Data from: On the use of double quantile regression and visual assessment to estimate performance constraints

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.