RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY

TLDR

Classification procedures are widely used in ecology, yet random forests—a powerful, high‑accuracy, variable‑importance, interaction‑modeling, multi‑purpose algorithm—remain largely unknown in the field. The authors compared random forest accuracy to four other common classifiers across datasets of invasive plant species, rare lichen species, and cavity‑nesting bird sites in the United States. Random forests achieved high accuracy in all cases, and the variables deemed most important for invasive plant species matched established ecological knowledge.

Abstract

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.