Rescuing Botany: using citizen-science and mobile apps in the classroom and beyond

Chozas, Sergio; Nunes, Alice; Serrano, Helena Cristina; Ascensão, Fernando; Tapia, Susana; Máguas, Cristina; Branquinho, Cristina

doi:10.1038/s44185-023-00011-9

Cited by 4 publications

(3 citation statements)

References 39 publications

(29 reference statements)

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“…Although the species in our data are not subject to these fluctuations, it is likely that observers going out to observe more attractive species during these months will also record other species. Organised special events, often held in the spring, such as Fascination of Plants Day (https://plantday18may.org/), the Spanish Flora Biomarathon, the Portuguese Flora Bioblitz, Invasive species week, BioBlitzes, may also influence recording activity (Márquez-Corro, 2021;Chozas et al, 2023;Tiago et al, 2024a).…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, citizen science initiatives play a significant role in ecological research, conservation planning, and policymaking by providing large-scale, geographically diverse datasets that are otherwise difficult and expensive to obtain (Hochachka et al, 2012;Callaghan et al, 2019). Furthermore, the engagement of the public in scientific research fosters a greater awareness and understanding of environmental issues, promoting conservation efforts at a community level (Bonney et al, 2016, Chozas et al, 2023.…”

Section: Introductionmentioning

confidence: 99%

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Drivers of temporal bias in biodiversity recording by citizen scientists

Rosário,

Tiago,

Chozas

et al. 2024

Preprint

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Citizen science data is increasingly important for ecological research, biodiversity conservation and monitoring. However, these data often suffer from biases due to uneven recording efforts by citizen scientists. Biases caused by intra-annual differences in recording activity can be particularly severe, hindering the use of citizen science data in research areas such as population dynamics and phenology. Therefore, understanding the factors driving recording activity is essential.In this study, we provide a detailed assessment of how weather and calendar-related factors influence biodiversity recording activity by citizen scientists at a daily resolution. To perform this, we analyse the recording patterns for six tree species in the Iberian Peninsula, which maintain a fairly consistent appearance throughout the year. Observation data were collected from iNaturalist, a leading platform for citizen-science data. We used boosted regression trees (BRT) to compare observed recording activity patterns with those expected by chance. Our analysis included a comprehensive set of explanatory variables, such as day of the week, month, holidays, temperature, accumulated precipitation, wind intensity, and snow depth.The BRT models effectively identified the drivers of recording activity, with the correlation between predicted and observed temporal patterns (left out of model training) ranging from 0.47 to 0.96, depending on the species. The day of the week, daily temperature, and month of the year consistently emerged as the main drivers. Recording activity was higher on weekends, to some extent on Fridays, and during the spring months. Extreme low and high temperatures generally correlated with lower recording activity, although there were exceptions. Wind speed and precipitation had a moderate influence, with higher wind intensity and accumulated precipitation leading to decreased activity. Holidays and accumulated snow had very minor relevance across species.Our findings show that citizen scientists record more frequently on weekends, during mild weather, and in spring. By addressing these biases, we can maximize the utility of citizen-collected data for research and applied purposes, ensuring robust and reliable conclusions that enhance ecological understanding and conservation efforts.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Drivers of temporal bias in biodiversity recording by citizen scientists

Rosário,

Tiago,

Chozas

et al. 2024

Preprint

Self Cite

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“…iNaturalist is an easy-to-use platform that can offer an effective learning tool to citizens (Chozas et al 2023). It also comes at no cost, making it accessible to everyone.…”

Section: Discussionmentioning

confidence: 99%

Can we trust iNaturalist in lichenology? Evaluating the effectiveness and reliability of artificial intelligence in lichen identification

Munzi,

Isocrono,

Ravera

2023

The Lichenologist

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AbstractiNaturalist is a widely-utilized platform for data collection and sharing among non-professional volunteers and is widely employed in citizen science. This platform's data are also used in scientific studies for a wide range of purposes, including tracking changes in species distribution, monitoring the spread of alien-invasive species, and assessing the impacts of urbanization and land-use change on biodiversity. Lichens, due to their year-round presence on trees, soil and rocks, and their diverse shapes and colours, have captured the attention of iNaturalist users, and lichen records are widely represented on the platform. However, due to the complexity of lichen identification, the use of data collected by untrained, or poorly trained volunteers in scientific investigation poses concerns among lichenologists. To address these concerns, this study assessed the reliability of lichen identification by iNaturalist users by comparing records on the platform with identifications carried out by experts (experienced lichenologists) in three cities where citizen science projects were developed. Results of this study caution against the use of unchecked data obtained from the platform in lichenology, demonstrating substantial inconsistency between results gathered by iNaturalist users and experts.

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Macroecological Data

Diniz-Filho

2023

The Macroecological Perspective

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Rescuing Botany: using citizen-science and mobile apps in the classroom and beyond

Cited by 4 publications

References 39 publications

Drivers of temporal bias in biodiversity recording by citizen scientists

Drivers of temporal bias in biodiversity recording by citizen scientists

Can we trust iNaturalist in lichenology? Evaluating the effectiveness and reliability of artificial intelligence in lichen identification

Macroecological Data

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