DOI: 10.5061/dryad.bzkh189m9
title: Predicting the breeding distribution of wader species across climatic and environmental gradients

description: 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.
description: Species occurrence data in the lowland areas (&lt;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 (&gt;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 &gt;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 <em>dbarts</em> in the R package
 <em>embarcadero</em>, 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)
description: # Predicting the breeding distribution of wader species across climatic
 and environmental gradients
 [https://doi.org/10.5061/dryad.bzkh189m9](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/](http://www.R-project.org/).