Spatiotemporal Analysis of Cryptosporidium Species/Genotypes and Relationships with Other Zoonotic Pathogens in Surface Water from Mixed use Watersheds.

Wilkes, G.A., Ruecker, N.J., Neumann, N.F., Gannon, V.P.J., Jokinen, C.C., Sunohara, M., Topp, E., Pintar, K.D.M., Edge, T.A., and Lapen, D.R. (2013). "Spatiotemporal Analysis of Cryptosporidium Species/Genotypes and Relationships with Other Zoonotic Pathogens in Surface Water from Mixed use Watersheds.", Applied and Environmental Microbiology, 79(2), pp. 434-448. doi : 10.1128/AEM.01924-12  Access to full text


Nearly 690 raw surface water samples were collected during a 6 year period from multiple watersheds in the South Nation River basin, Ontario Canada. Cryptosporidium oocysts in water samples were enumerated, sequenced and genotyped by detailed phylogenetic analysis. The resulting species and genotypes were assigned to broad known host and human infection risk classes. Wildlife/unknown, livestock, avian, and human host classes occurred in 21, 13, 3, and <1% of sampled surface water, respectively. Cryptosporidium andersoni was the most commonly detected livestock species, while Muskrat I and II genotypes were the most dominant wildlife genotypes. The presence of Giardia spp., Salmonella spp., Campylobacter spp., and E. coli O157:H7 was evaluated in all water samples. The greatest (significant) odds of Giardia spp., Campylobacter spp. and Salmonella spp. in water was associated, respectively, with the presence of livestock (odds ratio (OR) =3.1), avian (OR=4.3), and livestock (OR=9.3) host classes. Classification and regression tree analyses (CART) were used to group generalized host and human infection risk classes on the basis of a broad range of environmental and land use variables, while tracking co-occurrence of zoonotic pathogens in these groupings. The occurrence of livestock associated Cryptosporidium was most strongly related to agricultural water pollution in the fall (conditions also associated with elevated odds of other zoonotic pathogens occurring in water), whereas wildlife/unknown sources of Cryptosporidium were geospatially associated with smaller watercourses where urban/rural development was relatively lower. Conditions that support wildlife may not necessarily increase overall human infection risks associated with Cryptosporidium, since most Cryptosporidium classed as wildlife in this study (e.g., Muskrat I and II genotype) do not pose significant infection risks to humans. Consequently, from a human health perspective, land use practices in agricultural watersheds that create opportunities for wildlife to flourish, should not be refuted solely on their potential to increase relative proportions of wildlife fecal contamination in surface water. The present study suggests that mitigating livestock fecal pollution in surface water in this region would likely reduce human infection risks associated with Cryptosporidium and other zoonotic pathogens.

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