Mechanistic Modeling Reveals Adaptive Photosynthetic Strategies of Eichhornia crassipes: Implications for Aquatic Plant Physiology and Invasion Dynamics

1. Introduction

Aquatic macrophytes play pivotal roles in ecosystem functioning by mediating biogeochemical cycles and creating habitat heterogeneity within aquatic environments [1]. However, their photosynthetic traits fundamentally differ from terrestrial plants due to the unique environmental constraints, including rapid light attenuation in water [2], thermal fluctuations [3], and variable nutrient bioavailability [4]. Floating macrophytes such as Eichhornia crassipes (Mart.) Solms (water hyacinth) face a distinct challenge wherein their leaves, positioned at the air-water interface, are exposed to high light at the surface while simultaneously contending with self-shading and turbidity-driven light limitation [2]. This dual pressure necessitates adaptive strategies to balance photoprotection against intense sunlight with efficient light harvesting under low-light conditions—a dynamic critical to their ecological success.

E. crassipes, a free-floating perennial monocot native to South America, exemplifies a striking ecological paradox. Introduced to China as an ornamental species in the early 20th century, it has since become a pervasive invasive plant in ponds, reservoirs, and rivers [1]. Despite its notorious capacity for rapid proliferation, which often disrupts aquatic ecosystems, E. crassipes exhibits extraordinary physiological plasticity, enabling tolerance to a wide range of environmental stressors [5,6]. Central to this adaptability is its sophisticated photosynthetic apparatus, which integrates morphological traits (e.g., large floating leaves, vertical petioles) and biochemical adjustments to optimize light capture and carbon assimilation [6,7].

Plant leaves absorb, excite, transmit, and convert light energy based on the intrinsic properties of their light-harvesting pigments, including spatial structure and charge distribution [8]. The ecological dominance of E. crassipes appears closely associated with its ability to regulate photosynthesis [6], including strategies to tolerate or avoid light stress through increased leaf area, optimized photosynthetic efficiency via chloroplast arrangement and stomatal regulation, and vertical growth for enhanced light capture [6,9,10]. Under high light, its glossy leaves minimize photodamage through reflective surfaces and thermal dissipation mechanisms, while in shaded or turbid conditions, increased pigment density and chloroplast reconfiguration enhance light absorption efficiency [6,8]. These adaptations are governed by photochemical processes in photosystem II (PSII), where spatial organization of pigments and charge separation dynamics in reaction centers determine the quantum efficiency of electron transport [11,12]. Chlorophyll fluorescence analysis, a non-invasive method for probing PSII dynamics [13], has proven instrumental in characterizing these mechanisms in terrestrial plants. Recent advances integrating gas exchange, chlorophyll fluorescence, and photosynthetic modeling have clarified photoadaptive strategies in terrestrial systems by precisely characterizing photosynthetic capacity, electron transport dynamics, and PSII down-regulation [14,15,16,17,18]. However, analogous studies on aquatic macrophytes like E. crassipes are lacking, hindering our understanding of their unique photoadaptive physiology.

While terrestrial plants prioritize sustained light utilization, aquatic species like E. crassipes must balance efficient low-light harvesting with robust photoprotection. Previous studies highlight its capacity to maintain high photosynthetic efficiency across light gradients [6,9,19], yet the photophysical mechanisms enabling this plasticity remain unresolved. This study presents a comprehensive analysis of photosynthetic performance in E. crassipes using simultaneous measurements of chlorophyll fluorescence and gas exchange parameters using LI-6400 portable photosynthesis system. The purpose is to: a) quantify key photosynthetic light response characteristics; b) evaluate the applicability of three established photosynthetic models (rectangular hyperbola, non-rectangular hyperbola, and mechanistic models) for aquatic plants; c) investigate physiological foundation for its invasive success. Our findings provide novel insights into the photobiological adaptations of aquatic macrophytes and establish methodological frameworks for analyzing aquatic photosynthesis.

2. Materials and Methods

2.1. Plant Material

Mature E. crassipes plants were used for the experiment. They grew naturally a eutrophic pond adjacent to Jinggangshan University, Ji’an City, Jiangxi Province, China (27.09°N, 115.03°E; elevation 381.6 m). The plants were in a phase of vigorous vegetative growth, reaching heights of 31-52 cm and displaying 5-8 leaves with well-developed root system.

2.2. Gas Exchange and Chlorophyll Fluorescence Measurements

Measurements were taken on clear days in July 2019, from 8:30-11:30 and 14:00-17:30, at an average daytime temperature of 36°C. In this region, photosynthetically active radiation (PAR) typically reached around 2200 μmol photons m^‒2 s^‒1 in summer. Five to seven biologically independent plants with uniform growth were randomly selected, with fully expanded leaves from the upper canopy designated for measurement.

Prior to measurements, selected leaves were light-adapted for one hour under natural light. Simultaneous recordings of chlorophyll fluorescence and gas exchange parameters were obtained using a LI-6400 portable photosynthesis system equipped with a 6400-40 leaf chamber fluorometer (Li-Cor INC., USA). The open-path system maintained controlled conditions, including a CO₂ concentration of 380 μmol mol^‒1, relative humidity of 50–70%, and air temperature within ± 1°C. A 16-step light intensity (I) gradient (2400, 2200, 2000, 1800, 1600, 1400, 1200, 1000, 800, 600, 400, 200, 150, 100, 50, and 0 μmol photons m^‒2 s^‒1) was applied using the embedded “Flr Light Curve” automated protocol. Each light step included a 120-180 s equilibration period, followed by automated reference/sample cell matching. Steady-state measurements of net photosynthetic rate (P_n), electron transport rate (J), stomatal conductance (g_s), transpiration rate (T_r), intercellular CO₂ concentration (C_i), and PSII quantum efficiency (Φ_PSII), and non-photochemical quenching (NPQ) were recorded.

2.3. Photosynthesis Models and Calculations

2.3.1. Rectangular Hyperbola (RH) Model

The rectangular hyperbola model [20] is one of the classic models used to describe the photosynthetic light-response curve of plants. This model is characterized by its simplicity, minimal parameter requirements, and ease of computation. The mathematical representation of the RH model is given by:

$P_{\mathrm{n}}={\displaystyle \frac{\alpha I{P}_{\mathrm{nmax}}}{\alpha I{+P}_{\mathrm{nmax}}}}-R_{\mathrm{d}}$ (1)

where P_nmax represents the maximum net photosynthetic rate, α is the initial slope of P_n-I response curve, and R_d is the dark respiration rate. Additionally, this model has been employed to characterize the response of J to I.

2.3.2. Non-Rectangular Hyperbola (NRH) Model

The NRH model [21,22], by introducing a curvature parameter (θ) to correct the “convexity” between low and high light intensities, thereby improving the accuracy of light-response curve fitting. When combined with the Farquhar–von Caemmerer–Berry (FvCB) biochemical model [23], the NRH model has become one of the core frameworks for modeling plant photosynthesis. The relationship between P_n and I in the NRH model is expressed as:

$P_{\mathrm{n}}={\displaystyle \frac{\alpha I+P_{\mathrm{nmax}}-\sqrt{(\alpha I+P_{\mathrm{nmax}}{)}^2-4\alpha \theta I{P}_{\mathrm{nmax}}}}{2\theta }}-R_{\mathrm{d}}$ (2)

where θ represents the convexity of curve, and other parameters are as previously defined. Similar to the RH model, this model has also been applied to describe the response of J to I.

2.3.3. Photosynthetic Mechanistic Model (Ye Model)

The Ye model [24,25] is a mechanistic representation of the photosynthetic process, explicitly incorporating primary photophysical and photochemical reactions, including light absorption, exciton resonance transfer, quantum energy level transitions, and de-excitation. The model integrates photochemical reactions, exciton transfer, and physiological heat dissipation mechanisms. Through statistical weighting parameters, it quantitatively describes the partitioning of absorbed energy among photochemical reactions, thermal dissipation, and fluorescence. Importantly, this model can directly account for photoinhibition effects and provides an accurate representation of photosynthesis across the entire range of light intensities, from low light to saturation and inhibition [26,27,28]. The mathematical formulation of the Ye model is given by:

$P_{\mathrm{n}}={\displaystyle \frac{{\alpha }^{\mathrm{\text{'}}}{\beta }^{\mathrm{\text{'}}}{N}_0{\sigma }_{\mathrm{i}\mathrm{k}}\phi \eta }{S}}\times {\displaystyle \frac{1-\frac{\left(1-\frac{g_{\mathrm{i}}}{g_{\mathrm{k}}}\right){\sigma }_{\mathrm{i}\mathrm{k}}\tau }{{\xi }_3+\left({\xi }_1{k}_{\mathrm{P}}+{\xi }_2{k}_{\mathrm{D}}\right)\tau }I}{1+\frac{\left(1+\frac{g_{\mathrm{i}}}{g_{\mathrm{k}}}\right){\sigma }_{\mathrm{i}\mathrm{k}}\tau }{{\xi }_3+\left({\xi }_1{k}_{\mathrm{P}}+{\xi }_2{k}_{\mathrm{D}}\right)\tau }I}}I-R_{\mathrm{d}}$ (3)

where α′ represents the light energy distribution coefficient between PSII and PSI (dimensionless), β′ is the leaf light absorption coefficient (dimensionless), N₀ is the number of light-harvesting pigment molecules, σ_ik is the intrinsic light absorption cross-section of light-harvesting pigment molecules (m²), φ denotes the exciton utilization efficiency (dimensionless), η is the efficiency of photosynthetic electron transport (its reciprocal represents the number of electron required to assimilate one CO₂ molecule), S represents the measured leaf area (m²). g_i and g_k are the energy level degeneracies of the light-harvesting pigment molecule in ground and excited states, respectively (dimensionless). ξ₁, ξ₂ and ξ₃ are the statistical weighting factors for exciton transfer to photochemistry, heat dissipation, and fluorescence, respectively (dimensionless). k_P is the photoreaction rate constant (s⁻¹). k_D is the heat dissipation rate constant (s⁻¹). τ represents the average lifetime of the light-harvesting pigment molecule in the lowest excited state (s⁻¹) [24,25].

According to Equation (3), P_n depends on multiple biophysical parameters, including α′, β′, N₀, σ_ik, φ, η, S, g_i, g_k, ξ₁, ξ₂, ξ₃, k_P, k_D, and τ. Under steady-state conditions, these parameters are intrinsic to the plant species but may vary under different environmental conditions. To facilitate practical applications, three aggregate parameters are introduced to simplify Equation (3): ${\alpha }_{\mathrm{p}}={\displaystyle \frac{{\alpha }^{\text{'}}{\beta }^{\text{'}}{N}_0{\sigma }_{\mathrm{i}\mathrm{k}}\phi \eta }{S}}$ (μmol electrons (μmol photons)⁻¹), ${\beta }_{\mathrm{p}}={\displaystyle \frac{\left(1-\frac{g_{\mathrm{i}}}{g_{\mathrm{k}}}\right){\sigma }_{\mathrm{i}\mathrm{k}}\tau }{{\xi }_3+\left({\xi }_1{k}_{\mathrm{P}}+{\xi }_2{k}_{\mathrm{D}}\right)\tau }}$ (m² s (μmol photons)⁻¹), and ${\gamma }_{\mathrm{p}}={\displaystyle \frac{\left(1+\frac{g_{\mathrm{i}}}{g_{\mathrm{k}}}\right){\sigma }_{\mathrm{i}\mathrm{k}}\tau }{{\xi }_3+\left({\xi }_1{k}_{\mathrm{P}}+{\xi }_2{k}_{\mathrm{D}}\right)\tau }}$ (m² s (μmol photons)⁻¹).

$P_{\mathrm{n}}={\alpha }_{\mathrm{p}}{\displaystyle \frac{1-{\beta }_{\mathrm{p}}I}{1+{\gamma }_{\mathrm{p}}I}}I-R_{\mathrm{d}}$ (4)

where α_p is the initial slope of the P_n-I response curve, while β_p and γ_p are parameters characterizing light limitation and light saturation, respectively.

The photosynthetic quantum efficiency (P′_n), defined as the number of CO₂ molecules fixed per photon absorbed at a given I, is derived from Equation (4) as:

$P_{\mathrm{n}}^{\mathrm{\text{'}}}={\displaystyle \frac{{\gamma }_{\mathrm{p}}-{\alpha }_{\mathrm{p}}{I}^2}{{\left({\gamma }_{\mathrm{p}}+{\beta }_{\mathrm{p}}I+{\alpha }_{\mathrm{p}}{I}^2\right)}^2}}$ (5)

The maximum P_n (P_nmax) is determined by:

$P_{\mathrm{nmax}}={\alpha }_{\mathrm{p}}{\left({\displaystyle \frac{\sqrt{{\beta }_{\mathrm{p}}+{\gamma }_{\mathrm{p}}}-\sqrt{{\beta }_{\mathrm{p}}}}{{\gamma }_{\mathrm{p}}}}\right)}^2-R_{\mathrm{d}}$ (6)

while the saturation I (I_sat) is given by:

$I_{\mathrm{sat}}={\displaystyle \frac{\sqrt{\raisebox{1ex}{${(\beta }_{\mathrm{p}}+{\gamma }_{\mathrm{p}})$}\!\left/ \!\raisebox{-1ex}{${\beta }_{\mathrm{p}}$}\right.}-1}{{\gamma }_{\mathrm{p}}}}$ (7)

Ye et al. (2013) also established the relationship between J and I in their photosynthetic mechanistic model using the following equation:

$J={\alpha }_{\mathrm{e}}{\displaystyle \frac{1-{\beta }_{\mathrm{e}}I}{1+{\gamma }_{\mathrm{e}}I}}I$ (8)

where α_e is the initial slope of the J-I response curve, β_e is PSII dynamics down-regulation coefficient, and γ_e are the saturation coefficient.

The maximum J (J_max) is determined by:

$J_{\max }={\alpha }_{\mathrm{e}}{\left({\displaystyle \frac{\sqrt{{\beta }_{\mathrm{e}}+{\gamma }_{\mathrm{e}}}-\sqrt{{\beta }_{\mathrm{e}}}}{{\gamma }_{\mathrm{e}}}}\right)}^2$ (9)

while the saturation I (I_e-sat) is given by:

$I_{\mathrm{e}-\mathrm{sat}}={\displaystyle \frac{\sqrt{\raisebox{1ex}{${(\beta }_{\mathrm{e}}+{\gamma }_{\mathrm{e}})$}\!\left/ \!\raisebox{-1ex}{${\beta }_{\mathrm{e}}$}\right.}-1}{{\gamma }_{\mathrm{e}}}}$ (10)

Additionally, by incorporating chlorophyll content (unit: mg m⁻²), Equation (8) can be used to simulate J-I curves, allowing the extraction of key traits characterizing light-harvesting pigment molecules, including the total photosynthetic pigment molecules (N₀), the eigen-absorption cross-section of photosynthetic pigment molecules (σ_ik), the minimum average lifetime of the lowest excited-state photosynthetic pigment molecules (τ_min), the effective absorption cross-section of pigment molecules (σ′_ik), and the total excited-state pigment molecules (N_k) [25]. Moreover, building upon the P_n-I and J-I mechanistic models, Ye and Yang et al. developed quantitative models describing the light response of light-use efficiency (LUE) [29], carboxylation efficiency (CE), intrinsic and instantaneous WUE (WUE_i and WUE_inst, respectively) [30], Φ_PSII and NPQ [18]. These interconnected models provide a comprehensive framework for quantifying plant photosynthetic physiology, as detailed previously.

2.4. Statistical Analysis

Non-linear regression was performed to fit P_n-I, J-I, NPQ-I, Φ_PSII-I, LUE-I, CE-I, WUE_i-I, and WUE_inst-I curves using the Photosynthesis Model Simulation Software (PMSS, Jinggangshan University) (http://photosynthetic.sinaapp.com/index.html-Chinese/English version). Goodness of fit of the three models was evaluated by the coefficient of determination (R² = 1 - SSE/SST, where SST is the total sum of squares and SSE is the error sum of squares), Akaike’s information criterion (AIC), and Mean absolute Error (MAE = ${\displaystyle \frac{1}{n}}\sum _{i=1}^n\left|\widehat{y_i}-y_i\right|$, where $\widehat{y_i}$ is the fitted values from model and $y_i$ is the measured values). A one-way analysis of variance (ANOVA) was conducted using SPSS Statistics 24.0 to compare differences between the model-fitted and measured values, with statistical significance set at p < 0.05. The ratio of J_max to P_nmax can be used to estimate the apparent number of electrons required to assimilate one molecule of CO₂ (n_a), providing an indicator of the photosynthetic electron utilization efficiency in plant leaves. Data are presented as mean ± SE (n = 4).

3. Results

3.1. Photosynthetic and Electron Transport Responses

The photosynthetic light-response dynamics of E. crassipes were rigorously assessed through coupled chlorophyll fluorescence and gas exchange analyses. Figure 1A–C illustrates the light-dependent progression of net photosynthetic rate (P_n), which exhibited a hyperbolic increase with increasing I, saturating at approximately 2000 μmol photons m^‒2 s^‒1 without photoinhibition. The Ye mechanistic model demonstrated superior goodness-of-fit (R² = 0.9963, MAE = 0.40, AIC = 4.34) in replicating the P_n-I curve (Figure 1C), with no statistically significant deviation between modeled and observed P_nmax (24.64 ± 1.08 vs. 24.70 ± 1.01 μmol CO₂ m⁻² s⁻¹, p > 0.05, Table 1). In contrast, both rectangular (RH) and non-rectangular hyperbola (NRH) models overestimated P_nmax by 45.7% and 36.4%, respectively (p < 0.05), and failed to yield saturation light intensity (I_sat), highlighting their limitations in capturing high-light dynamics (Figure 1A-B, Table 1).

Parallel analysis of electron transport rate (J) revealed a biphasic response to I, peaking at 186.07 ± 10.04 μmol CO₂ m⁻² s⁻¹, and then gradually declined, indicating dynamic downregulation of PSII activity (Figure 1D–F). The Ye model precisely simulated this trajectory (R² = 0.9979, MAE = 2.12, AIC = 19.29), whereas RH and NRH models diverged markedly at supra-optimal light intensity, overestimating J_max by 24.7% and 1.5%, respectively (Table 1). This also leaded to a significant overestimation of the value of apparent electron requirement for CO₂ assimilation (n_a) in both models. However, the n_a value from the Ye model closely aligned with the measured (Table 1).

3.2. Quantum Yield and Photophysical Traits of Light-Harvesting Pigment Molecules

Photosynthetic quantum efficiency (P′_n), reflecting the efficiency of light energy conversion, decreased from 0.023 ± 0.002 to –0.47×10⁻³ μmol CO₂ μmol photons⁻¹ with increasing I, indicating diminished light utilization (Figure 2A). The negative P′_n at I = 2400 μmol photons m⁻² s⁻¹ indicates that respiratory CO₂ release exceeded photosynthetic CO₂ fixation, which was supported by the stabilization of respiratory rate in the light (R_Light) at 2.97% of P_n under saturating light (Figure 2B). Mechanistic modeling of J-I curves resolved intrinsic photophysical parameters of light-harvesting pigments. As shown in Table 1, the total photosynthetic pigment pool (N₀ = 9.46 ± 0.08 × 10¹⁶) and eigen-absorption cross-section (σ_ik = 1.91 ± 0.04 × 10⁻²¹ m²) underscored robust light-capturing capacity. The minimum average lifetime of the lowest excited-state pigment molecules (τ_min) was 11.53 ± 1.27 ms. The total excited-state pigment molecules (N_k) increased with I (Figure 2C), suggesting a gradual shift toward energy dissipation rather than abrupt saturation. Despite a 73.8% reduction in effective absorption cross-section (σ′_ik) under high light (0.5 × 10⁻²¹ m² at I = 2000 μmol photons m⁻² s⁻¹, Figure 2D), residual absorption capacity remained sufficient to sustain photosynthetic activity, reflecting adaptive plasticity in light harvesting.

3.3. Photoprotection and Metabolic Efficiency Dynamics

Non-photochemical quenching (NPQ) increased monotonically to 1.375 ± 0.062 at I = 2350 μmol photons m⁻² s⁻¹ (Figure 3A), consistent with enhanced thermal dissipation under high light. This photoprotective response coincided with a decline in PSII quantum efficiency (Φ_PSII) from 0.762 ± 0.007 to 0.412 ± 0.011 (Figure 3B), suggesting reduced photochemical efficiency as light saturation intensified. Light-use efficiency (LUE) followed a typical peaked response, with a sharp rise at low light and a gradual decline beyond saturation at approximately 200 μmol photons m⁻² s⁻¹ (Figure 3C), while carboxylation efficiency (CE) reached its maximum 0.085 mol m⁻² s⁻¹ at I = 2200 μmol photons m⁻² s⁻¹ (Figure 3D), reflecting distinct thresholds for light capture and CO₂ assimilation. Water-use metrics exhibited analogous saturation patterns, with intrinsic water-use efficiency (WUE_i) plateauing at I = 1600 μmol photons m⁻² s⁻¹ (45.91 ± 6.28 μmol mol⁻¹, Figure 3E) and instantaneous WUE (WUE_inst) stabilizing earlier at I = 1400 μmol photons m⁻² s⁻¹ (1.96 ± 0.29 mmol⁻¹, Figure 3F).

Critically, derivative models from the Ye mechanism framework (e.g., NPQ-I, Φ_PSII-I, CE-I, WUE_i-I, WUE_inst-I) accurately simulated photoprotective and metabolic responses (R² > 0.99) (Figure 3). Fitted NPQ_max, CE_max, WUE_i-max, and WUE_inst-max, and their saturation light intensities, closely matched measured values (p > 0.05) (Table 2). While the Φ_PSII-I model slightly underestimated Φ_PSII-max, the LUE-I model only moderately captured LUE changes, exhibiting larger discrepancies compared to other parameters.

4. Discussion

4.1. Applicability of Ye Mechanistic Model in Aquatic Plants Photosynthesis

Our results demonstrate the superior performance of Ye mechanistic model in the simulating photosynthetic light-response curves of E. crassipes compared to traditional empirical models (RH, NRH), aligning with previous studies that have validated the Ye model in various plant species, including terrestrial plants [26,27,28,30,31], cyanobacteria [32], and eukaryotic algae [33]. The Ye model’s advantage lies in its detailed incorporation of both photophysical and photochemical processes. Specifically, it accounts for light absorption (σ_ik), exciton transfer efficiency (φ), and energy dissipation dynamics (k_D, τ), all critical for accurately representing aquatic photosynthesis in variable light conditions.

While the RH and NRH models provided reasonable approximations under low light, they fell short in capturing high-light saturation and the dynamic down-regulation of PSII. These models overestimated the P_nmax by 36–46% and the J_max by 1.5–24.7% because they assume linear or hyperbolic light responses without accounting for the underlying physiological mechanisms of photosynthesis [16]. These findings align with prior critiques of empirical models in terrestrial systems [16,34,35,36], which lack the flexibility to simulate dynamic photoinhibition or species-specific photoprotective strategies. In contrast, the Ye model’s parameterization of N_k (total excited-state pigments), τ_min (the minimum average lifetime of excited-state pigments), and σ′_ik (effective absorption cross-section)—explains how E. crassipes rapidly dissipates excess energy as heat under high light, thereby avoiding photodamage. In addition, the close alignment between modeled and observed J_max/P_nmax ratio (n_a = 7.46 vs. 7.52) in E. crassipes underscores the Ye model’s ability to quantify electron transport efficiency. This ratio reflects the balance between linear electron flow (driven by PSII activity) and carboxylation efficiency, a relationship inherently modulated by large N₀ and adaptive NPQ [37]. Traditional models, which oversimplify electron transport as a static function of light, fail to resolve these interdependencies. The Ye model’s mechanistic basis thus provides a critical tool for studying aquatic plants, where environmental variability (e.g., light fluctuations, nutrient gradients) demands precise representation of energy allocation and stress responses.

4.2. Evolutionary Adaptations of E. crassipes

The photosynthetic performance of E. crassipes reveals evolutionary adaptations that differentiate it from native aquatic macrophytes. As shown in Table 3, its P_nmax and I_sat exceed values reported for other aquatic plants like Nymphoides peltate, Nelumbo nucifera, and Phragmites australis [38,39,40], which show P_nmax values < 20 μmol CO₂ m⁻² s⁻¹ and earlier photoinhibition thresholds. Even its photosynthetic capacity exceeds that of some common C₃ plants such as Oryza sativa [41], Tamarix ramosissima [42], Solanum lycopersicum L. [43], Malus pumila Mill. [44], and Glycine max L. (Merr.) [45]. The I_sat is close to C₄ maize [19] and exceeds C₄ sorghum [16]. These traits suggest that E. crassipes has evolved more efficient mechanisms for light absorption and energy dissipation, making it particularly competitive in nutrient-rich, high-light environments. The plant’s large photosynthetic pigment pool (9.46 × 10¹⁶ molecules m⁻²) and high eigen-absorption cross-section (1.91 × 10⁻²¹ m²) enhance photon absorption efficiency, which is coupled with dynamic photoprotective responses, such as NPQ, to prevent photodamage under high light. Interestingly, regional variation in photosynthetic capacity, such as the higher P_nmax observed in E. crassipes populations in Nanjing, China (32.03°N, 118.88°E; elevation 10 m) [19] and in Federal University of Lavras, state of Minas Gerais, Brazil. (19.913°S, 43.941°W; elevation 830 m) [6], indicates the species’ ability to adapt to local light conditions. This adaptability contrasts sharply with native species like N. peltate [38], which exhibit more rigid physiological responses to environmental gradients, further emphasizing the ecological flexibility of E. crassipes.

Table 3. E. crassipes compared to other plants in photosynthetic ability.

Plants	Pnmax	Isat	Jmax	Ie-sat	Reference
E. crassipes	23.1–30.8	―	―	―	[6]
E. crassipes	34.5 ± 0.72	2358 ± 69	―	―	[19]
E. crassipes	24.70 ± 1.01	2200.0 ± 81.7	186.1 ± 10.0	1750.0 ± 170.8	This study
Nymphoides peltate	12.66	219.98	―	―	[38]
Nelumbo nucifera	7.1–9.2	―	―	―	[39]
Phragmites australis	9.0~19.5	924.1–2186.3	―	―	[40]
Oryza sativa L.	17.51–27.89	≈ 2000	―	―	[41]
Oryza sativa L. (Kitaake)	19.56 ± 0.62	1641 ± 32.0	―	―	[19]
Tamarix ramosissima	17.2–24.4	957–1360	―	―	[42]
Solanum lycopersicum L.	6.34–17.82	―	―	―	[43]
Malus pumila Mill.	15.25–20.29	1413.8–1874.9	―	―	[44]
Glycine max L. (Merr.)	19.73	1800	143.51 ± 5.21	1601.6 ± 0.64	[45]
Zea mays L. (Nongda 108)	30.36 ± 0.42	2550 ± 37.0	―	―	[19]
Sorghum bicolor L. (KFJT-4)	37.49 ± 0.90	1866.7 ± 33.3	170.15 ± 4.45	1640.0 ± 74.83	[16,18]
Sorghum bicolor L. (KFJT-1)	―	―	133.84 ± 5.52	1600.0 ± 63.24	[18]

Table 3. E. crassipes compared to other plants in photosynthetic ability.

Plants	Pnmax	Isat	Jmax	Ie-sat	Reference
E. crassipes	23.1–30.8	―	―	―	[6]
E. crassipes	34.5 ± 0.72	2358 ± 69	―	―	[19]
E. crassipes	24.70 ± 1.01	2200.0 ± 81.7	186.1 ± 10.0	1750.0 ± 170.8	This study
Nymphoides peltate	12.66	219.98	―	―	[38]
Nelumbo nucifera	7.1–9.2	―	―	―	[39]
Phragmites australis	9.0~19.5	924.1–2186.3	―	―	[40]
Oryza sativa L.	17.51–27.89	≈ 2000	―	―	[41]
Oryza sativa L. (Kitaake)	19.56 ± 0.62	1641 ± 32.0	―	―	[19]
Tamarix ramosissima	17.2–24.4	957–1360	―	―	[42]
Solanum lycopersicum L.	6.34–17.82	―	―	―	[43]
Malus pumila Mill.	15.25–20.29	1413.8–1874.9	―	―	[44]
Glycine max L. (Merr.)	19.73	1800	143.51 ± 5.21	1601.6 ± 0.64	[45]
Zea mays L. (Nongda 108)	30.36 ± 0.42	2550 ± 37.0	―	―	[19]
Sorghum bicolor L. (KFJT-4)	37.49 ± 0.90	1866.7 ± 33.3	170.15 ± 4.45	1640.0 ± 74.83	[16,18]
Sorghum bicolor L. (KFJT-1)	―	―	133.84 ± 5.52	1600.0 ± 63.24	[18]

4.3. Synergistic Efficiency Metrics Supports Invasiveness of E. crassipes

The integration of key efficiency metrics—light-use efficiency (LUE), water-use efficiency (WUE), and carboxylation efficiency (CE)—provides a comprehensive physiological framework for understanding the invasiveness of E. crassipes. The species’ high LUE_max (0.030 mol mol⁻¹) at low light intensity (approximately 200 µmol photons m⁻² s⁻¹), coupled with a sharp decline in LUE at higher light levels, indicates its strategic shift toward photoprotection rather than carbon fixation as light intensity increases. This plasticity enables E. crassipes to thrive under variable light conditions while maintaining efficient resource utilization. Moreover, the species exhibits superior WUE, as reflected in both intrinsic WUE (WUE_i) and instantaneous WUE (WUE_inst). These metrics suggest that E. crassipes effectively regulates stomatal conductance to balance CO₂ uptake and water conservation, a critical strategy for survival in eutrophic environments. This efficient use of light and water, combined with the species’ high J_max, places E. crassipes in direct competition with native macrophytes like P. australis, which exhibit lower CE and less effective photoprotective mechanisms [40]. However, under high light (>1600 μmol photons m⁻² s⁻¹), as observed in most C₃ plants, E. crassipes also exhibited significant PSII dynamic down-regulation [18,46].

5. Conclusions

In conclusion, this study highlights the utility of the Ye mechanistic model in elucidating the complex photobiological adaptations that contribute to the ecological success of E. crassipes. By integrating chlorophyll fluorescence and gas exchange data, we provide a holistic view of the species’ photosynthetic performance, linking molecular-level mechanisms to its ecosystem-level invasiveness. The findings underscore the critical role of efficient light-harvesting and energy dissipation in enabling E. crassipes to dominate eutrophic, high-light environments. Future research should extend this approach to other aquatic macrophytes, particularly invasive species, to identify universal physiological traits that predict ecological success and inform management strategies in aquatic ecosystems.