Plant competition in the Amazon rainforest: insights from multi-annual field inventory and 3D terrestrial-LiDAR - Ciccalè, Pietro (2024)
Quantifying plant competition is essential for predicting species responses to global change and informing conservation and management strategies in forest ecosystems like the Amazon rainforest. Plant competition is often measured through Plant Competition Indices (PCIs), which quantify competitive interactions. In this study, following a detailed analysis of the structure and dynamics of six AmazonFACE project plots, three traditional PCIs were calculated: two distance-dependent indices (the Hegyi index and Modified Area Potentially Available) and one distance-independent index (Basal Area of Larger Trees). These indices were evaluated to identify the best model for predicting relative growth rate (RGR) of trees over a five-year period (2016-2021). Generalized Additive Models (GAMs) with smoothing function were selected, after 10-fold cross-validation. Results revealed variability in R-squared (R²) values over time for the three indices. The three indices resulted in relatively low explanatory power in most cases. Additionally, high-resolution data from a Terrestrial Laser Scanner (TLS) were used to obtain detailed 3D representations of 90 segmented trees in the first plot. Metrics such as diameter at breast height (DBH), height, crown volume (CV) and crown projection area (CPA) were extracted. Nonlinear models based on DBH were employed to estimate height and crown metrics for insegmented trees. Three TLS-derived PCIs (modified Hegyi index, crown-size competition index, and crown-volume ratio) were then calculated and compared with traditional PCIs in predicting RGR. Indices incorporating crown metrics showed superior predictive power, highlighting the potential of TLS-derived PCIs to improve growth predictions.