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. 2019 Jun 21;16(12):2206.
doi: 10.3390/ijerph16122206.

Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis

Yingnan Niu  1   2 , Rendong Li  3 , Juan Qiu  4 , Xingjian Xu  5 , Duan Huang  6   7 , Qihui Shao  8   9 , Ying Cui  10   11
Affiliations

Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis

Yingnan Niu et al. Int J Environ Res Public Health. .

Abstract

Schistosomiasis is a snail-borne parasitic disease endemic to the tropics and subtropics, whose distribution depends on snail prevalence as determined by climatic and environmental factors. Here, dynamic spatial and temporal patterns of Oncomelania hupensis distributions were quantified using general statistics, global Moran's I, and standard deviation ellipses, with Maxent modeling used to predict the distribution of habitat areas suitable for this snail in Gong'an County, a severely affected region of Jianghan Plain, China, based on annual average temperature, humidity of the climate, soil type, normalized difference vegetation index, land use, ditch density, land surface temperature, and digital elevation model variables; each variable's contribution was tested using the jackknife method. Several key results emerged. First, coverage area of O. hupensis had changed little from 2007 to 2012, with some cities, counties, and districts alternately increasing and decreasing, with ditch and bottomland being the main habitat types. Second, although it showed a weak spatial autocorrelation, changing negligibly, there was a significant east-west gradient in the O. hupensis habitat area. Third, 21.9% of Gong'an County's area was at high risk of snail presence; and ditch density, temperature, elevation, and wetting index contributed most to their occurrence. Our findings and methods provide valuable and timely insight for the control, monitoring, and management of schistosomiasis in China.

Keywords: Maxent; Oncomelania hupensis; spatiotemporal changes; suitable habitats.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Location of the study area in China and O. hupensis snail samples.
Figure 2
Figure 2
Gross O. hupensis snail habitat area.
Figure 3
Figure 3
Coverage areas of O. hupensis in different land use types. (a) Tianmen City; (b) Jiangling County; (c) Qianjiang City; (d) Xiantao City; (e) Shashi District; (f) Songzi City; (g) Gong’an County; (h) Honghu City; (i) Jingzhou District; (j) Shishou City; (k) Jianli County.
Figure 3
Figure 3
Coverage areas of O. hupensis in different land use types. (a) Tianmen City; (b) Jiangling County; (c) Qianjiang City; (d) Xiantao City; (e) Shashi District; (f) Songzi City; (g) Gong’an County; (h) Honghu City; (i) Jingzhou District; (j) Shishou City; (k) Jianli County.
Figure 3
Figure 3
Coverage areas of O. hupensis in different land use types. (a) Tianmen City; (b) Jiangling County; (c) Qianjiang City; (d) Xiantao City; (e) Shashi District; (f) Songzi City; (g) Gong’an County; (h) Honghu City; (i) Jingzhou District; (j) Shishou City; (k) Jianli County.
Figure 4
Figure 4
The SDE (standard deviation ellipse) of O. hupensis snail areas of Jianghan Plain, China, from 2007 to 2012.
Figure 5
Figure 5
Values from the AUC (area under curve) testing.
Figure 6
Figure 6
Predicted potential geographic distributions for O. hupensis snail using all occurrence records and environmental variables, by applying Maxent modeling at a 1-km resolution.
Figure 7
Figure 7
Results of jackknife testing of each variables’ contribution to modeling the O. hupensis habitat suitability distribution. Note: Bio_1, the annual average temperature; Bio_3, humidity of the climate; Bio_4, soil type; Bio_5, normalized difference vegetation index; Bio_6, land use; Bio_7, ditch density; Bio_8, land surface temperature; Bio_9, digital elevation model.
Figure 8
Figure 8
Response curves of the four major environment variables. The x-axis and y-axis represent the value of a factor and the probability of O. hupensis occurrence, respectively. Note: Bio_1, annual average temperature; Bio_3, humidity of the climate; Bio_7, ditch density; Bio_9, digital elevation model. (a) ditch density; (b) temperature; (c) digital elevation model; (d) humidity of the climate.
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