Data Quality of National Monitoring Schemes: Filling the Gap between Specialists and the General Public

Worldwide, large-scale biodiversity monitoring schemes are developing and involve many non-specialist volunteers. If the opening of schemes to non-specialists allows for the gathering of huge amounts of data, their quality represents a controversial issue. In the framework of the French Garden Butterfly Observatory (FGBO), we studied non-specialist volunteer identification errors based on identifications provided during a one-shot experiment. With 3492 butterfly pictures sent by 554 non-specialist volunteers, we directly measured identification errors and misidentification rates for each butterfly species or species group targeted by the FGBO. The results showed that when non-specialist volunteers identified butterflies at the species level, identification errors (i.e., the misidentification rate) reached 20.9%. It was only 5.0% when FGBO species groups were used. This study provides novel insights into the trade-off between data quantity and quality provided by non-specialist volunteers and shows that if protocols, research questions and identification levels are adapted, participatory monitoring schemes relying on non-specialists represent a powerful and reliable tool to study common species at a large scale and on a long-term basis.

Authors

Institutions

Publication Details

Journal
Land
Published
2024-08-09
DOI
https://doi.org/10.3390/land13081252
Citations
5
Primary Topic
Plant and animal studies
Type
article
Field-Weighted Citation Impact
5.15
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Data Quality of National Monitoring Schemes: Filling the Gap between Specialists and the General Public

Benjamin Bergerot, Benoît Fontaine
5 citations
Land
Plant and animal studies
5.15
article

Data Quality of National Monitoring Schemes: Filling the Gap between Specialists and the General Public

Benjamin Bergerot, Benoît Fontaine
article en
5 citations

Abstract

Worldwide, large-scale biodiversity monitoring schemes are developing and involve many non-specialist volunteers. If the opening of schemes to non-specialists allows for the gathering of huge amounts of data, their quality represents a controversial issue. In the framework of the French Garden Butterfly Observatory (FGBO), we studied non-specialist volunteer identification errors based on identifications provided during a one-shot experiment. With 3492 butterfly pictures sent by 554 non-specialist volunteers, we directly measured identification errors and misidentification rates for each butterfly species or species group targeted by the FGBO. The results showed that when non-specialist volunteers identified butterflies at the species level, identification errors (i.e., the misidentification rate) reached 20.9%. It was only 5.0% when FGBO species groups were used. This study provides novel insights into the trade-off between data quantity and quality provided by non-specialist volunteers and shows that if protocols, research questions and identification levels are adapted, participatory monitoring schemes relying on non-specialists represent a powerful and reliable tool to study common species at a large scale and on a long-term basis.

LandVol. 13(8)
Centre National de la Recherche Scientifique (FR), Ecosystèmes, Biodiversité, Evolution (FR), Centre d'Écologie et des Sciences de la Conservation (FR), Institut de Recherche pour le Développement (FR), Structure et Instabilité des Génomes (FR), Université de Rennes (FR)
Life in Land
Openalex Percentile: Top 5%
Plant and animal studies
5.15
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.