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Trophic Magnification Factors: Modeling biomagnification in aquatic food webs

Gioutlakis, Michail (2013) BION14 20122
Degree Projects in Biology
Abstract
In this study the hypothesis that particular groups of organisms showing common physiological characteristics play an important role for the calculation of Trophic Magnification Factors (TMFs) of anthropogenic pollutants is tested. For this purpose data were collected from previous studies of biomagnification and analysed. Monte Carlo simulations of the linear regression TMF model were run for mercury and PCB- 138 and Biomagnification Factors (BMFs) were calculated. For mercury, the 95% limits of TMF distribution were 1.98 and 2.18. Sensitivity analysis revealed that the trophic position and concentration in phytoplankton, zooplankton, invertebrates and fish organisms were the most influential parameters for the model output. The results... (More)
In this study the hypothesis that particular groups of organisms showing common physiological characteristics play an important role for the calculation of Trophic Magnification Factors (TMFs) of anthropogenic pollutants is tested. For this purpose data were collected from previous studies of biomagnification and analysed. Monte Carlo simulations of the linear regression TMF model were run for mercury and PCB- 138 and Biomagnification Factors (BMFs) were calculated. For mercury, the 95% limits of TMF distribution were 1.98 and 2.18. Sensitivity analysis revealed that the trophic position and concentration in phytoplankton, zooplankton, invertebrates and fish organisms were the most influential parameters for the model output. The results of BMF calculation suggested also that mercury concentration in fish that feed on zooplankton was 3 to 5 times higher in comparison with the concentration in their prey which was the highest BMF in the food web. For PCB- 138 the 95% limits of TMF distribution were 2.5 and 5. The concentration in organisms of all trophic levels and their lipid content significantly affected TMF. The highest BMF resulted for fish- eating birds, where concentration of PCB- 138 could be 40 times higher than the concentration in their diet. Uncertainty of TMF and BMF depended not only on the number of available data (n), but also on the variability of the input parameters. Database construction and modeling of biomagnification indexes can be useful for predicting the fate of toxicants in biota in different ecosystems for which field data are not available. (Less)
Popular Abstract
Predicting which chemicals are putting top predators at risk

A wide variety of chemical substances have been introduced in the environment as a result of human activity. For some of them, the measured concentration in top predators is higher than the concentration in organisms found in the base of the food chain. This phenomenon is called biomagnification. The potential for biomagnification of a contaminant is as important as its toxicity and persistence when it comes to environmental regulations.

To determine if a contaminant biomagnifies or not, concentrations should be analysed in organisms that occupy different trophic levels in a food chain. If contaminant concentration increase from bottom level organisms to top predators, the... (More)
Predicting which chemicals are putting top predators at risk

A wide variety of chemical substances have been introduced in the environment as a result of human activity. For some of them, the measured concentration in top predators is higher than the concentration in organisms found in the base of the food chain. This phenomenon is called biomagnification. The potential for biomagnification of a contaminant is as important as its toxicity and persistence when it comes to environmental regulations.

To determine if a contaminant biomagnifies or not, concentrations should be analysed in organisms that occupy different trophic levels in a food chain. If contaminant concentration increase from bottom level organisms to top predators, the substance biomagnifies. Except from calculating to which extent the particular ecosystem is threatened from a contaminant, generalising is also important. In other words, it is very useful to be able to predict the biomagnification potential of a chemical in unexplored ecosystems.

Is it possible to use databases and mathematical tools in order to predict the concentrations of chemicals in top predators? If yes, which data are important for making this kind of predictions? These are the questions that this study aimed to answer. For this purpose, data from relevant previously published studies were gathered in databases and re- analysed. This was done for two contaminants: mercury and PCB- 138.

The results of this study indicate that the ability to predict biomagnification potential and predictions accuracy differed between contaminants. More specifically, between the two chemicals that were examined, it was easier to predict the behaviour of the mercury than it is for PCB -138. Also, data for organisms that occupy a few trophic levels would be enough for drawing conclusions for mercury. This was not the case for PCB- 138 since in order to make meaningful predictions data were needed from all the trophic levels of a food chain. These differences could be attributed to the different chemical properties of the two contaminants.

Knowledge of biology as well as empirical observations lead to the conclusion that many factors are affecting biomagnification. Factors that have to do with the biology of organisms and characteristics of the ecosystems that they live in. Understanding the effect of these factors and taking them into consideration, can make our predictions more conservative and accurate.


Advisor: Olof Berglund
MasterĀ“s Degree Project 45 credits in Ecotoxicology, 2013
Department of Biology, Lund University (Less)
Please use this url to cite or link to this publication:
author
Gioutlakis, Michail
supervisor
organization
course
BION14 20122
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
5431986
date added to LUP
2015-05-25 13:45:13
date last changed
2015-05-25 13:45:13
@misc{5431986,
  abstract     = {In this study the hypothesis that particular groups of organisms showing common physiological characteristics play an important role for the calculation of Trophic Magnification Factors (TMFs) of anthropogenic pollutants is tested. For this purpose data were collected from previous studies of biomagnification and analysed. Monte Carlo simulations of the linear regression TMF model were run for mercury and PCB- 138 and Biomagnification Factors (BMFs) were calculated. For mercury, the 95% limits of TMF distribution were 1.98 and 2.18. Sensitivity analysis revealed that the trophic position and concentration in phytoplankton, zooplankton, invertebrates and fish organisms were the most influential parameters for the model output. The results of BMF calculation suggested also that mercury concentration in fish that feed on zooplankton was 3 to 5 times higher in comparison with the concentration in their prey which was the highest BMF in the food web. For PCB- 138 the 95% limits of TMF distribution were 2.5 and 5. The concentration in organisms of all trophic levels and their lipid content significantly affected TMF. The highest BMF resulted for fish- eating birds, where concentration of PCB- 138 could be 40 times higher than the concentration in their diet. Uncertainty of TMF and BMF depended not only on the number of available data (n), but also on the variability of the input parameters. Database construction and modeling of biomagnification indexes can be useful for predicting the fate of toxicants in biota in different ecosystems for which field data are not available.},
  author       = {Gioutlakis, Michail},
  language     = {eng},
  note         = {Student Paper},
  title        = {Trophic Magnification Factors: Modeling biomagnification in aquatic food webs},
  year         = {2013},
}