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

Methods in Ecology and Evolution

By:
, ,
DOI: 10.1111/j.2041-210X.2011.00124.x

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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.

Additional Publication Details

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):
Leetown Science Center
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
116
Last page:
128
Country:
United States