Longitudinal deep sequencing informs vector selection and future deployment strategies for transmissible vaccines.

Vaccination is a powerful tool in combating infectious diseases of humans and companion animals. In most wildlife, including reservoirs of emerging human diseases, achieving sufficient vaccine coverage to mitigate disease burdens remains logistically unattainable. Virally vectored "transmissible" vaccines that deliberately spread among hosts are a potentially transformative, but still theoretical, solution to the challenge of immunising inaccessible wildlife. Progress towards real-world application is frustrated by the absence of frameworks to guide vector selection and vaccine deployment prior to major in vitro and in vivo investments in vaccine engineering and testing. Here, we performed deep sequencing on field-collected samples of Desmodus rotundus betaherpesvirus (DrBHV), a candidate vector for a transmissible vaccine targeting vampire bat-transmitted rabies. We discovered 11 strains of DrBHV that varied in prevalence and geographic distribution across Peru. The phylogeographic structure of DrBHV strains was predictable from both host genetics and landscape topology, informing long-term DrBHV-vectored vaccine deployment strategies and identifying geographic areas for field trials where vaccine spread would be naturally contained. Multistrain infections were observed in 79% of infected bats. Resampling of marked individuals over 4 years showed within-host persistence kinetics characteristic of latency and reactivation, properties that might boost individual immunity and lead to sporadic vaccine transmission over the lifetime of the host. Further, strain acquisitions by already infected individuals implied that preexisting immunity and strain competition are unlikely to inhibit vaccine spread. Our results support the development of a transmissible vaccine targeting a major source of human and animal rabies in Latin America and show how genomics can enlighten vector selection and deployment strategies for transmissible vaccines.

Sublethal effects of parasitism on ruminants can have cascading consequences for ecosystems.

Parasitic infections are common, but how they shape ecosystem-level processes is understudied. Using a mathematical model and meta-analysis, we explored the potential for helminth parasites to trigger trophic cascades through lethal and sublethal effects imposed on herbivorous ruminant hosts after infection. First, using the model, we linked negative effects of parasitic infection on host survival, fecundity, and feeding rate to host and producer biomass. Our model, parameterized with data from a well-documented producer­caribou­helminth system, reveals that even moderate impacts of parasites on host survival, fecundity, or feeding rate can have cascading effects on ruminant host and producer biomass. Second, using meta-analysis, we investigated the links between helminth infections and traits of free-living ruminant hosts in nature. We found that helminth infections tend to exert negative but sublethal effects on ruminant hosts. Specifically, infection significantly reduces host feeding rates, body mass, and body condition but has weak and highly variable effects on survival and fecundity. Together, these findings suggest that while helminth parasites can trigger trophic cascades through multiple mechanisms, overlooked sublethal effects on nonreproductive traits likely dominate their impacts on ecosystems. In particular, by reducing ruminant herbivory, pervasive helminth infections may contribute to a greener world.

Diversification of mammalian deltaviruses by host shifting.

Hepatitis delta virus (HDV) is an unusual RNA agent that replicates using host machinery but exploits hepatitis B virus (HBV) to mobilize its spread within and between hosts. In doing so, HDV enhances the virulence of HBV. How this seemingly improbable hyperparasitic lifestyle emerged is unknown, but it underpins the likelihood that HDV and related deltaviruses may alter other host-virus interactions. Here, we show that deltaviruses diversify by transmitting between mammalian species. Among 96,695 RNA sequence datasets, deltaviruses infected bats, rodents, and an artiodactyl from the Americas but were absent from geographically overrepresented Old World representatives of each mammalian order, suggesting a relatively recent diversification within the Americas. Consistent with diversification by host shifting, both bat and rodent-infecting deltaviruses were paraphyletic, and coevolutionary modeling rejected cospeciation with mammalian hosts. In addition, a 2-y field study showed common vampire bats in Peru were infected by two divergent deltaviruses, indicating multiple introductions to a single host species. One vampire bat-associated deltavirus was detected in the saliva of up to 35% of individuals, formed phylogeographically compartmentalized clades, and infected a sympatric bat, illustrating horizontal transmission within and between species on ecological timescales. Consistent absence of HBV-like viruses in two deltavirus-infected bat species indicated acquisitions of novel viral associations during the divergence of bat and human-infecting deltaviruses. Our analyses support an American zoonotic origin of HDV and reveal prospects for future cross-species emergence of deltaviruses. Given their peculiar life history, deltavirus host shifts will have different constraints and disease outcomes compared to ordinary animal pathogens.

Ecological conditions predict the intensity of Hendra virus excretion over space and time from bat reservoir hosts.

The ecological conditions experienced by wildlife reservoirs affect infection dynamics and thus the distribution of pathogen excreted into the environment. This spatial and temporal distribution of shed pathogen has been hypothesised to shape risks of zoonotic spillover. However, few systems have data on both long-term ecological conditions and pathogen excretion to advance mechanistic understanding and test environmental drivers of spillover risk. We here analyse three years of Hendra virus data from nine Australian flying fox roosts with covariates derived from long-term studies of bat ecology. We show that the magnitude of winter pulses of viral excretion, previously considered idiosyncratic, are most pronounced after recent food shortages and in bat populations displaced to novel habitats. We further show that cumulative pathogen excretion over time is shaped by bat ecology and positively predicts spillover frequency. Our work emphasises the role of reservoir host ecology in shaping pathogen excretion and provides a new approach to estimate spillover risk.

Functional traits explain waterbirds' host status, subtype richness, and community-level infection risk for avian influenza.

Species functional traits can influence pathogen transmission processes, and consequently affect species' host status, pathogen diversity, and community-level infection risk. We here investigated, for 143 European waterbird species, effects of functional traits on host status and pathogen diversity (subtype richness) for avian influenza virus at species level. We then explored the association between functional diversity and HPAI H5Nx occurrence at the community level for 2016/17 and 2021/22 epidemics in Europe. We found that both host status and subtype richness were shaped by several traits, such as diet guild and dispersal ability, and that the community-weighted means of these traits were also correlated with community-level risk of H5Nx occurrence. Moreover, functional divergence was negatively associated with H5Nx occurrence, indicating that functional diversity can reduce infection risk. Our findings highlight the value of integrating trait-based ecology into the framework of diversity-disease relationship, and provide new insights for HPAI prediction and prevention.

Single-cell analysis of localized prostate cancer patients links high Gleason score with an immunosuppressive profile.

BACKGROUND: Evading immune surveillance is a hallmark for the development of multiple cancer types. Whether immune evasion contributes to the pathogenesis of high-grade prostate cancer (HGPCa) remains an area of active inquiry. METHODS: Through single-cell RNA sequencing and multicolor flow cytometry of freshly isolated prostatectomy specimens and matched peripheral blood, we aimed to characterize the tumor immune microenvironment (TME) of localized prostate cancer (PCa), including HGPCa and low-grade prostate cancer (LGPCa). RESULTS: HGPCa are highly infiltrated by exhausted CD8+ T cells, myeloid cells, and regulatory T cells (TRegs). These HGPCa-infiltrating CD8+ T cells expressed high levels of exhaustion markers including TIM3, TOX, TCF7, PD-1, CTLA4, TIGIT, and CXCL13. By contrast, a high ratio of activated CD8+  effector T cells relative to TRegs and myeloid cells infiltrate the TME of LGPCa. HGPCa CD8+  tumor-infiltrating lymphocytes (TILs) expressed more androgen receptor and prostate-specific membran antigen yet less prostate-specific antigen than the LGPCa CD8+  TILs. The PCa TME was infiltrated by macrophages but these did not clearly cluster by M1 and M2 markers. CONCLUSIONS: Our study reveals a suppressive TME with high levels of CD8+ T cell exhaustion in localized PCa, a finding enriched in HGPCa relative to LGPCa. These studies suggest a possible link between the clinical-pathologic risk of PCa and the associated TME. Our results have implications for our understanding of the immunologic mechanisms of PCa pathogenesis and the implementation of immunotherapy for localized PCa.

Virus isolation data improve host predictions for New World rodent orthohantaviruses.

Identifying reservoir host species is crucial for understanding the ecology of multi-host pathogens and predicting risks of pathogen spillover from wildlife to people. Predictive models are increasingly used for identifying ecological traits and prioritizing surveillance of likely zoonotic reservoirs, but these often employ different types of evidence for establishing host associations. Comparisons between models with different infection evidence are necessary to guide inferences about the trait profiles of likely hosts and identify which hosts and geographical regions are likely sources of spillover. Here, we use New World rodent-orthohantavirus associations to explore differences in the performance and predictions of models trained on two types of evidence for infection and onward transmission: RT-PCR and live virus isolation data, representing active infections versus host competence, respectively. Orthohantaviruses are primarily carried by muroid rodents and cause the diseases haemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS) in humans. We show that although boosted regression tree (BRT) models trained on RT-PCR and live virus isolation data both performed well and capture generally similar trait profiles, rodent phylogeny influenced previously collected RT-PCR data, and BRTs using virus isolation data displayed a narrower list of predicted reservoirs than those using RT-PCR data. BRT models trained on RT-PCR data identified 138 undiscovered hosts and virus isolation models identified 92 undiscovered hosts, with 27 undiscovered hosts identified by both models. Distributions of predicted hosts were concentrated in several different regions for each model, with large discrepancies between evidence types. As a form of validation, virus isolation models independently predicted several orthohantavirus-rodent host associations that had been previously identified through empirical research using RT-PCR. Our model predictions provide a priority list of species and locations for future orthohantavirus sampling. More broadly, these results demonstrate the value of multiple data types for predicting zoonotic pathogen hosts. These methods can be applied across a range of systems to improve our understanding of pathogen maintenance and increase efficiency of pathogen surveillance.

Plasticity in female timing may explain earlier breeding in a North American songbird.

Many species have shifted their breeding phenology in response to climate change. Identifying the magnitude of phenological shifts and whether climate-mediated selection drives these shifts is key for determining species' resilience to climate change. Birds are a strong model for studying phenological shifts due to numerous long-term research studies; however, generalities pertaining to drivers of phenological shifts will emerge only as we add study species that differ in life history and geography. We investigated 32 years of reproductive timing in a non-migratory population of dark-eyed juncos Junco hyemalis. We predicted that plasticity in reproductive timing would allow females to breed earlier in warmer springs. We also predicted that selection would favour earlier breeding and asked whether the temperatures throughout the breeding season would predict the strength of selection. To test these predictions, we examined temporal changes in the annual median date for reproductive onset (i.e. first egg date) and we used a sliding window analysis to identify spring temperatures driving these patterns. Next, we explored plasticity in reproductive timing and asked whether selection favoured earlier breeding. Lastly, we used a sliding window analysis to identify the time during the breeding season that temperature was most associated with selection favouring earlier breeding. First egg dates occurred earlier over time and strongly covaried with April temperatures. Furthermore, individual females that bred in at least 3 years typically bred earlier in warmer Aprils, exhibiting plastic responses to April temperature. We also found significant overall selection favouring earlier breeding (i.e. higher relative fitness with earlier first egg dates) and variation in selection for earlier breeding over time. However, temperature across diverse climatic windows did not predict the strength of selection. Our findings provide further evidence for the role of phenotypic plasticity in shifting phenology in response to earlier springs. We also provide evidence for the role of selection favouring earlier breeding, regardless of temperature, thus setting the stage for adaptive changes in female breeding phenology. We suggest for multi-brooded birds that advancing first egg dates likely increase the length of the breeding season, and therefore, reproductive success.

Surveying the Vampire Bat (Desmodus rotundus) Serum Proteome: A Resource for Identifying Immunological Proteins and Detecting Pathogens.

Bats are increasingly studied as model systems for longevity and as natural hosts for some virulent viruses. Yet the ability to characterize immune mechanisms of viral tolerance and to quantify infection dynamics in wild bats is often limited by small sample volumes and few species-specific reagents. Here, we demonstrate how proteomics can overcome these limitations by using data-independent acquisition-based shotgun proteomics to survey the serum proteome of 17 vampire bats (Desmodus rotundus) from Belize. Using just 2 µL of sample and relatively short separations of undepleted serum digests, we identified 361 proteins across 5 orders of magnitude. Levels of immunological proteins in vampire bat serum were then compared to human plasma via published databases. Of particular interest were antiviral and antibacterial components, circulating 20S proteasome complex and proteins involved in redox activity. Lastly, we used known virus proteomes to putatively identify Rh186 from Macacine herpesvirus 3 and ORF1a from Middle East respiratory syndrome-related coronavirus, indicating that mass spectrometry-based techniques show promise for pathogen detection. Overall, these results can be used to design targeted mass-spectrometry assays to quantify immunological markers and detect pathogens. More broadly, our findings also highlight the application of proteomics in advancing wildlife immunology and pathogen surveillance.

Interaction between race and prostate cancer treatment benefit in the Veterans Health Administration.

BACKGROUND: Studies have demonstrated that Black men may undergo definitive prostate cancer (CaP) treatment less often than men of other races, but it is unclear whether they are avoiding overtreatment of low-risk disease or experiencing a reduction in appropriate care. The authors' aim was to assess the role of race as it relates to treatment benefit in access to CaP treatment in a single-payer population. METHODS: The authors used the Veterans Health Administration (VHA) Corporate Data Warehouse to perform a retrospective cohort study of veterans diagnosed with low- or intermediate-risk CaP between 2011 and 2017. RESULTS: The authors identified 35,427 men with incident low- or intermediate-risk CaP. When they controlled for covariates, Black men had 1.05 times the odds of receiving treatment in comparison with non-Black men (P < .001), and high-treatment-benefit men had 1.4 times the odds of receiving treatment in comparison with those in the low-treatment-benefit group (P < .001). The interaction of race and treatment benefit was significant, with Black men in the high-treatment-benefit category less likely to receive treatment than non-Black men in the same treatment category (odds ratio, 0.89; P < .001). CONCLUSIONS: Although race does appear to influence the receipt of definitive treatment in the VHA, this relationship varies in the context of the patient's treatment benefit, with Black men receiving less definitive treatment in high-benefit situations. The influence of patient race at high treatment benefit levels invites further investigation into the driving forces behind this persistent disparity in this consequential group.

COVID-19 outcomes in hospitalized patients with active cancer: Experiences from a major New York City health care system.

BACKGROUND: The authors sought to study the risk factors associated with severe outcomes in hospitalized coronavirus disease 2019 (COVID-19) patients with cancer. METHODS: The authors queried the New York University Langone Medical Center's records for hospitalized patients who were polymerase chain reaction-positive for severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) and performed chart reviews on patients with cancer diagnoses to identify patients with active cancer and patients with a history of cancer. Descriptive statistics were calculated and multivariable logistic regression was used to determine associations between clinical, demographic, and laboratory characteristics with outcomes, including death and admission to the intensive care unit. RESULTS: A total of 4184 hospitalized SARS CoV-2+ patients, including 233 with active cancer, were identified. Patients with active cancer were more likely to die than those with a history of cancer and those without any cancer history (34.3% vs 27.6% vs 20%, respectively; P < .01). In multivariable regression among all patients, active cancer (odds ratio [OR], 1.89; CI, 1.34-2.67; P < .01), older age (OR, 1.06; CI, 1.05-1.06; P < .01), male sex (OR for female vs male, 0.70; CI, 0.58-0.84; P < .01), diabetes (OR, 1.26; CI, 1.04-1.53; P = .02), morbidly obese body mass index (OR, 1.87; CI, 1.24-2.81; P < .01), and elevated D-dimer (OR, 6.41 for value >2300; CI, 4.75-8.66; P < .01) were associated with increased mortality. Recent cancer-directed medical therapy was not associated with death in multivariable analysis. Among patients with active cancer, those with a hematologic malignancy had the highest mortality rate in comparison with other cancer types (47.83% vs 28.66%; P < .01). CONCLUSIONS: The authors found that patients with an active cancer diagnosis were more likely to die from COVID-19. Those with hematologic malignancies were at the highest risk of death. Patients receiving cancer-directed therapy within 3 months before hospitalization had no overall increased risk of death. LAY SUMMARY: Our investigators found that hospitalized patients with active cancer were more likely to die from coronavirus disease 2019 (COVID-19) than those with a history of cancer and those without any cancer history. Patients with hematologic cancers were the most likely among patients with cancer to die from COVID-19. Patients who received cancer therapy within 3 months before hospitalization did not have an increased risk of death.

Body size affects immune cell proportions in birds and non-volant mammals, but not bats.

Powered flight has evolved several times in vertebrates and constrains morphology and physiology in ways that likely have shaped how organisms cope with infections. Some of these constraints probably have impacts on aspects of immunology, such that larger fliers might prioritize risk reduction and safety. Addressing how the evolution of flight may have driven relationships between body size and immunity could be particularly informative for understanding the propensity of some taxa to harbor many virulent and sometimes zoonotic pathogens without showing clinical disease. Here, we used a comparative framework to quantify scaling relationships between body mass and the proportions of two types of white blood cells - lymphocytes and granulocytes (neutrophils/heterophils) - across 63 bat species, 400 bird species and 251 non-volant mammal species. By using phylogenetically informed statistical models on field-collected data from wild Neotropical bats and from captive bats, non-volant mammals and birds, we show that lymphocyte and neutrophil proportions do not vary systematically with body mass among bats. In contrast, larger birds and non-volant mammals have disproportionately higher granulocyte proportions than expected for their body size. Our inability to distinguish bat lymphocyte scaling from birds and bat granulocyte scaling from all other taxa suggests there may be other ecological explanations (i.e. not flight related) for the cell proportion scaling patterns. Future comparative studies of wild bats, birds and non-volant mammals of similar body mass should aim to further differentiate evolutionary effects and other aspects of life history on immune defense and its role in the tolerance of (zoonotic) infections.

Synzootics.

Ecologists increasingly recognise coinfection as an important component of emergent epidemiological patterns, connecting aspects of ecoimmunology, behaviour, ecosystem function and even extinction risk. 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. 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.

Taxonomic resolution affects host-parasite association model performance.

Identifying the factors that structure host­parasite interactions is fundamental to understand the drivers of species distributions and to predict novel cross-species transmission events. More phylogenetically related host species tend to have more similar parasite associations, but parasite specificity may vary as a function of transmission mode, parasite taxonomy or life history. Accordingly, analyses that attempt to infer host−parasite associations using combined data on different parasite groups may perform quite differently relative to analyses on each parasite subset. In essence, are more data always better when predicting host−parasite associations, or does parasite taxonomic resolution matter? Here, we explore how taxonomic resolution affects predictive models of host−parasite associations using the London Natural History Museum's database of host­helminth interactions. Using boosted regression trees, we demonstrate that taxon-specific models (i.e. of Acanthocephalans, Nematodes and Platyhelminthes) consistently outperform full models in predicting mammal-helminth associations. At finer spatial resolutions, full and taxon-specific model performance does not vary, suggesting tradeoffs between phylogenetic and spatial scales of analysis. Although all models identify similar host and parasite covariates as important to such patterns, our results emphasize the importance of phylogenetic scale in the study of host­parasite interactions and suggest that using taxonomic subsets of data may improve predictions of parasite distributions and cross-species transmission. Predictive models of host­pathogen interactions should thus attempt to encompass the spatial resolution and phylogenetic scale desired for inference and prediction and potentially use model averaging or ensemble models to combine predictions from separately trained models.

Reactivation of latent infections with migration shapes population-level disease dynamics.

Annual migration is common across animal taxa and can dramatically shape the spatial and temporal patterns of infectious disease. Although migration can decrease infection prevalence in some contexts, these energetically costly long-distance movements can also have immunosuppressive effects that may interact with transmission processes in complex ways. Here, we develop a mechanistic model for the reactivation of latent infections driven by physiological changes or energetic costs associated with migration (i.e. 'migratory relapse') and its effects on disease dynamics. We determine conditions under which migratory relapse can amplify or reduce infection prevalence across pathogen and host traits (e.g. infectious periods, virulence, overwinter survival, timing of relapse) and transmission phenologies. We show that relapse at either the start or end of migration can dramatically increase prevalence across the annual cycle and may be crucial for maintaining pathogens with low transmissibility and short infectious periods in migratory populations. Conversely, relapse at the start of migration can reduce the prevalence of highly virulent pathogens by amplifying culling of infected hosts during costly migration, especially for highly transmissible pathogens and those transmitted during migration or the breeding season. Our study provides a mechanistic foundation for understanding the spatio-temporal patterns of relapsing infections in migratory hosts, with implications for zoonotic surveillance and understanding how infection patterns will respond to shifts in migratory propensity associated with environmental change. Further, our work suggests incorporating within-host processes into population-level models of pathogen transmission may be crucial for reconciling the range of migration-infection relationships observed across migratory species.

Artificial light at night amplifies seasonal relapse of haemosporidian parasites in a widespread songbird.

Urban habitats can shape interactions between hosts and parasites by altering not only exposure rates but also within-host processes. Artificial light at night (ALAN) is common in urban environments, and chronic exposure can impair host immunity in ways that may increase infection. However, studies of causal links between this stressor, immunity, and infection dynamics are rare, particularly in migratory animals. Here, we experimentally tested how ALAN affects cellular immunity and haemosporidian parasite intensity across the annual cycle of migrant and resident subspecies of the dark-eyed junco (Junco hyemalis). We monitored an experimental group exposed to light at night and a control group under natural light/dark cycles as they passed through short days simulating early spring to longer days simulating the breeding season, followed by autumn migration. Using generalized additive mixed models, we show that ALAN increased inflammation, and leucocyte counts were greatest in early spring and autumn. At the start of the experiment, few birds had active infections based on microscopy, but PCR revealed many birds had chronic infections. ALAN increased parasitaemia across the annual cycle, with strong peaks in spring and autumn that were largely absent in control birds. As birds were kept in indoor aviaries to prevent vector exposure, this increased parasitaemia indicates relapse of chronic infection during costly life-history stages (i.e. reproduction). Although the immunological and parasitological time series were in phase for control birds, cross-correlation analyses also revealed ALAN desynchronized leucocyte profiles and parasitaemia, which could suggest a general exaggerated inflammatory response. Our study shows how a common anthropogenic influence can shape within-host processes to affect infection dynamics.

Expanding host specificity and pathogen sharing beyond viruses.

Most emerging pathogens of humans can infect multiple host species (Woolhouse & Gowtage-Sequeria, 2005). This simple fact has motivated multiple large-scale, comparative analyses of the drivers of pathogen sharing and zoonotic pathogen richness among hosts as well as the factors determining the zoonotic potential of pathogens themselves. However, most of this work focuses on viruses, limiting a broader understanding of how host range varies within and between pathogen groups. In this issue of Molecular Ecology, Shaw et al. (2020) compile a comprehensive data set of host-pathogen associations across viruses and bacteria and test whether previous patterns observed in the former occur in the latter. They find most viruses and bacteria are specialists, and viruses are more likely to be generalists; however, generalist bacteria encompass multiple host orders, whereas viral sharing occurs more within host orders. Lastly, the authors demonstrate that many factors previously identified as predictors of zoonotic richness for viruses occur for bacteria and that host phylogenetic similarity is a primary determinant of cross-species transmission. However, pathogen sharing with humans was more common and more weakly related to phylogenetic distance to Homo sapiens for bacteria compared to viruses, suggesting the former could pose greater spillover risks across host orders. This work represents a key advance in our understanding of host specificity and pathogen sharing beyond viruses.

Demographic and environmental drivers of metagenomic viral diversity in vampire bats.

Viruses infect all forms of life and play critical roles as agents of disease, drivers of biochemical cycles and sources of genetic diversity for their hosts. Our understanding of viral diversity derives primarily from comparisons among host species, precluding insight into how intraspecific variation in host ecology affects viral communities or how predictable viral communities are across populations. Here we test spatial, demographic and environmental hypotheses explaining viral richness and community composition across populations of common vampire bats, which occur in diverse habitats of North, Central and South America. We demonstrate marked variation in viral communities that was not consistently predicted by a null model of declining community similarity with increasing spatial or genetic distances separating populations. We also find no evidence that larger bat colonies host greater viral diversity. Instead, viral diversity follows an elevational gradient, is enriched by juvenile-biased age structure, and declines with local anthropogenic food resources as measured by livestock density. Our results establish the value of linking the modern influx of metagenomic sequence data with comparative ecology, reveal that snapshot views of viral diversity are unlikely to be representative at the species level, and affirm existing ecological theories that link host ecology not only to single pathogen dynamics but also to viral communities.

Ecological and evolutionary drivers of haemoplasma infection and bacterial genotype sharing in a Neotropical bat community.

Most emerging pathogens can infect multiple species, underlining the importance of understanding the ecological and evolutionary factors that allow some hosts to harbour greater infection prevalence and share pathogens with other species. However, our understanding of pathogen jumps is based primarily around viruses, despite bacteria accounting for the greatest proportion of zoonoses. Because bacterial pathogens in bats (order Chiroptera) can have conservation and human health consequences, studies that examine the ecological and evolutionary drivers of bacterial prevalence and barriers to pathogen sharing are crucially needed. Here were studied haemotropic Mycoplasma spp. (i.e., haemoplasmas) across a species-rich bat community in Belize over two years. Across 469 bats spanning 33 species, half of individuals and two-thirds of species were haemoplasma positive. Infection prevalence was higher for males and for species with larger body mass and colony sizes. Haemoplasmas displayed high genetic diversity (21 novel genotypes) and strong host specificity. Evolutionary patterns supported codivergence of bats and bacterial genotypes alongside phylogenetically constrained host shifts. Bat species centrality to the network of shared haemoplasma genotypes was phylogenetically clustered and unrelated to prevalence, further suggesting rare-but detectable-bacterial sharing between species. Our study highlights the importance of using fine phylogenetic scales when assessing host specificity and suggests phylogenetic similarity may play a key role in host shifts not only for viruses but also for bacteria. Such work more broadly contributes to increasing efforts to understand cross-species transmission and the epidemiological consequences of bacterial pathogens.

Macroimmunology: The drivers and consequences of spatial patterns in wildlife immune defence.

The prevalence and intensity of parasites in wild hosts varies across space and is a key determinant of infection risk in humans, domestic animals and threatened wildlife. Because the immune system serves as the primary barrier to infection, replication and transmission following exposure, we here consider the environmental drivers of immunity. Spatial variation in parasite pressure, abiotic and biotic conditions, and anthropogenic factors can all shape immunity across spatial scales. Identifying the most important spatial drivers of immunity could help pre-empt infectious disease risks, especially in the context of how large-scale factors such as urbanization affect defence by changing environmental conditions. We provide a synthesis of how to apply macroecological approaches to the study of ecoimmunology (i.e. macroimmunology). We first review spatial factors that could generate spatial variation in defence, highlighting the need for large-scale studies that can differentiate competing environmental predictors of immunity and detailing contexts where this approach might be favoured over small-scale experimental studies. We next conduct a systematic review of the literature to assess the frequency of spatial studies and to classify them according to taxa, immune measures, spatial replication and extent, and statistical methods. We review 210 ecoimmunology studies sampling multiple host populations. We show that whereas spatial approaches are relatively common, spatial replication is generally low and unlikely to provide sufficient environmental variation or power to differentiate competing spatial hypotheses. We also highlight statistical biases in macroimmunology, in that few studies characterize and account for spatial dependence statistically, potentially affecting inferences for the relationships between environmental conditions and immune defence. We use these findings to describe tools from geostatistics and spatial modelling that can improve inference about the associations between environmental and immunological variation. In particular, we emphasize exploratory tools that can guide spatial sampling and highlight the need for greater use of mixed-effects models that account for spatial variability while also allowing researchers to account for both individual- and habitat-level covariates. We finally discuss future research priorities for macroimmunology, including focusing on latitudinal gradients, range expansions and urbanization as being especially amenable to large-scale spatial approaches. Methodologically, we highlight critical opportunities posed by assessing spatial variation in host tolerance, using metagenomics to quantify spatial variation in parasite pressure, coupling large-scale field studies with small-scale field experiments and longitudinal approaches, and applying statistical tools from macroecology and meta-analysis to identify generalizable spatial patterns. Such work will facilitate scaling ecoimmunology from individual- to habitat-level insights about the drivers of immune defence and help predict where environmental change may most alter infectious disease risk.