Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Author: search.filters.author.Hayman DTSSubject: search.filters.subject.Mammals

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    Mammal-related Cryptosporidium infections in endemic reptiles of New Zealand.
    (Springer Nature, 2023-05-01) Garcia-R JC; Pita AB; Velathanthiri N; Pas A; Hayman DTS
    New Zealand's endemic reptile fauna is highly threatened and pathogens causing infectious diseases may be a significant risk to already endangered species. Here, we investigate Cryptosporidium infection in captive endemic New Zealand reptiles. We found two mammal-related Cryptosporidium species (C. hominis and C. parvum) and six subtypes from three gp60 families (Ib, Ig and IIa) in 12 individuals of captive endemic Tuatara, Otago and Grand skinks, and Jewelled and Rough geckos. Cryptosporidium serpentis was identified in two Jewelled geckos using 18S. In New Zealand, C. hominis and C. parvum are associated with infections in humans and introduced domestic animals but have also been recently found in wildlife. Our finding of Cryptosporidium infection in endemic reptiles can help inform strategies to monitor the conservation of species and manage potential introductions of pathogens to in-situ and ex-situ populations.
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    Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots.
    (Springer Nature Limited, 2023-10-27) Muylaert RL; Wilkinson DA; Kingston T; D'Odorico P; Rulli MC; Galli N; John RS; Alviola P; Hayman DTS
    The emergence of SARS-like coronaviruses is a multi-stage process from wildlife reservoirs to people. Here we characterize multiple drivers-landscape change, host distribution, and human exposure-associated with the risk of spillover of zoonotic SARS-like coronaviruses to help inform surveillance and mitigation activities. We consider direct and indirect transmission pathways by modeling four scenarios with livestock and mammalian wildlife as potential and known reservoirs before examining how access to healthcare varies within clusters and scenarios. We found 19 clusters with differing risk factor contributions within a single country (N = 9) or transboundary (N = 10). High-risk areas were mainly closer (11-20%) rather than far ( < 1%) from healthcare. Areas far from healthcare reveal healthcare access inequalities, especially Scenario 3, which includes wild mammals and not livestock as secondary hosts. China (N = 2) and Indonesia (N = 1) had clusters with the highest risk. Our findings can help stakeholders in land use planning, integrating healthcare implementation and One Health actions.