OJIP Chlorophyll Fluorescence, Ecological Traits, and Phylogenetic Analysis of 263 Vascular Plant Species

Plant sampling and chlorophyll fluorescence measurements Plant material was collected from natural and semi-natural vegetation in the Dnipropetrovsk and Zaporizhzhia regions of Ukraine. The sampling was designed to represent the regional diversity of vascular plants and included 263 species occurring across a broad range of habitat conditions. For each species, fully developed, visually healthy leaves of typical morphology were collected from randomly selected adult individuals. A minimum of 10 independent measurements were obtained for each species, with each measurement representing leaves from different plants. Leaves showing visible signs of mechanical damage, herbivory, disease, senescence, or other abnormalities were excluded. The resulting dataset comprised 2,667 individual chlorophyll fluorescence measurements across 263 species. Chlorophyll a fluorescence induction was measured using an FLS 10s fluorometer [1,2]. Measurements were performed on intact, fully developed leaves after dark adaptation to ensure that the photosynthetic electron transport chain was oxidised. A saturating actinic light pulse-initiated chlorophyll fluorescence induction, and the fast fluorescence transient was recorded with excitation at 460–485 nm and fluorescence detection within the 600–1000 nm spectral range. Each measurement comprised 470 sampling points, providing a high-resolution record of the polyphasic OJIP fluorescence transient and allowing identification of the characteristic O, J, I, and P phases. The principal fluorescence parameters and JIP-test indices derived from the recorded OJIP transients, together with their calculation formulas, are presented in Table 1. Table 1. Calculation of OJIP fluorescence parameters used in the analysis [3]. Parameter Formula Definition Fv/Fo Potential activity of PSII Fv/Fm Maximum quantum yield of primary PSII photochemistry (Fm – Fst)/Fst Relative decline from maximum to steady–state fluorescence K Relative fluorescence at 300 μs VJ Relative variable fluorescence at the J step VI Relative variable fluorescence at the I step phiEo Quantum yield of electron transport ABS/RC Absorption flux per active reaction center TR0/RC Trapped energy flux per active reaction center ET0/RC Electron transport flux per active reaction center DI0/RC Dissipated energy flux per active reaction center ABS/CS0 Absorption flux per excited cross–section at t = 0 TR0/CS0 Trapped energy flux per excited cross–section at t = 0 ET0/CS0 Electron transport flux per excited cross–section at t = 0 DI0/CS0 Dissipated energy flux per excited cross–section at t = 0 RC/CS0 Density of active PSII reaction centers per excited cross–section PIABS RC/ABS × Performance index on absorption basis Note: F0, minimum fluorescence at 20 μs; F300μs, fluorescence at 300 μs; FJ, fluorescence at 2 μs; FI, fluorescence at 30 μs; Fm, maximum fluorescence; Fst, fluorescence at 10 s; Fv = Fm − F0; φP0 = Fv/Fm; ψE0 = 1 − VJ; φE0 = φP0 × ψE0; M0 = 4(F300μs − F0)/(Fm − F0); RC/ABS = 1/(ABS/RC). PIABS was calculated as the performance index on an absorption basis from the RC/ABS, φP0, and ψE0 components. Species ecological characteristics and functional strategies Species ecological characteristics were quantified using indicator values for light, temperature, continentality, soil moisture, soil acidity, nutrient availability, salinity, naturalness, and hemeroby. The Ellenberg-type indicator scales [4] and the naturalness and hemeroby scales [5] were used after regional adaptation to the environmental conditions of the study area [5–7]. Two complementary indicators represented moisture conditions. Edaphic moisture was characterised using the regionally adapted conventional Ellenberg-type moisture scale, reflecting species preferences along the soil moisture gradient. In addition, topographic moisture was represented by a separately developed indicator scale reflecting species associations with landscape-position-related water redistribution and moisture accumulation [7]. Thus, edaphic and topographic moisture were treated as distinct ecological dimensions rather than alternative estimates of the same moisture gradient. Plant ecological strategies were characterised according to Grime’s CSR framework using the relative contributions of competitive (C), stress-tolerant (S), and ruderal (R) strategies [8]. Because C, S, and R represent compositional data constrained to a constant sum, they were not included in the models as three independent predictors. Instead, the CSR composition was transformed into two isometric log-ratio (ILR) coordinates using the compositions package in R [9,10]. These orthogonal coordinates were subsequently used as continuous predictors in the statistical analyses. Based on their relationships with the original CSR components, CSR_ilr1 primarily represented an S–C contrast, whereas CSR_ilr2 primarily represented an R–S contrast. Life form was included as a categorical species-level trait based on the Raunkiaer life-form classification [11]. The 263 species were assigned to eight life-form categories: chamaephytes (Ch, n = 5), geophytes (G, n = 26), herbaceous hydrophytes (Hd, n = 3), helophytes (Hel, n = 12), hemicryptophytes (HKr, n = 102), nanophanerophytes (nPh, n = 11), phanerophytes (Ph, n = 25), and therophytes (T, n = 79). Life form was treated as a categorical predictor in subsequent analyses. References 1. Voronenko, O. Fluorometer “FLS 10s.” Cybern. Comput. Technol. 2024, 87–95, doi:10.34229/2707-451X.24.3.9. 2. Tutova, H.; Lisovets, O.; Kunakh, O.; Zhukov, O. Chlorophyll Fluorescence Traits Reveal Ecological Specialization of Plant Species through Photosynthetic Functional Syndromes. Regul. Mech. Biosyst. 2026, 17, e26104, doi:10.15421/0226104. 3. Estrada, F.; Gonzàlez‐Meler, M.A.; Dias de Oliveira, E.A.; del Pozo, A.; Lobos, G.A. Morphophysiological Plant Phenotyping for the Development of Plant Breeding Under Drought and Heat Conditions: A Practical Approach. Food Energy Secur. 2025, 14, doi:10.1002/fes3.70030. 4. Dengler, J.; Jansen, F.; Chusova, O.; Hüllbusch, E.; Nobis, M.P.; Van Meerbeek, K.; Axmanová, I.; Bruun, H.H.; Chytrý, M.; Guarino, R.; et al. Ecological Indicator Values for Europe (EIVE) 1.0. Veg. Classif. Surv. 2023, 4, 7–29, doi:10.3897/VCS.98324. 5. Tutova, H.; Lisovets, O.; Kunakh, O.; Zhukov, O. From Expert-Based Scores to Community-Derived Indicator Values: Reconstruction of Species Hemeroby Scales from Vegetation Composition. Regul. Mech. Biosyst. 2026, 17, e26055, doi:10.15421/0226055. 6. Tutova, H.; Lisovets, O.; Kunakh, O.; Zhukov, O. From Species Traits to Community Gradients: Validation of Borhi-Di-like Naturalness Indicators in Steppe Vegetation. Biosyst. Divers. 2026, 34, e2608, doi:10.15421/012608. 7. Tutova, H.; Lisovets, O.; Kunakh, O.; Zhukov, O. Empirically Calibrated Phytoindication Scales Resolve Complementary Edaphic and Topographic Components of the Moisture Regime. Diversity 2026, 18, 507, doi:10.3390/d18090507. 8. Pierce, S.; Negreiros, D.; Cerabolini, B.E.L.; Kattge, J.; Díaz, S.; Kleyer, M.; Shipley, B.; Wright, S.J.; Soudzilovskaia, N.A.; Onipchenko, V.G.; et al. A Global Method for Calculating Plant Ecological Strategies Applied across Biomes World‐wide. Funct. Ecol. 2017, 31, 444–457, doi:10.1111/1365-2435.12722. 9. Egozcue, J.J.; Pawlowsky-Glahn, V.; Mateu-Figueras, G.; Barceló-Vidal, C. Isometric Logratio Transformations for Compositional Data Analysis. Math. Geol. 2003, 35, 279–300, doi:10.1023/A:1023818214614. 10. van den Boogaart, K.G.; Tolosana-Delgado, R. Analyzing Compositional Data with R; 2013; ISBN 9783642368097. 11. Raunkiaer, C. The Life Forms of Plants and Statistical Geography; Oxford University Press: London, 1934;