Applying additive modeling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages
Methods in Ecology and Evolution
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Abstract
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.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Applying additive modeling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages |
| Series title | Methods in Ecology and Evolution |
| DOI | 10.1111/j.2041-210X.2011.00124.x |
| Volume | 3 |
| Issue | 1 |
| Year Published | 2012 |
| Language | English |
| Publisher | Wiley |
| Contributing office(s) | Coop Res Unit Leetown |
| Larger Work Type | Article |
| Larger Work Subtype | Journal Article |
| Larger Work Title | Methods in Ecology and Evolution |
| First page | 116 |
| Last page | 128 |
| Country | United States |
| Google Analytic Metrics | Metrics page |