Diagnosing underdetermination in stable isotope mixing models.

Stable isotope mixing models (SIMMs) provide a powerful methodology for quantifying relative contributions of several sources to a mixture. They are widely used in the fields of ecology, geology, and archaeology. Although SIMMs have been rapidly evolved in the Bayesian framework, the underdeterminat...

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Autores principales:	Yutaka Osada, Jun Matsubayashi, Ichiro Tayasu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6cb04a94d2ad4c7782429c44e0ee1fd3
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id	oai:doaj.org-article:6cb04a94d2ad4c7782429c44e0ee1fd3
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spelling	oai:doaj.org-article:6cb04a94d2ad4c7782429c44e0ee1fd32021-12-02T20:17:25ZDiagnosing underdetermination in stable isotope mixing models.1932-620310.1371/journal.pone.0257818https://doaj.org/article/6cb04a94d2ad4c7782429c44e0ee1fd32021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257818https://doaj.org/toc/1932-6203Stable isotope mixing models (SIMMs) provide a powerful methodology for quantifying relative contributions of several sources to a mixture. They are widely used in the fields of ecology, geology, and archaeology. Although SIMMs have been rapidly evolved in the Bayesian framework, the underdetermination of mixing space remains problematic, i.e., the estimated relative contributions are incompletely identifiable. Here we propose a statistical method to quantitatively diagnose underdetermination in Bayesian SIMMs, and demonstrate the applications of our method (named β-dependent SIMM) using two motivated examples. Using a simulation example, we showed that the proposed method can rigorously quantify the expected underdetermination (i.e., intervals of β-dependent posterior) of relative contributions. Moreover, the application to the published field data highlighted two problematic aspects of the underdetermination: 1) ordinary SIMMs was difficult to quantify underdetermination of each source, and 2) the marginal posterior median was not necessarily consistent with the joint posterior peak in the case of underdetermination. Our study theoretically and numerically confirmed that β-dependent SIMMs provide a useful diagnostic tool for the underdetermined mixing problem. In addition to ordinary SIMMs, we recommend reporting the results of β-dependent SIMMs to obtain a biologically feasible and sound interpretation from stable isotope data.Yutaka OsadaJun MatsubayashiIchiro TayasuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0257818 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yutaka Osada Jun Matsubayashi Ichiro Tayasu Diagnosing underdetermination in stable isotope mixing models.
description	Stable isotope mixing models (SIMMs) provide a powerful methodology for quantifying relative contributions of several sources to a mixture. They are widely used in the fields of ecology, geology, and archaeology. Although SIMMs have been rapidly evolved in the Bayesian framework, the underdetermination of mixing space remains problematic, i.e., the estimated relative contributions are incompletely identifiable. Here we propose a statistical method to quantitatively diagnose underdetermination in Bayesian SIMMs, and demonstrate the applications of our method (named β-dependent SIMM) using two motivated examples. Using a simulation example, we showed that the proposed method can rigorously quantify the expected underdetermination (i.e., intervals of β-dependent posterior) of relative contributions. Moreover, the application to the published field data highlighted two problematic aspects of the underdetermination: 1) ordinary SIMMs was difficult to quantify underdetermination of each source, and 2) the marginal posterior median was not necessarily consistent with the joint posterior peak in the case of underdetermination. Our study theoretically and numerically confirmed that β-dependent SIMMs provide a useful diagnostic tool for the underdetermined mixing problem. In addition to ordinary SIMMs, we recommend reporting the results of β-dependent SIMMs to obtain a biologically feasible and sound interpretation from stable isotope data.
format	article
author	Yutaka Osada Jun Matsubayashi Ichiro Tayasu
author_facet	Yutaka Osada Jun Matsubayashi Ichiro Tayasu
author_sort	Yutaka Osada
title	Diagnosing underdetermination in stable isotope mixing models.
title_short	Diagnosing underdetermination in stable isotope mixing models.
title_full	Diagnosing underdetermination in stable isotope mixing models.
title_fullStr	Diagnosing underdetermination in stable isotope mixing models.
title_full_unstemmed	Diagnosing underdetermination in stable isotope mixing models.
title_sort	diagnosing underdetermination in stable isotope mixing models.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6cb04a94d2ad4c7782429c44e0ee1fd3
work_keys_str_mv	AT yutakaosada diagnosingunderdeterminationinstableisotopemixingmodels AT junmatsubayashi diagnosingunderdeterminationinstableisotopemixingmodels AT ichirotayasu diagnosingunderdeterminationinstableisotopemixingmodels
_version_	1718374349168181248

Diagnosing underdetermination in stable isotope mixing models.

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