Combined Generalized Additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake

The over-proliferation of phytoplankton has been a public concern for the last several decades. To evaluate the importance of different environmental factors on phytoplankton biomass variations with their complex response relationships in the large eutrophic Lake Okeechobee, the nonlinear methods in...

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Autores principales:	Jinpeng Zhang, Mengmeng Zhi, Ying Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Eutrophication Lake Okeechobee Random Forest Nutrients Generalized Additive Model Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/c1aaa9b9a8554f278d54e3408deedf1e
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spelling	oai:doaj.org-article:c1aaa9b9a8554f278d54e3408deedf1e2021-12-01T04:58:53ZCombined Generalized Additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake1470-160X10.1016/j.ecolind.2021.108082https://doaj.org/article/c1aaa9b9a8554f278d54e3408deedf1e2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21007470https://doaj.org/toc/1470-160XThe over-proliferation of phytoplankton has been a public concern for the last several decades. To evaluate the importance of different environmental factors on phytoplankton biomass variations with their complex response relationships in the large eutrophic Lake Okeechobee, the nonlinear methods including Generalized Additive Model (GAM) and Random Forest (RF) algorithm were employed. A long-term monitoring dataset of 8 sampling sites ranging from January 1996 to December 2010 was applied to explore the driven factors and response relationships of phytoplankton biomass from the different scales in Lake Okeechobee. Results showed a spatially heterogeneous distribution of phytoplankton biomass there, and the western and southern littoral zones occurred heavier algal blooms than the central pelagic zones. Spearman’s correlation results denoted Chlorophyll a (Chla) was negatively correlated with phosphorus in different scales. In the aspect of temporal variations, phytoplankton biomass appeared a three-peak-two-valley variation trend there, which peaked in 1997, 2003, and 2010, respectively. On the lake-wide scale, the RF model indicated that the inorganic nutrients were the primary predictors of phytoplankton biomass, while underwater light availability factors were followed to play essential roles in the prediction. On the local scale, total nitrogen (TN) was the top predictor in the western near-shore zone, while other zones were nitrate and nitrite (NO23) or phosphate (PO4). GAM results suggested that phytoplankton biomass had positive response to TN increase in both lake-wide and local scales. However, its response to water temperature (WT) appeared spatial heterogeneity. This study provided a new perspective to evaluate the primary predictors of phytoplankton biomass with their response relationships from lake-wide scale and local scale in the large shallow eutrophic lake, and the importance of taking spatial heterogeneity into account for lake water quality management was stressed.Jinpeng ZhangMengmeng ZhiYing ZhangElsevierarticleEutrophicationLake OkeechobeeRandom ForestNutrientsGeneralized Additive ModelEcologyQH540-549.5ENEcological Indicators, Vol 130, Iss , Pp 108082- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Eutrophication Lake Okeechobee Random Forest Nutrients Generalized Additive Model Ecology QH540-549.5
spellingShingle	Eutrophication Lake Okeechobee Random Forest Nutrients Generalized Additive Model Ecology QH540-549.5 Jinpeng Zhang Mengmeng Zhi Ying Zhang Combined Generalized Additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake
description	The over-proliferation of phytoplankton has been a public concern for the last several decades. To evaluate the importance of different environmental factors on phytoplankton biomass variations with their complex response relationships in the large eutrophic Lake Okeechobee, the nonlinear methods including Generalized Additive Model (GAM) and Random Forest (RF) algorithm were employed. A long-term monitoring dataset of 8 sampling sites ranging from January 1996 to December 2010 was applied to explore the driven factors and response relationships of phytoplankton biomass from the different scales in Lake Okeechobee. Results showed a spatially heterogeneous distribution of phytoplankton biomass there, and the western and southern littoral zones occurred heavier algal blooms than the central pelagic zones. Spearman’s correlation results denoted Chlorophyll a (Chla) was negatively correlated with phosphorus in different scales. In the aspect of temporal variations, phytoplankton biomass appeared a three-peak-two-valley variation trend there, which peaked in 1997, 2003, and 2010, respectively. On the lake-wide scale, the RF model indicated that the inorganic nutrients were the primary predictors of phytoplankton biomass, while underwater light availability factors were followed to play essential roles in the prediction. On the local scale, total nitrogen (TN) was the top predictor in the western near-shore zone, while other zones were nitrate and nitrite (NO23) or phosphate (PO4). GAM results suggested that phytoplankton biomass had positive response to TN increase in both lake-wide and local scales. However, its response to water temperature (WT) appeared spatial heterogeneity. This study provided a new perspective to evaluate the primary predictors of phytoplankton biomass with their response relationships from lake-wide scale and local scale in the large shallow eutrophic lake, and the importance of taking spatial heterogeneity into account for lake water quality management was stressed.
format	article
author	Jinpeng Zhang Mengmeng Zhi Ying Zhang
author_facet	Jinpeng Zhang Mengmeng Zhi Ying Zhang
author_sort	Jinpeng Zhang
title	Combined Generalized Additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake
title_short	Combined Generalized Additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake
title_full	Combined Generalized Additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake
title_fullStr	Combined Generalized Additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake
title_full_unstemmed	Combined Generalized Additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake
title_sort	combined generalized additive model and random forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/c1aaa9b9a8554f278d54e3408deedf1e
work_keys_str_mv	AT jinpengzhang combinedgeneralizedadditivemodelandrandomforesttoevaluatetheinfluenceofenvironmentalfactorsonphytoplanktonbiomassinalargeeutrophiclake AT mengmengzhi combinedgeneralizedadditivemodelandrandomforesttoevaluatetheinfluenceofenvironmentalfactorsonphytoplanktonbiomassinalargeeutrophiclake AT yingzhang combinedgeneralizedadditivemodelandrandomforesttoevaluatetheinfluenceofenvironmentalfactorsonphytoplanktonbiomassinalargeeutrophiclake
_version_	1718405625744982016

Combined Generalized Additive model and Random Forest to evaluate the influence of environmental factors on phytoplankton biomass in a large eutrophic lake

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