Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages

dc.contributor.authorMaloney, Kelly O.
dc.contributor.authorSchmid, Matthias
dc.contributor.authorWeller, Donald E.
dc.date.accessioned2012-03-14T14:26:42Z
dc.date.available2012-03-14T14:26:42Z
dc.date.issued2012
dc.description.abstract1. 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.
dc.format.extent116–128
dc.identifier2041-210X
dc.identifier.citationMaloney, 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>.
dc.identifier.issn2041-210X
dc.identifier.urihttp://hdl.handle.net/10088/18172
dc.relation.ispartofMethods in Ecology and Evolution 3 (1)
dc.titleApplying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages
dc.typearticle
sro.description.unitSERC
sro.identifier.doi10.1111/j.2041-210X.2011.00124.x
sro.identifier.itemID108617
sro.identifier.refworksID56455
sro.identifier.urlhttps://repository.si.edu/handle/10088/18172

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