Revealing biases in insect observations: A comparative analysis between academic and citizen science data

Citizen Science is a powerful tool for biodiversity research, as it facilitates data recording at large scales that would otherwise be impossible to cover by standard academic research. Despite its benefits, the accuracy of citizen science data remains a subject of concern among scientists, with var...

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Veröffentlicht in:PloS one 2024-07, Vol.19 (7), p.e0305757
Hauptverfasser: Díaz-Calafat, Joan, Jaume-Ramis, Sebastià, Soacha, Karen, Álvarez, Ana, Piera, Jaume
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Jaume-Ramis, Sebastià
Soacha, Karen
Álvarez, Ana
Piera, Jaume
description Citizen Science is a powerful tool for biodiversity research, as it facilitates data recording at large scales that would otherwise be impossible to cover by standard academic research. Despite its benefits, the accuracy of citizen science data remains a subject of concern among scientists, with varying results reported so far. Neither citizen science data nor academic records are immune to biases, which can significantly impact the quality and reliability of observations. Here, using insects in the Iberian Peninsula as a case study, we compare data collected by participatory platforms to those obtained through academic research projects, and assess their taxonomic, spatial, temporal, and environmental biases. Results show a prominent taxonomic bias in both academic and citizen science data, with certain insect orders receiving more attention than others. These taxonomic biases are conserved between different participatory platforms, as well as between groups of users with different levels of contribution performance. The biases captured by leading contributors in participatory platforms mirrored those of sporadic users and academic data. Citizen science data had higher spatial coverage and less spatial clustering than academic data, showing also clearer trends in temporal seasonality. Environmental coverage over time was more stable in citizen science than in academic records. User behaviour, preference, taxonomical expertise, data collection methodologies and external factors may contribute to these biases. This study shows the multifaceted nature of biases present in academic records and citizen science platforms. The insights gained from this analysis emphasize the need for careful consideration of these biases when making use of biodiversity data from different sources. Combining academic and citizen science data enhances our understanding of biodiversity, as their integration offers a more comprehensive perspective than relying solely on either dataset alone, especially since biases in these two types of data are not always the same.
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Citizen science data had higher spatial coverage and less spatial clustering than academic data, showing also clearer trends in temporal seasonality. Environmental coverage over time was more stable in citizen science than in academic records. User behaviour, preference, taxonomical expertise, data collection methodologies and external factors may contribute to these biases. This study shows the multifaceted nature of biases present in academic records and citizen science platforms. The insights gained from this analysis emphasize the need for careful consideration of these biases when making use of biodiversity data from different sources. Combining academic and citizen science data enhances our understanding of biodiversity, as their integration offers a more comprehensive perspective than relying solely on either dataset alone, especially since biases in these two types of data are not always the same.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>39024343</pmid><doi>10.1371/journal.pone.0305757</doi><tpages>e0305757</tpages><orcidid>https://orcid.org/0000-0002-5823-2176</orcidid><orcidid>https://orcid.org/0000-0002-6857-2553</orcidid><oa>free_for_read</oa></addata></record>
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subjects Animals
Archives & records
Bias
Biodiversity
Biological diversity
Biology and Life Sciences
Case studies
Citizen Science
Citizen scientists
Clustering
Comparative analysis
Computer and Information Sciences
Data analysis
Data collection
Data recording
Datasets
Ecology
Ecology and Environmental Sciences
Ekologi
Filosofi
Humans
Insecta - classification
Insects
Metadata
Philosophy
Platforms
Research projects
Science
Science Policy
Scientists
Seasonal variations
Social Sciences
Spain
Spatial data
Taxonomy
title Revealing biases in insect observations: A comparative analysis between academic and citizen science data
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