Pest Risk Analysis (PRA) Workpackage

  Sitka spruce is by far the most economically important species in Irish forestry, so any new disease or pest could have a devastating effect on the forest industry in Ireland. The ability to predict and pre-empt future threats is essential to protecting Ireland’s forest estate which includes the potential effects of climate change Under the EU Plant Health Directive (Council Directive 2000/29/EC), Ireland has been granted “protected zone status” for thirteen harmful forest pests and diseases, with oak processionary moth and pine wood nematode soon to be added. This has considerable benefits to the forestry sector directly. However, maintenance of protected zone status is dependent on effective PRA and monitoring. The use of PRA will help identify the threats, thus helping Ireland to develop strategies to prevent the degeneration of Irish forests as a result of these emerging pests and pathogens, for example using increased bio-security and phytosanitary measures, with immediate impact, while the full impact of the research will likely be realised through the future (long-term impact) sustainable growth of the Irish forest estate.

Different modelling techniques are applied within the FORM project to aid in the identification of high risk pests to Irish forestry under both current and future climates. Global pest data is being analysed using machine learning techniques to identify high risk pests for Irish forestry, which can help to prioritise species for PRA. The establishment and spread of alien pest species is being assessed using bioclimatic envelope mapping in conjunction with spread models based on the life-history of selected pest species. In addition, current climate data is being employed, along with future climate simulations to assess how the suitability of Irish stands for pest species might change under a warming climate.

Pest Risk Analyses are conducted to decide if and how pests should be regulated in order to prevent invasion or establishment in a target region. However, PRAs can not be carried out for every pest. Two modelling approaches have aided in the identification of high risk pest species for further analysis: (1) Hierarchical clustering (HC) and (2) Self Organising Maps (SOMs). Both of these approaches enable the clustering of regions based on the similarity of their pest assemblages, which can aid in the objective identification of target species for analysis within a pest risk framework. The presence of a species within a specific region indicates that suitable biotic and abiotic conditions exist for that pest, and the grouping of regions based on a species’ presence or absence indicates a level of similarity of those same conditions. Those regions with similar pest assemblages are then likely to exhibit similar biotic and abiotic conditions, which would likely facilitate the establishment of a species from one region to another (if a pathway for entry was present). Work carried out in the FORM project identified a number of high risk pests using both of these approaches, which can then be flagged for PRA or for protected zone status. Maps such as the one below (Figure 1) illustrates the clustering of one of the insect pest groups analysed. Maps like this aid in the prioritization of risk allocation to certain pest species and ultimately facilitate an increase in national knowledge regarding our risk of invasion of damaging forest pests.

Figure 1: Clustering of geographic regions based on their respective pest species assemblages.

Fabio Stergulc, Università di Udine,

Great spruce bark beetle (Dendroctonus micans): serious pest of spruce, Ireland has a protected zone, widespread in rest of Europe. This image nicely shows the larvae under the bark.

William M. Ciesla, Forest Health Management International,

Spruce broom rust, a North American fungal disease of spruce regulated by the EU.

Edward H. Holsten, USDA Forest Service,

Spruce bud scale.

William M. Ciesla, Forest Health Management International,

Defoliation by Western Spruce budworm.