Biology subjects

Fountain-Jones, N.

Publications and source records attributed to Fountain-Jones, N..

5 recordsLinked to original sources

bioRxiv2026

Dispersal, adaptation and persistence of H5N1 in the sub-Antarctic and Antarctica

High pathogenicity avian influenza virus (HPAIV) H5N1 reached the sub-Antarctic and Antarctica in 2023, subsequently spreading to remote locations within this region where it had devastating impacts on seal, penguin and albatross populations. The threat to marine wildlife over this broad area exemplifies the need to understand H5N1 long-distance dispersal and evolution. We obtained 104 novel viral genomic sequences from samples that we collected at South Georgia, Kerguelen, Crozet, Prince Edward, Falklands/Malvinas Islands and the Antarctic Peninsula in a region spanning 8,000 kilometers. Using recent phylogeographic modeling advances we show that H5N1 spread encompassed numerous transmission events between distant locations, accumulating mammalian-adaptive mutations in the process. Seals are the most affected species, but we reveal that the long-distance eastward virus dispersal better aligns with the long-distance movements of large petrels and albatrosses. The risk of H5N1 endemisation, dispersal to other locations and ongoing evolution are highly concerning.

microbiology↗

bioRxiv2026

High pathogenicity avian influenza virus H5N1 clade 2.3.4.4b in Antarctica: Multiple Introductions and the First Confirmed Infection of Ice-Dependent Seals

Highly pathogenic avian influenza (HPAI) H5N1 clade 2.3.4.4b has expanded rapidly across the Southern Ocean since 2023, causing extensive mortality in sub-Antarctic wildlife. Yet its penetration into Antarctica and impacts on ice-dependent species remain poorly resolved primarily due to surveillance constraints. We report the first confirmed H5N1 infection in an Antarctic ice-dependent seal (crabeater seal; Lobodon carcinophaga) and document mortality of crabeater seals across the northern Weddell Sea during November-December 2024. Combining genomic, serological and observational data across nine species, we detected H5N1 RNA in a crabeater seal and a kelp gull (Larus dominicanus), and recovered complete HA, NA and M2 gene sequences from both. Phylogenetic analyses allowed us to identify at least two independent introductions of HPAI H5N1 clade 2.3.4.4b into the northern Antarctic Peninsula region. Serology provided strong evidence of prior exposure in scavenging birds, but no detectable H5 immunity in penguins or pinnipeds. Together, the results demonstrate ongoing novel viral incursions into Antarctica, likely facilitated by at-sea processes e.g. animal interactions on ice floes, that remain invisible to land-based surveillance. These findings highlight the vulnerability of ice-dependent pinnipeds to HPAI H5N1 clade 2.3.4.4b and the urgent need for expanded integrated Antarctic monitoring frameworks that pair serology, opportunistic carcass sampling and genomic sequencing.

ecology↗

bioRxiv2020

Machine-learning model led design to experimentally test species thermal limits: the case of kissing bugs (Triatominae)

Species Distribution Modelling (SDM) determines habitat suitability of a species across geographic areas using macro-climatic variables; however, micro-habitats can buffer or exacerbate the influence of macro-climatic variables, requiring links between physiology and species persistence. Experimental approaches linking species physiology to micro-climate are complex, time consuming and expensive. E.g., what combination of exposure time and temperature is important for a species thermal tolerance is difficult to judge a priori. We tackled this problem using an active learning approach that utilized machine learning methods to guide thermal tolerance experimental design for three kissing-bug species (Hemiptera: Reduviidae: Triatominae), vectors of the parasite causing Chagas disease. As with other pathogen vectors, triatomines are well known to utilize micro-habitats and the associated shift in microclimate to enhance survival. Using a limited literature-collected dataset, our approach showed that temperature followed by exposure time were the strongest predictors of mortality; species played a minor role, and life stage was the least important. Further, we identified complex but biologically plausible nonlinear interactions between temperature and exposure time in shaping mortality, together setting the potential thermal limits of triatomines. The results from this data led to the design of new experiments with laboratory results that produced novel insights of the effects of temperature and exposure for the triatomines. These results, in turn, can be used to better model micro-climatic envelope for the species. Here we demonstrate the power of an active learning approach to explore experimental space to design laboratory studies testing species thermal limits. Our analytical pipeline can be easily adapted to other systems and we provide code to allow practitioners to perform similar analyses. Not only does our approach have the potential to save time and money: it can also increase our understanding of the links between species physiology and climate, a topic of increasing ecological importance. Author summarySpecies Distribution Modelling determines habitat suitability of a species across geographic areas using macro-climatic variables; however, micro-habitats can buffer or exacerbate the influence of macro-climatic variables, requiring links between physiology and species persistence. We tackled the problem of the combination of exposure time and temperature (a combination difficult to judge a priori) in determining species thermal tolerance, using an active learning approach that utilized machine learning methods to guide thermal tolerance experimental design for three kissing-bug species, vectors of the parasite causing Chagas disease. These bugs are found in micro-habitats with associated shifts in microclimate to enhance survival. Using a limited literature-collected dataset, we showed that temperature followed by exposure time were the strongest predictors of mortality, that species played a minor role, that life stage was the least important, and a complex nonlinear interaction between temperature and exposure time in shaping mortality of kissing bugs. These results led to the design of new laboratory experiments to assess the effects of temperature and exposure for the triatomines. These results can be used to better model micro-climatic envelope for species. Our active learning approach to explore experimental space to design laboratory studies can also be applied to other environmental conditions or species.

physiology↗

bioRxiv2019

Urbanization impacts apex predator gene flow but not genetic diversity across an urban-rural divide

Apex predators are important indicators of intact natural ecosystems. They are also sensitive to urbanization because they require broad home ranges and extensive contiguous habitat to support their prey base. Pumas (Puma concolor) can persist near human developed areas, but urbanization may be detrimental to their movement ecology, population structure, and genetic diversity. To investigate potential effects of urbanization in population connectivity of pumas, we performed a landscape genomics study of 134 pumas on the rural Western Slope and more urbanized Front Range of Colorado, USA. Over 12,000 single nucleotide polymorphisms were genotyped using double-digest, restriction site-associated DNA sequencing (ddRADseq). We investigated patterns of gene flow and genetic diversity, and tested for correlations between key landscape variables and genetic distance to assess the effects of urbanization and other landscape factors on gene flow. Levels of genetic diversity were similar for the Western Slope and Front Range, but effective population sizes were smaller, genetic distances were higher, and there was more overall population substructure in the more urbanized Front Range. Forest cover was strongly positively associated with puma gene flow on the Western Slope, while impervious surfaces restricted gene flow and more open, natural habitats enhanced gene flow on the Front Range. Landscape genomic analyses revealed differences in puma movement and gene flow patterns in rural versus urban settings. Our results highlight the utility of dense, genome-scale markers to document subtle impacts of urbanization on a wide-ranging carnivore living near a large urban center.

genomics↗

bioRxiv2019

How to make more from exposure data? An integrated machine learning pipeline to predict pathogen exposure

O_LIPredicting infectious disease dynamics is a central challenge in disease ecology. Models that can assess which individuals are most at risk of being exposed to a pathogen not only provide valuable insights into disease transmission and dynamics but can also guide management interventions. Constructing such models for wild animal populations, however, is particularly challenging; often only serological data is available on a subset of individuals and non-linear relationships between variables are common.\nC_LIO_LIHere we take advantage of the latest advances in statistical machine learning to construct pathogen-risk models that automatically incorporate complex non-linear relationships with minimal statistical assumptions from ecological data with missing values. Our approach compares multiple machine learning algorithms in a unified environment to find the model with the best predictive performance and uses game theory to better interpret results. We apply this framework on two major pathogens that infect African lions: canine distemper virus (CDV) and feline parvovirus.\nC_LIO_LIOur modelling approach provided enhanced predictive performance compared to more traditional approaches, as well as new insights into disease risks in a wild population. We were able to efficiently capture and visualise strong non-linear patterns, as well as model complex interactions between variables in shaping exposure risk from CDV and feline parvovirus. For example, we found that lions were more likely to be exposed to CDV at a young age but only in low rainfall years.\nC_LIO_LIWhen combined with our data calibration approach, our framework helped us to answer questions about risk of pathogen exposure which are difficult to address with previous methods. Our framework not only has the potential to aid in predicting disease risk in animal populations, but also can be used to build robust predictive models suitable for other ecological applications such as modelling species distribution or diversity patterns.\nC_LI

ecology↗