Abstract
A precise prediction of the response in soil respiration (Rs) to climate warming requires the temperature-driven portion of the response to be characterized separately from the global, undecomposed response. Eliminating non-temperature-driven effects, which tend to confound the response in Rs, is possible with Singular Spectrum Analysis (SSA), a non-parametric approach to timeseries analysis. Rs and ancillary data were collected at three microsites in a desert-shrubland ecosystem in northwest China over a three-year period (i.e., 2013–2015). The microsites include desert-sites with non-biocrusted, lichen-crusted, and moss-crusted soil surfaces, or NC, LC, and MC, respectively. Results revealed that temperature sensitivity in Rs (i.e., Q10) based on the original, unprocessed Rs-data and a seasonal equation relating Rs to temperature were similar among microsites, with similar amplitudes in Rs. High-frequency bins of Rs, with sub-signals with a period < 90 days, obtained by subtracting the low-frequency bins from timeseries of Rs, were positively correlated with soil moisture (θ) at all microsites. Whereas, the same high-frequency bins were individually decoupled from soil temperature at LC and MC. This suggests that the influence of θ on the response in Rs to temperature dominated the Rs-measurements over the shorter term. After eliminating the non-temperature effects on Rs with SSA, Q10 tended to produce lower values, i.e., 1.40, 1.28, and 1.34 for NC, LC and MC, respectively, when compared to cases where non-temperature effects were not removed, yielding a mean of 2.28. Q10-values estimated with SSA-processed Rs-data were consistent with those published at the ecosystem level (i.e., Re ~ 1.40). The research suggests that SSA-adjusted Q10-values tend to be similar with those at the ecosystem scale. Future theoretical work is anticipated using a convergent temperature-only-based value of Q10 between Rs and Re across ecosystems.
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