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- Institut für Ernährungswissenschaft (116)
- Department Linguistik (99)
The European Alps are amongst the regions with highest glacier mass loss rates over the last decades. Under the threat of ongoing climate change, the ability to predict glacier mass balance changes for water and risk management purposes has become imperative. This raises an urgent need for reliable glacier models. The European Alps do not only host glaciers, but also numerous caves containing carbonate formations, called speleothems. Previous studies have shown that those speleothems also grew during times when the cave was covered by a warm-based glacier. In this thesis, I utilise speleothems from the European Alps as archives of local, environmental conditions related to mountain glacier evolution.
Previous studies have shown that speleothem isotope data from the Alps can be strongly affected by in-cave processes. Therefore, part of this thesis focusses on developing an isotope evolution model, which successfully reproduces differences between contemporaneous growing speleothems. The model is used to propose correction approaches for prior calcite precipitation effects on speleothem oxygen isotopes (δ18O). Applications on speleothem records from caves outside of the Alps demonstrate that corrected δ18O agrees better with other records and climate model simulations.
Existing speleothem growth histories and carbon isotope (δ13C) records from Alpine caves located at different elevations are used to infer soil vs. glacier cover and the thermal regime of the glacier over the last glacial cycle. The compatibility with glacier evolution models is statistically assessed. A general agreement between speleothem δ13C-derived information on soil vs. glacier presence and modelled glacier coverage is found. However, glacier retreat during Marine Isotope Stage (MIS) 3 seems to be underestimated by the model. Furthermore, speleothem data provides evidence of surface temperature above the freezing point which is, however, not fully reproduced by the simulations.
History of glacier cover and their thermal regime is explored for the high-elevation cave system Melchsee-Frutt in the Swiss Alps. Based on new (MIS 9b – MIS 7b, MIS 2) and available speleothem δ13C (MIS 7a – 5d) data, warm-based glacier cover is inferred for MIS 8, 7d, 6, and 2. Also a short period of cold-based ice coverage is found for early MIS 6. In a detailed multi-proxy analysis (δ18O, δ13C, Mg/Ca and Sr/Ca), millennial-scale changes in the glacier-related source of the water infiltrating in the karst during MIS 8 and 7d are found and linked to Northern Hemisphere climate variability.
While speleothem records from high-elevation cave sites in the Alps exhibit huge potential for glacier reconstruction, several limitations remain, which are discussed throughout this thesis. Ultimately, recommendations are given to further leverage subglacial speleothems as an archive of glacier dynamics.
Galaxy morphology is a fossil record of how galaxies formed and evolved and can be regarded as a function of the dynamical state of a galaxy. It encodes the physical processes that dominate its evolutionary history, and is strongly aligned with physical properties like stellar mass, star formation rate and local environment. At a distance of ∼50 and 60 kpc, the Magellanic Clouds represent the nearest interacting pair of dwarf irregular galaxies to the Milky Way, rendering them an important test bed for galaxy morphology in the context of galaxy interactions and the effect of the local environment in which they reside. The Large Magellanic Cloud is classified as the prototype for Magellanic Spiral galaxies, with one prominent spiral arm, an offset bar and an inclined rotating disc while the Small Magellanic Cloud is classified as a dwarf Irregular galaxy and is known for its unstructured shape and large depth across the line–of–sight. Resolved stellar populations are powerful probes of a wide range of astrophysical phenomena, the proximity of the Magellanic Clouds allows us to resolve their stellar populations to individual stars that share coherent chemical and age distributions. The coherent properties of resolved stellar populations enable us to analyse them as a function of position within the Magellanic Clouds, offering a picture of the growth of the galaxies’ substructures over time and yielding a comprehensive view of their morphology. Furthermore, investigating the kinematics of the Magellanic Clouds offers valuable insights into their dynamics and evolutionary history. By studying the motions and velocities of stars within these galaxies, we can trace their past interactions, with the Milky Way or with each other and unravel the complex interplay of forces that have influenced the Magellanic Clouds’ formation and evolution.
In Chapter 2, the VISTA survey of the Magellanic Clouds was employed to generate unprecedented high-resolution morphological maps of the Magellanic Clouds in the near-infrared. Utilising colour-magnitude diagrams and theoretical evolutionary models to segregate stellar populations, this approach enabled a comprehensive age tomography of the galaxies. It revealed previously uncharacterised features in their central regions at spatial resolutions of 0.13 kpc (Large Magellanic Cloud) and 0.16 kpc (Small Magellanic Cloud), the findings showcased the impact of tidal interactions on their inner regions. Notably, the study highlighted the enhanced coherent structures in the Large Magellanic Cloud, shedding light on the significant role of the recent Magellanic Clouds’ interaction 200 Myr ago in shaping many of the fine structures. The Small Magellanic Cloud revealed asymmetry in younger populations and irregularities in intermediate-age ones, pointing towards the influence of past tidal interactions.
In Chapter 3, an examination of the outskirts of the Magellanic Clouds led to the identification of new substructures through the use of near-infrared photometry from the VISTA Hemisphere Survey and multi-dimensional phase-space information from Gaia. The distances and proper motions of these substructures were investigated. This analysis revealed the impact of past Magellanic Clouds’ interactions and the influence of the Milky Way’s tidal field on the morphology and kinematics of the Magellanic Clouds. A bi-modal distance distribution was identified within the luminosity function of the red clump stars in the Small Magellanic Cloud, notably in its eastern regions, with the foreground substructure being attributed to the Magellanic Clouds’ interaction around 200 Myr ago. Furthermore, associations with the Counter Bridge and Old Bridge were uncovered through the detection of background and foreground structures in various regions of the SMC.
In chapter 4, a detailed kinematic analysis of the Small Magellanic Cloud was conducted using spectra from the European Southern Observatory Science Archive Facility. The study reveals distinct kinematics in the Wing and bar regions, attributed to interactions with the Large Magellanic Cloud and variations in star formation history. Notably, velocity disparities are observed in the bar’s young main sequence stars, aligning with specific star-forming episodes, and suggesting potential galactic stretching or tidal stripping, as corroborated by proper motion studies.
As followers are becoming more educated and better connected, empowering leadership has gained traction in recent times as an alternative to traditional top-down models of leadership. Several scholars have investigated the relationship between empowering leadership and other variables in different contexts. As most previous studies have focused on the positive aspects of empowering leadership, research on its potential dark side is scarce. Furthermore, no previous study has examined whether and how the transfer of workload from followers to leaders can occur over time, which I proposed can lead to emotional exhaustion and work-family conflict among leaders. Therefore, I proposed that despite the positive outcomes of empowering leadership for both followers and leaders, it may also trigger negative outcomes capable of affecting the well-being of leaders. Drawing on the Conservation of Resources (COR) theory, Job Demand-Resources (JD-R) theory, and Too-Much-of-a-Good-Thing (TMGT) effect model, I investigated this idea. Using follower workload as a moderator, I proposed that the relationship between empowering leadership and leader workload is positive when follower workload is high and negative when follower workload is low. In addition, I examined how empowering leadership interacts with follower workload to affect leader emotional exhaustion and work-family conflict, mediated by leader workload. I proposed that this interaction results in a negative relationship between empowering leadership and both outcomes when follower workload is low, and a positive relationship when it is high.
I tested these hypotheses using data from a three-wave time-lagged design field study with 65 leader-follower dyads consisting of civil servants from different administrative entities of India and Pakistan. The time lag between each study variable was four weeks. At Time 1 (T1), followers answered questions about demographic characteristics, virtual interaction with their leaders, their workload, and the extent to which their leaders practice empowering leadership. At the same time, leaders answered questions about demographic characteristics and their job satisfaction. At Time 2 (T2), leaders provided data on their own workload. Finally, at Time 3 (T3), leaders rated their emotional exhaustion and work-family conflict. A moderated mediation model was tested using PROCESS Model 7 in R. The findings of the study reveal that a significant increase in follower workload through empowering leadership will also increase the leader's workload. Consequently, this increased leader workload leads to a crossover of this interactive effect onto the level of emotional exhaustion and work-family conflict experienced by leaders.
This research offers various contributions to the leadership literature. While empowering leadership has been commonly associated with positive outcomes, my study reveals that it can also lead to negative outcomes. In addition, it shifts the focus of existing research from the effect of empowering leadership on followers to the consequences that it might have for leaders themselves. Overall, my research underscores the need for leaders to consider the potential counterproductive effects of empowering leadership and tailor their approach accordingly.
Human-induced climate change is impacting the global water cycle by, e.g., causing changes in precipitation patterns, evapotranspiration dynamics, cryosphere shrinkage, and complex streamflow trends. These changes, coupled with the increased frequency and severity of extreme hydrometeorological events like floods, droughts, and heatwaves, contribute to hydroclimatic disasters, posing significant implications for local and global infrastructure, human health, and overall productivity.
In the tropical Andes, climate change is evident through warming trends, glacier retreats, and shifts in precipitation patterns, leading to altered risks of floods and droughts, e.g., in the upper Amazon River basin. Projections for the region indicate rising temperatures, potential glacier disappearance or substantial shrinkage, and altered streamflow patterns, highlighting challenges in water availability due to these expected changes and growing human water demand. The evolving trends in hydroclimatic conditions in the tropical Andes present significant challenges to socioeconomic and environmental systems, emphasizing the need for a comprehensive understanding to guide effective adaptation policies and strategies in response to the impacts of climate change in the region.
The main objective of this thesis is to investigate current hydrological dynamics in the tropical Andes of Peru and Ecuador and their responses to climate change. Given the scarcity of hydrometeorological data in the region, this objective was accomplished through a comprehensive data preparation and analysis in combination with hydrological modeling using the Soil and Water Assessment Tool (SWAT) eco-hydrological model. In this context, the initial steps involved assessing, identifying, and/or generating more reliable climate input data to address data limitations.
The thesis introduces RAIN4PE, a high-resolution precipitation dataset for Peru and Ecuador, developed by merging satellite, reanalysis, and ground-based data with surface elevation through the random forest method. Further adjustments of precipitation estimates were made for catchments influenced by fog/cloud water input on the eastern side of the Andes using streamflow data and applying the method of reverse hydrology. RAIN4PE surpasses other global and local precipitation datasets, showcasing superior reliability and accuracy in representing precipitation patterns and simulating hydrological processes across the tropical Andes. This establishes it as the optimal precipitation product for hydrometeorological applications in the region.
Due to the significant biases and limitations of global climate models (GCMs) in representing key atmospheric variables over the tropical Andes, this study developed regionally adapted GCM simulations specifically tailored for Peru and Ecuador. These simulations are known as the BASD-CMIP6-PE dataset, and they were derived using reliable, high-resolution datasets like RAIN4PE as reference data. The BASD-CMIP6-PE dataset shows notable improvements over raw GCM simulations, reflecting enhanced representations of observed climate properties and accurate simulation of streamflow, including high and low flow indices. This renders it suitable for assessing regional climate change impacts on agriculture, water resources, and hydrological extremes.
In addition to generating more accurate climatic input data, a reliable hydrological model is essential for simulating watershed hydrological processes. To tackle this challenge, the thesis presents an innovative multiobjective calibration framework integrating remote sensing vegetation data, baseflow index, discharge goodness-of-fit metrics, and flow duration curve signatures. In contrast to traditional calibration strategies relying solely on discharge goodness-of-fit metrics, this approach enhances the simulation of vegetation, streamflow, and the partitioning of flow into surface runoff and baseflow in a typical Andean catchment. The refined hydrological model calibration strategy was applied to conduct reliable simulations and understand current and future hydrological trajectories in the tropical Andes.
By establishing a region-suitable and thoroughly tested hydrological model with high-resolution and reliable precipitation input data from RAIN4PE, this study provides new insights into the spatiotemporal distribution of water balance components in Peru and transboundary catchments. Key findings underscore the estimation of Peru's total renewable freshwater resource (total river runoff of 62,399 m3/s), with the Peruvian Amazon basin contributing 97.7%. Within this basin, the Amazon-Andes transition region emerges as a pivotal hotspot for water yield (precipitation minus evapotranspiration), characterized by abundant rainfall and lower atmospheric water demand/evapotranspiration. This finding underlines its paramount role in influencing the hydrological variability of the entire Amazon basin.
Subsurface hydrological pathways, particularly baseflow from aquifers, strongly influence water yield in lowland and Andean catchments, sustaining streamflow, especially during the extended dry season. Water yield demonstrates an elevation- and latitude-dependent increase in the Pacific Basin (catchments draining into the Pacific Ocean), while it follows an unimodal curve in the Peruvian Amazon Basin, peaking in the Amazon-Andes transition region. This observation indicates an intricate relationship between water yield and elevation.
In Amazon lowlands rivers, particularly in the Ucayali River, floodplains play a significant role in shaping streamflow seasonality by attenuating and delaying peak flows for up to two months during periods of high discharge. This observation underscores the critical importance of incorporating floodplain dynamics into hydrological simulations and river management strategies for accurate modeling and effective water resource management.
Hydrological responses vary across different land use types in high Andean catchments. Pasture areas exhibit the highest water yield, while agricultural areas and mountain forests show lower yields, emphasizing the importance of puna (high-altitude) ecosystems, such as pastures, páramos, and bofedales, in regulating natural storage.
Projected future hydrological trajectories were analyzed by driving the hydrological model with regionalized GCM simulations provided by the BASD-CMIP6-PE dataset. The analysis considered sustainable (low warming, SSP1-2.6) and fossil fuel-based development (high-end warming, SSP5-8.5) scenarios for the mid (2035-2065) and end (2065-2095) of the century. The projected changes in water yield and streamflow across the tropical Andes exhibit distinct regional and seasonal variations, particularly amplified under a high-end warming scenario towards the end of the century. Projections suggest year-round increases in water yield and streamflow in the Andean regions and decreases in the Amazon lowlands, with exceptions such as the northern Amazon expecting increases during wet seasons. Despite these regional differences, the upper Amazon River's streamflow is projected to remain relatively stable throughout the 21st century. Additionally, projections anticipate a decrease in low flows in the Amazon lowlands and an increased risk of high flows (floods) in the Andean and northern Amazon catchments.
This thesis significantly contributes to enhancing climatic data generation, overcoming regional limitations that previously impeded hydrometeorological research, and creating new opportunities. It plays a crucial role in advancing hydrological model calibration, improving the representation of internal hydrological processes, and achieving accurate results for the right reasons. Novel insights into current hydrological dynamics in the tropical Andes are fundamental for improving water resource management. The anticipated intensified changes in water flows and hydrological extreme patterns under a high-end warming scenario highlight the urgency of implementing emissions mitigation and adaptation measures to address the heightened impacts on water resources.
In fact, the new datasets (RAIN4PE and BASD-CMIP6-PE) have already been utilized by researchers and experts in regional and local-scale projects and catchments in Peru and Ecuador. For instance, they have been applied in river catchments such as Mantaro, Piura, and San Pedro to analyze local historical and future developments in climate and water resources.
Prediction is often regarded as a central and domain-general aspect of cognition. This proposal extends to language, where predictive processing might enable the comprehension of rapidly unfolding input by anticipating upcoming words or their semantic features. To make these predictions, the brain needs to form a representation of the predictive patterns in the environment. Predictive processing theories suggest a continuous learning process that is driven by prediction errors, but much is still to be learned about this mechanism in language comprehension. This thesis therefore combined three electroencephalography (EEG) experiments to explore the relationship between prediction and implicit learning at the level of meaning.
Results from Study 1 support the assumption that the brain constantly infers und updates probabilistic representations of the semantic context, potentially across multiple levels of complexity. N400 and P600 brain potentials could be predicted by semantic surprise based on a probabilistic estimate of previous exposure and a more complex probability representation, respectively.
Subsequent work investigated the influence of prediction errors on the update of semantic predictions during sentence comprehension. In line with error-based learning, unexpected sentence continuations in Study 2 ¬– characterized by large N400 amplitudes ¬– were associated with increased implicit memory compared to expected continuations. Further, Study 3 indicates that prediction errors not only strengthen the representation of the unexpected word, but also update specific predictions made from the respective sentence context. The study additionally provides initial evidence that the amount of unpredicted information as reflected in N400 amplitudes drives this update of predictions, irrespective of the strength of the original incorrect prediction.
Together, these results support a central assumption of predictive processing theories: A probabilistic predictive representation at the level of meaning that is updated by prediction errors. They further propose the N400 ERP component as a possible learning signal. The results also emphasize the need for further research regarding the role of the late positive ERP components in error-based learning. The continuous error-based adaptation described in this thesis allows the brain to improve its predictive representation with the aim to make better predictions in the future.
Data preparation stands as a cornerstone in the landscape of data science workflows, commanding a significant portion—approximately 80%—of a data scientist's time. The extensive time consumption in data preparation is primarily attributed to the intricate challenge faced by data scientists in devising tailored solutions for downstream tasks. This complexity is further magnified by the inadequate availability of metadata, the often ad-hoc nature of preparation tasks, and the necessity for data scientists to grapple with a diverse range of sophisticated tools, each presenting its unique intricacies and demands for proficiency.
Previous research in data management has traditionally concentrated on preparing the content within columns and rows of a relational table, addressing tasks, such as string disambiguation, date standardization, or numeric value normalization, commonly referred to as data cleaning. This focus assumes a perfectly structured input table. Consequently, the mentioned data cleaning tasks can be effectively applied only after the table has been successfully loaded into the respective data cleaning environment, typically in the later stages of the data processing pipeline.
While current data cleaning tools are well-suited for relational tables, extensive data repositories frequently contain data stored in plain text files, such as CSV files, due to their adaptable standard. Consequently, these files often exhibit tables with a flexible layout of rows and columns, lacking a relational structure. This flexibility often results in data being distributed across cells in arbitrary positions, typically guided by user-specified formatting guidelines.
Effectively extracting and leveraging these tables in subsequent processing stages necessitates accurate parsing. This thesis emphasizes what we define as the “structure” of a data file—the fundamental characters within a file essential for parsing and comprehending its content. Concentrating on the initial stages of the data preprocessing pipeline, this thesis addresses two crucial aspects: comprehending the structural layout of a table within a raw data file and automatically identifying and rectifying any structural issues that might hinder its parsing. Although these issues may not directly impact the table's content, they pose significant challenges in parsing the table within the file.
Our initial contribution comprises an extensive survey of commercially available data preparation tools. This survey thoroughly examines their distinct features, the lacking features, and the necessity for preliminary data processing despite these tools. The primary goal is to elucidate the current state-of-the-art in data preparation systems while identifying areas for enhancement. Furthermore, the survey explores the encountered challenges in data preprocessing, emphasizing opportunities for future research and improvement.
Next, we propose a novel data preparation pipeline designed for detecting and correcting structural errors. The aim of this pipeline is to assist users at the initial preprocessing stage by ensuring the correct loading of their data into their preferred systems. Our approach begins by introducing SURAGH, an unsupervised system that utilizes a pattern-based method to identify dominant patterns within a file, independent of external information, such as data types, row structures, or schemata. By identifying deviations from the dominant pattern, it detects ill-formed rows. Subsequently, our structure correction system, TASHEEH, gathers the identified ill-formed rows along with dominant patterns and employs a novel pattern transformation algebra to automatically rectify errors. Our pipeline serves as an end-to-end solution, transforming a structurally broken CSV file into a well-formatted one, usually suitable for seamless loading.
Finally, we introduce MORPHER, a user-friendly GUI integrating the functionalities of both SURAGH and TASHEEH. This interface empowers users to access the pipeline's features through visual elements. Our extensive experiments demonstrate the effectiveness of our data preparation systems, requiring no user involvement. Both SURAGH and TASHEEH outperform existing state-of-the-art methods significantly in both precision and recall.
Due to their sessile lifestyle, plants are constantly exposed to pathogens and possess a multi-layered immune system that prevents infection. The first layer of immunity called pattern-triggered immunity (PTI), enables plants to recognise highly conserved molecules that are present in pathogens, resulting in immunity from non-adaptive pathogens. Adapted pathogens interfere with PTI, however the second layer of plant immunity can recognise these virulence factors resulting in a constant evolutionary battle between plant and pathogen. Xanthomonas campestris pv. vesicatoria (Xcv) is the causal agent of bacterial leaf spot disease in tomato and pepper plants. Like many Gram-negative bacteria, Xcv possesses a type-III secretion system, which it uses to translocate type-III effectors (T3E) into plant cells. Xcv has over 30 T3Es that interfere with the immune response of the host and are important for successful infection. One such effector is the Xanthomonas outer protein M (XopM) that shows no similarity to any other known protein. Characterisation of XopM and its role in virulence was the focus of this work.
While screening a tobacco cDNA library for potential host target proteins, the vesicle-associated membrane protein (VAMP)-associated protein 1-2 like (VAP12) was identified. The interaction between XopM and VAP12 was confirmed in the model species Nicotiana benthamiana and Arabidopsis as well as in tomato, a Xcv host. As plants possess multiple VAP proteins, it was determined that the interaction of XopM and VAP is isoform specific.
It could be confirmed that the major sperm protein (MSP) domain of NtVAP12 is sufficient for binding XopM and that binding can be disrupted by substituting one amino acid (T47) within this domain. Most VAP interactors have at least one FFAT (two phenylalanines [FF] in an acidic tract) related motif, screening the amino acid sequence of XopM showed that XopM has two FFAT-related motifs. Substitution of the second residue of each FFAT motif (Y61/F91) disrupts NtVAP12 binding, suggesting that these motifs cooperatively mediate this interaction. Structural modelling using AlphaFold further confirmed that the unstructured N-terminus of XopM binds NtVAP12 at its MSP domain, which was further confirmed by the generation of truncated XopM variants.
Infection of pepper leaves, with a XopM deficient Xcv strain did not result in a reduction of virulence in comparison to the Xcv wildtype, showing that the function of XopM during infection is redundant. Virus-induced gene silencing of NbVAP12 in N. benthamiana plants also did not affect Xcv virulence, which further indicated that interaction with VAP12 is also non-essential for Xcv virulence. Despite such findings, ectopic expression of wildtype XopM and XopMY61A/F91A in transgenic Arabidopsis seedlings enhanced the growth of a non-pathogenic Pseudomonas syringae pv. tomato (Pst) DC3000 strain. XopM was found to interfere with the PTI response allowing Pst growth independent of its binding to VAP. Furthermore, transiently expressed XopM could suppress reactive oxygen species (ROS; one of the earliest PTI responses) production in N. benthamiana leaves. The FFAT double mutant XopMY61A/F91A as well as the C-terminal truncation variant XopM106-519 could still suppress the ROS response while the N-terminal variant XopM1-105 did not. Suppression of ROS production is therefore independent of VAP binding. In addition, tagging the C-terminal variant of XopM with a nuclear localisation signal (NLS; NLS-XopM106-519) resulted in significantly higher ROS production than the membrane localising XopM106-519 variant, indicating that XopM-induced ROS suppression is localisation dependent.
To further characterise XopM, mass spectrometry techniques were used to identify post-translational modifications (PTM) and potential interaction partners. PTM analysis revealed that XopM contains up to 21 phosphorylation sites, which could influence VAP binding. Furthermore, proteins of the Rab family were identified as potential plant protein interaction partners. Rab proteins serve a multitude of functions including vesicle trafficking and have been previously identified as T3E host targets. Taking this into account, a model of virulence of XopM was proposed, with XopM anchoring itself to VAP proteins to potentially access plasma membrane associated proteins. XopM possibly interferes with vesicle trafficking, which in turn suppresses ROS production through an unknown mechanism.
In this work it was shown that XopM targets VAP proteins. The data collected suggests that this T3E uses VAP12 to anchor itself into the right place to carry out its function. While more work is needed to determine how XopM contributes to virulence of Xcv, this study sheds light onto how adapted pathogens overcome the immune response of their hosts. It is hoped that such knowledge will contribute to the development of crops resistant to Xcv in the future.
The order of destruction
(2024)
This book studies sugarcane monoculture, the dominant form of cultivation in the colonial Caribbean, in the later 1600s and 1700s up to the Haitian Revolution. Researching travel literature, plantation manuals, Georgic poetry, letters, and political proclamations, this book interprets texts by Richard Ligon, Henry Drax, James Grainger, Janet Schaw, and Toussaint Louverture. As the first extended investigation into its topic, this book reads colonial Caribbean monoculture as the conjunction of racial capitalism and agrarian capitalism in the tropics. Its eco-Marxist perspective highlights the dual exploitation of the soil and of enslaved agricultural producers under the plantation regime, thereby extending Marxist analysis to the early colonial Caribbean. By focusing on textual form (in literary and non-literary texts alike), this study discloses the bearing of monoculture on contemporary writers' thoughts. In the process, it emphasizes the significance of a literary tradition that, despite its ideological importance, is frequently neglected in (postcolonial) literary studies and the environmental humanities. Located at a crossroads of disciplines and perspectives, this study will be of interest to literary critics and historians working in the early Americas, to students and scholars of agriculture, colonialism, and (racial) capitalism, to those working in the environmental humanities, and to Marxist academics. It will be of great interest to scholars and researchers of language and literature, post-colonial studies, cultural studies, diaspora studies, and the Global South studies