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  • 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 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), for further details.

  • 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)

  • bioRad provides standardized methods for extracting and reporting biological signals from weather radars. It includes functionality to inspect low-level radar data, process these data into meaningful biological information on animal speeds and directions at different altitudes in the atmosphere, visualize these biological extractions, and calculate further summary statistics.

  • Read and write Frictionless Data Packages. A 'Data Package' ( is a simple container format and standard to describe and package a collection of (tabular) data. It is typically used to publish FAIR ( and open datasets.

  • 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 ( and from the Global Inventory of Floras and Traits ( 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.

  • The package provides features to manage the complete workflow for biodiversity data cleaning: uploading data, gathering input from users (in order to adjust cleaning procedures), cleaning data and finally, generating various reports and several versions of the data. It facilitates user-level data cleaning, designed for the inexperienced R users.

  • A tool for Quality Controlling Darwin Core based datasets according to the EMODnet Biology guidelines. The tool performs a thorough QC on OBIS-env datasets and occurrence core datasets. It can use an IPT resource URL as input. Quality controlling a dataset is fundamental in order to ensure its appropriate usage. The EMODnetBiocheck R package is developed in the framework of the LifeWatch and EMODnet Biology projects, and managed by the EurOBIS (European Ocean Biodiversity Information System) Data Management Team at the Flanders Marine Institute (VLIZ). It helps users to Quality Control their (marine) biological datasets by performing a varied number of quality checks on both published and unpublished datasets. This R package also allows a thorough visual exploration of the dataset, while highlighting potential issues within the dataset. The R package can be used on: i) public IPT resources; ii) loaded data tables. The only requirement to use the R package is the existence of an Occurrence table in the dataset, although the analysis reaches its full potential using an IPT resource with OBIS-ENV data format (Core: "Event"; Extensions: "Occurrence" and "Extended Measurements or Facts").

  • mregions2 provides access to the data from in R. It uses both the Marine Regions Gazetteer Web Services and the Marine Regions OGC Web Services in R. mregions2 superseedes the previous mregions R package.

  • 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.