Including community composition in biodiversity–productivity models

Studies on biodiversity and ecosystem functioning (BEF) have elicited debate over the interpretation of the positive relationship between species richness and plant productivity. Manipulating richness cannot be achieved without affecting composition; it is thus essential to consider the latter in statistical models.We firstly review existing approaches that use species richness as an explanatory variable and propose modifications to improve their performance. We use an original data set to illustrate the analyses. The classical method where composition is coded as a factor with a level for each different species mixture can be improved by defining the levels using clustering. Methods based on ordinations reduce the dimensionality of plant composition and use the new coordinates as fixed effects; they provide a much better fit to our observations.Secondly, we develop a new method where composition is included as a similarity matrix affecting the residual variance–covariance. Similarity in composition between plots is treated in the same way as shared evolutionary history between species in phylogenetic regression. We find that it outperforms the other models.We discuss the different approaches and suggest that our method is particularly suited for observational studies or for manipulative studies where plant diversity is not kept constant by weeding. By treating species composition in an intuitive and sensible way, it offers a valuable and powerful complement to existing models

The relative contributions of species richness and species composition to ecosystem functioning

How species diversity influences ecosystem functioning has been the subject of many experiments and remains a key question for ecology and conservation biology. However, the fact that diversity cannot be manipulated without affecting species composition makes this quest methodologically challenging. Here, we evaluate the relative importance of diversity and of composition on biomass production, by using partial Mantel tests for one variable while controlling for the other. We analyse two datasets, from the Jena (2002–2008) and the Grandcour (2008–2009) Experiments. In both experiments, plots were sown with different numbers of species to unravel mechanisms underlying the relationship between biodiversity and ecosystem functioning (BEF). Contrary to Jena, plots were neither mowed nor weeded in Grandcour, allowing external species to establish. Based on the diversity–ecosystem functioning and competition theories, we tested two predictions: 1) the contribution of composition should increase with time; 2) the contribution of composition should be more important in non-weeded than in controlled systems. We found support for the second hypothesis, but not for the first. On the contrary, the contribution of species richness became markedly more important few years after the start of the Jena Experiment. This result can be interpreted as suggesting that species complementarity, rather than intraspecific competition, is the driving force in this system. Finally, we explored to what extent the estimated relative importance of both factors varied when measured on different spatial scales of the experiment (in this case, increasing the number of plots included in the analyses). We found a strong effect of scale, suggesting that comparisons between studies, and more generally the extrapolation of results from experiments to natural situations, should be made with caution

Source apportionment of polychlorinated biphenyls (PCBs) using different receptor models: A case study on sediment from the Portland Harbor Superfund Site (PHSS), Oregon, USA

Multivariate modelling techniques are used by a wide variety of investigations in environmental chemistry. It is surprisingly rare for studies to show a detailed understanding of uncertainties created by modelling or how uncertainties in chemical analysis impact model outputs. It is common to use untrained multivariate models for receptor modelling. These models produce a slightly different output each time they are run. The fact that a single model can provide different results is rarely acknowledged. In this manuscript, we attempt to address this by investigating differences that can be generated using four different receptor models (NMF, ALS, PMF & PVA) to perform source apportionment of polychlorinated biphenyls (PCBs) in surface sediments from Portland Harbor. Results showed that models generally had a strong agreement and identified the same main signatures that represented commercial PCB mixtures, however, subtle differences were identified by; different models, same models but with a different number of end members (EM), and the same model with the same number of end members. As well as identifying different Aroclor-like signatures, the relative proportion of these sources also varied. Depending on which method is selected it may have a significant impact on conclusions of a scientific report or litigation case and ultimately, allocation on who is responsible for paying for remediation. Therefore, care must be taken to understand these uncertainties to select a method that produces consistent results with end members that can be chemically explained. We also investigated a novel approach to use our multivariate models to identify inadvertent sources of PCBs. By using a residual plot produced from one of our models (NMF) we were able to suggest the presence of approximately 30 different potentially inadvertently produced PCBs which account for 6.6 % of the total PCBs in Portland Harbor sediments

Polychlorinated biphenyl (PCB) concentrations and profiles in marine mammals from the North Atlantic Ocean

Polychlorinated biphenyls (PCBs) can provide crucial information into the bioaccumulation and biomagnification of POPs in marine mammals. Muscle tissue samples were obtained for detailed PCB congener specific analysis of all 209 PCBs in 11 species of marine mammals stranded across the coast of the UK between 2010 and 2013. At least 145 PCB congeners were found in each individual. The highest concentrations of PCBs were recorded in a killer whale (318 mg/kg lipid) and the highest toxic equivalent in a Risso's dolphin (1687 pg/g TEQ2005 wet). Concentrations of PCBs in the majority of samples exceeded toxic thresholds (9 mg/kg lipid) for marine mammals, highlighting the health risk they face from PCB exposure. Many PCB profiles did not fit typical ‘Aroclor’ signatures, but instead indicated patterns of congeners that are resistant to biotransformation and elimination. However, this study identified a novel PCB signature in a sei whale that has not yet been previously observed in marine mammals. The whale had a PCB profile that included lighter and inadvertent PCB congeners such as PCB 11, suggesting that the main source of exposure was through atmospheric deposition, rather than terrestrial discharges. Seven subsamples were chosen for chiral analysis of PCB 95, 136 and 149. The enantiomer fractions (EFs) of C-PCBs 95 and 149 were non racemic suggesting there may be enantiomer selective metabolism in marine mammals. Although there has been a shift in the literature towards emerging pollutants, this study acts as a stark reminder that PCBs continue to pose a significant risk to wildlife

Understanding negative biodiversity–ecosystem functioning relationship in semi-natural wildflower strips

Studies on biodiversity–ecosystem functioning (BEF) in highly controlled experiments often yield results incompatible with observations from natural systems: experimental results often reveal positive relationships between diversity and productivity, while for natural systems, zero or even negative relationships have been reported. The discrepancy may arise due to a limited or closed local species pool in experiments, while natural systems in meta-community contexts experience dynamic processes, i.e., colonization and extinctions. In our study, we analysed plant community properties and above-ground biomass within a semi-natural (i.e., not weeded) experiment in an agricultural landscape. Eleven replicates with four different diversity levels were created from a species pool of 20 wildflower species. We found an overall significant negative relationship between total diversity and productivity. This relationship likely resulted from invasion resistance: in plots sown with low species numbers, we observed colonization by low-performing species; colonization increased species richness but did not contribute substantially to productivity. Interestingly, when analysing the biomass of the sown and the colonizer species separately, we observed in both cases positive BEF relationships, while this relationship was negative for the whole system. A structural equation modelling approach revealed that higher biomass of the sown species was linked to higher species richness, while the positive BEF relationship of the colonizers was indirect and constrained by the sown species biomass. Our results suggest that, in semi-natural conditions common in extensive agroecosystems, the negative BEF relationship results from the interplay between local dominant species and colonization from the regional species pool by subordinate species

ESNOQ, Proteomic Quantification of Endogenous S-Nitrosation

S-nitrosation is a post-translational protein modification and is one of the most important mechanisms of NO signaling. Endogenous S-nitrosothiol (SNO) quantification is a challenge for detailed functional studies. Here we developed an ESNOQ (Endogenous SNO Quantification) method which combines the stable isotope labeling by amino acids in cell culture (SILAC) technique with the detergent-free biotin-switch assay and LC-MS/MS. After confirming the accuracy of quantification in this method, we obtained an endogenous S-nitrosation proteome for LPS/IFN-γ induced RAW264.7 cells. 27 S-nitrosated protein targets were confirmed and using our method we were able to obtain quantitative information on the level of S-nitrosation on each modified Cys. With this quantitative information, over 15 more S-nitrosated targets were identified than in previous studies. Based on the quantification results, we found that the S-nitrosation levels of different cysteines varied within one protein, providing direct evidence for differences in the sensitivity of cysteine residues to reactive nitrosative stress and that S-nitrosation is a site-specific modification. Gene ontology clustering shows that S-nitrosation targets in the LPS/IFN-γ induced RAW264.7 cell model were functionally enriched in protein translation and glycolysis, suggesting that S-nitrosation may function by regulating multiple pathways. The ESNOQ method described here thus provides a solution for quantification of multiple endogenous S-nitrosation events, and makes it possible to elucidate the network of relationships between endogenous S-nitrosation targets involved in different cellular processes

The -786T>C promoter polymorphism of the NOS3 gene is associated with prostate cancer progression

<p>Abstract</p> <p>Background</p> <p>There is no biological or epidemiological data on the association between <it>NOS3 </it>promoter polymorphisms and prostate cancer. The polymorphisms in the promoter region of <it>NOS3 </it>gene may be responsible for variations in the plasma NO, which may promote cancer progression by providing a selective growth advantage to tumor cells by angiogenic stimulus and by direct DNA damage.</p> <p>Methods</p> <p>This study aimed evaluating the <it>NOS3 </it>promoter polymorphisms by PCR-SSCP and sequencing, associating genotypes and haplotypes with <it>NOS3 </it>expression levels through semi-quantitative RT-PCR, and with <it>PCA</it>3 mRNA detection, a specific tumor biomarker, in the peripheral blood of pre-surgical samples from 177 patients; 83 PCa and 94 BPH.</p> <p>Results</p> <p>Three novel SNPs were identified -764A>G, -714G>T and -649G>A in the <it>NOS3 </it>gene promoter region, which together with the -786T>C generated four haplotypes (N, T, C, A). <it>NOS3 </it>gene expression levels were affected by the -786T>C polymorphism, and there was a 2-fold increase in <it>NOS3 </it>levels favored by the incorporation of each C allele. <it>NOS3 </it>levels higher than 80% of the constitutive gene expression level (<it>B2M</it>) presented a 4-fold increase in PCa occurrence.</p> <p>Conclusion</p> <p>The -786T>C polymorphism was the most important promoter alteration of the <it>NOS3 </it>gene that may affect the PCa progression, but not its occurrence, and the incorporation of the C allele is associated with increased levels of <it>NOS3 </it>transcripts. The <it>NOS3 </it>transcript levels presented a bimodal behavior in tumor development and may be used as a biomarker together with the <it>PCA3 </it>marker for molecular staging of the prostate cancer.</p