Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy

Bayesian inference of microbial soil respiration models is often based on the assumptions that the residuals are independent (i.e., no temporal or spatial correlation), identically distributed (i.e., Gaussian noise), and have constant variance (i.e., homoscedastic). In the presence of model discrepancy, as no model is perfect, this study shows that these assumptions are generally invalid in soil respiration modeling such that residuals have high temporal correlation, an increasing variance with increasing magnitude of CO2 efflux, and non-Gaussian distribution. Relaxing these three assumptions stepwise results in eight data models. Data models are the basis of formulating likelihood functions of Bayesian inference. This study presents a systematic and comprehensive investigation of the impacts of data model selection on Bayesian inference and predictive performance. We use three mechanistic soil respiration models with different levels of model fidelity (i.e., model discrepancy) with respect to the number of carbon pools and the explicit representations of soil moisture controls on carbon degradation; therefore, we have different levels of model complexity with respect to the number of model parameters. The study shows that data models have substantial impacts on Bayesian inference and predictive performance of the soil respiration models such that the following points are true: (i) the level of complexity of the best model is generally justified by the cross-validation results for different data models; (ii) not accounting for heteroscedasticity and autocorrelation might not necessarily result in biased parameter estimates or predictions, but will definitely underestimate uncertainty; (iii) using a non-Gaussian data model improves the parameter estimates and the predictive performance; and (iv) accounting for autocorrelation only or joint inversion of correlation and heteroscedasticity can be problematic and requires special treatment. Although the conclusions of this study are empirical, the analysis may provide insights for selecting appropriate data models for soil respiration modeling.</p

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

The net ecosystem exchange (NEE) is the difference between ecosystem CO2 assimilation and CO2 losses to the atmosphere. Ecosystem respiration (R-eco), the efflux of CO2 from the ecosystem to the atmosphere, includes the soil-to-atmosphere carbon flux (i.e., soil respiration; R-soil) and aboveground plant respiration. Therefore, R-soil is a fraction of R-eco and theoretically has to be smaller than R-eco at daily, seasonal, and annual scales. However, several studies estimating R-eco with the eddy covariance technique and measuring R-soll within the footprint of the tower have reported higher R-soil than R-eco, at different time scales. Here, we compare four different and contrasting ecosystems (from forest to grasslands, and from boreal to semiarid) to test if measurements of R-eco are consistently higher than R-soil. In general, both fluxes showed similar temporal patterns, but R-eco, was not consistently higher than R-soil from daily to annual scales across sites. We identified several issues that apply for measuring NEE and measuring/upscaling R-soil that could result in an underestimation of R-eco and/or an overestimation of R-soil. These issues are discussed based on (a) nighttime measurements of NEE, (b) R-soil measurements, and (c) the interpretation of the functional relationships of these fluxes with temperature (i.e., Q(10)). We highlight that there is still a need for better integration of R-soil with eddy covariance measurements to address challenges related to the spatial and temporal variability of R-eco, and R-soil.Peer reviewe

The effect of elevated atmospheric CO2 and drought on sources and sinks of isoprene in a temperate and tropical rainforest mesocosm

Isoprene is the most abundant volatile hydrocarbon emitted by many tree species and has a major impact on tropospheric chemistry, leading to formation of pollutants and enhancing the lifetime of methane, a powerful greenhouse gas. Reliable estimates of global isoprene emission from different ecosystems demand a clear understanding of the processes of both production and consumption. Although the biochemistry of isoprene production has been studied extensively and environmental controls over its emission are relatively well known, the study of isoprene consumption in soil has been largely neglected. Here, we present results on the production and consumption of isoprene studied by measuring the following different components: (1) leaf and soil and (2) at the whole ecosystem level in two distinct enclosed ultraviolet light-depleted mesocosms at the Biosphere 2 facility: a cottonwood plantation with trees grown at ambient and elevated atmospheric CO2 concentrations and a tropical rainforest, under well watered and drought conditions. Consumption of isoprene by soil was observed in both systems. The isoprene sink capacity of litter-free soil of the agriforest stands showed no significant response to different CO2 treatments, while isoprene production was strongly depressed by elevated atmospheric CO2 concentrations. In both mesocosms, drought suppressed the sink capacity, but the full sink capacity of dry soil was recovered within a few hours upon rewetting. We conclude that soil uptake of atmospheric isoprene is likely to be modest but significant and needs to be taken into account for a comprehensive estimate of the global isoprene budget. More studies investigating the capacity of soils to uptake isoprene in natural conditions are clearly needed

Temperature response surfaces for mortality risk of tree species with future drought

Widespread, high levels of tree mortality, termed forest die-off, associated with drought and rising temperatures, are disrupting forests worldwide. Drought will likely become more frequent with climate change, but even without more frequent drought, higher temperatures can exacerbate tree water stress. The temperature sensitivity of drought-induced mortality of tree species has been evaluated experimentally for only single-step changes in temperature (ambient compared to ambient + increase) rather than as a response surface (multiple levels of temperature increase), which constrains our ability to relate changes in the driver with the biological response. Here we show that time-to-mortality during drought for seedlings of two western United States tree species, Pinus edulis (Engelm.) and Pinus ponderosa (Douglas ex C. Lawson), declined in continuous proportion with increasing temperature spanning a 7.7 °C increase. Although P. edulis outlived P. ponderosa at all temperatures, both species had similar relative declines in time-to-mortality as temperature increased (5.2% per °C for P. edulis; 5.8% per °C for P. ponderosa). When combined with the non-linear frequency distribution of drought duration—many more short droughts than long droughts—these findings point to a progressive increase in mortality events with global change due to warming alone and independent of additional changes in future drought frequency distributions. As such, dire future forest recruitment patterns are projected assuming the calculated 7–9 seedling mortality events per species by 2100 under business-as-usual warming occur, congruent with additional vulnerability predicted for adult trees from stressors like pathogens and pests. Our progressive projection for increased mortality events was driven primarily by the non-linear shape of the drought duration frequency distribution, a common climate feature of drought-affected regions. These results illustrate profound benefits for reducing emissions of carbon to the atmosphere from anthropogenic sources and slowing warming as rapidly as possible to maximize forest persistence.Peer reviewedPlant Biology, Ecology and Evolutio

Controlled Experiments of Hillslope Coevolution at the Biosphere 2 Landscape Evolution Observatory: Toward Prediction of Coupled Hydrological, Biogeochemical, and Ecological Change

Understanding the process interactions and feedbacks among water, porous geological media, microbes, and vascular plants is crucial for improving predictions of the response of Earth’s critical zone to future climatic conditions. However, the integrated coevolution of landscapes under change is notoriously difficult to investigate. Laboratory studies are limited in spatial and temporal scale, while field studies lack observational density and control. To bridge the gap between controlled laboratory and uncontrollable field studies, the University of Arizona built a macrocosm experiment of unprecedented scale: the Landscape Evolution Observatory (LEO). LEO comprises three replicated, heavily instrumented, hillslope-scale model landscapes within the environmentally controlled Biosphere 2 facility. The model landscapes were designed to initially be simple and purely abiotic, enabling scientists to observe each step in the landscapes’ evolution as they undergo physical, chemical, and biological changes over many years. This chapter describes the model systems and associated research facilities and illustrates how LEO allows for tracking of multiscale matter and energy fluxes at a level of detail impossible in field experiments. Initial sensor, sampler, and soil coring data are already providing insights into the tight linkages between water flow, weathering, and microbial community development. These interacting processes are anticipated to drive the model systems to increasingly complex states and will be impacted by the introduction of vascular plants and changes in climatic regimes over the years to come. By intensively monitoring the evolutionary trajectory, integrating data with mathematical models, and fostering community-wide collaborations, we envision that emergent landscape structures and functions can be linked, and significant progress can be made toward predicting the coupled hydro-biogeochemical and ecological responses to global change

Soil Respiration in Relation to Photosynthesis of Quercus mongolica Trees at Elevated CO2

Knowledge of soil respiration and photosynthesis under elevated CO2 is crucial for exactly understanding and predicting the carbon balance in forest ecosystems in a rapid CO2-enriched world. Quercus mongolica Fischer ex Ledebour seedlings were planted in open-top chambers exposed to elevated CO2 (EC = 500 µmol mol−1) and ambient CO2 (AC = 370 µmol mol−1) from 2005 to 2008. Daily, seasonal and inter-annual variations in soil respiration and photosynthetic assimilation were measured during 2007 and 2008 growing seasons. EC significantly stimulated the daytime soil respiration by 24.5% (322.4 at EC vs. 259.0 mg CO2 m−2 hr−1 at AC) in 2007 and 21.0% (281.2 at EC vs. 232.6 mg CO2 m−2 hr−1 at AC) in 2008, and increased the daytime CO2 assimilation by 28.8% (624.1 at EC vs. 484.6 mg CO2 m−2 hr−1 at AC) across the two growing seasons. The temporal variation in soil respiration was positively correlated with the aboveground photosynthesis, soil temperature, and soil water content at both EC and AC. EC did not affect the temperature sensitivity of soil respiration. The increased daytime soil respiration at EC resulted mainly from the increased aboveground photosynthesis. The present study indicates that increases in CO2 fixation of plants in a CO2-rich world will rapidly return to the atmosphere by increased soil respiration

Individual tree and stand-level carbon and nutrient contents across one rotation of loblolly pine plantations on a reclaimed surface mine

While reclaimed loblolly pine (Pinus taeda L.) plantations in east Texas, USA have demonstrated similar aboveground productivity levels relative to unmined forests, there is interest in assessing carbon (C) and nutrients in aboveground components of reclaimed trees. Numerous studies have previously documented aboveground biomass, C, and nutrient contents in loblolly pine plantations; however, similar data have not been collected on mined lands. We investigated C, N, P, K, Ca, and Mg aboveground contents for first-rotation loblolly pine growing on reclaimed mined lands in the Gulf Coastal Plain over a 32-year chronosequence and correlated elemental rates to stand age, stem growth, and similar data for unmined lands. At the individual tree level, we evaluated elemental contents in aboveground biomass components using tree size, age, and site index as predictor variables. At the stand-level, we then scaled individual tree C and nutrients and fit a model to determine the sensitivity of aboveground elemental contents to stand age and site index. Our data suggest that aboveground C and nutrients in loblolly pine on mined lands exceed or follow similar trends to data for unmined pine plantations derived from the literature. Diameter and height were the best predictors of individual tree stem C and nutrient contents (R ≥ 0.9473 and 0.9280, respectively) followed by stand age (R ≥ 0.8660). Foliage produced weaker relationships across all predictor variables compared to stem, though still significant (P ≤ 0.05). The model for estimating stand-level C and nutrients using stand age provided a good fit, indicating that contents aggrade over time predictably. Results of this study show successful modelling of reclaimed loblolly pine aboveground C and nutrients, and suggest elemental cycling is comparable to unmined lands, thus providing applicability of our model to related systems

Agrivoltaic system design tools for managing trade-offs between energy production, crop productivity and water consumption

Agrivoltaic systems that locate crop production and photovoltaic energy generation on the same land have the potential to aid the transition to renewable energy by reducing the competition between food, habitat, and energy needs for land while reducing irrigation requirements. Experimental efforts to date have not adequately developed an understanding of the interaction among local climate, array design and crop selection sufficient to manage trade-offs in system design. This study simulates the energy production, crop productivity and water consumption impacts of agrivoltaic array design choices in arid and semi-arid environments in the Southwestern region of the United States. Using the Penman–Monteith evapotranspiration model, we predict agrivoltaics can reduce crop water consumption by 30%–40% of the array coverage level, depending on local climate. A crop model simulating productivity based on both light level and temperature identifies afternoon shading provided by agrivoltaic arrays as potentially beneficial for shade tolerant plants in hot, dry settings. At the locations considered, several designs and crop combinations exceed land equivalence ratio values of 2, indicating a doubling of the output per acre for the land resource. These results highlight key design axes for agrivoltaic systems and point to a decision support tool for their development