ENMTools 1.0: an R package for comparative ecological biogeography. Issue 4 (19th January 2021)

• Record Type: Journal Article
• Authors: Warren, Dan L.; Matzke, Nicholas J.; Cardillo, Marcel; Baumgartner, John B.; Beaumont, Linda J.; Turelli, Michael; Glor, Richard E.; Huron, Nicholas A.; Simões, Marianna; Iglesias, Teresa L.; Piquet, Julien C.; Dinnage, Russell
• Is Part Of: Ecography. Volume 44:Issue 4(2021)
• Page Start: 504
• Page End: 511
• Publication Date: 2021-01-19
• Subjects: biogeography -- ecology -- ENM -- ENMTools -- SDM; Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/ecog.05485
• Languages: English
• ISSNs: 0906-7590

Abstract

Abstract : The ENMTools software package was introduced in 2008 as a platform for making measurements on environmental niche models (ENMs, frequently referred to as species distribution models or SDMs), and for using those measurements in the context of newly developed Monte Carlo tests to evaluate hypotheses regarding niche evolution. Additional functionality was later added for model selection and simulation from ENMs, and the software package has been quite widely used. ENMTools was initially implemented as a Perl script, which was also compiled into an executable file for various platforms. However, the package had a number of significant limitations; it was only designed to fit models using Maxent, it relied on a specific Perl distribution to function, and its internal structure made it difficult to maintain and expand. Subsequently, the R programming language became the platform of choice for most ENM studies, making ENMTools less usable for many practitioners. Here we introduce a new R version of ENMTools that implements much of the functionality of its predecessor as well as numerous additions that simplify the construction, comparison and evaluation of niche models. These additions include new metrics for model fit, methods of measuring ENM overlap, and methods for testing evolutionary hypotheses. The new version of ENMTools is also designed to work within the expanding universe of R tools for ecological biogeography, and as such includes greatly simplified interfaces for analyses from several other R packages.