Soil respiration on an aging managed heathland: identifying an appropriate empirical model for predictive purposes

Abstract

Heathlands are cultural landscapes which are managed through cyclical cutting, burning or grazing practices. Understanding the carbon (C) fluxes from these ecosystems provides information on the optimal management cycle time to maximise C uptake and minimise C output. The interpretation of field data into annual C loss values requires the use of soil respiration models. These generally include model variables related to the underlying drivers of soil respiration, such as soil temperature, soil moisture and plant activity. Very few studies have used selection procedures in which structurally different models are calibrated, then validated on separate observation datasets and the outcomes critically compared. We present thorough model selection procedures to determine soil heterotrophic (microbial) and autotrophic (root) respiration for a heathland chronosequence and show that soil respiration models are required to correct the effect of experimental design on soil temperature. Measures of photosynthesis, plant biomass, photosynthetically active radiation, root biomass, and microbial biomass did not significantly improve model fit when included with soil temperature. This contradicts many current studies in which these plant variables are used (but not often tested for parameter significance). We critically discuss a number of alternative ecosystem variables associated with soil respiration processes in order to inform future experimental planning and model variable selection at other heathland field sites. The best predictive model used a generalized linear multi-level model with soil temperature as the only variable. Total annual soil C loss from the young, middle and old communities was calculated to be 650, 462 and 435 g C m⁻² yr⁻¹, respectively

Can current moisture responses predict soil CO<sub>2</sub> efflux under altered precipitation regimes? A synthesis of manipulation experiments

“…Possible mechanisms to explain this lag effect could be the Birch effect as described above, or the decomposition of roots that died during drought-induced senescence. Moreover, plants can allocate large but variable fractions of their photosynthates belowground (Vicca et al, 2012b), with potentially rapid and strong effects on the autotrophic component of SCE (Bahn et al, 2008;Högberg et al, 2001;Kuzyakov and Gavrichkova, 2010). Hence, the results from this experiment also emphasize the need to separate autotrophic from heterotrophic respiration to fully explore the exact mechanisms underlying SCE responses.…”

“…For this and further analyses, experiments with no more than 10 data points were discarded. The four models (which have been used previously, see, for example, Curiel Yuste et al, 2003;Kopittke et al, 2013) were log (SR) = a + bST + cSWC,…”

Comparing ecosystem and soil respiration: Review and key challenges of tower-based and soil measurements

“…Efforts have been made to develop diffusivity models for estimating R soil with the soil gradient method (Maier and Schack-Kirchner, 2014;Moldrup et al, 2001), but an empirical calibration with chamber measurements is still necessary (Figure 3, Issue 2.d) for reliable long-term R soil measurements (Roland et al, 2015;Sánchez-Cañete et al, 2017). In studies where non-steady state chambers are used, R soil is usually estimated from the rate of change of CO 2 concentrations in relation to time using linear or exponential models (Kopittke et al, 2013;Pihlatie et al, 2013). However, recent studies have shown the usefulness of hierarchical Bayesian models in order to improve R soil estimates (Ogle et al, 2016).…”

Annual balances and extended seasonal modelling of carbon fluxes from a temperate fen cropped to festulolium and tall fescue under two‐cut and three‐cut harvesting regimes

“…However, ER models based only on temperature often show a lack of fit to seasonal data due to confounding contributions from biomass dynamics and therefore some seasonal studies include cluster-wise ER modelling for distinct plant phenological stages to overcome such confounding effects (Beetz et al, 2013;Poyda et al, 2016). Another approach has been to use instantaneous GPP measured under ambient PAR as a biomass proxy (Larsen et al, 2007;Kopittke et al, 2013). However, GPP is strongly sensitive to PAR, and coupling ER with GPP therefore also makes ER sensitive to diel PAR fluctuations which may not be generally valid.…”