Geometric models |
Uses Euclidean distances between the isotopic composition of consumers and sources in a bivariate distribution space of δ13C and δ15N values; Estimates the proportional contribution of each source |
Simple calculations that only require the isotope values of the consumers and their prey items |
Cannot precisely identify specific contributions, due to the possibility of multiple combinations of sources; Tends to overestimate the rare prey species and underestimate the common prey species |
113113 Kline Junior, T. C.; Goering, J. J.; Mathisen, O. A.; Poe, P. H.; Parker, P. L.; Scalan, R. S.; Can. J. Fish. Aquat. Sci. 1993, 50, 2350. [Crossref] Crossref...
114114 Ben-David, M.; Flynn, R. W.; Schell, D. M.; Oecologia 1997, 111, 280. [Crossref] Crossref...
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Linear mixing models |
Uses linear mass balance equations to determine the proportion of the contribution of (n + 1) potential sources, (n = number of isotopes in the system) |
Can provide an exact contribution of the sources for the consumers; Requires a relatively small number of input parameters in the model |
Only determines the contribution to (n + 1) potential food sources; Cannot deal with complex systems |
116116 Schwarcz, H. P.; J. Archaeol. Sci. 1991, 18, 261. [Crossref] Crossref...
117117 Phillips, D. L.; Gregg, J. W.; Oecologia 2001, 127, 171. [Crossref] Crossref...
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IsoError |
Permits incorporating the errors and isotopic correlation of the sources in the model |
Establishes confidence intervals around the estimates of the variances between consumers and prey items |
Does not include any premises. Does not apply to complex systems with multiple sources |
9292 Phillips, D. L.; Gregg, J. W.; Oecologia 2003, 136, 261. [Crossref] Crossref...
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IsoSource |
Calculates the distribution of the frequencies and the contribution of the sources in systems where the sources exceed (n + 1), (n = number of isotopes) |
Examines all possible combinations that can result in the observed isotopic value and determines the range of possible contributions |
Provides a probabilistic solution instead of the exact proportion of the contributions. Does not incorporate variability in the model |
9292 Phillips, D. L.; Gregg, J. W.; Oecologia 2003, 136, 261. [Crossref] Crossref...
9393 Phillips, D. L.; Newsome, S. D.; Gregg, J. W.; Oecologia 2005, 144, 520. [Crossref] Crossref...
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IsoConc |
Performs calculations depending on concentrations; Includes the elemental concentration of the sources (e.g., C and N) and digestibility in the model |
Assumes that the contribution of the source is proportional to its biomass multiplied by the elemental contribution |
Hard to apply in practice for generalist organisms. Does not permit including the inherent errors in the model |
9191 Phillips, D. L.; Koch, P. L.; Oecologia 2002, 130, 114. [Crossref] Crossref...
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MixSIR, SIAR and MixSIAR Bayesian Mixing Models |
Examines the probability distributions of source contributions, with associated uncertainties; Incorporates variability in the parameters and provides a credibility interval |
Encompasses complex systems with multiple possible sources. Allows including the standard deviation in the corrections by the trophic enrichment factor; Enables including optional information (e.g., elemental concentrations) |
The interpretation of the data is sensitive to the variations of the trophic enrichment factor and the presence of highly similar sources |
9696 Moore, J. W.; Semmens, B. X.; Ecology Letters 2008, 11, 470. [Crossref] Crossref...
2626 Parnell, A. C.; Inger, R.; Bearhop, S.; Jackson, A. L.; PLoS One 2010, 5, 1. [Crossref] Crossref...
9797 Stock, B.; Jackson, A.; Ward, E.; Venkiteswaran, J.; MixSIAR, version 3.1; R Core Team, Austria, 2016. [Crossref] Crossref...
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IsotopeR Bayesian mixing model |
The hierarchical structure of the model permits making statistical inferences at the individual level in the population |
Incorporates the resources of the other models, including measurement errors, dependence on concentration and isotopic correlation |
The interpretation of the data is sensitive to the variations of the trophic enrichment factor and the presence of highly similar sources |
9898 Hopkins, J. B.; Ferguson, J. M.; PLoS One 2012, 7, e28478. [Crossref] Crossref...
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SIBER Bayesian model |
Utilizes quantitative metrics of the isotopic dispersion to make inferences about the structure of the community; Includes uncertainties associated with the sampling, generating robust measures of the breadth of the isotopic niche occupied by the species |
The metrics calculated generate impartial ellipses in relation to the sample size; Permits comparing groups with different sample sizes and isotopic niches between different systems and communities; Enables performing meta-analysis studies |
Recommends a minimum sample number of 10 per member group of the community |
2828 Jackson, A. L.; Inger, R.; Parnell, A. C.; Bearhop, S.; J. Anim. Ecol. 2011, 80, 595. [Crossref] Crossref...
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SIDER Bayesian model |
Uses a phylogenetic regression model based on a compiled dataset to impute (estimate) a TEF of a consumer; Uses all the information in the data, i.e. phylogenetic information, tissue type, repeated measures of the same species, diet and environment type, weighted accordingly by the estimated correlation structures |
Provide a TEF estimate (mean and standard deviation) specific to the species of interest; Compatible with all the major Bayesian stable isotope mixing models including MixSIAR, IsotopeR, SIAR, and MixSIR |
The species may be one present in the provided dataset, or it may be a new species, but it must be recognized as present in the phylogeny (terrestrial and marine birds and mammals) |
100100 Healy, K.; Guillerme, T.; Kelly, S. B. A.; Inger, R.; Bearhop, S.; Jackson, A. L.; Ecography 2018, 41, 1393. [Crossref] Crossref...
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