Ball, J. G. C.; Jaffer, S.; Laybros, A.; Prieur, C.; Jackson, T.; Madhavapeddy, A.; Barbier, N.; Vincent, G.; Coomes, D. A.

Airborne imaging spectroscopy enables species-level classification in hyperdiverse tropical forests, but accuracy varies enormously among species. We asked which ecological and evolutionary attributes make a tropical tree species spectrally separable. Using 3,256 field-verified crowns spanning 169 species in a hyperdiverse moist forest in French Guiana, we tested seven hypothesised determinants of classification accuracy at species, pairwise, and individual-crown scales using random forest, beta regression, elastic net, and binomial GLMM analyses. Phenological regularity -- the strength and consistency of seasonal leaf-cycling -- was the single strongest predictor of separability, emerging as the top-ranked variable across all analyses. The presence of congeneric species in the classification pool also reduced accuracy, while broader phylogenetic isolation contributed in multivariate models. At the crown level, crown area was the strongest predictor of correct classification, while liana infestation reduced odds of correct identification by 38\%. Leaf chemical traits did not predict separability. It is the consistency of a species' ecological signal -- its phenological rhythm, spatial sampling, and freedom from canopy contamination -- rather than any single functional trait, that determines whether it can be reliably mapped from imaging spectroscopy.