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Remotely sensed localised primary production anomalies predict the burden and community structure of infection in long‐term rodent datasets

cris.lastimport.scopus2025-10-23T06:58:58Z
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dc.abstract.enThe increasing frequency and cost of zoonotic disease emergence due to global change have led to calls for the primary surveillance of wildlife. This should be facilitated by the ready availability of remotely sensed environmental data, given the importance of the environment in determining infectious disease dynamics. However, there has been little evaluation of the temporal predictiveness of remotely sensed environmental data for infection reservoirs in vertebrate hosts due to a deficit of corresponding high-quality long-term infection datasets. Here we employ two unique decade-spanning datasets for assemblages of infectious agents, including zoonotic agents, in rodents in stable habitats. Such stable habitats are important, as they provide the baseline sets of pathogens for the interactions within degrading habitats that have been identified as hotspots for zoonotic emergence. We focus on the enhanced vegetation index (EVI), a measure of vegetation greening that equates to primary productivity, reasoning that this would modulate infectious agent populations via trophic cascades determining host population density or immunocompetence. We found that EVI, in analyses with data standardised by site, inversely predicted more than one-third of the variation in an index of infectious agent total abundance. Moreover, in bipartite host occupancy networks, weighted network statistics (connectance and modularity) were linked to total abundance and were also predicted by EVI. Infectious agent abundance and, perhaps, community structure are likely to influence infection risk and, in turn, the probability of transboundary emergence. Thus, the present results, which were consistent in disparate forest and desert systems, provide proof-of-principle that within-site fluctuations in satellite-derived greenness indices can furnish useful forecasting that could focus primary surveillance. In relation to the well-documented global greening trend of recent decades, the present results predict declining infection burden in wild vertebrates in stable habitats; but if greening trends were to be reversed, this might magnify the already upwards trend in zoonotic emergence.
dc.affiliationWydział Leśny i Technologii Drewna
dc.affiliation.instituteKatedra Entomologii i Fitopatologii Leśnej
dc.contributor.authorJackson, Joseph A.
dc.contributor.authorBajer, Anna
dc.contributor.authorBehnke-Borowczyk, Jolanta
dc.contributor.authorGilbert, Francis S.
dc.contributor.authorGrzybek, Maciej
dc.contributor.authorAlsarraf, Mohammed
dc.contributor.authorBehnke, Jerzy M.
dc.date.access2025-06-25
dc.date.accessioned2025-10-07T11:51:16Z
dc.date.available2025-10-07T11:51:16Z
dc.date.copyright2023-07-06
dc.date.issued2023
dc.description.abstract<jats:title>Abstract</jats:title><jats:p>The increasing frequency and cost of zoonotic disease emergence due to global change have led to calls for the primary surveillance of wildlife. This should be facilitated by the ready availability of remotely sensed environmental data, given the importance of the environment in determining infectious disease dynamics. However, there has been little evaluation of the temporal predictiveness of remotely sensed environmental data for infection reservoirs in vertebrate hosts due to a deficit of corresponding high‐quality long‐term infection datasets. Here we employ two unique decade‐spanning datasets for assemblages of infectious agents, including zoonotic agents, in rodents in stable habitats. Such stable habitats are important, as they provide the baseline sets of pathogens for the interactions within degrading habitats that have been identified as hotspots for zoonotic emergence. We focus on the enhanced vegetation index (EVI), a measure of vegetation greening that equates to primary productivity, reasoning that this would modulate infectious agent populations via trophic cascades determining host population density or immunocompetence. We found that EVI, in analyses with data standardised by site, inversely predicted more than one‐third of the variation in an index of infectious agent total abundance. Moreover, in bipartite host occupancy networks, weighted network statistics (connectance and modularity) were linked to total abundance and were also predicted by EVI. Infectious agent abundance and, perhaps, community structure are likely to influence infection risk and, in turn, the probability of transboundary emergence. Thus, the present results, which were consistent in disparate forest and desert systems, provide proof‐of‐principle that within‐site fluctuations in satellite‐derived greenness indices can furnish useful forecasting that could focus primary surveillance. In relation to the well‐documented global greening trend of recent decades, the present results predict declining infection burden in wild vertebrates in stable habitats; but if greening trends were to be reversed, this might magnify the already upwards trend in zoonotic emergence.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if10,8
dc.description.number19 ( October 2023)
dc.description.points200
dc.description.versionfinal_published
dc.description.volume29
dc.identifier.doi10.1111/gcb.16898
dc.identifier.eissn1365-2486
dc.identifier.issn1354-1013
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/5249
dc.identifier.weblinkhttps://onlinelibrary.wiley.com/doi/full/10.1111/gcb.16898
dc.languageen
dc.relation.ispartofGlobal Change Biology
dc.relation.pages5568-5581
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOTHER
dc.subject.encommunity networks
dc.subject.enconnectance
dc.subject.enEVI
dc.subject.engreening
dc.subject.eninfectious agents
dc.subject.enmodularity
dc.subject.enparasites
dc.subject.entime series
dc.subject.enwild rodent
dc.subject.enzoonotic reservoir
dc.titleRemotely sensed localised primary production anomalies predict the burden and community structure of infection in long‐term rodent datasets
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue19
oaire.citation.volume29