Supplementary file 1_Selection-aware metrics provide complementary decision-level information in plant genomic prediction.docx

The r-best and candidate-metric-best models disagreed in 25%–55% of evaluation contexts, but large differences in candidate metrics among near-r models (|Δr| ≤ 0.02) were uncommon. Nevertheless, models with nearly identical r did not always prioritize the same candidates: LightGBM and rrBLUP/ridge shared, on average, 72.9% of the top 5% candidates in maize and 68.8% in switchgrass. These results indicate that Pearson’s r remains informative for broad model comparison but does not fully describe decision-level behavior.

Phenotype-referenced candidate diagnostics are therefore best used as targeted complements when models have similar predictive ability, selection intensity is high, or candidate identity is consequential across environments.</p>