Timber harvesting can disadvantage plants that rely on living mature forest as a recolonisation seed source rather than the soil seed bank. Nearby mature forest can supply seeds and shaded microclimates, accelerating post-harvest ecological succession through edge effects (‘mature forest influence’). We tested whether time since harvesting and proximity to mature forest affected plant species composition in wet eucalypt forest. Surveys were conducted in regenerating clearcut sites of three age classes (∼7, ∼27, and ∼45 years post-harvest), using transects extending across the mature forest–clearcut boundary. Plant composition differed among age classes, with older forests supporting more mature forest indicator species and fewer young forest indicator species, consistent with expected successional trajectories. Richness of mature forest indicator species increased with proximity to mature forest edges, providing evidence of mature forest influence. Richness of young forest indicator species showed no change across the edge distance gradient. Community composition varied with distance from mature forest, with the edge influence extending approximately 17 m in ∼7-year-old sites, 25 m in ∼27-year-old sites, and 27 m in ∼45-year-old sites. Similar depths of influence in ∼27 and ∼45 year old age classes likely reflect seed dispersal limits, suggesting reproductive maturity may be needed for deeper recolonisation of mature forest plants. Although mature forest influence was important, it had less impact on community composition than other factors like soil chemistry and topography. Our findings suggest that variable retention forestry that retains mature forest near to harvest areas should accelerate succession through increased seed availability and shading.

Abstract High Pathogenicity Avian Influenza (HPAI) H5N1 clade 2.3.4.4b has spread beyond birds to affect seals across the Southern Ocean and sub-Antarctic region, with southern elephant seals ( Mirounga leonina ) particularly devastated. The virus, likely introduced via spillover from infected migratory birds, has killed tens of thousands of adult seals and pups throughout most of their range, though Macquarie Island remains unaffected so far. We used twenty years of elephant seal movement data from the southern Indian and Pacific oceans to assess whether seal-to-seal transmission could spread HPAI H5N1 between breeding colonies, despite the vast distances separating them (Marion Island, Iles Crozet, Iles Kerguelen, and Macquarie Island). There was substantial overlap in seals’ at-sea distributions during their winter post-moult trips, when seals travel for weeks at average speeds of 3.5 km/h. Two transmission pathways were examined: (1) terrestrial “stepping stone” routes, where infected seals could pass the virus between colonies during short intervals to remain infectious were feasible from Marion Island to Kerguelen but not from Kerguelen to Macquarie Island; and (2) at-sea encounters between seals, which occurred frequently enough to enable transmission. The findings suggest that once established at Macquarie Island, the virus could potentially spread further to New Zealand’s sub-Antarctic islands and mainland New Zealand. While seal-to-seal transmission appears possible, we conclude this is unlikely. Nonetheless, understanding at-sea contact rates enhances knowledge of H5N1 epidemiology and demonstrates the value of combining long-term population monitoring with movement data to understand wildlife disease dynamics.

Avian influenza viruses (AIV) have a remarkable capacity to adapt through rapid mutation and cross-species transmission, posing a major global pandemic threat. The distinct spatial patterns of outbreaks suggest that the relationship between disease emergence and ecological drivers is complex, non-linear, and varies with virus pathogenicity and host characteristics. Here, we used a multi-algorithm machine learning statistical framework and 27 ecological features to analyze over 50,000 outbreaks reported across Eurasia and Africa from 2004 to 2025, stratifying the data into highly pathogenic (HPAI), low-pathogenic (LPAI), domestic, and wild-bird subsets. Our models achieved high predictive performance (accuracies ≥ 84%) and revealed distinct, strata-specific risk profiles driven by strong non-linear interactions. For the all-AIV and wild-bird cohorts, vegetation indices and wetland proximity were the most important predictors of spatial risk. Conversely, domestic duck and chicken densities dominated predictions for the LPAI and domestic poultry subsystems, while proximity to LPAI outbreaks was the strongest driver of HPAI risk. Although underreporting in wildlife and developing regions limits data quality, this interpretable framework provides a flexible platform to support risk-based surveillance and to optimize targeted interventions at regional and global scales.

Tick-borne diseases cause a high morbidity in the USA with Ixodes scapularis as the main vector, responsible for the majority of tick-borne diseases in the USA. Understanding disease dynamics requires not just human disease surveillance but also surveillance of the ticks themselves. This study collected 355 I. scapularis adults from 16 sites across Minnesota, Wisconsin and Iowa by drag cloths during 2017-2019. Ticks were tested using 16S sequencing and targeted PCR to identify human relevant tick-borne disease agents. We detected 168 Borrelia burgdorferi positive, 2 Borrelia mayonii positive, 33 Anaplasma phagocytophilum Human Active positive, 1 Anaplasma phagocytophilum Variant-1 positive, 14 Ehrlichia muris positive and 5 Borrelia miyamotoi positive ticks. These results add surveillance data on adult I. scapularis adults in the Upper Midwest.

The ability to simulate realistic networks based on empirical data is an important task across scientific disciplines, from epidemiology to computer science. Often simulation approaches involve selecting a suitable network generative model such as Erdös-Rényi or small-world. However, few tools are available to quantify if a particular generative model is suitable for capturing a given network structure or organization. We utilize advances in interpretable machine learning to classify simulated networks by our generative models based on various network attributes, using both primary features and their interactions. Our study underscores the significance of specific network features and their interactions in distinguishing generative models, comprehending complex network structures, and the formation of real-world networks.

1. Detecting patterns in animal social behaviour and movement is complicated by the diversity of ecological, evolutionary, environmental, and biological drivers of these behaviours such as migration, foraging, assortative mixing, socio-ecological factors, and human influence. 2. Addressing these complexities requires a multidisciplinary approach. Recent advances in network analysis and machine learning offer powerful tools for examining and interpreting complex network structures, aiding in the identification and quantification of movement patterns and the prediction of behavioural changes. 3. Here we use a comparative approach leveraging network analysis and machine learning techniques to assess commonalities in standard theoretical social structures governing networks across the animal kingdom. We investigate how these theoretical structures explain social organization at different scales, from entire populations to smaller groups. By leveraging interpretable machine learning techniques, we examine the predictive power of species and network construction techniques in predicting structural features of animal social networks. 4. We found that geometric graphs are the frequently predicted network model across the animal kingdom. These graphs represent both spatial and social processes and are formed by positioning individuals uniformly on a 2D plane, with links established based on proximity within a specified distance. In particular, geometric graphs demonstrate structural similarities with interaction types and data collection methods. For example, we found that this graph model had strong structural similarities with networks derived from physical contact and spatial proximity data. Networks with small-world properties, in contrast, were rare across all interaction types and collection methods. Additionally, the occurrence of these networks is influenced by the identity of species and sampling duration. Although incorporating species identity into the classification model did not improve the accuracy of the prediction, it enabled us to account for the varying dependencies of biological characteristics on specific behaviours. 5. This study highlights the value of predictive modelling for uncovering ecological drivers of animal network structures. By focusing on standard theoretical models that are well established in network science, we connect animal social networks to broader theoretical findings, while recognizing that more tailored models combining multiple generative models or network properties may offer deeper insights into network structures. We also emphasize the importance of considering how the specific methods used to build networks for each taxon could influence the biological inferences that can be drawn from those networks. ### Competing Interest Statement The authors have declared no competing interest. Australian Research Council Discovery Project Grant, DP190102020

Free-roaming rosy-faced lovebirds (Agapornis roseicollis) were first documented in the greater Phoenix area (Arizona, USA) in the mid-1980s and have since established small populations. In blood and cloacal swab samples from 69 lovebirds at four locations, we identified the presence of beak and feather disease virus (BFDV), aves polyomavirus (APyV), and psittacine adenovirus 5 (PsAdV-5). Additionally, we identified a novel aviadenovirus, hereby referred to as psittacine adenovirus 12 (PsAdV-12). PsAdV-12 is most closely related to members of the species Aviadenovirus senegalense and Aviadenovirus rubri, sharing ∼64 % genome-wide pairwise identity. Incidence of each virus across all lovebirds varied, with BFDV being the highest at 68.1 % overall, whereas a much lower infection rate was observed for APyV (8.6 %), PsAdV-5 (5.7 %), and PsAdV-12 (2.8 %). Our analysis supports an introduction of BFDV-infected lovebirds into the greater Phoenix area in the 1980s followed by radiation to three circulating distinct lineages; APyVs showed very little diversification.

Parelaphostrongylus tenuis causes ungulate morbidity and mortality in eastern and central North America, but no reference genome sequence exists to facilitate research. Here, we present a P. tenuis genome assembly and annotation, generated with PacBio and Illumina technologies. The assembly is 491 Mbp, with 7285 scaffolds and 185 kb N50.

The distinctive notodontine moth genus Gallaba Walker, 1865 is confined to eastern and southern Australia where multiple species inhabit forests, woodlands and heathlands from sea level to montane forests of at least 1650m. The continental island of Tasmania supports four Gallaba species (including these two new endemics) at the current global southern limit of Notodontidae. Two distinctive new species are described from montane and subalpine Tasmania: Gallaba constellata sp. nov. and Gallaba kirkpatricki sp. nov. Both exhibit distinctive phenotypes including extraordinarily long rami in the male antennae, but their phylogenetic relationships to the rest of the genus must await a full revision of Gallaba and allied genera. We find no evidence for earlier claims that G. duplicata Walker, 1865 occurs in Tasmania. It was likely confused with the phenotypically similar Hobartina amblyiodes (Turner, 1931) and we remove it from the Tasmanian checklist. Biological information about Gallaba is very limited, but we have discovered that the larva of Gallaba kirkpatricki sp. nov. feeds on the pandaniform tall shrub Richea pandanifolia (Ericaceae: Epacridoideae). The Ericaceae is a novel hostplant family for Australian Notodontinae, although it is sparingly used by notodontines in tropical Asia. The mesic habitat and foodplants of these species are vulnerable to fire risk in montane areas which has been increasing rapidly in frequency and extent in recent years due to drying of the environment caused by climate change.

The global impact of the SARS-CoV-2 pandemic has been uneven, with some regions experiencing significant excess mortality while others have been relatively unaffected. Yet factors which predict this variation remain enigmatic, particularly at large spatial scales. We aimed to uncover the key drivers of excess mortality across countries and regions to help understand the factors contributing to the varied impacts of the pandemic worldwide. We used spatially explicit Bayesian models that integrate environmental, socio-demographic and endemic disease data at the country level to provide robust global estimates of excess SARS-CoV-2 mortality (P-scores) for the years 2020 and 2021. We find that urbanization, gross domestic product (GDP) and spatial patterns are strong predictors of excess mortality, with countries characterized by low GDP but high urbanization experiencing the highest levels of excess mortality. Intriguingly, we also observed that the prevalence of malaria and human immunodeficiency virus (HIV) are associated with country-level SARS-CoV-2 excess mortality in Africa and the Western Pacific, whereby countries with low HIV prevalence but high malaria prevalence tend to have lower levels of excess mortality. While these associations are correlative in nature at the macro-scale, they emphasize that patterns of endemic disease and socio-demographic factors are needed to understand the global dynamics of SARS-CoV-2. Our study identifies factors associated with variation in excess mortality across countries, providing insights into why some were more impacted by the pandemic than others. By understanding these predictors, we can better inform global outbreak management strategies, such as targeting medical resources to highly urban countries with low GDP and high HIV prevalence to reduce mortality during future outbreaks.

IntroductionContinued SARS-CoV-2 infection among immunocompromised individuals is likely to play a role in generating genomic diversity and the emergence of novel variants. Antiviral treatments such as molnupiravir are used to mitigate severe COVID-19 outcomes, but the extended effects of these drugs on viral evolution in patients with chronic infections remain uncertain. This study investigates how molnupiravir affects SARS-CoV-2 evolution in immunocompromised patients with prolonged infections.MethodsThe study included five immunocompromised patients treated with molnupiravir and four patients not treated with molnupiravir (two immunocompromised and two non-immunocompromised). We selected patients who had been infected by similar SARS-CoV-2 variants and with high-quality genomes across timepoints to allow comparison between groups. Throat and nasopharyngeal samples were collected in patients up to 44 days post treatment and were sequenced using tiled amplicon sequencing followed by variant calling. The UShER pipeline and University of California Santa Cruz genome viewer provided insights into the global context of variants. Treated and untreated patients were compared, and mutation profiles were visualised to understand the impact of molnupiravir on viral evolution.FindingsPatients treated with molnupiravir showed a large increase in low-to-mid-frequency variants in as little as 10 days after treatment, whereas no such change was observed in untreated patients. Some of these variants became fixed in the viral population, including non-synonymous mutations in the spike protein. The variants were distributed across the genome and included unique mutations not commonly found in global omicron genomes. Notably, G-to-A and C-to-T mutations dominated the mutational profile of treated patients, persisting up to 44 days post treatment.InterpretationMolnupiravir treatment in immunocompromised patients led to the accumulation of a distinctive pattern of mutations beyond the recommended 5 days of treatment. Treated patients maintained persistent PCR positivity for the duration of monitoring, indicating clear potential for transmission and subsequent emergence of novel variants.FundingAustralian Research Council.

Dispersal is an important process that is widely studied across species, and it can be influenced by intrinsic and extrinsic factors. Intrinsic factors commonly assessed include the sex and age of individuals, while landscape features are frequently-tested extrinsic factors. Here, we investigated the effects of both sex and landscape composition and configuration on genetic distances among bare-nosed wombats (Vombatus ursinus)-one of the largest fossorial mammals in the world and subject to habitat fragmentation, threats from disease, and human persecution including culling as an agricultural pest. We analyzed a data set comprising 74 Tasmanian individuals (30 males and 44 females), genotyped for 9,064 single-nucleotide polymorphisms. We tested for sex-biased dispersal and the influence of landscape features on genetic distances including land use, water, vegetation, elevation, and topographic ruggedness. We detected significant female-biased dispersal, which may be related to females donating burrows to their offspring due to the energetic cost of excavation, given their large body sizes. Land use, waterbodies, and elevation appeared to be significant landscape predictors of genetic distance. Land use potentially reflects land clearing and persecution over the last 200 years. If our findings based on a limited sample size are valid, retention and restoration of nonanthropogenic landscapes in which wombats can move and burrow may be important for gene flow and maintenance of genetic diversity.

Islands play a central role in understanding the ecological and evolutionary processes that shape life but are rarely used to untangle the processes that shape human, animal, and environmental health. Islands, with their discrete human and animal populations, and often well-studied ecological networks, serve as ideal natural laboratories for exploring the complex relationships that shape health across biomes. Relatively long coastlines and, in some cases, low lying topography also make islands sentinels for climate change. In this article, we examine the potential of islands as valuable laboratories and research locations for understanding the One Health nexus. By delving into the challenges faced in island settings, we provide valuable insights for researchers and policymakers aiming to globally promote and apply One Health principles. Ultimately, recognizing the interconnected health of humans, animals, and the environment on islands contributes to efforts aimed at promoting global health and sustainability.

ABSTRACT Wildlife is the source of many emerging infectious diseases. Several viruses from the order Nidovirales have recently emerged in wildlife, sometimes with severe consequences for endangered species. The order Nidovirales is currently classified into eight suborders, three of which contain viruses of vertebrates. Vertebrate coronaviruses (suborder Cornidovirineae ) have been extensively studied, yet the other major suborders have received less attention. The aim of this minireview was to summarize the key findings from the published literature on nidoviruses of vertebrate wildlife from two suborders: Arnidovirineae and Tornidovirineae . These viruses were identified either during investigations of disease outbreaks or through molecular surveys of wildlife viromes, and include pathogens of reptiles and mammals. The available data on key biological features, disease associations, and pathology are presented, in addition to data on the frequency of infections among various host populations, and putative routes of transmission. While nidoviruses discussed here appear to have a restricted in vivo host range, little is known about their natural life cycle. Observational field-based studies outside of the mortality events are needed to facilitate an understanding of the virus-host-environment interactions that lead to the outbreaks. Laboratory-based studies are needed to understand the pathogenesis of diseases caused by novel nidoviruses and their evolutionary histories. Barriers preventing research progress include limited funding and the unavailability of virus- and host-specific reagents. To reduce mortalities in wildlife and further population declines, proactive development of expertise, technologies, and networks should be developed. These steps would enable effective management of future outbreaks and support wildlife conservation.

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.

Our understanding of wildlife multihost pathogen transmission systems is often incomplete due to the difficulty of observing contact between hosts. Understanding these interactions can be critical for preventing disease-induced wildlife declines. The proliferation of high-throughput sequencing technologies provides new opportunities to better explore these cryptic interactions. Parelaphostrongylus tenuis, a multihost parasite, is a leading cause of death in some moose (Alces alces) populations threatened by local extinction in the midwestern and northeastern US and southeastern Canada. Moose contract P. tenuis by consuming infected gastropod intermediate hosts, but little is known about which gastropod species moose consume. To gain more insight, we used a genetic metabarcoding approach on 258 georeferenced and temporally stratified moose fecal samples collected May-October 2017-20 from a declining population in the north-central US. We detected moose consumption of three species of gastropods across five positive samples. Two of these (Punctum minutissimum and Helisoma sp.) have been minimally investigated for the ability to host P. tenuis, while one (Zonitoides arboreus) is a well-documented host. Moose consumption of gastropods documented herein occurred in June and September. Our findings prove that moose consume gastropod species known to become infected by P. tenuis and demonstrate that fecal metabarcoding can provide novel insight on interactions between hosts of a multispecies pathogen transmission system. After determining and improving test sensitivity, these methods may also be extended to document important interactions in other multihost disease systems.

Spatially heterogeneous landscape factors such as urbanisation can have substantial effects on the severity and spread of wildlife diseases. However, research linking patterns of pathogen transmission to landscape features remains rare. Using a combination of phylogeographic and machine learning approaches, we tested the influence of landscape and host factors on feline immunodeficiency virus (FIVLru) genetic variation and spread among bobcats (Lynx rufus) sampled from coastal southern California. We found evidence for increased rates of FIVLru lineage spread through areas of higher vegetation density. Furthermore, single-nucleotide polymorphism (SNP) variation among FIVLru sequences was associated with host genetic distances and geographic location, with FIVLru genetic discontinuities precisely correlating with known urban barriers to host dispersal. An effect of forest land cover on FIVLru SNP variation was likely attributable to host population structure and differences in forest land cover between different populations. Taken together, these results suggest that the spread of FIVLru is constrained by large-scale urban barriers to host movement. Although urbanisation at fine spatial scales did not appear to directly influence virus transmission or spread, we found evidence that viruses transmit and spread more quickly through areas containing higher proportions of natural habitat. These multiple lines of evidence demonstrate how urbanisation can change patterns of contact-dependent pathogen transmission and provide insights into how continued urban development may influence the incidence and management of wildlife disease.