Data and R script for: "Niche and spatial segregation define the distribution of sympatric red wood ant species"

This record contains the environmental predictor rasters, the occurrence/response dataset and the R script used in the analyses of Elia Nalini , Felix Puff , Marco Mina , Carlo Polidori , Julia Seeber, Elia Guariento, “Niche and spatial segregation define the distribution of sympatric red wood ant species”, OIKOS, 10.1002/oik.12550. The study investigates niche differentiation and local co-occurrence among seven co-occurring red wood ant species of the Formica rufa group in South Tyrol (Italy), combining niche overlap and similarity analyses with spatial segregation analyses at a fine spatial scale. Nine environmental predictors were retained after variable selection (see Table 1). All rasters share the same extent, resolution (100 m) and coordinate reference system (WGS 84, EPSG:4326). Files R_script_Niche_Modelling.R – R script reproducing the analyses (data preparation, predictor extraction, niche modelling, figures). Tested with R [versione]; required packages: [es. terra, …]. Dataset.xlsx – [occurrence data (presences and absences) and related coordinates]. MEAN_TEMP_ST.tif – Mean air temperature 2011–2020 (°C), from Crespi et al. (2021). MEAN_PREC_ST.tif – Mean annual precipitation 2011–2020 (mm), from Crespi et al. (2021). Summer.tif – Average potential solar radiation in summer (June–August) (W/m²), from Tscholl et al. (2021). SLOPE_ST.tif – Slope inclination (°), derived in QGIS from the Copernicus DEM. GRASSLAND.tif – Proportion of grassland cover, from Corine Land Cover 2018 (© Copernicus). BUILDINGS.tif – Proportion of settlement cover, from Corine Land Cover 2018 (© Copernicus). BROAD_LEAVES.tif – Tree density in deciduous forest habitat: Corine Land Cover broadleaved forest mask × Tree Cover Density 2018 (© Copernicus). CONIFERS.tif – Tree density in coniferous forest habitat: Corine Land Cover coniferous forest mask × Tree Cover Density 2018 (© Copernicus). COARSE.tif – Volumetric fraction of coarse fragments >2 mm (cm³/dm³), from SoilGrids 2.0. Usage: place all files in the same folder, set it as the working directory and run the script. Rasters derived from third-party data are redistributed under the terms of their original licences; please also cite the original sources (Crespi et al. 2021; Tscholl et al. 2021; Copernicus Land Monitoring Service; Poggio et al. 2021 for SoilGrids).

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-28
DOI
https://doi.org/10.5281/zenodo.23723676
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
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article

Data and R script for: "Niche and spatial segregation define the distribution of sympatric red wood ant species"

Julia Seeber, Marco Mina, Carlo Polidori, Elia Guariento et al.
1 links
Zenodo (CERN European Organization for Nuclear Research)
Species Distribution and Climate Change
article

Data and R script for: "Niche and spatial segregation define the distribution of sympatric red wood ant species"

Julia Seeber, Marco Mina, Carlo Polidori, Elia Guariento, Felix Puff, Elia Nalini
article en

Abstract

This record contains the environmental predictor rasters, the occurrence/response dataset and the R script used in the analyses of Elia Nalini , Felix Puff , Marco Mina , Carlo Polidori , Julia Seeber, Elia Guariento, “Niche and spatial segregation define the distribution of sympatric red wood ant species”, OIKOS, 10.1002/oik.12550. The study investigates niche differentiation and local co-occurrence among seven co-occurring red wood ant species of the Formica rufa group in South Tyrol (Italy), combining niche overlap and similarity analyses with spatial segregation analyses at a fine spatial scale. Nine environmental predictors were retained after variable selection (see Table 1). All rasters share the same extent, resolution (100 m) and coordinate reference system (WGS 84, EPSG:4326). Files R_script_Niche_Modelling.R – R script reproducing the analyses (data preparation, predictor extraction, niche modelling, figures). Tested with R [versione]; required packages: [es. terra, …]. Dataset.xlsx – [occurrence data (presences and absences) and related coordinates]. MEAN_TEMP_ST.tif – Mean air temperature 2011–2020 (°C), from Crespi et al. (2021). MEAN_PREC_ST.tif – Mean annual precipitation 2011–2020 (mm), from Crespi et al. (2021). Summer.tif – Average potential solar radiation in summer (June–August) (W/m²), from Tscholl et al. (2021). SLOPE_ST.tif – Slope inclination (°), derived in QGIS from the Copernicus DEM. GRASSLAND.tif – Proportion of grassland cover, from Corine Land Cover 2018 (© Copernicus). BUILDINGS.tif – Proportion of settlement cover, from Corine Land Cover 2018 (© Copernicus). BROAD_LEAVES.tif – Tree density in deciduous forest habitat: Corine Land Cover broadleaved forest mask × Tree Cover Density 2018 (© Copernicus). CONIFERS.tif – Tree density in coniferous forest habitat: Corine Land Cover coniferous forest mask × Tree Cover Density 2018 (© Copernicus). COARSE.tif – Volumetric fraction of coarse fragments >2 mm (cm³/dm³), from SoilGrids 2.0. Usage: place all files in the same folder, set it as the working directory and run the script. Rasters derived from third-party data are redistributed under the terms of their original licences; please also cite the original sources (Crespi et al. 2021; Tscholl et al. 2021; Copernicus Land Monitoring Service; Poggio et al. 2021 for SoilGrids).

Zenodo (CERN European Organization for Nuclear Research)
Eurac Research (IT), University of Milan (IT), Universität Innsbruck (AT)
Academy of Finland
Life on land
Openalex Percentile: Top 5%
Species Distribution and Climate Change
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