Model inter-comparison between statistical and dynamic model assessments of the long-term stability of blanket peat in Great Britain (1940-2099)

We compared output from 3 dynamic process-based models (DMs: ECOSSE, MILLENNIA and the Durham Carbon Model) and 9 bioclimatic envelope models (BCEMs; including BBOG ensemble and PEATSTASH) ranging from simple threshold to semi-process-based models. Model simulations were run at 4 British peatland sites using historical climate data and climate projections under a medium (A1B) emissions scenario from the 11-RCM (regional climate model) ensemble underpinning UKCP09. The models showed that blanket peatlands are vulnerable to projected climate change; however, predictions varied between models as well as between sites. All BCEMs predicted a shift from presence to absence of a climate associated with blanket peat, where the sites with the lowest total annual precipitation were closest to the presence/absence threshold. DMs showed a more variable response. ECOSSE predicted a decline in net C sink and shift to net C source by the end of this century. The Durham Carbon Model predicted a smaller decline in the net C sink strength, but no shift to net C source. MILLENNIA predicted a slight overall increase in the net C sink. In contrast to the BCEM projections, the DMs predicted that the sites with coolest temperatures and greatest total annual precipitation showed the largest change in carbon sinks. In this model inter-comparison, the greatest variation in model output in response to climate change projections was not between the BCEMs and DMs but between the DMs themselves, because of different approaches to modelling soil organic matter pools and decomposition amongst other processes. The difference in the sign of the response has major implications for future climate feedbacks, climate policy and peatland management. Enhanced data collection, in particular monitoring peatland response to current change, would significantly improve model development and projections of future change.

Bioclimatic envelope model of climate change impacts on blanket peatland distribution in Great Britain

Blanket peatlands are rain-fed mires that cover the landscape almost regardless of topography. The geographical extent of this type of peatland is highly sensitive to climate. We applied a global process-based bioclimatic envelope model, PeatStash, to predict the distribution of British blanket peatlands. The model captures the present areal extent (Kappa = 0.77) and is highly sensitive to both temperature and precipitation changes. When the model is run using the UKCIP02 climate projections for the time periods 2011–2040, 2041–2070 and 2071–2100, the geographical distribution of blanket peatlands gradually retreats towards the north and the west. In the UKCIP02 high emissions scenario for 2071–2100, the blanket peatland bioclimatic space is ~84% smaller than contemporary conditions (1961–1990); only parts of the west of Scotland remain inside this space. Increasing summer temperature is the main driver of the projected changes in areal extent. Simulations using 7 climate model outputs resulted in generally similar patterns of declining aereal extent of the bioclimatic space, although differing in degree. The results presented in this study should be viewed as a first step towards understanding the trends likely to affect the blanket peatland distribution in Great Britain. The eventual fate of existing blanket peatlands left outside their bioclimatic space remains uncertain.

An investigation of the impact of time of generation on carbon savings from PV systems in Great Britain

PV only generates electricity during daylight hours and primarily generates over summer. In the UK, the carbon intensity of grid electricity is higher during the daytime and over winter. This work investigates whether the grid electricity displaced by PV is high or low carbon compared to the annual mean carbon intensity using carbon factors at higher temporal resolutions (half-hourly and daily). UK policy for carbon reporting requires savings to be calculated using the annual mean carbon intensity of grid electricity. This work offers an insight into whether this technique is appropriate. Using half hourly data on the generating plant supplying the grid from November 2008 to May 2010, carbon factors for grid electricity at half-hourly and daily resolution have been derived using technology specific generation emission factors. Applying these factors to generation data from PV systems installed on schools, it is possible to assess the variation in the carbon savings from displacing grid electricity with PV generation using carbon factors with different time resolutions. The data has been analyzed for a period of 363 to 370 days and so cannot account for inter-year variations in the relationship between PV generation and carbon intensity of the electricity grid. This analysis suggests that PV displaces more carbon intensive electricity using half-hourly carbon factors than using daily factors but less compared with annual ones. A similar methodology could provide useful insights on other variable renewable and demand-side technologies and in other countries where PV performance and grid behavior are different.

Sector, region or function? A MAD reassessment of real estate diversification in Great Britain

This paper re-examines whether it is more advantageous in terms of risk reduction to diversify by sector or region by comparing the performance of the ‘conventional’ regional classification of the UK with one based on modern socio-economic criteria using a much larger real estate data set than any previous study and the MAD portfolio approach. The general conclusion of this analysis is that property market sectors still dominate regions, however defined and so should be the first level of analysis when developing a portfolio diversification strategy. This is in line with previous research. When the performance of Functional groups is compared with the ‘conventional’ administrative regions the results here show that, when functionally based, groupings can in some cases provide greater risk reduction. In addition the underlying characteristics of these functional groups may be much more insightful and acceptable to real estate portfolio managers in considering the assets that a portfolio might contain.

Identifying antimicrobial resistance genes of human clinical relevance within Salmonella isolated from food animals in Great Britain

To investigate the occurrence of antimicrobial resistance genes of human clinical relevance in Salmonella isolated from livestock in Great Britain. Two hundred and twenty-five Salmonella enterica isolates were characterized using an antimicrobial resistance gene chip and disc diffusion assays. Plasmid profiling, conjugation experiments and identification of Salmonella genomic island 1 (SGI1) were performed for selected isolates. Approximately 43% of Salmonella harboured single or multiple antimicrobial resistance genes with pig isolates showing the highest numbers where 96% of Salmonella Typhimurium harboured one or more resistance genes. Isolates harbouring multiple resistances divided into three groups. Group 1 isolates harboured ampicillin/streptomycin/sulphonamide/tetracycline resistance and similar phenotypes. This group contained isolates from pigs, cattle and poultry that were from several serovars including Typhimurium, 4,[5],12:i:-, Derby, Ohio and Indiana. All Group 2 isolates were from pigs and were Salmonella Typhimurium. They contained a non-sul-type class 1 integron and up to 13 transferrable resistances. All Group 3 isolates harboured a class 1 integron and were isolated from all animal species included in the study. Most isolates were Salmonella Typhimurium and harboured SGI1. Salmonella isolated from livestock was shown to harbour antimicrobial resistance genes although no or little resistance to third-generation cephalosporins or ciprofloxacin, respectively, was detected. The preponderance in pigs of multidrug-resistant Salmonella Typhimurium makes it important to introduce control measures such as improved biosecurity to ensure that they do not pass through the food chain and limit human therapeutic options.

A high prevalence of antimicrobial resistant Escherichia coli isolated from pigs and a low prevalence of antimicrobial resistant E-coli from cattle and sheep in Great Britain at slaughter

The incidence of antimicrobial resistance and expressed and unexpressed resistance genes among commensal Escherichia coli isolated from healthy farm animals at slaughter in Great Britain was investigated. The prevalence of antimicrobial resistance among the isolates varied according to the animal species; of 836 isolates from cattle tested only 5.7% were resistant to one or more antimicrobials, while only 3.0% of 836 isolates from sheep were resistant to one or more agents. However, 92.1% of 2480 isolates from pigs were resistant to at least one antimicrobial. Among isolates from pigs, resistance to some antimicrobials such as tetracycline (78.7%), sulphonamide (66.9%) and streptomycin (37.5%) was found to be common, but relatively rare to other agents such as amikacin (0.1%), ceftazidime ( 0.1%) and coamoxiclav (0.2%). The isolates had a diverse range of resistance gene profiles, with tet(B), sul2 and strAB identified most frequently. Seven out of 615 isolates investigated carried unexpressed resistance genes. One trimethoprim-susceptible isolate carried a complete dfrA17 gene but lacked a promoter for it. However, in the remaining six streptomycin-susceptible isolates, one of which carried strAB while the others carried aadA, no mutations or deletions in gene or promoter sequences were identified to account for susceptibility. The data indicate that antimicrobial resistance in E. coli of animal origin is due to a broad range of acquired genes.

The sources of phosphorus in the waters of Great Britain

Total phosphorus (TP) and soluble reactive phosphorus (SRP) loads to watercourses of the River Basin Districts (RBDs) of Great Britain (GB) were estimated using inventories of industrial P loads and estimates of P loads from sewage treatment works and diffuse P loads calculated using region-specific export coefficients for particular land cover classes combined with census data for agricultural stocking densities and human populations. The TP load to GB waters was estimated to be 60 kt yr(-1), of which households contributed 73, agriculture contributed 20, industry contributed 3, and 4 came from background sources. The SRP load to GB waters was estimated to be 47 kt yr(-1), of which households contributed 78, agriculture contributed 13, industry contributed 4, and 6 came from background Sources. The 'average' area-normalized TP and SRP loads to GB waters approximated 2.4 kg ha(-1) yr(-1) and 1.8 kg ha(-1) yr(-1), respectively. A consideration of uncertainties in the data contributing to these estimates suggested that the TP load to GB waters might lie between 33 and 68 kt yr(-1), with agriculture contributing between 10 and 28 of the TP load. These estimates are consistent with recent appraisals of annual TP and SRP loads to GB coastal waters and area-normalized TP loads from their catchments. Estimates of the contributions of RBDs to these P loads were consistent with the geographical distribution of P concentrations in GB rivers and recent assessments of surface waters at risk from P Pollution.

Species distribution models for crop pollination: a modelling framework applied to Great Britain

Insect pollination benefits over three quarters of the world's major crops. There is growing concern that observed declines in pollinators may impact on production and revenues from animal pollinated crops. Knowing the distribution of pollinators is therefore crucial for estimating their availability to pollinate crops; however, in general, we have an incomplete knowledge of where these pollinators occur. We propose a method to predict geographical patterns of pollination service to crops, novel in two elements: the use of pollinator records rather than expert knowledge to predict pollinator occurrence, and the inclusion of the managed pollinator supply. We integrated a maximum entropy species distribution model (SDM) with an existing pollination service model (PSM) to derive the availability of pollinators for crop pollination. We used nation-wide records of wild and managed pollinators (honey bees) as well as agricultural data from Great Britain. We first calibrated the SDM on a representative sample of bee and hoverfly crop pollinator species, evaluating the effects of different settings on model performance and on its capacity to identify the most important predictors. The importance of the different predictors was better resolved by SDM derived from simpler functions, with consistent results for bees and hoverflies. We then used the species distributions from the calibrated model to predict pollination service of wild and managed pollinators, using field beans as a test case. The PSM allowed us to spatially characterize the contribution of wild and managed pollinators and also identify areas potentially vulnerable to low pollination service provision, which can help direct local scale interventions. This approach can be extended to investigate geographical mismatches between crop pollination demand and the availability of pollinators, resulting from environmental change or policy scenarios.