# Variations in heterotrophic seedling growth parameters of 28 plant species and their relationships with seed traits

Elias Soltani, Keyvan Maleki, Nathalie Colbach, Antoine Gardarin, Jay Ram Lamichhane

Lamichhane

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## To cite this version:

Elias Soltani, Keyvan Maleki, Nathalie Colbach, Antoine Gardarin, Jay Ram Lamichhane. Variations in heterotrophic seedling growth parameters of 28 plant species and their relationships with seed traits. Environmental and Experimental Botany, 2025, 236, pp.106170. 10.1016/j.envexpbot.2025.106170. hal-05094244

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https://hal.inrae.fr/hal-05094244v1

Submitted on 2 Jun 2025

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Research paper

# Variations in heterotrophic seedling growth parameters of 28 plant species and their relationships with seed traits

Elias Soltani<sup>a, ●</sup>, Keyvan Maleki<sup>b</sup>, Nathalie Colbach<sup>c, ●</sup>, Antoine Gardarin<sup>d</sup>,
Jay Ram Lamichhane<sup>e, *, ●</sup>

## ARTICLE INFO

Keywords:

Arable cropping systems

Annual plants

Inter- and intra-specific variation

Seedling emergence

Temperate climates

Thermal time dynamics

## ABSTRACT

The timing of seedling emergence, influenced by heterotrophic growth, is a critical determinant for the competitive success of annual plants in arable cropping systems under temperate climates. While the heterotrophic growth phase is crucial for seedling establishment under field conditions, it has been much less explored compared to other growth phases. We address this gap by: i) analyzing the heterotrophic growth of primary seminal roots and shoots, focusing on their inter- and intra-specific diversity and growth synchrony, and ii) investigating the relationship between seed traits and heterotrophic growth parameters to explore how these traits influence growth dynamics. We synthesized data from 28 plant species, including 13 crops, 11 weeds, and four model plants, combining new datasets and published data. Inter-specific variation in heterotrophic root growth was assessed across all 28 species, while intra-specific variation in heterotrophic growth was examined in detail for seven species. We also explored the relationships between heterotrophic growth parameters (for both root and shoot) and seed traits, including seed mass and hydrothermal time model parameters — base water potential (Ψ<sub>b</sub>) and base temperature (T<sub>b</sub>) for germination. Our results showed that the maximum seedling length increased with an increase in seed mass or a decrease in either Ψ<sub>b</sub> or T<sub>b</sub>. The thermal time to mid-elongation, maximal shoot growth rate, and growth synchrony were significantly correlated to seed mass in weeds. This study enhances our ability to predict plant performance in agricultural systems, informs weed management strategies, and supports ecological forecasting in changing environments.

## 1. Introduction

The timing of seedling emergence is a critical determinant for the competitive success of annual plants in arable cropping systems under temperate climates. Plant species with rapid seedling emergence and high vigor gain a distinct advantage by pre-emptively occupying space and thus securing resources in the future, often leading to the competitive exclusion of species characterized by slow seedling emergence (Gioria and Osborne, 2014; Smith et al., 2021). The timing of seedling emergence is determined initially by seasonal seed dormancy (for weeds) or sowing date (for crops), followed by seed germination and the subsequent heterotrophic growth beneath the soil surface, with the latter receiving comparatively less research attention than the former

(Colbach and Dürr, 2003; Gardarin et al., 2010, 2016; Dürr et al., 2016; Finch-Savage et al., 1998). Although selective breeding has led to commercial cultivars with high germination rates and speed, as evidenced by observations under field conditions (Finch-Savage et al., 1998; Lamichhane et al., 2020, 2022), there has been no focus to date on traits required for enhanced seedling emergence despite the challenges posed by environmental variability.

The heterotrophic growth phase, during which seedlings rely on seed reserves rather than photosynthesis, is a critical phase for determining initial development and vigor (Taiz and Zeiger, 2010). This phase determines the initial development and early vigor of the plant after emergence, which can have lasting effects on crop yield and quality. The transition from heterotrophic to autotrophic growth, in which the plant

<footnote><sup>a</sup> College of Agricultural Technology (Aburaihan Campus), University of Tehran, Tehran, Iran</footnote>

<footnote><sup>b</sup> Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH, USA</footnote>

<footnote><sup>c</sup> Agroécologie, INRAE, Institut Agro, Université Bourgogne, Dijon F-21000, France</footnote>

<footnote><sup>d</sup> Université Paris-Saclay, AgroParisTech, INRAE, UMR Agronomie, Palaiseau 91120, France</footnote>

<footnote><sup>e</sup> Université de Toulouse, INRAE, INPT, EI PURPAN, AGIR, Castanet-Tolosan, France</footnote>

<footnote>* Corresponding author.
E-mail address: jay-ram.lamichhane@inrae.fr (J.R. Lamichhane).</footnote>
begins to photosynthesize and relies on external carbon sources, is a pivotal moment in the plant's life cycle. Understanding this transition and the factors influencing it is essential for improving agricultural practices and outcomes. Research has shown that environmental conditions during the heterotrophic phase, such as temperature and soil moisture, can significantly impact the rate of seedling growth in the soil and the timing of the transition to autotrophy (Dürr et al., 2016; Gardarin et al., 2016). Additionally, the nutritional content of the seed itself plays a role in the duration and success of heterotrophic growth, with larger seeds generally providing a more substantial energy reserve for the developing seedlings (Gardarin et al., 2016). Surprisingly, except in a few cases (Dürr et al., 2016; Gardarin et al., 2016), most studies documenting the variation in heterotrophic growth patterns of weeds and crops have been limited to a single or a few species (Mohanty and Painuli, 2004; Gardarin et al., 2010; Gupta et al., 2012; Shi et al., 2016; Brim-DeForest et al., 2022), thereby hindering our understanding of the interspecific variability in heterotrophic growth.

Heterotrophic growth parameters, i.e., root and shoot elongation, the rate at which they grow, and their synchrony, are important traits determining the success of seedling establishment (Pflugfelder et al., 2022). In contrast to germination synchrony (Maleki et al., 2023), heterotrophic growth synchrony (the coordinated development of a plant's shoot and root systems in the absence of light) has been little studied in temperate arable cropping systems. Indeed, while pre-emergent shoot elongation has been studied in many crop and weed species (Dürr et al., 2016; Gardarin et al., 2016), pre-emergent root growth has rarely been investigated. Root length is associated with water and nutrient uptake (Lynch, 1995; Palta and Watt, 2009), and inter- and intra-specific variability for root traits has been reported during the autotrophic phase of different plant species (Lynch and van Beem, 1993; McPhee, 2005; Manavalan et al., 2010; Chen et al., 2012; Carton et al., 2018). Despite the importance of this information to optimize tillage for seedling establishment in farming systems (Watt et al., 2005) or to choose plant species or cultivars adapted to environmental conditions, particularly with climate change, there are still knowledge gaps. For crops, a cultivar characterized by a rapid primary seminal root elongation rate and elevated maximum growth may be of potential interest across drought-prone environments to search for water at greater soil depths (Dayoub et al., 2022, 2021). Likewise, for weeds, understanding and predicting the success and timing of seedling emergence and establishment across different environmental conditions is essential to understand crop-weed interactions and the resulting crop yield losses and weed seed production (Fahad et al., 2015).

Pre-emergent seedling growth and the resulting plant establishment and its timing are driven by soil temperature and moisture, depending on species- and variety-specific parameters. As pre-emergent growth relies on the seed for carbon, these parameters are potentially linked to seed functional traits such as seed mass (Gardarin et al., 2010; 2016; Larson et al., 2016; Guzmán et al., 2023; Maleki et al., 2024a). Indeed, species traits, which include morpho-physiological and phenological characteristics, are pivotal in influencing plant performance (Violle et al., 2007; Colbach et al., 2020). To explore this complex set of interactions, mechanistic models such as FLORSYS (Gardarin et al., 2012) can be used as conceptual frameworks to characterize and compare species from experiments. In particular, these models (1) simulate pre-emergent growth (following germination) from hydrothermal time and parameters such as base temperature ($T_b$) and base water potential ($\Psi_b$), maximum shoot and root length as well as shoot and root elongation rates, (2) link these parameters to species traits such as seed mass. The hydrothermal-time model allows evaluating seed or seedling response to reduced water potential and temperature (Bradford and Still, 2004; Bewley et al., 2012; Powell, 2022). Moreover, higher $T_b$ and $\Psi_b$ have also been shown to be linked to higher germination and/or growth rates (Gardarin et al., 2016; Bradford and Still, 2004; Soltani and Farzaneh, 2014; Soltani et al., 2017; Powell, 2022). However, to date, the range of species investigated with this approach remains limited. No

study has investigated the relationship between $\Psi_b$ and heterotrophic growth, nor the relationship between root heterotrophic growth and seed mass, $\Psi_b$, and $T_b$. Moreover, the correlations among heterotrophic growth parameters have been little investigated, particularly in terms of growth synchrony.

The objective of the present study was to carry out comparative ecology of the heterotrophic seedling growth, in two steps: i) analyze the primary seminal root and shoot heterotrophic growth, focusing on their inter- and intra-specific diversity and the synchrony of their growth patterns, and ii) investigate the relationship between seed mass and hydrothermal time model parameters ($\Psi_b$ and $T_b$) with heterotrophic growth parameters, exploring how these fundamental traits or parameters influence seedling growth dynamics. Through this scrutiny, we aim to assess the adaptive strategies of different plant species, discerning potential trade-offs between these traits and growth patterns. By synthesizing pre-existing data with new datasets, we performed a comprehensive analysis of heterotrophic growth. We quantified growth synchrony across species and tested links between heterotrophic growth parameters, seed mass, and hydrothermal time model parameters. To better understand how evolutionary lineage and functional groupings influence growth dynamics, we explicitly assessed these relationships across different plant classifications, including crops versus weeds, monocotyledonous ("monocots") versus dicotyledonous ("dicots") species, and legumes versus non-legumes. The latter two classifications were included due to their contrasting seed structures, nutrient reserves, and germination strategies, which may differentially affect early heterotrophic growth.

## 2. Materials and methods

### 2.1. Principle

We analyzed the heterotrophic growth of 28 annual plant species. The study encompassed 13 crops, 11 weed species, and four model species, with different varieties or populations for seven of them (Table 1). Heterotrophic growth data for 12 of the plant species were obtained from experiments described in the current study, while data for the remaining 16 species were sourced from external references (Table 1). A Weibull equation was fitted to all data to estimate the heterotrophic growth parameters for these species. Subsequently, these heterotrophic growth parameters were correlated to seed mass and hydrothermal time model parameters, and multivariate analyses were conducted on the data.

### 2.2. Plant species inter- and intra-specific variation: data collection

#### 2.2.1. Experimental data

Our experimental data included the heterotrophic growth of 12 plant species referred to as "new datasets" in Table 1. In this experiment, 12 *Triticum aestivum*, eight *Brassica napus*, four *Beta vulgaris*, four *Pisum sativum*, two *Phaseolus vulgaris*, and two *Daucus carota* cultivars were used to study intra-specific variability in heterotrophic growth. *T. aestivum* cultivars used in the study were Alsace 22, Fidel, Thésée, Génial, Courtot, Scipion, Soissons, Ventura, Eole, Hynovaléa, DN122, and RE001. *B. napus* cultivars used were Colizée, Groß Lüsewitzer, Jupiter, Lipton, Savannah, SW Gospel, Campala, and Cap Vert. *B. vulgaris* cultivars were Véga, Avantage, Amélie, and Monatuno. For *D. carota*, two cultivars were used: Tim Tom and Véga. *P. sativum* cultivars used were Champagne, Baccara, and Térèse, while *P. vulgaris* cultivars used were Booster and Inter (Table S1). For each crop species/cultivar, we used a single seed lot coming from commercial lots characterized by a high germination percentage.

The heterotrophic growth dynamics of hypocotyl, epicotyl, or coleoptile - based on the plant species used (hereafter referred to as shoot) - and radicle (hereafter referred to as root) were measured using destructive measurements as described previously (Dürr and Boiffin,
<table><caption>**Table 1**
Plant species included in this study, key features, and source of data.</caption><thead><tr><th>Plants</th><th>Scientific name</th><th>Class</th><th>Seed mass (mg)</th><th>Legume Status</th><th>Cultivar/genotype/population*</th><th>Source</th></tr></thead><tbody><tr><td><strong>Crops</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Carrot</td><td><i>Daucus carota</i> L.</td><td>Dicot</td><td>0.99</td><td>No</td><td>2</td><td>New datasets</td></tr><tr><td>Common bean</td><td><i>Phaseolus vulgaris</i> L.</td><td>Dicot</td><td>330.07</td><td>Yes</td><td>2</td><td>New datasets</td></tr><tr><td>Faba bean</td><td><i>Vicia faba</i> L.</td><td>Dicot</td><td>994.48</td><td>Yes</td><td>1</td><td>Tribouillois et al. (2016)</td></tr><tr><td>Flax</td><td><i>Linum usitatissimum</i> L.</td><td>Dicot</td><td>5.67</td><td>No</td><td>1</td><td>Gardarin et al. (2016)</td></tr><tr><td>Maize</td><td><i>Zea mays</i> L.</td><td>Monocot</td><td>246.36</td><td>No</td><td>1</td><td>New datasets</td></tr><tr><td>Mustard</td><td><i>Sinapis alba</i> L.</td><td>Dicot</td><td>0.97</td><td>No</td><td>1</td><td>Dorsainvil et al. (2005)</td></tr><tr><td>Oilseed rape</td><td><i>Brassica napus</i> L.</td><td>Dicot</td><td>3.47</td><td>No</td><td>8</td><td>New datasets</td></tr><tr><td>Pea</td><td><i>Pisum sativum</i> L.</td><td>Dicot</td><td>215.88</td><td>Yes</td><td>4</td><td>Raveneau et al. (2011)</td></tr><tr><td>Soybean</td><td><i>Glycine max</i> Merr.</td><td>Dicot</td><td>175.43</td><td>Yes</td><td>1</td><td>New datasets</td></tr><tr><td>Sugar beet</td><td><i>Beta vulgaris</i> L.</td><td>Dicot</td><td>17.59</td><td>No</td><td>4</td><td>New datasets</td></tr><tr><td>Sunflower</td><td><i>Helianthus annuus</i> L.</td><td>Dicot</td><td>44.88</td><td>No</td><td>1</td><td>New datasets</td></tr><tr><td>Wheat</td><td><i>Triticum aestivum</i> L.</td><td>Monocot</td><td>38.78</td><td>No</td><td>12</td><td>New datasets</td></tr><tr><td>Wheat</td><td><i>Triticum turgidum</i> L.</td><td>Monocot</td><td>41.41</td><td>No</td><td>1</td><td>Gardarin et al. (2016)</td></tr><tr><td><strong>Weeds</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Blackgrass</td><td><i>Alopecurus myosuroides</i> Huds.</td><td>Monocot</td><td>2.01</td><td>No</td><td>13</td><td>Colbach and Dürr (2003)</td></tr><tr><td>Cut-leaved geranium</td><td><i>Geranium dissectum</i> L.</td><td>Dicot</td><td>2.36</td><td>No</td><td>1</td><td>New datasets</td></tr><tr><td>False chamomile</td><td><i>Matricaria perforata</i> Mérat.</td><td>Dicot</td><td>0.36</td><td>No</td><td>1</td><td>New datasets</td></tr><tr><td>Italian Ryegrass</td><td><i>Lolium multiflorum</i> Lam.</td><td>Monocot</td><td>3.09</td><td>No</td><td>1</td><td>Gardarin et al. (2016)</td></tr><tr><td>Ivy-leaved speedwell</td><td><i>Veronica L. hederifolia</i> L.</td><td>Dicot</td><td>3.95</td><td>No</td><td>1</td><td>New datasets</td></tr><tr><td>Slim amaranth</td><td><i>Amaranthus hybridus</i> L.</td><td>Dicot</td><td>0.57</td><td>No</td><td>1</td><td>Gardarin et al. (2010)</td></tr><tr><td>Wild spinach</td><td><i>Chenopodium album</i> L.</td><td>Dicot</td><td>0.59</td><td>No</td><td>1</td><td>Gardarin et al. (2010)</td></tr><tr><td>Pale persicaria</td><td><i>Polygonum lapathifolium</i> L.</td><td>Dicot</td><td>2.29</td><td>No</td><td>1</td><td>Gardarin et al. (2010)</td></tr><tr><td>Cleavers</td><td><i>Galium aparine</i> L.</td><td>Dicot</td><td>8.62</td><td>No</td><td>1</td><td>Gardarin et al. (2010)</td></tr><tr><td>Wild oat</td><td><i>Avena fatua</i> L.</td><td>Monocot</td><td>18.30</td><td>No</td><td>1</td><td>Gardarin et al. (2010)</td></tr><tr><td>Hairy crabgrass</td><td><i>Digitaria sanguinalis</i> L.</td><td>Monocot</td><td>0.52</td><td>No</td><td>1</td><td>Gardarin et al. (2010)</td></tr><tr><td><strong>Model/perennial plants</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Thale cress</td><td><i>Arabidopsis thaliana</i> Heynh.</td><td>Dicot</td><td>0.02</td><td>No</td><td>1</td><td>New datasets</td></tr><tr><td>Black clover</td><td><i>Medicago lupulina</i></td><td>Dicot</td><td>1.54</td><td>Yes</td><td>1</td><td>Gardarin et al. (2016)</td></tr><tr><td>Tall fescue</td><td><i>Festuca arundinacea</i> Schreb</td><td>Monocot</td><td>2.30</td><td>No</td><td>1</td><td>Gardarin et al. (2016)</td></tr><tr><td>Barrelclover</td><td><i>Medicago truncatula</i> Gaertn.</td><td>Dicot</td><td>4.09</td><td>Yes</td><td>1</td><td>Brunel et al. (2009)</td></tr></tbody></table>

1995; Gardarin et al., 2010). Germinated seeds (radicle ≤ 2 mm) of each plant species were placed at a depth of 3 cm in polyethylene pots (five seeds per pot; 30 pots in total; diameter = 10.5 cm; h = 11.5 cm) filled with 500 ml white sand (150–210 µm; 1.47 kg dm⁻³ bulk density). For both crops and weeds, we planted germinated seeds for two main reasons: (1) seed dormancy can considerably affect the germination of weeds (Baskin and Baskin, 2014); so by planting germinated seeds we eliminated the risk of confusing delays or reduction in germination with those in heterotrophic growth, and (2) for the results to be comparable between weeds and crops, we used the same method for crops as well. The moisture gravimetric content was raised to 0.20 g g⁻¹ before sowing by watering the sand with 100 ml of a nutrient solution (Saglio and Pradet, 1980; concentration of 0.20 g g⁻¹). To replenish the water lost through evaporation, the pots were monitored and weighed daily, and demineralized water was added as needed. All the pots were incubated in a dark growth chamber at 20 ± 1 °C and 80 % relative humidity. The pots were kept in the dark to simulate pre-emergence heterotrophic growth, and they were covered with aluminium foil to prevent light and water loss.

Shoot and root growth were measured daily, starting the day after sowing, by harvesting seedlings from three pots at each sampling date. Plant organs were separated from the sand and washed under tap water, and root and shoot lengths were measured. The measurements continued until no further growth was observed (destructive harvests - shoot and root growth reached plateau values). Time was calculated as thermal time after sowing using Tb determined for each species. We extracted T<sub>b</sub> and Ψ<sub>b</sub> for germination of each species from the literature (Tables S2 and S3).

### 2.2.2. External data from the literature

To more accurately assess interspecific variations and investigate the relationships between heterotrophic seedling growth parameters, seed mass, and hydrothermal time model parameters, we gathered data on the heterotrophic growth patterns of 16 distinct plant species from the

literature. This dataset encompasses five crops, eight weeds, and three model plants, as detailed in Table 1. These data were collected from previously published articles by the author's group or their collaborators. We collected all the available data on the heterotrophic growth of major crops and weed species from papers published on these species, extracting information regarding heterotrophic growth curves. We conducted a reanalysis of the data extracted from published papers to obtain heterotrophic growth curves for the data derived from these sources. The reason for reanalyzing the extracted data was to ensure that heterotrophic growth parameters were estimated using the same statistical model as for the new datasets, allowing for consistency and comparability in subsequent analyses. This was necessary because some use a different model, for instance, Gompertz, and other possible reasons include variations in data interpretation and model fitting techniques.

### 2.3. Data analysis

#### 2.3.1. A conceptual framework for analyzing heterotrophic growth

To quantify the heterotrophic growth of shoot and root, we fitted a Weibull equation to shoot and radicle lengths versus thermal time (TT) since sowing, using the *nlsLM* function of R, which is part of the *minpack* *lm* package (Elzhov et al., 2016). The Weibull equation is considered to be a suitable model for estimating the heterotrophic growth parameters (Colbach and Dürr, 2003; Gardarin et al., 2010; 2016):

$$ \text{Length} = m[1 - e^{-\ln2(x/x_{50})b}] \quad (1) $$

where m and x<sub>50</sub> denote the final length of root or shoot (mm) and the thermal time (°C days) required to reach 50 % of the final length (thermal time to mid-elongation). The b parameter (no unit) is curve-shaped. Thermal time TT (°C days) was computed as the daily sum from planting for those days above base temperature (Bonhomme, 2000).

<footnote>* More information about the Cultivar/genotype/population of each species is available in table S1.</footnote>
$$TTd = \sum_{i=0}^{d} [T_i - T_b] \qquad (2)$$

T represents the daily temperature (°C) on a given day during the experiment, and T<sub>b</sub> (°C) is the species base temperature for germination (Table S2).

The maximum growth rate (V<sub>max</sub>, mm/°Cdays) at the inflection point according to Eq. (1) was determined using the following equation:

$$V_{\text{max}} = \left[ \frac{m \times \ln 2}{X} \right] \times \left[ \frac{b - 1}{b \times \ln 2} \right] \frac{b - 1}{b} \times \exp \left[ \frac{1 - b}{b} \right] \qquad (3)$$

where m, b, and x50 are the same parameters as defined for Eq. (1).

### 2.3.2. Intra-specific variation in the parameters for heterotrophic growth

Data analysis was performed in R (version 4.3.1; R Core Team, 2021) to investigate differences in heterotrophic growth parameters across plant species. A generalized linear mixed model (GLMm), was fitted using species as the fixed effect predictor and populations/cultivars as random effects. Pairwise comparisons of estimated marginal means were conducted using the emmeans package (Lenth, 2020), specifying type = "response" to obtain back-transformed means on the original scale. Tukey adjustments were applied to control for multiple comparisons among species. Results were visualized using compact letter displays from the multcomp (Graves et al., 2015) and multcompView packages, where non-significant groups were assigned the same letter.

### 2.3.3. Heterotrophic growth synchrony of root and shoot

Heterotrophic growth synchrony measures the extent to which the growth periods of shoots and roots overlap more than what random growth patterns would predict. A species exhibits a high degree of growth synchrony when the shoots and roots of the species grow for longer periods simultaneously than what was expected to happen randomly. We determined the synchrony of heterotrophic growth using the following equation, which is calculated using germinationmetrics package implemented within the R environment, R version 4.3.1 (Aravind et al., 2021; Maleki et al., 2023). The synchrony index GS, is given by:

$$GS = \left[ \sum (C_{ni.2}) \right] / \left( C_{\sum ni.2} \right) \qquad (4)$$

In this equation, GS ranges from 0 (minimal overlap) up to 1 (perfect overlap) and captures the growth progression (growth in mm) within specific time intervals by considering the combinatorial possibilities of growth events occurring in pairs. The function measures the intensity of growth by evaluating how growth events pair up within the same or nearby time intervals. C<sub>ni</sub> denotes the number of possible pairs of growth events (the measurement intervals in which growth is recorded) for shoots and/or roots that can occur on time i and C<sub>ni.2</sub> is the total number of possible pairs across the entire period considered. In this equation, n represents the number of growth events (e.g., growth increments recorded in mm) occurring within a specific time interval. Σ indicates that the summation runs across all time intervals i in the dataset. C<sub>ni.2</sub> is estimated as follows:

$$C_{ni.2} = \frac{[n_i(n_i - 1)]}{2} \qquad (9)$$

Z is more concerned with the clustering of growth occurrences in pairs rather than their distribution over time. A higher Z value indicates a greater degree of overlap or clustering of growth events in pairs, suggesting that growth events are not only frequent but also tend to occur close together, enhancing the perception of intense growth bursts. Growth is continuous, but the model analyzes growth by examining the clustering within measurement intervals. So, each "growth event" represents the observed growth within a specific interval, allowing us to evaluate how closely these growth intervals pair up in terms of their timing and extent. This approach captures the intensity and overlap of growth increments within the continuous growth process. To explore

interspecific variation in growth synchrony parameters, we classified plant species into distinct categories based on functional traits and species features, i.e., legume vs. non-legume, monocot vs. dicot, and seasonal affiliation. Pairwise comparisons between these groups were conducted using t-tests to assess statistically significant differences. To compare synchrony values among different plant species and categories (crops-weeds, legume-non-legume, monocotyledon-dicotyledon, and autumn/winter-spring-summer), we used a t-test to compare two groups in terms of GS values.

### 2.3.4. Relationships of heterotrophic seedling growth parameters with seed mass

We sought to identify relationships between the heterotrophic growth parameters - maximal length (m), thermal time to mid-elongation (X<sub>50</sub>), maximal growth rate (V<sub>max</sub>), and growth synchrony (GS) - and seed mass and hydrothermal time model parameters. To facilitate this, we collated seed mass data from the literature (Table 1). For species absent from these publications, we retrieved data from the Seed Information Database (SER, INSR, RBGK, 2023).

Using the statistical software R, we conducted regression analyses to evaluate the association between seed mass, germination parameters (Ψ<sub>b</sub> and T<sub>b</sub>), and heterotrophic growth parameters. This involved fitting linear models to the data using the lm function from R's base package (R Core Team, 2023), allowing us to quantify the relationship's strength through the determination of coefficient (R<sup>2</sup>) values. The regression approach was straightforward, focusing on the predictive value of seed mass for the aforementioned growth parameters.

### 2.3.5. Multivariate analysis

To examine relationships across ecological traits and growth parameters of different species used in the current study, principal component analysis (PCA) was conducted. The PCA was performed on seed traits and heterotrophic growth parameters using the prcomp function from the base stats package in R version 4.3.1 (R Core Team, 2023). This function was chosen for its ability to handle standardized data efficiently, ensuring that the variance of each trait contributes equally to the analysis. Data were scaled and subjected to principal coordinates analysis (PCoA) based on Bray-Curtis dissimilarity using the vegdist and cmdscale functions in the vegan R package (Oksanen et al., 2013). Species scores along the first two axes were visualized using ggplot2 and ggforce, with group-specific ellipses drawn using geo-mark ellipse to illustrate multivariate dispersion patterns. To see if there were any statistically significant relationships among plant categories (crops, weeds, and model plants) in terms of growth synchrony, a t-test was used to compare each group.

## 3. Results

### 3.1. Variation in shoot and root growth across plant species: integrating new datasets and published data

Our results demonstrated a high-quality data fit, as indicated by the CV, R<sup>2</sup>, and RMSE values (Tables 2 and 3). Most species exhibited R<sup>2</sup> values above 0.97, signifying a strong relationship between the model and the observed data. Additionally, the RMSE values indicated minimal prediction errors, further validating the model's accuracy. Among heterotrophic growth parameters, maximal length and thermal time to mid-elongation (X<sub>50</sub>) showed the greatest variation among different species for shoot and root growth (Tables 2 and 3). When comparing plant groups (crops, weeds, and model plants), crops generally had longer maximal lengths and higher thermal times to mid-elongation values. In contrast, weeds and model plants tended to have shorter maximal lengths and lower thermal time to mid-elongation values.

Among the species used in the present experiments, Glycine max exhibited the highest maximal shoot length (245 mm), which was significantly greater than that of all other species except Phaseolus
<caption>**Table 2**</caption>

Heterotrophic shoot growth parameters from new datasets, color-coded by magnitude (green = highest values, red = lowest values). The parameters were estimated by fitting a Weibull equation to shoot growth data. For statistical analysis, a GLMM model with Tukey adjustment was used; the groups not significantly different share the same letter. The parameters from published data are available in Table S4.

<table><caption>**Table 3**</caption><thead><tr><th>Species</th><th>Maximal length (m, mm)</th><th>Thermal time to mid-elongation (X50; °C days)</th><th>Shape parameter b (no unit)</th><th>Maximal growth rate (mm/°Cdays)</th><th>R<sup>2</sup></th><th>RMSE</th></tr></thead><tbody><tr><td><strong>Crops</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Glycine max</td><td>245.39 ± 4.8<sup>a</sup></td><td>191.57 ± 2.62<sup>a</sup></td><td>2.42 ± 0.37</td><td>0.020<sup>a</sup></td><td>0.98</td><td>9.897</td></tr><tr><td>Helianthus annuus</td><td>86.02 ± 1.30<sup>ab</sup></td><td>109.9 ± 2.27<sup>bc</sup></td><td>1.50 ± 0.46</td><td>0.013<sup>a</sup></td><td>0.97</td><td>5.121</td></tr><tr><td>Beta vulgaris</td><td>71.01 ± 0.88<sup>a</sup></td><td>82.70 ± 0.91<sup>a</sup></td><td>5.99 ± 0.47</td><td>0.103<sup>a</sup></td><td>0.99</td><td>3.400</td></tr><tr><td>Daucus carota</td><td>75.93 ± 1.65<sup>a</sup></td><td>90.43 ± 1.53<sup>a</sup></td><td>3.14 ± 0.18</td><td>0.050<sup>a</sup></td><td>0.99</td><td>2.599</td></tr><tr><td>Triticum aestivum</td><td>67.78 ± 1.38<sup>ab</sup></td><td>82.58 ± 1.28<sup>a</sup></td><td>5.30 ± 0.61</td><td>0.089<sup>a</sup></td><td>0.99</td><td>3.163</td></tr><tr><td>Brassica napus</td><td>128.20 ± 3.31<sup>cd</sup></td><td>73.05 ± 1.66<sup>a</sup></td><td>3.35 ± 0.27</td><td>0.043<sup>a</sup></td><td>0.98</td><td>6.820</td></tr><tr><td>Zea mays</td><td>136.18 ± 1.52<sup>bc</sup></td><td>68.93 ± 0.66<sup>a</sup></td><td>3.42 ± 0.13</td><td>0.045<sup>a</sup></td><td>0.99</td><td>2.703</td></tr><tr><td>Phaseolus vulgaris</td><td>186.47 ± 5.78<sup>ab</sup></td><td>185.2 ± 7.02<sup>a</sup></td><td>1.24 ± 0.01</td><td>0.010<sup>a</sup></td><td>0.98</td><td>2.848</td></tr><tr><td>CV</td><td>1.96</td><td>1.85</td><td>-</td><td>10.00</td><td>-</td><td>-</td></tr><tr><td>Mean</td><td>122.97</td><td>110.54</td><td>-</td><td>0.046</td><td>-</td><td>-</td></tr><tr><td><strong>Weeds</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Matricaria perforata</td><td>22.87 ± 0.64<sup>a</sup></td><td>14.44 ± 1.43<sup>a</sup></td><td>2.62 ± 0.26</td><td>0.462<sup>b</sup></td><td>0.99</td><td>0.871</td></tr><tr><td>Geranium dissectum</td><td>141.81 ± 1.63<sup>bc</sup></td><td>80.83 ± 1.05<sup>a</sup></td><td>2.75 ± 0.12</td><td>1.282<sup>a</sup></td><td>0.99</td><td>3.048</td></tr><tr><td>Veronica hederifolia</td><td>123.02 ± 5.10<sup>cde</sup></td><td>114.6 ± 1.23<sup>b</sup></td><td>1.57 ± 0.24</td><td>0.490<sup>b</sup></td><td>0.99</td><td>4.979</td></tr><tr><td>CV</td><td>2.70</td><td>1.86</td><td>-</td><td>10.00</td><td>-</td><td>-</td></tr><tr><td>Mean</td><td>95.90</td><td>79.95</td><td>-</td><td>0.744</td><td>-</td><td>-</td></tr><tr><td><strong>Model plants</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Arabidopsis thaliana</td><td>18.96 ± 0.27<sup>a</sup></td><td>55.85 ± 0.92<sup>a</sup></td><td>3.53 ± 0.24</td><td>0.272<sup>b</sup></td><td>0.98</td><td>2.691</td></tr></tbody></table>

Heterotrophic root growth parameters from new datasets, color-coded by magnitude (green = highest values, red = lowest values). The parameters were estimated by fitting a Weibull equation to root growth data. For statistical analysis, a GLMM model with Tukey adjustment was used; the groups not significantly different share the same letter. The parameters from published data are available in Table S5.

<table><thead><tr><th>Species</th><th>Maximal length (m, mm)</th><th>Thermal time to mid-elongation (X50; °C days)</th><th>Shape parameter b (no unit)</th><th>Maximal growth rate (mm/°Cdays)</th><th>R<sup>2</sup></th><th>RMSE</th></tr></thead><tbody><tr><td><strong>Crops</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Glycine max</td><td>52.88 ± 4.48<sup>a</sup></td><td>191.57 ± 2.62<sup>a</sup></td><td>2.42 ± 0.37</td><td>0.009<sup>a</sup></td><td>0.98</td><td>3.066</td></tr><tr><td>Helianthus annuus</td><td>56.04 ± 1.30<sup>cd</sup></td><td>270.19 ± 2.27<sup>a</sup></td><td>1.50 ± 0.46</td><td>0.009<sup>a</sup></td><td>0.96</td><td>3.700</td></tr><tr><td>Beta vulgaris</td><td>49.24 ± 0.65<sup>cd</sup></td><td>64.52 ± 1.19<sup>a</sup></td><td>3.29 ± 0.24</td><td>0.052<sup>a</sup></td><td>0.99</td><td>1.760</td></tr><tr><td>Daucus carota</td><td>43.43 ± 0.92<sup>a</sup></td><td>58.83 ± 1.31<sup>a</sup></td><td>3.87 ± 0.42</td><td>0.076<sup>ab</sup></td><td>0.98</td><td>2.230</td></tr><tr><td>Triticum aestivum</td><td>99.34 ± 6.75<sup>b</sup></td><td>92.63 ± 4.67<sup>ab</sup></td><td>3.80 ± 1.11</td><td>0.053<sup>a</sup></td><td>0.97</td><td>8.246</td></tr><tr><td>Brassica napus</td><td>56.15 ± 1.68<sup>ab</sup></td><td>35.70 ± 4.12<sup>a</sup></td><td>2.88 ± 1.23</td><td>0.099<sup>cd</sup></td><td>0.80</td><td>5.354</td></tr><tr><td>Zea mays</td><td>122.90 ± 4.57<sup>a</sup></td><td>62.09 ± 2.40<sup>a</sup></td><td>2.57 ± 0.29</td><td>0.040<sup>a</sup></td><td>0.99</td><td>5.355</td></tr><tr><td>CV</td><td>3.91</td><td>3.82</td><td>-</td><td>9.98</td><td>-</td><td>-</td></tr><tr><td>Mean</td><td>70.56</td><td>110.79</td><td>-</td><td>0.048</td><td>-</td><td>-</td></tr><tr><td><strong>Weeds</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Matricaria perforata</td><td>5.81 ± 0.65<sup>a</sup></td><td>24.44 ± 5.04<sup>a</sup></td><td>1.39 ± 0.48</td><td>0.117<sup>c</sup></td><td>0.92</td><td>0.851</td></tr><tr><td>Geranium dissectum</td><td>132.20 ± 9.39<sup>a</sup></td><td>99.67 ± 8.65<sup>a</sup></td><td>1.42 ± 0.09</td><td>0.611<sup>a</sup></td><td>0.99</td><td>3.536</td></tr><tr><td>Veronica hederifolia</td><td>111.94 ± 8.32<sup>ab</sup></td><td>220.52 ± 9.52<sup>b</sup></td><td>1.46 ± 0.22</td><td>0.471<sup>b</sup></td><td>0.99</td><td>5.030</td></tr><tr><td>CV</td><td>8.03</td><td>11.21</td><td>-</td><td>9.87</td><td>-</td><td>-</td></tr><tr><td>Mean</td><td>83.65</td><td>114.87</td><td>-</td><td>0.339</td><td>-</td><td>-</td></tr></tbody></table>

vulgaris (p < 0.05). *Arabidopsis thaliana* and *Matricaria perforata* had the shortest shoots (19 mm and 23 mm, respectively). *Phaseolus vulgaris* had the second-longest maximal shoot length but was not significantly different from *Glycine max* (p < 0.05) (Table 2; Fig. 1). In published data, *Avena fatua* exhibited the longest shoot of 287 mm (Table S4; Fig S2). Among the crops, *G. max* and *Linum usitatissimum* had the longest shoots, whereas *Triticum turgidum* and *T. aestivum* had the shortest. Weeds displayed a wide range of shoot lengths, with *A. fatua* and *Galium aparine* having the longest and *M. perforata* and *Amaranthus hybridus* the shortest (Tables 2, S3; Figs. 1, S2).

In terms of thermal time to mid-elongation (X50), *G. max* and *P. vulgaris* required the most time (192°C days and 185°C days,

respectively), whereas the species with the shortest shoots, *M. perforata* and *A. thaliana*, also had the shortest X50 values (44°C days and 56°C days, respectively; Tables 2, S4). Among weeds, *G. aparine* had the highest thermal time to mid-elongation, whereas *A. hybridus* had the lowest. The maximal growth rate also varied widely. Among crops, *B. vulgaris* and *T. aestivum* had the highest growth rates, while *P. vulgaris* and *Helianthus annuus* had the lowest (Tables 2, S4). Weeds generally exhibited higher growth rates, with *Lolium multiflorum* and *A. fatua* showing the highest rates among all species studied (p < 0.05).

The maximal growth rate also varied significantly among species (p < 0.05), with *Geranium dissectum* showing the highest rate (1.3 mm/°C day), followed by *M. perforata* (0.46 mm/°C day) and *Veronica*
<figure><img src="image_4.png" /><figcaption>Fig. 1. Examples of heterotrophic shoot and root growth vs thermal time for weeds, crops, and model species, with Weibull equations (solid lines) fitted to observations (round or square points, each point representing the mean of five replicates per date). For the other species, see Tables S4, S5 and Figs. S1-S5 in the supplementary file online.</figcaption></figure>

hederifolia (0.49 mm/°C day; Tables 2, S4). In contrast, *P. vulgaris* and *H. annuus* had the slowest growth rates (0.01 mm/°C day and 0.01 mm/°C day, respectively). These results highlight the variability in heterotrophic shoot growth strategies among species, with some prioritizing rapid elongation while others exhibiting slower but sustained growth.

The maximal root length values varied significantly among species (p < 0.05). For maximal root length, *G. dissectum* and *Zea mays* exhibited the longest roots, with mean values of 132 mm and 123 mm,

respectively, significantly exceeding those of most other species (Tables 3, S5; Figs. 1, S4). *V. hederifolia* and *T. aestivum* formed an intermediate group, while *B. napus* and *M. perforata* had significantly shorter roots (Tables 3, S4; Figs. 1, S5), with *M. perforata* exhibiting the shortest (6.81 mm).

The X50 also varied significantly among species (p < 0.05), with *H. annuus* having the highest value (270°C days), followed by *V. hederifolia* (221°C days) and *G. max* (192°C days) (Tables 3, S5). In

<figure><img src="image_5.png" /><figcaption>Fig. 2. Intra-specific variation in pre-emergent growth of shoots vs thermal time for crops, with Weibull equations (solid lines) fitted to observations (round or square points, each point representing the mean of five replicates per date). For clarity, only two varieties/populations are indicated per species.</figcaption></figure>
contrast, *M. perforata* and *B. napus* required the least thermal time for mid-elongation, at 24°C days and 36°C days, respectively. The growth rates varied widely among species. Weeds generally exhibited higher growth rates compared to crops and model plants. *Polygonum lapathifolium* and *A. hybridus* had the highest growth rates among weeds (Tables 3, S5).

## 3.2. Intraspecific variation in shoot growth parameters

Intra-specific variations in shoot growth across the tested crop species are illustrated in Figs. 2 and 3. As for maximal growth length, we observed a range varying from 50 mm to 220 mm. *B. napus* (120–220 mm) and *P. vulgaris* (80–200 mm) showed the highest intra-specific variation for shoot growth. Followed by *B. napus* and *P. vulgaris*, *P. sativum* showed the longest shoot growth, with a range varying from 150 to 190 mm. A lower intra-specific variation was observed for *D. carota* that ranged from 80 mm to 90 mm (Figs. 2, 3).

Concerning thermal time to mid-elongation (X50), *P. vulgaris* and *D. carota*, for example, exhibited much variation in thermal time required to reach 50 % of shoot growth, with a range varying from 50 to 250 °C days, respectively (Fig. 3). *B. vulgaris* and *T. aestivum* exhibited lower values of thermal time, ranging from 60 to 90 °C days, respectively. Regarding growth rate, *B. vulgaris* exhibited much variation, which was eight-fold compared to other species, suggesting a range varying from 0.42 to 0.65 mm °C days⁻¹ (Fig. 3). Other species showed similar variation among different cultivars or populations.

## 3.3. Heterotrophic growth synchrony

The results of the comparative analyses of growth synchrony across different categorical variables - crops versus weeds, monocots versus dicots, legumes versus non-legumes, and autumn/winter versus spring/summer crops are presented in Fig. 4. The comparison between crops and weeds revealed a statistically significant difference in shoot growth synchrony (p < 0.001), with weeds demonstrating higher median growth synchrony than crops, although there was great variability among weed species. In contrast, root growth synchrony did not differ significantly between the two groups. Despite variation observed for

other groups (monocot/dicot, legume/non-legume, and autumn/winter versus spring/summer), the difference was not statistically significant (Fig. 4).

## 3.4. Relationships between seed traits and heterotrophic seedling growth parameters

Seed mass showed a positive relationship with maximal shoot growth length (p = 0.0086; R² = 0.23) and a positive but non-significant relationship with thermal time to mid-elongation (p = 0.0552; R² = 0.33) (Fig. 5). No significant relationships were observed between seed mass and maximal shoot growth rate or shoot growth synchrony (Fig. 5). For crops, none of the heterotrophic shoot growth parameters showed a significant relationship with seed mass (Fig S6). In contrast, for weeds, maximal length (p = 0.0013; R²=0.59) and growth synchrony (p = 0.0489; R²=0.59) had significant positive relationships with seed mass, while thermal time to mid-elongation (p = 0.0690; R²=0.29) and maximal shoot growth rate (p = 0.0739; R²=0.22) showed positive relationships with seed mass but were not significant at the 0.05 level (Fig. 6S). Similarly, when comparing monocotyledonous and dicotyledonous plants, the results indicated no significant relationship between heterotrophic shoot growth parameters and seed mass in monocots. However, in dicots, maximal shoot length (p = 0.0108; R²=0.29) had a significant positive relationship with seed mass, while thermal time to mid-elongation (p = 0.0613; R²=0.17) showed a positive relationship and maximal shoot growth rate (p = 0.0943; R²=0.19) showed a negative relationship with seed mass, though neither was significant at the 0.05 level (Fig. 6S).

Seed mass was also positively correlated with maximal root length (p = 0.0046; R²=0.43; Fig. 5). When comparing crops and weeds, seed mass had a significant relationship with maximal root length in crops (p = 0.0500; R²=0.35), but in weeds, no heterotrophic root growth parameters were related to seed mass (Fig. S7). In monocots, none of the heterotrophic root growth parameters had a significant relationship with seed mass, but in dicots, maximal root length (p = 0.0227; R²=0.33) was significantly related to seed mass (Fig. S7).

The base water potential for germination was negatively correlated with the maximum root (p = 0.0077; R²=0.62) and shoot length

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Intra-specific variation in the parameters for heterotrophic growth of crops, including (a) maximum shoot length, (b) thermal time required to attain 50 % of growth, and (c) maximal shoot growth rate. The parameters were estimated using the Weibull function, which was fitted separately to each cultivar. The resulting growth parameters were then plotted to illustrate the variation among different cultivars or populations. The box plots represent the distribution of parameter values, where the horizontal line inside each box indicates the median, the boxes represent the interquartile range (IQR), and the whiskers extend to 1.5 times the IQR or the most extreme data points within this range. The GLMM model with Tukey adjustment was used for statistical analysis, and groups that were not significantly different were assigned the same letter. The number of cultivars/populations can be found in Table 1. The error bars represent standard errors.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Synchrony of root and shoot growth patterns of different plant species. To compare categories, a *t*-test was used. The error bars represent standard errors.</figcaption></figure>

(p = 0.0277; R²=0.61). In contrast, the base temperature ($T_b$) for germination was negatively correlated only to the maximum root length (p = 0.0308; R²=0.49; Fig. 5). The relationship between the moisture and temperature thresholds for germination and other heterotrophic growth parameters was not significant (Fig. 5).

**3.5. Trait-based clustering of heterotrophic seedling growth across plant categories**

The first principal component (PC1) accounted for 35.3 % of the total variance in the dataset, primarily capturing a gradient of traits such as maximum growth rate, maximum growth length, and $T_b$. The second principal component (PC2) explained an additional 22 % of the variance and was mainly associated with seed mass and water potential thresholds, together accounting for 57.3 % of the total variance (Fig. 6a; Table S6). These components highlight the role of seed mass, $T_b$, and growth synchrony in differentiating plant strategies.

Principal Coordinates Analysis (PCoA; Fig. 6b) provided additional insight into trait combinations by ecological identity. Crops, weeds, and model plants largely overlapped in trait space, with only one crop and one weed species separated from the rest. Notably, weeds showed the broadest dispersion along PC1, reflecting diverse strategies in traits related to growth and germination under suboptimal conditions. In contrast, model plants clustered tightly within the trait space defined by crops and weeds, suggesting a narrower range of trait syndromes. Crops showed intermediate trait variability, generally trending toward high seed mass and lower germination thresholds.

**4. Discussion**

**4.1. New insights into heterotrophic root and shoot growth variability**

Previous studies have analyzed the heterotrophic shoot growth of seedlings and the interspecific diversity of heterotrophic growth across different species (Colbach and Dürr, 2003; Gardarin et al., 2010; Dürr et al., 2016; Gardarin et al., 2016; Finch-Savage et al., 1998; Lamichhane et al., 2022, 2020). However, we extended our knowledge to examine the intraspecific diversity of seedling growth in seven different species. Additionally, the diversity of heterotrophic root growth has not been thoroughly investigated previously. By focusing on both inter- and intra-specific diversity, we provide a more comprehensive

understanding of the growth patterns and synchrony between roots and shoots.

Although synchrony models have been widely used to quantify the synchrony of phenological traits, including germination, dormancy, and flowering (Qiu et al., 2018; Aravind et al., 2021; Maleki et al., 2023), we developed a growth synchrony model describing the synchronization process of heterotrophic growth in different plant species. Synchrony patterns varied considerably among and within crops. Variable growth synchrony patterns might have evolved as a response to environmental conditions. While synchronized growth in a particular environment or at a specific time can help species efficiently utilize available resources, it also increases the risk of seedlings competing with each other (Gioria and Osborne, 2014). In agricultural systems, however, uniform seedling growth and emergence are typically desirable because they support the timely application of management practices such as mechanical and chemical weeding, and they also help crops gain a competitive advantage over weeds by emerging earlier and faster (Fahad et al., 2015). To mitigate the negative effects of excessive synchrony, such as heightened seedling competition under suboptimal conditions, seeding rates can be adjusted based on field characteristics to support robust crop establishment. Thus, a balanced interpretation is necessary, as the best approach depends on the specific context and involves a compromise between different factors.

Past research has primarily focused on the relationships between heterotrophic shoot growth and seed mass and $T_b$ (Gardarin et al., 2016). In our study, we not only strengthened these existing relationships by adding new data but also extended the analysis to include root growth. Furthermore, we explored the relationships between heterotrophic growth parameters (separately for shoots and roots) and hydrothermal time model parameters.

**4.2. Heterotrophic seedling growth and seed mass: aligning with and expanding on previous research**

Our findings align with previous research that reported considerable variation in heterotrophic shoot growth parameters (Finch-Savage et al., 1998; Colbach and Dürr, 2003; Gardarin et al., 2010; Dürr et al., 2016; Gardarin et al., 2016; Lamichhane et al., 2020, 2022). Our results indicated that crops like *Z. mays* exhibited the longest final root lengths, indicative of robust root systems developed through selective breeding and genetic optimization. These characteristics are influenced by the
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Relationships between heterotrophic shoot/root growth parameters of plant species and seed traits (seed mass, base temperature for germination ($T_b$), and base water potential for germination ($\Psi_b$)). P values and $R^2$ values are shown in each panel to indicate the significance of the relationships. The grey areas represent confidence intervals. Heterotrophic shoot growth parameters and heterotrophic shoot growth synchrony (a, c, e) and heterotrophic root growth parameters and heterotrophic root growth synchrony (b, d, f) in relation to seed mass (a, b), base temperature $T_b$ (c, d) and base water potential $\Psi_b$ (e, f).</figcaption></figure>

initial seed mass, which provides the energy for heterotrophic growth. In contrast, the shorter root lengths and greater thermal requirement variability observed in weeds such as *M. perforata*, *A. hybridus*, and *D. sanguinalis* may reflect the smaller seed mass, limiting the initial energy reserve for root development. Weeds may have been indirectly selected for traits that enhance their resilience, such as deeper rooting to access water during dry periods (MacLaren et al., 2020).

The relationship between maximum shoot/root length and seed mass

generally presented a positive correlation, suggesting that large-seeded plant species tend to produce longer root/shoot lengths. However, exceptions exist, such as *V. faba* with a large seed mass but short shoot length, and *L. usitatissimum* with a small seed mass but long shoot length. Larger seeds can produce seedlings with longer roots under limited nutrient supply (Albert et al., 2023). However, under sufficient nutrient conditions, shoot length can be significantly increased in larger-seeded species compared to root length. Therefore, while seed mass can be a
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Principal component analysis (a; PCA) and Principal Coordinates Analysis (b; PCoA) of seed traits measurements for weeds, model plants, and crops. The data shown describes the first two principal components. Only the shoot data was used. GS: growth synchrony. ML: maximum growth length. MGR: maximum growth rate. Tb: base temperature for germination. X50: thermal time required to attain 50 % of growth. Ψb: base water potential for germination.</figcaption></figure>

predictor of plant growth, it is not the sole determinant, and under-
standing the interplay of various biological, genetic, and environmental
factors is crucial (Moles et al., 2005; Moles and Leishman, 2008;
Finch-Savage and Bassel, 2016). Larger seeds often contain more re-
serves, supporting greater initial growth rates and providing a compet-
itive advantage (Cicek, 2007).

Our findings suggest that genetic selection and breeding have played
a crucial role in shaping the heterotrophic growth patterns of crop
species compared to weeds. Crops have undergone intensive artificial
selection for traits that enhance seedling vigor, including larger seed size
and faster pre-emergence growth, which improve their competitive
ability in managed agricultural environments (Moles et al., 2005;
Finch-Savage and Bassel, 2016). In contrast, weeds, which have not been
subject to targeted breeding, exhibit a broader range of adaptive traits
shaped by agricultural techniques, including variable seed dormancy
and slower but more resource-efficient early growth (Westoby et al.,
1996; Baskin and Baskin, 2014), as indicated by higher growth syn-
chrony in weeds. While crops exhibited higher mean heterotrophic
growth rates, they also showed greater variability. However, this pattern
may partly reflect a general statistical relationship between mean and
variance, and not necessarily indicate a functional difference unless
further tested.

Contrary to expectations, our results showed no significant rela-
tionship between seed mass and heterotrophic shoot growth parameters
in crops. In contrast, weeds exhibited positive correlations between seed
mass and both maximal shoot length and growth synchrony (Fig. S6),
suggesting that seed size in weed species may play a more consistent role
in shaping early biomass accumulation. This pattern likely reflects
different ecological strategies: weed species tend to adopt a 'play often
with lower investment' strategy—producing many small seeds with
variable emergence timing—to spread risk in disturbed environments.
Crops, by contrast, are typically sown in well-prepared habitats, where a
'play rarely but invest heavily' strategy (i.e., larger seeds with higher
per-emergence success) is more advantageous (Donohue et al., 2010).
That said, exceptions among crops, such as large-seeded species with
relatively short heterotrophic shoot lengths, suggest that breeding pro-
grams may also target other seedling traits beyond elongation capacity,
including architecture and stress resilience. Understanding the genetic
and ecological basis of these differences could help refine breeding
strategies that improve early crop establishment, while balancing
trade-offs between seedling competition, resilience, and resource use
efficiency (Sadras, 2007).

We found that larger weed seeds contribute to more robust and co-
ordinated growth. While larger seeds generally produced longer shoots,
the thermal time for heterotrophic growth also increased with seed

mass. This implies that although larger seeds lead to greater shoot
lengths, they require more time to achieve this growth. However, this
does not necessarily mean that the maximal growth rate is lower, as the
correlation between maximum shoot length and later mid-elongation is
expected and can result in similar maximal growth rates. This corrob-
orates previous findings that larger chickpea seeds resulted in enhanced
root and shoot growth and a higher relative growth rate (but not the
maximal growth rate; Soltani et al., 2002). Despite faster consumption of
seed reserves, the conversion efficiency to plant tissue was uniform
across seed sizes, highlighting the advantage of larger seeds in providing
energy more rapidly to support growth. Seedling growth rate is influ-
enced by seed size and cell volume, but this relationship can be altered
by environmental conditions and genetic factors (Sexton et al., 1997).
Gardarin et al. (2016) demonstrated that seedling growth rate was
positively associated with seed size due to the larger dry mass of seeds
leading to increased shoot elongation. Yet, this is not a definitive rule, as
seed reserve location and seedling shoot diameter also significantly
impact germination and emergence success. These insights collectively
suggest that while seed size is crucial, it is not the sole determinant of
seedling growth rate, and other traits must be considered to understand
seedling establishment dynamics.

4.3. The role of Ψ<sub>b</sub> and T<sub>b</sub> in shaping heterotrophic seedling growth and growth synchrony

We developed a growth synchrony model describing the synchroni-
zation process of heterotrophic growth in different plant species. Vari-
able growth synchrony patterns might have evolved as a response to
environmental conditions. While synchronized growth can help species
efficiently utilize resources, it also increases the risk of seedlings
competing with each other. Thus, a balanced interpretation is necessary,
as the best approach depends on the specific context and involves a
compromise between different factors. Understanding synchrony pat-
terns in growth and phenological transitions among plant species is
crucial due to the role synchronous events can play in competition for
essential resources such as light, water, and nutrients. More synchronous
events may outcompete asynchronous ones when they share similar
resource requirements and phenological timing (Bogdziewicz et al.,
2020).

The significant negative relationship between Ψb and the maximum
lengths of both roots and shoots highlights the critical importance of a
seed's ability to germinate under lower water potential. Higher Ψb
values, indicating a strong dependence on moisture availability, facili-
tate greater elongation of roots and shoots (Gardarin et al., 2010; 2016).
This is crucial for the establishment and survival of seedlings, as longer
roots can access deeper water reserves and longer shoots can enhance photosynthetic capacity (Gardarin et al., 2010; 2016). The significant negative slopes observed for the relationships between Ψb and the maximum lengths of roots and shoots indicate that as Ψb decreases, the lengths of these structures increase. This finding is particularly relevant in water-limited environments, where species with lower Ψb thresholds may have a competitive advantage due to their enhanced growth capabilities under such conditions.

High-vigor seeds typically have lower Ψb, enabling them to germinate and emerge more effectively under water-limited conditions (Soltani and Farzaneh, 2014; Powell, 2022). Soltani and Farzaneh (2014) found that only Ψb was a reliable predictor of germination, emergence, and early seedling vigor in cotton, while other parameters like hydromime constant (θH) and standard deviation of Ψb(50) (σΨb) were not. Similarly, Soltani et al. (2017) reported a negative correlation between Ψb and both the rate and final emergence of 19 genotypes of oilseed rape (B. napus) when data from various stress conditions were combined. In our study, species with more negative Ψb for germination exhibited greater maximum root and shoot lengths. Generally, longer seedling lengths are considered an indicator of seed vigor. Seeds that produce larger seedlings tend to have higher vigor, enhancing their ability to germinate and rapidly establish under field conditions.

We found that T<sub>b</sub> was only correlated to the maximum root length. An increase in T<sub>b</sub> significantly reduced the maximum root length during heterotrophic growth. Walne and Reddy (2022) found that root growth in corn had the lowest optimum temperature compared to other growth processes, indicating that higher temperatures can inhibit root elongation. Engels (1994) investigated the effects of different root zone temperatures on maize and spring wheat grown in nutrient solutions at a constant air temperature of 20°C. The study found that, in wheat, the shoot-to-root dry matter ratio decreased at lower root zone temperatures, while in maize, it increased. In both species, dry matter partitioning to the shoot was increased when the shoot base temperature, and thus shoot activity, was increased at low root zone temperatures. Our results also showed that plants with lower T<sub>b</sub> exhibited greater and more synchronized root elongation compared to those with higher T<sub>b</sub> (Fig. 5). Maleki et al. (2024b) support these findings, reporting that an increase in T<sub>b</sub> raises the optimal and ceiling temperatures for germination. Consequently, plants adapted to warm regions tend to have higher cardinal temperatures compared to plants adapted to cold regions (Bürger et al., 2020; Sousaraei et al., 2021), which generally exhibit greater root growth. The variability in T<sub>b</sub> and heterotrophic growth among populations and species further highlights the significance of intraspecific differences for research and modeling. For instance, studies on arable weed species have shown that seeds from colder climates exhibit higher T<sub>b</sub> (Bürger et al., 2020). This variability underscores the need to integrate intraspecific differences into growth simulation models to improve predictions of plant performance in diverse environments.

## 4.4. Practical Implications of the findings for agriculture

The relationship between maximal growth rate and seed mass is complex and often non-significant or negative due to two primary mechanisms: resource allocation trade-offs and ecological strategies. Larger seeds provide greater initial nutrient reserves, supporting early heterotrophic growth and establishment by supplying energy before the plant transitions to photosynthesis (Green and Juniper, 2004; Quero et al., 2007; Daws et al., 2007). However, this initial investment in survival may limit resources available for rapid growth, leading to slower growth rates. In contrast, smaller seeds, with fewer nutrient reserves, rely on photosynthesis earlier, promoting faster growth rates. These differences illustrate the trade-offs between early survival and growth strategies. Ecologically, large-seeded species are often conservative, prioritizing survival and establishment under stressful conditions (Laughlin, 2023). Conversely, small-seeded species exhibit

opportunistic strategies, characterized by rapid growth to exploit favorable conditions. This aligns with the ecological classification of plant strategies, where large-seeded species are adapted to resilience and small-seeded species to competitive environments (Laughlin, 2023).

Intra-specific variation in heterotrophic growth parameters among genotypes, varieties, and populations presents significant opportunities for crop improvement. For example, selecting genotypes with faster heterotrophic growth and stronger seedlings can enhance field crop establishment and productivity (Subramanian et al., 2024). Larger seedlings benefit from a greater photosynthetic area, enabling higher radiation capture and photosynthesis, which promotes rapid growth and yield under normal conditions (McCann and Sage, 2022; Murchie and Burgess, 2022; Sreekanta et al., 2024). Crops with faster heterotrophic growth and larger seedlings can better compete with biotic stressors like weeds by closing their canopy earlier and preventing the light from reaching weeds (Aharon et al., 2021; Xu et al., 2023). However, under abiotic stress, larger seedlings can have disadvantages, such as increased water loss due to higher transpiration rates from extensive leaf and stem structures, particularly in dryland ecosystems (Chirino et al., 2011; Gang et al., 2019). In these conditions, plants often allocate more resources to root growth, enhancing their ability to access deeper water sources and survive (Hanslin et al., 2019; Claeys and Inzé, 2013).

## 4.5. Limitations of the study

While this study provides valuable insights into the heterotrophic growth dynamics of different plant species, it has three key limitations. First, our experiments were conducted in growth chamber conditions, which may not fully replicate the complexities of field environments. However, mechanistic models such as SIMPLE (Dürr et al., 2001) and ALOMYSYS (Colbach et al., 2007) tested against field observations performed well. Therefore, further research is needed to investigate the impact of various stresses, such as soil compaction, infections by soil-borne pathogens, and other factors, under controlled conditions to complement our current results. Additionally, comparing our findings with field data will help evaluate the validity of results obtained from controlled conditions. Second, it primarily focused on heterotrophic shoot and root growth, leaving other aspects of seedling emergence (such as seed germination and seedling survival) unexplored, which highlights the importance of considering other important factors influencing seedling emergence in future studies. Third, we aimed to understand what happens in arable cropping systems, and our results cover this well. However, to gain a broader perspective, it is necessary to include wild types and forest trees. Caution is warranted, as previous work has shown that correlations established over a broader range of species and habitats do not always apply within a specific habitat, such as arable cropping systems (Colbach et al., 2020). The representation of the studied species in relation to the global plant kingdom is limited. Future studies are thus needed to: (i) broaden the scope to encompass a wider range of plant types, including more wild and endemic species, to provide a more comprehensive understanding of growth dynamics in nature, (ii) understand the genetic and physiological mechanisms underlying the observed growth variations, and (iii) pinpoint ecological consequences of these different growth strategies in natural ecosystems that may offer further insights into the dynamics of plant communities.

## 5. Conclusions and perspectives

This study enhances our understanding of heterotrophic seedling growth by examining inter- and intra-specific diversity and the relationship between root and shoot dynamics across seven plant species. Using a novel growth synchrony model, we identified variable synchrony patterns, highlighting their adaptive significance in different environments. Our findings extend previous research by integrating heterotrophic root growth with established shoot growth parameters. We observed correlations between seed mass, hydrothermal time model
parameters, and heterotrophic growth dynamics, providing insights into resource allocation under varying conditions. While seed mass is a key trait influencing early growth, it is important to note that seed mass can vary considerably within a species due to factors such as environmental conditions, plant-to-plant differences, and positional variation on the plant. Seed mass may also fluctuate from year to year, influenced by factors like nutrient availability, climate, and stress conditions during seed development (Colbach and Dürr, 2003). These factors can contribute to variability in the observed relationships between seed mass and growth parameters in this study. The negative relationship between Ψb and root/shoot length emphasizes the importance of moisture availability in seedling establishment, especially in water-limited environments. Variability in Tb influences root elongation, showing how plants from different climates adapt their growth strategies.

Our results have ecological and agricultural implications. Large-seeded crops, while robust in resource-rich environments, may face trade-offs in water-limited conditions. Smaller seeds with faster heterotrophic growth rates are suited to transient favorable conditions. This balance informs crop breeding programs to enhance stress resilience and resource-use efficiency. Understanding growth strategies among plant species aids in predicting species coexistence and ecosystem resilience. The diversity in heterotrophic growth dynamics underscores biodiversity's importance in maintaining ecosystem functions and offers insights for conservation and ecological restoration.

## CRediT authorship contribution statement

**Jay Ram Lamichhane:** Writing – review & editing, Visualization, Validation, Supervision, Methodology, Funding acquisition, Conceptualization. **Keyvan Maleki:** Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation. **Elias Soltani:** Writing – original draft, Visualization, Validation, Resources, Methodology, Investigation, Formal analysis, Data curation. **Antoine Gardarin:** Writing – review & editing, Visualization, Validation, Supervision. **Nathalie Colbach:** Writing – review & editing, Visualization, Supervision.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

We thank Carolyne Dürr who kindly provided most of the dataset used in this study as well as Pierre Pluchard, Jacques Legouis, Grégory Delhemmes, Bruno Desprez, and Louis Delesalle for contributing to this dataset. We also thank C. Dominiarzick, C. Leforestier, and Lydie Ledroit for their technical assistance during the experiments. JRL is partially supported by Plant2Pro® Carnot Institute in the frame of its 2024 call for projects. Plant2Pro® is supported by ANR (amendment #24-CARN-0024-01 relating to agreement 20-CARN-0024-01). ES is supported by the French Government and Campus France through a scholarship (File No: 164205X) for his research stay at INRAE, Toulouse, France.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.envexpbot.2025.106170.

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