Shaw, J. O.; Dunhill, A. M.; Beckerman, A. P.; Dunne, J. A.; Hull, P. M.

Food webs provide quantitative insights into the structure and dynamics of ecological communities. Previous work has shown their utility in understanding community responses to modern and ancient perturbations, including anthropogenic change and mass extinctions. However, few ancient food webs have been reconstructed due to difficulties assessing trophic interactions amongst extinct species derived from an incomplete fossil record. We present and assess the Paleo Food web Inference Model (PFIM). PFIM uses functional trait data--predictive of interactions in modern ecosystems and commonly available for fossil organisms--to reconstruct ancient food webs. We test the model by (i) applying it to four modern ecosystems with empirical constrained food webs to directly compare PFIM-constructed networks to their empirical counterparts, (ii) by carefully comparing discrepancies between PFIM-inferred and empirical webs in one of those systems, and (iii) by comparing networks describing feasible trophic interactions (\"feasible webs\") with networks to which we superimpose characteristic interaction distributions derived from modern theory (\"realized webs\"). As a proof of concept, we then apply the method to faunal data from two Cambrian fossil deposits to reconstruct ancient trophic systems. PFIM-inferred feasible food webs successfully predict ~70% of trophic interactions across four modern systems. Furthermore, inferred food webs with enforced interaction distributions (i.e., realized webs) accurately predict ~90% of interactions. Comparisons with a global database of trophic interactions and other food web models, suggest that under sampling of empirical webs accounts for up to 21% of the remaining differences between PFIM and empirical food webs. Food webs can be reasonably approximated by inferring trophic interactions based upon life habit traits. This study provides the foundation to use trait-based inference models across the fossil record to examine ancient food webs and community evolution.