Bio-ORACLE offers essential physical, chemical, biological and topographic data layers with global extent and uniform resolution for modelling the distribution of marine biodiversity. The layers can be used in Species Distribution Modelling to predict the distribution of biodiversity, address niche-based questions, unravel biogeographic patterns, and support the conservation of global marine biodiversity at the global. The availability of data under the Shared Socioeconomic Pathway scenarios of CMIP6 allows projecting the implications of future change to marine biodiversity, contributing to inform climate policy. Usage notes Bio-ORACLE provides 26 physical, chemical, biological and topographic marine data layers, with global coverage, uniform grid system, at a spatial resolution of 0.05 degrees, and a temporal resolution of 10 decadal steps, from 2000 to 2100. 19 essential physical, chemical and biological variables, presented as 6 statistics* for surface and benthic** conditions, under present-day conditions and the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-6.0 and SSP5-8.5 scenarios; 7 topographic layers (including bathymetric and rugosity profiles of the seabed). * the average, maximum and minimum records of a given decade, long-term average of the yearly maxima and minima of a given decade (e.g., the average temperature of the warmest month in the period 2000-2010), and range, which represents the average absolute difference between the maximum and minimum records per year. ** because focal cells at 0.05 degree resolution comprise a wide range of depth values, the benthic layers were developed for the minimum, average and maximum depth within focal cells. All layers are archived as NetCDF (network Common Data Form) and deposited into an ERDDAP server to facilitate filtering and downloading of the layers in common data formats. Additionally, Python (pyo_oracle) and R (biooracler) packages were developed for facilitated data retrieval and improved integration in available frameworks of bioclimatic modelling. Such packages act as clients for ERDDAP’s REST API, an interoperable web protocol for data transfer, and thus can be used for integration into most generic web-based applications.

NICHE Vlaanderen (Nature Impact Assessment of Changes in Hydro-Ecological Systems) is a hydro-ecological model that predicts the potential occurrence of (ground)water-dependent vegetation types in an area based on information about the (abiotic) site conditions. NICHE Vlaanderen can be used to evaluate the impact of changes in the water management to groundwater-dependent vegetation. The model is based on a number of location factors that are important for the vegetation: soil type, hydrology, nutrient availability and acidity. Based on calculated abiotic properties of the location NICHE Vlaanderen determines whether certain vegetation types can develop. An additional flooding module allows the user to test whether the predicted vegetations are compatible with a particular flooding regime. This project is a redevelopment of an existing ArcGIS plugin in Python, without external non-open source dependencies.

The goal of MarineSPEED is to provide a benchmark data set for presence-only species distribution modeling (SDM) in order to facilitate reproducible and comparable SDM research. It contains species occurrences (coordinates) from a wide diversity of marine species and associated environmental data from Bio-ORACLE and MARSPEC. Some additional information about MarineSPEED can be found in the R Shiny viewer at https://rshiny.lifewatch.be/marinespeed/.

The goal of sdmpredictors is to make environmental data, commonly used for species distribution modelling (SDM), also called ecological niche modelling (ENM) or habitat suitability modelling, easy to use in R. sdmpredictors gives access to several environmental datasets, including the the Bio-ORACLE Marine Data Layers for Bioclimatic Modelling. LifeWatch hosts the website https://www.bio-oracle.org/ and supports the development of these layers. The package contains methods for getting downloading raster data for the current climate but also for future and paleo climatic conditions. These rasters and then loaded into R.