Implications of short-range spatial variation of soil bulk density for adequate field-sampling protocols: methodology and results from two contrasting soils

Soil bulk density (BD) is measured during soil monitoring. Because it is spatially variable, an appropriate sampling protocol is required. This paper shows how information on short-range variability can be used to quantify uncertainty of estimates of mean BD and soil organic carbon on a volumetric basis (SOCv) at a sampling site with different sampling intensities. We report results from two contrasting study areas, with mineral soil and with peat. More sites should be investigated to develop robust protocols for national-scale monitoring, but these results illustrate the methodology. A 20 × 20-m2 monitoring site was considered and sampling protocols were evaluated under geostatistical models of our two study areas. At sites with local soil variability comparable to our mineral soil, sampling at 16 points (4 × 4 square grid of interval 5 m) would achieve a root mean square error (RMSE) of the sample mean value of both BD and SOCv of less than 5% of the mean (topsoil and subsoil). Pedotransfer functions (PTFs) gave predictions of mean soil BD at a sample site, comparable to our study area on mineral soil, with similar precision to a single direct measurement of BD. On peat soils comparable to our second study area, the mean BD for the monitoring site at depth 0–50 cm would be estimated with RMSE to be less than 5% of the mean with a sample of 16 cores, but at greater depths this criterion cannot be achieved with 25 cores or fewer

Scope to predict soil properties at within-field scale from small samples using proximally sensed γ-ray spectrometer and EM induction data

Spatial predictions of soil properties are needed for various purposes. However, the costs associated with soil sampling and laboratory analysis are substantial. One way to improve efficiencies is to combine measurement of soil properties with collection of cheaper-to-measure ancillary data. There are two possible approaches. The first is the formation of classes from ancillary data. A second is the use of a simple predictive linear model of the target soil property on the ancillary variables. Here, results are presented and compared where proximally sensed gamma-ray (γ-ray) spectrometry and electromagnetic induction (EMI) data are used to predict the variation in topsoil properties (e.g. clay content and pH). In the first instance, the proximal data is numerically clustered using a fuzzy k-means (FKM) clustering algorithm, to identify contiguous classes. The resultant digital soil maps (i.e. k = 2–10 classes) are consistent with a soil series map generated using traditional soil profile description, classification and mapping methods at a highly variable site near the township of Shelford, Nottinghamshire UK. In terms of prediction, the calculated expected value of mean squared prediction error (i.e. σ2p,C) indicated that values of k = 7 and 8 were ideal for predicting clay and pH. Secondly, a linear mixed model (LMM) is fitted in which the proximal data are fixed effects but the residuals are treated as a combination of a spatially correlated random effect and an independent and identically distributed error. In terms of prediction, the expected value of the mean squared prediction error from a regression (σ2p,R) suggested that the regression models were able to predict clay content, better than FKM clustering. The reverse was true with respect to pH, however. We conclude that both methods have merit. In the case of the clustering the approach is able to account for soil properties which have non-linearity's with the ancillary data (i.e. pH), whereas the LMM approach is best when there is a strong linear relationship (i.e. clay)

Pasture age impacts soil fungal composition while bacteria respond to soil chemistry

Pasture is a globally important managed habitat providing both food and income. The way in which it is managed leads to a wide range of impacts on soil microbial communities and associated soil health. While there have been several studies comparing pasture farming to other forms of land use, we still have limited understanding of how the soil microbial communities vary between pasture farms and according to management practices. Here we present the results of a field survey across 56 UK livestock farms that are managed by members of the Pasture fed Livestock Association, using amplicon sequencing of the 16S and ITS regions to characterise the soil bacterial and fungal community within fields that have been under pasture for differing durations. We show that grazing management intensity has only limited effects upon microbial community structure, while the duration of pasture since ploughing (ranging from 1 year to over 100 years) impacted the fungal community structure. The impact of management duration was conditional upon soil physicochemical properties, particularly pH. Plant community effects on upon soil bacterial and fungal composition appear to also interact with the soil chemistry, highlighting the importance of plant-soil interactions in determining microbial community structure. Analyses of microbial indicators revealed proportionally more fungal taxa that responded to multiple ecosystem health associated properties than bacterial taxa. We also identified several fungal taxa that both acted as indicators of soil health related properties within our dataset and showed differentiation between grassland types in a national survey, indicating the generality of some fungal indicators to the national level. Members of the Agaricomycetes were associated with multiple indicators of soil health. Our results show the importance of maintaining grassland for the development of plant-soil interactions and microbial community structure with concomitant effects on soil and general ecosystem health

Soil bacterial and fungal communities show within field heterogeneity that varies by land management and distance metric

Increasing interest in the use of microbial metrics to evaluate soil health raises the issue of how fine-scale heterogeneity can affect microbial community measurements. Here we analyse bacterial and fungal communities of over 100 soil samples across 17 pasture farms and evaluate beta diversity at different scales. We find large variation in microbial communities between different points in the same field, and if Aitchison distance is used we find that within-field variation is as high as between-farm variation. However, if Bray-Curtis or Jaccard distance are used this variation is partially explained by differences in soil pH and vegetation and is higher under mob grazing for fungi. Hence, field scale variation in microbial communities can impact the evaluation of soil health

Divergent national-scale trends of microbial and animal biodiversity revealed across diverse temperate soil ecosystems

Soil biota accounts for ~25% of global biodiversity and is vital to nutrient cycling and primary production. There is growing momentum to study total belowground biodiversity across large ecological scales to understand how habitat and soil properties shape belowground communities. Microbial and animal components of belowground communities follow divergent responses to soil properties and land use intensification; however, it is unclear whether this extends across heterogeneous ecosystems. Here, a national-scale metabarcoding analysis of 436 locations across 7 different temperate ecosystems shows that belowground animal and microbial (bacteria, archaea, fungi, and protists) richness follow divergent trends, whereas β-diversity does not. Animal richness is governed by intensive land use and unaffected by soil properties, while microbial richness was driven by environmental properties across land uses. Our findings demonstrate that established divergent patterns of belowground microbial and animal diversity are consistent across heterogeneous land uses and are detectable using a standardised metabarcoding approach

Citalopram reduces aggregation of ATXN3 in a YAC transgenic mouse model of Machado-Joseph disease

Machado-Joseph disease, also known as spinocerebellar ataxia type 3, is a fatal polyglutamine disease with no disease-modifying treatment. The selective serotonin reuptake inhibitor citalopram was shown in nematode and mouse models to be a compelling repurposing candidate for Machado-Joseph disease therapeutics. We sought to confirm the efficacy of citalopram to decrease ATXN3 aggregation in an unrelated mouse model of Machado-Joseph disease. Four-week-old YACMJD84.2 mice and non-transgenic littermates were given citalopram 8 mg/kg in drinking water or water for 10 weeks. At the end of treatment, brains were collected for biochemical and pathological analyses. Brains of citalopram-treated YACMJD84.2 mice showed an approximate 50% decrease in the percentage of cells containing ATXN3-positive inclusions in the substantia nigra and three examined brainstem nuclei compared to controls. No differences in ATXN3 inclusion load were observed in deep cerebellar nuclei of mice. Citalopram effect on ATXN3 aggregate burden was corroborated by immunoblotting analysis. While lysates from the brainstem and cervical spinal cord of citalopram-treated mice showed a decrease in all soluble forms of ATXN3 and a trend toward reduction of insoluble ATXN3, no differences in ATXN3 levels were found between cerebella of citalopram-treated and vehicle-treated mice. Citalopram treatment altered levels of select components of the cellular protein homeostatic machinery that may be expected to enhance the capacity to refold and/or degrade mutant ATXN3. The results here obtained in a second independent mouse model of Machado-Joseph disease further support citalopram as a potential drug to be repurposed for this fatal disorder.This work was funded by Becky Babcox Research Fund/pilot research award G015617, University of Michigan to M.C.C. and NINDS/NIH R01NS038712 to H.L.P. The work performed at the University of Minho was funded by the European Regional Development Funds (FEDER), through the Competitiveness Factors Operational Programme (COMPETE), and by National funds, through the Foundation for Science and Technology (FCT), under the scope of the project POCI-01-0145-FEDER-007038. This article was developed under the scope of the project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Program (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the FEDER. This work was also supported by FCT and COMPETE through the projects [PTDC/SAU-GMG/112617/2009] (to P.M.) and [EXPL/BIM-MEC/ 0239/2012] (to A.T.C.); by FCT through the project [POCI-01-0145- FEDER-016818 (PTDC/NEU-NMC/3648/2014)] (to P.M.); by National Ataxia Foundation (to P.M. and to A.T.C.); and by Ataxia UK (to P.M.). S.D.S. and A.T.C. were supported by fellowships from FCT, SFRH/BD/ 78388/2011 and SFRH/BPD/102317/2014, respectively. FCT fellowships are co-financed by POPH, QREN, Governo da República Portuguesa and EU/FSE

The handbook for standardised field and laboratory measurements in terrestrial climate-change experiments and observational studies

Climate change is a worldwide threat to biodiversity and ecosystem structure, functioning, and services. To understand the underlying drivers and mechanisms, and to predict the consequences for nature and people, we urgently need better understanding of the direction and magnitude of climate‐change impacts across the soil–plant–atmosphere continuum. An increasing number of climate‐change studies is creating new opportunities for meaningful and high‐quality generalisations and improved process understanding. However, significant challenges exist related to data availability and/or compatibility across studies, compromising opportunities for data re‐use, synthesis, and upscaling. Many of these challenges relate to a lack of an established “best practice” for measuring key impacts and responses. This restrains our current understanding of complex processes and mechanisms in terrestrial ecosystems related to climate change

Evolutionary Reconstructions of the Transferrin Receptor of Caniforms Supports Canine Parvovirus Being a Re-emerged and Not a Novel Pathogen in Dogs

Parvoviruses exploit transferrin receptor type-1 (TfR) for cellular entry in carnivores, and specific interactions are key to control of host range. We show that several key mutations acquired by TfR during the evolution of Caniforms (dogs and related species) modified the interactions with parvovirus capsids by reducing the level of binding. These data, along with signatures of positive selection in the TFRC gene, are consistent with an evolutionary arms race between the TfR of the Caniform clade and parvoviruses. As well as the modifications of amino acid sequence which modify binding, we found that a glycosylation site mutation in the TfR of dogs which provided resistance to the carnivore parvoviruses which were in circulation prior to about 1975 predates the speciation of coyotes and dogs. Because the closely-related black-backed jackal has a TfR similar to their common ancestor and lacks the glycosylation site, reconstructing this mutation into the jackal TfR shows the potency of that site in blocking binding and infection and explains the resistance of dogs until recent times. This alters our understanding of this well-known example of viral emergence by indicating that canine parvovirus emergence likely resulted from the re-adaptation of a parvovirus to the resistant receptor of a former host

Inactivation of PNKP by mutant ATXN3 triggers apoptosis by activating the DNA damage-response pathway in SCA3.

Spinocerebellar ataxia type 3 (SCA3), also known as Machado-Joseph disease (MJD), is an untreatable autosomal dominant neurodegenerative disease, and the most common such inherited ataxia worldwide. The mutation in SCA3 is the expansion of a polymorphic CAG tri-nucleotide repeat sequence in the C-terminal coding region of the ATXN3 gene at chromosomal locus 14q32.1. The mutant ATXN3 protein encoding expanded glutamine (polyQ) sequences interacts with multiple proteins in vivo, and is deposited as aggregates in the SCA3 brain. A large body of literature suggests that the loss of function of the native ATNX3-interacting proteins that are deposited in the polyQ aggregates contributes to cellular toxicity, systemic neurodegeneration and the pathogenic mechanism in SCA3. Nonetheless, a significant understanding of the disease etiology of SCA3, the molecular mechanism by which the polyQ expansions in the mutant ATXN3 induce neurodegeneration in SCA3 has remained elusive. In the present study, we show that the essential DNA strand break repair enzyme PNKP (polynucleotide kinase 3'-phosphatase) interacts with, and is inactivated by, the mutant ATXN3, resulting in inefficient DNA repair, persistent accumulation of DNA damage/strand breaks, and subsequent chronic activation of the DNA damage-response ataxia telangiectasia-mutated (ATM) signaling pathway in SCA3. We report that persistent accumulation of DNA damage/strand breaks and chronic activation of the serine/threonine kinase ATM and the downstream p53 and protein kinase C-d pro-apoptotic pathways trigger neuronal dysfunction and eventually neuronal death in SCA3. Either PNKP overexpression or pharmacological inhibition of ATM dramatically blocked mutant ATXN3-mediated cell death. Discovery of the mechanism by which mutant ATXN3 induces DNA damage and amplifies the pro-death signaling pathways provides a molecular basis for neurodegeneration due to PNKP inactivation in SCA3, and for the first time offers a possible approach to treatment.This study was funded by NIH grant NS073976 to TKH and a John Sealy Grant to PSS