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The Arctic environments constitute rich and dynamic ecosystems, dominated by microorganisms extremely well adapted to survive and function under severe conditions. A range of physiological adaptations allow the microbiota in these habitats to withstand low temperatures, low water and nutrient availability, high levels of UV radiation, etc. In addition, other adaptations of clear competitive nature are directed at not only surviving but thriving in these environments, by disrupting the metabolism of neighboring cells and affecting intermicrobial communication. Since Arctic microbes are bioindicators which amplify climate alterations in the environment, the Arctic region presents the opportunity to study local microbiota and carry out research about interesting, potentially virulent phenotypes that could be dispersed into other habitats around the globe as a consequence of accelerating climate change. In this context, exploration of Arctic habitats as well as descriptions of the microbes inhabiting them are abundant but microbial competitive strategies commonly associated with virulence and pathogens are rarely reported. In this project, environmental samples from the Arctic region were collected and microorganisms (bacteria and fungi) were isolated. The clinical relevance of these microorganisms was assessed by observing the following virulence markers: ability to grow at a range of temperatures, expression of antimicrobial resistance and production of hemolysins. The aim of this project is to determine the frequency and relevance of these characteristics in an effort to understand microbial adaptations in habitats threatened by climate change. The isolates obtained and described here were able to grow at a range of temperatures, in some cases more than 30 °C higher than their original isolation temperature. A considerable number of them consistently expressed compounds capable of lysing sheep and bovine erythrocytes on blood agar at different incubation temperatures. Ethanolic extracts of these bacteria were able to cause rapid and complete lysis of erythrocyte suspensions and might even be hemolytic when assayed on human blood. In silico analyses showed a variety of resistance elements, some of them novel, against natural and synthetic antimicrobial compounds. In vitro experiments against a number of antimicrobial compounds showed resistance phenotypes belonging to wild-type populations and some non-wild type which clearly denote human influence in the acquisition of antimicrobial resistance. The results of this project demonstrate the presence of virulence-associated factors expressed by microorganisms of natural, non-clinical environments. This study contains some of the first reports, to the best of our knowledge, of hemolytic microbes isolated from the Arctic region. In addition, it provides additional information about the presence and expression of intrinsic and acquired antimicrobial resistance in environmental isolates, contributing to the understanding of the evolution of relevant pathogenic species and opportunistic pathogens. Finally, this study highlights some of the potential risks associated with changes in the polar regions (habitat melting and destruction, ecosystem transition and re-colonization) as important indirect consequences of global warming and altered climatic conditions around the planet.
The need to develop sustainable resource management strategies for semi-arid and arid rangelands is acute as non-adapted grazing strategies lead to irreversible environmental problems such as desertification and associated loss of economic support to society. In such vulnerable ecosystems, successful implementation of sustainable management strategies depends on well-founded under-standing of processes at different scales that underlay the complex system dynamic. There is ample evidence that, in contrast to traditional sectoral approaches, only interdisciplinary research does work for resolving problems in conservation and natural resource management. In this thesis I combined a range of modeling approaches that integrate different disciplines and spatial scales in order to contribute to basic guidelines for sustainable management of semi-arid and arid range-lands. Since water availability and livestock management are seen as most potent determinants for the dynamics of semi-arid and arid ecosystems I focused on (i) the interaction of ecological and hydro-logical processes and (ii) the effect of farming strategies. First, I developed a grid-based and small-scaled model simulating vegetation dynamics and inter-linked hydrological processes. The simulation results suggest that ecohydrological interactions gain importance in rangelands with ascending slope where vegetation cover serves to obstruct run-off and decreases evaporation from the soil. Disturbances like overgrazing influence these positive feedback mechanisms by affecting vegetation cover and composition. In the second part, I present a modeling approach that has the power to transfer and integrate ecological information from the small scale vegetation model to the landscape scale, most relevant for the conservation of biodiversity and sustainable management of natural resources. I combined techniques of stochastic modeling with remotely sensed data and GIS to investigate to which ex-tent spatial interactions, like the movement of surface water by run-off in water limited environments, affect ecosystem functioning at the landscape scale. My simulation experiments show that overgrazing decreases the number of vegetation patches that act as hydrological sinks and run-off increases. The results of both simulation models implicate that different vegetation types should not only be regarded as provider of forage production but also as regulator of ecosystem functioning. Vegetation patches with good cover of perennial vegetation are capable to catch and conserve surface run-off from degraded surrounding areas. Therefore, downstream out of the simulated system is prevented and efficient use of water resources is guaranteed at all times. This consequence also applies to commercial rotational grazing strategies for semi-arid and arid rangelands with ascending slope where non-degraded paddocks act as hydrological sinks. Finally, by the help of an integrated ecological-economic modeling approach, I analyzed the relevance of farmers’ ecological knowledge for longterm functioning of semi-arid and arid grazing systems under current and future climatic conditions. The modeling approach consists of an ecological and an economic module and combines relevant processes on either level. Again, vegetation dynamics and forage productivity is derived by the small-scaled vegetation model. I showed that sustainable management of semi-arid and arid rangelands relies strongly on the farmers’ knowledge on how the ecosystem works. Furthermore, my simulation results indicate that the projected lower annual rainfall due to climate change in combination with non-adapted grazing strategies adds an additional layer of risk to these ecosystems that are already prone to land degradation. All simulation models focus on the most essential factors and ignore specific details. Therefore, even though all simulation models are parameterized for a specific dwarf shrub savanna in arid southern Namibia, the conclusions drawn are applicable for semi-arid and arid rangelands in general.
Climate change fundamentally transforms glaciated high-alpine regions, with well-known cryospheric and hydrological implications, such as accelerating glacier retreat, transiently increased runoff, longer snow-free periods and more frequent and intense summer rainstorms. These changes affect the availability and transport of sediments in high alpine areas by altering the interaction and intensity of different erosion processes and catchment properties.
Gaining insight into the future alterations in suspended sediment transport by high alpine streams is crucial, given its wide-ranging implications, e.g. for flood damage potential, flood hazard in downstream river reaches, hydropower production, riverine ecology and water quality. However, the current understanding of how climate change will impact suspended sediment dynamics in these high alpine regions is limited. For one, this is due to the scarcity of measurement time series that are long enough to e.g. infer trends. On the other hand, it is difficult – if not impossible – to develop process-based models, due to the complexity and multitude of processes involved in high alpine sediment dynamics. Therefore, knowledge has so far been confined to conceptual models (which do not facilitate deriving concrete timings or magnitudes for individual catchments) or qualitative estimates (‘higher export in warmer years’) that may not be able to capture decreases in sediment export. Recently, machine-learning approaches have gained in popularity for modeling sediment dynamics, since their black box nature tailors them to the problem at hand, i.e. relatively well-understood input and output data, linked by very complex processes.
Therefore, the overarching aim of this thesis is to estimate sediment export from the high alpine Ötztal valley in Tyrol, Austria, over decadal timescales in the past and future – i.e. timescales relevant to anthropogenic climate change. This is achieved by informing, extending, evaluating and applying a quantile regression forest (QRF) approach, i.e. a nonparametric, multivariate machine-learning technique based on random forest.
The first study included in this thesis aimed to understand present sediment dynamics, i.e. in the period with available measurements (up to 15 years). To inform the modeling setup for the two subsequent studies, this study identified the most important predictors, areas within the catchments and time periods. To that end, water and sediment yields from three nested gauges in the upper Ötztal, Vent, Sölden and Tumpen (98 to almost 800 km² catchment area, 930 to 3772 m a.s.l.) were analyzed for their distribution in space, their seasonality and spatial differences therein, and the relative importance of short-term events. The findings suggest that the areas situated above 2500 m a.s.l., containing glacier tongues and recently deglaciated areas, play a pivotal role in sediment generation across all sub-catchments. In contrast, precipitation events were relatively unimportant (on average, 21 % of annual sediment yield was associated to precipitation events). Thus, the second and third study focused on the Vent catchment and its sub-catchment above gauge Vernagt (11.4 and 98 km², 1891 to 3772 m a.s.l.), due to their higher share of areas above 2500 m. Additionally, they included discharge, precipitation and air temperature (as well as their antecedent conditions) as predictors.
The second study aimed to estimate sediment export since the 1960s/70s at gauges Vent and Vernagt. This was facilitated by the availability of long records of the predictors, discharge, precipitation and air temperature, and shorter records (four and 15 years) of turbidity-derived sediment concentrations at the two gauges. The third study aimed to estimate future sediment export until 2100, by applying the QRF models developed in the second study to pre-existing precipitation and temperature projections (EURO-CORDEX) and discharge projections (physically-based hydroclimatological and snow model AMUNDSEN) for the three representative concentration pathways RCP2.6, RCP4.5 and RCP8.5.
The combined results of the second and third study show overall increasing sediment export in the past and decreasing export in the future. This suggests that peak sediment is underway or has already passed – unless precipitation changes unfold differently than represented in the projections or changes in the catchment erodibility prevail and override these trends. Despite the overall future decrease, very high sediment export is possible in response to precipitation events. This two-fold development has important implications for managing sediment, flood hazard and riverine ecology.
This thesis shows that QRF can be a very useful tool to model sediment export in high-alpine areas. Several validations in the second study showed good performance of QRF and its superiority to traditional sediment rating curves – especially in periods that contained high sediment export events, which points to its ability to deal with threshold effects. A technical limitation of QRF is the inability to extrapolate beyond the range of values represented in the training data. We assessed the number and severity of such out-of-observation-range (OOOR) days in both studies, which showed that there were few OOOR days in the second study and that uncertainties associated with OOOR days were small before 2070 in the third study. As the pre-processed data and model code have been made publically available, future studies can easily test further approaches or apply QRF to further catchments.