<scp>iPhenology:</scp> Using open‐access citizen science photos to track phenology at continental scale

Klinger, Yves P.; Eckstein, Lutz; Kleinebecker, Till

doi:10.1111/2041-210x.14114

Cited by 6 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Such community-sourced data have significantly contributed to the accumulation of ecosystem information. These datasets have been instrumental in assessing the impacts of climate change and urbanization on phenology (Fuccillo Battle et al, 2022;Klinger et al, 2023), detecting distribution changes including invasive alien species (Larson et al, 2020;Roy et al, 2023;Wallace & Bargeron, 2014), exploring large-scale geographic variations in traits (Atsumi & Koizumi, 2017;Leighton et al, 2016), and estimating species distributions (Chandler et al, 2017;Feldman et al, 2021;Johnston et al, 2018;Steen et al, 2019). Moreover, the utilization of machine learning to describe population trends based on community-sourced data (Fink et al, 2023) offers opportunities for conducting time-series analyses.…”

Section: Introductionmentioning

confidence: 99%

Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating community-sourced data

Atsumi,

Nishida,

Ushio

et al. 2023

Preprint

View full text Add to dashboard Cite

Kunming-Montreal Global Biodiversity Framework increased the demand for biodiversity distribution data. To gather species observation from the public, we introduced a mobile application called 'Biome' in Japan. By employing species identification algorithms and gamification elements, Biome has gathered >5M observations since its launch in 2019. However, cloud-sourced data often exhibit spatial and taxonomic biases. Species distribution models (SDMs) enable infer species distribution while accommodating such bias. We investigated Biome data's quality and how incorporating Biome data influences the performance of SDMs. Species identification accuracy of Biome data exceeds 95% for birds, reptiles, mammals, and amphibians, but seed plants, molluscs, and fishes scored below 90%. The distributions of 132 terrestrial plants and animals across Japan were modeled, and their accuracy was improved by incorporating Biome data into traditional survey data. For endangered species, traditional survey data required >2,000 records to build accurate models (Boyce index >0.9), though only ca.300 records were required when Biome was blended. The unique data distributions may explain this improvement: Biome data covers urban-natural gradients uniformly, while traditional data is biased towards natural areas. Combining multiple data sources offers insights into species distributions across Japan, aiding protected area designation and ecosystem service assessment.

show abstract

Section: Introductionmentioning

confidence: 99%

Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating community-sourced data

Atsumi,

Nishida,

Ushio

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Phenology: Nature’s Calendar

Kalita,

Sharma

2023

Reson

View full text Add to dashboard Cite

Predicting the timing of ecological phenomena across regions using citizen science data

Capinha

Ceia-Hasse

de-Miguel

et al. 2023

Preprint

View full text Add to dashboard Cite

Spatial predictions of intra-annual ecological variation enhance ecological understanding and inform decision-making. Unfortunately, it is often challenging to use statistical or machine learning techniques to make such predictions, due to the scarcity of systematic, long-term observational data. Conversely, opportunistic time-stamped observation records, supported by highly informative data such as photographs, are increasingly available for diverse ecological phenomena in many regions. However, a general framework for predicting such phenomena using opportunistic data remains elusive. Here, we introduce a novel framework that leverages the concept of relative phenological niche to model observation records as a sample of temporal environmental conditions in which the represented ecological phenomenon occurs. We demonstrate its application using two distinct, management-relevant, ecological events: the emergence of the adult stage of the invasive Japanese beetle (Popillia japonica), and of fruiting bodies of the winter chanterelle mushroom (Craterellus tubaeformis). The framework accounts for spatial and temporal biases in observation data, and it contrasts the temporal environmental conditions (e.g., in temperature, precipitation, wind speed, etc.) associated with the observation of these events to those available in their occurrence locations. To discriminate between the two sets of conditions, we employ machine-learning algorithms (boosted regression trees and random forests). The proposed approach can accurately predict the temporal dynamics of ecological events across large geographical scales. Specifically, it successfully predicted the intra-annual timing of occurrence of adult Japanese beetles and of winter chanterelle mushrooms across Europe and North America. We further validate the approach by successfully predicting the timing of occurrence of adult Japanese beetles in Northern Italy, a recent hotspot of invasion in continental Europe, and the winter chanterelle mushroom in Denmark, a country with a high number of records of this mushroom. These results were also largely insensitive to temporal bias in recording effort. Our results highlight the potential of opportunistic observation data to predict the temporal variation of a wide range of ecological phenomena in near real-time. Furthermore, the conceptual and methodological framework is intuitive and easily applicable for the large number of ecologists already using machine-learning and statistical-based predictive approaches.

show abstract

iPhenology: Using open‐access citizen science photos to track phenology at continental scale

Cited by 6 publications

References 28 publications

Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating community-sourced data

Boosting biodiversity monitoring using smartphone-driven, rapidly accumulating community-sourced data

Phenology: Nature’s Calendar

Predicting the timing of ecological phenomena across regions using citizen science data

Contact Info

Product

Resources

About