A statistical resampling method to calculate biomagnification factors exemplified with organochlorine data from herring (Clupea harengus) muscle and guillemot (Uria aalge) egg from the Baltic sea.
A novel method for calculating biomagnification factors is presented and demonstrated using contaminant concentration data from the Swedish national monitoring program regarding organochlorine contaminants (OCs) in herring (Clupea harengus) muscle and guillemot (Uria aalge) egg, sampled from 1996 to 1999 from the Baltic Sea. With this randomly sampled ratios (RSR) method, biomagnification factors (BMF(RSR)) were generated and denoted with standard deviation (SD) as a measure of the variation. The BMFRsR were calculated by randomly selecting one guillemot egg out of a total of 29 and one herring out of a total of 74, and the ratio was determined between the concentration of a given OC in that egg and the concentration of the same OC in that herring. With the resampling technique, this was performed 50 000 times for any given OC, and from this new distribution of ratios, BMF(RSR) for each OC were calculated and given as geometric mean (GM) with GM standard deviation (GMSD) range, arithmetic mean (AM) with AMSD range, and minimum (BMF(MIN)) as well as maximum (BMF(MAX)) biomagnification factors. The 14 analyzed OCs were p,p'DDT and its metabolites p,p'DDE and p,p'DDD, polychlorinated biphenyls (PCB congeners: CB28, CB52, CB101, CB118, CB138, CB153, and CB180), hexachlorocyclohexane isomers (alpha-, beta-, and gammaHCH), and hexachlorobenzene (HCB). Multivariate data analysis (MVDA) methods, including principal components analysis (PCA), partial least squares regression (PLS), and PLS discriminant analyses (PLS-DA), were first used to extract information from the complex biological and chemical data generated from each individual animal. MVDA were used to model similarities/dissimilarities regarding species (PCA, PLS-DA), sample years (PLS), and sample location (PLS-DA) to give a deeper understanding of the data that the BMF modeling was based upon. Contaminants that biomagnify, that had BMF(RSR) significantly higher than one, were p,p'DDE, CB118, HCB, CB138, CB180, CB153, ,betaHCH, and CB28. The contaminants that did not biomagnifywere p,p'DDT, p,p'DDD, alphaHCH, CB101, and CB52. Eventual biomagnification for gammaHCH could not be determined. The BMF(RSR) for OCs present in herring muscle and guillemot egg showed a broad span with large variations for each contaminant. To be able to make reliable calculations of BMFs for different contaminants, we emphasize the importance of using data based upon large numbers of, as well as well-defined, individuals.
Lundstedt-Enkel
Katrin
K
Environmental Toxicology, Department of Physiology and Developmental Biology, Evolutionary Biology Centre, Uppsala University, Norbyvägen 18A, SE-752 36, Sweden. Katrin.Lundstedt-Enkel@ebc.uu.se
Tysklind
Mats
M
Trygg
Johan
J
Schüller
Peter
P
Asplund
Lillemor
L
Eriksson
Ulla
U
Häggberg
Lisbeth
L
Odsjö
Tjelvar
T
Hjelmberg
Mats
M
Olsson
Mats
M
Orberg
Jan
J
D000818:Animals; D001572:Benzofurans / Q000032:analysis*; D001717:Birds; D004531:Eggs / Q000032:analysis*; D005399:Fishes; D015999:Multivariate Analysis; D009132:Muscles / Q000737:chemistry*; D000072317:Polychlorinated Dibenzodioxins / Q000031:analogs & derivatives* / Q000032:analysis; D011108:Polymers / Q000032:analysis*; D014874:Water Pollutants, Chemical / Q000032:analysis*
D001572:Benzofurans; D000072317:Polychlorinated Dibenzodioxins; D011108:Polymers; D014874:Water Pollutants, Chemical; C016068:polychlorodibenzofuran