Cultivating Resilience in Dryland Soils: An Assisted Migration Approach to Biological Soil Crust Restoration.

Land use practices and climate change have driven substantial soil degradation across global drylands, impacting ecosystem functions and human livelihoods. Biological soil crusts, a common feature of dryland ecosystems, are under extensive exploration for their potential to restore the stability and fertility of degraded soils through the development of inoculants. However, stressful abiotic conditions often result in the failure of inoculation-based restoration in the field and may hinder the long-term success of biocrust restoration efforts. Taking an assisted migration approach, we cultivated biocrust inocula sourced from multiple hot-adapted sites (Mojave and Sonoran Deserts) in an outdoor facility at a cool desert site (Colorado Plateau). In addition to cultivating inoculum from each site, we created an inoculum mixture of biocrust from the Mojave Desert, Sonoran Desert, and Colorado Plateau. We then applied two habitat amelioration treatments to the cultivation site (growth substrate and shading) to enhance soil stability and water availability and reduce UV stress. Using marker gene sequencing, we found that the cultivated mixed inoculum comprised both local- and hot-adapted cyanobacteria at the end of cultivation but had similar cyanobacterial richness as each unmixed inoculum. All cultivated inocula had more cyanobacterial 16S rRNA gene copies and higher cyanobacterial richness when cultivated with a growth substrate and shade. Our work shows that it is possible to field cultivate biocrust inocula sourced from different deserts, but that community composition shifts toward that of the cultivation site unless habitat amelioration is employed. Future assessments of the function of a mixed inoculum in restoration and its resilience in the face of abiotic stressors are needed to determine the relative benefit of assisted migration compared to the challenges and risks of this approach.

Peat loss collocates with a threshold in plant-mycorrhizal associations in drained peatlands encroached by trees.

Drainage-induced encroachment by trees may have major effects on the carbon balance of northern peatlands, and responses of microbial communities are likely to play a central mechanistic role. We profiled the soil fungal community and estimated its genetic potential for the decay of lignin and phenolics (class II peroxidase potential) along peatland drainage gradients stretching from interior locations (undrained, open) to ditched locations (drained, forested). Mycorrhizal fungi dominated the community across the gradients. When moving towards ditches, the dominant type of mycorrhizal association abruptly shifted from ericoid mycorrhiza to ectomycorrhiza at c. 120 m from the ditches. This distance corresponded with increased peat loss, from which more than half may be attributed to oxidation. The ectomycorrhizal genus Cortinarius dominated at the drained end of the gradients and its relatively higher genetic potential to produce class II peroxidases (together with Mycena) was positively associated with peat humification and negatively with carbon-to-nitrogen ratio. Our study is consistent with a plant-soil feedback mechanism, driven by a shift in the mycorrhizal type of vegetation, that potentially mediates changes in aerobic decomposition during postdrainage succession. Such feedback may have long-term legacy effects upon postdrainage restoration efforts and implication for tree encroachment onto carbon-rich soils globally.

Evidence for older carbon loss with lowered water tables and changing plant functional groups in peatlands.

A small imbalance in plant productivity and decomposition accounts for the carbon (C) accumulation capacity of peatlands. As climate changes, the continuity of peatland net C storage relies on rising primary production to offset increasing ecosystem respiration (ER) along with the persistence of older C in waterlogged peat. A lowering in the water table position in peatlands often increases decomposition rates, but concurrent plant community shifts can interactively alter ER and plant productivity responses. The combined effects of water table variation and plant communities on older peat C loss are unknown. We used a full-factorial 1-m3 mesocosm array with vascular plant functional group manipulations (Unmanipulated Control, Sedge only, and Ericaceous only) and water table depth (natural and lowered) treatments to test the effects of plants and water depth on CO2 fluxes, decomposition, and older C loss. We used Δ14 C and δ13 C of ecosystem CO2 respiration, bulk peat, plants, and porewater dissolved inorganic C to construct mixing models partitioning ER among potential sources. We found that the lowered water table treatments were respiring C fixed before the bomb spike (1955) from deep waterlogged peat. Lowered water table Sedge treatments had the oldest dissolved inorganic 14 C signature and the highest proportional peat contribution to ER. Decomposition assays corroborated sustained high rates of decomposition with lowered water tables down to 40 cm below the peat surface. Heterotrophic respiration exceeded plant respiration at the height of the growing season in lowered water table treatments. Rates of gross primary production were only impacted by vegetation, whereas ER was affected by vegetation and water table depth treatments. The decoupling of respiration and primary production with lowered water tables combined with older C losses suggests that climate and land-use-induced changes in peatland hydrology can increase the vulnerability of peatland C stores.

Nitrogen deposition stimulated winter nitrous oxide emissions from bare sand more than biological soil crusts in cold desert ecosystem.

Dryland ecosystems are often nitrogen-limited, and small nitrogen inputs may produce large responses to dryland ecological processes, such as gaseous nitrogen emission. The effect of increased anthropogenic nitrogen deposition on N2O and NO emissions in desert ecosystems is unclear, especially in non-growing seasons when the surface is covered with snow. In this study, nitrogen applications were performed on biological soil crusts (lichen crust and moss crust, bare sand for control) in the Gurbantunggut Desert, Northwest China. We measured the fluxes of N2O and NO and related nitrogen cycle functional gene abundances in winter for three-years period. Nitrogen addition significantly affected N2O emissions and increased the abundances of key functional gene for nitrogen cycle, while it only slightly influenced NO emissions. These effects of nitrogen addition depended on composition of biological soil crusts. For bare sand and lichen crust, nitrogen addition significantly increased N2O emissions, whereas for moss crust, only a negligible effect was observed. Meanwhile, significant differences in nitrogen cycle functional gene abundances were found among different composition of biological soil crusts. Abundance of amoA, narG, and nosZ genes were highly related to N2O and NO emissions. Thus, our results indicate that gaseous nitrogen emissions were generally increased by nitrogen addition through their effects on related functional microbial groups. The effects were regulated by composition of biological soil crusts which can buffer the effects of increasing nitrogen addition during winter.

What is a biocrust? A refined, contemporary definition for a broadening research community.

Studies of biological soil crusts (biocrusts) have proliferated over the last few decades. The biocrust literature has broadened, with more studies assessing and describing the function of a variety of biocrust communities in a broad range of biomes and habitats and across a large spectrum of disciplines, and also by the incorporation of biocrusts into global perspectives and biogeochemical models. As the number of biocrust researchers increases, along with the scope of soil communities defined as 'biocrust', it is worth asking whether we all share a clear, universal, and fully articulated definition of what constitutes a biocrust. In this review, we synthesize the literature with the views of new and experienced biocrust researchers, to provide a refined and fully elaborated definition of biocrusts. In doing so, we illustrate the ecological relevance and ecosystem services provided by them. We demonstrate that biocrusts are defined by four distinct elements: physical structure, functional characteristics, habitat, and taxonomic composition. We describe outgroups, which have some, but not all, of the characteristics necessary to be fully consistent with our definition and thus would not be considered biocrusts. We also summarize the wide variety of different types of communities that fall under our definition of biocrusts, in the process of highlighting their global distribution. Finally, we suggest the universal use of the Belnap, Büdel & Lange definition, with minor modifications: Biological soil crusts (biocrusts) result from an intimate association between soil particles and differing proportions of photoautotrophic (e.g. cyanobacteria, algae, lichens, bryophytes) and heterotrophic (e.g. bacteria, fungi, archaea) organisms, which live within, or immediately on top of, the uppermost millimetres of soil. Soil particles are aggregated through the presence and activity of these often extremotolerant biota that desiccate regularly, and the resultant living crust covers the surface of the ground as a coherent layer. With this detailed definition of biocrusts, illustrating their ecological functions and widespread distribution, we hope to stimulate interest in biocrust research and inform various stakeholders (e.g. land managers, land users) on their overall importance to ecosystem and Earth system functioning.

Multiple resource limitation of dryland soil microbial carbon cycling on the Colorado Plateau.

Understanding interactions among biogeochemical cycles is increasingly important as anthropogenic alterations of global climate and of carbon (C), nitrogen (N), and phosphorus (P) cycles interactively affect the Earth system. Ecosystem processes in the dryland biome, which makes up over 40% of Earth's terrestrial surface, are often distinctively sensitive to small changes in resource availability, likely because levels of many resources are low. However, data also suggest that simultaneous changes in the availability of multiple resources may be necessary to affect a response in these low-resource systems, offering an opportunity to test patterns and controls of co-limitation, serial limitation, and individual limitation in soil environments. While drylands may play a governing role in key aspects of Earth's C cycle, and while an improved understanding of resource limitation could substantially improve our forecasts of dryland responses to change, our understanding of interacting controls on soil C cycle processes remains notably poor in these dry systems. Here, we address multiple fundamental hypotheses of resource controls over ecosystem function to test how water, C, N, and P regulate soil C cycling individually and interactively in a dryland ecosystem on the Colorado Plateau. Using a series of laboratory incubations, we found that, while water, C, and N limited C cycling through serial limitation, water alone resulted in an extremely small respiratory response from target organisms, whereas water + C resulted in a dramatic increase in soil C cycling, suggesting a degree of functional co-limitation. Nitrogen additions alone resulted in no changes to soil C cycling, but when N was added in concert with water and C, N greatly increased soil C cycling rates relative to additions of water and C without N. Phosphorus additions had no effect on the C cycle either alone or synergistically. These patterns were consistent with the stoichiometry of the system and interactions among resources were surprising in ways that inform our understanding of critical theories in ecology, such as the Transient Maxima Hypothesis, supporting the suggestion that multiple resource limitation explains pulse-dynamic C cycling in drylands better than water limitation alone.

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors.

Quantifying temperature and moisture at the soil surface is essential for understanding how soil surface biota respond to changes in the environment. However, at the soil surface these variables are highly dynamic and standard sensors do not explicitly measure temperature or moisture in the upper few millimeters of the soil profile. This paper describes methods for manufacturing simple, inexpensive sensors that simultaneously measure the temperature and moisture of the upper 5 mm of the soil surface. In addition to sensor construction, steps for quality control, as well as for calibration for various substrates, are explained. The sensors incorporate a Type E thermocouple to measure temperature and assess soil moisture by measuring the resistance between two gold-plated metal probes at the end of the sensor at a depth of 5 mm. The methods presented here can be altered to customize probes for different depths or substrates. These sensors have been effective in a variety of environments and have endured months of heavy rains in tropical forests as well as intense solar radiation in deserts of the southwestern U.S. Results demonstrate the effectiveness of these sensors for evaluating warming, drying, and freezing of the soil surface in a global change experiment.

Beyond simple linear mixing models: process-based isotope partitioning of ecological processes.

Stable isotopes are valuable tools for partitioning the components contributing to ecological processes of interest, such as animal diets and trophic interactions, plant resource use, ecosystem gas fluxes, streamflow, and many more. Stable isotope data are often analyzed with simple linear mixing (SLM) models to partition the contributions of different sources, but SLM models cannot incorporate a mechanistic understanding of the underlying processes and do not accommodate additional data associated with these processes (e.g., environmental covariates, flux data, gut contents). Thus, SLM models lack predictive ability. We describe a process-based mixing (PBM) model approach for integrating stable isotopes, other data sources, and process models to partition different sources or process components. This is accomplished via a hierarchical Bayesian framework that quantifies multiple sources of uncertainty and enables the incorporation of process models and prior information to help constrain the source-specific proportional contributions, thereby potentially avoiding identifiability issues that plague SLM models applied to "too many" sources. We discuss the application of the PBM model framework to three diverse examples: temporal and spatial partitioning of streamflow, estimation of plant rooting profiles and water uptake profiles (or water sources) with extension to partitioning soil and ecosystem CO2 fluxes, and reconstructing animal diets. These examples illustrate the advantages of the PBM modeling approach, which facilitates incorporation of ecological theory and diverse sources of information into the mixing model framework, thus enabling one to partition key process components across time and space.

Does declining carbon-use efficiency explain thermal acclimation of soil respiration with warming?

Enhanced soil respiration in response to global warming may substantially increase atmospheric CO2 concentrations above the anthropogenic contribution, depending on the mechanisms underlying the temperature sensitivity of soil respiration. Here, we compared short-term and seasonal responses of soil respiration to a shifting thermal environment and variable substrate availability via laboratory incubations. To analyze the data from incubations, we implemented a novel process-based model of soil respiration in a hierarchical Bayesian framework. Our process model combined a Michaelis-Menten-type equation of substrate availability and microbial biomass with an Arrhenius-type nonlinear temperature response function. We tested the competing hypotheses that apparent thermal acclimation of soil respiration can be explained by depletion of labile substrates in warmed soils, or that physiological acclimation reduces respiration rates. We demonstrated that short-term apparent acclimation can be induced by substrate depletion, but that decreasing microbial biomass carbon (MBC) is also important, and lower MBC at warmer temperatures is likely due to decreased carbon-use efficiency (CUE). Observed seasonal acclimation of soil respiration was associated with higher CUE and lower basal respiration for summer- vs. winter-collected soils. Whether the observed short-term decrease in CUE or the seasonal acclimation of CUE with increased temperatures dominates the response to long-term warming will have important consequences for soil organic carbon storage.

Ecological forecasting and data assimilation in a data-rich era.

Several forces are converging to transform ecological research and increase its emphasis on quantitative forecasting. These forces include (1) dramatically increased volumes of data from observational and experimental networks, (2) increases in computational power, (3) advances in ecological models and related statistical and optimization methodologies, and most importantly, (4) societal needs to develop better strategies for natural resource management in a world of ongoing global change. Traditionally, ecological forecasting has been based on process-oriented models, informed by data in largely ad hoc ways. Although most ecological models incorporate some representation of mechanistic processes, today's models are generally not adequate to quantify real-world dynamics and provide reliable forecasts with accompanying estimates of uncertainty. A key tool to improve ecological forecasting and estimates of uncertainty is data assimilation (DA), which uses data to inform initial conditions and model parameters, thereby constraining a model during simulation to yield results that approximate reality as closely as possible. This paper discusses the meaning and history of DA in ecological research and highlights its role in refining inference and generating forecasts. DA can advance ecological forecasting by (1) improving estimates of model parameters and state variables, (2) facilitating selection of alternative model structures, and (3) quantifying uncertainties arising from observations, models, and their interactions. However, DA may not improve forecasts when ecological processes are not well understood or never observed. Overall, we suggest that DA is a key technique for converting raw data into ecologically meaningful products, which is especially important in this era of dramatically increased availability of data from observational and experimental networks.