Consistent trait-temperature interactions drive butterfly phenology in both incidental and survey data.

Autor: Larsen EA; Department of Biology, Georgetown University, Regents Hall 501, Washington DC, 20057, USA. elise.larsen@georgetown.edu., Belitz MW; Florida Museum of Natural History, University of Florida, Gainesville, FL, 32611, USA.; University of Florida Biodiversity Institute, Gainesville, FL, 32603, USA., Guralnick RP; Florida Museum of Natural History, University of Florida, Gainesville, FL, 32611, USA., Ries L; Department of Biology, Georgetown University, Regents Hall 501, Washington DC, 20057, USA.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Aug 04; Vol. 12 (1), pp. 13370. Date of Electronic Publication: 2022 Aug 04.
DOI: 10.1038/s41598-022-16104-7
Abstrakt: 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.
(© 2022. The Author(s).)
Databáze: MEDLINE
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