Archaeology’s Search for History Hidden in Ice

A wide range of research shows that nutrient availability strongly influences terrestrial carbon (C) cycling and shapes ecosystem responses to environmental changes and hence terrestrial feedbacks to climate. Nonetheless, our understanding of nutrient controls remains far from complete and poorly quantified, at least partly due to a lack of informative, comparable, and accessible datasets at regional-to-global scales. A growing research infrastructure of multi-site networks are providing valuable data on C fluxes and stocks and are monitoring their responses to global environmental change and measuring responses to experimental treatments. These networks thus provide an opportunity for improving our understanding of C-nutrient cycle interactions and our ability to model them. However, coherent information on how nutrient cycling interacts with observed C cycle patterns is still generally lacking. Here, we argue that complementing available C-cycle measurements from monitoring and experimental sites with data characterizing nutrient availability will greatly enhance their power and will improve our capacity to forecast future trajectories of terrestrial C cycling and climate. Therefore, we propose a set of complementary measurements that are relatively easy to conduct routinely at any site or experiment and that, in combination with C cycle observations, can provide a robust characterization of the effects of nutrient availability across sites. In addition, we discuss the power of different observable variables for informing the formulation of models and constraining their predictions. Most widely available measurements of nutrient availability often do not align well with current modelling needs. This highlights the importance to foster the interaction between the empirical and modelling communities for setting future research priorities

Interactions of Climate, Carbon, and Nutrient Cycling in Wet Tropical Forest

Tropical forests play a substantial role in the global carbon (C) cycle and are projected to experience significant environmental change, highlighting the importance of understanding the factors that control C and N cycling in this biome. Yet interactions between biogeochemical and abiotic variables, notably species diversity and precipitation, remain poorly resolved in the tropics. In a wet lowland tropical forest in southwest Costa Rica I examined how: i) biologically generated chemical diversity affected soil C mineralization; ii) abiotic and chemical factors control litter decomposition; and iii) potential changes in precipitation and labile C inputs may alter nitrous oxide (N2O) emissions. I investigated how inter-specific chemical variation in soluble dissolved organic matter (DOM) affected heterotrophic soil respiration in a series of laboratory incubations. Following DOM additions, soil respiration rates varied by as much as an order of magnitude; largely driven by variation in the C: phosphorus (P) ratio of DOM. These results suggest that tropical tree species composition may influence soil C storage and mineralization via variation in plant litter chemistry. Subsequently, I assessed how variation in precipitation and litter chemistry affect rates of leaf litter decay. Despite exceptionally high rainfall, simulated throughfall reductions consistently slowed rates of litter decomposition. Overall, variation between species was greater than variation induced by throughfall reductions, and was best explained by initial litter solubility and lignin: P ratios. These results support a model of litter decomposition where mass loss rates are positively correlated with rainfall, and reemphasize the importance of litter solubility and P availability in controlling decomposition rates. Finally, I examined how changes in precipitation and labile C availability may affect soil N2O production. Experimentally reducing throughfall significantly increased emissions of N2O; at least part of this response was driven by an increase in the concentration of litter-to-soil inputs of dissolved organic C under the drier conditions, as well as a positive relationship between leaf litter inputs and soil N2O production. These results highlight the importance of understanding the potential direct effects of changing precipitation on soil biogeochemistry (e.g., altered soil redox conditions), and also the indirect effects resulting from interactions between hydrologic, C, and N cycles

Controls Over Leaf Litter Decomposition in Wet Tropical Forests

Tropical forests play a substantial role in the global carbon (C) cycle and are projected to experience significant changes in climate, highlighting the importance of understanding the factors that control organic matter decomposition in this biome. In the tropics, high temperature and rainfall lead to some of the highest rates of litter decomposition on earth, and given the near-optimal abiotic conditions, litter quality likely exerts disproportionate control over litter decomposition. Yet interactions between litter quality and abiotic variables, most notably precipitation, remain poorly resolved, especially for the wetter end of the tropical forest biome. We assessed the importance of variation in litter chemistry and precipitation in a lowland tropical rain forest in southwest Costa Rica that receives \u3e5000 mm of precipitation per year, using litter from 11 different canopy tree species in conjunction with a throughfall manipulation experiment. In general, despite the exceptionally high rainfall in this forest, simulated throughfall reductions consistently suppressed rates of litter decomposition. Overall, variation between species was greater than that induced by manipulating throughfall and was best explained by initial litter solubility and lignin:P ratios. Collectively, these results support a model of litter decomposition in which mass loss rates are positively correlated with rainfall up to very high rates of mean annual precipitation and highlight the importance of phosphorus availability in controlling microbial processes in many lowland tropical forests

Experimental Drought in a Tropical Rain Forest Increases Soil Carbon Dioxide Losses to the Atmosphere

Climate models predict precipitation changes for much of the humid tropics, yet few studies have investigated the potential consequences of drought on soil carbon (C) cycling in this important biome. In wet tropical forests, drought could stimulate soil respiration via overall reductions in soil anoxia, but previous research suggests that litter decomposition is positively correlated with high rainfall fluxes that move large quantities of dissolved organic matter (DOM) from the litter layer to the soil surface. Thus, reduced rainfall could also limit C delivery to the soil surface, reducing respiration rates. We conducted a throughfall manipulation experiment to investigate how 25% and 50% reductions in rainfall altered both C movement into soils and the effects of those DOM fluxes on soil respiration rates. In response to the experimental drought, soil respiration rates increased in both the −25% and −50% treatments. Throughfall fluxes were reduced by 26% and 55% in the −25% and −50% treatments, respectively. However, total DOM fluxes leached from the litter did not vary between treatments, because the concentrations of leached DOM reaching the soil surface increased in response to the simulated drought. Annual DOM concentrations averaged 7.7 ± 0.8, 11.2 ± 0.9, and 15.8 ± 1.2 mg C/L in the control, −25%, and −50% plots, respectively, and DOM concentrations were positively correlated with soil respiration rates. A laboratory incubation experiment confirmed the potential importance of DOM concentration on soil respiration rates, suggesting that this mechanism could contribute to the increase in CO2 fluxes observed in the reduced rainfall plots. Across all plots, the data suggested that soil CO2 fluxes were partially regulated by the magnitude and concentration of soluble C delivered to the soil, but also by soil moisture and soil oxygen availability. Together, our data suggest that declines in precipitation in tropical rain forests could drive higher CO2 fluxes to the atmosphere both via increased soil O2 availability and through responses to elevated DOM concentrations

Divergent patterns of experimental and model-derived permafrost ecosystem carbon dynamics in response to Arctic warming

In the last few decades, temperatures in the Arctic have increased twice as much as the rest of the globe. As permafrost thaws in response to this warming, large amounts of soil organic matter may become vulnerable to decomposition. Microbial decomposition will release carbon (C) from permafrost soils, however, warmer conditions could also lead to enhanced plant growth and C uptake. Field and modeling studies show high uncertainty in soil and plant responses to climate change but there have been few studies that reconcile field and model data to understand differences and reduce uncertainty. In this work, we evaluate gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem C exchange (NEE) from eight years of experimental soil warming in moist acidic tundra against equivalent fluxes from the Community Land Model (CLM) during simulations parameterized to reflect the field conditions associated with this manipulative field experiment. Over the eight-year experimental period, soil temperatures and thaw depths increased with warming in field observations and model simulations. However, the field and model results do not agree on warming effects on water table depth; warming created wetter soils in the field and drier soils in the models. In the field, initial increases in growing season GPP, Reco, and NEE to experimentally-induced permafrost thaw created a higher C sink capacity in the first years followed by a stronger C source in years six through eight. In contrast, both models predicted linear increases in GPP, Reco, and NEE with warming. The divergence of model results from field experiments reveals the role subsidence, hydrology, and nutrient cycling play in influencing the C flux responses to permafrost thaw, a complexity that the models are not structurally able to predict, and highlight challenges associated with projecting C cycle dynamics across the Arctic

Divergent controls of soil organic carbon between observations and process-based models

The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships a priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions

N and P constrain C in ecosystems under climate change: role of nutrient redistribution, accumulation, and stoichiometry

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Rastetter, E., Kwiatkowski, B., Kicklighter, D., Plotkin, A., Genet, H., Nippert, J., O’Keefe, K., Perakis, S., Porder, S., Roley, S., Ruess, R., Thompson, J., Wieder, W., Wilcox, K., & Yanai, R. N and P constrain C in ecosystems under climate change: role of nutrient redistribution, accumulation, and stoichiometry. Ecological Applications, (2022): e2684, https://doi.org/10.1002/eap.2684.We use the Multiple Element Limitation (MEL) model to examine responses of 12 ecosystems to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in precipitation. Ecosystems respond synergistically to elevated CO2, warming, and decreased precipitation combined because higher water-use efficiency with elevated CO2 and higher fertility with warming compensate for responses to drought. Response to elevated CO2, warming, and increased precipitation combined is additive. We analyze changes in ecosystem carbon (C) based on four nitrogen (N) and four phosphorus (P) attribution factors: (1) changes in total ecosystem N and P, (2) changes in N and P distribution between vegetation and soil, (3) changes in vegetation C:N and C:P ratios, and (4) changes in soil C:N and C:P ratios. In the combined CO2 and climate change simulations, all ecosystems gain C. The contributions of these four attribution factors to changes in ecosystem C storage varies among ecosystems because of differences in the initial distributions of N and P between vegetation and soil and the openness of the ecosystem N and P cycles. The net transfer of N and P from soil to vegetation dominates the C response of forests. For tundra and grasslands, the C gain is also associated with increased soil C:N and C:P. In ecosystems with symbiotic N fixation, C gains resulted from N accumulation. Because of differences in N versus P cycle openness and the distribution of organic matter between vegetation and soil, changes in the N and P attribution factors do not always parallel one another. Differences among ecosystems in C-nutrient interactions and the amount of woody biomass interact to shape ecosystem C sequestration under simulated global change. We suggest that future studies quantify the openness of the N and P cycles and changes in the distribution of C, N, and P among ecosystem components, which currently limit understanding of nutrient effects on C sequestration and responses to elevated CO2 and climate change.This material is based on work supported by the National Science Foundation under Grant No. 1651722 as well through the NSF LTER Program 1637459, 2220863 (ARC), 1637686 (NWT), 1832042 (KBS), 2025849 (KNZ), 1636476 (BNZ), 1637685 (HBR), 1832210 (HFR), 2025755 (AND). We also acknowledge NSF grants 1637653 and 1754126 (INCyTE RCN), and DOE grant DESC0019037. We also acknowledge support through the USDA Forest Service Hubbard Brook Experimental Forest, North Woodstock, New Hampshie (USDA NIFA 2019-67019-29464) and Pacific Northwest Research Station, Corvallis, Oregon

Soil organic carbon models need independent time-series validation for reliable prediction

Numerical models are crucial to understand and/or predict past and future soil organic carbon dynamics. For those models aiming at prediction, validation is a critical step to gain confidence in projections. With a comprehensive review of ~250 models, we assess how models are validated depending on their objectives and features, discuss how validation of predictive models can be improved. We find a critical lack of independent validation using observed time series. Conducting such validations should be a priority to improve the model reliability. Approximately 60% of the models we analysed are not designed for predictions, but rather for conceptual understanding of soil processes. These models provide important insights by identifying key processes and alternative formalisms that can be relevant for predictive models. We argue that combining independent validation based on observed time series and improved information flow between predictive and conceptual models will increase reliability in predictions