DOI: 10.5061/dryad.zpc866tdx
title: Experimental assessment of cross-species transmission in a natural multihost–multivector–multipathogen community

description: Vector-borne pathogens (VBPs), many of which cause major suffering
 worldwide, often circulate in diverse wildlife communities comprising
 multiple reservoir host and/or vector species. However, the complexities
 of these systems make it challenging to determine the contributions these
 different species make to transmission. We experimentally manipulated
 transmission within a natural multihost–multipathogen–multivector system,
 by blocking flea-borne pathogen transmission from either of two
 co-occurring host species (bank voles and wood mice). Through genetic
 analysis of the resulting infections in the hosts and vectors, we show
 that both host species likely act together to maintain the overall flea
 community, but cross-species pathogen transmission is relatively rare –
 most pathogens were predominantly found in only one host species, and
 there were few cases where targeted treatment affected pathogens in the
 other host species. However, we do provide experimental evidence of some
 reservoir-spillover dynamics whereby reductions of some infections in one
 host species are obtained by blocking transmission from the other host
 species. Overall, despite the apparent complexity of such systems, we show
 there can be ‘covert simplicity’, whereby pathogen transmission is
 primarily dominated by single host species, potentially facilitating the
 targeting of key hosts for control, even in diverse ecological
 communities.
description: As described in Methods section and Supplementary Information of
 associated paper.
description: # Experimental assessment of cross-species transmission in a natural
 multihost–multivector–multipathogen community
 [https://doi.org/10.5061/dryad.8cz8w9gxn](https://doi.org/10.5061/dryad.8cz8w9gxn) Data collected from co-occurring bank voles and wood mice on sampling grids in the North West of England, May to December, 2013-2014. ## Description of the data and file structure 1) DATA TO CREATE FIG 1: Barplots of host and flea sharing by variants: \- "bart\_tryp\_table\.csv": Breakdown of number of samples of each Trypanosome species or Bartonella variant (column A), found in wood mice (col B) and bank voles (col C) \- "Flea\_Data\.csv": Breakdown of Bartonella variants (col B) found in fleas feeding on either wood mice or bank voles (col A). 2) DATA FILES TO CREATE FIG 2: GLMM model prediction plots for each parasite type in mice and voles on the different treatment grids \- "FLEA\_model\_dat\.csv": Data on flea infestion (presence/absence) on captured individual mice and voles (each row as a separate capture). Additional metadata relating to each record are: Species: WM = wood mouse; BV = bank vole Year: 2013, 2014 Site: Rode Hall, Wirral Treatment.on.grid2: Grid-level treatment: 1.C = Control (no treatment) 2.M = Mouse-treatment 3.V = Vole-treatment 4.5 = 50:50 treatment Sex: M = Male; F = Female sex.act: reproductive status (active; not active) Flea.present: 0 = absent; 1 = present time.of.year: early = sessions 1-4 (summer) mid = sessions 5-8 (late summer/early autumn) late = sessions 9-11 (late autumn/early winter) Age2: A = Adult; Y = young (juvenile/sub-adult) \- "TRYP\_model\_dat\.csv": As with "Flea\_model\_dat.csv", but in addition: Tryp.final: Detectable trypanosome infection (0 = Absent; 1 = present) \- "BART\_model\_dat\.csv": As with "Flea\_model\_dat.csv", but in addtion: wm.bart: Wood mouse-specific Bartonella infection (0 = Absent; 1 = present) bv.bart: Bank vole-specific Bartonella infection (0 = Absent; 1 = present) shared.bart: Shared mouse/vole Bartonella infection (0 = Absent; 1 = present) ## Code/Software 1) R code to create FIG 1: Barplots of host and flea sharing by variants: \- "BartTrypTable Final\.R": Code to process the above data files to generate Figure 1 in the associated paper 2) R CODE TO CREATE FIG 2: GLMM model prediction plots for each parasite type in mice and voles on the different treatment grids \- "GLMM ALL\.R": Code to process the above data files\, run the relevant GLMMs and posthoc tests\, and generate Figure 2 in the associated paper