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Successful export sectors in manufacturing and agribusiness are important drivers of structural transformation in Sub-Sahara African countries. Backed by industrial policies and active state involvement, a small number of successful productive export sectors has emerged in Sub-Saharan Africa. This thesis asks the question: How do politics shape the promotion of export-driven industrialisation and firm-level upgrading in Sub-Saharan Africa? It exemplifies this question with an in-depth, qualitative study of the cashew processing industry in Mozambique in the period from 1991 until 2019. Mozambique used to be one of the world’s largest producers and processors of cashew nuts in the 1960s and 1970s. At the end of the 20th century, the cashew processing industry broke down completely but has re-emerged as one of the country’s few successful agro-processing exports.
The thesis draws on theoretical approaches from the fields of political science, notably the political settlements framework, global value chain analysis and the research on technological capabilities to explore why the Mozambican Government supported the cashew processing industry and how Mozambican cashew processors acquired the technological capabilities needed to access the global cashew value chain and to upgrade. It makes an important theoretical contribution by linking the political settlements framework and the literature on upgrading in global value chains to study how politics shaped productive sector promotion and upgrading in the Mozambican cashew processing industry. The findings of the thesis are based on extensive primary data, including 58 expert interviews and 10 firm surveys, that was collected in Mozambique in 2018 as well as a broad base of secondary literature.
The thesis argues that the Mozambican Government supported the cashew processing industry because it became important for the Government’s political survival. Promoting the cashew sector formed part of an electoral strategy for the ruling FRELIMO coalition and a means to keep FRELIMO factions united by offering economic opportunities to key constituencies. In 1999, it adopted a protectionist cashew law that created strong incentives for cashew processing in Mozambique. This not only facilitated the re-emergence of the cashew processing industry after its breakdown. The law and the active involvement of the National Cashew Institute (INCAJU) also affected the governance of the local cashew value chain, the creation of backward linkages, and the upgrading paths of cashew processors. The findings of the thesis suggest that the cashew law reduced the pressure on the cashew processing industry to upgrade. The law further created opportunities for formal and informal rent creation for members of the political elite and lower level FRELIMO officials that prevented a far-reaching reform of the law. The thesis shows that international buyers do not promote upgrading among Sub-Sahara African firms in global value chains with market-based or modular governance. Moreover, firms that operate in countries where industrial policies are not enforced effectively cannot draw on the support of government institutions to enhance their capabilities and to upgrade. Firms therefore mainly depended on costly learning channels at firm level, e.g. learning by doing or hiring skilled labour, and/or on technical assistance from donors to build the technological capabilities needed to access global value chains and to remain competitive.
The findings of the thesis suggest that researchers, governments, development practitioners and consultants need to rethink their understanding of upgrading in GVCs in four ways. First, they need to move away from understanding upgrading in terms of moving towards more complex, higher value-added activities in GVCs (functional upgrading). Instead, it is important to consider the potential of other, more realistic types of upgrading for firms in low-income countries, such reducing risks by diversifying suppliers and buyers or increasing rewards by making production processes more efficient. Second, they need to replace an overly positive view on upgrading that neglects possible side-effects at sector and/or country level. Third, GVC participation on its own does not promote upgrading among local supplier firms in Sub-Saharan Africa. The interests of lead firms and Sub-Sahara African supplier firms may not be aligned or even conflicting. Targeted industrial policies and the creation of institutions that effectively promote capability building among firms therefore become even more important. Finally, upgrading needs to be understood as a process that is not only shaped by interactions between firms, but also by local domestic politics.
The findings of the thesis are highly relevant for scholars from the fields of political science, development studies, and economics. Its practical implications and tools, e.g. a technological capabilities matrix for the cashew industry, are of interest for development practitioners, members of public institutions in Sub-Sahara African countries, local entrepreneurs, and representatives of local business associations that are involved in promoting export sectors and upgrading among local firms.
Human population pressure increased with the population growth around the NNP and Cyamudongo with disturbance impacts on the forests isolating populations into fragments and today, Cyamudongo natural forest is located a way at a distance of at least 8.5 km horizontal distance to Nyungwe main block with a surface area estimated at 300 ha. Under Cyamudongo project implementation, there was a need to understand how the flora diversity responded to human imposed challenges and to forest restoration initiatives. Three physiognomic landscapes forest were identified and considered for three phases of vegetation survey in Cyamudongo and related to the closest area of Nyungwe main block. In this study, 15 transects were laid in each physiognomic forest landscape and 10 and 5 plots were set respectively in Cyamudongo and Gasumo study area. In total, 315 phytosociological relevés were performed and the Braun-Blanquet methods used for three times vegetation surveys. Species life-forms and chorophyte were evaluated and tree species dbh and height have been measured. Data were subjected to different statistical analyses using different softwares such as PAST, R 3.5.2, and SPSS. The mapping was done using Arc GIS and the Multi-Spectral Remote Sensing used to find NDVI for the vegetation classification.
NDVI trends showed that there has been fluctuations in vegetation classifications of the studied area. In this study, 494 vascular plant species from 106 families were harbored in the study area and distributed differently among forest landscapes and study phases. Although, 43.54% were common to Cyamudongo and Gasumo landscapes while 48.54 % of species diversity were hold only by Cyamudongo and 7.92% confined to Gasumo and 12 in total were found new records for Rwanda while several others suspected require detailed research for identification showing how the flora diversity of Cyamudongo is of special interest and extremely important for discoveries.
The finding of the study on diversity indices, the PCA, CA and the Cluster analysis, all statistical analyses (MANOVA, ANOVA) and life form spectra unanimously showed that the anthropogenic disturbance shaped the vegetation cover, the floristic composition, the species diversity, the forest landscapes community structure, the life form spectrum and the phytoclimate of Cyamudongo and Gasumo forest landscapes. Although, the vegetation analysis couldn’t clearly identify communities and sub-communities at the initial and final vegetation surveys and cluster groups were heterogeneous as well as overlapping and species associations not clearly defined due to the high level of similarities in species composition among forest landscapes and vegetation surveys. The species diversity was found high in secondary forest and Gasumo landscape forest and low in the primary forest and the buffer zone of Cyamudongo and the disturbance with gaps openings was found to be associated to the species diversity with a seasonal variation. The patterns of dbh for the buffer zone and of the size classes of all landscapes with an inverted ‘J’ indicated a healthy regeneration in the forest landscapes and tree species explained a good regeneration and recruitment capacity. Different shapes in the pattern of dbh with respectively an inverted ‘J’, ‘J ‘and ‘U’ for the buffer zone, primary and together the secondary and Gasumo forest landscapes indicated differences in the landscapes health and degree of regeneration and recruitment capacity.
Findings from differents measuements showed at which extend human activities have shaped the flora diversity and structure of forest landcapes studied. For instance, disturbances due human activities were daily oberved and trees were logged by neighboring communities such as Batwa populations at Cyamudongo and local populations at Gasumo. Some species were evenly observed targeted for their barks such as Ocotea usambarensis, Parinari excelsa for medicines and many others for their wood quality, fire wood collection and for agricultural purposes.
In the period of Cyamudongo project implementation, important achievements included the increase of forest biomass and therefore the photosynthetic capacity and the evapotranspiration potential that influence the rainfall regime; the regulation of weather conditions and then species diversity; supporting local communities and limiting human activities; raising awareness on conservation and protection of biodiversity and improving of living conditions of neighboring populations by providing paid employment and so to restore to the Cyamudongo forest ecosystem functions. Moreover, Cyamudongo forest remains vulnerable as surrounded by local communities with a high population pressure relying on forest resources for its survival. Cyamudongo harbors a high level of endemism and is a small hotspot for biodiversity conservation. It is therefore recommended to strengthen conservation and protection measures and continue the support of local communities.
Despite the significant presence of neuroactive substances in the environment, bioassays that allow to detect diverse groups of neuroactive mechanisms of action are not well developed and not properly integrated into environmental monitoring and chemical regulation. Therefore, there is a need to develop testing methods which are amenable for fast and high-throughput neurotoxicity testing. The overall goal of this thesis work is to develop a test method for the toxicological characterization and screening of neuroactive substances and their mixtures which could be used for prospective and diagnostic hazard assessment.
In this thesis, the behavior of zebrafish embryos was explored as a promising tool to distinguish between different neuroactive mechanisms of action. Recently, new behavioral tests have been developed including photomotor response (PMR), locomotor response (LMR) and spontaneous tail coiling (STC) tests. However, the experimental parameters of these tests lack consistency in protocols such as exposure time, imaging time, age of exposure, endpoint parameter etc. To understand how experimental parameters may influence the toxicological interpretation of behavior tests, a systematic review of existing behavioral assays was conducted in Chapter 2. Results show that exposure concentration and exposure duration highly influenced the comparability between different test methods and the spontaneous tail coiling (STC) test was selected for further testing based on its relative higher sensitivity and capacity to detect neuroactive substances (Chapter 2).
STC is the first observable motor activity generated by the developing neural network of the embryo which is assumed to occur as a result of the innervation of the muscle by the primary motor neurons. Therefore, STC could be a useful endpoint to detect effect on the muscle innervation and also the on the whole nervous system. Consequently, important parameters of the STC test were optimized and an automated workflow to evaluate the STC with the open access software KNIME® was developed (Chapter 3).
To appropriately interpret the observed effect of a single chemical and especially mixture effects, requires the understanding of toxicokinetics and biotransformation. Most importantly, the biotransformation capacity of zebrafish embryos might be limited and this could be a challenge for assessment of chemicals such as organophosphates which require a bioactivation step to effectively inhibit the acetylcholinesterase (AChE) enzyme. Therefore, the influence of the potential limited biotransformation on the toxicity pathway of a typical organophosphate, chlorpyrifos, was investigated in Chapter 5. Chlorpyrifos could not inhibit AChE and this was attributed to possible lack of biotransformation in 24 hpf embryos (Chapter 5).
Since neuroactive substances occur in the environment as mixtures, it is therefore more realistic to assess their combined effect rather than individually. Therefore, mixture toxicity was predicted using the concentration addition and independent action models. Result shows that mixtures of neuroactive substances with different mechanisms of action but similar effects can be predicted with concentration addition and independent action (Chapter 4). Apart
from being able to predict the combined effect of neuroactive substances for prospective risk assessment, it is also important to assess in retrospect the combined neurotoxic effect of environmental samples since neuroactive substances are the largest group of chemicals occurring in the environment. In Chapter 6, the STC test was found to be capable of detecting neurotoxic effects of a wastewater effluent sample. Hence, the STC test is proposed as an effect based tool for monitoring environmental acute and neurotoxic effects.
Overall, this thesis shows the utility and versatility of zebrafish embryo behavior testing for screening neuroactive substances and this allows to propose its use for prospective and diagnostic hazard assessment. This will enhance the move away from expensive and demanding animal testing. The information contained in this thesis is of great potential to provide precautionary solutions, not only for the exposure of humans to neuroactive chemicals but for the environment at large.
Die Raytracing-Beschleunigung durch dedizierte Datenstrukturen ist schon lange ein wichtiges Thema der Computergrafik. Im Allgemeinen werden dafür zwei unterschiedliche Ansätze vorgeschlagen: räumliche und richtungsbezogene Beschleunigungsstrukturen. Die vorliegende Arbeit stellt einen innovativen kombinierten Ansatz dieser beiden Bereiche vor, welcher weitere Beschleunigung der Strahlenverfolgung ermöglicht. Dazu werden moderne räumliche Datenstrukturen als Basisstrukturen verwendet und um vorberechnete gerichtete Sichtbarkeitsinformationen auf Basis von Schächten innerhalb einer originellen Struktur, dem Line Space, ergänzt.
Im Laufe der Arbeit werden neuartige Ansätze für die vorberechneten Sichtbarkeitsinformationen vorgeschlagen: ein binärer Wert, der angibt, ob ein Schacht leer oder gefüllt ist, sowie ein einzelner Vertreter, der als repräsentativer Kandidat die tatsächliche Oberfläche approximiert. Es wird gezeigt, wie der binäre Wert nachweislich in einer einfachen, aber effektiven Leerraumüberspringungs-Technik (Empty Space Skipping) genutzt wird, welche unabhängig von der tatsächlich verwendeten räumlichen Basisdatenstruktur einen Leistungsgewinn beim Raytracing von bis zu 40% ermöglicht. Darüber hinaus wird gezeigt, dass diese binären Sichtbarkeitsinformationen eine schnelle Technik zur Berechnung von weichen Schatten und Umgebungsverdeckung auf der Grundlage von Blockerapproximationen ergeben. Obwohl die Ergebnisse einen gewissen Ungenauigkeitsfehler enthalten, welcher auch dargestellt und diskutiert wird, zeigt sich, dass eine weitere Traversierungsbeschleunigung von bis zu 300% gegenüber der Basisstruktur erreicht wird. Als Erweiterung zu diesem Ansatz wird die repräsentative Kandidatenvorberechnung demonstriert, welche verwendet wird, um die indirekte Lichtberechnung durch die Integration von kaum wahrnehmbaren Bildfehlern signifikant zu beschleunigen. Schließlich werden Techniken vorgeschlagen und bewertet, die auf zweistufigen Strukturen und einer Nutzungsheuristik basieren. Diese reduzieren den Speicherverbrauch und die Approximationsfehler bei Aufrechterhaltung des Geschwindigkeitsgewinns und ermöglichen zusätzlich weitere Möglichkeiten mit Objektinstanziierungen und starren Transformationen.
Alle Beschleunigungs- und Speicherwerte sowie die Näherungsfehler werden gemessen, dargestellt und diskutiert. Insgesamt zeigt sich, dass durch den Line Space eine deutliche Erhöhung der Raytracing Leistung auf Kosten eines höheren Speicherverbrauchs und möglicher Annäherungsfehler erreicht wird. Die vorgestellten Ergebnisse zeigen damit die Leistungsfähigkeit des kombinierten Ansatzes und eröffnen weitere Möglichkeiten für zukünftige Arbeiten.
In der Dissertation “Leben am und vom Rhein. Aspekte der Alltagsgeschichte in St. Goar und St. Goarshausen vom Späten Mittelalter bis zum Ende des 17. Jahrhunderts” untersucht der Autor Richard Lange die Historie “kleiner Leute” in zwei Städten am Mittelrhein.
Die Studie konzentriert sich dabei auf die Geschichte jener Berufe, die direkt vom Rhein abhängig waren, also in erster Linie auf das Zollpersonal, die Kranbediensteten sowie die Salmenfischer. Hinzu kommen einige weitere Berufszweige wie Treidler, Fährleute, Schiffsleute sowie Händler und Betreiber von Wirtshäusern.
Für all diese Gruppen wird, soweit anhand der Quellen möglich, der Alltag ihres Berufes nachgezeichnet. Auf diesem Wege wird versucht, das bunte Bild, das sich auf dem Rhein bisweilen bot, darzustellen und gleichzeitig aufzuzeigen, in welcher nicht zu unterschätzenden Weise der Rhein das ganze Leben in St. Goar und St. Goarshausen über die Jahrhunderte hinweg beeinflusste.
The stands surveyed are among the last closed canopy forests in Rwanda. Their exploration began in the early twentieth century and is still ongoing. Previous studies were mainly concerned with plant sociological issues and presented references to environmental factors in anecdotal form, at best using indirect ordination methods. The present study undertakes a classification of the vegetation with numerical methods and establishes quantitative relationships of the species’ distributional structure to environmental parameters using spatially explicit procedures. For this purpose, 94 samples were taken in 100 m² hexagonal plots. Of these, 70 samples are from Nyungwe, 14 are from Gishwati, and 10 are from Cyamudongo. Given the homogeneity of the terrain and vegetation, all vegetation types encountered, all types of stands, and all vegetation strata were included. The beta diversity is expressed by an average Bray-Curtis dissimilarity of 0.92, and in JOST’S (2007) numbers equivalents, 37.90 equally likely samples would be needed to represent the diversity encountered. Within the survey, 1198 species in 127 families were collected. Among the specimens are 6 local endemics and 40 Albertine Rift endemics. Resulting from UPGMA and FCM-NC, 20 to 40 plant communities were established depending on the level of resolution. It can be inferred by means of a Mantel correlogram that the mean zone of influence of a single vegetation stand, as sampled by a 100 m² plot in Nyungwe Forest, ranges between 0.016 and 3.42 km. Of the communities compiled using FCM-NC and UPGMA, 50% consist of individual samples. Beyond undersampling, natural small-scale discontinuities are reflected by this result. Partial db-RDA resulted in an explained variation of 9.60% and 14.41% for environmental and soil factors, respectively. Utilising variation partitioning analyses based on CCA and tb-RDA, between 21.70% and 37.80% of the variation in vegetation data could be explained. The spatially structured fraction of these parameters accounts for between 30.50% and 49.80% of the explained variation (100%). The purely environmental parameters account for a share of 10.30% to 16.30%, whereby the lower limit originates from the unimodal approach and has lost its statistical significance. The soil variables, also after partial analysis, account for a share of 19.00% to 35.70%. While the residual impact of the climatic parameters is hardly significant, the effect of the soil properties is prevalent. In general, the spatially structured fraction of the parameters is predominant here. While on the broad-scale climatic factors, the altitude a.s.l. and the geology are determining factors, some soil parameters and matrix components also show their impacts here. In the mid-range of the scale, it is the forest matrix, the soil types, and the geology that determine species distribution. While in the fine range of the scale, some unrecorded parameters seem to have an effect, there are also neutral processes that determine species composition.
Enterprise collaboration platforms are increasingly gaining importance in organisations. Integrating groupware functionality and enterprise social software (ESS), they have substantially been transforming everyday work in organisations. While traditional collaboration systems have been studied in Computer Supported Cooperative Work (CSCW) for many years, the large-scale, infrastructural and heterogeneous nature of enterprise collaboration platforms remains uncharted. Enterprise collaboration platforms are embedded into organisations’ digital workplace and come with a high degree of complexity, ambiguity, and generativity. When introduced, they are empty shells with no pre-determined purposes of use. They afford interpretive flexibility, and thus are shaping and being shaped by and in their social context. Outcomes and benefits emerge and evolve over time in an open-ended process and as the digital platform is designed through use. In order to make the most of the platform and associated continuous digital transformation, organisations have to develop the necessary competencies and capabilities.
Extant literature on enterprise collaboration platforms has proliferated and provide valuable insights on diverse topics, such as implementation strategies, adoption hurdles, or collaboration use cases, however, they tend to disregard their evolvability and related multiple time frames and settings. Thus, this research aims to identify, investigate, and theorise the ways that enterprise collaboration platforms are changing over time and space and the ways that organisations build digital transformation capabilities. To address this research aim two different case study types are conducted: i) in-depth longitudinal qualitative case study, where case narratives and visualisations capturing hard-to-summarise complexities in the enterprise collaboration platform evolution are developed and ii) multiple-case studies to capture, investigate, and compare cross-case elements that contribute to the shaping of enterprise collaboration platforms in different medium-sized and large organisations from a range of industries. Empirical data is captured and investigated through a multi-method research design (incl. focus groups, surveys, in-depth interviews, literature reviews, qualitative content analysis, descriptive statistics) with shifting units of analysis. The findings reveal unique change routes with unanticipated outcomes and transformations, context-specific change strategies to deal with multiple challenges (e.g. GDPR, works council, developments in the technological field, competing systems, integration of blue-collar workers), co-existing platform uses, and various interacting actors from the immediate setting and broader context. The interpretation draws on information infrastructure (II) as a theoretical lens and related sociotechnical concepts and perspectives (incl. inscriptions, social worlds, biography of artefacts). Iteratively, a conceptual model of the building of digital transformation capabilities is developed, integrating the insights gained from the study of enterprise collaboration platform change and developed monitoring change tools (e.g. MoBeC framework). It assists researchers and practitioners in understanding the building of digital transformation capabilities from a theoretical and practical viewpoint and organisations implement the depicted knowledge in their unique digital transformation processes.
Human action recognition from a video has received growing attention in computer vision and has made significant progress in recent years. Action recognition is described as a requirement to decide which human actions appear in videos. The difficulties involved in distinguishing human actions are due to the high complexity of human behaviors as well as appearance variation, motion pattern variation, occlusions, etc. Many applications use human action recognition on captured video from cameras, resulting in video surveillance systems, health monitoring, human-computer interaction, and robotics. Action recognition based on RGB-D data has increasingly drawn more attention to it in recent years. RGB-D data contain color (Red, Green, and Blue (RGB)) and depth data that represent the distance from the sensor to every pixel in the object (object point). The main problem that this thesis deals with is how to automate the classification of specific human activities/actions through RGB-D data. The classification process of these activities utilizes a spatial and temporal structure of actions. Therefore, the goal of this work is to develop algorithms that can distinguish these activities by recognizing low-level and high-level activities of interest from one another. These algorithms are developed by introducing new features and methods using RGB-D data to enhance the detection and recognition of human activities. In this thesis, the most popular state-of-the-art techniques are reviewed, presented, and evaluated. From the literature review, these techniques are categorized into hand-crafted features and deep learning-based approaches. The proposed new action recognition framework is based on these two categories that are approved in this work by embedding novel methods for human action recognition. These methods are based on features extracted from RGB-D data that are
evaluated using machine learning techniques. The presented work of this thesis improves human action recognition in two distinct parts. The first part focuses on improving current successful hand-crafted approaches. It contributes into two significant areas of state-of-the-art: Execute the existing feature detectors, and classify the human action in the 3D spatio-temporal domains by testing a new combination of different feature representations. The contributions of this part are tested based on machine learning techniques that include unsupervised and supervised learning to evaluate this suitability for the task of human action recognition. A k-means clustering represents the unsupervised learning technique, while the supervised learning technique is represented by: Support Vector Machine, Random Forest, K-Nearest Neighbor, Naive Bayes, and Artificial Neural Networks classifiers. The second part focuses on studying the current deep-learning-based approach and how to use it with RGB-D data for the human action recognition task. As the first step of each contribution, an input video is analyzed as a sequence of frames. Then, pre-processing steps are applied to the video frames, like filtering and smoothing methods to remove the noisy data from each frame. Afterward, different motion detection and feature representation methods are used to extract features presented in each frame. The extracted features
are represented by local features, global features, and feature combination besides deep learning methods, e.g., Convolutional Neural Networks. The feature combination achieves an excellent accuracy performance that outperforms other methods on the same RGB-D datasets. All the results from the proposed methods in this thesis are evaluated based on publicly available datasets, which illustrate that using spatiotemporal features can improve the recognition accuracy. The competitive experimental results are achieved overall. In particular, the proposed methods can be better applied to the test set compared to the state-of-the-art methods using the RGB-D datasets.
Speziell in Anwendungen mit intensiver Temperatur- und Korrosionsbeanspruchung finden vermehrt Phosphate als sogenannte chemische Binder für Hochleistungskeramiken Verwendung. Konkret ist die Summe der Reaktionsverläufe während des Bindemechanismus in Folge einer thermisch-induzierten Aushärtung und somit die Wirkungsweise von Phosphatbindern prinzipiell innerhalb der Fachliteratur nicht eindeutig untersucht. Innerhalb dieser Arbeit wurden aufbauend auf einer umfangreichen strukturanalytischen Prüfungsanordnung (Festkörper-NMR, RBA, REM-EDX) einer exemplarischen phosphatgebundenen Al₂O₃-MgAl₂O₄-Hochtemperaturkeramikzusammensetzung unter Einbeziehung verschiedenartiger anorganischer Phosphate grundlegende Bindemechanismen charakterisiert. Mechanisch-physikochemische Eigenschaftsuntersuchungen (STA, Dilatometrie, DMA, KBF) deckten zudem den Einfluss der eingesetzten Phosphate auf die Eigenschaftsentwicklungen der Feuerfestkeramiken bezüglich des Abbindeverhaltens, der Biegefestigkeit sowie der thermischen Längenänderung auf, welche mit Strukturänderungen korreliert wurden. Es wurde gezeigt, dass sich Bindemechanismen bei Verwendung von Phosphaten temperaturgeleitet (20 °C ≤ T ≤ 1500 °C) grundsätzlich aus zwei parallel ablaufenden Reaktionsabfolgen zusammensetzen, wobei die sich entwickelnden Phosphatphasen innerhalb der Keramikmasse quantitativ und qualitativ bezüglich ihrer Bindewirkung bewertet wurden. Zum einen wurde die Bildung eines festigkeitssteigernden Bindenetzwerks aus Aluminiumphosphaten meist amorpher Struktur identifiziert und charakterisiert. Dieses bindungsfördernde, dreidimensionale Aluminiumphosphatnetzwerk baut sich innerhalb der Initialisierungs- und Vernetzungsphasen temperaturgeleitet kontinuierlich über multiple Vernetzungsreaktionen homogen auf. Zum anderen werden Reaktionsabfolgen durch parallel ablaufende Strukturumwandlungen nicht aktiv-bindender Phosphatspezies wie Magnesium-, Calcium- oder Zirkoniumphosphate ergänzt, welche lediglich thermische Umwandlungsreaktionen der Ausgangsphosphate darstellen. Vermehrt bei T > 800 °C geht das phosphatische Bindenetzwerk Festkörperreaktionen mit MgAl₂O₄ unter Ausbildung und Agglomeration von Magnesium-Orthophosphat-Sinterstrukturen ein. Die Bildung dieser niedrigschmelzenden Hochtemperaturphasen führt zu einem teilweisen Bruch des Bindenetzwerks.
Wildbienen sind unerlässlich für die Bestäubung von Wild- und Kulturpflanzen. Die zunehmende Intensivierung der Landwirtschaft führte jedoch sowohl zu einer Verringerung und Fragmentierung als auch zu einer Wertminderung der von ihnen benötigten Lebensräume innerhalb der letzten Jahrzehnte. Die damit einhergehenden Verluste von Bestäubern und ihrer Bestäubung stellt die weltweite Nahrungsmittelproduktion vor eine immense Herausforderung. Zur Förderung von Wildbienen ist die Verfügbarkeit von Blüteressourcen essentiell. Die Blühdauer einzelner Ressourcen ist jedoch zeitlich begrenzt und hat, je nach Blütezeitpunkt, unterschiedliche Effekte auf Bestäuber und deren Bestäubung.
Um Wildbienen als Bestäuber in Agrarlandschaften effizient fördern und nutzen zu können, identifizierten wir deshalb die artspezifischen Schlüsselressourcen dreier ausgewählter Wildbienen und deren räumliche und zeitliche Verfügbarkeit (KAPITEL 2, 3 & 4). Wir untersuchten, welche Habitatstypen diese Ressourcen überwiegend bereitstellen (KAPITEL 3 & 4). Wir untersuchten zudem, ob Blütenressourcenkarten, die auf der Nutzung dieser Schlüsselressourcen und deren räumlich zeitlicher Verfügbarkeit basieren, die Abundanzen und die Entwicklung der ausgewählten Wildbienen (KAPITEL 3 & 4) und die Bestäubung (KAPITEL 5) besser erklären als Habitatkarten, die die Verfügbarkeit von Blüteressourcen nur indirekt beschreiben.
Für jede der untersuchten Arten konnten wir unterschiedliche, im frühen Saisonverlauf (April/Mai) überwiegend holzige im späteren Verlauf (Juni/Juli) auch zunehmend krautige, Schlüsselarten identifizieren (KAPITEL 2, 3 & 4). Die Wildobst- und Wildheckengehölze unserer Agrarlandschaften stellten rund 75% der Blütenressourcen für Erdhummeln, 60% für Rote Mauerbienen und 55% für Gehörnte Mauerbienen bereit, obwohl sie einen Flächenanteil von nur 3% ausmachten (KAPITEL 3 & 4). Obstplantagen stellten zusätzlich rund 35% des Blütenangebots für Gehörnte Mauerbienen auf 4% der Fläche bereit (KAPITEL 3). Wir konnten zeigen, dass beide Mauerbienenarten von der Ressourcenverfügbarkeit in den umliegenden Landschaften profitierten (KAPITEL 3). Bei Erdhummeln zeigte sich dieser Zusammenhang jedoch nicht (KAPITEL 4). Stattdessen waren die Gewichtszunahme ihrer Kolonien, die Anzahlen der darin ausgebildeten Königinnenzellen und die Überlebensdauer der Kolonie mit zunehmender Nähe zum Wald höher. Ebenfalls auf die beiden Mauerbienenarten wirkte sich die Waldnähe positiv aus (KAPITEL 3). Daneben profitierten Rote Mauerbienen durch krautige halbnatürliche Habitate. Nachteilig wirkten sich Siedlungsflächen auf die Gehörnten Mauerbienen, und Ackerland auf die Roten Mauerbienen aus. Habitatkarten erklärten die Abundanzen der Gehörnten Mauerbienen gleich gut wie Blütenressourcenkarten, jedoch wurden die Abundanzen der Roten Mauerbienen deutlich besser durch Schlüsselressourcen erklärt. Die Bestäubung der Ackerbohne erhöhte sich mit höheren Anteilen früher Blütenressourcen (KAPITEL 5). Dabei zeigte sich keine messbare Reduktion der Bestäubung durch gleichzeitig blühende Ressourcen. Habitatkarten erklärten die Bestäubung der Ackerbohne auch besser als Blütenressourcenkarten. Dabei nahm die Bestäubung mit zunehmenden Anteilen an Siedlungsflächen in den Landschaften zu und reduzierte sich mit zunehmenden Anteilen von Ackerland.
Unsere Ergebnisse verdeutlichen die Wichtigkeit der räumlich-zeitlichen Verfügbarkeit bestimmter Schlüsselarten als Ressourcenpflanzen von Wildbienen in Agrarlandschaften. Sie zeigen, dass Habitatkarten detaillierten Blütenressourcenkarten in der Vorhersage der Entwicklung von Wildbienen und deren Bestäubung voraus oder zumindest ebenbürtig sind. Dennoch ermöglichen es
Blütenressourcenkarten, genauere Schlüsse zwischen den einzelnen Ressourcen und den untersuchten Organismen zu ziehen. Die Nähe zu Waldrändern wirkte sich positiv auf jede der drei untersuchten Wildbienenarten aus. Neben der reinen Nahrungsverfügbarkeit scheinen jedoch weitere Faktoren das Vorkommen von Wildbienen in Agrarlandschaften mitzubestimmen.