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DataWe used temperature data, rainfall data, and data on the incidence of malaria collected from 1901 to 2015 for 43 African countries to construct networks to determine the relationships between transmission of malaria and climate change elements, especially temperature and rainfall. Data resolution is given by the latitude and longitude of the capital city for every country in Africa. Temperature and rainfall data are provided in terms of monthly averages in the country wise. The nodes in the network represent the country, and the edges in the network represent the relationship between countries. We collected malaria data from Harvard Dataverse35 and the world malaria report from the WHO31. Data for temperature and rainfall were obtained from the Climate Change Knowledge Portal of the World Bank Group36.Network generation and analysisThe networks were constructed by using the threshold method where the network depends on the mean, standard deviation, and the real number ((n)) used to control the features of the network. Therefore, data for temperature, rainfall, and the incidence of malaria were divided into six groups mostly comprising ranges of 20 years (1900–1920, 1921–1940, 1941–1960, 1961–1980, 1981–2000) as well as the period from 2001 to 2015. The missing data in Malaria incidence data are filled by the average amount of malaria incidence collected per year.In Table S1, a malaria report from the World Health Organization shows that the rate of death is directly proportional to the incidence of malaria35. The death toll in Africa from malaria is about 98% of world deaths from malaria. Such deaths in African regions decrease thanks to efforts the WHO, governments, and the private sector have been conducting to prevent them. Weather and climate are among the factors that drive increases in malaria infections in different areas.We consider networks based on the threshold method (see the “Methods and Materials” section below). First, we fill the missing malaria incidence data, and we calculate normalized Pearson correlation coefficients of three-time series between two countries. Then, we obtain a correlation matrix for the countries. We estimate the average value of the correlation coefficients from the time intervals 1901–1920, 1921–1940, 1941–1960, 1961–1980, 1981–2000, and 2001–2015 for three time series: temperature, rainfall, and incidence of malaria. We summarize the averages and standard deviations of the correlation coefficients, as shown in Table S2. The mean values from the correlation in temperature are high, compared to those for rainfall and the incidence of malaria. The standard deviations in temperature and rainfall are large, but the standard deviation for the incidence of malaria is small.We chose an ad hoc threshold value of the correlation coefficients to generate sparse networks. The characteristic values for (n) of the threshold are given in Table S3. We consider three types of thresholds in order to observe changes in the networks according to the threshold.Let us define the normalized variance of each time series. We considered time series (T_{i} left( t right)), (M_{i} left( t right)), and (R_{i} left( t right)) in country (i) for temperature, the incidence of malaria, and rainfall, respectively. We define normalized variance as$$r_{ij} = frac{{x_{i} left( t right)x_{j} left( t right) – x_{i} left( t right)x_{j} left( t right)}}{{sigma_{i} sigma_{j} }}$$
(1)
where (x_{i} left( t right)) = (T_{i} left( t right)), (M_{i} left( t right)), (R_{i} left( t right)). We obtained a Pearson correlation matrix for each time series as follows:$$R_{S} = left[ {begin{array}{*{20}c} {r_{11} } & cdots & {r_{1N} } \ vdots & {r_{ij} } & vdots \ {r_{N1} } & cdots & {r_{NN} } \ end{array} } right]$$
(2)
where (S = T, M, R).We calculated the average value, (overline{r }), and the standard deviation, (sigma), for the correlation coefficients of the matrix. We applied the threshold method to generate a sparse network from the correlation matrix. Two countries are connected in the correlation network if and only if the value of the correlation coefficient is greater than, or equal to, the threshold value:$$r_{{ij}} = left{ {begin{array}{*{20}c} 1 & {{text{if}};r_{{ij}} ge bar{r}{text{ + n}}sigma } \ 0 & {{text{otherwise}}} \ end{array} } right.$$
(3)
where (r_{ij}) is the correlation coefficient between two countries, and (n) is an element of real numbers ((n in {mathbb{R}})). The value of (n) determines whether the network is sparsely or densely connected.We use Python programming language, packages, numpy for mathematical functions and random number generator, pandas for data analysis and manipulations, networkx for creation, manipulation, and studying the structure of the complex network, matplotlib for visualization and plotting graph and base map for map projection and visualization of geographic information. More

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Rabbits have had a disastrous impact on Australian agriculture and native plants.Credit: Bettman/Getty

A genomic analysis has helped to show that Australia’s invasive rabbit population probably originated from a shipment of two dozen wild English rabbits that arrived near Melbourne on Christmas Day, 1859. The study also finds that the herd’s wild ancestry probably gave it an advantage over previous arrivals.Rabbits have invaded most of the Australian continent and have had a disastrous impact on ecosystems, threatening some 300 species of plants and animals, and causing hundreds of millions of dollars’ worth of damage to the agriculture industry a year. “That single event triggered this enormous catastrophe, ecologically and economically, in Australia,” says Francis Jiggins, an evolutionary geneticist at the University of Cambridge, UK, and study co-author.Breeding like rabbitsHistorical records suggest that the first European rabbits (Oryctolagus cuniculus) in Australia arrived in Sydney in 1788, with the first colonizers. Ships bringing rabbits continued to dock along the coast for decades, but it wasn’t until the second half of the nineteenth century that the population expanded significantly, spreading across the country at a rate of 100 kilometres a year.Historical records also suggest that the rabbit expansion followed a shipment of animals that arrived for a certain Thomas Austin at Barwon Park, southwest of what is now Melbourne. His brother had trapped them around their family property in Baltonsborough in southwest England.Joel Alves, an evolutionary geneticist at the University of Oxford, UK, and his colleagues wanted to find out whether genomic data corroborated the records. They analysed genetic data from 179 wild rabbits caught across Australia and in New Zealand, France and the United Kingdom, as well as 8 domestic rabbits of different breeds.They found that most rabbits in mainland Australia were genetically similar, with mixed wild and domestic ancestry. Australian rabbits also shared more rare alleles with rabbits from southwest England than with those from elsewhere in the United Kingdom, suggesting they originated in Baltonsborough. Looking specifically at mitochondrial DNA, which is inherited from the mother, the researchers concluded that most mainland Australian rabbits descended from about five females, introduced from Europe.The researchers also found that the rabbits’ genetic diversity declined the further from Barwon Park the animals were caught, and that alleles that are rare or absent in wild rabbits increased. The researchers say these patterns are consistent with the idea that most rabbits across Australia originated from Barwon Park. The team report their findings in the Proceedings of the National Academy of Sciences on 22 August1.“This is a very exciting paper on a very important and well-studied topic,” says Martin Nuñez, who researches ecological invasions at the University of Houston in Texas. Using genetics to understand how unwanted animal invasions start can help to predict future invasions, he says.Perfect stormOverall, the team says that the rabbits’ wild ancestry was an important factor in triggering their invasion of the continent. “Wild rabbits are different,” says Alves. They exhibit traits such as fleeing stressful environments and burrow-digging, meaning that they were probably better at evading predators and surviving in difficult terrain than are domestic rabbits, he says. Historical records suggest that Austin requested wild rabbits, and that previous arrivals were largely domestic breeds.The expansion of Australian pastoral lands and widespread suppression of predators around that time would have also helped their expansion. “It was like a perfect storm,” says Alves. “You have the right rabbits in the right place at the right time, with the right changes in the environment.”“The genetic analyses appear very sound,” says rabbit geneticist Amy Iannella, a consultant based in Adelaide, Australia. She adds that although the country’s rabbit populations probably originated in Barwon Park, their rapid expansion might have been aided by people transporting the animals to other parts of the country, where they also began spreading. Rabbits are typically communal animals that rely on shelter for survival and juveniles rarely travel further than 1 kilometre, she says. “The idea of rabbits moving fast enough at the invasion front to colonize Australia so quickly from a single release, well that feels extreme to me, given what we know about rabbit ecology.” More

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