Subterranean animals act as ecosystem engineers, for example, through soil perturbation and herbivory, shaping their environments worldwide. As the occurrence of animals is often linked to above-ground features such as plant species composition or landscape textures, satellite-based remote sensing approaches can be used to predict the distribution of subterranean species. Here, we combine insitu collected vegetation composition data with remotely sensed data to improve the prediction of a subterranean species across a large spatial scale. We compared three machine learning-based modeling strategies, including field and satellitebased remote sensing data to different extents, in order to predict the distribution of the subterranean giant root-rat GRR, Tachyoryctes macrocephalus, an endangered rodent species endemic to the Bale Mountains in southeast Ethiopia. We included no, some and extensive fieldwork data in the modeling to test how these data improved prediction quality. We found prediction quality to be particularly dependent on the spatial coverage of the training data. Species distributions were best predicted by using texture metrics and eyeball-selected data points of landscape marks created by the GRR. Vegetation composition as a predictor showed the lowest contribution to model performance and lacked spatial accuracy. Our results suggest that the time-consuming collection of vegetation data in the field is not necessarily required for the prediction of subterranean species that leave traceable above-ground landscape marks like the GRR. Instead, remotely sensed and spatially eyeball-selected presence data of subterranean species could profoundly enhance predictions. The usage of remote sensing-derived texture metrics has great potential for improving the distribution modeling of subterranean species, especially in arid ecosystems.

PurposeLow-status expatriates (LSEs), a highly vulnerable group, have been significantly affected by the ongoing COVID-19 pandemic. This study explores the stressors that continue to impact LSEs in Germany and their access to support during the pandemic.Design/methodology/approachAn interpretivist, qualitative research approach was employed. The authors utilised a multi-level data collection strategy that combined interview and questionnaire data from 16 expatriates and 16 social actors. The data were analysed using a directed content analysis method.FindingsLSEs experienced high levels of stress that were further exacerbated by the introduction of COVID-19 control measures that were intended to slow the spread of the virus. LSEs are particularly vulnerable due to their overrepresentation in precarious professions and the associated job insecurity. Critically, external support from employers and social actors is generally lacking, leaving LSEs to rely on their own personal coping strategies in difficult times.Research limitations/implicationsThe earlier Expatriate crisis Framework highlights the importance of external support for expatriates. However, this framework does not sufficiently account for personal coping strategies that are particularly important for individuals that cannot access such external support (e.g. LSEs). Herein, the authors offer a revised framework that is more applicable to LSEs.Practical implicationsCurrent practices are problematic, necessitating policy changes at both governmental and organisational levels.Originality/valueThis study provides unique insights into the ways in which the pandemic has affected the already precarious position of LSEs and identifies the importance of personal coping strategies in the absence of external sources of support.