间特征在松材线虫病传播预测中的重要性，并验证元胞自动机模型在处理复杂时空数据和更精细尺度空间数据预测方面的高度适用性。结论: 木材运输是驱动松材线虫病传播扩散的关键因素，而温度与降水的差异也在显著程度上影响其发生风险。作为一种融合空间异质性与时间动态特征的建模方法，元胞自动机模型在处理复杂生态数据与入侵物种风险评估方面展现出较高的适用性与灵活性，可为松材线虫病的精准防控与高效管理提供有力的技术支撑。

关键词: 松材线虫病, 传播预测模型, 大数据, 数据挖掘, 元胞自动机

Abstract:

Objective: In this study, multi-source data comprising natural climate variables, anthropogenic activity indicators, and geospatial features is used to analyze the key factors influencing the spread and expansion of pine wilt disease (PWD). Focusing on the ecological invasion process of PWD of ‘introduction-colonization-expansion’, a predictive model applicable at a fine spatial scale is constructed, aiming to achieve precise identification and early warning of high-risk outbreak areas of pine wilt disease. Method: This study utilized subcompartment-level outbreak records of PWD in Jiangsu Province published by the National Forestry and Grassland Administration of China. Based on the ecological characteristics and spatial distribution patterns of PWD, a total of 25 influencing variables were selected, covering natural climate conditions, human activity, and spatial features. Principal Component Analysis (PCA) was used for dimensionality reduction, and Spearman correlation analysis and the Apriori data mining algorithm were applied to examine the interactions between each influencing factor and the occurrence of PWD. Bayesian estimation was employed to enhance the feature of the variables. A Grey Wolf Optimizer-Cellular Automata (GWO-CA) model was constructed to simulate the spatiotemporal spread of PWD. The model’s predictive performance was further evaluated through horizontal comparison with five mainstream machine learning models, with precision, recall, and AUC as evaluation metrics. Result: The Grey Wolf Optimizer-Cellular Automata model developed in this study exhibited excellent performance in predicting the new occurrence of pine wilt disease in subcompartment. The model achieved a recall rate of 78.5%, and significantly outperformed the other five mainstream machine learning models. Additionally, the model yielded an AUC value of 89.0%, indicating a high level of predictive accuracy and discriminative ability in identifying new outbreak locations. This study also underscored the critical role of geospatial features in forecasting the spread of pine wilt disease, and confirmed the strong suitability of cellular automata for modeling complex spatiotemporal data, especially at fine spatial scales. Conclusion: This study has identified timber transportation as a key driver of the spread of pine wood nematode, and temperature and precipitation differences also exert significant influence on outbreak risk. As a modeling approach that integrates spatial heterogeneity and temporal dynamics, the Cellular Automata model has proven to be highly adaptable and effective for complex ecological data analysis and invasive species risk assessment. It offers robust technical support for the precise prevention and efficient management of pine wilt disease.

Key words: pine wilt disease, spread prediction model, big data, cellular automata, data mining

中图分类号:

S763.18

周宏威,李永正,郭文辉,陈怡帆,胡浩昌,张思岩,崔迪,陈雨茉. 基于元胞自动机模型的松材线虫病小班尺度预测[J]. 林业科学, 2026, 62(1): 133-143.

Hongwei Zhou,Yongzheng Li,Wenhui Guo,Yifan Chen,Haochang Hu,Siyan Zhang,Di Cui,Yumo Chen. Prediction of Subcompartment-Scale Spread of Pine Wilt Disease Based on Cellular Automata Model[J]. Scientia Silvae Sinicae, 2026, 62(1): 133-143.

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影响因子类型 Environmental factor type	影响因子 Environmental factor	缩写 Abbreviation	数据来源 Data source
生物气候因子 Bioclimatic factor	19项生物气候因子 19 Bioclimatic factors	Bio1-Bio19	https://data.cma.cn/
地理环境因子 Geography factor	海拔 Elevation	Ele	https://data.cma.cn/
人为影响因子 Human influence	人口 Population	Pop	https://www.resdc.cn/
	地区生产总值 Gross domestic product	GDP	https://www.resdc.cn/
	道路密度 Road density	R.dens	http://geodata.pku.edu.cn
	木材运输量 Pine wood transportation	PWT	国家林业和草原局生物灾害防控中心 Center for Biological Disaster Prevention and Control, National Forestry and Grassland Administration
	疫区木材运输量 Infected pine wood transportation	IPWT

项集长度 Length	频繁项集 Frequent items	支持度 Support	频率 Occurrences
1	疫区木材运输量 Infected pine wood transportation: 中 Middle（3.11~8.52）	0.546	1 601
1	木材运输量 Pine wood transportation: 较高 Relatively high（349.15~2 951.08）	0.454	1 329
1	木材运输量 Pine wood transportation: 中 Middle（200.00~349.15）	0.423	1 238
1	疫区木材运输量 Infected pine wood transportation: 较高 Relatively high（8.52~167.83）	0.311	913
1	木材运输量 Pine wood transportation: 高 High（6 386.88 ~ 8 478.69）	0.208	610
2	降水类影响因子 Synthetic precipitation class: 低 Low（?1.94~?1.62），疫区木材运输量 Infected pine wood transportation: 中 Middle（3.11~8.52）	0.394	1 155
2	疫区木材运输量 Infected pine wood transportation: 较高 Relatively high（8.52~167.83），木材运输量 Pine wood transportation: 高 High（6 386.88~8 478.69）	0.312	913
2	降水类影响因子 Synthetic precipitation class: 低 Low（?1.94~?1.62），木材运输量 Pine wood transportation: 中 Middle（200.00~349.15）	0.188	551
2	温度类影响因子 Synthetic temperature class: 较低 Relatively low（?1.75~?1.43），疫区木材运输量Infected pine wood transportation: 中 Middle（3.11~8.52）	0.168	492
2	木材运输量 Pine wood transportation: 中 Middle（200.00~349.15），温度类影响因子 Synthetic temperature class: 较低 Relatively low（?1.75~?1.43）	0.156	455

项集长度 Length	频繁项集 Frequent items	支持度 Support	频率 Frequency
1	Bio5: 较高Relatively high（54.32~54.42）	0.337	423
1	Bio6: 高High（0.20~1.02）	0.327	410
1	Bio11: 中Middle（14.65~14.82）	0.316	396
1	Bio11: 较高Relatively high（14.99~20.75）	0.274	343
1	Bio6: 中（0.16~0.20）	0.271	340
2	Bio16: 较高Relatively high（809.40~834.40）， Bio18: 较高Relatively high（809.40~834.40）	0.229	287
2	Bio16: 高High（801.20~809.40），Bio18: 高High（801.20~809.40）	0.226	283
2	Bio1: 较高Relatively high（26.39~49.54）， Bio10: 较高Relatively high（37.53~119.48）	0.226	283
2	Bio1: 较高Relatively high（26.39~49.54），Bio11: 较高Relatively high（14.99~20.75）	0.217	272
2	Bio5: 较高Relatively high（54.32~54.42），Bio8: 较高Relatively high（37.84~38.24）	0.213	267

预测模型 Predict model	准确率 Accuracy	召回率 Recall	精确率 Precision	F1分数 F1 score	曲线下面积 AUC
CA	0.979	0.785	0.382	0.491	0.890
SVM	0.967	0.147	0.062	0.088	0.781
RF	0.989	0.270	0.517	0.355	0.699
CART	0.987	0.298	0.360	0.326	0.988
GLM	0.991	0.273	0.736	0.398	0.986
ANN	0.991	0.288	0.701	0.408	0.988

[1]	陈婷婷,程浩,叶建仁,李映,倪安顺,张娇. 松材线虫移动状况与早期诊断及治疗效果[J]. 林业科学, 2025, 61(7): 241-250.
[2]	李俊楠,付煜,陈润恺,徐云,蔡梦玲,罗圣洁,张继峰,吴松青,张飞萍. 松天牛小首螨的研究Ⅳ：环境及培养条件对寄生率的影响[J]. 林业科学, 2025, 61(5): 161-170.
[3]	徐有城,万兴永,陈兵,赵凤君,刘晓东,叶江霞. 基于精细化风速场模拟的山地林火蔓延[J]. 林业科学, 2025, 61(4): 56-68.
[4]	李志红,张威,赵新康,舒金平,王浩杰. 浙江省不同发生区松材线虫表型变异[J]. 林业科学, 2025, 61(1): 137-149.
[5]	谢婉滢,刘文萍,王晗. 无人机松林图像早期松材线虫病害检测[J]. 林业科学, 2024, 60(9): 124-133.
[6]	李俊楠,陈润恺,付煜,蔡梦玲,黄炳荣,徐云,吴松青,张飞萍. 松天牛小首螨的研究Ⅲ.携播特性[J]. 林业科学, 2022, 58(9): 128-137.
[7]	倪安顺,王永春,杨丹,孙学书,叶建仁. 一种新型复配药剂2%阿维菌素·6%氟吡菌酰胺对松材线虫毒力药效[J]. 林业科学, 2022, 58(8): 18-25.
[8]	李俊楠,陈润恺,付煜,蔡梦玲,黄炳荣,徐云,吴松青,张飞萍. 松天牛小首螨的研究Ⅱ.雌成螨的移动和趋性[J]. 林业科学, 2022, 58(8): 149-156.
[9]	李俊楠,朱禹臻,陈润恺,付煜,蔡梦玲,黄炳荣,徐云,吴松青,张飞萍. 松天牛小首螨的研究Ⅰ.生物学特性[J]. 林业科学, 2022, 58(6): 79-87.
[10]	王旻嘉,叶建仁,涂煜昇,杜方平. 处理松材线虫病疫木的木腐真菌筛选[J]. 林业科学, 2021, 57(6): 93-102.
[11]	叶建仁. 松材线虫病在中国的流行现状、防治技术与对策分析[J]. 林业科学, 2019, 55(9): 1-10.
[12]	孙婷玉, 王艳丽, 沈李元, 吴小芹, 朱丽华, 叶建仁. 培养基成分对黑松体胚发育成熟的影响[J]. 林业科学, 2019, 55(4): 178-186.
[13]	姚伍, 郑催云, 陈红梅, 刘侃诚, 韩正敏. 福建三明市应用Smal-007菌剂防治松材线虫病的效果[J]. 林业科学, 2018, 54(1): 168-173.
[14]	林丽, 周蕾, 潘珺, 康李鹏, 叶建仁, 朱丽华. 无菌和带菌松材线虫对赤松的致病性[J]. 林业科学, 2017, 53(5): 82-87.
[15]	田静, 邢艳秋, 姚松涛, 曾旭婧, 焦义涛. 基于元胞自动机和BP神经网络算法的Landsat-TM遥感影像森林类型分类比较[J]. 林业科学, 2017, 53(2): 26-34.