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

Author:

Jackson Joseph A.1ORCID,Bajer Anna2ORCID,Behnke‐Borowczyk Jolanta3ORCID,Gilbert Francis S.4ORCID,Grzybek Maciej5ORCID,Alsarraf Mohammed2ORCID,Behnke Jerzy M.4ORCID

Affiliation:

1. School of Science, Engineering and Environment University of Salford Manchester UK

2. Department of Eco‐Epidemiology of Parasitic Diseases, Institute of Developmental Biology and Biomedical Sciences, Faculty of Biology University of Warsaw Warsaw Poland

3. Department of Forest Pathology, Faculty of Forestry Poznań University of Life Sciences Poznań Poland

4. School of Life Sciences University of Nottingham, University Park Nottingham UK

5. Department of Tropical Parasitology, Institute of Maritime and Tropical Medicine Medical University of Gdansk Gdynia Poland

Abstract

AbstractThe 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.

Funder

Narodowe Centrum Nauki

Uniwersytet Warszawski

Publisher

Wiley

Subject

General Environmental Science,Ecology,Environmental Chemistry,Global and Planetary Change

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