Titlebook: Machine Learning for Ecology and Sustainable Natural Resource Management; Grant Humphries,Dawn R. Magness,Falk Huettmann Book 2018 Springe

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Landscape Applications of Machine Learning: Comparing Random Forests and Logistic Regression in Multe conifer forest. Visual inspection of the predicted occurrence probability maps shows that random forest produces predictions that are more discriminatory, with higher range of predicted probability and higher spatial heterogeneity than logistic regression. The logistic regression model has an AUC

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Breaking Away from ‘Traditional’ Uses of Machine Learning: A Case Study Linking Sooty Shearwaters (, correlation of r > 0.8 for SOI values from 0 to 14 months after peak chick size. A combination of parameters and regions best explain the variation in the SOI data, however the most important variables are those that represent general turbulence in the sub-Antarctic water and Polar front regions (i

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Machine Learning Techniques for Quantifying Geographic Variation in Leach’s Storm-Petrel (,) Vocaliz handling. We found that random forests from the h2o and ‘randomForest’ packages in R performed best with regards to accuracy, ‘randomForest’ and ‘gbm’ performing best with regards to speed, and ‘tensor forest’ and ‘h2o’ implementations performing best with regards to memory. Furthermore, we were ab

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