Introduction

The biomass development of submerged aquatic plants varies both inter-specifically (Gillard et al., 2021) and intra-specifically among macrophyte communities (Dumont et al., 2019). The variability depends on geographic location, seasonality, and local controlling factors such as temperature, light availability, pH and trophic state (Dar et al., 2014). Temperature is usually considered the main factor that determines the rates of metabolic processes (Berry & Björkman, 1980; Li et al., 2020), influencing the distribution of plants in aquatic and terrestrial ecosystems. In aquatic environments with low thermal variations (e.g., tropical and subtropical aquatic systems), environmental factors affecting macrophyte growth such as light availability and trophic state can prevail over the effect of temperature (Madsen & Sand-Jensen, 1991; Riis et al., 2012; Ersoy et al., 2020).

In aquatic environments exposed to anthropogenic actions, temporal variations in light intensity, temperature and chemical properties of water (e.g., availability of nutrient elements, presence of toxic elements or substances, turbidity, pH) also result from artificial processes, which can directly interfere with the selection and growth of macrophytes (Cristofor et al., 2003). Various factors that contribute to the failure or low growth of submerged aquatic plant species include: high water turbulence, low acidity, high turbidity, extreme temperatures, and low redox potential. The frequency and duration of these factors reduce biodiversity and select species that are not always the most favorable to keep ecosystem services untouched (Thomaz, 2023). Environments with more stable physical and chemical conditions, in turn, tend to favor the establishment of species with more predictable characteristics, making the selection of control actions more objective, specific and simplified.

Photosynthetic rates of submerged macrophyte species are susceptible to several short-term events: (1) passage of turbidity plumes (usual causes: vertical water circulation, effluent adductions, rainfall and surface runoff); Hall Jr. et al. (2015); (2) changes in the intensity of photosynthetically active radiation (PAR) due to the passage of clouds and change of solar radiation angle (circadian and seasonal variations); Barko & Smart (1981); (3) PAR absorption and blocking of the electron transfer by colored dissolved compounds (e.g., humic substances); Padial & Thomaz (2008); Steinberg et al. (2008); (4) pH changes (e.g., the introduction of organic and inorganic residues, the addition of deep water due to breakdown of thermal stratification); (5) temperature variations (daily and seasonal), thermal pollution (e.g., effluent supply from a thermoelectric power plant), water supply from thermal sources (Majewski & Miller, 1979) and (6) water level changes (Hussner et al., 2008; Taveira, 2022). Although various natural or anthropogenic adducts are rich in nutrient elements, there is a possibility that they contribute to the inhibition of photosynthetic rates due to the predominant composition (e.g., metals, toxic compounds) and the possible presence of color (Gimenes et al., 2020; Machado et al., 2020). These events, which constitute the forcing functions of photosynthesis, modulate plant growth, expressed in temporal variations of growth rates (Carr et al., 1997; Yoshida et al., 2022).

Aquatic plants have a doubling time of a few days to a few weeks (Bianchini Jr. et al., 2015) and the knowledge of environmental changes on a low time scale (e.g., day, week) may be necessary to understand or predict successful establishment and maintenance of species. Short-term environmental alterations and the respective response time of macrophytes are also important information to properly establish the time interval of the algebraic equations that deal with the growth of aquatic plants, to derive more accurate results (Chapra & Canale, 2010). In a short-term time scale, environments with changes in the physical and chemical characteristics of water due to anthropogenic actions (e.g., hydroelectric reservoirs, aquatic environments impacted by agricultural activities, aquatic ecosystems exposed to erosion processes) may not provide adequate support for the desired establishment of a native species, to the detriment of another, not always desired (e.g., invasive species). Ultimately, the chemical and physical variability of the aquatic environment, resulting from anthropogenic actions, can induce the establishment of undesirable species, with negative repercussions on native biodiversity (Salgado et al., 2019).

The distribution, richness and density of aquatic species can be mapped using satellite imagery and field assessments. This is useful information for the protection and management of aquatic plants. In addition, mathematical models that deal with the growth of these organisms provide information on the bathymetric and chemical characteristics of sediments and water. To perform simulations, besides information on the physical and chemical characteristics of the environment, it is necessary to know the ecophysiological parameters of the target species, such as: (1) range of variation of growth rates; (2) tolerance limits for solar irradiation, temperature and pH; (3) growth dependence relationships on nutrient elements; (4) relations of the effects of elements and toxic substances on growth rates (Bowie et al., 1985; Jørgensen & Fath, 2010). Mathematical models that deal with the growth of phytoplankton and submerged aquatic plants tend to discriminate temperature from other variables, considering it to be the most important factor (e.g., Eq. 1; Bowie et al., 1985; Carr et al., 1997).

$$NP={NP}_{max}\left({T}_{ref}\right)\times f\left(T\right)\times f\left(L, P,N,C,Si\right)$$
(1)

where NP = Net photosynthetic rate (mg O2 g−1 DM h−1); \({NP}_{max}\times \left({T}_{ref}\right)\) = maximum net photosyntetic rate at particular reference temperature \(\left({T}_{ref}\right)\) under optimal conditions of saturated light intensity and excess nutrients (mg O2 g−1 DM h−1); \(f\left(T\right)\) = temperature function for growth; T = temperature (°C); \(f\left(L, P,N,C,Si\right)\) = growth limiting function for light and nutrients; L = light intensity; P = available inorganic phosphorus concentration (mass volume−1); N = available inorganic nitrogen concentration (mass volume−1); C = available inorganic carbon concentration (mass volume−1); Si = available inorganic silicon concentration (mass volume−1).

Beyond the use of mathematical models in management programs for aquatic plants, they have been used to describe scenarios involving: (1) potential dispersal (local and regional) of macrophytes (Strange et al., 2019; Cordeiro et al., 2020; Jetter et al., 2021); (2) the management of populations to provide raw material for methane production (Kuar et al., 2018); (3) changes in water quality due to nonpredatory mortality (or decomposition) and herbicide use (e.g., release of colored organic compounds and nutrients, dissolved oxygen consumption; Bianchini Jr. et al., 2014; Song et al., 2021); (4) establishment of the biomass mechanical removal effort and the respective costs (e.g., raw materials, fuel, equipment sizing and hours of work; Mahujchariyawong & Ikeda, 2001; Weber et al., 2021); (5) assessment of changes in ecosystem services in the aquatic environment due to different degrees of incidence of macrophytes (Janssen et al., 2021); (6) evaluation of the macrophyte interferences in the multiple uses of aquatic environments (Verhofstad & Bakker, 2019; García-López et al., 2023) and (7) mapping of potential areas that may harbor disease vectors (Mackay et al., 2016).

Mathematical models are also essential tools for the monitoring and proper management of aquatic environments given the effects of climate change on macrophytes growth (e.g., changes in temperature, euphotic zone, intensities and regime of water supply, trophic state) and, consequently, on biodiversity (Silveira & Thiébaut, 2017; He et al., 2018; Reitsema et al., 2018). Due to its use in wide-ranging activities, the theoretical basis of the models that describe the growth of aquatic plants is already well established (Carr et al., 1997); however, the parameters that address the physiology of various macrophyte species often still need to be determined.

Egeria najas Planchon 1849 (narrow-leaf elodea) is a submerged and rooted angiosperm native to the tropical region of South America and cultivated in Europe (Cook & Urmi-Körnig, 1984). This species is adapted to low light intensities (120–300 µmol m−2 s−1) and can live under conditions of low availability of free CO2 and high pH (Tavechio & Thomaz, 2003). E. najas can obtain nutrients from sediments when concentrations are low in the water column. This species presents a greater affinity for nitrogen than phosphorus and has competitive advantages in low-fertility environments (Thomaz et al., 2007). As described for Egeria densa Planch. (Morgan et al., 2023), it is usually rooted in the bottom mud but can be found as a free-floating mat or as fragments with stems near the surface of the water (Duarte et al., 1994). The growth of E. najas with and without sediment showed similar growth coefficients, suggesting that this species uses both the nutrients available in the sediment and in the water (Bianchini et al., 2006), a trait that has been verified for other species of this genus (Yarrow et al., 2009). This species is widely distributed and plays a crucial role in several Neotropical aquatic systems (lakes, oxbow lakes, artificial lakes, floodplains).

For species of the Hydrocharitaceae family (e.g., E. densa, Hydrilla verticillata (L.f.) Royle and Elodea canadensis Michx.), the participation of C4 cycle enzymes (NADP-ME and PEPC) in CO2 fixation, under high light intensities and temperatures (300 µmol m−2 s−1 and 30 °C), and low carbon concentrations, confer adaptive advantages on the metabolism of these plants (Casati et al., 2000; Lara et al., 2002). The Hydrocharitaceae family has great invasive potential and worldwide distribution, thus subsidizing several trophic chains in different environments. Due to its great potential for growth and dispersal, E. najas often causes problems in artificial reservoirs, reducing multiple uses and interfering with the operation of hydroelectric plants (Martins et al., 2003; Verhofstad & Bakker, 2019; Florêncio et al., 2021). Even though several ecophysiological aspects have already been addressed for the Hydrocharitaceae family, this study specifically aims to describe the effects of short-term variations of abiotic factors (i.e., pH, light, nutrient availability, and temperature) that occur in aquatic ecosystems on the photosynthetic activity of E. najas. We hypothesize that among the four abiotic factors that control E. najas growth, in the short-term, temperature will be the most determinant for defining the intensity of photosynthetic activity (i.e., small temperature variations will lead to expressive changes in photosynthetic rates, more than the changes in other variables).

Material and methods

E. najas plants and water samples were collected in the summer (December to February) at Óleo Lagoon (21° 36′ S and 47° 49′ W), an oxbow lake sited in the floodplain of the Mogi-Guaçu River, located at the Ecological Station of Jataí, municipality of Luís Antônio, (São Paulo State, Brazil). Plants were taken to the laboratory (Bioensaios e Modelagem Matemática, DHb, UFSCar) and washed with tap water to remove periphyton and adhered material. Then, after 1 day, the plants were used in experiments related to photosynthetic activity. The biomass of E. najas was estimated from linear regression between length and dry mass (DM); for this purpose, 70 stem fragments (between 2.0 and 32.0 cm) were used. After determining the lengths, the fragments were dried (60 °C) until constant mass.

Incubation time of Egeria najas

To estimate a suitable time to measure the gross photosynthetic rate (GP), respiration rate (R) and net photosynthetic rate (NP) of E. najas, pre-tests with the light and dark bottles method (Vollenweider, 1971) were employed. The plants (apical shoots ≈ 7 cm long ≡ 31 mg DM) were incubated under non-limiting light intensity (i.e., 177 µmol m−2 s−1 PAR; Tavechio & Thomaz, 2003) for 0.5, 1, 2, 3, 4, and 5 h at 25 °C (temperature value within the variation range of Óleo Lagoon \(\left(\overline{x}=23.5\pm 4.2 \,^{\circ}{\text{C}}\right)\), and close to the optimal growth temperature of E. najas (26.3 ºC); Pezzato et al., 2023). The water sample from Óleo Lagoon was used in the photosynthetic assays (pH: 4.16 ± 0.08; electrical conductivity: 17.0 ± 0.0 µS cm−1; total alkalinity: 0.07 ± 0.01 meq l−1; dissolved inorganic carbon: 1.16 ± 0.08 mg l−1; free CO2: 1.06 ± 0.07 mg l−1; HCO3−: 0.10 ± 0.00 mg l−1; Total nitrogen (TN): 0.40 mg l−1; N-NH4: 0.02 mg l−1; Total phosphorous (TP): 0.03 mg l−1; Fe: 3.22 mg l−1; Mg: 0.40 mg l−1; K: 0.61 mg l−1).

Three incubations (i.e., BOD bottles; volume ≈ 300 ml; 177 µmol m−2 s−1 PAR) were employed for each experimental period (i.e., 0.5, 1, 2, 3, 4, and 5 h). For each time, three incubations were also prepared and kept in the dark to determine the respiration rates. Dissolved oxygen concentrations (DO) were determined by the Winkler titrimetric method (Golterman et al., 1978), and calculations of GP, NP, and R were made according to the equations described by Littler & Arnold (1985). The same mass of apical shoots (≈ 31 mg DM) and number of incubations (3 light and 3 dark bottles for each experimental condition) were also used in the photosynthetic assays where the effect of different light intensities, pH and temperature were evaluated.

For the selected experimental conditions (i.e., plant species, biomass, temperature, and light intensity), net photosynthesis rates were significantly higher when incubated from 1 to 5 h (p < 0.001) compared to half an hour of incubation. Variations in NP values between 1 and 5 h were not significant (Fig. 1 and supplementary material section; S1). Based on these results, the exposure time of 1 h was adopted for the experiments with pH, light intensity and temperature.

Fig. 1
figure 1

Average values of the net photosynthesis rates of Egeria najas as a function of incubation time (at 25 °C and 117 µmol m−2 s−1 PAR); bars refer to standard deviations

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Photosynthetic rate and light intensities

The response of photosynthetic rates of E. najas to light was assessed with incubations (n = 3) with different light intensities (PAR: 10, 16, 27, 45, 75, 100, 177, 275, 390 µmol m−2 s−1) at 25 °C and an exposure time of 1 h. Light intensities were obtained using four 9 w led tubular lamps and a black polyethylene net (Menendez & Sanches, 1998). In addition, to compare the availability of the nutrients on photosynthetic rates, two media were used in these evaluations: (1) water sample from the Óleo Lagoon and (2) diluted (10%) nutrient medium: pH 5.87 ± 0.05; electrical conductivity: 190 ± 0.05 µS cm−1; total alkalinity: 0.14 ± 0.01 meq l−1; dissolved inorganic carbon: 0.85 ± 0.10 mg l−1; free CO2: 0.70 ± 0.11 mg l−1; HCO3−: 0.15 ± 0.03 mg l−1; TN: 21.0 mg l−1; N-NH4: 1.40 mg l−1; TP: 3.10 mg l−1; Fe: 0.07 mg l−1; Mg: 4.80 mg l−1; K: 23.4 mg l−1 (Hoagland & Arnon, 1950). The nutrient medium was used to determine the NP of unrooted E. najas without the limitations due to nutritional deficiencies of the lagoon water. Six incubations were also prepared with apical shoots of E. najas (3 with a water sample from the Óleo Lagoon and 3 with Hoagland-Arnon medium) to be kept in the dark to determine respiratory rates in the 2 different trophic conditions.

The results referring to the experiment of photosynthetic rates and light intensity were fitted to the formulation proposed by Steele (1965). For this purpose, the NP values were adjusted to Eq. 2 with an iterative algorithm (Fylstra et al., 1998). GP, NP and R calculations were derived from DO concentrations (Littler & Arnold (1985) that were measured by the titrimetric method (Golterman et al., 1978).

$$NP={NP}_{max}\times \frac{I}{{I}_{s}}{e}^{\left(1-\frac{I}{{I}_{s}}\right)}$$
(2)

where NP = net photosynthetic rate; NPmax = maximum value of NP; I = light intensity; Is = optimum (saturating) light intensity.

pH response experiment

To verify the effect of pH on NP, incubations (n = 3) with the Óleo Lagoon water sample and apical shoots (≈ 31 mg DM) were maintained at 25 °C and 177 µmol m−2 s−1 (PAR), respectively (Tavecchio & Thomaz, 2003). The experiment was conducted with three pH values (4.0, 6.5 and 8.5). Hydrochloric acid (HCl) or potassium hydroxide (KOH) 0.5 M solutions were added to the water samples to adjust the pH. For each pH value, 3 incubations with apical shoots of E. najas were also prepared to be kept in the dark to determine respiratory rates.

Photosynthetic rate for plants acclimatized at different temperatures

Four cultures of E. najas were maintained for ca. 7 weeks at 15, 20, 25 and 30 °C to assess the short-term effect of temperature on NP (Fig. 2). These parental cultures were prepared, with plants harvested in the Óleo Lagoon, and planted in borosilicate containers (area: 250 cm2; volume: 6.0 L) containing diluted (10%) nutrients medium (Hoagland & Arnon, 1950). Parental cultures were kept under controlled conditions (germination chamber Tecnal, model TE-401) of temperature, and 12 h photoperiod, in non-limiting light intensity [177 µmol m−2 s−1 (PAR); Tavecchio & Thomaz, 2003]. Parental cultures were prepared and maintained according to the experimental procedures suggested by Machado et al. (2020). Medium nutrients were replenished every ten days.

Fig. 2
figure 2

Experimental design to assess the short-term effect of temperature on the net photosynthetic rate, gross photosynthetic rate and respiratory rate of Egeria najas; where n is equal to the number of flasks (light and dark) used in each 1-h incubation (at 15, 20, 25 and 30 °C)

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Using the light and dark bottles method, three plants (≈ 31 mg DM apical shoots) from each temperature treatment (15, 20, 25 and 30 °C) were incubated to measure NP. These values were used to calculate the parameters of the optimum temperature function (Shugart et al., 1974): Q10 (ratio of reaction rates at 10 °C temperature increments; Sierra, 2012), Topt (optimum temperature); Tmax (maximum temperature; the temperature at which NP is null); therefore, an iterative algorithm (Fylstra et al., 1998) was used to calculate these parameters. The optimal temperature curve describes the increments of NP and GP with increasing temperature until the respective optimal temperatures are reached, after which the rates decrease. The temperature function parameters (i.e., Q10, Topt and Tmax) were calculated from the NP values of 1-h incubations (at 15, 20, 25 and 30 °C) of apical shoots of plants from parental cultures. R was also used to calculate Q10 (temperature coefficient) due to respiration; therefore, the Arrhenius approach was used (Eq. 3; Jones, 2014).

$${Q}_{10}={\left(\frac{{k}_{2}}{{k}_{1}}\right)}^{\left(\frac{10}{\left({T}_{2}-{T}_{1}\right)}\right)}$$
(3)

where k1 = reaction rate (e.g., R) at temperature T1; k2 = reaction rate at temperature T2; Q10 = ratio of reaction rates at 10 °C temperature increments.

Data analysis

Statistical tests were performed to evaluate differences in incubation time, light intensity, nutrient availability, temperature, and pH conditions. The normalities of the NP datasets were verified by normality of each NP data set was verified using the D'Agostino-Pearson test. Once the normality of the data was confirmed, the analysis of variance (ANOVA) was performed to verify the occurrence of significant differences between pH, nutrient availability and light intensity and NP. For optimal temperature curve comparisons, the Kruskal–Wallis test was performed once normality was not confirmed. A P-level of 0.05 was considered to be statistically significant in this case. The post hoc used for multiple comparisons was Dunn multiple comparison tests for temperature differences. The Tukey post hoc was performed according to incubation time, light intensity, nutrient availability, and pH conditions. Statistical analyses were performed using the open-source software PAST (version 4.03).

Results

The increase in pH (4.0, 6.5 and 8.5) promoted a highly significant continuous decrease in net photosynthesis rates (P < 0.001); NP = 4.22, 1.74 and 0.0 mg O2 g−1 DM h−1, respectively. Compared to incubations carried out at pH 4, the NP at pH 6.5 decreased by ca. 60% and at 8.5, by 100%. E. najas showed greater affinity for free CO2 and illustrated a reduction in the photosynthetic performance when the inorganic carbon form was 50% free CO2 and 50% HCO3− (i.e., pH 6.5) and showed NP null when the HCO3− was predominant (Fig. 3a).

Fig. 3
figure 3

Changes of the net photosynthetic rate of Egeria najas related with (i) medium pH (a); (ii) light intensity in sample water from Óleo Lagoon (b) (iii) light intensity in nutrient artificial medium (c); Relationship between GP and NP of E. najas from incubations with temperature variations (d). Dots refer to mean values and bars (±) to standard deviations. The lines correspond to the values calculated from the non-linear (b, c) and linear (d) regressions. GP: gross photosynthetic rate; NP: net photosynthetic rate; NPmax: maximum value of NP; I: light intensity; Is: optimum (saturating) light intensity; r2 = determination coefficient

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The fitting of NP as a function of light intensity using water from Óleo Lagoon resulted in an optimum light intensity (Is) of 157.6 µmol m−2 s−1 and a maximum net photosynthetic rate (NPmax) of 1.06 mg O2 g−1 DM h−1 (Fig. 3b). In contrast, when E. najas was placed in the Hoagland–Arnon medium, these values were significantly increased with Is = 204.2 µmol m−2 s−1 and NPmax of 3.05 mg O2 g−1 DM h−1 (Fig. 3c). Similarly, NP of E. najas was also higher in the medium solution. The light response curves were significantly different (F = 8.9; df = 9; P ≤ 0.005). However, when excluding the effect of nutrient availability (i.e., dividing NP by NPmax in each condition), the light and NP/NPmax curves (ranging from zero to 1) were similar (F = 1.1; df = 15; P ≤ 0.005; P > 0.05), showing that the differences between the light versus NP curves were essentially due to the nutrient contents of the culture medium (i.e., Óleo Lagoon water sample and Hoagland–Arnon medium).

When considering all temperature variations, the comparison of gross photosynthesis rates with net photosynthesis rates, showed that NP responds to 79% of the GP, and therefore, R is equal to 21%. These proportions were also maintained when the temperatures coincided with those of the parent cultures selected (Fig. 3d).

In general, the optimal temperature functions did not present very evident differences (P ≥ 0.05) as a function of cultivation temperatures (Fig. 4). A significant difference (P ≤ 0.05) was registered between the optimal temperature curves of plants grown at 20 and 25 °C (Fig. 4b, c; S1); at 20 °C the lowest NP values occurred and at 25 °C the highest. Significant differences (P ≤ 0.001) were also noted between the curves of plants grown at 20 °C and without temperature variation (Fig. 4b, e). However, the acclimatization of the cultures interfered with the values of the parameters of the optimal temperature curve (i.e., Q10, Topt and Tmax). Topt values slightly increased with increasing temperature of parent cultures; thus, the culture acclimatized to 15 °C presented Topt = 26.7 °C and the culture acclimated to 30 °C presented Topt = 29.5 °C, the other cultures generated intermediate and practically ascending values of Topt. On the other hand, the respective Q10 values tended to continuously decrease; Q10 of the culture at 15 °C = 1.71 and that of the culture at 30 °C = 1.42. The Tmax values (i.e., the temperature at which NP = zero) did not show apparent variation as a function of the temperature of the parent cultures, however, it always varied little (between 30 and 32 °C).

Fig. 4
figure 4

Temperature response curves for the net photosynthetic rate of Egeria najas. Where: (a) temperature optimum curve for plants acclimatized at 15 °C and incubated at 15, 20, 25, and 30 °C; (b) temperature optimum curve for plants acclimatized at 20 °C and incubated at 15, 20, 25, and 30 °C; (c) temperature optimum curve for plants acclimatized at 25 °C and incubated at 15, 20, 25, and 30 °C; (d) temperature optimum curve for plants acclimatized at 30 °C and incubated at 15, 20, 25, and 30 °C; (e) temperature optimum curve for plants acclimatized and incubated at 15, 20, 25, and 30 °C, respectively. Dots refer to mean values and bars (±) to standard deviations. The lines correspond to the values calculated from the non-linear regressions. NP: net photosynthetic rate; NPmax: maximum value of NP; Q10: ratio of reaction rates (NP) at 10 °C temperature increments; Topt: optimum temperature; Tmax: maximum temperature (NP = zero); r2 = determination coefficient

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The values of the net photosynthesis rates at the optimal temperature (NPmax) varied between 3.99 (ref. culture acclimated to 20 °C; Fig. 4b) and 7.68 mg O2 g−1 DM h−1 (ref. plants acclimatized and incubated at 15, 20, 25, and 30 °C, respectively; Fig. 4e); however, there was no clear trend of NPmax variation as a function of the parent cultures temperature. Selecting only the NPs from incubations in which the temperatures of the parent cultures were not changed (i.e., incubations at 15, 20, 25 and 30 °C from the cultures acclimated at 15, 20, 25 and 30 °C, respectively) for the fit of the temperature curve, the highest value of Q10 (2.23) and NPmax (7.68 mg O2 g−1 DM h−1) are obtained, and intermediate values for Tmax (31.1 °C) was verified. This situation directly compares the net photosynthesis rates of acclimated cultures (e.g., E. najas acclimatized and incubated at 15 °C) without the imposed interferences of temperature change (e.g., E. najas acclimated at 15 °C and incubated at 20, 25 and 30 °C).

The values of Tmax, Topt and Q10 defined by GP and NP were always close and convergent. As verified for the Q10 calculated by the NP variations, the Q10 values referring to the GP also tended to decrease with the increased temperature of the acclimatized culture. On the other hand, the Q10 values related to respiration did not show a definite trend as a function of the temperature of the parental cultures (Table 1). According to the direct measures of GP and NP (Fig. 3d), the average value of NPmax was 82% of GPmax (Table 1).

Table 1 Parameters (GPmax, NPmax, Tmax, Topt, Q10) calculated from gross photosynthesis (GP), respiration (R) and net photosynthesis (NP) rates variations
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Discussion

Different abiotic factors (e.g., inorganic carbon and nutrient availability, light intensity, photoperiod, and temperature) contribute to controlling the growth of submerged aquatic plants. Such factors are changed temporally and, consequently, alternate in the definition of photosynthesis rates (Hussner et al., 2016; Ersoy et al., 2020; Petracco et al., 2022). Information dealing with physiological variables is relevant to understanding the effects of short-term events (e.g., period: day-week) on aquatic plant growth. Such events can assist with the successful establishment, colonization or disappearance of these macrophytes (Jørgensen & Fath, 2010). The changes (pH, light intensity, temperature, and nutrient availability) imposed in the experiments exemplify the rapid response of photosynthetic activity that E. najas exhibits to chemical and physical environmental changes, and the NP values obtained (Figs. 3 and 4) were of the same order of magnitude as those typically verified for this species and gender (Kahara & Vermaat, 2003; Pezzato & Camargo, 2004; Petracco et al., 2022).

After 1 h of exposure, E. najas practically already reached the maximum performance of a photosynthetic activity, which was maintained for several consecutive hours. This is within the NP:GP ratio generally found for this genus (e.g., 0.78; Yoshida et al., 2022). These characteristics and the Topt values (23.8–29.5 °C) point out that E. najas quickly responds to short-term abiotic changes. Therefore E. najas has the potential to survive and grow in various aquatic environments in tropical and subtropical regions. In addition, thermal pollution and climate change could aid its establishment in new regions. However, as several abiotic factors act together in the development of submerged macrophytes, formulations that deal with net photosynthetic rates can include (e.g., pH, sediment nutrients for rooted species) or suppress elements and factors in the growth limiting function (Eq. 1), according to the aquatic plant life form and predominant features of the environment. If necessary (i.e., when using the exponential or sigmoid models), the NP and NPmax parameters (Eq. 1) can be changed to the growth rate constant (µ; time−1; Yoshida et al. 2022).

The available forms of inorganic carbon and elements vary according to environmental factors, such as pH, oxidation state, degree of dissociation, and macrophytes and algae capacity to assimilate elements from different (organic or inorganic) compounds (Sand-Jensen, 1989; Hussner et al., 2016; Bottino et al., 2018). Inorganic carbon speciation is closely linked to the pH of aquatic environments; the preferred assimilation forms of C3 plants are “free” CO2 (i.e., CO2 and H2CO3) which predominate in acidic media. Bicarbonate ion (HCO3−), which predominates at pH above 6.5 (Golterman et al., 1978; Langmuir, 1997), not always is the preferred or even available form for the assimilation of inorganic carbon. The experiment clearly showed the (indirect) effect of pH on the photosynthetic activity of E. najas. Similar decreases in photosynthetic activity as a function of pH were observed for Egeria (Kahara & Vermaat, 2003); however, such activity was still recorded after pH 8 (Pierini & Thomaz, 2004). The NP was null at pH 8 because the acid condition is predominant (pH 5.2 ± 0.5; range: 4.6–5.8) in the location where the plants were harvested (Óleo Lagoon; Petracco et al., 2022).

A comparison of the light response curves showed the effect of irradiation and nutritional availability for the E. najas photosynthetic activity. It was found that under the same conditions, NP irradiance was significantly lower (2.9-fold), limited by the amount of nutrients, and possibly for some high value of the assimilation half-saturation constants (Christiansen et al., 2016; Kim et al., 2018). In the case of this species, rooted individuals can attenuate nutrient shortages in properly illuminated environments (e.g., 160–200 µmol m−2 s−1). Overall, the NP values obtained in the experiments were of the same magnitude as those verified in Óleo Lagoon. The NP of E. najas in the Óleo Lagoon (0—22 mg O2 g−1 DM h−1; Petracco, 2006) are mainly influenced by the flood pulse and light intensity, being generally higher in the dry period. The flood pulse defines the nutrient input, the increase in turbidity and the relative depth of the plant establishment. In the dry period (winter) there is greater light availability, and temperatures are high enough for the occurrence of high NP (Petracco et al., 2022).

The optimal temperature function describes that for E. najas, CO2 fixation and reduction occur slowly at low temperatures, increasing rapidly with warming up to an optimal value (NPopt range: 23.8–29.5 °C). At supraoptimal temperatures, the CO2/O2 ratio is modified in favor of oxygen and therefore, the carboxylation work of RUBP carboxylase/oxygenase decreases (Larcher, 2003). Extreme temperature values (in this study, NPmax range: 30–32 °C) interrupt entirely the absorption of CO2 (Nievola et al., 2017); if favorable conditions occur again, the plant will only gradually recover (Larcher, 2003). Above 35 or 40 °C, denaturation of most plant enzymes rapidly occurs, and low temperatures can also denature certain enzymes (Salisbury & Ross, 1992).

According to NPopt and NPmax ranges, the temperature amplitude in tropical aquatic environments (e.g., Óleo Lagoon: 19.1 ± 1.2 to 27.3 ± 2.1; Petracco, 2006; Pezzato, 2007) is favorable to the occurrence of this species, as corroborated by the results obtained in these photosynthesis experiments. In this sense, the thermal conditions in tropical regions favor the presence of this species throughout the year, with no defined periods of growth and senescence, decreasing the importance of temperature in the determination of NP values, increasing the influence of light (turbidity), pH and trophic state on the photosynthetic activity of this species. In this case, as E. najas can be fixed in sediments by roots, the effects of the trophic state (i.e., possible nutrient deficiencies) can also be attenuated, suggesting that at low lake water nutrient concentrations, E. najas can survive on the nutrient pools in the sediment porewater, as verified for others submerged species (Christiansen et al., 2016).

Since Q10 is proportional to the activation energy (Ea), the greater its value, the greater the dependence on temperature for the metabolic process to overcome Ea. However, photosynthesis is catalyzed by enzymes that mitigate the role of temperature (i.e., increased vibration and collision of molecules) in overcoming the Ea barrier (Jones, 2014). At low temperatures, Q10 values are higher; at these temperatures, the set of enzymatic reactions is the limiting factor for the reaction rate. At high temperatures, the values of Q10 are small since, in this situation, it is the physical processes that limit the rate of the reaction (e.g., the diffusion rate of solutes in water); Larcher (2003). In these experiments, the downward trend in Q10 values as a function of increasing temperature was more evident for photosynthetic activities (GP and NP) than respiratory rates. In addition, there was an evident difference in the Q10 values (both GP and NP and R) of plants subjected to thermal stress in relation to those of parent cultures. The Q10 values of the plants maintained at ever the same temperature were always higher. Lower sensitivity to temperature variations (i.e., lower Q10 values) suggests that E. najas may not rapidly generate biomass accumulation in response to temperature increases that begin in spring. On the other hand, they may indicate greater resistance to NP decline in decreasing temperature (autumn–winter). Considering the intensity and regime of seasonal temperature variations in aquatic environments, this attribute can constitute an adaptive advantage.

Optimal temperature curves obtained from acclimatized cultures generally mimic the effect of short-term thermal changes on the growth of E. najas. Comparing the Q10 values obtained directly from plants kept at a constant temperature (2.23) and those that suffered thermal stress, it is verified that short-term thermal alterations do not prevent the adequate development of the species. The decreasing values of Q10 can point out that, after short-term thermal stress, the NP limitation was mainly related to physical processes other than temperature. Temperature also affects reactions by changing the shape of the enzyme, which determines its ability to combine with the substrate and causes catalysis. Various enzymes differ greatly in their response to temperature. Thus, some enzymes function optimally at any given temperature while others function below ideal. The metabolism of an organism varies greatly with temperature once this force function defines the action of enzymes that control rate-limiting reactions (Burke et al., 1988). Considering the geographic distribution and occurrence of the genus Egeria in both hot and cold regions (Cook & Urmi-Konig, 1984), this genus has physiological, morphological and anatomical characteristics capable of tolerating and adapting more efficiently to changes in temperature than many other submerged aquatic macrophyte species.

The parameters of the optimal temperature curve obtained from plants subjected to thermal stress (NP-Topt; NP-Tmax; Table 1) were close to those obtained from acclimatized plants; however, in parental cultures, these parameters were calculated from the growth rates, obtained from the sigmoid model fit (Pezzato et al., 2023). This result is particularly important in practice, as shows that short-term NP determinations (e.g., hours) are as effective and accurate in describing growth as long-term experiments (e.g., months). In addition, it shows the importance of this methodology (i.e., determination of NP) to update growth parameters as a function of short-term environmental changes; this aspect is extremely important for simulations or monitoring that aim to describe the growth of macrophytes as a function of short-term changes (e.g., the passage of a turbidity plume, abrupt temperature changes generated by thermal pollution, accidental effects of toxic substances releases).

Conclusion

Our results showed that E. najas presented quick NP responses to short-term changes for all factors considered (pH, light and temperature), which may constitute a relevant adaptive advantage of this species. In tropical and subtropical aquatic environments where low temperature and pH variations occur in relation to light intensity and trophic state, temperature and acidity may become secondary factors for the growth and maintenance of E. najas, as verified for cyanobacterial biomass (Bonilla et al., 2023). Thus, for individuals of submerged and rooted E. najas, light can become the main factor for growth and maintenance, as these plants are not affected by the trophic state since they can supply the demands for nutrients from the sediments. On the other hand, the trophic state acts directly on the NP of unrooted individuals, intensifying photosynthetic activity with increased nutrient availability.

E. najas did not require high light intensity to achieve good photosynthetic performance (i.e., high NP values). This characteristic also contributes to its spread, even more so, in aquatic environments where the intensity of incident light is normally high throughout the year (e.g., tropical and subtropical aquatic systems). For the rooted forms, this performance can be reduced mainly by the occurrence of shading due to the intense growth of algae and other floating species [e.g., Eichhornia crassipes (Mart.) Solms (Pontederiaceae), Pistia stratiotes Linnaeus (Araceae), Salvinia molesta DS Mitch (Salvinaceae)]. For the unrooted individuals (that can act as propagules) the results showed that rapidly increasing light availability in a more fertile environment constituted a situation of extreme advantage for these individuals (i.e., expressive increase in NP values).

The results of the experiments carried out suggest that in tropical and subtropical aquatic environments, short-term changes in the set of variables formed by light and trophic states can overcome the effects of short-term variations in temperature and pH for the growth and maintenance of E. najas. Knowledge of these characteristics helps build predictive models of the dispersal and growth of this species as a function of the prevailing abiotic characteristics of the environment. Predicting the potential of colonization of E. najas is crucial for helping public policies and management strategies to avoid species that have a high potential for growth to spread under stressful or changing environmental conditions.