Empirical nitrogen and sulfur critical loads of U.S. tree species and their uncertainties with machine learning.

ABSTRACT: Critical loads (CLs) of atmospheric deposition for nitrogen (N) and sulfur (S) are used to support decision making related to air regulation and land management. Frequently, CLs are calculated using empirical methods, and the certainty of the results depends on accurate representation of underlying ecological processes. Machine learning (ML) models perform well in empirical modeling of processes with non-linear characteristics and significant variable interactions. We used bootstrap ensemble ML methods to develop CL estimates and assess uncertainties of CLs for the growth and survival of 108 tree species in the conterminous United States. We trained ML models to predict tree growth and survival and characterize the relationship between deposition and tree species response. Using four stati

SUBMITTER: Pavlovic NR 

PROVIDER: S-EPMC10241478 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature