ECOSSE: Estimating Carbon in Organic Soils - Sequestration and Emissions: Final Report

Background Climate change, caused by greenhouse gas ( GHG) emissions, is one of the most serious threats facing our planet, and is of concern at both UK and devolved administration levels. Accurate predictions for the effects of changes in climate and land use on GHG emissions are vital for informing land use policy. Models which are currently used to predict differences in soil carbon (C) and nitrogen (N) caused by these changes, have been derived from those based on mineral soils or deep peat. None of these models is entirely satisfactory for describing what happens to organic soils following land-use change. Reports of Scottish GHG emissions have revealed that approximately 15% of Scotland's total emissions come from land use changes on Scotland's high carbon soils; the figure is much lower for Wales. It is therefore important to reduce the major uncertainty in assessing the carbon store and flux from land use change on organic soils, especially those which are too shallow to be deep peats but still contain a large reserve of C. In order to predict the response of organic soils to external change we need to develop a model that reflects more accurately the conditions of these soils. The development of a model for organic soils will help to provide more accurate values of net change to soil C and N in response to changes in land use and climate and may be used to inform reporting to UKGHG inventories. Whilst a few models have been developed to describe deep peat formation and turnover, none have so far been developed suitable for examining the impacts of land-use and climate change on the types of organic soils often subject to land-use change in Scotland and Wales. Organic soils subject to land-use change are often (but not exclusively) characterised by a shallower organic horizon than deep peats (e.g. organo-mineral soils such as peaty podzols and peaty gleys). The main aim of the model developed in this project was to simulate the impacts of land-use and climate change in these types of soils. The model is, a) be driven by commonly available meteorological data and soil descriptions, b) able to simulate and predict C and N turnover in organic soils, c) able to predict the impacts of land-use change and climate change on C and N stores in organic soils in Scotland and Wales. In addition to developing the model, we have undertaken a number of other modelling exercises, literature searches, desk studies, data base exercises, and experimentation to answer a range of other questions associated with the responses of organic soils in Scotland and Wales to climate and land-use change. Aims of the ECOSSE project The aims of the study were: To develop a new model of C and N dynamics that reflects conditions in organic soils in Scotland and Wales and predicts their likely responses to external factors To identify the extent of soils that can be considered organic in Scotland and Wales and provide an estimate of the carbon contained within them To predict the contribution of CO 2, nitrous oxide and methane emissions from organic soils in Scotland and Wales, and provide advice on how changes in land use and climate will affect the C and N balance In order to fulfil these aims, the project was broken down into modules based on these objectives and the report uses that structure. The first aim is covered by module 2, the second aim by module 1, and the third aim by modules 3 to 8. Many of the modules are inter-linked. Objectives of the ECOSSE project The main objectives of the project were to: Describe the distribution of organic soils in Scotland and Wales and provide an estimate of the C contained in them Develop a model to simulate C and N cycling in organic soils and provide predictions as to how they will respond to land-use, management and climate change using elements of existing peat, mineral and forest soil models Provide predictive statements on the effects of land-use and climate change on organic soils and the relationships to GHG emissions, including CO 2, nitrous oxide and methane. Provide predictions on the effects of land use change and climate change on the release of Dissolved Organic Matter from organic soils Provide estimates of C loss from scenarios of accelerated erosion of organic soils Suggest best options for mitigating C and N loss from organic soils Provide guidelines on the likely effects of changing land-use from grazing or semi-natural vegetation to forestry on C and N in organic soils Use the land-use change data derived from the Countryside Surveys of Scotland and Wales to provide predictive estimates for changes to C and N balance in organic soils over time

Elecciones en Jalisco 2018: hallazgos y consideraciones

Esta obra aborda el análisis del proceso electoral de 2018 en México desde la experiencia multidisciplinaria de académicos, periodistas y personajes que contendieron con un cargo público y, a diferencia de otros estudios que concentran su atención en el escenario nacional, hace énfasis en la interpretación de las particularidades de los sufragios en el estado de Jalisco y de cómo sus efectos incidieron en la realidad inmediata de la ciudadanía. Está dirigido a investigadores, analistas y estudiantes de carreras afines a las ciencias políticas.ITESO, A.C

Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression

Copyright © 2009 The Authors. Copyright © ECOGRAPHY 2009.A major focus of geographical ecology and macro ecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regressions, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modelling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; “OLS models” hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation

Carbon fluxes from plants through soil organisms determined by field 13CO2 pulse-labelling in an upland grassland

The main findings of research into carbon (C) fluxes from plants to soil micro-organisms using in situ 13CO2 pulse-labelling on upland grassland at the NERC Soil Biodiversity Thematic Programme field site in Southern Scotland are reviewed. From 1999 to 2003 the site was the focus of a unique and intensive programme of stable isotope tracing of C flux through rhizodeposition to soil microbiota and stable isotope probing of microbial biomarker compounds. We review the findings published to date, and highlight the novel ways in which the pulse-labelling approach has been applied to further understand C fluxes in the rhizosphere and mycorrhizophere in this grassland. The most important achievements from these studies, many of which are the first field measurements of their kind, include: (1) quantification of C flux from recent photosynthate into roots, soil microbial populations and soil respiration over time periods of hours to months; (2) analysis of diurnal control of root exudation and respiration linked to photoperiod and photosynthetic activity; (3) measurements of C flux from plants directed through mycorrhizal fungal networks; (4) establishing the importance C flow from recent photosynthate into soil fungi, revealed by 13C enrichment of phospholipid fatty acid biomarker molecules (PLFA); (5) detection of the disruptive effects of fungal-feeding microarthropods on 13CO2 respiration in the mycorrhizosphere; (6) measurement of 13C enrichment into soil microbial DNA and RNA and the rates of turnover of RNA; (7) identification of soil micro-organisms most enriched with 13C by sequence analysis of ‘heavy’ RNA separated by density-gradient centrifugation; and (8) estimates of the effects of liming on C flux into and through upland grassland, and its effects on C cycling by soil micro-organisms. In reviewing all these findings we highlight the strengths and limitations of the in situ 13C technique. We also explain how the new insights gained from these studies emphasise the complex temporal dynamics of recent photosynthate entering the soil through different pathways and the role of multi-trophic interactions between soil biota in determining the fate of recently fixed carbon in grassland

Functional principal component data analysis: A new method for analysing microbial community fingerprints

A common approach to molecular characterisation of microbial communities in natural environments is the amplification of small subunit (SSU) rRNA genes or genes encoding enzymes essential for a particular ecosystem function. A range of ‘fingerprinting’ techniques are available for the analysis of amplification products of both types of gene enabling quantitative or semi-quantitative analysis of relative abundances of different community members, and facilitating analysis of communities from large numbers of samples, including replicates. Statistical models that have been applied in this context suffer from a number of unavoidable limitations, including lack of distinction between closely adjacent bands or peaks, particularly when these differ significantly in intensity or size. Current approaches to the analysis of banding structures derived from gels are typically based on standard multivariate analysis methods such as principal component analysis (PCA) which do not consider structure of DGGE gels but treat the intensity of each band as independent from the other bands, ignoring local neighbourhood structures. This paper assesses whether a new statistical analytical technique, based on functional data analysis (FDA) methods, improves the discriminatory ability of molecular fingerprinting techniques. The approach regards band intensities as a mathematical function of the location on the gel and explicitly includes neighbourhood structure in the analysis. A simulation study clearly reveals the weaknesses of the standard PCA approach as opposed to the FDA approach, which is then used to analyse experimental DGGE data

Flux and turnover of fixed carbon in soil microbial biomass of limed and unlimed plots of an upland grassland ecosystem

The influence of liming on rhizosphere microbial biomass C and incorporation of root exudates was studied in the field by in situ pulse labelling of temperate grassland vegetation with 13CO2 for a 3-day period. In plots that had been limed (CaCO3 amended) annually for 3 years, incorporation into shoots and roots was, respectively, greater and lower than in unlimed plots. Analysis of chloroform-labile C demonstrated lower levels of 13C incorporation into microbial biomass in limed soils compared to unlimed soils. The turnover of the recently assimilated 13C compounds was faster in microbial biomass from limed than that from unlimed soils, suggesting that liming increases incorporation by microbial communities of root exudates. An exponential decay model of 13C in total microbial biomass in limed soils indicated that the half-life of the tracer within this carbon pool was 4.7 days. Results are presented and discussed in relation to the absolute values of 13C fixed and allocated within the plant–soil syste