Spatial autocorrelation (Global Moran’s I) of Hendra virus spill-overs by distance.

<p>A peak Z Score at 40 km suggests that spatial processes exist at this distance to produce pronounced spatial clustering.</p

Identification and modelling of spatial and environmental variables for Hendra virus spill-overs using OLS^a and GWR^b.

a<p>For the OLS models, estimates correspond to the standardised coefficient and the standard error in parentheses.</p>b<p>For the GWR model, estimates correspond to the standardised mean coefficient and the standard error in parentheses.</p><p>*P<0.10,</p><p>**P<0.05,</p><p>***P<0.01 are statistically significant levels of the OLS model.</p

The final GWR model for the spill-over of Hendra virus in eastern Australia, with predicted (A) and residual (B) values.

<p>The density of flying-foxes <i>P. alecto</i> and <i>P. conspicillatus</i> had the strongest positive correlation with reported Hendra virus spill-overs (A). An absence of spatial autocorrelation of the residuals suggests additional (as yet unidentified) local risk factors play a role in Hendra virus spill-over from flying-foxes to horses (B).</p

Equine property locations and Hendra virus spillover hot spots.

<p>(A) Forty reported Hendra virus equine cases September 1994 to December 2012 and 1,189 randomly selected control horse properties. (B) Hot spot analysis (Getis-Ord Gi*) identified areas of significant clustering of spill-overs (Z Score>1.96 SD) along the central and northern coasts of eastern Australia.</p

Map of eastern Australia illustrating the spatial distribution of the 1431 respondents from the target study population of Queensland and New South Wales, and indicating reported Hendra virus case locations.

<p>Map of eastern Australia illustrating the spatial distribution of the 1431 respondents from the target study population of Queensland and New South Wales, and indicating reported Hendra virus case locations.</p

The number of respondents completing the questionnaire from each Australian state or territory with industry sector composition.

<p>(QLD = Queensland, NSW = New South Wales, VIC = Victoria, SA = South Australia, WA = Western Australia, ACT = Australian Capital Territory, TAS = Tasmania, and NT = Northern Territory.)</p

Flying-Fox Roost Disturbance and Hendra Virus Spillover Risk

<div><p>Bats of the genus <i>Pteropus</i> (flying-foxes) are the natural host of Hendra virus (HeV) which periodically causes fatal disease in horses and humans in Australia. The increased urban presence of flying-foxes often provokes negative community sentiments because of reduced social amenity and concerns of HeV exposure risk, and has resulted in calls for the dispersal of urban flying-fox roosts. However, it has been hypothesised that disturbance of urban roosts may result in a stress-mediated increase in HeV infection in flying-foxes, and an increased spillover risk. We sought to examine the impact of roost modification and dispersal on HeV infection dynamics and cortisol concentration dynamics in flying-foxes. The data were analysed in generalised linear mixed models using restricted maximum likelihood (REML). The difference in mean HeV prevalence in samples collected before (4.9%), during (4.7%) and after (3.4%) roost disturbance was small and non-significant (P = 0.440). Similarly, the difference in mean urine specific gravity-corrected urinary cortisol concentrations was small and non-significant (before = 22.71 ng/mL, during = 27.17, after = 18.39) (P= 0.550). We did find an underlying association between cortisol concentration and season, and cortisol concentration and region, suggesting that other (plausibly biological or environmental) variables play a role in cortisol concentration dynamics. The effect of roost disturbance on cortisol concentration approached statistical significance for region, suggesting that the relationship is not fixed, and plausibly reflecting the nature and timing of disturbance. We also found a small positive statistical association between HeV excretion status and urinary cortisol concentration. Finally, we found that the level of flying-fox distress associated with roost disturbance reflected the nature and timing of the activity, highlighting the need for a ‘best practice’ approach to dispersal or roost modification activities. The findings usefully inform public discussion and policy development in relation to Hendra virus and flying-fox management.</p></div

Relationship between 21 roosts and 18 regions¹ monitored in the eastern Australian states of Queensland and New South Wales between September 2011 and November 2012.

<p>¹ ‘Region’ defines the geographic location of roosts for the purposes of analysis, and may constitute a single roost or multiple roosts. Roosts appearing in more than one region contributed disturbance data and baseline data at different time points.</p><p>² Roosts subjected to permitted disturbance. [Sydney (RBG) = Sydney Royal Botanic Gardens.]</p><p>³ Roosts known to receive, or putatively receiving, flying-foxes from a disturbed roost in the same region. [Sydney (CP) = Sydney Centennial Park.]</p><p>⁴Collinsville is a primary roost based on DMP application (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125881#pone.0125881.s003" target="_blank">S1 Table</a>), but the sole sampling event at this roost was in the context of it putatively receiving flying-foxes following the Barcaldine roost dispersal, thus it is a secondary roost in this context.</p><p>Relationship between 21 roosts and 18 regions<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125881#t001fn001" target="_blank">¹</a> monitored in the eastern Australian states of Queensland and New South Wales between September 2011 and November 2012.</p

Adjusted overall effect of roost disturbance on the prevalence of HeV (A) and on USG-corrected cortisol concentration (B) in pooled urine samples from flying-foxes roosts in the eastern Australian states of Queensland and New South Wales between September 2011 and November 2012.

<p>Error bars represent the mean ± one standard error, obtained by back-transforming variance from the logistic scale.</p

Adjusted mean HeV excretion prevalence (A) and adjusted USG-corrected urinary cortisol concentration (B) in six regions before, during and after permitted flying-fox roost disturbances in the eastern Australian states of Queensland and New South Wales between September 2011 and November 2012.

<p>Error bars represent the mean ± one standard error, obtained by back-transforming variance from the logistic scale. Approximate variance is used where HeV excretion prevalence is zero during (Gayndah 2011, SRBG) or after (Charters Towers) disturbance (A). Duaringa was excluded as HeV excretion prevalence was zero before, during and after disturbance (A). Respective baseline values using the same y-axis scale are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125881#pone.0125881.s001" target="_blank">S1 Fig</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0125881#pone.0125881.s002" target="_blank">S2 Fig</a>.</p