Predictive pollen-based biome modeling using machine learning

• Published in: PloS one Vol. 13; no. 8; p. e0202214
• Main Authors: Sobol, Magdalena K., Finkelstein, Sarah A.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.08.2018; Public Library of Science (PLoS)
• Subjects: Africa; Algorithms; Aridity; Artificial intelligence; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Classification; Classifiers; Climate Change; Computer and Information Sciences; Data processing; Datasets; Decision analysis; Decision Trees; Discriminant Analysis; Earth; Earth science; Earth Sciences; Ecology and Environmental Sciences; Ecosystem; Environmental aspects; Environmental changes; Forests; Fossil pollen; Fossils; Grasslands; Learning algorithms; Logistic Models; Machine learning; Methods; Models, Statistical; Neural networks; Neural Networks, Computer; Open systems; Physical Sciences; Pollen; Pollen - classification; Predictions; Regression analysis; Research and Analysis Methods; Statistical analysis; Statistical methods; Statistics, Nonparametric; Supervised Machine Learning; Support Vector Machine; Support vector machines; Vegetation; Vegetation changes; Vegetation cover; Africa; Canada; Kenya; Botswana
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0202214

This paper investigates suitability of supervised machine learning classification methods for classification of biomes using pollen datasets. We assign modern pollen samples from Africa and Arabia to five biome classes using a previously published African pollen dataset and a global ecosystem classification scheme. To test the applicability of traditional and machine-learning based classification models for the task of biome prediction from high dimensional modern pollen data, we train a total of eight classification models, including Linear Discriminant Analysis, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Classification Decision Tree, Random Forest, Neural Network, and Support Vector Machine. The ability of each model to predict biomes from pollen data is statistically tested on an independent test set. The Random Forest classifier outperforms other models in its ability correctly classify biomes given pollen data. Out of the eight models, the Random Forest classifier scores highest on all of the metrics used for model evaluations and is able to predict four out of five biome classes to high degree of accuracy, including arid, montane, tropical and subtropical closed and open systems, e.g. forests and savanna/grassland. The model has the potential for accurate reconstructions of past biomes and awaits application to fossil pollen sequences. The Random Forest model may be used to investigate vegetation changes on both long and short time scales, e.g. during glacial and interglacial cycles, or more recent and abrupt climatic anomalies like the African Humid Period. Such applications may contribute to a better understanding of past shifts in vegetation cover and ultimately provide valuable information on drivers of climate change.