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Code and data for manuscript: Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir.
<p>This is the source code and data required to reproduce data analysis and figures from the manuscript, "Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir". </p>
Data for: Spillover effects of organic agriculture on pesticide use on nearby fields
<p><span>The environmental impacts of organic agriculture are only partially understood and </span><span>whether such practices have spillover effects </span><span>on pests or pest control activity </span><span>on nearby fields remains unknown</span><span>. Using </span><span>roughly 13,000 field observations per year from 2013-2019 in Kern County, CA , </span><span>we estimate that organic crop producers benefit from surrounding organic fields, decreasing overall pesticide use and pesticides targeting insect pests. </span><span><span>Conventional fields, in contrast, tend to increase pesticide use as the area of surrounding organic production increases.</span></span></p>
Expansion of the early warning system for avian influenza in the EU to evaluate the risk of spillover from wild birds to poultry
<p>GIF 1: Animated map of the spatiotemporal variations of the estimated number of infectious wild birds between February 2023 and March 2024</p> <p>GIF 2: Animated map of the spatiotemporal variations of transmission rate from wild birds to indoor chicken between February 2023 and March 2024</p> <p>GIF 3: Animated map of the spatiotemporal variations of transmission rate from wild birds to outdoor chicken between February 2023 and March 2024</p> <p>GIF 4: Animated map of the spatiotemporal variations of transmission rate from wild birds to indoor duck between February 2023 and March 2024</p> <p>GIF 5: Animated map of the spatiotemporal variations of transmission rate from wild birds to outdoor duck between February 2023 and March 2024</p> <p>GIF 6: Animated map of the spatiotemporal variations of the probability of HPAI introduction into indoor chicken farms between February 2023 and March 2024</p> <p>GIF 7: Animated map of the spatiotemporal variations of the probability of HPAI introduction into outdoor chicken farms between February 2023 and March 2024</p> <p>GIF 8: Animated map of the spatiotemporal variations of the probability of HPAI introduction into indoor duck farms between February 2023 and March 2024</p> <p>GIF 9: Animated map of the spatiotemporal variations of the probability of HPAI introduction into outdoor duck farms between February 2023 and March 2024</p> <p>GIF 10: Animated map of the spatiotemporal variations of the probability of HPAI introduction into poultry farms between February 2023 and March 2024 (scenario A)</p> <p>GIF 11: Animated map of the spatiotemporal variations of the probability of HPAI introduction into poultry farms between February 2023 and March 2024 (scenario B)</p>
Extended Data Figure 7 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 7 | Global distribution of viral and host species richness for primates (order Primates). a, Observed total viral richness (for n = 71 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 73);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3e); g, global host species richness for Primates (n = 400); h, host species richness for Primates in our database (n = 98);i, primate species with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in primates. Red/pink colours in panel i highlight areas with poor viral surveillance in primate species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 4 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 4 | Global distribution of viral and host species richness for wild carnivores (order Carnivora). a, Observed total viral richness (for n = 55 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 55); e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3b); g, global host species richness for Carnivora (n = 276);h, host species richness for Carnivora in our database (n = 79); i, species of the order Carnivora with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in carnivores. Red/pink colours in panel i highlight areas with poor viral surveillance in carnivore species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 3 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 3 | Global distribution of viral and host species richness for all wild mammals. a, Observed total viral richness (for n = 576 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 584);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3a); g, global mammal species richness (n = 5,290); h, mammal richness for species in our database (n = 753);i, mammal species with no described viruses in the literature. Warmer colours (larger values) in panels c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in mammals. Red/pink colours in panel i highlight areas with poor viral surveillance in mammal species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Figure 4 in Host and viral traits predict zoonotic spillover from mammals
Figure 4 | Traits that predict zoonotic potential of a virus. a, Box plot of maximum phylogenetic host breadth per virus (PHB, see methods) for each of 586 mammalian viruses, aggregated by 28 viral families. Individual points represent viral species, colour-coded by zoonotic status. Box plots coloured and sorted by the proportion of zoonoses in each viral family. b–d, Partial effect plots for the best-fit GAM to predict the zoonotic potential of a virus. b, Maximum PHB. Viruses that infect a phylogenetically broader range of hosts are more likely to be zoonotic. c, Research effort (log, number of PubMed citations per viral species). d, Whether or not a virus replicates in the cytoplasm or is vector-borne. Viral genome length and whether or not a virus is enveloped improved the overall predictive power but were non-significant and are not shown (see Extended Data Table 1).
Extended Data Figure 2 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 2 | Heat map of observed total viral richness by mammalian order and viral family. Dataset includes 754 mammalian species and 586 unique ICTV recognized viral species. Heat map aggregated by rows and columns to group taxa with similar levels of observed viral richness.
Figure 3 in Host and viral traits predict zoonotic spillover from mammals
Figure 3 | Global distribution of the predicted number of 'missing zoonoses' by order. Warmer colours highlight areas predicted to be of greatest value for discovering novel zoonotic viruses. a, All wild mammals (n = 584 spp. included in the best-fit model). b, Carnivores (order Carnivora, n = 55).c, Even-toed ungulates (order Cetartiodactyla, n = 70). d, Bats (order Chiroptera, n = 157).e, Primates (order Primates, n = 73). f, Rodents (order Rodentia, n = 183). Hatched regions represent areas where model predictions deviate systematically for the assemblage of species in that grid cell (approximately 18 km × 18 km, see Methods). Animal silhouettes from PhyloPic.
Extended Data Figure 6 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 6 | Global distribution of viral and host species richness for bats (order Chiroptera). a, Observed total viral richness (for n = 156 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 157);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3d); g, global host species richness for Chiroptera (n = 1117);h, host species richness for Chiroptera in our database (n = 192);i, species of the order Chiroptera with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in bats. Red/pink colours in panel i highlight areas with poor viral surveillance in bat species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 1 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 1 | Conceptual model of zoonotic spillover, viral richness, and summary of models. a, Conceptual model of zoonotic spillover showing primary risk factors examined, colour-coded according to generalized additive models used. b, Conceptual model of observed, predicted, and actual viral richness in mammals. c, GAMs used in our study to address specific components of a and b, colour-coded by model. Variables listed with 'or' under each GAM covaried and were provided as competing terms in model selection, and those in bold were included in the best-fit model using all host–virus associations. Significant variables from each best-fit GAM are noted with an asterisk. Zoonotic viral spillover first depends on the underlying total viral richness in mammal populations and the ecological, taxonomic, and life-history traits that govern this diversity (GAM 1). Second, host- and virus-specific factors may facilitate viral spillover. We examine the relative importance of host phylogenetic distance to humans, ecological opportunity for contact, or other species-specific life-history and taxonomic traits (GAM 2), and identify viral traits associated with a higher likelihood of an observed virus being zoonotic (GAM 3). We estimate the total and zoonotic viral richness per host species using GAMs 1 and 2, and calculate the missing viruses and missing zoonoses under a scenario of increased research effort (b, Methods). Owing to imperfect surveillance in both humans and wildlife and biases in viral detection, there may be uncertainty in the exact proportion of viruses that are zoonotic (b, light grey), and also between the actual, or true, viral richness (dotted lines) and the predicted maximum viral richness per host (dashed line).
Extended Data Figure 9 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 9 | Order-level phylogenies showing residuals from zoonoses model. a–e, Subtrees from cytochrome b maximum likelihood phylogeny for 558 mammal species (constrained to order-level topology of mammal supertree) for bats (a), carnivores (b), even-toed ungulates (c), rodents (d) and primates (e). Species included have at least one described virus association and available genetic data. Wildlife species names and terminal branches are colour-coded by the residuals (predicted minus observed) from the best-fit GAM to predict the number of zoonotic viruses using all data. Species with residual values between −1 and 1 (black) are accurately predicted within one virus. Warm colours represent species with positive residuals (orange>1 to 3; red>3). Cool colours represent species with negative residuals (green <−1 to−3; blue<−3). Marine mammals, domestic animals, and species with missing data and not included in the best-fit models are shown in grey.
Figure 2 in Host and viral traits predict zoonotic spillover from mammals
Figure 2 | Host traits that predict total viral richness (top row) and proportion of zoonotic viruses (bottom row) per wild mammal species. Partial effect plots show the relative effect of each variable included in the best-fit GAM, given the effect of the other variables. Shaded circles represent partial residuals; shaded areas, 95% confidence intervals around mean partial effect. a–e, Best model for total viral richness includes: a, number of disease-related citations per host species (research effort, log); b, phylogenetic eigenvector regression (PVR) of body mass (log); c, geographic range area of each species (log km2); d, number of sympatric mammal species overlapping with at least 20% area of target species range; and e, mammalian orders. f–i, Best model for proportion of zoonoses includes: f, research effort (log); g, phylogenetic distance from humans (cytochrome b tree constrained to the topology of the mammal supertree28); h, ratio of urban to rural human population within species range; and i, three mammalian orders. Bats are the only order with a significantly larger proportion of zoonotic viruses than would be predicted by the other variables in the all-data model. Three additional mammalian orders, and whether or not a species is hunted, improved the overall predictive power of the best zoonotic virus model but were non-significant and are not shown (see Extended Data Table 1).
Extended Data Figure 8 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 8 | Global distribution of viral and host species richness for rodents (order Rodentia). a, Observed total viral richness (for n = 178 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 183);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3f); g, global host species richness for Rodentia (n = 2206);h, host species richness for Rodentia in our database (n = 221); i, rodent species with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in wild rodents. Red/pink colours in panel i highlight areas with poor viral surveillance in rodent species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Extended Data Figure 5 in Host and viral traits predict zoonotic spillover from mammals
Extended Data Figure 5 | Global distribution of viral and host species richness for wild even-toed ungulates (order Cetartiodactyla). a, Observed total viral richness (for n = 70 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 70);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3c); g, global host species richness for Cetartiodactyla (n = 229);h, host species richness for Cetartiodactyla in our database (n = 105);i, species of the order Cetartiodactyla with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in even-toed ungulates. Red/pink colours in panel i highlight areas with poor viral surveillance in even-toed ungulates species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).
Disentangling the influence of reservoir abundance and pathogen shedding on zoonotic spillover of the Leptospira agent in urban informal settlements
<p>The following datasets were used to carry out the analyses in "Disentangling the influence of reservoir abundance and pathogen shedding on zoonotic spillover of the Leptospira agent in urban informal settlements". </p> <p>rat_abundance.csv - dataset for rat abundance model.</p> <p>rat_shedding.csv - dataset for individual rat shedding model.</p> <p>human_infections.csv - dataset for human infections (coordinates and valley removed for anonymity).</p> <p>prediction_grid.csv - prediction dataset.</p> <p>Codebook for repository data.pdf - codebook for the included datasets.</p>
Fig. 1 in From Galapagos doves to passerines: Spillover of Haemoproteus multipigmentatus
Fig. 1. Map of the Galapagos Islands indicating sampling sites during our haemosporidian survey 2013‾2015. Prevalence percentages are provided only for those sites where Haemoproteus multipigmentatus was found.
Fig. 2. a in From Galapagos doves to passerines: Spillover of Haemoproteus multipigmentatus
Fig. 2. a. Mean prevalence of Haemoproteus multipigmentatus in passerines on sampling sites on Santiago. b. Mean relative abundance of doves and passerines on sampling sites on Santiago. Bars indicate standard error. (Sullivan was sampled only in 2014 and 2015).
Fig. 1 in Spillover and spillback risks of ectoparasites by an invasive squirrel Callosciurus erythraeus in Kanto region of Japan
Fig. 1. Location map of the study areas. The Pallas's squirrels examined in this study were collected by extermination programs operated by the local governments of Yokohama and Yokosuka. Detailed information on the sampling localities is refrained due to the intension of the cooperative organizations.
Fig. 3. NMDS plot for the parasite infracommunity composition recovered from 52 in Spillover and spillback risks of ectoparasites by an invasive squirrel Callosciurus erythraeus in Kanto region of Japan
Fig. 3. NMDS plot for the parasite infracommunity composition recovered from 52 host individuals. The influence of each parasite's abundance by discriminating the developmental stage on the score components of the two axes is represented by broken lines. The relationships with environmental variables are indicated by gray arrows. Abbreviations are as follows: M: male, F: female, YH: Yokohama, YS: Yokosuka, HfN: nymph of Haemaphysalis flava, HfL: larva of H. flava, Lep: larva of Leptotrombidium spp., EkA: adult of Enderleinellus kumadai, EkL: larva of E. kumadai, NoA: adult of Neohaematopinus callosciuri, NoL: larva of N. callosciuri, Ca: Ceratophyllus anisus, Ci: Ceratophyllus indages indages.
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