Elemental concentrations and in vitro bioaccessibility in Canadian background soils

Elemental concentrations and bioaccessibility were determined in background soils collected in Canada as part of the North American Geochemical Landscapes Project. The concentrations of As, Cr, Cu, Co, Ni and Zn were higher in the C-horizon (parent material) compared to 0–5 cm (surface soil), and this observation along with the regional distribution suggested that most of the variability in concentrations of these elements were governed by the bedrock characteristics. Unlike the above-stated elements, Pb and Cd concentrations were higher in the surface layer reflecting the potential effects of anthropogenic deposition. Elemental bioaccessibility was variable decreasing in the order Cd > Pb > Cu > Zn > Ni > Co > As > Cr for the surface soils. With the exception of As, bioaccessibility was generally higher in the C-horizon soils compared to the 0–5 cm soils. The differences in metal bioaccessibility between the 0–5 cm and the C-horizon and among the provinces may reflect geological processes and speciation. The mean, median or 95th percentile bioaccessibility for As, Cr, Cu, Co, Ni and Pb were all below 100 %, suggesting that the use of site-specific bioaccessibility results for these elements will yield more accurate estimation of the risk associated with oral bioavailability for sites where soil ingestion is the major contributor of human health risk.     

Arsenic and other trace elements in groundwater and human urine in Ha Nam province,the Northern Vietnam: contamination characteristics and risk assessment

The contamination characteristics of arsenic and other trace elements in groundwater and the potential risks of arsenic from the groundwater were investigated. Elevated contamination of arsenic, barium and manganese was observed in tube-well water of two villages (Chuyen Ngoai and Chau Giang) in Ha Nam province in the Northern Vietnam. Concentrations of As in the groundwater ranged from 12.8 to 884 µg/L with mean values in Chuyen Ngoai and Chau Giang were 614.7 and 160.1 µg/L, respectively. About 83 % of these samples contained As concentrations exceeding WHO drinking water guideline of 10 μg/L. The mean values of Mn and Ba in groundwater from Chuyen Ngoai and Chau Giang were 300 and 657 μg/L and 650 and 468 μg/L, respectively. The mean value of Ba concentration in groundwater in both Chuyen Ngoai and Chau Giang was about 22 % of the samples exceeded the WHO guideline (700 µg/L). Arsenic concentrations in human urine of residents from Chuyen Ngoai and Chau Giang were the range from 8.6 to 458 µg/L. The mean values of Mn and Ba in human urine of local people from Chuyen Ngoai were 46.9 and 62.8 μg/L, respectively, while those in people from Chau Giang were 25.9 and 45.9 μg/L, respectively. The average daily dose from ingesting arsenic for consuming both untreated and treated groundwater is from 0.02 to 11.5 and 0.003 to 1.6 μg/kg day, respectively. Approximately, 57 % of the families using treated groundwater and 64 % of the families using untreated groundwater could be affected by elevated arsenic exposure.     

Metal-contaminated potato crops and potential human health risk in Bolivian mining highlands

This study assessed metals in irrigation water, soil and potato crops impacted by mining discharges, as well as potential human health risk in the high desert near the historic mining center of Potosí, Bolivia. Metal concentrations were compared with international concentration limit guidelines. In addition, an ingested average daily dose and minimum risk level were used to determine the hazard quotient from potato consumption for adults and children. Irrigation water maximum concentrations of Cd, Pb and Zn in mining-impacted sites were elevated 20- to 1100-fold above international concentration limit guidelines. Agricultural soils contained total metal concentrations of As, Cd, Pb and Zn that exceeded concentration limits in agricultural soil guidelines by 22-, 9-, 3- and 12-fold, respectively. Potato tubers in mining-impacted sites had maximum concentrations of As, Cd, Pb and Zn that exceeded concentration limits in commercially sold vegetables by 9-, 10-, 16- and fourfold, respectively. Using conservative assumptions, hazard quotients (HQ) for potatoes alone were elevated for As, Cd and Pb among children (range 1.1–71.8), in nearly all of the mining-impacted areas; and for As and Cd among adults (range 1.2–34.2) in nearly all of the mining-impacted areas. Only one mining-impacted area had a Pb adult HQ for potatoes above 1 for adults. Toxic trace elements in a major regional dietary staple may be a greater concern than previously appreciated. Considering the multitude of other metal exposure routes in this region, it is likely that total HQ values for these metals may be substantially higher than our estimates.     

Quantity and biochemical composition of sedimentary organic matter around offshore gas extraction platforms of the Adriatic Sea

Gas platforms can exert relevant effects on various ecosystem properties of the hosting area, modifying patterns of productivity and particle sedimentation. We hypothesised that the presence of gas platforms is associated with higher organic matter (OM) contents and we tested the null hypothesis by which benthic trophic conditions do not vary significantly among gas structures with different architectural characteristics. To test these hypotheses, we investigated sedimentary OM contents and biochemical composition at increasing distance from two distinct structures: one subsea well-site and one four-leg platform (FLP). We found that sediments surrounding the structures were characterised by similar OM contents, but different biochemical compositions, and that sedimentary OM compositional change with increasing distance from the structure is confirmed only at the FLP. These results suggest that: (i) benthic eutrophication is not consistent among different structures; (ii) OM biochemical composition changes are more evident than those in OM quantity; and (iii) the effect, wherever present, disappears at a very narrow distance from the platform. As biochemical composition of OM reflects food availability for benthic organisms, our results suggest that the enrichment of macrozoobenthic communities around these structures could be the consequence of increased nutritional quality of resources.     

Nearest neighbor methods for testing reflexivity

Nearest neighbor (NN) methods are widely employed for drawing inferences about spatial point patterns of two or more classes. We introduce a method for testing reflexivity in the NN structure (i.e., NN reflexivity) based on a contingency table which will be called reflexivity contingency table (RCT) henceforth. The RCT is based on the NN relationships among the data points and was used for testing niche specificity in literature, but we demonstrate that it is actually more appropriate for testing the NN reflexivity pattern. We derive the asymptotic distribution of the entries of the RCT under random labeling and introduce tests of reflexivity based on these entries. We also consider Pielou’s approach on RCT and show that it is not appropriate for completely mapped spatial data. We determine the appropriate null hypotheses and the underlying conditions/assumptions required for all tests considered. We investigate the finite sample performance of the tests in terms of empirical size and power by extensive Monte Carlo simulations and illustrate the methods on two real-life ecological data sets.     

Fourth-corner correlation is a score test statistic in a log-linear trait–environment model that is useful in permutation testing

Ecologists wish to understand the role of traits of species in determining where each species occurs in the environment. For this, they wish to detect associations between species traits and environmental variables from three data tables, species count data from sites with associated environmental data and species trait data from data bases. These three tables leave a missing part, the fourth-corner. The fourth-corner correlations between quantitative traits and environmental variables, heuristically proposed 20 years ago, fill this corner. Generalized linear (mixed) models have been proposed more recently as a model-based alternative. This paper shows that the squared fourth-corner correlation times the total count is precisely the score test statistic for testing the linear-by-linear interaction in a Poisson log-linear model that also contains species and sites as main effects. For multiple traits and environmental variables, the score test statistic is proportional to the total inertia of a doubly constrained correspondence analysis. When the count data are over-dispersed compared to the Poisson or when there are other deviations from the model such as unobserved traits or environmental variables that interact with the observed ones, the score test statistic does not have the usual chi-square distribution. For these types of deviations, row- and column-based permutation methods (and their sequential combination) are proposed to control the type I error without undue loss of power (unless no deviation is present), as illustrated in a small simulation study. The issues for valid statistical testing are illustrated using the well-known Dutch Dune Meadow data set.     

A graphical assessment and spatial clustering of the Deepwater Horizon oil spill impact on Laughing Gulls

The Deepwater Horizon oil spill occurred in the Gulf of Mexico on April 20, 2010. Considered the largest accidental marine oil spill in history, oil flowed for three months and approximately five million barrels of oil spilled through by mid-July 2010. In this article, we analyze bird data to assess the impact of the oil spill on the Gulf wildlife. Particularly, we want to determine in which regions Laughing Gulls were mostly affected by the oil spill, and whether those regions spatially shifted throughout the year 2010. Though our data sets have some limitations to apply statistical analysis methods, we obtained very interesting results. Our analyses showed the general consistency of the results based on two population data sets (from the 2011 Data Expo and from the eBird community) and justified the use of both publicly available data sets. We showed that the closer the surface oil spill area approached to the Laughing Gulls habitats, the more significant clusters of bird cases were observed.     

Investigating the link between $$\hbox {PM}_{2.5}$$ and atmospheric profile variables via penalized functional quantile regression

Fine particulate matter (\(\hbox {PM}_{2.5}\)) events negatively affect the health of numerous persons globally each year. Previous works have described the association between air pollution and surface-level meteorological conditions; however, there has been less focus on the task of linking air pollution events with meteorological conditions at higher levels of the atmosphere. Working within the functional data framework, we develop a penalized functional quantile regression (PFQR) procedure to model conditional quantiles of a continuous response based on a functional covariate, with the ability to penalize selected derivatives of the estimated coefficient function. Our aim is to investigate the relationship between atmospheric profile variables (APVs), assumed to be functional, and key quantiles of the conditional distribution of surface-level \(\hbox {PM}_{2.5}\). Via a simulation study, we find that the performance of our PFQR procedure compares favorably to other related approaches. We conclude with an analysis of \(\hbox {PM}_{2.5}\) data at two Southeastern US locations, Columbia, SC and Tampa, FL, where we estimate the coefficient functions for the APVs corresponding to both ‘typical’ and ‘high’ \(\hbox {PM}_{2.5}\) events. As we believe that the true coefficient functions are smooth and may be exactly zero over subsets of their domains, we impose penalties on the 0th and 2nd derivatives. Our analysis indicates that the corresponding atmospheric conditions differ between the two locations, and that the conditions differ seasonally within location.     

Comparing canopy leaf temperature of three Central European tree species based on simultaneous confidence bands for penalized splines

Temperature is an important physical factor that is known to strongly affect biodiversity as well as ecosystems and their functioning. However, research in this area is still relatively limited; this may also be attributed to the multitude of influencing factors and the complexity of the statistics involved. This study analyzes the differences between the surface temperature of three Central European broadleaf tree species. A better understanding of these differences may help to elucidate the role of microclimate in biodiversity. We consider a time series of high-resolution thermal images taken from a meteorological observation tower and calculate mean canopy leaf temperatures for beech, ash and maple (Fagus silvatica, Fraxinus excelsior and Acer pseudoplatanus). In a first step, comparable image areas are extracted from the thermal image sections of the crown of each tree species avoiding shadow areas, branches, etc. We used an automatic segmentation technique, the Otsu thresholding. Extracted canopy leaf temperature values were then processed and the resulting temperature profiles estimated by O’Sullivan penalized splines. For comparing the differences in canopy leaf temperature over time, we propose the construction of simultaneous confidence bands. The analyses show that there are significant—though small—differences in canopy surface temperature between the three tree species.     

Estimation bias under model selection for distance sampling detection functions

Many simulation studies have examined the properties of distance sampling estimators of wildlife population size. When assumptions hold, if distances are generated from a detection model and fitted using the same model, they are known to perform well. However, in practice, the true model is unknown. Therefore, standard practice includes model selection, typically using model comparison tools like Akaike Information Criterion. Here we examine the performance of standard distance sampling estimators under model selection. We compare line and point transect estimators with distances simulated from two detection functions, hazard-rate and exponential power series (EPS), over a range of sample sizes. To mimic the real-world context where the true model may not be part of the candidate set, EPS models were not included as candidates, except for the half-normal parameterization. We found median bias depended on sample size (being asymptotically unbiased) and on the form of the true detection function: negative bias (up to 15% for line transects and 30% for point transects) when the shoulder of maximum detectability was narrow, and positive bias (up to 10% for line transects and 15% for point transects) when it was wide. Generating unbiased simulations requires careful choice of detection function or very large datasets. Practitioners should collect data that result in detection functions with a shoulder similar to a half-normal and use the monotonicity constraint. Narrow-shouldered detection functions can be avoided through good field procedures and those with wide shoulder are unlikely to occur, due to heterogeneity in detectability.