Corresponding author: Miguel Alvarez ( malvarez@uni-bonn.de ) Academic editor: Florian Jansen
© 2021 Miguel Alvarez, Michael Curran, Itambo Malombe.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Alvarez M, Curran M, Malombe I (2021) SWEA-Dataveg: A vegetation database for sub-Saharan Africa. Vegetation Classification and Survey 2: 59-63. https://doi.org/10.3897/VCS/2021/64911
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SWEA-Dataveg is a vegetation-plot database collecting observations mainly in sub-Saharan Africa but also open to the rest of the African continent. To date this database contains more than 5,500 plot observations provided by 47 sources (projects, monographs, and articles). While the database is stored in PostgreSQL (including the PostGIS extension), the R-package “vegtable” implements a suitable exchange format. In this article we assess the current content of SWEA-Database and introduce its history and future as a repository of data for syntaxonomic assessments and macroecological research.
ecoinformatics, plant biodiversity, taxlist, syntaxonomy, vegetation ecology, vegtable
In sub-Saharan Africa as elsewhere, documenting and classifying vegetation has become an urgent task to enable the proper assessment of endangered ecosystems (
The database SWEA-Dataveg (
This report briefly displays the current status of the vegetation-plot database SWEA-Dataveg (GIVD AF-00-006) and its applications in the research of vegetation ecology and biogeography in sub-Saharan Africa.
The idea of establishing a vegetation-plot database started during a visit to the 8th Meeting on Vegetation Databases, held at the University of Greifswald, Germany, in 2009. The project was officially launched in 2010 and the first report was published in 2012 with a small collection of 206 plots originally stored in a Microsoft-Access database (
In 2015, and in the context of a collaborative activity between the SWEA-Project and the ETH-Zürich, Switzerland, SWEA-Dataveg migrated to the software Turboveg (
After the first releases of the packages “taxlist” and “vegtable” at CRAN in 2017 (see
Currently, the database contains 5,552 plot observations (relevés) collected from 47 sources, including projects, journal articles and monographs. These observations contain records of 3,530 plant species belonging to 1,318 genera and 216 families. The dominant families are Leguminosae (402 species; 10.4%), Poaceae (393 species; 10.2%), Compositae (290 species; 7.5%), and Cyperaceae (212 species; 5.5%).
According to record date and year of publication, the oldest observations are from 1937 (
The current version of SWEA-Dataveg is stored in a PostgreSQL database, including the PostGIS extension for geo-referenced information. Plot observations are organized in a table called “header” and linked to several tables analogous to the popup tables of Turboveg (
All plots included in the database are geo-referenced. A logical variable called “validation_coordinates” indicates whether these coordinates were provided by the authors as coordinate values or in a detailed map (“true”), or if they are inferred from the description of locality (“false”). Observations have been undertaken in 12 countries with 2,804 plots (51%) sampled in Kenya, 986 (18%) in the Democratic Republic of the Congo, 467 (8%) in Ethiopia, and 425 (8%) in Tanzania. The rest of the plots were collected in Uganda, Togo, Rwanda, South Africa, Burundi, Congo-Brazzaville, Benin, and Zambia (see Figure
SWEA-Dataveg attempts to collect as much of the information originally published with plot observations as possible. Besides information on plot size, recording dates and locations (coordinates and descriptions of localities), additional data on slope inclination, exposition, elevation, total vegetation cover, soil physical and chemical properties and remarks, if provided by the sources, are digitized and stored. From all observations, 79% are stored with a sampling date, 64% with coordinates, 58% with information on plot size, and 21% with information on soil physical or chemical properties (Figure
The associated taxonomic list is supported by five sources referred to as taxon views (see
All data sources are supported by a private soft copy of the relevant published article to enable cross-validation of fidelity of data stored in the database. Digitization procedures strive to resemble the data published in the original source.
Projects attempting to derive critical assessments of classifications in the context of the Braun-Blanquet approach (e.g.
Besides all of these features, the development of the R-packages “taxlist” and “vegtable” (
At present, data is accessible only after special agreements with the custodian. While data stored from ongoing projects are highly restricted at least during the life-span of the respective projects, we expect to be able to make data freely available from already published works. The preferred format for exchange is an R-Image including a vegtable object (
From its origins, SWEA-Dataveg focused on a preliminary classification of wetland vegetation in East Africa (
In the specific case of Kenya, a model describing plant biodiversity and spatial conservation prioritization was performed for the Kenyan subset and included a pool of bioclimatic, macroecological and economic factors as explanatory variables (
SWEA-Dataveg also supported the design of ecological assessment and monitoring methods, such as an adaptation of the WET-Health approach by
Ongoing projects are dealing with distribution models of invasive species in Eastern Africa, in particular on Prosopis juliflora (Sw.) DC. (
In addition to inclusion in the Global Index of Vegetation-Plot Databases (
The implementation of a multiple-taxon views approach, for instance considering discrepancies among different projects involved in the African Plant Database (https://www.ville-ge.ch/musinfo/bd/cjb/africa/recherche.php) and some regional floras (e.g. Flora of Tropical East Africa,
We also seek to integrate an electronic document library, which is at present housed in a separated database formatted as a BibTeX file and linked to respective data sources as well as taxonomic and syntaxonomic authorities.
The database is currently maintained in the context of the project “Future Invasions” within the Collaborative Research Centre “Future Rural Africa” (http://www.futureruralafrica.de/). We thank Mrs. Emilia Lösche for her support accessing the valuable collections of the Library of the Geographical Department at the University of Bonn in Germany. Several students have supported the work of digitizing data and testing assessments by the developed R-packages, to whom we are very thankful.