Comparing nearshore benthic and pelagic prey as mercury sources to lake fish: the importance of prey quality and mercury content

Mercury (Hg) bioaccumulation in fish poses well-known health risks to wildlife and humans through fish consumption. Yet fish Hg concentrations are highly variable, and key factors driving this variability remain unclear. One little studied source of variation is the influence of habitat-specific feeding on Hg accumulation in lake fish. However, this is likely important because most lake fish feed in multiple habitats during their lives, and the Hg and caloric content of prey from different habitats can differ. This study used a three-pronged approach to investigate the extent to which habitat-specific prey determine differences in Hg bioaccumulation in fish. This study first compared Hg concentrations in common nearshore benthic invertebrates and pelagic zooplankton across five lakes and over the summer season in one lake, and found that pelagic zooplankton generally had higher Hg concentrations than most benthic taxa across lakes, and over a season in one lake. Second, using a bioenergetics model, the effects of prey caloric content from habitat-specific diets on fish growth and Hg accumulation were calculated. This model predicted that the consumption of benthic prey results in lower fish Hg concentrations due to higher prey caloric content and growth dilution (high weight gain relative to Hg from food), in addition to lower prey Hg levels. Third, using data from the literature, links between fish Hg content and the degree of benthivory, were examined,

Diet and Toenail Arsenic Concentrations in a New Hampshire Population with Arsenic-Containing Water

Background: Limited data exist on the contribution of dietary sources of arsenic to an individual\u27s total exposure, particularly in populations with exposure via drinking water. Here, the association between diet and toenail arsenic concentrations (a long-term biomarker of exposure) was evaluated for individuals with measured household tap water arsenic. Foods known to be high in arsenic, including rice and seafood, were of particular interest. Methods: Associations between toenail arsenic and consumption of 120 individual diet items were quantified using general linear models that also accounted for household tap water arsenic and potentially confounding factors (e.g., age, caloric intake, sex, smoking) (n = 852). As part of the analysis, we assessed whether associations between log-transformed toenail arsenic and each diet item differed between subjects with household drinking water arsenic concentrations \u3c1 μg/L versus ≥1 μg/L

Diet and toenail arsenic concentrations in a New Hampshire population with arsenic-containing water

Abstract Background Limited data exist on the contribution of dietary sources of arsenic to an individual’s total exposure, particularly in populations with exposure via drinking water. Here, the association between diet and toenail arsenic concentrations (a long-term biomarker of exposure) was evaluated for individuals with measured household tap water arsenic. Foods known to be high in arsenic, including rice and seafood, were of particular interest. Methods Associations between toenail arsenic and consumption of 120 individual diet items were quantified using general linear models that also accounted for household tap water arsenic and potentially confounding factors (e.g., age, caloric intake, sex, smoking) (n = 852). As part of the analysis, we assessed whether associations between log-transformed toenail arsenic and each diet item differed between subjects with household drinking water arsenic concentrations <1 μg/L versus ≥1 μg/L. Results As expected, toenail arsenic concentrations increased with household water arsenic concentrations. Among the foods known to be high in arsenic, no clear relationship between toenail arsenic and rice consumption was detected, but there was a positive association with consumption of dark meat fish, a category that includes tuna steaks, mackerel, salmon, sardines, bluefish, and swordfish. Positive associations between toenail arsenic and consumption of white wine, beer, and Brussels sprouts were also observed; these and most other associations were not modified by exposure via water. However, consumption of two foods cooked in water, beans/lentils and cooked oatmeal, was more strongly related to toenail arsenic among those with arsenic-containing drinking water (≥1 μg/L). Conclusions This study suggests that diet can be an important contributor to total arsenic exposure in U.S. populations regardless of arsenic concentrations in drinking water. Thus, dietary exposure to arsenic in the US warrants consideration as a potential health risk

DryadDataKarimietal2010.doc

NOTE: THIS FILE CONTAINS ERRORS. Please use the updated version of the file, found at http://dx.doi.org/10.5061/dryad.1858/

Data from: Multielement stoichiometry in aquatic invertebrates: when growth dilution matters

Element concentrations in organisms can be variable, often causing deviations from otherwise consistent, taxon-specific multielement stoichiometries. Such variation can have considerable ecological consequences, yet physiological mechanisms remain unclear. We tested the influence of somatic growth dilution (SGD) on multiple element concentrations under different bioenergetic conditions. SGD occurs when rapid individual growth causes a disproportional gain in biomass relative to gain of a specific element. SGD can strongly affect elements in various organisms, but we lack a general framework to unify results across studies and assess its overall importance. We derived the general conditions that trigger SGD from an element accumulation model. We parameterized the model with bioenergetic and element-specific rates summarized from the literature to compare SGD effects on 15 elements (nonessential metals, essential trace elements, macronutrients) in three aquatic invertebrate taxa. For all taxa, we found that SGD: (1) occurs to some degree for all 15 elements over realistic ranges of growth and ingestion rates, and (2) has the greatest effect on elements with low efflux (excretion) rates, including certain nonessential metals (e.g., MeHg, Po), essential trace elements and macronutrients (e.g., N, Fe). Thus, SGD can strongly affect concentrations of a spectrum of elements under natural conditions. These results provide a framework for predicting variation in elemental composition of animals

Contrasting Food Web Factor and Body Size Relationships with Hg and Se Concentrations in Marine Biota

<div><p>Marine fish and shellfish are primary sources of human exposure to mercury, a potentially toxic metal, and selenium, an essential element that may protect against mercury bioaccumulation and toxicity. Yet we lack a thorough understanding of Hg and Se patterns in common marine taxa, particularly those that are commercially important, and how food web and body size factors differ in their influence on Hg and Se patterns. We compared Hg and Se content among marine fish and invertebrate taxa collected from Long Island, NY, and examined associations between Hg, Se, body length, trophic level (measured by δ¹⁵N) and degree of pelagic feeding (measured by δ¹³C). Finfish, particularly shark, had high Hg content whereas bivalves generally had high Se content. Both taxonomic differences and variability were larger for Hg than Se, and Hg content explained most of the variation in Hg:Se molar ratios among taxa. Finally, Hg was more strongly associated with length and trophic level across taxa than Se, consistent with a greater degree of Hg bioaccumulation in the body over time, and biomagnification through the food web, respectively. Overall, our findings indicate distinct taxonomic and ecological Hg and Se patterns in commercially important marine biota, and these patterns have nutritional and toxicological implications for seafood-consuming wildlife and humans.</p></div

Taxonomic differences in Hg, Se and molar Hg:Se ratios (ppm, wet weight), shown in approximately decreasing values from top to bottom, according to statistically significant taxonomic differences (taxa not connected by the same letter(s)).

a<p>Mako Shark, <i>Isurus oxyrinchus.</i>; Thresher Shark, <i>Alopias vulpinus</i>; Blue Crab, <i>Callinectes sapidus</i>; Striped Bass, <i>Morone saxatilis</i>; Summer Flounder, <i>Paralichthys dentatus</i>; Long-finned squid, <i>Loligo pealei</i>; Atlantic Silverside, <i>Menidia menidia</i>; Bluefish, <i>Pomatomus saltatrix</i>; Scup, <i>Stenotomus chrysops</i>; Weakfish, <i>Cynoscion regalis</i>; Winter Flounder, <i>Pseudopleuronectes americanus</i>; Menhaden, <i>Brevoortia tyrannus</i>; Blue Mussel, <i>Mytilus edulis</i>; Ribbed Mussel, <i>Geukensia demissa</i>; Angelwing Clam, <i>Cyrtopleura costata</i>; Eastern Oyster, <i>Crassostrea virginica</i>; Killifish, <i>Fundulus sp.</i>, Softshell Clam, <i>Mya arenaria</i>; Hardshell clam, <i>Mercenaria mercenaria</i>; Surf clam, <i>Spisula solidissima</i>; Razor Clam, <i>Ensis directus</i>.</p>b<p>Welch ANOVA for Hg: F(21,29.6) = 77.31, P<0.0001;</p>c<p>Welch ANOVA for Se: F(21,26.5) = 15.90, P<0.0001;</p>d<p>Welch ANOVA for Hg:Se ratio: F(21,27.5) = 76.2, P<0.0001.</p

Principal component eigenvalues, percent variance explained and variable loadings (loadings with an absolute value >0.4 in bold).

<p>Principal component eigenvalues, percent variance explained and variable loadings (loadings with an absolute value >0.4 in bold).</p