In January 2020, increased mortality was reported in a small broiler breeder flock in County Fermanagh, Northern Ireland. Gross pathological findings included coelomitis, oophoritis, salpingitis, visceral gout, splenomegaly, and renomegaly. Clinical presentation included inappetence, pronounced diarrhoea, and increased egg deformation. These signs, in combination with increased mortality, triggered a notifiable avian disease investigation. High pathogenicity avian influenza virus (HPAIV) was not suspected, as mortality levels and clinical signs were not consistent with HPAIV. Laboratory investigation demonstrated the causative agent to be a low-pathogenicity avian influenza virus (LPAIV), subtype H6N1, resulting in an outbreak that affected 15 premises in Northern Ireland. The H6N1 virus was also associated with infection on 13 premises in the Republic of Ireland and six in Great Britain. The close genetic relationship between the viruses in Ireland and Northern Ireland suggested a direct causal link whereas those in Great Britain were associated with exposure to a common ancestral virus. Overall, this rapidly spreading outbreak required the culling of over 2 million birds across the United Kingdom and the Republic of Ireland to stamp out the incursion. This report demonstrates the importance of investigating LPAIV outbreaks promptly, given their substantial economic impacts.

Plant pests (including pathogens) threaten forests, reduce carbon sequestration, disrupt trade, and are costly to manage. Traditionally, forest surveys involve the visual inspection of trees for symptoms of disease; however, this process is time consuming and by the time symptoms are observed, the disease may be widespread. New methods of surveillance are needed to help plant health authorities monitor and protect forests from disease. Previous research has shown that metabarcoding of environmental DNA (eDNA) can be used to identify plant pests. This study collected rainwater samples from five forest sites across Northern Ireland every month for a year to examine whether eDNA metabarcoding could help authorities detect plant diseases in forests. Metabarcoding of the internal transcribed spacer (ITS) region was used to determine the fungal and oomycete profile of rainwater samples that passed through the canopy of spruce, pine, oak, and ash trees, along with a non-tree field trap. In total, 65 known plant fungal and oomycete pests were detected; seven were regulated pests, and two were pests that had not been previously identified in Northern Ireland. This work demonstrates that metabarcoding of eDNA from rainwater can detect plant pests and could be used in forest surveillance programmes.