Mindfulness is a popular technique that helps people to get closer to their self. However, recent findings indicate that mindfulness may not benefit everybody. In the present research, we hypothesized that mindfulness promotes alienation from the self among individuals with low abilities to self-regulate affect (state-oriented individuals) but not among individuals with high abilities to self-regulate affect (action-oriented individuals). In two studies with participants who were mostly naïve to mindfulness practices (70% indicated no experience; N1 = 126, 42 men, 84 women, 0 diverse, aged 17–86 years, Mage = 31.87; N2 = 108, 30 men, 75 women, 3 diverse, aged 17–69 years, Mage = 28.00), we tested a mindfulness group (five-minute mindfulness exercise) against a control group (five-minute text reading). We operationalized alienation as lower consistency in repeated preference judgments and a lower tendency to adopt intrinsic over extrinsic goal recommendations. Results showed that, among state-oriented participants, mindfulness led to significantly lower consistency of preference judgments (Study 1) and lower adoption of intrinsic over extrinsic goals (Study 2) compared to text reading. The alienating effect was absent among action-oriented participants. Thus, mindfulness practice may alienate psychologically vulnerable people from their self and hamper access to preferences and intrinsic goals. We discuss our findings within Personality-Systems-Interactions (PSI) theory.

Environmental DNA (eDNA) metabarcoding promises to be a cost- and time-efficient monitoring tool to detect interactions of arthropods with plants. However, observation-based verification of the eDNA-derived data is still required to confirm the reliability of those detections, i.e., to verify whether the arthropods have previously interacted with the plant. Here, we conducted a comparative analysis of the performance of eDNA metabarcoding and video camera observations to detect arthropod communities associated with sunflowers (Helianthus annuus, L.). We compared the taxonomic composition, interaction type, and diversity by testing for an effect of arthropod interaction time and occupancy on successful taxon recovery by eDNA. We also tested if prewashing of the flowers successfully removed eDNA deposition from before the video camera recording, thus enabling a reset of the community for standardized monitoring. We find that eDNA and video camera observations recovered distinct communities, with about a quarter of the arthropod families overlapping. However, the overlapping taxa comprised ~90% of the interactions observed by the video camera. Interestingly, eDNA metabarcoding recovered more unique families than the video cameras, but approximately two-thirds of those unique observations were of rare species. The eDNA-derived families were biased toward plant sap-suckers, showing that such species may deposit more eDNA than, for example, transient pollinators. We also find that prewashing of the flower heads did not suffice to remove all eDNA traces, suggesting that eDNA on plants may be more temporally stable than previously thought. Our work highlights the great potential of eDNA as a tool to detect plant-arthropod interactions, particularly for specialized and frequently interacting taxa.

Amphibians globally suffer from emerging infectious diseases like chytridiomycosis caused by the continuously spreading chytrid fungi. One is Batrachochytrium salamandrivorans (Bsal) and its disease ‒ the ‘salamander plague’ ‒ which is lethal to several caudate taxa. Recently introduced into Western Europe, long distance dispersal of Bsal, likely through human mediation, has been reported. Herein we study if Alpine salamanders (Salamandra atra and S. lanzai) are yet affected by the salamander plague in the wild. Members of the genus Salamandra are highly susceptible to Bsal leading to the lethal disease. Moreover, ecological modelling has shown that the Alps and Dinarides, where Alpine salamanders occur, are generally suitable for Bsal. We analysed skin swabs of 818 individuals of Alpine salamanders and syntopic amphibians at 40 sites between 2017 to 2022. Further, we compiled those with published data from 319 individuals from 13 sites concluding that Bsal infections were not detected. Our results suggest that the salamander plague so far is absent from the geographic ranges of Alpine salamanders. That means that there is still a chance to timely implement surveillance strategies. Among others, we recommend prevention measures, citizen science approaches, and ex situ conservation breeding of endemic salamandrid lineages.

This dissertation examines the relevance of regimes for stock markets. In three research articles, we cover the identification and predictability of regimes and their relationships to macroeconomic and financial variables in the United States. The initial two chapters contribute to the debate on the predictability of stock markets. While various approaches can demonstrate in-sample predictability, their predictive power diminishes substantially in out-of-sample studies. Parameter instability and model uncertainty are the primary challenges. However, certain methods have demonstrated efficacy in addressing these issues. In Chapter 1 and 2, we present frameworks that combine these methods meaningfully. Chapter 3 focuses on the role of regimes in explaining macro-financial relationships and examines the state-dependent effects of macroeconomic expectations on cross-sectional stock returns. Although it is common to capture the variation in stock returns using factor models, their macroeconomic risk sources are unclear. According to macro-financial asset pricing, expectations about state variables may be viable candidates to explain these sources. We examine their usefulness in explaining factor premia and assess their suitability for pricing stock portfolios. In summary, this dissertation improves our understanding of stock market regimes in three ways. First, we show that it is worthwhile to exploit the regime dependence of stock markets. Markov-switching models and their extensions are valuable tools for filtering the stock market dynamics and identifying and predicting regimes in real-time. Moreover, accounting for regime-dependent relationships helps to examine the dynamic impact of macroeconomic shocks on stock returns. Second, we emphasize the usefulness of macro-financial variables for the stock market. Regime identification and forecasting benefit from their inclusion. This is particularly true in periods of high uncertainty when information processing in financial markets is less efficient. Finally, we recommend to address parameter instability, estimation risk, and model uncertainty in empirical models. Because it is difficult to find a single approach that meets all of these challenges simultaneously, it is advisable to combine appropriate methods in a meaningful way. The framework should be as complex as necessary but as parsimonious as possible to mitigate additional estimation risk. This is especially recommended when working with financial market data with a typically low signal-to-noise ratio.