A new tool based on faecal stanols can distinguish specific mammalian species in modern and past environments

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Preferential degradation of polyphenols from Sphagnum - 4-isopropenylphenol as a proxy for past hydrological conditions in Sphagnum-dominated peat

The net accumulation of remains of Sphagnum spp. is fundamental to the development of many peatlands. The effect of polyphenols from Sphagnum on decomposition processes is frequently cited but has barely been studied. The central area of the Rödmossamyran peatland (Sweden) is an open lawn that consists mostly of Sphagnum spp. with a very low contribution from vascular plants. In order to determine the effects of decay on sphagnum phenols, 53 samples of a 2.7 m deep core from this lawn were analysed with pyrolysis gas chromatography-mass spectrometry (pyrolysis-GC/MS) and compared with more traditional decomposition proxies such as C/N ratio, UV light transmission of alkaline peat extracts, and bulk density. Factor Analysis of 72 quantified pyrolysis products suggested that the variation in 4-isopropenylphenol was largely determined by aerobic decomposition instead of Sphagnum abundance. In order to evaluate the effects of aerobic decay in Sphagnum peat, down-core records from different climatic regions were compared using molecular markers for plant biopolymers and C/N ratio. These included markers for lignin from vascular plants ((di)methoxyphenols), polyphenols from Sphagnum spp. (4-isopropenylphenol), and cellulose (levoglucosan). Our results indicate that polyphenols from Sphagnum are preferentially degraded over polysaccharides; consequently the variability of the marker for sphagnum acid, 4-isopropenylphenol, was found indicative of decomposition instead of reflecting the abundance of Sphagnum remains. The fact that 4-isopropenylphenol is aerobically degraded in combination with its specificity for Sphagnum spp. makes it a consistent indicator of past hydrological conditions in Sphagnum-dominated peat. In contrast, the variability of C/N records in Sphagnum-dominated peat was influenced by both vegetation shifts and decomposition, and the dominant effect differed between the studied peatlands. Our results provide direction for modelling studies that try to predict possible feedback mechanisms between peatlands and future climate change, and indicate that the focus in Sphagnum decay studies should be on carbohydrates rather than on phenolic compounds

Industrial-era lead and mercury contamination in southern Greenland implicates North American sources

We would like to thank Jesús R. Aboal (Universidade de Santiago de Compostela) and Kjell Billström (Naturhistoriska Riksmuseet) for access to the laboratory facilities; Antonio Rodríguez López helped with laboratory work. This research was done under the framework of the projects CGL2010-20672 (Plan Nacional I+D+i, Spanish Ministerio de Economía y Competitividad), R2014/001 and GPC2014-009 (Dirección Xeral I+D, Xunta de Galicia). The authors gratefully acknowledge the financial support of the UK Leverhulme Trust Footprints on the Edge of Thule programme award for core collection and associated environmental research.Peer reviewedPostprin

Introducing Global Peat-Specific Temperature and pH Calibrations Based on brGDGT Bacterial Lipids

Glycerol dialkyl glycerol tetraethers (GDGTs) are membrane-spanning lipids from Bacteria and Archaea that are ubiquitous in a range of natural archives and especially abundant in peat. Previous work demonstrated that the distribution of bacterial branched GDGTs (brGDGTs) in mineral soils is correlated to environmental factors such as mean annual air temperature (MAAT) and soil pH. However, the influence of these parameters on brGDGT distributions in peat is largely unknown. Here we investigate the distribution of brGDGTs in 470 samples from 96 peatlands around the world with a broad mean annual air temperature (−8 to 27 °C) and pH (3–8) range and present the first peat-specific brGDGT-based temperature and pH calibrations. Our results demonstrate that the degree of cyclisation of brGDGTs in peat is positively correlated with pH, pH = 2.49 × CBTpeat + 8.07 (n = 51, R2 = 0.58, RMSE = 0.8) and the degree of methylation of brGDGTs is positively correlated with MAAT, MAATpeat (°C) = 52.18 × MBT5me′ − 23.05 (n = 96, R2 = 0.76, RMSE = 4.7 °C). These peat-specific calibrations are distinct from the available mineral soil calibrations. In light of the error in the temperature calibration (∼4.7 °C), we urge caution in any application to reconstruct late Holocene climate variability, where the climatic signals are relatively small, and the duration of excursions could be brief. Instead, these proxies are well-suited to reconstruct large amplitude, longer-term shifts in climate such as deglacial transitions. Indeed, when applied to a peat deposit spanning the late glacial period (∼15.2 kyr), we demonstrate that MAATpeat yields absolute temperatures and relative temperature changes that are consistent with those from other proxies. In addition, the application of MAATpeat to fossil peat (i.e. lignites) has the potential to reconstruct terrestrial climate during the Cenozoic. We conclude that there is clear potential to use brGDGTs in peats and lignites to reconstruct past terrestrial climate. © 2017 The Author

An application of kernel methods to variety identification based on SSR markers genetic fingerprinting

<p>Abstract</p> <p>Background</p> <p>In crop production systems, genetic markers are increasingly used to distinguish individuals within a larger population based on their genetic make-up. Supervised approaches cannot be applied directly to genotyping data due to the specific nature of those data which are neither continuous, nor nominal, nor ordinal but only partially ordered. Therefore, a strategy is needed to encode the polymorphism between samples such that known supervised approaches can be applied. Moreover, finding a minimal set of molecular markers that have optimal ability to discriminate, for example, between given groups of varieties, is important as the genotyping process can be costly in terms of laboratory consumables, labor, and time. This feature selection problem also needs special care due to the specific nature of the data used.</p> <p>Results</p> <p>An approach encoding SSR polymorphisms in a positive definite kernel is presented, which then allows the usage of any kernel supervised method. The polymorphism between the samples is encoded through the Nei-Li genetic distance, which is shown to define a positive definite kernel between the genotyped samples. Additionally, a greedy feature selection algorithm for selecting SSR marker kits is presented to build economical and efficient prediction models for discrimination. The algorithm is a filter method and outperforms other filter methods adapted to this setting. When combined with kernel linear discriminant analysis or kernel principal component analysis followed by linear discriminant analysis, the approach leads to very satisfactory prediction models.</p> <p>Conclusions</p> <p>The main advantage of the approach is to benefit from a flexible way to encode polymorphisms in a kernel and when combined with a feature selection algorithm resulting in a few specific markers, it leads to accurate and economical identification models based on SSR genotyping.</p

Inorganic geochemistry of lake sediments: A review of analytical techniques and guidelines for data interpretation

Inorganic geochemistry is a powerful tool in paleolimnology. It has become one of the most commonly used techniques to analyze lake sediments, particularly due to the development and increasing availability of XRF core scanners during the last two decades. It allows for the reconstruction of the continuous processes that occur in lakes and their watersheds, and it is ideally suited to identify event deposits. How earth surface processes and limnological conditions are recorded in the inorganic geochemical composition of lake sediments is, however, relatively complex. Here, we review the main techniques used for the inorganic geochemical analysis of lake sediments and we offer guidance on sample preparation and instrument selection. We then summarize the best practices to process and interpret bulk inorganic geochemical data. In particular, we emphasize that log-ratio transformation is critical for the rigorous statistical analysis of geochemical datasets, whether they are obtained by XRF core scanning or more traditional techniques. In addition, we show that accurately interpreting inorganic geochemical data requires a sound understanding of the main components of the sediment (organic matter, biogenic silica, carbonates, lithogenic particles) and mineral assemblages. Finally, we provide a series of examples illustrating the potential and limits of inorganic geochemistry in paleolimnology. Although the examples presented in this paper focus on lake and fjord sediments, the principles presented here also apply to other sedimentary environments

The Sol Genomics Network (solgenomics.net): growing tomatoes using Perl

The Sol Genomics Network (SGN; http://solgenomics.net/) is a clade-oriented database (COD) containing biological data for species in the Solanaceae and their close relatives, with data types ranging from chromosomes and genes to phenotypes and accessions. SGN hosts several genome maps and sequences, including a pre-release of the tomato (Solanum lycopersicum cv Heinz 1706) reference genome. A new transcriptome component has been added to store RNA-seq and microarray data. SGN is also an open source software project, continuously developing and improving a complex system for storing, integrating and analyzing data. All code and development work is publicly visible on GitHub (http://github.com). The database architecture combines SGN-specific schemas and the community-developed Chado schema (http://gmod.org/wiki/Chado) for compatibility with other genome databases. The SGN curation model is community-driven, allowing researchers to add and edit information using simple web tools. Currently, over a hundred community annotators help curate the database. SGN can be accessed at http://solgenomics.net/