Scientists are continuously developing new methods to project the impacts of climate change. Various consequences of climate change have already been observed in terrestrial and marine environments, such as those affecting geographic range location and boundaries, body size, life history, and phenology. Extant models often estimate a species' bio-climate envelope (the specific conditions for a viable population) on which an estimated environmental shift due to climate change is imposed in order to estimate potential re-distribution of the species' range. However, traditional bio-envelope models may underestimate species' vulnerability to climate change. By contrast, the Integrative Conceptual Framework for Assessing Relative Endangerment due to Climate Change (ICFARECC) approach suggests that species traits known from first principles are better able to estimate a species' vulnerability to climate change. My objective was to apply the ICFARECC framework to a complex but intensively-studied marine ecosystem, the North Sea. I evaluated ICFARECC strengths, limitations, and ability to provide distinctive information by comparing ICFARECC findings to the existing IUCN approach. I analyzed primary literature and public governmental reports to collect the data needed for 20 dominant North Sea fisheries species from which I was able to obtain 57% of the data required for the ICFARECC framework. ICFARECC analysis indicated that most North Sea fisheries species are not very vulnerable to climate change. Moreover, I found that the ICFARECC vulnerability scores were not correlated to extant IUCN criteria, suggesting that the two models provide distinct information. Data availability varied per Category and species. The lack of physiological data in Category II may have resulted in an underestimation of climate change vulnerabilities. This study suggests some improvements to the framework, including adjustments to the terminology of criteria and thresholds and suggestions for a new format to make the framework more user-friendly.