Assessing the geographic specificity of pH prediction by classification and regression trees.

Soil pH effects a wide range of critical biogeochemical processes that dictate plant growth and diversity. Previous literature has established the capacity of classification and regression trees (CARTs) to predict soil pH, but limitations of CARTs in this context have not been fully explored. The cu...

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Autores principales:	Jacob Egelberg, Nina Pena, Rachel Rivera, Christina Andruk
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dc5a9484cb8f4f3b928cfad97cd70d4b
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spelling	oai:doaj.org-article:dc5a9484cb8f4f3b928cfad97cd70d4b2021-12-02T20:15:05ZAssessing the geographic specificity of pH prediction by classification and regression trees.1932-620310.1371/journal.pone.0255119https://doaj.org/article/dc5a9484cb8f4f3b928cfad97cd70d4b2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255119https://doaj.org/toc/1932-6203Soil pH effects a wide range of critical biogeochemical processes that dictate plant growth and diversity. Previous literature has established the capacity of classification and regression trees (CARTs) to predict soil pH, but limitations of CARTs in this context have not been fully explored. The current study collected soil pH, climatic, and topographic data from 100 locations across New York's Temperate Deciduous Forests (in the United States of America) to investigate the extrapolative capacity of a previously developed CART model as compared to novel CART and random forest (RF) models. Results showed that the previously developed CART underperformed in terms of predictive accuracy (RRMSE = 14.52%) when compared to a novel tree (RRMSE = 9.33%), and that a novel random forest outperformed both models (RRMSE = 8.88%), though its predictions did not differ significantly from the novel tree (p = 0.26). The most important predictors for model construction were climatic factors. These findings confirm existing reports that CART models are constrained by the spatial autocorrelation of geographic data and encourage the restricted application of relevant machine learning models to regions from which training data was collected. They also contradict previous literature implying that random forests should meaningfully boost the predictive accuracy of CARTs in the context of soil pH.Jacob EgelbergNina PenaRachel RiveraChristina AndrukPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255119 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jacob Egelberg Nina Pena Rachel Rivera Christina Andruk Assessing the geographic specificity of pH prediction by classification and regression trees.
description	Soil pH effects a wide range of critical biogeochemical processes that dictate plant growth and diversity. Previous literature has established the capacity of classification and regression trees (CARTs) to predict soil pH, but limitations of CARTs in this context have not been fully explored. The current study collected soil pH, climatic, and topographic data from 100 locations across New York's Temperate Deciduous Forests (in the United States of America) to investigate the extrapolative capacity of a previously developed CART model as compared to novel CART and random forest (RF) models. Results showed that the previously developed CART underperformed in terms of predictive accuracy (RRMSE = 14.52%) when compared to a novel tree (RRMSE = 9.33%), and that a novel random forest outperformed both models (RRMSE = 8.88%), though its predictions did not differ significantly from the novel tree (p = 0.26). The most important predictors for model construction were climatic factors. These findings confirm existing reports that CART models are constrained by the spatial autocorrelation of geographic data and encourage the restricted application of relevant machine learning models to regions from which training data was collected. They also contradict previous literature implying that random forests should meaningfully boost the predictive accuracy of CARTs in the context of soil pH.
format	article
author	Jacob Egelberg Nina Pena Rachel Rivera Christina Andruk
author_facet	Jacob Egelberg Nina Pena Rachel Rivera Christina Andruk
author_sort	Jacob Egelberg
title	Assessing the geographic specificity of pH prediction by classification and regression trees.
title_short	Assessing the geographic specificity of pH prediction by classification and regression trees.
title_full	Assessing the geographic specificity of pH prediction by classification and regression trees.
title_fullStr	Assessing the geographic specificity of pH prediction by classification and regression trees.
title_full_unstemmed	Assessing the geographic specificity of pH prediction by classification and regression trees.
title_sort	assessing the geographic specificity of ph prediction by classification and regression trees.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/dc5a9484cb8f4f3b928cfad97cd70d4b
work_keys_str_mv	AT jacobegelberg assessingthegeographicspecificityofphpredictionbyclassificationandregressiontrees AT ninapena assessingthegeographicspecificityofphpredictionbyclassificationandregressiontrees AT rachelrivera assessingthegeographicspecificityofphpredictionbyclassificationandregressiontrees AT christinaandruk assessingthegeographicspecificityofphpredictionbyclassificationandregressiontrees
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Assessing the geographic specificity of pH prediction by classification and regression trees.

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