julilili42/papers huggingface dataset:
Variability of mineral protection of organic matter in thawing permafrost peatlands
2022-03-27

&amp;lt;p&amp;gt;Permafrost preserves huge amounts of carbon in Arctic soils including peatlands, which are common in high latitudes. The potential for carbon release from these peatlands upon permafrost thaw is still a big uncertainty for climate predictions. Protection of organic matter against microbial degradation by association with minerals such as iron minerals has been identified as an important stabilization mechanism for organic carbon in soils [1]. In a permafrost peatland in northern Sweden (Stordalen mire) up to 20% of organic carbon was found associated with iron minerals in oxic peat layers [2]. However, upon thaw and collapse of frozen peat, reducing conditions cause microbial iron reduction and dissolution of minerals, therefore releasing associated carbon. Despite the prevalence of peatlands in the permafrost zone, little is known about the variability and overall importance of mineral protection in permafrost peatlands, and it is still uncertain how this will change upon collapse of palsas (frozen peat mounds). Following optimization of a protocol for Fe-OC quantification from peat, we sampled peat cores and pore water from different thawing palsas in the Tornetr&amp;amp;#228;sk area of northern Sweden to quantify iron-carbon associations across different sites and estimate the changes in geochemistry upon permafrost thaw. Understanding these changes and differences between peatlands will help to predict the role of permafrost peatlands for carbon emissions triggered by permafrost thaw across larger geographical areas.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[1] Kaiser and Guggenberger (2000), Org. Geochem., 31, 711-725. [2] Patzner et al. (2020), Nat. Commun., 11, 6329.&amp;lt;/p&amp;gt;
Explainable deep neural networks for exploring spatial variability of soil properties in Germany
2022-03-27

&amp;lt;p&amp;gt;Digital soil mapping approaches predict soil properties based on the relationship between soil observations and related environmental covariates using machine learning models. In this research, we applied deep neural networks to predict the spatial distribution of soil properties in Germany using 1976 soil observations and 170 environmental covariates which are derived from several sources (e.g., remote sensing data). However, a major problem with using deep neural networks is that the exact contribution of environmental covariates in the overall result is unknown. To address this issue and improve the interpretability of deep neural networks, several model-agnostic interpretation tools (i.e., post hoc analyses and techniques) are used to understand previously trained &amp;quot;black-box models&amp;quot; or their predictions. For example, a permutation feature importance technique ranked remote sensing images as the most important predictors to explain the spatial variability of soil organic carbon in the study area. This is the first study to use deep neural networks with explainable algorithms to explore and visualize the spatial distribution of soil properties in Germany.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Keywords&amp;lt;/strong&amp;gt;: explainable machine learning; deep neural networks; soil properties; Germany&amp;lt;/p&amp;gt;
Spatial prediction of soil type maps with Neural Networks including quantification of model uncertainty
2022-03-27

&amp;lt;p&amp;gt;Artificial neural networks (ANN), which are mainly used in pattern and image recognition, have now found a wide range of applications. In recent years, different variants of ANN have also been increasingly used in the geosciences. They have proven to be a useful tool for complex questions that also involve a large amount of data. In their basic form, however, deep-learning algorithms do not provide interpretable predictive uncertainty. In the geosciences in particular, they have been used more as black-box models that require interpretation by an expert or do not allow for specific interpretation. Therefore, we implement in our explorative study on soil classification a Bayesian deep learning approach (i.e. a method to add uncertainty to deep networks) known as last layer Laplace approximation. This is a technique that can be applied as a post-hoc &amp;quot;add-on&amp;quot; without destroying the otherwise good performance of deep classifiers. &amp;lt;br&amp;gt;Our target soil type variable provides us with a large amount of information about soil processes and properties, which is a great advantage since it would take a lot of time and money to collect all this information individually. At the same time, soil maps are in high demand by authorities, construction companies or farmers. In our study area around T&amp;amp;#252;bingen in southern Germany, there are 39 different soil types, determined according to the German soil systematics, which we consider individually for the prediction, but also combine into superordinate categories with similar properties, which is possible at low computational cost under the Laplace approximation. In addition to the underlying soil map, remotely sensed variables such as satellite imagery, a digital elevation model and its derivatives, and climate data are used as input to the model, which is designed to learn the relationship between these and the soil type. &amp;amp;#160;As a test case, we then explicitly include the Swabian Jura as a prediction region for the environment. This region is characterised by very different soil types due to its extremely different development and the resulting geology, climate and terrain. &amp;lt;br&amp;gt;Our goal is then to enrich soil type maps with a structured uncertainty, which is estimated to be high in the area of the Swabian Jura. This will help to better understand the causality of machine learning models in soil science and their transferability to regions other than the training and validation area.&amp;lt;/p&amp;gt;
Biological soil crusts as a mechanism to protect areas under sandization processes in southern Brazil
2022-03-27

&amp;lt;p&amp;gt;Biological Soil Crusts (biocrusts) are communities of algae, lichens, mosses, cyanobacteria, and other nonvascular organisms living in the soil surfaces. Biocrusts are a key factor in the protection of arid and semiarid ecosystems and, therefore, playing a major role against desertification. Biocrusts are also of profound importance in sand dune areas, as they are recognized as the first colonizers after environmental disturbances and can help preventing sediment remobilization. Moreover, biocrusts have shown to be of importance in soil protection against erosion, but also nutrient cycling in the Pampa biome in Brazil. Here, natural geomorphological processes and soil misuse led to the expansion of sediment remobilization areas, generating a severe problem - the difficulty of fixing field vegetation and crops. The present study investigates the behavior and interrelationships of biocrusts specifically in areas that suffer sandization in the Brazilian Pampa biome and verifies their relationship with soils and soil organisms. We analyzed biocrusts in three consecutive stages. Starting with a taxonomic exploration of the dominant component of cyanobacteria, proceeding to its characterization and finally determining its importance. We investigated two study sites in S&amp;amp;#227;o Francisco de Assis and samples were collected in May 2016 and October 2019. The sites are characterized by sandy soils that suffer gullying, one without human intervention and the other one with artificially stabilized ravines. The analysis of biological material was carried out with microscopy, and it could be determined that the composition consists of 13 taxa of cyanobacteria and one filamentous species, &amp;lt;em&amp;gt;Stigonema &amp;lt;/em&amp;gt;sp., could be specifically highlighted. These black to dark green spotty communities on the soil surface played an important role in particle aggregation, which can be granulated and show macroscopic forms. When analyzing the location of the biocrusts within the topography, it was observed that they occur in more humid places, occupying the same positions in all relief compartments. Biocrusts mostly develop on the top of the gullies or on upper and more stabilized slopes, especially when facing southern orientation. Taking into account the biocrust cover, we can identify different morphologies such as smooth, rolling and pinnacle blocks, which showed us different combinations according to the degree of evolution related to the micromorphology of the relief. We found that the presence of these biocrusts as an element of nutrient source and balance generator can lead to a reduction of soil erosion and was thus of outmost importance for the restoration of this biome.&amp;lt;/p&amp;gt;
Effect of root hairs and benzoxazinoids on maize microbiome and its enzymatic activity in the rhizosphere
2022-03-27

&amp;lt;p&amp;gt;Root morphology and the composition of root exudates shape the spatial organization and various processes in the rhizosphere. For instance, root hairs are essential for plant nutrition, while secondary plant metabolites (i.e. benzoxazinoids) ensure plant defence from herbivore and fungal infection. Nevertheless, it is still unknown to which extent root hairs and benzoxazinoids may change the microbiome and enzymatic activities, as well as formation of rhizosphere hot- and coldspots.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;To study the effect of root hairs and benzoxasinoids on the rhizosphere microbiome structure and its enzymatic activities we compared mutants with defective root hairs &amp;lt;em&amp;gt;rth3 &amp;lt;/em&amp;gt;or with reduced benzoxazinoids &amp;lt;em&amp;gt;bx1&amp;lt;/em&amp;gt; with the corresponding wild-type (WT) maize.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Root hairs increased acid phosphatase activity by 80 % promoting mineralization of organic phosphorus sources to available forms in the hotspots. In the coldspots, broken root hairs in WT facilitated the intensive microbial hotspots with up to two times higher &amp;amp;#946;-glucosidase and chitinase activities, compared to &amp;lt;em&amp;gt;rth3&amp;lt;/em&amp;gt;.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The presence of benzoxazinoids in root exudates strongly supported plant defence against pathogenic fungi (i.e., genus &amp;lt;em&amp;gt;Fusarium&amp;lt;/em&amp;gt; and &amp;lt;em&amp;gt;Gibberella&amp;lt;/em&amp;gt;) while the total microbial biomass remained unaffected. In response to the presence of pathogenic fungi, &amp;lt;em&amp;gt;bx1&amp;lt;/em&amp;gt; exuded 70 % more chitinase for defence purpose to partly compensate for benzoxazinoids deficiency, which was however, less efficient against pathogens than the presence of benzoxazinoids.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Overall, we conclude that: i) root hairs facilitate better plant nutrition at the shortage of available nutrients (i.e., coldspots), while; ii) the presence of benzoxazinoids in exudates protect plant from pathogenic microorganisms. This two root traits are promising for plant breeding of genotypes suitable for sustainable agriculture and organic farming.&amp;lt;/p&amp;gt;
Constrained Generative Adversarial Networks for Improving Earth System Model Precipitation
2022-03-27

&amp;lt;p&amp;gt;The simulation of precipitation in numerical Earth system models (ESMs) involves various processes on a wide range of scales, requiring high temporal and spatial resolution for realistic simulations. This can lead to biases in computationally efficient ESMs that have a coarse resolution and limited model complexity. Traditionally, these biases are corrected by relating the distributions of historical simulations with observations [1]. While these methods successfully improve the modelled statistics, unrealistic spatial features that require a larger spatial context are not addressed.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here we apply generative adversarial networks (GANs) [2] to transform precipitation of the CM2Mc-LPJmL ESM [3] into a bias-corrected and more realistic output. Feature attribution shows that the GAN has correctly learned to identify spatial regions with the largest bias during training. Our method presents a general bias correction framework that can be extended to a wider range of ESM variables to create highly realistic but computationally inexpensive simulations of future climates. We also discuss the generalizability of our approach to projections from CMIP6, given that the GAN is only trained on historical data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[1] A.J. Cannon et al. &amp;quot;Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?.&amp;quot; Journal of Climate 28.17 (2015): 6938-6959.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[2] I. Goodfellow et al. &amp;quot;Generative adversarial nets.&amp;quot; Advances in neural information processing systems 27 (2014).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;[3] M. Dr&amp;amp;#252;ke et al. &amp;quot;CM2Mc-LPJmL v1.0: Biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model.&amp;quot; Geoscientific Model Development 14.6 (2021): 4117--4141.&amp;lt;/p&amp;gt;
Investigating the effects of bryophytes on carbon cycling in a temperate forest ecosystem from stable isotope composition
2022-03-27

&amp;lt;p&amp;gt;Nonvascular plants such as bryophytes are often overlooked; however, they are important players at the soil-atmosphere interface and affecting water exchange, nutrient fluxes or carbon storage. Bryophytes also act as soil stabilizers in a variety of ecosystems and thus contribute significantly to the mitigation of soil erosion. Nevertheless, these stabilizing effects are not completely understood, with two distinctions: Firstly, bryophytes form a physical protective barrier that prevents direct drop impact on soil. Secondly, they fix carbon and thus contribute to soil carbon storage, which in turn enhances soil aggregation. Both factors result in a reduction of soil erosion, although it is unclear to what extent. As bryophytes showed a high impact on carbon assimilation and soil carbon storage in boreal environments, gaining an understanding of these effects in a temperate forest can be a key factor in assessing the overall state of that ecosystem.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, we used the stable carbon isotope ratio (&amp;amp;#948;&amp;lt;sup&amp;gt;13&amp;lt;/sup&amp;gt;C) analysis as an approach to evaluate the effect of bryophytes on soil organic carbon (SOC). Furthermore, we investigated the influence of SOC on aggregate size. Soil substrates and bryophyte species were sampled in temperate forests in southern Germany with different kinds of parent material. Five sites were located in Sch&amp;amp;#246;nbuch Nature Park next to T&amp;amp;#252;bingen and one site in the Black Forest close to Freiburg. Each study site consisted of three treatments: bryophyte-covered patches, bare undisturbed soil and partial disturbed soil from forest management. In this context, it was hypothesized that there is a significant contribution of bryophytes to SOC, which is reflected by a change in isotopic signatures and aggregate sizes. Consequently, bryophyte-covered soils are assumed to exhibit higher SOC contents, form larger soil aggregates and for this reason be more resistant to soil erosion, and the contribution of bryophytes to SOC can be estimated based on the differences in the &amp;amp;#948;&amp;lt;sup&amp;gt;13&amp;lt;/sup&amp;gt;C of bryophytes compared to C&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; plants.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Preliminary results of two study sites revealed a distinct positive correlation between SOC and aggregate size, whereby a contribution of bryophytes to SOC could not yet be established based on &amp;amp;#948;&amp;lt;sup&amp;gt;13&amp;lt;/sup&amp;gt;C values. This could be due to the different ecological structure of the two studied sites, the similarity of carbon isotope signatures of C&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; plants and bryophytes, the alteration of isotope signature as a result of decomposition, and the combination of all these factors. Laboratory and data analysis of the four remaining sites is currently ongoing, so further results will be presented at EGU 2022.&amp;lt;/p&amp;gt;
Hierarchical Soil Classification using Gaussian Processes
2022-03-27

&amp;lt;p&amp;gt;The goal of this work is to perform soil classification with uncertainty quantification for a structured treatment of the output classes. Uncertainty can help in this setting to make predictions more informative with regard to class relationships. This is of particular interest due to the often highly related nature of the distinguished soil types. Incorporating knowledge about class structure into the model also provides opportunity for improving the model accuracy. Our main focus, however, is to enable modellers to better understand and work with this structure during analysis.&amp;lt;br&amp;gt;For example, post-hoc aggregation of class labels into supersets facilitates applications such as letting the model choose an ontological level on which it can confidently distinguish the output class. It can likewise be used to determine the combined probability of specified classes that share a property of interest.&amp;lt;br&amp;gt;Technically, this works by learning a latent Gaussian distribution, for example using a Gaussian Process model, and mapping it to a distribution over the class probabilities. We demonstrate this approach, explore possible applications for exploiting uncertainty information, in particular with regard to the class hierarchy, and compare the performance of different model variants in terms of accuracy and calibration.&amp;lt;/p&amp;gt;
Intracellular energy storage mediating soil microbial resource stress
2022-03-27

&amp;lt;p&amp;gt;A diverse range of soil microorganisms accumulate energy to secure their future needs under resource fluctuation or deficiency. Microbial intracellular storage can substantially mediate the stress of resource variability across time, thereby supporting growth and reproduction. Microbial storage is well known in industrial applications and under pure culture conditions, yet few studies address its importance in the soil. To evaluate how widespread microbial energy storage is in soil, we quantified the contents of two intracellular storage compounds, polyhydroxybutyrate (PHB) and triacylglycerides (TAGs), from seven permanent grasslands in Germany differing in field management (grazing/mowing and fertilizing) and soil types. In winter 2021, soil was collected from two depths, 5-10 cm called topsoil, and &amp;gt;30 cm called subsoil, to capture different soil carbon inputs from grass roots. The storage compound contents were determined by gas chromatography&amp;amp;#8211;mass spectrometry (GC-MS). We hypothesized that the carbon input controls the storage compound levels. From topsoil to subsoil, as root carbon inputs (estimated from the fresh root weight) drop with depth, microbial storage levels follow suit. Dissolved organic carbon (DOC) was measured to qualify carbon availability to microorganisms, and microbial biomass carbon (MBC) was to assess microbial biomass. The root weight in the topsoil was 20-50 times higher than in the subsoil, while MBC and DOC contents were 3-4 and 1.5-2.5 times higher, respectively. Storage levels and MBC decreased with depth, and showed a positive correlation with DOC. This experiment allowed us to quantify intracellular storage occurrence in soils and to understand how its distribution related to root carbon input. These results point out that microbial intracellular carbon storage might accumulate according to the available carbon level (root carbon inputs) for microorganisms. Thus, this carbon plays a pivotal role for microbial ecology of soils as it prepares the microbial cells to survive throughout the winter when less carbon is provided by plants.&amp;lt;/p&amp;gt;
Ice rise and ice rumple dynamics, and the consequences for ice sheet evolution
2022-03-27

&amp;lt;p&amp;gt;The Antarctic contribution to sea level projections remains poorly constrained, particularly due to the complex dynamical response of the ice sheet to changes in external forcing in coastal regions. In our study we investigate ice rises and ice rumples, features which form in ice shelves where ice is locally grounded due to elevated bed topography. As a consequence, upstream ice is buttressed, regulating the flow of ice. Ice rises and ice rumples differ from one another in their characteristic flow regimes, with ice rises having a local, radial flow regime and ice rumples having a flow regime predominantly aligned with that of the surrounding ice shelf. Ice rises cause the surrounding ice shelf to flow either side of the feature and thereby cause a greater degree of buttressing.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Using a three-dimensional, isothermal, &amp;amp;#160;full Stokes, idealised model setup (Elmer/Ice), we investigate the response of ice rises and ice rumples to sea level change, mimicking a glacial cycle. During sea level increase, a transition from ice rise to ice rumple occurs, and with a subsequent decrease in sea level, hysteretic behaviour is observed, i.e. the current grounded area, dome position and flow regime are dependent on the past state of the system. The hysteretic behaviour seen in the ice rise-rumple system is reflected in the upstream ice shelf and is likely to have an effect on continental grounding line dynamics. These findings have important implications for the initialisation and transient simulation of ice rises and ice rumples within continental-scale ice sheet models given that the evolution of these features is important for the timing and magnitude of sea level projections.&amp;lt;/p&amp;gt;