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Remote sensing-supported mapping of the activity of asubterranean landscape engineer across an afro-alpineecosystem
Subterranean animals act as ecosystem engineers, for example, through soil per-turbation and herbivory, shaping their environments worldwide. As the occur-rence of animals is often linked to above-ground features such as plant speciescomposition or landscape textures, satellite-based remote sensing...
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| Asıl Yazarlar: | , , , , , , , , , , , , |
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| Materyal Türü: | Makale |
| Dil: | İngilizce |
| Baskı/Yayın Bilgisi: |
Philipps-Universität Marburg
2022
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| Konular: | |
| Online Erişim: | PDF Tam Metin |
| Etiketler: |
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| Özet: | Subterranean animals act as ecosystem engineers, for example, through soil per-turbation and herbivory, shaping their environments worldwide. As the occur-rence of animals is often linked to above-ground features such as plant speciescomposition or landscape textures, satellite-based remote sensing approaches canbe used to predict the distribution of subterranean species. Here, we combine in-situ collected vegetation composition data with remotely sensed data to improvethe prediction of a subterranean species across a large spatial scale. We comparedthree machine learning-based modeling strategies, including field and satellite-based remote sensing data to different extents, in order to predict the distributionof the subterranean giant root-rat GRR,Tachyoryctes macrocephalus, an endan-gered rodent species endemic to the Bale Mountains in southeast Ethiopia. Weincluded no, some and extensive fieldwork data in the modeling to test how thesedata improved prediction quality. We found prediction quality to be particularlydependent on the spatial coverage of the training data. Species distributions werebest predicted by using texture metrics and eyeball-selected data points of land-scape marks created by the GRR. Vegetation composition as a predictor showedthe lowest contribution to model performance and lacked spatial accuracy. Ourresults suggest that the time-consuming collection of vegetation data in the fieldis not necessarily required for the prediction of subterranean species that leavetraceable above-ground landscape marks like the GRR. Instead, remotely sensedand spatially eyeball-selected presence data of subterranean species could pro-foundly enhance predictions. The usage of remote sensing-derived texture metricshas great potential for improving the distribution modeling of subterranean spe-cies, especially in arid ecosystems. |
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| Diğer Bilgileri: | Gefördert durch den Open-Access-Publikationsfonds der UB Marburg. |
| DOI: | 10.1002/rse2.303 |