Introduction
Emerging zoonotic diseases account for close to 13% of known human pathogens (Taylor et al.
2001; Woolhouse and Gowtage-Sequeria
2005). These diseases along with other emerging pathogens that affect crops and domestic animals can have extensive socio-economic consequences (Jones et al.
2008), especially when they adapt to and transmit among their new hosts (Taylor et al.
2001). Four diseases that have spilled over from bats to humans and have resulted in epidemics are Ebola and Marburg viruses in Africa (Leroy et al.
2005) and severe acute respiratory syndrome (SARS)
Coronavirus and Nipah virus in east Asia (Chua
2003; He et al.
2004; Leroy et al.
2005; Li et al.
2005; Wang and Eaton
2007). Some of these outbreaks have had long-term devastating consequences, from the loss of thousands of human lives to the collapse of the already imperilled public health systems that prevent, control and treat other diseases (Chang et al.
2004; Plucisnki et al.
2015).
Hendra virus (HeV, Paramyxoviridae:
Henipavirus) is another bat-borne virus that spills over into domestic animals, in its case horses, and then people with high case fatality rates of 50–75% (Halpin et al.
2011; Smith et al.
2014; Edson et al.
2015; Martin et al.
2016). It was discovered in 1994 in a Brisbane suburb in Queensland, Australia, with two of the four Australian mainland flying fox species,
Pteropus alecto and
P. conspicillatus, as its major reservoir hosts (Halpin et al.
2011; Smith et al.
2014; Edson et al.
2015; Martin et al.
2016), although antibodies against HeV are commonly found in
P. scapulatus and
P. poliocephalus (Young et al.
1996; Plowright et al.
2008). HeV is closely related to Nipah virus, which is also a
Henipavirus from pteropodid bats. Nipah virus occurs in east Asia and spills over to pigs and humans, where it has been able to cause epidemic disease outbreaks with case fatality rates close to 41% in humans (Chong et al.
2002; Chua
2003). In Bangladesh, spillover occurs directly to humans with even higher case fatality rates (Luby et al.
2009). The proven ability of henipaviruses to cause epidemic outbreaks with high case fatality rates make spillover mitigation highly necessary.
Mitigation and prevention of impacts of disease spillover depends on our understanding of the transmission process and ability to predict it (Plowright et al.
2017). Mechanistic models of infectious diseases have proven useful frameworks to make informed decisions towards controlling and mitigating the impacts of epidemics (Wickwire
1977). These methods require high-quality longitudinal data rarely available for pathogens that originate in wild animals (Woodroffe
1999).The poor understanding of HeV dynamics in bats (Plowright et al.
2015) limit our ability to directly predict HeV levels in those populations. Nevertheless, prediction can be made with alternative methods to mechanistic models at lower spatial and temporal resolutions. These methods are based on readily available data and can be used to model the response of the system of interest (Peterson
2006).
One approach to identify areas at risk from emerging infectious diseases is to model the ecological niche of the causal agent and its reservoir host with spatiality explicit climatic data, and to use the model to predict their geographic distribution (Escobar and Craft
2016). The process of niche modelling consists of relating the climatic conditions of locations where organisms are able to breed and persist with the prevailing climatic conditions of areas where species could occur (Soberón et al.
2005). The relationships between a species’ presence and climate are usually established with statistical models that ultimately represent a measure of environmental suitability. The spatial representation of environmental suitability helps visualisation of the model’s estimates in the form of maps (Peterson
2006). Assuming that the organisms’ niches being modelled do not undergo a climatic niche shift, models can be used to predict future distributions under climate change scenarios (Pearman et al.
2008). For instance, using these methods many diseases have been predicted to impact wider areas with climate change, expanding or shifting from tropical to subtropical areas (Lafferty
2009). Therefore, identifying areas at risk and anticipating the potential impacts of climate change on HeV spillover is critical to adequately allocate resources and mitigate risk.
Ecological niche modelling has been applied with varying degrees of success to investigate the distribution of the zoonotic niches of bat-borne viruses. For instance when Peterson et al. (
2004) initially predicted areas at risk of Marburg disease spillover in Africa, left out wide areas that were later on shown to be at risk in updated models with improved methods and data (Peterson et al.
2006; Pigott et al.
2015). Previous ecological niche modelling studies of
Henipavirus spillover systems have focused on answering ecological and epidemiological questions (Hahn et al.
2014), identifying reservoir hosts (Martin et al.
2016); identifying new populations at risk (Walsh
2015); or generating broad predictions of risk (Daszak et al.
2013). While their contribution towards improving our understanding has been valuable, none have focused on forecasting areas at risk of spillover in time, which is essential to anticipate the effects of climate change and inform public health measures (Braks et al.
2013).
In order to be able to predict the consequences of climate change, models must rely on climatic data that can be projected into the future, such as those resulting from global circulation models (Hijmans et al.
2005; Beaumont et al.
2008; Wiens et al.
2009). Empirical evidence suggests that HeV spillover is related to climate by several different mechanisms acting at different temporal and spatial scales. From broad to fine: the spatial and temporal abundance patterns of HeV reservoir hosts, flying foxes, are related to climatic suitability (Martin et al.
2016); the spatial dynamics of bats are largely governed by food resources that are dependent on climate (Hudson et al.
2010; Giles et al.
2016); the levels of HeV shedding may be linked to low food productivity and availability after severe weather events (Plowright et al.
2008; McFarlane et al.
2011; Páez et al.
2017; Peel et al.
2017); and lastly HeV survival in microclimates which might facilitate indirect transmission, is also dependent on climate (Martin et al.
2015,
2017).
In this study we present two models that estimate the areas at risk of Hendra virus spillover to horses under current and future climatic conditions. The models represent the climatic requirements for the presence of HeV’s reservoir hosts and the climatic conditions that have facilitated HeV’s transmission to horses. We used current and predicted future climatic conditions to project the statistical models and predict areas that could be at risk now and by year 2050 according to two representative greenhouse gas concentration pathways.
Discussion
Under climate change, suitability for HeV spillover could expand southwards. In addition to a southward expansion, some scenarios predicted inland expansion in the
P. alecto HeV spillover system. However, while the total area at risk of spillover was predicted to increase, the average probability of spillover in these areas could slightly decrease, especially in the
P. conspicillatus system. There was high uncertainty of future risk in areas north of the current distribution of spillover. In areas currently inhabited by
P. conspicillatus, P. alecto was predicted to remain stable or expand. In areas where both
P. alecto and
P. conspicillatus were predicted to co-occur, average probability of exceeding the intensity threshold was predicted to decrease with respect to both species.
P. alecto’s expansion indicates that additional mitigation efforts should be allocated where risk has been predicted to increase (marked as red in the consensus maps in Figs.
5,
6). In addition, the expansion of
P. alecto into
P. conspicillatus territory suggests that
P. alecto may replace or become the more predominant HeV host in those areas.
The current forecasted area at risk of HeV spillover to horses is wider than the area that contains the detected HeV spillover events. Based on both
P. alecto and
P. conspicillatus, areas farther north than previously recognised were predicted to be at risk. Absence of spillover detection in these areas is probably due to the very low density of horses (Fig.
2) and relative lack of disease surveillance. While the effect of horse density on risk of spillover seems negligible (McFarlane et al.
2011) or negative depending on the spatial scale (Martin et al.
2018), the presence of horses is conditional for spillover (Plowright et al.
2015).
Previous niche modelling studies of
Henipavirus hosts predicted broad areas at risk in Australia (Daszak et al.
2013). Our results differ from these predictions because we used the actual spillover events to fit the model and because we narrowed down the number of reservoir hosts from four to two. Before 2014 it was unclear which bat species were more relevant for HeV epidemiology. Recent findings have provided epidemiological (Smith et al.
2014; Edson et al.
2015), and ecological (Martin et al.
2016) support for
P. alecto and
P. conspicillatus as the main HeV reservoir hosts. Hence, we have predicted smaller areas at risk.
Poisson point processes have infrequently been used to model the spatial pattern of spillover of bat-borne viruses. Walsh (
2015) modelled the spatial pattern of Nipah virus spillover to humans as a point process model in response to human footprint, the presence of bat reservoirs and environmental factors (vegetation). One key difference with our study is that we focused on modelling HeV spillover driven by environmental factors in order to be able to project the models into climate change scenarios. This enabled us to explore the potential consequences of climate change for HeV spillover. With that in mind we used reservoir host density (statistically equivalent to the human footprint variable in Walsh (
2015)) as an offset term to specifically model the isolated effect of climate. We decided to take this approach because of the lack of data to predict future horse density and distribution, which precludes its inclusion as a HeV spillover predictor.
Previous studies of the zoonotic niche of bat-borne viruses including Marburg and Ebola viruses (Peterson et al.
2004,
2006; Pigott et al.
2014,
2015) and Nipah virus (Peterson
2013) have used machine learning methods. Interpretation of these models and the risk management implications of the predictions were thus limited to visual analysis of the geographic patterns and associated climatic factors. The transparent nature and control over model selection that Poisson point processes confer result in better understanding of the likely biological meaning of statistical associations (Renner et al.
2015; Taylor et al.
2015). However, definitive interpretation is dependent on understanding of the underlying biological mechanisms (Walsh
2015).
Although the final models are complex due to a lack of understanding of the interaction of flying foxes and horses, the statistical associations in the models of the
P. alecto system are similar with those found in Martin et al. (
2018). First, most of the variables’ interactions that were kept in the model represent rainfall (
bio12) and its seasonality (
bio15). These two interact with
Maxent.p.alecto indicating that interactions between rainfall, its variability, and the probable presence of this bat species are important for spillover. Such effects could be due to the climatic differences between areas used for foraging and establishing a roost (Tidemann et al.
1999; Vardon et al.
2001). In fact, high suitability for
P. alecto is not enough to explain spillover because
Maxent.p.alecto alone had a negative effect which is reversed when it interacts with rainfall levels (Table
1). We can infer from these associations that rainfall levels and their variability with respect to seasonal extremes could be broad-scale correlates of HeV spillover risk (Páez et al.
2017; Martin et al.
2018).
In the
P. conspicillatus system, the small number of spillover events, nine, limits the number of variables and their interactions that could be included in the model. From the final model structure only
Maxent.p.conspicillatus and
bio2 (mean diurnal temperature range) had significant effects (Table
2). Given the cubic exponent affecting the positive effect of
Maxent.p.conspicillatus, we infer that transmission from this species to horses occurs in areas where climatic suitability is very high for
P. conspicillatus.
There was complete agreement among climate change scenarios that there could be a southward increase in suitability for spillover caused by the response of
P. alecto to climate change. However, the already observed southward expansion of
P. alecto is faster than predicted by changing climate (Roberts et al.
2012), suggesting that other non-climatic factors like urbanisation (Plowright et al.
2011; Tait et al.
2014) are also affecting the presence and density of the bat species. To date the southernmost recorded spillover events lie within the limits of the current potential distribution of spillover (blue areas in left panels of Fig.
5). This shows that even when
P. alecto is capable of occupying areas beyond its optimal climatic niche, spillover and spillover risk occur within the areas with the highest climatic suitability in most cases, most likely due to higher potential densities of
P. alecto [climatic suitability is correlated with bat density and spillover risk (Smith et al.
2014; Martin et al.
2016)]. Hence, as climatic suitability for
P. alecto continues to increase southwards, potential for higher population densities could increase southwards as well as HeV spillover risk.
The cause of predicted expansions under climate change with high agreement might be related to the higher temperatures expected at higher altitude and lower latitudes (Lafferty
2009), particularly in Australia (Williams et al.
2003). This is consistent with the predictions of tropical diseases expanding or shifting into subtropical areas (Lafferty
2009). The lower agreement on the inland expansion indicates that the effect of altitude is less clear among climate change scenarios. In fact, some of the scenarios indicate that there could be a contraction towards the coast. Consequently, to adequately assess if there will be expansion or contraction to and from the coast, flying fox monitoring programs are required.
In model projections, we identified overpredictions (Figs.
3,
5). These could be due to the inclusion of areas that are not usually available to
P. alecto (Soberón and Peterson
2005). Accessible areas are usually defined by physical barriers, however, in the absence of such evident barriers for pteropodid bats in Australia we assumed that climate could act as a barrier through its effects on bat physiology. While the assumption could be valid, the choice of climatic regions clearly did not eliminate inaccessible areas that could be suitable, at least according to some climatic factors. Alternatively, the most relevant climatic factors that restrict the distribution of bats and HeV spillover might have been discarded in the search for variables that were less likely to impact the model’s transference to climate change scenarios (Owens et al.
2013).
The ultimate implications of the southward and probable inland expansion are a greater number of horses at spillover risk. Depending on the representative concentration pathway (RCP), and based on the 2007 horse census, there could be at least 112–165,000 more horses at risk (175–260% increase). Because there is considerable uncertainty around the potential outcomes of climate change on disease occurrence in new areas more research is needed, first to verify predictions and then to better manage the consequences (Braks et al.
2013). Furthermore, the ultimate spillover risk scenario by 2050 will also depend on horse densities and socio-economic processes, and how these processes interact with climate change. Therefore, one potential area of ecological and epidemiological research is the role of novel ecological interactions between flying foxes and other organisms such as food sources that could experience distributional shifts and impacts as result of human activities. We need to understand if these novel interactions and processes affect the dynamics of bat populations, HeV, and spillover risk (Williams et al.
2003; Sala et al.
2009). Consequently, we emphasise the need to undertake regular risk assessments to quantify HeV exposure in horse populations and to consider the potential consequences of a larger horse population at risk.
In light of the potentially larger horse population at risk, it is clear that direct intervention of the HeV spillover system is necessary to mitigate risk in response to climate change such as extending vaccine coverage regardless of the uncertainties involved (Beaumont et al.
2008). However, a more holistic approach would include reduction of greenhouse gas emissions. Such a management strategy would positively impact all levels of organisation of the HeV spillover system studied here and prevent other predicted consequences of climate change. For instance, the Australian tropics are predicted to experience large biodiversity losses (Williams et al.
2003) and grasslands in southern Australia could experience increased variability in productivity which could affect the cattle and potentially the horse industry (Cullen et al.
2009).
Our models predicted that spillover frequency could decrease in response to climate change with respect to P. conspicillatus. However, the P. alecto HeV spillover system was predicted to remain or increase within the current areas suitable for P. conspicillatus. Therefore, these areas of tropical north Queensland could experience a replacement of reservoir host species that may result in different epidemiological processes that would benefit from different mitigation strategies. We recommend, therefore, that an area of research be the development of specific management strategies for the different flying fox species relevant to Hendra virus spillover. These management strategies would anticipate and better manage flying fox species’ replacements and changes in the epidemiology of HeV spillover.
The predicted shrinking of the distribution of
P. conspicillatus could also affect the dynamics of many ecosystem processes because flying foxes are important pollinators and seed dispersers. The absence of such ecosystem services could result in further biodiversity loss. Such loss of ecosystem services occurs even before bats become extinct (McConkey and Drake
2006). Therefore serious additional conservation issues may arise as a result of
P. conspicillatus decline that could affect HeV epidemiology.
Predicting HeV spillover with the methods we used carries considerable uncertainty. Sources of uncertainty may be related to: (1) the type of presence only data used limits the number of analytical methods that can be used and hampers the identification of limiting factors; (2) the effects of climate on HeV spillover act at several different levels of ecological organisation and are not well understood, for instance temperature, humidity and ground vegetation might also limit the available pathways for HeV transmission to horses (Martin et al.
2017), and temperature can regulate the flowering status of native plants (Hudson et al.
2010), the main source of food for flying foxes; (3) flying fox species distributions do not depend entirely on climate (Tidemann et al.
1999; Vardon et al.
2001), but are greatly affected by native plant phenology (Giles et al.
2016), and have an apparently innate preference for fragmented and urbanised landscapes (Tait et al.
2014); (4) the predicted distributions of flying foxes in response to climate change do not account for other organisms’ shifting distributions that affect bats’ distributions. Other organisms shifting distributions might give rise to novel and unpredictable interactions and effects on bats’ distributions (Eby et al.
1999; Eby and Law
2008; Giles et al.
2016); and (5) climate change could also affect horse behaviour and susceptibility to diseases. For instance, horses have limited thermal tolerance. Exceeding their comfort levels can alter their behaviour (Castanheira et al.
2010) and increase the frequency of interaction with tree shaded areas (Jørgensen and Bøe
2007), which is where HeV is usually excreted (Field et al.
2011). All of these issues warrant further research to increase understanding of HeV epidemiology and bat virus spillover in general.
The strength of our approach lies in its generality. Possible improvements to our models to make them more specific might involve: (1) including a model of bat distribution that better accounts for the effect of urbanisation (Tait et al.
2014); (2) including other biological interactions that are crucial for bat species (Giles et al.
2016) that can be transferred to climate change scenarios; and (3) establishing direct links between climatic factors and the levels of HeV infection in bats. Such an analysis would likely result in smaller areas and populations predicted to be at risk.
Spillover of diseases from wild to domestic animals and humans comprises several levels of ecological organisation. The first level includes the distribution of the reservoir host, and then the distribution of the causal agent within the reservoir host (Plowright et al.
2015,
2017). By including the additional layer of spillover host distribution as an offset during model fit, we have modelled the direct effect of climate (Taylor et al.
2015) on the biological processes that affect the reservoir and the causal agent and result in HeV spillover. Therefore, the models represent the underlying risk to any spillover hosts present in the areas predicted to be at risk due to the presence of the reservoir host and the effects of climate on the HeV spillover system. The 20% omission threshold indicates that within these areas at least 80% of spillover cases could occur. The precise location and timing of spillover cases will depend on processes that occur at finer scales like the fraction of susceptible horses (e.g. unvaccinated) that are effectively exposed (Plowright et al.
2015; Martin et al.
2015,
2017). Consequently the models should only be used to improve understanding of spillover risk, identify areas to allocate resources for mitigation and inform research activities.