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Soil organic carbon (SOC) plays a crucial role in the global carbon cycle and in maintaining soil functions in the context of land use and climate change. Understanding the spatial distribution of SOC is essential for the management of agricultural land to optimize soil health and carbon storage. In this study, we investigated the spatial distribution of SOC in an agricultural lowland area of the Lombardy region, Italy, using machine learning (ML) techniques combined with residual kriging. ML models, including the artificial neural network (ANN), extreme learning machine (ELM), and random forest (RF), were trained on 120 SOC observations and eight environmental variables to predict SOC values across the study area. The performance of this ML approach was assessed using a ten-fold nested cross-validation process. The ELM and RF models showed better predictive performances based on the concordance correlation coefficient and root mean square error (RMSE), with RF slightly outperforming ELM based on the RMSE. The residuals of each iteration from the ML models were interpolated by ordinary kriging (OK) and added to the ML-based trend model in a hybrid regression-kriging approach. This approach which accounted for the spatial autocorrelation of the prediction residuals, resulting in a marginally improved prediction accuracy in the ML models. In addition, we found that vertical distance to the channel network and channel network base level are important predictor variables that should be considered in future digital soil models for SOC in lowland areas, given their importance in this study. Furthermore, this study highlights that predicted SOC values were low, particularly in Luvisols, which can be explained by the long history of agricultural land use depleting SOC due to agricultural management and loss of organic plant residues. The prediction maps depicted spatial variation and patterns of SOC in the study area. Our findings may help to refine soil management practices and contribute to improving soil health and carbon sequestration in agricultural lowland areas.

Abstract Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering new opportunities to improve our understanding of the complex Earth system. IML goes beyond conventional machine learning by not only making predictions but also seeking to elucidate the reasoning behind those predictions. The combination of predictive power and enhanced transparency makes IML a promising approach for uncovering relationships in data that may be overlooked by traditional analysis. Despite its potential, the broader implications for the field have yet to be fully appreciated. Meanwhile, the rapid proliferation of IML, still in its early stages, has been accompanied by instances of careless application. In response to these challenges, this paper focuses on how IML can effectively and appropriately aid geoscientists in advancing process understanding—areas that are often underexplored in more technical discussions of IML. Specifically, we identify pragmatic application scenarios for IML in typical geoscientific studies, such as quantifying relationships in specific contexts, generating hypotheses about potential mechanisms, and evaluating process‐based models. Moreover, we present a general and practical workflow for using IML to address specific research questions. In particular, we identify several critical and common pitfalls in the use of IML that can lead to misleading conclusions, and propose corresponding good practices. Our goal is to facilitate a broader, yet more careful and thoughtful integration of IML into Earth science research, positioning it as a valuable data science tool capable of enhancing our current understanding of the Earth system.

Abstract Annual antler growth begins in the spring and is completed by late summer for male caribou (Rangifer tarandus groenlandicus) from the Qamanirjuaq herd (Nunavut, Canada), aligned with both the spring migration and a seasonal dietary shift. Antlers may provide a non‐lethal means of studying short‐ and long‐term changes in caribou ecology through incorporated isotopes of carbon (δ13C) and nitrogen (δ15N). We sampled the antlers of 12 male caribou from the Qamanirjuaq herd culled in September 1967. We predicted that serial sampling of antlers would reflect the known seasonal dietary change from lichen to grass‐like and shrub diet based on rumen contents from individuals culled during the same period. The δ13C and δ15N were analyzed in food sources and every 3 cm along each antler's length. The carbon isotope compositions of collagen (δ13Ccol) varied by ~0.5‰ among individuals and within antlers, while the carbon isotope compositions of antler bioapatite (δ13CCO3) increased by 1–1.5‰ from pedicle to tip. Values of δ15Ncol increased within antlers by 1–3‰ from pedicle to tip and varied by 3‰ among the individuals sampled. Antler collagen was lower in δ15Ncol by ~1‰ relative to bone collagen. Bayesian mixing models were conducted to test for changes in dietary proportions from antler isotope compositions. Mixing models did not indicate significant dietary shifts for any individual during antler formation, showing consistently mixed diets of fungi, horsetail, lichen, and woody plants. Increases in δ15Ncol in antler tissue could, therefore, correspond to subtle seasonal dietary changes and/or the physiological stress of antler tissue development.

Although the COVID-19 crisis is a worldwide threat to individuals' physical health and psychological well-being, not all people are equally susceptible to increased ill-being. One potentially important factor in individuals' vulnerability (versus resilience) to ill-being in the face of stress is emotion regulation. On the basis of Self-Determination Theory, this study examined the role of three emotion regulation styles in individuals' mental health during the COVID-19 crisis, that is, integration, suppression, and dysregulation. Participants were 6584 adults (77% female, M.sub.age = 45.16 years) who filled out well-validated measures of emotion regulation, depression, anxiety, life satisfaction, and sleep quality. To examine naturally occurring combinations of emotion regulation strategies, hierarchical k-means clustering was performed, yielding 3 profiles: (a) low scores on all strategies (indicating rather low overall levels of worry; 27%), (b) high scores on integration only (41%), and (c) high scores on suppression and dysregulation (32%). Participants in the profiles scoring high on suppression and dysregulation displayed a less favorable pattern of outcomes (high ill-being, low life satisfaction, and poorer sleep quality) compared to the other two groups. Between-cluster differences remained significant even when taking into account the corona-related worries experienced by people. Overall, the findings underscore the important role of emotion regulation in individuals' mental health during mentally challenging periods such as the COVID-19 crisis. Practical implications and directions for future research are discussed., Author(s): Joachim Waterschoot [sup.1] , Sofie Morbée [sup.1] , Branko Vermote [sup.1] , Katrijn Brenning [sup.1] , Nele Flamant [sup.1] , Maarten Vansteenkiste [sup.1] , Bart Soenens [sup.1] Author Affiliations: [...]

Excess ice, found as massive ice and within icy sediments, is an important variable to quantify as it is a dominant control on the terrain and geotechnical response to permafrost thaw. A large amount of permafrost borehole data are available from the Tuktoyaktuk Coastlands; however, field geotechnical assessments typically only involve the estimation of visible ice. To add significant value to these data sets, a cryostratigraphic data set collected along the Inuvik–Tuktoyaktuk Highway (566 boreholes) is used to develop a beta regression model which predicts the excess ice content of icy sediments based on interval depth, visible ice content, material type, and Quaternary deposits. The resulting predictions are compared to recorded massive ice intervals and show that ground ice within icy sediments can contribute up to 65% of the excess ice and potential thaw strain within the first 10 m from the surface in this area. This study shows the general applicability of this approach and indicates that comparable, quantitative data on ground ice conditions should be collected with drilling programs to derive geotechnical variables and reduce modelling uncertainties so that ground ice data are available for quantitative analysis.

ABSTRACTMammals exhibit daily fasting and feeding patterns that produce a fluctuating environment in the gut. By colonizing germfree mice with Bacteroides thetaiotaomicron and examining gene expression through a time-restricted feeding cycle, we demonstrate that this prominent gut commensal exhibits gene expression patterns characteristic of a stringent response and increases ppGpp levels during the host-fasting phase of the feeding regimen. Mutants unable to produce (p)ppGpp fail to produce this transcriptional response, exhibit unrestrained chromosomal replication, and cannot maintain population size during the host-fasting phase. Additionally, B. thetaiotaomicron requires (p)ppGpp to utilize host glycans both in vitro and in vivo, and mutants unable to produce (p)ppGpp display deficiencies in mucus layer colonization during the host-fasting phase. Complete gut microbial communities from mice and humans also increase ppGpp levels in this manner, demonstrating that this response appears to be general across species and conserved across mammalian gut communities. Together, these results identify an intracellular signal that allows gut microbes to coordinate their physiology with a time-restricted feeding regimen of their host.IMPORTANCEMammals do not eat continuously, instead concentrating their feeding to a restricted portion of the day. This behavior presents the mammalian gut microbiota with a fluctuating environment with consequences for host-microbiome interaction, infection risk, immune response, drug metabolism, and other aspects of health. We demonstrate that in mice, gut microbes elevate levels of an intracellular signaling molecule, (p)ppGpp, during the fasting phase of a time-restricted feeding regimen. Disabling this response in a representative human gut commensal species significantly reduces colonization during this host-fasting phase. This response appears to be general across species and conserved across mammalian gut communities, highlighting a pathway that allows healthy gut microbiomes to maintain stability in an unstable environment.

The dynamics of forest recovery after windthrows (i.e., broken or uprooted trees by wind) are poorly understood in tropical forests. The Northwestern Amazon (NWA) is characterized by a higher occurrence of windthrows, greater rainfall, and higher annual tree mortality rates (~2%) than the Central Amazon (CA). We combined forest inventory data from three sites in the Iquitos region of Peru, with recovery periods spanning 2, 12, and 22 years following windthrow events. Study sites and sampling areas were selected by assessing the windthrow severity using remote sensing. At each site, we recorded all trees with a diameter at breast height (DBH) ≥ 10 cm along transects, capturing the range of windthrow severity from old-growth to highly disturbed (mortality > 60%) forest. Across all damage classes, tree density and basal area recovered to >90% of the old-growth values after 20 years. Aboveground biomass (AGB) in old-growth forest was 380 (±156) Mg ha−1 . In extremely disturbed areas, AGB was still reduced to 163 (±68) Mg ha−1 after 2 years and 323 (± 139) Mg ha−1 after 12 years. This recovery rate is ~50% faster than that reported for Central Amazon forests. The faster recovery of forest structure in our study region may be a function of its higher productivity and adaptability to more frequent and severe windthrows. These varying rates of recovery highlight the importance of extreme wind and rainfall on shaping gradients of forest structure in the Amazon, and the different vulnerabilities of these forests to natural disturbances whose severity and frequency are being altered by climate change.

The dynamics of forest recovery after windthrows (i.e., broken or uprooted trees by wind) are poorly understood in tropical forests. The Northwestern Amazon (NWA) is characterized by a higher occurrence of windthrows, greater rainfall, and higher annual tree mortality rates (~2%) than the Central Amazon (CA). We combined forest inventory data from three sites in the Iquitos region of Peru, with recovery periods spanning 2, 12, and 22 years following windthrow events. Study sites and sampling areas were selected by assessing the windthrow severity using remote sensing. At each site, we recorded all trees with a diameter at breast height (DBH) ≥ 10 cm along transects, capturing the range of windthrow severity from old-growth to highly disturbed (mortality > 60%) forest. Across all damage classes, tree density and basal area recovered to >90% of the old-growth values after 20 years. Aboveground biomass (AGB) in old-growth forest was 380 (±156) Mg ha−1 . In extremely disturbed areas, AGB was still reduced to 163 (±68) Mg ha−1 after 2 years and 323 (± 139) Mg ha−1 after 12 years. This recovery rate is ~50% faster than that reported for Central Amazon forests. The faster recovery of forest structure in our study region may be a function of its higher productivity and adaptability to more frequent and severe windthrows. These varying rates of recovery highlight the importance of extreme wind and rainfall on shaping gradients of forest structure in the Amazon, and the different vulnerabilities of these forests to natural disturbances whose severity and frequency are being altered by climate change.

The assessment of uncertainties in landslide susceptibility modelling in a changing environment is an important, yet often neglected, task. In an Austrian case study, we investigated the uncertainty cascade in storylines of landslide susceptibility emerging from climate change and parametric landslide model uncertainty. In June 2009, extreme events of heavy thunderstorms occurred in the Styrian Basin, triggering thousands of landslides. Using a storyline approach, we discovered a generally lower landslide susceptibility for the pre-industrial climate, while for the future climate (2071–2100) a potential increase of 35 % in highly susceptible areas (storyline of much heavier rain) may be compensated for by much drier soils (−45 % areas highly susceptible to landsliding). However, the estimated uncertainties in predictions were generally high. While uncertainties related to within-event internal climate model variability were substantially lower than parametric uncertainties in the landslide susceptibility model (ratio of around 0.25), parametric uncertainties were of the same order as the climate scenario uncertainty for the higher warming levels (+3 and +4 K). We suggest that in future uncertainty assessments, an improved availability of event-based landslide inventories and high-resolution soil and precipitation data will help to reduce parametric uncertainties in landslide susceptibility models used to assess the impacts of climate change on landslide hazard and risk.

Transferability of knowledge from well-investigated areas to a new study region is gaining importance in landslide hazard research. Considering the time-consuming compilation of landslide inventories as a prerequisite for landslide susceptibility mapping, model transferability can be key to making hazard-related information available to stakeholders in a timely manner. In this paper, we compare and combine two important transfer-learning strategies for landslide susceptibility modeling: case-based reasoning (CBR) and domain adaptation (DA). Care-based reasoning gathers knowledge from previous similar situations (source areas) and applies it to solve a new problem (target area). Domain adaptation, which is widely used in computer vision, selects data from a source area that has a similar distribution to the target area. We assess the performances of single- and multiple-source CBR, DA, and CBR–DA strategies to train and combine landslide susceptibility models using generalized additive models (GAMs) for 10 study areas with various resolutions (1, 10, and 25 m) located in Austria, Ecuador, and Italy. The performance evaluation shows that CBR and combined CBR–DA based on our proposed similarity criterion were able to achieve performances comparable to benchmark models trained in the target area itself. Particularly the CBR strategies yielded favorable results in both single- and multi-source strategies. Although DA tended to have overall lower performances than CBR, it had promising results in scenarios where the source–target similarity was low. We recommend that future transfer-learning research for landslide susceptibility modeling can build on the similarity criterion we used, as it successfully helped to transfer landslide susceptibility models by identifying suitable source regions for model training.

Accurate subseasonal weather forecasts, from 2 weeks up to a season, can help reduce costs and impacts related to weather and corresponding extremes. The quality of weather forecasts has improved considerably in recent decades as models represent more details of physical processes, and they benefit from assimilating comprehensive Earth observation data as well as increasing computing power. However, with ever-growing model complexity, it becomes increasingly difficult to pinpoint weaknesses in the forecast models' process representations which is key to improving forecast accuracy. In this study, we use a comprehensive set of observation-based ecological, hydrological, and meteorological variables to study their potential for explaining temperature forecast errors at the weekly timescale. For this purpose, we compute Spearman correlations between each considered variable and the forecast error obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal-to-seasonal (S2S) reforecasts at lead times of 1–6 weeks. This is done across the globe for the time period 2001–2017. The results show that temperature forecast errors globally are most strongly related with climate-related variables such as surface solar radiation and precipitation, which highlights the model's difficulties in accurately capturing the evolution of the climate-related variables during the forecasting period. At the same time, we find particular regions in which other variables are more strongly related to forecast errors. For instance, in central Europe, eastern North America and southeastern Asia, vegetation greenness and soil moisture are relevant, while in western South America and central North America, circulation-related variables such as surface pressure relate more strongly with forecast errors. Overall, the identified relationships between forecast errors and independent Earth observations reveal promising variables on which future forecasting system development could focus by specifically considering related process representations and data assimilation.

The area affected by a landslide analogous to an Alpine foreland event in 2009 could be up to 45% larger under 4 K of global warming, or 10% if warming is limited in line with the Paris Agreement, according to simulations of event storylines with varying climate, soil moisture and land use.

Although mentoring programs for female STEM students are often carried out with a great deal of passion on the part of program managers and mentors, robust results on their effects are often missing. However, regular evaluations are indispensable for an efficient allocation of resources towards gender balances in STEM. To accomplish this requirement, empirically valid and easy-to-use evaluation concepts are needed. We therefore develop an evaluation concept which corresponds to a Logic Chart, capturing three levels of expected effects (output—outcome—impact). On each level of impact, we derive a set of success indicators that can be measured by qualitative methods. A major advantage of our evaluation design is that the effect of a mentoring program can be observed directly after the end of the program. Furthermore, the results provide information about different channels of impact (e.g., reduced stereotypes or increased self-efficacy) and hence offer concrete indications for the further development of the program.

Abstract The repeated expansion of East Asian steppe cultures was a key driver of Eurasian history, forging new social, economic, and biological links across the continent. Climate has been suggested as important driver of these poorly understood cultural expansions, but paleoclimate records from the Mongolian Plateau often suffer from poor age control or ambiguous proxy interpretation. Here, we use a combination of geochemical analyses and comprehensive radiocarbon dating to establish the first robust and detailed record of paleohydrological conditions for Lake Telmen, Mongolia, covering the past ~ 4000 years. Our record shows that humid conditions coincided with solar minima, and hydrological modeling confirms the high sensitivity of the lake to paleoclimate changes. Careful comparisons with archaeological and historical records suggest that in the vast semi-arid grasslands of eastern Eurasia, solar minima led to reduced temperatures, less evaporation, and high biomass production, expanding the power base for pastoral economies and horse cavalry. Our findings suggest a crucial link between temperature dynamics in the Eastern Steppe and key social developments, such as the emergence of pastoral empires, and fuel concerns that global warming enhances water scarcity in the semi-arid regions of interior Eurasia.

Although abundant research has demonstrated associations between psychologically controlling parenting and adolescent problem behavior, little is known about the moderating role of adolescent personality herein. This study examined whether adolescent personality moderates the associations between parental psychological control and internalizing and externalizing problems at the level of within-person change. A 3-wave longitudinal design (N = 198 families, M[subscript age] adolescent = 14.89 years, M[subscript age] mother = 45.14 years, Mage father = 46.79 years), with 1-year intervals between waves, and using multi-informant assessment was conducted. Multilevel analyses demonstrated that changes in parental psychological control related positively to changes in multi-informant scores of both internalizing and externalizing problems. Evidence for the moderating role of personality was found for only 1 out of 25 interactions in a variable-centered approach and for 2 out of 8 interactions in a person-centered approach. The interactions obtained indicated that a mature personality (i.e., higher scores on emotional stability or membership in a resilient profile in comparison to an overcontrolled profile) buffered against the detrimental effects of psychologically controlling parenting on problem behaviors. Overall, the number of interactions was limited, suggesting only a modest moderating effect of adolescent personality.