A range of coronaviruses have been identified in over 100 bat species in Asia, Europe, Africa, Australia and America (Ge et al., 2015) . The similarity of SARS-CoV-2 (cause of the current COVID-19 pandemic, Li et al., 2020) to bat SARS-CoV-like coronaviruses makes it likely that bats may have been reservoir hosts for the SARS-CoV-2 progenitor (Andersen et al. 2020 , Boni et al. 2020 , Zhou et al. 2020 . Coronaviruses are an ancient viral lineage with an estimated mean time of the most recent common ancestor (tMRCA) of approximately 293 million years ago (range 190-489 mya) , which roughly coincides with the inferred tMRCA of birds and mammals (Wertheim et al. 2013) . It is therefore likely that co-evolutionary relationships exist between coronaviruses and their natural hosts (Vijaykrishna et al. 2007 , Wang et al. 2011 , Zhang et al. 2013 . Coronaviruses and bats would be expected to have ecological features in common and the coronavirus environment should mirror that of its natural hosts. A number of recent papers have highlighted possible links between the incidence of SARS-CoV and climatic variables at various spatial scales (Tan et al. 2005 , Yuan et al. 2006 ) and specifically, SARS-CoV-2 (Araujo and Naimi 2020, Moriyama et al. 2020 ). Here we examine the relationship between climatic variables and the incidence of COVID-19 (and hence SARS-CoV-2) using data from Italy, Spain and Australia. We link the observed associations with the ecology of the natural coronavirus hosts, specifically bats, and argue that these reflect deep time co-evolutionary ecological relationships.
We compiled a database of bat species known to have been infected by coronaviruses (from Ge et al. 2015) . We also generated a database of bat species to which we added ecological and behavioural features: 545 species for which data were available (Wilson and Mittermeier 2019) . This allowed us to examine if bats known to have been infected by coronaviruses had particular ecological and behavioural characteristics.
We selected Italy and Spain for an analysis of COVID-19 incidence, being two countries which had been significantly affected by COVID-19 at an early stage and which had a number of regions within each country: (a) 20 for Italy; and (b) 19 for Spain. We analysed daily reported new cases for a period of seven weeks following lockdown (data sources:
http://www.protezionecivile.gov.it/ and https://elpais.com/sociedad/2020/03/30/actualidad/1585589827_546714.html). The logic behind the exercise was to examine the effect of a national lockdown (acting as a standardized control between regions) on COVID-19 incidence, the assumption being that any differences between regions would have to implicate variables other than lockdown itself. As seasonal climatic and environmental variables were being examined, it was decided to contrast the results with those of a southern hemisphere country. We chose Australia as it offered a range of regions (8 states and territories) that would permit analysis. In this case we examined daily new cases listed on the website of the John Hopkins University Center for Systems Science and Engineering (CSSE, 2020) . Several studies have examined cases using four time delays in relation to weather conditions: zero, three, seven and fourteen days (Chen et al. 2020 , Liu et al. 2020 . We adopted the same approach to time lags for this part of our study. We understand that there may have been errors in reporting of daily cases, which may have added noise to our analyses. Mortality data may have been more accurate than incidence data, although also subject to reporting errors, but would not have reflected the reach of the virus in each region as the majority of cases do not end in death.
For each region within the three countries, we compiled a database of daily climatological variables obtained from the OpenWeather website (OpenWeather, 2020) , coinciding with the seven-week period from the commencement of lockdown in each case. Climatological data included daily Ultraviolet Index (UVI) data, temperature (mean, maximum and minimum), relative humidity, wind speed and direction, rainfall, and cloud cover for each country/territory.
These data were derived and summarised from hourly data. For each territory, the station closest to the capital of the territory was chosen.
Multivariate statistics, using Microsoft Excel and SPSS, were used to analyse the data. Where zero cases were reported for a particular day, we added a constant (0.01) to each value prior to log transformation, in order to deal with the log of zero in our stepwise multiple regression models. With regards to temperature, we used Kelvin scale to avoid zeros when log transforming. Details of models used and variables entered are provided, as appropriate, in Appendix 1.
It could be inferred from the long period of coronavirus-bat coevolution, spanning millions of years (Mao et al. 2010 , You et al. 2010 , Latinne et al. 2020 , Wertheim et al. 2013 , that bats and coronaviruses would share common features of climatic tolerance shaped by a coshared environment. Among the features of the bat-coronavirus environment, given the generalised nocturnal behaviour of bats, absence of solar radiation would appear to be the most widespread and prevalent variable.
Of the ultraviolet (UV) radiation reaching the Earth's surface it I, s UV-B (280 to 315 nm) which causes damage and mutations to living organisms (Flenley 2007) . UV-B is therefore of particular interest to us and its absence would be the constant feature of all organisms living in darkness ( Figure 1 ). In addition to darkness (Rowse et al. 2016) , a wide range of temperatures (but not exceeding ~35 o C), and high relative humidity (between 60 and 100%; Perry 2013) also appear as regular features of bat cave environments. In seeking patterns linking SARS-CoV-2 to environmental variables, we would expect the closest relationships to be between the distribution of the virus and these particular variables, especially UV-B. Our null hypothesis is therefore that there is no relationship between (a) ultraviolet radiation (UVR); (b) a broad temperature range, excluding very high temperatures, and (c) high relative humidity, and the incidence of SARS-CoV-2. Failure to uphold the null hypothesis would, instead, implicate some or all of these climatic and environmental variables with the incidence of SARS-CoV-2.
Bats, coronaviruses and the shared environment Ge et al. (2015) list the bat species recorded with coronavirus infection. We have used this list to examine the broad ecological and behavioural characteristics of bats which have been identified with coronavirus infection.
A comparison by social status of number of species detected with coronavirus infection, with those that were not infected, shows that the gregarious species were more likely to be infected than the less social species (one-way χ 2 4 = 44.241; p= 0.000). Similarly, bat species inhabiting caves were more likely to be infected than those not inhabiting caves (one-way χ 2 1 = 24.387; p= 0.000). Bats harbouring coronaviruses are therefore predominantly highly gregarious cave dwellers that gather to roost in large numbers and at high densities (Tables 1 & 2) . Wilson & Mittermaier (2019) and coronavirus incidence from Ge et al. (2015) . 1 Caves offer a sheltered environment capable of accepting large numbers of bats. The large gatherings and dense clustering presumably provide the context for viral transmission within the caves (Kuzmin et al. 2011 , Willoughby et al. 2017 and the use of a cave by various species, in some cases in very close proximity, raises the possibility of interspecific viral exchange (Messenger et al. 2003 , Kuzmin et al. 2011 . It is notable that among the fruit bats (Pteropodidae), a family noted for typically roosting on trees, the four known cave dwelling species have all been reported with coronavirus infections, three of these species being wellknown for gathering in very large numbers (up to a million individuals; Wilson and Mittermeier 2019) in roosts (Table 2) . 
We examined two countries which had been significantly affected by COVID-19 in the initial stages of the pandemic (Ceylan 2020 , Gatto 2020 . In Italy, national lockdown commenced on 9 th March, 2020 and, in Spain on 14 th March, 2020. National lockdowns offered a "natural" experiment that permitted testing the spread of SARS-CoV-2 within regions in each country by providing a control. Our null hypothesis was that there would be no differences in the spread of the virus, or of its rate of control, between regions within each country as the conditions of national lockdown applied across the regions and could therefore be regarded as a constant.
The mean number of new daily cases (MNNDC) reported for Italian regions during the seven-
week period following lockdown varied considerably ( Figure 2 ). These regions separated into three clusters that had non-overlapping incidence: (a) a north to north-west cluster (b) a north-east and east-central cluster; and (c) a southern cluster with low incidence (Figure 2a) . These results suggest that factors other than lockdown must have been at work and that these varied regionally. MNNDC per region was strongly correlated with latitude (Pearson correlation = 0.909; P = 0.000) and with longitude (Pearson correlation = -0.708; P = 0.000) but not with altitude (Pearson correlation = 0.094; P = 0.694). The trend was for high to low MNNDC on a northwest to south-east axis. The strongest environmental correlates of latitude were relative humidity (RH; Pearson correlation = -0.933; P = 0.000) and UV (Pearson correlation = 0.814, P = 0.000). No environmental variables were correlated with longitude. Using stepwise multiple regression, the model that best explained the regional distribution of daily new cases had relative humidity as sole explanatory variable ( Figure 2b ). The relationship held valid for zero, three-day, seven-day and fourteen-day time lags (Appendix 1). RH and UV were strongly correlated (Pearson correlation = 0.706, P = 0.000). Removing RH from the model, UV emerged as the single explanatory variable ( Figure 2c ). In Italy, the highest regional MNNDC were therefore associated with low RH and low UV.
In Spain MNNDC for Spanish regions during the seven-week period following lockdown also varied considerably (Figure 3a ). The regions separated into three clusters: (a) a central inland cluster with high incidence; (b) a northern and eastern cluster with intermediate incidence comprising; and (c) a southern cluster with low incidence ( Figure 3a ). As in Italy, factors other than lockdown must have been at work at regional scales. Using stepwise multiple regression, the model that best explained the regional distribution of (c) the highest regional daily rates of new cases were therefore associated with low MMDT and low UV. Our results for Italy and Spain can be expected from a virus associated with nocturnal and cave-dwelling host species.
The observed correlations between UV and temperature, in particular, follow the gradient from lockdown forwards as they occurred during the northern hemisphere spring, which is time of rising UV and increasing temperature. Even though each region's post-lockdown curve is different, it could be suggested that our observations simply reflect the natural progression from lockdown. For this reason, we look at a southern hemisphere country, which would have faced autumnal conditions of decreasing UV and temperatures during lockdown. We selected MNNDC per region was strongly correlated with latitude (Pearson correlation = 0.732; P = 0.039) but not with longitude (Pearson correlation = 0.382; P = 0.351) or altitude (Pearson correlation = 0.221; P = 0.6). There was therefore a trend from large numbers of daily new cases to low numbers on a south to north gradient. The strongest environmental correlates of latitude were UV (Pearson correlation = -0.934; P = 0.001) and temperature (highest with mean minimum daily temperature, Pearson correlation = 0.927, P = 0.001). Using stepwise multiple regression, the model that best explained the regional distribution of daily new cases had UV as sole explanatory variable ( Figure 4b ). The relationship held valid for zero, three-day, sevenday and fourteen-day time lags (Appendix 1). In Australia, the highest regional daily rates of new cases were therefore associated with low UV, conditions which we would expect the virus to favour.
Alpha-(α-) and beta (β-) coronaviruses regularly infect bats and other mammals, including humans, the latter having been identified from fewer hosts and showing less genetic diversity than the former (Ge at al. 2015) . SARS-CoV-2 is a betacoronavirus (Wassenaar and Zou 2020) of the subgenus Sarbecoronavirus (Boni et al. 2020) . Rhinolophidae and Hipposeridaeappear to have had an important role in the evolution of β-coronaviruses and Vespertilionidae and Miniopteridae with α-coronaviruses (Latinne et al. 2020) .
Bats are reservoirs of coronaviruses (Guan et al. 2003 , Li et al. 2005 , Corman et al. 2014 , Ge et al. 2015 , Schneeberger and Voigt 2016 , Anthony et al. 2017 , Forni et al. 2017 , Tao et al. 2017 , Lau et al. 2018 , Cui et al. 2019 ) and host the highest coronavirus diversity among mammals (Drexler et al. 2014 , Wong et al. 2019 . It seems that bats may well have been the progenitors of SARS-CoV-2 with phylogenetic analysis implicating horseshoe bats (Rhinolophus spp.) in eastern Asia as the most likely candidates (Latinne et al. 2020 , Boni et al. 2020 ).
Cave-dwelling bats are often faithful to their roost sites and will occupy a cave for life and even for hundreds of generations (Altringham 2011) . Temperature, humidity and cavity size are considered the most important factors determining the choice of caves as roosts by bats and microclimate is regulated further by the behaviour of the bats themselves, and these vary according to species, geography and season (Twente 1955 , Dwyer 1971 , Raesly and Gates 1987 , Perry 2013 . In tropical bats, physical protection from predators, relative constancy of temperatures (typically lower than in the exterior and moving to cooler parts of the roost during heat stress), lower levels of illumination and high humidity (though not the prime determining factor) determine the choice of roosts (Usman 1988). Although warm roosts provide significant benefits to bats (Dechmann et al. 2004) , extremely high temperatures are stressful (Downs et al. 2015) and are avoided except in extraordinary circumstances (Bondarenco et al. 2014 ). In cave-dwelling bats, ambient temperatures in the >38 o C would appear to be on the limit of tolerance (Czenze et al. 2019) . Given the use of caves with ambient temperatures ranging between 2 and 10 o C for hibernation in temperate environments (Perry 2013 Our results are therefore in keeping with the observed characteristics of the bat-coronavirus ecological niche, specifically showing a consistent aversion to daylight (UV-B), modulated, in some cases, by a preference for cool and moderately humid climatic conditions.
The SARS-CoV-2 ecological niche -ultraviolet radiation, temperature and relative humidity Coronaviruses have been found to display marked winter seasonality comparable to the pattern seen with influenza viruses (Gaunt et al. 2010) . Recently, Schuit and colleagues (2020) confirmed epidemiological findings that sunlight levels were inversely correlated with influenza transmission, a finding that was suggested could assist in improved understanding of the spread of the virus under varied environmental conditions. Solar radiation, principally through the action of UVR, is known to have a direct effect on pathogen fitness including viral infections (Abhimanyu and Coussens 2017) . It is a major factor threatening the life and activity of many microorganisms suspended in the atmosphere (Madronich et al. 2018) . Airborne infectious animal viruses, including a coronavirus and an adenovirus, have been shown to have UV susceptibility, being higher in viral aerosols than in viral liquid suspensions (Walker and Ko 2007). SARS-CoV was inactivated by UV light at 254nm under laboratory conditions (Darnell et al. 2004) . UV light irradiation for 60 min on the SARS-CoV in culture medium resulted in the destruction of viral infectivity (Duan et al. 2003) .
There is an emerging literature on the impact of climatic factors such as temperature (e.g. Huang et al. 2020 , Prata et al. 2020 ) and UV on SARS-CoV-2 at country (Sehra et al. 2020 , Takagi et al. 2020 ) and global levels (Gunthe et al. 2020) . Our data support and expand these preliminary results, and provide an evolutionary backdrop to the nature of the SARS-CoV-2 ecological niche and its origins.
An early worldwide study linked the outbreak of the COVID-19 to temperature, wind speed and relative humidity in combination as predictors of the pandemic situation. SARS-CoV-2 transmission reached a peak when the air temperature was 8.07 ℃, or when the wind speed was 16.1 mile/hr, or when the visibility was 2.99 statute miles to nearest tenth, or when the relative humidity was 64.6% (Chen et al. 2020) . Liu et al. (2020) found that low temperatures, a mild diurnal temperature range and low humidity favoured the transmission of SARS-CoV-2. Another study in China showed that the incidence of the COVID-19 outbreak decreased as temperature increased, peaking at 10 o C (Shi et al. 2020) . In a worldwide analysis, Sjadi et al.
(2020) found that the eight cities with substantial community spread of COVID-19 as of 10 th March, 2020, were located on a narrow band, roughly on the 30° N to 50° N corridor. These cities had consistently similar weather patterns, consisting of mean temperatures of between 5 and 11°C, combined with low specific humidity (3-6 g/kg) and low absolute humidity (4-7 g/m3). COVID-19 deaths in Wuhan, China, were positively associated with diurnal temperature range and negatively with absolute humidity (Ma et al. 2020) . A global comprehensive ecogeographical analysis demonstrated that although cases of COVID-19 were reported all over the world, most outbreaks displayed a pattern of clustering in relatively cool and dry areas (Araujo and Naimi 2020). Our results support these conclusions.
Our results demonstrate a clear association between UVR and the incidence of SARS-CoV-2, with temperature and relative humidity being significant but not generalised variables. Our results are therefore consistent with the direct effects of UVR on viral inactivation. In this regard, the long evolutionary association between coronaviruses and nocturnal mammals may be a reflection of the ecological niche of the coronaviruses, natural selection having effectively delimited their niche outside the scope of ultraviolet radiation (Wertheim et al. 2013) .
If In this paper we have shown clear relationships between SARS-CoV-2 and UVR, temperature and humidity but it is also clear that lockdown, an imposed form of social distancing, has been the key overriding factor in flattening the disease growth curve. Thus the significance of UVR and other climatic variables must be seen in a co-evolutionary context and providing the backdrop (Araujo and Naimi 2020). The lesson could be learnt, however, from the behaviour of bats. Thus human activities resembling those of highly social cave-dwelling bats (e.g. large nocturnal gatherings or high density indoor activities) will only serve to exacerbate the problem of COVID-19.
All authors contributed to the manuscript and results. Geraldine Finlayson and Clive Finlayson participated in the conceptual design, data analysis, and drafting of the manuscript. Stewart
Finlayson contributed to the data on bats and coronaviruses, and data analysis. T  20  20  20  20  20  20  20  20  20  20   TMIN  20  20  20  20  20  20  20  20  20  20   TMAX  20  20  20  20  20  20  20  20  20  20   HUM  20  20  20  20  20  20  20  20  20  20   WINDS  20  20  20  20  20  20  20  20  20  20   WINDD  20  20  20  20  20  20  20  20  20  20  RAIN  20  20  20  20  20  20  20  20  20  20   CLOUDS  20  20  20  20  20  20  20  20 20 20 T  20  20  20  20  20  20  20  20  20  20   TMIN  20  20  20  20  20  20  20  20  20  20   TMAX  20  20  20  20  20  20  20  20  20  20   51   HUMIDITY  20  20  20  20  20  20  20  20  20  20   WINDS  20  20  20  20  20  20  20  20  20  20   WINDD  20  20  20  20  20  20  20  20  20  20   RAIN  20  20  20  20  20  20  20  20  20  20   CLOUD  20  20  20  20  20  20  20  20  20 
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