Limits...
Source partitioning using stable isotopes: coping with too much variation.

Parnell AC, Inger R, Bearhop S, Jackson AL - PLoS ONE (2010)

Bottom Line: By accurately reflecting natural variation and uncertainty to generate robust probability estimates of source proportions, the application of Bayesian methods to stable isotope mixing models promises to enable researchers to address an array of new questions, and approach current questions with greater insight and honesty.We outline a framework that builds on recently published Bayesian isotopic mixing models and present a new open source R package, SIAR.The formulation in R will allow for continued and rapid development of this core model into an all-encompassing single analysis suite for stable isotope research.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematical Sciences, University College Dublin, Dublin, Ireland.

ABSTRACT

Background: Stable isotope analysis is increasingly being utilised across broad areas of ecology and biology. Key to much of this work is the use of mixing models to estimate the proportion of sources contributing to a mixture such as in diet estimation.

Methodology: By accurately reflecting natural variation and uncertainty to generate robust probability estimates of source proportions, the application of Bayesian methods to stable isotope mixing models promises to enable researchers to address an array of new questions, and approach current questions with greater insight and honesty.

Conclusions: We outline a framework that builds on recently published Bayesian isotopic mixing models and present a new open source R package, SIAR. The formulation in R will allow for continued and rapid development of this core model into an all-encompassing single analysis suite for stable isotope research.

Show MeSH
Proportion of 1000 simulated data sets where true values lie inside 95% intervals.The model performs well for all of the different scenarios considered. The figure shows the deterioration of model predictions as the number of sources is increased. Performance can be improved by increasing the number of isotopes used.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2837382&req=5

pone-0009672-g002: Proportion of 1000 simulated data sets where true values lie inside 95% intervals.The model performs well for all of the different scenarios considered. The figure shows the deterioration of model predictions as the number of sources is increased. Performance can be improved by increasing the number of isotopes used.

Mentions: The values reported in Figure 2 show the estimated proportion of the 1000 data sets inside the 95% credibility interval.


Source partitioning using stable isotopes: coping with too much variation.

Parnell AC, Inger R, Bearhop S, Jackson AL - PLoS ONE (2010)

Proportion of 1000 simulated data sets where true values lie inside 95% intervals.The model performs well for all of the different scenarios considered. The figure shows the deterioration of model predictions as the number of sources is increased. Performance can be improved by increasing the number of isotopes used.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2837382&req=5

pone-0009672-g002: Proportion of 1000 simulated data sets where true values lie inside 95% intervals.The model performs well for all of the different scenarios considered. The figure shows the deterioration of model predictions as the number of sources is increased. Performance can be improved by increasing the number of isotopes used.
Mentions: The values reported in Figure 2 show the estimated proportion of the 1000 data sets inside the 95% credibility interval.

Bottom Line: By accurately reflecting natural variation and uncertainty to generate robust probability estimates of source proportions, the application of Bayesian methods to stable isotope mixing models promises to enable researchers to address an array of new questions, and approach current questions with greater insight and honesty.We outline a framework that builds on recently published Bayesian isotopic mixing models and present a new open source R package, SIAR.The formulation in R will allow for continued and rapid development of this core model into an all-encompassing single analysis suite for stable isotope research.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematical Sciences, University College Dublin, Dublin, Ireland.

ABSTRACT

Background: Stable isotope analysis is increasingly being utilised across broad areas of ecology and biology. Key to much of this work is the use of mixing models to estimate the proportion of sources contributing to a mixture such as in diet estimation.

Methodology: By accurately reflecting natural variation and uncertainty to generate robust probability estimates of source proportions, the application of Bayesian methods to stable isotope mixing models promises to enable researchers to address an array of new questions, and approach current questions with greater insight and honesty.

Conclusions: We outline a framework that builds on recently published Bayesian isotopic mixing models and present a new open source R package, SIAR. The formulation in R will allow for continued and rapid development of this core model into an all-encompassing single analysis suite for stable isotope research.

Show MeSH