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Comparing biodiversity databases: Greater Caribbean reef fishes as a case study
Chollett, I.; Robertson, D.R. (2020). Comparing biodiversity databases: Greater Caribbean reef fishes as a case study. Fish Fish. 21(6): 1195-1212. https://dx.doi.org/10.1111/faf.12497
In: Fish and Fisheries. Blackwell Science: Oxford. ISSN 1467-2960; e-ISSN 1467-2979
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| Trefwoorden |
Completeness
Geography > Biogeography
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| Author keywords |
beta diversity, data quality, global aggregators, spatial biases |
| Auteurs | | Top |
- Chollett, I.
- Robertson, D.R.
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| Abstract |
There is a widespread need for reliable biodiversity databases for science and conservation. Among the many public databases available, we lack guidance as to how their data quality varies. Here, we compare species distribution data for a well known regional reef fish fauna extracted from five global online databases that supply “as is” data (GBIF, OBIS, IDigBio, FishNet2 and FishBase) and our own curated regional database (STRI, Smithsonian Tropical Research Institute) using quantitative criteria, and assess how they affect biogeographical analyses. We first describe the databases and quantify overlap between them. We then describe variation in the geographical distributions of species records and species richness and in the completeness of local species lists. Finally, we assess the consequences of using these different databases in biogeographical analyses by comparing patterns of species turnover (beta diversity) and bioregionalization. The databases vary considerably in size and show high overlap in species lists, but low overlap in georeferenced species records. Levels of completeness of local inventories are spatially heterogeneous and low in most databases. Spatial biases produced artefactual variation in patterns of species turnover and delineation of bioregions in all databases. Although not the largest, STRI database has the most complete geographic coverage of data, showed relatively low turnover and the clearest biogeographic regionality. Incorporating data from a wide range of other sources, curating data to reduce errors, and assessing effects of spatial biases in data is critical to obtaining an accurate picture of the geography of biodiversity and its change. |
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