Data from: On the use of double quantile regression and visual assessment to estimate performance constraints
Data files
Apr 11, 2025 version files 10.19 MB
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All_analyses_except_warm_up_model.Rmd
32.45 KB
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Cardoso_2A.csv
50.70 KB
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README.md
7.17 KB
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Supp_material_7.7_Notesdf.csv
10.08 MB
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Warm_up_model.Rmd
23.04 KB
Abstract
Quantile regression (QR) is a widely used tool for estimating performance limits in behavioral data. In Vazquez-Cardona et al. (2023, Behavioral Ecology), we applied mixed QR to estimate motor constraints in Adelaide’s warbler song, and measured performance as deviations from these estimated limits. A recent critique (Cardoso, 2024, Behavioral Ecology) raised two concerns about that analysis: (1) QR-based deviation scores arbitrarily emphasize one of the two variables in the trade-off, and (2) uneven sampling may produce the illusion of constraint, compromising the validity of visual assessments and QR. Here, we evaluate these claims by applying double quantile regression (DQR)—Cardoso’s proposed remedy—and by testing whether rarefaction alters the observed constraints. We introduce a novel method to characterize DQR results using bisecting lines and show that, for some distributions, DQR improves fit to the constrained edge. However, DQR performs poorly under some conditions and can falsely identify constraints in unconstrained data. Rarefaction analyses reveal that uneven sampling does not account for the appearance of constraint in our bird song data, which show sharp, bounded edges unlike the smooth gradients of unconstrained simulations. Finally, we demonstrate that the warm-up effect in vocal performance, previously reported in Vazquez-Cardona et al. (2023), is robust to both DQR-based estimation and rarefaction. We conclude that while DQR can be a useful complement to QR, visual inspection remains essential, and caution is warranted when applying DQR to datasets with complex geometries or potential false boundaries.
Author Information
Name: David Logue
ORCID: 0000-0003-3020-7101
Institution: University of Lethbridge
Address: 4401 University Drive / Lethbridge, AB T1K 3M4 / Canada
Email: david.logue@uleth.ca
Name: Juleyska Vazquez-Cardona
Institution: University of Lethbridge
Address: 4401 University Drive / Lethbridge, AB T1K 3M4 / Canada
Email: j.vazquezcardona@uleth.ca
Name: Tyler Bonnell
Institution: University of Calgary
Address: 2500 University Dr NW / Calgary, AB T2N 1N4 / Canada
Email: tyler.bonnell@ucalgary.ca
- Date of data collection:
Adelaide’s warbler note data are from recordings during the periods 2012-03-03 - 2012-06-19 and 2017-04-13 - 2017-05-17
Other data were synthesized
- Geographic location of data collection:
Adelaide’s warbler note data are from the Cabo Rojo National Wildlife Refuge (U.S. Fish and Wildlife Service; 17.98 N, 67.17 W) in western Puerto Rico - Information about funding sources that supported the collection of the data:
Data collection was funded by a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada (grant number RGPIN-2015-06553) to David M. Logue
SHARING/ACCESS INFORMATION
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Licenses/restrictions placed on the data: None
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Links to publications that cite or use the data:
The Adelaide’s warbler note-level data (Supp_material_7.7_Notesdf.csv) are also used here: https://academic.oup.com/beheco/article/34/4/621/7140380 - Cardoso’s data (Cardoso_2A.csv) were also used here: https://academic.oup.com/beheco/article/35/3/arae015/7628475
- Links to other publicly accessible locations of the data:
The Adelaide’s warbler note-level data can also be found here: https://datadryad.org/dataset/doi:10.5061/dryad.5x69p8d7z
Cardoso_2A.csv comes from Cardoso_2024Behavioral_Ecology–_Dryad_dataset.xlsx which is available at https://datadryad.org/dataset/doi:10.5061/dryad.fxpnvx112. - Links/relationships to supplementary data sets:
NA - Was data derived from another source?
Adelaide’s warbler data were collected and scored by Logue’s lab.
Cardoso’s synthesized data were generated by G. Cardoso for his 2024 manuscript linked above. - Recommended citation for this dataset:
Logue, D.M., Vazquez-Cardona, J., Bonnell, T.R. (2025) Data and code for “On the use of double quantile regression and visual assessment to estimate performance constraints.”
DATA & FILE OVERVIEW
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File List: <list all files (or folders, as appropriate for dataset organization) contained in the dataset, with a brief description>
A. Filename: Supp_material_7.7_Notesdf.csv
Brief description: A csv file that can be read by Microsoft Excel or R Studio. This file contains all the Adelaide warbler data.B. Filename: Cardoso_2A.csv
Brief description: Synthesized data from Cardoso 2024C. Filename: All analyses except warm up model.Rmd
Brief description: A markdown file that can be read by R studio. This file contains all the code for analyses and data visualizations except those relating to the warm-up model.
D. Filename: Warm up model.Rmd
Brief description: A markdown file that can be read by R studio. This file contains all the code for analyses related to the warm-up model. - Relationship between files:
Data in A and B are used in C. Data in A are used in D - Additional related data collected that was not included in the current data package:
Cardoso’s synthesized data can be found here:
https://datadryad.org/dataset/doi:10.5061/dryad.fxpnvx112
METHODOLOGICAL INFORMATION
- Description of methods used for collection/generation of data:
Collection of Adelaide’s warbler data is described in Vazquez-Cardona, Juleyska, et al. “Vocal performance increases rapidly during the dawn chorus in Adelaide’s warbler (Setophaga adelaidae).” Behavioral Ecology 34.4 (2023): 621-630.
Cardoso’s synthesized data are described in Cardoso, Goncalo C. “Warm-up and metrics of song performance: a commentary on Vazquez-Cardona et al.” Behavioral Ecology 35.3 (2024): arae015. -
Methods for processing the data:
Data scoring is described in Vazquez-Cardona, Juleyska, et al. “Vocal performance increases rapidly during the dawn chorus in Adelaide’s warbler (Setophaga adelaidae).” Behavioral Ecology 34.4 (2023): 621-630.Data analysis is described in Logue, D.M., Vazquez-Cardona, J., Bonnell, T.R. (2025) On the use of double quantile regression and visual assessment for estimating performance constraints: A reply to Cardoso 2024. Behavioral Ecology
- Instrument- or software-specific information needed to interpret the data:
Data can be analysed in R Studio (Posit team (2025). RStudio: Integrated Development Environment for R. Posit Software, PBC, Boston, MA. URL http://www.posit.co/.)
DATA-SPECIFIC INFORMATION FOR: Supp_material_7.7_Notesdf.csv
- Number of variables: 18 rows
- Number of cases/rows: 91284 cases, 91285 rows. Each row represents a note.
- Variable List:
Index: Numbers in ascending order 1-91284
SongID.x: Bird ID and song number. This is a unique ID for each song. (BirdID+Day+SongNumber)
Bird.x: Bird ID.
Day: Ordinal number of recording day for that bird ID.
Element.Number.x: Note number within the song.
NotesLessOne: Total number of notes in that song, minus one.
Sunrise: Sunrise time in seconds since midnight.
Julian: Julian date (days since last December 31).
Time.song: Time the song began in seconds since midnight.
Order.x: Ordinal number of song for that bird on that day.
RecDate.x: Recording date (YYYY-MM-DD)
Song_Type: Song type appended to bird ID and year (ID_year_SongType)
Year.x: Year of recording
NoteID: A unique ID for each note (SongID.x.NoteNumber)
Length.y: Note duration in milliseconds
Gap_after.y: Duration of silent gap after the focal note, in milliseconds
PeakFreqMaxMinRatio.y: The ratio of the note’s maximum peak frequency to its minimum peak frequency. “NA” for the last note in each song.
PeakFreqGapRatio.y: The ratio of the greater of the following variables to the lesser: end peak frequency of the current note, beginning peak frequency of the subsequent note. “NA” for the last note in each song.
DATA-SPECIFIC INFORMATION FOR: Cardoso_2A.csv
- Number of variables: 2
- Number of cases/rows: 2046 cases, 2047 rows
- Variable List:
X: A synthetic random variable with uneven sampling (sample size decreases with X). See Fig. 2 caption from Cardoso 2024 for details.
Y: A synthetic random variable with uneven sampling (sample size decreases with Y). See Fig. 2 caption from Cardoso 2024 for details.