Towards predictive ecological biogeography: Predicting vegetation type using fine-scale measurements of the abiotic environment

Abstract

One of the goals of ecological biogeography is to accurately predict where major vegetation types, such as forests or grasslands, can establish themselves. Alas, our current understanding of the mechanisms by which environmental variables jointly influence vegetation types does not allow for such predictions. Here, we compile a database of local measurements of soil, weather, topography, and bedrock lithology for 6404 sites across the continental United States, which we complement with information about the vegetation around the measurement sites. By applying machine learning methods to a subset of the sites, we are able to train a machine that predicts - using only information about abiotic variables - the presence of forests or grasslands with nearly 90% accuracy on the rest of the sites. We find that while weather variables are statistically important for these predictions, a few soil variables prove to be the most powerful predictors. By separating the combined contributions of weather and soil, we show that soil texture is a better predictor under dry conditions than under wet conditions, while pH is important predictor regardless of the weather. Furthermore, from 136 sites undergoing grassland-to-forest succession, our analysis indicates that soil pH changes with a certain lag relative to vegetation change. Sites that have recently undergone a transition exhibit a higher pH, closer to that of grasslands, despite the presence of trees. The pH then decreases by about one unit over a 30-year period of forest development. Ultimately, our results indicate that fine-scale environmental measurements and historical ecological data can serve as a basis for quantitative bio-geographical predictions, which could eventually guide targeted conservation interventions.