Different bee-flower survey methods impact conservation recommendations: Comparing citizen science and academic surveys
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F25%3A10506616" target="_blank" >RIV/00216208:11310/25:10506616 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=SxV4ncwHev" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=SxV4ncwHev</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.26786/1920-7603(2025)834" target="_blank" >10.26786/1920-7603(2025)834</a>
Alternative languages
Result language
angličtina
Original language name
Different bee-flower survey methods impact conservation recommendations: Comparing citizen science and academic surveys
Original language description
Promoting diverse and abundant flowering plants in cities is essential to counteract the decline in wild bee diversity due to urbanisation. To support effective conservation, it is crucial to identify which flowers best enhance wild bee abundance and species richness. This requires large datasets and robust analytical tools. Here, we use a plant selection tool developed by M'Gonigle et al. (2016), which recommends flower mixes that maximise pollinator species richness based on visitation data. We analysed bee-flower interaction data from the Brussels Capital Region (Belgium), comparing two contrasting sources: (1) citizen science records and (2) standardised academic surveys. We evaluated the bipartite networks of these datasets and their combination and generated optimised flower mixes from each using the plant selection tool. Our results show that dataset composition and inherent biases strongly influence outcomes. The bipartite networks differed substantially (compositional difference = 0.86), mainly due to rewiring of bee-flower interactions (0.69). Consequently, the flower mixes derived from each dataset overlapped by only 7% when optimising for species richness. The combined dataset network more closely resembled the citizen science data (WN = 0.106) than the academic survey data (WN = 0.590). These findings highlight the substantial impact of data collection methods on ecological recommendations. Awareness of such biases is essential for making sound, evidence-based conservation decisions. To support wider application, we developed a free app that allows users to create flower mixes optimized for pollinator abundance, species richness, or both, using our dataset or their own.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10613 - Zoology
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Pollination Ecology
ISSN
1920-7603
e-ISSN
1920-7603
Volume of the periodical
38
Issue of the periodical within the volume
12
Country of publishing house
CA - CANADA
Number of pages
12
Pages from-to
171-182
UT code for WoS article
001627850300012
EID of the result in the Scopus database
2-s2.0-105010039614