Experimentally derived δ¹³C and δ¹⁵N discrimination factors for gray wolves and the impact of prior information in Bayesian mixing models
Stable isotope analysis of diet has become a common tool in conservation research. However, the multiple sources of uncertainty inherent in this analysis framework involve consequences that have not been thoroughly addressed. Uncertainty arises from the choice of trophic discrimination factors, and...
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description | Stable isotope analysis of diet has become a common tool in conservation research. However, the multiple sources of uncertainty inherent in this analysis framework involve consequences that have not been thoroughly addressed. Uncertainty arises from the choice of trophic discrimination factors, and for Bayesian stable isotope mixing models (SIMMs), the specification of prior information; the combined effect of these aspects has not been explicitly tested. We used a captive feeding study of gray wolves (Canis lupus) to determine the first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰ for δ13C and 3.04‰ for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It may be difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable. |
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However, the multiple sources of uncertainty inherent in this analysis framework involve consequences that have not been thoroughly addressed. Uncertainty arises from the choice of trophic discrimination factors, and for Bayesian stable isotope mixing models (SIMMs), the specification of prior information; the combined effect of these aspects has not been explicitly tested. We used a captive feeding study of gray wolves (Canis lupus) to determine the first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰ for δ13C and 3.04‰ for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It may be difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0119940</identifier><identifier>PMID: 25803664</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Alces alces ; Animals ; Autoimmune diseases ; Bayes Theorem ; Bayesian analysis ; Canidae ; Canis lupus ; Carbon Isotopes - analysis ; Conservation ; Decision analysis ; Diet ; Discrimination ; Ecosystem biology ; Ecosystems ; Estimates ; Foxes ; Mathematical models ; Natural resources ; Nitrogen Isotopes - analysis ; Parameter estimation ; Prey ; Stable isotopes ; Uncertainty ; Wolves</subject><ispartof>PloS one, 2015, Vol.10 (3), p.e0119940-e0119940</ispartof><rights>2015 Derbridge et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Derbridge et al 2015 Derbridge et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3710-59445c519a4c17b781c2abc19576223cfac509255125abe9a35a8e49ba3e71723</citedby><cites>FETCH-LOGICAL-c3710-59445c519a4c17b781c2abc19576223cfac509255125abe9a35a8e49ba3e71723</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4372554/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4372554/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,729,782,786,866,887,2106,2932,4028,23875,27932,27933,27934,53800,53802</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25803664$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Bump, Joseph K.</contributor><creatorcontrib>Derbridge, Jonathan J</creatorcontrib><creatorcontrib>Merkle, Jerod A</creatorcontrib><creatorcontrib>Bucci, Melanie E</creatorcontrib><creatorcontrib>Callahan, Peggy</creatorcontrib><creatorcontrib>Koprowski, John L</creatorcontrib><creatorcontrib>Polfus, Jean L</creatorcontrib><creatorcontrib>Krausman, Paul R</creatorcontrib><title>Experimentally derived δ¹³C and δ¹⁵N discrimination factors for gray wolves and the impact of prior information in Bayesian mixing models</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Stable isotope analysis of diet has become a common tool in conservation research. However, the multiple sources of uncertainty inherent in this analysis framework involve consequences that have not been thoroughly addressed. Uncertainty arises from the choice of trophic discrimination factors, and for Bayesian stable isotope mixing models (SIMMs), the specification of prior information; the combined effect of these aspects has not been explicitly tested. We used a captive feeding study of gray wolves (Canis lupus) to determine the first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰ for δ13C and 3.04‰ for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It may be difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable.</description><subject>Alces alces</subject><subject>Animals</subject><subject>Autoimmune diseases</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Canidae</subject><subject>Canis lupus</subject><subject>Carbon Isotopes - analysis</subject><subject>Conservation</subject><subject>Decision analysis</subject><subject>Diet</subject><subject>Discrimination</subject><subject>Ecosystem biology</subject><subject>Ecosystems</subject><subject>Estimates</subject><subject>Foxes</subject><subject>Mathematical models</subject><subject>Natural resources</subject><subject>Nitrogen Isotopes - analysis</subject><subject>Parameter estimation</subject><subject>Prey</subject><subject>Stable isotopes</subject><subject>Uncertainty</subject><subject>Wolves</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNptkktuFDEURUsIREJgBwgsMWHSjb_l8gQJWgEiRTCBsfXK5eo4ctmNXd2kh-yAvQADplkAi2AlOKlOlCBG_p17_a79quoxwXPCJHlxGtcpgJ-vYrBzTIhSHN-p9olidFZTzO7emO9VD3I-xViwpq7vV3tUNJjVNd-vvh2erWxygw0jeL9FXVlsbId-_zj_df59gSBM8z9ff75HncumsC7A6GJAPZgxpoz6mNAywRZ9iX5j86VmPLHIDatCoNijVXKFcaGQw6R1Ab2Grc0OAhrcmQtLNMTO-vywuteDz_bRbjyoPr05_Lh4Nzv-8PZo8ep4Zkp4PBOKc2EEUcANka1siKHQGqKErCllptQmsKJCECqgtQqYgMZy1QKzkkjKDqqnk-_Kx6x3j5k1qeuaYSUbUYijieginOoSYYC01RGcvtyIaakhjc54q9uOS9uzRnad4cYoVe6vRUfBttDSnhWvl7vb1u1gO1OeO4G_ZXr7JLgTvYwbzZksIXgxeL4zSPHz2uZRD-UzrPcQbFxPdatGSi4L-uwf9P_p-ESZFHNOtr8uhmB90WBXKn3RYHrXYEX25GaQa9FVR7G_ozLWvw</recordid><startdate>2015</startdate><enddate>2015</enddate><creator>Derbridge, Jonathan J</creator><creator>Merkle, Jerod A</creator><creator>Bucci, Melanie E</creator><creator>Callahan, Peggy</creator><creator>Koprowski, John L</creator><creator>Polfus, Jean L</creator><creator>Krausman, Paul R</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>2015</creationdate><title>Experimentally derived δ¹³C and δ¹⁵N discrimination factors for gray wolves and the impact of prior information in Bayesian mixing models</title><author>Derbridge, Jonathan J ; Merkle, Jerod A ; Bucci, Melanie E ; Callahan, Peggy ; Koprowski, John L ; Polfus, Jean L ; Krausman, Paul R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3710-59445c519a4c17b781c2abc19576223cfac509255125abe9a35a8e49ba3e71723</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Alces alces</topic><topic>Animals</topic><topic>Autoimmune diseases</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Canidae</topic><topic>Canis lupus</topic><topic>Carbon Isotopes - 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However, the multiple sources of uncertainty inherent in this analysis framework involve consequences that have not been thoroughly addressed. Uncertainty arises from the choice of trophic discrimination factors, and for Bayesian stable isotope mixing models (SIMMs), the specification of prior information; the combined effect of these aspects has not been explicitly tested. We used a captive feeding study of gray wolves (Canis lupus) to determine the first experimentally-derived trophic discrimination factors of C and N for this large carnivore of broad conservation interest. Using the estimated diet in our controlled system and data from a published study on wild wolves and their prey in Montana, USA, we then investigated the simultaneous effect of discrimination factors and prior information on diet reconstruction with Bayesian SIMMs. Discrimination factors for gray wolves and their prey were 1.97‰ for δ13C and 3.04‰ for δ15N. Specifying wolf discrimination factors, as opposed to the commonly used red fox (Vulpes vulpes) factors, made little practical difference to estimates of wolf diet, but prior information had a strong effect on bias, precision, and accuracy of posterior estimates. Without specifying prior information in our Bayesian SIMM, it was not possible to produce SIMM posteriors statistically similar to the estimated diet in our controlled study or the diet of wild wolves. Our study demonstrates the critical effect of prior information on estimates of animal diets using Bayesian SIMMs, and suggests species-specific trophic discrimination factors are of secondary importance. When using stable isotope analysis to inform conservation decisions researchers should understand the limits of their data. It may be difficult to obtain useful information from SIMMs if informative priors are omitted and species-specific discrimination factors are unavailable.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25803664</pmid><doi>10.1371/journal.pone.0119940</doi><oa>free_for_read</oa></addata></record> |
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subjects | Alces alces Animals Autoimmune diseases Bayes Theorem Bayesian analysis Canidae Canis lupus Carbon Isotopes - analysis Conservation Decision analysis Diet Discrimination Ecosystem biology Ecosystems Estimates Foxes Mathematical models Natural resources Nitrogen Isotopes - analysis Parameter estimation Prey Stable isotopes Uncertainty Wolves |
title | Experimentally derived δ¹³C and δ¹⁵N discrimination factors for gray wolves and the impact of prior information in Bayesian mixing models |
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