The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Estimating species diversity and distribution in the era of Big Data: to what extent can we trust public databases?
Open accessAbstract
Aim: Massive digitalization of natural history collections is now leading to a steep accumulation of publicly available species distribution data. However, taxonomic errors and geographical uncertainty of species occurrence records are now acknowledged by the scientific community – putting into question to what extent such data can be used to unveil correct patterns of biodiversity and distribution. We explore this question through quantitative and qualitative analyses of uncleaned versus manually verified datasets of species distribution records across different spatial scales. Location: The A merican tropics. Methods: As test case we used the plant tribe C inchoneae (R ubiaceae). We compiled four datasets of species occurrences: one created manually and verified through classical taxonomic work, and the rest derived from GBIF under different cleaning and filling schemes. We used new bioinformatic tools to code species into grids, ecoregions, and biomes following WWF's classification. We analysed species richness and altitudinal ranges of the species. Results: Altitudinal ranges for species and genera were correctly inferred even without manual data cleaning and filling. However, erroneous records affected spatial patterns of species richness. They led to an overestimation of species richness in certain areas outside the centres of diversity in the clade. The location of many of these areas comprised the geographical midpoint of countries and political subdivisions, assigned long after the specimens had been collected. Main conclusion: Open databases and integrative bioinformatic tools allow a rapid approximation of large‐scale patterns of biodiversity across space and altitudinal ranges. We found that geographic inaccuracy affects diversity patterns more than taxonomic uncertainties, often leading to false positives, i.e. overestimating species richness in relatively species poor regions. Public databases for species distribution are valuable and should be more explored, but under scrutiny and validation by taxonomic experts. We suggest that database managers implement easy ways of community feedback on data quality.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Terrestrial ecoregions of the world: a new map of life on Earth
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., et al. (2001) · BioScience · doi:10.1641/0006-3568(2001)051[0933:teotwa]2.0.co;2 · Cited by 8,488
How Many Species Are There on Earth and in the Ocean?
Mora, C., Tittensor, D. P., Adl, S., Simpson, A. G. B., & Worm, B. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001127 · Cited by 3,045 · cited 2×
“…able to correctly identify species of many understudied organism groups, and that there are millions of species yet to be described it would not be feasible to require a peer-review of the accuracy of identifications before making occurrence data publicly available (Dov, 2007; Mora et al., 2011).”
Background(2010) · Cited by 1,762
Olson, D. M., & Dinerstein, E. (1998) · Conservation Biology · doi:10.1046/j.1523-1739.1998.012003502.x · Cited by 1,573
Mapping Species Distributions: Spatial Inference and Prediction
Franklin, J. (2010) · doi:10.1017/cbo9780511810602 · Cited by 1,534
New developments in museum-based informatics and applications in biodiversity analysis
GRAHAM, C., FERRIER, S., HUETTMAN, F., MORITZ, C., & PETERSON, A. (2004) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2004.07.006 · Cited by 1,115
Spatial bias in the GBIF database and its effect on modeling species' geographic distributions
Beck, J., Böller, M., Erhardt, A., & Schwanghart, W. (2014) · Ecological Informatics · doi:10.1016/j.ecoinf.2013.11.002 · Cited by 739
Tracing the impact of the Andean uplift on Neotropical plant evolution
Antonelli, A., Nylander, J. A. A., Persson, C., & Sanmartín, I. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0811421106 · Cited by 652
Self-citationGlobal patterns of plant diversity and floristic knowledge
Kier, G., Mutke, J., Dinerstein, E., Ricketts, T. H., Küper, W., Kreft, H., & Barthlott, W. (2005) · Journal of Biogeography · doi:10.1111/j.1365-2699.2005.01272.x · Cited by 623
Cited by
Data integration enables global biodiversity synthesis
Heberling, J. M., Miller, J. T., Noesgaard, D., Weingart, S. B., & Schigel, D. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2018093118 · Cited by 380
Amazonian tree species threatened by deforestation and climate change
Gomes, V. H. F., Vieira, I. C. G., Salomão, R. P., & ter Steege, H. (2019) · Nature Climate Change · doi:10.1038/s41558-019-0500-2 · Cited by 209
The global distribution of known and undiscovered ant biodiversity
Kass, J. M., Guénard, B., Dudley, K. L., Jenkins, C. N., Azuma, F., Fisher, B. L., Parr, C. L., Gibb, H., Longino, J. T., Ward, P. S., et al. (2022) · Science Advances · doi:10.1126/sciadv.abp9908 · Cited by 194
To increase trust, change the social design behind aggregated biodiversity data
Franz, N. M., & Sterner, B. W. (2018) · Database · doi:10.1093/database/bax100 · Cited by 57
Metadata
- Authors
- Maldonado, C., Molina, C. I., Zizka, A., Persson, C., Taylor, C. M., Albán, J., Chilquillo, E., Rønsted, N., & Antonelli, A.
- Venue
- Global Ecology and Biogeography
- Publisher
- Wiley
- Published
- 2015-05-25
- Volume
- 24
- Issue
- 8
- Pages
- 973-984
- ISSN
- 1466-822X (ISSN-L)
- 1466-8238
- OpenAlex
- W2111628353
- Cited by
- 371
- References
- 38
- FWCI
- 16.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Estimating species diversity and distribution in the era of Big Data: to what extent can we trust public databases? https://app.cochat.ai/papers/10.1111/geb.12326
- The paper
- Maldonado, C., Molina, C. I., Zizka, A., Persson, C., Taylor, C. M., Albán, J., Chilquillo, E., Rønsted, N., & Antonelli, A. (2015). Estimating species diversity and distribution in the era of Big Data: to what extent can we trust public databases? Global Ecology and Biogeography. https://doi.org/10.1111/geb.12326
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.