Projecting community trophic structures for the last 120,000 years

Studying past community dynamics can provide valuable insights for anticipating future changes in the world's biota. However, the existing fossil record is too sparse to enable continuous temporal reconstructions of wholesale community dynamics. In this study, we utilise machine learning to reconstruct Late Quaternary community structure, leveraging the climate-trophic structure relationship. We followed a four-stage approach: 1) identify and map trophic structure units (TSUs) at the global scale based on the guild richness and composition of extinct and extant terrestrial mammal species weighing over 3 kg; 2) train a random forest model to predict TSUs based on contemporary climatic conditions; 3) hindcast the global distribution of TSUs using climatic conditions as reconstructed over the past 120,000 years; and 4) compare TSU hindcasts against elements of community trophic structure as estimated with the fossil record. Models project significant shifts in the geographical distribution..., Our approach consists of four stages: 1) identify and map in a 1 × 1° grid cell surface the community TSUs that emerge from the raw data on species distributions, with species reclassified into guilds so that patterns of co-occurrence among guilds are examined instead of species co-occurrences; 2) train a random forests model to predict the community TSUs in every grid cell given contemporary climatic conditions; 3) use the trained model to hindcast the global distribution of community TSUs based on past climate data; and 4) assess the usefulness of hindcasts by examining the match between hindcasted community TSU transitions and observed community transitions in the fossil record. 
Identification of community TSUs
Following Mendoza and Araújo (2022), we characterized the trophic structure of mammal communities in every 1 × 1° grid cell based on the number of species from each trophic guild that occurred therein. This resulted in a data matrix (with n trophic guilds as columns and m gri...,