| Peer-Reviewed

Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China

Received: 1 July 2016     Published: 5 July 2016
Views:       Downloads:
Abstract

Analysis of multi-scale spatial variance and anisotropy was the crucial that should be taken into consideration in optimization of spatial sampling. The aim of this study was to analyse the effect of sampling unit size on perception of spatial variance of Oncomelania snail, the unique schistosomiasis intermediate host. A "push-broom" method was used to investigate Oncomelania snail density in an experimental field, south-western Poyang lake, China, obtaining 22,500 sample cells distributed continuously whole-covered on a square of 50m*50m. Combinations of different number of basic sample cells served as spatial sample unit sizes (sample cells from 1*1 to 17*17). Geo-statistics was used to calculate the parameters range, nugget, sill of anisotropy variograms for different sample unit sizes to obtain the characteristics of their spatial variance. The results showed that the spatial variance had obvious sample unit size effects. The range had no relationship with the spatial unit sizes (about 50m), but the nugget and sill were associated with the sampling unit sizes. The nugget and the ratio of nugget by sill were inversely associated with sample unit sizes and the random fraction over total spatial variance decreases when sample unit sizes changed from 1*1 to 9*9. The nugget effect became stronger when sample unit sizes changed from 10*10 to 17*17, tallying with the semi-variogram theory. Otherwise, the sill and the difference between sill and the nugget were the biggest when the spatial unit sizes was 8*8. The study implied that the possible optimal sample unit size for explaining the spatial autocorrelation might be at combinations of 8*8 to 10*10 cells for this study field. In conclusion, when in the survey sampling should clearly choose the appropriate sample unit size.

Published in Science Discovery (Volume 4, Issue 3)
DOI 10.11648/j.sd.20160403.12
Page(s) 165-172
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Oncomelania snail, Sample Unit Size Effects, Spatial Variance, Geo-statistics

References
[1] 张法升,刘作新,张颖等. 农田土壤有机质空间变异的尺度效应[J].中国科学院研究生院学报,2009,26(3):350-356。
[2] Carroll S S and Pearson D L. The effects of scale and sample size on the accuracy of spatial predictions of tiger beetle (Cicindelidae) species richness[J]. Ecography 1998,21: 40l-414.
[3] Dungan J L, Perry J N, Dale M R T,et al. A balanced view of scale in spatial statistical analysis [J]. Ecography, 2002,25: 626–640.
[4] Vallejos R and Osorio F. Effective sample size of spatial process models[J]. Spatial statistics, 2014 (9): 66–92.
[5] Griff D A. Effective geographic sample size in the presence of spatial autocorrelation[J]. Annals of the Association of American geographers, 2005,95(4): 740-760.
[6] Wagenet R J. Scale issues in agroecological research chains[J] .Nutrient Cycling in Agroecosystems. 1998, 50: 23-34.
[7] 祝俊祥,王卷乐.基于半方差函数的复杂场景地物适宜尺度分析[J].地理与地理信息科学,2015, 31(4): 33-37。
[8] 潘瑜春,刘巧芹,阎波杰,等.采样尺度对土壤养分空间变异分析的影响[J].土壤通报,2010,41(2): 257-262。
[9] 霍霄妮,李红,张微微,等.北京耕作土壤重金属多尺度空间结构[J].农业工程学报.2009,25(3): 223-229。
[10] 王圣伟,冯娟,刘刚,等.多嵌套空间尺度农田土壤重金属空间变异研究[J].农业机械学报,2013, 44(6): 128-135。
[11] 葛剑平, 郭海燕,仲莉娜,地统计学在生态学中的应用(I)─基本理论和方法[J].东北林业大学学报, 995(02): 88-94.
[12] 李琳一,袁涛,陈旭.农业抽样调查中统计理论和抽样方法选取研究[J].上海农业学报,2011,27(4): 5-8。
[13] 陈雄文,张新时,周广胜等.中国东北样带(NECT)森林区域中主要树种空间分布特征[J].林业科学, 2000,36(6): 35-38。
[14] Vaclavik T, Kupfer J A and Meentemeyer R K. Accounting for multi-scale spatial autocorrelation improves performance of invasive species distribution modeling (iSDM) [J]. Journal of Biogeography, 2012 (39): 42–55.
[15] 潘杰,王涛,宗世祥等.红脂大小蠹种群空间格局第统计学分析及抽样技术 [J].生态学报,2011,31(1): 195-202。
[16] 周灿芳,余世孝,郑业鲁等.种群分布格局测定的样方尺度效应[J].广西植物, 2003, 23(1): 19-22。
[17] 中华人民共和国卫生部地方病防治司,血吸虫病防治手册 [M]. 1990,上海:上海科学技术出版社.29-51。
[18] 王延安,伍卫平,华政辉等,全国血吸虫病抽样调查样本量估算方法的探讨[J].中国寄生虫学与寄生虫病杂志, 1998(03): 76-77。
[19] 王延安,伍卫平,华政辉等血吸虫感染度几何均数的换算公式 [J]. 中国寄生虫学与寄生虫病杂志,1998(02):38。
[20] Cressie, N., Statistics for spatial data [M]. 1993, Wiley Interscience
[21] 王广德,过常龄,“Krige”空间内插技术在地理学中的应用 [J].地理学报,1987(04):366-375。
[22] 李天生,周国法,空间自相关与分布型指数研究 [J]. 生态学报,1994(03): 327-331。
[23] Rossi, R. E., Mulla D. J. and Journel A. G., Geostatistical tools for modeling and interpreting ecological spatial dependence [J]. Ecological. Monographs, 1992. 62(2): p. 277-314.
[24] 王红梅,王仲良,王堃等,华北农牧交错带农田-草地景观镶嵌体土壤水分空间异质 [J].生态学报, 2013(19): 6287-6294。
[25] Lichstein J W, Smons T R.and Shriner S A. Spatial autocorrelation and autoregressive models in ecology [J]. Ecological Monographs, 2002(72): p. 445-463.
[26] 赵安、孙九林、胡飞.微地理环境钉螺空间分布的各向异性克里格研究[J].武汉大学学报(理学版),2007,53(S1): 197-200。
[27] Fortin M J. Effects of sampling unit resolution on the estimation of spatial autocorrelation[J]. Ecoscience, 1999,6(4): 636-641.
[28] Clark I. Statistics or geostatistics? Sampling error or nugget effect?[J]. The Journal of The Southern African Institute of Mining and Metallurgy,2010,10: 307-312.
[29] 尤海舟,刘兴良,缪宁等.川滇高山栎种群不同海拔空间格局的尺度效应及个体间空间关联[J].生态学报,2010,30(15): 4004-4011。
[30] Hill B G, Mcnew R W, Young J H and Ruth W E. The effects of sampling-unit size in some southwestern oklahoma cotton insects[J]. Environmental Entomology, 1975,4(3): 491-494.
[31] Cox G.W.(蒋有绪译).普通生态学实验手册[M].北京:科学出版社,1979。
[32] Kershaw K A. Quantitative and Dynamic Ecology[M]. London, Edward Arnold Publishers,1964.
[33] Tobin P C. Estimation of the spatial autocorrelation function: consequences of sampling dynamic populations in space and time [J]. Ecography, 2004(27): 767-775.
[34] Kershaw K A. Quantitative and Dynamic Ecology[M]. Edward Arnold Publishers, 1964. London.
[35] Bellehumeur C and Legendre P. Aggregation of sampling units: an analytical solution to predict variance[J]. Geographical analysis, 1997, 29(3): 258-266.
[36] Rossi J P, Nuutinen A. The effect of sampling unit size on the perception of the spatial pattern of earthworm (Lumbricus terrestris L.) middens[J]. Applied Soil Ecology, 2004, 27: 189–196.
[37] 薛冬冬,佘光辉,温小荣等,基于地统计分析的南京钟山风景区景观格局尺度效应分析[J].西南林业大学学报, 2012(01): 30-35+111。
[38] 岳文泽,徐建华,谈文琦等, 城市景观多样性的空间尺度分析—以上海市外环线以内区域为例[J].生态学报, 2005(01): 122-128。
Cite This Article
  • APA Style

    Liu Qing, Zhao An, Ma Yukuan, Li Cui, Zhang Wenxin. (2016). Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China. Science Discovery, 4(3), 165-172. https://doi.org/10.11648/j.sd.20160403.12

    Copy | Download

    ACS Style

    Liu Qing; Zhao An; Ma Yukuan; Li Cui; Zhang Wenxin. Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China. Sci. Discov. 2016, 4(3), 165-172. doi: 10.11648/j.sd.20160403.12

    Copy | Download

    AMA Style

    Liu Qing, Zhao An, Ma Yukuan, Li Cui, Zhang Wenxin. Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China. Sci Discov. 2016;4(3):165-172. doi: 10.11648/j.sd.20160403.12

    Copy | Download

  • @article{10.11648/j.sd.20160403.12,
      author = {Liu Qing and Zhao An and Ma Yukuan and Li Cui and Zhang Wenxin},
      title = {Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China},
      journal = {Science Discovery},
      volume = {4},
      number = {3},
      pages = {165-172},
      doi = {10.11648/j.sd.20160403.12},
      url = {https://doi.org/10.11648/j.sd.20160403.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sd.20160403.12},
      abstract = {Analysis of multi-scale spatial variance and anisotropy was the crucial that should be taken into consideration in optimization of spatial sampling. The aim of this study was to analyse the effect of sampling unit size on perception of spatial variance of Oncomelania snail, the unique schistosomiasis intermediate host. A "push-broom" method was used to investigate Oncomelania snail density in an experimental field, south-western Poyang lake, China, obtaining 22,500 sample cells distributed continuously whole-covered on a square of 50m*50m. Combinations of different number of basic sample cells served as spatial sample unit sizes (sample cells from 1*1 to 17*17). Geo-statistics was used to calculate the parameters range, nugget, sill of anisotropy variograms for different sample unit sizes to obtain the characteristics of their spatial variance. The results showed that the spatial variance had obvious sample unit size effects. The range had no relationship with the spatial unit sizes (about 50m), but the nugget and sill were associated with the sampling unit sizes. The nugget and the ratio of nugget by sill were inversely associated with sample unit sizes and the random fraction over total spatial variance decreases when sample unit sizes changed from 1*1 to 9*9. The nugget effect became stronger when sample unit sizes changed from 10*10 to 17*17, tallying with the semi-variogram theory. Otherwise, the sill and the difference between sill and the nugget were the biggest when the spatial unit sizes was 8*8. The study implied that the possible optimal sample unit size for explaining the spatial autocorrelation might be at combinations of 8*8 to 10*10 cells for this study field. In conclusion, when in the survey sampling should clearly choose the appropriate sample unit size.},
     year = {2016}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Sample Unit Size Effects of Oncomelania hupensis snail on Its Spatial Statistics in the Marshland schistosomiasis Epidemic Area in China
    AU  - Liu Qing
    AU  - Zhao An
    AU  - Ma Yukuan
    AU  - Li Cui
    AU  - Zhang Wenxin
    Y1  - 2016/07/05
    PY  - 2016
    N1  - https://doi.org/10.11648/j.sd.20160403.12
    DO  - 10.11648/j.sd.20160403.12
    T2  - Science Discovery
    JF  - Science Discovery
    JO  - Science Discovery
    SP  - 165
    EP  - 172
    PB  - Science Publishing Group
    SN  - 2331-0650
    UR  - https://doi.org/10.11648/j.sd.20160403.12
    AB  - Analysis of multi-scale spatial variance and anisotropy was the crucial that should be taken into consideration in optimization of spatial sampling. The aim of this study was to analyse the effect of sampling unit size on perception of spatial variance of Oncomelania snail, the unique schistosomiasis intermediate host. A "push-broom" method was used to investigate Oncomelania snail density in an experimental field, south-western Poyang lake, China, obtaining 22,500 sample cells distributed continuously whole-covered on a square of 50m*50m. Combinations of different number of basic sample cells served as spatial sample unit sizes (sample cells from 1*1 to 17*17). Geo-statistics was used to calculate the parameters range, nugget, sill of anisotropy variograms for different sample unit sizes to obtain the characteristics of their spatial variance. The results showed that the spatial variance had obvious sample unit size effects. The range had no relationship with the spatial unit sizes (about 50m), but the nugget and sill were associated with the sampling unit sizes. The nugget and the ratio of nugget by sill were inversely associated with sample unit sizes and the random fraction over total spatial variance decreases when sample unit sizes changed from 1*1 to 9*9. The nugget effect became stronger when sample unit sizes changed from 10*10 to 17*17, tallying with the semi-variogram theory. Otherwise, the sill and the difference between sill and the nugget were the biggest when the spatial unit sizes was 8*8. The study implied that the possible optimal sample unit size for explaining the spatial autocorrelation might be at combinations of 8*8 to 10*10 cells for this study field. In conclusion, when in the survey sampling should clearly choose the appropriate sample unit size.
    VL  - 4
    IS  - 3
    ER  - 

    Copy | Download

Author Information
  • School of Geography and Environmental Sciences, Jiangxi Normal University, Nanchang City, China

  • School of Geography and Environmental Sciences, Jiangxi Normal University, Nanchang City, China

  • School of Geography and Environmental Sciences, Jiangxi Normal University, Nanchang City, China

  • School of Geography and Environmental Sciences, Jiangxi Normal University, Nanchang City, China

  • School of Geography and Environmental Sciences, Jiangxi Normal University, Nanchang City, China

  • Sections