Hatching with the enemy: Daphnia diapausing eggs hatch in the presence of fish kairomones

Summary. Infochemicals are known to play a key role in mediating predator-prey interactions, both in aquatic and terrestrial communities. However, state-dependent variation may exist in how effectively individuals can use this information, depending on genotype, life stage and experience. For our study, we used the predator-prey model system fish-waterflea Daphnia magna Straus (Cladocera, Daphniidae). Adult Daphnia use fish-derived infochemicals, so-called kairomones, as indicators of predation risk, and exhibit a spectrum of morphological, behavioural and life-history responses to the presence of fish kairomones. Here, we investigate whether diapausing eggs, an embryonic resting stage in the life cycle of D. magna, also use fish kairomones and tune their hatching to the risk of fish predation, as reported for diapausing stages of dinoflagellates. In two laboratory experiments, we studied hatching proportion and time until hatching of D. magna diapausing eggs in the absence and presence of fish kairomones. D. magna families differed significantly in their response to the presence of fish kairomones; some families reduced hatching proportion, whereas others increased it. Our results imply genotype-dependent differences in the hatching reactions to fish kairomones as observed for other traits in adult Daphnia.     

Value functions for environmental pollutants: A technique for enhancing the assessment of expert judgements

This paper presents a new procedure to assess value functions for environmental pollutants. It has been particularly designed for eliciting expert-based value functions for pollutant substances. The main features of the model are the possibility to integrate direct and indirect assessment techniques, the possibility to make explicit assessment uncertainties and to avoid forcing quantitative judgements. The main aim of the procedure is to improve the reliability of the assessment but also to make the expert's task easier by using qualitative judgements more frequently. The procedure has been developed especially for soil pollutants, for which an application is described.     

Bayesian model-based cluster analysis for predicting macrofaunal communities

To predict macrofaunal community composition from environmental data a two-step approach is often followed: (1) the water samples are clustered into groups on the basis of the macrofauna data and (2) the groups are related to the environmental data, e.g. by discriminant analysis. For the cluster analysis in step 1 many hard, seemingly arbitrary choices have to be made that nevertheless influence the solution (similarity measure, clustering strategy, number of clusters). The stability of the solution is often of concern, e.g. in clustering by the program. In the discriminant analysis of step 2 it can occur that a water sample is misclassified on the basis of the environmental data but on further inspection happens to be a borderline case in the cluster analysis. One would then rather reclassify such a sample and iterate the two steps. Bayesian latent class analysis is a flexible, extendable model-based cluster analysis approach that recently has gained popularity in the statistical literature and that has the potential to address these problems. It allows the macrofauna and environmental data to be modelled and analyzed in a single integrated analysis. An exciting extension is to incorporate in the analysis prior information on the habitat preferences of the macrofauna taxa such as is available in lists of indicator values. The output of the analysis is not a hard assignment of water samples to clusters but a probabilistic (fuzzy) assignment. The number of clusters is determined on the basis of the Bayes factor. A standard feature of the Bayesian method is to make predictions and to assess their uncertainty. We applied this approach to a data set consisting of 70 water samples, 484 macrofauna taxa and four environmental variables for which previously a five cluster solution had been proposed. The standard for Bayesian estimation, the Gibbs sampler, worked fine on a subset with only 12 selected taxa but did not converge on the full set with 484 taxa. This is due to many configurations in which the assignment probabilities are all very close to either 0 or 1. This convergence problem is comparable with the local optima problem in classical cluster optimization algorithms, including the EM algorithm used in Latent Gold, a Windows program for latent class analysis. The convergence problem needs to be solved before the benefits of Bayesian latent class analysis can come to fruition in this application. We discuss possible solutions.     

Higher Cd adsorption on biogenic elemental selenium nanoparticles

Cadmium (Cd) is a carcinogenic metal contaminating the environment and ending up in wastewaters. There is therefore a need for improved methods to remove Cd by adsorption. Biogenic elemental selenium nanoparticles have been shown to adsorb Zn, Cu and Hg, but these nanoparticles have not been tested for Cd removal. Here we studied the time-dependency and adsorption isotherm of Cd onto biogenic elemental selenium nanoparticles using batch adsorption experiments. We measured ζ-potential values to assess the stability of nanoparticles loaded with Cd. Results show that the maximum Cd adsorption capacity amounts to 176.8 mg of Cd adsorbed per g of biogenic elemental selenium nanoparticles. The ζ-potential of Cd-loaded nanoparticles became less negative from ?32.7 to ?11.7 mV when exposing nanoparticles to an initial Cd concentration of 92.7 mg L^?1. This is the first study that demonstrates the high Cd uptake capacity of biogenic elemental selenium nanoparticles, of 176.8 mg g^?1, when compared to that of traditional adsorbents such as carboxyl-functionalized activated carbon, of 13.5 mg g^?1. An additional benefit is the easy solid–liquid separation by gravity settling due to coagulation of Cd-loaded biogenic elemental selenium nanoparticles.     

Spatial variability in characteristics and trace metal levels in sediments from watercourses

Assessing sediment quality requires sampling designs which address variability. Often however, only one sample or composited sample is collected at a sample site. Existing studies on sediment variability or sampling strategies primarily concern lake or marine sediments. Two rivers and one canal were sampled to assess sediment variability. Further, it was determined if sediment contamination in running water could be predicted by using visual criteria and/or knowledge of the presence of depositional and erosion areas. Metal concentrations and sediment characteristics were measured in different visually distinct areas of the river. At all sample sites the coefficient of variance was relatively high for most sediment characteristics (1.4‐ > 100%) and metal levels (15.3–63.6%). In one river the majority of sediment characteristics variation was between two sediment types and detected by visually distinct differences. Significant differences in cadmium and zinc concentrations were also detected. Contrary to what was expected, cadmium, and zinc levels were highest in the coarse fraction. No differences were found in the second river. In the canal a greater concentration of fine grained sediments and metals were found in the deep areas. It was not possible to predict sediment areas with the highest levels of metal contamination using visual criteria or knowledge of the erosion and sedimentation pattern of the river.     

A novel method to determine buffer strip effectiveness on deep soils

Unfertilized buffer strips (BS) generally improve surface water quality. High buffer strip effectiveness (BSE) has been reported for sloping shallow aquifers, but experimental data for plain landscapes with deeply permeable soils is lacking. We tested a novel method to determine BSE on a 20-m-deep, permeable sandy soil. Discharge from soil to ditch was temporarily collected in an in-stream reservoir to measure its quantity and quality, both for a BS and a reference (REF) treatment. Treatments were replicated once for the first, and three times for the next three leaching seasons. No significant BSE was obtained for nitrogen and phosphorus species in the reservoirs. Additionally, water samples were taken from the upper groundwater below the treatments. The effect of BS for nitrate was much bigger in upper groundwater than in the reservoirs that also collected groundwater from greater depths that were not influenced by the treatments. We conclude that measuring changes in upper groundwater to assess BSE is only valid under specific hydrogeological conditions. We propose an alternative experimental set-up for future research, including extra measurements before installing the BS and REF treatments to deal with spatial and temporal variability. The use of such data as covariates will increase the power of statistical tests by decreasing between-reservoir variability.     

The addition of organic carbon and nitrate affects reactive transport of heavy metals in sandy aquifers

Organic carbon introduction in the soil to initiate remedial measures, nitrate infiltration due to agricultural practices or sulphate intrusion owing to industrial usage can influence the redox conditions and pH, thus affecting the mobility of heavy metals in soil and groundwater. This study reports the fate of Zn and Cd in sandy aquifers under a variety of plausible in-situ redox conditions that were induced by introduction of carbon and various electron acceptors in column experiments. Up to 100% Zn and Cd removal (from the liquid phase) was observed in all the four columns, however the mechanisms were different. Metal removal in column K1 (containing sulphate), was attributed to biological sulphate reduction and subsequent metal precipitation (as sulphides). In the presence of both nitrate and sulphate (K2), the former dominated the process, precipitating the heavy metals as hydroxides and/or carbonates. In the presence of sulphate, nitrate and supplemental iron (Fe(OH)(3)) (K3), metal removal was also due to precipitation as hydroxides and/or carbonates. In abiotic column, K4, (with supplemental iron (Fe(OH)(3)), but no nitrate), cation exchange with soil led to metal removal. The results obtained were modeled using the reactive transport model PHREEQC-2 to elucidate governing processes and to evaluate scenarios of organic carbon, sulphate and nitrate inputs.     

Biological alkylation and colloid formation of selenium in methanogenic UASB reactors

Bioalkylation and colloid formation of selenium during selenate removal in upflow anaerobic sludge bed (UASB) bioreactors was investigated. The mesophilic (30 degrees C) UASB reactor (pH = 7.0) was operated for 175 d with lactate as electron donor at an organic loading rate of 2 g COD L(-1) d(-1) and a selenium loading rate of 3.16 mg Se L(-1) d(-1). Combining sequential filtration with ion chromatographic analysis for selenium oxyanions and solid phase micro extraction gas chromatography mass spectrometry (SPME-GC-MS) for alkylated selenium compounds allowed to entirely close the selenium mass balance in the liquid phase for most of the UASB operational runtime. Although selenate was removed to more than 98.6% from the liquid phase, a less efficient removal of dissolved selenium was observed due to the presence of dissolved alkylated selenium species (dimethylselenide and dimethyldiselenide) and colloidal selenium particles in the effluent. The alkylated and the colloidal fractions contributed up to 15 and 31%, respectively, to the dissolved selenium concentration. The size fractions of the colloidal dispersion were: 4 to 0.45 mum: up to 21%, 0.45 to 0.2 mum: up to 11%, and particles smaller than 0.2 mum: up to 8%. Particles of 4 to 0.45 mum were formed in the external settler, but did not settle. SEM-EDX analysis showed that microorganisms form these selenium containing colloidal particles extracellularly on their surface. Lowering the temperature by 10 degrees C for 6 h resulted in drastically reduced selenate removal efficiencies (after a delay of 1.5 d), accompanied by the temporary formation of an unknown, soluble, organic selenium species. This study shows that a careful process control is a prerequisite for selenium treatment in UASB bioreactors, as disturbances in the operational conditions induce elevated selenium effluent concentrations by alkylation and colloid formation.