Published June 21, 2019 | Version v1
Journal article Open

Forewarned is forearmed: harmonized approaches for early detection of potentially invasive pests and pathogens in sentinel plantings

  • 1. University of Tuscia, Viterbo, Italy
  • 2. Technische Universität Braunschwei, Braunschweig, Germany
  • 3. INRA, Forest Zoology Research Unit, Orléans, France
  • 4. Sukachev Institute of Forest, Russian Academy of Sciences (Siberian Branch), Krasnoyarsk, Russia
  • 5. National Research Institute of Rural Engineering, Water and Forests, Ariana, Tunisia
  • 6. Nature Research Centre, Institute of Botany, Vilnius, Lithuania
  • 7. Agricultural University of Tirana, Tirana, Albania
  • 8. Ukrainian Research Institute of Forestry and Forest Melioration, Kharkiv , Ukraine|Swedish University of Agricultural Sciences, Alnarp, Sweden
  • 9. Isparta Applied Science University, Isparta, Turkey
  • 10. Estonian University of Life Sciences, Tartu, Estonia
  • 11. CABI, Ecosystems Management, and Risk Analysis and Invasion Ecology, Delèmont, Switzerland
  • 12. CABI, Ecosystems Management, and Risk Analysis and Invasion Ecology, Delémont, Switzerland
  • 13. University of Belgrade, Belgrade, Serbia
  • 14. Slovenian Forestry Institute, Department of Forest protection, Ljubljana, Slovenia
  • 15. University of Agriculture in Krakow, Krakow, Poland
  • 16. CABI, Risk Analysis and Invasion Ecology, Delémont, Switzerland
  • 17. Siberian Federal University, Krasnoyarsk, Russia|Sukachev Institute of Forest, Russian Academy of Sciences (Siberian Branch), Krasnoyarsk, Russia
  • 18. Ukrainian National Forestry University, Lviv, Ukraine
  • 19. Saint Petersburg State Forest Technical University, St. Petersburg, Russia
  • 20. Cardinal Stefan Wyszynski University, Warsaw, Poland
  • 21. Agri-Food and Biosciences Institut, Belfast, United Kingdom
  • 22. Swiss Federal Institute for Forest, Snow and Landscape Research, Birmensdorf, Switzerland
  • 23. CNR, Institute for Sustainable Plant Protection, Sesto fiorentino, Italy
  • 24. Norwegian Institute of Bioeconomy Research, Plant Health and Biotechnology, Ås, Norway
  • 25. University of Tartu, Tartu, Estonia
  • 26. Natural Resources Institute, Kuopio, Finland
  • 27. Swedish University of Agricultural Sciences, Alnarp, Sweden
  • 28. University of Aberdeen, Aberdeen, United Kingdom
  • 29. Hellenic Agricultural Organization 'Demeter', Naoussa, Greece

Description

The number of invasive alien pest and pathogen species affecting ecosystem functioning, human health and economies has increased dramatically over the last decades. Discoveries of invasive pests and pathogens previously unknown to science or with unknown host associations yet damaging on novel hosts highlights the necessity of developing novel tools to predict their appearance in hitherto naïve environments. The use of sentinel plant systems is a promising tool to improve the detection of pests and pathogens before introduction and to provide valuable information for the development of preventative measures to minimize economic or environmental impacts. Though sentinel plantings have been established and studied during the last decade, there still remains a great need for guidance on which tools and protocols to put into practice in order to make assessments accurate and reliable. The sampling and diagnostic protocols chosen should enable as much information as possible about potential damaging agents and species identification. Consistency and comparison of results are based on the adoption of common procedures for sampling design and sample processing. In this paper, we suggest harmonized procedures that should be used in sentinel planting surveys for effective sampling and identification of potential pests and pathogens. We also review the benefits and limitations of various diagnostic methods for early detection in sentinel systems, and the feasibility of the results obtained supporting National Plant Protection Organizations in pest and commodity risk analysis.

Files

34276__1_203514_LE_312147.pdf

Files (1.9 MB)

Name Size Download all
md5:3b7bc00b8cdff1b113c98fb0a7dcb75a
1.7 MB Preview Download
md5:6cd93f072ff6a898467520d03129b426
211.8 kB Preview Download

Linked records