Shifts in rhizosphere microbial communities and enzyme activity of Poa alpina across an alpine chronosequence

This study quantifies the influence of Poa alpina on the soil microbial community in primary succession of alpine ecosystems, and whether these effects are controlled by the successional stage. Four successional sites representative of four stages of grassland development (initial, 4 years (non-vegetated); pioneer, 20 years; transition, 75 years; mature, 9500 years old) on the Rotmoos glacier foreland, Austria, were sampled. The size, composition and activity of the microbial community in the rhizosphere and bulk soil were characterized using the chloroform-fumigation extraction procedure, phospholipid fatty acid (PLFA) analysis and measurements of the enzymes beta-glucosidase, beta-xylosidase, N-acetyl-beta-glucosaminidase, leucine aminopeptidase, acid phosphatase and sulfatase. The interplay between the host plant and the successional stage was quantified using principal component (PCA) and multidimensional scaling analyses. Correlation analyses were applied to evaluate the relationship between soil factors (C-org, N-t, C/N ratio, pH, ammonium, phosphorus, potassium) and microbial properties in the bulk soil. In the pioneer stage microbial colonization of the rhizosphere of P. alpina was dependent on the reservoir of microbial species in the bulk soil. As a consequence, the rhizosphere and bulk soil were similar in microbial biomass (ninhydrin-reactive nitrogen (NHR-N)), community composition (PLFA), and enzyme activity. In the transition and mature grassland stage, more benign soil conditions stimulated microbial growth (NHR-N, total amount of PLFA, bacterial PLFA, Gram-positive bacteria, Gram-negative bacteria), and microbial diversity (Shannon index H) in the rhizosphere either directly or indirectly through enhanced carbon allocation. In the same period, the rhizosphere microflora shifted from a G(-) to a more G(+), and from a fungal to a more bacteria-dominated community. Rhizosphere beta-xylosidase, N-acetyl-beta-glucosaminidase, and sulfatase activity peaked in the mature grassland soil, whereas rhizosphere leucine aminopeptidase, beta-glucosidase, and phosphatase activity were highest in the transition stage, probably because of enhanced carbon and nutrient allocation into the rhizosphere due to better growth conditions. Soil organic matter appeared to be the most important driver of microbial colonization in the bulk soil. The decrease in soil pH and soil C/N ratio mediated the shifts in the soil microbial community composition (bacPLFA, bacPLFA/fungPLFA, G(-), G(+)/G(-)). The activities of beta-glucosidase, beta-xylosidase and phosphatase were related to soil ammonium and phosphorus, indicating that higher decomposition rates enhanced the nutrient availability in the bulk soil. We conclude that the major determinants of the microllora vary along the successional gradient: in the pioneer stage the rhizosphere microflora was primarily determined by the harsh soil environment; under more favourable environmental conditions, however, the host plant selected for a specific microbial community that was related to the dynamic interplay between soil properties and carbon supply. (C) 2004 Elsevier Ltd. All rights reserved.

Statistical methods for examining genetic influences of resistance to anti-epileptic drugs

It is generally accepted that genetics may be an important factor in explaining the variation between patients’ responses to certain drugs. However, identification and confirmation of the responsible genetic variants is proving to be a challenge in many cases. A number of difficulties that maybe encountered in pursuit of these variants, such as non-replication of a true effect, population structure and selection bias, can be mitigated or at least reduced by appropriate statistical methodology. Another major statistical challenge facing pharmacogenetics studies is trying to detect possibly small polygenic effects using large volumes of genetic data, while controlling the number of false positive signals. Here we review statistical design and analysis options available for investigations of genetic resistance to anti-epileptic drugs.

The development and application of capillary electrophoresis methods for food analysis

Capillary electrophoresis (CE) offers the analyst a number of key advantages for the analysis of the components of foods. CE offers better resolution than, say, high-performance liquid chromatography (HPLC), and is more adept at the simultaneous separation of a number of components of different chemistries within a single matrix. In addition, CE requires less rigorous sample cleanup procedures than HPLC, while offering the same degree of automation. However, despite these advantages, CE remains under-utilized by food analysts. Therefore, this review consolidates and discusses the currently reported applications of CE that are relevant to the analysis of foods. Some discussion is also devoted to the development of these reported methods and to the advantages/disadvantages compared with the more usual methods for each particular analysis. It is the aim of this review to give practicing food analysts an overview of the current scope of CE.

An ex-ante evaluation of the economic impact of Bt cotton adoption by spanish farmers facing the EU cotton sector reform

Cotton production in the European Union (EU) is limited to areas of Greece and Southern Spain (Andalusia). The 2004 reform of the EU cotton policy severely affected the profitability of the crop. In this article we analyze how the introduction of genetically modified (GM), insect-resistant cotton varieties (Bt cotton) might help EU cotton farmers to increase profitability and therefore face the cotton policy reform. We first study farmers’ attitudes toward adoption of Bt cotton varieties through a survey conducted in Andalusia (Southern Spain). The results show a positive attitude of Andalusian cotton farmers toward the Bt cotton varieties. Second, we perform an ex-ante analysis of the effects of introducing Bt cotton in Andalusia. Finally, we integrate the analysis of the effects of Bt cotton with the analysis of the EU cotton reform. Our results show that despite the significant economic benefits of Bt cotton, the current policy reform is likely to jeopardize the profitability of cotton production in the EU.

Inequality and GM crops: a case study of Bt cotton in India

Critics of genetically modified (GM) crops often contend that their introduction enhances the gap between rich and poor farmers, as the former group are in the best position to afford the expensive seed as well as provide other inputs such as fertilizer and irrigation. The research reported in this paper explores this issue with regard to Bt cotton (cotton with the endotoxtin gene from Bacillus thuringiensis conferring resistance to some insect pests) in Jalgaon, Maharashtra State, India, spanning the 2002 and 2003 seasons. Questionnaire–based survey results from 63 non–adopting and 94 adopting households of Bt cotton were analyzed, spanning 137 Bt cotton plots and 95 non–Bt cotton plots of both Bt adopters and non–adopters. For these households, cotton income accounted for 85 to 88% of total household income, and is thus of vital importance. Results suggest that in 2003 Bt adopting households have significantly more income from cotton than do non–adopting households (Rp 66,872 versus Rp 46,351) but inequality in cotton income, measured with the Gini coefficient (G), was greater amongst non–adopters than adopters. While Bt adopters had greater acreage of cotton in 2003 (9.92 acres versus 7.42 for non–adopters), the respective values of G were comparable. The main reason for the lessening of inequality amongst adopters would appear to be the consistency in the performance of Bt cotton along with the preferred non–Bt cultivar of Bt adopters—Bunny. Taking gross margin as the basis for comparison, Bt plots had 2.5 times the gross margin of non–Bt plots of non–adopters, while the advantage of Bt plots over non–Bt plots of adopters was 1.6 times. Measured in terms of the Gini coefficient of gross margin/acre it was apparent that inequality was lessened with the adoption of Bunny (G = 0.47) and Bt (G = 0.3) relative to all other non–Bt plots (G = 0.63). Hence the issue of equality needs to be seen both in terms of differences between adopters and non–adopters as well as within each of the groups.

Predictive empirical modelling of Orobanche life cycle and seed ecology

Abstract 1.7.4

Identification of trade-offs underlying the primary strategies of plants

Question: What are the key physiological and life-history trade-offs responsible for the evolution of different suites of plant traits (strategies) in different environments? Experimental methods: Common-garden experiments were performed on physiologically realistic model plants, evolved in contrasting environments, in computer simulations. This allowed the identification of the trade-offs that resulted in different suites of traits (strategies). The environments considered were: resource rich, low disturbance (competitive); resource poor, low disturbance (stressed); resource rich, high disturbance (disturbed); and stressed environments containing herbivores (grazed). Results: In disturbed environments, plants increased reproduction at the expense of ability to compete for light and nitrogen. In competitive environments, plants traded off reproductive output and leaf production for vertical growth. In stressed environments, plants traded off vertical growth and reproductive output for nitrogen acquisition, contradicting Grime's (2001) theory that slow-growing, competitively inferior strategies are selected in stressed environments. The contradiction is partly resolved by incorporating herbivores into the stressed environment, which selects for increased investment in defence, at the expense of competitive ability and reproduction. Conclusion: Our explicit modelling of trade-offs produces rigorous testable explanations of observed associations between suites of traits and environments.

Overview of mathematical approaches used to model bacterial chemotaxis I: the single cell

Mathematical modeling of bacterial chemotaxis systems has been influential and insightful in helping to understand experimental observations. We provide here a comprehensive overview of the range of mathematical approaches used for modeling, within a single bacterium, chemotactic processes caused by changes to external gradients in its environment. Specific areas of the bacterial system which have been studied and modeled are discussed in detail, including the modeling of adaptation in response to attractant gradients, the intracellular phosphorylation cascade, membrane receptor clustering, and spatial modeling of intracellular protein signal transduction. The importance of producing robust models that address adaptation, gain, and sensitivity are also discussed. This review highlights that while mathematical modeling has aided in understanding bacterial chemotaxis on the individual cell scale and guiding experimental design, no single model succeeds in robustly describing all of the basic elements of the cell. We conclude by discussing the importance of this and the future of modeling in this area.

Overview of mathematical approaches used to model bacterial chemotaxis II: bacterial populations

We review the application of mathematical modeling to understanding the behavior of populations of chemotactic bacteria. The application of continuum mathematical models, in particular generalized Keller-Segel models, is discussed along with attempts to incorporate the microscale (individual) behavior on the macroscale, modeling the interaction between different species of bacteria, the interaction of bacteria with their environment, and methods used to obtain experimentally verified parameter values. We allude briefly to the role of modeling pattern formation in understanding collective behavior within bacterial populations. Various aspects of each model are discussed and areas for possible future research are postulated.