KBAscope: key biodiversity area identification in R

Key Biodiversity Areas (KBAs) represent the largest global network of sites critical to the persistence of biodiversity, which have been identified against standardised quantitative criteria. Sites that hold very high biodiversity value or potential are given specific attention on site-based conserv...

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Veröffentlicht in:Ecography (Copenhagen) 2024-09, Vol.2024 (9), p.n/a
Hauptverfasser: Spiliopoulou, Konstantina, Rigal, François, Plumptre, Andrew J., Trigas, Panayiotis, Paragamian, Kaloust, Hochkirch, Axel, Lymberakis, Petros, Portolou, Danae, Stoumboudi, Maria, Triantis, Kostas
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Sprache:eng
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Zusammenfassung:Key Biodiversity Areas (KBAs) represent the largest global network of sites critical to the persistence of biodiversity, which have been identified against standardised quantitative criteria. Sites that hold very high biodiversity value or potential are given specific attention on site-based conservation targets of the Kunming-Montreal Global Biodiversity Framework (GBF), and KBAs are already used in indicators for the GBF and the Sustainable Development Goals. However, most of the species that trigger KBA status are birds and to maximise benefits for biodiversity under the actions taken to fulfil the GBF, countries need to update their KBAs to represent important sites across multiple taxa. Here we introduce KBAscope, an R package to identify potential KBAs using multiple taxonomic groups. KBAscope provides flexible, user-friendly functions to edit species data (population, range maps, area of occupancy, area of habitat and localities); apply KBA criteria; and generate outputs to support the delineation and validation of KBAs. The details of the analysis – such as the spatial units tested or the KBA criteria applied – can be decided according to the scope of the analysis. We demonstrate the functionality of KBAscope by using it to identify potential KBAs in Greece based on multiple terrestrial taxonomic groups and four sizes of grid cells (4 km2, 25 km2, 100 km2, 225 km2).
ISSN:0906-7590
1600-0587
DOI:10.1111/ecog.07061