Remote sensing‐supported mapping of the activity of a subterranean landscape engineer across an afro‐alpine ecosystem
Abstract 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...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-04-01
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| Series: | Remote Sensing in Ecology and Conservation |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/rse2.303 |
| _version_ | 1797842277474762752 |
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| author | Luise Wraase Victoria M. Reuber Philipp Kurth Mekbib Fekadu Sebsebe Demissew Georg Miehe Lars Opgenoorth Ulrike Selig Zerihun Woldu Dirk Zeuss Dana G. Schabo Nina Farwig Thomas Nauss |
| author_facet | Luise Wraase Victoria M. Reuber Philipp Kurth Mekbib Fekadu Sebsebe Demissew Georg Miehe Lars Opgenoorth Ulrike Selig Zerihun Woldu Dirk Zeuss Dana G. Schabo Nina Farwig Thomas Nauss |
| author_sort | Luise Wraase |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-09T16:45:18Z |
| format | Article |
| id | doaj.art-00eb6f82d7474ce6952635eb93bd4f6d |
| institution | Directory Open Access Journal |
| issn | 2056-3485 |
| language | English |
| last_indexed | 2024-04-09T16:45:18Z |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Remote Sensing in Ecology and Conservation |
| spelling | doaj.art-00eb6f82d7474ce6952635eb93bd4f6d2023-04-22T17:18:04ZengWileyRemote Sensing in Ecology and Conservation2056-34852023-04-019219520910.1002/rse2.303Remote sensing‐supported mapping of the activity of a subterranean landscape engineer across an afro‐alpine ecosystemLuise Wraase0Victoria M. Reuber1Philipp Kurth2Mekbib Fekadu3Sebsebe Demissew4Georg Miehe5Lars Opgenoorth6Ulrike Selig7Zerihun Woldu8Dirk Zeuss9Dana G. Schabo10Nina Farwig11Thomas Nauss12Department of Geography, Environmental Informatics Philipps‐Universität Marburg Deutschhausstraße 12 35032 Marburg GermanyDepartment of Biology, Conservation Ecology Philipps‐Universität Marburg Karl‐von‐Frisch‐Straße 8 35034 Marburg GermanyDepartment of Geography, Environmental Informatics Philipps‐Universität Marburg Deutschhausstraße 12 35032 Marburg GermanyDepartment of Biology, Plant Ecology and Geobotany Philipps‐Universität Marburg Karl‐von‐Frisch‐Straße 8 35034 Marburg GermanyDepartment of Plant Biology and Biodiversity Management, College of Natural and Computational Sciences Addis Ababa University Addis Ababa EthiopiaDepartment of Geography, Vegetation Geography Philipps‐Universität Marburg Deutschhausstraße 10 35032 Marburg GermanyDepartment of Biology, Plant Ecology and Geobotany Philipps‐Universität Marburg Karl‐von‐Frisch‐Straße 8 35034 Marburg GermanyDepartment of Geography, Environmental Informatics Philipps‐Universität Marburg Deutschhausstraße 12 35032 Marburg GermanyDepartment of Plant Biology and Biodiversity Management, College of Natural and Computational Sciences Addis Ababa University Addis Ababa EthiopiaDepartment of Geography, Environmental Informatics Philipps‐Universität Marburg Deutschhausstraße 12 35032 Marburg GermanyDepartment of Biology, Conservation Ecology Philipps‐Universität Marburg Karl‐von‐Frisch‐Straße 8 35034 Marburg GermanyDepartment of Biology, Conservation Ecology Philipps‐Universität Marburg Karl‐von‐Frisch‐Straße 8 35034 Marburg GermanyDepartment of Geography, Environmental Informatics Philipps‐Universität Marburg Deutschhausstraße 12 35032 Marburg GermanyAbstract 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.https://doi.org/10.1002/rse2.303afro‐alpine ecosystemsecosystem engineermachine learningremote sensingspecies distributionsubterranean animals |
| spellingShingle | Luise Wraase Victoria M. Reuber Philipp Kurth Mekbib Fekadu Sebsebe Demissew Georg Miehe Lars Opgenoorth Ulrike Selig Zerihun Woldu Dirk Zeuss Dana G. Schabo Nina Farwig Thomas Nauss Remote sensing‐supported mapping of the activity of a subterranean landscape engineer across an afro‐alpine ecosystem Remote Sensing in Ecology and Conservation afro‐alpine ecosystems ecosystem engineer machine learning remote sensing species distribution subterranean animals |
| title | Remote sensing‐supported mapping of the activity of a subterranean landscape engineer across an afro‐alpine ecosystem |
| title_full | Remote sensing‐supported mapping of the activity of a subterranean landscape engineer across an afro‐alpine ecosystem |
| title_fullStr | Remote sensing‐supported mapping of the activity of a subterranean landscape engineer across an afro‐alpine ecosystem |
| title_full_unstemmed | Remote sensing‐supported mapping of the activity of a subterranean landscape engineer across an afro‐alpine ecosystem |
| title_short | Remote sensing‐supported mapping of the activity of a subterranean landscape engineer across an afro‐alpine ecosystem |
| title_sort | remote sensing supported mapping of the activity of a subterranean landscape engineer across an afro alpine ecosystem |
| topic | afro‐alpine ecosystems ecosystem engineer machine learning remote sensing species distribution subterranean animals |
| url | https://doi.org/10.1002/rse2.303 |
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