Data from: Modelling amphibian road crossing points in a dynamic environment
Data files
Mar 28, 2025 version files 42.18 KB
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occurencedata.csv
40.22 KB
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README.md
1.96 KB
Mar 28, 2025 version files 42.10 KB
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occurencedata.csv
40.22 KB
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README.md
1.88 KB
Abstract
Roads are ubiquitous infrastructures that have detrimental effects on wildlife and contribute to increased mortality and fragmentation of animal populations. Although several mitigation measures are available to reduce road impacts, their planning rarely considers the dynamic nature of the environment, which may reflect into temporal variation in habitat suitability and connectivity for animal species. Consequently, the effectiveness of such measures may fall short of expectations. By combining high-resolution satellite imagery and connectivity modelling, we propose a generalizable approach to identify the most probable crossing points across a barrier at different time snapshots. This information may be pivotal in planning mitigation measures that can take into account dynamic components. We collected occurrence data of three farmland-adapted anurans and high spatiotemporal definition Sentinel-2 multispectral images to build habitat suitability models that capture the relationship between the environment and the species within the study area (an agricultural area crossed by a highway). We then projected the models onto six snapshots from two subsequent amphibian breeding seasons. Finally, we used circuit theory-based connectivity models for each snapshot/species to identify the areas with the highest probability of highway crossing in each snapshot. We found remarkable differences over time for each species, both in suitability and connectivity. Furthermore, the distribution and relative importance of the crossing points changed greatly between the two years as well as within the same. Some crossing points were stable over time, while others were important for a specific snapshot.
Synthesis and applications. Not considering the spatiotemporal variability of the environment can lead to a loss of crucial information when modelling the localities at which the predicted flows of animals intersect the highway. The dynamics of roads-affected ecosystems can be taken into account using freely available remote sensing data. This can be an important element in achieving the goal of maintaining connectivity and minimizing wildlife mortality.
Dataset DOI: 10.5061/dryad.cz8w9gjg2
Description of the data and file structure
Reported data were the basis for Habitat Suitability Modelling and subsequent Connectivity Models.
Data includes the occurrence data of the three amphibian target species (Hyla intermedia, Pelophylax synkl. esculentus, Bufotes viridis), collected in 2021 and 2022. The values of the Sentinel-2 spectral bands we used as predictors are Copernicus Sentinel data [2025] and can be downloaded from the Copernicus Browser (https://browser.dataspace.copernicus.eu/).
Files and variables
File: occurencedata.csv
Description:
Occurrence points of the three target species. Observations missing important information have already been removed from this dataset. Here you can find the data we used for all the modelling steps. They are the occurrence data of the three amphibians species we collected in 2021 and 2022.
Variables
- prg: progressive number of the observation
- lon: longitude in Coordinate reference system (CRS) “EPSG: 4326”
- lat: latitude in Coordinate reference system (CRS) “EPSG: 4326”
- spp: species: Pelesc = Pelophylax synkl. esculentus; Hylint = Hyla intermedia; Bufvir = Bufotes viridis
- num: number of observed individuals
- date: date of the observation
- month: month of the observation
- year: year of the observation
- time: time of day of the observation
Code/software
We analysed the data in R environment, using the package terra for managing spatial data and package dismo for Habitat Suitability Modelling. Connectivity analyses were performed using the software Circuitscape 5 in the programming language Julia. Codes and scripts can be made available on request.