The package estmates the importance and the relative contribution of factors to explain species distribution by using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.

Framework for microclimate and mechanistic niche models. It provides an interface to a suite of biophysical modelling algorithms including the Niche Mapper system.

The package provides a framework for generating virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. It integrates the existing methodological approaches with the objective of generating virtual species distributions with increased ecological realism.

The package contains a number of discrete functions, each of which can be used to assess a particular form of bias, uncertainty or coverage. Generally, users must pass their occurrence data to the functions along with a list of time periods into which the outputs will be split. The functions generally return a list with two elements: a ggplot2 object, and the data that underpins that plot.

Automated assessment of accuracy and geographical status of georeferenced biological data. The methods rely on reference regions, namely checklists and range maps. The package includes functions to obtain data from the Global Biodiversity Information Facility (https://www.gbif.org) and from the Global Inventory of Floras and Traits (https://gift.uni-goettingen.de/home). Alternatively, the user can input their own data. Furthermore, it provides easy visualisation of the data and the results through the plotting functions. It is especially suited for large datasets. The reference for the methodology is: Arlé et al. https://doi.org/10.1111/2041-210X.13629

The package performs spatial analyses of species' niche overlap in e-espace (environment or climate space). This software is named after Alexander von Humboldt, who was a notable Prussian geographer, explorer, and naturalist. He is widely recognized for his works on botanical geography that laid the foundation for the field of biogeography. His greatest legacy is his sweeping idea about the interconnectedness of the world, however it would be wrong to see Humboldt as only a "big picture" man. Much of Humboldt's work was based on measurements - and lots of them. He used the best technology of the day to measure everything he could - temperature, humidity, the magnetic field. And alongside this were observations of rock and soil, fungi, insects, plants, animals and people. Humboldt fused all of this together to show the links of dependency in nature connecting species distributions to key environmental variables. Since Humboldt, Lyell, Darwin, Mendel, and Grinnell brought us geology, evolution, genetics and niche theory, respectively. Using these new concepts and modern tools, we continue to pursue Humboldt's basic ideas regarding drivers of the geographic distributions of species. This package builds upon the framework introduced by Dr. Olivier Broennimann that was published in 2012 in the paper entitled "Measuring ecological niche overlap from occurrence and spatial environmental data" in the journal Global Ecology and Biogeography (issue 21: pgs 481-497). For some functions, Humboldt builds upon their the framework, updating, supplementing to, and improving their supplied R code (as a derivative work). In most cases these scripts are entirely diffent and most analogous scripts represent completely different calcuations. For most other functions, they are entirely new, such as 'humboldt.g2e', 'humboldt.doitall', 'humboldt.plot.overlap', 'humboldt.espace.correction', 'humboldt.top.env', 'humboldt.background.test', 'humboldt.pnt.index' to name a few.

The package provides individual-based simulations of spatial eco-evolutionary dynamics. It is based on the software RangeShifter, making it flexible and fast. RangeShiftR models the processes of demography, dispersal and evolution in an inter-dependent way, offering substantial complexity in the corresponding modelling choices. It is entirely open-source and aims to facilitate the application of individual-based and mechanistic modelling to eco-evolutionary questions.

The package provides a single function with all arguments necessary to calculate ENMs: variable collinearity control, bias control, accessible area delimitation, pseudoabsence allocation, data partition, several algorithms, thresholds, evaluation metrics, over-prediction, ensemble modelling, and projections over time and space.

The package allows creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments. Moreover, it permits to investigate and visualise the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019), https://doi.org/10.1111/2041-210X.13107.

The package provides a simple method to display and characterise the multidimensional ecological niche of a species. Moreover, the method estimates the optimums and amplitudes along each niche dimension (index D). It gives also an estimation of the degree of niche overlapping between species. See Kleparski and Beaugrand (2022), https://doi.org/10.1002/ece3.8830 for further details.