Spatial dynamics of pathogen transmission in communally roosting species: Impacts of changing habitats on bat-virus dynamics
Cite this dataset
Lunn, Tamika et al. (2021). Spatial dynamics of pathogen transmission in communally roosting species: Impacts of changing habitats on bat-virus dynamics [Dataset]. Dryad. https://doi.org/10.5061/dryad.n02v6wwxh
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.