dc.contributor.author |
Maloney, Kelly O. |
en |
dc.contributor.author |
Schmid, Matthias |
en |
dc.contributor.author |
Weller, Donald E. |
en |
dc.date.accessioned |
2012-03-14T14:26:42Z |
|
dc.date.available |
2012-03-14T14:26:42Z |
|
dc.date.issued |
2012 |
|
dc.identifier.citation |
Maloney, Kelly O., Schmid, Matthias, and Weller, Donald E. 2012. "<a href="https://repository.si.edu/handle/10088/18172">Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages</a>." <em>Methods in Ecology and Evolution</em>. 3 (1):116–128. <a href="https://doi.org/10.1111/j.2041-210X.2011.00124.x">https://doi.org/10.1111/j.2041-210X.2011.00124.x</a> |
en |
dc.identifier.issn |
2041-210X |
|
dc.identifier.uri |
http://hdl.handle.net/10088/18172 |
|
dc.description.abstract |
1. 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. |
en |
dc.relation.ispartof |
Methods in Ecology and Evolution |
en |
dc.title |
Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages |
en |
dc.type |
Journal Article |
en |
dc.identifier.srbnumber |
108617 |
|
dc.identifier.doi |
10.1111/j.2041-210X.2011.00124.x |
|
rft.jtitle |
Methods in Ecology and Evolution |
|
rft.volume |
3 |
|
rft.issue |
1 |
|
rft.spage |
116 |
|
rft.epage |
128 |
|
dc.description.SIUnit |
SERC |
en |
dc.description.SIUnit |
Peer-reviewed |
en |
dc.citation.spage |
116 |
|
dc.citation.epage |
128 |
|