| 基于MaxEnt模型的双条杉天牛全球适生区预测及分析 |
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| 引用本文：孙漂,李涵,郝恩华,陆鹏飞,乔海莉.基于MaxEnt模型的双条杉天牛全球适生区预测及分析.植物保护学报,2024,51(6):1506-1517 |
| DOI：10.13802/j.cnki.zwbhxb.2024.2023110 |
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| 中文摘要:为防止双条杉天牛Semanotus bifasciatus进一步扩散及制订相关检疫措施，收集双条杉天牛现有的分布点数据，从19个生物气候变量中筛选关键的生物气候变量，优化调控倍频及特征组合等模型参数，利用优化后的MaxEnt模型预测当前气候条件下及未来气候条件下双条杉天牛在全球的适生区。结果显示：共筛选148个分布点数据；优化后的调控倍频为0.2，特征组合为二次型Q；预测模型受测试者工作特征曲线（receiver operating characteristic curve，ROC）与横坐标围成的区域面积（area under ROC curve，AUC）值为0.982，表明模型的预测精度较高；最干季均温和最冷季均温是影响双条杉天牛分布的主要因素；未来气候条件低强迫情景SSP126下双条杉天牛在全球的适生区面积增大，且呈现向北扩张的趋势。 |
| 中文关键词:双条杉天牛 MaxEnt模型 气候变化 潜在地理分布 |
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| Prediction and analysis of global suitability of juniper bark borer Semanotus bifasciatus based on MaxEnt model |
| Author Name | Affiliation | E-mail | | Sun Piao | State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China | | | Li Han | State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China | | | Hao Enhua | State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China | | | Lu Pengfei | State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing 100083, China | lpengfei224@126.com | | Qiao Haili | Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100193, China | hlqiao@implad.ac.cn |
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| Abstract:To prevent the further spread of juniper bark borer Semanotus bifasciatus and to establish relevant quarantine measures, distribution data of S. bifasciatus were collected. Key environmental variables were selected from 19 bioclimatic variables, and model parameters such as regularization multiplier and feature combinations were optimized. The MaxEnt model was then used to predict the global suitable habitat of S. bifasciatus under current and future climate scenarios. The results showed that a total of 148 distribution data points were screened; the optimized regularization multiplier was 0.2, and the feature combination was quadratic (Q). The area under the receiver operating characteristic curve (AUC) value of the prediction model was 0.982, indicating high prediction accuracy. The mean temperatures of the driest and coldest quarter were the main factors affecting the distribution of S. bifasciatus. Under the low-forcing scenario SSP126 for future climate conditions, the suitable habitat area for S. bifasciatus globally is expected to increase, showing a trend of northward expansion. |
| keywords:Semanotus bifasciatus MaxEnt model climate change potential geographic distribution |
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