Seasonal variation in enzyme activities and temperature sensitivities in Arctic tundra soils

Arctic soils contain large amounts of organic matter due to very slow rates of detritus decomposition. The first step in decomposition results from the activity of extracellular enzymes produced by soil microbes. We hypothesized that potential enzyme activities are low relative to the large stocks of organic matter in Arctic tundra soils, and that enzyme activity is low at in situ temperatures. We measured the potential activity of six hydrolytic enzymes at 4 and 20 °C on four sampling dates in tussock, intertussock, shrub organic, and shrub mineral soils at Toolik Lake, Alaska. Potential activities of N‐acetyl glucosaminidase, β‐glucosidase, and peptidase tended to be greatest at the end of winter, suggesting that microbes produced enzymes while soils were frozen. In general, enzyme activities did not increase during the Arctic summer, suggesting that enzyme production is N‐limited during the period when temperatures would otherwise drive higher enzyme activity in situ. We also detected seasonal variations in the temperature sensitivity (Q₁₀) of soil enzymes. In general, soil enzyme pools were more sensitive to temperature at the end of the winter than during the summer. We modeled potential in situβ‐glucosidase activities for tussock and shrub organic soils based on measured enzyme activities, temperature sensitivities, and daily soil temperature data. Modeled in situ enzyme activity in tussock soils increased briefly during the spring, then declined through the summer. In shrub soils, modeled enzyme activities increased through the spring thaw into early August, and then declined through the late summer and into winter. Overall, temperature is the strongest factor driving low in situ enzyme activities in the Arctic. However, enzyme activity was low during the summer, possibly due to N‐limitation of enzyme production, which would constrain enzyme activity during the brief period when temperatures would otherwise drive higher rates of decomposition.     

Power of the scan statistic in detecting a changed segment in a Bernoulli sequence

Fu & Curnow (1990) derive recursive equations to find thelevel of significance and power of a likelihood ratio test fora changed segment of specified length, based on the scan statistic,the maximum number of successes within the specified length.Their method is computationally feasible for segment lengthsof 20 or less. We present and evaluate highly accurate approximationsas well as bounds for the power function of this test that arecomputationally feasible even for very large segment lengths.We also evaluate power when the duration of the increased lengthused in the test statistic does not correspond to the actuallength.