Carbohydrate metabolism in lactic acid bacteria

The term “lactic acid bacteria” is discussed. An overview of the following topics is given: main pathways of homo- and heterofermentation of hexoses, i.e. glycolysis, bifidus pathway, 6-phosphogluconate pathway; uptake and dissimilation of lactose (tagatose pathway); fermentation of pentoses and pentitols; alternative fates of pyruvate, i.e. splitting to formate and acetate, CO₂ and acetate or formation of acetoin and diacetyl; lactate oxidation; biochemical basis for the formation of different stereoisomers of lactate.     

Scale sensitivity of synthetic long‐term vegetation time series derived through overlay of short‐term field records

Questions: Is change in cover of dominant species driving the velocity of succession or is it species turnover (1)? Is the length of the time‐step chosen in sampling affecting our recognition of the long‐term rate of change (2)1 Location: 74 permanent plots located in the Swiss National Park, SE Switzerland, ca. 1900 m a.s.l. Methods: We superimpose several time‐series from permanent plots to one single series solely based on compositional dissimilarity. As shown earlier (Wildi & Schütz 2000) this results in a synthetic series covering about 400 to 650 yr length. Continuous power transformation of cover‐percentage scores is used to test if the dominance or the presence‐absence of species is governing secondary succession from pasture to forest. The effect of time step length is tested by sub‐samples of the time series. Results: Altering the weight of presence‐absence versus dominance of species affects the emerging time frame, while altering time step length is uncritical. Where species turnover is fast, different performance scales yield similar results. When cover change in dominant species prevails, the solutions vary considerably. Ordinations reveal that the synthetic time series seek for shortest paths of the temporal pattern whereas in the real system longer lasting alternatives exist. Conclusions: Superimposing time series differs from the classical space‐for‐time substitution approach. It is a computation‐based method to investigate temporal patterns of hundreds of years fitting between direct monitoring (usually limited to decades) and the analysis of proxy‐data (for time spans of thousands of years and more).