Biology subjects

Carlson, C. J.

Publications and source records attributed to Carlson, C. J..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Data proliferation, reconciliation, and synthesis in viral ecology

The fields of viral ecology and evolution have rapidly expanded in the last two decades, driven by technological improvements, and motivated by efforts to discover potentially zoonotic wildlife viruses under the rubric of pandemic prevention. One consequence has been a massive proliferation of host-virus association data, which comprise the backbone of research in viral macroecology and zoonotic risk prediction. These data remain fragmented across numerous data portals and projects, each with their own scope, structure, and reporting standards. Here, we propose that synthesis of host-virus association data is a central challenge to improve our understanding of the global virome and develop foundational theory in viral ecology. To illustrate this, we build an open reconciled mammal-virus database from four key published datasets, applying a standardized taxonomy and metadata. We show that reconciling these datasets provides a substantially richer view of the mammal virome than that offered by any one individual database. We argue for a shift in best practice towards the incremental development and use of synthetic datasets in viral ecology research, both to improve comparability and replicability across studies, and to facilitate future efforts to use machine learning to predict the structure and dynamics of the global virome.

ecology↗

bioRxiv2021

Viral competence data improves rodent reservoir predictions for American orthohantaviruses

Identifying reservoir host species is crucial for understanding the risk of pathogen spillover from wildlife to people. Orthohantaviruses are zoonotic pathogens primarily carried by rodents that cause the diseases hemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS) in humans. Given their diversity and abundance, many orthohantaviruses are expected to be undiscovered, and several host relationships remain unclear, particularly in the Americas. Despite the increasing use of predictive models for understanding zoonotic reservoirs, explicit comparisons between different evidence types for demonstrating host associations, and relevance to model performance in applied settings, have not been previously made. Using multiple machine learning methods, we identified phylogenetic patterns in and predicted unidentified reservoir hosts of New World orthohantaviruses based on evidence of infection (RT-PCR data) and competence (live virus isolation data). Infection data were driven by phylogeny, unlike competence data, and boosted regression tree (BRT) models using competence data displayed higher accuracy and a narrower list of predicted reservoirs than those using infection data. Eight species were identified by both BRT models as likely orthohantavirus hosts, with a total of 98 species identified by our infection models and 14 species identified by our competence models. Hosts predicted by competence models are concentrated in the northeastern United States (particularly Myodes gapperi and Reithrodontomys megalotis) and northern South America (several members of tribe Oryzomyini) and should be key targets for empirical monitoring. More broadly, these results demonstrate the value of infection competence data for predictive models of zoonotic pathogen hosts, which can be applied across a range of settings and host-pathogen systems. Author SummaryHuman diseases with wildlife origins constitute a significant risk for human health. Orthohantaviruses are viruses found primarily in rodents that cause disease with high rates of mortality and other complications in humans. An important step in disease prevention is to identify which rodent species carry and transmit orthohantaviruses. By incorporating species relatedness and evidence of different levels of host capacity to be infected and transmit virus, we used predictive modeling to determine unidentified rodent hosts of orthohantaviruses. Models using host competence data outperformed models using host infection data, highlighting the importance of stronger data in model optimization. Our results highlighted roughly a dozen key target species to be monitored that are concentrated in two geographic regions--northeastern United States and northern South America. More broadly, the approaches used in this study can be applied to a variety of other host-pathogen systems that threaten public health.

ecology↗

bioRxiv2021

Urban-adapted mammal species have more known pathogens

The world is rapidly urbanising, inviting mounting concern that urban environments will experience increased zoonotic disease risk. Urban animals could have more frequent contact with humans, and therefore may transmit more zoonotic parasites; however, these animals have a specific set of underlying traits that may determine their parasite burdens while predisposing them to urban living, and they may be subject to more intense research effort, both of which could complicate our ability to reliably identify the role of urbanisation in driving zoonotic risk. Here, we test whether urban mammal species host more known zoonotic parasites, investigating the potential underlying drivers while accounting for a correlated suite of phenotypic, taxonomic, and geographic predictors. We found that urban-adapted mammals have more documented parasites, and more zoonotic parasites specifically: despite comprising only 157 of the 2792 investigated species (6%), urban mammals provided 39% of known host-parasite combinations and showed consistently higher viral discovery rates throughout the last century. However, contrary to predictions, much of the observed effect was attributable to research effort rather than to urban adaptation status itself, and urban-adapted species in fact hosted fewer zoonoses than expected given their total observed parasite richness. We conclude that extended historical contact with humans has had a limited impact on the number of observed zoonotic parasites in urban-adapted mammals; instead, their greater observed zoonotic richness likely reflects sampling bias arising from proximity to humans, which supports a near-universal underlying pattern of conflation between zoonotic risk, research effort, and synanthropy. These findings underscore the need to resolve the ecological mechanisms underlying links between anthropogenic change, sampling bias, and observed wildlife disease dynamics.

ecology↗

bioRxiv2020

Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization

Despite global investment in One Health disease surveillance, it remains difficult--and often very costly--to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can be used to guide sampling prioritization, but predictions from any given model may be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of likely reservoir hosts. In the first quarter of 2020, we generated an ensemble of eight statistical models that predict host-virus associations and developed priority sampling recommendations for potential bat reservoirs and potential bridge hosts for SARS-CoV-2. Over more than a year, we tracked the discovery of 40 new bat hosts of betacoronaviruses, validated initial predictions, and dynamically updated our analytic pipeline. We find that ecological trait-based models perform extremely well at predicting these novel hosts, whereas network methods consistently perform roughly as well or worse than expected at random. These findings illustrate the importance of ensembling as a buffer against variation in model quality and highlight the value of including host ecology in predictive models. Our revised models show improved performance and predict over 400 bat species globally that could be undetected hosts of betacoronaviruses. Although 20 species of horseshoe bats (Rhinolophus spp.) are known to be the primary reservoir of SARS-like viruses, we find at least three-fourths of plausible betacoronavirus reservoirs in this bat genus might still be undetected. Our study is the first to demonstrate through systematic validation that machine learning models can help optimize wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

ecology↗

bioRxiv2020

Climate change will drive novel cross-species viral transmission

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 surveillance 1-3 . 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 species 4, 5 . In some cases, this will inevitably facilitate spillover into humans 6, 7 --a possible mechanistic link between global environmental change and emerging zoonotic disease 8 . Here, we map potential hotspots of viral sharing, using a phylogeographic model of the mammal-virus network, and projections of potential geographic range shifts for 3,139 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 novel cross-species transmission of their viruses an estimated 4,000 times. Counter to expectations, holding warming under 2{degrees}C within the century does not reduce new viral sharing, due to greater potential 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 species range shifts, especially in tropical countries that harbor the most emerging zoonoses.

ecology↗

bioRxiv2019

What would it take to describe the global diversity of parasites?

How many parasites are there on Earth? Here, we use helminth parasites to high-light how little is known about parasite diversity, and how insufficient our current approach will be to describe the full scope of life on Earth. Using the largest database of host-parasite associations and one of the worlds largest parasite collections, we estimate a global total of roughly 100,000 to 350,000 species of helminth endoparasites of vertebrates, of which 85% to 95% are unknown to science. The parasites of amphibians and reptiles remain the most poorly described, but the majority of undescribed species are likely parasites of birds and bony fish. Missing species are disproportionately likely to be smaller parasites of smaller hosts in undersampled countries. At current rates, it would take centuries to comprehensively sample, collect, and name vertebrate helminths. While some have suggested that macroecology can work around existing data limitations, we argue that patterns described from a small, biased sample of diversity arent necessarily reliable, especially as host-parasite networks are increasingly altered by global change. In the spirit of moon-shots like the Human Genome Project and the Global Virome Project, we consider the idea of a Global Parasite Project: a global effort to transform parasitology and inventory parasite diversity at an unprecedented pace.

ecology↗

bioRxiv2019

The two extinctions of the Carolina parakeet

Due to climate change and habitat conversion, estimates of the number of species extinctions over the next century are alarming. Coming up with solutions for conservation will require many different approaches, including exploring the extinction processes of recently extinct species. Given that parrots are the most threatened group of birds, information regarding parrot extinction is especially pressing. While most recent parrot extinctions have been island endemics, the Carolina parakeet (Conuropsis carolinensis) had an 18th-century range covering nearly half of the present-day United States, despite which, they went extinct in the 20th century. The major cause of their extinction remains unknown. As a first step to determining what caused their extinction, we used a newly published, extensive dataset of Carolina parakeet observations combined with a Bayesian extinction estimating model to determine the most likely date of their extinction. By considering each of the two subspecies independently, we found that they went extinct ~30 years apart: the western subspecies (C. c. ludovicianus) around 1914 and the eastern subspecies (C. c. carolinensis) either in the late 1930s or mid-1940s. Had we only considered all observations together, this pattern would have been obscured, missing a major clue to the Carolina parakeets extinction. Since the Carolina parakeet was a wide-ranging species that went extinct during a period of rapid agricultural and industrial expansion, conditions that mirror those presently occurring in many parts of the world where parrot diversity is highest, any lessons we can glean from their disappearance may be vital to modern parrot conservation efforts.

ecology↗

bioRxiv2019

embarcadero: Species distribution modelling with Bayesian additive regression trees in R

O_LIembarcadero is an R package of convenience tools for species distribution modelling with Bayesian additive regression trees (BART), a powerful machine learning approach that has been rarely applied to ecological problems. C_LIO_LILike other classification and regression tree methods, BART estimates the probability of a binary outcome based on a set of decision trees. Unlike other methods, BART iteratively generates sets of trees based on a set of priors about tree structure and nodes, and builds a posterior distribution of estimated classification probabilities. So far, BARTs have yet to be applied to species distribution modelling. C_LIO_LIembarcadero is a workflow wrapper for BART species distribution models, and includes functionality for easy spatial prediction, an automated variable selection procedure, several types of partial dependence visualization, and other tools for ecological application. The embarcadero package is available open source on Github and intended for eventual CRAN release. C_LIO_LITo show how embarcadero can be used by ecologists, I illustrate a BART workflow for a virtual species distribution model. The supplement includes a more advanced vignette showing how BART can be used for mapping disease transmission risk, using the example of Crimean-Congo haemorrhagic fever in Africa. C_LI

ecology↗

bioRxiv2019

Zygomorphic flowers have fewer visitors

Botanists have long identified bilaterally symmetrical (zygomorphic) flowers with more specialized pollination interactions than radially symmetrical (actinomorphic) flowers. Zygomorphic flowers facilitate more precise contact with pollinators, guide pollinator behaviour, and exclude less effective pollinators. However, whether zygomorphic flowers are actually visited by a smaller subset of available pollinator species has not been broadly evaluated. We compiled 53,609 floral visitation records in 159 communities and classified the plants floral symmetry. Globally and within individual communities, plants with zygomorphic flowers are indeed visited by fewer species. At the same time, zygomorphic flowers share a somewhat larger proportion of their visitor species with other co-occurring plants, and have particularly high sharing with co-occurring plants that also have zygomorphic flowers. Visitation sub-networks for zygomorphic species also show differences that may arise from reduced visitor diversity, including greater connectance, greater web asymmetry, and lower coextinction robustness of both plants and visitor species -- but these changes do not necessarily translate to whole plant-visitor communities. These results provide context for widely documented associations between zygomorphy and diversification and imply that species with zygomorphic flowers may face greater risk of extinction due to pollinator loss.

ecology↗

bioRxiv2019

Rapid range shifts in African Anopheles mosquitoes over the last century

Facing a warming climate, many tropical species-including the arthropod vectors of several infectious diseases-will be displaced to higher latitudes and elevations. These shifts are frequently forecasted for the future, but rarely documented in the present day. Here, we use one of the most comprehensive datasets ever compiled by medical entomologists to track the observed range limits of African malaria mosquito vectors (Anopheles spp.) from 1898 to 2016. Using a simple regression approach, we estimate that these species ranges gained an average of 6.5 meters of elevation per year, and the southern limits of their ranges moved polewards 4.7 kilometers per year. These shifts are consistent with the local velocity of climate change, and might help explain the incursion of malaria transmission into new areas over the past few decades. Confirming that climate change underlies these shifts, and applying similar methods to other disease vectors, are important directions for future research.

ecology↗

bioRxiv2018

The global distribution of Bacillus anthracis and associated anthrax risk to humans, livestock, and wildlife

Bacillus anthracis is a spore-forming, Gram-positive bacterium responsible for anthrax, an acute and commonly lethal infection that most significantly affects grazing livestock, wild ungulates and other herbivorous mammals, but also poses a serious threat to human health1, 2. The geographic extent of B. anthracis endemism is still poorly understood, despite multi-decade research on anthrax epizootic and epidemic dynamics around the world3, 4. Several biogeographic studies have focused on modeling environmental suitability for anthrax at local or national scales5-9, but many countries have limited or inadequate surveillance systems, even within known endemic regions. Here we compile an extensive global occurrence dataset for B. anthracis, drawing on confirmed human, livestock, and wildlife anthrax outbreaks. With these records, we use boosted regression trees10, 11 to produce the first map of the global distribution of B. anthracis as a proxy for anthrax risk. Variable contributions to the model support pre-existing hypotheses that environmental suitability for B. anthracis depends most strongly on soil characteristics such as pH that affect spore persistence, and the extent of seasonal fluctuations in vegetation, which plays a key role in transmission for herbivores12, 13. We apply the global model to estimate that 1.83 billion people (95% credible interval: 0.59--4.16 billion) live within regions of anthrax risk, but most of that population faces little occupational exposure to anthrax. More informatively, a global total of 63.8 million rural poor livestock keepers (95% CI: 17.5--168.6 million) and 1.1 billion livestock (95% CI: 0.4--2.3 billion) live within vulnerable regions. Human risk is concentrated in rural areas, and human and livestock vulnerability are both concentrated in rainfed systems throughout arid and temperate land across Eurasia, Africa, and North America. We conclude by mapping where anthrax risk overlaps with vulnerable wild ungulate populations, and therefore could disrupt sensitive conservation efforts for species like bison, pronghorn, and saiga that coincide with anthrax-prone, mixed-agricultural landscapes. Anthrax is a zoonotic disease caused by the Gram-positive bacterium Bacillus anthracis, a generalist soil-transmitted pathogen found on every inhabited continent14, and several islands including Haiti and parts of the Philippines and Indonesia. Worldwide, an estimated 20,000 to 100,000 cases of anthrax occur annually, mostly in poor rural areas15. In clinical presentations of anthrax, case fatality rates are a function of exposure pathway. Respiratory exposure from spore inhalation is important the context of bioterrorism, but is highly uncommon, and accounts for a negligible fraction of the global burden of anthrax cases. Cutaneous exposure to B. anthracis accounts for the majority of human cases worldwide, and typically presents with low mortality; gastrointestinal exposure accounts for the remainder and presents with intermediate to high fatality rates. Cutaneous and gastrointestinal cases of anthrax are most commonly caused by handling and slaughtering infected livestock, or butchering and eating contaminated meat; untreated gastrointestinal cases likely account for most human mortality from anthrax.14-16

epidemiology↗

bioRxiv2018

Leveraging pathogen community distributions to understand outbreak and emergence potential

Understanding pathogen outbreak and emergence events has important implications to the management of infectious disease. Apart from preempting infectious disease events, there is considerable interest in determining why certain pathogens are consistently found in some regions, and why others spontaneously emerge or reemerge over time. Here, we use a trait-free approach which leverages information on the global community of human infectious diseases to estimate the potential for pathogen outbreak, emergence, and re-emergence events over time. Our approach uses pairwise dissimilarities among pathogen distributions between countries and country-level pathogen composition to quantify pathogen outbreak, emergence, and re-emergence potential as a function of time (e.g., number of years between training and prediction), pathogen type (e.g., virus), and transmission mode (e.g., vector-borne). We find that while outbreak and re-emergence potential are well captured by our simple model, prediction of emergence events remains elusive, and sudden global emergences like an influenza pandemic seem beyond the predictive capacity of the model. While our approach allows for dynamic predictability of outbreak and re-emergence events, data deficiencies and the stochastic nature of emergence events may preclude accurate prediction. Together, our results make a compelling case for incorporating a community ecological perspective into existing disease forecasting efforts.

epidemiology↗

bioRxiv2018

Spatial extinction date estimation: a novel method for reconstructing spatiotemporal patterns of extinction and identifying potential zones of rediscovery

O_LIThe estimation of extinction dates from limited and incomplete sighting records is a key challenge in conservation (when experts are uncertain whether a species has gone extinct) and historical ecology (when the date and mechanism of extinction is controversial).\nC_LIO_LIWe introduce a spatially-explicit method of interpolating extinction date estimators, allowing users to estimate spatiotemporal surfaces of population persistence from georeferenced sighting data of variable quality.\nC_LIO_LIWe present the R package spatExtinct, which produces spatially-explicit extinction date surfaces from geolocated sightings, including options for custom randomization schemes to improve accuracy with limited datasets. We use simulations to illustrate the sensitivity of the method to parameterization, and apply the method to identify potential areas where Bachmans warbler (Vermivora bachmanii) might be rediscovered.\nC_LIO_LIOur method, and the spatExtinct package, has the potential to help describe and differentiate different drivers of extinction for historical datasets, and could be used to identify possible regions of population persistence for species with an uncertain extinction status, improving on non-spatial or imprecise methods that are currently in use.\nC_LI

ecology↗

bioRxiv2018

Zika Virus Outbreak, Barbados, 2015 - 2016

Barbados is a Caribbean island country of approximately 285,000 people, with a thriving tourism industry. In 2015, Zika spread rapidly throughout the Americas, and its proliferation through the Caribbean islands followed suit. Barbados reported its first confirmed autochthonous Zika transmission to the Pan American Health Organization (PAHO) in January 2016, a month before the global public health emergency was declared. Following detection of suspected Zika cases on Barbados in 2015, 926 individuals were described as suspected cases, and 147 lab confirmed cases were reported through December 2016, the end of the most recent epidemiological year. In this short report, we describe the epidemiological characteristics of 926 clinical case records which were originally suspected as cases of Zika, and which were subsequently sent for testing and confirmation; 147 were found positive for Zika, using RT-PCR methods, another 276 tested negative, and the remaining 503 were either pending results or still in the suspected category. Women were represented at about twice the rate of men in case records where sex was reported (71.9%), and confirmed cases (78.2%), and 19 of the confirmed positive cases were children under the age of 10.

epidemiology↗

bioRxiv2018

Using movement data to estimate contact rates in a simulated environmentally-transmitted disease system

Agent-based models have become important tools in ecology, particularly in the study of infectious disease dynamics. Simulations of near-continuous movement paths guided by empirical data offer new avenues of investigation into disease transmission. Here, we simulate the spatiotemporal transmission dynamics of anthrax, the acute disease caused by the bacterium Bacillus anthracis, a pathogen transmitted primarily via environmental reservoirs. We explore how calculations of the probabilities of contact between a host and infectious reservoirs are affected by the scale and method of analysis. At both the landscape and individual scales, empirical movement tracks offer previously unattainable estimates of impacts of movement decisions on contact rate metrics. However, the analytical method selected for the calculation of the probability of contact has notable impacts on the resulting estimates, with convex polygons virtually canceling out variation, and unions of local convex hulls (LoCoH methods) and space-time prisms reflecting reasonable variation, but differing in the magnitude of their estimates. The explicit consideration of behavioral states along movement pathways also impacts evaluations of exposure risk, though its effects differ across methods of analysis. Ultimately, simulations demonstrate that the incorporation of movement data into pathogen transmission analyses helps clarify the role of movement processes underlying the observed dynamics of infectious disease.

ecology↗

bioRxiv2017

Going through the motions: incorporating movement analyses into disease research

Though epidemiology dates back to the 1700s, most mathematical representations of epidemics still use transmission rates averaged at the population scale, especially for wildlife diseases. In simplifying the contact process, we ignore the heterogeneities in host movements that complicate the real world, and overlook their impact on spatiotemporal patterns of disease burden. Movement ecology offers a set of tools that help unpack the transmission process, letting researchers more accurately model how animals within a population interact and spread pathogens. Analytical techniques from this growing field can also help expose the reverse process: how infection impacts movement behaviors, and therefore other ecological processes like feeding, reproduction, and dispersal. Here, we synthesize the contributions of movement ecology in disease research, with a particular focus on studies that have successfully used movement-based methods to quantify individual heterogeneity in exposure and transmission risk. Throughout, we highlight the rapid growth of both disease and movement ecology, and comment on promising but unexplored avenues for research at their overlap. Ultimately, we suggest, including movement empowers ecologists to pose new questions expanding our understanding of host-pathogen dynamics, and improving our predictive capacity for wildlife and even human diseases.

ecology↗

bioRxiv2017

The Parasite Extinction Assessment & Red List: an open-source, online biodiversity database for neglected symbionts

Parasite conservation is a rapidly growing field at the intersection of ecology, epidemiology, parasitology, and public health. The overwhelming diversity of parasitic life on earth, and recent work showing that parasites and other symbionts face severe extinction risk, necessitates infrastructure for parasite conservation assessments. Here, we describe the release of the Parasite Extinction Assessment & Red List (PEARL) version 1.0, an open-access database of conservation assessments and distributional data for almost 500 macroparasitic invertebrates. The current approach to vulnerability assessment is based on range shifts and loss from climate change, and will be expanded as additional data (e.g., host-parasite associations and coextinction risk) is consolidated in PEARL. The web architecture is also open-source, scalable, and extensible, making PEARL a template for more eZcient red listing for other high-diversity, data-de1cient groups. Future iterations will also include new functionality, including a user-friendly open data pository and automated assessment and re-listing.

ecology↗

bioRxiv2017

Climate change drives uncertain global shifts in potential distribution and seasonal risk of Aedes-transmitted viruses

AbstractForecasting the impacts of climate change on Aedes-borne viruses--especially dengue, chikungunya, and Zika--is a key component of public health preparedness. We apply an empirically parameterized Bayesian transmission model of Aedes-borne viruses for the two vectors Aedes aegypti and Ae. albopictus as a function of temperature to predict cumulative monthly global transmission risk in current climates, and compare with projected risk in 2050 and 2080 based on general circulation models (GCMs). Our results show that if mosquito range shifts track optimal temperatures for transmission (26-29 {degrees}C), we can expect poleward shifts in Aedes-borne virus distributions. However, the differing thermal niches of the two vectors produce different patterns of shifts under climate change. More severe climate change scenarios produce proportionally worse population exposures from Ae. aegypti, but not from Ae. albopictus in the most extreme cases. Expanding risk of transmission from both mosquitoes will likely be a serious problem, even in the short term, for most of Europe; but significant reductions are also expected for Aedes albopictus, most noticeably in southeast Asia and west Africa. Within the next century, nearly a billion people are threatened with new exposure to both Aedes spp. in the worst-case scenario; but massive net losses in risk are noticeable for Ae. albopictus, especially in terms of year-round transmission, marking a global shift towards more seasonal risk across regions. Many other complicating factors (like mosquito range limits and viral evolution) exist, but overall our results indicate that while climate change will lead to both increased and new exposures to vector-borne disease, the most extreme increases in Ae. albopictus transmission are predicted to occur at intermediate climate change scenarios.\n\nAuthor SummaryThe established scientific consensus indicates that climate change will severely exacerbate the risk and burden of Aedes-transmitted viruses, including dengue, chikungunya, Zika, West Nile virus, and other significant threats to global health security. Here, we show that the story is more complicated, first and foremost due to differences between the more heat-tolerant Aedes aegypti and the more heat-limited Ae. albopictus. Almost a billion people could face their first exposure to viral transmission from either mosquito in the worst-case scenario, especially in Europe and high-elevation tropical and subtropical regions. On the other hand, while year-round transmission potential from Ae. aegypti is likely to expand (especially in south Asia and sub-Saharan Africa), Ae. albopictus loses significant ground in the tropics, marking a global shift towards seasonal risk as the tropics eventually become too hot for transmission by Ae. albopictus. Complete mitigation of climate change to a pre-industrial baseline could protect almost a billion people from arbovirus range expansions; but middle-of-the-road mitigation may actually produce the greatest expansion in the potential for viral transmission by Ae. albopictus. In any scenario, mitigating climate change also shifts the burden of both dengue and chikungunya (and potentially other Aedes transmitted viruses) from higher-income regions back onto the tropics, where transmission might otherwise start to be curbed by rising temperatures.

epidemiology↗