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Article

Seasonal Ecophysiological Dynamics of Erythroxylum pauferrense in an Open Ombrophilous Forest of the Brazilian Atlantic Forest

by
João Everthon da Silva Ribeiro
1,*,
Ester dos Santos Coêlho
1,
Francisco Romário Andrade Figueiredo
1,
Walter Esfrain Pereira
2,
Thiago Jardelino Dias
2,
Marlenildo Ferreira Melo
1,
Lindomar Maria da Silveira
1,
Aurélio Paes Barros Júnior
1 and
Manoel Bandeira de Albuquerque
2
1
Postgraduate Program in Phytotechnics, Universidade Federal Rural do Semi-Árido, Mossoró 59625-900, Brazil
2
Postgraduate Program in Agronomy, Universidade Federal da Paraíba, Areia 58397-000, Brazil
*
Author to whom correspondence should be addressed.
Climate 2024, 12(9), 128; https://doi.org/10.3390/cli12090128
Submission received: 8 August 2024 / Revised: 20 August 2024 / Accepted: 21 August 2024 / Published: 25 August 2024
(This article belongs to the Special Issue Forest Ecosystems under Climate Change)
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Abstract

:
Seasonal forests are characterized by seasonal dynamics that influence the growth and ecophysiology of forest species. Erythroxylum pauferrense is an understory species endemic to the Northeastern region of Brazil, with a distribution limited to Paraíba, Brazil. In this study, how the physiological characteristics of E. pauferrense vary in response to seasonal changes in an open ombrophilous forest of the Brazilian Atlantic Forest was investigated. Precipitation, air and soil temperature, and leaf area index were monitored and correlated with gas exchange, chlorophyll fluorescence, chlorophyll indices, and leaf morphofunctional attributes. The results show that E. pauferrense exhibits ecophysiological plasticity, adjusting its photosynthesis rates, stomatal conductance, and water use efficiency according to seasonal changes. During the rainy season, photosynthesis and stomatal conductance were higher than in the dry season, indicating more excellent photosynthetic activity due to increased water availability. Water use efficiency varied, with more efficient use in the dry season, which is crucial for survival in conditions of low water availability. Thus, this study contributes to understanding the ecology of endemic understory species in seasonal tropical forests, such as Erythroxylum pauferrense.

1. Introduction

Tropical forests, with their unparalleled productivity and diverse species, form a unique and vital global ecosystem. Their complex ecological niche network is a key player in carbon cycling, storing and processing approximately half of the carbon fixed by plants, a crucial element for global climate regulation. Within tropical forests, seasonal forests stand out for their unique seasonal dynamics, with a cycle that includes a rainy and dry season [1,2]. During the dry season, many tree species demonstrate an increase in leaf deciduity, possibly as a strategy to minimize water loss and address the water deficit associated with low rainfall [3,4,5,6].
The adaptability of plants to harsh environmental conditions, known as physiological plasticity, is a critical survival strategy. This plasticity allows plants to adjust their physiological and morphological functions in response to environmental changes, such as variations in water availability, irradiance, and temperature. Water availability, for example, is a determining factor in the regulation of photosynthesis and the opening and closing of stomata, directly influencing plant growth and physiology [7,8,9]. In water-scarce environments, plants develop adaptation mechanisms such as stomatal closure to reduce transpiration, increased water-use efficiency, and modification of root morphology to exploit available water resources better [10,11,12]. Irradiance also plays a critical role, varying dramatically in the understory of tropical forests, where light can range from 10 μmol m−2 s−1 in shaded areas to 1000 μmol m−2 s−1 in clearings, being of fundamental importance for photosynthesis-related processes [13,14,15].
In dense forests, the competition for light is intense, and understory plants develop adaptations, such as thinner and larger leaves, with chloroplasts optimized to capture the diffuse light that penetrates the canopy [16,17]. In addition to these factors, temperature affects a wide range of physiological processes in plants, including the rate of photosynthesis, respiration, transpiration, and phenological development [18,19]. Plants in variable temperature environments develop strategies to optimize their physiological performance under different thermal conditions [20].
In addition to abiotic factors, the forest’s canopy structure and spatiotemporal dynamics affect the availability of light and other environmental conditions in the understory. Leaf area index (LAI), visible sky fraction (VFS), and photosynthetically active radiation (PAR) are critical parameters for assessing irradiance and canopy structure [21,22,23]. Advanced techniques such as hemispherical photographs and solar radiation meters are used to quantify these variables and better understand understory conditions [24,25].
Several studies have investigated the interaction between climatic factors and plant ecophysiology. For example, the research has shown how variations in precipitation and temperature affect plants’ growth dynamics and phenology in different biomes. Studies on physiological plasticity have often investigated how different species respond to specific stresses and how these responses vary with location and environmental conditions [26,27,28,29]. However, the literature still needs a detailed understanding of how understory species, such as Erythroxylum pauferrense Plowman, adjust to seasonal variations in their natural environment.
Erythroxylum pauferrense Plowman (Erythroxylaceae), or ‘guarda-orvalho’, is an endemic plant of northeastern Brazil and is classified as a “Least Concern” (LC), according to the International Union for Conservation of Nature’s (IUCN) Red List [30]. This species plays an essential ecological role in conserving genetic resources and seed dispersal [31,32]. Despite its ecological value, more knowledge is required about how its ecophysiological characteristics vary in response to seasonal and site-specific conditions. This study hypothesizes that (i) the ecophysiological plasticity of E. pauferrense varies significantly in response to seasonal changes, (ii) the water availability and irradiance are the main abiotic factors that influence the physiological responses of E. pauferrense, and (iii) the physiological characteristics of E. pauferrense, such as photosynthesis rate, stomatal conductance, and water use efficiency, show significant variations between the rainy and dry seasons. In this study the physiological characteristics of E. pauferrense and how they vary in response to seasonal changes in an open ombrophilous forest of the Brazilian Atlantic Forest are investigated.

2. Materials and Methods

2.1. Study Area and Site Characterization

The research was carried out between November 2017 and July 2018 in the Mata do Pau-Ferro State Park, located in the municipality of Areia, state of Paraíba, Northeast Brazil (6°58′12″ S and 35°42′15″ W) (Figure 1). The site is located in the microregion of Brejo Paraibano and the mesoregion of Agreste Paraibano, with a tropical climate (hot and humid) with autumn–winter rainfall, classified according to Alvares et al. (2013) [33] as As. Its altitude varies between 400 and 600 m, with an average annual temperature of 22 °C and a rainfall index of around 1400 mm [1].
The research site is a highly threatened fragment of humid forest in the Atlantic Forest, known as “Brejo de Altitude”. The fragment has an area of approximately 600 hectares, located five kilometers west of the seat of the municipality of Areia. It is one of the few forest remnants of the Brejos de Altitude in the Brazilian Northeast.
The experiment’s rainfall and air temperature data were collected monthly at the Meteorological Station of the Center for Agrarian Sciences, Federal University of Paraíba, Campus II (Areia Station—A310, OMM code 81877), about 3.5 linear km away from the study area.
The months were selected and grouped for data collection according to each year’s season (dry, rainy, and intermediate). Thus, the evaluations were carried out monthly during the dry (November and December 2017 and January 2018), rainy (February, March, and April 2018), and intermediate (May, June, and July 2018) seasons (Figure 1), and the seasons were chosen according to the shoulder thermal diagram constructed by Ribeiro et al. (2018) [1]. Meteorological data collection was carried out on the last day of each month to allow a more representative analysis of monthly averages and trends over time.

2.2. Collection of Ecophysiological Data

Echophysiological analyses were performed monthly in 20 adult individuals, with a mean diameter at the breast height of 18.7 ± 1.02 cm and a mean height of 2.8 ± 0.32 m, respectively.

2.2.1. Gas Exchanges

For the gas exchange analyses, the net assimilation rate of CO2 (A) (μmol CO2 m−2 s−1), stomatal conductance (gs) (mol m−2 s−1), transpiration (E) (mmol H2O m−2 s−1), and internal concentration of CO2 (Ci) (μmol mol−1) were measured. These data calculated the instantaneous water use efficiency (WUE, A/E) and carboxylation efficiency (iCE, A/Ci). The evaluations were conducted on non-detached, healthy, and fully expanded leaves in the plants’ middle third. A portable infrared gas analyzer (IRGA) (LI-6400XT, Licor Inc., Lincoln, NE, USA) was used to take the readings, on days with favorable weather conditions (open sky with total luminosity), between 11 a.m. and 12 p.m., as recommended by Ribeiro et al. (2020) [34]. A 6 cm2 leaf chamber with a natural light sensor attached, air humidity between 50 and 60%, and airflow of 300 μmol s−1 and 400 μmol mol−1 of atmospheric CO2 was used.

2.2.2. Chlorophyll a Fluorescence

The chlorophyll fluorescence variables analyzed were initial fluorescence (F0), maximum fluorescence (Fm), variable fluorescence (Fv), and a maximum quantum yield of the PSII (Fv/Fm). The measurements were carried out on healthy leaves in the middle third of the plants, adapted to the dark through leaf tweezers for 30 min. The analyses used a portable continuous excitation fluorimeter (OS-30p, Opti-sciences, Inc., Hudson, NH, USA) from 11:00 am to 12:00 pm.

2.2.3. Chlorophyll Leaf Indices

The indices of chlorophyll a (Chl a), chlorophyll b (Chl b), and total chlorophyll (T chl) were measured using a portable chlorophyll meter (ClorofiLOG® CFL 1030, Falker, Porto Alegre, RS, Brazil) in four leaves located in the middle third of the plants. Subsequently, the ratio between the chlorophyll a and b (Chl a/Chl b) indices was calculated. The results were expressed using the Falker Chlorophyll Index (FCI).

2.2.4. Morphofunctional Attributes and Water Relations

To evaluate the morphofunctional attributes of the leaves, 10 leaf discs were collected in the middle third of the plants (diameter of 1 cm2) in each individual from 5:00 am to 6:00 am. This time was chosen to collect discs with greater cellular turgidity and less water loss due to transpiration. Then, the discs were packed in plastic bags and sent to the Plant Ecology Laboratory, Federal University of Paraíba, Campus II. First, the discs’ fresh mass (FM) was measured using a digital scale (0.0001 g). Subsequently, to obtain the turgid mass (TM), the discs were arranged in Petri dishes and hydrated in distilled water for 24 h at room temperature, reaching the maximum turgidity of the tissues [35]. Next, the thickness of the leaf blade (Thi) (mm) was measured using a digital caliper (±0.001 mm) [35]. To determine the dry mass (DM), the discs were stored in Kraft paper and placed in an oven at 65° for 72 h.
With the results obtained, the leaf mass per unit area (LMA) (g m−2) was calculated, with the ratio between dry mass and disc area [36] and succulence (Suc) (g m−2) calculated by the differences between turgid mass and dry mass divided by leaf disc area [37]. To calculate the leaf density (Den) (mg mm−3), the following formula was used: Den = LMA/Thi [38].
The relative water content (RWC) was determined according to Barrs and Weatherley (1962) [39] from the following equation: RWC = [(FM − DM)/(TM − DM)] × 100. The percentage of leaf moisture (LM) was determined according to the formula proposed by Slavik (1974) [40]: %LM = [(FM − DM)/FM] × 100.

2.2.5. Electrolyte Leakage

The quantification of electrolyte leakage (EL) was performed in the same period as the morphofunctional attributes, according to the methodology described by Bajji et al. (2002) [41]. A total of 10 leaf discs per individual (1 cm2) were used, which were washed immediately after cutting, dried on absorbent paper, and placed in test tubes with a lid containing 10 mL of distilled water at 25 °C for 6 h, under occasional agitation. After this period, the extract’s initial electrical conductivity (EC1) was determined by a portable conductivity meter (CD-850, Instrutherm, São Paulo, SP, Brazil). Subsequently, the test tubes were subjected to 90 °C for 60 min, and then the extract’s final electrical conductivity (EC2) was determined. Electrolyte leakage was expressed as a percentage and calculated using the following equation: EL (%) = (EC1/EC2) × 100.

2.3. Environmental Data Collection

2.3.1. Soil Characterization

For the physicochemical characterization of the soil in the experimental area, a simple soil sample (0–20 cm depth) was collected under the canopy of each individual of E. pauferrense and then homogenized to obtain a composite sample, the results of which are presented in Table 1. P and K+ were extracted using the Mehlich method; Na+, Al3+, Ca2+, and Mg2+ were determined using KCl; and H+ + Al3+ was measured using calcium acetate. The soil has a clayey texture, and the pH was classified as low, indicating high acidity, which may limit nutrient availability (Table 1). Phosphorus levels were low, while potassium, calcium, magnesium, the sum of bases (SBs), cation exchange capacity (CEC), and soil organic matter (SOM) were classified as medium (Table 1). These results indicate moderate overall fertility.

2.3.2. Soil Moisture and Temperature

The determination of soil moisture (SM) was performed using the gravimetric method, recommended by Klein (2008) [42], through the following equation: SM (%) = SW − SD × 100/SD, where SM = soil moisture in %; SW = wet mass of the sample (g); SD = dry mass of the sample (g). Soil temperature was measured directly at the surface (ST0cm) and at a depth of 20 cm (ST20cm) using a portable digital infrared thermometer (MT6, Raytek, Wilmington, NC, USA). Soil samples for moisture determination and temperature data were collected simultaneously as the ecophysiological evaluations.

2.3.3. Canopy Structure Indices

The leaf area index (LAI), visible sky fraction (VSF), and photosynthetically active radiation (PAR) were estimated above the E. pauferrense individuals in the understory, using digital hemispherical images, with a forest canopy analyzer (CI-110, CID Inc., Washington, DC, USA). The data were collected under diffuse light conditions (early morning or late afternoon), optimizing the accuracy of the device in order to observe the maximum contrast between the leaves and the sky [43,44].

2.4. Data Analysis

A mixed-effect analysis of variance (ANOVA—F test) (p ≤ 0.01 and p ≤ 0.05) was used to evaluate the differences between the ecophysiological and environmental variables in the different months and seasons of the year, with repeated measures in time. The means were then grouped by the Scott–Knott test, assuming an error of 5% of probability (p ≤ 0.05).
To verify the correlations between the environmental variables—Group I (canopy structure indexes, soil temperature, soil moisture, precipitation, and air temperature) and ecophysiological variables—Group II (gas exchange, chlorophyll and fluorescence, chlorophyll indexes, morphofunctional attributes, and water relations), multivariate analyses were performed through canonical correlation analysis (CCA) and principal component analysis (PCA). The Wilks’ lambda multivariate significance test (approximation of the F distribution) (p ≤ 0.01 and p ≤ 0.05) was used to analyze the significance of the canonical roots. The analyses used the R v.3.4.3 software [45].

3. Results and Discussion

3.1. Environmental Variables

The accumulated rainfall during the study period was 933.80 mm, with values above 139 mm in the rainy season (February to April), with the highest rainfall recorded in April, with 224.7 mm (Figure 1). The period with the lowest precipitation was between November 2017 and January 2018 (dry season: 96.40 mm), with the month of November recording the lowest accumulation of precipitation (Figure 2a). The average air temperature was 22.79 °C, with little oscillation between the seasons, with the highest values observed in the dry season (23.53 °C) (Figure 2b). According to the environmental variables, the total rainfall recorded in the area was lower than other studies carried out in the municipality of Areia, as found by Ribeiro et al. (2018) [1]. Their study, conducted between September 2015 and August 2016, recorded 1130.7 mm in the region studied, which aligns with the findings of the present study. According to these authors, the dry season in the region extends from September to February, thus corroborating the data of the present study, except for the month of February, which presented above-average precipitation (139.4 mm).
Regarding the forest canopy structure indexes, it was observed that the leaf area index (LAI) was higher in the intermediate season, increasing by 35.56% and 32.39% compared to the dry and rainy seasons, respectively (Figure 2c). The fraction of visible sky showed a different behavior from the LAI, with the highest values obtained during the dry season, decreasing in the following seasons (Figure 2d). The photosynthetically active radiation showed a similar trend to the fraction of visible sky, registering the highest values during the dry season, decreasing in the following seasons, with the lowest values observed in the intermediate season (Figure 2e). The decrease in the leaf area index (LAI) and increase in the visible sky fraction (VSF) and photosynthetically active radiation (PAR) recorded in the dry season may have happened due to the fact that the studied area is a semideciduous seasonal forest, in such a way as to cause the partial loss of canopy leaves in this season, evidencing the seasonal influence on the forest structure in the studied area.
Soil moisture varied considerably throughout the seasons, similar to the precipitation and leaf area index, with maximum values recorded in the rainy season and lower values found during the dry season (Figure 2f). Regarding soil temperature, the surface temperature (ST0cm) and the depth of 20 cm (ST20cm) showed the same trend, with the highest values recorded in the driest months and the lowest in the rainy season (Figure 2g,h).

3.2. Ecophysiological Traits

Net CO2 assimilation (A), stomatal conductance (gs), and transpiration (E) showed significantly higher values during the rainy season compared to the dry and intermediate seasons (Figure 3a–c). This behavior can be attributed to the greater availability of water in the soil and the higher humidity of the air during the rainy season, which favor the opening of the stomata and, consequently, the greater assimilation of CO2 [2,46]. Previous studies have indicated that water availability is a critical factor influencing stomatal conductance and photosynthesis rate [47,48,49].
During the dry season, the decrease in A, gs, and E can be associated with greater water restriction, negatively interfering with the physiological system of plants, causing stomatal closure, damage to the photosynthetic apparatus, and reduced activity of Rubisco (Ribulose-1,5-bisphosphate carboxylase oxygenase) [50]. The lower stomatal conductance at this station limits CO2 input, resulting in lower net CO2 assimilation. This behavior is consistent with the literature, which suggests that plants partially close their stomata during drought conditions to minimize transpiration and conserve water [51,52]. In addition, the reduction in the transpiration rate (E) during the dry season is a strategy of the plants to reduce the rapid depletion of the water available in the soil, tolerating the water deficit through osmotic adjustment in the turgidity of the cells [53,54,55].
The internal concentration of CO2 (Ci) was higher in the rainy and intermediate seasons, with increases of 21.25% and 17.61%, respectively, compared to the dry season (Figure 3d). This increase may be related to the higher stomatal conductance observed in stations with greater water availability, allowing a greater diffusion of CO2 into the leaves [56]. The higher internal concentration of CO2 suggests that photosynthesis is not limited by the availability of CO2 during these seasons but rather by other factors, possibly including the availability of light and the intrinsic photosynthetic capacity of the plant [57,58]. During the dry season, the closure of the stomata is possibly the main factor that restricts the photosynthetic performance of plants, affecting the resistance of the mesophyll to CO2 diffusion in the substomatal chamber [59,60].
The water use efficiency (WUE) did not show significant differences between the dry and rainy seasons (Figure 4a), indicating that the ability of the plants to use the available water was not affected by water availability. This result suggests that plants have adaptive mechanisms to maintain a relatively constant water use efficiency, regardless of seasonal conditions [61]. However, an increase of approximately 15% in WUE was observed during the intermediate season (Figure 4a). This increase may be related to a combination of factors, such as greater water availability in the soil and more favorable weather conditions, which may have optimized the use of water by the plants [62]. The increases observed for WUE in the months with greater water availability in the soil and more excellent canopy cover (LAI) are reflections of the high values in the net assimilation of CO2 and transpiration, evidencing the influence of environmental conditions on the gas exchange in the species [1]. This improvement in WUE during the mid-season may reflect an adaptive response from plants to maximize water use efficiency under more balanced conditions [63].
On the other hand, the instantaneous carboxylation efficiency (iCE) was higher during the rainy season (Figure 4b), which is related to the greater availability of water, facilitating the carboxylation process. High values of internal CO2 concentration associated with net CO2 assimilation possibly indicate an increase in iCE [64]. The lower iCE values observed in the dry season corroborate the hypothesis that water limitation negatively affects carboxylation efficiency, possibly due to the closure of the stomata and the reduction in CO2 availability for the photosynthetic process [65]. These results highlight the importance of environmental conditions in determining the physiological strategies of plants. The ability to maintain a stable WUE and the variation in iCE in response to seasonal conditions emphasize the complexity of plant adaptive mechanisms due to changes in resource availability.
The fluorescence parameters of chlorophyll, including maximum fluorescence (Fm), variable fluorescence (Fv), and PSII quantum yield (Fv/Fm), showed similar trends over the different months and seasons (Figure 5b–d). The highest values were recorded during the rainy season and with low light availability (PAR), with significant increases of 57.9% and 55.6% (Fm), 73.4% and 61.1% (Fv), and 36.8% and 12.4% (Fv/Fm) in the dry and intermediate seasons, respectively (Figure 5b–d). These results indicate that wetter conditions and lower light intensity during the rainy season are more favorable for the photosynthetic efficiency of plants, reflecting a greater capacity to capture light and convert it into chemical energy [66].
On the other hand, the initial fluorescence (F₀) was higher in the dry season, and its values decreased by 55% and 51% in the rainy and intermediate periods, respectively (Figure 5a). The increase in F₀ during the dry season may indicate photoinhibitory stress or damage to the photosynthetic apparatus, possibly due to greater light exposure and lower water availability, resulting in a lower efficiency in the use of light for photosynthesis. The initial fluorescence (F₀) was higher in the period of greatest plant stress (dry season), decreasing over the months. High values of this variable possibly indicate a destruction of the PSII reaction center or inability to transfer excitation energy from the antenna to the reaction centers [67]. Unfavorable environmental conditions can modify the structure of photosynthetic pigments in the PSII, increasing the F₀ values [68].
The reduction in maximum fluorescence (Fm) in the dry season may be related to the decrease in quinone due to the inactivation of PSII in the thylakoid membranes, influencing the flow of electrons between photosystems [69]. Such a decline alters the photochemical activities of the leaves since a higher Fm acts directly in the transfer of energy to the formation of the reducer NADPH, ATP, and reduced ferredoxin, thus promoting a greater capacity for CO2 assimilation in the biochemical process of photosynthesis [64,70,71]. The higher variable fluorescence (Fv) recorded in the middle of the rainy season possibly provided a greater capacity in the transfer of electrons removed from the molecules of photosynthetic pigments, which may be indicative of more excellent stabilization of individuals during the rainy season [72].
The maximum quantum yield of PSII (Fv/Fm) allows us to infer the maximum efficiency with which the light energy absorbed by PSII is used to reduce quinone A activity, thus becoming an indicator of photochemical performance [70]. In the present study, it was observed that there were significant differences in the months in the two areas, with wide variation between the values. Individuals that register values between 0.75 and 0.85 quantum electrons−1 in Fv/Fm have the photosynthetic apparatus intact, indicating that the plants evaluated during the rainy season presented a quantum yield with adequate values [73]. On the other hand, the plants in the dry season presented Fv/Fm values lower than 0.75 quantum electrons−1, indicating a situation of stress and reduction in photosynthetic potential, thus causing possible photoinhibitory damage to the PSII due to the seasonal variation in luminosity and water availability [64].
The Fv/F₀ ratio is the ratio between the captured energy flow and the dissipated energy. It is a possible indicator of the maximum efficiency in the photochemical process in the PSII and/or the potential photosynthetic activity, with ideal values ranging between 4 and 6 quantum electrons−1. Based on these values, Silva et al. (2015) [64] observed that in the present study, the individuals were under stress conditions during the dry season, with values lower than 4 quantum electrons−1.
The indices of chlorophyll a, chlorophyll b, and total chlorophyll showed similar behavior in the different seasons studied, with increases observed in the rainy season and lower canopy cover (<LAI) (Figure 6a–c). This increase in chlorophyll levels during the rainy season may be related to the greater availability of water and nutrients, which favor the synthesis of chlorophyll and, consequently, the photosynthetic capacity of plants. The greater availability of water during the rainy season facilitates chlorophyll synthesis and improves cell hydration, enabling better cell expansion and, consequently, a larger leaf area for light interception.
In addition, during the rainy season, the lower light intensity due to the higher cloud cover can lead plants to increase chlorophyll production to maximize the capture of available light. The presence of water in the soil also facilitates the absorption of essential nutrients such as nitrogen and magnesium, which are critical components in the structure of the chlorophyll molecule. This combination of factors contributes to a significant increase in chlorophyll a, b, and total levels, reflecting the adaptation of plants to optimize photosynthesis under conditions of lower light intensity and greater water availability.
On the other hand, in the dry season, water limitations and higher light intensity can lead to faster chlorophyll degradation since water stress can induce the production of reactive oxygen species (ROS) that damage photosynthetic pigments. In addition, plants can reduce chlorophyll synthesis to minimize light absorption and prevent photo-oxidative damage. The decrease in chlorophyll indices during the dry season reflects a strategy of plant survival in response to environmental stress, ensuring that limited water and nutrient resources are used more efficiently.
The ratio between chlorophyll a and b (Chl a/Chl b) showed the highest values in the dry season (Figure 6d), during which the highest incidence of luminosity was observed in the understory. This behavior can be explained by adapting plants to high light conditions, where chlorophyll a, which is more efficient in capturing light, is favored over chlorophyll b. The reduction in the Chl a/Chl b in the rainy season with the lower incidence of light occurred mainly due to the significant increase in chlorophyll b content [74], which acts as an accessory pigment, capturing energy and transferring it to chlorophyll a, directly assisting in light absorption [50]. This process represents a mechanism of adaptation of plants to the environmental conditions found in the rainy season, effectively acting on the photochemical reactions of photosynthesis [75].
Leaf mass per unit area (LMA), leaf blade thickness (Thi), succulence (Suc), and leaf density (Den) showed inverse behavior of the other ecophysiological variables analyzed, with the highest values recorded in the dry season, when higher light intensity was observed under the plants (Figure 7). The increase in these leaf attributes during the dry season suggests an adaptation of the plants to cope with water stress and high luminosity through increased leaf thickness and density, which can improve water retention and protection against damage caused by excess light.
Corroborating the present study, some researchers have stated that species found in unfavorable conditions, such as more significant water restrictions, tend to present high LMA values. These plants tend to grow slowly and demonstrate tolerance to stresses [76,77]. On the other hand, plants with low LMA values generally have faster growth and higher ecophysiological performance [78]. Niinemets (2001) [79] pointed out that thicker sheets are often found in environments with high light intensity. Increasing leaf thickness in the driest and sunniest season is considered an adjustment of the plants to avoid further heating, reduce water loss through transpiration, and control leaf carbon gain [80].
The increase in succulence during the dry season can be explained by mechanisms regulating water storage in the leaves, avoiding dehydration and desiccation [81]. Succulence reflects the water storage capacity per unit of leaf area, contributing to water stress tolerance [82,83]. In addition, the higher leaf density during the dry season may be associated with the plants’ low photosynthetic performance. Leaves with smaller cells, fewer intercellular spaces, and thicker cell walls reduce CO2 diffusion into the leaf and, consequently, photosynthetic capacity [84,85]. This adaptation can be a strategy to minimize water loss and optimize survival in harsh environmental conditions.
The relative water content (RWC) and leaf moisture (LM) showed similar behaviors in the different seasons, with the highest values observed in the season of more significant water availability (the rainy and intermediate seasons) (Figure 8a,b). These results are consistent with the increased water availability during these seasons, allowing plants to maintain a more favorable water status. Variables related to leaf water status, such as RWC and LM, are critical indicators of plant water conditions, reflecting the ability of plants to retain and use the water available in the environment. However, electrolyte extravasation (EL) revealed a different situation. During the dry season, a significant increase in EL was observed, indicating greater damage to cell membranes (Figure 8c). The values were 47.9% and 26.6% higher than in the rainy and intermediate seasons (Figure 8c).
This increase in EL during the dry season suggests more severe stress and cellular damage associated with dehydration and high light intensity. Dehydration of protoplasm and exposure to harsh environmental conditions can lead to lipid peroxidation, compromising the integrity of cell membranes and resulting in increased electrolyte leakage [86,87]. These results indicate that although greater water availability is beneficial for maintaining water status, reduced humidity and increased luminosity can promote significant cellular damage. E. pauferrense plants appear sensitive to these environmental conditions, with dehydration and light stress adversely affecting the membrane integrity and cell functioning.

3.3. Influence of Environmental Factors on the Ecophysiology of E. pauferrense

Table 2 presents the results of the Wilks’ lambda multivariate test for different canonical functions, which assesses the significance of the associations between variables of two data sets. The first canonical function shows a very high correlation (R2 = 0.9828) and a significant Fa value (Fa = 9.74) with p-value < 0.0001, indicating a strong and statistically significant association between the sets of variables (Table 2). Subsequent functions also show significant, albeit slightly smaller, correlations, with all functions statistically significant up to the seventh (p ≤ 0.05). The eighth canonical function is not significant (p = 0.5381) (Table 2), suggesting that it does not contribute significantly to the relationship between the variables.
The results of the canonical correlation analyses show significant associations between soil abiotic factors and plant physiological parameters, with some strong correlations and others moderate (Table 3). For example, soil moisture (SM) has a significant positive correlation with relative water content (RWC) and leaf moisture (LM), indicating that wetter soils better support plant hydration. Rainfall is also strongly correlated with these water attributes in the leaves (Table 3), highlighting the importance of water availability. In contrast, soil temperatures at 0 cm (ST0cm) and 20 cm (ST20cm) show negative correlations with the maximum quantum efficiency of the PSII (Fv/Fm) (Table 3), suggesting that higher soil temperatures may reduce photosynthetic efficiency. Other abiotic factors, such as visible sky fraction (VSF) and photosynthetically active radiation (PAR), have significant negative correlations with variables such as stomatal conductance (gs) and variable fluorescence (Fv), indicating that greater exposure to light may lead to more significant stress on plants (Table 3). These results highlight the complex interactions between environmental conditions and physiological responses of plants.
Principal component analysis (PCA) reveals that the first two principal components together explain 83.11% of the variation in the data: the first principal component (PC1) explains 60.67%, and the second principal component (PC2) explains 22.44% (Figure 9). The seasons of the year (dry, rainy, and intermediate) significantly influence the physiological and environmental variables of the plants. The rainy season (Feb, Mar, Apr) is associated with variables such as the net assimilation of CO2 (A), instantaneous carboxylation efficiency (iCE), stomatal conductance (gs), photosynthetic efficiency (Fv/Fm), and precipitation (Rain) (Figure 9). The dry season (Nov, Dec, Jan) is related to variables such as initial fluorescence (F0), chlorophyll a/b ratio (Chl a/Chl b), leaf mass per unit area (LMA), density (Den), soil temperature at 0 cm and 20 cm (ST0cm, ST20cm), photosynthetically active radiation (PAR), visible sky fraction (VSF), leaf area index (LAI), and air temperature (Tair) (Figure 9), indicating responses to abiotic stresses and structural adaptation. In the intermediate season (May, Jun, Jul), a balance is observed, with variables such as water use efficiency (WUE), leaf area index (LAI), transpiration (EL), and soil moisture (SM) being more influential (Figure 9).
The Pearson correlation matrix showed significant relationships between the environmental and ecophysiological variables of plants, with the significance levels indicated in Figure 10. Strong positive correlations were observed, such as between instantaneous carboxylation efficiency (iCE) and stomatal conductance (gs) (r = 0.72, p < 0.01), maximum fluorescence (Fm) and maximum quantum yield of PSII (Fv/Fm) (r = 0.75, p < 0.01), and leaf moisture (LM), relative water content (RWC) (r = 0.89, p < 0.01), initial fluorescence (F0) and chlorophyll a/b ratio (Chl a/Chl b) (r = 0.40, p < 0.05) (Figure 10). Moderate correlations included intercellular CO2 concentration (Ci) and net CO2 assimilation (A) (r = 0.36, p < 0.05), as well as positive correlations between precipitation (Rain) and net CO2 assimilation (A) (r = 0.68, p < 0.05) (Figure 10). These results highlight the significant influence of environmental variables on plant physiological processes, indicating either adverse or beneficial effects, and provide a solid foundation for further investigations into plant growth and physiology under different environmental conditions.

4. Conclusions

The individuals of E. pauferrense show phenotypic plasticity in response to seasonal variations, with significant changes in gas exchange, chlorophyll fluorescence, chlorophyll indexes, morphofunctional attributes, and water relations, showing a lower ecophysiological development in the dry season (more significant water deficit and luminosity). Seasonality influences the ecophysiological traits of E. pauferrense, with a more significant influence on soil moisture, precipitation, and leaf area index on gas exchange, chlorophyll fluorescence, and chlorophyll indexes. In this sense, the more excellent canopy opening in the dry season (higher irradiance) and lower soil moisture promoted low values of net CO2 assimilation and high values in morphofunctional attributes and electrolyte extravasation. The results highlight the importance of water availability in gas exchange and the efficiency of water use by plants. During the rainy season, the greater availability of water in the soil positively influenced gas exchange and improved photosynthetic efficiency.
In contrast, the dry season imposes significant limitations, forcing plants to adopt survival strategies that include reducing transpiration and CO2 assimilation. These findings are essential for understanding plant ecology in environments with pronounced seasonal variability. They may inform management practices in tropical and subtropical forests, especially in climate change scenarios that predict altered precipitation patterns. In addition, knowledge about the physiological responses of plants to variable environmental conditions can contribute to the development of predictive models that consider the influence of climate on ecosystem functions.

Author Contributions

Conceptualization, J.E.d.S.R. and M.B.d.A.; methodology, J.E.d.S.R.; software, W.E.P. and J.E.d.S.R.; validation, J.E.d.S.R., E.d.S.C. and F.R.A.F.; formal analysis, J.E.d.S.R. and W.E.P.; investigation, T.J.D.; resources, M.B.d.A.; data curation, E.d.S.C. and F.R.A.F.; writing—original draft preparation, J.E.d.S.R., W.E.P., A.P.B.J. and M.F.M.; writing—review and editing, J.E.d.S.R., L.M.d.S. and M.F.M.; visualization, W.E.P. and T.J.D.; supervision, J.E.d.S.R. and M.B.d.A.; project administration, J.E.d.S.R. and M.B.d.A.; funding acquisition, J.E.d.S.R. and M.B.d.A. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.

Data Availability Statement

The original contributions presented in the study are included in the article.

Acknowledgments

We thank the Universidade Federal da Paraíba (UFPB) for its support during the research.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ribeiro, J.E.d.S.; Barbosa, A.J.S.; Lopes, S.d.F.; Pereira, W.E.; Albuquerque, M.B.d. Seasonal variation in gas exchange by plants of Erythroxylum simonis Plowman. Acta Bot. Bras. 2018, 32, 287–296. [Google Scholar] [CrossRef]
  2. Mendes, K.R.; Campos, S.; da Silva, L.L.; Mutti, P.R.; Ferreira, R.R.; Medeiros, S.S.; Perez-Marin, A.M.; Marques, T.V.; Ramos, T.M.; de Lima Vieira, M.M. Seasonal variation in net ecosystem CO2 exchange of a Brazilian seasonally dry tropical forest. Sci. Rep. 2020, 10, 9454. [Google Scholar] [CrossRef]
  3. Derroire, G.; Powers, J.S.; Hulshof, C.M.; Cárdenas Varela, L.E.; Healey, J.R. Contrasting patterns of leaf trait variation among and within species during tropical dry forest succession in Costa Rica. Sci. Rep. 2018, 8, 285. [Google Scholar] [CrossRef]
  4. Marques, T.V.; Mendes, K.; Mutti, P.; Medeiros, S.; Silva, L.; Perez-Marin, A.M.; Campos, S.; Lúcio, P.S.; Lima, K.; dos Reis, J. Environmental and biophysical controls of evapotranspiration from Seasonally Dry Tropical Forests (Caatinga) in the Brazilian Semiarid. Agric. For. Meteorol. 2020, 287, 107957. [Google Scholar] [CrossRef]
  5. Wu, J.; Serbin, S.P.; Ely, K.S.; Wolfe, B.T.; Dickman, L.T.; Grossiord, C.; Michaletz, S.T.; Collins, A.D.; Detto, M.; McDowell, N.G. The response of stomatal conductance to seasonal drought in tropical forests. Glob. Chang. Biol. 2020, 26, 823–839. [Google Scholar] [CrossRef] [PubMed]
  6. Chaturvedi, R.K.; Tripathi, A.; Raghubanshi, A.S.; Singh, J.S. Functional traits indicate a continuum of tree drought strategies across a soil water availability gradient in a tropical dry forest. For. Ecol. Manag. 2021, 482, 118740. [Google Scholar] [CrossRef]
  7. Sun, Y.; Wang, C.; Chen, H.Y.H.; Ruan, H. Response of plants to water stress: A meta-analysis. Front. Plant Sci. 2020, 11, 978. [Google Scholar] [CrossRef]
  8. Seleiman, M.F.; Al-Suhaibani, N.; Ali, N.; Akmal, M.; Alotaibi, M.; Refay, Y.; Dindaroglu, T.; Abdul-Wajid, H.H.; Battaglia, M.L. Drought stress impacts on plants and different approaches to alleviate its adverse effects. Plants 2021, 10, 259. [Google Scholar] [CrossRef] [PubMed]
  9. Wu, J.; Wang, J.; Hui, W.; Zhao, F.; Wang, P.; Su, C.; Gong, W. Physiology of plant responses to water stress and related genes: A review. Forests 2022, 13, 324. [Google Scholar] [CrossRef]
  10. Dresselhaus, T.; Hückelhoven, R. Biotic and abiotic stress responses in crop plants. Agronomy 2018, 8, 267. [Google Scholar] [CrossRef]
  11. Zandalinas, S.I.; Mittler, R.; Balfagón, D.; Arbona, V.; Gómez-Cadenas, A. Plant adaptations to the combination of drought and high temperatures. Physiol. Plant. 2018, 162, 2–12. [Google Scholar] [CrossRef] [PubMed]
  12. Raza, A.; Ashraf, F.; Zou, X.; Zhang, X.; Tosif, H. Plant adaptation and tolerance to environmental stresses: Mechanisms and perspectives. In Plant Ecophysiology and Adaptation under Climate Change: Mechanisms and Perspectives I: General Consequences and Plant Responses; Springer: Singapore, 2020; pp. 117–145. [Google Scholar]
  13. Mathur, S.; Jain, L.; Jajoo, A. Photosynthetic efficiency in sun and shade plants. Photosynthetica 2018, 56, 354–365. [Google Scholar] [CrossRef]
  14. Morales, A.; Kaiser, E. Photosynthetic acclimation to fluctuating irradiance in plants. Front. Plant Sci. 2020, 11, 268. [Google Scholar] [CrossRef] [PubMed]
  15. Shi, Y.; Ke, X.; Yang, X.; Liu, Y.; Hou, X. Plants response to light stress. J. Genet. Genom. 2022, 49, 735–747. [Google Scholar] [CrossRef]
  16. Blondeel, H.; Perring, M.P.; Depauw, L.; De Lombaerde, E.; Landuyt, D.; De Frenne, P.; Verheyen, K. Light and warming drive forest understorey community development in different environments. Glob. Chang. Biol. 2020, 26, 1681–1696. [Google Scholar] [CrossRef]
  17. Deng, J.; Fang, S.; Fang, X.; Jin, Y.; Kuang, Y.; Lin, F.; Liu, J.; Ma, J.; Nie, Y.; Ouyang, S. Forest understory vegetation study: Current status and future trends. For. Res. 2023, 3, 1–23. [Google Scholar]
  18. Hu, S.; Ding, Y.; Zhu, C. Sensitivity and responses of chloroplasts to heat stress in plants. Front. Plant Sci. 2020, 11, 375. [Google Scholar] [CrossRef] [PubMed]
  19. Qi, L.; Shi, Y.; Terzaghi, W.; Yang, S.; Li, J. Integration of light and temperature signaling pathways in plants. J. Integr. Plant Biol. 2022, 64, 393–411. [Google Scholar] [CrossRef]
  20. Jagadish, S.V.K.; Way, D.A.; Sharkey, T.D. Plant heat stress: Concepts directing future research. Plant Cell Environ. 2021, 44, 1992–2005. [Google Scholar] [CrossRef] [PubMed]
  21. Brown, L.A.; Meier, C.; Morris, H.; Pastor-Guzman, J.; Bai, G.; Lerebourg, C.; Gobron, N.; Lanconelli, C.; Clerici, M.; Dash, J. Evaluation of global leaf area index and fraction of absorbed photosynthetically active radiation products over North America using Copernicus Ground Based Observations for Validation data. Remote Sens. Environ. 2020, 247, 111935. [Google Scholar] [CrossRef]
  22. Jiménez-Leyva, A.; Gutiérrez, A.; Ojeda-Contreras, Á.J.; Vargas, G.; Esqueda, M.; Orozco-Avitia, J.-A. Seasonal phenology, shade reliance, and ecophysiology of wild Capsicum annuum var. glabriusculum in Sonoran Desert. J. Arid Environ. 2022, 201, 104736. [Google Scholar] [CrossRef]
  23. Fernandes, R.; Brown, L.; Canisius, F.; Dash, J.; He, L.; Hong, G.; Huang, L.; Le, N.Q.; MacDougall, C.; Meier, C. Validation of Simplified Level 2 Prototype Processor Sentinel-2 fraction of canopy cover, fraction of absorbed photosynthetically active radiation and leaf area index products over North American forests. Remote Sens. Environ. 2023, 293, 113600. [Google Scholar] [CrossRef]
  24. De Bock, A.; Belmans, B.; Vanlanduit, S.; Blom, J.; Alvarado-Alvarado, A.A.; Audenaert, A. A review on the leaf area index (LAI) in vertical greening systems. Build. Environ. 2023, 229, 109926. [Google Scholar] [CrossRef]
  25. Yamaguchi, H.; Yasutake, D.; Hirota, T.; Nomura, K. Nondestructive Measurement Method of Leaf Area Index Using Near-infrared Radiation and Photosynthetically Active Radiation Transmitted through a Leafy Vegetable Canopy. HortScience 2023, 58, 16–22. [Google Scholar] [CrossRef]
  26. Zhu, L.; Bloomfield, K.J.; Hocart, C.H.; Egerton, J.J.G.; O’Sullivan, O.S.; Penillard, A.; Weerasinghe, L.K.; Atkin, O.K. Plasticity of photosynthetic heat tolerance in plants adapted to thermally contrasting biomes. Plant Cell Environ. 2018, 41, 1251–1262. [Google Scholar] [CrossRef] [PubMed]
  27. Gil, K.E.; Park, C.M. Thermal adaptation and plasticity of the plant circadian clock. New Phytol. 2019, 221, 1215–1229. [Google Scholar] [CrossRef]
  28. Lauriks, F.; Salomón, R.L.; De Roo, L.; Goossens, W.; Leroux, O.; Steppe, K. Limited plasticity of anatomical and hydraulic traits in aspen trees under elevated CO2 and seasonal drought. Plant Physiol. 2022, 188, 268–284. [Google Scholar] [CrossRef]
  29. Grossman, J.J. Phenological physiology: Seasonal patterns of plant stress tolerance in a changing climate. New Phytol. 2023, 237, 1508–1524. [Google Scholar] [CrossRef] [PubMed]
  30. IUCN. The IUCN Red List of Threatened Species. Version 2024-1. Available online: https://www.iucnredlist.org (accessed on 22 August 2024).
  31. Fabricante, J.R. Sociabilidade de espécies da Mata Atlântica com a exótica invasora Artocarpus heterophyllus Lam. Rev. Biol. Neotrop./J. Neotrop. Biol. 2013, 10, 18–25. [Google Scholar] [CrossRef]
  32. da Silva Ribeiro, J.E.; Leite, A.P.; Nóbrega, J.S.; Alves, E.U.; Bruno, R.d.L.A.; de Albuquerque, M.B. Temperatures and substrates for germination and vigor of Erythroxylum pauferrense Plowman seeds. Acta Scientiarum. Biol. Sci. 2019, 41, e46030. [Google Scholar]
  33. Alvares, C.A.; Stape, J.L.; Sentelhas, P.C.; Gonçalves, J.L.d.M.; Sparovek, G. Köppen’s climate classification map for Brazil. Meteorol. Z. 2013, 22, 711–728. [Google Scholar] [CrossRef] [PubMed]
  34. da Silva Ribeiro, J.E.; Figueiredo, F.R.A.; dos Santos Coêlho, E.; de Albuquerque, M.B.; Pereira, W.E. Environmental factors variation in physiological aspects of Erythroxylum pauferrense. Rev. Bosque 2020, 41, 157–164. [Google Scholar] [CrossRef]
  35. Rosado, B.H.P.; Mattos, E.A.d. Variação temporal de características morfológicas de folhas em dez espécies do Parque Nacional da Restinga de Jurubatiba, Macaé, RJ, Brasil. Acta Bot. Bras. 2007, 21, 741–752. [Google Scholar] [CrossRef]
  36. Poorter, H.; Niinemets, Ü.; Poorter, L.; Wright, I.J.; Villar, R. Causes and consequences of variation in leaf mass per area (LMA): A meta-analysis. New Phytol. 2009, 182, 565–588. [Google Scholar] [CrossRef]
  37. Kluge, M.; Ting, I.P. Crassulacean Acid Metabolism: Analysis of an Ecological Adaptation; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2012; Volume 30. [Google Scholar]
  38. Witkowski, E.T.F.; Lamont, B.B. Leaf specific mass confounds leaf density and thickness. Oecologia 1991, 88, 486–493. [Google Scholar] [CrossRef] [PubMed]
  39. Barrs, H.D.; Weatherley, P.E. A re-examination of the relative turgidity technique for estimating water deficits in leaves. Aust. J. Biol. Sci. 1962, 15, 413–428. [Google Scholar] [CrossRef]
  40. Slavík, B.; Jarvis, M.S. Methods of Studying Plant Water Relations; Academia Publishing House of the Czechoslovak Academy of Sciences: Prague, Czech Republic, 1974. [Google Scholar]
  41. Bajji, M.; Kinet, J.-M.; Lutts, S. The use of the electrolyte leakage method for assessing cell membrane stability as a water stress tolerance test in durum wheat. Plant Growth Regul. 2002, 36, 61–70. [Google Scholar] [CrossRef]
  42. Klein, V.A. Física do Solo; SBCS: Viçosa, Brazil, 2008. [Google Scholar]
  43. Whitford, K.R.; Colquhoun, I.J.; Lang, A.R.G.; Harper, B.M. Measuring leaf area index in a sparse eucalypt forest: A comparison of estimates from direct measurement, hemispherical photography, sunlight transmittance and allometric regression. Agric. For. Meteorol. 1995, 74, 237–249. [Google Scholar] [CrossRef]
  44. Galvani, E.; de Lima, N.G.B. Fotografias hemisféricas em estudos microclimáticos: Referencial teórico-conceitual e aplicações. Ciência E Nat. 2014, 36, 215–221. [Google Scholar] [CrossRef]
  45. R Core Team. R Development Core Team R: A Language and Environment for Statistical Computing 2023; R Core Team: Vienna, Austria, 2023. [Google Scholar]
  46. Cernusak, L.A. Gas exchange and water-use efficiency in plant canopies. Plant Biol. 2020, 22, 52–67. [Google Scholar] [CrossRef]
  47. Lavoie-Lamoureux, A.; Sacco, D.; Risse, P.A.; Lovisolo, C. Factors influencing stomatal conductance in response to water availability in grapevine: A meta-analysis. Physiol. Plant. 2017, 159, 468–482. [Google Scholar] [CrossRef] [PubMed]
  48. Li, S.-l.; Tan, T.-T.; Fan, Y.-F.; Raza, M.A.; Wang, Z.-l.; Wang, B.-B.; Zhang, J.-w.; Tan, X.-m.; Ping, C.; Shafiq, I. Responses of leaf stomatal and mesophyll conductance to abiotic stress factors. J. Integr. Agric. 2022, 21, 2787–2804. [Google Scholar] [CrossRef]
  49. Liao, Q.; Ding, R.; Du, T.; Kang, S.; Tong, L.; Li, S. Stomatal conductance drives variations of yield and water use of maize under water and nitrogen stress. Agric. Water Manag. 2022, 268, 107651. [Google Scholar] [CrossRef]
  50. Taiz, L.; Zeiger, E.; Møller, I.M.; Murphy, A. Fisiologia e Desenvolvimento Vegetal; Artmed Editora: Porto Alegre, Brazil, 2017. [Google Scholar]
  51. Agurla, S.; Gahir, S.; Munemasa, S.; Murata, Y.; Raghavendra, A.S. Mechanism of stomatal closure in plants exposed to drought and cold stress. In Survival Strategies in Extreme Cold and Desiccation: Adaptation Mechanisms and Their Applications; Springer: Singapore, 2018; pp. 215–232. [Google Scholar]
  52. Buckley, T.N. How do stomata respond to water status? New Phytol. 2019, 224, 21–36. [Google Scholar] [CrossRef]
  53. Campelo, D.d.H.; Lacerda, C.F.; Sousa, J.A.; Correia, D.; Bezerra, A.M.E.; Araújo, J.D.M.; Neves, A.L.R. Leaf gas exchange and efficiency of photosystem II in adult plants of six forest species as function of the water supply in the soil. Rev. Árvore 2015, 39, 973–983. [Google Scholar] [CrossRef]
  54. Bijalwan, P.; Sharma, M.; Kaushik, P. Review of the effects of drought stress on plants: A systematic approach. Preprints 2022, 2022020014. [Google Scholar] [CrossRef]
  55. Pamungkas, S.S.T.; Farid, N. Drought stress: Responses and mechanism in plants. Rev. Agric. Sci. 2022, 10, 168–185. [Google Scholar] [CrossRef] [PubMed]
  56. Gago, J.; Daloso, D.M.; Carriquí, M.; Nadal, M.; Morales, M.; Araújo, W.L.; Nunes-Nesi, A.; Perera-Castro, A.V.; Clemente-Moreno, M.J.; Flexas, J. The photosynthesis game is in the” inter-play”: Mechanisms underlying CO2 diffusion in leaves. Environ. Exp. Bot. 2020, 178, 104174. [Google Scholar] [CrossRef]
  57. Wehr, R.; Munger, J.W.; McManus, J.B.; Nelson, D.D.; Zahniser, M.S.; Davidson, E.A.; Wofsy, S.C.; Saleska, S.R. Seasonality of temperate forest photosynthesis and daytime respiration. Nature 2016, 534, 680–683. [Google Scholar] [CrossRef]
  58. Wenzel, S.; Cox, P.M.; Eyring, V.; Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 2016, 538, 499–501. [Google Scholar] [CrossRef]
  59. Martin-StPaul, N.; Delzon, S.; Cochard, H. Plant resistance to drought depends on timely stomatal closure. Ecol. Lett. 2017, 20, 1437–1447. [Google Scholar] [CrossRef]
  60. Huber, A.E.; Melcher, P.J.; Piñeros, M.A.; Setter, T.L.; Bauerle, T.L. Signal coordination before, during and after stomatal closure in response to drought stress. New Phytol. 2019, 224, 675–688. [Google Scholar] [CrossRef] [PubMed]
  61. Leakey, A.D.B.; Ferguson, J.N.; Pignon, C.P.; Wu, A.; Jin, Z.; Hammer, G.L.; Lobell, D.B. Water use efficiency as a constraint and target for improving the resilience and productivity of C3 and C4 crops. Annu. Rev. Plant Biol. 2019, 70, 781–808. [Google Scholar] [CrossRef] [PubMed]
  62. Kochhar, S.L.; Gujral, S.K. Plant Physiology: Theory and Applications; Cambridge University Press: Cambridge, UK, 2020. [Google Scholar]
  63. Farooq, M.; Hussain, M.; Ul-Allah, S.; Siddique, K.H.M. Physiological and agronomic approaches for improving water-use efficiency in crop plants. Agric. Water Manag. 2019, 219, 95–108. [Google Scholar] [CrossRef]
  64. Silva, F.G.d.; Dutra, W.F.; Dutra, A.F.; Oliveira, I.M.d.; Filgueiras, L.; Melo, A.S.d. Gas exchange and chlorophyll fluorescence of eggplant grown under different irrigation depths. Rev. Bras. De Eng. Agrícola Ambient. 2015, 19, 946–952. [Google Scholar] [CrossRef]
  65. Cotton, C.A.R.; Edlich-Muth, C.; Bar-Even, A. Reinforcing carbon fixation: CO2 reduction replacing and supporting carboxylation. Curr. Opin. Biotechnol. 2018, 49, 49–56. [Google Scholar] [CrossRef]
  66. Wang, X.; Wang, F.; Sang, Y.; Liu, H. Full-spectrum solar-light-activated photocatalysts for light–chemical energy conversion. Adv. Energy Mater. 2017, 7, 1700473. [Google Scholar] [CrossRef]
  67. Kalaji, H.M.; Schansker, G.; Brestic, M.; Bussotti, F.; Calatayud, A.; Ferroni, L.; Goltsev, V.; Guidi, L.; Jajoo, A.; Li, P. Frequently asked questions about chlorophyll fluorescence, the sequel. Photosynth. Res. 2017, 132, 13–66. [Google Scholar] [CrossRef]
  68. Perboni, A.T.; Martinazzo, E.G.; Silva, D.M.; Bacarin, M.A. Baixas temperaturas sobre a fluorescência da clorofila a em plantas de diferentes híbridos de canola. Ciênc. Rural 2014, 45, 215–222. [Google Scholar] [CrossRef]
  69. Konrad, M.L.F.; Silva, J.A.B.d.; Furlani, P.R.; Machado, E.C. Trocas gasosas e fluorescência da clorofila em seis cultivares de cafeeiro sob estresse de alumínio. Bragantia 2005, 64, 339–347. [Google Scholar] [CrossRef]
  70. Baker, N.R. Chlorophyll fluorescence: A probe of photosynthesis in vivo. Annu. Rev. Plant Biol. 2008, 59, 89–113. [Google Scholar] [CrossRef] [PubMed]
  71. Suassuna, J.F.; de Melo, A.S.; Costa, F.S.; Fernandes, P.D.; Ferreira, R.S.; da Silva Sousa, M.S. Eficiência fotoquímica e produtividade de frutos de meloeiro cultivado sob diferentes lâminas de irrigação. Semin. Ciênc. Agrár. 2011, 32, 1251–1262. [Google Scholar] [CrossRef]
  72. Souza, J.T.A.; da Silva Ribeiro, J.E.; de Farias Ramos, J.P.; de Sousa, W.H.; Araújo, J.S.; Lima, G.F.C.; Dias, J.A. Rendimento quântico e eficiência de uso da água de genótipos de palma forrageira no Semiárido brasileiro. Arch. De Zootec. 2019, 68, 268–273. [Google Scholar] [CrossRef]
  73. Silva, L.d.A.; Brito, M.E.B.; Sá, F.V.d.S.; Moreira, R.C.L.; Soares Filho, W.d.S.; Fernandes, P.D. Mecanismos fisiológicos em híbridos de citros sob estresse salino em cultivo hidropônico. Rev. Bras. Eng. Agríc. Ambient. 2014, 18, 1–7. [Google Scholar] [CrossRef]
  74. Kouřil, R.; Dekker, J.P.; Boekema, E.J. Supramolecular organization of photosystem II in green plants. Biochim. Biophys. Acta (BBA)-Bioenerg. 2012, 1817, 2–12. [Google Scholar] [CrossRef]
  75. de Souza, R.R.; Cavalcante, M.Z.B.; Silva, E.M.; Amaral, G.C.; Brito, L.P.S.; Avelino, R.C. Alterações morfofisiológicas e crescimento de helicônias em função de diferentes ambientes de sombreamento. Comun. Sci. 2016, 7, 214–222. [Google Scholar] [CrossRef]
  76. Adler, P.B.; Salguero-Gómez, R.; Compagnoni, A.; Hsu, J.S.; Ray-Mukherjee, J.; Mbeau-Ache, C.; Franco, M. Functional traits explain variation in plant life history strategies. Proc. Natl. Acad. Sci. USA 2014, 111, 740–745. [Google Scholar] [CrossRef] [PubMed]
  77. Díaz, S.; Kattge, J.; Cornelissen, J.H.C.; Wright, I.J.; Lavorel, S.; Dray, S.; Reu, B.; Kleyer, M.; Wirth, C.; Colin Prentice, I. The global spectrum of plant form and function. Nature 2016, 529, 167–171. [Google Scholar] [CrossRef]
  78. Reich, P.B. The world-wide ‘fast–slow’plant economics spectrum: A traits manifesto. J. Ecol. 2014, 102, 275–301. [Google Scholar] [CrossRef]
  79. Niinemets, Ü. Global-scale climatic controls of leaf dry mass per area, density, and thickness in trees and shrubs. Ecology 2001, 82, 453–469. [Google Scholar] [CrossRef]
  80. Petter, G.; Wagner, K.; Wanek, W.; Sánchez Delgado, E.J.; Zotz, G.; Cabral, J.S.; Kreft, H. Functional leaf traits of vascular epiphytes: Vertical trends within the forest, intra-and interspecific trait variability, and taxonomic signals. Funct. Ecol. 2016, 30, 188–198. [Google Scholar] [CrossRef]
  81. Larcher, W.; de Assis Prado, C.H.B. Ecofisiologia Vegetal; RiMa Artes e textos São Carlos: São Carlos, Brazil, 2000. [Google Scholar]
  82. Caparrotta, S.; Masi, E.; Atzori, G.; Diamanti, I.; Azzarello, E.; Mancuso, S.; Pandolfi, C. Growing spinach (Spinacia oleracea) with different seawater concentrations: Effects on fresh, boiled and steamed leaves. Sci. Hortic. 2019, 256, 108540. [Google Scholar] [CrossRef]
  83. Ma, F.; Barrett-Lennard, E.G.; Tian, C.Y. Changes in cell size and tissue hydration (‘succulence’) cause curvilinear growth responses to salinity and watering treatments in euhalophytes. Environ. Exp. Bot. 2019, 159, 87–94. [Google Scholar] [CrossRef]
  84. Boeger, M.R.T.; Wisniewski, C.; Reissmann, C.B. Nutrientes foliares de espécies arbóreas de três estádios sucessionais de floresta ombrófila densa no sul do Brasil. Acta Bot. Bras. 2005, 19, 167–181. [Google Scholar] [CrossRef]
  85. Cabral, R.D.C.; Melo Junior, J.C.F.d.; Matilde-Silva, M. Plasticidade morfoanatômica foliar em Smilax campestris (Smilacaceae) em gradiente ambiental de restinga, SC, Brasil. Hoehnea 2018, 45, 173–183. [Google Scholar] [CrossRef]
  86. Peloso, A.d.F.; Tatagiba, S.D.; Reis, E.F.d.; Pezzopane, J.E.M.; Amaral, J.F.T.d. Limitações fotossintéticas em folhas de cafeeiro arábica promovidas pelo déficit hídrico. Coffee Sci. 2017, 12, 389–399. [Google Scholar] [CrossRef]
  87. de Almeida, J.A.S.; Lopes, A.R.; Pantano, A.P.; Silvarolla, M.B.; Mistro, J.C. Análise morfofisiológica de plantas de Coffea arabica L. em períodos de seca e de precipitação. Agrometeoros 2018, 26, 173–180. [Google Scholar] [CrossRef]
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Figure 1. Climatic data (precipitation and temperature) collected at the Meteorological Station of the Center for Agrarian Sciences, Federal University of Paraíba, municipality of Areia, state of Paraíba, Brazil, for the period from September 2017 to August 2018 and the expected average of the last 30 years (1987–2016). Adapted from Ribeiro et al. (2018) [1].
Figure 1. Climatic data (precipitation and temperature) collected at the Meteorological Station of the Center for Agrarian Sciences, Federal University of Paraíba, municipality of Areia, state of Paraíba, Brazil, for the period from September 2017 to August 2018 and the expected average of the last 30 years (1987–2016). Adapted from Ribeiro et al. (2018) [1].
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Figure 2. Rainfall (a), temperature (b), leaf area index (c), visible sky fraction (d), photosynthetically active radiation (PAR) (e), soil moisture (f), soil surface temperature (ST0 cm) (g), and soil temperature at a depth of 20 cm (ST20 cm) (h) at different seasons (D: dry, R: rainy, and I: intermediate), in the Mata do Pau-Ferro State Park, Areia, Paraíba, Brazil. Lowercase letters compare the seasons (rainy, dry, and intermediate) using the Scott–Knott test at a 5% probability level.
Figure 2. Rainfall (a), temperature (b), leaf area index (c), visible sky fraction (d), photosynthetically active radiation (PAR) (e), soil moisture (f), soil surface temperature (ST0 cm) (g), and soil temperature at a depth of 20 cm (ST20 cm) (h) at different seasons (D: dry, R: rainy, and I: intermediate), in the Mata do Pau-Ferro State Park, Areia, Paraíba, Brazil. Lowercase letters compare the seasons (rainy, dry, and intermediate) using the Scott–Knott test at a 5% probability level.
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Figure 3. Net CO2 assimilation (a), stomatal conductance (b), transpiration (c), and internal CO2 concentration (d) in individuals of E. pauferrense in different seasons (rainy, dry, and intermediate). Uppercase letters compare the seasons (rainy, dry, and intermediate), and lowercase letters compare the months of seasons using the Scott–Knott test at a 5% probability level.
Figure 3. Net CO2 assimilation (a), stomatal conductance (b), transpiration (c), and internal CO2 concentration (d) in individuals of E. pauferrense in different seasons (rainy, dry, and intermediate). Uppercase letters compare the seasons (rainy, dry, and intermediate), and lowercase letters compare the months of seasons using the Scott–Knott test at a 5% probability level.
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Figure 4. Instantaneous water use efficiency (a) and instantaneous carboxylation efficiency (b) in individuals of E. pauferrense at different seasons (rainy, dry, and intermediate). Uppercase letters compare the seasons (rainy, dry, and intermediate), and lowercase letters compare the months of seasons using the Scott–Knott test at a 5% probability level.
Figure 4. Instantaneous water use efficiency (a) and instantaneous carboxylation efficiency (b) in individuals of E. pauferrense at different seasons (rainy, dry, and intermediate). Uppercase letters compare the seasons (rainy, dry, and intermediate), and lowercase letters compare the months of seasons using the Scott–Knott test at a 5% probability level.
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Figure 5. Initial fluorescence (a), maximum fluorescence (b), variable fluorescence (c), and maximum quantum yield of PSII (d) in individuals of E. pauferrense at different seasons (rainy, dry, and intermediate). Uppercase letters compare the seasons (rainy, dry, and intermediate), and lowercase letters compare the months of seasons using the Scott–Knott test at a 5% probability level.
Figure 5. Initial fluorescence (a), maximum fluorescence (b), variable fluorescence (c), and maximum quantum yield of PSII (d) in individuals of E. pauferrense at different seasons (rainy, dry, and intermediate). Uppercase letters compare the seasons (rainy, dry, and intermediate), and lowercase letters compare the months of seasons using the Scott–Knott test at a 5% probability level.
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Figure 6. Chlorophyll a (a), chlorophyll b (b), total chlorophyll (c), and the ratio between chlorophyll a and chlorophyll b (Chl a/Chl b ratio) (d) in individuals of E. pauferrense in different seasons (rainy, dry, and intermediate). Uppercase letters compare the seasons (rainy, dry, and intermediate), and lowercase letters compare the months of seasons using the Scott–Knott test at a 5% probability level.
Figure 6. Chlorophyll a (a), chlorophyll b (b), total chlorophyll (c), and the ratio between chlorophyll a and chlorophyll b (Chl a/Chl b ratio) (d) in individuals of E. pauferrense in different seasons (rainy, dry, and intermediate). Uppercase letters compare the seasons (rainy, dry, and intermediate), and lowercase letters compare the months of seasons using the Scott–Knott test at a 5% probability level.
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Figure 7. Leaf mass per unit area (a), leaf blade thickness (b), succulence (c), and density (d) in individuals of E. pauferrense at different seasons (rainy, dry, and intermediate). Uppercase letters compare the seasons (rainy, dry, and intermediate), and lowercase letters compare the months of seasons using the Scott–Knott test at a 5% probability level.
Figure 7. Leaf mass per unit area (a), leaf blade thickness (b), succulence (c), and density (d) in individuals of E. pauferrense at different seasons (rainy, dry, and intermediate). Uppercase letters compare the seasons (rainy, dry, and intermediate), and lowercase letters compare the months of seasons using the Scott–Knott test at a 5% probability level.
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Figure 8. Relative content of water (a), leaf moisture (b), and electrolyte leakage (c) in individuals of E. pauferrense at different seasons (rainy, dry, and intermediate). Uppercase letters compare the seasons (rainy, dry, and intermediate), and lowercase letters compare the months of seasons using the Scott–Knott test at a 5% probability level.
Figure 8. Relative content of water (a), leaf moisture (b), and electrolyte leakage (c) in individuals of E. pauferrense at different seasons (rainy, dry, and intermediate). Uppercase letters compare the seasons (rainy, dry, and intermediate), and lowercase letters compare the months of seasons using the Scott–Knott test at a 5% probability level.
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Figure 9. Principal component analysis (PC1 and PC2) between Group I and Group II variables. Table 3 presents abbreviations.
Figure 9. Principal component analysis (PC1 and PC2) between Group I and Group II variables. Table 3 presents abbreviations.
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Figure 10. Pearson’s correlation between ecophysiological and environmental variables. * Significant correlation at 5% (p < 0.05); ** Significant correlation at 1% (p < 0.01).
Figure 10. Pearson’s correlation between ecophysiological and environmental variables. * Significant correlation at 5% (p < 0.05); ** Significant correlation at 1% (p < 0.01).
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Table 1. Physicochemical analysis of soil at 0–20 cm depth in the experimental area.
Table 1. Physicochemical analysis of soil at 0–20 cm depth in the experimental area.
pH (H2O)PK+Na+H+ + Al3+Al3+Ca2+Mg2+SBsCECVOMSandSiltClay
mg dm−3cmolc dm−3%g kg−1g kg−1
4.976.96104.890.089.740.54.553.558.4418.1846.1258.55640167193
SBs: sum of bases; CEC: cation exchange capacity; V: base saturation; OM: organic matter.
Table 2. Wilks’ lambda multivariate test.
Table 2. Wilks’ lambda multivariate test.
Canonical FunctionR2FaDF1DF2p-Value
10.98289.74152481.21<0.0001
20.94726.20126430.55<0.0001
30.86754.16102377.57<0.0001
40.71083.0080322.04<0.0001
50.62502.4960263.76<0.0001
60.56672.1042202.490.0004
70.54591.68261380.0300
80.25410.9112700.5381
Fa: approximate value of F; DF1: degrees of freedom of treatments; DF2: Degrees of freedom from error.
Table 3. Canonical correlations and first canonical pair between the characteristics of groups I and II.
Table 3. Canonical correlations and first canonical pair between the characteristics of groups I and II.
VariablesCanonical Pair
Group I
Abiotic factors
Leaf area index (LAI)0.2740
Visible sky fraction (VSF)−0.5823
Photosynthetically active radiation (PAR)−0.8019
Soil moisture (SM)0.7173
Soil temperature—0 cm (ST0cm)−0.7750
Soil temperature—20 cm (ST20cm)−0.8065
Air temperature (Tair)−0.3253
Rainfall (Rain)0.8939
Group II
Gas exchange
Net assimilation rate of CO2 (A)0.5874
Stomatal conductance (gs)0.6334
Transpiration rate (E)0.5084
Internal concentration of CO2 (Ci)0.6707
Instantaneous water use efficiency (WUE)0.1051
Instantaneous carboxylation efficiency (iCE)0.4465
Chlorophyll content
Chlorophyll a (Chl a)0.4761
Chlorophyll b (Chl b) 0.4445
Total chlorophyll (T chl)0.4956
Chlorophyll a/b ratio (Chl a/Chl b)−0.3999
Chlorophyll a fluorescence
Initial fluorescence (F0)−0.9179
Maximum fluorescence (Fm)0.6240
Variable fluorescence (Fv)0.7087
Maximum quantum yield of PSII (Fv/Fm)0.9073
Morphofunctional attributes and water relations
Leaf mass per unit area (LMA)−0.6198
Succulence (SUC)−0.4930
Leaf thickness (Thi)−0.3254
Leaf density (Den)−0.7536
Relative water content (RWC)0.9171
Leaf moisture (LM)0.9156
Electrolyte leakage (EL)0.2955
Wilk’s lambda 0.00002970
Cumulative variance (%)65.78
R20.9828
Significance**
** Significant at 1% probability by the chi-square test; R = canonical correlation.
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MDPI and ACS Style

Ribeiro, J.E.d.S.; Coêlho, E.d.S.; Figueiredo, F.R.A.; Pereira, W.E.; Dias, T.J.; Melo, M.F.; Silveira, L.M.d.; Barros Júnior, A.P.; Albuquerque, M.B.d. Seasonal Ecophysiological Dynamics of Erythroxylum pauferrense in an Open Ombrophilous Forest of the Brazilian Atlantic Forest. Climate 2024, 12, 128. https://doi.org/10.3390/cli12090128

AMA Style

Ribeiro JEdS, Coêlho EdS, Figueiredo FRA, Pereira WE, Dias TJ, Melo MF, Silveira LMd, Barros Júnior AP, Albuquerque MBd. Seasonal Ecophysiological Dynamics of Erythroxylum pauferrense in an Open Ombrophilous Forest of the Brazilian Atlantic Forest. Climate. 2024; 12(9):128. https://doi.org/10.3390/cli12090128

Chicago/Turabian Style

Ribeiro, João Everthon da Silva, Ester dos Santos Coêlho, Francisco Romário Andrade Figueiredo, Walter Esfrain Pereira, Thiago Jardelino Dias, Marlenildo Ferreira Melo, Lindomar Maria da Silveira, Aurélio Paes Barros Júnior, and Manoel Bandeira de Albuquerque. 2024. "Seasonal Ecophysiological Dynamics of Erythroxylum pauferrense in an Open Ombrophilous Forest of the Brazilian Atlantic Forest" Climate 12, no. 9: 128. https://doi.org/10.3390/cli12090128

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