<p>Limited data can challenge assessing fish vulnerability to overfishing. In response to this challenge, risk assessment methods such as Productivity-Susceptibility Analysis (PSA) are commonly used tools to assess fish vulnerability. PSA measures vulnerability based on life-history traits, catchability and current management of species. This method has been applied in fisheries throughout the world using various forms of parametrization. Traditionally, the intrinsic growth rate of populations (<i>r</i>) is a notable productivity attribute as it theoretically integrates many of the other life-history attributes, yet it is often used alongside the other attributes instead of replacing them. This study assesses species vulnerability based on alternative assumptions regarding the use of <i>r.</i> To do this, we employ a novel PSA approach using meta-analytical attribute estimates to score productivity probabilistically based on prediction densities to account for uncertainty. This approach was used to estimate fish vulnerability across 185 species in three cases of artisanal bottom-set gillnetting in Brazil. Including or excluding <i>r</i> while using other attributes resulted in no difference in vulnerability outcomes. Using <i>r</i> alone as productivity resulted in slightly higher contrast in vulnerability scores compared to using other attributes, with regression <i>R</i>² between study areas varying from 0.64 to 0.82. In all cases, most species were classified as low vulnerability (50% to 65%), and most high vulnerability species were sharks and rays. Further, snappers and groupers were consistently classified with moderate vulnerability across scenarios. We propose a novel PSA approach for data-limited contexts and make recommendations of productivity parametrization using this approach.</p> Graphical abstract <p></p>