An Study On Spatial Distribution Pattern And Sampling Optimization Of Oncomlania Snail

Oncomelania hupensis is the only intermediate host of Schistosoma japonicum, which plays an important role in the popularity and spread of schistosomiasis. The research can help understand the snail population ecological characteristics, and also provide the basis for snail survey and the molluscicidal planning, and the prevention and control of schistosomiasis as well. Accurately identifying the Oncomelania hupensis’ spatial distribution pattern and taking measures to exterminate them have vital significance to blocking the spread of the schistosomiasis.Use was made of a "push-broom" method to record the snail number at all spatially continuous cells(each is of 0.33m*0.33 m in size) in a quadrate field of 50m*50m size in November 2013 in the Chayegang Marshland near Henghu Farm of Xinjian County in the Poyang Lake region. The classical statistics and geostatistics were adoptd to research the spatial pattern of the snail distribution at different sample sizes of the experimental field. Comparison of the sampling errors was made between traditional sampling method and geostatistics sampling method to seek the best sampling method. The results and conclusions are as follows:(1)6 cluster indicators and two regression models were employed to study the spatial distribution pattern and sampling technique. The calculation results show that, the snail populations are in aggregated distribution pattern in the all sample unit sizes(from 13m*13m to 173m*173m); the basic component of the snail distribution is Individual group, the aggregation strength increases with the increase of the snail density, which is introduced by aggregation behavior habits of the snail and environmental factors, or snail itself.(2) Through the comparison of global spatial autocorrelation, the snail populations are in aggregated distribution pattern(HH area or LL area proximity) in the all sample unit sizes(from 13m*13m to 173m*173m), the degree of the spatial positive correlation is high. The correlation reaches to the highest value 0.919 at the sample size of 6*6 cells(or 2m*2m).(3) The local Moran scatter plots and local Moran’I indicates that local spatialautocorrelation is significant in the all sample unit sizes(from 13m×13m to 173m×173m)). The snail populations are in aggregated distribution pattern(“high-high” and “low-low” spatial aggregation). It intuitively reflects the overshadowed information by the global spatial analysis. The smaller is the sample size the more clearly shown the changes of snail distribution in the experimental foeld, which is more impressive in demonstrating the spatial instability of the snail distribution.(4)Comparing the results of three different sampling methods, we find the spatially stratified sampling based on Elevation as a spatial ancillary data needs fewest samples and produces smallest absolute errors. This implies we could adopt this method in future snail surveys(5)The optimum number of sample calculation formula in different snail densities is N=(5.4951/m+0.7591)/D2 and that of the Sequential sampling mode is T0(n)=m0n±(5.4951m0+ 0.7591m02)n, providing scientific grounds for optimizing the field snail sampling scenarios.