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The precise and accurate assessment of carbon dioxide (CO2) exchange is crucial to identify terrestrial carbon (C) sources and sinks and for evaluating their role within the global C budget. The substantial uncertainty in disentangling the management and soil impact on measured CO2 fluxes are largely ignored especially in cropland. The reasons for this lies in the limitation of the widely used eddy covariance as well as manual and automatic chamber systems, which either account for short-term temporal variability or small-scale spatial heterogeneity, but barely both. To address this issue, we developed a novel robotic chamber system allowing for dozens of spatial measurement repetitions, thus enabling CO2 exchange measurements in a sufficient temporal and high small-scale spatial resolution. The system was tested from 08th July to 09th September 2019 at a heterogeneous field (100 m x 16 m), located within the hummocky ground moraine landscape of northeastern Germany (CarboZALF-D). The field is foreseen for a longer-term block trial manipulation experiment extending over three erosion induced soil types and was covered with spring barley. Measured fluxes of nighttime ecosystem respiration (R-eco) and daytime net ecosystem exchange (NEE) showed distinct temporal patterns influenced by crop phenology, weather conditions and management practices. Similarly, we found clear small-scale spatial differences in cumulated (gap-filled) R-eco, gross primary productivity (GPP) and NEE fluxes affected by the three distinct soil types. Additionally, spatial patterns induced by former management practices and characterized by differences in soil pH and nutrition status (P and K) were also revealed between plots within each of the three soil types, which allowed compensating for prior to the foreseen block trial manipulation experiment. The results underline the great potential of the novel robotic chamber system, which not only detects short-term temporal CO2 flux dynamics but also reflects the impact of small-scale spatial heterogeneity.
Students of computer science studies enter university education with very different competencies, experience and knowledge. 145 datasets collected of freshmen computer science students by learning management systems in relation to exam outcomes and learning dispositions data (e. g. student dispositions, previous experiences and attitudes measured through self-reported surveys) has been exploited to identify indicators as predictors of academic success and hence make effective interventions to deal with an extremely heterogeneous group of students.
Im Sommersemester 2022 wurde erstmalig die Lehrveranstaltung „Chemieunterricht für heterogene Lerngruppen“ angeboten. Diese Lehrveranstaltung ist im Rahmen von PSI-Potsdam im Schwerpunkt „Inklusion und Heterogenität“ entstanden. Die Lehrveranstaltung wurde synchron online durchgeführt. Die Studierenden erhielten viele Möglichkeiten zur Diskussion sowie zur selbstständigen Erstellung von Unterrichtsmaterialien. Zum erfolgreichen Abschluss des Moduls erstellten die Studierenden ein Portfolio zu einem selbst gewählten Thema. Die Lehrveranstaltung wurde von den Studierenden sehr gut bewertet.
Fractures serve as highly conductive preferential flow paths for fluids in rocks, which are difficult to exactly reconstruct in numerical models. Especially, in low-conductive rocks, fractures are often the only pathways for advection of solutes and heat. The presented study compares the results from hydraulic and tracer tomography applied to invert a theoretical discrete fracture network (DFN) that is based on data from synthetic cross-well testing. For hydraulic tomography, pressure pulses in various injection intervals are induced and the pressure responses in the monitoring intervals of a nearby observation well are recorded. For tracer tomography, a conservative tracer is injected in different well levels and the depth-dependent breakthrough of the tracer is monitored. A recently introduced transdimensional Bayesian inversion procedure is applied for both tomographical methods, which adjusts the fracture positions, orientations, and numbers based on given geometrical fracture statistics. The used Metropolis-Hastings-Green algorithm is refined by the simultaneous estimation of the measurement error’s variance, that is, the measurement noise. Based on the presented application to invert the two-dimensional cross-section between source and the receiver well, the hydraulic tomography reveals itself to be more suitable for reconstructing the original DFN. This is based on a probabilistic representation of the inverted results by means of fracture probabilities.
Fractures serve as highly conductive preferential flow paths for fluids in rocks, which are difficult to exactly reconstruct in numerical models. Especially, in low-conductive rocks, fractures are often the only pathways for advection of solutes and heat. The presented study compares the results from hydraulic and tracer tomography applied to invert a theoretical discrete fracture network (DFN) that is based on data from synthetic cross-well testing. For hydraulic tomography, pressure pulses in various injection intervals are induced and the pressure responses in the monitoring intervals of a nearby observation well are recorded. For tracer tomography, a conservative tracer is injected in different well levels and the depth-dependent breakthrough of the tracer is monitored. A recently introduced transdimensional Bayesian inversion procedure is applied for both tomographical methods, which adjusts the fracture positions, orientations, and numbers based on given geometrical fracture statistics. The used Metropolis-Hastings-Green algorithm is refined by the simultaneous estimation of the measurement error’s variance, that is, the measurement noise. Based on the presented application to invert the two-dimensional cross-section between source and the receiver well, the hydraulic tomography reveals itself to be more suitable for reconstructing the original DFN. This is based on a probabilistic representation of the inverted results by means of fracture probabilities.
Understanding animal performance in heterogeneous or variable environments is a central question in ecology. We combine modelling and experiments to test how temperature and food availability variance jointly affect life-history traits of ectotherms. The model predicts that as mean temperatures move away from the ectotherm's thermal optimum, the effect size of joint thermal and food variance should become increasingly sensitive to their covariance. Below the thermal optimum, performance should be positively correlated with food–temperature covariance and the opposite is predicted above it. At lower temperatures, covariance should determine whether food and temperature variance increases or decreases performance compared to constant conditions. Somewhat stronger than predicted, the covariance effect below the thermal optimum was confirmed experimentally on an aquatic ectotherm (Daphnia magna) exposed to diurnal food and temperature variance with different amounts of covariance. Our findings have important implications for understanding ectotherm responses to climate-driven alterations of thermal mean and variance.
Cross-national variation in the relationship between welfare generosity and single mother employment
(2022)
Reform of the U.S. welfare system in 1996 spurred claims that cuts to welfare programs effectively incentivized single mothers to find employment. It is difficult to assess the veracity of those claims, however, absent evidence of how the relationship between welfare benefits and single mother employment generalizes across countries. This study combines data from the European Union Labour Force Survey and the U.S. Current Population Survey (1992-2015) into one of the largest samples of single mothers ever, testing the relationships between welfare generosity and single mothers’ employment and work hours. We find no consistent evidence of a negative relationship between welfare generosity and single mother employment outcomes. Rather, we find tremendous cross-national heterogeneity, which does not clearly correspond to well-known institutional variations. Our findings demonstrate the limitations of single country studies and the pervasive, salient interactions between institutional contexts and social policies.
Die differenzierte und individuelle Förderung der Leseflüssigkeit in heterogenen Gruppen erfordert ein hohes Maß an diagnostischer Kompetenz. Diese kann nicht allein durch die Vermittlung von Wissen gefördert werden, sondern muss zusätzlich durch praxisnahe Übungen sukzessive ausgebildet werden. Um die Diagnosekompetenz von Masterstudierenden der Grundschulpädagogik (Deutsch) an der Universität zu Potsdam zu fördern, wurde im Rahmen des Projekts PSI-Potsdam ein Seminar entwickelt und durchgeführt, das nach der Idee des Blended Learning mit digitalen Übungen angereichert ist und eine Förderung der Diagnosekompetenz über einen längeren Zeitraum gewährleisten soll. In dem Beitrag werden nach einer theoretischen Einführung sowie einer Darlegung der Relevanz des Seminarkonzepts für Lehrkräfte der Primarstufe Deutsch die Entwicklung und Einbettung der Übungstools im Seminar dargestellt sowie deren praktischer Einsatz diskutiert.
Rooted in Eccles and colleagues' expectancy-value theory, this study aimed to examine how expectancies and different facets of task value combine to diverse profiles of motivational beliefs, how such complex profiles develop across a school year, and how they relate to gender and career plans. Despite abundant research on the association between gender and motivational beliefs, there is a paucity of knowledge regarding the gendered development of student motivational belief profiles in specific domains. Using latent-transition analysis in a sample of N = 751 ninth to tenth graders (55.9% girls), we investigated girls' and boys' development of motivational belief profiles (profile paths) in mathematics across a school year. We further analyzed the association between these profile paths and math-related career plans. The results revealed four motivational belief profiles: high motivation (intrinsic and attainment oriented), balanced above average motivation, average motivation (attainment and cost oriented), and low motivation (cost oriented). Girls were less likely than expected by chance to remain in the high motivation profile, while the opposite was true for boys. The math-relatedness of students' career plans was significantly higher in the "stable high motivation" profile path than in all other stable profile paths.
Give chance a chance
(2019)
A large part of biodiversity theory is driven by the basic question of what allows species to coexist in spite of a confined number of niches. A substantial theoretical background to this question is provided by modern coexistence theory (MCT), which rests on mathematical approaches of invasion analysis to categorize underlying mechanisms into factors that reduce either niche overlap (stabilizing mechanisms) or the average fitness differences of species (equalizing mechanisms). While MCT has inspired biodiversity theory in the search for these underlying mechanisms, we feel that the strong focus on coexistence causes a bias toward the most abundant species and neglects the plethora of species that are less abundant and often show high local turnover. Given the more stochastic nature of their occurrence, we advocate a complementary cross-level approach that links individuals, small populations, and communities and explicitly takes into account (1) a more complete inclusion of environmental and demographic stochasticity affecting small populations, (2) intraspecific trait variation and behavioral plasticity, and (3) local heterogeneities, interactions, and feedbacks. Focusing on mechanisms that drive the temporary coviability of species rather than infinite coexistence, we suggest a new approach that could be dubbed coviability analysis (CVA). From a modeling perspective, CVA builds on the merged approaches of individual-based modeling and population viability analysis but extends them to the community level. From an empirical viewpoint, CVA calls for a stronger integration of spatiotemporal data on variability and noise, changing drivers, and interactions at the level of individuals. The resulting large volumes of data from multiple sources could be strongly supported by novel techniques tailored to the discovery of complex patterns in high-dimensional data. By complementing MCT through a stronger focus on the coviability of less common species, this approach can help make modern biodiversity theory more comprehensive, predictive, and relevant for applications.