Wild birds are suspected to have played a role in highly pathogenic avian influenza (HPAI) H5N1 outbreaks in West Bengal. Cluster analysis showed that H5N1 was introduced in West Bengal at least 3 times between 2008 and 2010. We simulated the introduction of H5N1 by wild birds and their contact with poultry through a stochastic continuous-time mathematical model. Results showed that reducing contact between wild birds and domestic poultry, and increasing the culling rate of infected domestic poultry communities will reduce the probability of outbreaks. Poultry communities that shared habitat with wild birds or those indistricts with previous outbreaks were more likely to suffer an outbreak. These results indicate that wild birds can introduce HPAI to domestic poultry and that limiting their contact at shared habitats together with swift culling of infected domestic poultry can greatly reduce the likelihood of HPAI outbreaks.

Emerging infectious diseases in humans are frequently caused by pathogens originating from animal hosts, and zoonotic disease outbreaks present a major challenge to global health. To investigate drivers of virus spillover, we evaluated the number of viruses mammalian species have shared with humans. We discovered that the number of zoonotic viruses detected in mammalian species scales positively with global species abundance, suggesting that virus transmission risk has been highest from animal species that have increased in abundance and even expanded their range by adapting to human-dominated landscapes. Domesticated species, primates and bats were identified as having more zoonotic viruses than other species. Among threatened wildlife species, those with population reductions owing to exploitation and loss of habitat shared more viruses with humans. Exploitation of wildlife through hunting and trade facilitates close contact between wildlife and humans, and our findings provide further evidence that exploitation, as well as anthropogenic activities that have caused losses in wildlife habitat quality, have increased opportunities for animal-human interactions and facilitated zoonotic disease transmission. Our study provides new evidence for assessing spillover risk from mammalian species and highlights convergent processes whereby the causes of wildlife population declines have facilitated the transmission of animal viruses to humans.

Flaviviruses continue to cause globally relevant epidemics and have emerged or re-emerged in regions that were previously unaffected. Factors determining emergence of flaviviruses and continuing circulation in sylvatic cycles are incompletely understood. Here we identify potential sylvatic reservoirs of flaviviruses and characterize the macro-ecological traits common to known wildlife hosts to predict the risk of sylvatic flavivirus transmission among wildlife and identify regions that could be vulnerable to outbreaks. We evaluate variability in wildlife hosts for zoonotic flaviviruses and find that flaviviruses group together in distinct clusters with similar hosts. Models incorporating ecological and climatic variables as well as life history traits shared by flaviviruses predict new host species with similar host characteristics. The combination of vector distribution data with models for flavivirus hosts allows for prediction of  global vulnerability to flaviviruses and provides potential targets for disease surveillance in animals and humans.

[This corrects the article DOI: 10.1371/journal.pone.0208057.].

Despite the popular use of hummingbird feeders, there are limited studies evaluating the effects of congregation, sharing food resources and increased contact when hummingbirds visit feeders in urban landscapes. To evaluate behavioral interactions occurring at feeders, we tagged 230 individuals of two species, Anna's and Allen's Hummingbirds, with passive integrated transponder tags and recorded their visits with RFID transceivers at feeders. For detecting the presence of tagged birds, we developed an RFID equipped feeding station using a commercially available antenna and RFID transceiver. Data recorded included the number of feeder visits, time spent at the feeder, simultaneous feeder visitation by different individuals, and identifying which feeders were most commonly visited by tagged birds. For the study period (September 2016 to March 2018), 118,017 detections were recorded at seven feeding stations located at three California sites. The rate of tagged birds returning to RFID equipped feeders at least once was 61.3% (141/230 birds). Females stayed at feeders longer than males per visit. We identified primary, secondary and tertiary feeders at Sites 2 and 3, according to the frequency of visitation to them, with a mean percentage of 86.9% (SD±19.13) visits to a primary feeder for each tagged hummingbird. During spring and summer, hummingbirds visited feeders most often in morning and evening hours. Feeder visits by males overlapped in time with other males more frequently than other females. The analysis of the contact network at the feeders did not distinguish any significant differences between age or sex. Although most hummingbirds visited the feeders during the daytime, our system recorded night feeder visitations (n = 7 hummingbirds) at one site. This efficient use of RFID technology to characterize feeder visitations and contact networks of hummingbirds in urban habitats could be used in the future to elucidate behaviors, population dynamics and community structure of hummingbirds visiting feeders.

Background

Understanding the effects of herd management practices on the prevalence of multidrug-resistant pathogenic Salmonella and commensals Enterococcus spp. and Escherichia coli in dairy cattle is key in reducing antibacterial resistant infections in humans originating from food animals. Our objective was to explore the herd and cow level features associated with the multi-drug resistant, and resistance phenotypes shared between Salmonella, E. coli and Enterococcus spp. using machine learning algorithms.

Methods

Randomly collected fecal samples from cull dairy cows from six dairy farms in central California were tested for multi-drug resistance phenotypes of Salmonella, E. coli and Enterococcus spp. Using data on herd management practices collected from a questionnaire, we built three machine learning algorithms (decision tree classifier, random forest, and gradient boosting decision trees) to predict the cows shedding multidrug-resistant Salmonella and commensal bacteria.

Results

The decision tree classifier identified rolling herd average milk production as an important feature for predicting fecal shedding of multi-drug resistance in Salmonella or commensal bacteria. The number of culled animals, monthly culling frequency and percentage, herd size, and proportion of Holstein cows in the herd were found to be influential herd characteristics predicting fecal shedding of multidrug-resistant phenotypes based on random forest models for Salmonella and commensal bacteria. Gradient boosting models showed that higher culling frequency and monthly culling percentages were associated with fecal shedding of multidrug resistant Salmonella or commensal bacteria. In contrast, an overall increase in the number of culled animals on a culling day showed a negative trend with classifying a cow as shedding multidrug-resistant bacteria. Increasing rolling herd average milk production and spring season were positively associated with fecal shedding of multidrug- resistant Salmonella. Only six individual cows were detected sharing tetracycline resistance phenotypes between Salmonella and either of the commensal bacteria.

Discussion

Percent culled and culling rate reflect the increase in culling over time adjusting for herd size and were associated with shedding multidrug resistant bacteria. In contrast, number culled was negatively associated with shedding multidrug resistant bacteria which may reflect producer decisions to prioritize the culling of otherwise healthy but low-producing cows based on milk or beef prices (with respect to dairy beef), amongst other factors. Using a data-driven suite of machine learning algorithms we identified generalizable and distant associations between antimicrobial resistance in Salmonella and fecal commensal bacteria, that can help develop a producer-friendly and data-informed risk assessment tool to reduce shedding of multidrug-resistant bacteria in cull dairy cows.

Avian pox is a common avian virus that in its cutaneous form can cause characteristic lesions on a bird's dermal surfaces. Detection of avian pox in free-ranging birds historically relied on observations of visual lesions and/or histopathology, both which can underestimate avian pox prevalence. We compared traditional visual observation methods for avian pox with molecular methods that utilize minimally invasive samples (blood, toenail clipping, feathers, and dermal swabs) in an ecologically important group of birds, hummingbirds. Specifically, avian pox prevalence in several species of hummingbirds were examined across multiple locations using three different methods: (1) visual inspection of hummingbirds for pox-like lesions from a long-term banding data set, (2) qPCR assay of samples from hummingbird carcasses from wildlife rehabilitation centers, and (3) qPCR assay of samples from live-caught hummingbirds. A stark difference in prevalences among these three methods was identified, with an avian pox prevalence of 1.5% from banding data, 20.4% from hummingbird carcasses, and 32.5% from live-caught hummingbirds in California. This difference in detection rates underlines the necessity of a molecular method to survey for avian pox, and this study establishes one such method that could be applied to other wild bird species. Across all three methods, Anna's hummingbirds harbored significantly higher avian pox prevalence than other species examined, as did males compared with females and birds caught in Southern California compared with Northern California. After hatch-year hummingbirds also harbored higher avian pox prevalences than hatch-year hummingbirds in the California banding data set and the carcass data set. This is the first study to estimate the prevalence of avian pox in hummingbirds and address the ecology of this hummingbird-specific strain of avian pox virus, providing vital information to inform future studies on this charismatic and ecologically important group of birds.

The ability to rapidly detect and respond to wildlife morbidity and mortality events is critical for reducing threats to wildlife populations. Surveillance systems that use pre-diagnostic clinical data can contribute to the early detection of wildlife morbidities caused by a multitude of threats, including disease and anthropogenic disturbances. Here, we demonstrate proof of concept for use of a wildlife disease surveillance system, the 'Wildlife Morbidity and Mortality Event Alert System', that integrates pre-diagnostic clinical data in near real-time from a network of wildlife rehabilitation organizations, for early and enhanced detection of unusual wildlife morbidity and mortality events. The system classifies clinical pre-diagnostic data into relevant clinical classifications based on a natural language processing algorithm, generating alerts when more than the expected number of cases is recorded across the rehabilitation network. We demonstrated the effectiveness and efficiency of the system in alerting to events associated with both common and emerging diseases. Tapping into this readily available unconventional general surveillance data stream offers added value to existing wildlife disease surveillance programmes through a relatively efficient, low-cost strategy for the early detection of threats.

Peninsular bighorn sheep (Ovis canadensis nelsoni) are found exclusively in Southern California and Baja Mexico. They are federally endangered due to multiple threats, including introduced infectious disease. From 1981 - 2017, we conducted surveillance for 16 pathogens and estimated population sizes, adult survival, and lamb survival. We used mixed effects regression models to assess disease patterns at the individual and population levels. Pathogen infection/exposure prevalence varied both spatially and temporally. Our findings indicate that the primary predictor of individual pathogen infection/exposure was the region in which an animal was captured, implying that transmission is driven by local ecological or behavioral factors. Higher Mycoplasma ovipneumoniae seropositivity was associated with lower lamb survival, consistent with lambs having high rates of pneumonia-associated mortality, which may be slowing population recovery. There was no association between M. ovipneumoniae and adult survival. Adult survival was positively associated with population size and parainfluenza-3 virus seroprevalence in the same year, and orf virus seroprevalence in the previous year. Peninsular bighorn sheep are recovering from small population sizes in a habitat of environmental extremes, compounded by infectious disease. Our research can help inform future pathogen surveillance and population monitoring for the long-term conservation of this population.

Ebola virus is highly lethal for great apes. Estimated mortality rates up to 98% have reduced the global gorilla population by approximately one-third. As mountain gorillas (Gorilla beringei beringei) are endangered, with just over 1000 individuals remaining in the world, an outbreak could decimate the population. Simulation modeling was used to evaluate the potential impact of an Ebola virus outbreak on the mountain gorilla population of the Virunga Massif. Findings indicate that estimated contact rates among gorilla groups are high enough to allow rapid spread of Ebola, with less than 20% of the population projected to survive at 100 days post-infection of just one gorilla. Despite increasing survival with vaccination, no modeled vaccination strategy prevented widespread infection. However, the model projected that survival rates greater than 50% could be achieved by vaccinating at least half the habituated gorillas within 3 weeks of the first infectious individual.