‘envar’: An R package to retrieve and process environmental variables for macroecology and biogeography

‘envar’: An R package to retrieve and process environmental variables for macroecology and biogeography | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 January 2026 V1 Latest version Share on ‘envar’: An R package to retrieve and process environmental variables for macroecology and biogeography Authors : Andrea Simoncini 0000-0002-9856-6111 [email protected] , Michele Bertoncini 0000-0002-1866-0045 , Andrea Cerofolini 0009-0009-4439-3014 , Andrea Dalpasso , Gentile Francesco Ficetola , Falaschi Mattia 0000-0002-4511-4816 , and Elia Lo Parrino Authors Info & Affiliations https://doi.org/10.22541/au.176918612.23247936/v1 340 views 117 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The selection of environmental variables is a cornerstone of biogeographical analyses. However, most studies use standard sets of predictors, without fully leveraging the variety of available environmental data. Here, we present ‘envar’, an R package to retrieve and process 396 global variables at ~ 1 km resolution from 25 different sources, describing climate and land cover (past, current, and future), landscape heterogeneity, habitats, soil characteristics, topography, freshwater/marine environments, human impacts, and protected areas. Through ‘envar’, the user defines a study area - as custom points/polygon, country/continent, ecological boundary, or species range -, a buffer in km, a coordinate reference system, the resolution, the variables to download, and the correlation and extrapolation checks. The output is one or multiple raster(s) with the downloaded and processed variables, or a table with the extracted variables over the specified points, and optionally information on correlation and extrapolation. We also illustrate the package potential to streamline the development of Species Distribution Models (SDMs), using as case study the butterfly Parnassius apollo in the European Alps. We use ‘envar’ to: 1) create a raster template with a buffer around occurrences where bias-corrected background points are selected, 2) extract predictors for occurrences and background points across Europe checking for collinearity, and 3) obtain predictors for the European Alps checking for extrapolation. ‘envar’ can foster the selection of a set of uncorrelated proximal variables tailored to the specific research question, across all of biogeography and macroecology. Its use can also improve the quality of SDMs by limiting the issues related to niche truncation, collinearity, extrapolation, and heterogeneous sampling effort. Supplementary Material File (main_text.docx) Download 3.99 MB Information & Authors Information Version history V1 Version 1 23 January 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords biooracle chelsa ecosystems functional maxent worldclim Authors Affiliations Andrea Simoncini 0000-0002-9856-6111 [email protected] University of Milan View all articles by this author Michele Bertoncini 0000-0002-1866-0045 Università di Pisa View all articles by this author Andrea Cerofolini 0009-0009-4439-3014 Università degli Studi di Firenze View all articles by this author Andrea Dalpasso University of Milan View all articles by this author Gentile Francesco Ficetola University of Milan View all articles by this author Falaschi Mattia 0000-0002-4511-4816 Università degli Studi di Milano View all articles by this author Elia Lo Parrino University of Milan View all articles by this author Metrics & Citations Metrics Article Usage 340 views 117 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Andrea Simoncini, Michele Bertoncini, Andrea Cerofolini, et al. ‘envar’: An R package to retrieve and process environmental variables for macroecology and biogeography. Authorea . 23 January 2026. DOI: https://doi.org/10.22541/au.176918612.23247936/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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