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Reed, Jodie ... et al.-- 12 pages, 4 figures, supplementary data and supplementary material https://doi.org/10.1093/icesjms/fsw150, Trophic level (TL)-based indicators are commonly used to track the ecosystem effects of fishing as the selective removal of organisms from the food web may result in changes to the trophic structure of marine ecosystems. The use of a fixed TL per species in the calculation of TL-based indicators has been questioned, given that species’ TLs vary with ontogeny, as well as over time and space. We conducted a model-based assessment of the performance of fixed TL-based indicators vs. variable TL-based indicators for tracking the effects of fishing pressure. This assessment considered three TL-based indicators (the trophic level of the landed catch (TLc), the marine trophic index (MTI) and the trophic level of the surveyed community (TLsc)), three fishing scenarios that targeted specific model groups (the low TL scenario (LTL), the high TL scenario (HTL) and a scenario encompassing broad-scale exploitation (ALL)) and ten contrasting marine ecosystems with four types of ecosystem modelling approaches that differ in their structure and assumptions. Results showed that, overall, variable TL-based indicators have a greater capacity for detecting the effects of fishing pressure than fixed TL-based indicators. Across TL-based indicators, TLsc displayed the most consistent response to fishing whether fixed or variable species' TLs were used, as well as the highest capacity for detecting fishing effects. This result supports previous studies that promote the use of survey-based indicators over catch-based indicators to explore the impacts of fishing on the structure of marine ecosystems. Across fishing scenarios, the low trophic level fishing scenario (LTL) resulted in the lowest consistency between fixed and variable TL-based indicator responses and the lowest capacity of TL-based indicators for detecting fishing effects. Overall, our results speak to the need for caution when interpreting TL-based indicator trends, and knowledge of the broader context, such as fishing strategies and exploitation history, This study was funded by Euromarine and the EMIBIOS project (FRB Fondation pour la Recherche sur la Biodiversité, Contract no. APP-SCEN-2010-II). JR was funded by the French-South African ICEMASA program and IRD. LS was supported through the South African Research Chair Initiative, funded through the South African Department of Science and Technology (DST) and administered by the South African National Research Foundation (NRF), as well as through additional funding granted by IRD. AG was supported by NOAA’s Integrated Ecosystem Assessment (IEA) programme (http://www.noaa.gov/iea/). GH was funded by IRD-DPF PhD fellowships program of the Institut de Recherche pour le Développement (IRD). JEH was supported by a Beaufort Marine Research Award carried out under the Sea Change Strategy and the Strategy for Science Technology and Innovation (2006–2013), with the support of the Marine Institute, funded under the Marine Research Sub-Programme of the Irish National Development Plan 2007–2013. JJH was supported by the Natural Environment Research Council and Department for Environment, Food and Rural Affairs under the project MERP: Grant no. NE/L003279/1, Marine Ecosystems Research Programme