Plant susceptibility to ozone: A tower of Babel?

Journal Pre-proofs Plant susceptibility to ozone: A Tower of Babel? Evgenios Agathokleous, Costas J Saitanis PII: DOI: Reference:

S0048-9697(19)34954-X https://doi.org/10.1016/j.scitotenv.2019.134962 STOTEN 134962

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Science of the Total Environment

Received Date: Revised Date: Accepted Date:

13 May 2019 11 October 2019 11 October 2019

Please cite this article as: E. Agathokleous, C.J. Saitanis, Plant susceptibility to ozone: A Tower of Babel?, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134962

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Discussion Plant susceptibility to ozone: A Tower of Babel? Evgenios Agathokleous1* and Costas J Saitanis2 1Institute

of Ecology, Key Laboratory of Agrometeorology of Jiangsu Province, School of Applied Meteorology,

Nanjing University of Information Science & Technology, Nanjing, Jiangsu, 210044, China 2Lab

of Ecology and Environmental Science, Agricultural University of Athens, Iera Odos 75, Athens, 11855,

Greece *correspondence: [email protected], ORCID: 0000-0002-0058-4857

Abstract: In a world with climate change and environmental pollution, modern Biology is concerned with organismic susceptibility. At the same time, policy and decision makers seek information about organismic susceptibility. Therefore, information about organismic susceptibility may have far-reaching implications to the entire biosphere that can extend to several forthcoming generations. Here, we review a sample of approximately 200 published peer-reviewed articles dealing with plant response to ground-level ozone to understand how the information about susceptibility is communicated. A fuzzy and often incorrect terminology was used to describe the responsiveness of plants to ozone. Susceptibility was classified too arbitrarily and this was reflected to the approximately 50 descriptive words that were used to characterize susceptibility. The classification of susceptibility was commonly based on calculated probability (p) value. This practice is inappropriate as p values do not provide any basis for effect or susceptibility magnitude. To bridge the gap between science and policy decision making, classification of susceptibility should be done using alternative approaches, such as effect size estimates in conjunction with multivariate ordination statistics.

Keywords: dose-response; effect size; organismic sensitivity terminology; plant resistance; species tolerance; statistical significance level

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Body

1. Introduction Modern Biology is more than ever concerned with the biotic response to any kinds of stressinducing agents, at any levels of organization (from atoms to ecosystems or beyond). At the same time, policy and decision makers more than ever need information about organismic sensitivity/susceptibility. Hence, it is of central interest to find if there is an effect of certain levels of a stress-inducing agent or how much sensitive/susceptible an organism to some levels of stressinducing agent is. Organismic sensitivity/susceptibility is an evolutionarily-attained trait that permits the survival and existence of organisms on Earth. That is, organisms undergo biological and behavioral modifications in response to environmental challenges. However, not all organisms have equal ability to react to environmental challenges, and some organisms may be more sensitive/susceptible than others. Complex interactions in given environments may lead to higher ability for adaptive responses in some organisms or to maladaptive responses in other organisms.

2. What is organismic sensitivity and susceptibility? Before attempting to answer this question, it is essential to define organismic stress. There is hitherto no accepted unified definition (Jansen and Potters, 2017), and this situation is undeniably fair enough considering the high complexity of the stress biology. For instance, the exposition of the American Institute of Stress (https://www.stress.org/what-is-stress) begins with the statement “stress is not a useful term for scientists because it is such a highly subjective phenomenon that it defies definition” (see also Levine (1985)) One of the definitions given for stress is “a functional state or, in other words, the dynamic response of the whole organism” (Larcher, 2003). Here, we will adopt a recent definition from systems biology (proposed for various forms of life) stating that “stress occurs when a biological control system detects a failure to control a fitness-critical variable, which may be either internal or external to the organism” (Del Guidice et al., 2018; a historical assessment and further analysis of stress definition can be also found therein and in Jansen and Potters, 2017). Sensitivity is so a general term that is used in various disciplines, such as analytical method performance, statistics, psychology, mechanical engineering and medical tests (Ekins and 2

Edwards, 1997; Boyce and Ellis, 2005). The Oxford English Dictionary (OED; Oxford University Press) defines sensitivity as “the degree to which a device, test, or procedure responds to small amounts of, or slight changes in, that to which it is designed to respond” (further discussed in Ekins and Edwards, 1997). In engineering the sensitivity of a device describes the ability of “sensing” of a device (sensor). In medical tests, sensitivity is defined as “the ability of the test to correctly identify those patients with the disease” and quantifies how to trust the outcome of a test concerning a binary question; the inverse is called specificity (Lalkhen and McCluske, 2008; Maxim et al., 2014). In statistics, a variable is considered sensitive against the changes of another variable according to the absolute value of the regression slope (e.g. a function of a linear relationship). Sensitivity is also a function of the organismic stress biology. According to MerriamWebster Dictionary (2019) (https://www.merriam-webster.com/) sensitivity is “the capacity of an organism or sense organ to respond to stimulation”. However, there is no some widely accepted definition of organismic sensitivity. With regard to organismic sensitivity, Boyce and Ellis (2005) state “…heightened stress reactivity may reflect, not simply exaggerated arousal under challenge, but rather an increased biological sensitivity to context, with potential for negative health effects under conditions of adversity and positive effects under conditions of support and protection”. The unclear meaning of the term sensitivity (and its adjective sensitive) has been a matter of controversy in the history of science (Ekins and Edwards, 1997). We would say that organismic “sensitivity” can be defined as the response of an organism (i.e., biological deviation) above or below a homeostatic state (control) of a set of biological traits, after sensing some environmental stress-inducing agents. In order for an organism to respond to some stress-inducing agent, it must, first, sense the agent and, then, activate stress signaling (Sandermann et al., 1998; Zhang et al., 2018). Biological variation will also increase in response to environmental stimuli, an evolutionarily-attained organismic feature of plasticity that increases the genetic pool and promotes evolutionary rescue in stochastic environments (Costantini et al., 2010; Ashander et al., 2016; Rowiński and Rogell, 2017). The increase in biological variation would be only up to a point, after which increases in stress would reduce plasticity by eliminating those genotypes incapable of dealing with severe stresses. Environmental stress-inducing agents may cause negative effects on organismic health, depending on the adversity and duration of stress and the ability of an organism to undergo and manage the stress situation; positive effects may also be triggered by low-level challenges (Boyce and Ellis, 2005; Ellis and Boyce, 2008; Costantini et al., 2010; Bellini and De Tullio, 2019; Malea et al., 2019; Muszyńska and Labudda, 2019; Saitanis and Agathokleous, 2019). Therefore, sensitivity is a general term that can be used to

3

denote how fast an organism senses environmental challenges and how dynamically it responds (in terms of speed, magnitude, variation and complexity). Susceptibility is used in the microbiological, pharmacological and medical literatures to denote the organismal predisposition to be inhibited or adversely affected by or die of a xenobiotic/drug (Rodloff et al., 2008). The same term is used in the plant science literature (often interchangeably with sensitivity) to denote the extent of negative (inhibitory or adverse) effects induced by diseases or environmental challenges (Lapin and Van den Ackerveken, 2013). Organisms negatively affected by stress-inducing agents would be considered "susceptible”. In the studies dealing with ozone effects on plants, commonly assessed effects are visible injury and inhibition of growth (e.g. plant height and leaf number and size) and productivity (e.g. biomasses and seed yields). Sensitivity includes the degree of response per unit of stress imposed, and susceptibility is the predisposition to respond to a particular degree of stress. Hence, in many cases, plants might exhibit a significant positive correlation between susceptibility and sensitivity, i.e., plants that are less susceptible to ozone may also display lower sensitivity, at least over some portion of the range of stress imposed. Likewise, susceptible plants may also be (highly) sensitive to ozone. Sensitivity is used hereafter to refer to any stress responses, which may even lead to protection via conditioning processes that enhance the “coping machinery”, whereas susceptibility is used hereafter to refer to negative/adverse effects that can harm the organismal fitness. Ecotoxicology is principally concerned with susceptibility. Sensitivity and susceptibility to environmental stressors (including climate change) are of high ecological relevance because they can drive numerous ecological functions and processes such as various forms of competition, species spatial distribution, success in the environment and community composition. Susceptibility is important not only for science but also for policy and decision making (e.g. selection of tree species in urban green spaces and reforestation programs). But really, how is sensitivity/susceptibility of organisms classified? Do we, scientists, communicate correct information to policy and decision makers?

3. Sensitivity/susceptibility of plants to ozone: a review To understand how sensitivity/susceptibility is classified and if sensitivity/susceptibility information is communicated correctly to policy and decision makers, as an example, we used 195 articles, published in a wide spectrum of journals, dealing with the plant response to ozone (Agathokleous et al., 2015a,b). Each article was searched by using the keywords “sensitiv”, 4

“susceptib”, “tolera” and “resist”. Each article was reviewed again regarding the characterization of plant sensitivity/susceptibility to ozone. The authors of the original publications characterized taxa studied in their experiments or taxa investigated in other studies (third parties). In the latter case, authors of the reviewed articles characterized species sensitivity/susceptibility based on characterization that was done by third parties or based on their own judgement. We created a database including the descriptive words used for characterization and the references. In this database, we added the term “ozonophobic” that we have coined earlier (Agathokleous et al., 2015a). Forty seven descriptive words were traced, which were cited 447 times in total (Table 1). This is not surprising considering that there is no widely accepted terminology to categorize plant sensitivity/susceptibility to ozone (Agathokleous et al., 2015a). 3.1 Indefinable terminology This review reveals incorrectly used terminology (Table 1). Although the preceding discussion suggests that susceptibility and sensitivity have different meanings, “sensitivity” and “susceptibility” to ozone are used interchangeably (Table 1). To avoid misinterpretation, we suggest that “susceptibility” is preferred over “sensitivity” when a study is concerned with toxicological aspects. This paper is also primarily concerned with susceptibility, but we will use both words (sensitivity/susceptibility) when we refer to the reviewed literature. Another example of indefinable use of terminology is that of the terms “tolerant” and “resistant”, which are used interchangeably to denote that an organism is equipped with defense mechanisms that permit it to respond to ozone effects but with no significant negative effects to fitness. Tolerance and resistance are not synonyms (Schafer, 1971; Strauss and Agrawal, 1999; Blum, 2017). According to Merriam-Webster Dictionary, “tolerance is the capacity of the body to endure or become less responsive to a substance (such as a drug) or a physiological insult especially with repeated use or exposure”, whereas resistance is a) “the inherent ability of an organism to resist harmful influences (such as disease, toxic agents, or infection)” or b) “the capacity of a species or strain of microorganism to survive exposure to a toxic agent (such as a drug) formerly effective against it”. Among the several meanings given to tolerance of plants to diseases, an appropriate definition is the ability of plants to “endure severe disease without severe losses in yield or quality” (Schafer, 1971). In more detail, tolerance is defined as the “capacity of a cultivar resulting in less yield or quality loss relative to disease severity or pathogen development when compared with other cultivars or crops” (Schafer, 1971). This definition is in agreement with the definition of tolerance given by Levitt (1972), i.e. “resistance via the plant's ability 'to come to thermodynamic equilibrium with the stress' without being killed.” The term resistance 5

encompasses several phenomena among which tolerance (Schafer, 1971), and it seems tolerance is “an intermediate level of observable resistance somewhere between immunity and full susceptibility” (Schafer, 1971). Resistance of plants to stress is a consequence of stress avoidance or stress tolerance (Taylor, 1978). A brief browsing in the ScienceDirect database (https://www.sciencedirect.com; Elsevier B.V.) shows that tolerance is used for both abiotic (more frequently) (Wu, 2018; Afzal et al., 2019; Farhangi-Abriz and Ghassemi-Golezani, 2019) and biotic (less frequently) (Cooper and Jones, 1983; Šedivá et al., 2019; Vitale and Best, 2019) stress-inducing agents. The inverse is observed with the use of the term resistance that is mainly used for biotic stresses. The term resistant is also used for abiotic stresses (Taylor, 1978; Acevedo and Fereres, 1993), even for the case of ozone-induced stress in plants (Engle and Gabelman, 1976; Ting and Gabelman, 1971). In particular, it is used to characterize the tobacco Bel-B, the snap bean R123 and the white clover NC-R genotypes, which perform quite well in atmospheres with elevated ozone levels (Heggestad, 1991; Heagle et al., 1995; Saitanis, 2003; Burkey et al., 2005; Salvatori et al., 2013). For this reason, these genotypes serve as “controls” when exposed simultaneously with their counterpart susceptible genotypes (Bel-W3, S156 and NC-S respectively, which are called ozone bioindicators) in biomonitoring investigations (Saitanis and Karandinos, 2001; Saitanis, 2003). In some cases, the terms resistant and tolerant are used in the same report to characterize the response of cultivars to ozone (Burkey and Carter, 2009) and other air pollutants (Heggestad and Heck, 1971), with the term resistant seeming to have the meaning of even more tolerant. However, the resistance terminology “was established in relation to diseases caused by extracellular pathogens” (Cooper and Jones, 1983) and is used more frequently for plant (Schafer, 1971; Cooper and Jones, 1983) and animal (Raberg, et al., 2007) parasitic diseases (biotic stresses), to show the ability of an organism to prevent, impede or restrict the growth and spreading of parasites. In medicine, resistance also describes the capability of the host to eradicate pathogens (McCarville and Ayres, 2018). Thus, often researchers preferably use the term “tolerance/tolerant” with reference to abiotic stress-inducing agents and the term resistance/resistant with reference to biotic diseases (e.g. Xiong and Yang, 2003). 3.2 Fuzzy classification of sensitivity/susceptibility This

analysis

reveals

an

arbitrary,

subjective

and

fuzzy

classification

of

sensitivity/susceptibility (Fig. 1). For example, in a study, plants cultivated in closed chambers with clean air were characterized as sensitive/susceptible because they showed characteristic ozone injury after a 5-hour exposure to 200 ppb ozone. However, in this case, were the plants susceptible 6

to ozone or was the ozone exposure unnaturally high? To put it in other words, would we argue that humans are sensitive to amoxicillin if a group of people had progressively-developed skin rashes (not anaphylaxis) after receiving orally 4 g of amoxicillin per day, in a short time after the first dose? or, is it the dose which is unnaturally too high? A further sample of 32 articles dealing with the sensitivity/susceptibility of Bel-W3 tobacco cultivar to ozone (Supplementary Materials 1) revealed that different terms are used to characterize the sensitivity/susceptibility of not only different species but also of a particular cultivar of the same species. For example, in a review paper, the characterizations super-sensitive, sensitive and extremely sensitive were used to refer to Bel-W3 sensitivity/susceptibility to ozone (Heggestad, 1991). It is not uncommon that even the same authors use different characterizations in different reports to refer to the sensitivity/susceptibility of the same cultivar to ozone (e.g. Lorenzini et al., 1995; Nali et al., 2007; Pellegrini et al., 2014; Saitanis, 2001; Saitanis et al., 2003, 2015). 3.3 Ranking sensitivity/susceptibility classification based on p-value Unsurprisingly, the characterization of sensitivity/susceptibility in the reviewed articles was commonly based on p values (commonly at a level of α=0.05). Commonly, just a plain p value was reported to claim the existence of a significant effect and subjectively characterize sensitivity/susceptibility as per the magnitude. Modern science is largely framed around p values – so is the field of dose-response. The use of the p value has taken more severe dimensions than originally envisioned by its inventor, R.A. Fisher, and is associated with several misconceptions that have been identified over the years and for a plethora of scientific disciplines (Amrhein et al., 2019; Goodman, 2008; Johnson, 2013; Nuzzo, 2014; Senn, 2001). Practically speaking, a p value says nothing more than that some observation deserves attention for further experimentation or, in other words, it indicates chances for some difference in response if the study is repeated (Goodman, 2008). Since p values cannot denote any magnitude of effect, basing classification of sensitivity/susceptibility upon p values is inappropriate and misleading. We do not seek to disapprove the use of p values [everything has its usefulness, so do p values; see also Ioannidis (2019)], but to highlight that the characterization of sensitivity/susceptibility cannot rely on p values. Hitherto the common practice is that a subjective classification of the effect follows a statistically significant ozone effect; however, the ranking of sensitivity/susceptibility needs quantitative support based on an objective practice.

7

4. Basing the ranking of sensitivity/susceptibility on effect size Effect size estimates can support classification of sensitivity/susceptibility and policy and decision-making. Meanwhile, effect sizes facilitate cumulative science and can be also used to estimate the sample size required to achieve an acceptable power (Kirk, 2007; Durlak, 2009; Lakens, 2013). For example, Cohen’s d can be utilized to estimate the effect size using equation 1 (Cohen, 1988); hereafter, the Greek letter delta (δ) will be used, instead of d, to refer to the population value (Hunter and Schmidt, 2004): 𝛿=

(

)

[𝐶𝑖(𝑇𝑅𝑇) ― 𝐶𝑖(𝐶𝑇𝑅𝐿)] 2

(eq. 1)

[(𝑛𝑖(𝑇𝑅𝑇) ― 1) ∗ (𝑆𝐷𝑖(𝑇𝑅𝑇)) + (𝑛𝑖(𝐶𝑇𝑅𝐿) ― 1) ∗ (𝑆𝐷𝑖(𝐶𝑇𝑅𝐿))2]/(𝑛𝑖(𝑇𝑅𝑇) + 𝑛𝑖(𝐶𝑇𝑅𝐿) ― 2)

where the numerator represents the difference between the means of two groups and the denominator the pooled standard deviation. Ci, ni, SDi, denote the mean score, sample size, and standard deviation, respectively, in the plants of an experimental condition [treatment, TRT (elevated ozone level group), or control, CTRL (low ozone level group)], of the i case. Formula 1 can be corrected for estimate bias based on Hedges and Olkin (1985). Hence, the final equation would be: 𝛿𝑢𝑛𝑏𝑖𝑎𝑠𝑒𝑑 =

(

) × (1 ―

[𝐶𝑖(𝑇𝑅𝑇) ― 𝐶𝑖(𝐶𝑇𝑅𝐿)]

[(𝑛𝑖(𝑇𝑅𝑇) ― 1) ∗ (𝑆𝐷𝑖(𝑇𝑅𝑇))2 + (𝑛𝑖(𝐶𝑇𝑅𝐿) ― 1) ∗ (𝑆𝐷𝑖(𝐶𝑇𝑅𝐿))2]/(𝑛𝑖(𝑇𝑅𝑇) + 𝑛𝑖(𝐶𝑇𝑅𝐿) ― 2)

3

) (eq. 2)

4(𝑛𝑖(𝑇𝑅𝑇) + 𝑛𝑖(𝐶𝑇𝑅𝐿) ― 2) ― 1

Effect sizes should be reported in conjunction with confidence intervals to account for the extent of uncertainty in small-subsample studies and to permit less biased comparisons of agreement among separate studies (Hunter and Schmidt, 2004). Noteworthy, the Publication Manual of the American Psychological Association also highlights the necessity of primary studies to report effect size estimates and confidence intervals (Hunter and Schmidt, 2004), and some journals require inclusion of effect sizes and confidence intervals in the manuscripts (Durlak, 2009). An approximate confidence interval for δunbiased can be easily calculated using 𝛿𝑢𝑛𝑏𝑖𝑎𝑠𝑒𝑑 ―(𝑧𝛼𝑆𝐷𝑖(𝑇𝑅𝑇)/ 𝑛𝑖(𝑇𝑅𝑇)) ≤ 𝛿𝑢𝑛𝑏𝑖𝑎𝑠𝑒𝑑 ≤ 𝛿𝑢𝑛𝑏𝑖𝑎𝑠𝑒𝑑 + (𝑧𝛼𝑆𝐷𝑖(𝐶𝑇𝑅𝐿)/ 𝑛𝑖(𝐶𝑇𝑅𝐿)) 2

2

(eq. 3) where zα/2 represents the two-tailed critical value that cuts off the upper α/2 zone of the Laplace– Gauss distribution (Kirk, 2007). This calculation is simple but can facilitate the interpretations. Different types of confidence interval exist and can be found elsewhere (Kirk, 2007). Cohen’s δ is the most widely used statistic in meta-analyses of experimental or intervention studies (Hunter and Schmidt, 2004) due to a number of features contributing to its popularity (Kirk, 2007). It can find further applications, in conjunction with multivariate statistics (such as cluster analysis and principal component analysis), when an array of response variables and/or species, 8

genotypes or cultivars are incorporated in the experimental testing (Saitanis et al., 2014). Multivariate

statistics

have

been

widely

used

in

studies

concerned

with

plant

sensitivity/susceptibility to ozone (Nali et al., 2005; Pandey et al., 2014; Saitanis et al., 2014, 2015; Fatima et al., 2019). Application of multivariate statistics based on Cohen’s δ may also be useful for ranking species/cultivar sensitivity/susceptibility to ozone (Saitanis et al., 2014). Examples of applying and interpreting effect size estimates in studies on gas treatment effects on plants can be found elsewhere (Agathokleous et al., 2016a,b), including a) their use in multivariate statistics (Saitanis et al., 2014) and b) the mean effect size of many plant traits as an overall estimator of the effect size of experimental treatment such as ozone (Agathokleous et al., 2016c). If Cohen’s δ or other effect size estimators are adopted, descriptive words are still required. According to Cohen (1988), absolute δ values within the arbitrary segments 0.00-0.20, 0.20-0.50, 0.50-0.80 and 0.80+ indicate trivial, small, moderate and large effect size, respectively. For classification of susceptibility, the same segments can be used to indicate no susceptibility (or nonsusceptible), modest susceptibility (modestly susceptible), moderate susceptibility (or moderately susceptible), and high susceptibility (or highly susceptible), respectively. Likewise, the same segments can be used to indicate insensitivity (or insensitive), modest sensitivity (modestly sensitive), moderate sensitivity (or moderately sensitive), and high sensitivity (or highly sensitive), respectively, for studies dealing with sensitivity. Different effect size indices along with indicative segments of sensitivity/susceptibility ranking can be found in Cohen (1992), and the terminology used for sensitivity/susceptibility would be modified according to the one we propose here using Cohen’s δ. Estimation of Cohen’s δ and its magnitude interpretation can be facilitated by an MS Excel file application provided along with this paper (Supplementary Materials 2). In this calculator, the Cohen’s U3 index for converting the effects into percentile gain appearing in the experimental condition of interest (Cohen, 1977), and the overlapping coefficient (Reiser and Faraggi, 1999) are also included. The latter improving indices can be used for reporting purposes as well, and relevant examples can be found elsewhere (Agathokleous et al., 2016a,b). When groups of plants (different species, cultivars or genotypes) are inter-compared for their susceptibility to ozone, commonly several plant response traits are measured (e.g. visible injury, photosynthesis, biomass production, seeds yield, etc.), most of which are highly intercorrelated as being influenced by the same factor. For example, ozone causes visible symptoms that appear as leaf necrosis (which reduces photosynthetic surface) and chlorosis (which reduces chlorophyll content); both of these lead to reduced photosynthesis, which, in turn, reduces biomass production, leaf size, number of leaves, seed yield, etc. All these inter-correlated variables, when submitted to some ordination statistics (e.g. Principal Component Analysis, Redundancy 9

Analysis), are expected to display high loadings on one (usually the first) principal component that would be considered the latent factor of “susceptibility to ozone”. The scores of the subjects (plants) on the latent factor can be further analyzed to estimate the effect size and, based on Cohen’s δ segments, to characterize the plant groups (cultivars – species - genotypes) as per their susceptibility magnitude. By following this procedure, the susceptibility of the whole organism (i.e. plant susceptibility to ozone) can be assessed based on the response of some -sensitive to ozone- vital organismic response parameters. It should be highlighted here that p values are still useful for objective decision as to whether the treatments imposed resulted in a finding that would not have reasonably occurred by chance. Combining p values with Cohen’s δ would provide more support for any conclusions that researchers reach from their studies.

5. Standardizing the language used for ranking susceptibility as per the magnitude We fully understand the complexity of the issue of classifying organisms as per their susceptibility to environmental stress-inducing agents (and even more for gases); however, inconsistent and subjective characterization of plant susceptibility may have severe implications to policy makers and other stakeholders. If effect size/magnitude estimates were fully adopted, descriptive words would still be required for aspects of discussion with non-scientists or scientists not specialized in the research field (e.g. decision makers). In the previous section, a standardized system for ranking susceptibility as per the magnitude is provided. This system can be applied regardless of the experimental system and for any conditions. To be able to characterize an organism’s susceptibility to ozone in general, retrospective analyses may be conducted where the response of dozens of species, cultivars or genotypes will be analyzed as a function of the dose/exposure using justified ranges of dose/exposure across the full dose-response spectrum. By finding an average (or median) magnitude of response, the susceptibility of particular species or genotypes may be classified as per the deviation of the magnitude of response from the magnitude of the average response. In doing so, numerous interacting factors, that can alter the effects of ozone, would need to be factored in, such as ambient temperature (Albertine and Manning, 2009; Hartikainen et al., 2012; Mäenpää et al., 2011), nitrogen availability in the soil (Bielenberg et al., 2001; Carriero et al., 2016; Karnosky et al., 1992; Li et al., 2019) (note: a recent meta-analysis revealed that soil nitrogen load typically does not change ozone effects on (semi-)natural vegetation; Feng et al., 2019), water content or availability 10

in the soil (Dusart et al., 2019; Landi et al., 2019; Li et al., 2019; Zhang et al., 2019) and vapor pressure deficit (Moura et al., 2014; Nussbaum and Fuhrer, 2000; Piikki et al., 2008). In addition, aspects of the ozone exposure should be accounted for, such as exposure timing within a day, exposure duration, exposure profile (acute versus chronic exposure), temporal fluctuation of ozone levels, or even stomatal gas exchange (conductance) and ozone uptake. Although incorporating too many environmental variables could potentially impede the development of any sort of classifications, when, except of ozone level and exposure duration, few other major environmental factor, like optimum irrigation, soil pH, N-nutrition, temperature and agrochemicals avoidance) are at -more or less- appropriate for the species levels, a meta-analysis can summarize about the overall response and classification of the species/cultivars. Considering the aforementioned limitations in characterizing susceptibility, it is equally important to draw conclusions for the organismic susceptibility with care. It is important that the experimental conditions follow the characterization of susceptibility in a clear manner. For example, a study would conclude “genotype X of species Y is moderately susceptible to an ozone exposure of 70 ppb × 30 d × 8 h d-1 when grown in a field with well-irrigated brown podzolic soil with no fertilization” instead of “genotype X of species Y is moderately susceptible to ozone”. The former denotes that genotype X is moderately susceptible to ozone under particular experimental conditions, whereas the latter communicates that genotype X is moderately susceptible to ozone overall, no matter what the ozone regime is or what the experimental conditions are. Therefore, the latter conclusion may present a more “severe” picture to a non-science audience. For instance, a non-science audience may interpret the latter conclusion as genotype X is moderately susceptible to local ambient ozone. This would be even more important when two genotypes of the same species or two different species are compared under the aforementioned ozone exposure but the one is moderately susceptible while the other is highly susceptible. Not stating the experimental conditions may mislead that the one species is more susceptible than the other when actually the opposite would be true in local conditions. Care should be exercised such that accurate conclusions are drawn, especially in the abstracts of papers which can be read by a non-scientific audience, including the public, thus making them more vulnerable to misinterpretation (when the entire script is not read carefully). In a similar fashion, susceptibility should be claimed for the particular genotype or cultivar used in the research but not for the species overall because of the genotype-specific susceptibility of plants to ozone (Dumont et al., 2014; Frei et al., 2012; Salvatori et al., 2013; Singh et al., 2018; Yuan et al., 2015; Pandey et al., 2019). Before assigning a relative susceptibility label to a species, a wide array of cultivars or genotypes should be tested (Heagle, 1989); however, such a perspective 11

may be constrained by funding limitations. Comparisons of the relative susceptibility between species should be also done only if all, or most of, the cultivars or genotypes existing locally, regionally or globally were incorporated in the study, and the scale at which the species susceptibility to ozone occur should be clearly mentioned. For example, a statement could be “species X is moderately susceptible while species Y is non-susceptible to ozone at the local conditions (35 ppb 12-h ozone mean × 90 d; well-irrigated brown podzolic soil; no fertilization)”. Furthermore, the classification of susceptibility should be based on the overall response across an array of vital traits of major concern, such as visible leaf injury and yields (fruits/seeds and or stems, leaves, roots biomass) that can be critical to fitness, in order to reflect the overall organismic susceptibility by all means, but not based on a single trait studied exclusively or selected among several assessed. When based on a single endpoint (i.e. plant trait), the classification may differ depending on which the endpoint was, and, thus, no conclusion can be deduced about the overall classification. Concerning the visible injury, that is widely used in relevant studies, it should be pointed out that low (or the lack of) visible leaf injury (in terms of necrosis, discoloration, etc.), by itself, is inadequate for denoting organismic susceptibility. The presence of visible leaf injury obviously denotes susceptibility but the lack of visible leaf injury does not denote tolerance to ozone. It should be noted that we are not suggesting against the use of visible leaf injury in the framework of biomonitoring but only against its sole use for ranking organismic susceptibility. To this end, organismic susceptibility should be ranked based on a set of traits ranging from cellular to individual levels. To account for this issue, if susceptibility is argued based on one or more assessed traits, it should be clarified when drawing conclusions. For example, an appropriate conclusion would be “chlorophyll fluorescence traits of cultivar X (species Y) were moderately sensitive to an ozone exposure of 70 ppb × 30 d × 8 h d-1 when grown in a field with well-irrigated brown podzolic soil with no fertilization” but not “cultivar X (species Y) was moderately susceptible to an ozone exposure of 70 ppb × 30 d × 8 h d-1 when grown in a field with well-irrigated brown podzolic soil with no fertilization”. This is important because in the latter case a moderate susceptibility of cultivar X might be implied based solely on chlorophyll fluorescence traits, while the organismic susceptibility might be modest. Classification based on an array of traits is also important when comparing susceptibility among organisms because some organisms may follow different stress response strategies than other organisms (Boeckler et al., 2011; Karabourniotis et al., 2014; Tiwari, 2017; Tiwari and Agrawal, 2017). It should be noted that when effect sizes are used for estimating the susceptibility magnitude, absolute values should be used to account for arithmetically negative responses which may translate to biologically

12

positive outcomes (e.g. decreased malondialdehyde content) and vice versa (e.g. quantum yield of regulated non-photochemical energy loss as heat, ΦNPQ; Bayçu et al., 2018).

6. Conclusions and perspectives This study revealed an indefinable terminology used for characterizing plant susceptibility and sensitivity to ozone. An attempt was made herein to provide the basis for correcting the use of terminology in the future, and some recommendations are made as to the way the relevant terminology can be used and how results describing susceptibility and sensitivity can be reported. A subjective and fuzzy classification of susceptibility and sensitivity to ozone was also found, with the characterization of susceptibility and sensitivity in the reviewed articles commonly based upon plain p values obtained from standard statistical hypothesis testing. Despite the fact that p values do not denote magnitude of sensitivity/susceptibility, a large proportion of the characterizations used are magnitude-indicative characterizations. In order to improve the communication and understanding among scientists and between scientists and decision makers, promote cumulative science, and enhance the societal translation of scientific findings, the terminology should always be used correctly, the current statistical techniques should be strengthened and the characterization of the degree of susceptibility (claimed or implied) with no valid statistical support (e.g. effect sizes) should be discontinued. We suggest the utilization of the Cohen’s δ segments, solely or in conjunction with multivariate ordination statistics, as a more objective process for susceptibility classification. An Excel file is available online along with this paper (Supplementary Materials 2), which we hope will facilitate the application of Cohen’s δ in relevant studies. We believe that the use of Cohenʼs δ will be a useful objective tool for the inter-comparison and characterization of the relative susceptibility of plant genetic groups (species, subspecies, cultivars, varieties, genotypes) to ozone, under the presupposition that the inter-compared plant groups have been submitted to the same ozone treatment in terms of exposure level and duration, stage of plant growth, and growth conditions. If the experimental conditions differ, the intercomparison is difficult –if not impossible. Hence, for an absolute inter-comparison and characterization of different plant groups to ozone-induced stress, standardized experimentation protocols should be developed. This is well beyond the scope of this paper; however, the present paper provides an important perspective to achieve this in the future.

13

Acknowledgements: These understandings would not be possible without the efforts and outstanding contribution of several research groups across the globe, whose research provided the opportunity to move the field forward. EA acknowledges multi-year support from the Research Fund of International Young Scientists (Grant No. 31950410547) of the National Natural Science Foundation of China (NSFC) and The Startup Foundation for Introducing Talent of Nanjing University of Information Science & Technology (NUIST), Nanjing, China (Grant No. 1411021901008). The authors declare that there are no conflicts of interest. Authors’ contributions: EA and CJS conceived the ideas, drafted and revised the article critically for important intellectual content, and gave final approval for publication.

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Table 1. Characterization of plant sensitivity to ozone along with the frequency (v) that appeared in a sample of 195 peer-reviewed, SCI articles (Agathokleous et al., 2015b). More than 30% of the characterization occurrences reflected magnitude-indicative characterizations; null/neutral magnitude is excluded from the counts of magnitude-indicative characterizations. Note: This study, the recent extensive documentation of plant stimulation by low doses/exposures of ozone, and the advanced understanding of the low-dose effects overall in the recent years, suggest that the term ozonophobic (Agathokleous et al., 2015a) and alike terms that conjure up a dichotomous situation are not supported by the current scientific understanding and should not be used. Characterization

v

Characterization

v

Neutral

1

Not very sensitive

1

Ozonophobic (antonym=

1

Very sensitive

19

Resistance (or resistant)

48

Relatively high sensitivity

2

Good resistance

1

High sensitivity (or highly

29

ozonophilic)

sensitive)

23

Moderately resistant

1

Great sensitivity

5

Particularly resistant

1

Hyper sensitive

1

Very resistant

1

Extreme sensitivity (or extremely

7

sensitive) Highly resistant

2

Not susceptible

1

Extremely resistant

1

Relatively susceptible

1

Insensitive (or non-sensitive or

14

Susceptibility (or susceptible)

58

Relatively insensitive

2

Moderate susceptibility

1

Nearly insensitive

1

Particularly susceptible

2

Potentially sensitive

3

High susceptibility (or high

5

insensitivity)

degree of susceptibility or highly susceptible) Relatively sensitive

4

Extreme susceptibility

1

Suspected sensitive

1

Intolerant

1

Sensitive

123

Tolerance (or tolerant)

64

At least sensitive

2

Relatively tolerant

4

Slightly sensitive

1

Good tolerance

1

Low sensitivity

4

Well tolerant

1

Enhanced sensitivity

1

Fairly tolerant

1

Especially sensitive

3

Very tolerant

1

Intermediate sensitivity (or

7

Quite tolerant

1

7

High tolerance (or highly tolerant)

5

intermediately sensitive) Moderate sensitivity (or moderately sensitive) Particularly sensitive

5

Sum of characterizations

447

Sum of magnitude-indicative characterizations

135

Magnitude-indicative characterizations (% of total)

30.2 %

24

Fig. 1. Characterization of plant susceptibility (and sensitivity) to ozone. The current classification of plant susceptibility to ozone resembles the Tower of Babel where a higher power confounded the speech of the constructors (united humanity) so that it was impossible to understand each other and, thus, to complete the project (tower); the constructors are scattered around the world. The way the susceptibility of plants has been classified not only confounds the communication and understanding among scientists and between scientists and decision makers, but may also impede cumulative science and the societal translation of scientific findings. The painting of the Tower of Babel (aka Little Babel) is accredited to Pieter Bruegel the Elder, 1565, Museum Boijmans Van Beuningen, Rotterdam.

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