MECHANISTIC MODEL OF VEGETATION STRUCTURE AND ANIMAL COMMUNITY INTERACTIONS-INTEGRATING GEDI & TLS DATA IN THE MADINGLEY MODEL

Abstract

Background Vegetation structure is increasingly recognized as a key variable to explain ecosystems states and dynamics. New Remote Sensing tools are available to complement labor intensive field investigations and consider the global biogeography of this parameter. Objectives We propose to model the processes explaining the interaction between vegetation structure and animal community assembly globally, while requiring minimal computing power, based on the most fundamentals assumptions. Methods We integrate spaceborne (GEDI: Global Ecosystem Dynamics Investigation) and ground based (TLS: Terrestrial Laser Scanning) Lidar data in the Madingley general ecosystem model. We compare the outcome of this integration to previous version and to the TetraDensity estimate of animal biomass and Elton traits database for arboreality. Results Animal biomass density simulated by Madingley is closer to global estimates when integrating vegetation structure. The strength of this effect increases with higher cohort body mass and varies with local environmental conditions and stochastic processes. Simulated proportion of arboreality across cohorts is consistently higher than observations. This is consistent with the divergence of biases between model and database. Conclusions Our results concur with our hypotheses about the role of vegetation structure on animal community assembly, as it reduces total animal biomass abundance. However, assessing the accuracy of its relative weight is challenging. While we have global products about arboreality and animal biomass density, they represent modern day ecosystem state, including anthropogenic activity, while Madingley simulates potential ecosystem optimum. Therefore, we call for further research in this field and for challenging modelling attempts to compare with.