Photosynthetic efficiency and stress indices in medicinal Bulbine natalensis Baker respond to warming and drought futures for Southern Africa

Photosynthetic efficiency and stress indices in medicinal Bulbine natalensis Baker respond to warming and drought futures for Southern Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Photosynthetic efficiency and stress indices in medicinal Bulbine natalensis Baker respond to warming and drought futures for Southern Africa Philile Patience Ngcobo, Tukayi Kudanga, Ignatious Matimati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6728719/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate warming in Southern Africa is predicted to range between 1.1 ˚ and 4.5˚ by 2050, with an increased frequency, duration and severity of droughts, which affect plant physiology. We used two types of open-top warming chambers, which passively warmed by 1.3–2.1˚ (OTC30) and by 2.2–3.1˚ (OTC50) above ambient temperatures, to simulate daytime warming for a year. Bulbine natalensis Baker, a valued medicinal plant in Southern Africa, was grown at low, moderate and high soil moisture levels under Ambient, OTC30 or OTC50 to test the impacts of drought and warming on the chlorophyll concentrations and efficiency of the photosystem (PSII) across seasons. While warming with OTC30 had insignificant effects, OTC50 led to 5% lower quantum yields of PSII (Fv/Fm), 14% lower performance index (PI (abs)), and a 53% rise in energy dissipation (Dio/CS) of PSII. Although plants displayed more stress and less chlorophyll concentrations during warmer Spring and Summer than cooler Autumn and Winter, the seasonal stress did not materially reduce PSII efficiency. Chlorophyll concentrations peaked with higher soil moisture during the cooler, but not the warmer seasons. Overall, B. natalensis was more temperature- than drought-sensitive, possibly due to its high leaf-water storage capacity. Given that all OTC30- and OTC50-warmed plants survived, thermal thresholds of B. natalensis were not evident. The 3.1˚ passive warming (OTC50), which matched the 2050s median for Southern Africa, caused significant plant stress but not mortality. Therefore, studying optimal temperatures for maximum photosynthesis (Topt) and critical temperature thresholds (Tcrit) for PSII function in B. natalensis is recommended. Medicinal plants bulbine climatic warming PSII efficiency O-J-I-P test chlorophyll concentration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1.0 Introduction Medicinal plants contribute substantially to the well-being of people in Southern Africa, with approximately 100 million people in 2005 estimated to be using medicinal plants, which amounted to an annual quantity of ca. 700,000 tonnes of plant material (Mander and McKenzie 2005 ). Climate change-induced warming and drought in Southern Africa may, however, reduce plant species richness (Thuiller et al. 2006 ; Sommer et al. 2010 ; Yates et al. 2010 ; Moncrieff et al. 2016 ), resulting in serious threats of medicinal plant extinctions in the wild (Tshabalala et al. 2022 ). Climate warming in Southern Africa is predicted to range between 1.1˚C and 4.5˚C by 2050 (Hulme et al. 2001 ), this accompanied by an increase in frequency, duration and severity of drought events (Spinoni et al. 2014 ). Understanding the impact of both warming and drought on the physiology of medicinal plants is therefore essential to predict their vulnerability to the predicted drought and warming scenarios or future species range shifts in the wild (Tshabalala et al. 2022 ). Medicinal plants are already diminishing at an alarming rate, partly because of changes to their natural habitats (Kariuki et al. 2018 ). Bulbine natalensis Baker, commonly known as ibhucu (IsiZulu) or rooiwortel (Afrikaans), is a medicinal plant that is widely distributed across central, eastern and northern South Africa, as well as in parts of Zimbabwe and Mozambique (Bodede and Prinsloo 2020 ). It is highly valued and extensively used for medicinal purposes (Musara and Bosede 2020 ). The medicinal benefits of B. natalensis are well-documented, such as treatment of skin ailments (Moteetee et al. 2019 ; Coopoosamy 2011 ; Bodede and Prinsloo 2020 ), infections of the gastrointestinal tract (Coopoosamy 2011 ; Mathabe et al. 2006 ), urinary tract infections (Moteetee et al. 2019 ), back pain, ringworm, diabetes, convulsions, burns, rheumatism, wound healing and opportunistic fungal infections in HIV/AIDS patients (Bodede and Prinsloo 2020 ). Despite the medicinal value of B. natalensis , its response to warmer temperatures and drought is yet to be tested through experimentation. Although higher plants have evolved physiological plasticity that provides a fitness advantage in novel environmental scenarios, such as climate warming and drought, the changes may be extreme enough to drive vulnerable species beyond their tolerable ranges, even in the most plastic species (Anderson et al. 2012 ). Experimental manipulations to simulate the predicted climatic scenarios can thus provide useful information on how plant species will likely respond to their novel environments (Schwinning et al. 2022 ). Scenarios of heat and drought stress can have a greater combined effect than when the conditions are experienced independently (Dreesen et al. 2012 ). Therefore, experimental simulations should test both warming and drought to evaluate their effects on the survival, and efficiency of the photosystem PSII and biomass of key native medicinal plants, such as B. natalensis . Generally, plants respond to heat and drought stress in a variety of ways, with their photosynthetic systems (PSII) being the most vulnerable (Huo et al. 2015 ; Min et al. 2016 ). Drought, heat, and combinations of these stress conditions mostly affect stomatal conductance, photosynthetic activity, cellular oxidative conditions, metabolomic profiles, and molecular signalling mechanisms (Sato et al. 2024 ). Heat elevates water consumption or loss and evaporation of soil moisture. Conversely, during drought, plants can partially or completely close their stomata to reduce transpiration (Schulze et al. 1973 ), resulting in elevated leaf surface temperatures (Sato et al. 2024 ). Thus, a period of protracted water stress increases temperature-sensitive carbon starvation or sudden hydraulic failure under extreme water stress (Adams et al. 2009 ). Consequently, drought stress results in significant quantitative changes in plant organelles and ultrastructure, including chloroplast enlargement, rupture, disintegration, plastid, and changes in starch grain number and size (Zellnig et al. 2010 ). It also decreases photosynthesis, inhibits photoelectron transport, and photophosphorylation (Ohashi et al. 2006 ). Chlorophyll (Chl) a fluorescence emitted by illuminated plants is measurable through non-invasive methods, such as fluorometers, and its kinetics provide key information on the structure and function of the photosynthetic apparatus (Murchie and Lawson 2013 ; Strasser et al. 2010 ). When leaves are dark-adapted, with all the PSII centres open and there is no non-photochemical quenching (NPQ), the kinetics of increase in fluorescence is measurable between the two extremes, starting with the point of illumination (Fo) to the maximal fluorescence (Fm) when all reaction centres (RCs) are closed. The kinetics display a multi-phase rise with distinct intermediate steps, that are conventionally named from O to P, also commonly known as O-J-I-P test (Strasser et al. 2010 ). A simplified schematic depiction of events leading to the determination of key parameters in fluorescence kinetics after excitation of a dark-adapted leaf are presented in Fig. 1 . The excitation energy after flashing a leaf is trapped by light harvesting complexes (LHCII antennas) and used for photochemistry or dissipated as either heat energy or as fluorescence. The O-J-I-P kinetics of the florescence transient, its rate of trapping and dissipation enables the efficiency of PSII to be inferred. A glossary of the key information measured from dark-adapted leaves of B. natalensis is summarised in Table 1 . Table 1 Glossary, formulae and description of the parameters of the OJIP-test used in our analysis of Chl a fluorescence transient O-J-I-P emitted by dark-adapted photosynthetic Bulbine natalensis leaf samples. Parameter Formula Description Fv/Fm \(\:\text{F}\text{v}/\text{F}\text{m}=\frac{(\text{F}\text{m}-\text{F}\text{o})}{\text{F}\text{m}}\) Ratio of variable fluorescence (Fv) to maximum fluorescence (Fm), which indicates the maximum quantum efficiency of PSII photochemistry (Maxwell and Johnson 2000 ; Kalaji et al. 2012 ). Pi (abs) \(\:\text{P}\text{i}\left(\text{a}\text{b}\text{s}\right)=\frac{{\gamma\:}\text{R}\text{c}}{1-{\gamma\:}\text{R}\text{c}\:}\:\bullet\:\:\frac{\text{ф}\text{P}\text{o}}{1-\text{ф}\text{P}\text{o}}\:\bullet\:\frac{\text{ѱ}\text{E}\text{o}}{1-\text{ѱ}\text{E}\text{o}}\) Performance index on an absorption basis of PSII. High values indicate that the plant is efficiently trapping light energy and converting it to chemical energy. Optimal conditions are associated with the least amount of stress, while low Pi (abs) suggests plant stress (Kalaji et al. 2012 ). DIo/Cs \(\:{\text{D}\text{I}}_{0}=\frac{\left({\text{F}}_{\text{m}}-{\text{F}}_{0}\right)-({\text{F}}_{\text{m}}-{\text{F}}_{\text{t}})}{\text{C}\text{S}}\) Energy dissipation per cross section area (CS). Shows how much of the absorbed light energy is dissipated as heat or fluorescence rather than being used for photochemistry. Ft is the fluorescence measured at time =(t) Mo \(\:\text{M}\text{o}=\frac{4\:\bullet\:({\text{F}}_{300{\mu\:}\text{s}}-{\text{F}}_{0})}{{\text{F}}_{\text{v}}}\) Approximated initial slope of the fluorescence transient. High Mo indicates rapid initial rate of electron transport from the primary electron acceptor (Q A ) to the secondary acceptor (Q B ) in PSII. Usually a sign of a highly efficient and active photosynthetic apparatus (Strasser et al. 2010 ). TRo/Rc \(\:\text{T}\text{R}\text{o}/\text{R}\text{C}=\frac{{\text{F}}_{\text{m}}-{\text{F}}_{0}}{{\text{F}}_{\text{m}}}\) Trapped energy flux (leading to Q A reduction) per reaction centre (RC) leading to the reduction of the primary electron acceptor Q A in PSII. High values suggest efficiency of light energy trapping by the PSII. Data are normalized to RCs (Force et al. 2003 ). Vj \(\:{\text{V}}_{\text{j}}=\frac{{\text{F}}_{\text{j}}-{\text{F}}_{0}}{{\text{F}}_{\text{m}}-{\text{F}}_{0}}\) Relative variable fluorescence at the J-step (2 ms after the onset of illumination). It highlights the state of the photosynthetic electron transport chain, the reduction state of the primary electron acceptor (Q A ) in PSII. High values suggest accumulation of reduced form (Q A − ), often associated with stress or damage impairing normal electron flow to Q B and beyond in PSII. Combined effects of drought and heat can cause detrimental effects on stomatal conductance (Zandalinas et al. 2016 ). Heat stress increases transpiration rates due to higher rates of stomatal conductance, increased permeability of the cuticular tissues and decreased water density (Sadok et al. 2021 ). Therefore, when a plant is under persistent water stress, its stomata shrink or close to conserve water causing an increase in leaf surface temperatures (Zandalinas et al. 2018 ). This stomatal closure reduces the amount of carbon dioxide that the plant can absorb, which can have an impact on photosynthesis (Ashraf and Harris 2004 ). Reduced moisture availability affects the photosynthetic apparatus and causes adverse changes in photosynthetic pigments (Fu and Huang 2001 ). Furthermore, chlorophyll concentration in plants decreases when leaves are exposed to heat stress, resulting in chlorosis or leaf senescence (Rossi et al. 2017 ). Many studies on Bulbine natalensis have focused on its medicinal benefits, such as phytochemicals, antioxidants, and antibacterial properties (Ghuman, 2019), but limited studies have focused on the species’ response to climatic warming and drought. It was hypothesized in this study that the projected climatic warming and drought scenarios for Southern Africa (Hulme et al. 2001 ) are likely to influence the efficiency of the Bulbine natalensis PSII, biomass yields and its survival, which potentially impacts on productivity of the species in environments that have warmed beyond the maximum tolerable temperatures and water stress thresholds. To test the hypothesis, this study explored the indicators of photosynthetic efficiency (see Table 1 ) in B. natalensis plants over twelve months under ambient and warmer temperatures simulating (Hulme et al. 2001 ) predictions for Southern Africa and at different soil moisture levels. 2.0 Materials and methods 2.1 Plant culture Thirty-six 150 mm PVC pots (Grovida Horticultural Products, South Africa) were each filled with thoroughly mixed Durban University of Technology Nursery mix, which is a mixture of the 40% v/v of grade 30/10 sand (Consol Minerals, Cape Town, RSA), 30% v/v of Garden Master® premium potting soil (Grovida Horticultural Products, South Africa), and 30% v/v of Garden Master® compost (Grovida Horticultural Products, South Africa). Bulbine natalensis (Baker) seedlings sourced from the Silverglen Medicinal Plant Nursery (eThekwini Municipality, Durban, RSA) were planted in each of the pots and watered to field capacity. The pots were kept under humid ambient environmental conditions for 14 d to acclimatize, with a similar irrigation frequency. Established potted B. natalensis plants were then randomly allocated to different treatment combinations of the experiment. To simulate warmer temperatures, daytime passive heating was achieved using two sizes of hexagonal open-top chambers (OTCs) constructed after the design of Musil et al. (2005). The larger of the chambers (OTC50) were 46 cm high with distances between parallel sides of 120 cm at their bases and 72 cm at their apices, while the smaller chambers (OTC30) were 36 cm high with distances between parallel sides of 120 cm at their bases and 72 cm at their apices. Such hexagonal open-top chambers are extensively used in simulating elevated temperatures and have received biotic validation (Hollister and Webber 2001 ). A well-ventilated and partially clad rainout tunnel prevented rain from reaching the experiment while allowing free ambient air circulation from all the sides. To test the hypothesis, a 3x3 factorial experiment with four replicates was set up at Department of Horticulture Nursery at the Durban University of Technology, in Durban, KwaZulu Natal, which comprised of three temperature levels (Ambient, OTC30, OTC50) and three weekly soil moisture levels of Low (75 ml plant − 1 ), Moderate (112.5 ml plant − 1 ) and High (150 ml plant − 1 ). Air temperatures in each warming chamber were recorded at 30-min intervals using synchronized miniature thermocouple data loggers (Spectrum Technologies Inc., Plainfield, Illinois, USA) fitted in ventilated radiation shields and positioned in the middle of each warming treatment at 15 cm above the ground. After acclimatization, the thirty-six potted B. natalensis plants were randomly allocated to the experimental plots and monitored for a year from September 2022 to end of August 2023. Irrigation treatments were administered twice weekly, and the volumetric water content recorded at 30-min intervals using synchronised soil moisture sensors that were interfaced with Watchdog data loggers (Spectrum Technologies Inc., Plainfield, Illinois, USA). 2.2 Estimation of Chlorophyll content and O-J-I-P fluorescence test To estimate chlorophyll concentration of each B. natalensis plant, a portable SPAD 502 Plus (Konica Minolta) was used at monthly intervals on the three youngest fully developed leaves from the experiment and then averaged, as in Khan et al. ( 2003 ). The approach of Ling et al. ( 2011 ) was used to convert the SPAD readings to chlorophyll concentration. In summary, the three youngest fully developed B. natalensis leaves from other non-experimental plants were marked and their SPAD readings recorded using twenty replicates. A cork-borer (0.5 cm diameter) was used for sampling the measured leaves, which were then weighed, and their chlorophyll immediately extracted in dimethyl formamide. A Jenway 7305 UV/Vis spectrophotometer (Lasec Group, Durban, South Africa) was used for measuring chlorophyll concentration. Relationships between the SPAD values and chlorophyll concentrations were used for determining the calibration curves used on experimental B. natalensis plants, following the method of Ling et al. ( 2011 ). Kinetics of chlorophyll fluorescence were measured on dark-adapted B. natalensis leaves in the experiment using an OSI30p + fluorometer (Optisciences Inc., Hudson, USA), commonly referred to as the J-I-P test. The leaves were dark-adapted using leaf clamps for ca. 30 minutes to allow for the complete relaxation of all photosynthetic processes. The OSI30p + fluorometer emits a series of light pulses to induce chlorophyll fluorescence, capturing the OJIP transient. The recorded transient reveals the photochemical efficiency and energy fluxes within the photosystem II (PSII). The recorded fluorescence data were used to derive parameters listed in Table 1 , which provide insights into the photosynthetic performance and stress responses of B. natalensis . 2.3 Biomass Measurements To quantify the biomasses of the B. natalensis , plants were gently excavated under water in 90-Litre ‘No Ash’ bins (Crazy Plastics, Durban, RSA) to remove any soil media or compost particles without breaking the roots. The excavated wet plants were blotted with paper towel, then divided into below ground (roots) and above ground (shoots) parts using secateurs, before separately placing them in clearly labelled paper bags. Thus, each plant had one bag with roots and another bag with shoots to enable determination of the ratio of shoot to root biomasses. All the plants were dried at 70˚ C for 72 h in an oven (Ecotherm, Labotec, RSA) or until their masses remained constant. The oven-dried samples were then weighed on a Radwag Precision Balance (Lasec, Cape Town, RSA) to determine their dry masses. 2.4 Statistical analysis All data ( https://doi.orgDOI : 10.17632/pbv5knfgcx.1 ) were analysed using R version 4.3.2 (R Core Team 2023 ). Linear mixed effects models were fitted to predict the effect of the warming scenario, soil moisture levels and the seasons on the response variables measured (see glossary in Table 1 ). The dredge function of the MuMIn package was then used for selecting the most parsimonious model, after ranking the models based on their Akaike Information Criterion (AIC) values. Post-hoc treatment differences were determined by comparing treatment means using the emmeans package using Tukey HSD. 3.0 Results 3.1 Ambient, OTC30 and OTC50 Microenvironments The averaged annual daytime maximum air temperatures at 13H00 SAST in the Ambient, OTC30 and OTC50 microenvironments for the year were 28.2°C, 29.8°C and 30.3°C, respectively. However, the differences between the Ambient and the warming treatments (OTC30, OTC50) were more amplified across the months (Fig. 2 ). The Ambient air temperatures fall within the measured average daily maximum air temperatures of 24.7°C and 32.2°C at 13H00 SAST during the same period at Durban South Wentworth Station (-29.9340˚ 30.9880˚), which is 11.5 km away. Noteworthy was that the highest average midday air temperatures inside the OTC30 and OTC50 microenvironments respectively warmed by 1.3–2.1˚ and 2.2–3.1˚ higher than for the Ambient microenvironment across the months (Fig. 2 a – 2 l). Although the Ambient, OTC30 and OTC50 were randomized within the same rainout tunnel covering ca . 300 m 2 , they experienced different air temperature scenarios (Fig. 2 ). 3.2 Chlorophyll concentration A general linear model (estimated using ML) predicted levels of chlorophyll concentration with warming, soil moisture and season (formula = Chlorophyll ~ 1 + Season + Warming + Moisture + Season * Warming + Season * Moisture + Season * Warming * Moisture, data = df, na.action = na.fail). However, the most parsimonious model (Table 2 ) dropped warming and its interaction terms and fitted chlorophyll content with soil moisture and season (formula: Chlorophyll ~ 1 + Moisture + Season + Moisture: Season). The model's explanatory power was substantial (R 2 = 0.35). Its intercept, corresponding to low soil moisture and Autumn, was at 0.20 mg.g − 1 (95% CI [0.15,0.25], t (420) = 8.27, p < .001). The effects of moderate and high soil moisture levels on chlorophyll concentrations were statistically significant (p < 0.05) and positive in Autumn and Winter. Thus, chlorophyll concentrations became higher when soil moisture was higher. While the effects of winter on chlorophyll concentration were not significantly different from Autumn, both Summer and Spring had significant and negative effects on the chlorophyll concentrations (Fig. 3 ). Despite the positive effects of moderate and high soil moisture in Autumn and Winter, when these moderate and high soil moisture levels were experienced in both Summer and Spring, they resulted in significant and negative effects on the chlorophyll concentrations relative to Autumn. Table 2 Model (estimated using ML) for seasonal chlorophyll concentration of Bulbine natalensis grown under a rainout tunnel with free air flow from all sides at Low (75 ml plant − 1 ), Moderate (112.5 ml plant − 1 ) and High (150 ml plant − 1 ) soil moisture levels. The model’s intercept corresponds to Moisture = Low and Season = Autumn. Probability values in bold are significant at p < 0.05. Predictors Estimates Confidence Intervals P-values (Intercept) 0.2 0.15–0.25 < 0.001 Moisture [Moderate] 0.11 0.04–0.18 0.001 Moisture [High] 0.22 0.15–0.28 < 0.001 Season [Spring] -0.08 -0.15 – -0.02 0.016 Season [Summer] -0.07 -0.13 – -0.00 0.047 Season [Winter] 0.01 -0.06–0.08 0.776 Moisture [Moderate] × Season [Spring] -0.1 -0.19 – -0.01 0.036 Moisture [High] × Season [Spring] -0.2 -0.30 – -0.11 < 0.001 Moisture [Moderate] × Season [Summer] -0.09 -0.19 – -0.00 0.049 Moisture [High] × Season [Summer] -0.19 -0.29 – -0.10 < 0.001 Moisture [Moderate] × Season [Winter] 0 -0.09–0.10 0.985 Moisture [High] × Season [Winter] -0.03 -0.12–0.07 0.566 Observations 432 R 2 0.35 3.3 Chlorophyll fluorescence O-J-I-P test 3.3.1 Variable to maximum fluorescence (Fv/Fm), performance index (PI (abs)) and Energy dissipation per cross section (DIo/CS) in B. natalensis The parsimonious models for Fv/Fm, PI(Abs) and DIo/CS fitted a linear model (estimated using ML) to predict the parameters with Season and Warming (formula: Fv/Fm or PI (abs) or Dio/CS ~ 1 + Season + Warming) (Table 3 ). Although these models’ explanatory powers were weak (Table 3 ), they had some terms with significant effects at p < 0.05 (Fig. 4 ). The models’ intercepts corresponded to Autumn season and Ambient temperatures. Within these models, the effects of seasons Spring and Summer were statistically significant and negative on Fv/Fm (which is positively correlated with plant stress), but positive on DIo/CS (a parameter indicating how much of the absorbed light energy is dissipated as heat or fluorescence rather than being used for photochemistry). Summer, Spring and Winter seasons had statistically significant and negative effects on the performance index of PSII PI(Abs) relative to Autumn. Warming with the OTC50 had significant negative effects on Fv/Fm and PI(Abs) relative to Ambient air, but had positive effects on DIo/CS. On the contrary, the OTC30 chamber did not have statistically significant effects on all three parameters relative to Ambient air control. Thus, seasonal estimates for DIo/CS, generally ranked in the descending order, Summer > Spring > Winter > Autumn (Fig. 4 A-C), while only OTC50 led to significantly lower estimates. The OTC30 estimates for Fv/Fm, PI(Abs) and DIo/CS were not distinguishable from Ambient estimates. OTC50 plants had a 5% lower Fv/Fm than Ambient plants, while their efficiency of PSII, PI (abs) was lower by 14% and DIo/CS rose by 53%. Table 3 General Linear Models (estimated using ML) for Fv/Fm, PI(Abs) and DIo/CS of Bulbine natalensis grown under a rainout tunnel with free air flow from all sides and exposed to ambient air temperatures (Ambient) and warming in hexagonal open-top chambers (OTC30 and OTC50). OTC30 warmed to Ambient + 1.3˚-2.1˚ and OTC50 to Ambient + 2.2˚-3.1˚ (Fig. 2 ). The intercept corresponds to Moisture = Low and Season = Autumn. P-values in bold are significant at p < 0.05. Predictors Estimates Confidence Intervals P-values Fv/Fm (Intercept) 0.77 0.75–0.80 < 0.001 Season [Spring] -0.05 -0.07 – -0.02 0.001 Season [Summer] -0.11 -0.14 – -0.08 < 0.001 Season [Winter] -0.01 -0.04–0.02 0.441 Warming [OTC30] -0.01 -0.03–0.01 0.451 Warming [OTC50] -0.04 -0.07 – -0.02 0.001 Observations 432 R 2 0.17 PI(Abs) (Intercept) 7.68 6.89–8.48 < 0.001 Season [Spring] -1.89 -2.80 – -0.97 < 0.001 Season [Summer] -2.7 -3.62 – -1.79 < 0.001 Season [Winter] -0.92 -1.84 – -0.00 0.049 Warming [OTC30] -0.21 -1.00–0.59 0.61 Warming [OTC50] -1.05 -1.84 – -0.26 0.01 Observations 432 R 2 0.10 DIo/CS (Intercept) 0.4 0.25–0.54 < 0.001 Season [Spring] 0.19 0.02–0.36 0.025 Season [Summer] 0.38 0.21–0.55 < 0.001 Season [Winter] 0.06 -0.11–0.23 0.476 Warming [OTC30] -0.04 -0.18–0.11 0.612 Warming [OTC50] 0.21 0.06–0.35 0.006 Observations 432 R 2 0.08 3.3.2 Initial slope of florescence transient (Mo), Trapped energy flux (TRo/RC) and Relative variable fluorescence at the J-step (Vj) in B. natalensis The initial slope of the florescence transient (Mo), which indicates the initial rate of electron transport from the primary electron acceptor (Q A ) to the secondary acceptor (Q B ) in PSII (Fig. 1 ), was affected by seasons and by warming. Autumn had the lowest estimates of Mo of 0.29. The effects of Summer and Spring seasons on Mo were statistically significant and positive, while the winter effects were non-significant relative to Autumn (Table 4 ). Although warming in the OTC30 did not significantly affect Mo, warming in OTC50 had a significant and positive effect on the Mo values relative to the Ambient microenvironment. The trapped energy flux (leading to Q A reduction) per reaction centre (RC) (TRo/RC), which suggests efficiency of light energy trapping by the PSII, was affected by the seasonal and the warming effects (Table 4 ). Like with Mo, the effects of Summer and Spring seasons on TRo/RC were significant and positive, relative to Autumn. Winter effects were, however, non-significant on the TRo/RC relative to Autumn values. Effects of warming in the OTC30 did not significantly affect TRo/RC values, while the effect of warming in OTC50 was significant and positive on the trapped energy flux per reaction centre relative to the Ambient microenvironment. The relative variable fluorescence at the J-step (Vj), which highlights the accumulation of reduced form (Q A − ) when electron flow to Q B is impaired (Table 1 , Fig. 1 ), was affected by seasonal effects, warming effects and soil moisture effects (Table 4 , Fig. 5 ). Moderate soil moisture effects were statistically significant and negative on the Vj values measured. Like in the case of TRo/RC and Mo, the Spring and Summer effects were statistically significant and positive, relative to Autumn. Winter effects were, however, insignificant at p = 0.05. Table 4 General Linear Models (estimated using ML) for initial slope of florescence transient (Mo), trapped energy flux (TRo/RC), and relative variable fluorescence at the J-step (Vj) in B. natalensis grown under a rainout tunnel (with free ambient air circulation from all sides) exposed to Ambient temperatures (Control) or warming by hexagonal open-top chambers (OTC30 and OTC50). Elevated temperatures were OTC30 (Ambient + 1.3˚-2.1˚) and OTC50 (Ambient + 2.2˚-3.1˚). The model’s intercept corresponds to Season = Autumn, Warming = Ambient. Probability values in bold are significant at p < 0.05. Predictors Estimates Confidence Intervals P-values Mo (Intercept) 0.29 0.25–0.32 < 0.001 Season [Spring] 0.07 0.03–0.12 0.001 Season [Summer] 0.07 0.03–0.12 0.001 Season [Winter] 0.04 -0.01–0.08 0.083 Warming [OTC30] -0.03 -0.07–0.01 0.165 Warming [OTC50] 0.04 0.01–0.08 0.025 Observations 432 R 2 0.06 TRo/RC (Intercept) 0.23 0.20–0.25 < 0.001 Season [Spring] 0.04 0.02–0.07 0.001 Season [Summer] 0.03 0.00–0.06 0.027 Season [Winter] 0.02 -0.01–0.05 0.152 Warming [OTC30] -0.02 -0.04–0.01 0.161 Warming [OTC50] 0.02 0.00–0.05 0.046 Observations 432 R 2 0.05 Vj (Intercept) 0.23 0.20–0.25 < 0.001 Moisture [Moderate] -0.02 -0.04 – -0.00 0.025 Moisture [High] -0.01 -0.03–0.01 0.161 Season [Spring] 0.05 0.03–0.07 < 0.001 Season [Summer] 0.05 0.03–0.07 < 0.001 Season [Winter] 0.01 -0.01–0.03 0.231 Warming [OTC30] -0.01 -0.03–0.01 0.302 Warming [OTC50] 0.01 -0.01–0.03 0.177 Observations 432 R 2 0.10 3.4 Plant biomass Soil moisture levels had significant effects on the aboveground, belowground and the total biomasses of B. natalensis (Table 5 ; Fig. 6 ). Estimates of shoot, root and total biomasses in one-year old B. natalensis at low soil moistures (Low) were 2.7 g, 5.7 g and 8.4 g respectively (Table 5 ). The effects of moderate soil moisture were statistically non-significant, however, the effects of High (150 ml) soil moisture were highly significant and positive. Table 5 General Linear Models (GLMs) for shoot, root, and total biomasses in B. natalensis grown under a rainout tunnel (with natural lighting and free ambient air circulation from all sides) exposed to Low (75 ml plant − 1 ), Moderate (112.5 ml plant − 1 ) and High (150 ml plant − 1 ) soil moistures. The model’s intercept corresponds to Low soil moisture levels. Probability values in bold are significant at p < 0.05. Predictors Estimates Confidence Intervals P-values Shoot dry mass (g) (Intercept) 2.66 2.09–3.23 < 0.001 Moisture [Moderate] 0.42 -0.39–1.22 0.312 Moisture [High] 1.41 0.60–2.22 0.001 Observations 36 R 2 0.27 Root dry mass (g) (Intercept) 5.73 4.84–6.61 < 0.001 Moisture [Moderate] 0.01 -1.25–1.27 0.99 Moisture [High] 1.87 0.62–3.13 0.003 Observations 36 R 2 0.26 Total dry mass (g) (Intercept) 8.38 7.10–9.67 < 0.001 Moisture [Moderate] 0.43 -1.39–2.24 0.646 Moisture [High] 3.28 1.47–5.10 < 0.001 Observations 36 R 2 0.31 Shoot: Root (g g − 1 ) (Intercept) 0.47 0.39–0.54 < 0.001 Warming [OTC30] 0.12 0.01–0.22 0.030 Warming [OTC50] 0.04 -0.06–0.15 0.402 Observations 36 R 2 0.13 4.0 Discussion Ambient air temperatures at our experimental site over the one-year period represented the average climatic conditions for the area, as supported by the similar ranges of average daily maximum air temperatures at the nearby (11.5 km) Durban Wentworth Weather Station. The significantly higher average midday air temperatures inside the OTC30 and OTC50 microenvironments across the months, which were above the Ambient by 1.3–2.1˚ and 2.2–3.1˚ respectively, confirmed the passive warming of ambient temperatures (Fig. 2 ). Such elevation of temperatures falls within the predicted climate futures for A2-high scenarios for 2020s, calculated as median of 7 GCM experiments for geographic coordinates 29° S to 30° S, 30° E to 31° E (Hulme et al. 2001 ) where B. natalensis grows in the wild. Although the Ambient (control) and the OTC30 and OTC50 microenvironments were randomised across a 300 m 2 rainout tunnel, the significant differences in mid-day air temperatures of the microenvironments (Fig. 2 ) confirm the efficacy of the used OTC30 and OTC50 chambers in passively elevating air temperatures. We tested and found evidence for our hypothesis that the projected climatic warming and drought scenarios simulated for Southern Africa (Hulme et al. 2001 ) are likely to influence the chlorophyll concentration, efficiency of photosystem (PSII) and biomass yields in Bulbine natalensis . We further evaluated the effects of limiting soil moisture and seasonal effects on the measured photosynthetic efficiency and biomass parameters. A complex interaction emerged between soil moisture levels (Low, Moderate and High) and seasonal effects on the chlorophyll concentration (Fig. 3 ), with clear separation between the hotter seasons (Summer and Spring) and the relatively cooler seasons (Autumn and Winter). Overall, the chlorophyll concentration was lowest during the hot seasons (Summer and Spring), and the soil moisture levels did not significantly affect chlorophyll concentrations in the hot seasons. Unlike the hotter Summer and Spring seasons, the chlorophyll concentrations increased at higher soil moisture levels during the cooler Winter and Autumn. This suggests that although elevating soil moisture levels had a significant impact during cooler seasons, the increased moisture levels could not mitigate the stress effects of high temperatures during the hotter Summer and Spring seasons. Bulbine natalensis is known for its pointed thickset leaves that can reserve water, thus making it a drought tolerant medicinal plant for water-wise gardens (Musara and Bosede 2020 ). The results imply that B. natalensis is more sensitive to temperature stress than to variations in soil moisture, especially during the hotter parts of the year. Thus, during cooler seasons, the increased soil moisture enhanced chlorophyll concentrations of this temperature-sensitive species. The decrease in chlorophyll concentration under low moisture stress was expected from the associated increase in enzymatic chlorophyll degradation (Ma et al., 2018). The first evidence of the effects of warming on the efficiency of photosystem (PSII) was that warming by 2.2–3.1˚ above ambient using OTC50s had highly significant and negative impacts on the quantum yield of PSII (Fv/Fm) and its performance index (PI (abs)). Thus, warming resulted in 5% lower maximum quantum efficiency of PSII photochemistry in B. natalensis , which concurs with expectations based on literature from other species (Kalaji et al., 2012 ; Maxwell and Johnson, 2000 ). Secondly, the negative performance index on an absorption basis of PSII (PI(Abs)), which dropped by 14% in OTC50, indicates that the warmed plants were less efficiently trapping light energy and converting it to chemical energy relative to the Ambient plants. Generally, optimal growing conditions are associated with the least amount of stress, while low PI (abs) suggests plant stress (Kalaji et al., 2012 ). The observed significant and positive effects of warming (OTC50) on DIo/CS of B. natalensis also support the study hypothesis. DIo/CS, which is energy dissipation per cross section area (CS) (Table 1 ), shows how much of the absorbed light energy is dissipated as heat or fluorescence rather than being used for photochemistry. Thus, the 53% rise in Dio/CS of OTC50 B. natalensis suggests higher energy dissipation than those plants in the Ambient control treatment, which implies gradual impairment of PSII activity. In addition, the significant and negative seasonal effects of the hotter Summer and Spring on quantum yield of PSII (Fv/Fm) and its performance index (PI (abs)) can be associated with the temperature sensitivity of the B. natalensis PSII. Southern Africa has its highest temperatures during Summer and Spring, with Winter having the lowest average temperatures (Van der Walt and Fitchett 2020 ). It is likely that the negative Summer and Spring effects on Fv/Fm and its performance index, PI (abs), suggest temperature-induced plant stress or gradual impairment of PSII. As seasonal effects can be reversible with the advent of tolerable climatic conditions, inferences from this study had to be confined to the imposed warming and drought scenarios. As all plants survived the warming and drought treatments, it is also likely that B. natalensis can survive the warmer conditions predicted for Southern Africa, at least up to the simulated 3.1˚C (Hulme et al. 2001 ). However, to determine the critical heat tolerances of B. natalensis , we propose further studies on the critical temperatures (Tcrit) beyond which B. natalensis leaves will not recover, such as the approaches of Slot et al. ( 2021 ) and Cook et al. ( 2024 ). The observed statistically significant and positive effects of the hotter Summer and Spring seasons on Mo, TRo/RC and Vj relative to cooler seasons suggest the seasonal effects on the PSII photochemistry of B. natalensis . High Mo and TRo/RC values, which indicate enhanced photosynthetic activity (Strasser et al. 2010 ), during Summer and Spring were expected due to the associated increase in temperatures and sunlight during these seasons. However, the significant and negative Summer and Spring effects on Fv/Fm and PI (abs) also suggest a photosynthetic apparatus gradually experiencing stress. Generally, high temperatures and intense light can lead to photoinhibition, where PSII is damaged faster than its repair, reducing the efficiency of photosynthesis (Strasser et al. 2010 ). Although Fv/Fm and PI(abs) in Spring and Summer were significantly lower than Autumn, the seasonal Summer and Spring Fv/Fm estimates of 0.66 and 0.72 respectively (Table 3 ) are close to the expected optimal values of ca . 0.8 (Maxwell and Johnson 2000 ), which suggests that the Summer and Spring stress in B. natalensis plants had not severely damaged the PSII beyond its rate of repair. Mo is the approximated initial slope of the O-J-I-P fluorescence transient, and TRo/RC is the trapped energy per reaction centre (Table 1 ). Therefore, high Mo suggests rapid initial rate of electron transport from the primary electron acceptor (QA) to the secondary acceptor (QB) in PSII, while high TRo/RC suggests efficiency of light energy trapping by the PSII (Force et al. 2003 ) (Fig. 1 ). Thus, higher Mo and TRo/RC in Summer and Spring imply a more efficient and active photosynthetic apparatus (Strasser et al., 2010 ), despite the statistically significant drop in quantum yield of PSII photochemistry (Fv/Fm) and performance Index based on absorbance (PI (abs)) (Fig. 4 A and 4 B). Drought has well-known negative impacts on biomass accumulation, which explains the observed accumulation at moderate and high soil moisture levels (Fig. 6 ). However, warming did not significantly impact biomass accumulation but impacted the biomass allocation (allometry) by altering shoot:root ratio (Table 5 ). The positive effects of warming on shoot:root ratios indicate more allocation towards shoots in B. natalensis at warmer temperatures than towards the roots. Thus, where medicinal extracts are from belowground organs, such as in B. natalensis , warmer temperatures may result in reduced root yields. 5.0 Conclusion We tested and found evidence that the projected climatic warming and drought scenarios for Southern Africa, based on Hulme et al. ( 2001 ), are likely to influence the chlorophyll concentration, efficiency of photosystem (PSII) and biomass yields in B. natalensis across different seasons. The effects of limiting soil moisture and seasonal effects were evident on the measured O-J-I-P test parameters, which are Fv/Fm, PI (abs), Dio/CS, Mo, TRo/RC and Vj. Although plants displayed more stress and less chlorophyll concentrations during hotter (Spring and Summer) than cooler seasons (Autumn, Winter), the stress did not translate to serious impairment of the efficiency of PSII. Chlorophyll concentrations increased at higher moisture levels during the cooler, but not during the hotter seasons. Bulbine natalensis was more sensitive to temperature stress than drought, possibly due to its underground storage bulb and its ability to reserve water in leaves. Given that all plants in OTC30 and OTC50 survived the passive warming over a year, the critical thermal thresholds of B. natalensis were not reached. The predicted warming by up to 3.1˚, which matches the median for 7 GCM experiments for Southern Africa according to Hulme et al. ( 2001 ) may cause plant stress and reduce PSII efficiency but was not intense enough to cause plant mortality in B. natalensis. Therefore, we recommend further studies on the critical temperatures (Tcrit) beyond which B. natalensis and other indigenous wild plants will not recover. Declarations Competing interests The authors declare no competing interests. Funding We gratefully acknowledge support from the Durban University of Technology’s institutional funding (FEVO 201800). The funders did not have any influence on the study design, data collection, interpretation, and publication of the results. Author Contribution I.M.: Conceptualization (equal); Investigation (equal); Data curation (equal); Formal analysis (lead); Writing - review and editing (equal). P.P.N.: Conceptualization (equal), Investigation (equal), Data curation (equal), Writing - original draft (lead), Writing - review and editing (equal). T.K.: Conceptualization (equal), Investigation (equal), Data curation (equal), Writing - review and editing (equal). Acknowledgement We thank Nokuzola Patience Phungula, Thagen Anumanthoo, Lindani Blessing Khanyile, Nosipho Nokwindla, Alfred Andile Mkhize and Snothi Njabulo Mdunge of the Horticulture Department of Durban University of Technology, who provided technical support during the establishment of the experiment. Data availability Data are available on the following Mendeley Data link:. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6728719","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464102001,"identity":"8e02f292-791f-4841-950f-42906c77a900","order_by":0,"name":"Philile Patience Ngcobo","email":"","orcid":"","institution":"Durban University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Philile","middleName":"Patience","lastName":"Ngcobo","suffix":""},{"id":464102002,"identity":"d51fc791-c4f9-40bf-9eed-1aaaca3fa84a","order_by":1,"name":"Tukayi Kudanga","email":"","orcid":"","institution":"Durban University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Tukayi","middleName":"","lastName":"Kudanga","suffix":""},{"id":464102003,"identity":"eb3b5e1b-6139-450d-81ed-80e74faeb702","order_by":2,"name":"Ignatious Matimati","email":"data:image/png;base64,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","orcid":"","institution":"University of Tasmania","correspondingAuthor":true,"prefix":"","firstName":"Ignatious","middleName":"","lastName":"Matimati","suffix":""}],"badges":[],"createdAt":"2025-05-23 02:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6728719/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6728719/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83764693,"identity":"03643796-7922-4598-abce-06feb3a79028","added_by":"auto","created_at":"2025-06-02 10:38:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172242,"visible":true,"origin":"","legend":"\u003cp\u003eA) Electron transport within the PSII reaction centre complex. B) The excitation energy intercepted by the light harvesting complex (LHCII Antenna) after a light pulse provides excitation energy to a dark-adapted leaf, which can be used for photochemistry (electron transport), dissipated as heat or as fluorescence. Energy dissipation can be measured per cross section area and used together with OJIP transient kinetics. C) The O-J-I-P kinetics of the dissipated florescence transient can reveal the efficiency of PSII. The measured (Fo, Fj, Fi and Fm) and derived parameters (Table 1) can provide key information on the status of PSII.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6728719/v1/efd3cceda13955c4ec3d8206.png"},{"id":83764694,"identity":"643e2c11-06b4-4150-9b6a-b00a27bbef53","added_by":"auto","created_at":"2025-06-02 10:38:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":249901,"visible":true,"origin":"","legend":"\u003cp\u003eDiurnal monthly air temperatures of current ambient climatic conditions (Ambient) and inside two types of hexagonal open-top chambers (OTC30 and OTC50) which raised mid-day air temperatures from September 2022 – August 2023 by 1.3-2.1˚ and 2.2-3.1˚ respectively. Each circle and bar represent monthly mean ± se.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6728719/v1/4140dd3931b0ae7d5ddefe28.png"},{"id":83764692,"identity":"67879165-2dcc-4baa-ad51-f8139ade144e","added_by":"auto","created_at":"2025-06-02 10:38:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16979,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variations in chlorophyll content (mg g\u003csup\u003e-1\u003c/sup\u003e) of \u003cem\u003eBulbine natalensis \u003c/em\u003egrown under greenhouse conditions, in low (75 ml plant\u003csup\u003e-1\u003c/sup\u003e; n=36), moderate (112.5 ml plant\u003csup\u003e-1\u003c/sup\u003e; n=36) and high (150 ml plant\u003csup\u003e-1\u003c/sup\u003e; n=36) soil moisture levels. Bars represent mean ± standard deviation (n=12).\u0026nbsp; Means with different letters are significantly different after a TukeyHSD post-hoc test at p=0.05.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6728719/v1/fc21b764873f0f4f967bd8a7.png"},{"id":83764695,"identity":"71655c15-902f-493f-a5dc-ce9379cc87fc","added_by":"auto","created_at":"2025-06-02 10:38:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35899,"visible":true,"origin":"","legend":"\u003cp\u003eA-C.) Seasonal effects and D-F.) warming effects on Fv/Fm, PI(Abs) and DIo/CS of \u003cem\u003eBulbine natalensis\u003c/em\u003e grown under a rainout tunnel (with free air flow from all sides) exposed to Ambient air temperatures or warming using hexagonal open-top chambers OTC30 (Ambient +1.3˚-2.1˚) and OTC50 (Ambient +2.2˚-3.1˚). Bars represent mean± standard deviation (seasons n=108; warming n=144). Means with different letters are significantly different after a TukeyHSD post-hoc test at p=0.05.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6728719/v1/a9792f0855575cd56453ee0d.png"},{"id":83764684,"identity":"e7562575-6f74-4da3-ab95-b2f0869c7001","added_by":"auto","created_at":"2025-06-02 10:38:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":33527,"visible":true,"origin":"","legend":"\u003cp\u003eA-C.) Seasonal effects and D-E.) warming effects on Mo, TRo/RC and Vj of \u003cem\u003eBulbine natalensis\u003c/em\u003e grown under a rainout tunnel (with free air flow from all sides) exposed to natural light and Ambient air temperatures or warming by hexagonal open-top chambers OTC30 (Ambient +1.3˚-2.1˚) and OTC50 (Ambient +2.2˚-3.1˚). Bars represent mean± standard deviation (seasons n=108; warming n=144). Means with different letters are significantly different after a TukeyHSD post-hoc test at p=0.05.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6728719/v1/cc5899d013879cc256d3ac4a.png"},{"id":83764689,"identity":"07a814c5-321e-47a2-b836-f31369125a98","added_by":"auto","created_at":"2025-06-02 10:38:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":19052,"visible":true,"origin":"","legend":"\u003cp\u003eA) Above-ground, B) belowground and C) total biomasses of \u003cem\u003eBulbine natalensis\u003c/em\u003e at low (75 ml plant\u003csup\u003e-1\u003c/sup\u003e; n=36), moderate (112.5 ml plant\u003csup\u003e-1\u003c/sup\u003e; n=36) and high (150 ml plant\u003csup\u003e-1\u003c/sup\u003e; n=36) soil moistures. Bars represent mean± standard error (n=144).\u0026nbsp; Means with different letters are significantly different after a TukeyHSD post-hoc test at p=0.05.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6728719/v1/d71ff16cf6e8a6527d311f3d.png"},{"id":88297762,"identity":"26dba810-e584-42cf-bbc1-c9a732cc91c6","added_by":"auto","created_at":"2025-08-05 03:46:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1624790,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6728719/v1/3a72d1fb-e5ee-4ead-bbc6-77c9cf628522.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Photosynthetic efficiency and stress indices in medicinal Bulbine natalensis Baker respond to warming and drought futures for Southern Africa","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eMedicinal plants contribute substantially to the well-being of people in Southern Africa, with approximately 100\u0026nbsp;million people in 2005 estimated to be using medicinal plants, which amounted to an annual quantity of \u003cem\u003eca.\u003c/em\u003e 700,000 tonnes of plant material (Mander and McKenzie \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Climate change-induced warming and drought in Southern Africa may, however, reduce plant species richness (Thuiller et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Sommer et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Yates et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Moncrieff et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), resulting in serious threats of medicinal plant extinctions in the wild (Tshabalala et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Climate warming in Southern Africa is predicted to range between 1.1˚C and 4.5˚C by 2050 (Hulme et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), this accompanied by an increase in frequency, duration and severity of drought events (Spinoni et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Understanding the impact of both warming and drought on the physiology of medicinal plants is therefore essential to predict their vulnerability to the predicted drought and warming scenarios or future species range shifts in the wild (Tshabalala et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Medicinal plants are already diminishing at an alarming rate, partly because of changes to their natural habitats (Kariuki et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eBulbine natalensis\u003c/em\u003e Baker, commonly known as \u003cem\u003eibhucu\u003c/em\u003e (IsiZulu) or \u003cem\u003erooiwortel\u003c/em\u003e (Afrikaans), is a medicinal plant that is widely distributed across central, eastern and northern South Africa, as well as in parts of Zimbabwe and Mozambique (Bodede and Prinsloo \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is highly valued and extensively used for medicinal purposes (Musara and Bosede \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The medicinal benefits of \u003cem\u003eB. natalensis\u003c/em\u003e are well-documented, such as treatment of skin ailments (Moteetee et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Coopoosamy \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bodede and Prinsloo \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), infections of the gastrointestinal tract (Coopoosamy \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Mathabe et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), urinary tract infections (Moteetee et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), back pain, ringworm, diabetes, convulsions, burns, rheumatism, wound healing and opportunistic fungal infections in HIV/AIDS patients (Bodede and Prinsloo \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite the medicinal value of \u003cem\u003eB. natalensis\u003c/em\u003e, its response to warmer temperatures and drought is yet to be tested through experimentation.\u003c/p\u003e \u003cp\u003eAlthough higher plants have evolved physiological plasticity that provides a fitness advantage in novel environmental scenarios, such as climate warming and drought, the changes may be extreme enough to drive vulnerable species beyond their tolerable ranges, even in the most plastic species (Anderson et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Experimental manipulations to simulate the predicted climatic scenarios can thus provide useful information on how plant species will likely respond to their novel environments (Schwinning et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Scenarios of heat and drought stress can have a greater combined effect than when the conditions are experienced independently (Dreesen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, experimental simulations should test both warming and drought to evaluate their effects on the survival, and efficiency of the photosystem PSII and biomass of key native medicinal plants, such as \u003cem\u003eB. natalensis\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eGenerally, plants respond to heat and drought stress in a variety of ways, with their photosynthetic systems (PSII) being the most vulnerable (Huo et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Min et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Drought, heat, and combinations of these stress conditions mostly affect stomatal conductance, photosynthetic activity, cellular oxidative conditions, metabolomic profiles, and molecular signalling mechanisms (Sato et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Heat elevates water consumption or loss and evaporation of soil moisture. Conversely, during drought, plants can partially or completely close their stomata to reduce transpiration (Schulze et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1973\u003c/span\u003e), resulting in elevated leaf surface temperatures (Sato et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, a period of protracted water stress increases temperature-sensitive carbon starvation or sudden hydraulic failure under extreme water stress (Adams et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Consequently, drought stress results in significant quantitative changes in plant organelles and ultrastructure, including chloroplast enlargement, rupture, disintegration, plastid, and changes in starch grain number and size (Zellnig et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It also decreases photosynthesis, inhibits photoelectron transport, and photophosphorylation (Ohashi et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChlorophyll (Chl) a fluorescence emitted by illuminated plants is measurable through non-invasive methods, such as fluorometers, and its kinetics provide key information on the structure and function of the photosynthetic apparatus (Murchie and Lawson \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Strasser et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). When leaves are dark-adapted, with all the PSII centres open and there is no non-photochemical quenching (NPQ), the kinetics of increase in fluorescence is measurable between the two extremes, starting with the point of illumination (Fo) to the maximal fluorescence (Fm) when all reaction centres (RCs) are closed. The kinetics display a multi-phase rise with distinct intermediate steps, that are conventionally named from O to P, also commonly known as O-J-I-P test (Strasser et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A simplified schematic depiction of events leading to the determination of key parameters in fluorescence kinetics after excitation of a dark-adapted leaf are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The excitation energy after flashing a leaf is trapped by light harvesting complexes (LHCII antennas) and used for photochemistry or dissipated as either heat energy or as fluorescence. The O-J-I-P kinetics of the florescence transient, its rate of trapping and dissipation enables the efficiency of PSII to be inferred. A glossary of the key information measured from dark-adapted leaves of \u003cem\u003eB. natalensis\u003c/em\u003e is summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGlossary, formulae and description of the parameters of the OJIP-test used in our analysis of Chl a fluorescence transient O-J-I-P emitted by dark-adapted photosynthetic \u003cem\u003eBulbine natalensis\u003c/em\u003e leaf samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFv/Fm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{F}\\text{v}/\\text{F}\\text{m}=\\frac{(\\text{F}\\text{m}-\\text{F}\\text{o})}{\\text{F}\\text{m}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRatio of variable fluorescence (Fv) to maximum fluorescence (Fm), which indicates the maximum quantum efficiency of PSII photochemistry (Maxwell and Johnson \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Kalaji et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePi (abs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{P}\\text{i}\\left(\\text{a}\\text{b}\\text{s}\\right)=\\frac{{\\gamma\\:}\\text{R}\\text{c}}{1-{\\gamma\\:}\\text{R}\\text{c}\\:}\\:\\bullet\\:\\:\\frac{\\text{ф}\\text{P}\\text{o}}{1-\\text{ф}\\text{P}\\text{o}}\\:\\bullet\\:\\frac{\\text{ѱ}\\text{E}\\text{o}}{1-\\text{ѱ}\\text{E}\\text{o}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerformance index on an absorption basis of PSII. High values indicate that the plant is efficiently trapping light energy and converting it to chemical energy. Optimal conditions are associated with the least amount of stress, while low Pi (abs) suggests plant stress (Kalaji et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIo/Cs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{D}\\text{I}}_{0}=\\frac{\\left({\\text{F}}_{\\text{m}}-{\\text{F}}_{0}\\right)-({\\text{F}}_{\\text{m}}-{\\text{F}}_{\\text{t}})}{\\text{C}\\text{S}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnergy dissipation per cross section area (CS). Shows how much of the absorbed light energy is dissipated as heat or fluorescence rather than being used for photochemistry. Ft is the fluorescence measured at time =(t)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{M}\\text{o}=\\frac{4\\:\\bullet\\:({\\text{F}}_{300{\\mu\\:}\\text{s}}-{\\text{F}}_{0})}{{\\text{F}}_{\\text{v}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eApproximated initial slope of the fluorescence transient. High Mo indicates rapid initial rate of electron transport from the primary electron acceptor (Q\u003csub\u003eA\u003c/sub\u003e) to the secondary acceptor (Q\u003csub\u003eB\u003c/sub\u003e) in PSII. Usually a sign of a highly efficient and active photosynthetic apparatus (Strasser et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRo/Rc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{T}\\text{R}\\text{o}/\\text{R}\\text{C}=\\frac{{\\text{F}}_{\\text{m}}-{\\text{F}}_{0}}{{\\text{F}}_{\\text{m}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrapped energy flux (leading to Q\u003csub\u003eA\u003c/sub\u003e reduction) per reaction centre (RC) leading to the reduction of the primary electron acceptor Q\u003csub\u003eA\u003c/sub\u003e in PSII. High values suggest efficiency of light energy trapping by the PSII. Data are normalized to RCs (Force et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{V}}_{\\text{j}}=\\frac{{\\text{F}}_{\\text{j}}-{\\text{F}}_{0}}{{\\text{F}}_{\\text{m}}-{\\text{F}}_{0}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelative variable fluorescence at the J-step (2 ms after the onset of illumination). It highlights the state of the photosynthetic electron transport chain, the reduction state of the primary electron acceptor (Q\u003csub\u003eA\u003c/sub\u003e) in PSII. High values suggest accumulation of reduced form (Q\u003csub\u003eA\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), often associated with stress or damage impairing normal electron flow to Q\u003csub\u003eB\u003c/sub\u003e and beyond in PSII.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCombined effects of drought and heat can cause detrimental effects on stomatal conductance (Zandalinas et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Heat stress increases transpiration rates due to higher rates of stomatal conductance, increased permeability of the cuticular tissues and decreased water density (Sadok et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, when a plant is under persistent water stress, its stomata shrink or close to conserve water causing an increase in leaf surface temperatures (Zandalinas et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This stomatal closure reduces the amount of carbon dioxide that the plant can absorb, which can have an impact on photosynthesis (Ashraf and Harris \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Reduced moisture availability affects the photosynthetic apparatus and causes adverse changes in photosynthetic pigments (Fu and Huang \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Furthermore, chlorophyll concentration in plants decreases when leaves are exposed to heat stress, resulting in chlorosis or leaf senescence (Rossi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany studies on \u003cem\u003eBulbine natalensis\u003c/em\u003e have focused on its medicinal benefits, such as phytochemicals, antioxidants, and antibacterial properties (Ghuman, 2019), but limited studies have focused on the species\u0026rsquo; response to climatic warming and drought. It was hypothesized in this study that the projected climatic warming and drought scenarios for Southern Africa (Hulme et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) are likely to influence the efficiency of the \u003cem\u003eBulbine natalensis\u003c/em\u003e PSII, biomass yields and its survival, which potentially impacts on productivity of the species in environments that have warmed beyond the maximum tolerable temperatures and water stress thresholds. To test the hypothesis, this study explored the indicators of photosynthetic efficiency (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in \u003cem\u003eB. natalensis\u003c/em\u003e plants over twelve months under ambient and warmer temperatures simulating (Hulme et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) predictions for Southern Africa and at different soil moisture levels.\u003c/p\u003e"},{"header":"2.0 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant culture\u003c/h2\u003e \u003cp\u003eThirty-six 150 mm PVC pots (Grovida Horticultural Products, South Africa) were each filled with thoroughly mixed Durban University of Technology Nursery mix, which is a mixture of the 40% v/v of grade 30/10 sand (Consol Minerals, Cape Town, RSA), 30% v/v of Garden Master\u0026reg; premium potting soil (Grovida Horticultural Products, South Africa), and 30% v/v of Garden Master\u0026reg; compost (Grovida Horticultural Products, South Africa). \u003cem\u003eBulbine natalensis\u003c/em\u003e (Baker) seedlings sourced from the Silverglen Medicinal Plant Nursery (eThekwini Municipality, Durban, RSA) were planted in each of the pots and watered to field capacity. The pots were kept under humid ambient environmental conditions for 14 d to acclimatize, with a similar irrigation frequency. Established potted \u003cem\u003eB. natalensis\u003c/em\u003e plants were then randomly allocated to different treatment combinations of the experiment.\u003c/p\u003e \u003cp\u003eTo simulate warmer temperatures, daytime passive heating was achieved using two sizes of hexagonal open-top chambers (OTCs) constructed after the design of Musil et al. (2005). The larger of the chambers (OTC50) were 46 cm high with distances between parallel sides of 120 cm at their bases and 72 cm at their apices, while the smaller chambers (OTC30) were 36 cm high with distances between parallel sides of 120 cm at their bases and 72 cm at their apices. Such hexagonal open-top chambers are extensively used in simulating elevated temperatures and have received biotic validation (Hollister and Webber \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). A well-ventilated and partially clad rainout tunnel prevented rain from reaching the experiment while allowing free ambient air circulation from all the sides.\u003c/p\u003e \u003cp\u003eTo test the hypothesis, a 3x3 factorial experiment with four replicates was set up at Department of Horticulture Nursery at the Durban University of Technology, in Durban, KwaZulu Natal, which comprised of three temperature levels (Ambient, OTC30, OTC50) and three weekly soil moisture levels of Low (75 ml plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Moderate (112.5 ml plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and High (150 ml plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Air temperatures in each warming chamber were recorded at 30-min intervals using synchronized miniature thermocouple data loggers (Spectrum Technologies Inc., Plainfield, Illinois, USA) fitted in ventilated radiation shields and positioned in the middle of each warming treatment at 15 cm above the ground. After acclimatization, the thirty-six potted \u003cem\u003eB. natalensis\u003c/em\u003e plants were randomly allocated to the experimental plots and monitored for a year from September 2022 to end of August 2023. Irrigation treatments were administered twice weekly, and the volumetric water content recorded at 30-min intervals using synchronised soil moisture sensors that were interfaced with Watchdog data loggers (Spectrum Technologies Inc., Plainfield, Illinois, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Estimation of Chlorophyll content and O-J-I-P fluorescence test\u003c/h2\u003e \u003cp\u003eTo estimate chlorophyll concentration of each \u003cem\u003eB. natalensis\u003c/em\u003e plant, a portable SPAD 502 Plus (Konica Minolta) was used at monthly intervals on the three youngest fully developed leaves from the experiment and then averaged, as in Khan et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The approach of Ling et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) was used to convert the SPAD readings to chlorophyll concentration. In summary, the three youngest fully developed \u003cem\u003eB. natalensis\u003c/em\u003e leaves from other non-experimental plants were marked and their SPAD readings recorded using twenty replicates. A cork-borer (0.5 cm diameter) was used for sampling the measured leaves, which were then weighed, and their chlorophyll immediately extracted in dimethyl formamide. A Jenway 7305 UV/Vis spectrophotometer (Lasec Group, Durban, South Africa) was used for measuring chlorophyll concentration. Relationships between the SPAD values and chlorophyll concentrations were used for determining the calibration curves used on experimental \u003cem\u003eB. natalensis\u003c/em\u003e plants, following the method of Ling et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKinetics of chlorophyll fluorescence were measured on dark-adapted \u003cem\u003eB. natalensis\u003c/em\u003e leaves in the experiment using an OSI30p\u0026thinsp;+\u0026thinsp;fluorometer (Optisciences Inc., Hudson, USA), commonly referred to as the J-I-P test. The leaves were dark-adapted using leaf clamps for ca. 30 minutes to allow for the complete relaxation of all photosynthetic processes. The OSI30p\u0026thinsp;+\u0026thinsp;fluorometer emits a series of light pulses to induce chlorophyll fluorescence, capturing the OJIP transient. The recorded transient reveals the photochemical efficiency and energy fluxes within the photosystem II (PSII). The recorded fluorescence data were used to derive parameters listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which provide insights into the photosynthetic performance and stress responses of \u003cem\u003eB. natalensis\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Biomass Measurements\u003c/h2\u003e \u003cp\u003eTo quantify the biomasses of the \u003cem\u003eB. natalensis\u003c/em\u003e, plants were gently excavated under water in 90-Litre \u0026lsquo;No Ash\u0026rsquo; bins (Crazy Plastics, Durban, RSA) to remove any soil media or compost particles without breaking the roots. The excavated wet plants were blotted with paper towel, then divided into below ground (roots) and above ground (shoots) parts using secateurs, before separately placing them in clearly labelled paper bags. Thus, each plant had one bag with roots and another bag with shoots to enable determination of the ratio of shoot to root biomasses. All the plants were dried at 70˚ C for 72 h in an oven (Ecotherm, Labotec, RSA) or until their masses remained constant. The oven-dried samples were then weighed on a Radwag Precision Balance (Lasec, Cape Town, RSA) to determine their dry masses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.orgDOI\u003c/span\u003e\u003cspan address=\"https://doi.orgDOI\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17632/pbv5knfgcx.1\u003c/span\u003e\u003cspan address=\"10.17632/pbv5knfgcx.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were analysed using R version 4.3.2 (R Core Team \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Linear mixed effects models were fitted to predict the effect of the warming scenario, soil moisture levels and the seasons on the response variables measured (see glossary in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The \u003cem\u003edredge\u003c/em\u003e function of the \u003cem\u003eMuMIn\u003c/em\u003e package was then used for selecting the most parsimonious model, after ranking the models based on their Akaike Information Criterion (AIC) values. Post-hoc treatment differences were determined by comparing treatment means using the \u003cem\u003eemmeans\u003c/em\u003e package using Tukey HSD.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Ambient, OTC30 and OTC50 Microenvironments\u003c/h2\u003e \u003cp\u003eThe averaged annual daytime maximum air temperatures at 13H00 SAST in the Ambient, OTC30 and OTC50 microenvironments for the year were 28.2\u0026deg;C, 29.8\u0026deg;C and 30.3\u0026deg;C, respectively. However, the differences between the Ambient and the warming treatments (OTC30, OTC50) were more amplified across the months (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Ambient air temperatures fall within the measured average daily maximum air temperatures of 24.7\u0026deg;C and 32.2\u0026deg;C at 13H00 SAST during the same period at Durban South Wentworth Station (-29.9340˚ 30.9880˚), which is 11.5 km away. Noteworthy was that the highest average midday air temperatures inside the OTC30 and OTC50 microenvironments respectively warmed by 1.3\u0026ndash;2.1˚ and 2.2\u0026ndash;3.1˚ higher than for the Ambient microenvironment across the months (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea \u0026ndash; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003el). Although the Ambient, OTC30 and OTC50 were randomized within the same rainout tunnel covering \u003cem\u003eca\u003c/em\u003e. 300 m\u003csup\u003e2\u003c/sup\u003e, they experienced different air temperature scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Chlorophyll concentration\u003c/h2\u003e \u003cp\u003eA general linear model (estimated using ML) predicted levels of chlorophyll concentration with warming, soil moisture and season (formula\u0026thinsp;=\u0026thinsp;Chlorophyll\u0026thinsp;~\u0026thinsp;1\u0026thinsp;+\u0026thinsp;Season\u0026thinsp;+\u0026thinsp;Warming\u0026thinsp;+\u0026thinsp;Moisture\u0026thinsp;+\u0026thinsp;Season * Warming\u0026thinsp;+\u0026thinsp;Season * Moisture\u0026thinsp;+\u0026thinsp;Season * Warming * Moisture, data\u0026thinsp;=\u0026thinsp;df, na.action\u0026thinsp;=\u0026thinsp;na.fail). However, the most parsimonious model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) dropped warming and its interaction terms and fitted chlorophyll content with soil moisture and season (formula: Chlorophyll\u0026thinsp;~\u0026thinsp;1\u0026thinsp;+\u0026thinsp;Moisture\u0026thinsp;+\u0026thinsp;Season\u0026thinsp;+\u0026thinsp;Moisture: Season). The model's explanatory power was substantial (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.35). Its intercept, corresponding to low soil moisture and Autumn, was at 0.20 mg.g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (95% CI [0.15,0.25], t \u003csub\u003e(420)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8.27, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). The effects of moderate and high soil moisture levels on chlorophyll concentrations were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and positive in Autumn and Winter. Thus, chlorophyll concentrations became higher when soil moisture was higher. While the effects of winter on chlorophyll concentration were not significantly different from Autumn, both Summer and Spring had significant and negative effects on the chlorophyll concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Despite the positive effects of moderate and high soil moisture in Autumn and Winter, when these moderate and high soil moisture levels were experienced in both Summer and Spring, they resulted in significant and negative effects on the chlorophyll concentrations relative to Autumn.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel (estimated using ML) for seasonal chlorophyll concentration of \u003cem\u003eBulbine natalensis\u003c/em\u003e grown under a rainout tunnel with free air flow from all sides at Low (75 ml plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Moderate (112.5 ml plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and High (150 ml plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) soil moisture levels. The model\u0026rsquo;s intercept corresponds to Moisture\u0026thinsp;=\u0026thinsp;Low and Season\u0026thinsp;=\u0026thinsp;Autumn. Probability values in bold are significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConfidence Intervals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u0026ndash;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture [Moderate]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026ndash;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture [High]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u0026ndash;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason [Spring]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.15\u0026nbsp;\u0026ndash;\u0026nbsp;-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason [Summer]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.13\u0026nbsp;\u0026ndash;\u0026nbsp;-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.047\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason [Winter]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.06\u0026ndash;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture [Moderate] \u0026times; Season [Spring]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.19\u0026nbsp;\u0026ndash;\u0026nbsp;-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture [High] \u0026times; Season [Spring]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.30\u0026nbsp;\u0026ndash;\u0026nbsp;-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture [Moderate] \u0026times; Season [Summer]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.19\u0026nbsp;\u0026ndash;\u0026nbsp;-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture [High] \u0026times; Season [Summer]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.29\u0026nbsp;\u0026ndash;\u0026nbsp;-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture [Moderate] \u0026times; Season [Winter]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.09\u0026ndash;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoisture [High] \u0026times; Season [Winter]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.12\u0026ndash;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Chlorophyll fluorescence O-J-I-P test\u003c/h2\u003e \u003cp\u003e \u003cb\u003e3.3.1 Variable to maximum fluorescence (Fv/Fm), performance index (PI (abs)) and Energy dissipation per cross section (DIo/CS) in\u003c/b\u003e \u003cb\u003eB. natalensis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe parsimonious models for Fv/Fm, PI(Abs) and DIo/CS fitted a linear model (estimated using ML) to predict the parameters with Season and Warming (formula: Fv/Fm or PI (abs) or Dio/CS\u0026thinsp;~\u0026thinsp;1\u0026thinsp;+\u0026thinsp;Season\u0026thinsp;+\u0026thinsp;Warming) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although these models\u0026rsquo; explanatory powers were weak (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), they had some terms with significant effects at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The models\u0026rsquo; intercepts corresponded to Autumn season and Ambient temperatures. Within these models, the effects of seasons Spring and Summer were statistically significant and negative on Fv/Fm (which is positively correlated with plant stress), but positive on DIo/CS (a parameter indicating how much of the absorbed light energy is dissipated as heat or fluorescence rather than being used for photochemistry). Summer, Spring and Winter seasons had statistically significant and negative effects on the performance index of PSII PI(Abs) relative to Autumn. Warming with the OTC50 had significant negative effects on Fv/Fm and PI(Abs) relative to Ambient air, but had positive effects on DIo/CS. On the contrary, the OTC30 chamber did not have statistically significant effects on all three parameters relative to Ambient air control. Thus, seasonal estimates for DIo/CS, generally ranked in the descending order, Summer\u0026thinsp;\u0026gt;\u0026thinsp;Spring\u0026thinsp;\u0026gt;\u0026thinsp;Winter\u0026thinsp;\u0026gt;\u0026thinsp;Autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C), while only OTC50 led to significantly lower estimates. The OTC30 estimates for Fv/Fm, PI(Abs) and DIo/CS were not distinguishable from Ambient estimates. OTC50 plants had a 5% lower Fv/Fm than Ambient plants, while their efficiency of PSII, PI (abs) was lower by 14% and DIo/CS rose by 53%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral Linear Models (estimated using ML) for Fv/Fm, PI(Abs) and DIo/CS of \u003cem\u003eBulbine natalensis\u003c/em\u003e grown under a rainout tunnel with free air flow from all sides and exposed to ambient air temperatures (Ambient) and warming in hexagonal open-top chambers (OTC30 and OTC50). OTC30 warmed to Ambient\u0026thinsp;+\u0026thinsp;1.3˚-2.1˚ and OTC50 to Ambient\u0026thinsp;+\u0026thinsp;2.2˚-3.1˚ (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The intercept corresponds to Moisture\u0026thinsp;=\u0026thinsp;Low and Season\u0026thinsp;=\u0026thinsp;Autumn. P-values in bold are significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConfidence Intervals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFv/Fm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u0026ndash;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Spring]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u0026nbsp;\u0026ndash;\u0026nbsp;-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Summer]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.14\u0026nbsp;\u0026ndash;\u0026nbsp;-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Winter]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.04\u0026ndash;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u0026ndash;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u0026nbsp;\u0026ndash;\u0026nbsp;-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI(Abs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.89\u0026ndash;8.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Spring]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.80\u0026nbsp;\u0026ndash;\u0026nbsp;-0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Summer]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.62\u0026nbsp;\u0026ndash;\u0026nbsp;-1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Winter]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.84\u0026nbsp;\u0026ndash;\u0026nbsp;-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.00\u0026ndash;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.84\u0026nbsp;\u0026ndash;\u0026nbsp;-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDIo/CS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u0026ndash;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Spring]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u0026ndash;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Summer]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u0026ndash;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Winter]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u0026ndash;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.18\u0026ndash;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u0026ndash;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3.2 Initial slope of florescence transient (Mo), Trapped energy flux (TRo/RC) and Relative variable fluorescence at the J-step (Vj) in B. natalensis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe initial slope of the florescence transient (Mo), which indicates the initial rate of electron transport from the primary electron acceptor (Q\u003csub\u003eA\u003c/sub\u003e) to the secondary acceptor (Q\u003csub\u003eB\u003c/sub\u003e) in PSII (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), was affected by seasons and by warming. Autumn had the lowest estimates of Mo of 0.29. The effects of Summer and Spring seasons on Mo were statistically significant and positive, while the winter effects were non-significant relative to Autumn (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Although warming in the OTC30 did not significantly affect Mo, warming in OTC50 had a significant and positive effect on the Mo values relative to the Ambient microenvironment.\u003c/p\u003e \u003cp\u003eThe trapped energy flux (leading to Q\u003csub\u003eA\u003c/sub\u003e reduction) per reaction centre (RC) (TRo/RC), which suggests efficiency of light energy trapping by the PSII, was affected by the seasonal and the warming effects (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Like with Mo, the effects of Summer and Spring seasons on TRo/RC were significant and positive, relative to Autumn. Winter effects were, however, non-significant on the TRo/RC relative to Autumn values. Effects of warming in the OTC30 did not significantly affect TRo/RC values, while the effect of warming in OTC50 was significant and positive on the trapped energy flux per reaction centre relative to the Ambient microenvironment.\u003c/p\u003e \u003cp\u003eThe relative variable fluorescence at the J-step (Vj), which highlights the accumulation of reduced form (Q\u003csub\u003eA\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) when electron flow to Q\u003csub\u003eB\u003c/sub\u003e is impaired (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), was affected by seasonal effects, warming effects and soil moisture effects (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Moderate soil moisture effects were statistically significant and negative on the Vj values measured. Like in the case of TRo/RC and Mo, the Spring and Summer effects were statistically significant and positive, relative to Autumn. Winter effects were, however, insignificant at p\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral Linear Models (estimated using ML) for initial slope of florescence transient (Mo), trapped energy flux (TRo/RC), and relative variable fluorescence at the J-step (Vj) in \u003cem\u003eB. natalensis\u003c/em\u003e grown under a rainout tunnel (with free ambient air circulation from all sides) exposed to Ambient temperatures (Control) or warming by hexagonal open-top chambers (OTC30 and OTC50). Elevated temperatures were OTC30 (Ambient\u0026thinsp;+\u0026thinsp;1.3˚-2.1˚) and OTC50 (Ambient\u0026thinsp;+\u0026thinsp;2.2˚-3.1˚). The model\u0026rsquo;s intercept corresponds to Season\u0026thinsp;=\u0026thinsp;Autumn, Warming\u0026thinsp;=\u0026thinsp;Ambient. Probability values in bold are significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConfidence Intervals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u0026ndash;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Spring]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026ndash;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Summer]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026ndash;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Winter]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u0026ndash;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u0026ndash;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u0026ndash;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRo/RC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u0026ndash;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Spring]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u0026ndash;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Summer]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u0026ndash;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Winter]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u0026ndash;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.04\u0026ndash;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u0026ndash;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVj\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u0026ndash;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoisture [Moderate]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.04\u0026nbsp;\u0026ndash;\u0026nbsp;-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoisture [High]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u0026ndash;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Spring]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026ndash;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Summer]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u0026ndash;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason [Winter]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u0026ndash;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.03\u0026ndash;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u0026ndash;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Plant biomass\u003c/h2\u003e \u003cp\u003eSoil moisture levels had significant effects on the aboveground, belowground and the total biomasses of \u003cem\u003eB. natalensis\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Estimates of shoot, root and total biomasses in one-year old \u003cem\u003eB. natalensis\u003c/em\u003e at low soil moistures (Low) were 2.7 g, 5.7 g and 8.4 g respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The effects of moderate soil moisture were statistically non-significant, however, the effects of High (150 ml) soil moisture were highly significant and positive.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral Linear Models (GLMs) for shoot, root, and total biomasses in \u003cem\u003eB. natalensis\u003c/em\u003e grown under a rainout tunnel (with natural lighting and free ambient air circulation from all sides) exposed to Low (75 ml plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Moderate (112.5 ml plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and High (150 ml plant\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) soil moistures. The model\u0026rsquo;s intercept corresponds to Low soil moisture levels. Probability values in bold are significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConfidence Intervals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eShoot dry mass (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.09\u0026ndash;3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoisture [Moderate]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.39\u0026ndash;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoisture [High]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60\u0026ndash;2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRoot dry mass (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.84\u0026ndash;6.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoisture [Moderate]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.25\u0026ndash;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoisture [High]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u0026ndash;3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTotal dry mass (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.10\u0026ndash;9.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoisture [Moderate]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.39\u0026ndash;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoisture [High]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47\u0026ndash;5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eShoot: Root\u003c/p\u003e \u003cp\u003e(g g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u0026ndash;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u0026ndash;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWarming [OTC50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.06\u0026ndash;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eAmbient air temperatures at our experimental site over the one-year period represented the average climatic conditions for the area, as supported by the similar ranges of average daily maximum air temperatures at the nearby (11.5 km) Durban Wentworth Weather Station. The significantly higher average midday air temperatures inside the OTC30 and OTC50 microenvironments across the months, which were above the Ambient by 1.3\u0026ndash;2.1˚ and 2.2\u0026ndash;3.1˚ respectively, confirmed the passive warming of ambient temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Such elevation of temperatures falls within the predicted climate futures for A2-high scenarios for 2020s, calculated as median of 7 GCM experiments for geographic coordinates 29\u0026deg; S to 30\u0026deg; S, 30\u0026deg; E to 31\u0026deg; E (Hulme et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) where \u003cem\u003eB. natalensis\u003c/em\u003e grows in the wild. Although the Ambient (control) and the OTC30 and OTC50 microenvironments were randomised across a 300 m\u003csup\u003e2\u003c/sup\u003e rainout tunnel, the significant differences in mid-day air temperatures of the microenvironments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) confirm the efficacy of the used OTC30 and OTC50 chambers in passively elevating air temperatures.\u003c/p\u003e \u003cp\u003eWe tested and found evidence for our hypothesis that the projected climatic warming and drought scenarios simulated for Southern Africa (Hulme et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) are likely to influence the chlorophyll concentration, efficiency of photosystem (PSII) and biomass yields in \u003cem\u003eBulbine natalensis\u003c/em\u003e. We further evaluated the effects of limiting soil moisture and seasonal effects on the measured photosynthetic efficiency and biomass parameters. A complex interaction emerged between soil moisture levels (Low, Moderate and High) and seasonal effects on the chlorophyll concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), with clear separation between the hotter seasons (Summer and Spring) and the relatively cooler seasons (Autumn and Winter). Overall, the chlorophyll concentration was lowest during the hot seasons (Summer and Spring), and the soil moisture levels did not significantly affect chlorophyll concentrations in the hot seasons. Unlike the hotter Summer and Spring seasons, the chlorophyll concentrations increased at higher soil moisture levels during the cooler Winter and Autumn. This suggests that although elevating soil moisture levels had a significant impact during cooler seasons, the increased moisture levels could not mitigate the stress effects of high temperatures during the hotter Summer and Spring seasons. \u003cem\u003eBulbine natalensis\u003c/em\u003e is known for its pointed thickset leaves that can reserve water, thus making it a drought tolerant medicinal plant for water-wise gardens (Musara and Bosede \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The results imply that \u003cem\u003eB. natalensis\u003c/em\u003e is more sensitive to temperature stress than to variations in soil moisture, especially during the hotter parts of the year. Thus, during cooler seasons, the increased soil moisture enhanced chlorophyll concentrations of this temperature-sensitive species. The decrease in chlorophyll concentration under low moisture stress was expected from the associated increase in enzymatic chlorophyll degradation (Ma et al., 2018).\u003c/p\u003e \u003cp\u003eThe first evidence of the effects of warming on the efficiency of photosystem (PSII) was that warming by 2.2\u0026ndash;3.1˚ above ambient using OTC50s had highly significant and negative impacts on the quantum yield of PSII (Fv/Fm) and its performance index (PI (abs)). Thus, warming resulted in 5% lower maximum quantum efficiency of PSII photochemistry in \u003cem\u003eB. natalensis\u003c/em\u003e, which concurs with expectations based on literature from other species (Kalaji et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Maxwell and Johnson, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Secondly, the negative performance index on an absorption basis of PSII (PI(Abs)), which dropped by 14% in OTC50, indicates that the warmed plants were less efficiently trapping light energy and converting it to chemical energy relative to the Ambient plants. Generally, optimal growing conditions are associated with the least amount of stress, while low PI (abs) suggests plant stress (Kalaji et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The observed significant and positive effects of warming (OTC50) on DIo/CS of \u003cem\u003eB. natalensis\u003c/em\u003e also support the study hypothesis. DIo/CS, which is energy dissipation per cross section area (CS) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), shows how much of the absorbed light energy is dissipated as heat or fluorescence rather than being used for photochemistry. Thus, the 53% rise in Dio/CS of OTC50 \u003cem\u003eB. natalensis\u003c/em\u003e suggests higher energy dissipation than those plants in the Ambient control treatment, which implies gradual impairment of PSII activity.\u003c/p\u003e \u003cp\u003eIn addition, the significant and negative seasonal effects of the hotter Summer and Spring on quantum yield of PSII (Fv/Fm) and its performance index (PI (abs)) can be associated with the temperature sensitivity of the \u003cem\u003eB. natalensis\u003c/em\u003e PSII. Southern Africa has its highest temperatures during Summer and Spring, with Winter having the lowest average temperatures (Van der Walt and Fitchett \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). It is likely that the negative Summer and Spring effects on Fv/Fm and its performance index, PI (abs), suggest temperature-induced plant stress or gradual impairment of PSII. As seasonal effects can be reversible with the advent of tolerable climatic conditions, inferences from this study had to be confined to the imposed warming and drought scenarios. As all plants survived the warming and drought treatments, it is also likely that \u003cem\u003eB. natalensis\u003c/em\u003e can survive the warmer conditions predicted for Southern Africa, at least up to the simulated 3.1˚C (Hulme et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). However, to determine the critical heat tolerances of \u003cem\u003eB. natalensis\u003c/em\u003e, we propose further studies on the critical temperatures (Tcrit) beyond which \u003cem\u003eB. natalensis\u003c/em\u003e leaves will not recover, such as the approaches of Slot et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Cook et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observed statistically significant and positive effects of the hotter Summer and Spring seasons on Mo, TRo/RC and Vj relative to cooler seasons suggest the seasonal effects on the PSII photochemistry of \u003cem\u003eB. natalensis\u003c/em\u003e. High Mo and TRo/RC values, which indicate enhanced photosynthetic activity (Strasser et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), during Summer and Spring were expected due to the associated increase in temperatures and sunlight during these seasons. However, the significant and negative Summer and Spring effects on Fv/Fm and PI (abs) also suggest a photosynthetic apparatus gradually experiencing stress. Generally, high temperatures and intense light can lead to photoinhibition, where PSII is damaged faster than its repair, reducing the efficiency of photosynthesis (Strasser et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Although Fv/Fm and PI(abs) in Spring and Summer were significantly lower than Autumn, the seasonal Summer and Spring Fv/Fm estimates of 0.66 and 0.72 respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) are close to the expected optimal values of \u003cem\u003eca\u003c/em\u003e. 0.8 (Maxwell and Johnson \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), which suggests that the Summer and Spring stress in \u003cem\u003eB. natalensis\u003c/em\u003e plants had not severely damaged the PSII beyond its rate of repair. Mo is the approximated initial slope of the O-J-I-P fluorescence transient, and TRo/RC is the trapped energy per reaction centre (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, high Mo suggests rapid initial rate of electron transport from the primary electron acceptor (QA) to the secondary acceptor (QB) in PSII, while high TRo/RC suggests efficiency of light energy trapping by the PSII (Force et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Thus, higher Mo and TRo/RC in Summer and Spring imply a more efficient and active photosynthetic apparatus (Strasser et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), despite the statistically significant drop in quantum yield of PSII photochemistry (Fv/Fm) and performance Index based on absorbance (PI (abs)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eDrought has well-known negative impacts on biomass accumulation, which explains the observed accumulation at moderate and high soil moisture levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). However, warming did not significantly impact biomass accumulation but impacted the biomass allocation (allometry) by altering shoot:root ratio (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The positive effects of warming on shoot:root ratios indicate more allocation towards shoots in \u003cem\u003eB. natalensis\u003c/em\u003e at warmer temperatures than towards the roots. Thus, where medicinal extracts are from belowground organs, such as in \u003cem\u003eB. natalensis\u003c/em\u003e, warmer temperatures may result in reduced root yields.\u003c/p\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eWe tested and found evidence that the projected climatic warming and drought scenarios for Southern Africa, based on Hulme et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), are likely to influence the chlorophyll concentration, efficiency of photosystem (PSII) and biomass yields in \u003cem\u003eB. natalensis\u003c/em\u003e across different seasons. The effects of limiting soil moisture and seasonal effects were evident on the measured O-J-I-P test parameters, which are Fv/Fm, PI (abs), Dio/CS, Mo, TRo/RC and Vj. Although plants displayed more stress and less chlorophyll concentrations during hotter (Spring and Summer) than cooler seasons (Autumn, Winter), the stress did not translate to serious impairment of the efficiency of PSII. Chlorophyll concentrations increased at higher moisture levels during the cooler, but not during the hotter seasons. \u003cem\u003eBulbine natalensis\u003c/em\u003e was more sensitive to temperature stress than drought, possibly due to its underground storage bulb and its ability to reserve water in leaves. Given that all plants in OTC30 and OTC50 survived the passive warming over a year, the critical thermal thresholds of \u003cem\u003eB. natalensis\u003c/em\u003e were not reached. The predicted warming by up to 3.1˚, which matches the median for 7 GCM experiments for Southern Africa according to Hulme et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) may cause plant stress and reduce PSII efficiency but was not intense enough to cause plant mortality in \u003cem\u003eB. natalensis.\u003c/em\u003e Therefore, we recommend further studies on the critical temperatures (Tcrit) beyond which \u003cem\u003eB. natalensis\u003c/em\u003e and other indigenous wild plants will not recover.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eWe gratefully acknowledge support from the Durban University of Technology\u0026rsquo;s institutional funding (FEVO 201800). The funders did not have any influence on the study design, data collection, interpretation, and publication of the results.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI.M.: Conceptualization (equal); Investigation (equal); Data curation (equal); Formal analysis (lead); Writing - review and editing (equal). P.P.N.: Conceptualization (equal), Investigation (equal), Data curation (equal), Writing - original draft (lead), Writing - review and editing (equal). T.K.: Conceptualization (equal), Investigation (equal), Data curation (equal), Writing - review and editing (equal).\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Nokuzola Patience Phungula, Thagen Anumanthoo, Lindani Blessing Khanyile, Nosipho Nokwindla, Alfred Andile Mkhize and Snothi Njabulo Mdunge of the Horticulture Department of Durban University of Technology, who provided technical support during the establishment of the experiment.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eData are available on the following Mendeley Data link:. Ngcobo, Philile Patience; Kudanga, Tukayi; Matimati, Ignatious (2025), \u0026ldquo;Photosynthetic efficiency and stress responses in medicinal Bulbine natalensis Baker under simulated warming and drought futures for Southern Africa.\u0026rdquo;, Mendeley Data, V1, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17632/pbv5knfgcx.1\u003c/span\u003e\u003cspan address=\"10.17632/pbv5knfgcx.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. No datasets were generated or analysed during the current study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams, H.D., Guardiola-Claramonte, M., Barron-Gafford, G.A., Villegas, J.C., Breshears, D.D., Zou, C.B., Troch, P.A., Huxman, T.E., 2009. Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought. 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Plant adaptations to the combination of drought and high temperatures. Physiol Plant 162, 2-12. https://doi.org/10.1111/ppl.12540\u003c/li\u003e\n\u003cli\u003eZellnig, G., Perktold, A., Zechmann, B., 2010. Fine structural quantification of drought-stressed Picea abies (L.) organelles based on 3D reconstructions. Protoplasma 243, 129-136. https://doi.org/10.1007/s00709-009-0058-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Medicinal plants, bulbine, climatic warming, PSII efficiency, O-J-I-P test, chlorophyll concentration","lastPublishedDoi":"10.21203/rs.3.rs-6728719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6728719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate warming in Southern Africa is predicted to range between 1.1 ˚ and 4.5˚ by 2050, with an increased frequency, duration and severity of droughts, which affect plant physiology. We used two types of open-top warming chambers, which passively warmed by 1.3\u0026ndash;2.1˚ (OTC30) and by 2.2\u0026ndash;3.1˚ (OTC50) above ambient temperatures, to simulate daytime warming for a year. \u003cem\u003eBulbine natalensis\u003c/em\u003e Baker, a valued medicinal plant in Southern Africa, was grown at low, moderate and high soil moisture levels under Ambient, OTC30 or OTC50 to test the impacts of drought and warming on the chlorophyll concentrations and efficiency of the photosystem (PSII) across seasons. While warming with OTC30 had insignificant effects, OTC50 led to 5% lower quantum yields of PSII (Fv/Fm), 14% lower performance index (PI (abs)), and a 53% rise in energy dissipation (Dio/CS) of PSII. Although plants displayed more stress and less chlorophyll concentrations during warmer Spring and Summer than cooler Autumn and Winter, the seasonal stress did not materially reduce PSII efficiency. Chlorophyll concentrations peaked with higher soil moisture during the cooler, but not the warmer seasons. Overall, \u003cem\u003eB. natalensis\u003c/em\u003e was more temperature- than drought-sensitive, possibly due to its high leaf-water storage capacity. Given that all OTC30- and OTC50-warmed plants survived, thermal thresholds of \u003cem\u003eB. natalensis\u003c/em\u003e were not evident. The 3.1˚ passive warming (OTC50), which matched the 2050s median for Southern Africa, caused significant plant stress but not mortality. Therefore, studying optimal temperatures for maximum photosynthesis (Topt) and critical temperature thresholds (Tcrit) for PSII function in \u003cem\u003eB. natalensis\u003c/em\u003e is recommended.\u003c/p\u003e","manuscriptTitle":"Photosynthetic efficiency and stress indices in medicinal Bulbine natalensis Baker respond to warming and drought futures for Southern Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 10:38:29","doi":"10.21203/rs.3.rs-6728719/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17e7346a-75b3-49ee-8720-8030746c8bd0","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-05T03:38:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 10:38:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6728719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6728719","identity":"rs-6728719","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}