Leopoldo Javier Iannone

Fabrice Roux
email: fabrice.roux@inrae.fr

Jaishree Harihar

Dominique Subrahmaniam

Fabrice Roby

Roux

Toward Unifying Evolutionary Ecology and Genomics to Understand Positive Plant-Plant Interactions Within Wild Species

Keywords: intraspecific variation, genotype-by-genotype interactions, kin cooperation, diversity-productivity relationship, overyielding

In a local environment, plant networks include interactions among individuals of different species and among genotypes of the same species. While interspecific interactions are recognized as main drivers of plant community patterns, intraspecific interactions have recently gained attention in explaining plant community dynamics. However, an overview of intraspecific genotype-by-genotype interaction patterns within wild plant species is still missing. From the literature, we identified 91 experiments that were mainly designed to investigate the presence of positive interactions based on two contrasting hypotheses. Kin selection theory predicts partisan help given to a genealogical relative. The rationale behind this hypothesis relies on kin/non-kin recognition, with the positive outcome of kin cooperation substantiating it. On the other hand, the elbowroom hypothesis supports intraspecific niche partitioning leading to positive outcome when genetically distant genotypes interact. Positive diversity-productivity relationship rationalizes this hypothesis, notably with the outcome of overyielding. We found that both these hypotheses have been highly supported in experimental studies despite their opposite predictions between the extent of genetic relatedness among neighbors and the level of positive interactions. Interestingly, we identified a highly significant effect of breeding system, with a high proportion of selfing species associated with the presence of kin cooperation. Nonetheless, we identified several shortcomings regardless of the species considered, such as the lack of a reliable estimate of genetic relatedness among genotypes and ecological characterization of the natural habitats from which genotypes were collected, thereby impeding the identification of selective drivers of positive interactions. We therefore propose a framework combining evolutionary ecology and genomics to establish the eco-genomic landscape of positive GxG interactions in wild plant species.

INTRODUCTION

During the course of its life cycle, a plant can interact directly or indirectly -consecutively and/or concurrently -with multiple neighboring plants. Plant social networks include interactions among individuals of different species (i.e., interspecific interactions) and among genotypes of the same species (i.e., intraspecific interactions) in a local environment. Plant-plant interactions play an important role in regulating the diversity and structure of plant communities and ultimately ecosystem functioning, through their effects on resource availability and habitat structure [START_REF] Brooker | Plant -plant interactions and environmental change[END_REF][START_REF] Martorell | Testing the roles of competition, facilitation and stochasticity on community structure in a species-rich assemblage[END_REF]. Studying the mechanisms underlying plant-plant interactions is therefore essential to understand the dynamics of plant communities, which may in turn help to predict the resilience of plant species to anthropogenic-related global changes [START_REF] Subrahmaniam | The genetics underlying natural variation of plant-plant interactions, a beloved but forgotten member of the family of biotic interactions[END_REF]. For instance, ongoing climate warming has resulted in modifications of plant assemblages due to increase of plant biomass, reduced diversity [START_REF] Baldwin | Warming increases plant biomass and reduces diversity across continents, latitudes, and species migration scenarios in experimental wetland communities[END_REF] and shifts in the distribution areas of plant species [START_REF] Gilman | A framework for community interactions under climate change[END_REF][START_REF] Singer | Interspecific interactions affect species and community responses to climate shifts[END_REF].

Plant-plant interactions can be divided into four main categories depending on the net benefit and cost associated with the interaction [START_REF] Subrahmaniam | The genetics underlying natural variation of plant-plant interactions, a beloved but forgotten member of the family of biotic interactions[END_REF]. Firstly, competitive interactions (-/-) are those that come with a significant cost for both partners (benefit < 0, cost > 0 for both partners). Competition is characterized by reciprocal negative effects on plant growth or fitness caused by the presence of neighbors [START_REF] Keddy | Competition[END_REF]. Since all plants share few basic requirements, limitations of resources such as the availability of nutrients, water or light could drive competition among plants [START_REF] Turkington | The growth, distribution and neighbour relationships of Trifolium repens in a permanent pasture. IV. Fine-scale biotic differentiation[END_REF][START_REF] Chaney | The costs and benefits of tolerance to competition in Ipomoea purpurea, the common morning glory[END_REF]. Second, asymmetric interactions (-/+) yield benefit to one of the partners, at the cost of the other interactor (benefit < 0 and cost > 0 for the helper; benefit > 0 and cost < 0 for the receiver). Parasitic plants are the prime example of this kind of behavior. In addition, plants releasing allelochemicals, which negatively influence the physiology of their neighbors, can be grouped under this category. Third, commensal interactions (0/+) are those that are beneficial for one of the partners, but there is no cost associated with providing such aid (benefit = 0 and cost = 0 for helper; benefit > 0 and cost = 0 for the receiver of the help). Many examples of such interactions exist at the interspecific level, like nurse plant effects in deserts or climbing plants that use the stems of other plants to avoid shade [START_REF] Padilla | The role of nurse plants in restoration[END_REF][START_REF] Gianoli | The behavioural ecology of climbing plants[END_REF]. Lastly, individuals can also reciprocally benefit (+/+) from being associated with a partner (such that the net benefit > 0 for each plant partner). One of the first examples demonstrating this mutually beneficial relationship came from a leguminous shrub, Retama sphaerocarpa and its understory species Marrubium vulgare [START_REF] Pugnaire | Facilitation between higher plant species in a semiarid environment[END_REF]. Increasing resource availability leading to facilitative benefits of being underneath the shrub, was reciprocated by improved water relationships due to the understory species in this study.

Estimating the relative importance of these broad categories in explaining patterns of plant communities is still under debate and mainly focused on interactions at the interspecific level. Interspecific competitive interactions have been traditionally recognized as the major factors driving the structure [START_REF] Goldberg | Patterns and consequences of interspecific competition in natural communities -a review of field experiments with plants[END_REF], diversity [START_REF] Chesson | Mechanisms of maintenance of species diversity[END_REF] and dynamics of plant communities [START_REF] Tilman | The resource-ratio hypothesis of plant succession[END_REF]. However, the role of positive interactions among species (including both commensal interactions and reciprocal helping interactions) in regulating the composition of communities, has recently gained attention [START_REF] Bertness | Positive interactions in communities[END_REF][START_REF] Callaway | Positive interactions among plants[END_REF][START_REF] Brooker | The balance between positive and negative plant interactions and its relationship to environmental gradients: a model[END_REF][START_REF] Dormann | Facilitation and competition in the high Arctic: the importance of the experimental approach[END_REF][START_REF] Bruno | Inclusion of facilitation into ecological theory[END_REF][START_REF] Kotowska | Plant genetic diversity yields increased plant productivity and herbivore performance[END_REF][START_REF] Wendling | Specific interactions leading to transgressive overyielding in cover crop mixtures[END_REF]. In particular, positive interactions among species have been put forward to explain overyielding, a phenomenon which corresponds to the increase in productivity of species when grown in a mixture, as opposed to monoculture [START_REF] Harper | Population Biology of Plants[END_REF][START_REF] Vandermeer | The interference production principle: ecological theory for agriculture[END_REF][START_REF] Loreau | Does functional redundancy exist?[END_REF][START_REF] Schmid | Biodiversity effects and transgressive overyielding[END_REF]. Also being increasingly recognized is the notion that studying Genotype-by-Genotype (GxG) interactions (also referred as intergenotypic interactions) at the intraspecific level might be a prerequisite for understanding eco-evolutionary patterns of plant communities [START_REF] Hughes | Ecological consequences of genetic diversity[END_REF]. Indeed, a huge number of genotypes of varying levels of genetic relatedness can co-exist within a local population, even in the case of highly selfing species. For instance, a recent study on Arabidopsis thaliana revealed that the genetic diversity observed within a local population can represent almost one-sixth of the genetic diversity at the worldwide scale [START_REF] Frachon | Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time[END_REF]. Consequently, the patterns of interactions between different genotypes within one population are bound to vary as well.

Several meta-analyses have been carried out to understand patterns of GxG interactions at the interspecific level in herbaceous wild plant species [START_REF] Maestre | Is the change of plantplant interactions with abiotic stress predictable? A meta-analysis of field results in arid environments[END_REF] as well as in trees [START_REF] Piotto | A meta-analysis comparing tree growth in monocultures and mixed plantations[END_REF][START_REF] Zhang | Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis[END_REF]. However, to our knowledge, an overview of GxG interaction patterns at the intraspecific level within wild plant species is still missing from literature. We therefore aimed to make a synthesis on such interactions. By surveying the literature, we quickly observed that most studies have been dedicated to testing the presence of positive interactions within different plant species. Moreover, studies on these positive GxG interactions have been based upon testing two main non-exclusive hypotheses with opposite relationships between the extent of genetic relatedness among neighbors and the level of positive interactions [START_REF] File | Plant kin recognition enhances abundance of symbiotic microbial partner[END_REF]: kin/non-kin recognition or elbow-room. Rooted in evolutionary biology concepts, the first hypothesis is based on the kin selection theory advocating that individuals increase their inclusive fitness by modifying their behavior to help a relative, thereby leading to kin cooperation [START_REF] Hamilton | The genetical evolution of social behavior[END_REF]. On the other hand, rooted in ecological concepts, the elbow-room hypothesis assumes that intraspecific resource partitioning occurs and increases as the genetic distance between neighbors increases [START_REF] Argyres | Neighbor relatedness and competitive performance in Impatiens capensis (Balsaminaceae): a test of the resource partitioning hypothesis[END_REF]. The resulting expected positive relationship between genotypic diversity and productivity corresponds to overyielding at the intraspecific level.

After highlighting the nature of the data of 78 identified articles and identifying the main caveats before extracting trends from these data, we tested whether kin cooperation and overyielding observed within our sample dataset was dependent upon specific features of a species (e.g., breeding system, mode of seed dispersal, life cycle etc.). Finally, we introduce several avenues that deserve to be explored to obtain a thorough picture of GxG interaction patterns within wild species. We particularly stress the need to integrate genomics and evolutionary ecology to fully understand the complexity of intraspecific positive genetic interactions in wild plant populations.

LITERATURE SURVEY AND HYPOTHESES TESTED

For this review, we only focused on studies looking at intraspecific interactions within wild populations of herbaceous species. We made this choice because the number of generations of wild herbaceous species is clearly lesser than the one of trees. Therefore, fitness proxies can be better estimated during their life cycle. Several keywords were used to gather these studies, i.e., 'GxG interactions, ' 'intraspecific interactions, ' 'intraspecific variation, ' 'intra/inter-population variation, ' and 'group selection.' The websites inspected included Google Scholar, Web of Science, Sci-hub, as well as the web application Researcher1 . Although we tried to do a comprehensive analysis to include a maximum number of studies reporting intraspecific GxG interactions, the list is certainly not exhaustive, and some studies may have been overlooked. We gathered a list of 78 articles published in the last 36 years and including 91 experiments (Supplementary Table 1). Interestingly, we observed a sharp increase in cumulative number of experimental papers published over the years, notably in the last 15 years (Figure 1A). This illustrates the rising interest in examining intraspecific GxG interactions within wild plant species.

The list includes 47 species belonging to 19 botanical families (Figure 1B). The most commonly studied botanical families are the Brassicaceae (36%), Fabaceae (12%), Asteraceae (11%), and Poaceae (11%). However, there is a significant bias in the family Brassicaceae toward A. thaliana as it constitutes about ∼88% of the studies from this family. Upon removing this species, the relative proportion of botanical families studied is more consistent with the number of studied species within each family (Figure 1C and Supplementary Table 1).

The reported experiments can be categorized upon testing either the 'kin/non-kin recognition' hypothesis (∼58% of the experiments) where the differential response of a genotype was tested in the presence/absence of a relative genotype (kin) vs. a stranger genotype in pairwise experiments, or the elbow-room (or 'genotypic diversity-productivity relationship') hypothesis (31% of the experiments) where fitness proxies were compared between monocultures using multiple kin individuals (compound intra-genotypic interactions) and mixtures of different genotypes (compound inter-genotypic interactions) (Figure 2A and Supplementary Table 1).

The remaining experiments (∼11%) were grouped under the category 'Others'. These experiments aimed at (i) characterizing the genetic architecture underlying GxG interactions [START_REF] Botto | Seasonal and plant-density dependency for quantitative trait loci affecting flowering time in multiple populations of Arabidopsis thaliana[END_REF][START_REF] Mutic | Indirect genetic effects from ecological interactions in Arabidopsis thaliana[END_REF], (ii) studying the effects of GxG interactions on intra-individual traits such as genome size variation (Šmarda et al., 2010) and transcriptomic profiles [START_REF] Bowsher | Transcriptomic responses to conspecific and congeneric competition in co-occurring Trifolium[END_REF], (iii) studying extended phenotypes such as root exudate profiles [START_REF] Badri | Root secreted metabolites and proteins are involved in the early events of plant-plant recognition prior to competition[END_REF] and soil microbial communities [START_REF] Burghardt | Legacy of prior host and soil selection on rhizobial fitness in planta[END_REF][START_REF] Fitzpatrick | Soil microbes alter plant fitness under competition and drought[END_REF], (iv) testing the effect of adding a neighbor plant on genotype-byenvironment interactions (i.e., GxExG instead of GxGxE) [START_REF] Cahill | Plants integrate information about nutrients and neighbors[END_REF], (v) examining local adaptation of genotypes [START_REF] Linhart | Intrapopulation differentiation in annual plants. III. The contrasting effects of intra-and interspecific competition[END_REF][START_REF] Espeland | Facilitation across stress gradients: the importance of local adaptation[END_REF], and (vi) investigating individual vs. group selection in wild plant populations [START_REF] Goodnight | The influence of environmental variation on group and individual selection in a cress[END_REF][START_REF] Donohue | The influence of neighbor relatedness on multilevel selection in the Great Lakes sea rocket[END_REF]. For the latter, we need to stress that the existence of group selection is still controversial [START_REF] Nowak | Five rules for the evolution of cooperation[END_REF][START_REF] Nowak | The evolution of eusociality[END_REF][START_REF] Rousset | Much ado about nothing: nowak et al.'s charge against inclusive fitness theory[END_REF][START_REF] Kramer | Kin and multilevel selection in social evolution: a never-ending controversy?[END_REF] and it will not be addressed in this review. (A) Doughnut plot describing the distribution of the papers according to the rationale of studying GxG interactions. (B) Relationship between the number of genetic lines and the number of populations according to three geographic scales. For better illustration, the two outliers with 60 genotypes each from a different population, are not incorporated in the figure [START_REF] Fitzpatrick | Soil microbes alter plant fitness under competition and drought[END_REF][START_REF] Turner | Effects of genomic and functional diversity on stand-level productivity and performance of nonnative Arabidopsis[END_REF].

HIGHLIGHTS OF THE NATURE OF THE DATA AND IDENTIFICATION OF CAVEATS Life-History Traits

The list of 47 species is divided roughly equally between annuals (∼46%) and perennials (∼54%) (Supplementary Table 1) and is dominated by selfing species that make up about 44% of the dataset. The remaining species represent species with mixed breeding systems (∼27%), outcrossing species (∼19%) and clonal species (∼10%). Allochory (assisted seed dispersal) is predominant in this list as about ∼60% of the listed species exhibited this mode of seed dispersal, while only ∼40% of listed species have an autochorous (self) mode of seed dispersal (Supplementary Table 1).

Nature and Extent of Genetic Variation

Around 86% of the experiments listed were based on genotypes collected in natural populations (Supplementary Table 1). With the main goal of dissecting the underlying genetic and molecular mechanisms of GxG interactions, the remaining experiments were based on (i) experimental populations such as F 2 populations, families of Recombinant Inbred Lines (RILs) or Introgression lines (IL, ∼9%) [START_REF] Goodnight | The influence of environmental variation on group and individual selection in a cress[END_REF][START_REF] Griffing | Genetic analysis of plant mixtures[END_REF][START_REF] Botto | Seasonal and plant-density dependency for quantitative trait loci affecting flowering time in multiple populations of Arabidopsis thaliana[END_REF][START_REF] Mutic | Indirect genetic effects from ecological interactions in Arabidopsis thaliana[END_REF][START_REF] Willis | Genetic variation in tolerance of competition and neighbour suppression in Arabidopsis thaliana[END_REF][START_REF] Latzel | Epigenetic diversity increases the productivity and stability of plant populations[END_REF][START_REF] Wuest | A plant biodiversity effect resolved to a single chromosomal region[END_REF][START_REF] Hauri | Chemical diversity rather than cultivar diversity predicts natural enemy control of herbivore pests[END_REF], and (ii) mutant lines (∼5%) [START_REF] Cahill | Differential genetic influences on competitive effect and response in Arabidopsis thaliana[END_REF][START_REF] Crepy | Photoreceptor-mediated kin recognition in plants[END_REF][START_REF] Wagg | Intraspecific and intergenerational differences in plant-soil feedbacks[END_REF][START_REF] Zhang | Facilitation from an intraspecific perspectivestress tolerance determines facilitative effect and response in plants[END_REF]; Supplementary Table 1). Unsurprisingly, given the great amount of genetic resources publicly available, all these experiments concern A. thaliana, with the exception of Trifolium pratense [START_REF] Wagg | Intraspecific and intergenerational differences in plant-soil feedbacks[END_REF] and Solanum lycopersicum [START_REF] Hauri | Chemical diversity rather than cultivar diversity predicts natural enemy control of herbivore pests[END_REF].

For experiments based on genotypes collected in natural populations, we observed a clear tradeoff between the number of genotypes used and the number of populations sampled (Figure 2B). The number of natural genetic lines used to evaluate GxG interactions was highly variable among experiments, ranging from 2 to 72 (mean ∼12) (Figure 2B). On average, these lines have been collected from about four populations (min = 1, max = 60) (Figure 2B). In wild plant populations, intraspecific neighbors share common space over generations, and this increases their probability for repeated interactions. Consequently, positive interactions are more likely to evolve between members of a single population rather than between members of different populations [START_REF] Nowak | Five rules for the evolution of cooperation[END_REF]. Accordingly, most experiments that do not employ A. thaliana are based on natural genetic lines from a single population (∼75%) or sampled at a regional scale (between 2 and 14 populations, ∼11%). On the other hand, an opposite trend is observed across the 29 experiments performed on A. thaliana. Almost 90% of the experiments on this species utilized genotypes coming from worldwide collections. The main hypothesis to explain this bias in using worldwide genotypes in A. thaliana might be related to its predominantly selfing breeding system, which initially suggested that most populations were monomorphic [START_REF] Platt | The scale of population structure in Arabidopsis thaliana[END_REF]. Therefore, the large public collections of genotypes that are available for A. thaliana mostly correspond to one representative genotype per population. However, progressively, a substantial number of studies challenged this view by revealing extensive genetic diversity within populations [START_REF] Le Corre | Variation at two flowering time genes within and among populations of Arabidopsis thaliana: comparison with markers and traits[END_REF][START_REF] Jorgensen | Functional variation in a disease resistance gene in populations of Arabidopsis thaliana[END_REF][START_REF] Bomblies | Local-scale patterns of genetic variability, outcrossing, and spatial structure in natural stands of Arabidopsis thaliana[END_REF][START_REF] Platt | The scale of population structure in Arabidopsis thaliana[END_REF][START_REF] Kronholm | Genetic basis of adaptation in Arabidopsis thaliana: local adaptation at the seed dormancy QTL DOG1[END_REF][START_REF] Brachi | Investigation of the geographical scale of adaptive phenological variation and its underlying genetics in Arabidopsis thaliana[END_REF][START_REF] Roux | The genetics underlying natural variation in the biotic interactions of Arabidopsis thaliana: the challenges of linking evolutionary genetics and community ecology[END_REF][START_REF] Frachon | Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time[END_REF][START_REF] Frachon | A genomic map of climate adaptation in Arabidopsis thaliana at a micro-geographic scale[END_REF][START_REF] Frachon | Adaptation to plant communities across the genome of Arabidopsis thaliana[END_REF][START_REF] Fulgione | Madeiran Arabidopsis thaliana reveals ancient long-range colonization and clarifies demography in Eurasia[END_REF], thereby giving an opportunity of studying more relevant GxG interactions in A. thaliana at the local scale.

Based on all the experiments listed in this survey, we nonetheless identified a major shortcoming of the plant material used to study GxG interactions, regardless of the identity of the species considered. As previously mentioned, testing both the kin/non-kin recognition and elbow-room hypotheses requires an estimation of the degree of genetic relatedness among interacting genotypes. Kin selection theory predicts partisan help given to close relatives. By contrast, according to the elbow-room hypothesis, genetically close relatives will compete for the same resources and increasing genetic distance between genotypes can translate into increasing niche partitioning. Testing for these contrasting predictions would require integrating information about the extent of genetic relatedness among interacting genotypes. However, this crucial information has been poorly considered in these experiments (but see [START_REF] Crutsinger | Plant genotypic diversity predicts community structure and governs an ecosystem process[END_REF][START_REF] Crutsinger | Intraspecific diversity and dominant genotypes resist plant invasions[END_REF], which may question the realism of having tested interactions between different genotypes in some studies, in particular when genotypes were originating from a single population (Supplementary Table 1).

Relevant Growth Conditions?

Performing experiments in controlled and field conditions are complementary [START_REF] Bergelson | Towards identifying genes underlying ecologically relevant traits in Arabidopsis thaliana[END_REF][START_REF] Brachi | Linkage and association mapping of Arabidopsis thaliana flowering time in nature[END_REF]. Experiments conducted under controlled conditions drastically reduce environmental noise, thereby allowing the establishment of a direct link between phenotypic observations and genotype performance under a given set of stable environmental conditions. On the other hand, in the field, plants are exposed to a greater but more ecologically realistic range of abiotic and biotic fluctuations than typically encountered in controlled conditions. Nonetheless, encompassing all these environmental fluctuations requires the field experiments to be repeated over several years.

In our survey, ∼82% of the experiments were conducted under controlled conditions (Supplementary Table 1). Out of these, ∼55 and ∼20% of the experiments were conducted in greenhouse conditions and growth chambers (including root chambers and growth tunnels), respectively. The remaining experiments (∼7%) were performed under in vitro conditions. On the other hand, few experiments (∼14%) had been conducted under field conditions, fewer in the native habitats (only two reported experiments, Supplementary Table 1). Finally, four experiments (∼4%) were conducted in both greenhouse and field conditions [START_REF] Espeland | Facilitation across stress gradients: the importance of local adaptation[END_REF][START_REF] Andersson | Indirect genetic effects from competition in the clonal herb Sedum album (Crassulaceae)[END_REF][START_REF] Ehlers | Competitor relatedness, indirect soil effects and plant coexistence[END_REF]. The type of growth conditions used to study intraspecific GxG interactions is therefore strongly biased in favor of controlled conditions, notably when compared to other types of biotic interactions. For instance, in a recent review on Genome-Wide Association studies (GWAS) performed on plant -pathogen interactions, 60% of the studies were conducted in controlled conditions (greenhouse/growth chambers) and 40% under field conditions [START_REF] Bartoli | Genome-wide association studies in plant pathosystems: toward an ecological genomics approach[END_REF].

Of particular note, around 68% of the experiments in our survey tested the effect of a particular environmental factor on GxG interactions, either in controlled or field conditions (Supplementary Table 1). Around 55% of these experiments tested the effect of abiotic factors, including light quality, nutrient status, CO 2 concentration and drought. On the other hand, ∼50% of these experiments tested the effect of biotic factors, which mainly encompassed density and soil conditioning by one or a mixture of genotypes, in particular to investigate the diversity-productivity relationship [START_REF] Bukowski | Intraspecific plant-soil feedback and intraspecific overyielding in Arabidopsis thaliana[END_REF][START_REF] Semchenko | Intraspecific genetic diversity modulates plant-soil feedback and nutrient cycling[END_REF][START_REF] Bukowski | The strength of negative plant-soil feedback increases from the intraspecific to the interspecific and the functional group level[END_REF]. Unfortunately, the treatments applied may not have been ecologically relevant. Indeed, in our survey, only ∼19% of the experiments loosely described the ecology of the populations used in the experiments (Supplementary Table 1). At most, only a rough description of habitats from which the genotypes were collected, was given. Since interspecific positive plant-plant interactions have been postulated to evolve in natural settings including a certain level of abiotic and/or biotic stress (i.e., 'stress gradient hypothesis'; [START_REF] Bertness | Positive interactions in communities[END_REF][START_REF] Brooker | The balance between positive and negative plant interactions and its relationship to environmental gradients: a model[END_REF][START_REF] Bruno | Inclusion of facilitation into ecological theory[END_REF], this is of particular importance to finely describe the ecology of native habitats from which genotypes are collected. This may in turn allow testing the stress gradient hypothesis at the intraspecific level.

A Large Diversity of Phenotyped Traits

All the traits measured across the 91 experiments are listed in Supplementary Table 2. To assess GxG interactions, an average of 3.5 traits per study have been directly measured on plants (min = 1, max = 10). We divided the list of traits into four broad categories, each relating to a distinct eco-function of the plant (i.e., root related traits, shoot related traits, life history traits and seed production related traits). While ∼34% of the experiments scored life history related traits (e.g., germination and flowering timing), ∼53 and ∼76% of the experiments measured root (e.g., root length and biomass) and shoot (e.g., plant height and dry biomass) related traits, respectively. Seed production related traits (e.g., number of fruits and number of seeds per fruit) were measured in ∼40% of the experiments. Interestingly, most experiments focused on collecting phenotypic information using either two (∼46%) or three (∼20%) categories. About 40% of the experiments looked at both root and shoot related phenotypes, while ∼29% of experiments focused on both shoot and life history related traits. Only four experiments (∼4%) focused on all four categories [START_REF] Willson | Sibling competition in plants: an experimental study[END_REF][START_REF] Linhart | Intrapopulation differentiation in annual plants. III. The contrasting effects of intra-and interspecific competition[END_REF][START_REF] Masclaux | Competitive ability not kinship affects growth of Arabidopsis thaliana accessions[END_REF].

Another interesting category of traits corresponds to extended phenotypes, which contains traits that were not directly phenotyped on the interacting individuals but rather looked at alteration of the immediate environment in GxG interactions. In our survey, ∼14% of studies examined such extended phenotypes, which mainly focused on either studying soil microbial communities [START_REF] File | Plant kin recognition enhances abundance of symbiotic microbial partner[END_REF][START_REF] Wagg | Intraspecific and intergenerational differences in plant-soil feedbacks[END_REF][START_REF] Abdala-Roberts | Traits underlying community consequences of plant intra-specific diversity[END_REF][START_REF] Burghardt | Legacy of prior host and soil selection on rhizobial fitness in planta[END_REF][START_REF] Tomiolo | Identity and provenance of neighbors, genotype-specific traits and abiotic stress affect intraspecific interactions in the annual legume Medicago truncatula[END_REF], tritrophic interactions [START_REF] Crutsinger | Plant genotypic diversity predicts community structure and governs an ecosystem process[END_REF][START_REF] Crutsinger | Intraspecific diversity and dominant genotypes resist plant invasions[END_REF][START_REF] Kotowska | Plant genetic diversity yields increased plant productivity and herbivore performance[END_REF][START_REF] Karban | Kin recognition affects plant communication and defence[END_REF][START_REF] Abdala-Roberts | Traits underlying community consequences of plant intra-specific diversity[END_REF][START_REF] Hauri | Chemical diversity rather than cultivar diversity predicts natural enemy control of herbivore pests[END_REF] or biochemical properties of plant secreted compounds [START_REF] Badri | Root secreted metabolites and proteins are involved in the early events of plant-plant recognition prior to competition[END_REF][START_REF] Kalske | Insect herbivory selects for volatile-mediated plant-plant communication[END_REF].

When considering experiments testing the kin/non-kin recognition hypothesis, root related traits were phenotyped significantly more often than in experiments testing the diversity-productivity relationship (contingency chi-squared test, χ 2 = 154.59, df = 3, P = 2.68 E-33, Figure 3). A potential explanation for this stems from the extensive literature on kin recognition studies, where many studies have demonstrated kin recognition by roots and subsequent nepotistic response across the plant kingdom (reviewed in Supplementary Tables 1,2). On the other hand, experiments testing the diversity-productivity relationship examined more shoot and seed production related traits than experiments pertaining to kin/non-kin recognition (Figure 3). A potential explanation for this observation could be linked to the difficulties associated with measuring individual specific root response in experiments testing for the diversityproductivity relationship. Often, to measure specific root traits, the total root biomass of several genotypes present in a pot has been considered as a dependent measure, which obviously impedes the estimation of the relative contribution of each individual genotype in the pot. Therefore, aboveground traits could be an easier measure for quantifying productivity.

Plant neighbor interactions are often dynamic in nature, and can switch from competitive to positive interactions depending on (i) the environmental stress perceived [START_REF] Bertness | Positive interactions in communities[END_REF], (ii) life stages of interacting species [START_REF] Walker | An invader alters germination and growth of a native dominant tree in Hawaï[END_REF][START_REF] Kellman | Facilitation of tree seed-ling establishment in a sand dune succession[END_REF][START_REF] Chapin | Mechanisms of primary succession following deglaciation at Glacier Bay[END_REF]Pugnaire et al., 1996a), or (iii) the physiological capacity of the interacting species (e.g., facilitation by improving plant water uptake under canopy shade or soil oxygenation; [START_REF] Callaway | Oxygenation of the soil rhizosphere by Typha latifolia and its facilitative effects on other species[END_REF][START_REF] Holmgren | The interplay of facilitation and competition in plant communities[END_REF]. In all the experiments listed in our survey, this potential switch of interactions at the intraspecific level has been overlooked. Testing for such complex dynamic GxG interactions would require phenotypic measurements of various plant compartments at multiple time points.

Direct vs. Indirect GxG Interactions

We further categorized the experiments depending on whether the interactions tested were direct or indirect. We classified the interactions to be indirect when the experiment did not include placing at least two genotypes in a pot together or to allow any direct contact between them. In our survey, such indirect interactions have been addressed in ∼10% of the 91 experiments (Supplementary Table 1). Indirect interactions between genotypes may occur by two main processes. Firstly, soil conditioning can mediate indirect interactions between genotypes. There is mounting evidence suggesting the role of root exudates in identity recognition of neighbors. In our survey, two studies confirmed kin recognition by showing alteration of root response upon external root exudate application in indirect GxG interactions [START_REF] Biedrzycki | Root exudates mediate kin recognition in plants[END_REF][START_REF] Semchenko | Plant root exudates mediate neighbour recognition and trigger complex behavioural changes[END_REF]. Furthermore, the composition of root exudates, which is genotype specific, may define the assembly of distinct soil microbial communities in the rhizosphere. Consequently, different genotypes in one population can be associated with different microbiota. Therefore, similar to soil microbiota mediated plant soil feedback (PSF) that has been abundantly documented at interspecific level and recognized in driving ecosystem processes [START_REF] Klironomos | Feedback with soil biota contributes to plant rarity and invasiveness in communities[END_REF][START_REF] Kardol | Temporal variation in plant-soil feedbacks controls succession[END_REF][START_REF] Kardol | Microbe-mediated plant-soil feedbacks cause historical contingency effects in plant community assembly[END_REF][START_REF] Reinhart | Soil biota and invasive plants[END_REF][START_REF] Petermann | Janzen Connell effects are widespread and strong enough to maintain diversity[END_REF], soil microbiota may have a central role in mediating GxG interactions in natural plant communities. In our survey, four experiments reported indirect interactions for which the effect of soil microbiota conditioning by one genotype is tested on the growth response of another genotype [START_REF] Bukowski | Intraspecific plant-soil feedback and intraspecific overyielding in Arabidopsis thaliana[END_REF][START_REF] Wagg | Intraspecific and intergenerational differences in plant-soil feedbacks[END_REF][START_REF] Bukowski | The strength of negative plant-soil feedback increases from the intraspecific to the interspecific and the functional group level[END_REF][START_REF] Burghardt | Legacy of prior host and soil selection on rhizobial fitness in planta[END_REF]. One of these studies suggested a role in maintaining genotypes coexistence [START_REF] Bukowski | The strength of negative plant-soil feedback increases from the intraspecific to the interspecific and the functional group level[END_REF], which is in line with recent theoretical demonstration of the putative role of microbes in altruism between organisms [START_REF] Lewin-Epstein | Microbes can help explain the evolution of host altruism[END_REF]. Yet, the genuine role of PSF in shaping evolution of plant populations is largely unexplored [START_REF] Wagg | Intraspecific and intergenerational differences in plant-soil feedbacks[END_REF], and investigation of the role of microbiota on GxG interactions represents a future challenge.

Secondly, indirect interactions between genotypes can also be mediated by volatile organic compounds (VOCs) produced by plants both aboveground and underground. VOCs are known to act in neighbor detection [START_REF] Ninkovic | Who is my neighbor? Volatile cues in plant interactions[END_REF] and to affect intraspecific competition in crops [START_REF] Ninkovic | Volatile communication between barley plants affects biomass allocation[END_REF] and trees [START_REF] Ormeño | Effect of intraspecific competition and substrate type on terpene emissions from some Mediterranean plant species[END_REF]. In our survey, only three experiments focused on interactions through aerial VOCs [START_REF] Karban | Kin recognition affects plant communication and defence[END_REF] in Artemisia tridentata; Kalske et al., 2019 in Solidago altissima;and Shimola and Bidart, 2019 in A. thaliana). The cocktail of VOCs has been found to be highly variable among individuals and has been proposed to help distinguishing kin from stranger genotypes [START_REF] Karban | Deciphering the language of plant communication: volatile chemotypes of sagebrush[END_REF]. Still, there is a caveat in understanding the exact role of VOCs in indirect GxG interactions in wild species, and how they may affect intraspecific communication between genotypes under natural conditions.

BOTH KIN COOPERATION AND OVERYIELDING ARE SUPPORTED BY EXPERIMENTS

We divided the major conclusions from the 81 experiments underlying the two major rationales (kin/non-kin recognition vs. diversity-productivity relationship) into (i) kin cooperation (KC) for studies demonstrating positive interactions between kin genotypes, (ii) overyielding (OY) for studies where the mixture of genotypes was confirmed to be more productive than monocultures, and (iii) neutral for studies where no significant differences were observed between the treatments specified in the experiments. Among this set of 81 experiments, only ∼17% found no significant differences between treatments, while ∼50% reported KC and ∼33% studies reported OY (Supplementary Table 1). The relative proportion of KC, OY and neutral interactions in the survey can be matched to the two main rationales tested in the experiments. Experiments testing for kin/non-kin recognition confirmed more KC, while experiments aiming at testing diversity-productivity relationships, corroborated more cases of OY (contingency chisquared test, χ 2 = 31.68, df = 2, P = 1.32 E-07; Figure 4). However, we need to stress that the number of studies reporting no differences could be more than what is listed in this survey, owing to the high tendency of not publishing non-significant results.

Species having low dispersal rate have been theoretically proposed to allow for evolution of kin selection mechanisms as interacting individuals are likely to be relatives [START_REF] Hamilton | The genetical evolution of social behavior[END_REF]. Moreover, species having strong spatial population structure have been poised to have positive frequency dependent interactions among genetically related individuals (Ehlers and Bilde, 2019). However, in our survey, when considering all the experiments, the relative proportion of KC, OY or neutral interactions was not found dependent on the mode of seed dispersal (autochory vs. allochory, contingency chi-squared test, χ 2 = 2.03, df = 2, P = 0.362; Supplementary Figure 1) or on the geographical scale of sampling (one population vs. multiple populations, contingency chi-squared test, χ 2 = 3.34, df = 2, P = 0.188; Supplementary Figure 2). Removing A. thaliana from the list yielded similar results for these proportions (autochory vs. allochory, contingency chi-squared test, χ 2 = 0.67, df = 2, P = 0.714; one population vs. multiple populations, contingency chi-squared test, χ 2 = 1.08, df = 2, P = 0.582; Supplementary Figures 1,2).

In species with a high selfing rate, the presence of neighbors of high genetic relatedness in the vicinity of seed-dispersing mother plants is expected in natural populations, which may allow for the prevalence of kin selection mechanisms for increasing one's inclusive fitness [START_REF] Dudley | Kin recognition and competition in plants[END_REF][START_REF] Ehlers | Inclusive fitness, asymmetric competition and kin selection in plants[END_REF]. The relative proportions between the three types of interactions (KC, OY or neutral) were significantly dependent on the breeding system of the plant species studied (selfing and clonal vs. outcrossing and mixed, χ 2 = 22.23, P = 1.48 E-05). KC was detected in 56.9 and 31.8% of the experiments conducted on species with a selfing/clonal and outcrossing/mixed breeding system, respectively (Figure 5). This effect remained significant even upon the removal of A. thaliana from the list (χ 2 = 15.04, P = 5.39 E-04).

Species having an annual life cycle are known to be commonly composed of genetically related individuals, especially in cases of limited seed dispersal and self-fertilization [START_REF] Cheplick | Genetic relatedness and competition in Triplasis purpurea (Poaceae): resource partitioning or kin selection?[END_REF]. Therefore, kin selection mechanisms may prevail for annual plants. In our survey, the relative proportions between the three types of interactions (KC, OY or neutral) were found to be dependent on the annual vs. perennial life history strategy of the plant species considered (χ 2 = 7.36, P = 0.025), with KC detected in 59.7 and 36.4% of the experiments conducted on annual and perennial species, respectively (Figure 6). This difference between these two life history strategies was however not significant upon removal of A. thaliana from the list of species considered (χ 2 = 4.80, P = 0.091; Figure 6).

At the interspecific level, increase in magnitude of positive interactions has been linked to varying levels of abiotic [START_REF] Maestre | Refining the stress-gradient hypothesis for competition and facilitation in plant communities[END_REF] and biotic stresses [START_REF] Smit | Inclusion of biotic stress (consumer pressure) alters predictions from the stress gradient hypothesis[END_REF] studies. In our survey, we identified two studies reporting a shift from KC to OY occurring between the same genotypes in response to a particular environmental factor. Firstly, in A. thaliana, genotypes performed better with kin under ambient CO 2 concentrations, while genotypic mixtures were favored under elevated CO 2 concentrations [START_REF] Andalo | Inter-and intragenotypic competition under elevated carbon dioxide in Arabidopsis thaliana[END_REF]. Secondly, in Phalaris arundinacea, genotypes performed better with kin in disturbed plots (with all native vegetation removed), while genotypic mixtures were confirmed to perform better in undisturbed plots [START_REF] Collins | Small-scale genotypic richness stabilizes plot biomass and increases phenotypic variance in the invasive grass Phalaris arundinacea[END_REF]. Both experiments provide evidence for the existence of similar dynamics of intraspecific positive interactions among different species in varying environments.

in various

FUTURE AVENUES

Despite the demonstration of positive interactions in 36 species (Supplementary Table 1), the underlying adaptive genetic architecture is still an open question. In our survey, this challenge was addressed in only one study, with the identification of a single major QTL underlying kin cooperation in A. thaliana [START_REF] Wuest | A plant biodiversity effect resolved to a single chromosomal region[END_REF]. While informative, the low number of RILs used (N = 37) precluded a proper characterization of the genetic architecture [START_REF] Keurentjes | Development of a near-isogenic line population of Arabidopsis thaliana and comparison of mapping power with a recombinant inbred line population[END_REF]. In addition, remaining questions include (i) testing whether polymorphic genes involved in positive interactions have been shaped by natural selection and (ii) identifying the ecological factors driving adaptive KC or OY [START_REF] Subrahmaniam | The genetics underlying natural variation of plant-plant interactions, a beloved but forgotten member of the family of biotic interactions[END_REF][START_REF] Frachon | Adaptation to plant communities across the genome of Arabidopsis thaliana[END_REF]. Here, we therefore introduce a framework in order to establish a genomic map of local adaptive positive plant-plant interactions (Figure 7).

The first step would be collecting genetic material from multiple local populations (Figure 7A). Then, the genomic characterization of the plant material could be conducted either by genome-wide SNP characterization or whole genome sequencing methodologies. This will not only allow estimating the genetic relatedness among genotypes within and between local populations but will also be essential to run GWA mapping (see below, Figure 7B). Simultaneously, an extensive in situ characterization of both abiotic (e.g., climate, soil physico-chemical properties) and biotic (e.g., plant communities, herbivore communities, as well as microbiota including commensal, pathogenic and symbiotic microbes) factors should be conducted for each population (Figure 7C). While abiotic stress characterization is facilitated by publicly available databases for climate and dedicated platforms for soil physico-chemical properties, characterizing biotic factors can still be strenuous and time consuming. However, with the ever-decreasing cost of next-generation sequencing (NGS) technologies, describing the taxonomic and functional networks (microbes, plants, insects. . .) of hundreds of wild plant populations is not out of reach [START_REF] Bartoli | In situ relationships between microbiota and potential pathobiota in Arabidopsis thaliana[END_REF][START_REF] Frachon | Adaptation to plant communities across the genome of Arabidopsis thaliana[END_REF]. While collecting plant material and accumulating genomic, phenotypic and ecological data for tens to hundreds of populations represents a monumental task, this has already been achieved for a few wild plants including A. thaliana (Fournier-Level et al., 2011;[START_REF] Hancock | Adaptation to climate across the Arabidopsis thaliana genome[END_REF][START_REF] Brachi | Investigation of the geographical scale of adaptive phenological variation and its underlying genetics in Arabidopsis thaliana[END_REF][START_REF] Bartoli | In situ relationships between microbiota and potential pathobiota in Arabidopsis thaliana[END_REF][START_REF] Frachon | A genomic map of climate adaptation in Arabidopsis thaliana at a micro-geographic scale[END_REF][START_REF] Frachon | Adaptation to plant communities across the genome of Arabidopsis thaliana[END_REF] and sunflower [START_REF] Todesco | Massive haplotypes underlie ecotypic differentiation in sunflowers[END_REF].

In order to estimate the natural variation of GxG interaction patterns among populations, the next step would be to conduct common garden experiments by estimating differences at the intra-population level between intragenotypic interactions (monoculture) and inter-genotype pairwise interactions (Figure 7D). Although the best strategy would be to perform experiments in the native habitats from which the populations have been collected, this approach may not be manageable for a larger number of populations.

Kin recognition and elbow-room hypotheses make opposite predictions about the relationship between genetic distance and the extent of positive interactions between two genotypes (Figure 7E). Having genome sequence information of genotypes, linking level of positive interactions to the genomic distance of interacting genotypes could help validating either hypothesis for explaining positive interactions between populations (Figure 7E). Additionally, identification of ecological factors associated with positive interactions between populations could be of interest in understanding how positive interactions (KC or OY) may evolve under different environmental selection pressures in natural populations (Figure 7F) and designing experiments to test the relevance of the stress gradient hypothesis at the intraspecific level. As a next step, a GWA mapping approach could be adopted to fine map genomic regions associated with natural variation in the level of positive interactions among populations (Figure 7G). GWA mapping has previously proven to be a powerful approach to identify QTLs associated with plantplant interactions at the intraspecific and interspecific levels in many crop species [START_REF] Subrahmaniam | The genetics underlying natural variation of plant-plant interactions, a beloved but forgotten member of the family of biotic interactions[END_REF] as well as in A. thaliana [START_REF] Baron | The genetics of intra-and interspecific competitive response and effect in a local population of an annual plant species[END_REF][START_REF] Frachon | Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time[END_REF][START_REF] Frachon | Adaptation to plant communities across the genome of Arabidopsis thaliana[END_REF]. The detected QTLs can in turn help to reliably discriminate between the three main hypotheses about the genetic architecture underlying positive interactions within species, that is (i) kin selection wherein a large chunk of the genome is shared among cooperating partners (polygenic architecture; [START_REF] Hamilton | The genetical evolution of social behavior[END_REF], (ii) Green beard effect with cooperative genotypes sharing the same allele at one or few QTLs identified [START_REF] Dawkins | The Sefish Gene[END_REF], and (iii) complementarity where cooperative genotypes have different alleles at single or few QTLs identified (D 'Souza et al., 2018).

Such QTLs can then allow identifying signatures of selection at the genomic level (Figure 7H). For instance, the type and strength of selection can be addressed by testing for any overlaps and enrichments between QTLs underlying positive interactions and genomic regions showing signatures of long-term (e.g., hard sweeps) or short-term selection (e.g., local adaptation with F ST ) [START_REF] Fournier-Level | A map of local adaptation in Arabidopsis thaliana[END_REF][START_REF] Hancock | Adaptation to climate across the Arabidopsis thaliana genome[END_REF][START_REF] Horton | Genome-wide patterns of genetic variation in worldwide Arabidopsis thaliana accessions from the RegMap panel[END_REF][START_REF] Brachi | Co-selected genes determine adaptive variation in herbivore resistance throughout the native range of Arabidopsis thaliana[END_REF][START_REF] Frachon | Intermediate degrees of synergistic pleiotropy drive adaptive evolution in ecological time[END_REF]; Figure 7H).

After confirming the adaptive status of the genetic basis associated with natural variation of positive interactions, one could take the final steps toward understanding the genetic and molecular mechanisms (Figure 7I). For this, QTL cloning can be achieved by characterization of mutants (T-DNA, EMS. . .) or generation of transgenic lines with gain or loss of functions of the gene(s) of interest.

Altogether, combining ecology and evolutionary biology, along with quantitative genetics, genomics and molecular biology represents an unprecedented opportunity to establish the genetic and molecular picture of positive GxG interactions in wild plant species in their natural ecological landscape.

FIGURE 1 |

 1 FIGURE 1 | Surveying GxG interactions within herbaceous wild species. (A) Cumulative number of papers on GxG interactions in wild plant species from 1985 to 2021. (B) Doughnut plots describing the distribution of the major botanical families used in the study of intraspecific GxG interactions. (C) Doughnut plots describing the distribution of the major botanical families used in the study of intraspecific GxG interactions upon removal of Arabidopsis thaliana.

FIGURE 2 |

 2 FIGURE 2 | Classification of experimental studies on GxG interactions.(A) Doughnut plot describing the distribution of the papers according to the rationale of studying GxG interactions. (B) Relationship between the number of genetic lines and the number of populations according to three geographic scales. For better illustration, the two outliers with 60 genotypes each from a different population, are not incorporated in the figure[START_REF] Fitzpatrick | Soil microbes alter plant fitness under competition and drought[END_REF][START_REF] Turner | Effects of genomic and functional diversity on stand-level productivity and performance of nonnative Arabidopsis[END_REF].

FIGURE 3 |

 3 FIGURE 3 | Stacked barplots illustrating the distribution of studies based on trait categories according to different rationales. The number of traits studied within each category are indicated within each stack.

FIGURE 4 |

 4 FIGURE 4 | Stacked barplots illustrating the relative proportion of kin cooperation (KC), overyielding (OY) and neutral interactions between the 'diversity-productivity relationship' and 'kin/non-kin recognition' rationales. The number of studies supporting each type of interaction are indicated within each stack.

FIGURE 5 |

 5 FIGURE 5 | Stacked barplots illustrating the different outcomes of GxG according to the breeding system of species studied. MIXED + OUTCROSSING: species with an outcrossing breeding system or with a mixed breeding system. SELFING + CLONAL: species with a selfing breeding system or a vegetative reproduction. SELFING W/O A.T. + CLONAL: species with a selfing breeding system (without considering Arabidopsis thaliana) or a vegetative reproduction.

FIGURE 6 |

 6 FIGURE 6 | Stacked barplots illustrating the different outcomes of GxG interactions according to the annual vs. perennial strategy of species studied (with and without including Arabidopsis thaliana). The number of studies supporting each type of interaction are indicated within each stack.

FIGURE 7 |

 7 FIGURE 7 | Future avenues for studying GxG interactions among wild plant populations. (A) Collecting multiple genotypes from several natural populations. (B) Genome sequencing/Genome-wide SNP characterization of all genotypes coming from different populations. (C) Characterizing abiotic and biotic ecological factors. (D) Conducting common garden experiments to dissect fitness outcomes in pairwise interactions among genotypes from one population (intragenotype and intra-population intergenotype combinations). Different genotypes are represented by different shapes. (E) Linking genomic distance with levels of positive interactions exhibited in pairwise GxG interactions. (F) Identifying putative ecological driver(s) associated with among-population variation of positive interactions. (G) Genome Wide Association mapping to identify genomic regions underlying positive interactions. (H) Enrichment analysis in signatures of selection, e.g., spatial genetic differentiation estimated by F ST . (I) Cloning and functional characterization of genes underlying QTLs associated with positive interactions.

Frontiers in Plant Science | www.frontiersin.org

July 2021 | Volume 12 | Article 683373

https://www.researcher-app.com/

FUNDING

This work was funded by a Ph.D. fellowship from the LABEX TULIP to HJS (ANR-10-LABX-41).

.

AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2021. 683373/full#supplementary-material Supplementary Table 1 | Survey of GxG interaction studies in herbaceous wild plant species. The first six columns (columns A-F) indicate the serial number, the list of different plant species studied, their botanical family, main breeding system, life cycle and natural seed dispersal mode. Next six columns (columns G-L) describe the genetic material used including the main type of genetic variation studied (here experimental variation indicates use of F 2 population, Recombinant Inbred Lines or Introgression Lines, while artificial variation includes use of mutant lines), number of genetic lines sampled (here NA indicates not available), number of populations sampled (here NA indicates not available and NR indicates not relevant), the scale of sampling (here WW indicates worldwide sampling), whether genetic differences between the lines were tested or assumed, and the habitats from which these lines were sampled from. Following 15 columns (columns M-AA) are divided to describe the experimental design of the studies including the main rationale, whether the GxG interactions were tested directly or indirectly, the experimental design (here solo refers to treatments where genotypes were grown alone, intra refers to 'intragenotype' treatment where two individuals of the same genotype were placed together and inter refers to 'intergenotype' treatment where two different genotypes were placed together; in the 'inter' column, 'x' in red indicates a mixed polyculture treatment) and conditions under which the experiments were performed (here GH indicates greenhouse and GC indicates growth chamber). Additionally, details about the specific treatments applied and the traits measured are listed. Next three columns (columns AB-AD) detail the main ecological questions addressed in each study, major findings with respect to intraspecific plant-plant interactions and the main type of positive interaction observed in each experimental study. Here KC indicates kin cooperation, OY indicates overyielding, coop indicates studies where positive interactions were identified. However, due to lack of genetic information, these latter studies could not be categorized in either KC or OY. The last three columns (columns AE-AG) list the reference name, year of publication, and the journal of publication [START_REF] Tonsor | Relatedness and intraspecific competition in Plantago lanceolata[END_REF][START_REF] Mahall | Root communication among desert shrubs[END_REF][START_REF] Burt | Tests of sib diversification theories of outcrossing in Impatiens capensis: effects of inbreeding and neighbour relatedness on production and infestation[END_REF][START_REF] Thomas | The genetic component in plant size hierarchies: norms of reaction to density in a Polygonum species[END_REF][START_REF] Mahall | Effects of regional origin and genotype on intraspecific root communication in the desert shrub Ambrosia dumosa (Asteraceae)[END_REF][START_REF] Ronsheim | Population-level specificity in the plant-mycorrhizae association alters intraspecific interactions among neighboring plants[END_REF][START_REF] Rautiainen | The effect of within-genet and between-genet competition on sexual reproduction and vegetative spread in Potentilla anserina ssp[END_REF][START_REF] Falik | Physiologically-mediated self/nonself root discrimination in Trifolium repens has mixed effects on plant performance[END_REF][START_REF] Dudley | Kin recognition in an annual plant[END_REF][START_REF] Semchenko | Effects of physical connection and genetic identity of neighbouring ramets on root-placement patterns in two clonal species[END_REF][START_REF] Lankinen | Root competition influences pollen competitive ability in Viola tricolor: effects of presence of a competitor beyond resource availability?[END_REF][START_REF] Murphy | Kin recognition: competition and cooperation in Impatiens (Balsaminaceae)[END_REF][START_REF] Aguilera | No accession-specific effect of rhizosphere soil communities on the growth and competition of Arabidopsis thaliana accessions[END_REF][START_REF] Bhatt | Kin recognition, not competitive interactions, predicts root allocation in young Cakile edentula seedling pairs[END_REF][START_REF] Biernaskie | Evidence for competition and cooperation among climbing plants[END_REF][START_REF] Markham | Effect of neighbour presence and soil volume on the growth of Andropogon gerardii Vitman[END_REF][START_REF] Drummond | Genotypic diversity effects on the performance of Taraxacum officinale populations increase with time and environmental favorability[END_REF][START_REF] Milla | Kinship rivalry does not trigger specific allocation strategies in Lupinus angustifolius[END_REF][START_REF] Creissen | Stabilization of yield in plant genotype mixtures through compensation rather than complementation[END_REF][START_REF] Tomimatsu | Effects of genotypic diversity of Phragmites australis on primary productivity and water quality in an experimental wetland[END_REF][START_REF] Yang | Root contact responses and the positive relationship between intraspecific diversity and ecosystem productivity[END_REF][START_REF] Corliss | Evolutionary potential for increased invasiveness: high-performance Polygonum cespitosum genotypes are competitively superior in full sun[END_REF][START_REF] Huber | Genotypic and phenotypic diversity does not affect productivity and drought response in competitive stands of Trifolium repens[END_REF][START_REF] Palmer | Kin recognition is a nutrient-dependent inducible phenomenon[END_REF][START_REF] El-Gawad | Does a plant detect its neighbor if it is kin or stranger? Evidence from a common garden experiment[END_REF][START_REF] Jie | Kin interactions of Arabidopsis based on the integrated performance of plants[END_REF][START_REF] Li | Relatives growing together: pair density and kinship[END_REF][START_REF] Torices | Kin discrimination allows plants to modify investment towards pollinator attraction[END_REF][START_REF] Baker | Transgenerational effects of parental light environment on progeny competitive performance and lifetime fitness[END_REF][START_REF] Smith | Early sibling conflict may ultimately benefit the family[END_REF][START_REF] Tomiolo | Intraspecific interactions in the annual legume Medicago minima are shaped by both genetic variation for competitive ability and reduced competition among kin[END_REF]Zhang et al., 2019;[START_REF] Goddard | Evidence for root kin recognition in the clonal plant species Glechoma hederacea[END_REF][START_REF] Yamawo | Outcome of interspecific competition depends on genotype of conspecific neighbours[END_REF].

Supplementary Table 2 | List of different traits measured in GxG interaction studies listed in the literature survey. The serial number and list of species studied in the 91 experiments are listed in the first two columns (columns A and B). The next four columns (columns C-F) describe the measured trait categories that have been divided into root, shoot, life history and seed production related traits. Following this, the columns (columns G-J) summarize the total number of traits studied in each trait category within the experiments. The next two columns detail the extended phenotypes, if recorded within the experiment (columns K and L). Following this, the total number of traits recorded within the experiment are listed (column M). Lastly, the two columns (columns N and O) list the reference name, and the year of publication.

Conflict of Interest:

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Darine Al-Mohtar
email: darinemohtar@gmail.com

Amani Ramzi Daou
email: amani.daou2@gmail.com

Nour El Madhoun
email: nour.el-madhoun@epita.fr

Rachad Maallawi
email: rachad.maallawi@gmail.com

A Secure Blockchain-Based Architecture for the COVID-19 Data Network

Keywords: Blockchain, COVID-19, PCR, Security, Smartcontracts, Ethereum, Traceability

The COVID-19 pandemic has impacted the world economy and mainly all activities where social distancing cannot be respected. In order to control this pandemic, screening tests such as PCR have become essential. For example, in the case of a trip, the traveler must carry out a PCR test within 72 hours before his departure and if he is not a carrier of the COVID-19, he can therefore travel by presenting, during check-in and boarding, the negative result sheet to the agent. The latter will then verify the presented sheet by trusting: (a) the medical biology laboratory, (b) the credibility of the traveler for not having changed the PCR result from "positive to negative". Therefore, this confidence and this verification are made without being based on any mechanism of security and integrity, despite the great importance of the PCR test results to control the COVID-19 pandemic. Consequently, we propose in this paper a blockchainbased decentralized trust architecture that aims to guarantee the integrity, immutability and traceability of COVID-19 test results. Our proposal also aims to ensure the interconnection between several organizations (airports, medical laboratories, cinemas, etc.) in order to access COVID-19 test results in a secure and decentralized manner.

I. INTRODUCTION

The COVID-19 pandemic is a health crisis caused by an emerging infectious disease. It has impacted the world economy, mainly all activities where social distancing cannot be respected, for example in airports, concerts, theaters, and so on. Rapid and precise screening tests such as "PCR (Polymerase Chain Reaction)" and "Immunochromatography" are essential for an effective response to this pandemic because they help to identify infections and then to mitigate them. Indeed, the results of these tests have become very important, for example in the case of a trip: the traveler needs to perform a PCR test within 72 hours before his departure to detect whether he is a carrier or not of the COVID-19. If the result is negative, the person can therefore travel by presenting the negative result sheet to the agent, during check-in and boarding, to ensure that he does not have the COVID-19. The agent will then check the result sheet presented by the traveler by trusting: (a) a centralized system "the medical biology laboratory" which provided the result of the PCR test to the traveler, and (b) the credibility of the traveler for not having changed the PCR test result from "positive to negative" [START_REF] Kiang | Routine asymptomatic testing strategies for airline travel during the covid-19 pandemic: a simulation study[END_REF] [START_REF] Munoz | Lockdown during covid-19 pandemic: Impact on road traffic noise and on the perception of sound environment in france[END_REF].

In fact, the trust and verification procedure performed by the agent is not based on any security and integrity mechanism, despite the great importance of securing PCR test results to combat the COVID-19 pandemic. The biggest issue in this context is that laboratories send the results through mails to the people who have been tested. These people then print their results to present them later to organizations (airport, station, etc.) and this process presents both following vulnerabilities:

• Vulnerability (1): failure to ensure the integrity of PCR test results [START_REF] Long | Diagnosis of the coronavirus disease (covid-19): rrtpcr or ct?[END_REF]:

-For example, in the case of a trip (airplane or train), the agent cannot guarantee the integrity of the PCR test results, given by travelers, because he does not have access to internal laboratory data. However, if the agent "really" wants to confirm the integrity of a PCR test result, he then needs to call the laboratory but this is not really possible when traveling. -In the case of a positive result of a PCR test, a malicious traveler can modify the result of his test from "positive to negative" easily and without any control: "this is because the result is presented in a simple PDF file". The agent cannot subsequently detect these changes except in the case where he calls the laboratory to confirm the result. -A malicious traveler can also create his own document of the PCR test result with the name of a known laboratory in his country and a forged signature. The agent also cannot detect these changes except if he calls the laboratory to confirm the result. -Indeed, there is an absolute risk that people who are in the same airport (or in the same train station) and in the same airplane (or the same train) will be contaminated if a malicious traveler changes the result of his PCR test from "positive to negative" or creates his own PCR test result document. -The first fraud attempts based on false negative PCR results have already appeared in [START_REF]Covid-19. un trafic de faux certificats de tests négatifs démantelé à l'aéroport de roissy[END_REF]. Therefore, the integrity of PCR test results is essential.

• Vulnerability (2): impossibility of finding the history of all PCR tests carried out by a person and therefore there 978-1-6654-0722-9/21/$31.00 ©2021 IEEE is a lack of traceability: in the case of a trip (airplane or train), when the traveler arrives in the country of destination, he once again needs to show the agent in that country the PCR test result carried out in the country of departure, and do another PCR test within the next 72 hours. So there is no absolute traceability to know the history of all PCR tests previously performed by a person. These tests were carried out in different countries and in completely independent laboratories [START_REF] Salg | A globally available covid-19-template for clinical imaging studies[END_REF]. Indeed, these vulnerabilities are also present in the case of another type of COVID-19 test such as Immunochromatography test (see section II-A2). For vaccination, Vulnerability (1) is also strongly present in the case of verification if the person is vaccinated or not. Accordingly, our goal in this paper is to design a blockchain based decentralized trust architecture that aims to: (a) guarantee the integrity and immutability of the COVID-19 test results while sharing them in a decentralized and secure way between laboratories and other organizations, (b) ensure the traceability of all the results of COVID-19 tests carried out by people in national and international laboratories. We note that in this paper we will not address the case of vaccination because even though its verification procedure may be the same as the verification procedure of the COVID-19 test results, we however have not studied this context in this paper. In addition, vaccination is not mandatory today but is strongly recommended, and if there are currently some countries that open their borders without restriction to vaccinated people, there are other destinations that also require presenting a negative result of a PCR test [START_REF] Moreau | Faut-il faire un test pcr même si l'on est vacciné contre le covid pour voyager ?[END_REF].

This paper is organized as follows. In section II, we introduce an overview of COVID-19 tests and blockchain technology. In section III, we describe our proposed blockchain-based architecture. The last section concludes the paper. II. GENERALITIES A. COVID-19 Tests 1) PCR (Polymerase Chain Reaction): it allows to detect if a person is a carrier or not of the COVID-19, with a response: "negative search" or "positive search". For a trip for example, it must be undergone 72 hours before departure (it is only valid for 72 hours) [START_REF] Lan | Positive rt-pcr test results in patients recovered from covid-19[END_REF].

2) Immunochromatography: it is the COVID-19 Serology test. It is used for the qualitative detection of Immunoglobulin G (IgG) and Immunoglobulin M (IgM) at the same time. IgG is a type of antibody representing about 75% of serum antibodies in humans [START_REF] Long | Antibody responses to sarscov-2 in patients with covid-19[END_REF]. IgM is a class of antibody representing about 10% of the immunoglobulins in an immunoserum [START_REF] Cassaniti | Performance of vivadiag covid-19 igm/igg rapid test is inadequate for diagnosis of covid-19 in acute patients referring to emergency room department[END_REF]. As shown in Fig. 1, the level of IgM antibodies begins to increase approximately one week after the initial infection, while IgG antibodies appear later than IgM (generally within 14 days of infection) and can last 6 months or even several years. This means that IgG is used as an indicator of a previous infection. Patients who are infected with SARS-CoV-2 can be quickly identified by simultaneous monitoring of IgM and IgG [START_REF]Sars-cov-2 (covid-19) : Test rapide igg/igm pour le diagnostic[END_REF].

B. State of the Art

The choice of a centralized solution was generally applied for the storage and management of health data such as in [START_REF] Koutli | Secure iot e-health applications using vicinity framework and gdpr guidelines[END_REF] [START_REF] Sengupta | Secure data management in cloudlet assisted iot enabled e-health framework in smart city[END_REF] [13] and [START_REF] Georgiou | Cloud computing framework for e-health security requirements and security policy rules case study: A european cloud-based health system[END_REF]. Indeed, with the urgency of the COVID-19 health crisis and the lack of skills on the market, the application of decentralized solutions, such as blockchain technology, was not considered, although they respond to several security aspects [START_REF] Madhoun | A decision tree for building it applications[END_REF] and also to the decentralization of our health system: ARS (Agence Régionale de Santé), hospitals, laboratory, and so on [START_REF] Gao | A survey of blockchain: Techniques, applications, and challenges[END_REF].

1) Blockchain Technology: it is one of the solutions that seems quite apt to decentralize digitized medical networks in a reliable, secure and robust manner. In fact, it is proposed today as a new technical infrastructure to decentralize IT applications [START_REF] Maldonado-Ruiz | An innovative and decentralized identity framework based on blockchain technology[END_REF] [START_REF] Madhoun | Going beyond the blockchain hype: In which cases are blockchains useful for it applications?[END_REF]. The Bitcoin was the first implementation of the blockchain technology to exchange, in a decentralized manner, cryptocurrencies. The customers appreciated the Bitcoin application because of its absolute security that is guaranteed thanks to the blockchain technology [START_REF] Madhoun | A decision tree for building it applications[END_REF] [START_REF] Maldonado-Ruiz | 3bi-ecc: a decentralized identity framework based on blockchain technology and elliptic curve cryptography[END_REF]. So, this security comes firstly from the intrinsic main property of blockchain that allows two nodes to execute transactions together without going through a Trusted Third Party (TTP), and secondly from the fact that the history of all these transactions is stored in a distributed and an immutable data ledger among blockchain nodes (it is impossible to modify the underlying records in the blockchain ledger) [START_REF] Gao | A survey of blockchain: Techniques, applications, and challenges[END_REF].

There are several types of blockchains and there is a dichotomy between them [START_REF] Beck | Governance in the blockchain economy: A framework and research agenda[END_REF] [START_REF] Yang | Public and private blockchain in construction business process and information integration[END_REF]. For a permissionless public blockchain (Bitcoin, Ethereum, etc.), anyone, without revealing his true identity, can be part of such a network and ensure that peer-to-peer transactions are secure and consistent, while remaining private and available only on network nodes that support blockchain. Decentralization is strong but the lack of control of network participants can lead to a risk on the infrastructure [START_REF] Gao | A survey of blockchain: Techniques, applications, and challenges[END_REF] [START_REF] Hatin | Welcome to the jungle: A reference model for blockchain, dlt and smart-contracts[END_REF]. For a permissioned private blockchain (Hyperledger Fabric, Hyperledger Besu, etc.), each participant who joins the network, to contribute to the infrastructure, must first register his identity and the identity of the resources that contribute to the entire network. This is necessary so that a malicious entity, such as a hidden node, can be detected. Due to this registration process, there is also a need for an efficient node identity management mechanism in addition to the immutable blockchain ledger keeping. In this case, the decentralization of the network is weaker but thwarted by the trust placed in the participants [START_REF] Gao | A survey of blockchain: Techniques, applications, and challenges[END_REF].

III. PROPOSED ARCHITECTURE

In this paper, we propose an innovative blockchain-based architecture that will help prevent, fight and control the spread of the COVID-19 pandemic as follows :

A. Architecture Prototype

We have developed a prototype of our architecture based on the use of the Ethereum public blockchain and smartcontracts (see section II-B2). Our prototype needs a graphical and user-friendly interface to allow data manipulation. This interface must communicate with the core of the blockchain that allows the creation of the blocks and their diffusion (see section III-B). In our prototype, we identify three actors (see Fig. 2):

• The user (person/patient): who wishes to benefit from a service requiring the presentation of a negative COVID-19 test result. • The laboratory: that performs the COVID-19 tests and guarantees their validity.

• The organization: it is the entity (airport, cinema, shopping center, etc.) that aims to ensure that all participants are not infected with COVID-19.

In our proposed prototype, we consider two main use cases "PCR Tests" and "Immunochromatography Tests": After performing a PCR test or an Immunochromatography test, the laboratory and from the graphical interface creates a block containing all the data specific to the test, where only the eligible organization and the user that can obtain the data encapsulated in this block. The eligible organization that wishes to access the data must establish a smart-contract with the laboratory. The primary key of this smart-contract is the person's identifier communicated by the person himself. Fig. 2 shows the flow of exchanges between the three actors.

B. Prototype Software Components

Several software components need to be integrated together in order to provide an end to end prototype. Our prototype is divided into 3 main components (see Fig. 3): off-chain component, on-chain component and integration component. We describe these components as follows:

• Off-Chain Component (Graphical Interface): it is developed through HTML and CSS languages. Three main pages are ensured via this interface: It is implemented via smart-contracts "solidity language on the Ethereum blockchain [START_REF] Modi | Solidity programming essentials: A beginner's guide to build smart contracts for ethereum and blockchain[END_REF]" (see section II-B2) . It is the main skeleton of our prototype. • Integration Component: this component ensures a secure connection, thanks to TLS protocol, between the core of the blockchain and the graphical interface. It is developed through JavaScript language and allows to communicate the values entered on the graphical interface with the core of the blockchain. Two main functions can be performed: get to read and set to write.

C. On-Chain Component (Core of the Blockchain)

we have used multiple smart-contracts in our application for more clarity of the code, organization and classification of the required data. We have also used inheritance to combine our Patient, Labs and Organizations contracts into one main contract, whose basic functionality is to set, edit and get the test results for each patient.

As illustrated in Fig- 4, we defined for the Patient contract a structure that contains the username and a Boolean value to indicate whether this patient's id exists or not. We also defined two mappings: "Patients" that maps the hashed id to the user's information and "patientByAddr" that maps the user's address (device's public key) to the hashed id, and an array "patientsList" that contains the hashed ids of all the patients registered on this Blockchain. The function "setPatient" is called upon signing up to set the information of the patient.

As illustrated in Fig- 5, the Lab contract contains a structure with all the details of a lab. It also has three mappings: "isLab" to indicate whether an address corresponds to a lab or not, "labInfo" that maps the lab's id to its details, and "labByAddr" to map the different addresses of a lab to its id. The function "setLab" sets the information of a lab when first signing up. "getLabId" returns the id of this lab if it is registered on the device calling the function (returns 0 otherwise). This is used upon logging in such that if the id returned is 0, then "setLabId " is called to set the id to the currently used device's address. Our Main contract inherits all three contracts. We developed two functions: "setPCRresults" and "setSerum" to set the results' details, and can only be accessed by labs and only if the patient is registered on the network. We developed also two functions "getAllPCR" and "getAllSerum" that allow labs to return a list of ids for patients that have been tested in the lab calling each function. Then the front-end code can take this array and call the "getPCRresult" and "getSerumResult" in a loop, which in turn returns all results for tests done in this lab. That being said, the getters of the results take into consideration the sender of the function, and set the id of the patient accordingly: in case the patient is calling, the id is automatically set to this patient's id depending on the address; in case the lab is calling, the id is set to the _patientId given as an argument, but converted to integer type (the argument is the patient's hashed id given as string); otherwise (in case of an organization), searching for a patient's id hashes the given value and uses it as id of the test results to be returned.

IV. CONCLUSION AND FUTURE WORK

In this paper, we proposed a blockchain-based decentralized trust architecture that aims to guarantee the integrity, immutability and traceability of COVID-19 test results. Our proposal also aims to ensure the interconnection between several organizations (airports, medical laboratories, cinemas, etc.) in order to access COVID-19 test results in a secure and decentralized manner. For future work, we aim to extend this paper by improving our smart-contracts and our prototype. In addition, we aim to study and develop a solution for the third case study which is "The vaccination".

Fig. 1 .

 1 Fig.1. IgM and IgG after Infection[START_REF]Sars-cov-2 (covid-19) : Test rapide igg/igm pour le diagnostic[END_REF]

Fig. 2 .

 2 Fig. 2. Exchange Flow

Fig. 3 .

 3 Fig. 3. Prototype Software Components

Fig. 4 .Fig. 5 .

 45 Fig. 4. Patient Smart-Contract

Fig. 6 .

 6 Fig. 6. Organisation Smart-Contract