Mimicking natural wind conditions using the variable boundary layer method reveals species-specific variability in gas exchange dynamics

Mimicking natural wind conditions using the variable boundary layer method reveals species-specific variability in gas exchange dynamics | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 22 January 2025 V1 Latest version Share on Mimicking natural wind conditions using the variable boundary layer method reveals species-specific variability in gas exchange dynamics Authors : Ariel Joseph , Adi Yaaran , Bar Ben Zeev , Uri Hochberg 0000-0002-7649-7004 , Or Shapira , and Yotam Zait 0000-0003-4266-1635 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.173751962.23312884/v1 488 views 235 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Leaf gas exchange and evaporation are governed by the water vapor pressure gradient and resistances, including stomatal and boundary layers. Wind speed plays a critical role in modifying these processes, yet the effects of natural wind variability on photosynthesis and stomatal behavior remain understudied. We used a variable boundary layer method to simulate natural wind variability and compared its effects with constant high wind conditions, commonly employed in gas exchange research. Our results showed that angiosperms were highly sensitive to changes in wind. Grapevines benefitted from increased wind, with photosynthesis improving by 14% and stomatal conductance increasing by 20% under high wind. Wheat displayed extreme sensitivity, with stomatal conductance increasing by 15% as wind intensified and decreasing similarly as it weakened, driven by mechanical interactions between guard and epidermal cells. In banana, stomatal conductance under constant high wind was 36% lower than under variable wind. In contrast, gymnosperms and ferns exhibited minimal responses, maintaining stable gas exchange due to structural adaptations like sunken stomata and limited guard-epidermal cell interactions. These findings emphasize the need to incorporate realistic wind dynamics in gas exchange studies. Ignoring wind variability can lead to inaccurate measurements, especially in sensitive angiosperms, and misinterpretation of their physiological performance. Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex Introduction The rate of evaporation from a plant leaf is primarily governed by the water vapor pressure gradient between the leaf’s internal sites of evaporation and the surrounding air beyond the leaf boundary layer (Ting & Loomis, 1965). This process is also influenced by resistance along the diffusion pathway, which includes the leaf cells (Gaastra, 1959), stomata (Darwin & Pertz, 1911), and the leaf surface boundary layer (Bange, 1953). The leaf boundary layer is a thin, layer of air moving mainly in parallel to the leaf surface slowing down as it gets closer to the surface. Acting as a buffer, this layer regulates the exchange of heat, and gases between the leaf and the atmosphere (Nobel, 2020). Wind speed plays a crucial role in determining boundary layer thickness, with higher speeds thinning the layer and enhancing water loss, while lower speeds maintain a thicker boundary layer, reducing these rates (Huang et al., 2015; Nobel, 1975). The extent of these effects varies significantly between species, influenced by leaf morphology \RL and surface properties like trichomes, (Renard & Demessemacker, 1983; Shaw, 1977; Siqueira et al., 2012). Commercial gas exchange systems often use high fan speeds to better mix the measurement chamber gases thus resulting in artificially high boundary layer conductance (gb.l)(Busch et al., 2024). Therefore, in many cases such as low wind conditions or species with large leaves, or high wind sensitivity, leaf level gas exchange measurements may not accurately reflect natural stomatal conductance and assimilation rates (Benz & Martin, 2006; chreuder et al., 2001) Fluctuations in wind speed further compound this complexity by dynamically altering boundary layer conductance, affecting both gas exchange rates and stomatal responses (Aphalo & Jarvis, 1993; Schymanski & Or, 2016). Stomatal responses to environmental factors such as vapor pressure deficit (VPD) and wind are strongly influenced by the interactions between the epidermal and guard cells (McAdam & Brodribb, 2014; Mott & Franks, 2001; Raschke, 1970; Shapira et al., 2024). Understanding the mechanical interactions between epidermal and guard cells is critical for accurately interpreting gas exchange data and assessing plant physiological performance under natural environmental conditions. Mechanical advantage describes the relationship of epidermal cells (or subsidiary cells) and the guard cells. The epidermal cells exert a physical effect on the guard cells during water loss or gain (DeMichele & Sharpe, 1973; Glinka, 1971). Guard cells need to displace adjacent subsidiary cells laterally to achieve stomatal opening (Franks et al., 1998; Mott & Franks, 2001). If the subsidiary cells maintain high turgor, their mechanical advantage can limit or prevent stomatal opening, even when the guard cells have high turgor pressure. The reduction of subsidiary cell turgor enables rapid and wide stomatal movement (Buckley et al., 2011; Hanson et al., 2013; McAdam & Brodribb, 2015). Assuming no direct water loss from the guard cells (Nonami et al., 1990), when transpiration-driven water loss reduces turgor pressure, the guard cells maintain their turgor more effectively than subsidiary cells, modulating stomatal opening when water potential drops and closing when water potential rises (Franks & Farquhar, 2007; Mott & Franks, 2001). Recent work by Shapira et al. (2024) demonstrated that wind speed affects stomatal dynamics largely through passive mechanisms i.e ”wrong-way” response which passively opens stomata presumably due to decreased epidermal turgor caused by increased water loss. This effect significantly increased stomatal kinetics, with a 42% increase in opening rate under high wind speeds compared to low wind in Vicia faba . However, studies that have tested this effect under fluctuating wind conditions are absent. Quantifying the bias introduced by using high winds when natural wind speeds are low is essential, as this may wrongly estimate stomatal status and distort gas exchange measurements, leading to inaccurate conclusions about physiological performance. The use of gas exchange systems enables researchers to configure measurements better to track ambient conditions, such as light, temperature, and humidity, providing insights into plant physiological processes under natural or simulated environments (Lawson & Vialet-Chabrand, 2019; Matthews et al., 2019; McAusland et al., 2016; Suwannarut et al., 2023). For example, configuring a light source to replicate ambient light levels, as measured by an attached light sensor, ensures that photosynthetic responses are recorded under conditions that closely mimic those in the natural environment (Matthews et al., 2017). Similarly, integrating real-time wind speed tracking into experimental setups can provide a more realistic and comprehensive assessment of gas exchange dynamics. However, research that systematically monitors wind speed and stomatal responses across diverse species and environments remains limited. This study investigates the effects of wind-induced variations in boundary layer conductance on gas exchange using a new method allowing online tracking and adjustment of ambient wind speed inside the chamber (Variable Boundary Layer Setup). We measured a range of species, including the angiosperms; banana ( Musa acuminata ), grapevine ( Vitis vinifera cv. Cabernet Sauvignon ), tomato ( Solanum lycopersicum ), the gymnosperm Pinus halepensis and ferns; Nephrolepis exaltata and Adiantum capillus-veneris . These species were selected for their contrasting morphologies, stomatal behaviors, and ecological adaptations. We hypothesize that survey gas exchange measurements using commercial systems produce an artifactual result due to neglecting the effect of wind speed on the boundary layer conductance. Specifically, we aim to evaluate the impacts of wind variability on gas exchange parameters in the field under native environmental conditions and in controlled greenhouse settings. Understanding these dynamics will enhance the accuracy of photosynthesis and stomatal conductance measurements and improve modeling and water use efficiency (WUE) assessments. Materials and Methods Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex Plant material and growth conditions Gas exchange measurements were conducted on seven species: banana ( Musa acuminata cv. Grand Nain), wheat ( Triticum durum ), grapevine ( Vitis vinifera ), Pinus halepensis , and two ferns (Nephrolepis exaltata and Adiantum capillus-veneris ). The plants were grown under varying environmental conditions to simulate different wind regimes and growth environments. One year old Banana plants were cultivated in an open-field high-wind climate at the experimental farm of the Robert H. Smith Faculty of Agriculture, Food and Environment in Rehovot, Israel (31°54’25”N, 34°48’00”E), where relative humidity during the day ranged from 32% to 50%, solar radiation was 800–1500 µmol m⁻² s⁻¹, and temperatures were between 25°C and 32°C. Grapevines were also grown in an open-field high-wind environment at Barkan Vineyards in Kibbutz Hulda, Israel (31°50’26”N, 34°53’02”E), under conditions of 40%–65% relative humidity, 800–2000 µmol m⁻² s⁻¹ solar radiation, and temperatures ranging from 30°C to 35°C. One year old Banana, Triticum durum and 2-years-old Pinus halepensis and 12 weeks old wheat plants were cultivated in a low-wind-controlled greenhouse at the Faculty of Agriculture, Rehovot, Israel. Greenhouse conditions included day/night temperatures of 28–25°C/22–20°C, relative humidity of ~50% during the day and ~90% at night, and photosynthetically active radiation (PAR) of 850–1000 µmol m⁻² s⁻¹. These plants were grown in 5L pots filled with Green 20 potting soil (Even-Ari, Israel), fertilized with a 6-6-6 NPK fertilizer solution and Osmocote, and irrigated three times daily for 10 minutes to ensure well-watered conditions. Ferns ( Nephrolepis exaltata and Adiantum capillus-veneris ) were grown in an indoor growth chamber under controlled conditions, with PAR set at 350 µmol m⁻² s⁻¹, day/night temperatures of 28–25°C/22–20°C, and relative humidity similar to the greenhouse (~60% during the day and ~80% at night). Gas exchange measurements Variable boundary layer setup was conducted using the LI-6800 Portable Photosynthesis System, coupled with an omnidirectional air velocity transducer (model 8475, TSI, Singapore) placed under the abaxial leaf surface to measure wind speed outside the leaf boundary layer ( Fig. 1 ). The transducer was connected via the LI-6800F’s auxiliary channel, converting voltage output to wind speed (m s⁻¹) to control the leaf chamber wind speed through the fan. A second LI-6800 system applied constant wind (8000 rpm), enabling comparison between variable and constant wind speeds. Leaf chamber sizes were 2 cm² for the ferns, wheat, tomato, and Pinus halepensis , and 6 cm² for other species. Environmental conditions remained constant across measurements (flow rate: 530 μmol s -1 , RH: 60%, CO₂: 420 μmol mol -1 , PAR: 1000 μmol photons m -2 s -1 , except for ferns, measured at PAR 350 μmol photons m -2 s -1 ). The transformation from wind speed to fan speed was calibrated according to Shapira et al. (2024), with measurements taken every minute from fully expanded, healthy leaves. The boundary layer conductance ( g bl ) was corrected according to the filter paper method described in Shapira et al. (2024). To measure air velocity, we used an omnidirectional air velocity transducer to capture the electrical voltage signal (V) generated by the sensor. The signal was then converted into wind speed (rpm) for integration into the LI-6800 gas exchange system. A correlation was established between fan speed (rpm) and the average voltage (V) received from the sensor. Using the resulting data, a linear trend line was generated with the equation y=0.0004x−0.2571 (R 2 =0.9891), where y represents voltage and x represents fan speed. Within the LI-6800, we defined a new variable (V1) calculated by multiplying the voltage by the coefficient derived from the trend line. This enabled the system to measure variable wind speeds in real time. To convert fan speed (rpm) into wind speed (m/s), a second calibration was performed. A correlation plot was created by measuring wind speed in constant upward intervals of fan speed and wind speed. From this plot, a second linear trend line was generated with the equation y=0.0002x−0.1475 (R 2 =0.9893), where y represents wind speed in m/s and x represents fan speed in rpm. This equation was subsequently used to compute wind speed during experiments. Measurement of response in stomatal conductance to leaf excision To assess the mechanical advantage levels of the studied species, we quantified the characteristics of stomatal movements during the response to leaf excision and calculated the opening level during the initial phase (wrong way response). The experimental duration was standardized to 40 minutes for all species, with uniform conditions maintained across the instrument settings. Each plant was connected to the LI-6800 system and allowed to stabilize for 30 minutes. Following stabilization, the leaf inside the chamber was excised 3 minutes after initiating the program, and measurements were recorded at 1-minute intervals for the remainder of the experiment. All other parameters were kept constant, as described above. To calculate the change in stomatal conductance ( g sw ) during the ”wrong way” response, the following formula was used: % change in g sw = ( g sw at t gsw max post-excision- g sw at t 0 excision)/ g sw at t gsw max post excision) ×100. Guard cell and epidermal cell morphology analysis Guard cell and epidermal cell sizes were measured to calculate the guard cell-to-epidermal cell size ratio. Images were captured using an ECHO microscope (Model: REB-01-E2, BICO) and analyzed with ImageJ software (http://rsb.info.nih.gov/ij/), following the methodology outlined by Yaaran et al. (2019). Figure 1: Traditional vs. variable boundary layer gas exchange setups. The variable boundary layer setup employs a gas exchange chamber attached to a fully expanded leaf, with airflow regulated to simulate varying natural wind conditions. Under high wind conditions (a), the leaf boundary layer (illustrated by light blue coloring) diminishes. When measuring gas exchange on leaves exposed to high wind conditions, both the variable boundary layer setup (b) and traditional setup (c) use high mixing fan speeds, leading to low boundary resistance that closely mimics real environmental conditions. In contrast, leaves exposed to low wind conditions naturally develop a thicker boundary layer (d). Here, the two setups differ significantly. The variable boundary layer setup (e) uses low mixing fan speeds in the chamber to maintain a thicker boundary layer, better simulating the natural environment. The traditional setup, however, applies high mixing fan speeds (f), artificially thinning the boundary layer and creating a mismatch between chamber conditions and the natural environment. This discrepancy can distort photosynthesis and stomatal conductance measurements, leading to potential over- or underestimations of gas exchange rates. Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex Impact of wind speed on leaf gas exchange and water use efficiency Under open plantation conditions, natural wind speed in grapevine ( Vitis vinifera ) fluctuated between 0 and 3 m s⁻¹ during variable wind conditions, while constant high wind was maintained at 1.6 m s⁻¹ ( Fig. 2a ) Comparing these methods, net photosynthesis (Aₙ) under constant wind reached a stable peak of ~14 μmol CO₂ m⁻² s⁻¹, with a gradual decline around midday. In contrast, under variable wind, Aₙ peaked at a slightly lower value of declined more sharply throughout the day ( Fig. 2b ). Stomatal conductance ( g sw ) followed a similar trend: under constant high wind, g sw was stable at than the ~0.15 mol H₂O m⁻² s⁻¹ observed under variable wind ( Fig. 2c ). In the fern Nephrolepis exaltata , which was subjected to the same wind regimes, both Aₙ and g sw remained steady regardless of the wind conditions ( Fig. 2 d,e,f ). The responses of Triticum durum and Pinus halepensis to wind variability revealed a stark contrast (Fig. 3). While Pinus halepensis showed minimal sensitivity, with stomatal conductance ( g sw ) remaining steady and photosynthesis (Aₙ) exhibiting only minor fluctuations ( Fig. 3a–c ), Triticum durum displayed significant responsiveness to wind changes. Under constant high wind, Aₙ stabilized at peak values, but variable wind led to an initial 13% decrease in Aₙ, followed by a gradual midday decline ( Fig. 3d ). Similarly, g sw under constant high wind remained steady, while variable wind induced transient early peaks and subsequent fluctuations that mirrored changes in the wind regime ( Fig. 3e ). When comparing the wind-in wind-out method to constant high wind in banana plants ( Musa acuminata ) grown in high wind (field) and low wind (greenhouse) environments, similar response patterns were observed, but the effects were significantly more pronounced in the low wind environment ( Fig. 4 ). Under high wind conditions, net photosynthesis ( A n ) and stomatal conductance (g sw ) were suppressed, with A n peaking at 14 μmol CO₂ m⁻² s⁻¹ represent a 12.5% reduction compared to variable wind ( Fig 4b ), and g sw at 0.14 mol H₂O m⁻² s⁻¹, 36% lower than under variable wind ( Fig 4c ). In the low wind environment, the impact of the wind-in wind-out method was even more prominent: A n peaked at 12 μmol CO₂ m⁻² s⁻¹ under variable wind, three times higher than the 4 μmol CO₂ m⁻² s⁻¹ recorded under constant wind ( Fig. 4e ). Similarly, g sw reached 0.13 mol H₂O m⁻² s⁻¹ under variable wind, more than double the 0.06 mol H₂O m⁻² s⁻¹ observed under constant wind (Fig. 4f) . Furthermore, banana plants in the low wind environment exhibited pronounced periodic stomatal oscillations with a consistent time period of 10 minutes. These oscillations were more sharply defined under constant high wind, highlighting the dynamic and environment-specific responses of banana plants to wind variability. Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex Species-specific sensitivities to wind conditions Among the species measured, wheat exhibited the highest sensitivity to wind variability, followed by tomato and grapevine, all of which showed overestimations of gas exchange rates under variable wind conditions compared to constant high wind conditions. In contrast, banana was highly sensitive to wind variability but exhibited underestimations of photosynthetic rates under constant high wind conditions. Fern and Pinus halepensis s showed minimal or no variance in response to wind conditions, ( Fig. 5a ). Stomatal conductance ( g sw ) generally mirrored these trends: wheat, tomato, and grapevine showed pronounced fluctuations under variable wind, leading to overestimations, while banana displayed greater variability and underestimations under constant high wind. Fern and Pinus halepensis , however, maintained stable gas exchange rates and were largely unaffected by wind variability ( Fig. 5b ). We examined the relationship between wind speed and photosynthesis rate ( A n ) ( Fig. 6a ) and g sw ( Fig. 6b ) across species. Triticum durum and Vitis vinifera showed a positive trend, with both A n and g sw increasing under higher wind speeds. Solanum lycopersicum exhibited a moderate increase in both parameters, reflecting a less pronounced but positive response to wind. In contrast, Musa acuminata displayed a negative trend, with A n and g sw decreasing under high wind conditions. Minimal changes were observed in Pinus halepensis and the ferns Nephrolepis exaltata and Adiantum capillus-veneris , indicating low sensitivity to wind across these species. Stomatal response to leaf excision: assessing mechanical advantage and ’wrong-way’ opening To study the relationship between wind effects on gas exchange and leaf epidermal structure, we examined the g sw response to leaf excision across six plant species, focusing on the role of mechanical advantage and the ”wrong-way” stomatal response ( Fig. 7 a,b ). Musa acuminata exhibited the most pronounced ”wrong-way” response, characterized by a rapid g sw increase peaking within 5 minutes, followed by a sharp decline. This behavior indicates a strong mechanical advantage, where epidermal cells transiently open stomata due to rapid changes in turgor pressure. Solanum lycopersicum showed a moderate peak in g sw , suggesting a less pronounced mechanical influence. Vitis vinifera and Triticum durum displayed a slower, steadier gₛ w response, reflecting an intermediate level of mechanical advantage. In contrast, Nephrolepis exaltata and Pinus halepensis maintained stable g sw levels with minimal change, indicating a lack of mechanical advantage and no ”wrong-way” response. Next, we calculated the stomatal size-to-epidermal cell ratio across species, linking structural traits to stomatal behavior ( Fig. 7c ). Angiosperms generally exhibit higher ratios, associated with greater mechanical influence on stomatal movement, whereas ferns and gymnosperms tend to have lower ratios, limiting mechanical effects. Solanum lycopersicum had the lowest ratio. Interestingly, this low ratio did not align with its observed ”wrong-way” stomatal behaviour. Discussion: This study shows the importance of considering wind speed variability when measuring gas exchange parameters like stomatal conductance and CO₂ assimilation. Ignoring wind effects can lead to over- or underestimation of plant physiological responses ( Figs. 2-4 ). Including wind as a key environmental factor, along with CO₂ concentration, light, temperature, and humidity, helps better reflect natural conditions in plant studies. Our results highlight the interplay between wind dynamics and leaf morphology in shaping stomatal kinetics and species-specific responses ( Figs. 5-7 ). The variation between species can be explained by differences in the stomatal complex, specifically how guard cells interact mechanically with surrounding epidermal cells (Pichaco et al., 2024; Shapira et al., 2024). Species with a higher stomatal-to-epidermal cell size ratio were more sensitive to wind-induced changes in g sw , likely because of the stronger mechanical influence of epidermal cells (Pichaco et al., 2024). On the other hand, ferns, which have the smallest ratios, showed little response due to the lack of mechanical advantage (McAdam & Brodribb, 2015) and luck of wrong way response ( Fig. 7 ). Grapevine demonstrated a higher tolerance to wind and, in some cases, even benefited from increased wind speeds ( Fig. 2 ). The enhanced CO₂ uptake under high wind led to improved gas exchange, consistent with the findings of Schymanski and Or (2016), who reported that increased wind speeds can improve grapevine water use efficiency by enhancing CO₂ uptake and mitigating heat stress through convective cooling. The conflicting effects of wind on gas exchange across species raise the question of why some species benefit from wind while others do not. One potential explanation, lies in how wind influences both transpiration and leaf temperature. For instance, Huang et al. (2015) demonstrated that wind increases transpiration under low light by thinning the boundary layer but can reduce it under high light due to surface heating. In water-limited conditions, the threshold where cooling shifts to heating occurs more readily, suggesting that wind effect on photosynthesis may enhance or inhibit gas exchange depending on light levels and water availability. Wheat, among the angiosperm species tested, demonstrated the most pronounced responsiveness to wind variability. Its stomatal conductance ( g sw ) increased nearly perfectly in response to changes in wind speed ( Fig. 3 ). This sensitivity likely reflects wheat adaptation to open-field environments, where wind plays a significant role in driving gas exchange. It was demonstrated that increasing wind speeds from 1 to 3.5 m s⁻¹ resulted in higher transpiration rates in grasses, including during night-time, primarily due to an increase in both stomatal and cuticular conductance (Grace, 1974). Further, it has been shown that wind exposure in wheat leads to significantly higher leaf water stress compared to sheltered conditions (Frank and Willis,1972). Exposed wind plants also exhibit consistently high stomatal conductance throughout the day, suggesting increased transpiration water loss and reduced efficiency in regulating water use. We suggest that this strong responsiveness in grasses introduces a potential risk of overestimating gas exchange parameters in controlled experiments where wind conditions are artificially constant or excessively high. Ferns and certain gymnosperms, such as Pinus halepensis , demonstrate minimal sensitivity to wind, largely due to their lack of mechanical advantage in stomatal complexes ( Figs. 2,3 ). Ferns, which lack strong guard-epidermal interactions, show limited response to wind-induced changes in stomatal conductance ( g sw ), indicating that wind variability is less critical for these species ( Figs. 5,6 ). Similarly, Pinus halepensis , with its sunken stomata (Boddi et al., 2002), experiences an additional layer of isolation that shields guard cells from direct environmental fluctuations, contributing to its hyposensitivity to wind. This species also does not exhibit the “wrong-way” stomatal response observed in many angiosperms during wind exposure or after leaf excision, ( Fig. 7 ) (Buckley et al., 2011; Shapira et al., 2024) suggest that these structural characteristics minimize the influence of wind on stomatal behaviour, allowing stable gas exchange despite fluctuating environmental conditions. Interestingly, banana, a large-leafed tropical species adapted to high-humidity, low-wind environments, we observed significant sensitivity to wind variability. Under variable wind conditions, banana exhibited an initial increase in Aₙ and g sw , followed by pronounced oscillations and an overall decline in both parameters. Compared to constant high wind, variable wind conditions led to higher Aₙ and g sw in low-wind greenhouse environments, but the responses were unstable, showing marked fluctuations ( Fig. 4 ). These findings align with the adaptation of banana to humid conditions, where thicker boundary layers moderate transpiration (Robinson & Bower, 1988; Tanny et al., 2006, 2010; Turner et al., 2008; Zait et al., 2017). High wind disrupted these boundary layers, increasing water loss and reducing turgor pressure (Zimmermann et al., 2010), leading to oscillatory stomatal behavior and an overall decline in photosynthetic capacity compared to low-wind conditions. Thus, we suggest that these stomatal oscillations do not occur in bananas natural habitat (humid tropics) and that measurements of gas exchange in banana often misrepresent both the absolute values of gas exchange parameters and the diurnal patterns of these parameters. These findings are consistent with studies on coffee plants, another broad-leafed species from tropical, wind-sensitive habitats, which showed decreases in A n (20%) under high wind conditions (Monteiro Reis et al., 2018). These results demonstrate that measuring gas exchange in banana or other large-leafed tropical species, under high wind speeds or elevated fan speeds may not accurately reflect natural conditions, as such environments artificially disrupt the plants adaptations to still air and humid settings. Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex Conclusion Our findings emphasize the importance of accounting for variable wind speeds in gas exchange studies, particularly for low wind speed environments and for large-leaf species with thick boundary layers. Traditional setups using constant high wind often fail to capture the natural variability plants experience, potentially leading to inaccurate measurements. Physiological assessments can better reflect plant responses to environmental changes by incorporating realistic wind fluctuations. Using the variable boundary layer setup, provides important knowledge into how different species respond to natural wind variability. Further research into the interplay between wind, stomatal dynamics, and gas exchange is essential for addressing the impacts of climate variability in regions where wind plays a significant role. not-yet-known not-yet-known not-yet-known unknown Acknowledgment This study was supported by the Israel Science Foundation (ISF) grant 2076/23 to Y.Z. not-yet-known not-yet-known not-yet-known unknown Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement All data generated or analyzed in this study are included in this published article. References: Aphalo, P. J., & Jarvis, P. G. (1993) The boundary layer and the apparent responses of stomatal conductance to wind speed and to the mole fractions of CO2 and water vapour in the air. Plant, Cell & Environment , 16, 771–783. https://doi.org/10.1111/j.1365-3040.1993.tb00499.x Bange, G. G. J. (1953) On the quantitative explanation of stomatal transpiration. 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Plant Biology , 12, 424–436. https://doi.org/10.1111/j.1438-8677.2009.00235.x Figure legends: Figure 1: Traditional vs. variable boundary layer gas exchange setups. The variable boundary layer setup employs a gas exchange chamber attached to a fully expanded leaf, with airflow regulated to simulate varying natural wind conditions. Under high wind conditions (a), the leaf boundary layer (illustrated by light blue coloring) diminishes. When measuring gas exchange on leaves exposed to high wind conditions, both the variable boundary layer setup (b) and traditional setup (c) use high mixing fan speeds, leading to low boundary resistance that closely mimics real environmental conditions. In contrast, leaves exposed to low wind conditions naturally develop a thicker boundary layer (d). Here, the two setups differ significantly. The variable boundary layer setup (e) uses low mixing fan speeds in the chamber to maintain a thicker boundary layer, better simulating the natural environment. The traditional setup, however, applies high mixing fan speeds (f), artificially thinning the boundary layer and creating a mismatch between chamber conditions and the natural environment. This discrepancy can distort photosynthesis and stomatal conductance measurements, leading to potential over- or underestimations of gas exchange rates. Figure 2: Plant responses to variable and constant wind conditions. Wind speed, stomatal conductance (𝑔 𝑠𝑤 ), and net photosynthesis (A n ) were measured in Vitis vinifera L . (a–c) and Nephrolepis exaltata (d–f). Variable wind conditions, generated using the wind-in wind-out method, are represented by orange triangles, while high, constant wind at 1.6 m s -1 is represented by blue circles. Panels (a, d) show wind speed; (b, e), stomatal conductance; and (c, f), net photosynthesis. Data presented are from a single experiment representative of three independent measurements showing a consistent pattern. Figure 3: Plant responses to variable and constant wind conditions. Wind speed, stomatal conductance ( g sw ), and net photosynthesis (An ) were measured in the gymnosperm Pinus halepensis (a–c) \RL and Triticum durum (d–f) and the Variable wind conditions, generated using the wind-in wind-out method, are represented by orange triangles, while high, constant wind at 1.6 m s −1 is represented by blue circles. Panels (a, d) show wind speed; (b, e), stomatal conductance; and (c, f), net photosynthesis. Data presented are from a single experiment, representative of three independent measurements showing a consistent pattern. Figure 4: Comparison of Musa acuminata grown under high-wind field environments (a–c) and low-wind greenhouse environments (d–f) responses to variable and constant wind conditions. Variable wind conditions, generated using the wind-in wind-out method, are represented by orange triangles, while high, constant wind at 1.6 m s -1 is represented by blue circles. Panels (a, d) show wind speed; (b, e), stomatal conductance ( g sw ); and (c, f), net photosynthesis ( A n ). Data presented are from a single experiment, representative of three independent measurements showing a consistent pattern. Figure 5: Changes in photosynthesis rate (Δ A n ) (a) and stomatal conductance (Δ g sw ) (b) across different species in response to high wind versus variable wind conditions. The delta (Δ) values were calculated as the difference between measurements taken under high wind and variable wind conditions at the same time of day. Each box plot represents the range of responses for each species, with positive values indicating an increase and negative values indicating a decrease in response to high wind. Species represented include Adiantum capillus-veneris, Nephrolepis exaltata, Pinus halepensis, Musa acuminata, Triticum durum, Solanum lycopersicum, and Vitis vinifera L. Figure 6: Relationship between photosynthesis rate (Aₙ) (a) and stomatal conductance ( gₛ w ) (b) as a function of wind speed (0–3 m s⁻¹) for various species. In (a), Aₙ is described by the following equations: Triticum durum (y = 20.4 + 3.4x, p < 0.001), Solanum lycopersicum (y = 19.6 + 1.88x, p < 0.001), Musa acuminata (y = 15.9 - 2.34x, p < 0.001), Vitis vinifera (y = 9.53 + 2.06x, p < 0.001), Pinus halepensis (y = 0.869 + 0.117x, p = 0.385), Nephrolepis exaltata (y = 2.55 - 0.367x, p < 0.001), and Adiantum capillus-veneris (y = 0.536 + 0.791x, p < 0.001). In (b), gₛw is represented by: Triticum durum (y = 0.298 + 0.213x, p < 0.0001), Solanum lycopersicum (y = 0.523 + 0.0236x, p < 0.0001), Musa acuminata (y = 0.168 - 0.0362x, p < 0.0001), Vitis vinifera (y = 0.0504 + 0.0715x, p < 0.0001), Pinus halepensis (y = 0.0577 + 0.0242x, p < 0.0001), Nephrolepis exaltata (y = 0.2063 + 0.00953x, p < 0.0001), and Adiantum capillus-veneris (y = 0.021 + 0.00585x, p < 0.0001). Figure 7: (a) Stomatal conductance ( g sw ) response to leaf excision across six species: Nephrolepis exaltata, Pinus halepensis , Triticum durum , Musa acuminata, Solanum lycopersicum, and Vitis vinifera . The vertical dashed line indicates the time of leaf excision (at 3 minutes). Data points represent the mean ± SE of three biological replicates. (b) Percentage of the ”wrong-way” response, calculated as the difference between the g sw at the time of excision and the peak g sw observed 3-5 minutes after excision. (c) Ratio of stomatal guard cell size to epidermal cell size across the studied species. Certainly! Apologies for the previous omissions. Below is the complete LaTeX document that includes all the requested sections, arguments, code snippets, and proofs, organized logically into a single cohesive document. “‘latex Figure 2: Plant responses to variable and constant wind conditions. Wind speed, stomatal conductance (𝑔 𝑠𝑤 ), and net photosynthesis (A n ) were measured in Vitis vinifera L . (a–c) and Nephrolepis exaltata (d–f). Variable wind conditions, generated using the wind-in wind-out method, are represented by orange triangles, while high, constant wind at 1.6 m s -1 is represented by blue circles. Panels (a, d) show wind speed; (b, e), stomatal conductance; and (c, f), net photosynthesis. Data presented are from a single experiment representative of three independent measurements showing a consistent pattern. Figure 3: Plant responses to variable and constant wind conditions. Wind speed, stomatal conductance ( g sw ), and net photosynthesis (An ) were measured in the gymnosperm Pinus halepensis (a–c) \RL and Triticum durum (d–f) and the Variable wind conditions, generated using the wind-in wind-out method, are represented by orange triangles, while high, constant wind at 1.6 m s −1 is represented by blue circles. Panels (a, d) show wind speed; (b, e), stomatal conductance; and (c, f), net photosynthesis. Data presented are from a single experiment, representative of three independent measurements showing a consistent pattern. Figure 4: Comparison of Musa acuminata grown under high-wind field environments (a–c) and low-wind greenhouse environments (d–f) responses to variable and constant wind conditions. Variable wind conditions, generated using the wind-in wind-out method, are represented by orange triangles, while high, constant wind at 1.6 m s -1 is represented by blue circles. Panels (a, d) show wind speed; (b, e), stomatal conductance ( g sw ); and (c, f), net photosynthesis ( A n ). Data presented are from a single experiment, representative of three independent measurements showing a consistent pattern. Figure 5: Changes in photosynthesis rate (Δ A n ) (a) and stomatal conductance (Δ g sw ) (b) across different species in response to high wind versus variable wind conditions. The delta (Δ) values were calculated as the difference between measurements taken under high wind and variable wind conditions at the same time of day. Each box plot represents the range of responses for each species, with positive values indicating an increase and negative values indicating a decrease in response to high wind. Species represented include Adiantum capillus-veneris, Nephrolepis exaltata, Pinus halepensis, Musa acuminata, Triticum durum, Solanum lycopersicum, and Vitis vinifera L. not-yet-known not-yet-known not-yet-known unknown Figure 6: Relationship between photosynthesis rate (Aₙ) (a) and stomatal conductance (gₛw ) (b) as a function of wind speed (0–3 m s⁻¹) for various species. In (a), Aₙ is described by the following equations: Triticum durum (y = 20.4 + 3.4x, p < 0.001), Solanum lycopersicum (y = 19.6 + 1.88x, p < 0.001), Musa acuminata (y = 15.9 - 2.34x, p < 0.001), Vitis vinifera (y = 9.53 + 2.06x, p < 0.001), Pinus halepensis (y = 0.869 + 0.117x, p = 0.385), Nephrolepis exaltata (y = 2.55 - 0.367x, p < 0.001), and Adiantum capillus-veneris (y = 0.536 + 0.791x, p < 0.001). In (b), gₛw is represented by: Triticum durum (y = 0.298 + 0.213x, p < 0.0001), Solanum lycopersicum (y = 0.523 + 0.0236x, p < 0.0001), Musa acuminata (y = 0.168 - 0.0362x, p < 0.0001), Vitis vinifera (y = 0.0504 + 0.0715x, p < 0.0001), Pinus halepensis (y = 0.0577 + 0.0242x, p < 0.0001), Nephrolepis exaltata (y = 0.2063 + 0.00953x, p < 0.0001), and Adiantum capillus-veneris (y = 0.021 + 0.00585x, p < 0.0001). not-yet-known not-yet-known not-yet-known unknown Figure 7: (a) Stomatal conductance (gsw) response to leaf excision across six species: Nephrolepis exaltata, Pinus halepensis, Triticum durum, Musa acuminata, Solanum lycopersicum, and Vitis vinifera . The vertical dashed line indicates the time of leaf excision (at 3 minutes). Data points represent the mean ± SE of three biological replicates. (b) Percentage of the ”wrong-way” response, calculated as the difference between the gsw at the time of excision and the peak gsw observed 3-5 minutes after excision. (c) Ratio of stomatal guard cell size to epidermal cell size across the studied species. Information & Authors Information Version history V1 Version 1 22 January 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords boundary layer gas exchange stomata stomatal conductance transpiration wind Authors Affiliations Ariel Joseph Hebrew University of Jerusalem Robert H Smith Faculty of Agriculture Food and Environment View all articles by this author Adi Yaaran Hebrew University of Jerusalem Robert H Smith Faculty of Agriculture Food and Environment View all articles by this author Bar Ben Zeev Hebrew University of Jerusalem Robert H Smith Faculty of Agriculture Food and Environment View all articles by this author Uri Hochberg 0000-0002-7649-7004 Newe Ya'ar Research Center View all articles by this author Or Shapira Migal Galilee Technology Center View all articles by this author Yotam Zait 0000-0003-4266-1635 [email protected] Hebrew University of Jerusalem Robert H Smith Faculty of Agriculture Food and Environment View all articles by this author Metrics & Citations Metrics Article Usage 488 views 235 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ariel Joseph, Adi Yaaran, Bar Ben Zeev, et al. Mimicking natural wind conditions using the variable boundary layer method reveals species-specific variability in gas exchange dynamics. Authorea . 22 January 2025. DOI: https://doi.org/10.22541/au.173751962.23312884/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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