| 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. |
| Genre: | Article |
| ProdID: | 70043474 |
| Citation Author: | Kelly O Maloney; Schmid, Matthias ; Weller, Donald E. |
| Citation Contributing Office: | Leetown Science Center |
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| Citation End Page: | 128 |
| Citation Issue: | 1 |
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| Citation Language: | English |
| Citation Larger Work Title: | Methods in Ecology and Evolution |
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| Citation Month: | 2 |
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| Citation Publisher: | Wiley |
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| Citation Search Results Text: | Applying additive modeling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages; 2012; Article; Journal; Methods in Ecology and Evolution; Kelly O Maloney; Schmid, Matthias ; Weller, Donald E. |
| Citation Start Page: | 116 |
| Citation Volume: | 3 |
| Citation Year: | 2012 |
| Type: | citation/reference |
| Text: | Applying additive modeling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages; 2012; Article; Journal; Methods in Ecology and Evolution; Kelly O Maloney; Schmid, Matthias ; Weller, Donald E. |
| URL (THUMBNAIL): | http://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg |
| URL (DIGITAL OBJECT IDENTIFIER): | http://dx.doi.org/10.1111/j.2041-210X.2011.00124.x |
| Date Other: | Thu, 1 Mar 2012 00:00 -0600 |
| Publisher: | Wiley |
