Ecological Network Inference From Long-Term Presence-Absence Data

Abstract Ecological communities are characterized by complex networks of trophic and nontrophic interactions, which shape the dy-namics of the community. Machine learning and correlational methods are increasingly popular for inferring networks from co-occurrence and time series data, particularly i...

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Autores principales:	Elizabeth L. Sander, J. Timothy Wootton, Stefano Allesina
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0a1c1fafb39a47679b06e032395d6927
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id	oai:doaj.org-article:0a1c1fafb39a47679b06e032395d6927
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spelling	oai:doaj.org-article:0a1c1fafb39a47679b06e032395d69272021-12-02T16:07:58ZEcological Network Inference From Long-Term Presence-Absence Data10.1038/s41598-017-07009-x2045-2322https://doaj.org/article/0a1c1fafb39a47679b06e032395d69272017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-07009-xhttps://doaj.org/toc/2045-2322Abstract Ecological communities are characterized by complex networks of trophic and nontrophic interactions, which shape the dy-namics of the community. Machine learning and correlational methods are increasingly popular for inferring networks from co-occurrence and time series data, particularly in microbial systems. In this study, we test the suitability of these methods for inferring ecological interactions by constructing networks using Dynamic Bayesian Networks, Lasso regression, and Pear-son’s correlation coefficient, then comparing the model networks to empirical trophic and nontrophic webs in two ecological systems. We find that although each model significantly replicates the structure of at least one empirical network, no model significantly predicts network structure in both systems, and no model is clearly superior to the others. We also find that networks inferred for the Tatoosh intertidal match the nontrophic network much more closely than the trophic one, possibly due to the challenges of identifying trophic interactions from presence-absence data. Our findings suggest that although these methods hold some promise for ecological network inference, presence-absence data does not provide enough signal for models to consistently identify interactions, and networks inferred from these data should be interpreted with caution.Elizabeth L. SanderJ. Timothy WoottonStefano AllesinaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Elizabeth L. Sander J. Timothy Wootton Stefano Allesina Ecological Network Inference From Long-Term Presence-Absence Data
description	Abstract Ecological communities are characterized by complex networks of trophic and nontrophic interactions, which shape the dy-namics of the community. Machine learning and correlational methods are increasingly popular for inferring networks from co-occurrence and time series data, particularly in microbial systems. In this study, we test the suitability of these methods for inferring ecological interactions by constructing networks using Dynamic Bayesian Networks, Lasso regression, and Pear-son’s correlation coefficient, then comparing the model networks to empirical trophic and nontrophic webs in two ecological systems. We find that although each model significantly replicates the structure of at least one empirical network, no model significantly predicts network structure in both systems, and no model is clearly superior to the others. We also find that networks inferred for the Tatoosh intertidal match the nontrophic network much more closely than the trophic one, possibly due to the challenges of identifying trophic interactions from presence-absence data. Our findings suggest that although these methods hold some promise for ecological network inference, presence-absence data does not provide enough signal for models to consistently identify interactions, and networks inferred from these data should be interpreted with caution.
format	article
author	Elizabeth L. Sander J. Timothy Wootton Stefano Allesina
author_facet	Elizabeth L. Sander J. Timothy Wootton Stefano Allesina
author_sort	Elizabeth L. Sander
title	Ecological Network Inference From Long-Term Presence-Absence Data
title_short	Ecological Network Inference From Long-Term Presence-Absence Data
title_full	Ecological Network Inference From Long-Term Presence-Absence Data
title_fullStr	Ecological Network Inference From Long-Term Presence-Absence Data
title_full_unstemmed	Ecological Network Inference From Long-Term Presence-Absence Data
title_sort	ecological network inference from long-term presence-absence data
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/0a1c1fafb39a47679b06e032395d6927
work_keys_str_mv	AT elizabethlsander ecologicalnetworkinferencefromlongtermpresenceabsencedata AT jtimothywootton ecologicalnetworkinferencefromlongtermpresenceabsencedata AT stefanoallesina ecologicalnetworkinferencefromlongtermpresenceabsencedata
_version_	1718384637627072512

Ecological Network Inference From Long-Term Presence-Absence Data

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