Mycorrhizal ecology and evolution: the past, the present, and the future.

1406 I. 1407 II. 1408 III. 1410 IV. 1411 V. 1413 VI. 1416 VII. 1418 1418 References 1419 SUMMARY: Almost all land plants form symbiotic associations with mycorrhizal fungi. These below-ground fungi play a key role in terrestrial ecosystems as they regulate nutrient and carbon cycles, and influence soil structure and ecosystem multifunctionality. Up to 80% of plant N and P is provided by mycorrhizal fungi and many plant species depend on these symbionts for growth and survival. Estimates suggest that there are c. 50 000 fungal species that form mycorrhizal associations with c. 250 000 plant species. The development of high-throughput molecular tools has helped us to better understand the biology, evolution, and biodiversity of mycorrhizal associations. Nuclear genome assemblies and gene annotations of 33 mycorrhizal fungal species are now available providing fascinating opportunities to deepen our understanding of the mycorrhizal lifestyle, the metabolic capabilities of these plant symbionts, the molecular dialogue between symbionts, and evolutionary adaptations across a range of mycorrhizal associations. Large-scale molecular surveys have provided novel insights into the diversity, spatial and temporal dynamics of mycorrhizal fungal communities. At the ecological level, network theory makes it possible to analyze interactions between plant-fungal partners as complex underground multi-species networks. Our analysis suggests that nestedness, modularity and specificity of mycorrhizal networks vary and depend on mycorrhizal type. Mechanistic models explaining partner choice, resource exchange, and coevolution in mycorrhizal associations have been developed and are being tested. This review ends with major frontiers for further research.

Experiments on substratum roughness, grainsize and volume influence on the motion and spreading of rock avalanches

The main objective of the research is to link granular physics with the modelling of rock avalanches. Laboratory experiments consist to find a convenient granular material, i.e. grainsize and physical behaviour, and testing it on simple slope geometry. When the appropriate sliding material is selected, we attempted to model the debris avalanche and the spreading on a slope with different substratum to understand the relationship between the volume and the reach angle, i.e. angle of the line joining the top of the scar and the end of the deposit. For a better understanding of the mass spreading, the deposits are scanned with a laser scanner. Datasets are compared to see how the grain size and volume influence a debris avalanche. The relationship between the roughness and grainsize of the substratum shows that the spreading of the sliding mass is increased when the roughness of the substratum starts to be equivalent or greater than the grainsize of the flowing mass. The runout distance displays a more complex relationship, because a long runout distance implies that grains are less spread. This means that if the substratum is too rough the distance diminishes, as well if it is too smooth because the effect on the apparent friction decreases. Up to now our findings do not permit to validate any previous model (Melosh, 1987; Bagnold 1956).

Uranium-enrichment in soils and plants in the vicinity of a pitchblende vein at La Creusaz-Les Marecottes (W of Martigny, Valais, Switzerland)

In the NE part of the Aiguilles Rouges Massif near Martigny, at the eastern contact of the Variscan Vallorcine granite to adjacent gneisses, a series of pitchblende (UO2)-veins occur. This paper determines the level of enrichment and mobility of uranium in soils situated in the vicinity of such a UO2-vein 7 km west of Martigny. Within an area of 50 x 100 m, situated on a relatively steep slope and characterized by a strong gramma-ray anomaly, six soil profiles including their plant cover and a reference soil profile outside the influence of the UO2-vein have been examined. The soil shows pH-values between 4 and 5 and is colluvial. The applied analytical methods for the metal contents include extraction methods, common for soil studies, and bulk analysis performed with X-ray fluorescence and ICP-MS. Uranium contents found in the uppermost 20 cm of the soil profiles vary from 2,500 ppm close to the vein to 15 ppm at the lowermost point of the study area. The reference soil has around 3 ppm uranium. At greater depth (20 to 40 cm) the U-content decreases to about half of the surface values, indicating a vertical transport of uranium within the soil profile. No systematic dependance of uranium-contents to grain size (amount of clay) nor to the amount of organic matter has been found. However, the good correlation between uranium and free iron oxide concentration suggests adsorption of uranium on iron oxy-hydroxides. The ashes of grass and mosses contain up to 90 ppm U, the blueberry and redwood only up to 3 ppm. Our observations suggest that at the surface the uranium is transported by downhill creep (solifluxion) of uranium-rich rock fragments. Liberated by oxidation of the uppermost fragments in a given soil column, the uranium migrates vertically until the conditions are favourable to adsorption onto Fe-oxy-hydroxides. However, as high U-contents of local surface water show, this adsorption does not lead to a significant retention of the uranium.

Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables

Questions Soil properties have been widely shown to influence plant growth and distribution. However, the degree to which edaphic variables can improve models based on topo-climatic variables is still unclear. In this study, we tested the roles of seven edaphic variables, namely (1) pH; (2) the content of nitrogen and of (3) phosphorus; (4) silt; (5) sand; (6) clay and (7) carbon-to-nitrogen ratio, as predictors of species distribution models in an edaphically heterogeneous landscape. We also tested how the respective influence of these variables in the models is linked to different ecological and functional species characteristics. Location The Western Alps, Switzerland. Methods With four different modelling techniques, we built models for 115 plant species using topo-climatic variables alone and then topo-climatic variables plus each of the seven edaphic variables, one at a time. We evaluated the contribution of each edaphic variable by assessing the change in predictive power of the model. In a second step, we evaluated the importance of the two edaphic variables that yielded the largest increase in predictive power in one final set of models for each species. Third, we explored the change in predictive power and the importance of variables across plant functional groups. Finally, we assessed the influence of the edaphic predictors on the prediction of community composition by stacking the models for all species and comparing the predicted communities with the observed community. Results Among the set of edaphic variables studied, pH and nitrogen content showed the highest contributions to improvement of the predictive power of the models, as well as the predictions of community composition. When considering all topo-climatic and edaphic variables together, pH was the second most important variable after degree-days. The changes in model results caused by edaphic predictors were dependent on species characteristics. The predictions for the species that have a low specific leaf area, and acidophilic preferences, tolerating low soil pH and high humus content, showed the largest improvement by the addition of pH and nitrogen in the model. Conclusions pH was an important predictor variable for explaining species distribution and community composition of the mountain plants considered in our study. pH allowed more precise predictions for acidophilic species. This variable should not be neglected in the construction of species distribution models in areas with contrasting edaphic conditions.

Transport of a biocontrol Pseudomonas fluorescens through 2.5-m deep outdoor lysimeters and survival in the effluent water

Application of wild-type or genetically-modified bacteria to the soil environment entails the risk of dissemination of these organisms to the groundwater. To measure vertical transport of bacteria under natural climatic conditions, Pseudomonas fluorescens strain CHA0 was released together with bromide as a mobile tracer at the surface of large outdoor lysimeters. Two experiments, one starting in autumn 1993 and the other in spring 1994 were performed. Shortly after a heavy rainfall in late spring 1994, the released bacteria were detected for the first time in effluent water from the 2.5-m-deep lysimeters in both experiments, i.e. 210 d and 21 d, respectively, after inoculation. Only a 10−9 to 10−8 fraction of the inoculum was recovered as culturable cells in the effluent water, but a larger fraction of the CHA0 cells was in a non-culturable state as detected with immunofluorescence microscopy. As much as 50% of the mobile tracer percolated through the lysimeters, indicating that, compared with bromide, bacterial cells were retained in soil. In the second part of this study, persistence of CHA0 in groundwater microcosms consisting of lysimeter effluent water was studied for 380 d. Survival of the inoculant as culturable cells was better under anaerobic than under aerobic conditions. However, a large fraction of the cells became non-culturable in both cases. When the experiment was performed with filter-sterilized effluent water, the total count of introduced bacteria did not decline with time. In conclusion, the biocontrol strain was transported in low numbers to a potential groundwater level under natural climatic conditions, but could persist for an extended period in groundwater microcosms.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

Impact of Pseudomonas fluorescens strain CHA0 and a derivative with improved biocontrol activity on the culturable resident bacterial community on cucumber roots

Information on the effects of released wild-type or genetically engineered bacteria on resident bacterial communities is important to assess the potential risks associated with the introduction of these organisms into agroecosystems. The rifampicin-resistant biocontrol strain Pseudomonas fluorescens CHA0-Rif and its derivative CHA0-Rif/pME3424, which has improved biocontrol activity and enhanced production of the antibiotics 2,4-diacetylphloroglucinol (Phl) and pyoluteorin (Plt), were introduced into soil microcosms and the culturable bacterial community developing on cucumber roots was investigated 10 and 52 days later. The introduction of either of the two strains led to a transiently enhanced metabolic activity of the bacterial community on glucose dimers and polymers as measured with BIOLOG GN plates, but natural succession between the two sampling dates changed the metabolic activity of the bacterial community more than did the inoculants. The introduced strains did not significantly affect the abundance of dominant genotypic groups of culturable bacteria discriminated by restriction analysis of amplified 16S rDNA of 2500 individual isolates. About 30-50% of the resident bacteria were very sensitive to Phl and Plt, but neither the wild-type nor CHA0-Rif/pME3424 changed the proportion of sensitive and resistant bacteria in situ. In microcosms with a synthetic bacterial community, both biocontrol strains reduced the population of a strain of Pseudomonas but did not affect the abundance of four other bacterial strains including two highly antibiotic-sensitive isolates. We conclude that detectable perturbations in the metabolic activity of the resident bacterial community caused by the biocontrol strain CHA0-Rif are (i) transient, (ii) similar for the genetically improved derivative CHA0-Rif/pME3424 and (iii) less pronounced than changes in the community structure during plant growth.