Unravelling Bias: A Sardinian perspective on taxonomic, spatial, and temporal biases in vascular plant biodiversity data from GBIF

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F25%3A103611" target="_blank" >RIV/60460709:41330/25:103611 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.ecoinf.2025.103289" target="_blank" >https://doi.org/10.1016/j.ecoinf.2025.103289</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ecoinf.2025.103289" target="_blank" >10.1016/j.ecoinf.2025.103289</a>

Result language

angličtina

Original language name

Unravelling Bias: A Sardinian perspective on taxonomic, spatial, and temporal biases in vascular plant biodiversity data from GBIF

Original language description

Biodiversity data are expanding rapidly, yet often exhibit significant biases that are rarely mapped or systematically analyzed to understand underlying drivers. This issue is particularly pressing in the era of citizen science, which now contributes a substantial share of biodiversity records. In this study, we assessed taxonomic, temporal, and spatial biases in vascular plant occurrence records from Sardinia, a Mediterranean biodiversity hotspot, using all available occurrence data for the region retrieved from the Global Biodiversity Information Facility (GBIF). The dataset encompasses a range of sources, from structured inventories to citizen science platforms. Biases were quantified using metrics such as species richness completeness, Pielou's evenness, and the Nearest Neighbor Index (NNI). After mapping these biases, we used Generalized Additive Models (GAMs) to explore their environmental drivers, including road density, the standard deviation of the Normalized Difference Vegetation Index (NDVI), and topographic roughness. Additionally, we evaluated the influence of structured data sources (e.g., Wikiplantbase) versus citizen science platforms (e.g., PlantNet and iNaturalist) on observed bias patterns. Spatial bias was the most prominent, followed by temporal and taxonomic biases. Road density and NDVI influenced both temporal and taxonomic biases, while topographic roughness affected temporal and spatial biases. Structured data mainly contributed to temporal bias, whereas citizen science data were more associated with spatial bias. Our findings highlight the importance of addressing biases in biodiversity data, particularly those introduced by citizen science, and provide a replicable framework for improving data quality and biodiversity monitoring at both sampling and interpretation stage.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10511 - Environmental sciences (social aspects to be 5.7)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Ecological Informatics

ISSN

1574-9541

e-ISSN

1574-9541

Volume of the periodical

90

Issue of the periodical within the volume

DEC 2025

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

11

Pages from-to

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UT code for WoS article

001538993300002

EID of the result in the Scopus database

2-s2.0-105009615121