Refining regional litterfall estimates in temperate South Korean forests: An application of random forest modeling

Mark Bryan Carayugan1*, Byung Bae Park1

(1Department of Environment and Forest Resources, Chungnam National University)

Abstract: Developing optimal models for forest litterfall, a pathway of carbon entry from plants to soil detrital systems, plays a major role in refining regional carbon budgets. However, current litterfall models developed using traditional regression techniques present considerable uncertainties as they overlook the non-linearity of regional litter relationships with various abiotic features. This study explores the potential of machine learning, notably random forest (RF), in improving regional litterfall estimates in South Korea using a consolidated dataset from 28 stands of varying geographic, climatic, and edaphic profiles. An RF regressor model, trained iteratively using total annual litterfall as input and latitude, evapotranspiration, temperature, soil organic carbon, and total nitrogen as target features, was evaluated against a traditional multiple linear regression model (MLR) using R² and RMSE as performance indicators. The final RF model improved the predictability of canopy litterfall by 19% relative to MLR, which captured 74% of regional litterfall variation. RF further reduced the prediction error incurred by MLR by more than half (64%), bringing the mean predicted litterfall value 29 g m⁻² closer to the observed mean value (610 g m⁻²). Climate provided the highest level of contribution to the RF model, with evapotranspiration and temperature demonstrating positive correlations with canopy litterfall. These findings contribute to the groundwork for fine-tuning regional carbon budgets, offering nuanced insights for minimizing adverse climate change impacts on temperate forest ecosystems.

Key words: carbon cycle, ecological modelling, litterfall, machine learning, random forest, temperate forest

Acknowledgement: This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT). (No. 2021R1A2C201017812)