Projecting community trophic structures for the last 120,000 years.

Abstract

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 terrestrial mammal species weighing over 3 kg; 2) train a random forest classifier to predict the observed distribution of 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 structure as estimated with the fossil record. Models project significant shifts in the geographical distribution of community trophic structures, with more pronounced changes occurring during the Pleistocene–Holocene transition. These shifts exhibit regional variations, particularly in Eurasia and North America, where the models project reductions in the distribution of less-complex trophic structures over the last 24 000 years. Hindcasts partially identified the alterations in community structure seen in the fossil record, demonstrating a match between the observed and predicted times of change in mammal community structure (between 24 and 8 ka BP). However, projections of trophic guilds diverged from fossil records during the Holocene. While the fossil record indicated a decrease in the number of grazers and carnivores, our models projected an increase in these numbers. Characterising community-wide responses to climatic changes is essential to address key questions about past and future impacts of such changes. Although further research is needed to refine the models, our approach offers a perspective for addressing the complex interactions among climate and trophic structures and model their distributions over time.