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).