Simulating biases in collection effort across population-level flowering periods
To simulate biases towards collection of specimens during the early or late portion of their local population-level flowering displays, we selected an individual at random within each population and year using both left- and right-skewed normal probability distributions. These distributions were constructed by modulating the parameter α in the python package scipy.stats.skewnorm v1.10.1 (Azzalini and Capitanio 1998), such that if the underlying plant population was treated as exhibiting a normal distribution (α = 0), samples were collected from that population with a left-skewed (α = -1.0) or right-skewed (α = -1.0) probability distribution (Fig. S1a). Once an individual was selected from these skewed distributions, the timing of sample collection from within the individual flowering durations of these ‘specimens’ were generated using similar methods as unbiased specimens. We then determined the accuracy of the model predictions generated from datasets exhibiting biased and unbiased sampling of local populations by comparing predicted population-level flowering onset and termination dates with the actual (i.e., known, simulated) flowering dates that were produced using a normal distribution. To minimize computation time, population-level biases were examined only for the subset of species for which phenological responsiveness to mean annual temperature equaled 4 days/˚C (representing moderate responsiveness to climate stimuli), intrapopulation variation was high (σ = 30), and individual flowering duration was moderate (30 days).