A set of functions for reproducible and shareable analysis of models from an online repository, comparing and diagnostic models. The package has been developed specifically to improve reproducibility and comparability of SDMs in R by allowing users to encode entire SDM analyses as repeatable and extensible workflows consisting of independently executable, community-contributed modules. The module-workflow structure enables scientists to more easily create and share components of their analysis, and then, access, modify, reuse and combine the components of others.

This package is a versatile tool that aims at (1) defining the minimum background extent necessary to fit Species Distribution Models reliable enough to extract ecologically relevant conclusions from them and (2) optimizing the modelling process in terms of computation demands. See Rotllan-Puig, X. & Traveset, A. (2021) https://www.sciencedirect.com/science/article/pii/S0304380020304191

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 allows users to create and evaluate ensembles of species distribution model (SDM) predictions. Functionality is offered through R functions or a GUI (R Shiny app). This tool can assist users in identifying spatial uncertainties and making informed conservation and management decisions.

A set of functions for preparing data, training and evaluating dismo models, and comparing ecological niches. It complements to dismo. The package includes functions for implementing species distribution models (SDMs) and ecological niche models (ENMs). The heart of the package is a set of "training" functions which automatically tune and parameterize a model based on several popular algorithms (e.g., MaxEnt, GLMs, BRTs, GAMs, etc.). Ancillary tools include estimation of spatial sampling bias, model evaluation, and calculation of biotic velocity (speed and direction at which a species' range moves through time).

The package includes several methods that helps users to create virtual distribution map. Such maps can be used for Species Distribution Modelling (SDM) studies, which use environmental data for sites of occurrence of a species to predict all the sites where the environmental conditions are suitable for the species to persist, and may be expected to occur.

A set of functions for error detection and correction in point data quality datasets that are used in species distribution modelling. Includes functions for parsing and converting coordinates into decimal degrees from various formats.

This service aims at running a species distribution modelling (sdm) service based on Maximum Entropy (maxent) algorithm. It is intended to predict the potential geographic distribution of a species based on environmental variables. Maxent algorithm is a probabilistic modelling technique used for making predictions based on incomplete information or limited data. It is based on the principle of maximum entropy, which states that when making predictions, one should choose the probability distribution that is the least informative, or maximally uncertain, while still satisfying a set of known constraints. The application calculates the distribution model of a species by taking as input files a vector of presence and a group of raster files that represent the background or environmental layers and providing five output files: elapid_object.ela, maxent_prediction_model.tif, maxent_prediction_plot.png, dependency_plots.png, and auc_score.txt. The application can take as input also a set of parameters (optional), which have default values in case the user does not want to configure them.

A user-friendly framework that enables the training and the evaluation of species distribution models (SDMs). The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the 'RStudio' viewer pane during their execution.

The package analyses species distribution models and evaluates their performance. It includes functions for variation partitioning, extracting variable importance, computing several metrics of model discrimination and calibration performance, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots.