Plant root architectural traits mediate a trade-off between suppression and tolerance of competitors

Plant root architectural traits mediate a trade-off between suppression and tolerance of competitors | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecology and Evolution This is a preprint and has not been peer reviewed. Data may be preliminary. 27 June 2025 V1 Latest version Share on Plant root architectural traits mediate a trade-off between suppression and tolerance of competitors Authors : Hugo Salinas 0000-0002-1734-2977 [email protected] , Erik Veneklaas , Elizabeth Trevenen , and Michael Renton Authors Info & Affiliations https://doi.org/10.22541/au.175102417.71298991/v1 Published Ecology and Evolution Version of record Peer review timeline 246 views 148 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Plants’ competitive ability involves both suppressing the growth of neighbours (competitive effect) and resisting or tolerating their suppression (competitive response). Competition for below-ground resources must be related to the ability of plants to acquire these resources, which is mediated by roots and their morphology. However, the role of root architecture in the competitive ability of plants, and in the possible trade-offs among growth potential, competitive suppression and competition tolerance involved, has not been extensively studied. We used a functional-structural root model coupled with an evolutionary algorithm to simulate the evolution of root architectures in five scenarios with different plant densities. We asked (1) does selection under different intraspecific competition scenarios result in different root architectures? and (2) do differences in these architectures result in differences in growth potential and competitive ability, i.e., competitive effect and response? Our results indicate that as the number of neighbours increases, selection on traits such as branching angles, gravitropism and branching probability result in root architectures that are deeper and sparser, resulting in lower shoot biomass. We also found a difference in competitive ability among architectures, with a trade-off between resistance to competition and maximum productivity (maximum shoot biomass): there is not a globally optimal strategy. Our findings have implications for management of invasive species, improvement of crop yield, and the study of species co-existence. Plant root architectural traits mediate a trade-off between suppression and tolerance of competitors Salinas Hugo 1 *, Veneklaas Erik J. 1 , Trevenen Elizabeth 1 , and Renton Michael 1 1 School of Biological Sciences, University of Western Australia, 35 Stirling Highway, Crawley, Perth, WA, 6009, Australia *Corresponding author, [email protected] Acknowledgements This work was supported by resources provided by the Pawsey Supercomputing Research Centre with funding from the Australian Government and the Government of Western Australia. Author contributions Conceptualization HS MR, Data Curation HS, Formal Analysis HS MR, Funding Acquisition MR, Investigation HS MR ET EV, Methodology HS MR ET EV, Project Administration HS MR, Resources MR, Software HS MR, Supervision MR, Visualization HS, Writing – Original Draft HS, Writing – Review & Editing HS MR ET EV Data availability statement Code and data are provided for peer-review in the figshare repository https://figshare.com/s/e26de7ad3632ca4e88b5 and will be published at the Zenodo repository upon acceptance. Abstract Plants’ competitive ability involves both suppressing the growth of neighbours (competitive effect) and resisting or tolerating their suppression (competitive response). Competition for below-ground resources must be related to the ability of plants to acquire these resources, which is mediated by roots and their morphology. However, the role of root architecture in the competitive ability of plants, and in the possible trade-offs among growth potential, competitive suppression and competition tolerance involved, has not been extensively studied. We used a functional-structural root model coupled with an evolutionary algorithm to simulate the evolution of root architectures in five scenarios with different plant densities. We asked (1) does selection under different intraspecific competition scenarios result in different root architectures? and (2) do differences in these architectures result in differences in growth potential and competitive ability, i.e., competitive effect and response? Our results indicate that as the number of neighbours increases, selection on traits such as branching angles, gravitropism and branching probability result in root architectures that are deeper and sparser, resulting in lower shoot biomass. We also found a difference in competitive ability among architectures, with a trade-off between resistance to competition and maximum productivity (maximum shoot biomass): there is not a globally optimal strategy. Our findings have implications for management of invasive species, improvement of crop yield, and the study of species co-existence. Keywords Plant competition, Root architecture, FSPM, plant traits, plant evolution, Donald’s ideotype. 1 Introduction Plants will rarely encounter optimal low-stress, low-disturbance, or low-competition conditions, and thus have evolved strategies to withstand non-optimal conditions during their growth. Grime (1977) identified three extremes of strategies that evolve under different levels of stress and disturbance: the ruderal strategy (low stress and high disturbance intensity), the stress-tolerant strategy (high stress and low disturbance intensity), and the competitive strategy (low stress and low-disturbance intensity). These strategies can be associated with certain traits. For example, plants with the stress-tolerant strategy tend to have long-lived, small, evergreen leaves (Grime, 1977). Under the competitive strategy, plant maximize fitness in the presence of competitors. As with stress and disturbances, competition intensity varies and different competition intensities should result in the selection of different traits, associated with different strategies. Such traits cause differences in competitive ability, which is defined a species’ ability to suppress the growth of neighbouring plants (competitive effect) and to resist suppression by neighbours (competitive response) (Goldberg, 1990, 1996; Wang et al ., 2010). Studying differences in species’ competitive abilities is critical for understanding plant community dynamics, as it is expected that differences in competitive ability eventually lead to species displacement in communities (Hardin, 1960; Hart et al., 2018). These differences can also affect yield in agricultural systems (Tokatlidis, 2017) and have implications for the management of invasive species (Joshi et al., 2014). In general, traits that increase a plant’s ability to deplete resources, thus making them unavailable to neighbours, increase its competitive effect and traits that allow plants to tolerate or avoid resource depletion by neighbours increase their competitive response (Goldberg, 1990). Above-ground plant traits such as wood density, and specific leaf area have been shown to affect the competitive interactions among trees worldwide (Kunstler et al ., 2016). The effect of below-ground traits on competitive ability is less understood (Cahil and Lamb, 2007). Roots are the organs that allow plants to forage for below-ground resources, including water and nutrients (De Kroon et al ., 2009; York et al ., 2013). How effectively roots access these resources will directly affect plant fitness (Moreau et al., 2022), and likely a plant’s competitive ability. Previous theoretical research found that traits important for optimal resource acquisition include root branching angles (Ho et al., 2004), shoot-root biomass allocation (Iwasa and Roughgarden, 1984; Ikegami et al., 2008), and root length per soil depth interval (Jung et al ., 2019). Previous work has tended to study the effect of individual root traits on plant performance, but the efficiency of a root system relies on the interaction of multiple traits, which may have synergistic or antagonistic effects (York et al., 2013). Since the whole root system is under natural selection, metrics reflecting properties of the overall root system should be more indicative of plant efficiency and competitive ability than individual traits (Fitter et al., 1987). Root architecture is the spatial configuration and arrangement of roots within the root system (Fitter, 1987; Lynch, 1995; Manschadi et al ., 2008). It is the result of multiple root traits that are often individually studied and considered important (Cahil and Lamb, 2007, Ravenek et al ., 2016). Root architecture is determined by a genetic component that is modified by environmental signals (Hodge, 2009). Thus, resource limitations should exert a selective pressure on root architectural traits to maximise performance under different conditions. For example, in dry soils, deep roots with greater branching in deeper soil sections is most efficient, whereas in wet soils, having shallower roots with many branches is a better strategy (Draye et al ., 2010). Root architecture may therefore be a better indicator of plant efficiency and competitive ability than individual traits. However, its effects on a plant’s competitive ability have not been extensively evaluated. While it is informative to study optimal rooting strategies under specific conditions, it is important to understand how optimal rooting strategies perform under different conditions, as it is unlikely that a strategy is optimal in every condition. Previous research has found that traits that maximise growth when plants are alone are different to those that maximise growth in competition, for example in plant and leaf size of cereals (Hamblin and Donald, 1974) and root biomass of wheat (Zhu and Zhang, 2013). This relates to what Van Der Bom et al . (2020) called ‘characterising the ecological niche’: understanding and accounting for trade-offs between root architecture, environmental settings, and performance. Simulation models can help to overcome some of the challenges involved in evaluating root systems to study the relationship between root morphology and performance (Wasson, 2012). For example, Javaux et al . (2008) used a 3D model of roots to show that water extraction profiles may not correlate to root density if root radial conductance is large. Renton and Poot (2014) found that specialised root systems with deeper roots evolve in dry shallow soils where deep water is occasionally available. Rangarajan et al. (2018) found that fitness depends on the interaction of root traits such as root angle and basal root whorl number; the fitness landscape of plans is complex. It is also possible to use models to study the relationship between root traits and competition; for example Rubio et al. (2001) used a root model representing three bean genotypes with shallow, intermediate or deep basal roots, to show that the effect of competition for phosphorus is greater among architectures that explore the same regions of the soil. Understanding of the relationship between the competitive ability of plants and root system architecture is limited, especially in the context of evolution. To help address this, we aimed to study how root architectures that result from selection under different competition conditions vary in their competitive ability. Specifically, we asked two questions. (1) Does selection under different competition scenarios result in different root architecture traits and root morphology? We hypothesized that scenarios of increasing competitive pressure will result in the selection of root traits that result in deeper root systems that better avoid resource depleted sections in the shallow soil. (2) Can differences in root architecture traits result in differences in growth potential, and competitive effect and response (tendency to suppress growth of neighbours, and resistance to suppression)? We hypothesized that root architecture trait differences would result in different access to sections of the soil and thus different growth potentials. Moreover, architectures that maximise growth potential will be wider and denser and will therefore overlap more with competitors and thus have a higher competitive effect and lower competitive response (be less tolerant of competition). 2 Methods We use the individual-based model root model developed by Renton et al. (in press), adapted from Renton and Poot (2014). This model simulates plant roots and their growth as they forage for water in the soil. 2. 1 Plant structure In the model, plants are composed of two biomass compartments (g): below-ground (root) and above-ground (shoot and reproductive organs). Root biomass has an explicit three-dimensional structure, while above-ground biomass does not. Each root system is composed of branches. Each branch is composed of a set of sequentially joined linear segments, each of the same size ( SegmentLength ). Plants are initialised with a single root branch, with a single root segment heading downward and no shoot biomass. 2.2 Model dynamics The model simulates the growth of a set of plants over discrete time-steps. At each timestep, the soil recharges, then plants take up water, and then they grow. Plants gather water from the soil; the amount of water they obtain is the sum obtained across the voxels they occupy. Water is then converted into biomass, a fraction of which is allocated to above-ground and the rest to below ground biomass. Roots grow in length by adding more segments, and by branching with a certain probability, this is controlled by architecture parameters (Table 1). For further details on how structure, resource uptake and growth are represented in the model, see Renton and Poot (2014) and Renton et al. (2025). The same values for model constants were used for all simulations in this study (Table 2). Roots grow inside a three-dimensional 20 × 20 × 140 cm soil volume (world) comprised of 10 × 10 × 70 cubic voxels (x, y, z), each with a side length of 20 mm. Each voxel can hold a fraction of its volume in water: they have a moisture content ranging from 0 mm 3 to 20 3 × whc = 800 mm 3 of water. The moisture content in the soil is affected by uptake by the plants, diffusion, evaporation, and recharge. Diffusion is simulated by moving part of the water content in a voxel to the voxels surrounding it. The diffusible water in a voxel is a fraction diffuseperday of its total water content, and 1/6 of this amount moves to each of its six neighbouring voxels. There is no diffusion up from the top layer or down from the bottom layer. Evaporation removes a fraction of the moisture of the top layer of soil voxels. Recharge fills the water content of every voxel to its maximum capacity. Border conditions are avoided by using a wraparound effect on the x and y dimensions for diffusion and the roots. 2.3 Genotype and species Each plant has a ‘genotype’, where a genotype means a set of parameter values that affect its growth (Table 1). These parameter values do not change during the growth of the individual plant, but may vary between individuals. Two individual plants with the same genotype will likely have similar root architectures but will vary due to model stochasticity and interactions with their environment. A set of similar genotypes that are the result of a single evolutionary simulation (see below) is called a ‘species’ (Section 2.5). 2.4 Competition scenarios We defined five scenarios of varying competition intensity (Fig. 1). These considered a target plant developing alone or surrounded by one to four neighbours. The target plant was placed in the middle of the world. The neighbours were placed 50 mm from the target plant (Fig. 1). The neighbours had the same genotype; that of the target plant in the evolutionary simulations, but could be different to the target plant in the final competition experiments (see below). jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf 2.5 Evolutionary algorithm We used an evolutionary algorithm (Ashlock, 2006) to find root architectures that result in efficient foraging under each of the five competition scenarios. Above-ground biomass was used as the measure of fitness since it includes reproductive organs. The evolutionary algorithm consisted of defining a population of N = 15 genotypes, each corresponding to a set of parameter values chosen randomly within the sampling ranges in Table 1. This population evolved across 500 generations. At the beginning of each generation, the genotypes were duplicated and added to the population with a random change in all its parameter values (see supplementary material). Then, for each of the 30 genotypes in the population (15 original plus 15 copies with mutations) we performed a growth simulation under a competition scenario, with the genotype of any neighbours set to be the same as that of the target plant. The growth simulations ran for 150 time-steps/days, after which above-ground biomass of the target plant was recorded. The 15 genotypes with the highest above-ground biomass of the 30 became the starting population for the next generation. For each competition scenario (S 0 , S 1 , S 2 , S 3 , and S 4 ), we performed five independent replicates of this simulated evolution process. We summarize the differences in root parameters across time and scenarios with a nmMDS on all the root parameter values for a subset of the generations. For each competition scenario, the final populations resulting from the replicate evolutionary simulations were combined, giving a total of 150 genotypes, and the 30 genotypes with the highest final above-ground biomass were extracted. We thus obtained one group of 30 genotypes for each of the five selection scenarios; we call these genotype groups ‘species’: with species Sp 0 , Sp 1 , Sp 2 , Sp 3 and Sp 4 corresponding to scenarios S 0 , S 1 , S 2 , S 3 , and S 4 respectively. When it was required to simulate the growth of a plant from a given species, one of the 30 genotypes from the corresponding species was randomly assigned. 2.6 Root morphology comparisons To compare the morphology of roots across species we performed simulations of plants growing alone. At the end of each 150 time-step simulation we collected morphological measures: maximum root horizontal length (mm), root depth (mm), root biomass (g), total root length per unit of biomass (mm g -1 ), number of branches per root length (mm -1 ), root density per voxel (g voxel -1 ) and fractal dimension (Tatsumi et al., 1989). We used a Principal Components Analysis of the standardized measures to analyse the similarities of the root morphologies corresponding to each species. 2.7 Competition experiments Finally, we used virtual competition experiments to evaluate the performance (final shoot biomass) of the species when grown alone or in the presence of neighbours. We used a factorial experimental design varying the species of the target plant (Sp 0 to Sp 4 ), the species of the competing neighbours (Sp 0 to Sp 4 ) and the number of neighbours (0 to 4), resulting in (5×5×4) + 5 = 105 virtual experiments. These experiments thus considered the same competition scenarios as the evolution simulations (see Section 2.2), but with varying species of the target plant and neighbours. 100 replicates of each of these 105 experiments were performed. These experiments again ran for 150 time-steps. Only the final biomass of the target plant was considered. 3 Results 3.1 Evolutionary algorithm Mean above-ground biomass of plants increased over the generations of the evolutionary runs, indicating that the evolutionary simulations were successfully selecting for root architectures that better optimized resource uptake under the different competition scenarios. Different ranges of biomass were reached in different competition scenarios, with greater biomass in scenarios with fewer competitors (Fig. 2). The fitness landscapes of the parameter values differed across scenarios and included patterns such as multiple equally optimal peaks (Fig. S1 A), mostly flat landscapes (Fig. S1 B) and one clear optimal peak (Fig. S1 C). Across generations the genotypes from the same scenario and evolutionary run tended to be more similar to each other than to those in other scenarios and runs, as shown by points with the same shape and colour tending to be closer with increasing generation in Figure 3. 3.2 Root morphology comparisons The evolved species varied in their root architectures, with a gradient in root morphology according to the competition scenario in which genotypes were selected, whereas plants of the same species were morphologically similar to each other even across replicate evolutionary runs (Figure 2 and 4). Selection under less competition resulted in higher root biomass, root length, fractal dimension, number of branches, and lower root depth. Root systems from each of the species clearly differed in the sections of the soil that they occupy. Sp 0 tends to have more root biomass in the shallow sections of the soil, and close to its base root (Fig. 4), while Sp 4 has sparser deeper roots (Fig. 4). 3.3 Competition experiments The maximum mean above-ground biomass (36.7 g) was that of plants from Sp 0 when growing alone, while the lowest (12.0 g) was also from Sp 0 but under competition with four neighbours from Sp 0 . For each species, plants had maximum above-ground biomass when they grew without competition (Fig. 5). Above-ground biomass was lower when competing with a higher number of neighbours and when competing with species selected under less competition (such as Sp 0 and Sp 1 ). Intra-specific competition with four neighbours reduced the biomass of Sp 0 from 36.7 to 12.0 g on average, while the same level of intra-specific competition reduced the biomass of Sp 4 from 22.6 to 16.6 g (Fig. 7). 4 Discussion Our results support our first hypothesis that scenarios of increasing competitive pressure will result in the selection of root traits that result in deeper root systems. We found that species that evolved under the same competition scenario converged to similar morphological root traits, even across different replicates of the evolutionary algorithm, and that these differed from plants developed under different competition scenarios (Fig. 3 and 5). This indicates that there were certain root architectures that performed better in each of the competition scenarios, as indicated by the different fitness landscapes across scenarios (Supplementary material). Species evolved strategies to optimize the trade-off between maximising water extraction whilst minimising investment into root biomass, with these strategies forming a gradient corresponding to the density of neighbours. At the high density extreme of this gradient, represented by Sp 4 , roots systems were selected to be less dense and with deeper roots to avoid allocating root biomass to soil sections that would be under high competition (Fig. 4 and 5). At the other extreme of this gradient of species, exemplified by Sp 0, the lack of competition resulted in the selection of a denser and shallower root system, as long deep roots were not needed (Fig. 4 and 5). Our results also support our second hypothesis that root architectures that maximise growth potential will be denser and therefore will overlap more with competitors and thus will have a higher competitive effect and lower competitive response. For all species, target plants had lower shoot biomass when competing with Sp 0 , and higher shoot biomass when competing with Sp 4 . This indicates that Sp 0 had a higher competitive effect, i.e., a greater ability to suppress the growth of neighbours. This finding is consistent with a review of 72 studies on herbaceous plants (Garbowski et al. , 2020), which found that specific root length was correlated with higher competition effect due to a rapid depletion of resources that negatively affected neighbours. This result is also consistent with the fact that, more generally, plant size has been identified as a main indicator of competitive effect (Cahill and Lamb, 2007). Our results also indicate that the species had a different competitive response, or resistance to competition. For example, under the highest competitive pressure (Vs. 4 individuals of S 0 ), Sp 0 had an average biomass of 12.0 g, whereas Sp 4 had an average of 14.4 (Fig. 6). Therefore, Sp 4 had a greater resistance to competition than Sp 0 . Similarly, in the experiments of Semchenko et al. (2018) on 26 temperate grassland species, plants with deeper roots, and lower specific root length were more resistant to competition. Species also differed in their growth potential (shoot biomass when growing alone), with Sp 0 having the highest and Sp 4 the lowest. The selection of traits that increased growth potential (higher shoot biomass in the absence of competition) seems to grant these plants a higher suppression ability. However, the trade-off for these traits is a reduced performance under competition. A similar trade-off between strategies that maximise growth potential and resistance to competition has been found in a game-theoretical analysis by Zhang et al. (1999). Using in-situ permanent plots, Tracey and Aarssen (2011) found a trade-off between minimum and maximum reproductive plant size indicating that the higher the potential size of plants, the more they are vulnerable to competition. Our findings indicate that a potential mechanism underlying this trade-off is differences in root architecture. This trade-off indicates that the efficiency of a root architecture depends on the context in which the plants develop: there is not a globally optimal strategy. The gradient of strategies that evolved reflect two avenues to greater fitness: growth potential (high above-ground biomass when growing alone) and resistance to competition (limited reduction in above-ground biomass due to competition). Species S 0 has the highest growth potential but the lowest resistance to competition. The maximisation of potential growth must involve traits conflicting with those needed to maximise resistance to competition (Schluter et al., 1991). This is to be expected as the peaks of the fitness landscapes already indicate conflicting optimal values for the root traits. Such trade-offs underpin well-known ecological strategies (Grime, 1977). The pioneering conceptual theory of Donald (1968) suggests that genotypes with a “communal type” will have lower yield in isolation but will have a higher stand yield than plants with a high yield in isolation, suggesting that competition is the mechanism of the trade-off. Our “communal type” (Sp 4 ) has lower shoot biomass than any of the other species when grown alone but performed better under high competition. The main ecological implication of our findings is that root architectures can cause density- dependent effects on plant fitness. In a community composed of Sp 0 and Sp 4 , plants from Sp 0 attain more biomass when growing under low density and should increase their population size. Then, as high intra-specific competition heavily affects Sp 0 , there would be a reduction in population size. On the other hand, Sp 4 , would experience lower average growth rate, but could resist higher competition before experiencing population size reduction. General theory suggests that species coexistence will be facilitated if the effect of intraspecific competition is stronger than that of interspecific competition (Chesson, 2000; Adler et al., 2018). Our results thus suggest that differences in root architectural traits could favour species coexistence. The results also suggest that some root architectural traits could make species more effective invaders, as suggested by Blank (2010). Our theoretical approach allowed us to simulate below-ground competition and to conclude that differences in root architecture can affect the competitive ability of plants. Our species can be ranked across one gradient of strategies: different levels of competition. Similar gradients have been described for other traits. For example, the ‘leaf economic spectrum’ has species with a rapid acquisition and turnover of leaf carbon at one end, and species that efficiently present the carbon they acquire more slowly at the other (Wright et al 2004, Laughlin et al 2010). Bergmann et al . (2020) argued that as below-ground resource uptake may involve collaborations with other organisms, root strategies may be better understood as a multidimensional space. It remains to be studied how root architectures would differ when multiple factors drive their evolution. For example, architecture traits can also affect mycorrhizal colonization (Li et al. , 2017), which has important consequences for plant fitness. Competition for belowground resources that likely have different spatial distributions and dynamics, e.g. water vs. nitrogen vs. phosphorus should result in the selection of other strategies with other potential trade-offs (Van Der Bom et al. , 2020). 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Colours indicate the competition scenario; this colour key is used in other figures to indicate the five different species (Sp 0 - Sp 4 ) that result from the five competition scenarios.Figure 2: Mean shoot biomass (g) per population of 20 plants across the 500 generations of the evolutionary simulations. Each line corresponds to a replicate run of the evolutionary algorithm. Figure 3: nmMDS showing similarities/dissimilarities of the 10 evolvable root parameters across generations of the evolutionary simulation. The data correspond to a subset of 10 genotypes per 15 evenly spaced generations per run. The different colours indicate the competition scenario in which the genotypes evolved. The shade of the colour indicates the generation, ranging from 0 (black) to 500 (colours in legend). Symbols indicate to which of the 5 evolutionary runs a genotype belonged. Figure 4: Examples of the root architecture of the five species (Sp 0 - Sp 4 ) selected at the end of the evolutionary simulations under the five competition scenarios (S 0 - S 4 ). For these examples, a single plant of each species grew alone without competition for 150 days.Figure 5: PCA of emergent root morphological measures of the species evolved at the end of the evolutionary simulations. Each point represents a single individual plant grown alone without competition for 150 days. Colours represent the species of the plant (Sp 0 -Sp 4 ); different symbols indicate the five different replicate model runs in which the genotypes were selected. The morphological measures used for the PCA were standardized and are: maximum root horizontal length (mm), root depth (mm), root biomass (g), total root length per unit of biomass (mm g -1 ), number of branches per root length (mm -1 ), root density per voxel (g voxel -1 ) and fractal dimension.Figure 6: Above-ground biomass for the competition experiments. The mean of the target plant from 50 repetitions of each experiment is shown. Columns correspond to the species of the target plant, and rows to competition scenario, differing in species and number of neighbours .Figure 7: Mean above-ground biomass for plants of species Sp 0 -Sp 4 growing with 0-4 intra-specific neighbours. Lines represent means across 50 repetitions per experiment, and shaded bands are the 25 th and 75 th quantiles. rotang Angle of horizontal rotation from one root lateral to the next [0, 1.5] branchang Angle of vertical rotation from one root lateral to the next [0, 1.5] gravitrop Strengh of gravitropism [0, 0.3] basezonelength Length of the base zone [5, 30] basezonep Probability of branching in base zone [0, 1] maxorder Maximum root order [2, 8] orderweightings Priority of growth per root order [0.5, 2 ] kshoot1 Controls allocation to shoot biomass per allocable biomass [-5, 5] kshoot2 Controls allocation to shoot biomass per allocable biomass [-0.1, 0.03] Table 1: Evolvable architecture parameters and initial value sampling range for the evolutionary algorithm. waterToBiomass Biomass obtained per volume of water 0.00001 g mm -3 biomassPerVolume Biomass per unit of root tissue volume 0.001 g mm -3 diffuseperday Proportion of diffusible water per voxel 0.1 waterTransEffiiciency Efficiency of water transport from the roots 0.002 waterUptakePerRootLength Potential volume of water uptake per mm of root length 10 mm -3 maxElongationRate Maximum daily root elongation 5 mm SegmentLength Length of root segment 2 mm soilvoxelsize Side length of soil voxel 20 mm Evaporation rate Fraction of water content lost to evaporation in the top soil layer 0.5 whc Soil water holding capacity, fraction 0.1 jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Table 2: Model general constant parameter values Information & Authors Information Version history V1 Version 1 27 June 2025 Peer review timeline Published Ecology and Evolution Version of Record 22 Jan 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Ecology and Evolution Keywords evolutionary ecology plants terrestrial theory Authors Affiliations Hugo Salinas 0000-0002-1734-2977 [email protected] The University of Western Australia School of Biological Sciences View all articles by this author Erik Veneklaas University of Western Australia View all articles by this author Elizabeth Trevenen The University of Western Australia School of Biological Sciences View all articles by this author Michael Renton University of Western Australia View all articles by this author Metrics & Citations Metrics Article Usage 246 views 148 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Hugo Salinas, Erik Veneklaas, Elizabeth Trevenen, et al. Plant root architectural traits mediate a trade-off between suppression and tolerance of competitors. Authorea . 27 June 2025. 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