Predicting the breeding distribution of wader species across climatic and environmental gradients
Data files
Jul 25, 2025 version files 27.76 MB
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aspect.tif
5.25 MB
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distance_to_water.tif
5.25 MB
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elevation.tif
2.62 MB
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occurrence_generalised.xlsx
195.35 KB
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precipitation.tif
5.25 MB
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README.md
2.98 KB
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slope.tif
5.25 MB
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status.xlsx
9.52 KB
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veg_cover_reclass.tif
1.31 MB
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veg_height_reclass.tif
2.62 MB
Abstract
Developing and applying species distribution models is particularly urgent for species currently facing high levels of environmental change. Waders (Charadrii) are among the most species rich groups of birds breeding at higher latitudes and the one where species are declining fastest. Iceland holds a large part of the global population of several wader species breeding sympatrically across unusual climate and environmental gradients. This provides an opportunity to assess synergy in habitat selection and occurrence of species usually occupying wide latitudinal ranges from temperate to arctic. In addition, the Icelandic wader habitats are facing significant and increasing pressures from anthropogenic sources putting in risk these populations. We built distribution models for several waders with the aim of identifying synergies in occurrence which allows better prioritisation of conservation efforts.
https://doi.org/10.5061/dryad.bzkh189m9
Description of the data and file structure
This repository archives the data and code associated with the manuscript titled: "Predicting the breeding distribution of wader species across climatic- and environmental gradients". The materials are organised into species data, containing the occurrence of nine Icelandic breeding waders derived from georeferenced point and transect counts collected across Iceland, all environmental raster layers resampled to a pixel resolution of ca. 400 x 400 meters width and a spatial reference of WGS84, and the R script used to generate the species distribution models presented in the manuscript.
Below is a breakdown of the file contents.
Files and variables
File: occurrence_generalised.xlsx
Description: This Excel file contains the occurrence data of each of the nine Icelandic breeding waders included in this study. Columns include:
- Species: name of the species
- Latitude.Y: latitude of the point
- Longitude.X: longitude of the point
The precision of the geographic coordinates has been generalised by reducing the number of decimal places according to the status of each species:
- Near threatened: coordinates rounded to 0.01 degrees;
- Vulnerable: coordinates rounded to 0.1 degrees;
- Endangered: coordinates rounded to 1 degree.
File: status.xlsx
Description: This Excel file contains the Icelandic and European status of all the species included in the study.
File: precipitation.tif
Description: This raster layer represents mean June precipitation (ml).
Files: aspect.tif
Description: This raster layer represents aspect (degrees).
Files: slope.tif
Description: This raster layer represents slope (%).
Files: elevation.tif
Description: This raster layer represents elevation (m).
File: veg_cover_reclass.tif
Description: This raster layer represents vegetation cover (%).
File: veg_height_reclass.tif
Description: This raster layer represents vegetation height (cm).
File: distance_to_water.tif
Description: This raster layer represents distance to permanent water (m).
Code/software
Recommended Software for Data Analysis
All analyses in this repository were performed using R version 4.0.2. The script sdmcode.R, which processes the occurrence and environmental data to generate species distribution models using Bayesian Additive Regression Trees (BART), is thoroughly annotated to guide through the workflow. The following R packages were loaded before running the scripts:
- embarcadero
- raster
Reference
R Core Team (2020). A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.
Species occurrence data in the lowland areas (<200m a.s.l.) of Iceland were derived from georeferenced point count data (standardized 5 min. point counts with a 200m radius) carried out during during the breeding season (May and June) from 2007 to 2019. Species occurrence data in the highland areas (>200m a.s.l.) were derived from georeferenced transect counts carried out during June and July from 1999 to 2004. For each transect, we used the species counted in the midpoint of the transect and excluded counts recorded with distance to the focal bird >200 m or counts recorded with no distance. All counts were transformed to presence data, so the species occurrence dataset consists of a set of longitude and latitude coordinates.
Species distribution models (SDMs) were generated using Bayesian Additive Regression Trees (BART) following the methodology/code described in Carlson (2020). BART models were run with the default parameters as implemented in dbarts in the R package embarcadero, using 200 trees and 1000 back-fitting Marcov Chain Monte Carlo (MCMC) iterations. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and true skill statistics (TSS)