DOI: 10.5061/dryad.vhhmgqp5d
title: Data from: On the use of double quantile regression and visual assessment to estimate performance constraints

description: 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.
description: # Data and code for On the use of double quantile regression and visual
 assessment to estimate performance constraints 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](mailto: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](mailto: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](mailto: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 * Licenses/restrictions placed on the data: None - 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](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](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](https://datadryad.org/dataset/doi:10.5061/dryad.5x69p8d7z) Cardoso_2A.csv comes from Cardoso_2024_Behavioral_Ecology_--_Dryad_dataset.xlsx which is available at [https://datadryad.org/dataset/doi:10.5061/dryad.fxpnvx112](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** 1. File List: 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 2024 C. 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. 2. Relationship between files: Data in A and B are used in C. Data in A are used in D 3. 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](https://datadryad.org/dataset/doi:10.5061/dryad.fxpnvx112) **METHODOLOGICAL INFORMATION** 1. 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. 2. 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** 3. 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/](http://www.posit.co/).) **DATA-SPECIFIC INFORMATION FOR: Supp_material_7.7_Notesdf.csv** 1. Number of variables: 18 rows 2. Number of cases/rows: 91284 cases, 91285 rows. Each row represents a note. 3. 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** 1. Number of variables: 2 2. Number of cases/rows: 2046 cases, 2047 rows 3. 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.