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Quantifying how global change impacts wild populations remains challenging, especially for species poorly represented by systematic datasets. Here, we infer climate change effects on masting by Joshua trees (Yucca brevifolia and Y. jaegeriana), keystone perennials of the Mojave Desert, from 15 years of crowdsourced observations. We annotated phenophase in 10,212 geo‐referenced images of Joshua trees on the iNaturalist crowdsourcing platform, and used them to train machine learning models predicting flowering from annual weather records. Hindcasting to 1900 with a trained model successfully recovers flowering events in independent historical records and reveals a slightly rising frequency of conditions supporting flowering since the early 20th Century. This reflects increased variation in annual precipitation, which drives masting events in wet years—but also increasing temperatures and drought stress, which may have net negative impacts on recruitment. Our findings reaffirm the value of crowdsourcing for understanding climate change impacts on biodiversity. [ABSTRACT FROM AUTHOR]

Beginning December 2016, sylvatic yellow fever (YF) outbreaks spread into southeastern Brazil, and Minas Gerais state experienced two sylvatic YF waves (2017 and 2018). Following these massive YF waves, we screened 187 free-living non-human primate (NHPs) carcasses collected throughout the state between January 2019 and June 2021 for YF virus (YFV) using qPCR. One sample belonging to a Callithrix, collected in June 2020, was positive for YFV. The viral strain belonged to the same lineage associated with 2017–2018 outbreaks, showing the continued enzootic circulation of YFV in the state. Next, using data from 781 NHPs carcasses collected in 2017–18, we used generalized additive mixed models (GAMMs) to identify the spatiotemporal and host-level drivers of YFV infection and intensity (an estimation of genomic viral load in the liver of infected NHP). Our GAMMs explained 65% and 68% of variation in virus infection and intensity, respectively, and uncovered strong temporal and spatial patterns for YFV infection and intensity. NHP infection was higher in the eastern part of Minas Gerais state, where 2017–2018 outbreaks affecting humans and NHPs were concentrated. The odds of YFV infection were significantly lower in NHPs from urban areas than from urban-rural or rural areas, while infection intensity was significantly lower in NHPs from urban areas or the urban-rural interface relative to rural areas. Both YFV infection and intensity were higher during the warm/rainy season compared to the cold/dry season. The higher YFV intensity in NHPs in warm/rainy periods could be a result of higher exposure to vectors and/or higher virus titers in vectors during this time resulting in the delivery of a higher virus dose and higher viral replication levels within NHPs. Further studies are needed to better test this hypothesis and further compare the dynamics of YFV enzootic cycles between different seasons. Author summary: In 2017 and 2018 massive sylvatic yellow fever (YF) outbreaks took place in Minas Gerais Brazil. To investigate yellow fever virus (YFV) circulation following these massive outbreaks, we investigated samples from 187 free-living non-human primate (NHPs) collected between January 2019 and June 2021. One sample belonging to a Callithrix, collected in June 2020 was positive for YFV. This virus was closely related to YFV from previous outbreaks (2017–2018) showing the continued enzootic circulation of YFV in the state. Next, we investigated the drivers of YFV infection and intensity (an estimation of viral load in each infected NHP) during the 2017–18 outbreaks. The odds of YFV infection in NHPs were lower in urban areas compared to rural ones as expected in sylvatic cycles. There were strong temporal and spatial patterns were observed for YFV infection and intensity, especially in the eastern part of Minas Gerais state. The higher YFV infection and intensity observed during the warm/rainy season (as opposed to the cold/dry one) could be related to higher exposure to vectors and/or higher virus titers in vectors during this time. The possible delivery of a higher virus dose in NHPs could lead to higher viral replication levels within NHPs explaining the higher intensity of infection during warm/rainy season. Further studies are needed to better test this hypothesis and further compare the dynamics of YFV enzootic cycles between different seasons. [ABSTRACT FROM AUTHOR]

The sugars that coat the outsides of viruses and host cells are key to successful disease transmission, but they remain understudied compared to other molecular features. Understanding the comparative zoology of glycosylation ‐ and harnessing it for predictive science ‐ could help close the molecular gap in zoonotic risk assessment. [ABSTRACT FROM AUTHOR]

Multinational epidemics of emerging infectious diseases are increasingly common, due to anthropogenic pressure on ecosystems and the growing connectivity of human populations. Early and efficient vaccination can contain outbreaks and prevent mass mortality, but optimal vaccine stockpiling strategies are dependent on pathogen characteristics, reservoir ecology, and epidemic dynamics. Here, we model major regional outbreaks of Nipah virus and Middle East respiratory syndrome, and use these to develop a generalized framework for estimating vaccine stockpile needs based on spillover geography, spatially-heterogeneous healthcare capacity and spatially-distributed human mobility networks. Because outbreak sizes were highly skewed, we found that most outbreaks were readily contained (median stockpile estimate for MERS-CoV: 2,089 doses; Nipah: 1,882 doses), but the maximum estimated stockpile need in a highly unlikely large outbreak scenario was 2–3 orders of magnitude higher (MERS-CoV: ∼87,000 doses; Nipah ∼ 1.1 million doses). Sensitivity analysis revealed that stockpile needs were more dependent on basic epidemiological parameters (i.e., death and recovery rate) and healthcare availability than any uncertainty related to vaccine efficacy or deployment strategy. Our results highlight the value of descriptive epidemiology for real-world modeling applications, and suggest that stockpile allocation should consider ecological, epidemiological, and social dimensions of risk. [ABSTRACT FROM AUTHOR]

Summary: Due to climate change and habitat conversion, estimates of the resulting levels of species extinction over the next century are alarming. Devising conservation solutions will require many different approaches, including examining the extinction processes of recently extinct species. Given that parrots are one of the most threatened groups of birds, information regarding parrot extinction is 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, yet mostly disappeared by the end of the 19th century. Despite a great deal of speculation, the major cause of its extinction remains unknown. Establishing the date when a species went extinct is one of the first steps in determining what caused their extinction. While there have been estimates of their extinction date, these analyses used a limited dataset and did not include observational data. We used a recently published, extensive dataset of Carolina Parakeet specimens and observations combined with a Bayesian extinction estimating model to determine the most likely extinction dates. By considering each of the two subspecies independently, we found that they went extinct ˜30 years apart: the western subspecies C. c. ludovicianus going extinct 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, possibly missing a major clue in solving the mystery of the parakeet's 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 occurring in many parts of the world where parrot diversity is highest, any progress we make in unraveling the mystery of their disappearance may be vital to modern conservation efforts. [ABSTRACT FROM AUTHOR]

After several pandemics over the last two millennia, the wildlife reservoirs of plague (Yersinia pestis) now persist around the world, including in the western United States. Routine surveillance in this region has generated comprehensive records of human cases and animal seroprevalence, creating a unique opportunity to test how plague reservoirs are responding to environmental change. Here, we test whether animal and human data suggest that plague reservoirs and spillover risk have shifted since 1950. To do so, we develop a new method for detecting the impact of climate change on infectious disease distributions, capable of disentangling long‐term trends (signal) and interannual variation in both weather and sampling (noise). We find that plague foci are associated with high‐elevation rodent communities, and soil biochemistry may play a key role in the geography of long‐term persistence. In addition, we find that human cases are concentrated only in a small subset of endemic areas, and that spillover events are driven by higher rodent species richness (the amplification hypothesis) and climatic anomalies (the trophic cascade hypothesis). Using our detection model, we find that due to the changing climate, rodent communities at high elevations have become more conducive to the establishment of plague reservoirs—with suitability increasing up to 40% in some places—and that spillover risk to humans at mid‐elevations has increased as well, although more gradually. These results highlight opportunities for deeper investigation of plague ecology, the value of integrative surveillance for infectious disease geography, and the need for further research into ongoing climate change impacts. [ABSTRACT FROM AUTHOR]

1. Ecologists increasingly recognise coinfection as an important component of emergent epidemiological patterns, connecting aspects of ecoimmunology, behaviour, ecosystem function and even extinction risk. 2. Building on syndemic theory in medical anthropology, we propose the term 'synzootics' to describe co-occurring enzootic or epizootic processes that produce worse health outcomes in wild animals. Using framing from syndemic theory, we describe how the synzootic concept offers new insights into the ecology and evolution of infectious diseases. 3. We then recommend a set of empirical criteria and lines of evidence that can be used to identify synzootics in nature. We conclude by exploring how synzootics could indirectly drive the emergence of novel pathogens in human populations. [ABSTRACT FROM AUTHOR]

Orthopoxviruses (OPVs), including the causative agents of smallpox and mpox have led to devastating outbreaks in human populations worldwide. However, the discontinuation of smallpox vaccination, which also provides cross-protection against related OPVs, has diminished global immunity to OPVs more broadly. We apply machine learning models incorporating both host ecological and viral genomic features to predict likely reservoirs of OPVs. We demonstrate that incorporating viral genomic features in addition to host ecological traits enhanced the accuracy of potential OPV host predictions, highlighting the importance of host-virus molecular interactions in predicting potential host species. We identify hotspots for geographic regions rich with potential OPV hosts in parts of southeast Asia, equatorial Africa, and the Amazon, revealing high overlap between regions predicted to have a high number of potential OPV host species and those with the lowest smallpox vaccination coverage, indicating a heightened risk for the emergence or establishment of zoonotic OPVs. Our findings can be used to target wildlife surveillance, particularly related to concerns about mpox establishment beyond its historical range.

Although climate change poses a well-established risk to human health, present-day health impacts, particularly those resulting from climate-induced behavioral changes, are under-quantified. Analyzing the U.S. West Coast wildfires of September 2020, we found that poor air quality drives people indoors, increasing the circulation of airborne pathogens like COVID-19. Indoor masking rates as low as 10% can mitigate this risk, offering a clear path to enhance public health responses during wildfires.

Pathogen evolution is one of the least predictable components of disease emergence, particularly in nature. Here, building on principles established by the geographic mosaic theory of coevolution, we develop a quantitative, spatially explicit framework for mapping the evolutionary risk of viral emergence. Driven by interest in diseases like Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and Coronavirus disease 2019 (COVID-19), we examine the global biogeography of bat-origin betacoronaviruses, and find that coevolutionary principles suggest geographies of risk that are distinct from the hotspots and coldspots of host richness. Further, our framework helps explain patterns like a unique pool of merbecoviruses in the Neotropics, a recently discovered lineage of divergent nobecoviruses in Madagascar, and-most importantly-hotspots of diversification in southeast Asia, sub-Saharan Africa, and the Middle East that correspond to the site of previous zoonotic emergence events. Our framework may help identify hotspots of future risk that have also been previously overlooked, like West Africa and the Indian subcontinent, and may more broadly help researchers understand how host ecology shapes the evolution and diversity of pandemic threats., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press.)