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dc.contributor.authorLitmanen, Jaakko J.
dc.contributor.authorPerälä, Tommi A.
dc.contributor.authorTaipale, Sami J.
dc.date.accessioned2021-03-11T10:42:19Z
dc.date.available2021-03-11T10:42:19Z
dc.date.issued2020
dc.identifier.citationLitmanen, J. J., Perälä, T. A., & Taipale, S. J. (2020). Comparison of Bayesian and numerical optimization-based diet estimation on herbivorous zooplankton. <i>Philosophical Transactions of the Royal Society B : Biological Sciences</i>, <i>375</i>(1804), Article 20190651. <a href="https://doi.org/10.1098/rstb.2019.0651" target="_blank">https://doi.org/10.1098/rstb.2019.0651</a>
dc.identifier.otherCONVID_35981923
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/74598
dc.description.abstractConsumer diet estimation with biotracer-based mixing models provides valuable information about trophic interactions and the dynamics of complex ecosystems. Here, we assessed the performance of four Bayesian and three numerical optimization-based diet estimation methods for estimating the diet composition of herbivorous zooplankton using consumer fatty acid (FA) profiles and resource library consisting of the results of homogeneous diet feeding experiments. The method performance was evaluated in terms of absolute errors, central probability interval checks, the success in identifying the primary resource in the diet, and the ability to detect the absence of resources in the diet. Despite occasional large inconsistencies, all the methods were able to identify the primary resource most of the time. The numerical optimization method QFASA using χ2(QFASA-CS) or Kullback­–Leibler (QFASA-KL) distance measures had the smallest absolute errors, most frequently found the primary resource, and adequately detected the absence of resources. While the Bayesian methods usually performed well, some of the methods produced ambiguous results and some had much longer computing times than QFASA. Therefore, we recommend using QFASA-CS or QFASA-KL. Our systematic tests showed that FA models can be used to accurately estimate complex dietary mixtures in herbivorous zooplankton.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherThe Royal Society Publishing
dc.relation.ispartofseriesPhilosophical Transactions of the Royal Society B : Biological Sciences
dc.rightsIn Copyright
dc.subject.otherQFASA
dc.subject.otherDaphnia
dc.subject.otherFASTAR
dc.subject.otherMixSIAR
dc.subject.otherbiotracers
dc.subject.otherfood web
dc.titleComparison of Bayesian and numerical optimization-based diet estimation on herbivorous zooplankton
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202103111945
dc.contributor.laitosBio- ja ympäristötieteiden laitosfi
dc.contributor.laitosDepartment of Biological and Environmental Scienceen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn0962-8436
dc.relation.numberinseries1804
dc.relation.volume375
dc.type.versionacceptedVersion
dc.rights.copyright© Authors, 2020
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber770884
dc.relation.grantnumber770884
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/770884/EU//COMPLEX-FISH
dc.subject.ysobayesilainen menetelmä
dc.subject.ysoestimointi
dc.subject.ysovesikirput
dc.subject.ysoravintoaineet
dc.subject.ysoplankton
dc.subject.ysoravintoverkot
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
jyx.subject.urihttp://www.yso.fi/onto/yso/p11349
jyx.subject.urihttp://www.yso.fi/onto/yso/p14681
jyx.subject.urihttp://www.yso.fi/onto/yso/p3939
jyx.subject.urihttp://www.yso.fi/onto/yso/p3053
jyx.subject.urihttp://www.yso.fi/onto/yso/p22082
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1098/rstb.2019.0651
dc.relation.funderEuroopan komissiofi
dc.relation.funderEuropean Commissionen
jyx.fundingprogramERC Consolidator Grantfi
jyx.fundingprogramERC Consolidator Granten
jyx.fundinginformationThis research was supported by funding from the Lake Vesijärvi Foundation and a research grant from the Finnish Cultural Foundation (grant no. 00200666) awarded to J.J.L., a research grant from the Kone foundation (grant no. 201905367) awarded to S.J.T., and T.A.P. was funded by the European Research Council (ERC) CoG project 770884 awarded to Anna Kuparinen.


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