Pre-cleaning effects on hair analysed by LA-ICP-MS.

Differences in element concentrations among cleaning treatments (unwashed UW = orange, ethanol EC = blue, nitric acid NC = green) and hair layers (external = lighter shade on left side, interior = darker shade on right side) for each element. Mean element concentrations across individuals (solid circles), standard errors (vertical bars) and hair-layer differences (solid gradient lines) illustrate the interactive effects among element, layer and cleaning from the best fit linear mixed effects model (S4 Table in S1 File). Note the three cleaning treatments within each layer are offset for clarity.</p

Variation in Hg, Mn and Pb of unwashed hair using LA-ICP-MS from 20 individuals.

Element concentration plotted across (spot drilling, cross-section, time in seconds as a proxy for depth) and along (line scans, external & interior layers) hair strands. Raw spot data (~0.3s instantaneous concentrations) and averaged line scan data (mean across 0.5cm sections) from every hair strand are plotted, and overlaid by a smoothed cubic spline across strands for each individual (curvy lines, λ = 0.05). Each individual is delineated by a unique colour. For all elements see S7, S8 Figs in S1 File.</p

Supplementary material.

Chronic exposure to toxic metals is a serious global health concern. However, population-wide biomonitoring is costly and carries several sampling constraints. Though hair sampling can be a useful way to assess environmental exposure, external contamination is a long-standing concern, and a pre-cleaning step prior to metal quantification has long been recommended despite a lack of evidence for its efficacy. In this study, we quantified the spatial distribution of 16 elements in unwashed human hair samples using Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS), then tested how two common pre-cleaning treatments (Triton-ethanol, Triton-nitric acid) affected metal content in external and interior layers of hair using LA-ICP-MS. We show that elements differ in their spatial distribution across hair and that pre-cleaning is not consistent in its effect on element concentrations and decreases interior concentrations of some elements. We demonstrate that differences among individuals can be quantified reliably with LA-ICP-MS analysis of interior concentrations of unwashed hair. Our study tests the widespread notion that pre-cleaning is essential in analyses of hair for environmental exposure to metals, and examines the benefits of a unified approach to analysis of metals in hair using LA-ICP-MS.</div

Inclusivity in global research.

Chronic exposure to toxic metals is a serious global health concern. However, population-wide biomonitoring is costly and carries several sampling constraints. Though hair sampling can be a useful way to assess environmental exposure, external contamination is a long-standing concern, and a pre-cleaning step prior to metal quantification has long been recommended despite a lack of evidence for its efficacy. In this study, we quantified the spatial distribution of 16 elements in unwashed human hair samples using Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS), then tested how two common pre-cleaning treatments (Triton-ethanol, Triton-nitric acid) affected metal content in external and interior layers of hair using LA-ICP-MS. We show that elements differ in their spatial distribution across hair and that pre-cleaning is not consistent in its effect on element concentrations and decreases interior concentrations of some elements. We demonstrate that differences among individuals can be quantified reliably with LA-ICP-MS analysis of interior concentrations of unwashed hair. Our study tests the widespread notion that pre-cleaning is essential in analyses of hair for environmental exposure to metals, and examines the benefits of a unified approach to analysis of metals in hair using LA-ICP-MS.</div

Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs

Leaf traits are frequently measured in ecology to provide a 'common currency' for predicting how anthropogenic pressures impact ecosystem function. Here, we test whether leaf traits consistently respond to experimental treatments across 27 globally distributed grassland sites across 4 continents. We find that specific leaf area (leaf area per unit mass)-a commonly measured morphological trait inferring shifts between plant growth strategies-did not respond to up to four years of soil nutrient additions. Leaf nitrogen, phosphorus and potassium concentrations increased in response to the addition of each respective soil nutrient. We found few significant changes in leaf traits when vertebrate herbivores were excluded in the short-term. Leaf nitrogen and potassium concentrations were positively correlated with species turnover, suggesting that interspecific trait variation was a significant predictor of leaf nitrogen and potassium, but not of leaf phosphorus concentration. Climatic conditions and pretreatment soil nutrient levels also accounted for significant amounts of variation in the leaf traits measured. Overall, we find that leaf morphological traits, such as specific leaf area, are not appropriate indicators of plant response to anthropogenic perturbations in grasslands

NutNet_foliar_data

This data includes leaf traits that were collected from the three to five most dominant species in each plot including specific leaf area, and leaf N, P and K concentrations. This file also include site level climatic and edaphic conditions: mean annual temperature, temperature seasonality, mean annual precipitation, precipitation seasonality, pre-treatment soil nitrogen by mass %, pre-treatment soil phosphorus by mass (ppm) and pre-treatment soil potassium by mass (ppm). A sheet is included in the data file explains each column and is called metadata

Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs.

Leaf traits are frequently measured in ecology to provide a 'common currency' for predicting how anthropogenic pressures impact ecosystem function. Here, we test whether leaf traits consistently respond to experimental treatments across 27 globally distributed grassland sites across 4 continents. We find that specific leaf area (leaf area per unit mass)-a commonly measured morphological trait inferring shifts between plant growth strategies-did not respond to up to four years of soil nutrient additions. Leaf nitrogen, phosphorus and potassium concentrations increased in response to the addition of each respective soil nutrient. We found few significant changes in leaf traits when vertebrate herbivores were excluded in the short-term. Leaf nitrogen and potassium concentrations were positively correlated with species turnover, suggesting that interspecific trait variation was a significant predictor of leaf nitrogen and potassium, but not of leaf phosphorus concentration. Climatic conditions and pretreatment soil nutrient levels also accounted for significant amounts of variation in the leaf traits measured. Overall, we find that leaf morphological traits, such as specific leaf area, are not appropriate indicators of plant response to anthropogenic perturbations in grasslands

Author Correction: Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs

Correction to: Nature Ecology & Evolution https://doi-org.proxy.library.uu.nl/10.1038/s41559-018-0790-1  published online 4 February 2019

Author correction: Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs

In the version of this Article originally published, there were unit conversion errors in the dataset when calculating specific leaf area (SLA) values at 10 of the 27 sites; errors were made when converting SLA from cm 2 g –1 to mm 2 g –1 and from mm 2 mg –1 to mm 2 g –1. This resulted in two incorrect data points: Phleum pratense in plot 27 at the Frue.ch site (SLA >300,000 mm 2 g –1) and Poa secunda in plot 31 at the shps.us site (SLA >60 mm 2 g –1). These two values were changed to ‘NA’ in the dataset, and therefore some reported estimates were changed, resulting in changes to the data points in Fig. 2a, the top bar of Fig. 3 and several values in the text. </p