title: Spatial analyses of wildlife contact networks.
abstract: Datasets from which wildlife contact networks of epidemiological importance can be inferred are becoming increasingly common. A largely unexplored facet of these data is finding evidence of spatial constraints on who has contact with whom, despite theoretical epidemiologists having long realized spatial constraints can play a critical role in infectious disease dynamics. A graph dissimilarity measure is proposed to quantify how close an observed contact network is to being purely spatial whereby its edges are completely determined by the spatial arrangement of its nodes. Statistical techniques are also used to fit a series of mechanistic models for contact rates between individuals to the binary edge data representing presence or absence of observed contact. These are the basis for a second measure that quantifies the extent to which contacts are being mediated by distance. We apply these methods to a set of 128 contact networks of field voles (Microtus agrestis) inferred from mark-recapture data collected over 7 years and from four sites. Large fluctuations in vole abundance allow us to demonstrate that the networks become increasingly similar to spatial proximity graphs as vole density increases. The average number of contacts, 〈k〉, was (i) positively correlated with vole density across the range of observed densities and (ii) for two of the four sites a saturating function of density. The implications for pathogen persistence in wildlife may be that persistence is relatively unaffected by fluctuations in host density because at low density 〈k〉 is low but hosts move more freely, and at high density 〈k〉 is high but transmission is hampered by local build-up of infected or recovered animals.
authors.affiliation: School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Victoria 3001, Australia stephen.davis@rmit.edu.au.
authors.forename: Stephen
authors.initials: S
authors.lastname: Davis
authors.affiliation: School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Victoria 3001, Australia.
authors.forename: Babak
authors.initials: B
authors.lastname: Abbasi
authors.affiliation: School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Victoria 3001, Australia.
authors.forename: Shrupa
authors.initials: S
authors.lastname: Shah
authors.affiliation: School of Biological Sciences, University of Aberdeen, Zoology Building, Tillydrone Avenue, Aberdeen AB24 2TZ, UK.
authors.forename: Sandra
authors.initials: S
authors.lastname: Telfer
authors.affiliation: Department of Evolution, Ecology and Behaviour, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK.
authors.forename: Mike
authors.initials: M
authors.lastname: Begon
mesh_terms: D000465:Algorithms; D000818:Animals; D000835:Animals, Wild; D003411:Arvicolinae / Q000502:physiology*; D001522:Behavior, Animal; D065928:Forests; D008954:Models, Biological; D015233:Models, Statistical; D011156:Population Density; D011157:Population Dynamics; D062206:Spatial Analysis
keywords: Microtus agrestis; dissimilarity measure; epidemiology; field vole; graph; mathematical model