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Management intensity modifies soil properties, e.g., organic carbon (C-org) concentrations and soil pH with potential feedbacks on plant diversity. These changes might influence microbial P concentrations (P-mic) in soil representing an important component of the Pcycle. Our objectives were to elucidate whether abiotic and biotic variables controlling P-mic concentrations in soil are the same for forests and grasslands, and to assess the effect of region and management on P-mic concentrations in forest and grassland soils as mediated by the controlling variables. In three regions of Germany, Schwabische Alb, Hanich-Dun, and Schorfheide-Chorin, we studied forest and grassland plots (each n=150) differing in plant diversity and land-use intensity. In contrast to controls of microbial biomass carbon (C-mic), P-mic was strongly influenced by soil pH, which in turn affected phosphorus (P) availability and thus microbial Puptake in forest and grassland soils. Furthermore, P-mic concentrations in forest and grassland soils increased with increasing plant diversity. Using structural equation models, we could show that soil C-org is the profound driver of plant diversity effects on P-mic in grasslands. For both forest and grassland, we found regional differences in P-mic attributable to differing environmental conditions (pH, soil moisture). Forest management and tree species showed no effect on P-mic due to a lack of effects on controlling variables (e.g., C-org). We also did not find management effects in grassland soils which might be caused by either compensation of differently directed effects across sites or by legacy effects of former fertilization constraining the relevance of actual practices. We conclude that variables controlling P-mic or C-mic in soil differ in part and that regional differences in controlling variables are more important for P-mic in soil than those induced by management.
In the last decade, there has been an increasing interest in compensating thermally induced errors to improve the manufacturing accuracy of modular tool systems. These modular tool systems are interfaces between spindle and workpiece and consist of several complicatedly formed parts. Their thermal behavior is dominated by nonlinearities, delay and hysteresis effects even in tools with simpler geometry and it is difficult to describe it theoretically. Due to the dominant nonlinear nature of this behavior the so far used linear regression between the temperatures and the displacements is insufficient. Therefore, in this study we test the hypothesis whether we can reliably predict such thermal displacements via nonlinear temperature-displacement regression functions. These functions are estimated firstly from learning measurements using the alternating conditional expectation (ACE) algorithm and then tested on independent data sets. First, we analyze data that were generated by a finite element spindle model. We find that our approach is a powerful tool to describe the relation between temperatures and displacements for simulated data. Next, we analyze the temperature-displacement relationship in a silent real experimental setup, where the tool system is thermally forced. Again, the ACE-algorithm is powerful to estimate the deformation with high precision. The corresponding errors obtained by using the nonlinear regression approach are 10-fold lower in comparison to multiple linear regression analysis. Finally, we investigate the thermal behavior of a modular tool system in a working milling machine and get again promising results. The thermally inducedaccuracy using this nonlinear regression analysis. Therefore, this approach seems to be very useful for the development of new modular tool systems. errors can be estimated with 1-2 micrometer
In the last decade, there has been an increasing interest in compensating thermally induced errors to improve the manufacturing accuracy of modular tool systems. These modular tool systems are interfaces between spindle and workpiece and consist of several complicatedly formed parts. Their thermal behavior is dominated by nonlinearities, delay and hysteresis effects even in tools with simpler geometry and it is difficult to describe it theoretically. Due to the dominant nonlinear nature of this behavior the so far used linear regression between the temperatures and the displacements is insufficient. Therefore, in this study we test the hypothesis whether we can reliably predict such thermal displacements via nonlinear temperature-displacement regression functions. These functions are estimated firstly from learning measurements using the alternating conditional expectation (ACE) algorithm and then tested on independent data sets. First, we analyze data that were generated by a finite element spindle model. We find that our approach is a powerful tool to describe the relation between temperatures and displacements for simulated data. Next, we analyze the temperature-displacement relationship in a silent real experimental setup, where the tool system is thermally forced. Again, the ACE-algorithm is powerful to estimate the deformation with high precision. The corresponding errors obtained by using the nonlinear regression approach are 10-fold lower in comparison to multiple linear regression analysis. Finally, we investigate the thermal behavior of a modular tool system in a working milling machine and get again promising results. The thermally induced errors can be estimated with 1-2${mu m}$ accuracy using this nonlinear regression analysis. Therefore, this approach seems to be very useful for the development of new modular tool systems.
Plain Language Summary Cassini flew through the gap between Saturn and its rings for 22 times before plunging into the atmosphere of Saturn, ending its 20-year mission. The radio and plasma waves instrument on board Cassini helped quantify the dust hazard in this previously unexplored region. The measured density of large dust particles was much lower than expected, allowing high-value science observations during the subsequent Grand Finale orbits.
We compiled global occurrence data sets of 13 congeneric sexual and apomictic species pairs, and used principal components analysis (PCA) and kernel smoothers to compare changes in climatic niche optima, breadths and unfilling/expansion between native and alien ranges. Niche change metrics were compared between sexual and apomictic species. All 26 species showed changes in niche optima and/or breadth and 14 species significantly expanded their climatic niches. However, we found no effect of the reproductive system on niche dynamics. Instead, species with narrower native niches showed higher rates of niche expansion in the alien ranges. Our results suggest that niche shifts are frequent in plant invasions but evolutionary potential may not be of major importance for such shifts. Niche dynamics rather appear to be driven by changes of the realized niche without adaptive change of the fundamental climatic niche.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
Amonchquite dyke, in the vicinity of Loch Roag, Lewis, Outer Hebrides has an unusually enriched chemistry, and contains a unique assemblage of megacrysts and xenoliths from the lithosphere of the Hebridean craton. A Ar-40/Ar-39 plateau age of 45.2 +/- 0.2 Ma (2 sigma) of a phlogopite megacryst from the dyke overlaps an earlier reported K-Ar age, and confirms that the British Palaeogene Igneous Province extended into the Eocene. Similar late low-volume melts were erupted in the Eocene and Oligocene in West and East Greenland, suggesting that such late-stage magmatic rejuvenescence is a widespread feature across the North Atlantic Igneous Province.
We used single-molecule FRET in combination with other biophysical methods and molecular simulations to investigate the effect of temperature on the dimensions of unfolded proteins. With singlemolecule FRET, this question can be addressed even under nearnative conditions, where most molecules are folded, allowing us to probe a wide range of denaturant concentrations and temperatures. We find a compaction of the unfolded state of a small cold shock protein with increasing temperature in both the presence and the absence of denaturant, with good agreement between the results from single-molecule FRET and dynamic light scattering. Although dissociation of denaturant from the polypeptide chain with increasing temperature accounts for part of the compaction, the results indicate an important role for additional temperaturedependent interactions within the unfolded chain. The observation of a collapse of a similar extent in the extremely hydrophilic, intrinsically disordered protein prothymosin suggests that the hydrophobic effect is not the sole source of the underlying interactions. Circular dichroism spectroscopy and replica exchange molecular dynamics simulations in explicit water show changes in secondary structure content with increasing temperature and suggest a contribution of intramolecular hydrogen bonding to unfolded state collapse.