Study becomes insight: Ecological learning from machine learning

[1]  P. Sen.  Estimates of the Regression Coefficient Based on Kendall's Tau , 1968 .

[2]  Anil K. Jain,et al.  Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Jordi Moya-Laraño,et al.  Plotting partial correlation and regression in ecological studies , 2008 .

[4]  Julian D. Olden,et al.  Assessing transferability of ecological models: an underappreciated aspect of statistical validation , 2012 .

[5]  Aaron C. Greenville,et al.  Habitat‐ and rainfall‐dependent biodiversity responses to cattle removal in an arid woodland–grassland environment. , 2014, Ecological applications : a publication of the Ecological Society of America.

[6]  Kristin K. Nicodemus,et al.  Letter to the Editor: On the stability and ranking of predictors from random forest variable importance measures , 2011, Briefings Bioinform..

[7]  George L. W. Perry,et al.  Using Machine Learning to Predict Geomorphic Disturbance: The Effects of Sample Size, Sample Prevalence, and Sampling Strategy , 2018, Journal of Geophysical Research: Earth Surface.

[8]  Achim Zeileis,et al.  BMC Bioinformatics BioMed Central Methodology article Conditional variable importance for random forests , 2008 .

[9]  Achim Zeileis,et al.  Bias in random forest variable importance measures: Illustrations, sources and a solution , 2007, BMC Bioinformatics.

[10]  A. Gelfand,et al.  Modelling species diversity through species level hierarchical modelling , 2005 .

[11]  Evgeny Putin,et al.  Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects , 2018, bioRxiv.

[12]  A. Magurran,et al.  Temperature-related biodiversity change across temperate marine and terrestrial systems , 2019, Nature Ecology & Evolution.

[13]  G. De’ath.  Boosted trees for ecological modeling and prediction. , 2007, Ecology.

[14]  James H Brown,et al.  A latitudinal diversity gradient in planktonic marine bacteria , 2008, Proceedings of the National Academy of Sciences.

[15]  Mitja Skudnik,et al.  A random forest model for basal area increment predictions from national forest inventory data , 2021 .

[16]  J. Pausas,et al.  Fire as a key driver of Earth's biodiversity , 2019, Biological reviews of the Cambridge Philosophical Society.

[17]  S. Grunwald,et al.  Accounting for two-billion tons of stabilized soil carbon. , 2019, The Science of the total environment.

[18]  Ribana Roscher,et al.  Explainable Machine Learning for Scientific Insights and Discoveries , 2019, IEEE Access.

[19]  Jerome H Friedman,et al.  Multiple additive regression trees with application in epidemiology , 2003, Statistics in medicine.

[20]  Daniel W. Apley,et al.  Visualizing the effects of predictor variables in black box supervised learning models , 2016, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

[21]  David W. Peterson,et al.  Fire frequency and tree canopy structure influence plant species diversity in a forest-grassland ecotone , 2007, Plant Ecology.

[22]  Fan Yang,et al.  An experimental study of the intrinsic stability of random forest variable importance measures , 2016, BMC Bioinformatics.

[23]  Ivan Glesk,et al.  Tuning machine learning models for prediction of building energy loads , 2019, Sustainable Cities and Society.

[24]  D. R. Cutler,et al.  Utah State University From the SelectedWorks of , 2017 .

[25]  N. Hanan,et al.  Fire in sub‐Saharan Africa: The fuel, cure and connectivity hypothesis , 2018 .

[26]  Niall P. Hanan,et al.  Woody cover in African savannas: the role of resources, fire and herbivory , 2008 .

[27]  Víctor Urrea,et al.  Letter to the Editor: Stability of Random Forest importance measures , 2011, Briefings Bioinform..

[28]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[29]  N. Hanan,et al.  Woody-biomass projections and drivers of change in sub-Saharan Africa , 2021, Nature Climate Change.

[30]  Bertrand Michel,et al.  Correlation and variable importance in random forests , 2013, Statistics and Computing.

[31]  Paulo J. G. Lisboa,et al.  Making machine learning models interpretable , 2012, ESANN.

[32]  A. Zeileis,et al.  Danger: High Power! – Exploring the Statistical Properties of a Test for Random Forest Variable Importance , 2008 .

[33]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[34]  Kwok-wing Chau,et al.  Effect of river flow on the quality of estuarine and coastal waters using machine learning models , 2018 .

[35]  Kerry A. Naish,et al.  A practical introduction to Random Forest for genetic association studies in ecology and evolution , 2018, Molecular ecology resources.

[36]  David R. B. Stockwell,et al.  Effects of sample size on accuracy of species distribution models , 2002 .

[37]  Tie-Yan Liu,et al.  LightGBM: A Highly Efficient Gradient Boosting Decision Tree , 2017, NIPS.

[38]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[39]  Trevor Hastie,et al.  Causal Interpretations of Black-Box Models , 2019, Journal of business & economic statistics : a publication of the American Statistical Association.

[40]  Bernd Bischl,et al.  iml: An R package for Interpretable Machine Learning , 2018, J. Open Source Softw..

[41]  Mathieu Marmion,et al.  Evaluation of consensus methods in predictive species distribution modelling , 2009 .

[42]  Qiuyan Yu,et al.  Toward Operational Mapping of Woody Canopy Cover in Tropical Savannas Using Google Earth Engine , 2020, Frontiers in Environmental Science.

[43]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[44]  Tim C. D. Lucas,et al.  A translucent box: interpretable machine learning in ecology , 2020 .

[45]  James C. Stegen,et al.  Evolving ecological networks and the emergence of biodiversity patterns across temperature gradients , 2012, Proceedings of the Royal Society B: Biological Sciences.

[46]  J Elith,et al.  A working guide to boosted regression trees. , 2008, The Journal of animal ecology.