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Due to their limited dispersal ability, fossorial species with predominantly belowground activity usually show increased levels of population subdivision across relatively small spatial scales. This may be exacerbated in harsh mountain ecosystems, where landscape geomorphology limits species’ dispersal ability and leads to small effective population sizes, making species susceptible to environmental change. The giant root-rat (Tachyoryctes macrocephalus) is a highly fossorial rodent confined to the afro-alpine ecosystem of the Bale Mountains in Ethiopia. Using mitochondrial and low-coverage nuclear genomes, we investigated 77 giant root-rat individuals sampled from nine localities across its whole ∼1,000 km2range. Our data revealed a distinct division into a northern and southern subpopulation, with no signs of gene flow, and higher nuclear genetic diversity in the south. Landscape genetic analyses of the mitochondrial genomes indicated that population subdivision was driven by steep slopes and elevation differences of up to 500 m across escarpments separating the north and south, potentially reinforced by glaciation of the south during the Late Pleistocene (∼42,000 to 16,000 years ago). Despite the pronounced subdivision observed at the range-wide scale, weak geographic structuring of sampling localities within subpopulations indicated gene flow across distances of at least 16 km, suggesting aboveground dispersal and high mobility for relatively long distances. Our study highlights how topographic barriers can lead to the genetic subdivision of fossorial species, despite their potential to maintain gene flow at the local scale. These factors can reduce genetic variability, which should be considered when developing conservation strategies.

The endangered giant root-rat (Tachyoryctes macrocephalus, also known as giant mole rat) is a fossorial rodent endemic to the afro-alpine grasslands of the Bale Mountains in Ethiopia. The species is an important ecosystem engineer with the majority of the global population found within 1000 km(2). Here, we present the first complete mitochondrial genome of the giant root-rat and the genus Tachyoryctes, recovered using shotgun sequencing and iterative mapping. A phylogenetic analysis including 15 other representatives of the family Spalacidae placed Tachyoryctes as sister genus to Rhizomys with high support. This position is in accordance with a recent study revealing the topology of the Spalacidae family. The full mitochondrial genome of the giant root-rat presents an important resource for further population genetic studies.

Ecosystem engineers continuously shape and re-shape the spatial and temporal structure of the environment. Burrowing animals are an important group of ecosystem engineers, because of their ability to rework sediments and soils with consequences for e.g. soil formation and vegetation patterns. Simultaneous, burrowing animals depend on climate, local soil characteristics and vegetation. The endemic Giant Molerat (GMR) is a burrowing animal and important ecosystem engineer in the Bale Mountains. As part of the Bale Mountain Exile Hypothesis Project, the aim of this study is to investigate (1) the interlinkages between GMR, climate and vegetation patterns as well as (2) to upscale the influence of GMR on the vegetation pattern across the plateau with Sentinel satellite data. Field data comprise 47 paired plots of 5m x 5m with and without GMR activity. Additionally, 1.500 independent GMR burrow openings have been mapped. For investigating interlinkages, all parameters are first pre-analysed for correlations and their dependencies (1). In the following these results, the remote sensing data and the individual variables are implemented into the prediction model. To increase the accuracy, an error correction of the model is pursued. For this, the area is calculated into likelihoods of areas influenced by GMR, based on the vegetation survey pairs serving as training areas for the correction. The corrected results are used as final input model in a machine learning-based classification approach using Random Forest with forward-feature selection and leave-feature-out option (2). In the following the results of this ongoing upscaling approach used for the Sanetti Plateau, Ethiopia is presented.