Assessing interannual variation in leaf chlorophyll dynamics using optical and destructive methods with mixed-effects and additive modelling

Accurate assessment of leaf chlorophyll is essential for understanding plant physiological responses to environmental variation. While solvent extraction provides precise chlorophyll concentrations, it is destructive and temporally limited. In contrast, portable optical meters such as the CCM-300 enable rapid, non-destructive measurements of chlorophyll fluorescence ratio (CFR), but their calibration against extracted pigments is often species- and season-specific. This study evaluated the reliability of CCM-300 measurements and reconstructed seasonal chlorophyll dynamics in Acer campestre across two contrasting summers in the United Kingdom.Paired CFR and acetone-extracted chlorophyll data collected in 2023 were used to develop calibration models. Random Forest regression achieved the best predictive accuracy (R² = 0.51, RMSE = 0.51 mg cm⁻²), although a simple linear model was adopted for cross-year projection due to its stability. Applying this calibration to daily 2022 CFR measurements generated a “virtual acetone” chlorophyll time series, allowing comparison with weekly destructive extractions in 2023. Both years exhibited mid-season chlorophyll plateaus followed by late-summer declines, but senescence occurred approximately ten days earlier in the warmer, drier 2022 season.Mixed-effects modelling of the 2022 data indicated positive effects of temperature (β = 0.0029 ± 0.0012 SE) and wind speed (β = 0.0053 ± 0.0021 SE) on CFR, whereas day of year and precipitation were not significant. A generalised additive model for 2023 explained 90% of deviance (adj. R² = 0.89) and revealed significant nonlinear effects of temperature, rainfall, and wind speed. Together, these results demonstrate that the CCM-300 can provide a robust non-destructive proxy for total chlorophyll when properly calibrated, and that Acer campestre chlorophyll dynamics are highly sensitive to interannual climatic variability.