ENMTools 1.0: an R package for comparative ecological biogeography

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.

Inferring the ecological niche of bat viruses closely related to SARS-CoV-2 using phylogeographic analyses of Rhinolophus species

“…For data set B, the following seven predictor bioclimatic variables were selected: Bio2 (mean diurnal range), Bio3, Bio7, Bio10, Bio13, Bio15, Bio17 (precipitation of the driest quarter), and Bio18. Ecological niche modelling was performed with the MaxEnt algorithm using ENMTools in R 34 . The MaxEnt approach was chosen for its ability to work with presence-only data sets and to produce results with a low sample size 35 .…”

The effects of climate change on Australia’s only endemic Pokémon: Measuring bias in species distribution models

“…We used the 19 Bioclim layers for both present and future projections. Future climate layers were obtained for four combinations of emissions scenario and climate model; A1B-CS, A2-CS, A1B-MR and A2-MR. Future climate projections were used for 2030, 2050, 2070, 2080, 2090 and 2100. Using the ENMTools r package (Warren, Matzke, et al, 2021), we constructed SDMs using six different algorithms; generalized additive models (GAM), generalized linear models (GLM), maximum entropy (Phillips et al, 2006), random forests, Bioclim (Nix & Busby, 1986) and Domain (Carpenter et al, 1993). As this study is based on a citizen science dataset and limited research funds are available for systematic sampling of Pokemon distributions, true absence data are not available for G. kangaskhani.…”

Genetic Diversity Analysis and Potential Distribution Prediction of Sophora moorcroftiana Endemic to Qinghai–Tibet Plateau, China

“…Therefore, preprocessing the climate data is necessary before building the maximum entropy model. ENMTools software was used to calculate the Pearson correlation coefficient matrix among the 19 present bioclimatic factors (territory in China) [22]. The threshold value of 0.80 was used to determine whether the correlation was significant.…”