analytical papers (3)

papers to read for analytical approach

PLANT DIVERSITY, SOIL MICROBIAL COMMUNITIES, AND ECOSYSTEM FUNCTION: ARE THERE ANY LINKS? — by Jenny Talbot — last modified 2008-03-06 16:20

A current debate in ecology centers on the extent to which ecosystem function depends on biodiversity. Here, we provide evidence from a long-term field manipulation of plant diversity that soil microbial communities, and the key ecosystem processes that they mediate, are significantly altered by plant species richness. After seven years of plant growth, we determined the composition and function of soil microbial communities beneath experimental plant diversity treatments containing 1–16 species. Microbial community biomass, respiration, and fungal abundance significantly increased with greater plant diversity, as did N mineralization rates. However, changes in microbial community biomass, activity, and composition largely resulted from the higher levels of plant production associated with greater diversity, rather than from plant diversity per se. Nonetheless, greater plant production could not explain more rapid N mineralization, indicating that plant diversity affected this microbial process, which controls rates of ecosystem N cycling. Greater N availability probably contributed to the positive relationship between plant diversity and productivity in the N-limited soils of our experiment, suggesting that plant–microbe interactions in soil are an integral component of plant diversity’s influence on ecosystem function.

A meta-analysis of the response of soil respiration, net nitrogen mineralization, and aboveground plant growth to experimental ecosystem warming — by Jenny Talbot — last modified 2008-03-06 16:23

Climate change due to greenhouse gas emissions is predicted to raise the mean global temperature by 1.0–3.5°C in the next 50–100 years. The direct and indirect effects of this potential increase in temperature on terrestrial ecosystems and ecosystem processes are likely to be complex and highly varied in time and space. The Global Change and Terrestrial Ecosystems core project of the International Geosphere-Biosphere Programme has recently launched a Network of Ecosystem Warming Studies, the goals of which are to integrate and foster research on ecosystem-level effects of rising temperature. In this paper, we use meta-analysis to synthesize data on the response of soil respiration, net N mineralization, and aboveground plant productivity to experimental ecosystem warming at 32 research sites representing four broadly defined biomes, including high (latitude or altitude) tundra, low tundra, grassland, and forest. Warming methods included electrical heat-resistance ground cables, greenhouses, vented and unvented field chambers, overhead infrared lamps, and passive nighttime warming. Although results from individual sites showed considerable variation in response to warming, results from the meta-analysis showed that, across all sites and years, 2–9 years of experimental warming in the range 0.3–6.0°C significantly increased soil respiration rates by 20% (with a 95% confidence interval of 18–22%), net N mineralization rates by 46% (with a 95% confidence interval of 30–64%), and plant productivity by 19% (with a 95% confidence interval of 15–23%). The response of soil respiration to warming was generally larger in forested ecosystems compared to low tundra and grassland ecosystems, and the response of plant productivity was generally larger in low tundra ecosystems than in forest and grassland ecosystems. With the exception of aboveground plant productivity, which showed a greater positive response to warming in colder ecosystems, the magnitude of the response of these three processes to experimental warming was not generally significantly related to the geographic, climatic, or environmental variables evaluated in this analysis. This underscores the need to understand the relative importance of specific factors (such as temperature, moisture, site quality, vegetation type, successional status, land-use history, etc.) at different spatial and temporal scales, and suggests that we should be cautious in “scaling up” responses from the plot and site level to the landscape and biome level. Overall, ecosystem-warming experiments are shown to provide valuable insights on the response of terrestrial ecosystems to elevated temperature.

Fuzzy classification of microbial biomass and enzyme activities in grassland soils — by Jenny Talbot — last modified 2008-03-06 16:43

Soil microbial properties are widely used as indicators of soil quality. The interpretation of soil microbial processes, however, is difficult because of their regional and seasonal heterogeneity as well as the lack of reference values. One possibility to overcome these limitations to apply the fuzzy set theory. This approach more realistically describes ecological systems because it considers natural ambiguity and complexity. The present study applies a fuzzy rule-based classification model to define soil quality based on soil microbial biomass, N-mineralisation, enzyme activity data (urease, xylanase, phosphatase, arylsulfatase) and soil organic matter. The data have been collected from different grassland sites in the European Union over a period of 20 years. The fuzzy model is based on a rule system derived from a training set using simulated annealing as an optimisation algorithm. For each variable, nine triangular fuzzy sets were defined for consideration as possible rule arguments. The model uses the t-norm for combination of arguments, product inference, the weighted sum as rule combination and the maximum method for defuzzification. The output is the assignment of membership of the object to a given soil quality class. The soil quality classes (very poor, poor, medium, high, very high) were defined by five heavy metal contamination levels (very high, high, medium, low, no). A predefined number of fuzzy rules were assessed using a simulated annealing algorithm. The fuzzy model was validated by a test file by assigning the soils to the quality class with the highest response value. The fuzzy model yielded an overall coincidence of 88.8% between observed and simulated results. The most sensitive index of soil quality was microbial biomass. N-mineralisation was a good indicator for the high-quality soils, while urease and arylsulfatase were important indicators for heavily contaminated, poor soil quality. Xylanase and phosphatase behaved ambivalently. Including soil organic carbon in the model decreased its effectiveness by 6.5%. We suggest that the presented fuzzy model based on soil microbial properties could be applied not only to soil degradation, upscaling and prediction, but also to judge the response of soils to environmental changes. r 2007 Elsevier Ltd. All rights reserved.