10.5061/DRYAD.N02V6WWXH
Lunn, Tamika
0000-0003-4439-2045
Griffith University
Lunn, Tamika
Griffith University
Peel, Alison
Griffith University
McCallum, Hamish
Griffith University
Eby, Peggy
Griffith University
Kessler, Maureen
Montana State University
Plowright, Raina
0000-0002-3338-6590
Montana State University
Restif, Olivier
University of Cambridge
Spatial dynamics of pathogen transmission in communally roosting species:
Impacts of changing habitats on bat-virus dynamics
Dryad
dataset
2021
aggregative behaviour
animal aggregation
communal roost
conspecific attraction
Henipavirus
heterogeneous mixing
pathogen transmission
roost size
FOS: Biological sciences
2021-06-29T00:00:00Z
2021-06-29T00:00:00Z
en
105749 bytes
2
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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.
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.