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.

The package allows to map species richness and endemism based on stacked species distribution models (SSDM). Individuals SDMs can be created using a single or multiple algorithms (ensemble SDMs). For each species, a SDM can yield a habitat suitability map, a binary map, a between-algorithm variance map, and can assess variable importance, algorithm accuracy, and between- algorithm correlation. Methods to stack individual SDMs include summing individual probabilities and thresholding then summing. Thresholding can be based on a specific evaluation metric or by drawing repeatedly from a Bernoulli distribution. The SSDM package also provides a user-friendly interface.

This service allows to filter the list of samples in the MasterARMS csv file on date range (start and end date in ISO format) and geographic location (lat and lon in decimal degrees). Moreover, also the selection of specific samples is permitted. It represents the Step 3 of the ARMS Workflow within the Internal Joint Initiative.

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.

It is a Support Vector Machine classifier trained for a 2-classes problem. It represents the Step 9 of the Ailanthus Workflow within the Internal Joint Initiative.

It is a tool package for running and analyzing species distribution models with Bayesian additive regression trees (BARTs), including basic model summary statistics and diagnostics, variable importance measures, and plotting.

Rhea: R scripts for the analysis of microbial profiles The importance of 16S rRNA gene amplicon profiles for understanding the influence of microbes in a variety of environments coupled with the steep reduction in sequencing costs led to a surge of microbial sequencing projects. Among available pipeline options for high-throughput 16S rRNA gene analysis, the R programming language and software environment for statistical computing stands out for its power and increased flexibility, and the possibility to adhere to most recent best practices and to adjust to individual project needs. The Rhea pipeline is a set of R scripts that encode a series of well-documented choices for the downstream analysis of Operational Taxonomic Units (OTUs) tables, including normalisation steps, alpha- and beta-diversity analysis, taxonomic composition, statistical comparisons, and calculation of correlations. Rhea is primarily a straightforward starting point for beginners, but can also be used as a framework for advanced users who can modify and expand the tool. Rhea is composed of 6 steps that can be run independently or as a set. Normalization Alpha-Diversity Beta-Diversity Taxonomic-Binning Serial-Group-Comparisons Correlations

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.

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.

Following the FAIR principles and best practices, the LifeWatch ERIC Training Catalogue hosts the metadata of relevant learning resources so that these can be shared, searched, discovered, accessed and reused. The LifeWatch ERIC training catalogue’s accurate and descriptive metadata allow all users to find the most appropriate and well-suited educational resources for their needs. Metadata are based on a subset of the IEEE Standard for Learning Object Metadata (IEEE 2002) that has been customised in order to be compliant with the EOSC Training Resource Profile - Data Model. The detail page of each single metadata record includes all the descriptive information and, on the right side of the page, a button “Start the course” that allows to access the resource and hence to start the training.