Boosted trees for ecological modeling and prediction.

By G. De’ath. Ecology, 2007

1,275 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Nature and Landscape Conservation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/771ec2c9ed5a85076a2f8ea5475db4637c668a40

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Citations in the corpus

Competition, species interaction and ageing control tree mortality in boreal forests (2011, methodology): “We interpreted the results by examining the relative influence of predictors from the best model, the predictive value plot of response to individual predictors and the main interactions in each model (De’ath 2007; Elith, Leathwick & Hastie 2008).”
Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees (2019, methodology): “EDT Boosted has been applied to many fields such as banking [49], ecology [50], and medical [51].”
Global hotspots and correlates of emerging zoonotic diseases (2017, methodology): “BRTs handle non-linear relationships and higher order interactions among many variables more robustly than many other modeling methods, and are robust to monotonic transformations of data 26, 60 .”
Ocean‐scale prediction of whale shark distribution (2012, methodology): “, 2006), regression methods such as boosted regression trees (De’ath, 2007; Elith et al., 2008), multivariate adaptive regression splines (Leathwick et al.”
Gradient boosting machines, a tutorial (2013, background): “However, partial dependence plots can provide a useful basis for interpretation that has been noted practical in different applications (De’ath, 2007; Hutchinson et al., 2011; Pittman and Brown, 2011).”
Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics (2011, methodology): “The use of boosting and sophisticated numerical and graphical presentation of model outputs in BRTs has alleviated the limited accuracy and difficult interpretation associated with traditional regression tree methods (De’ath, 2007; Elith et al., 2008).”
Thermogeography predicts the potential global range of the invasive European green crab (Carcinus maenas) (2010, methodology): “A BRT model fits a large collection of simple regression tree terms using a boosting algorithm (De’ath, 2007; Elith et al., 2008).”
Estimation of suspended sediment concentration and yield using linear models, random forests and quantile regression forests (2008, positive): “fied version of bagged trees (De’ath 2007).”
Towards a predictive framework for biocrust mediation of plant performance: A meta‐analysis (2019, methodology): “Boosted regression tree analysis additively fits and combines multiple trees using a forward stepwise procedure, thus improving accuracy (De'Ath, 2007).”
Species distribution models and mitochondrial DNA phylogeography suggest an extensive biogeographical shift in the high‐intertidal seaweed Pelvetia canaliculata (2014, methodology): “…models and predictive maps SDMs were developed using three machine-learning algorithms known for high predictive performance: boosted regression trees (BRT; De’ath, 2007), maximum entropy (Maxent; Phillips et al., 2006), and multivariate adaptive regression splines (MARS; Leathwick et al., 2005).”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, methodology): “The other top-ranked methods were boosted regression trees (BRT; De´ath 2007, Elith et al. 2008) and multivariate adaptive regression splines (MARS; Friedman 1991, Leathwick et al. 2006).”
Modeling Species Distribution and Change Using Random Forest (2011, methodology): “Although inference is a critical component in ecological modeling, the balance between accurate predictions and inference is the ultimate goal in ecological studies (Peters 1991; De’ath 2007).”