From training to implementation: building capacity in invasion science

Lorenzo Vilizzi 

Effective management of biological invasions requires scientifically robust decision-support tools and the capacity to apply them consistently. Risk identification is the first step of invasion risk analysis, yet comparatively little attention has been given to how such tools are transferred into operational use. This paper describes an international training programme based on the Invasiveness Screening Kit (ISK) decision-support tools, originating in 2023 from a commission by the International Trade Centre under the ARISE+ Malaysia project for training in the Aquatic Species Invasiveness Screening Kit (AS-ISK). The original four-day programme comprised four Modules subdivided into Sessions and Topics; this architecture was retained while content was updated to incorporate methodological and software developments and new applications. Between August 2023 and April 2026, ten training activities were delivered across academic, governmental, conservation and industry settings to 170 participants representing 50 institutions and organisations across 15 countries. Formal evaluation of the two ITC/ARISE+ Malaysia workshops documented mean pre- to post-training knowledge-score increases of 17.0 and 24.8 percentage points, with more than 80% of participants improving. Across successive deliveries, the curriculum progressed from risk-analysis concepts and screening protocol to hands-on ISK use, statistical analysis, interpretation and communication, while examples and exercises were adapted to context. Post-training trajectories were heterogeneous, with a multinational regional programme providing the clearest example of training embedded within coordinated implementation through a harmonised transboundary risk-identification exercise. Formal training, systematic updating, contextual adaptation and continuing technical support should be regarded as components of the implementation infrastructure required to translate invasion-risk science into reproducible decision support.