scholarly journals Bias in Diet Determination: Incorporating Traditional Methods in Bayesian Mixing Models

PLoS ONE ◽  
2013 ◽  
Vol 8 (11) ◽  
pp. e80019 ◽  
Author(s):  
Valentina Franco-Trecu ◽  
Massimiliano Drago ◽  
Federico G. Riet-Sapriza ◽  
Andrew Parnell ◽  
Rosina Frau ◽  
...  
2021 ◽  
Author(s):  
Christian Birkel ◽  
Alicia Correa Barahona ◽  
Clément Duvert ◽  
Sebastián Granados Bolaños ◽  
Andres Chavarría Palma ◽  
...  

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Robert S. Feranec ◽  
John P. Hart

Abstract Freshwater and marine fish have been important components of human diets for millennia. The Great Lakes of North America, their tributaries and smaller regional freshwater bodies are important Native American fisheries. The ethnohistorical record, zooarchaeological remains, and isotopic values on human bone and tooth collagen indicate the importance of fish in fourteenth- through seventeenth-century ancestral Wendat diets in southern Ontario, which is bordered by three of the Great Lakes. Maize (Zea mays ssp. mays) was the primary grain of Native American agricultural systems in the centuries prior to and following sustained European presence. Here we report new Bayesian dietary mixing models using previously published δ13C and δ15N values on ancestral Wendat bone and tooth collagen and tooth enamel. The results confirm previous estimates from δ13C values that ancestral Wendat diets included high proportions of maize but indicate much higher proportions of fish than has previously been recognized. The results also suggest that terrestrial animals contributed less to ancestral Wendat diets than is typically interpreted based on zooarchaeological records.


Ecosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
Author(s):  
Zaria Torres‐Poché ◽  
Miguel A. Mora ◽  
Thomas W. Boutton ◽  
Michael E. Morrow

PLoS ONE ◽  
2015 ◽  
Vol 10 (3) ◽  
pp. e0119940 ◽  
Author(s):  
Jonathan J. Derbridge ◽  
Jerod A. Merkle ◽  
Melanie E. Bucci ◽  
Peggy Callahan ◽  
John L. Koprowski ◽  
...  

2020 ◽  
Author(s):  
Marc Jürgen Silberberger ◽  
Katarzyna Koziorowska-Makuch ◽  
Karol Kuliński ◽  
Monika Kędra

Abstract. Stable isotope analysis has become one of the most widely used techniques in ecology. However, uncertainties about the effects of sample preservation and pre-treatment on the ecological interpretation of stable isotope data and especially on Bayesian stable isotope mixing models remain. Here, Bayesian mixing models were used to study how three different preservation methods (drying, freezing, formalin) and two pre-treatments (acidification, lipid removal) affect the estimation of diet composition for two benthic invertebrate species (Limecola balthica, Crangon crangon). Furthermore, commonly used mathematical lipid normalization and formalin correction were applied to check if they improve the model results. Preservation effects were strong on model outcomes for frozen as well as formalin preserved L. balthica samples, but not for C. crangon. Pre-treatment effects varied with species and preservation method and neither lipid normalization nor mathematical formalin correction consistently resulted in improved model outcomes. Our analysis highlights that particularly small changes in δ15N introduced by different preservation and pre-treatments display a so far unrecognized source of error in stable isotope studies. We conclude that mathematical correction of stable isotopes data should be avoided for Bayesian mixing models and that previously unaddressed effects of sample preservation (especially those arising from preservation by freezing) have potentially biased our understanding of the utilization of organic matter in aquatic food webs.


PLoS ONE ◽  
2014 ◽  
Vol 9 (5) ◽  
pp. e95580 ◽  
Author(s):  
Brandon Lee Drake ◽  
Wirt H. Wills ◽  
Marian I. Hamilton ◽  
Wetherbee Dorshow

2013 ◽  
Vol 10 (8) ◽  
pp. 10419-10459 ◽  
Author(s):  
M. Exner-Kittridge ◽  
J. L. Salinas ◽  
M. Zessner

Abstract. In this paper, a novel method for estimating gross gains and losses between streams and groundwater is developed and evaluated against two traditional approaches. These three streambank flux estimation methods are distinct in their assumptions on the spatial distribution of the inflowing and outflowing fluxes along the stream. The two traditional methods assume that the fluxes are independent and in a specific sequence, while the third and newly derived method assumes that both fluxes occur simultaneously and uniformly throughout the stream. The analytic expressions in connection to the underlying assumptions are investigated to evaluate the individual and mutual dynamics of the streambank flux estimation methods and to understand the causes for the different performances. The results show that the three methods produce significantly different results and that the mean absolute normalized error can have up to an order of magnitude difference between the methods. These differences between the streambank flux methods are entirely due to the assumptions of the streambank flux spatial dynamics of the methods, and the performances for a particular approach strongly decrease if its assumptions are not fulfilled. An assessment of the three methods through numerical simulations, representing a variety of streambank flux dynamics, show that the method introduced, considering simultaneous stream gains and losses, presents overall the highest performance. These streambank flux methods can also be used in conjunction with other end-member mixing models to acquire even more hydrologic information as both require the same type of input data.


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