Search

Plant pests may pose a significant threat to global agriculture, natural ecosystems and biodiversity, causing severe ecological and economic damage. Identifying regions more susceptible to pest introductions is crucial for developing effective prevention, early detection and outbreak response strategies. While historical data on pest introductions in the European Union (EU) exist, they are typically reported at the regional level. This broad aggregation has posed a challenge for accurate analysis in plant health research. This study addresses this gap by leveraging existing regional data to identify hotspots of pest introductions within the EU and the UK, through a Bayesian hierarchical spatial model. Specifically, we employed the Besag, York, and Mollié (BYM) model to identify higher risk regions (NUTS2) by incorporating covariates and spatial effects to consider information from neighbouring areas. The results showed a positive effect of annual average temperature, annual average precipitation, and human population density on the risk of pest introduction, highlighting the relevance of the spatial component. Our analysis pinpoints high-risk regions in southern Europe, particularly northern Italy. Additionally, the high human population density and documented pest introductions in the Netherlands contributed to its elevated risk. While limitations exist due to the regional nature of the data, this study represents a methodological advancement, demonstrating the effectiveness of spatial models and offering a robust framework for future studies using regional data. It also provides insights that can inform targeted prevention, early detection and preparedness strategies, ultimately contributing to safeguarding agriculture, natural ecosystems and biodiversity in the EU and UK. [ABSTRACT FROM AUTHOR]

Globalisation of trade in plants is a high-risk pathway that initiated many plant pest invasions. Models for ranking invasive pests are thus important to risk managers since they help them quickly adapt management strategies. Therefore, the Biological Organisms data Retrieval and Ranking system (BiOR2) process was developed and enabled high-throughput data acquisition connected to a risk assessment model. Its main characteristics are being objective, generic and based on sound science, namely pest risk analysis and multicriteria decision aid. The probability of entry, establishment and spread as well as the likely economic and environmental impacts were estimated separately through 24 independent criteria for which score calculation was precisely described. Moreover, data storing and processing in BiOR2 enable its further use for answering several risk-related questions. The behaviour of BiOR2 was explored regarding sensitivity as well as other features such as the 'fit for purpose' approach through an adaptable weighting system. The management of missing data and the expression of uncertainty in BiOR2 were also described. Moreover, BiOR2 was compared to previous models and expert statements in order to evaluate the modifications and deviations it generates with respect to these approaches respectively. The contribution to policy recommendations such as pest prioritisation was also investigated. In 2016, 278 pests have been assessed with BiOR2 for France and 110 pests for French Overseas Departments, which highlights its applicability to several geographic areas. Lastly, the fulfilment of suggested good practices by BiOR2 in the field of prioritisation studies was also verified. [ABSTRACT FROM AUTHOR]

Quarantine interception of invasive and nonindigenous insect pests at ports of entry is often impeded by the lack of robust identification methods. Because of their inconspicuous morphology and wax-covered bodies, mealybugs present a particular challenge. The present study employs DNA barcoding (658 base pairs near the 5′-terminus of the cytochrome c oxidase I gene) as a tool for their discrimination because of its proven utility in discriminating closely related species. The current study considers DNA barcodes from 914 mealybugs (Pseudococcidae) collected in 31 countries. Most (836) of these specimens were assigned to a named species, but others were only identified to a genus or family. Their sequence analysis revealed substantial COI diversity with maximum divergences reaching 27%. While the identified specimens included representatives of 62 species, the Barcode Index Number (BIN) system assigned the 914 sequences to 120 BINs, nearly doubling the putative species count and revealing cases of potential cryptic species and misidentifications. With a single exception, intraspecific divergence values for named species were less than their nearest-neighbor distances, but 13 species showed BIN splits and two species were merged in a BIN. High genetic diversity and presence of cryptic species in the known mealybugs, revealed in this study, underscore the limitations of morphology and potential utility of BINs for the rapid recognition of nonindigenous insect pests. [ABSTRACT FROM AUTHOR]