Effects of population-level collection biases on model
accuracy
Phenoclimate models derived from sample data that exhibited
population-level biases in collection timing (i.e., biases towards
collection of early or late individuals from within each population)
exhibited substantially higher MAE than models produced using unbiased
collections (Fig. 4). The greatest increases in MAE were observed among
predictions of flowering termination derived from collections biased
towards early-flowering individuals, and among predictions of flowering
onset derived from collections biased towards late-flowering
individuals. However, predictions of median (peak) flowering DOY derived
from early- or late-biased collections also exhibited significant
reductions in accuracy, with mean MAE among models derived from biased
collections sometimes exceeding two weeks (16.4 Days, Fig. 4). Moreover,
phenoclimate models appear to be highly sensitive to < 2 days
of population-level temporal biases in collections, with MAE of all
predictions exceeding 5 days even when skew was low (α = ± 0.25, Table
S3, Figs. S1b, S3), more than doubling the observed MAE of phenoclimate
models developed from unbiased collections.