Abstract: Objective To compare the effects of two common ecological niche models, maximum entropy (Maxent) and genetic algorithm for rule-set production (GARP), in prediction of the potential risk areas of epizootic plague. Methods Logistic regression was used to screen for climate and environment-related risk factors for the epizootic situation of Meriones unguiculatus. The Maxent and GARP models were independently used to predict the potential distribution of epizootic plague in M. unguiculatus. Results The following factors were screened out and significantly associated with epizootic plague in M. unguiculatus (P<0.05):elevation, seasonal variation in air temperature, the highest temperature in the hottest month, mean temperature of the driest quarter, mean temperature of the coldest quarter, annual precipitation, precipitation of the wettest quarter, precipitation of the hottest quarter, mean precipitation in February, mean precipitation in May, mean precipitation in August, and mean precipitation in September. The areas under the receiver operating characteristic curves (AUCs) for the training set and test set of the Maxent model were 0.989 and 0.987, respectively. AUCs for the training set and test set of the GARP model were 0.961 and 0.958, respectively. According to the Maxent model, the potential risk areas of epizootic plague accounted for 89.45% of the total area of the foci of M. unguiculatus plague, and the moderate-to-high risk areas accounted for 86.63% of the total area. The GARP model predicted that the potential and moderate-to-high risk areas accounted for 96.43% and 48.57% of the total area, respectively. Conclusion Both ecological niche models have good performance for accurately and reliably predicting the potential risk areas of the epizootic situation of M. unguiculatus plague. The Maxent model has more accurate prediction, while the GARP model predicts a larger spatial extent. Selection of predictive models can be made according to actual needs.

Key words: Plague, Genetic algorithm for rule-set production model, Maximum entropy model

摘要： 目的 比较2种常用的生态位模型最大熵（Maxent）模型和规则集遗传算法（GARP）模型在动物间鼠疫疫情潜在风险区预测中的应用效果。方法 采用logistic回归筛选长爪沙鼠动物间疫情与气候环境相关危险因素，利用Maxent和GARP模型分别预测长爪沙鼠动物间疫情潜在分布。结果 经筛选有海拔、温度季节变化、最热月份最高温度、最干季平均温度、最冷季平均温度、年降水量、最湿季降水量、最热季降水量、2月平均降水量、5月平均降水量、8月平均降水量、9月平均降水量与长爪沙鼠动物间疫情有统计学意义（P<0.05）。Maxent模型的受试者工作特征（ROC）曲线训练集和测试集的受试者工作特征曲线下面积（AUC值）分别为0.989和0.987；GARP模型ROC曲线训练集和测试集的AUC值分别为0.961和0.958。Maxent模型预测结果中，在长爪沙鼠鼠疫疫源地内预测为有动物间疫情潜在风险的面积占该疫源地总面积的89.45%，预测为动物间疫情潜在中、高风险区在长爪沙鼠鼠疫疫源地内面积占其总面积的86.63%；GARP模型预测结果中，在长爪沙鼠鼠疫疫源地内预测为有潜在风险的面积占该疫源地总面积的96.43%；预测为动物间疫情潜在中、高风险区在长爪沙鼠鼠疫疫源地内的面积占其总面积的48.57%。结论 利用2种生态位模型预测长爪沙鼠动物间疫情潜在风险区均有较好的效果，结果准确可靠。Maxent模型预测结果更为准确，GARP模型预测范围更为广泛，可根据实际需求选择预测模型。

关键词: 鼠疫, 规则集遗传算法模型, 最大熵模型