AUTHOR=Ebrahimi Aziz , Abbasi Akane O. , Liang Jingjing , Jacobs Douglass F. 

TITLE=Spatiotemporal trends of black walnut forest stocking under climate change

JOURNAL=Frontiers in Forests and Global Change

VOLUME=Volume 5 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2022.970379

DOI=10.3389/ffgc.2022.970379

ISSN=2624-893X

ABSTRACT=Basal area is a key measure of forest stocking and an important proxy of forest productivity in the face of climate change. Black walnut (Juglans nigra) is one of the most valuable timber species in North America. However, little is known regarding how the stocking of black walnut would change with differed bioclimatic conditions under climate change. In this study, we projected the current and future basal area of black walnut, in terms of basal area. We trained different machine learning models using more than 1.4 million tree records from 10,162 Forest Inventory and Analysis (FIA) sample plots and 42 spatially explicit bioclimate and other environmental attributes. We selected Random forest (RF) as a final model to estimate the basal area of black walnut under climate change, because RF had higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) than the other two models (XGBoost and linear regression). The most important variables to predict basal area were the mean annual temperature and precipitation, global evapotranspiration, topology, and human footprint. Under two emission scenarios (Representative Concentration Pathway 4.5 and 8.5), the RF model projected that black walnut stocking would increase in the northern part of the current range in the USA, with a potential shift of species distribution range. In conclusion, compared with XGBoost and linear regression, the random forest was an efficient and accurate tool for basal area prediction under climate change for black walnut. Our basal area models can be readily applied to forest management programs to predict tree growth based on future climate scenarios.