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Open AccessArticle

Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data

1
Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research—UFZ, 04318 Leipzig, Germany
2
Eurac Research, Institute for Earth Observation, 39100 Bozen/Bolzano, Italy
3
Gran Paradiso National Park, 10135 Turin, Italy
4
Karlsruhe Institute of Technology (KIT), Institute of Geography and Geoecology, 76131 Karlsruhe, Germany
*
Author to whom correspondence should be addressed.
Shared first authorship.
Remote Sens. 2020, 12(1), 80; https://doi.org/10.3390/rs12010080
Received: 1 December 2019 / Revised: 20 December 2019 / Accepted: 22 December 2019 / Published: 24 December 2019
(This article belongs to the Special Issue Remote Sensing for Biodiversity Mapping and Monitoring)
Mountain ecosystems are biodiversity hotspots that are increasingly threatened by climate and land use/land cover changes. Long-term biodiversity monitoring programs provide unique insights into resulting adverse impacts on plant and animal species distribution. Species distribution models (SDMs) in combination with satellite remote sensing (SRS) data offer the opportunity to analyze shifts of species distributions in response to these changes in a spatially explicit way. Here, we predicted the presence probability of three different rove beetles in a mountainous protected area (Gran Paradiso National Park, GPNP) using environmental variables derived from Landsat and Aster Global Digital Elevation Model data and an ensemble modelling approach based on five different model algorithms (maximum entropy, random forest, generalized boosting models, generalized additive models, and generalized linear models). The objectives of the study were (1) to evaluate the potential of SRS data for predicting the presence of species dependent on local-scale environmental parameters at two different time periods, (2) to analyze shifts in species distributions between the years, and (3) to identify the most important species-specific SRS predictor variables. All ensemble models showed area under curve (AUC) of the receiver operating characteristics values above 0.7 and true skills statistics (TSS) values above 0.4, highlighting the great potential of SRS data. While only a small proportion of the total area was predicted as highly suitable for each species, our results suggest an increase of suitable habitat over time for the species Platydracus stercorarius and Ocypus ophthalmicus, and an opposite trend for Dinothenarus fossor. Vegetation cover was the most important predictor variable in the majority of the SDMs across all three study species. To better account for intra- and inter-annual variability of population dynamics as well as environmental conditions, a continuation of the monitoring program in GPNP as well as the employment of SRS with higher spatial and temporal resolution is recommended. View Full-Text
Keywords: temporal analysis; species distribution model; Landsat; ASTER GDEM; ensemble modelling; protected area; Italian Alps; biodiversity monitoring temporal analysis; species distribution model; Landsat; ASTER GDEM; ensemble modelling; protected area; Italian Alps; biodiversity monitoring
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MDPI and ACS Style

Dittrich, A.; Roilo, S.; Sonnenschein, R.; Cerrato, C.; Ewald, M.; Viterbi, R.; Cord, A.F. Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data. Remote Sens. 2020, 12, 80. https://doi.org/10.3390/rs12010080

AMA Style

Dittrich A, Roilo S, Sonnenschein R, Cerrato C, Ewald M, Viterbi R, Cord AF. Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data. Remote Sensing. 2020; 12(1):80. https://doi.org/10.3390/rs12010080

Chicago/Turabian Style

Dittrich, Andreas; Roilo, Stephanie; Sonnenschein, Ruth; Cerrato, Cristiana; Ewald, Michael; Viterbi, Ramona; Cord, Anna F. 2020. "Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data" Remote Sens. 12, no. 1: 80. https://doi.org/10.3390/rs12010080

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