Data-Driven Analysis of Forest–Climate Interactions in the Conterminous United States

Olga Rumyantseva,Nikolay Strigul

doi:10.3390/cli9070108

Abstract

A predictive understanding of interactions between vegetation and climate has been a grand challenge in terrestrial ecology for over 200 years. Developed in recent decades, continental-scale monitoring of climate and forest dynamics enables quantitative examination of vegetation–climate relationships through a data-driven paradigm. Here, we apply a data-intensive approach to investigate forest–climate interactions across the conterminous USA. We apply multivariate statistical methods (stepwise regression, principal component analysis) including machine learning to infer significant climatic drivers of standing forest basal area. We focus our analysis on the ecoregional scale. For most ecoregions analyzed, both stepwise regression and random forests indicate that factors related to precipitation are the most significant predictors of forest basal area. In almost half of US ecoregions, precipitation of the coldest quarter is the single most important driver of basal area. The demonstrated data-driven approach may be used to inform forest-climate envelope modeling and the forecasting of large-scale forest dynamics under climate change scenarios. These results have important implications for climate, biodiversity, industrial forestry, and indigenous communities in a changing world.

Highlights

The Köppen classification is employed in the delineation of Bailey’s US ecoregions [14]. Another bioclimatic scheme, called the Holdridge life zone system, was proposed by Holdridge in 1947 [15,16], initially for tropical regions and later extended to boreal and temperate zones [17]. These bioclimatic classification schemes served as a scientific basis and inspiration for modern developments, including climate envelope models (CEMs), species distribution models (SDMs), and dynamic global vegetation models (DGVMs), within Earth system models (ESMs), focusing on both the understanding of vegetation formations and the prediction of climate effects across various temporal and spatial scales [18,19]
The correlation analysis and principal component analysis (PCA) allowed us to evaluate the intrinsic dimensionality of our data set and provided necessary ground information for stepwise regression modeling
We examined relationships between climate and forested ecosystems in the contiguous United States using a data-driven paradigm

Summary

Introduction

The Köppen classification is employed in the delineation of Bailey’s US ecoregions [14] Another bioclimatic scheme, called the Holdridge life zone system, was proposed by Holdridge in 1947 [15,16], initially for tropical regions and later extended to boreal and temperate zones [17]. These bioclimatic classification schemes served as a scientific basis and inspiration for modern developments, including climate envelope models (CEMs), species distribution models (SDMs), and dynamic global vegetation models (DGVMs), within Earth system models (ESMs), focusing on both the understanding of vegetation formations and the prediction of climate effects across various temporal and spatial scales [18,19]

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Conclusion