Department of Agriculture Food and the Marine

Streptococcus suis is a major pig pathogen with zoonotic potential, posing an occupational risk to farmers and meat handlers. We characterized 110 S. suis strains from diseased pigs in Ireland (2005-2022) using whole-genome sequencing to investigate population structure and phage-host dynamics. We identified 15 distinct serotypes, with serotypes 9 and 2 being the most dominant. In silico multi-locus sequence typing revealed high diversity within the collection, identifying several sequence types (STs), including 26 novel STs. Investigation of strain-level genomic clustering using PopPUNK against global S. suis genomes showed that the Irish isolates were phylogenetically dispersed across the broader global S. suis population rather than clustering in a single clonal group. The majority of Irish isolates fall within the ten established pathogenic lineages, including the highly virulent zoonotic lineage 1. A locally persistent clonal lineage was identified among Irish isolates, showing minimal genetic variation over a decade.Prophage analysis revealed novel viral taxa that were interspersed among known streptococcal phages, rather than clustering distinctly. Restriction-modification systems were the predominant anti-viral defence systems identified across genomes. CRISPR-Cas systems were present in limited strains but showed substantial targeting bias toward full-length prophages, indicating ongoing phage pressure. CRISPR spacers matched non-S. suis streptococcal phages, and phylogenomic analysis revealed that Vansinderenvirus phages clustered with S. suis rather than other Streptococcus thermophilus phages, suggesting evolutionary connections between phage lineages infecting different streptococci.This study presents the first comprehensive genomic characterization of S. suis in Ireland, revealing a diverse population with significant implications for animal and human health.

In the Republic of Ireland, the herd-level incidence of bovine tuberculosis (bTB), caused by Mycobacterium bovis, reached 6.40% by June 29th 2025, highlighting bTB’s risk to animal health, biosecurity and the economy. The complexity of bTB epidemiology, driven by multiple host species, undetected transmission and incomplete diagnostic sensitivity, makes surveillance and control challenging. Pathogen whole-genome sequencing (WGS) can clarify transmission dynamics but is constrained by the slow, variable mutation rate of M. bovis. This pilot case study integrates WGS with epidemiological data to elucidate transmission event pathways and could be a starting point for future automation. A decision-tree framework was developed to classify likely transmission event pathways by integrating M. bovis WGS sourced from the BTBGenie research project and epidemiological data. As proof-of-concept, one farm with multiple isolates was randomly selected from a national WGS database. Twenty-eight near-identical isolates (pairwise ≤ 3 SNP divergence), from other herds, were identified across databases of the Republic of Ireland and Northern Ireland, with full metadata was available for 16 isolates. These were analysed using TracebTB, a research tool linking national animal health, movement, wildlife and land management databases. Transmission event pathways for this case herd were classified as “local area transmission” (43.75%), “within-herd transmission” (12.5%), and “cattle movement-associated transmission” (also 43.75%), divided into between-herd (25%) and within-herd (18.8%) transmission. No evidence of residual within-herd transmission was found in this herd. Homebred animals served as spatial anchors, delineating the cluster’s ‘home range’ or kernel. A spatially distant homebred case, alongside the overall home range size, suggested an undetected movement-associated transmission event, likely via undetected carriers. Integrating WGS with detailed epidemiology enables identification of probable bTB transmission event pathways, revealing undetected infections and highlighting biosecurity concerns associated with undetected carriers. The decision-tree framework provides a scalable approach for retrospective outbreak investigation, targeted surveillance, and efficient resource allocation, particularly in high-risk systems such as Controlled Finishing Units. These findings highlight the importance of transboundary collaboration in persistent bTB hotspots. Automating this approach could support validation of disease epidemiological models, guide targeted interventions, and optimising resource allocation, supporting Ireland’s goal of bTB eradication.

Since the abolition of EU milk quotas in 2015, the Irish dairy industry has expanded with a 16.6% increase in calf births since 2011. Calf losses have major implications for the economic viability and sustainability of dairy enterprises. There is a paucity of literature on mortality in calves from birth to six months, particularly at a national herd level. Previous studies have tended to focus on herd size as a risk factor for calf mortality rather than the possible influence of herd expansion. The purpose of this study is to quantify any association between dairy herd expansion and the risk of being classified as a high or low calf mortality herd based on analysis of national dairy herd identification and movement records in Ireland from 2016-2020. In calves aged under 6months, herds that expanded > 20% (OR 1.23 95% CI: 1.10-1.37, p < 0.001) and > 45% (OR 1.22, 95% CI:1.09-1.36, p = 0.001), were more likely to have > 10% herd calf mortality, compared to herds that did not increase in size. Newly established herds were more likely to have poor mortality outcomes (OR 2.44, 95% CI: 1.82-3.29, p < 0.001) compared to herds that did not increase in size. Herd expansion < 20% was not associated with mortality outcome. Herd ordinal location and herd size were significant risk factors for > 10% herd calf mortality. This study has demonstrated that herds that have expanded > 20% over five years and newly established herds were more likely to have poor mortality outcomes. It suggests that new entrants into dairy farming may benefit from targeted emphasis on herd health management. The results also highlight the value of national data as a tool to determine optimisation of farm interventions and surveillance and policy decisions to prioritise animal health and welfare.

1. Land-use change can significantly alter wildlife movement patterns and behaviour which may increase the risk of zoonotic disease transmission, particularly diseases with wildlife maintenance hosts that intersect regularly with farmland such as bovine tuberculosis (bTB). 2. This study utilized an agent-based model (ABM) with high-resolution input data to simulate badger responses to clearfell forestry across 100 sites in Ireland. The model environment was constructed from multiple GIS datasets, including sett locations, habitat, farm boundaries, and forestry operations. Animal movement and behaviour in the model were parameterised using detailed analysis of GPS tracking data from over 200 collared badgers in the wild. 3. Our results show that badger density and herd type were the strongest predictors of alien encounters —instances where badgers move beyond their established territories and enter farms outside their usual home range. These encounters, if used as a proxy for bTB transmission risk, represent opportunities for badgers to interact with livestock, particularly cattle, increasing the likelihood of disease spread. Dairy herds, despite being fewer in number compared to beef and suckler enterprises, had a higher risk of alien encounters. Larger clearfell events significantly increased alien encounters, supporting the hypothesis that landscape disturbances elevate disease risk. 4. Contrary to expectations, badger dispersal distance did not significantly affect encounter rates. Instead, landscape heterogeneity, including clearfell size and farm density, played a more substantial role in modulating encounters. 5. Validation of the ABM using empirical bTB breakdown data confirmed a positive correlation between simulated badger encounter rates per study site and real world bTB outbreaks within those sites. 6. This research provides a novel spatial framework for predicting bTB risk in disturbed landscapes, offering a valuable tool for Ireland's efforts to mitigate wildlife-livestock disease transmission. By integrating high-resolution GIS data and GPS-tracked wildlife movements, this approach enhances the accuracy of agent-based models (ABMs) for applied ecology and wildlife conflict research. • Novel approach using agent-based models for ecological modeling with integrated GPS and GIS data. • Identifies dairy farms as significant, revealing spatial dynamics beyond traditional explanations of husbandry and farm management. • Highlights that badger and farm density outweigh disturbance effects in influencing encounter rates post-clearfell. • Strong model performance against real-world disease testing data, underscoring ABMs’ potential for applied ecological impact.

Causes of New World Camelid (168 alpacas; 7 llamas) mortality in Ireland based on review of passive surveillance findings of the Irish Veterinary Laboratory Service from 2010 till 2023 were analysed. The most frequently submitted age group were adults (58.8 %), followed by juveniles (20.6 %), crias (16.6 %), and aborted foeti (4 %). Regardless of age, 96 % were suitable for investigation, with identified aetiology in 77.4 % of cases. The pathology was systemic (30.9 %) or involving the alimentary (39.3 %), respiratory (8.9 %), urogenital (4.9 %), musculoskeletal and lymphatic (2.2 % each), integument (1.8 %), cardiocirculatory (1.3 %), nervous (0.9 %) system; 7.6 % were inconclusive. The aetiology was bacterial (44.4 %), parasitic (24.3 %), nutritional/metabolic/toxic (6.4 %), traumatic/mechanical (3.2 %), congenital (1.6 %), and neoplastic (0.5 %), while 19.6 % had unknown aetiology. The diagnostic rate was highest in crias (79.3 %) and lowest in the abortion group (0 %). Crias had mainly systemic and alimentary tract diseases (43.3 % and 26.6 %, respectively); juveniles had mainly alimentary illness and systemic diseases (51.3 % and 30.7 %, respectively); similarly for adults (40.2 % and 25.5 %, respectively). The primary aetiology detected was bacterial (45.5 %, 35.1 %, and 48.7 % for crias, juveniles and adults, respectively). Mycobacterium bovis was identified in 27.4 % of submissions, 25 % of which had lesions localised to a single organ (mainly lung), whilst 75 % had generalised tuberculosis, where the lung and liver were the most and second most commonly affected organs, respectively. Fasciola hepatica infection represented the second most common cause or co-cause of mortality. To the authors' knowledge, this is the most extensive published case series of M. bovis pathology in alpacas.

The dataset includes Pan-European maps of timber volume (Vol), above-ground biomass (AGB), and deciduous-coniferous proportion (DCP) with a pixel size of 10×10 m for the reference year 2020. In addition, a measure of prediction uncertainty is provided for each pixel. The maps have been created using a combination of a Sentinel-2 mosaic, Copernicus layers, and National Forest Inventory (NFI) data. The mapping was done with the k-Nearest Neighbour (kNN, k=7) approach with harmonized data of species-specific Vol and AGB from 14 NFIs consisting of approximately 151 000 field plots across Europe. The maps cover 40 European countries, forming a continuous coverage of the western part of the European continent. A sample of 1/3 of NFI plots was left out for validation, whereas 2/3 of the plots were used for mapping. Maps were created independently for 13 multi-country processing areas. Root-mean-squared-errors (RMSEs) for AGB ranged from 53 % in the Nordic processing area to 73 % in the South-Eastern area. The maps are on average nearly unbiased on European level (1.0 % of the mean AGB), but show significant overestimation for small biomass values (53 % bias for forests with AGB less than 150 t/ha) and underestimation for high biomass values (-55 % bias for forests with AGB higher than 500 t/ha). The created maps are the first of their kind as they are utilizing a large number of harmonized NFI plot observations and consistent remote sensing data for high-resolution forest attribute mapping. While the published maps can be useful for visualization and other purposes, they are primarily meant as auxiliary information in model-assisted estimation where model-related biases can be mitigated, and field-based estimates improved. Therefore, additional calibration procedures were not applied, and especially high Vol and AGB values tend to be underestimated. We therefore discourage from summarizing map values (pixel counting) over areas in interest, as this may inadvertently result in biased estimates.

Zymoseptoria tritici is an ascomycete fungus and the causal agent of Septoria tritici leaf blotch (STB) in wheat. Z. tritici secretes an array of effector proteins that are likely to facilitate host infection, colonisation and pycnidia production. In this study we demonstrate a role for Zt-11 as a Z. tritici effector during disease progression. Zt-11 is upregulated during the transition of the pathogen from the biotrophic to necrotrophic phase of wheat infection. Deletion of Zt-11 delayed disease development in wheat, reducing the number and size of pycnidia, as well as the number of macropycnidiospores produced by Z. tritici. This delayed disease development by the ΔZt-11 mutants was accompanied by a lower induction of PR genes in wheat, when compared to infection with wildtype Z. tritici. Overall, these data suggest that Zt-11 plays a role in Z. tritici aggressiveness and STB disease progression possibly via a salicylic acid associated pathway.