Statistical comparison of additive regression tree methods on ecological grassland data
نویسندگان
چکیده
Additive tree methods are widely used in ecology. To date most ecologists have boosted regression (BRT) methods. However, Bayesian additive (BART) models may offer advantages to previously unexamined. Here we test whether BART has some benefits over the BRT method. do this use two grassland data and 13 hydroclimatic land predictor variables. The dataset contained from a period of drought as well during recovery phase after drought. response variable was trend Enhanced Vegetation Index (EVI), which is an remotely sensed indicator degradation recovery. settable parameters both (BRT BART) were varied compare performance each evaluated using three prediction error statistics; root mean square (RMSE), absolute (MAE), coefficient determination (R2). best across assessed by inspecting relative importance variables statistics. exhibited similar selection abilities, but method generated with or more favourable statistics than (BART explained additional 10.17% 11.92% variation models). Our results indicate that BARTs be effective at modelling ecological BRTs. also had shorter run times, reasonable defaults its software implementation, greater functionality said beyond model building functions. Ecologists approaches benefit suggest their alongside commonly studies.
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ژورنال
عنوان ژورنال: Ecological Informatics
سال: 2021
ISSN: ['1878-0512', '1574-9541']
DOI: https://doi.org/10.1016/j.ecoinf.2020.101198