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TEASIng apart alien species risk assessments: a framework for best practices

  • Leung, Brian Department of Biology, McGill University, Montreal, Quebec, Canada - School of Environment, McGill University, Montreal, Quebec, Canada
  • Roura-Pascual, Nuria Departament de Ciències Ambientals, Facultat de Ciències, Universitat de Girona, Catalonia, Spain - Centre Tecnològic Forestal de Catalunya, Solsona, Catalonia, Spain
  • Bacher, Sven Departement of Biology, Ecology & Evolution Unit, University of Fribourg, Switzerland
  • Heikkilä, Jaakko MTT Economic Research, Helsinki, Finland
  • Brotons, Lluis Centre Tecnològic Forestal de Catalunya, Solsona, Catalonia, Spain
  • Burgman, Mark A. Department of Botany, University of Melbourne, Australia
  • Dehnen-Schmutz, Katharina School of Life Sciences, University of Warwick, Warwick, UK
  • Essl, Franz Environmental Agency Austria, Wien, Austria
  • Hulme, Philip E. The Bio-Protection Research Centre, Lincoln University, Christchurch, New Zealand
  • Richardson, David M. Centre for invasion Biology, Department of Botany and Zoology, Stellenbosch University, Matieland, South Africa
  • Sol, Daniel CREA,CEAB-CSIC, Universitat Autònoma de Barcelona, Catalonia, Spain
  • Vilà, Montserrat EBD-CSIC, Sevilla, Spain
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Published in:
  • Ecology Letters. - 2012, vol. 15, no. 12, p. 1475-1493
English Some alien species cause substantial impacts, yet most are innocuous. Given limited resources, forecasting risks from alien species will help prioritise management. Given that risk assessment (RA) approaches vary widely, a synthesis is timely to highlight best practices. We reviewed quantitative and scoring RAs, integrating < 300 publications into arguably the most rigorous quantitative RA framework currently existing, and mapping each study onto our framework, which combines Transport, Establishment, Abundance, Spread and Impact (TEASI). Quantitative models generally measured single risk components (78% of studies), often focusing on Establishment alone (79%). Although dominant in academia, quantitative RAs are underused in policy, and should be made more accessible. Accommodating heterogeneous limited data, combining across risk components, and developing generalised RAs across species, space and time without requiring new models for each species may increase attractiveness for policy applications. Comparatively, scoring approaches covered more risk components (50% examined < 3 components), with Impact being the most common component (87%), and have been widely applied in policy (< 57%), but primarily employed expert opinion. Our framework provides guidance for questions asked, combining scores and other improvements. Our risk framework need not be completely parameterised to be informative, but instead identifies opportunities for improvement in alien species RA.
Faculté des sciences et de médecine
Département de Biologie
  • English
Biological sciences
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