目的 研究简单随机抽样和空间分层抽样方法在诱蚊诱卵器监测的应用效果。方法 利用上海市2020年8月4日至9月1日138个诱蚊诱卵器的4次监测数据。使用ArcGIS 10.8软件计算全局空间自相关Moran's I指数和局部Moran's I指数评估样本空间相关性和异质性。采用蒙特卡罗（Monte Carlo）模拟1 000次的方法进行简单随机抽样和空间分层抽样，对抽样的结果计算绝对误差与抽样效率，评价不同抽样方法的精度和分层效率。空间抽样方法包括九宫格空间分层抽样、基于树冠面积的空间分层抽样和基于诱蚊诱卵器监测结果的空间分层抽样。结果 研究期间，4次监测的平均诱蚊诱卵指数为49.46。不同半径距离的全局空间自相关分析显示，空间自相关峰值半径为45 m，Moran's I指数为0.289，Z值为7.874（P<0.001）。局部空间自相关分析显示，在研究区域西北角呈现白纹伊纹高密度与高密度聚集区，西南角为低密度与低密度聚集区，东侧大部分为无聚集区。4种抽样方法绝对误差均随着抽样量的增加而逐步减小，其中基于诱蚊诱卵器监测结果的空间分层抽样绝对误差最小，抽样效率最高；其次为基于树冠面积的空间分层抽样和九宫格空间分层抽样。结论 空间分层抽样可以提高诱蚊诱卵器监测效率，不同的分层方法具有不同的效率值，基于先验知识的样点选择的空间分层抽样需做进一步研究。
Objective To study the effects of simple random sampling and spatially stratified sampling in mosq-ovitrap monitoring. Methods The data of 138 mosq-ovitraps from August 4 to September 1 of 2020 were taken as the whole population. ArcGIS 10.8 software was used to calculate global Moran's I and local Moran's I to evaluate the spatial correlation and heterogeneity of samples. The Monte Carlo simulation was run 1 000 times for simple random sampling and spatially stratified sampling. The absolute error and sampling efficiency were calculated to evaluate the precision and efficiency of stratification of different sampling methods. The spatial sampling methods included 3×3 grid stratified sampling, tree crown area-based stratified sampling, and stratified sampling based on mosq-ovitrap monitoring results. Results During the study, the mean mosq-ovitrap index of four monitoring activities was 49.46. The global spatial autocorrelation analysis of different radius distances showed that spatial autocorrelation peaked at a radius distance of 45 m, with the Moran's I index of 0.289 and Z value of 7.874 (P<0.001). The local spatial autocorrelation analysis showed that the northwest corner of the study area had clustering of high-density areas, the southwest corner had clustering of low-density areas, and most of the east had no clustering. The absolute error of the four sampling methods decreased gradually with the increase in sample size. Spatially stratified sampling based on mosq-ovitrap monitoring results had the smallest absolute error and the highest sampling efficiency, followed by tree crown area-based stratified sampling and 3×3 grid stratified sampling. Conclusion Spatially stratified sampling can improve the efficiency of mosq-ovitrap monitoring, and the efficiency varies among different stratified methods. Spatially stratified sampling based on prior knowledge needs further study.
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