dc.contributor.author | Heikkinen, Risto | |
dc.contributor.author | Hämäläinen, Heikki | |
dc.contributor.author | Kiljunen, Mikko | |
dc.contributor.author | Kärkkäinen, Salme | |
dc.contributor.author | Schilder, Jos | |
dc.contributor.author | Jones, Roger I. | |
dc.date.accessioned | 2022-10-17T10:48:54Z | |
dc.date.available | 2022-10-17T10:48:54Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Heikkinen, R., Hämäläinen, H., Kiljunen, M., Kärkkäinen, S., Schilder, J., & Jones, R. I. (2022). A Bayesian stable isotope mixing model for coping with multiple isotopes, multiple trophic steps and small sample sizes. <i>Methods in Ecology and Evolution</i>, <i>13</i>(11), 2586-2602. <a href="https://doi.org/10.1111/2041-210x.13989" target="_blank">https://doi.org/10.1111/2041-210x.13989</a> | |
dc.identifier.other | CONVID_159108838 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/83573 | |
dc.description.abstract | We introduce a Bayesian stable isotope mixing model for estimating the relative contributions of different dietary components to the tissues of consumers within food webs. The model is implemented with the probabilistic programming language Stan.
The model incorporates isotopes of multiple elements (e.g. C, N, H) for two trophic levels, when the structure of the food web is known. In addition, the model allows inclusion of latent trophic levels (i.e. for which no empirical data are available) intermediate between sources and measured consumers. Running the model in simulations driven by a real dataset from Finnish lakes, we tested the sensitivity of the posterior distributions by altering critical prior parameters and assumptions in the data-generating process.
Importantly, we found that the model estimations were particularly sensitive to the assigned prior value for ω (the fraction of H in aquatic consumer tissue that is derived from environmental water rather than diet) so that reliable empirical data for this parameter are required. When reliable information is not available for ω, we suggest that an uninformative prior should be used.
The proposed model and inferences are suitable for studies where resources for collecting new data are limited, but useful prior information for each specific trophic level is available from earlier studies. | en |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Wiley-Blackwell | |
dc.relation.ispartofseries | Methods in Ecology and Evolution | |
dc.rights | CC BY-NC 4.0 | |
dc.subject.other | Bayesian mixing model | |
dc.subject.other | informative prior | |
dc.subject.other | multiple levels | |
dc.subject.other | stable isotope | |
dc.title | A Bayesian stable isotope mixing model for coping with multiple isotopes, multiple trophic steps and small sample sizes | |
dc.type | article | |
dc.identifier.urn | URN:NBN:fi:jyu-202210174893 | |
dc.contributor.laitos | Bio- ja ympäristötieteiden laitos | fi |
dc.contributor.laitos | Matematiikan ja tilastotieteen laitos | fi |
dc.contributor.laitos | Informaatioteknologian tiedekunta | fi |
dc.contributor.laitos | Department of Biological and Environmental Science | en |
dc.contributor.laitos | Department of Mathematics and Statistics | en |
dc.contributor.laitos | Faculty of Information Technology | en |
dc.contributor.oppiaine | Tilastotiede | fi |
dc.contributor.oppiaine | Laskennallinen tiede | fi |
dc.contributor.oppiaine | Resurssiviisausyhteisö | fi |
dc.contributor.oppiaine | Akvaattiset tieteet | fi |
dc.contributor.oppiaine | Multiobjective Optimization Group | fi |
dc.contributor.oppiaine | Statistics | en |
dc.contributor.oppiaine | Computational Science | en |
dc.contributor.oppiaine | School of Resource Wisdom | en |
dc.contributor.oppiaine | Aquatic Sciences | en |
dc.contributor.oppiaine | Multiobjective Optimization Group | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 2586-2602 | |
dc.relation.issn | 2041-210X | |
dc.relation.numberinseries | 11 | |
dc.relation.volume | 13 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © 2022 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.grantnumber | 285619 | |
dc.relation.grantnumber | 351860 | |
dc.subject.yso | ravintoverkot | |
dc.subject.yso | isotoopit | |
dc.subject.yso | bayesilainen menetelmä | |
dc.format.content | fulltext | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p22082 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p6387 | |
jyx.subject.uri | http://www.yso.fi/onto/yso/p17803 | |
dc.rights.url | https://creativecommons.org/licenses/by-nc/4.0/ | |
dc.relation.doi | 10.1111/2041-210x.13989 | |
dc.relation.funder | Research Council of Finland | en |
dc.relation.funder | Research Council of Finland | en |
dc.relation.funder | Suomen Akatemia | fi |
dc.relation.funder | Suomen Akatemia | fi |
jyx.fundingprogram | Academy Project, AoF | en |
jyx.fundingprogram | Joint International Project, AoF | en |
jyx.fundingprogram | Akatemiahanke, SA | fi |
jyx.fundingprogram | KV-yhteishanke, SA | fi |
jyx.fundinginformation | Suomen Akatemia, Grant/Award Number: 285619 and 351860 | |
dc.type.okm | A1 | |