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Aim The trait-based vulnerability assessment (TVA) uses Boolean rules to assess species sensitivity, adaptive capacity and exposure to climate change to identify those that are climate-change vulnerable. The protocol is being increasingly used to assess climate-change impacts to a diversity of taxa, as it requires fewer data compared to niche and mechanistic models. However, uncertainty in TVA results remains unevaluated. We present the first quantitative investigation of the impacts of uncertainty on TVA, using global freshwater crayfish (574 species) as a representative data-poor taxon. Location Global. Methods To assess uncertainty in trait selection, we measured the completeness of information for each trait and how these contributed to the number of vulnerable species. To explore the sensitivity of TVA outcomes to arbitrary threshold selection, we randomly scored 25% species as high for quantitative traits and compared the results to the standard TVA. To investigate uncertainty in climate model selections, we tested the TVA using 66 alternative global climate scenarios. Results Given the structural rules used in TVA, as more traits are included in the protocol, more species are identified as vulnerable to climate change. Some traits also have more dominant contributions. Species vulnerability was relatively robust to arbitrary thresholds in quantitative trait variables. The number (79-156) and identity of vulnerable species varied depending on which climate scenario was selected. Ensemble means of climate models identified fewer vulnerable species, potentially softening the extremes of individual climate models. Main conclusions Assessors applying TVA across taxa and geographical scales should use ecological thresholds for quantitative traits, where possible; most importantly perform sensitivity analyses, including (a) critically assessing assumptions and correlations underpinning the selection of traits in different dimensions; and (b) capturing variability among climate-change models. Further research is required to fill data gaps that improve the robustness of TVA.