Despite recognition of the importance of soil bacteria to terrestrial ecosystem functioning there is little consensus on the factors regulating belowground biodiversity. Here we present a multi-scale spatial assessment of soil bacterial community profiles across Great Britain (> 1000 soil cores), and show the first landscape scale map of bacterial distributions across a nation. Bacterial diversity and community dissimilarities, assessed using terminal restriction fragment length polymorphism, were most strongly related to soil pH providing a large-scale confirmation of the role of pH in structuring bacterial taxa. However, while α diversity was positively related to pH, the converse was true for β diversity (between sample variance in α diversity). β diversity was found to be greatest in acidic soils, corresponding with greater environmental heterogeneity. Analyses of clone libraries revealed the pH effects were predominantly manifest at the level of broad bacterial taxonomic groups, with acidic soils being dominated by few taxa (notably the group 1 Acidobacteria and Alphaproteobacteria). We also noted significant correlations between bacterial communities and most other measured environmental variables (soil chemistry, aboveground features and climatic variables), together with significant spatial correlations at close distances. In particular, bacterial and plant communities were closely related signifying no strong evidence that soil bacteria are driven by different ecological processes to those governing higher organisms. We conclude that broad scale surveys are useful in identifying distinct soil biomes comprising reproducible communities of dominant taxa. Together these results provide a baseline ecological framework with which to pursue future research on both soil microbial function, and more explicit biome based assessments of the local ecological drivers of bacterial biodiversity.

Soil pH is a major determinant of microbial ecosystem processes and potentially a major driver of evolution, adaptation, and diversity of ammonia oxidizers, which control soil nitrification. Archaea are major components of soil microbial communities and contribute significantly to ammonia oxidation in some soils. To determine whether pH drives evolutionary adaptation and community structure of soil archaeal ammonia oxidizers, sequences of amoA , a key functional gene of ammonia oxidation, were examined in soils at global, regional, and local scales. Globally distributed database sequences clustered into 18 well-supported phylogenetic lineages that dominated specific soil pH ranges classified as acidic (pH <5), acido-neutral (5≤ pH <7), or alkalinophilic (pH ≥7). To determine whether patterns were reproduced at regional and local scales, amoA gene fragments were amplified from DNA extracted from 47 soils in the United Kingdom (pH 3.5–8.7), including a pH-gradient formed by seven soils at a single site (pH 4.5–7.5). High-throughput sequencing and analysis of amoA gene fragments identified an additional, previously undiscovered phylogenetic lineage and revealed similar pH-associated distribution patterns at global, regional, and local scales, which were most evident for the five most abundant clusters. Archaeal amoA abundance and diversity increased with soil pH, which was the only physicochemical characteristic measured that significantly influenced community structure. These results suggest evolution based on specific adaptations to soil pH and niche specialization, resulting in a global distribution of archaeal lineages that have important consequences for soil ecosystem function and nitrogen cycling.

No abstract

The utility of social media for both collecting and disseminating information during natural disasters is increasingly recognised. The rapid nature of urban flooding from intense rainfall means accurate surveying of peak depths and flood extents is rarely achievable, hindering the validation of urban flood models. This paper presents a real‐time modelling framework to identify areas likely to have flooded using data obtained only through social media. Graphics processing unit (GPU) accelerated hydrodynamic modelling is used to simulate flooding in a 48‐km2 area of Newcastle upon Tyne, with results automatically compared against flooding identified through social media, allowing inundation to be inferred elsewhere in the city with increased detail and accuracy. Data from Twitter during two 2012 flood events are used to test the framework, with the inundation results indicative of good agreement against crowd‐sourced and anecdotal data, even though the sample of successfully geocoded Tweets was relatively small.

No abstract

In the light of the long-standing concern in management regarding the gap that can arise between organizational policy prescriptions and line-management practice, the purpose of this article is to provide insights into the nature and causes of that gap. The particular focus of analysis is the line manager's role in facilitating the employment security of employees who have contracted serious illness, injuries or disabilities. More specifically, the article presents findings from four case-study organizations in the UK, and identifies a number of factors that militate against the ability of line managers to comply with organizational policies on the provision of workplace adjustments and employment security for ill or disabled workers. These factors include: contradictory policy requirements, weaknesses in training, lack of support from relevant internal and external specialists and various work and budgetary pressures. The authors suggest four areas of action that may begin to reduce the potential for conflicts between management and employees, and minimize the legal vulnerability of organizations in an area of increasing domestic and international regulation.

No abstract

Transport accessibility is an important driver of urban growth and key to the sustainable development of cities. This paper presents a simple GIS-based tool developed to allow the rapid analysis of accessibility by different transport modes. Designed to be flexible and use publicly-available data, this tool (built in ArcGIS) uses generalized cost to measure transport costs across networks including monetary and distance components. The utility of the tool is demonstrated on London, UK, showing the differing patterns of accessibility across the city by different modes. It is shown that these patterns can be examined spatially, by accessibility to particular destinations (e.g., employment locations), or as a global measure across a whole city system. A number of future infrastructure scenarios are tested, examining the potential for increasing the use of low-carbon forms of transport. It is shown that private car journeys are still the least cost mode choice in London, but that infrastructure investments can play a part in reducing the cost of more sustainable transport options.