The Atacama Desert is the driest non‐polar desert on Earth, presenting precarious conditions for biological activity. In the arid coastal belt, life is restricted to areas with fog events that cause almost daily wet–dry cycles. In such an area, we discovered a hitherto unknown and unique ground covering biocenosis dominated by lichens, fungi, and algae attached to grit‐sized (~6 mm) quartz and granitoid stones. Comparable biocenosis forming a kind of a layer on top of soil and rock surfaces in general is summarized as cryptogamic ground covers (CGC) in literature. In contrast to known CGC from arid environments to which frequent cyclic wetting events are lethal, in the Atacama Desert every fog event is answered by photosynthetic activity of the soil community and thus considered as the desert's breath. Photosynthesis of the new CGC type is activated by the lowest amount of water known for such a community worldwide thus enabling the unique biocenosis to fulfill a variety of ecosystem services. In a considerable portion of the coastal Atacama Desert, it protects the soil from sporadically occurring splash erosion and contributes to the accumulation of soil carbon and nitrogen as well as soil formation through bio‐weathering. The structure and function of the new CGC type are discussed, and we suggest the name grit–crust. We conclude that this type of CGC can be expected in all non‐polar fog deserts of the world and may resemble the cryptogam communities that shaped ancient Earth. It may thus represent a relevant player in current and ancient biogeochemical cycling.
Abstract. Large forms of sorted patterned ground belong to the most prominent geomorphic features of periglacial and permafrost environments of the mid-latitudes and polar regions, but they were hitherto unknown in the tropics. Here, we report on relict large sorted stone stripes (up to 1000 m long, 15 m wide, and 2 m deep) on the ca. 4000 m high central Sanetti Plateau of the tropical Bale Mountains in the southern Ethiopian Highlands. These geomorphic features are enigmatic since forms of patterned ground exceeding several metres are commonly associated with distinct seasonal ground temperatures, oscillating around 0 ∘C. To systematically investigate present frost phenomena and relict periglacial landforms in the Bale Mountains, we conducted extensive geomorphological mapping. The sorted stone stripes were studied in more detail by applying aerial photogrammetry, ground-penetrating radar measurements, and 36Cl surface exposure dating. In addition, we installed ground temperature data loggers between 3877 and 4377 m to analyse present frost occurrence and seasonal ground temperature variations. Superficial nocturnal ground frost was measured at 35–90 d per year, but the ground beneath the upper few centimetres remains unfrozen the entire year. Seasonal frost occurrence would require a mean annual ground temperature depression of about 11 ∘C, corresponding to an air temperature decrease of about 6–8 ∘C (relative to today) as inferred from a simple statistical ground temperature model experiment. Our results suggest the formation of the large sorted stone stripes under past periglacial conditions related to lateral and vertical frost sorting in the course of cyclic freezing and thawing of the ground. It is likely that the stone stripes formed either in proximity to a former ice cap on the Sanetti Plateau over the last glacial period due to seasonal frost heave and sorting or they developed over multiple cold phases during the Pleistocene. Although certain aspects of the genesis of the large sorted stone stripes remain unresolved, the presence of these geomorphic features provides independent evidence besides glacial landforms for unprecedented palaeoclimatic and palaeoenvironmental changes in the tropical Bale Mountains during the (Late) Pleistocene.
Unmanned aerial systems (UAS) are cost-effective, flexible and offer a wide range of applications. If equipped with optical sensors, orthophotos with very high spatial resolution can be retrieved using photogrammetric processing. The use of these images in multi-temporal analysis and the combination with spatial data imposes high demands on their spatial accuracy. This georeferencing accuracy of UAS orthomosaics is generally expressed as the checkpoint error. However, the checkpoint error alone gives no information about the reproducibility of the photogrammetrical compilation of orthomosaics. This study optimizes the geolocation of UAS orthomosaics time series and evaluates their reproducibility. A correlation analysis of repeatedly computed orthomosaics with identical parameters revealed a reproducibility of 99% in a grassland and 75% in a forest area. Between time steps, the corresponding positional errors of digitized objects lie between 0.07 m in the grassland and 0.3 m in the forest canopy. The novel methods were integrated into a processing workflow to enhance the traceability and increase the quality of UAS remote sensing.
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.
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