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Monitoring of cherry flowering phenology with Google Trends

Google Trends (GT) is an online tool designed for searching for changes over time. We assessed its use for evaluating changes in the timing of cherry flowering phenology, which is of intense interest to Japanese people. We examined the relationship between time-series of relative search volume (RSV:...

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Detalles Bibliográficos
Autores principales: Shin, Nagai, Kotani, Ayumi, Tei, Shunsuke, Tsutsumida, Narumasa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302780/
https://www.ncbi.nlm.nih.gov/pubmed/35862347
http://dx.doi.org/10.1371/journal.pone.0271648
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author Shin, Nagai
Kotani, Ayumi
Tei, Shunsuke
Tsutsumida, Narumasa
author_facet Shin, Nagai
Kotani, Ayumi
Tei, Shunsuke
Tsutsumida, Narumasa
author_sort Shin, Nagai
collection PubMed
description Google Trends (GT) is an online tool designed for searching for changes over time. We assessed its use for evaluating changes in the timing of cherry flowering phenology, which is of intense interest to Japanese people. We examined the relationship between time-series of relative search volume (RSV: relative change in search requests over time obtained from the GT access engine) and cherry flowering information published on websites (as ground truth) in relation to three famous ancient cherry trees. The time-series of RSV showed an annual bell-shaped seasonal variability, and the dates of the maximum RSV tended to correspond to the dates of full bloom. Our results suggest that GT allows monitoring of multiple famous cherry flowering sites where we cannot obtain long-term flowering data to evaluate the spatiotemporal variability of cherry flowering phenology.
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spelling pubmed-93027802022-07-22 Monitoring of cherry flowering phenology with Google Trends Shin, Nagai Kotani, Ayumi Tei, Shunsuke Tsutsumida, Narumasa PLoS One Research Article Google Trends (GT) is an online tool designed for searching for changes over time. We assessed its use for evaluating changes in the timing of cherry flowering phenology, which is of intense interest to Japanese people. We examined the relationship between time-series of relative search volume (RSV: relative change in search requests over time obtained from the GT access engine) and cherry flowering information published on websites (as ground truth) in relation to three famous ancient cherry trees. The time-series of RSV showed an annual bell-shaped seasonal variability, and the dates of the maximum RSV tended to correspond to the dates of full bloom. Our results suggest that GT allows monitoring of multiple famous cherry flowering sites where we cannot obtain long-term flowering data to evaluate the spatiotemporal variability of cherry flowering phenology. Public Library of Science 2022-07-21 /pmc/articles/PMC9302780/ /pubmed/35862347 http://dx.doi.org/10.1371/journal.pone.0271648 Text en © 2022 Shin et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Shin, Nagai
Kotani, Ayumi
Tei, Shunsuke
Tsutsumida, Narumasa
Monitoring of cherry flowering phenology with Google Trends
title Monitoring of cherry flowering phenology with Google Trends
title_full Monitoring of cherry flowering phenology with Google Trends
title_fullStr Monitoring of cherry flowering phenology with Google Trends
title_full_unstemmed Monitoring of cherry flowering phenology with Google Trends
title_short Monitoring of cherry flowering phenology with Google Trends
title_sort monitoring of cherry flowering phenology with google trends
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302780/
https://www.ncbi.nlm.nih.gov/pubmed/35862347
http://dx.doi.org/10.1371/journal.pone.0271648
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