scholarly journals Exploring Dietary Patterns in the Southernmost Limit of Prehispanic Agriculture in America by Using Bayesian Stable Isotope Mixing Models

2016 ◽  
Vol 57 (2) ◽  
pp. 230-239 ◽  
Author(s):  
Valeria Bernal ◽  
Paula N. Gonzalez ◽  
Florencia Gordón ◽  
S. Ivan Perez
Keyword(s):  
Stable Isotope ◽  
Dietary Patterns ◽  
Mixing Models ◽  
Isotope Mixing Models ◽  
Prehispanic Agriculture

scholarly journals Bayesian stable isotope mixing models

2013 ◽  
pp. n/a-n/a ◽  
Author(s):  
Andrew C. Parnell ◽  
Donald L. Phillips ◽  
Stuart Bearhop ◽  
Brice X. Semmens ◽  
Eric J. Ward ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Mixing Models ◽  
Isotope Mixing Models

scholarly journals Application of stable isotope mixing models for defining trophic biomagnification pathways of mercury and selenium

2014 ◽  
Vol 59 (4) ◽  
pp. 1181-1192 ◽  
Author(s):  
H. J. Jones ◽  
K. M. Swadling ◽  
E. C. V. Butler ◽  
L. A. Barry ◽  
C. K. Macleod
Keyword(s):  
Stable Isotope ◽  
Mixing Models ◽  
Isotope Mixing Models

Incorporating concentration dependence in stable isotope mixing models

Oecologia ◽  
2002 ◽  
Vol 130 (1) ◽  
pp. 114-125 ◽  
Author(s):  
Donald L. Phillips ◽  
Paul L. Koch
Keyword(s):  
Stable Isotope ◽  
Concentration Dependence ◽  
Mixing Models ◽  
Isotope Mixing Models

Dietary reconstruction of an early to middle Holocene human population from the central California coast: insights from advanced stable isotope mixing models

2004 ◽  
Vol 31 (8) ◽  
pp. 1101-1115 ◽  
Author(s):  
Seth D. Newsome ◽  
Donald L. Phillips ◽  
Brendan J. Culleton ◽  
Tom P. Guilderson ◽  
Paul L. Koch
Keyword(s):  
Stable Isotope ◽  
Human Population ◽  
Mixing Models ◽  
California Coast ◽  
Central California ◽  
Middle Holocene ◽  
Dietary Reconstruction ◽  
Isotope Mixing Models

scholarly journals Statistical basis and outputs of stable isotope mixing models: Comment on Fry (2013)

2013 ◽  
Vol 490 ◽  
pp. 285-289 ◽  
Author(s):  
BX Semmens ◽  
EJ Ward ◽  
AC Parnell ◽  
DL Phillips ◽  
S Bearhop ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Mixing Models ◽  
Isotope Mixing Models

A Bayesian framework for stable isotope mixing models

2012 ◽  
Vol 20 (3) ◽  
pp. 377-397 ◽  
Author(s):  
Erik B. Erhardt ◽  
Edward J. Bedrick
Keyword(s):  
Stable Isotope ◽  
Bayesian Framework ◽  
Mixing Models ◽  
Isotope Mixing Models

Stable isotope mixing models elucidate sex and size effects on the diet of a generalist marine predator

2015 ◽  
Vol 526 ◽  
pp. 213-225 ◽  
Author(s):  
RH Bjorkland ◽  
SF Pearson ◽  
SJ Jeffries ◽  
MM Lance ◽  
A Acevedo-Gutiérrez ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Size Effects ◽  
Mixing Models ◽  
Marine Predator ◽  
Isotope Mixing Models

scholarly journals Quantifying Inter- and Intra-Population Niche Variability Using Hierarchical Bayesian Stable Isotope Mixing Models

PLoS ONE ◽  
2009 ◽  
Vol 4 (7) ◽  
pp. e6187 ◽  
Author(s):  
Brice X. Semmens ◽  
Eric J. Ward ◽  
Jonathan W. Moore ◽  
Chris T. Darimont
Keyword(s):  
Stable Isotope ◽  
Mixing Models ◽  
Hierarchical Bayesian ◽  
Isotope Mixing Models

scholarly journals Best practices for use of stable isotope mixing models in food-web studies

2014 ◽  
Vol 92 (10) ◽  
pp. 823-835 ◽  
Author(s):  
Donald L. Phillips ◽  
Richard Inger ◽  
Stuart Bearhop ◽  
Andrew L. Jackson ◽  
Jonathan W. Moore ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Concentration Dependence ◽  
Probability Distributions ◽  
Mixing Models ◽  
Web Studies ◽  
Source Contributions ◽  
Spatio Temporal ◽  
Discrimination Factors ◽  
Isotope Mixing Models ◽  
User Friendly

Stable isotope mixing models are increasingly used to quantify consumer diets, but may be misused and misinterpreted. We address major challenges to their effective application. Mixing models have increased rapidly in sophistication. Current models estimate probability distributions of source contributions, have user-friendly interfaces, and incorporate complexities such as variability in isotope signatures, discrimination factors, hierarchical variance structure, covariates, and concentration dependence. For proper implementation of mixing models, we offer the following suggestions. First, mixing models can only be as good as the study and data. Studies should have clear questions, be informed by knowledge of the system, and have strong sampling designs to effectively characterize isotope variability of consumers and resources on proper spatio-temporal scales. Second, studies should use models appropriate for the question and recognize their assumptions and limitations. Decisions about source grouping or incorporation of concentration dependence can influence results. Third, studies should be careful about interpretation of model outputs. Mixing models generally estimate proportions of assimilated resources with substantial uncertainty distributions. Last, common sense, such as graphing data before analyzing, is essential to maximize usefulness of these tools. We hope these suggestions for effective implementation of stable isotope mixing models will aid continued development and application of this field.

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