Search

Simple Summary: Our study evaluates the global risk of Aeolesthes sarta establishment using mechanistic and correlational niche models. The pest, known to affect various hardwood trees, poses a threat to international trade due to its quarantine status. The CLIMEX model, based on species-specific physiological thresholds, and MaxEnt model, using species occurrences and climatic data, project its potential distribution. Both models align well with the current distribution, predicting broader ranges in the central and southern hemispheres, excluding extreme northern regions. Future climate changes may expand its range, particularly in Europe and North America, where its host species are present. Temperature and precipitation are key factors influencing its distribution. These models offer valuable insights for policymakers and trade negotiators to make science-based decisions on pest management and trade agreements, aiding in monitoring potential pest introductions globally. A precise evaluation of the risk of establishing insect pests is essential for national plant protection organizations. This accuracy is crucial in negotiating international trade agreements for forestry-related commodities, which have the potential to carry pests and lead to unintended introductions in the importing countries. In our study, we employed both mechanistic and correlative niche models to assess and map the global patterns of potential establishment for Aeolesthes sarta under current and future climates. This insect is a significant pest affecting tree species of the genus Populus, Salix, Acer, Malus, Juglans, and other hardwood trees. Notably, it is also categorized as a quarantine pest in countries where it is not currently present. The mechanistic model, CLIMEX, was calibrated using species-specific physiological tolerance thresholds, providing a detailed understanding of the environmental factors influencing the species. In contrast, the correlative model, maximum entropy (MaxEnt), utilized species occurrences and spatial climatic data, offering insights into the species' distribution based on observed data and environmental conditions. The projected potential distribution from CLIMEX and MaxEnt models aligns well with the currently known distribution of A. sarta. CLIMEX predicts a broader global distribution than MaxEnt, indicating that most central and southern hemispheres are suitable for its distribution, excluding the extreme northern hemisphere, central African countries, and the northern part of Australia. Both models accurately predict the known distribution of A. sarta in the Asian continent, and their projections suggest a slight overall increase in the global distribution range of A. sarta with future changes in climate temperature, majorly concentrating in the central and northern hemispheres. Furthermore, the models anticipate suitable conditions in Europe and North America, where A. sarta currently does not occur but where its preferred host species, Populus alba, is present. The main environmental variables associated with the distribution of A. sarta at a global level were the average annual temperature and precipitation rate. The predictive models developed in this study offer insights into the global risk of A. sarta establishment and can be valuable for monitoring potential pest introductions in different countries. Additionally, policymakers and trade negotiators can utilize these models to make science-based decisions regarding pest management and international trade agreements. [ABSTRACT FROM AUTHOR]

Increasing inter-continental trade of wood chips for biofuel represents a significant risk of introducing invasive pest species that can cause biome-scale impacts on forest ecosystems. Some potentially invasive species have the capacity to cause high tree mortality on the Eurasian continent and could cause significant impacts on biodiversity and ecosystem functions. Because eradication of established species is difficult, there is a need for scientific studies that can evaluate the reliability of current import control practices to ensure lowest possible risk of establishment of potentially harmful species. We used a stochastic simulation model and sensitivity analyses to evaluate the chance of detecting harmful pests in imported wood chips by sampling according to the current use of internationally accepted standards. As an example, we focused on the North American beetle Agrilus anxius (bronze birch borer) that can cause 100% mortality of European and Asian birch species in North America. We simulated the process from logging in North America to sampling the wood chips upon arrival in Europe. The probability of pest detection for current sampling protocols used by port inspectors was very low (<0.00005), while a 90% chance of detection may require sampling 27 million litres of wood chips per shipload. [ABSTRACT FROM AUTHOR]

Anoplophora glabripennis (Motschulsky, 1853), native to eastern Asia, is a destructive woodborer of many ornamental species, leading to the decline and the death of the attacked trees. In outbreak areas as Europe or North America, this pest is usually identified using morphological or molecular analyses of adult or larval specimens. However, the procedures for collecting A. glabripennis specimens from infested plants are too expensive and time consuming for routine screening. A noninvasive diagnostic tool based on frass discrimination is therefore crucial for the rapid identification of A. glabripennis at different development stages in the host. This article describes a rapid diagnostic protocol based on loop-mediated isothermal amplification (LAMP). DNA extracted from A. glabripennis frass was amplified with both visual and real-time LAMP and compared with those of nontarget species. The results show that the method is reliable and accurate and therefore could be a promising diagnostic tool in phytosanitary surveys., (© The Author(s) 2020. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)