Soil tillage affects the community structure of mycorrhizal fungi in maize roots

In this study we tested whether communities of arbuscular mycorrhizal fungi (AMF) colonizing the roots of maize (Zea mays L.) were affected by soil tillage practices (plowing, chiseling, and no-till) in a long-term field experiment carried out in Tanikon (Switzerland). AMF were identified in the roots using specific polymerase chain reaction (PCR) markers that had been developed for the AMF previously isolated from the soils of the studied site. A nested PCR procedure with primers of increased specificity (eukaryotic, then, fungal, then AMF species or. species-grouop specific) was used. Sequencing of amplified DNA confirmed that the DNA obtained from the maize roots was of AMF origin. Presence of particular AMF species or species-group was scored as a presence of a DNA product after PCR with specific primers. We also used single-strand conformation polymorphism analysis (SSCP), of amplified DNA samples to-check if the amplification of the DNA from maize roots matched the expected profile for a particular AMF isolate with a given specific primer pair. Presence of the genus Scutellospora, in maize roots was strongly reduced in plowed and chiseled soils. Fungi from the suborder Glomineae were more prevalent colonizers of maize roots growing in plowed soils, but were also present in the roots from other tillage treatments. These changes in community of AMF colonizing maize roots might be due to (1), the differences in tolerance to the tillage-induced disruption of the hyphae among the different AMF species, (2) changes in nutrient content of the soil, (3) changes in microbial activity, or (4) changes in weed populations in response to soil tillage. This is the first report on community composition of AMF in the roots of a field-grown crop plant (maize) as affected by soil tillage.

Derivation of soil-specific streaming potential electrical parameters from hydrodynamic characteristics of partially saturated soils

Water movement in unsaturated soils gives rise to measurable electrical potential differences that are related to the flow direction and volumetric fluxes, as well as to the soil properties themselves. Laboratory and field data suggest that these so-called streaming potentials may be several orders of magnitudes larger than theoretical predictions that only consider the influence of the relative permeability and electrical conductivity on the self potential (SP) data. Recent work has improved predictions somewhat by considering how the volumetric excess charge in the pore space scales with the inverse of water saturation. We present a new theoretical approach that uses the flux-averaged excess charge, not the volumetric excess charge, to predict streaming potentials. We present relationships for how this effective excess charge varies with water saturation for typical soil properties using either the water retention or the relative permeability function. We find large differences between soil types and the predictions based on the relative permeability function display the best agreement with field data. The new relationships better explain laboratory data than previous work and allow us to predict the recorded magnitudes of the streaming potentials following a rainfall event in sandy loam, whereas previous models predict values that are three orders of magnitude too small. We suggest that the strong signals in unsaturated media can be used to gain information about fluxes (including very small ones related to film flow), but also to constrain the relative permeability function, the water retention curve, and the relative electrical conductivity function.

Ecology and evolution of soil nematode chemotaxis.

Plants influence the behavior of and modify community composition of soil-dwelling organisms through the exudation of organic molecules. Given the chemical complexity of the soil matrix, soil-dwelling organisms have evolved the ability to detect and respond to these cues for successful foraging. A key question is how specific these responses are and how they may evolve. Here, we review and discuss the ecology and evolution of chemotaxis of soil nematodes. Soil nematodes are a group of diverse functional and taxonomic types, which may reveal a variety of responses. We predicted that nematodes of different feeding guilds use host-specific cues for chemotaxis. However, the examination of a comprehensive nematode phylogeny revealed that distantly related nematodes, and nematodes from different feeding guilds, can exploit the same signals for positive orientation. Carbon dioxide (CO(2)), which is ubiquitous in soil and indicates biological activity, is widely used as such a cue. The use of the same signals by a variety of species and species groups suggests that parts of the chemo-sensory machinery have remained highly conserved during the radiation of nematodes. However, besides CO(2), many other chemical compounds, belonging to different chemical classes, have been shown to induce chemotaxis in nematodes. Plants surrounded by a complex nematode community, including beneficial entomopathogenic nematodes, plant-parasitic nematodes, as well as microbial feeders, are thus under diffuse selection for producing specific molecules in the rhizosphere that maximize their fitness. However, it is largely unknown how selection may operate and how belowground signaling may evolve. Given the paucity of data for certain groups of nematodes, future work is needed to better understand the evolutionary mechanisms of communication between plant roots and soil biota.

The role of soil in vegetated gravelly river braid plains: more than just a passive response?

This paper reviews the role of alluvial soils in vegetated gravelly river braid plains. When considering decadal time scales of river evolution, we argue that it becomes vital to consider soil development as an emergent property of the developing ecosystem. Soil processes have been relatively overlooked in accounts of the interactions between braided river processes and vegetation, although soils have been observed on vegetated fluvial landforms. We hypothesise that soil development plays a major role in the transition (speed and pathway) from a fresh sediment deposit to a vegetated soil-covered landform. Disturbance (erosion and/or deposition), vertical sediment structure (process history), vegetation succession, biological activity and water table fluctuation are seen as the main controls on early alluvial soil evolution. Erosion and deposition processes may not only act as soil disturbing agents, but also as suppliers of ecosystem resources, because of their role in delivering and changing access (e.g. through avulsion) to fluxes of water, fine sediments and organic matter. In turn, the associated initial ecosystem may influence further fluvial landform development, such as through the trapping of fine-grained sediments (e.g. sand) by the engineering action of vegetation and the deposit stabilisation by the developing above and belowground biomass. This may create a strong feedback between geomorphological processes, vegetation succession and soil evolution which we summarise in a conceptual model. We illustrate this model by an example from the Allondon River (CH) and identify the research questions that follow.

Biological control of soil-borne pathogens by fluorescent pseudomonads.

Particular bacterial strains in certain natural environments prevent infectious diseases of plant roots. How these bacteria achieve this protection from pathogenic fungi has been analysed in detail in biocontrol strains of fluorescent pseudomonads. During root colonization, these bacteria produce antifungal antibiotics, elicit induced systemic resistance in the host plant or interfere specifically with fungal pathogenicity factors. Before engaging in these activities, biocontrol bacteria go through several regulatory processes at the transcriptional and post-transcriptional levels.

Effects of long-term irrigation with treated wastewater. Part I: Evolution of soil physico-chemical properties

The long-term impact of irrigation on a Mediterranean sandy soil irrigated with Treated wastewater (TWW) since 1980 was evaluated. The main soil properties (CEC, pH, size distribution, exchangeable cations and chloride, hydraulic conductivity) as well as the organic matter and Cu, Cr and Pb speciation in an irrigated soil and a non-irrigated control soil at various soil depths were monitored and compared during a 2 years experiment. In this first part, the evolution of the physico-chemical soil properties was described. The irrigation with TWW was beneficial with regard to water and nutrient supplying. All the exchangeable cations other than K(+) were higher in the irrigated soil than in the reference one. A part of the exchangeable cations was not fixed on the exchange complex but stored as labile salts or in concentrated soil solution. Despite the very sandy soil texture, both saturated and unsaturated hydraulic conductivity exhibited a significant diminution in the irrigated soil, but remained high enough to allow water percolation during rainy periods and subsequent leaching of accumulated salts, preventing soil salinization. In the irrigated soil, exchangeable sodium percentage (ESP) exhibited high values (20% on average) and the soil organic C was lower than in the reference. No significant effect was noticed on soil mineralogical composition due to irrigation. (C) 2010 Published by Elsevier Ltd.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.