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Comparing Like With Like in Biological Invasion Risk Analysis
1Department of Ecology and Vertebrate Zoology, Faculty of Biology and Environmental Protection, University of Lodz, Lodz, Poland.
Abstract:
Biological invasion decision-support methods are often compared under broad labels such as "risk assessment," even when they perform different functions. This Perspective argues that functional commensurability must be established before methods are compared. In biological invasion risk analysis, screening serves an initial risk identification function, flagging extant or horizon non-native species within a defined risk assessment area for prevention, surveillance, or more detailed evaluation. Comprehensive risk assessment examines entry or introduction, establishment, spread, and their ecological, economic, and socio-cultural consequences in greater detail. Risk management integrates risk with feasibility, cost, effectiveness, legal constraints, and stakeholder values. Impact classification and post-establishment population prioritization may contribute to these functions without being equivalent to them. Published comparisons show how conflating such functions can create false equivalence. The emerging Dispersal-Origin-Status-Impact (DOSI) literature provides an illustrative example through its comparisons with applications of the globally implemented Invasiveness Screening Kit (ISK) decision-support tools: population status and management priority are not the same quantities as prospective invasion risk screening, and species-based screening does not imply coarse spatial resolution. Forty-five peer-reviewed ISK-family studies report local or subnational screenings, including spatial resolutions comparable with or finer than DOSI applications. Valid comparisons require a shared decision objective, explicit spatial and temporal boundaries, independent evaluation evidence, and assessment of uncertainty and the consequences of error. These requirements permit methods to be evaluated for a common use, or combined within adaptive management, without treating disagreement between unlike outputs as evidence of superior performance.
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