Abstract
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.
Highlights
Mountain ecosystems are biodiversity hotspots with higher species richness and levels of endemism than adjacent lowlands as a result of steep environmental gradients over short distances that leadRemote Sens. 2020, 12, 80; doi:10.3390/rs12010080 www.mdpi.com/journal/remotesensingRemote Sens. 2020, 12, 80 to topographic, geologic, and climatic heterogeneity [1,2]
area under curve (AUC) and true skills statistics (TSS) values were highly concordant in assessing model performance, and the majority of species prediction models showed satisfactory accuracy levels based on the two evaluation metrics (i.e., AUC > 0.7 and TSS > 0.4, Table 3), and can be categorized as useful [86]
Dinothenarus fossor showed the highest number of well-performing models, probably because of the larger number of presence points in both time periods, compared to the other two species (Table 1)
Summary
Mountain ecosystems are biodiversity hotspots with higher species richness and levels of endemism than adjacent lowlands as a result of steep environmental gradients over short distances that leadRemote Sens. 2020, 12, 80; doi:10.3390/rs12010080 www.mdpi.com/journal/remotesensingRemote Sens. 2020, 12, 80 to topographic, geologic, and climatic heterogeneity [1,2]. 2020, 12, 80 to topographic, geologic, and climatic heterogeneity [1,2] Such physiographic complexity creates a mosaic of habitats and, a multitude of ecological niches. While data collection is a time-consuming endeavor by itself, accessibility is an additional major challenge in mountains. Such in situ data are indispensable for predicting species occurrence probability at larger scale using, e.g., species distribution models (SDMs, see [7] for detailed information). Since ecological niches in mountains are largely influenced by micro-topography [9], geospatial data created by employing spatial interpolation techniques (e.g., for climatic data, [10]) is often not capable of capturing such small-scale differences and heterogeneity
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