Journal Article

Remote sensing‐supported mapping of the activity of a subterranean landscape engineer across an afro‐alpine ecosystem

Kurth, Philipp, Schabo, Dana G., Nauss, Thomas, Reuber, Victoria M., Opgenoorth, Lars, Zeuss, Dirk, Miehe, Georg, Woldu, Zerihun, Fekadu, Mekbib, Sankey, Temuulen, Wraase, Luise, Selig, Ulrike, Farwig, Nina, Demissew, Sebsebe, Wu, Jin

2023

Overview

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 in‐situ 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 satellite‐based 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. We compared three machine learning‐based modeling strategies, which included field‐ and remote‐sensing data to a different extent, for predicting the distribution of a subterranean species. We used the endangered giant root‐rat Tachyoryctes macrocephalus, endemic to the afro‐alpine ecosystem of the Bale Mountains in Ethiopia, for demonstrating that remotely sensed and spatially eyeball‐selected presence data of subterranean species could profoundly enhance distribution predictions. Our results suggest that the time‐consuming collection of vegetation data in the field is not necessarily required for the distribution prediction of subterranean species that leave traceable above‐ground marks in the landscape.

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: afro‐alpine ecosystems / Animals / Composition / Data points / ecosystem engineer / Ecosystems / Endangered species / Endemic species / Engineers / Environmental impact / Fieldwork / Flowers & plants / Geographical distribution / Habitats / Herbivory / Landscape / Machine learning / Modelling / Mountains / National parks / Perturbation / Plant species / Predictions / Remote sensing / Species composition / species distribution / subterranean animals / Texture / Vegetation