The risk of vector transmission of Trypanosoma cruzi remains high in the State of Paraná.

BACKGROUND: Monitoring and analysing the infection rates of the vector of Trypanosoma cruzi, that causes Chagas disease, helps assess the risk of transmission. OBJECTIVES: A study was carried out on triatomine in the State of Paraná, Brazil, between 2012 and 2021 and a comparison was made with a previous study. This was done to assess the risk of disease transmission. METHODS: Ecological niche models based on climate and landscape variables were developed to predict habitat suitability for the vectors as a proxy for risk of occurrence. FINDINGS: A total of 1,750 specimens of triatomines were recorded, of which six species were identified. The overall infection rate was 22.7%. The areas with the highest risk transmission of T. cruzi are consistent with previous predictions in municipalities. New data shows that climate models are more accurate than landscape models. This is likely because climate suitability was higher in the previous period. MAIN CONCLUSION: Regardless of uneven sampling and potential biases, risk remains high due to the wide presence of infected vectors and high environmental suitability for vector species throughout the state and, therefore, improvements in public policies aimed at wide dissemination of knowledge about the disease are recommended to ensure the State remains free of Chagas disease.

Present and future distribution of bat hosts of sarbecoviruses: implications for conservation and public health.

Global changes in response to human encroachment into natural habitats and carbon emissions are driving the biodiversity extinction crisis and increasing disease emergence risk. Host distributions are one critical component to identify areas at risk of viral spillover, and bats act as reservoirs of diverse viruses. We developed a reproducible ecological niche modelling pipeline for bat hosts of SARS-like viruses (subgenus Sarbecovirus), given that several closely related viruses have been discovered and sarbecovirus-host interactions have gained attention since SARS-CoV-2 emergence. We assessed sampling biases and modelled current distributions of bats based on climate and landscape relationships and project future scenarios for host hotspots. The most important predictors of species distributions were temperature seasonality and cave availability. We identified concentrated host hotspots in Myanmar and projected range contractions for most species by 2100. Our projections indicate hotspots will shift east in Southeast Asia in locations greater than 2°C hotter in a fossil-fuelled development future. Hotspot shifts have implications for conservation and public health, as loss of population connectivity can lead to local extinctions, and remaining hotspots may concentrate near human populations.

ATLANTIC MAMMAL TRAITS: a data set of morphological traits of mammals in the Atlantic Forest of South America.

Measures of traits are the basis of functional biological diversity. Numerous works consider mean species-level measures of traits while ignoring individual variance within species. However, there is a large amount of variation within species and it is increasingly apparent that it is important to consider trait variation not only between species, but also within species. Mammals are an interesting group for investigating trait-based approaches because they play diverse and important ecological functions (e.g., pollination, seed dispersal, predation, grazing) that are correlated with functional traits. Here we compile a data set comprising morphological and life history information of 279 mammal species from 39,850 individuals of 388 populations ranging from -5.83 to -29.75 decimal degrees of latitude and -34.82 to -56.73 decimal degrees of longitude in the Atlantic forest of South America. We present trait information from 16,840 individuals of 181 species of non-volant mammals (Rodentia, Didelphimorphia, Carnivora, Primates, Cingulata, Artiodactyla, Pilosa, Lagomorpha, Perissodactyla) and from 23,010 individuals of 98 species of volant mammals (Chiroptera). The traits reported include body mass, age, sex, reproductive stage, as well as the geographic coordinates of sampling for all taxa. Moreover, we gathered information on forearm length for bats and body length and tail length for rodents and marsupials. No copyright restrictions are associated with the use of this data set. Please cite this data paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data.

ATLANTIC-CAMTRAPS: a dataset of medium and large terrestrial mammal communities in the Atlantic Forest of South America.

Our understanding of mammal ecology has always been hindered by the difficulties of observing species in closed tropical forests. Camera trapping has become a major advance for monitoring terrestrial mammals in biodiversity rich ecosystems. Here we compiled one of the largest datasets of inventories of terrestrial mammal communities for the Neotropical region based on camera trapping studies. The dataset comprises 170 surveys of medium to large terrestrial mammals using camera traps conducted in 144 areas by 74 studies, covering six vegetation types of tropical and subtropical Atlantic Forest of South America (Brazil and Argentina), and present data on species composition and richness. The complete dataset comprises 53,438 independent records of 83 species of mammals, includes 10 species of marsupials, 15 rodents, 20 carnivores, eight ungulates and six armadillos. Species richness averaged 13 species (±6.07 SD) per site. Only six species occurred in more than 50% of the sites: the domestic dog Canis familiaris, crab-eating fox Cerdocyon thous, tayra Eira barbara, south American coati Nasua nasua, crab-eating raccoon Procyon cancrivorus and the nine-banded armadillo Dasypus novemcinctus. The information contained in this dataset can be used to understand macroecological patterns of biodiversity, community, and population structure, but also to evaluate the ecological consequences of fragmentation, defaunation, and trophic interactions.

Threshold effect of habitat loss on bat richness in cerrado-forest landscapes.

Understanding how animal groups respond to contemporary habitat loss and fragmentation is essential for development of strategies for species conservation. Until now, there has been no consensus about how landscape degradation affects the diversity and distribution of Neotropical bats. Some studies demonstrate population declines and species loss in impacted areas, although the magnitude and generality of these effects on bat community structure are unclear. Empirical fragmentation thresholds predict an accentuated drop in biodiversity, and species richness in particular, when less than 30% of the original amount of habitat in the landscape remains. In this study, we tested whether bat species richness demonstrates this threshold response, based on 48 sites distributed across 12 landscapes with 9-88% remaining forest in Brazilian cerrado-forest formations. We also examined the degree to which abundance was similarly affected within four different feeding guilds. The threshold value for richness, below which bat diversity declines precipitously, was estimated at 47% of remaining forest. To verify if the response of bat abundance to habitat loss differed among feeding guilds, we used a model selection approach based on Akaike's information criterion. Models accounted for the amount of riparian forest, semideciduous forest, cerrado, tree plantations, secondary forest, and the total amount of forest in the landscape. We demonstrate a nonlinear effect of the contribution of tree plantations to frugivores, and a positive effect of the amount of cerrado to nectarivores and animalivores, the groups that responded most to decreases in amount of forest. We suggest that bat assemblages in interior Atlantic Forest and cerrado regions of southeastern Brazil are impoverished, since we found lower richness and abundance of different groups in landscapes with lower amounts of forest. The relatively higher threshold value of 47% suggests that bat communities have a relatively lower resistance to habitat degradation than other animal groups. Accordingly, conservation and restoration strategies should focus on increasing the amount of native vegetation of landscapes so as to enhance species richness of bats.

Impact of infectious diseases on wild bovidae populations in Thailand: insights from population modelling and disease dynamics.

The wildlife and livestock interface is vital for wildlife conservation and habitat management. Infectious diseases maintained by domestic species may impact threatened species such as Asian bovids, as they share natural resources and habitats. To predict the population impact of infectious diseases with different traits, we used stochastic mathematical models to simulate the population dynamics over 100 years for 100 times in a model gaur (Bos gaurus) population with and without disease. We simulated repeated introductions from a reservoir, such as domestic cattle. We selected six bovine infectious diseases; anthrax, bovine tuberculosis, haemorrhagic septicaemia, lumpy skin disease, foot and mouth disease and brucellosis, all of which have caused outbreaks in wildlife populations. From a starting population of 300, the disease-free population increased by an average of 228% over 100 years. Brucellosis with frequency-dependent transmission showed the highest average population declines (-97%), with population extinction occurring 16% of the time. Foot and mouth disease with frequency-dependent transmission showed the lowest impact, with an average population increase of 200%. Overall, acute infections with very high or low fatality had the lowest impact, whereas chronic infections produced the greatest population decline. These results may help disease management and surveillance strategies support wildlife conservation.

High connectivity and human movement limits the impact of travel time on infectious disease transmission.

The speed of spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic highlights the importance of understanding how infections are transmitted in a highly connected world. Prior to vaccination, changes in human mobility patterns were used as non-pharmaceutical interventions to eliminate or suppress viral transmission. The rapid spread of respiratory viruses, various intervention approaches, and the global dissemination of SARS-CoV-2 underscore the necessity for epidemiological models that incorporate mobility to comprehend the spread of the virus. Here, we introduce a metapopulation susceptible-exposed-infectious-recovered model parametrized with human movement data from 340 cities in China. Our model replicates the early-case trajectory in the COVID-19 pandemic. We then use machine learning algorithms to determine which network properties best predict spread between cities and find travel time to be most important, followed by the human movement-weighted personalized PageRank. However, we show that travel time is most influential locally, after which the high connectivity between cities reduces the impact of travel time between individual cities on transmission speed. Additionally, we demonstrate that only significantly reduced movement substantially impacts infection spread times throughout the network.

The coevolutionary mosaic of bat betacoronavirus emergence risk.

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.

Malaria Risk Drivers in the Brazilian Amazon: Land Use-Land Cover Interactions and Biological Diversity.

Malaria is a prevalent disease in several tropical and subtropical regions, including Brazil, where it remains a significant public health concern. Even though there have been substantial efforts to decrease the number of cases, the reoccurrence of epidemics in regions that have been free of cases for many years presents a significant challenge. Due to the multifaceted factors that influence the spread of malaria, influencing malaria risk factors were analyzed through regional outbreak cluster analysis and spatio-temporal models in the Brazilian Amazon, incorporating climate, land use/cover interactions, species richness, and number of endemic birds and amphibians. Results showed that high amphibian and bird richness and endemism correlated with a reduction in malaria risk. The presence of forest had a risk-increasing effect, but it depended on its juxtaposition with anthropic land uses. Biodiversity and landscape composition, rather than forest formation presence alone, modulated malaria risk in the period. Areas with low endemic species diversity and high human activity, predominantly anthropogenic landscapes, posed high malaria risk. This study underscores the importance of considering the broader ecological context in malaria control efforts.

Time lag effect on malaria transmission dynamics in an Amazonian Colombian municipality and importance for early warning systems.

Malaria remains a significant public health problem worldwide, particularly in low-income regions with limited access to healthcare. Despite the use of antimalarial drugs, transmission remains an issue in Colombia, especially among indigenous populations in remote areas. In this study, we used an SIR Ross MacDonald model that considered land use change, temperature, and precipitation to analyze eco epidemiological parameters and the impact of time lags on malaria transmission in La Pedrera-Amazonas municipality. We found changes in land use between 2007 and 2020, with increases in forested areas, urban infrastructure and water edges resulting in a constant increase in mosquito carrying capacity. Temperature and precipitation variables exhibited a fluctuating pattern that corresponded to rainy and dry seasons, respectively and a marked influence of the El Niño climatic phenomenon. Our findings suggest that elevated precipitation and temperature increase malaria infection risk in the following 2 months. The risk is influenced by the secondary vegetation and urban infrastructure near primary forest formation or water body edges. These results may help public health officials and policymakers develop effective malaria control strategies by monitoring precipitation, temperature, and land use variables to flag high-risk areas and critical periods, considering the time lag effect.

Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots.

The emergence of SARS-like coronaviruses is a multi-stage process from wildlife reservoirs to people. Here we characterize multiple drivers-landscape change, host distribution, and human exposure-associated with the risk of spillover of zoonotic SARS-like coronaviruses to help inform surveillance and mitigation activities. We consider direct and indirect transmission pathways by modeling four scenarios with livestock and mammalian wildlife as potential and known reservoirs before examining how access to healthcare varies within clusters and scenarios. We found 19 clusters with differing risk factor contributions within a single country (N = 9) or transboundary (N = 10). High-risk areas were mainly closer (11-20%) rather than far ( < 1%) from healthcare. Areas far from healthcare reveal healthcare access inequalities, especially Scenario 3, which includes wild mammals and not livestock as secondary hosts. China (N = 2) and Indonesia (N = 1) had clusters with the highest risk. Our findings can help stakeholders in land use planning, integrating healthcare implementation and One Health actions.

NeoBat Interactions: A data set of bat-plant interactions in the Neotropics.

Data papers and open databases have revolutionized contemporary science, as they provide the long-needed incentive to collaborate in large international teams and make natural history information widely available. Nevertheless, most data papers have focused on species occurrence or abundance, whereas interactions have received much less attention. To help fill this gap, we have compiled a georeferenced data set of interactions between 93 bat species of the family Phyllostomidae (Chiroptera) and 501 plant species of 68 families. Data came from 169 studies published between 1957 and 2007 covering the entire Neotropical Region, with most records from Brazil (34.5% of all study sites), Costa Rica (16%), and Mexico (14%). Our data set includes 2571 records of frugivory (75.1% of all records) and nectarivory (24.9%). The best represented bat genera are Artibeus (28% of all records), Carollia (24%), Sturnira (10.1%), and Glossophaga (8.8%). Carollia perspicillata (187), Artibeus lituratus (125), Artibeus jamaicensis (94), Glossophaga soricina (86), and Artibeus planirostris (74) were the bat species with the broadest diets recorded based on the number of plant species. Among the plants, the best represented families were Moraceae (17%), Piperaceae (15.4%), Urticaceae (9.2%), and Solanaceae (9%). Plants of the genera Cecropia (46), Ficus (42), Piper (40), Solanum (31), and Vismia (27) exhibited the largest number of interactions. These data are stored as arrays (records, sites, and studies) organized by logical keys and rich metadata, which helped to compile the information on different ecological and geographic scales, according to how they should be used. Our data set on bat-plant interactions is by far the most extensive, both in geographic and taxonomic terms, and includes abiotic information of study sites, as well as ecological information of plants and bats. It has already facilitated several studies and we hope it will stimulate novel analyses and syntheses, in addition to pointing out important gaps in knowledge. Data are provided under the Creative Commons Attribution 4.0 International License. Please cite this paper when the data are used in any kind of publication related to research, outreach, and teaching activities.

The Global Virome in One Network (VIRION): an Atlas of Vertebrate-Virus Associations.

Data that catalogue viral diversity on Earth have been fragmented across sources, disciplines, formats, and various degrees of open sharing, posing challenges for research on macroecology, evolution, and public health. Here, we solve this problem by establishing a dynamically maintained database of vertebrate-virus associations, called The Global Virome in One Network (VIRION). The VIRION database has been assembled through both reconciliation of static data sets and integration of dynamically updated databases. These data sources are all harmonized against one taxonomic backbone, including metadata on host and virus taxonomic validity and higher classification; additional metadata on sampling methodology and evidence strength are also available in a harmonized format. In total, the VIRION database is the largest open-source, open-access database of its kind, with roughly half a million unique records that include 9,521 resolved virus "species" (of which 1,661 are ICTV ratified), 3,692 resolved vertebrate host species, and 23,147 unique interactions between taxonomically valid organisms. Together, these data cover roughly a quarter of mammal diversity, a 10th of bird diversity, and ∼6% of the estimated total diversity of vertebrates, and a much larger proportion of their virome than any previous database. We show how these data can be used to test hypotheses about microbiology, ecology, and evolution and make suggestions for best practices that address the unique mix of evidence that coexists in these data. IMPORTANCE Animals and their viruses are connected by a sprawling, tangled network of species interactions. Data on the host-virus network are available from several sources, which use different naming conventions and often report metadata in different levels of detail. VIRION is a new database that combines several of these existing data sources, reconciles taxonomy to a single consistent backbone, and reports metadata in a format designed by and for virologists. Researchers can use VIRION to easily answer questions like "Can any fish viruses infect humans?" or "Which bats host coronaviruses?" or to build more advanced predictive models, making it an unprecedented step toward a full inventory of the global virome.

DarkCideS 1.0, a global database for bats in karsts and caves.

Understanding biodiversity patterns as well as drivers of population declines, and range losses provides crucial baselines for monitoring and conservation. However, the information needed to evaluate such trends remains unstandardised and sparsely available for many taxonomic groups and habitats, including the cave-dwelling bats and cave ecosystems. We developed the DarkCideS 1.0 ( https://darkcides.org/ ), a global database of bat caves and species synthesised from publicly available information and datasets. The DarkCideS 1.0 is by far the largest database for cave-dwelling bats, which contains information for geographical location, ecological status, species traits, and parasites and hyperparasites for 679 bat species are known to occur in caves or use caves in part of their life histories. The database currently contains 6746 georeferenced occurrences for 402 cave-dwelling bat species from 2002 cave sites in 46 countries and 12 terrestrial biomes. The database has been developed to be collaborative and open-access, allowing continuous data-sharing among the community of bat researchers and conservation biologists to advance bat research and comparative monitoring and prioritisation for conservation.

Community health and human-animal contacts on the edges of Bwindi Impenetrable National Park, Uganda.

Cross-species transmission of pathogens is intimately linked to human and environmental health. With limited healthcare and challenging living conditions, people living in poverty may be particularly susceptible to endemic and emerging diseases. Similarly, wildlife is impacted by human influences, including pathogen sharing, especially for species in close contact with people and domesticated animals. Here we investigate human and animal contacts and human health in a community living around the Bwindi Impenetrable National Park (BINP), Uganda. We used contact and health survey data to identify opportunities for cross-species pathogen transmission, focusing mostly on people and the endangered mountain gorilla. We conducted a survey with background questions and self-reported diaries to investigate 100 participants' health, such as symptoms and behaviours, and contact patterns, including direct contacts and sightings over a week. Contacts were revealed through networks, including humans, domestic, peri-domestic, and wild animal groups for 1) contacts seen in the week of background questionnaire completion, and 2) contacts seen during the diary week. Participants frequently felt unwell during the study, reporting from one to 10 disease symptoms at different intensity levels, with severe symptoms comprising 6.4% of the diary records and tiredness and headaches the most common symptoms. After human-human contacts, direct contact with livestock and peri-domestic animals were the most common. The contact networks were moderately connected and revealed a preference in contacts within the same taxon and within their taxa groups. Sightings of wildlife were much more common than touching. However, despite contact with wildlife being the rarest of all contact types, one direct contact with a gorilla with a timeline including concerning participant health symptoms was reported. When considering all interaction types, gorillas mostly exhibited intra-species contact, but were found to interact with five other species, including people and domestic animals. Our findings reveal a local human population with recurrent symptoms of illness in a location with intense exposure to factors that can increase pathogen transmission, such as direct contact with domestic and wild animals and proximity among animal species. Despite significant biases and study limitations, the information generated here can guide future studies, such as models for disease spread and One Health interventions.

The future of zoonotic risk prediction.

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Insights into the assembly rules of a continent-wide multilayer network.

How are ecological systems assembled? Identifying common structural patterns within complex networks of interacting species has been a major challenge in ecology, but researchers have focused primarily on single interaction types aggregating in space or time. Here, we shed light on the assembly rules of a multilayer network formed by frugivory and nectarivory interactions between bats and plants in the Neotropics. By harnessing a conceptual framework known as the integrative hypothesis of specialization, our results suggest that phylogenetic constraints separate species into different layers and shape the network's modules. Then, the network shifts to a nested structure within its modules where interactions are mainly structured by geographic co-occurrence. Finally, organismal traits related to consuming fruits or nectar determine which bat species are central or peripheral to the network. Our results provide insights into how different processes contribute to the assemblage of ecological systems at different levels of organization, resulting in a compound network topology.

Hantavirus host assemblages and human disease in the Atlantic Forest.

Several viruses from the genus Orthohantavirus are known to cause lethal disease in humans. Sigmodontinae rodents are the main hosts responsible for hantavirus transmission in the tropical forests, savannas, and wetlands of South America. These rodents can shed different hantaviruses, such as the lethal and emerging Araraquara orthohantavirus. Factors that drive variation in host populations may influence hantavirus transmission dynamics within and between populations. Landscape structure, and particularly areas with a predominance of agricultural land and forest remnants, is expected to influence the proportion of hantavirus rodent hosts in the Atlantic Forest rodent community. Here, we tested this using 283 Atlantic Forest rodent capture records and geographically weighted models that allow us to test if predictors vary spatially. We also assessed the correspondence between proportions of hantavirus hosts in rodent communities and a human vulnerability to hantavirus infection index across the entire Atlantic Forest biome. We found that hantavirus host proportions were more positively influenced by landscape diversity than by a particular habitat or agricultural matrix type. Local small mammal diversity also positively influenced known pathogenic hantavirus host proportions, indicating that a plasticity to habitat quality may be more important for these hosts than competition with native forest dwelling species. We found a consistent positive effect of sugarcane and tree plantation on the proportion of rodent hosts, whereas defaunation intensity did not correlate with the proportion of hosts of potentially pathogenic hantavirus genotypes in the community, indicating that non-defaunated areas can also be hotspots for hantavirus disease outbreaks. The spatial match between host hotspots and human disease vulnerability was 17%, while coldspots matched 20%. Overall, we discovered strong spatial and land use change influences on hantavirus hosts at the landscape level across the Atlantic Forest. Our findings suggest disease surveillance must be reinforced in the southern and southeastern regions of the biome where the highest predicted hantavirus host proportion and levels of vulnerability spatially match. Importantly, our analyses suggest there may be more complex rodent community dynamics and interactions with human disease than currently hypothesized.

The risk of vector transmission of Trypanosoma cruzi remains high in the State of Paraná

BACKGROUND Monitoring and analysing the infection rates of the vector of Trypanosoma cruzi, that causes Chagas disease, helps assess the risk of transmission. OBJECTIVES A study was carried out on triatomine in the State of Paraná, Brazil, between 2012 and 2021 and a comparison was made with a previous study. This was done to assess the risk of disease transmission. METHODS Ecological niche models based on climate and landscape variables were developed to predict habitat suitability for the vectors as a proxy for risk of occurrence. FINDINGS A total of 1,750 specimens of triatomines were recorded, of which six species were identified. The overall infection rate was 22.7%. The areas with the highest risk transmission of T. cruzi are consistent with previous predictions in municipalities. New data shows that climate models are more accurate than landscape models. This is likely because climate suitability was higher in the previous period. MAIN CONCLUSION Regardless of uneven sampling and potential biases, risk remains high due to the wide presence of infected vectors and high environmental suitability for vector species throughout the state and, therefore, improvements in public policies aimed at wide dissemination of knowledge about the disease are recommended to ensure the State remains free of Chagas disease.