Seasonality in Human Interest in Berry Plants Detection by Google Trends

Abstract

The phenology of berry-producing plants, particularly their harvest season, is of human interest and also reflects the ecosystem’s response to the changing environment. We investigated the seasonal dynamics of human interest in berries growing in boreal, subarctic and Arctic ecosystems, mainly in Russia, based on internet search data via Google Trends. There is a typical and culture-specific pattern of seasonal variations in search volume concerning berries across Russia, Finland, and Canada. Generally, the seasonal peak of search corresponds to the common berry harvest season across these countries. We discussed the potential and limitation for detecting ecological factors from the internet search data, in which physical phenomena and socio-cultural aspects are fundamentally superimposed, and its applicability to phenological studies.

Monitoring of cherry flowering phenology with Google Trends

“…RSV has a temporal resolution of 1 minute, 1 hour, 1 day, 1 week, or 1 month. In a given period, RSV = 100 when the number of target queries reaches the maximum and 0 when it is less than a certain threshold [ 11 , 22 ]. The target region can be specified; in Japan, the type of region with the highest spatial resolution is the prefecture.…”

“…We acquired RSV by “Topics” in all of Japan and by prefecture from 1 March to 30 April 2004 to 2021 ( Table 1 ). We did not acquire RSV by “Terms” because the time-series of RSV returned includes more noise than that by “Topics” [ 22 ]. In this case, the temporal resolution is daily, and the total number when RSV = 100 is 18.…”

“…The utility of GT has been shown in many studies, such as in the prediction of infection by COVID-19 [ 12 ], societal effects due to infection by COVID-19 [ 13 – 16 ], allergic rhinitis induced by Japanese cedar pollen [ 17 ], research and public interest in melanoma [ 18 ], global and country-specific interest in obesity, smoking, and alcoholism [ 19 ], societal concerns about pesticides [ 20 ], and Japanese citizens’ interest in insects [ 21 ]. However, there are few previous studies of plant phenology using this tool [ 22 ]. Despite lacking validation of ripening by using detailed in situ data, Kotani et al [ 22 ] reported the utility of GT for the spatiotemporal variability of people’s interest in berries in Finland, Russia, and Canada.…”

“…However, there are few previous studies of plant phenology using this tool [ 22 ]. Despite lacking validation of ripening by using detailed in situ data, Kotani et al [ 22 ] reported the utility of GT for the spatiotemporal variability of people’s interest in berries in Finland, Russia, and Canada. This fact suggests that time-series of RSV could be used as a proxy for cherry flowering phenology and its year-to-year variability.…”

Data mining by watching old documentary TV programs to learn about the relationships between people’s lives and the landscape in Sakha in the perestroika era at the end of the 1980s

“…For example, they highlighted subsistence topics such as reindeer grazing by the Chukchi and hunting by the Yukaghir. However, they did not focus on other subsistence issues, such as horse grazing (unique to the Yakutian people; Takakura, 2010Takakura, , 2016Nakada and Grigorev, 2019), fishing (Ksenofontov et al, 2017;Ksenofontov et al, 2018), and forest use (Fujioka et al, 2020;Kotani et al, 2021;Shin et al, 2022). These subsistence topics may not have been suitable or desirable as subjects of a TV program highlighting life in this part of Siberia in the 1980s.…”