The natural sciences, such as ecology and earth science, study complex interactions between biotic and abiotic systems in order to understand and make predictions. Machine-learning-based methods have an advantage over traditional statistical methods in studying these systems because the former do not impose unrealistic assumptions (such as linearity), are capable of inferring missing data, and can reduce long-term expert annotation burden. Thus, a wider adoption of machine learning methods in ecology and earth science has the potential to greatly accelerate the pace and quality of science. Despite these advantages, the full potential of machine learning techniques in ecology and earth science has not be fully realized.<br> This is largely due to 1) a lack of communication and collaboration between the machine learning research community and natural scientists, 2) a lack of communication about successful applications of machine learning in the natural sciences, 3) difficulty in validating machine learning models, and 4) the absence of machine learning techniques in a natural science education. These impediments can be overcome through financial support for collaborative work and the development of graduate-level educational materials about machine learning. Natural scientists who have not yet used machine learning methods can be introduced to these techniques through Random Forest, a method that is easy to implement and performs well. This manuscript will 1) briefly describe several popular machine learning tasks and techniques and their application to ecology and earth science, 2) discuss the limitations of machine learning, 3) discuss why ML methods are underutilized in natural science, and 4) propose solutions for barriers preventing wider ML adoption.
