This dataset was intially aimed for publication on GBIF (see details below), but we have now restricted it to a 'metadata' entry, and the corresponding ecosystem dataset is published on Zenodo: https://doi.org/10.5281/zenodo.7812549. It compiles data gathered on ecosystem-types and their distribution based on a series of field studies led by the author, in Seychelles and West and Central Africa (Senterre 2014, Senterre & Wagner 2014, Senterre 2016, Senterre et al. 2017, 2019, 2020, 2021a, 2022). The aims of this dataset are: 1. To share in an explicit and transparent way data on proposed taxonomies of ecosystems, i.e. conceptualizations of ecosystem-types, including explicit ecosystem names and management of synonymies. 2. To develop ecosystem red listing based on transparent and falsifiable distribution raw data, combining distribution modeling (maps) and in situ observation of individual stand occurrences. 3. To illustrate in detail how to deal with ecosystem data following the approach described in Senterre et al. (2021b) (i.e. "ecosystemology" approach). Although GBIF is currently not able to cater appropriately for ecosystem data and is designed in a species-centric view, GBIF is the largest repository of biodiversity data in the world and therefore it is relevant to at least explore the possibility of addressing that gap. In addition, as we will show here, we suggest that only a few additions and adjustments to the current GBIF structure would be required to integrate the treatment of ecosystem data in a standardized way, following the "ecosystemology" approach (ecosystem taxonomy) proposed by Senterre et al. 2021b (http://dx.doi.org/10.1016/j.ecocom.2021.100945). In the ‘sampling method’ section of these metadata, we present in detail the suggested needs for adjustments and additions in the GBIF structure, and we explain our short term strategy to publish an existing ecosystemology dataset using the current GBIF structure, by squeezing information within available and suitable fields of GBIF (mostly free text fields that are related to the ecosystem or habitat). Several fields are thus stored within a GBIF field by using the pipe separator (|). We then developed a series of R scripts that take the ecosystem data squeezed into the GBIF fields and that restore the tables needed to do an ecosystem taxonomy treatment (by splitting columns at the pipe separators). Finally, we compile ecosystem checklists, taxonomies and occurrence data into an R shiny application. In addition, we integrate the use of Google Earth Engine (EE) and we develop the method to integrate these with the GBIF dataset toward the production of complete distribution maps and their use in Red Listing of Ecosystems (RLE). The R scripts developed are available here: https://github.com/bsenterre/ecosystemology The corresponding shiny app is available here: https://shiny.bio.gov.sc/bioeco/ (earlier version : https://bsenterre.shinyapps.io/ecosystemology/)