Data from: Specialized flower visitation in montane butterflies is associated with positive population trajectories over time
Data files
Sep 03, 2025 version files 68.43 KB
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all_data_copy.csv
10.28 KB
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pht_rare.csv
37.98 KB
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plant_fam_sub.csv
1.69 KB
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README.md
2.70 KB
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revised_analysis_and_figs.R
15.78 KB
Abstract
Insect biodiversity is under threat from multiple stressors including climate change and extreme weather. For butterflies, nectar resource use is an understudied trait in relation to population trajectories and responses to global change. Here, we characterize nectar breadth for 50 species of montane butterflies occurring in the Sierra Nevada mountains of California and Nevada. These species displayed a wide spectrum of nectar use including relative specialists and extreme generalists. Further, we examined how nectar breadth and other species traits, including latent variables indicating ecological flexibility and dispersal potential, were indicative of long-term population trajectories, and responses to an extreme drought event from 2011-2015. Species that were more nectar-generalized were more likely to be declining, but nectar breadth did not predict how a species responded to extreme drought. Greater ecological flexibility as reflected in other traits was positively associated with population performance, while dispersal potential was negatively associated with population trajectories. Drought response was strongly associated with flight period, where species that fly later in the season are more susceptible to negative effects of drought. Our study highlights the importance of considering butterfly nectar breadth in predicting population resilience, and challenges assumptions about dietary generalism as a buffer against environmental change.
https://doi.org/10.5061/dryad.tb2rbp0cg
Description of the data and file structure
The data used consists of data from photographic observations from the authors and iNaturalist data from GBIF, and nectar breadth values derived from those observations. Species-level trends are derived from long-term monitoring of Sierra butterflies started by Dr. Art Shapiro, and contributed to by the Forister lab.
Files and variables
File: all_data_copy.csv
Description: NA values in the data represent values of variables that were not available due to absent information, or insufficient information (i.e., rarefied nectar breadth was not calculated for species with fewer than 10 observations).
Variables
- Butterfly: Butterfly species name
- n: number observations, i.e. number of photographs containing species
- trend: population trend coefficient, i.e. beta coefficient for each species both within each site and across all monitoring sites that represents the change in the probability of observation of the species throughout the time series.
- drought: drought response, i.e. an index of how a species performed pre- and post- drought (including drought years).
- rare: rarefied nectar breadth; estimated nectar breadth values for each species using rarefaction to a common sample size of 10.
- range: geographic range of a given species in meters squared
- wingspan: average wing span of a species in millimeters
- mean_dlf: mean date of last flight for a species, given by Julian date
- larva_plt: number larval host plant species, count
- abun: index of abundance, i.e. species-level index of abundance calculated as the average number of days a species was observed at each site, out of the number of days the site was visited from 2014-2022, and averaged across sites
- weed: affinity for weedy habitat, binary (0 = not associated with weedy habitat, 1 = associated with weedy habitat)
File: plant_fam_sub.csv
Description:
Variables
- SpScott: Butterfly species
- n: number plant families butterfly found on
File: "pht_rare.csv"
Description: Contains abundance matrix where the row names are the butterfly species,
column names are the plant species, and cell value contains the number of occurances of that butterfly on that plant
Code/software
File: revised_analysis_and_figs.R
Description: Contains all code for statistical analysis and figure creation.
All scripts were written and run using R (version 4.2.3).