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Detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques

Nowadays, remote sensing is being increasingly applied in ecology and conservation, and even underground animals can successfully be studied if they leave clear signs of their presence in the environment. In this work, by combining a field study, analysis of high-resolution aerial images, and machin...

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Autores principales: Łopucki, Rafał, Klich, Daniel, Kociuba, Piotr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117191/
https://www.ncbi.nlm.nih.gov/pubmed/35585229
http://dx.doi.org/10.1038/s41598-022-12405-z
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author Łopucki, Rafał
Klich, Daniel
Kociuba, Piotr
author_facet Łopucki, Rafał
Klich, Daniel
Kociuba, Piotr
author_sort Łopucki, Rafał
collection PubMed
description Nowadays, remote sensing is being increasingly applied in ecology and conservation, and even underground animals can successfully be studied if they leave clear signs of their presence in the environment. In this work, by combining a field study, analysis of high-resolution aerial images, and machine learning techniques, we investigated the interspecies relationships of two small burrowing mammals: the spotted souslik Spermophilus suslicus and the European mole Talpa europaea. The study was conducted for 3 years (2018–2020) at a 105-ha grass airfield where both species coexist (Poland). Both field studies and the analysis of aerial imagery showed that, in the period of low population numbers, the souslik avoided coexistence with the European mole, and the presence of the mole was found to reduce the area of the habitat suitable for the souslik. The presence of other burrowing species may be an important element in the habitat selectivity of the souslik, but this has not yet been included in the conservation guidelines for this species. We discuss the contribution of our results to the knowledge of the ecology of burrowing mammals and their interspecies relationships. We also assess the possibility of using remote sensing and deep learning methods in ecology and conservation of small burrowing mammals.
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spelling pubmed-91171912022-05-20 Detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques Łopucki, Rafał Klich, Daniel Kociuba, Piotr Sci Rep Article Nowadays, remote sensing is being increasingly applied in ecology and conservation, and even underground animals can successfully be studied if they leave clear signs of their presence in the environment. In this work, by combining a field study, analysis of high-resolution aerial images, and machine learning techniques, we investigated the interspecies relationships of two small burrowing mammals: the spotted souslik Spermophilus suslicus and the European mole Talpa europaea. The study was conducted for 3 years (2018–2020) at a 105-ha grass airfield where both species coexist (Poland). Both field studies and the analysis of aerial imagery showed that, in the period of low population numbers, the souslik avoided coexistence with the European mole, and the presence of the mole was found to reduce the area of the habitat suitable for the souslik. The presence of other burrowing species may be an important element in the habitat selectivity of the souslik, but this has not yet been included in the conservation guidelines for this species. We discuss the contribution of our results to the knowledge of the ecology of burrowing mammals and their interspecies relationships. We also assess the possibility of using remote sensing and deep learning methods in ecology and conservation of small burrowing mammals. Nature Publishing Group UK 2022-05-18 /pmc/articles/PMC9117191/ /pubmed/35585229 http://dx.doi.org/10.1038/s41598-022-12405-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Łopucki, Rafał
Klich, Daniel
Kociuba, Piotr
Detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques
title Detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques
title_full Detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques
title_fullStr Detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques
title_full_unstemmed Detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques
title_short Detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques
title_sort detection of spatial avoidance between sousliks and moles by combining field observations, remote sensing and deep learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117191/
https://www.ncbi.nlm.nih.gov/pubmed/35585229
http://dx.doi.org/10.1038/s41598-022-12405-z
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