key: cord-103460-5thh6syt
authors: Carlson, Colin J.; Albery, Gregory F.; Merow, Cory; Trisos, Christopher H.; Zipfel, Casey M.; Eskew, Evan A.; Olival, Kevin J.; Ross, Noam; Bansal, Shweta
title: Climate change will drive novel cross-species viral transmission
date: 2020-07-14
journal: bioRxiv
DOI: 10.1101/2020.01.24.918755
sha: 
doc_id: 103460
cord_uid: 5thh6syt

At least 10,000 species of mammal virus are estimated to have the potential to spread in human populations, but the vast majority are currently circulating in wildlife, largely undescribed and undetected by disease outbreak surveillance1,2,3. In addition, changing climate and land use are already driving geographic range shifts in wildlife, producing novel species assemblages and opportunities for viral sharing between previously isolated species4,5. In some cases, this will inevitably facilitate spillover into humans6,7—a possible mechanistic link between global environmental change and emerging zoonotic disease8. Here, we map potential hotspots of viral sharing, using a phylogeographic model of the mammal-virus network, and projections of geographic range shifts for 3,870 mammal species under climate change and land use scenarios for the year 2070. Range-shifting mammal species are predicted to aggregate at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, driving the cross-species transmission of novel viruses at least 4,000 times. Counter to expectations, holding warming under 2°C within the century does not reduce new viral sharing, due to greater range expansions—highlighting the need to invest in surveillance even in a low-warming future. Most projected viral sharing is driven by diverse hyperreservoirs (rodents and bats) and large-bodied predators (carnivores). Because of their unique dispersal capacity, bats account for the majority of novel viral sharing, and are likely to share viruses along evolutionary pathways that could facilitate future emergence in humans. Our findings highlight the urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking range shifts, especially in tropical countries that harbor the most emerging zoonoses.

dispersal limits). Even with dispersal limits, these first encounters are predicted to produce al-207 most one hundred new viral sharing events (RCP 2.6: 96 ± 2.3; RCP 8.5: 86 ± 3.9) that might 208 include ZEBOV, and which cover a much broader part of Africa than the current zoonotic niche 209 of Ebola 68 . Human spillover risk aside, this could expose several new wildlife species to a 210 deadly virus historically responsible for sizable primate die-offs 69 . Moreover, for zoonoses like emerging threats like ranavirus causing conservation concern, pathogen exchange among am-271 phibians may be especially important for conservation practitioners to understand 82 . Finally, 272 marine mammals are an important target given their exclusion here, especially after a recent 273 study implicating reduced Arctic sea ice in novel viral transmission between pinnipeds and sea 274 otters-a result that may be the first proof of concept for our proposed climate-disease link 83 .

Because hotspots of cross-species transmission are predictable, our study provides the first 276 template for how surveillance could target future hotspots of viral emergence in wildlife. In the 277 next decade alone, billions could be spent on virological work trying to identify and counteract 278 zoonotic threats before they spread from wildlife reservoirs into human populations 2 . These 

To implement the Grubb outlier tests for a given species we defined a distance matrix between 322 each record and the centroid of all records (in both environmental or geographic space, respec-323 tively) and determined whether the record with the largest distance was an outlier with respect 324 to all other distances, at a given statistical significance (p = 1e − 3, in order to exclude only 325 extreme outliers). If an outlier was detected it was removed and the test was repeated until no 326 additional outliers were detected. The WorldClim dataset is widely used in ecology, biodiversity, and agricultural projections 331 of potential climate change impacts. WorldClim makes data available for current and future 

For herbivores and omnivores, the maximum is estimated as D = 3.31M 0.65 . 514 We used mammalian diet data from the EltonTraits database 113 , and used the same cutoff as 515 Schloss to identify carnivores as any species with 10% or less plants in their diet. We used body 516 mass data from EltonTraits in the Schloss formula to estimate maximum generational dispersal, 517 and converted estimates to annual maximum dispersal rates by dividing by generation length, formula performs notably poorly for bats: for example, it would assign the largest bat in our 527 study, the Indian flying fox (Pteropus giganteus), a dispersal capacity lower than that of the gray 528 dwarf hamster (Cricetulus migratorius). Bats were instead given full dispersal in all scenarios: 529 14 given significant evidence that some bat species regularly cover continental distances 46,47 , and 530 that isolation by distance is uncommon within many bats' ranges 49 , we felt this was a defensible 531 assumption for modeling purposes. Moving forward, the rapid range shifts already observed 532 in many bat species (see main text) could provide an empirical reference point to fit a new allo-533 metric scaling curve (after standardizing those results for the studies' many different method-534 ologies). A different set of functional traits likely govern the scaling of bat dispersal, chiefly the 535 aspect ratio (length:width) of wings, which is a strong predictor of population genetic differ-536 entiation 49 . Migratory status would also be important to include as a predictor although here, 537 we exclude information on long-distance migration for all species (due to a lack of any real 538 framework for adding that information to species distribution models in the literature). . Using a linear model, we show that elevation (C), species richness (D), and land use (E) influence the number of new overlaps for bats and non-bats. Slopes for the elevation effect were generally steeply positive: a log 10 -increase in elevation was associated with between a 0.4-1.41 log 10 -increase in first encounters. Results are averaged across nine global climate models. Legends refer to scenarios: CL gives climate and land use change, while CLD add adds dispersal limits. 20

A. B. Extended Data Figure 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 viral sharing will occur disproportionately in bats.

Extended Data Figure 9 : Data processing workflow. Summary of species inclusion across the modeling pipeline for species distributions and viral sharing models. The final analyses in the main text use 3,139 species of placental mammals across all scenarios.

Extended Data Figure 10 : Species distribution modeling workflow for a single species. A focal species (the sand cat, Felis margarita) is displayed as an illustrative example. The present day climate prediction (top left) was clipped to the same continent according to the IUCN distribution (top right). This was then clipped according to Cervus elaphus land use (second row, left). The known dispersal distance of the red deer was used to buffer the climate distribution (second row, right). The future distribution predictions (RCP 2.6 shown as an example) are displayed in the bottom four panels, for each of the four pipelines: only climate (third row, left); climate + dispersal clip (third row, right); climate + land use clip (bottom row, left) and climate + land use + dispersal clip (bottom row, right). The four distributions clearly display the limiting effect of the dispersal filter (bottom right panels) in reducing the probability of novel species interactions (bottom left panels). The land use clip had little effect on this species as the entire distribution area was habitable for the red deer.

Bats as 'special' reservoirs for emerging zoonotic 805 pathogens

A comparison of bats and 808 rodents as reservoirs of zoonotic viruses: are bats special?

Are bats really 'special' as viral reservoirs? 811 what we know and need to know

Mass extinctions, biodiversity and mitochon-813 drial function: are bats 'special' as reservoirs for emerging viruses? Current Opinion in 814 Virology

Viral zoonotic risk is homogenous among taxonomic 816 orders of mammalian and avian reservoir hosts

Virological factors 819 that increase the transmissibility of emerging human viruses

Transmissibility of emerging viral 822 zoonoses

Origins of hiv and the aids pandemic. Cold Spring Harbor 824 perspectives in medicine 1

Origin and evolution of pathogenic coronaviruses