ArticleScientific reports2022
Consistent trait-temperature interactions drive butterfly phenology in both incidental and survey data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Hybrid machine learning approaches outperform mechanistic models of bloom timing in Eastern Redbud, Cercis canadensis.International journal of biometeorology · 2026Article
- Seasonal variation of butterflies from high altitude Rugezi marshland, Northern Rwanda.BMC zoology · 2026Article
- Potential for bird-insect phenological mismatch in a tri-trophic system.The Journal of animal ecology · 2025Article
- High voltinism, late-emerging butterflies are sensitive to interannual variation in spring temperature in North Carolina.Environmental entomology · 2025Article
- Pollinator activity and flowering in agricultural weeds in Sweden.Ecology and evolution · 2024Article
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- Article
- Demographic consequences of phenological asynchrony for North American songbirds.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Weather anomalies more important than climate means in driving insect phenology.Communications biology · 2023Article
- Quantifying phenological landmarks of migration shows nonuniform use of the Caribbean by shorebirds.Ecology and evolution · 2023Article
- Climate, landscape, and life history jointly predict multidecadal community mosquito phenology.Scientific reports · 2023Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Data availability limits phenological research at broad temporal and spatial extents. Butterflies are among the few taxa with broad-scale occurrence data, from both incidental reports and formal surveys. Incidental reports have biases that are challenging to address, but structured surveys are often limited seasonally and may not span full flight phenologies. Thus, how these data source compare in phenological analyses is unclear. We modeled butterfly phenology in relation to traits and climate using parallel analyses of incidental and survey data, to explore their shared utility and potential for analytical integration. One workflow aggregated "Pollard" surveys, where sites are visited multiple times per year; the other aggregated incidental data from online portals: iNaturalist and eButterfly. For 40 species, we estimated early (10%) and mid (50%) flight period metrics, and compared the spatiotemporal patterns and drivers of phenology across species and between datasets. For both datasets, inter-annual variability was best explained by temperature, and seasonal emergence was earlier for resident species overwintering at more advanced stages. Other traits related to habitat, feeding, dispersal, and voltinism had mixed or no impacts. Our results suggest that data integration can improve phenological research, and leveraging traits may predict phenology in poorly studied species.
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