Intraspecific competitive interactions rapidly evolve via spontaneous mutations

Using a mechanistic spatially explicit trait-based neighborhood-model, we quantify the impact of mutations on intraspecific spatial interactions to better understand mechanisms underlying the maintenance of genetic variation and the potential effects of these evolved interactions on the population dynamics of Arabidopsis thaliana. We use 100 twenty-fifth generation mutation accumulation (MA) lines (genotypes) derived from one founder genotype to study mutational effects on neighbor responses in a field experiment. We created individual-based maps (15,000 individuals), including phenotypic variation, to quantify mutational effects within genotypes versus between genotypes on reproduction and survival. At small-scale (within 80 cm of the focal plant), survival is enhanced but seed-set is decreased when a genotype is surrounded by different genotypes. At large-scale (within 200 cm of the focal plant), seed set is facilitated by different genotypes while the same genotype has either no effect or negative effects. The direction of the interactions among MA lines suggests that at small scale these interactions may contribute to the maintenance of genetic variation and at large scale contribute to the survival of the population. This may suggest, that, mutations potentially have immediate effects on population and community dynamics by influencing the outcome of competitive and faciliatory interactions among conspecifics.


Introduction
Mutations are the ultimate source of genetic diversity; thus, their contribution to genetic variation is central to evolutionary process (reviewed in Halligan and Keightley 2009;Nei 2013). Although mutations must at some time contribute to adaptive evolution, mutations have long been considered as secondary in their contribution to standing genetic variation relative to selection, gene flow and drift (Keightley and Lynch 2003). Given this perspective, it is not surprising that the contributions of mutations to eco-evolutionary processes is understudied. However, it is paradoxical to consider mutations as minor factors in adaptive response given that mutations and environmental context have played a prominent role in our understanding of evolutionary process since the earliest days of the neoDarwinian synthesis (Fisher 1930).
From an ecological perspective, it is important to note that mutations are expressed in individuals having specific spatial locations and experiencing specific environmental conditions (Weng et al. 2021). Thus, the spatial context of the appearance of new mutations may be important at very local spatial scales. In other words, whether the fitness consequences of a new mutation are beneficial, neutral, or deleterious may depend on microscale environmental variation. A component of this variation may be the extent to which mutation effect is determined by the genetic neighborhood of conspecifics. Thus, genetic diversity introduced by new mutations may potentially influence competitive outcomes at the intra and interspecific level. Consequently, our understanding of mutational contribution to an individual's fitness may require consideration of the local environment governing competitive relationships among genotypes. Evolutionary processes, i.e., recurrent mutations and drift, gene flow, frequency and balancing selection, hybridization and even epistasis (are often considered sources of genetic variation (Fenster et al. 1997; Barrett and Schluter 2007). However, the link between genetic diversity and foundational ecological processes such as intraspecific competitive interactions are rarely considered even though intraspecific competition is common (Notterbock et al. 2017b) and, depending on its intensity relative to interspecific competition, can influence population and community dynamics (Nottebrock et al. 2017a;Åkesson et al. 2021Ushio 2022. Intraspecific genetic variation contributing to individual trait variation and environmental adaptability may promote species coexistence by both increasing habitat heterogeneity and altering competitive hierarchies (Violle et al. 2012;Ehlers et al. 2016). However, intraspecific genetic variation may hinder species coexistence when intraspecific genetic variation is diminished by competition between individuals (Hart et al 2016). Thus, a complete understanding of evolutionary process requires linking intraspecific genetic diversity with spatial ecological interactions (Bolnick et al. 2011;Genung et al. 2011), even at the scale of plant-plant interactions. Previously, it has been shown that genetic variation of A. thaliana plants influences competition in common garden experiments (Violle et al. 2017). An unexplored gap in our knowledge is quantifying the role of novel mutations in contributing to the evolution of competitive interactions.
We investigate the link between evolutionary genetics and spatial ecological interactions of mutation accumulation lines (MA lines) in the model plant organism Arabidopsis thaliana (Brassicaceae) to advance our understanding of the fitness effects of new mutations. Previous studies demonstrate the potential for A. thaliana genotypes to establish competitive hierarchies, i.e., genotypes will suppress the growth and/or survivorship of other genotypes or their growth and/or survivorship will be suppressed by other genotypes (Damgaard 2004, Cahill et al. 2005, Bossdorf et al. 2009, Baron et al. 2015, but they focus on 1 3 interactions among genotypes from different populations. Here we take advantage of 25th generation A. thaliana mutation accumulation (MA) lines derived from a Columbia accession founder that were planted under field conditions in years 2004 and 2005 with spatial records of each individual each year (e.g., Rutter et al. 2010;Rutter et al. 2018) to quantify the potential contribution of de novo mutations to the generation of intraspecific competitive interactions (Fig. 1). Although it is challenging to track newly introduced mutations into natural populations, an experimental approach utilizing MA lines allows for the investigation of the effects of spontaneous mutations on both the performance of the individual and the potential consequences for ecological processes.
These previous field-based A. thaliana mutation studies consistently demonstrated that there is a relatively high frequency of beneficial mutations. Thus, rather than being immediately purged by purifying selection, new mutations may contribute to the genetic composition of the local population. We focus on the neighborhood effects of MA lines on focal plants from the same MA line (within genotypes) or different MA lines (between genotypes) to quantify the evolution of intraspecific genetic variation through the accumulation of spontaneous mutations. We implement trait-based neighborhood models (Nottebrock et al. 2017a, b;Lachmuth et al. 2018) to understand ecological mechanisms that may govern interactions within and between MA lines and which might influence the fitness effects of mutations.
Specifically, the hypothesis here is that genotypic composition of A. thaliana MA lines in a spatial neighborhood can affect individuals' fitness or survival. Thus, we ask: (i) how spontaneous mutations can shape ecological processes i.e. competition, facilitation and selection, depending on the genetic neighborhood among MA lines of A. thaliana plants and (ii) whether higher or lower genetic similarity in the neighborhood can alter individuals' fitness and survival. Answers to these questions also allow us to consider both the role of mutations to contributing to population genetic variation and to speculate on the genetic origins of population and community dynamics, contributing to a fuller understanding of the maintenance of biodiversity.

Methods
MA lines are generated from a single nearly homozygous individual founder and cultivated via limited effective population number. In the case of A. thaliana, this occurs through single seed descent, resulting in Ne = 1. Thus, typical MA line cultivation results in an unbiased sample of mutation effects ranging from deleterious to advantageous, although lethal mutations are excluded (Lynch and Walsh 1998). Note that we do not expect lethal mutations to contribute to genetic variation associated with selection response and lethal mutations should only minimally contribute to community level dynamics. Each MA line accumulates independent spontaneous mutations. After the propagation of a set of MA lines through multiple generations, the genetic differences among the MA lines and between those lines and the founder reflect the input of mutation. Significant MA line effects for multiple traits, including performance and trait measures, were found under both field and greenhouse conditions (Rutter et al. 2010(Rutter et al. , 2018Roles et al. 2016). Each of the MA lines in our experiment is fixed for an average of 20 different sequence level mutations, single nucleotide mutations (SNMs) and indels combined (Ossowski et al 2010;Rutter et al. 2012;Weng et al. 2019). Sequencing also revealed no evidence of selection affecting the retention of mutations during accumulation (Weng et al. 2019, Monroe et al. 2022). To 1 3 better understand interaction effects within and between MA lines and founders we are referring to intra-genetic and inter-genetic effects respectively throughout the manuscript.

MA lines and field experiments
We used survival and seed set data of A. thaliana MA lines and the founder as assessed in field experiments in 2004 and 2005 from Rutter et al. (2010Rutter et al. ( , 2012Rutter et al. ( and 2018 planted in a randomized design (Fig. 1). Rutter et al. (2010Rutter et al. ( , 2012 planted seedlings of 100 MA lines and the founder at the four-leaf stage, approximately two weeks post germination, into a secondary successional field at Blandy Experimental Farm (BEF) in Virginia (39°N, 78°W). The four-leaf stage refers to an early vegetative developmental period when the rosettes are just developing. Each of the 100 MA lines was used to found up to five sublines to spread seed production across multiple maternal plants to reduce the contribution of maternal effects to progeny performance. Similarly, the premutation founder genotype was represented by six sublines. All seed in the experiments were generated in 2003. In each planting, 7504 individuals were planted, 7000 individuals of 100 MA lines (70 replicates per MA line, 14 replicates per subline) and 504 individuals of the founder (14 replicates per subline). The subline progeny or the 14 replicates for each subline represent the grandoffspring of each MA line. The planting environment corresponds to a spring ephemeral life-history, where plants germinate and complete the life-cycle in the spring. Although little vegetation was present at planting, by the time of harvest a mixture of biennials and perennial plants covered the field, overtopping the A. thaliana plants. We did not characterize the surrounding vegetation for our plots. Thus, we assess mutational contribution to intraspecific interactions against a background of a varying plant community.
The plot was arranged in 14 spatial blocks with each containing 12 sub-blocks ( Fig. 1b) (total plot area approximately 35 × 25 m). Each block included one seedling from each subline (n = 7504 total individuals). We used the spatial information of each individual within the described design and created a raster of all plant individuals with R packages (raster, maps, maptools). We used individual-based maps neighborhood matrices with exact spatial and trait information of each genotype. If all five sublines did not produce enough seedlings to distribute in all blocks, seedlings from other sublines of the same MA line were overrepresented in blocks to maintain the same overall number of plants per MA line. Plants were censused weekly for survival and were harvested by late May at senescence. In 2004 a total of 5915 individuals including 394 founders survived. In 2005, a total of 4506 individuals survived including 302 founders. Plants were oven dried and biomass was measured. All fruits produced by each plant were counted and in combination with the average seed production per five randomly selected fruits. Following a regression of the average number of fertile seeds per fruit and the length of the fruit, we used seed set as response variable. For our analysis, we used measurements of two response variables representing an important part of the life history of A. thaliana to calculate direct neighborhood effects on: a) the survival of individuals and b) individual seed set from plants that survived and produced fruits.

Statistical analyses
We use spatially explicit trait-based neighborhood models that include intraspecific genetic variation and phenotypic variation expressed by plant trait biomass to analyze competition and facilitation between individuals of A. thaliana. We analyzed plant survival rate and focal seed set measurements from years 2004 and 2005 separately to ensure environmental effects of each year. For each year, we considered all individuals as focal plants in the analysis based on individual-based neighborhood matrices to analyze (1) rate of plant survival to reproduction and (2) seed set of those plants that survived to reproduction. We analyzed neighboring plants to focal plants in a radius of 80 cm (small-scale) or 200 cm (large-scale) of a given focal plant as two spatial scales in the neighborhood analyses ( Fig. 1) to quantify selection, competition or facilitation between plants depending on their genotypic and phenotypic variation. These spatial scales represent the size of the subblock and the block respectively and captured most of the environmental variation.
We used extensions of linear mixed models (package lme4, Bates et al. 2014) in R ver. 3.5.2 (www.r-proje ct. org) to conduct neighborhood analyses of focal seed set and survival. To ensure analyses of neighborhood models were valid, we tested different error distributions of the data. We assumed binomial errors for the analyses of plant survival and Poisson errors for analyses of logit-transformed seed set, because we used the number of seeds produced per plant individual. The mixed models described interactions among plants by including neighborhood indices as explanatory variables at two spatial scales in separate models. Neighborhood indices are spatial density effects of surrounding neighborhood plants that affect focal seed set and survival. For each plant, we used the Euclidian distance between the focal plant and the neighboring plants to compute response effects of within MA line or between MA line neighbors in a given radius around focal plants (Nottebrock et al. 2017b). The number of neighboring plants for a focal plant ranged from 3 (if the focal plant was at the corner of a subblock) to 8 (if the focal plant was internal to the subblock). Moreover, we used a neighborhood index that accounts for the decline of neighbor effects with distance from the focal plant (Uriarte et al. 2010) and summed the amount of biomass from all individuals in a radius of 80 cm or 200 cm respectively by a Gaussian interaction kernel (Lachmuth et al. 2018;Nottebrock et al. 2017a, b;Damgaard 2004). We used random effects of block and subblock to correct for environmental variation between local heterogeneous conditions. Importantly, we correct for between MA line effects by including MA lines as a random effect. That is, we account for the fact that different MA lines may have different effects on a focal plant by treating these as random effects. In addition, including a random slope of biomass on each random intercept corrects for the intraspecific phenotypic variation depending on local conditions (sub-block and block differences) of plant focal individuals. Moreover, the weighted neighborhood density by plant biomass accounts for environmental variation between neighboring plants. A detailed model description of survival and seed set models can be found in S1 supplementary text (Supplementary Material S1).
Neighborhood matrices of all individuals (individual-based maps) were used to analyze the effect of intra-and inter-genotypic neighbors on survival and focal seed set with spatial interaction kernels of neighborhood (plant biomass) density. By incorporating different genotypes and phenotypic variation, we can quantify how important genetic variation is for neighborhood models and if the phenotypic variation explains spatial interactions between individuals. We assume the consequences of genetic differences to be larger between MA lines than between any MA line with itself, or the founder with itself. This is a valid assumption since each MA line differs from the other by approximately 20 + 20 = 40 mutations, while any two replicates within a MA line or the founder lines will differ by one generation, ≤ 2 mutations. On average founders should differ from the individual MA lines by roughly half the difference of the number of mutations differentiating MA lines. Thus, we simulated line effects from parameters derived from MA lines as random effects with the R package 'merTools' and the function 'plotREsim' in R 2018. Not surprisingly, because the founder performance was always the average of all MA lines, we found no difference between founder and MA line effects on a focal plant (see results). Consequently, in many of our analyses we refer to the founder line and MA lines as just lines.
We used the biomass and height of neighbors to calculate trait-based neighborhood indices (Goldberg and Fleetwood, 1987;Goldberg and Landa, 1991;Cahill et al., 2005). We fitted spatially explicit trait-based neighborhood models at two different spatial scales for response variables (survival and seed set) for each of the two and both years. Essentially, we asked how the neighborhood composition of A. thaliana plants measured as biomass and genotype (within the same MA line or from a different MA line) affected our response variables for the focal plant. To address our objectives, we first analyzed models with differential effect in which within all MA lines and founder had a different effect on survival (A1, A2, Table 1) and seed set (B1, B2, Table1) than between MA line and founder neighbors at small-scale (s) and large-scale (l). In addition, we analyzed models with neutral effects on survival (A1, A2, Table 1) and seed set (B1, B2, Table 1) that included total neighbor density without the split between within and between MA line and founder neighbors at small-scale (s) and large-scale (l). To this end, all models were fitted with two separate neighborhood indices that were calculated from intra-and inter-genotypic neighbors. To justify the inclusion of individual plant biomass as trait-values for interacting plants in the model, we used AICc to compare the models with and without the trait-proxy (Burnham and Anderson 2002). We found that models perform generally better including biomass as a trait-proxy and calculated trait-based neighborhood indices for similar or different genotypes (MA lines) from biomass density (ΔAICc > 2, Supplementary Material S2). All spatially explicit trait-based neighborhood models contained random effects of subblock nested in block at block scale and subblock scale on the intercept, MA line identity on the intercept and the focal trait-value (plant biomass) on the slope. Additionally, because direct environmental variables were not measured during the field experiments, we included in each model the individual's biomass to correct for environmental conditions for spatial autocorrelation. All variables are scaled and centered to assure comparability between predictor variables. Models of differential and neutral effects for 2004 and 2005 (Table 1, A1, A2, B1, B2) are fitted at small-scale (80 cm scale) and at large-scale (200 cm scale). Hereafter, the 80 cm scale models are referred to as "small-scale" models and the 200 cm scale models are referred to as "large-scale" models. Neighborhood indices, intra-and Differential models split the neighbor responses into within genotypes and between genotypes. Neutral models describe the overall neighbor response without differentiating between genotypes. All models include random effects of plant biomass on the slope, subblock or block and MA-line on the intercept (see Fig. 1 for design), and respectively depending on small (s) or large-scale (l) (for more details see method section)

Year 2004
Year 2005  B2 (s,l) Neutral intergenotypic variation and total variation of biomass density are included as inverse density variables (1/1 + density). We compared models of differential and neutral effects through likelihood ratio tests (LRTs).

Results
At

Weighted neighbor effects of biomass density on MA lines and founders for plant survival
At the smaller-scale, survival rate is larger when a focal plant is surrounded by individuals from different lines (MA or founder) than when surrounded by plants of the same line for both years 2004 and 2005 (model A1 and A2; Table 2 and Fig. 2a). This finding is demonstrated by the superior performance of the differential model relative to the neutral model (year 2004: LRT: χ 2 1DF = 5.21, p < 0.05; year 2005: LRT: χ 2 1DF = 14.73, p < 0.001). The differential and neutral models of survival at large-scale have only non-significant effects ( Table 2). In addition, comparing the AIC we found that models at large-scale perform worse than at small-scale for both 2004 and 2005 (

Weighted neighbor effects of biomass density on genotypes for seed set
At both small and large scales, the differential model performs better than the neutral model: at small scales ( Fig. 2a). The difference between within and between line effects indicates that neighbors that are from a different line have a stronger competitive effect on seed set than within line neighbors at small-scale. In contrast, at large-scale seed set increases when surrounded by individuals from different lines but decreases when surrounded by individuals within the same line for both years 2004 and 2005 (model B1 and B2; Table 2 and Fig. 2b). Comparing the AIC between small and large-scales, models at large-scale for 2004 and 2005 perform better (Table 2).

MA line and founder effects on plant survival and seed set
The distance kernel at small-scale in 2004 indicates a reduction of seed set (competition) whereas at large-scale are consistent with an increase of seed set (facilitation). Neighboring plants from all genotypes of A. thaliana at small-scale reduce 50% of focal seed set Table 2 The coefficient and AIC from spatially explicit trait-based neighborhood models of the Arabidopsis thaliana field experiment in the years 2004 and 2005 Italics describe the predictor variable (weighted density) that was used to predict all response variables of models, because weighted density are more significant than non-weighted density (see Supplementary Material) Models include intraspecific genetic variation defined as intra-genotyptic (within genotypes) and intergenotypic (between genotypes) variation. Models are shown at two different spatial scales: weighted subblock density (80 cm radius) and weighted block density (200 cm radius). All scales include variables of neutral effects (absence of intra-and inter-genotypic variation (total sum of all neighboring genotypes)) and differential effects (presence of intra-and inter-genotypic variation (split of neighboring genotypes in intra-and inter-genotypic genotypes)). Thus, neighbor identity is split into intra-and inter-genotypes as differential effects or are combined as neutral effects, which includes all neighbors. P-value levels < 0.05*, < 0.005**, < 0.001*** and > 0.05 ns   The estimation of genotype effects (MA lines and founder) simulated as conditional means and expressed as odds ratios show hierarchical orders of MA line and founder competitive effects at small-scale (Fig. 3). We found no significance of simulated competitive effects of all MA lines and founder calculated from survival and seed set models at small-scale (ANOVA, p > 0.1, Fig. 4a). In addition, we found no significant correlation between competitive effects of MA lines and founder at large-scale. In addition, we did not find selective effects of MA lines and founder on the survival at small-scale (ANOVA, p > 0.1, Fig. 4b).
However, we found strong correlation between competitive effects of MA lines and founder at small-scale and large-scale indicating a genetic trade-off (ANOVA, F = 460.07, p < 0.0001, Fig. 4c).

Discussion
We are able to quantify individual spatial interactions between and within genotypes (MA lines and founder) using a mechanistic spatially explicit trait-based neighborhood model. We demonstrate intraspecific genetic variation due to spontaneous mutations can shape competitive abilities of genetically different individuals of A. thaliana. Notably, these differences arose in just 25 generations of mutation and in the absence of natural selection. Furthermore, these differences are so strong that we are able to quantify variation in competitive effects within our analytical framework despite treating the composition of the surrounding vegetation as a component of our random plot differences, that is, we did not control for the specific environmental effects of non-Arabidopsis species. Many of the mutations differentiating the MA lines also appear in nature, contributing to A. thaliana genetic polymorphism (Weng et al. 2019;Monroe et al. 2022). Thus, it may not be surprising that these very same mutations appear to contribute to dynamics having ecological implications. Below we discuss how these scale effects provide insight on the evolutionary processes depending on standing genetic variation and potential ecological processes underlying community assembly and coexistence.

Maintenance of genetic variation and fitness effects
Seed-dispersal is limited in space because the A. thaliana fruit do not dehiscently explode as they do in related species (Hofhuis and Hay 2017), although a seed bank ensures some dispersal through time (Falahati-Anbaren et al. 2014). Limited seed dispersal suggests that A. thaliana plants will typically interact with related individuals. Consequently, a relevant question is how mutations may contribute to these potential interactions. At small-scale, competitive effects of different lines have opposed impacts on survival and seed set. Plant survival is higher when surrounded by neighbors from different lines (MA and founder) suggesting that genetic diversity introduced by de novo mutation should be promoted by a frequency dependent process (Delph and Kelly 2013). Effects on focal seed sets were reversed between small and large scales: intragenotypic neighbors have a stronger effect on survival at small scale, but inter-genotypic neighbors have stronger competitive effects on focal seed sets at small-scale, whereas at large-scale seed set is facilitated by inter-genetic neighbors. At large-scale, the intraline effect on survival turns into less competitive effects on seed set. In contrast, different genotypes show stronger facilitative effects within the population at large-scale, maybe because local populations of genetically variable plants are more competitive against other species. Testing the validity of this explanation would require future experiments that monitor the surrounding plant community and genetic variation of individual plants. Overall, these results demonstrate the complexity of quantifying the fitness effects of mutations when tested in nature. Likely depending on density and other specific environmental conditions, mutations may contribute to increased survival of the rare genotype and may contribute to the persistence of local populations competing with other species. It is important to note that the contribution of mutation to promoting genotypic effects among neighbors may be less important in outcrossing organisms with greater post zygote dispersal. However, even highly outcrossing plants often have limited dispersal such that close relatives can be concentrated on a small spatial scale (e.g., Fenster 1991).

Potential community implications for mutation competitive and facultative effects.
We quantified stronger statistical signals of effect sizes between lines than within lines. This result suggests that niche variation among individuals due to mutations may result in niche expansion or reduction (Ehlers et al. 2016 A. thaliana of different genotypes show higher competition than within genotypes, when the survival of inter-genotypic neighbors is higher, which is perhaps due to the expansion of their individual niches due to more variation in plant traits. Antagonistic interactions between genotypes on different spatial scales may result in genetic trade-offs when mutations are advantageous or deleterious, which over many generations could provide a strong stabilizing force maintaining both species and genetic diversity in this system and may promote coexistence (Lankau 2008). Thus, spontaneous mutation could also provide an additional evolutionarily stabilizing effect on community dynamics. Our study demonstrates the potential for a fundamental evolutionary process, mutation, to have profound consequences for community structure. When A. thaliana is rare, selection would favor genotypes that compete well and enhance the population's survival relative to that of its interspecific competitor (Lankau 2009). In contrast, when A. thaliana is common and the interspecific competitor is rare, selection may favor A. thaliana genotypes that are good intraspecific competitors. This trade-off may result in a decrease in the interspecific competitive ability of A. thaliana, effectively increasing the competitor's fitness relative to that of A. thaliana (Lankau 2009, Lankau andStrauss 2007). However, the genetic trade-off between intra-and interspecific competitive abilities due to mutation remains unknown because our dataset only includes performance of A. thaliana individuals without quantifying the surrounding intraspecific environment (Chesson 2000;Adler et al. 2007).

Competitive hierarchies of MA lines and founder lines
Our result demonstrates scale dependent competition and facilitation (Nottebrock et al. 2017b) providing further evidence that in a relative short evolutionary time scale spontaneous mutations may change the competitive hierarchies between founders and specific MA lines due to advantageous or deleterious mutations (Rutter et al. 2010). In contrast to Masclaux et al. 2010, we found differential responses to similar genotypes vs different genotypes in A. thaliana depending on the neighbor effect on the plant survival or seed set. However, the interaction between similar genotypes and different genotypes seems to depend on the strength of the competitive abilities of the accession. Comparisons between MA lines and founder demonstrate that competitive hierarchies follow different orders and competitive effects occur at different life-history stages at different spatial scales. Ecoevolutionary processes might reflect spatial selection for diversified genotypes because of niche evolution and individuals of different genotypes that survived to reproduction have stronger competitive abilities (Ehlers et al. 2016). Moreover, the hierarchical order of competitive effects between MA lines and founders shows strong variation. Because our design reflected MA lines for only the Columbia founder, we must, however, be cautious in making general attributions of mutation effects on population dynamics.

Eco-evolutionary dynamics
Populations can adapt evolutionarily to their environment on a time scale equivalent to that of ecological processes and affect present day species interactions, i.e., coexistence (Fussmann et al. 2007). Yet, sustained, and rapid climate change could deplete genetic variance faster than it can be replenished by mutation (Fournier-Level et al. 2016). In our study, we show that only 25 generations are enough to influence present day plant interactions of A.
thaliana in the presence of other plant species. Possible effects of mutations on trait mechanisms might reflect the increased seed set of A. thaliana at large-scale, because of the presence of another plant species allowing the establishment and growth of A. thaliana (Ehlers et al. 2016). Many traits can influence competition and facilitation between individuals of different genotypes (e.g., Chapin et al. 1993;Caradus and Woodfield 1998;Hausmann et al. 2005). Intraspecific competition between A. thaliana individuals tends to be higher than between interspecific competitors (Lankau and Strauss 2007;Van Dam and Baldwin 1998;Tiffin 2002;Strauss and Irwin 2004). Such spatial mechanisms might be described by the scale difference of competition at small-scale and facilitation at large-scale between different A. thaliana genotypes. Competition and facilitation are therefore dependent on the extension or reduction of individual niches, which is based on trait changes due to spontaneous mutations. However, we are not able to directly link our results to benefactor or antagonistic plants, because we focused our experiment on A. thaliana densities and fitness components. Nevertheless, genotypic-specific interactions are well studied. Several studies of A. thaliana MA lines demonstrate mutation effects on plant traits such as leaf weight, flowering time, trichome density, number of leaves at bolting, duration of vegetative and flowering period (Camara and Pigliucci 1999;Rutter et al. 2010;Stearns and Fenster 2016). It is conceivable that the increase in variance of these traits through spontaneous mutations could contribute to the evolution of competitive hierarchies, which can happen over a short evolutionary time scale.

Conclusion
Rapid evolution of A. thaliana intraspecific competitive interactions due to spontaneous mutation alone appears to be of the size and direction necessary to promote the maintenance of genetic variation. Furthermore, mutation-based evolution of intraspecific competitive interactions may have consequences for population and community dynamics. In particular, our findings about the influence of mutations on competitive hierarchies among phenotypes in a given environment (Levine and HilleRisLambers 2009) may demonstrate that the discovery of the underlying eco-evolutionary nature of competition in A. thaliana supports abiotic or biotic ecological processes to form plant niches (Hutchinson 1957;Bruno et al. 2003;Colwell and Rangel 2009).Moreover, selection and trait evolution favor population survival of A. thaliana in the wild, which stresses the importance of combining spatial ecological and evolutionary mechanisms for our understanding of population and community dynamics to support the maintenance of biodiversity (Violle et al. 2017). Finally, non-neutral intraspecific processes may determine species coexistence, because genetic diversity is promoted by having stronger competitive abilities at small-scale and stabilizing population survival at large-scale . Depending on the time of environmental changes, species may adapt to environmental change by shifting their fundamental niches (Clark 2010). The combination of intraspecific genotypic variation and spatial interactions might advance our understanding of community dynamics, especially of rapid evolution (Koch et al. 2014;Turcotte and Levine 2016). Figure 1. a 100 Mutation accumulation lines (here example of four MA-lines) of Arabidopsis thaliana derived from a single founder (Columbia) after 25 generations with a mutation rate of ~ 0.7 per generation (Weng et al. 2019). Each MA line has a unique genotype that differs from the founder. Fundamental niche of individual genotypes (different dashed and dotted lines) and founder genotype (black line) are distributed within the hypothetical fundamental niche of a population of A. thaliana (bold black line). Axes represent niche axis (resource-use, environmental factors, etc.). b Planting design of A. thaliana in a natural field experiment showing spatial biomass variation of individual survivors in 2004. Each block consisted of 12 subblocks with each subblock consisting of 45 individuals planted in a rectangular design with planting distances of 10 cm. Thus, each subblock consisted of 6 columns and 7.5 rows of plants with plants within a row 10 cm apart and rows 10 cm apart. Each subblock was 1 m from the nearest subblock within a block and 2 m separated each block.
Differential models split the neighbor responses into within genotypes and between genotypes. Neutral models describe the overall neighbor response without differentiating between genotypes. All models include random effects of plant biomass on the slope, subblock or block and MA-line on the intercept (see Fig. 1 for design), and respectively depending on small (s) or large-scale (l) (for more details see method section).
Models are shown at two different spatial scales: weighted subblock density (80 cm radius) and weighted block density (200 cm radius). All scales include variables of neutral effects (absence of intra-and inter-genotypic variation (total sum of all neighboring genotypes)) and differential effects (presence of intra-and inter-genotypic variation (split of neighboring genotypes in intra-and inter-genotypic genotypes)). Thus, neighbor identity is split into intra-and inter-genotypes as differential effects or are combined as neutral effects, which includes all neighbors. p-value levels < 0.05*; < 0.005**; < 0.001*** and > 0.05 ns

Data Availability
The spatial data will be uploaded upon acceptance to the dryad database. Genetic data can be found at NCBI Short Read Archive (SRA) with the accession number of SRP133100 and https:// doi. org/ 10. 25386/ genet ics. 64560 65.