A compilation of class and cutting edge papers applying machine learning and data science in ecology.
Editor Nathan L. Brouwer
Machine Learning and Data Science in Ecology aggregates and highlights key publications related to machine learning in ecology, including classic unsupervised learning applications (e.g. PCA, NMDS), citation classics promoting data mining and ML to ecologists, and cutting edge developments in AI such as image classification and species distribution modeling. It will also highlight papers promoting data science tools and resources. While focused on ecology, this Peeriodical should be of interest more broadly to environmental scientists and biologists keeping abreast of 21st-century approaches to statistics and data analysis.
If you'd like to recommend a paper please contact me at nlb24@pitt.edu.
CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS (2000)
Glenn De'ath, Katharina E. Fabricius
http://dx.doi.org/10.1890/0012-9658(2000)081[3178:CARTAP]2.0.CO;2
Dec 30, 2020 - De'ath and Fabricius (2000) is an early paper drawing attention to the usefulness of supervised machine learning methods in ecology. According to Google Scholar it has been cited 3309 times as of Dec. 30th, 2020.