Applying additive modeling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages
March 1, 2012
Issues with ecological data (e.g. non-normality of errors, nonlinear relationships and autocorrelation of variables) and modelling (e.g. overfitting, variable selection and prediction) complicate regression analyses in ecology. Flexible models, such as generalized additive models (GAMs), can address data issues, and machine learning techniques (e.g. gradient boosting) can help resolve modelling issues. Gradient boosted GAMs do both. Here, we illustrate the advantages of this technique using data on benthic macroinvertebrates and fish from 1573 small streams in Maryland, USA.
Citation Information
| Publication Year | 2012 |
|---|---|
| Title | Applying additive modeling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages |
| DOI | 10.1111/j.2041-210X.2011.00124.x |
| Authors | Kelly O. Maloney, Matthias Schmid, Donald E. Weller |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Methods in Ecology and Evolution |
| Index ID | 70043474 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Coop Res Unit Leetown |