Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India

Over the past several decades, ecologists have been striving to develop models that accurately describe species-habitat relationships across ecological communities. Statistical models that explain ecological dynamics need to consider the nuances of the complex interactions between communities and ec...

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Autores principales:	Rubina Mondal, Anuradha Bhat
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Freshwater fish Artificial neural network Linear mixed models Multivariate adaptive regression splines Generalized additive models Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/a78da094134f4973968e85f2eee866c5
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id	oai:doaj.org-article:a78da094134f4973968e85f2eee866c5
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spelling	oai:doaj.org-article:a78da094134f4973968e85f2eee866c52021-12-01T04:55:52ZComparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India1470-160X10.1016/j.ecolind.2021.107922https://doaj.org/article/a78da094134f4973968e85f2eee866c52021-10-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21005872https://doaj.org/toc/1470-160XOver the past several decades, ecologists have been striving to develop models that accurately describe species-habitat relationships across ecological communities. Statistical models that explain ecological dynamics need to consider the nuances of the complex interactions between communities and ecological factors. Here, we used multiple linear mixed models (LMM), generalized additive models (GAM), multivariate adaptive regression splines (MARS), and artificial neural networks (ANN) to model species richness and diversity of freshwater fishes in eastern and central India. The models were based on fish abundance and associated ecological data over three years across the study regions. We developed global models using all predictors after removing highly correlated variables (Pearson’s r > 0.7). Results revealed conductivity, water temperature, and water velocity as the most important predictive factors of both species richness and diversity. We, then, built two subsets of selected factors to build predictive models for diversity and richness- one variable set containing common significant factors as revealed from the four different modeling methods used and the second, using an automatic feature selection technique. Amongst the modeling methods used in our study, ANN was found to create the best fit models for explaining nonlinearities between response variables and predictors. The importance of variable selection is highlighted, given that subset 1 (common consensual factors) creates more homogeneity in predictions compared to using subset 2 (automated feature selection). Contrary to similar studies in recent years, which show machine learning (ML) methods to typically outperform conventional methods, our results revealed that ANN performed at par with other methods in terms of predictive power. Our findings underline the need for a judicious choice of modeling techniques based on the availability of the data and the ecological communities being studied.Rubina MondalAnuradha BhatElsevierarticleFreshwater fishArtificial neural networkLinear mixed modelsMultivariate adaptive regression splinesGeneralized additive modelsEcologyQH540-549.5ENEcological Indicators, Vol 129, Iss , Pp 107922- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Freshwater fish Artificial neural network Linear mixed models Multivariate adaptive regression splines Generalized additive models Ecology QH540-549.5
spellingShingle	Freshwater fish Artificial neural network Linear mixed models Multivariate adaptive regression splines Generalized additive models Ecology QH540-549.5 Rubina Mondal Anuradha Bhat Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India
description	Over the past several decades, ecologists have been striving to develop models that accurately describe species-habitat relationships across ecological communities. Statistical models that explain ecological dynamics need to consider the nuances of the complex interactions between communities and ecological factors. Here, we used multiple linear mixed models (LMM), generalized additive models (GAM), multivariate adaptive regression splines (MARS), and artificial neural networks (ANN) to model species richness and diversity of freshwater fishes in eastern and central India. The models were based on fish abundance and associated ecological data over three years across the study regions. We developed global models using all predictors after removing highly correlated variables (Pearson’s r > 0.7). Results revealed conductivity, water temperature, and water velocity as the most important predictive factors of both species richness and diversity. We, then, built two subsets of selected factors to build predictive models for diversity and richness- one variable set containing common significant factors as revealed from the four different modeling methods used and the second, using an automatic feature selection technique. Amongst the modeling methods used in our study, ANN was found to create the best fit models for explaining nonlinearities between response variables and predictors. The importance of variable selection is highlighted, given that subset 1 (common consensual factors) creates more homogeneity in predictions compared to using subset 2 (automated feature selection). Contrary to similar studies in recent years, which show machine learning (ML) methods to typically outperform conventional methods, our results revealed that ANN performed at par with other methods in terms of predictive power. Our findings underline the need for a judicious choice of modeling techniques based on the availability of the data and the ecological communities being studied.
format	article
author	Rubina Mondal Anuradha Bhat
author_facet	Rubina Mondal Anuradha Bhat
author_sort	Rubina Mondal
title	Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India
title_short	Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India
title_full	Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India
title_fullStr	Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India
title_full_unstemmed	Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India
title_sort	comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern india
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/a78da094134f4973968e85f2eee866c5
work_keys_str_mv	AT rubinamondal comparisonofregressionbasedandmachinelearningtechniquestoexplainalphadiversityoffishcommunitiesinstreamsofcentralandeasternindia AT anuradhabhat comparisonofregressionbasedandmachinelearningtechniquestoexplainalphadiversityoffishcommunitiesinstreamsofcentralandeasternindia
_version_	1718405681312169984

Comparison of regression-based and machine learning techniques to explain alpha diversity of fish communities in streams of central and eastern India

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