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• W2271752941 abstract "Controversy and refutation are essential components of the scientific progress so we gratefully acknowledge Clavijo Michelangeli et al.'s (2016; in this issue of New Phytologist, pp. 377–379) comments. Our paper (Parent & Tardieu, 2012) was itself controversial. Our initial intention was to investigate genetic differences of temperature response of developmental rates (e.g. leaf elongation rate, reciprocal of the duration of phenological phases) and identify genomic regions associated to them as we did for other traits (Welcker et al., 2011; Dignat et al., 2013; Parent et al., 2015). To our own surprise, we did not detect any appreciable genetic variability on these patterns, although we explored genotypes from either tropical or temperate origins and from genetic groups that have diverged thousands of years ago. The following paragraphs address the points raised by Clavijo Michelangeli et al. thereby giving us the opportunity to discuss the usefulness of our approach. Deterministic models developed in plant science predict plant traits and performances under a wide range of environmental conditions by using explicit equations, with the species/cultivar specificities summarized by a vector of parameters (Parent & Tardieu, 2014). These representations open the way for comparing genotypes and species, although this exercise is still in its infancy (Hammer et al., 2006; Parent & Tardieu, 2014). It is essential to link these deterministic approaches to continuous phenotypic description of the worldwide biota performed in comparative ecology (Violle et al., 2014), which also begin to be addressed in agronomy/ecophysiology (Poorter et al., 2010). The latter descriptions have a long history, including the emergence of apparent ‘universal’ laws that may regulate plant diversification (Enquist et al., 2007). A dialogue between the two approaches has recently resulted in interesting debates about the leaf economics spectrum (Poorter et al., 2014), and needs to continue on the topic of adaptation to environmental effects. A condition for such cross-talk on adaptation is that common formalisms for environmental effects are used across disciplines (agronomy, ecology, plant physiology) and kingdoms (animals, plants). The main reason why we have adopted the equation of Johnson et al. (1942) with a thermodynamic basis is that it has been widely applied to plant and animal sciences at different levels of organization (see references in the original paper). When used in a context that differs from the activity of a given enzyme, this equation can be interpreted as the behaviour of a virtual enzyme that summarizes all biochemical reactions involved in the studied process, so its parameters would have a physiological meaning. Although this interpretation is a gruesome simplification, it has heuristic interests. In particular, it can help in identifying large-spectrum laws of plant functioning (Marquet et al., 2014) and facilitate meta-analyses using data from different disciplines and across kingdoms (Dell et al., 2011). As many other mechanistic equations, this equation cannot be directly used in a genetic analysis because it involves an excessive number of nonindependent parameters. Indeed, vectors of three parameters with appreciably different values resulted in very similar response curves because of correlations between parameters, thereby impeding genetic analyses. We had therefore to reduce the number of parameters by considering that the ratio between two parameters was constant. In addition, we have re-written the equation in such a way that parameter values and units are more intuitive for comparison between genotypes and species. However, the properties of the initial equation were kept so the final equation still has a general value with thermodynamically-sound parameters, which can be calculated from the parameters of Eqn 2 of the original paper. Hence, we argue that our formalism for temperature response has interesting properties because: (1) it is a representation of reality whose parameters have a biological meaning that can cross disciplines, (2) it is one of the most parsimonious available models, with two independent parameters only. We argue that the least mechanistic model is the one that is overparameterized because there is no direct correspondence between a given individual (e.g. genotype) and a unique vector of parameters. Occam's razor rule states that, among competing hypotheses that predict equally well, the one with the fewest assumptions should be selected. It has been widely used in modelling (e.g. Sinclair & Muchow, 1999). If a model with no genotypic variation of parameters does as well as a model assuming an organized genetic variation, then we should consider that the simplest model is the best. This rule is difficult to reconcile with statistics, in which the null hypothesis cannot be demonstrated. Instead of simply showing that a model considering no genetic differences in parameters applies to experimental data (the most frequent case in modelling papers), we did an exercise of transparency by comparing the accuracy of several models considering either that some parameter values are common, or that they differ between genotypes. The choice of a ∆BIC of five units as a criterion was made after a sensitivity analysis showing that it allows detection of a shift by 1°C in temperature responses or an overall difference of 5% between response curves (supporting information in the original paper). The model considering genetic variations on each parameter had a very small comparative advantage for explaining the variance compared to the model considering a unique set of parameters per species (i.e. no genetic variation). However, an intuitive observation of Figs 2 and 3 of the original paper suggests that it is reasonable to consider that: (1) tropical or temperate genotypes, (2) belonging to different genetic groups, (3) elite or traditional genotypes, and (4) lines or hybrids, have a common response of developmental rates to temperature in wheat, maize and rice. The same trend applied to other species via the re-analysis of published results. Furthermore, no genetic architecture has been observed for differences in temperature responses among genotypes of three mapping populations (Reymond et al., 2003; Welcker et al., 2011), thereby suggesting that the small genetic differences between parameters resulted from noise rather from an organized genetic variability. We do not claim that one will never find a genotype with a slightly different response, but that the observed differences (if they exist) can be considered as marginal compared with the large genetic variability observed for other processes such as the response of elongation to soil water potential or evaporative demand (Welcker et al., 2011; Dignat et al., 2013). The question addressed in the paper is not whether there are genetic sources of tolerance to heat or cold stresses (obviously there are, as shown by references in Clavijo Michelangeli et al.'s letter which mostly deal with the effects of stressing temperatures), but whether plant development rate is differently affected by temperature in different genotypes. The earlier paragraph suggests that this is not the case. Our conclusion is associated with limits that define the nonstressing environmental conditions in which a given species has evolved. (1) It is limited to the range of temperatures in which responses are reversible (typically 6–35°C for several cereals), that is, when rates at a given temperature can be considered as independent of the recent history of the plant. This is the case in Fig. 1 of our paper in which leaf elongation rate at 20°C had similar values regardless of temperature experienced earlier by plants. (2) It is limited to temperature scenarios in which cold or hot temperatures occur during a few hours in the morning (cold temperatures) or afternoon (hot temperatures). We are aware that long exposures to low or hot temperatures can lead to physiological disorders and protection mechanisms with an appreciable genetic variability (Kotak et al., 2007). The conclusion of the paper therefore applies to the range of temperatures and durations of exposures to low or hot temperature that are compatible with the area of adaptation of the studied species. For the last 30 years, agronomists and modellers have used thermal time to model the progression of plant development. A consensus is that a common equation for calculating thermal time can be used for all genotypes of a species, for example, with a common threshold temperature when a linear formalism is used. Genotypes differ in the thermal time required for reaching a given phenological stage, but not in the model for calculating this thermal time based on a common temperature response. We propose the argumentum ad absurdum that, if the temperature response clearly differed between genotypes, then at least some crop modellers would have proposed genotype-dependent temperature functions. This is not the case in reviews published on this topic (Porter & Gawith, 1999; Sanchez et al., 2014). Our conclusion is therefore not contradictory with published literature as long as we limit its scope to the range of temperatures with reversible responses to temperature, that is, temperature values within the climatic niche of the species under study. Concluding in a lack of genetic variability on a given trait is a risky exercise, because a single counter example would challenge the theory. However, we think that proposing simplifying hypotheses is a key process for both crop modelling and communication between disciplines. Indeed, each discipline creates its own simplifications so building common representations/models is an essential step for dialogue. This has been the case for the present model. Our hypothesis is compatible with experimental and published data, has long been used by crop modellers in a nonexplicit way, and considerably simplifies the comparison of species and genotypes because it provides a common ground by correcting time for temperature effects with a common formalism for all genotypes and species. It raises in addition an intriguing question: how can a trait have no genetic variability within a species, while differing between species? We have proposed that this is a question of time from divergence. If several processes need to be synchronized for plant viability, strong stabilizing selection will operate and any evolutionary change will need very long times, that is, most mutations affecting this synchronization will be highly deleterious. This is compatible with differences between species but not between genotypes. We are convinced that this theory will be challenged, perhaps shown to be wrong. It needs to be further tested with closely-related species that have never been subjected to a breeding process. Nevertheless we think that it merits to be considered for its simplifying effects on modelling and for the ecological and evolutionary questions that it raises. F.T. and B.P. were supported by the UE project DROPS, FP7-244374. C.V. was supported by the ERC Starting Grant Project ‘Ecophysiological and biophysical constraints on domestication in crop plants’ (grant ERC-StG-2014-639706-CONSTRAINTS)." @default.
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• W2271752941 date "2016-01-25" @default.
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• W2271752941 title "Towards parsimonious ecophysiological models that bridge ecology and agronomy" @default.
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