Elevational gradient is a critical environmental factor affecting forest carbon storage distribution in mountainous ecosystems, yet the nonlinear mechanisms and optimal elevation zones remain unclear. This study aims to reveal the distribution patterns of forest carbon storage along elevational gradients in Larix principis-rupprechtii forests, compare the predictive performance of different modeling approaches, and to explore the application potential of LiDAR technology in forest carbon storage assessment.

Based on field survey data from 35 plots and LiDAR data, this study applied linear regression, polynomial regression, mixed-effects models, and generalized additive models (GAM) to model and compare the relationship between carbon storage and elevation. Leave-one-out cross-validation was used to evaluate model predictive performance, and standard evaluation metrics such as the coefficient of determination (R²), standard error of estimation (SEE), and mean percentage error (MPE) were calculated.

Forest carbon storage of Larix principis-rupprechtii exhibited a significant inverted U-shaped distribution pattern along the elevational gradients (1 900-2 500 m), with an optimal elevation zone of 2 000-2 200 m and a theoretical optimal elevation of 2 165 m. Elevation was the dominant factor explaining carbon storage variation, accounting for 89.5% of the variance (η²=0.895, P<0.001). Among the four modeling approaches, GAM performed best (fitted R²=0.907, cross-validation R²=0.888, prediction standard error= 10.15 t/hm²) with only 1.86% overfitting. LiDAR-derived canopy density (Intensity_CV) emerged as an important predictor, significantly improving model prediction accuracy.

This study confirmed the nonlinear effects of elevational gradient on forest carbon storage, with GAM demonstrating clear advantages in modeling complex ecological relationships. LiDAR technology shows great potential in forest carbon storage assessment, providing technical support for establishing mountain forest carbon sink monitoring systems. The research findings will provide scientific basis for adaptive forest management under climate change and precision forest management based on elevational gradients.