Traits, phylogeny and host cell receptors predict Ebolavirus host status among African mammals.

We explore how animal host traits, phylogenetic identity and cell receptor sequences relate to infection status and mortality from ebolaviruses. We gathered exhaustive databases of mortality from Ebolavirus after exposure and infection status based on PCR and antibody tests. We performed ridge regressions predicting mortality and infection as a function of traits, phylogenetic eigenvectors and separately host receptor sequences. We found that mortality from Ebolavirus had a strong association to life history characteristics and phylogeny. In contrast, infection status related not just to life history and phylogeny, but also to fruit consumption which suggests that geographic overlap of frugivorous mammals can lead to spread of virus in the wild. Niemann Pick C1 (NPC1) receptor sequences predicted infection statuses of bats included in our study with very high accuracy, suggesting that characterizing NPC1 in additional species is a promising avenue for future work. We combine the predictions from our mortality and infection status models to differentiate between species that are infected and also die from Ebolavirus versus species that are infected but tolerate the virus (possible reservoirs of Ebolavirus). We therefore present the first comprehensive estimates of Ebolavirus reservoir statuses for all known terrestrial mammals in Africa.

Drivers of African Filovirus (Ebola and Marburg) Outbreaks.

Outbreaks of African filoviruses often have high mortality, including more than 11,000 deaths among 28,562 cases during the West Africa Ebola outbreak of 2014-2016. Numerous studies have investigated the factors that contributed to individual filovirus outbreaks, but there has been little quantitative synthesis of this work. In addition, the ways in which the typical causes of filovirus outbreaks differ from other zoonoses remain poorly described. In this study, we quantify factors associated with 45 outbreaks of African filoviruses (ebolaviruses and Marburg virus) using a rubric of 48 candidate causal drivers. For filovirus outbreaks, we reviewed >700 peer-reviewed and gray literature sources and developed a list of the factors reported to contribute to each outbreak (i.e., a "driver profile" for each outbreak). We compare and contrast the profiles of filovirus outbreaks to 200 background outbreaks, randomly selected from a global database of 4463 outbreaks of bacterial and viral zoonotic diseases. We also test whether the quantitative patterns that we observed were robust to the influences of six covariates, country-level factors such as gross domestic product, population density, and latitude that have been shown to bias global outbreak data. We find that, regardless of whether covariates are included or excluded from models, the driver profile of filovirus outbreaks differs from that of background outbreaks. Socioeconomic factors such as trade and travel, wild game consumption, failures of medical procedures, and deficiencies in human health infrastructure were more frequently reported in filovirus outbreaks than in the comparison group. Based on our results, we also present a review of drivers reported in at least 10% of filovirus outbreaks, with examples of each provided.

The ecology of zoonotic parasites in the Carnivora.

The order Carnivora includes over 300 species that vary many orders of magnitude in size and inhabit all major biomes, from tropical rainforests to polar seas. The high diversity of carnivore parasites represents a source of potential emerging diseases of humans. Zoonotic risk from this group may be driven in part by exceptionally high functional diversity of host species in behavioral, physiological, and ecological traits. We review global macroecological patterns of zoonotic parasites within carnivores, and explore the traits of species that serve as hosts of zoonotic parasites. We synthesize theoretical and empirical research and suggest future work on the roles of carnivores as biotic multipliers, regulators, and sentinels of zoonotic disease as timely research frontiers.

Opposing macroevolutionary and trait-mediated patterns of threat and naturalisation in flowering plants.

Due to expanding global trade and movement of people, new plant species are establishing in exotic ranges at increasing rates while the number of native species facing extinction from multiple threats grows. Yet, how species losses and gains globally may, together, be linked to traits and macroevolutionary processes is poorly understood. Here, we show that, adjusting for diversification rate and clade age, the proportion of threatened species across flowering plant families is negatively related to the proportion of naturalised species per family. Moreover, naturalisation is positively associated with range size, short generation time, autonomous seed production and interspecific hybridisation, but negatively with age and diversification, whereas threat is negatively associated with range size and hybridisation, and positively with biotic pollination, age and diversification rate. That we find such a pronounced signature of naturalisation and threat across plant families suggests that both trait syndromes have coexisted over deep evolutionary time and counter to intuition, that neither strategy is necessarily superior to the other over long evolutionary timespans.

Ecological indicators of mammal exposure to Ebolavirus.

Much of the basic ecology of Ebolavirus remains unresolved despite accumulating disease outbreaks, viral strains and evidence of animal hosts. Because human Ebolavirus epidemics have been linked to contact with wild mammals other than bats, traits shared by species that have been infected by Ebolavirus and their phylogenetic distribution could suggest ecological mechanisms contributing to human Ebolavirus spillovers. We compiled data on Ebolavirus exposure in mammals and corresponding data on life-history traits, movement, and diet, and used boosted regression trees (BRT) to identify predictors of exposure and infection for 119 species (hereafter hosts). Mapping the phylogenetic distribution of presumptive Ebolavirus hosts reveals that they are scattered across several distinct mammal clades, but concentrated among Old World fruit bats, primates and artiodactyls. While sampling effort was the most important predictor, explaining nearly as much of the variation among hosts as traits, BRT models distinguished hosts from all other species with greater than 97% accuracy, and revealed probable Ebolavirus hosts as large-bodied, frugivorous, and with slow life histories. Provisionally, results suggest that some insectivorous bat genera, Old World monkeys and forest antelopes should receive priority in Ebolavirus survey efforts. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Spatiotemporal Fluctuations and Triggers of Ebola Virus Spillover.

Because the natural reservoir of Ebola virus remains unclear and disease outbreaks in humans have occurred only sporadically over a large region, forecasting when and where Ebola spillovers are most likely to occur constitutes a continuing and urgent public health challenge. We developed a statistical modeling approach that associates 37 human or great ape Ebola spillovers since 1982 with spatiotemporally dynamic covariates including vegetative cover, human population size, and absolute and relative rainfall over 3 decades across sub-Saharan Africa. Our model (area under the curve 0.80 on test data) shows that spillover intensity is highest during transitions between wet and dry seasons; overall, high seasonal intensity occurs over much of tropical Africa; and spillover intensity is greatest at high (>1,000/km2) and very low (<100/km2) human population densities compared with intermediate levels. These results suggest strong seasonality in Ebola spillover from wild reservoirs and indicate particular times and regions for targeted surveillance.

Undiscovered Bat Hosts of Filoviruses.

Ebola and other filoviruses pose significant public health and conservation threats by causing high mortality in primates, including humans. Preventing future outbreaks of ebolavirus depends on identifying wildlife reservoirs, but extraordinarily high biodiversity of potential hosts in temporally dynamic environments of equatorial Africa contributes to sporadic, unpredictable outbreaks that have hampered efforts to identify wild reservoirs for nearly 40 years. Using a machine learning algorithm, generalized boosted regression, we characterize potential filovirus-positive bat species with estimated 87% accuracy. Our model produces two specific outputs with immediate utility for guiding filovirus surveillance in the wild. First, we report a profile of intrinsic traits that discriminates hosts from non-hosts, providing a biological caricature of a filovirus-positive bat species. This profile emphasizes traits describing adult and neonate body sizes and rates of reproductive fitness, as well as species' geographic range overlap with regions of high mammalian diversity. Second, we identify several bat species ranked most likely to be filovirus-positive on the basis of intrinsic trait similarity with known filovirus-positive bats. New bat species predicted to be positive for filoviruses are widely distributed outside of equatorial Africa, with a majority of species overlapping in Southeast Asia. Taken together, these results spotlight several potential host species and geographical regions as high-probability targets for future filovirus surveillance.

Rodent reservoirs of future zoonotic diseases.

The increasing frequency of zoonotic disease events underscores a need to develop forecasting tools toward a more preemptive approach to outbreak investigation. We apply machine learning to data describing the traits and zoonotic pathogen diversity of the most speciose group of mammals, the rodents, which also comprise a disproportionate number of zoonotic disease reservoirs. Our models predict reservoir status in this group with over 90% accuracy, identifying species with high probabilities of harboring undiscovered zoonotic pathogens based on trait profiles that may serve as rules of thumb to distinguish reservoirs from nonreservoir species. Key predictors of zoonotic reservoirs include biogeographical properties, such as range size, as well as intrinsic host traits associated with lifetime reproductive output. Predicted hotspots of novel rodent reservoir diversity occur in the Middle East and Central Asia and the Midwestern United States.

Two sides of the same coin? Rare and pest plants native to the United States and Canada.

Plant biodiversity is at risk, with as many as 10% of native species in the United States being threatened with extinction. Habitat loss has led a growing number of plant species to become rare or threatened, while the introduction or expansion of pest species has led some habitats to be dominated by relatively few, mostly nonindigenous, species. As humans continue to alter many landscapes and vegetation types, understanding how biological traits determine the location of species along a spectrum from vulnerability to pest status is critical to designing risk assessment protocols, setting conservation priorities, and developing monitoring programs. We used boosted regression trees to predict rarity (based on The Nature Conservancy global rankings) and pest status (defined as legal pest status) from data on traits for the native vascular flora of the United States and Canada including Hawaii, Puerto Rico, and the Virgin Islands (n approximately = 15,000). Categories were moderately to highly predictable (AUCpest = 0.87 on 25% holdout test set, AUCrarity = 0.80 on 25% holdout test set). Key predictors were chromosome number, ploidy, seed mass, and a suite of traits suggestive of specialist vs. generalist adaptations (e.g., facultative wetland habitat association and phenotypic variability in growth form and life history). Specifically, pests were associated with high chromosome numbers, polyploidy, and seed masses ranging from 0.1 to 100 mg, whereas rare species were associated with low chromosome numbers, low ploidy, and large (>1000 mg) seed masses. In addition, pest species were disproportionately likely to be facultatively associated with wetlands, and variable in growth form and life history, whereas rare species exhibited an opposite pattern. These results suggest that rare and pest species contrast along trait axes related to dispersal and performance in disturbed or novel habitats.

Why are some plant genera more invasive than others?

Determining how biological traits are related to the ability of groups of organisms to become economically damaging when established outside of their native ranges is a major goal of population biology, and important in the management of invasive species. Little is known about why some taxonomic groups are more likely to become pests than others among plants. We investigated traits that discriminate vascular plant genera, a level of taxonomic generality at which risk assessment and screening could be more effectively performed, according to the proportion of naturalized species which are pests. We focused on the United States and Canada, and, because our purpose is ultimately regulatory, considered species classified as weeds or noxious. Using contingency tables, we identified 11 genera of vascular plants that are disproportionately represented by invasive species. Results from boosted regression tree analyses show that these categories reflect biological differences. In summary, approximately 25% of variation in genus proportions of weeds or noxious species was explained by biological covariates. Key explanatory traits included genus means for wetland habitat affinity, chromosome number, and seed mass.

Time since introduction, seed mass, and genome size predict successful invaders among the cultivated vascular plants of Hawaii.

Extensive economic and environmental damage has been caused by invasive exotic plant species in many ecosystems worldwide. Many comparative studies have therefore attempted to predict, from biological traits, which species among the pool of naturalized non-natives become invasive. However, few studies have investigated which species establish and/or become pests from the larger pool of introduced species and controlled for time since introduction. Here we present results from a study aimed at quantifying predicting three classes of invasive species cultivated in Hawaii. Of 7,866 ornamental species cultivated in Hawaii between 1840 and 1999, 420 (5.3%) species naturalized, 141 (1.8%) have been classified as weeds, and 39 (0.5%) were listed by the state of Hawaii as noxious. Of the 815 species introduced >80 years ago, 253 (31%) have naturalized, 90 (11%) are classed as weeds, and 22 (3%) as noxious by the state of Hawaii. Using boosted regression trees we classified each group with nearly 90% accuracy, despite incompleteness of data and the low proportion of naturalized or pest species. Key biological predictors were seed mass and highest chromosome number standardized by genus which, when data on residence time was removed, were able to predict all three groups with 76-82% accuracy. We conclude that, when focused on a single region, screening for potential weeds or noxious plants based on a small set of biological traits can be achieved with sufficient accuracy for policy and management purposes.

Spatial genetic structure of the southeastern North American endemic, Ceratiola ericoides (Empetraceae).

Understanding the spatial distribution of genetic diversity (i.e., spatial genetic structure [SGS]) within plant populations can elucidate mechanisms of seed dispersal and patterns of recruitment that may play an important role in shaping the demography and spatial distribution of individuals in subsequent generations. Here we investigate the SGS of allozyme diversity in 2 populations of the southeastern North American endemic shrub, Ceratiola ericoides. The data suggest that the 2 populations have similar patterns of SGS at distances of 0-45 m that likely reflect the isolation by distance (IBD) model of seed dispersal. However, at distances >or=50 m, the pattern of SGS differs substantially between the 2 populations. Whereas one population continues to reflect the classical IBD pattern, the second population shows a marked increase in autocorrelation coefficient (r) values at 50-75 m. Furthermore, r values at these distances are as much as 33% higher than at 0-5 m where the highest r value would be predicted by IBD. A likely explanation is the differing frequencies of 2 fruit morphologies in these populations and the greater role that birds play in seed dispersal in the second population.

Genetic insights into the biogeography of the southeastern North American endemic, Ceratiola ericoides (Empetraceae).

The southeastern United States harbors an unusually large number of endemic plant taxa, which may reflect the refugial nature of the region during Pleistocene glacial maxima. Understanding the genetic diversity and structure of extant plant taxa can provide insights into the biogeographical processes that shaped them genetically. Here, we investigate the levels and partitioning of allozyme diversity in the southeastern North American endemic, Ceratiola ericoides, which displayed greater genetic variation and structure than other endemics. Central Florida populations represent a center of genetic diversity, whereas South Carolina and Georgia Fall Line sandhill populations have a subset of the Central Florida genetic diversity and may be relicts of a once continuous distribution. This much broader, continuous distribution throughout the southeastern United States occurred during glacial maxima when the scrub habitat, dominated by C. ericoides, expanded considerably owing to drier climatic conditions. Georgia Coastal Plain populations appear to have been independently founded more recently by propagules from Central Florida and the Fall Line sandhills because they have an even more limited subset of genetic diversity and greater genetic heterogeneity among populations. Since their establishment, coastal plain populations appear to have had little, if any, gene exchange among each other or with the relatively proximate Fall Line sandhill populations. These data underscore the importance of understanding the genetic composition and historical biogeography of species before intelligent management or restoration decisions can be made regarding their preservation.