letsR: a new R package for data handling and analysis in macroecology

TLDR

Large ecological datasets and increased computational power have enabled macroecological theory to be tested at broad spatial and temporal scales, yet tools are still needed to transform these data into actionable knowledge. Here we introduce letsR, an R package designed to handle and analyze macroecological data, including species distributions and environmental variables. letsR provides functions to retrieve species habitat use, description year, and IUCN RedList trends, builds presence–absence matrices, and generates species‑richness rasters, geographic mid‑points, and site‑based attributes, as demonstrated on tailless amphibians. Our example shows that letsR enables comprehensive macroecological analyses on a single computer platform, potentially saving researchers time and effort.

Abstract

Summary The current availability of large ecological data sets and the computational capacity to handle them have fostered the testing and development of theory at broad spatial and temporal scales. Macroecology has particularly benefited from this era of big data, but tools are still required to help transforming this data into information and knowledge. Here, we present ‘ letsR ’, a package for the R statistical computing environment, designed to handle and analyse macroecological data such as species’ geographic distributions (polygons in shapefile format and point occurrences) and environmental variables (in raster format). The package also includes functions to obtain data on species’ habitat use, description year and current as well as temporal trends in conservation status as provided by the IUCN RedList online data base. ‘ letsR ’ main functionalities are based on the presence–absence matrices that can be created with the package's functions and from which other functions can be applied to generate, for example species richness rasters, geographic mid‐points of species and species‐ and site‐based attributes. We exemplify the package's functionality by describing and evaluating the geographic pattern of species’ description year in tailless amphibians. All data preparation and most analyses were made using the ‘ letsR ’ functions. Our example illustrates the package's capability for conducting macroecological analyses under a single computer platform, potentially helping researchers to save time and effort in this endeavour.