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Climate change increases cross-species viral transmission risk

Abstract

At least 10,000 virus species have the ability to infect humans but, at present, the vast majority are circulating silently in wild mammals1,2. However, changes in climate and land use will lead to opportunities for viral sharing among previously geographically isolated species of wildlife3,4. In some cases, this will facilitate zoonotic spillover—a mechanistic link between global environmental change and disease emergence. Here we simulate potential hotspots of future viral sharing, using a phylogeographical model of the mammal–virus network, and projections of geographical range shifts for 3,139 mammal species under climate-change and land-use scenarios for the year 2070. We predict that species will aggregate in new combinations at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, causing the cross-species transmission of their associated viruses an estimated 4,000 times. Owing to their unique dispersal ability, bats account for the majority of novel viral sharing and are likely to share viruses along evolutionary pathways that will facilitate future emergence in humans. Notably, we find that this ecological transition may already be underway, and holding warming under 2 °C within the twenty-first century will not reduce future viral sharing. Our findings highlight an urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking the range shifts of species, especially in tropical regions that contain the most zoonoses and are experiencing rapid warming.

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Fig. 1: Climate change will drive novel viral sharing among mammal species.
Fig. 2: Bats disproportionately drive future novel viral sharing.
Fig. 3: Range expansions will expose naive hosts to zoonotic reservoirs.
Fig. 4: Novel viral sharing events coincide with human population centres.
Fig. 5: Projected timing of first encounters.

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Data availability

No original data were generated during our study. All raw datasets are available online, including the GBIF database of biodiversity occurrence data (https://www.gbif.org/), the IUCN Red List (https://www.iucnredlist.org/), the WorldClim climate dataset (https://worldclim.org/)52, the CHELSA climate dataset (https://chelsa-climate.org/cmip6/)84, the LUH2 land-use dataset (https://luh.umd.edu/)56, the USGS GMTED 2010 elevation dataset (https://www.usgs.gov/coastal-changes-andimpacts/gmted2010), the HP3 dataset of host–virus associations (https://github.com/ecohealthalliance/HP3; https://doi.org/10.5281/zenodo.596810) and a dataset of filovirus testing in bats61.

Code availability

Code to reproduce the study is deposited on Zenodo (https://doi.org/10.5281/zenodo.6463429) and is available at GitHub (https://github.com/viralemergence/iceberg). Additional code to generate the generalized additive mixed models used in this study, reused from ref. 10, are also available at GitHub (https://github.com/gfalbery/ViralSharingPhylogeography).

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Acknowledgements

This paper is the culmination of several years of idea development. We thank many people, including the entire Bansal laboratory, L. W. Alexander, K. Burgio, E. Dougherty, R. Garnier, W. Getz, P. Hitchens, C. Johnson and I. Ott; L. W. Alexander for sharing bat filovirus testing sources used to compile the Ebola subnetwork; and J. Hidasi-Neto for publicly available data-visualization code. C.J.C. was supported by the Georgetown Environment Initiative and the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the National Science Foundation DBI-1639145. C.J.C., G.F.A. and E.A.E. were supported by funding to the Verena Consortium including NSF BII 2021909 and a grant from Institut de Valorisation des Données (IVADO). C.M. acknowledges funding from National Science Foundation grant DBI-1913673. E.A.E., K.J.O. and N.R. were supported by the United States Agency for International Development (USAID) Emerging Pandemic Threats PREDICT project.

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C.J.C. and G.F.A. conceived the study. C.M., C.J.C. and C.H.T. developed SMDs. G.F.A., E.A.E., K.J.O. and N.R. developed the generalized additive models. G.F.A., C.J.C. and C.M.Z. integrated the predictions of species distributions and viral sharing patterns and designed visualizations. All of the authors contributed to writing the manuscript.

Corresponding authors

Correspondence to Colin J. Carlson or Gregory F. Albery.

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Extended data figures and tables

Extended Data Fig. 1 The mammal-virus network.

The present-day viral sharing network by mammal order inferred from modelled pairwise predictions of viral sharing probabilities. Edge width denotes the expected number of shared viruses (the sum of pairwise species-species viral sharing probabilities), with most sharing existing among the most speciose and closely related groups. Edges shown in the network are the top 25% of links. Nodes are sized by total number of species in that order in the host-virus association dataset, colour is scaled by degree. Silhouettes are from http://phylopic.org under Creative Commons license (creativecommons.org/licenses/by/3.0).

Extended Data Fig. 2 Predicted phylogeographical structure of viral sharing.

Phylogeographical prediction of viral sharing using a generalized additive mixed model. Viral sharing increases as a function of phylogenetic similarity (upper left) and geographical overlap (upper right), which have strong nonlinear interactions, shown in the contour map of joint effects (bottom left). Error bars are the 95% confidence interval for the estimated response. White contour lines denote 10% increments of sharing probability. Declines at high values of overlap may be an artefact of model structure and low sampling in the upper levels of geographical overlap, shown in a hexagonal bin chart of the raw data distribution (bottom right).

Extended Data Fig. 3 Outcomes by model formulation and climate change scenario.

Heatmaps displaying predicted changes across model formulations. (A) Range expansions were highest in non-dispersal-limited scenarios and in scenarios with lower levels of global warming. (B) The number of predicted first encounters was higher in non-dispersal-limited scenarios and in scenarios with lower levels of global warming. (C) The number of expected new viral sharing events was higher in non-dispersal-limited scenarios and in more severe RCPs. (D) The overall change in sharing probability (connectance) across the viral sharing network between the present day and the future scenarios; absolute changes may appear small, but an 0.4% increase in connectivity is notable on the scale of millions of possible pairwise combinations of species. Change is positive across all scenarios, being greatest in non-dispersal-limited scenarios and in scenarios with lower levels of global warming. Results are averaged across nine global climate models, with standard deviation indicated in parentheses underneath main statistics.

Extended Data Fig. 4 Geographical distribution of first encounters.

Predictions were carried out for four representative concentration pathways (RCPs), accounting for climate change and land use change, without (left) and with dispersal limits (right). Darker colours correspond to greater numbers of first encounters in the pixel. Results are averaged across nine global climate models.

Extended Data Fig. 5 Geographical distribution of first encounters in two global climate models.

Predictions were carried out for four representative concentration pathways (RCPs), accounting for climate change and land use change through pairing with shared socioeconomic pathways (SSPs) as detailed in the Methods. The two models selected are those with the highest (CanESM5) and lowest (MIROC6) effective climate sensitivity in the available CMIP6 set on WorldClim54. Darker colours correspond to greater numbers of first encounters in the pixel.

Extended Data Fig. 6 Geographical distribution of expected viral sharing events from first encounters.

Predictions were carried out for potential future distributions for four representative concentration pathways (RCPs), accounting for climate change and land use change, without (left) and with dispersal limits (right). Darker colours correspond to greater numbers of new viral sharing events in the pixel. Probability of new viral sharing was calculated by subtracting the species pair’s present sharing probability from their future sharing probability that our viral sharing GAMMs predicted. This probability was projected across the species pair’s range intersection, and then summed across all novel species pairs in each pixel. Results are averaged across nine global climate models.

Extended Data Fig. 7 Order-level heterogeneity in first encounters.

Dispersal stratifies the number of first encounters (RCP 2.6 with all range filters), where some orders have more than expected at random, based on the mean number of first encounters and order size (line). Results are averaged across nine global climate models.

Extended Data Fig. 8 Projected viral sharing from suspected Ebola reservoirs is dominated by bats.

Node size is proportional to (left) the number of suspected Ebola host species in each order, which connect to (middle) first encounters with potentially naive host species; and (right) the number of projected viral sharing events in each receiving group. (Node size denotes proportions out of 100% within each column total). While Ebola hosts will encounter a much wider taxonomic range of mammal groups than current reservoirs, the vast majority of future viral sharing will occur disproportionately in bats. (First encounters are averaged across GCMs to capture the maximum range of taxonomic diversity). Silhouettes are from http://phylopic.org under Creative Commons license (creativecommons.org/licenses/by/3.0).

Extended Data Fig. 9 Geographical distribution of first encounters over time without dispersal restrictions.

We show the RCP with the least mitigation (RCP 2.6) and most mitigation (RCP 8.5). Projections are made based on future climate and land use. Years are the start of each interval (2011-2040; 2041-2070; 2071-2100). Darker colours correspond to greater numbers of first encounters in the pixel. Results are averaged across five global climate models from CHELSA v2.1.

Extended Data Fig. 10 Geographical distribution of first encounters over time with dispersal restrictions.

We show the RCP with the least mitigation (RCP 2.6) and most mitigation (RCP 8.5). Projections are made based on future climate and land use. Years are the start of each interval (2011-2040; 2041-2070; 2071-2100). Darker colours correspond to greater numbers of first encounters in the pixel. Results are averaged across five global climate models from CHELSA v2.1.

Supplementary information

Supplementary Figs. 1–20.

Reporting Summary

Supplementary Table 1

Spreadsheet showing how particular IUCN habitat use classifications were reconciled to broader categories of land cover and use.

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Carlson, C.J., Albery, G.F., Merow, C. et al. Climate change increases cross-species viral transmission risk. Nature 607, 555–562 (2022). https://doi.org/10.1038/s41586-022-04788-w

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