Soil δ15N for South America

Soil nitrogen isotope composition (δ15N) is an essential tool for investigating ecosystem nitrogen balances, plant-microbe interactions, ecological niches, animal migration, food origins, and forensics. We applied a machine learning approach to ascertain a finer-scale understanding of geographic differences in soil δ15N, using the S. American continent as a test case. We use a robust training set spanning 278 geographic locations across the continent, spanning all major biomes. 10-fold cross-validation revealed that the RF method outperformed both the Cubist and GBM approaches. We created a biogeographic boundary map, which predicted an expected multi-scale spatial pattern of soil δ15N with a high degree of confidence performing substantially better than all previous approaches for the continent of South America. Therefore, the RF machine learning framework showed to be a great opportunity to explore a broad array of ecological, biogeochemical, and forensic issues through the lens of soil δ15N.