Mechanisms and consequences of change in aquatic microfauna communities

Anthropogenic influences on the natural environment are increasingly observed and we only start to comprehend how this will affect biodiversity and ecosystem functioning in the long run. This question is challenging because scientific approaches normally investigate only small parts of a community, focussing on particular taxa or the effect of a restricted number of environmental variables. The predictive power of these studies is questionable because reality is a lot more complex and direct as well as indirect interactions can lead to unexpected outcomes. Whole community approaches under natural environmental conditions are logistically impracticable in most ecosystems due to the sheer impossibility of sampling, for example, an entire forest. Phytotelma, such as bromeliads, provide an ideal solution for this dilemma. These small temporary water bodies contain communities of manageable sizes that can be easily sampled in naturally replicated micro-ecosystems. Most of the previous bromeliad studies have investigated the macrofauna living in bromeliads. Microfauna have been mostly neglected and therefore little is known about their community structure. Microfauna organisms - including flagellates, ciliates, amoeba, rotifers and crustaceans - are the part of the bromeliad-inhabiting communities that this dissertation focusses on. We used a community-level approach to explore community-structuring processes in bromeliad microfauna with the aim to better predict potential effects of environmental changes on biodiversity and ecosystem functioning. In a field survey along a canopy cover gradient (chapter 1) we investigated the effect of differences in sun-exposure in a restinga rainforest on microfauna community structure. We found strong differences in the environmental conditions which resulted in changes of habitat quality along the canopy cover gradient. This was shown to affect the community structure and beta diversity of bromeliad-inhabiting microfauna via differences in daily temperature fluctuations. With regard to the expected temperature increase through climate change, this result shows that it is not necessarily the direct effect of higher average temperatures that proposes a threat to natural communities but that indirect effects of climate change such as repeated short-time fluctuations in environmental conditions may decrease a habitat’s quality, and thus, lead to a loss of biodiversity and potentially ecological functions. To disentangle the effects of environmental change and trophic interactions on microfauna community structure we carried out a community-transplantation experiment along an elevational gradient (chapter 2). We used a full-factorial experimental design to particularly address potential interactions between environmental change and trophic interactions. The results showed that bromeliad-inhabiting microfauna communities are also shaped by predator presence and priority effects. Interacting effects played an important role in structuring communities, suggesting that we need to broaden our scientific approaches to fully understand the relationships in natural ecosystems and better predict consequences of human-induced changes. Though bromeliad plants grow mainly epiphytic, most bromeliad-related studies, including our field survey (chapter 1) and our field experiment (chapter 2), sample exclusively in the understory. Based on the assumption that sun-exposure increased with increasing height and thus leads to changed environmental conditions, we carried out a field survey sampling understory and canopy bromeliads using single-rope climbing techniques (chapter 3). The comparison of microfauna community structure in understory and canopy bromeliads revealed that no change in community structure occurs along the height gradient. This justifies the former bromeliad community approaches with exclusively understory samples. Finally, we conducted a field survey along three elevational gradients to determine if bromeliad-inhabiting communities change in a generalizable pattern along natural environmental gradients (chapter 4). There was no clear pattern detectable that would allow us to filter out driving environmental factors for community structure in bromeliads on regional scale. The lack of a clear environmental driver of community structure was probably at least partly due to the lack of environmental differences along two of the three gradients. We conclude from our results that microfauna communities are subject to complex interactions and that it is therefore important to use full-factorial approaches in future studies to disentangle the effects of potential drivers of community structure. So far, we could show that daily temperature fluctuations, predator presence, priority effects and oxygen saturation can play key roles in shaping microfauna communities, but we emphasize that these are strongly dependent on the surrounding environment making general predictions difficult. Human-induced environmental alterations such as climate change are likely to affect bromeliad-inhabiting microfauna communities via indirect effects which might result in alterations of important processes in regard to energy and matter fluxes on the ecosystem level. Based upon these results we recommend the integration of microfauna communities into conservation strategies

Modularized Active Learning Solution for Labelling Text Data for Business Environment Analysis

In today’s interconnected world, the pace of change is increasing gradually and the effects of an event can propagate and disrupt industries, organizations or companies more dramatically and quickly. Therefore, having a comprehensive overview of the environment is a precious asset for resilience and sustainable growth. One enabler of the above-mentioned interconnectedness is the rapid flow and vast availability of information in text form, which can be also used as the fundamental resource to understand the shifting environment. Hence, actors can be able to become aware of changes at an early stage. The underlying patterns to filter relevant information can be detected by learning from data, or more specifically machine learning. Natural language processing (NLP) techniques can be applied because text data is analyzed. However, to embed the expertise and perspective of the user into the initial model, data should be labeled. This requires valuable expert time from the organization for the labeling, thus it should be minimized. This study aims to present an efficient and user-friendly solution for data labeling. To achieve this, a modularized Active Learning-based backend is combined with an intuitive interface. The output of this labeling process will be used further to train a model for environment analysis. Nevertheless, the main focus of this paper is the development of a solution to maximize efficiency during data labeling for environment analysis. After an introduction to the problem, the overview of the suggested solution accompanied by a prototype will be demonstrated

TXS 0506+056 with Updated IceCube Data

Past results from the IceCube Collaboration have suggested that the blazar TXS 0506+056 is a potential source of astrophysical neutrinos. However, in the years since there have been numerous updates to event processing and reconstruction, as well as improvements to the statistical methods used to search for astrophysical neutrino sources. These improvements in combination with additional years of data have resulted in the identification of NGC 1068 as a second neutrino source candidate. This talk will re-examine time-dependent neutrino emission from TXS 0506+056 using the most recent northern-sky data sample that was used in the analysis of NGC 1068. The results of using this updated data sample to obtain a significance and flux fit for the 2014 TXS 0506+056 "untriggered" neutrino flare are reported

Galactic Core-Collapse Supernovae at IceCube: “Fire Drill” Data Challenges and follow-up

The next Galactic core-collapse supernova (CCSN) presents a once-in-a-lifetime opportunity to make astrophysical measurements using neutrinos, gravitational waves, and electromagnetic radiation. CCSNe local to the Milky Way are extremely rare, so it is paramount that detectors are prepared to observe the signal when it arrives. The IceCube Neutrino Observatory, a gigaton water Cherenkov detector below the South Pole, is sensitive to the burst of neutrinos released by a Galactic CCSN at a level >10σ. This burst of neutrinos precedes optical emission by hours to days, enabling neutrinos to serve as an early warning for follow-up observation. IceCube\u27s detection capabilities make it a cornerstone of the global network of neutrino detectors monitoring for Galactic CCSNe, the SuperNova Early Warning System (SNEWS 2.0). In this contribution, we describe IceCube\u27s sensitivity to Galactic CCSNe and strategies for operational readiness, including "fire drill" data challenges. We also discuss coordination with SNEWS 2.0

All-Energy Search for Solar Atmospheric Neutrinos with IceCube

The interaction of cosmic rays with the solar atmosphere generates a secondary flux of mesons that decay into photons and neutrinos – the so-called solar atmospheric flux. Although the gamma-ray component of this flux has been observed in Fermi-LAT and HAWC Observatory data, the neutrino component remains undetected. The energy distribution of those neutrinos follows a soft spectrum that extends from the GeV to the multi-TeV range, making large Cherenkov neutrino telescopes a suitable for probing this flux. In this contribution, we will discuss current progress of a search for the solar neutrino flux by the IceCube Neutrino Observatory using all available data since 2011. Compared to the previous analysis which considered only high-energy muon neutrino tracks, we will additionally consider events produced by all flavors of neutrinos down to GeV-scale energies. These new events should improve our analysis sensitivity since the flux falls quickly with energy. Determining the magnitude of the neutrino flux is essential, since it is an irreducible background to indirect solar dark matter searches

Recent neutrino oscillation results with the IceCube experiment

The IceCube South Pole Neutrino Observatory is a Cherenkov detector instrumented in a cubic kilometer of ice at the South Pole. IceCube’s primary scientific goal is the detection of TeV neutrino emissions from astrophysical sources. At the lower center of the IceCube array, there is a subdetector called DeepCore, which has a denser configuration that makes it possible to lower the energy threshold of IceCube and observe GeV-scale neutrinos, opening the window to atmospheric neutrino oscillations studies. Advances in physics sensitivity have recently been achieved by employing Convolutional Neural Networks to reconstruct neutrino interactions in the DeepCore detector. In this contribution, the recent IceCube result from the atmospheric muon neutrino disappearance analysis using the CNN-reconstructed neutrino sample are presented and compared to the existing worldwide measurements

Angular dependence of the atmospheric neutrino flux with IceCube data

IceCube Neutrino Observatory, the cubic kilometer detector embedded in ice of the geographic South Pole, is capable of detecting particles from several GeV up to PeV energies enabling precise neutrino spectrum measurement. The diffuse neutrino flux can be subdivided into three components: astrophysical, from extraterrestrial sources; conventional, from pion and kaon decays in atmospheric Cosmic Ray cascades; and the yet undetected prompt component from the decay of charmed hadrons. A particular focus of this work is to test the predicted angular dependence of the atmospheric neutrino flux using an unfolding method. Unfolding is a set of methods aimed at determining a value from related quantities in a model-independent way, eliminating the influence of several assumptions made in the process. In this work, we unfold the muon neutrino energy spectrum and employ a novel technique for rebinning the observable space to ensure sufficient event numbers within the low statistic region at the highest energies. We present the unfolded energy and zenith angle spectrum reconstructed from IceCube data and compare the result with model expectations and previous measurements