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While screening-level risk assessment (SLRA) tools for non-indigenous species generally provide managers with reliable information for decision making (e.g., a proposed species introduction should be allowed or rejected), the results are affected by several sources of uncertainty. In particular, model uncertainty, related to the influence of factors/questions included in SLRA tools has rarely been addressed. Here we undertook an investigation of model uncertainty using a detailed evaluation of the contribution of questions included in the Canadian Marine Invasive Screening Tool (CMIST) and determined if the tool can be made more accurate through a series of optimization procedures. Accuracy was defined as the fit between assessment scores and the results of an expert opinion survey of risk posed by 48 marine invertebrate species known to have been introduced into Canadian coastal waters. We first measured the contribution of each question to accuracy and removed the ones that did not improve the fit. We then derived optimal weights that adjust the contribution of each question to maximize accuracy. Eight of 17 questions were found not to improve accuracy, or even decreased it; removing these questions, followed by addition of weights made the tool gradually more accurate when all species were included. However, an independent cross-validation test showed these weights to be too variable to consistently improve fit; this was probably related to the relatively small number of species included in the tests. Tools that have previously been tested using a large number of species should be used to determine if addition of optimal weights can improve independent predictions. The evidence that risk assessment tools are over-parameterized is building and we suggest that currently used tools would benefit from a detailed evaluation of the value of questions they include. Careful selection of questions and weights, based on accuracy improvement and other elements (e.g., organizational mandate) could greatly benefit SLRA tools, by providing more accurate estimations of risk and accelerating assessments.

Optimizing screening protocols for non-indigenous species: are currently used tools over-parameterized?