DOI: 10.5061/dryad.n02v6wwxh
title: Spatial dynamics of pathogen transmission in communally roosting species: Impacts of changing habitats on bat-virus dynamics

description: 1. The spatial organisation of populations determines their pathogen
 dynamics. This is particularly important for communally roosting species,
 whose aggregations are often driven by the spatial structure of their
 environment. 2. We develop a spatially explicit model for virus
 transmission within roosts of Australian tree-dwelling bats (Pteropus
 spp.), parameterised to reflect Hendra virus. The spatial structure of
 roosts mirrors three study sites, and viral transmission between groups of
 bats in trees was modelled as a function of distance between roost trees.
 Using three levels of tree density to reflect anthropogenic changes in
 bats habitats, we investigate the potential effects of recent ecological
 shifts in Australia on the dynamics of zoonotic viruses in reservoir
 hosts. 3. We show that simulated infection dynamics in spatially
 structured roosts differ from that of mean-field models for equivalently
 sized populations, highlighting the importance of spatial structure in
 disease models of gregarious taxa. Under contrasting scenarios of
 flying-fox roosting structures, sparse stand structures (with fewer trees
 but more bats per tree) generate higher probabilities of successful
 outbreaks, larger and faster epidemics, and shorter virus extinction
 times, compared to intermediate and dense stand structures with more trees
 but fewer bats per tree. These observations are consistent with the
 greater force of infection generated by structured populations with less
 numerous but larger infected groups, and may flag an increased risk of
 pathogen spillover from these increasingly abundant roost types. 4.
 Outputs from our models contribute insights into the spread of viruses in
 structured animal populations, like communally roosting species, as well
 as specific insights into Hendra virus infection dynamics and spillover
 risk in a situation of changing host ecology. These insights will be
 relevant for modelling other zoonotic viruses in wildlife reservoir hosts
 in response to habitat modification and changing populations, including
 coronaviruses like SARS-CoV-2. 
description: To explore how infection dynamics are influenced by heterogeneity
 in stand structure, we applied spatially explicit and stochastic
 compartmental models to three empirical examples of flying-fox roost stand
 structures, representing sparse, intermediate, and dense stand structures
 respectively. Generation of the spatial model structure needs input of a
 distance matrix between tree-groups, and specification of how transmission
 is expected to relate to distance.  The distance
 matrices used in the manuscript are provided. These represent a tree stand
 structure, where the spatial arrangement of all overstory, canopy and
 midstory trees were mapped in a subplot (20x20 meters each) using an
 ultrasound distance instrument (Vertex Hypsometer, Haglöf Sweden). We did
 not map trees or shrubs in the understory as these are no suitable
 roosting habitat. Trees were mapped and tagged using tree survey methods
 described in the “Ausplots Forest Monitoring Network, Large Tree Survey
 Protocol”
 (https://portal.tern.org.au/tern-ausplots-forest-2012-2015/21755).
 Briefly, subplots were georeferenced at one corner. Distances were
 measured from the N/S or E/W subplot boundaries using an ultrasound
 distance instrument (Vertex Hypsometer, Haglöf Sweden, accurate to 10-30
 cm) along the defined orientation bearing. Trees within the subplot were
 then mapped with the X-Y coordinate in relation to the georeferenced
 corner (0,0). To achieve maximum accuracy with the Vertex Hypsometer, only
 distances of up to 10 meters were recorded. If a tree was greater than 10
 meters from the west/south origin (0 meter) subplot boundary, the tree was
 measured from the opposite (20 meter) subplot boundary, and the measured
 distance subtracted from 20 to give the distance from the origin boundary.
 Each tree was individually tagged and assigned a crown class following
 definitions in the Ausplots survey protocol. This approach allowed for
 precise spatial mapping of trees, with locations of trees within subplots
 accurate to 10-30 cm.
description: Three .csv files are provided: "matrix DTOW subplot
 5.csv"; "matrix DCLU subplot 4"; "matrix DLIS subplot
 2.csv" . These represent the sparse, intermediate, and dense stand
 structures respectively, which include the distances between 4 trees (tree
 #69-72), 32 trees (tree #90-#121), and 72 trees (tree #61-#132) in
 subplots, respectively. The data is structured as a standard distance
 matrix, where trees are named on the first row and first
 collumn.