New Insights into Leaf Physiological Responses to Ozone for Use in Crop Modelling

Abstract

Estimating food production under future air pollution and climate conditions in scenario analysis depends on accurately modelling ozone (O₃) effects on yield. This study tests several assumptions that form part of published approaches for modelling O₃ effects on photosynthesis and leaf duration against experimental data. In 2015 and 2016, two wheat cultivars were exposed in eight hemispherical glasshouses to O₃ ranging from 22 to 57 ppb (24 h mean), with profiles ranging from raised background to high peak treatments. The stomatal O₃ flux (Phytotoxic Ozone Dose, POD) to leaves was simulated using a multiplicative stomatal conductance model. Leaf senescence occurred earlier as average POD increased according to a linear relationship, and the two cultivars showed very different senescence responses. Negative effects of O₃ on photosynthesis were only observed alongside O₃-induced leaf senescence, suggesting that O₃ does not impair photosynthesis in un-senesced flag leaves at the realistic O₃ concentrations applied here. Accelerated senescence is therefore likely to be the dominant O₃ effect influencing yield in most agricultural environments. POD was better than 24 h mean concentration and AOT40 (accumulated O₃ exceeding 40 ppb, daylight hours) at predicting physiological response to O₃, and flux also accounted for the difference in exposure resulting from peak and high background treatments.

1. Introduction

The air pollutant ozone (O₃) reduces yield in many crops including wheat, rice, and soybean [1,2]. Ozone at the ground level forms from precursor gases—chiefly NO_x and volatile organic compounds (VOCs)—in chemical reactions catalysed by sunlight and heat [3]. Concentrations over much of the Earth’s land surface have approximately doubled since pre-industrial times, mainly due to anthropogenic emissions from vehicles, industry, and agriculture [4,5,6]. Annual mean surface O₃ concentrations have largely stabilized in Europe since the year 2000 as a result of emission control policies [7,8], but continued increase to 2050 is likely across South and East Asia [9,10]. The pattern of O₃ exposure across regions is also expected to change over coming decades. Short peak ‘episodes’ of very high concentrations are predicted to become more frequent in India and China [10,11], while in Europe and North America a decline in peak episode frequency, alongside steadily increasing annual mean O₃ concentrations, was observed between 1990 and 2010 [12]. Modelling suggests this decline in peak episode frequency in Europe and North America is likely to continue to 2050 [10].

Model-based estimates of yield loss under future climate and air pollution scenarios represent a powerful way of highlighting yield benefits that could come from reduced surface O₃ [13,14,15,16]. Global O₃-induced wheat yield loss for the year 2000 has been estimated as ranging from 5% to 26%, with potential additional losses of 1.5% to 10% predicted for 2030 [13,14]. However, nearly all large-scale assessments of O₃-induced yield loss for wheat published to date have followed an empirical approach, where O₃ concentration is simulated using a chemical transport model (CTM), concentration is linked to yield loss using response functions, and response is scaled up using crop production maps and agricultural statistics. An alternative is to use a process-based approach that could potentially produce more robust estimates of future yield, through inclusion of interactive effects between O₃, CO₂, and climate variables [17].

This process-based crop modelling approach builds on the development of methods for modelling O₃ flux into leaves that have been made over recent decades [18,19]. These methods provide an hourly estimate of O₃ dose reaching sites of damage in the leaf, creating the potential for O₃ effects to be integrated into crop simulation models in a dynamic way. Studies applying O₃ flux modelling have generally either used a multiplicative stomatal conductance (g_sto) algorithm—described in prior research [20,21,22]—or followed a semi-mechanistic approach where g_sto is estimated empirically from photosynthetic rate, which in turn is modelled using the biochemical model of Farquhar et al. [23], also described in [24,25]. Since most crop models simulate growth responses at daily (and less frequently, hourly) timesteps and can respond to a changing environment [26], integration of O₃ effects into crop models is feasible, if plant response to O₃ can be represented in the model formulation. Attempts have been made to integrate O₃ effects and crop modelling [25,27], but few estimates of O₃-induced crop yield loss using a dynamic approach have been published to date [28,29]. Reasons for slow progress include the challenge of upscaling responses from the leaf to the canopy, the need for species and cultivar-specific model parameters, and the incomplete understanding of physiological mechanisms driving O₃-induced yield reduction [17]. This paper goes some way to addressing these issues and identifying future research direction that would benefit from empirical investigation targeted towards developing the models that are currently in development.

It is well established that O₃ exposure can reduce yield in wheat [30,31,32] and can cause foliar injury, impaired photosynthesis, altered carbon translocation, and accelerated senescence [33,34]. However, the processes linking O₃ uptake to these responses are not fully understood and it is not clear which are most important in driving ultimate yield loss. Once O₃ has been taken up through stomata, reactions in the plant apoplast lead to the formation of reactive oxygen species (ROS), which can then react with and damage membranes and proteins [35]. Most plants have in-built defence mechanisms and can up-regulate antioxidants to detoxify ROS, but this comes at a carbon cost, meaning O₃ damage to productivity often occurs before visible symptoms appear [35,36]. An O₃-induced reduction in photosynthetic rate has been widely reported [32,37,38], but quantifying the extent to which this is a direct effect of O₃ on the photosynthetic mechanism, or indirect via changes to leaf pigmentation or g_sto, has been a challenge for experimentalists. Disentangling direct O₃ impacts on photosynthesis from the accelerated senescence response is also difficult. Some studies have observed reduced activity of the carbon-fixing enzyme ribulose-1,5-biphosphate carboxylase/oxygenase (rubisco) in response to O₃ [39,40,41], leading to the hypothesis that ‘instantaneous’ effects of O₃ on photosynthesis act via effects on this enzyme. The physiological mechanism underpinning the often-observed accelerated senescence response to O₃ [42,43,44,45,46] is also unknown, although it has been hypothesized that it relates to long-term respiratory costs associated with detoxification and repair [25].

Several approaches for modelling O₃ effects on photosynthesis and senescence have been published. In an early paper, Reich et al. proposed that ozone effects on a number of plant groups could be expressed via a linear relationship between exposure and growth [47]. Subsequent published approaches have attempted to model the separate effects of O₃ on productivity and senescence and have tried to account for differential sensitivity across species. A function for modelling ‘instantaneous’ suppression of photosynthesis was proposed by Martin et al. [27], who simulated a linear reduction in carboxylation capacity of rubisco (the parameter V_cmax in the model of Farquhar et al. [23]), above a threshold hourly flux value representative of the species or cultivar-specific detoxification capacity. A similar approach of O₃ acting on V_cmax was also used by Deckmyn et al. in their O₃ damage module for forest trees, alongside an overnight repair mechanism and a parameter representing the respiratory cost of detoxification [48]. Ewert and Porter [25] applied a version of the Martin et al. ‘short-term’ function alongside a ‘long-term’ algorithm for modelling O₃-induced senescence and assumed that ‘short-term’ photosynthetic suppression by O₃ occurs throughout the leaf lifespan. Their senescence function assumes a linear reduction in mature leaf lifespan as accumulated O₃ flux increases, and senescence comprises the final third of the mature leaf lifespan, during which time V_cmax is assumed to decline linearly. In this function, onset and completion of leaf senescence therefore move progressively earlier and closer together as accumulated O₃ flux increases. An alternative approach for modelling O₃-induced senescence is applied in the multiplicative DO₃SE model (Deposition of ozone for stomatal exchange), a g_sto model which estimates accumulated O₃ flux—known as the Phytotoxic Ozone Dose (POD)—to vegetation [18]. In this model, leaf senescence is induced by a threshold POD, which triggers curvilinear decline in leaf g_sto with a fixed shape but variable decline rate [20,49,50]. The POD ‘trigger’ can be parameterized according to the sensitivity of the cultivar or species.

The integration of O₃ damage functions, such as those described above, into crop models could improve yield estimates under O₃ stress. Model development must however be guided by experimental evidence that identifies damage mechanisms and their relative importance, informs parameterization, and indicates likely degree of error. Models must also be able to replicate the response to different patterns of O₃ exposure. For example, acute peaks in concentration have been observed to induce greater yield loss than consistent, moderate levels with the same 24-h mean exposure [51], and modelling methods need to be able to capture these nuances.

This study combines data from two independent O₃ exposure experiments on European wheat that took place at the same experimental facility in 2015 and 2016 and used consistent protocols for data collection. We model the accumulated O₃ flux to wheat flag leaves across different O₃ treatments, using the multiplicative DO₃SE model, to derive the POD₀SPEC metric of O₃ flux exposure (mmol m⁻² PLA day⁻¹). Exposure to O₃ flux is then considered alongside leaf chlorophyll, g_sto, and photosynthesis responses in order to test key assumptions underpinning published O₃ effect model functions. Firstly, with regards to O₃ effects on leaf senescence, we (i) examine whether inter-cultivar differences in response are captured by current senescence functions and (ii) whether leaf senescence begins at an accumulated O₃ flux ‘trigger’ value. Secondly, we examine whether O₃ reduces V_cmax before—and therefore independent of—onset of O₃-induced leaf senescence. Thirdly, we investigate if flux is a better predictor of the physiological response to O₃ than concentration-based metrics and whether flux can account for differences in the pattern of O₃ exposure (i.e., peak vs. background).

Our results reveal several insights about physiological responses to O₃ that can add to the evidence base for designing O₃ effect model functions. The two cultivars of European wheat studied here showed substantially different sensitivity to O₃ in terms of their senescence response, indicating the importance of cultivar-specific parameterization in senescence functions. The study also finds that O₃ effects on the photosynthetic mechanism are not observed in young flag leaves and are only seen following the onset of leaf senescence, suggesting that O₃-induced accelerated senescence is more important than direct effects on photosynthesis in determining final yield.

2. Results

2.1. Ozone Treatments in 2015 and 2016

This study combines data from two independent experiments, which took place at the Centre for Ecology and Hydrology (CEH) air pollution exposure facility in 2015 and 2016. Both experiments gathered data on the physiological response of wheat cultivars to O₃ exposure using consistent experimental design and data collection techniques. The main differences in these experiments were in the ozone concentration profiles to which plants were exposed. In 2015, two wheat cultivars, ‘Mulika’ and ‘Skyfall’, were exposed to eight O₃ treatments. The treatments varied in their profile, with some characterized by acute peaks in concentration and others by a consistent background level (described in full in Section 4.1). In 2016, cv. ‘Skyfall’ was exposed to five O₃ treatments, all with a ‘peak’ style profile. The treatments are named in this paper according to their severity and profile, these are ‘low background’ (LB), ‘low peak’ (LP), ‘medium background’ (MB), ‘medium peak’ (MP), ‘high background’ (HB), ‘high peak’ (HP), ‘very high background, (VHB), and ‘very high peak’ (VHP).

Table 1. Summary of ozone (O₃) treatments administered in the 2015 and 2016 experiments. 24-h mean, AOT40, and mean daily peak O₃ have been calculated over the full O₃ exposure period, whereas the mean POD₀SPEC, POD₀SPEC, and POD₆SPEC quantifies exposure in the flag leaf only (i.e., calculated over the period following flag leaf emergence).

Season	Ozone Treatment	24-h Mean (ppb)	AOT40 (ppm h)	Mean Daily Peak O3 (ppb)**	Mean Daily POD0SPEC (mmol m−2 PLA day−1)	POD0SPEC (mmol m−2 PLA)	POD6SPEC (mmol m−2 PLA)
2015	LB	26.94	0.002	33.21	0.43	22.87	6.64
	LP	30.39	0.02	36.44	0.46	25.19	8.17
	MB	37.42	4.19	47.74	0.57	29.91	13.03
	MP	40.39	14.51	67.59	0.69	30.99	15.95
	HB	50.06	12.49	56.73	0.71	31.10	15.8
	HP	50.14	28.56	91.90	0.82	31.79	18.48
	VHB	56.81	19.45	66.28	0.78	31.42	17.36
	VHP	55.73	40.03	116.55	1.07	32.16	20.55
2016	LP1	23.42	0.01	31.44	0.36	17.66	3.47
	LP2	22.05	0.03	30.73	0.34	17.11	2.93
	MP	30.41	6.003	55.75	0.54	27.36	11.90
	HP	39.72	21.25	81.04	0.78	31.87	17.39
	VHP	50.14	37.54	113.93	1.04	33.91	20.72

summarizes the O₃ treatments using several exposure indices including the 24-h mean (ppb) and seasonal AOT40 (ppm h). It also quantifies exposure using O₃ flux, or phytotoxic ozone dose (POD), which was modelled in this study using the multiplicative DO₃SE model (described in more detail in the Methodology and Appendix A). Several different flux metrics are included in the table. Because most plants have the ability to detoxify a small quantity of O₃, methods for quantifying O₃ dose often use a threshold for flux accumulation with the threshold value representing the detoxification capacity. Previous experimental work in wheat has found that a threshold of six produces the closest fit between exposure and yield [50]. We therefore applied a threshold of six when calculating accumulated flux, to produce the POD₆SPEC metric (where SPEC refers to the species-specific nature of the metric). However, as thresholds for physiological effects are not as well established, we have also calculated accumulated flux with no threshold, known hereafter as the POD₀SPEC. The mean daily POD₀SPEC was also calculated, to represent the average level of exposure on a given day in each treatment.

2.2. Effect of O₃ on Senescence

Leaf senescence in the two cultivars across different O₃ treatments was compared using chlorophyll content index (CCI) as a proxy measure for senescence. The growing seasons were divided into six segments or ‘bins’ of equal thermal time, and statistical analysis testing for the impact of O₃ was carried out within each time-bin, in order to identify the point in the season when O₃ effects on CCI can be seen to occur. CCI declined over the course of the growing seasons in both cultivars and in both years, and O₃ accelerated this senescence (Figure 1). A substantial difference in senescence response of the two cultivars was observed. In the 2015 experiment, cv. Mulika exhibited O₃-induced early senescence only in the highest treatment (VHP) (Figure 1A), whereas for cv. Skyfall in the same year, all treatments exhibited accelerated senescence relative to the lowest treatment (Figure 1B). In 2016, for Skyfall the three highest O₃ treatments exhibited accelerated senescence (Figure 1C). A statistical summary of this analysis is reported in

Table A3. Outcome of linear mixed models (LMM) analysis investigating which O₃ treatments in 2015 and 2016 exhibited an early decline in leaf chlorophyll (CCI) relative to the control treatments.

Cultivar–Year Combination	Ozone Treatment Code	Accelerated Senescence in Treatment Relative to Control? **	Mean POD0SPEC*Thermal Time Interaction Variable p-Value
Mulika, 2015	LP	No	n.e.
	MB	No	n.e.
	MP	No	n.e.
	HB	No	n.e.
	HP	No	n.e.
	VHB	No	n.e.
	VHP	Yes	p < 0.001
Skyfall, 2015	LP	Yes	p < 0.001
	MB	Yes	p < 0.0001
	MP	Yes	p < 0.0001
	HB	Yes	p < 0.0001
	HP	Yes	p < 0.0001
	VHB	Yes	p < 0.0001
	VHP	Yes	p < 0.0001
Skyfall, 2016	LP1	No	n.e.
	MP	Yes	p < 0.0001
	HP	Yes	p < 0.05
	VHP	Yes	p < 0.0001

** Control treatment was defined as the lowest treatment in terms of mean daily POD₀SPEC for each season (i.e., treatment LB in 2015 and treatment LP2 in 2016).; n.e. = no significant effect at p < 0.05.

of the Appendix A.

Analysis conducted within different thermal time groupings indicated that a significant negative effect of O₃ on CCI was observed substantially earlier in the season for Skyfall compared to Mulika. For Skyfall in 2015, accumulated POD₀SPEC was significantly negatively associated with flag leaf CCI from the third thermal time group onwards (1109–1337 °C days), after 25–36 days of O₃ exposure. For Mulika in 2015, accumulated POD₀SPEC was significantly negatively associated with CCI only at the fifth thermal time group (1568–1796 °C days), after 49–59 days of O₃ exposure. The limited CCI data for Skyfall in 2016 supports the 2015 results, with a significant negative association between accumulated POD₀SPEC and CCI observed in the 3rd and 4th thermal time-bins. A significant positive association between accumulated POD₀SPEC and CCI was observed for Skyfall in 2015, in thermal time group one (649–879 °C days) spanning the first 14 days of O₃ exposure. A statistical summary of the time-bin analysis is presented in

Table A4. Statistics summarising the effect of accumulated POD₀SPEC on flag leaf chlorophyll (CCI) in the six thermal time groups.

Cultivar–Year Combination	Thermal Time-Bin (°Cdays)	Number of Observations	POD0SPEC Variable p-Value	Direction of Effect
Mulika, 2015	649–879	44	n.e.
	880–1108	18	n.e.
	1109–1337	16	n.e.
	1338–1567	34	n.e.
	1568–1796	52	p < 0.01	−
	1797–2026	17	n.e.
Skyfall, 2015	649–879	36	p < 0.05	+ve
	880–1108	16	n.e.
	1109–1337	16	p < 0.05	−
	1338–1567	38	p < 0.05	−
	1568–1796	38	p < 0.001	−
	1797–2026	16	n.e.
Skyfall, 2016	649–879	0
	880–1108	0
	1109–1337	50	p < 0.001	−
	1338–1567	55	p < 0.01	−
	1568–1796	0
	1797–2026	0

n.e. = no significant effect at p < 0.05.

of the Appendix A.

The timing of leaf senescence onset and completion was influenced by O₃ exposure. For both cultivars in 2015, leaf senescence onset occurred earlier in O₃ treatments with higher mean daily POD₀SPEC, although this trend was only statistically significant for Mulika (Figure 2A). On average, O₃-induced senescence onset occurred later in the season for Mulika (1725 °C days) compared to Skyfall (1216 °C days). Senescence completion also occurred earlier in O₃ treatments with a higher mean daily POD₀SPEC in both cultivars, according to a linear relationship (Figure 2B). Senescence completion occurred at a similar thermal time on average for both cultivars (Mulika = 1841 °C days, Skyfall = 1867 °C days). The total duration of the O₃-induced senescence period was therefore longer for Skyfall than Mulika.

Skyfall also exhibited a linear reduction in the CCI-thermal time curve integral as the mean daily POD₀SPEC increased (Figure 2C). This indicates that Skyfall exhibited reduced CCI in the flag leaf throughout the post-anthesis period in elevated O₃. No significant association between mean exposure and curve integral was found for Mulika, although the highest treatment in terms of mean flux exposure (VHP) did exhibit a reduced integral compared to the other treatments.

2.3. Ozone Flux at Onset of Early Senescence

Leaf senescence data was also analysed to test whether a particular quantity of O₃ flux, or ‘trigger’ flux value, could be identified as inducing the onset of leaf senescence. Accumulated O₃ flux at leaf senescence onset for all 2015 treatments which exhibited significant accelerated senescence is shown in

Table 2. Accumulated flux (POD_YSPEC) at the onset of O₃-induced senescence for 2015 treatments which exhibited significant accelerated senescence.

Cultivar	O3 Treatment (2015)	POD0SPEC at Senescence Onset (mmol m−2)	POD6SPEC at Senescence Onset (mmol m−2)
Skyfall	LP	17.8	6.5
	MB	15.3	7.9
	MP	22.0	12.9
	HB	24.7	14.1
	HP	25.1	16.2
	VHB	22.9	14.4
	VHP	25.7	18.6
Mulika	VHP	30.1	20.6

. In the highest O₃ treatment (VHP), senescence onset occurred at a substantially lower accumulated POD₀SPEC for the cultivar Skyfall (25.7 mmol m⁻²) compared with Mulika (30.1 mmol m⁻²), indicating differential sensitivity across the cultivars. When accumulated POD₀SPEC and POD₆SPEC at senescence onset are compared across the different O₃ treatments for the cultivar Skyfall, senescence onset was observed to occur across a fairly wide range of accumulated flux (15.3–25.7 mmol m⁻² POD₀SPEC, 6.5–18.6 mmol m⁻² POD₆SPEC). The range of flux at senescence onset was more narrow when flux was calculated without a detoxification threshold (POD₀SPEC flux range = 10.4 mmol m⁻², POD₆SPEC flux range = 12.1 mmol m⁻²) and considerably more narrow when only the five highest O₃ treatments which exhibited the strongest accelerated senescence response are considered (POD₀SPEC flux range of five highest treatments = 3.7 mmol m⁻², POD₆SPEC flux range of five highest treatments = 5.7 mmol m⁻²).

2.4. Response of Photosynthesis and g_sto Over Time and in Elevated O₃

Figure 3 presents combined datasets for four leaf-level physiological parameters capable of short-term or ‘instantaneous’ change in response to environmental stimuli: A_sat (Light-saturated photosynthetic rate), V_cmax (maximum carboxylation capacity of rubisco), J_max (maximum rate of electron transport) and g_sto (stomatal conductance). Data from flag leaf measurements have been combined across both cultivars and both experiments, and the hue of each data point corresponds to the POD₀SPEC that had accumulated at the time of measurement (an equivalent figure indicating accumulated POD₆SPEC at time of measurement is shown as Figure A3 in the Appendix A). The average physiological values for high and low O₃-treated plants within each time-bin are also shown on the plots. The average ‘low’ value represents the mean value for the lowest 2015 treatment (LB) and lowest 2016 treatment (LP2) combined. The average ‘high’ value represents the mean value for the highest 2015 treatment (VHP) and the highest 2016 treatment (VHP) combined. A decline in the photosynthetic parameters (A_sat, V_cmax, J_max) was observed across the growing season, and this decline was accelerated in high O₃. g_sto did not decline over time in low O₃ but did decline throughout the season in high O₃.

The outcome of statistical analyses carried out on each cultivar–year combination and in each thermal time group, for the parameters A_sat, V_cmax, J_max, and g_sto as measured in the flag leaves, is shown in Figure 4. A statistical summary of this analysis is presented in

Table A5. Statistics summarising the effect of accumulated POD₀SPEC on flag leaf A_sat.

Cultivar–Year Combination	Thermal Time-Bin (°Cdays)	Number of Observations	POD0SPEC Variable p-Value	Direction of Effect
Mulika, 2015	649–879	9	n.e.
	880–1108	6	n.e.
	1109–1337	0
	1338–1567	0
	1568–1796	16	p < 0.05	−
	1797–2026	0
Skyfall, 2015	649–879	8	n.e.
	880–1108	8	p < 0.01	+ve
	1109–1337	0
	1338–1567	0
	1568–1796	7	p < 0.05	−
	1797–2026	8	p < 0.001	−
Skyfall, 2016	649–879	0
	880–1108	52	p < 0.001	+ve
	1109–1337	28	p < 0.05	−
	1338–1567	118	p < 0.05	−
	1568–1796	56	p < 0.001
	1797–2026	66	p < 0.001

n.e. = no significant effect at p < 0.05.

,

Table A6. Statistics summarising the effect of accumulated POD₀SPEC on flag leaf V_cmax.

Cultivar–Year Combination	Thermal Time-Bin (°Cdays)	Number of Observations	POD0SPEC Variable p-Value	Direction of Effect
Mulika, 2015	649–879	9	n.e.
	880–1108	6	n.e.
	1109–1337	0
	1338–1567	0
	1568–1796	16	p < 0.01	−
	1797–2026	0
Skyfall, 2015	649–879	8	n.e.
	880–1108	8	n.e.
	1109–1337	0
	1338–1567	0
	1568–1796	7	n.e.
	1797–2026	8	p < 0.001	−
Skyfall, 2016	649–879	0
	880–1108	54	p < 0.001	+ve
	1109–1337	28	n.e.
	1338–1567	118	n.e.
	1568–1796	56	p < 0.001	−
	1797–2026	66	p < 0.001	−

n.e. = no significant effect at p < 0.05.

,

Table A7. Statistics summarising the effect of accumulated POD₀SPEC on flag leaf J_max.

Cultivar–Year Combination	Thermal Time-Bin (°Cdays)	Number of Observations	POD0SPEC Variable p-Value	Direction of Effect
Mulika, 2015	649–879	9	p < 0.05	+ve
	880–1108	6	n.e.
	1109–1337	0
	1338–1567	0
	1568–1796	16	p < 0.01	−
	1797–2026	0
Skyfall, 2015	649–879	8	n.e.
	880–1108	8	n.e.
	1109–1337	0
	1338–1567	0
	1568–1796	7	p < 0.05	−
	1797–2026	8	p < 0.001	−
Skyfall, 2016	649–879	0
	880–1108	16	n.e.
	1109–1337	28	n.e.
	1338–1567	48	n.e.
	1568–1796	0
	1797–2026	14	n.e.

n.e. = no significant effect at p < 0.05.

and

Table A8. Statistics summarising the effect of accumulated POD₀SPEC on flag leaf g_sto.

Cultivar–Year Combination	Thermal Time-Bin (°Cdays)	Number of Observations	POD0SPEC Variable p-Value	Direction of Effect
Mulika, 2015	649–879	9	n.e.
	880–1108	6	n.e.
	1109–1337	0
	1338–1567	0
	1568–1796	15	p < 0.05	−
	1797–2026	0
Skyfall, 2015	649–879	8	n.e.
	880–1108	59	p < 0.01	+ve
	1109–1337	28
	1338–1567	116
	1568–1796	63	n.e.
	1797–2026	74	p < 0.001	−
Skyfall, 2016	649–879	8
	880–1108	7	n.e.
	1109–1337	0	n.e.
	1338–1567	0	p < 0.01	−
	1568–1796	7	p < 0.001	−
	1797–2026	8	p < 0.001	−

n.e. = no significant effect at p < 0.05.

of the Appendix A. Grey regions on plots denote the period following the observation of a significant negative effect of accumulated POD₀SPEC on flag leaf CCI. Across all cultivar–year combinations, no significant negative effects of accumulated POD₀SPEC on any of the instantaneous physiological parameters was observed before negative effects of accumulated POD₀SPEC on CCI were observed. A significant negative association of accumulated POD₀SPEC on the parameters V_cmax and J_max was not observed until the 5th thermal time-bin (1568–1796 °C days).

Some evidence of heightened physiological performance in the early-season in high O₃ was observed across both cultivars and both years, although the pattern was not consistent. A significant positive association between accumulated POD₀SPEC and physiology in either the first or second time-bin was observed for i) J_max in Mulika in 2015, ii) A_sat and g_sto in Skyfall in 2015, and iii) A_sat and V_cmax in Skyfall in 2016.

2.5. Comparison of O₃ Exposure Metrics for Predicting Physiological Response to O₃

For all measured physiological parameters (CCI, A_sat, V_cmax, J_max, g_sto) and for both cultivars, a flux-based metric of exposure was better at predicting physiological response of wheat to O₃, compared to the concentration-based metrics (24-h mean and AOT40) (

Table 3. Summary of linear mixed model regression analysis to determine whether accumulated POD₀SPEC, accumulated POD₆SPEC, 24-h mean, or AOT40 represent the best predictor of physiology in the 5^th thermal time-bin. The lowest AIC (Akaike Information Criterion) for each parameter and cultivar, indicating the best model, is highlighted in grey. The outcome of model selection to determine if the profile of O₃ exposure (i.e., peak vs. background) was important in the flux-based models is also shown.

Parameter	Cultivar	AIC: POD0SPEC	AIC: POD6SPEC	AIC: AOT40	AIC: 24-h Mean	O3 Profile Important in POD0SPEC Model?	O3 Profile Important in POD6SPEC Model?
CCI	Mulika	−62.8	−60.5	−59.2	−58.3	No	No
	Skyfall	3.7	10.4	12.3	15.0	No	No
Asat	Mulika	−0.5	1.3	4.1	3.2	No	No
	Skyfall	−82.9	−68.0	−48.1	−54.2	No	No
Vcmax	Mulika	6.1	6.9	9.9	9.0	No	No
	Skyfall	−63.0	−62.9	−34.6	−32.3	No	No
Jmax	Mulika	−1.7	0.2	3.2	2.3	No	No
	Skyfall	6.3	7.3	9.2	9.5	Yes	No
gsto	Mulika	13.4	14.8	17.3	16.5	No	No
	Skyfall	−19.1	−7.3	2.7	−1.6	No	No

). For four out of the ten model sets created in this analysis, the accumulated POD₀SPEC (i.e., without a threshold for accumulation) produced the best model fit. For the other six model sets, the accumulated POD₀SPEC and POD₆SPEC metrics were equally good at predicting physiological response. The O₃ flux metric with no threshold for accumulation was therefore equal to, or better than, the O₃ flux metric with a detoxification threshold at predicting the physiological response to O₃.

The inclusion of an explanatory variable describing the profile (i.e., peak or background) of O₃ exposure in the ‘best’ model did not improve fit in nine out of the ten model sets created with the accumulated POD₀SPEC metric, and in all models created with the accumulated POD₆SPEC metric. Using O₃ flux as the metric of exposure therefore accounts for differences in the O₃ exposure resulting from peak-dominated treatments and those featuring a consistent background level, in the majority of cases.

3. Discussion

The first aim of the analysis presented here was to assess whether published approaches for modelling O₃-induced senescence can account for inter-cultivar variation in response. Both cultivars exhibited accelerated senescence in response to O₃, but the pattern of response differed according to cultivar. In 2015, significant accelerated senescence was observed in seven O₃ treatments for Skyfall, but only in the highest treatment for Mulika, suggesting higher O₃ tolerance in Mulika (Figure 1). This differential tolerance is also indicated by the earlier appearance of significant O₃ effects on leaf CCI across all treatments for Skyfall compared to Mulika. Senescence completion occurred progressively earlier, hence total leaf duration became progressively shorter, in both cultivars as average O₃ flux (mean daily POD₀SPEC) in the treatment increased (Figure 2B), according to a linear relationship. Completion of leaf senescence occurred at a similar thermal time in both cultivars (Mulika = 1841 °C days, Skyfall = 1867 °C days), meaning that the total senescence duration was longer for Skyfall. While O₃-induced senescence in Mulika was characterised by a sudden drop in leaf CCI in the late-season, Skyfall exhibited a more gradual O₃-induced decline in CCI.

The linear relationship between mean flux and total leaf duration observed in this study for both cultivars gives support to the senescence function of Ewert and Porter [25], which assumes a linear decline in mature leaf lifespan as O₃ exposure increases. However, evidence presented here suggests that the duration of leaf senescence is likely to vary with O₃ exposure. Additionally, the differential senescence duration in the two cultivars suggests that a key assumption of the Ewert and Porter function—that leaf senescence comprises the final third of the mature leaf lifespan—may not hold true for all cultivars. For example, in 2015 for Skyfall, leaf senescence in the highest O₃ treatment comprised 76.7% of the total flag leaf lifespan (flag leaf emergence = 877 °C days, leaf senescence onset = 1075 °C days, senescence completion = 1725 °C days)—substantially greater than one third. The inter-cultivar variation in senescence response observed in this study would therefore only be captured by a model function that allows for the proportion of leaf lifespan comprising leaf senescence to be parameterised according to cultivar and ozone exposure. Our results suggest that to effectively model variation in the pattern of O₃-induced senescence, the timing of senescence onset, and the rate (or duration) of senescence, models need to be calibrated for particular species and cultivars.

However, the analysis in this paper highlights the uncertainty associated with the approach of Danielsson et al. [20] for modelling onset of O₃-induced senescence using a threshold of accumulated flux. Following this approach, senescence onset may occur at different points in time at different levels of mean exposure but should occur at approximately the same value of accumulated flux. This method was designed in the absence of a known mechanism for induction of senescence by O₃ but could be interpreted mechanistically if accumulated O₃ flux is assumed to be proportional to increased respiratory effort accumulated over the season, which has been proposed as a potential trigger for O₃-induced senescence [25]. For Skyfall, across the five highest O₃ treatments in 2015, onset occurred across a POD₀SPEC range of 22.0–25.7 mmol m⁻². Given the limitations associated with the method used to identify senescence onset—arbitrarily defined as a 10% reduction in leaf CCI relative to the control—as well as the inherent variation that exists between seedlings, this flux range can be considered relatively narrow. However, when all treatments that exhibited a significant O₃ effect on senescence are considered for Skyfall in 2015, the range of flux at senescence onset is considerably wider (17.8–25.7 mmol m⁻² POD₀SPEC). These results provide an estimate of the degree of error potentially associated with applying this approach in models and suggest that the accumulation of respiratory effort does not fully explain the triggering of O₃-induced senescence.

A second objective of this study was to test the hypothesis that O₃ reduces photosynthetic rate in the short term by reducing carboxylation capacity of rubisco (V_cmax). The assumption that O₃ reduces V_cmax is central to the ‘instantaneous’ O₃ effect function of Martin et al. [27]. A version of this function is also applied by Ewert and Porter [25], where O₃ reduces photosynthesis in a short-term and reversible way, in addition to and independent of the O₃ senescence effect. The analysis presented here found that a significant negative effect of O₃ on photosynthesis and g_sto was only observed concurrent with O₃-induced leaf senescence (Figure 4). This result was consistent across all cultivar–year combinations. For Skyfall in 2015 and 2016, significant negative effects of O₃ on A_sat were observed before a negative association between O₃ and V_cmax, suggesting that reduced carboxylation capacity is not responsible for the initial reduction in photosynthetic capacity observed in these experiments. There was therefore no evidence of an ‘instantaneous’ effect of O₃ on the photosynthetic mechanism, in the period preceding leaf senescence.

These results contradict several studies which observed short-term reduction in photosynthetic rate in response to O₃ [39,40,41]. One possible explanation for this contradiction is that instantaneous reduction of carboxylation capacity by O₃ is only relevant at acute concentrations. The reduced carboxylation efficiency reported by Farage et al. [41] was observed following 4–16 h of exposure at unrealistically high O₃ concentrations of 200–400 ppb, considerably higher hourly concentrations compared to those used in the experiments in this study, which more closely mimic ambient conditions (maximum hourly O₃ exposure of 117 ppb). The results presented here therefore indicate that accelerated senescence is likely to be more important than direct effects on photosynthesis for determining crop yield loss in most agricultural landscapes, where O₃ concentrations are typically moderate (ranging from ~20 to ~45 ppb) with occasional peaks in concentration [5,52]. Understanding and simulating the early senescence response to O₃ should therefore be the priority for O₃ experimentalists and modellers.

Alternatively, our results could be explained by a differential response to O₃ in younger and older leaves. Bernacchi et al. [53] and Morgan et al. [54] observed in field experiments with soybean that O₃ effects on photosynthesis and g_sto were not apparent in new fully expanded leaves and Reichenauer et al. [55] saw similar results in three wheat cultivars. Younger leaves may have a higher tolerance to O₃, or alternatively the O₃ effect on photosynthesis may be associated with a cumulative build-up of O₃ damage in leaves or leaf age. Either way, the age-dependency of O₃ effects is an important consideration in O₃ effects modelling. The function described by Ewert and Porter [25] for modelling short-term effects of O₃ on photosynthesis allows for leaf age to influence the rate of overnight recovery from O₃ damage but not the threshold for damage. The role of leaf age in determining O₃ flux thresholds would benefit from further investigation.

A surprising result from the data analysis is that O₃ had a significant positive effect on some physiological parameters early in the season. CCI, A_sat, V_cmax, and g_sto all exhibited a positive association with O₃ exposure for one or more of the cultivar–year combinations, in either the first or second thermal time group (up to 32 days following beginning of exposure). Stimulation of growth at low doses of a toxin is known as hormesis, and this phenomenon has been observed previously in a number of plants in response to exposure to low concentrations of O₃ [56]. Stimulation of photosynthesis and g_sto in wheat during the first few weeks of O₃ exposure was also observed by Mulholland et al. [57] in their open-top chamber experiment, although generally there are few reported cases of this phenomenon in crop species. Ozone-induced physiological stimulation could be an adaptive response associated with plant defence responses, for example, heightened g_sto and photosynthesis may enable the upregulation of antioxidant synthesis. Observations that yield can be stimulated at low O₃ exposure concentrations have also given rise to the theory that free radicals, at low concentrations, can act as growth promotors in plants [58]. An alternative hypothesis is that the observed early-season physiological boost in this study is related to disruption of stomatal control by O₃, as has been observed in some grassland species [59,60], leading to heightened g_sto and an associated boost in other physiological parameters. Our results suggest that a bi-phase hermetic dose-response curve, such as proposed by Agathokleous et al. [56], could be considered in ozone effect functions. More experimental data is needed in order to establish if the early-season physiological boost induced by O₃ in this study is consistent across other plant species and environments.

The third aim of this study was to test whether O₃ flux would be a better predictor of physiological response than concentration-based exposure (AOT40 and 24-h mean). Flux was superior at predicting the response to O₃ of five physiological variables (CCI, A_sat, V_cmax, J_max, g_sto) in regression models, for both cultivars (Table 3). Previous studies have reported that flux is better than AOT40 at predicting the spatial distribution of O₃ injury [61] and is a better predictor of the response of wheat yield, poplar biomass, and assimilation rate in urban trees to O₃ exposure [62,63,64]. However, few have compared the association between leaf-level physiology and different exposure metrics in crop species. Our results align with the general consensus in the O₃ research community that O₃ flux represents a more biologically relevant metric of O₃ exposure than ambient concentration [61,65,66,67] and indicate that O₃ flux should be the preferred metric of exposure in O₃ effect model functions. More surprising is the fact that the flux metric without an accumulation threshold, POD₀SPEC, was a better or equal predictor of physiological response compared to POD₆SPEC, which employs an accumulation threshold of six. POD₆SPEC produced the closest correlation between flux and yield of wheat in a previous analysis testing different flux accumulation thresholds [50] and has been applied in several assessments of O₃ impacts in wheat [22,68]. More research is therefore needed to establish how much the capacity to detoxify O₃ varies between cultivars and why the threshold flux required to induce leaf-level physiological changes appears to differ from the threshold required to reduce yield. This result also highlights the limitations of an empirical rather than a mechanistic approach to modelling ozone tolerance and detoxification capacity. The development and integration of mechanistic ozone detoxification modules in models, such as that proposed by Plöchl et al., could potentially lead to improved model accuracy [69].

The view that O₃ flux should be the metric of exposure in O₃ effect modelling is also supported by the fact that O₃ flux accounted for the different levels of exposure in treatments dominated by peaks in concentration, versus those characterised by a consistent background level, for the majority of physiological parameters. Flux is therefore likely to perform well as a predictor of physiological response across different world regions which are currently experiencing divergent trends in the pattern of O₃ exposure [10,11,12].

Limitations of this study need to be considered when interpreting and applying the results. Calculated values of O₃ flux were not verified by leaf-level measurements of gas flux through stomata. However, the decision to apply the multiplicative DO₃SE model in this study was based on the fact that fluxes produced by this model have previously been evaluated in several independent studies which have demonstrated the model’s predictive capability [19,21,22,70,71,72]. A further limitation is that estimates of the onset of leaf senescence were based on leaf chlorophyll content, which would have represented both the chlorophyll loss resulting from leaf injury, as well as chlorophyll loss relating to senescence. In addition, the analysis is based on only one crop species and two cultivars. As the variation in yield response to O₃ exhibited by different crops, and different cultivars within the same crop species, is well established [2,73], caution must be used when extrapolating results presented here to other wheat cultivars and crops. It should however be noted that the observation in this study that no O₃ effect on photosynthesis could be observed in young wheat leaves—indicating the senescence response is more important than direct effects on photosynthesis—is supported by previous work in other wheat cultivars [55] and by other experimental work in soybean [53,54]. Considerations when applying the results presented in this study, particularly when attempting to up-scale modelled responses from the leaf to canopy level, include the fact that the response observed in the wheat flag leaf may differ from the responses of lower-canopy leaves, and exposure to O₃ during early seedling and leaf development may also alter the sensitivity to O₃ observed in the flag leaf.

In conclusion, this study has shown that current approaches for modelling O₃ effects on leaf longevity and photosynthesis in crops have some limitations and are not fully supported by the experimental data presented here. Model functions representing O₃-induced senescence must allow for parameterisation of the timing of senescence onset and rate of senescence, if inter-cultivar variation in response is to be accurately simulated. Further research aimed at understanding the mechanistic ‘trigger’ of O₃-induced senescence should be a priority, as this understanding may allow for the development of a more effective mechanism in models for inducing the senescence response. The results also suggest an age-dependency in the response of photosynthesis to O₃—which is not currently fully considered in modelling methods—and indicate that acceleration of senescence is more important than direct effects of O₃ on photosynthesis in determining final O₃-induced yield loss, at O₃ concentrations that crops are likely to be exposed to on a day-to-day basis. Building functions that can accurately represent the O₃-induced senescence effect in crops should therefore be the priority for O₃ effect modellers.

4. Materials and Methods

4.1. Experimental Site and Treatments

Both experiments that provided data for this study took place at the CEH air pollution exposure facility in Abergwyngregyn, North Wales (53.2°N, 4.0°W). The two experiments were conducted independently and differ in terms of the number of cultivars and treatments, however, the experimental design was similar across both years and the same protocols for physiological measurements were applied. Data was therefore subsequently pooled across the two years to produce a more robust dataset. In 2015, two European wheat cultivars (Triticum aestivum L., ‘Mulika’ and ‘Skyfall’) were exposed to O₃ for 82 days. In 2016, only the more O₃-sensitive cultivar, ‘Skyfall’, was used and was exposed for 92 days. Timelines for sowing, emergence, O₃ exposure, and harvest in both experiments are presented in Figure A1 of the Appendix A. In both experiments, plants were grown in 25-litre containers (40 cm × 35 cm × 38 cm) filled with John Innes No.3 compost (Westland Horticulture Limited, Huntingdon, UK), and soil was inoculated shortly after sowing with microbial communities using a soil slurry taken from a nearby wheat field. Seeds were sown in rows 7 cm apart at a density of ~260 seedlings per square metre, which aligns with recommended seedling density for field conditions [74]. Four containers per cultivar–treatment combination were planted and placed alongside each other, producing a canopy of ~144 plants per cultivar–treatment combination. In both years, ammonium nitrate fertiliser was applied once mid-season (80 kg/ha). In 2015, fungicide (‘Unix’, cyprodinil, 1.6 kg/ha—Syngenta, Bracknell, UK) was applied once and insecticide (pyrethrum, 1 mL/L—Bayer, Monheim Am Rhein, Germany) applied three times. In 2016, fungicide was applied twice (1st application: Trifloxystrobin, 0.12 g/L—Bayer, Mondheim Am Rhein, Gernany; tebuconazole, 0.125 g/L—Bayer, Mondheim Am Rhein, Germany; 2nd application: cyprodinil, 2.25 g/L—Syngenta, Bracknell, UK) and insecticide applied once (thiachloprid, 0.15 g/L #x2014;Bayer, Mondheim Am Rhein, Germany).

Ozone exposure took place within ‘solar domes’, hemispherical glass domes three metres in diameter and two metres in height, described previously [59,75]. Air entering domes was carbon-filtered to remove O₃ and other air pollutants (i.e., NO_X and SO₂), and the solar dome site is an area with some of the lowest NO_X and SO₂ emissions in the country and is not downwind of any major sources [76], minimising any other pollutants that could have entered the domes after filtration. Following filtration, a precision-controlled quantity supplied by an O₃ generator (Dryden Aqua G11, Edinburgh, UK) linked to an oxygen concentrator (Sequal 10, Pure O2, Manchester, UK), was added to incoming air. Injection concentrations were determined by a computer-controlled O₃ injection system (Lab VIEW, version 8.6, National Instruments, Austin, TX, USA). Air within domes was circulated at a rate of two air changes per minute, and the O₃ concentration within each dome was recorded on a 30-min cycle using two O₃ analysers of matched calibration (Envirotech API 400A, St Albans, UK). Exposure profiles for each treatment are presented in Figure 5. Treatments spanned a range of seasonal mean concentrations and represented different O₃ exposure patterns, representing potential future profiles of increasing background or decreasing peak O₃. In 2015, four treatments consisted of a low night-time background level, with high peaks during the day, classified as ‘peak’ treatments, while the other four treatments comprised of consistent concentrations with only small peaks, classified as ‘background’ treatments. In 2016, all treatments were ‘peak’ in profile. Although O₃ treatments were not replicated, numerous studies have established the statistical validity of conducting unreplicated experiments using the solar dome facility [56,77,78], and previous work has shown that no solar dome effect on air or leaf temperature is detectable [79].

Climatic conditions fluctuated naturally in the solar domes according to ambient conditions. Air temperature, photosynthetically active radiation (PAR), relative humidity, and wind speed were monitored in one solar dome during both experiments using an automatic weather station (Skye instruments Ltd, Llandridod Wells, UK) to obtain data for stomatal flux modelling. Plants were well-watered throughout, and soil moisture content was continuously monitored in selected plant containers to a depth of 10 cm using Theta Probes (Delta-T Devices Ltd., Cambridge, UK).

4.2. Leaf Chlorophyll and Gas Exchange Measurements

Chlorophyll content was measured non-destructively as an index (chlorophyll content index, CCI) using CCM-200 and CCM-200+ instruments (Opti-sciences, Hudson, NH, USA). A regression line fit to paired measurements was used to standardise observations made using the two instruments. In 2015, 684 measurements were made over 70 days; in 2016, 105 measurements were made over 22 days.

To assess the effect of O₃ on photosynthetic capacity and g_sto, response curves of net photosynthetic rate (A) to intercellular CO₂ concentration (C_i), i.e., A–C_i curves, were constructed using a portable infrared gas analyser (Li-Cor 6400XT; LI-COR Biosciences, Lincoln, NE, USA). In 2015, measurements were made in the two lowest O₃ treatments at the beginning of exposure (20–26 May). Further measurements in the two lowest treatments (LB and LP) and two high O₃ treatments (VHB and VHP) were made in the mid-season (8–17 June) and late-season (16–24 July). Measurements were made in the youngest fully expanded leaf of randomly selected plants (represented by the flag leaf) from 28th May onwards. In 2016, four sets of A–C_i curve measurements were made at approximate two-weekly intervals spanning 6 June to 29 July. Measurements in 2016 were made in all treatments at each of the time intervals, except for the final measurement set in late July, when plants in HP and VHP treatments were too senesced for measurements to take place. All 2016 measurements were made in the flag leaf. For both years, four A–C_i measurements were made per treatment and per cultivar at each timepoint, and leaves were tagged following measurement so that the same leaf could be measured throughout the season.

All response curve measurements were conducted at a selected light saturation (minimum photosynthetic photon flux density = 1500 µmol m⁻² s⁻¹, LED light source), and sample chamber relative humidity was maintained between 50 and 80%. Photosynthetic rate and g_sto were allowed to stabilise in the leaf chamber at ambient CO₂ (400 µmol mol⁻¹). The A–C_i curve was constructed by measuring A at a minimum of nine air CO₂ concentrations, ranging from ca. 50 to 2000 µmol mol⁻¹. A_sat and associated g_sto values were determined from the ambient CO₂ measurements (400 µmol mol⁻¹) from each A–C_i curve.

Additional measurements of A_sat and associated g_sto were made in 2016 over six days (16 June, 1 July, 8 July, 14 July, 20 July, 26 July). Measurements were made at ambient CO₂ concentration (400 µmol mol⁻¹) under the same light and relative humidity conditions as described above.

4.3. Derivation of V_cmax and J_max

Maximum rate of carboxylation (V_cmax) and maximum rate of electron transport (J_max) were derived from A–C_i curves using the estimating utility and methodology described by Sharkey et al. [80]. Leaf temperature and atmospheric pressure were the input parameters, which were measured using the Licor 6400XT simultaneously with all photosynthesis measurements. V_cmax and J_max values calculated from curves were adjusted to 25 °C.

The V_cmax dataset was extended by applying the ‘one-point method’ of deriving V_cmax from A_sat as described by De Kauwe et al. [81]. Estimation of V_cmax when only A_sat is known using the one-point method relies on the assumption that photosynthetic rate at ambient CO₂ is rubisco-limited [81]. As the measurements of A at 400 µmol mol⁻¹ CO₂ in the measured A–C_i curves typically fell within the rubisco-limited section of the curve (i.e., before the transition point), this assumption was thought to be likely to hold true for the two cultivars used in this study. The one-point method also assumes, in the absence of a known daytime respiration rate (R_day), that R_day can be estimated as 1.5% of V_cmax. V_cmax was calculated from A_sat using the following equation:

$$V_{cmax}=A_{sat}\ast \left(\frac{C_i+K_m}{C_i- \Gamma \ast }-0.015\right)$$

(1)

where K_m is the Michaelis–Menten constant, given by:

$$K_m=K_c \ast \left(1+\frac{O_i}{K_O}\right)$$

(2)

The parameters K_c (Michaelis–Menten constant for CO₂), K_O (Michaelis–Menten constant for O₂) and Γ* (CO₂ compensation point in the absence of mitochondrial respiration) were estimated at 25 °C following the equations and constants published by Bernacchi et al. [82] describing their temperature dependence in the model species tobacco (Nicotiana tabacum, L.). Equations and constants used to derive these three parameters are listed in

Table A2. Equations used to derive the Michaelis–Menten constant for CO₂ (K_C), the Michaelis–Menten constant for O₂ (K_O), and the CO₂ compensation point in the absence of mitochondrial respiration (Γ*).

Parameter	Unit	Equation Used in Derivation
KC	µmol mol−1	$404.9\ast \exp \left(\frac{79403\left(T_K-298.15\right)}{298.15\ast R\ast T_K}\right)$
KO	mmol mol−1	$278.4\ast \exp \left(\frac{36380\left(T_K-298.15\right)}{298.15\ast R\ast T_K}\right)$
Γ*.	µmol mol−1	$42.75\ast \exp \left(\frac{37830\left(T_K-298.15\right)}{298.15\ast R\ast T_K}\right)$

T_K = leaf temperature in Kelvin; R = universal gas constant, 8.314 J mol⁻¹ K⁻¹.

of the Appendix A. O_i represents the intercellular concentration of O₂ (210 mmol mol⁻¹) [81].

The robustness of the one-point method was evaluated by comparing V_cmax values calculated from a subset of the measured A–C_i curves with the V_cmax values calculated from each corresponding A_sat value (i.e., the 400 µmol mol⁻¹ CO₂ value from each A–C_i curve). V_cmax values derived using both methods were adjusted to 25 °C. A very close association was observed between V_cmax values derived using the two methods (Figure 6, adjusted r² = 0.95, p < 0.001), indicating that the one-point method is robust for the cultivars used in this study. V_cmax values derived using the one-point method were therefore pooled with A–C_i-derived V_cmax values for analysis, and the potential error introduced through the use of two different derivation methods was accounted for in the statistical analysis by including—in model selection—an explanatory variable describing the derivation method (explained in more detail in Section 4.7.3).

4.4. Modelling O₃ Flux

Stomatal O₃ flux to the flag leaf was modelled in each treatment for both years to derive a measure of exposure that accounted for the environmental influence on O₃ uptake and could be tracked over time. Flux was modelled using the multiplicative g_sto module of the DO₃SE model [18], which has a published parameterisation for European wheat [49,50,83] and has been applied previously to model O₃ flux to this crop [22,84]. A summary of the DO₃SE algorithms and parameters used in this study are presented in Section A1 of the Appendix A.

Ozone flux for wheat is accumulated above a detoxification threshold of six in the DO₃SE methodology (producing the POD₆SPEC flux metric, species-specific phytotoxic O₃ dose above a threshold of 6 mmol m⁻² PLA s⁻¹—previously known as the POD₆, with “SPEC” referring to the species-specific version of the DO₃SE model) [83], as this threshold has produced the closest correlation between POD and wheat yield in previous experiments [50]. However, as thresholds of physiological effect in wheat have been far less studied, the POD₀SPEC (where no threshold for accumulation is applied, previously known as the POD₀) was also calculated, in order to avoid assuming a threshold of effect. Modelled POD₀SPEC and POD₆SPEC for 2015 and 2016 O₃ treatments are shown in Figure 7.

4.5. Alignment of Physiological Observations with O₃ Flux, and Calculation of Mean Flux Exposure (Mean Daily POD₀SPEC)

Each physiological observation (CCI, A_sat, V_cmax, J_max, g_sto) was aligned with the treatment-specific accumulated POD₀SPEC and POD₆SPEC on the day of measurement and at the exact time of measurement, wherever this data was available (referred to hereafter in this paper as ‘accumulated POD₀SPEC’ and ‘accumulated POD₆SPEC’). This was done to allow the impact of real-time O₃ flux exposure on physiology to be analysed. The mean daily POD₀SPEC (i.e., the average accumulation of flux per day, mmol m⁻² PLA day^−1,) was also calculated for each O₃ treatment to act as a metric of mean exposure intensity. Mean daily POD₀SPEC values for each O₃ treatment are presented in the results section in Table 1. Mean daily POD₀SPEC was calculated as the average of daily POD₀SPEC accumulation from A_start until the modelled onset of senescence.

4.6. Data Standardisation

The two experiments had different sowing and harvest calendars. In order to compare timings across the two experiments, time was therefore standardised by calculating thermal time from plant emergence onwards (daily mean temperature sum >0 °C). Physiological data was also standardised, by conversion from raw to relative values. This was done to account for differences in instrument calibration between years, and to account for differences in beginning of season ‘baseline’ physiology between cultivars. Relative values for the physiological observations were calculated by deriving a reference value for each parameter (CCI, A_sat, V_cmax, J_max, g_sto) and each cultivar–year combination (i.e., Mulika in 2015, Skyfall in 2015, Skyfall in 2016). The reference value—calculated as the 90th percentile value of all observations, spanning the whole season and all treatments—was used as the baseline for calculating relative change. Skyfall CCI data from 2016 comprised too few data points for the derivation of an individual reference value, 2016 and 2015 CCI data for Skyfall was therefore combined to produce a single reference value for Skyfall, as CCI data for Skyfall was found to not significantly differ by year (p = 0.06 in regression model). A comparison of CCI data for Skyfall measured in 2015 and 2016 can be found in Section A3 of the Appendix A.

4.7. Statistical Analysis

Statistical analysis was conducted in R version 3.3.2 [85], and either involved linear regression or linear mixed models (LMMs) using the package lme4 v1.17. Model selection was by AIC (Akaike Information Criterion). The model with the lowest AIC was considered the ‘best’ model of those fitted, and models differing in <2 AIC units from the best model were defined as having little empirical support [86]. Wherever relevant, a random factor describing solar dome number was included in models to account for multiple measurements made within domes, and unique pot ID was a random factor when analysis involved multiple measurements made from the same pot. p-values were obtained for terms in the optimal models using the R package lmerTest, v2.0-33 [87]. Assumptions of normality and even spread of residuals were checked using residual plots and data were transformed where necessary. Four key analyses were conducted as part of this study and are described in more detail below.

4.7.1. Identification of O₃ Treatments with Significantly Accelerated Senescence

Flag leaf CCI data was analysed in all O₃ treatments to test for accelerated senescence. Each elevated O₃ treatment was paired in turn with the control treatment for that experiment, and the significance of the thermal time/mean daily POD₀SPEC interaction term was tested using LMMs. Control treatments were defined as the lowest in terms of mean daily POD₀SPEC and comprised of treatment LB for the 2015 experiment and treatment LP2 for the 2016 experiment.

4.7.2. Analysis of O₃ Effect on the Timing of Senescence Onset and Completion

The impact of O₃ on leaf senescence onset and completion was examined using regression models fitted to each of the 2015 O₃ treatments (separately for the two cultivars). It was not possible to conduct this analysis for 2016, as 2016 CCI measurements only spanned 22 days. Regression models comprised of relative CCI as the dependent variable and thermal time as the independent variable, and the shape of response was determined by comparing linear, quadratic, and cubic models. The best model for each O₃ treatment was then used to determine i) thermal time at leaf senescence onset, ii) thermal time at senescence completion, and iii) the post-anthesis curve integral (i.e., area under the curve), as shown in Figure 8. Thermal time at senescence onset was aligned with the accumulated POD₀SPEC at that time for each O₃ treatment, to identify the accumulated flux ‘trigger’ values for senescence onset.

4.7.3. Analysis of Relative Timing of O₃ Effects on Different Aspects of Physiology

The effect of accumulated POD₀SPEC on CCI, A_sat, V_cmax, J_max, and g_sto during successive periods of the growing season was analysed, to identify when O₃ began to influence physiology. The range of thermal time spanned by flag leaf physiological measurements was divided into six thermal time-bins of equal width. The effect of accumulated POD₀SPEC on each parameter, within each time-bin and for each cultivar–year combination, was analysed by comparing model fit with and without accumulated POD₀SPEC as an explanatory variable. An additional explanatory variable was included in model selection for V_cmax, describing the derivation method (i.e., A–Ci curve or one-point method).

4.7.4. Comparison of Flux and Concentration-Based O₃ Exposure Metrics for Predicting Physiological Response

Accumulated POD₀SPEC, accumulated POD₆SPEC, 24-h mean concentration, and AOT40 (accumulated O₃ > 40 ppb during daylight hours) were compared in their ability to predict the response of CCI, A_sat, V_cmax, J_max, and g_sto during the 5th thermal time-bin. The 5th time-bin was selected for this analysis as most physiological parameters exhibited a response to O³ exposure during this time. For each physiological parameter, LMMs constructed with each of the metrics of O₃ exposure were compared for model fit. An explanatory variable describing whether O₃ had been administered as a ‘peak’ or ‘background’ profile was also included in model selection, to test whether the O₃ metric that produced the best model fit also accounted for different patterns of exposure.