Decoding pathogen ecological memory from chemical-stress phenotypes

Can standardized chemical stress reveal an ecological “memory” of host origin in plant pathogens? We profiled six coffee-associated Colletotrichum isolates using four phenolic-branched compounds and compared them with a previously characterized cacao panel. Quantitative morphology, hyperspectral imaging (HSI), and supervised machine learning (ML) yielded pathosystem-specific fingerprints under uniform, isotropic in vitro conditions. Circularity, a measure of edge symmetry, was the most informative morphological feature, and ML classified host origin with 87% accuracy in cross-validation. HSI detected dose-dependent physiological shifts, including changes near 1930 nm in the short-wave infrared consistent with water-associated bands. Multi-locus phylogeny showed the coffee isolates are polyphyletic, indicating that these fingerprints likely reflect convergent adaptation rather than shared ancestry. We propose a “chemical priors” framework in which long-term environmental exposure imprints stress-response pathways that become legible under simple, standardized probes. This integrative workflow supports scalable screening of eco-friendly antifungals and precision disease management.